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zTk!&lnTIn%?HEKv?isMj$H4_$%4$&CMcX;%8(XvxL9py zaj2m~5=C^Trx~+YMGl_DCUJ34wo05|1J}v(#p>W~#ww#cR`Xo&h`fBQcFy)?z?r23 z5-ha+u7xLB`R9tbSc92oM=K0w%*Oe4S(G_m$s!+rWzm7Qw6*X+HDF-7FHOW+^3wi2O}FFOx3h|4!bSW>b)5FtfpWhCmYXRLTXM1!9M%S$$fN>-7> zPXc$!(kqF0-&G4w#7$#0V|KI2zf2%l literal 0 HcmV?d00001 diff --git a/fall-2024/aicore/aic-501/00020/tweets_data.csv b/fall-2024/aicore/aic-501/00020/tweets_data.csv new file mode 100644 index 0000000..0119214 --- /dev/null +++ b/fall-2024/aicore/aic-501/00020/tweets_data.csv @@ -0,0 +1,27 @@ +Tweet_ID,Text,Sentiment,Language,Geolocation,Date,Emoji +1,"I love the beautiful beaches in Thailand!",Positive,English,Thailand,2023-11-01,😊 +2,"Not as amazing as the pictures, a bit disappointing.",Negative,English,Thailand,2023-11-02,😕 +3,"Just arrived in Bangkok, so excited to explore!",Positive,English,Thailand,2023-11-03,😃 +4,"The traffic here is terrible.",Negative,English,Thailand,2023-11-04,😤 +5,"I wish I could have stayed longer.",Neutral,English,Thailand,2023-11-05,😐 +6,"Incredible experience at Ayutthaya, the history is so rich!",Positive,English,Thailand,2023-11-06,🤩 +7,"Not much to see in Chiang Mai, disappointed.",Negative,English,Thailand,2023-11-07,😞 +8,"The street food here is amazing!",Positive,English,Thailand,2023-11-08,🍜 +9,"I’m stuck in traffic, Thailand’s roads are crazy.",Negative,English,Thailand,2023-11-09,😡 +10,"Bangkok is great, but I prefer the beaches.",Neutral,English,Thailand,2023-11-10,😐 +11,"Such a peaceful vacation, I’m so relaxed.",Positive,English,Thailand,2023-11-11,😌 +12,"Overcrowded beaches and too many tourists.",Negative,English,Thailand,2023-11-12,😒 +13,"Perfect weather for sightseeing today!",Positive,English,Thailand,2023-11-13,🌞 +14,"I’m not a fan of the nightlife scene here.",Negative,English,Thailand,2023-11-14,😩 +15,"I’ve been to Thailand before, but this trip feels different.",Neutral,English,Thailand,2023-11-15,🤔 +16,"The mountains are beautiful, and the hiking trails are perfect!",Positive,English,Thailand,2023-11-16,🏞️ +17,"I’ve seen better cities than Bangkok.",Negative,English,Thailand,2023-11-17,🙄 +18,"The food is a bit too spicy for me, but I love it!",Neutral,English,Thailand,2023-11-18,🌶️ +19,"What a beautiful sunrise at the beach.",Positive,English,Thailand,2023-11-19,🌅 +20,"I feel like I need more time to explore Thailand.",Neutral,English,Thailand,2023-11-20,🤗 +21,"The elephant sanctuary experience was unforgettable!",Positive,English,Thailand,2023-11-21,🐘 +22,"Too much noise in the city, can't enjoy myself.",Negative,English,Thailand,2023-11-22,😣 +23,"Love the local markets here, great shopping!",Positive,English,Thailand,2023-11-23,🛍️ +24,"I think I’ll skip the temples next time, I’m more into nature.",Neutral,English,Thailand,2023-11-24,🌿 +25,"Thailand has the best beaches in the world!",Positive,English,Thailand,2023-11-25,🏖️ + diff --git a/fall-2024/aicore/aic-501/00030/AIC-201-00030 - Thanawin Pattanaphol - 01324096.ipynb b/fall-2024/aicore/aic-501/00030/AIC-201-00030 - Thanawin Pattanaphol - 01324096.ipynb new file mode 100644 index 0000000..6a136c9 --- /dev/null +++ b/fall-2024/aicore/aic-501/00030/AIC-201-00030 - Thanawin Pattanaphol - 01324096.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from tensorflow.keras.utils import to_categorical\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam, SGD\n", + "from tensorflow.keras.callbacks import EarlyStopping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load dataset\n", + "iris_df = pd.read_csv('iris_dataset.csv')\n", + "\n", + "# Feature set (X) and target labels (y)\n", + "X_features = iris_df.iloc[:, :-1].values\n", + "y_labels = iris_df.iloc[:, -1].values\n", + "\n", + "# Split into training and testing sets (80/20)\n", + "X_train_set, X_test_set, y_train_set, y_test_set = train_test_split(X_features, y_labels, test_size=0.2, random_state=42, stratify=y_labels)\n", + "\n", + "# Standardize the feature data\n", + "scaler_instance = StandardScaler()\n", + "X_train_set = scaler_instance.fit_transform(X_train_set)\n", + "X_test_set = scaler_instance.transform(X_test_set)\n", + "\n", + "# Convert labels to one-hot encoded format\n", + "y_train_set_onehot = to_categorical(y_train_set, num_classes=3)\n", + "y_test_set_onehot = to_categorical(y_test_set, num_classes=3)\n", + "\n", + "# Early stopping configuration\n", + "early_stop_callback = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1st Model\n", + "Using Adam optimizer and two hidden layers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_1 = Sequential([\n", + " Dense(16, input_dim=X_train_set.shape[1], activation='relu'),\n", + " Dense(16, activation='relu'),\n", + " Dense(3, activation='softmax')\n", + "])\n", + "\n", + "model_1.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Training the first model\n", + "history_1 = model_1.fit(X_train_set, y_train_set_onehot, epochs=100, batch_size=8, validation_split=0.2, verbose=1, callbacks=[early_stop_callback])\n", + "\n", + "# Evaluate Model 1\n", + "test_loss_1, test_acc_1 = model_1.evaluate(X_test_set, y_test_set_onehot)\n", + "print(f\"Model 1 - Test Loss: {test_loss_1}, Test Accuracy: {test_acc_1}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2nd Model\n", + "Using SGD optimizer and two hidden layers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "model_2 = Sequential([\n", + " Dense(16, input_dim=X_train_set.shape[1], activation='relu'),\n", + " Dense(16, activation='relu'),\n", + " Dense(3, activation='softmax')\n", + "])\n", + "\n", + "model_2.compile(optimizer=SGD(learning_rate=0.01), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Training the second model\n", + "history_2 = model_2.fit(X_train_set, y_train_set_onehot, epochs=100, batch_size=8, validation_split=0.2, verbose=1, callbacks=[early_stop_callback])\n", + "\n", + "# Evaluate Model 2\n", + "test_loss_2, test_acc_2 = model_2.evaluate(X_test_set, y_test_set_onehot)\n", + "print(f\"Model 2 - Test Loss: {test_loss_2}, Test Accuracy: {test_acc_2}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3rd Model\n", + "Using Adam optimizer, Tanh activation, and three hidden layers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_3 = Sequential([\n", + " Dense(32, input_dim=X_train_set.shape[1], activation='tanh'),\n", + " Dense(32, activation='tanh'),\n", + " Dense(32, activation='tanh'),\n", + " Dense(3, activation='softmax')\n", + "])\n", + "\n", + "model_3.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Training the third model\n", + "history_3 = model_3.fit(X_train_set, y_train_set_onehot, epochs=100, batch_size=8, validation_split=0.2, verbose=1, callbacks=[early_stop_callback])\n", + "\n", + "# Evaluate Model 3\n", + "test_loss_3, test_acc_3 = model_3.evaluate(X_test_set, y_test_set_onehot)\n", + "print(f\"Model 3 - Test Loss: {test_loss_3}, Test Accuracy: {test_acc_3}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4th Model\n", + "Using SGD optimizer, Sigmoid activation, and one hidden layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_4 = Sequential([\n", + " Dense(64, input_dim=X_train_set.shape[1], activation='sigmoid'),\n", + " Dense(3, activation='softmax')\n", + "])\n", + "\n", + "model_4.compile(optimizer=SGD(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Training the fourth model\n", + "history_4 = model_4.fit(X_train_set, y_train_set_onehot, epochs=100, batch_size=8, validation_split=0.2, verbose=1, callbacks=[early_stop_callback])\n", + "\n", + "# Evaluate Model 4\n", + "test_loss_4, test_acc_4 = model_4.evaluate(X_test_set, y_test_set_onehot)\n", + "print(f\"Model 4 - Test Loss: {test_loss_4}, Test Accuracy: {test_acc_4}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___________" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fall-2024/aicore/aic-501/00030/ANN_Fashion_MNIST_Classification.ipynb b/fall-2024/aicore/aic-501/00030/ANN_Fashion_MNIST_Classification.ipynb new file mode 100644 index 0000000..bcf0470 --- /dev/null +++ b/fall-2024/aicore/aic-501/00030/ANN_Fashion_MNIST_Classification.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"Lz6TJu0o7UtX"},"source":["# **Tutorial**"]},{"cell_type":"markdown","metadata":{"id":"_1qlT7qtVyex"},"source":["This tutorial demonstrates image classification on Fashion MNIST dataset. We'll train a multiclass classifier to classify clothes such as sneakers, trousers, etc."]},{"cell_type":"markdown","metadata":{"id":"NUtmaHoT6C-v"},"source":["### **Step 1: Import libraries**"]},{"cell_type":"markdown","metadata":{"id":"VPvq3M1bX_zS"},"source":["This tutorial uses tf.keras, a high-level API to build and train models in TensorFlow. NumPy is used to do mathematical operations, and lastly, Matplotlib is used to show a figure.\n","\n"]},{"cell_type":"code","metadata":{"id":"OoBXQ6S1-JMR","colab":{"base_uri":"https://localhost:8080/"},"outputId":"fe3e9206-31d7-468e-e6ca-29f06476e99a","executionInfo":{"status":"ok","timestamp":1730106961791,"user_tz":-420,"elapsed":5804,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["import tensorflow as tf\n","\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","print(tf.__version__)"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["2.17.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"fM6fbmOM8wac"},"source":["### **Step 2: Import the Fasion MNIST dataset**"]},{"cell_type":"markdown","metadata":{"id":"N8uJNAweaSVq"},"source":["This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothes at low resolution (28 by 28 pixels), as seen here:\n","\n","\n"," \n"," \n","
\n"," \"Fashion\n","
\n"," Figure 1. Fashion-MNIST samples (by Zalando, MIT License).
 \n","
\n","\n","Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow.\n","\n","Note that the dataset is already splited as 60,000:10,000 (train:test) by the dataset provider.\n"]},{"cell_type":"code","metadata":{"id":"8FAH2bd6-eS5","outputId":"d0fa2716-3354-4f95-8e58-117a337c4489","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730106971014,"user_tz":-420,"elapsed":5732,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["fashion_mnist = tf.keras.datasets.fashion_mnist\n","\n","(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"]}]},{"cell_type":"markdown","metadata":{"id":"t9FDsUlxCaWW"},"source":["Loading the dataset returns four NumPy arrays:\n","\n","* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n","* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n","\n","The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 9. These correspond to the *class* of clothing the image represents:\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
LabelClass
0T-shirt/top
1Trouser
2Pullover
3Dress
4Coat
5Sandal
6Shirt
7Sneaker
8Bag
9Ankle boot
\n","\n","Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"]},{"cell_type":"code","metadata":{"id":"y6evzC_4_GWk","executionInfo":{"status":"ok","timestamp":1730106978258,"user_tz":-420,"elapsed":568,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n"," 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8NWVLtfZ82k1"},"source":["### **Step 3: Explore the data**"]},{"cell_type":"markdown","metadata":{"id":"MMAvnW-CdSo5"},"source":["Now, It is a good practice to explore the data and see what we are dealing with before starting to process it. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y8ztztA9_Kbm","outputId":"2e4c2a65-6b1d-4a89-cec4-8b993ccec495","executionInfo":{"status":"ok","timestamp":1730106980314,"user_tz":-420,"elapsed":537,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["train_images.shape"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(60000, 28, 28)"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"O-sr7Ca1fYED"},"source":["Likewise, since we have 60,000 training images, then there should be 60,000 labels in the training set accordingly"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DHO4_Xlp_PcB","outputId":"de2e4492-4bb4-4394-d46f-68d36f9a28cf","executionInfo":{"status":"ok","timestamp":1730106982295,"user_tz":-420,"elapsed":3,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["len(train_labels)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["60000"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"nw9HGlSkfsId"},"source":["Each label is an integer between 0-9"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FjcOlDC2_T-q","outputId":"fc7eb528-1536-452e-87df-b19039f67f2f","executionInfo":{"status":"ok","timestamp":1730106985443,"user_tz":-420,"elapsed":544,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["train_labels"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"MHTaS_iifycn"},"source":["There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7Yduv-XE_dzX","outputId":"710ac1d1-73a8-42eb-b4cd-6d26116fc7fa","executionInfo":{"status":"ok","timestamp":1730106987748,"user_tz":-420,"elapsed":553,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["test_images.shape"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 28, 28)"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"SB0y9KAfgpem"},"source":["And the test set contains 10,000 labels"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aPb4jRab_iLu","outputId":"af897e2b-f0b7-4156-cbbc-afbd77021d6d","executionInfo":{"status":"ok","timestamp":1730106989471,"user_tz":-420,"elapsed":2,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["len(test_labels)"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"nk9pTNIch2i6"},"source":["Each label is an integer between 0-9"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iv5CfoNr_lPe","outputId":"81d8be0b-6adb-44d9-e7b8-7dadd0d168d7","executionInfo":{"status":"ok","timestamp":1730106991495,"user_tz":-420,"elapsed":3,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["test_labels"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"Ep_jl9Wl_xwt"},"source":["### **Step 4: Preprocess the data**"]},{"cell_type":"markdown","metadata":{"id":"EyAqb3K6iDrV"},"source":["The data should be preprocessed before feeding it to the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255"]},{"cell_type":"code","metadata":{"id":"DOPdEt7Y__42","colab":{"base_uri":"https://localhost:8080/","height":430},"outputId":"7b478f5f-e787-4bb6-b13c-758e66e96304","executionInfo":{"status":"ok","timestamp":1730106994050,"user_tz":-420,"elapsed":616,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["plt.figure()\n","plt.imshow(train_images[0])\n","plt.colorbar()\n","plt.grid(False)\n","plt.show()"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["

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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"NKrPnl6QUask"},"source":["Therefore we normalize the images to 0-1 range which result in faster convergence while training the network."]},{"cell_type":"code","metadata":{"id":"87WmJUPtAYXN","executionInfo":{"status":"ok","timestamp":1730107909940,"user_tz":-420,"elapsed":632,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["train_images = train_images / 255.0\n","\n","test_images = test_images / 255.0"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tFeVxPUYUz4P"},"source":["Let's display some more images from the training set and display the class name below each image"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"id":"5ahwId_OAliV","outputId":"8cfbd8e0-b64f-4192-ded1-f8b7b7ef643c","executionInfo":{"status":"ok","timestamp":1730107914973,"user_tz":-420,"elapsed":2393,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["plt.figure(figsize=(10,10))\n","for i in range(25):\n"," plt.subplot(5,5,i+1)\n"," plt.xticks([])\n"," plt.yticks([])\n"," plt.grid(False)\n"," plt.imshow(train_images[i], cmap=plt.cm.binary)\n"," plt.xlabel(class_names[train_labels[i]])\n","plt.show()"],"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"hpXWPitg9BHj"},"source":["### **Step 5: Build the model** "]},{"cell_type":"markdown","metadata":{"id":"xF5Qz-Veea2L"},"source":["> Building the neural network requires configuring the layers of the model, then compiling the model.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"a_6XeK58fHKL"},"source":["**Set up the layers**\n","\n"]},{"cell_type":"code","metadata":{"id":"13r1s4MlA2Zx","executionInfo":{"status":"ok","timestamp":1730107921008,"user_tz":-420,"elapsed":1232,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"e7d32cac-1069-464a-c5a3-52f4e6e57b6e"},"source":["model = tf.keras.Sequential([\n"," tf.keras.layers.Flatten(input_shape=(28, 28)),\n"," tf.keras.layers.Dense(128, activation='relu'),\n"," tf.keras.layers.Dense(10, activation='softmax')\n","])"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(**kwargs)\n"]}]},{"cell_type":"markdown","metadata":{"id":"uE5eidOxVq64"},"source":["🤔 **TIPS:** Do you know that there are two ways to define a model?\n","\n","* **`Sequential Class`**: The simplest type of model, **a linear stack of layers**. However, we can't build complex networks such as multi-input or multi-output networks.\n","* **`Model Class`**: A model with the **Functional API** that allow us to create arbitrary graphs of layers, and as a result we can connect not only with the previous and next layers but also share feature information with other layers in the model, for instance, `ResNet`, `EfficientNet`.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"OGyj2rGTg0xj"},"source":["👀 **Layers Explanation:** What are those Flatten, Dense layers for?\n","\n","* **`Flatten`**: This layer is used to **reformats the data**. Note that it has no parameters to learn. In our case, it transforms the format of the images from a 2D array (of 28 * 28 pixels) to a 1D array (of 28 * 28 = 784 pixels). The reason behind using Flatten is that **the dense layer requires input in single-dimensional shape** i.e. 1D array.\n","* **`Dense`**: This layer is also known as fully connected layers. Each neuron receives input from all the neurons in the previous layer. **Dense layer is used to classify image based on output from previous layers**.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"FVTOhqSpj41J"},"source":["**Compile the model**"]},{"cell_type":"code","metadata":{"id":"AUYMjU9IA7lo","executionInfo":{"status":"ok","timestamp":1730107924622,"user_tz":-420,"elapsed":656,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["model.compile(optimizer='Adam',\n"," loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n"," metrics=['accuracy'])"],"execution_count":14,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VVYEsY34aXCE"},"source":["👀 **Compile Explanation:** What are those optimizer, loss, and metrics for?\n","\n","* **`Optimizer:`** Defines **how the model is updated** based on the past data and its loss function\n","* **`Loss:`** Defines **how inaccurate the model is** during training. We want to minimize this function. In addition, `from_logits=True` is used when the softmax function has not been applied in the final layer to produce a probability distribution but in our case we already specified activation function as softmax thus we set `from_logits=False`\n","* **`Metrics:`** **Monitors** the training and testing steps.\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"RkHP5lwN9HsW"},"source":["### **Step 6: Train the model** "]},{"cell_type":"markdown","metadata":{"id":"TYQNJIUQczWu"},"source":["**Feed the model**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GB09FPjUBbrI","outputId":"4ce86103-75d1-48eb-9dad-686122d4210c","executionInfo":{"status":"ok","timestamp":1730107968759,"user_tz":-420,"elapsed":40023,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["model.fit(train_images, train_labels, epochs=10)"],"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step - accuracy: 0.7821 - loss: 0.6348\n","Epoch 2/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8614 - loss: 0.3859\n","Epoch 3/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8750 - loss: 0.3459\n","Epoch 4/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step - accuracy: 0.8848 - loss: 0.3168\n","Epoch 5/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8916 - loss: 0.2941\n","Epoch 6/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step - accuracy: 0.8957 - loss: 0.2815\n","Epoch 7/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8988 - loss: 0.2690\n","Epoch 8/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9062 - loss: 0.2538\n","Epoch 9/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.9074 - loss: 0.2457\n","Epoch 10/10\n","\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1ms/step - accuracy: 0.9131 - loss: 0.2337\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"HZPVX0VEc5hf"},"source":["**Evaluate the model**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jVKTj049Bznr","outputId":"4cce5304-dd1b-42e3-fc07-2992e7c370e2","executionInfo":{"status":"ok","timestamp":1730107973424,"user_tz":-420,"elapsed":1947,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n","\n","print('Test accuracy:', test_acc)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 - 1s - 4ms/step - accuracy: 0.8813 - loss: 0.3433\n","Test accuracy: 0.8812999725341797\n"]}]},{"cell_type":"markdown","metadata":{"id":"PxDI5dv9dubf"},"source":["🤔 **TIPS:** Do you know that you can configure how you want to see the training and testing progress for each epoch?\n","\n","* **`verbose=0`**: show you nothing (slient mode)\n","* **`verbose=1`**: show an animated progress bar (default)\n","* **`verbose=2`**: show the number of epoch\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"RMeDbZRc9S_C"},"source":["### **Step 7: Use the trained model**"]},{"cell_type":"markdown","metadata":{"id":"bNxr6E1EjTdW"},"source":["Let's try to predict all images in the testing set by calling `model.predict` method."]},{"cell_type":"code","metadata":{"id":"MubeesSRCjv5","executionInfo":{"status":"ok","timestamp":1730107977721,"user_tz":-420,"elapsed":1639,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"488489d3-e27d-49ce-edee-a6a4ed14d8b9"},"source":["predictions = model.predict(test_images)"],"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]}]},{"cell_type":"markdown","metadata":{"id":"P5lPg-iQjvOx"},"source":["Here, the model has predicted all images and kept its answeres in *predictions* variable. Let's take a look at the first prediction."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8M2kqui0CoOn","outputId":"fdc87be6-d8bc-4e80-8ec5-ebf7b707773e","executionInfo":{"status":"ok","timestamp":1730107979873,"user_tz":-420,"elapsed":2,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["predictions[0]"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8.2384111e-10, 1.4830619e-10, 8.0443918e-10, 4.0465756e-12,\n"," 3.4603778e-10, 4.0334820e-05, 5.2069327e-10, 1.9924245e-03,\n"," 6.7655037e-08, 9.9796718e-01], dtype=float32)"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"JKRQtK-gkaUH"},"source":["A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different clothing. You can see which label has the highest confidence value."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OZ1fvAwvCv6l","outputId":"7fdf7357-11b4-434e-c842-2a5cdf12ad5e","executionInfo":{"status":"ok","timestamp":1730107981857,"user_tz":-420,"elapsed":2,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["np.argmax(predictions[0])"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"xWz3j-VglPxY","outputId":"29bc175b-3295-4435-b69e-b3e979c310d3","executionInfo":{"status":"ok","timestamp":1730107983919,"user_tz":-420,"elapsed":5,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["class_names[np.argmax(predictions[0])]"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Ankle boot'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"BdM0K8zQkzSr"},"source":["So, the model is most confident that this image is `class_names[9]`, or (ankle boot). Let's lookup the actual answer in the *test_labels* variable."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"olK4RuYRC1yX","outputId":"bebf7f04-473f-4f39-bd36-83e34aac2590","executionInfo":{"status":"ok","timestamp":1730107985765,"user_tz":-420,"elapsed":2,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["test_labels[0]"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"NtEDupeKl2OX","outputId":"26548d7a-e60d-41cd-ce44-f9fb9883ee67","executionInfo":{"status":"ok","timestamp":1730107987868,"user_tz":-420,"elapsed":6,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["class_names[test_labels[0]]"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Ankle boot'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"Gc5hBbMVmgGz"},"source":["Let's do something more fancy (optional)."]},{"cell_type":"code","metadata":{"id":"i6_eKfZOC-BY","executionInfo":{"status":"ok","timestamp":1730107989915,"user_tz":-420,"elapsed":2,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["def plot_image(i, predictions_array, true_label, img):\n"," true_label, img = true_label[i], img[i]\n"," plt.grid(False)\n"," plt.xticks([])\n"," plt.yticks([])\n","\n"," plt.imshow(img, cmap=plt.cm.binary)\n","\n"," predicted_label = np.argmax(predictions_array)\n"," if predicted_label == true_label:\n"," color = 'blue'\n"," else:\n"," color = 'red'\n","\n"," plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n"," 100*np.max(predictions_array),\n"," class_names[true_label]),\n"," color=color)\n","\n","def plot_value_array(i, predictions_array, true_label):\n"," true_label = true_label[i]\n"," plt.grid(False)\n"," plt.xticks(range(10))\n"," plt.yticks([])\n"," thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n"," plt.ylim([0, 1])\n"," predicted_label = np.argmax(predictions_array)\n","\n"," thisplot[predicted_label].set_color('red')\n"," thisplot[true_label].set_color('blue')"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fdXDK5LRnYfK"},"source":["Correct prediction labels are blue and incorrect predictions labels are red."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"id":"7RTJgALcDCkH","outputId":"f9d07d00-5e62-46be-b6d3-dcfc94aa1734","executionInfo":{"status":"ok","timestamp":1730107994421,"user_tz":-420,"elapsed":637,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["i = 0\n","plt.figure(figsize=(6,3))\n","plt.subplot(1,2,1)\n","plot_image(i, predictions[i], test_labels, test_images)\n","plt.subplot(1,2,2)\n","plot_value_array(i, predictions[i], test_labels)\n","plt.show()"],"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"id":"k4g45_1ADZHp","outputId":"21cb8835-9ce7-471a-c3b4-813ad9e98f7d","executionInfo":{"status":"ok","timestamp":1730107998586,"user_tz":-420,"elapsed":557,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["i = 12\n","plt.figure(figsize=(6,3))\n","plt.subplot(1,2,1)\n","plot_image(i, predictions[i], test_labels, test_images)\n","plt.subplot(1,2,2)\n","plot_value_array(i, predictions[i], test_labels)\n","plt.show()"],"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":859},"id":"LfsAkTd3DjyJ","outputId":"8629755d-172d-4cf4-889b-bda31d119342","executionInfo":{"status":"ok","timestamp":1730108005871,"user_tz":-420,"elapsed":3639,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["num_rows = 5\n","num_cols = 3\n","num_images = num_rows*num_cols\n","plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n","for i in range(num_images):\n"," plt.subplot(num_rows, 2*num_cols, 2*i+1)\n"," plot_image(i, predictions[i], test_labels, test_images)\n"," plt.subplot(num_rows, 2*num_cols, 2*i+2)\n"," plot_value_array(i, predictions[i], test_labels)\n","plt.tight_layout()\n","plt.show()"],"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"E8ijJreT7fe6"},"source":["# **Your Turn!**"]},{"cell_type":"markdown","metadata":{"id":"Cqd6EXwFFd99"},"source":["Let's try to change the model architecture and the optimizer to see the effects.\n","\n","🤯 Some of them will show an error, try to find the reason with your classmates.\n","\n","* Change the input shape (in this [section](#step_5))\n"," * e.g. (30, 30)\n","* Remove the first dense layer (in this [section](#step_5))\n","* Add more dense layers (in this [section](#step_5))\n","* Change the optimizers (in this [section](#step_5))\n"," * e.g., SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, Nadam, Ftrl\n","* Change the number of epochs (in this [section](#step_6))\n"," * e.g. 1, 10, 20\n","\n"]}]} \ No newline at end of file diff --git a/fall-2024/aicore/aic-501/00030/MNIST Handwriting Classification using ANN - Keras.ipynb b/fall-2024/aicore/aic-501/00030/MNIST Handwriting Classification using ANN - Keras.ipynb new file mode 100644 index 0000000..efd876c --- /dev/null +++ b/fall-2024/aicore/aic-501/00030/MNIST Handwriting Classification using ANN - Keras.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"HPR_nNQ94KJ-"},"source":["# Prepare Environment"]},{"cell_type":"code","metadata":{"id":"m15_JQeGuaTX","executionInfo":{"status":"ok","timestamp":1730172105657,"user_tz":-420,"elapsed":1108,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["from __future__ import absolute_import\n","from __future__ import division\n","from __future__ import print_function\n","\n","from IPython.display import display\n","\n","import matplotlib\n","import matplotlib.pyplot as plt\n","plt.rcParams[\"axes.grid\"] = False\n","%matplotlib inline"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"z--Q9_0x_0GP","executionInfo":{"status":"ok","timestamp":1730172143413,"user_tz":-420,"elapsed":4989,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["import numpy as np\n","import keras\n","import tensorflow as tf"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lXmN9g0ayjnx"},"source":["# Load MNIST dataset\n","\n","The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits.\n","\n","Ref: http://yann.lecun.com/exdb/mnist/"]},{"cell_type":"code","metadata":{"id":"LLM9sJikvkFn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730172147636,"user_tz":-420,"elapsed":1468,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"28b65b4d-c8f3-4e65-c916-191b0b454316"},"source":["from keras.datasets import mnist\n","\n","# Download MNIST dataset using `datasets` module in Keras\n","# Note: the data have already been split into training and test sets\n","(x_train, y_train), (x_test, y_test) = mnist.load_data()\n","\n","print(f'Training set: {x_train.shape}, {y_train.shape}')\n","print(f'Test set: {x_test.shape}, {y_test.shape}')"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Training set: (60000, 28, 28), (60000,)\n","Test set: (10000, 28, 28), (10000,)\n"]}]},{"cell_type":"markdown","metadata":{"id":"dsyiuNmM2PwN"},"source":["Let's look at some examples of the training and test sets."]},{"cell_type":"code","metadata":{"id":"xgrAQgknzX79","colab":{"base_uri":"https://localhost:8080/","height":352},"executionInfo":{"status":"ok","timestamp":1730172173520,"user_tz":-420,"elapsed":4126,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"f8501c04-56b4-4625-a770-5bee584fa274"},"source":["def plot_mnist_data(data, label, n_images):\n"," img_w = 28\n"," img_h = 28\n"," image = np.reshape(data, (-1, img_h, img_w))\n","\n"," f, axs = plt.subplots(1, n_images)\n"," f.set_figheight(15)\n"," f.set_figwidth(15)\n"," for i in range(len(axs)):\n"," axs[i].imshow(image[i], cmap=\"gray\")\n"," axs[i].set_title(f\"Label: {label[i]}\", fontsize=20)\n"," axs[i].tick_params(\n"," axis='both',\n"," which='both',\n"," bottom=False, top=False,left=False, right=False,\n"," labelbottom=False, labeltop=False, labelleft=False, labelright=False)\n"," plt.show()\n"," plt.close(\"all\")\n","\n","print(\"Training set\")\n","plot_mnist_data(x_train, y_train, n_images=8)\n","\n","print(\"Test set\")\n","plot_mnist_data(x_test, y_test, n_images=8)"],"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Training set\n"]},{"output_type":"display_data","data":{"text/plain":["
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set\n"]},{"output_type":"display_data","data":{"text/plain":["
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Data Preprocessing\n","As our model is going to take a single vector for each training example, we need to reshape the inputs (i.e., `x`) so that each 28x28 image becomes a single 784-dimensional vector."]},{"cell_type":"code","metadata":{"id":"-F9YbswP1YLF","executionInfo":{"status":"ok","timestamp":1730172189346,"user_tz":-420,"elapsed":415,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["# MNIST parameters\n","num_classes = 10\n","img_h, img_w = 28, 28\n","\n","# Reshape the input so that each 28x28 image becomes a single 784-dimensional vector\n","x_train = x_train.reshape(x_train.shape[0], img_h * img_w)\n","x_test = x_test.reshape(x_test.shape[0], img_h * img_w)\n","\n","# Convert from int to float format\n","x_train = x_train.astype('float32')\n","x_test = x_test.astype('float32')"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qetkkccz5ISk"},"source":["## Feature Scaling\n","\n","It is a common pratice to **normalize the range of independent variables or features of data**. This is mainly because many classifiers calculate the distance between two points by the Euclidean distance. If one of the features has a broad range of values, the distance will be governed by this particular feature. Therefore, the range of all features should be normalized **so that each feature contributes approximately proportionately to the final distance**.\n","\n","There are many other feature scaling techniques, which can be found in [here](https://en.wikipedia.org/wiki/Feature_scaling).\n","\n","In this MNIST example, we'll only scale the inputs to be in the range [0-1] rather than [0-255]."]},{"cell_type":"code","metadata":{"id":"1pnHXIU35HMU","executionInfo":{"status":"ok","timestamp":1730172229761,"user_tz":-420,"elapsed":430,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["# Scale the MNIST data to be in the range [0-1]\n","# Note: The maximum value of color value is 255\n","x_train /= 255\n","x_test /= 255"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AGJsAf6x7aRg"},"source":["## Convert from class data into one-hot format\n","\n","We also have to modify the labels to be in the one-hot format, i.e.\n","```\n","0 -> [1, 0, 0, 0, 0, 0, 0, 0, 0]\n","1 -> [0, 1, 0, 0, 0, 0, 0, 0, 0]\n","2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0]\n","etc.\n","```"]},{"cell_type":"code","metadata":{"id":"ZO3bA6ao7a3r","executionInfo":{"status":"ok","timestamp":1730172237256,"user_tz":-420,"elapsed":446,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["# Convert class data to one-hot format\n","y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n","y_test = tf.keras.utils.to_categorical(y_test, num_classes)"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f_PXJOEX7mnf"},"source":["Here are the shapes of the training and test sets after preprocessing."]},{"cell_type":"code","metadata":{"id":"PgwTaQm27f45","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730172246888,"user_tz":-420,"elapsed":433,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"ea81b1cd-d691-4a30-94f7-f4b286b08137"},"source":["print(f\"Training set: {x_train.shape} {y_train.shape}\")\n","print(f\"Test set: {x_test.shape} {y_test.shape}\")"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Training set: (60000, 784) (60000, 10)\n","Test set: (10000, 784) (10000, 10)\n"]}]},{"cell_type":"markdown","metadata":{"id":"PjZ-uOK07wPM"},"source":["# Define a Model\n","\n","We are going to define a neural network, or what is typically referred to as a deep learning model. Here, we will do a simple 3-layer fully-connected network."]},{"cell_type":"markdown","metadata":{"id":"QPlQ3OIv707z"},"source":["\n","\"Fully-connected"]},{"cell_type":"code","metadata":{"id":"UEzRx5l576Bd","executionInfo":{"status":"ok","timestamp":1730172314204,"user_tz":-420,"elapsed":599,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["from keras.models import Sequential\n","from keras.layers import *\n","\n","# Feed-forward neural network\n","model = Sequential()\n","\n","# Layer 1 - Hidden\n","# Number of neurons (or units) is 128.\n","# Activation function is rectified linear unit (ReLU).\n","model.add(Dense(128, activation='relu'))\n","\n","# Layer 2 - Hidden\n","# Number of neurons (or units) is 128.\n","# Activation function is rectified linear unit (ReLU).\n","model.add(Dense(128, activation='relu'))\n","\n","# Layer 3 - Output\n","# Number of neurons is 10 (i.e., number of output classes).\n","# Activation function is softmax.\n","# Note: the softmax function is commonly used in\n","# the classification problem to normalize a\n","# K-dimensional output vector into a probability\n","# distribution of classes.\n","num_classes = 10\n","model.add(Dense(num_classes, activation='softmax'))"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zQCt2mvz8ROs"},"source":["# Train a Model\n","\n","In this section, we will first define several parameters that will be used during the training.\n","\n","* `epochs`: the number of training epochs (one epoch means the model has seen the entire training samples one times).\n","* `batch_size`: the number of examples per one training step.\n","* `learning_rate`: a hyperparameter that defines the adjustment in the weights of our network with respect to the loss gradient.\n"]},{"cell_type":"code","metadata":{"id":"YjNa-rS28XqD","executionInfo":{"status":"ok","timestamp":1730172352486,"user_tz":-420,"elapsed":421,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["epochs = 20\n","batch_size = 256\n","learning_rate = 0.01"],"execution_count":10,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UHeeybzy-taV"},"source":["## Loss Function\n","\n","Before we train a model, we need to specify the **loss function**, `loss`, that will be used to quantify the error between the predicted and the target classes. As we would like to train our model to differentiate among 10 handwritten digits in MNIST dataset, a loss function that we can use is *cross-entropy*. Cross-entropy is a measure of how different your predicted distribution is from the target distribution (see [Wikipedia](https://en.wikipedia.org/wiki/Cross_entropy) for more details).\n","\n","In this exercise, we will use the cross-entropy.\n","\n","Keras also provides many other loss functions for other problems as well. You can read more [here](https://keras.io/losses/)."]},{"cell_type":"code","metadata":{"id":"P8OGmVLq-RNm","executionInfo":{"status":"ok","timestamp":1730172357003,"user_tz":-420,"elapsed":438,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["# Cross-entropy loss\n","loss = keras.losses.categorical_crossentropy"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LvObkjaY-32N"},"source":["## Optimizer\n","\n","Another component that we need to specify before the training is the **optimizer**, `optimizer`. The optimizers that are commonly used to train deep learning models are Stochastic Gradient Descent (SGD), Adam, RMSProp, Adadelta, etc. The list of optimizers provided by Keras can be found [here](https://keras.io/optimizers/).\n","\n","Here we will use SGD."]},{"cell_type":"code","metadata":{"id":"VTq6EcE1-4fo","executionInfo":{"status":"ok","timestamp":1730172363034,"user_tz":-420,"elapsed":1476,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["# Stochastic gradient descent (SGD)\n","optimizer = tf.keras.optimizers.SGD(learning_rate)"],"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VzG3YUL8_UdH"},"source":["## Compile Keras Model\n","\n","Next, we configures the model for training by calling."]},{"cell_type":"code","metadata":{"id":"CmCb0PgA_TJQ","executionInfo":{"status":"ok","timestamp":1730172367293,"user_tz":-420,"elapsed":411,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["model.compile(\n"," loss=loss,\n"," optimizer=optimizer,\n"," metrics=['accuracy'])"],"execution_count":13,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rkB6NPNuW0nE"},"source":["## Train a model"]},{"cell_type":"markdown","metadata":{"id":"QP4czTe_W7HC"},"source":["We are now ready to train our model. Let's start feeding the data to train the model and it will learn to classify digits."]},{"cell_type":"code","metadata":{"id":"LJP7WcsO_bZA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730172388242,"user_tz":-420,"elapsed":14444,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"2d467ca4-03d1-4326-c6ff-102ab997ed32"},"source":["hist = model.fit(\n"," x_train, y_train,\n"," batch_size=batch_size,\n"," epochs=epochs,\n"," verbose=1)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.2782 - loss: 2.1393\n","Epoch 2/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7634 - loss: 1.1343\n","Epoch 3/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8473 - loss: 0.6442\n","Epoch 4/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8695 - loss: 0.4980\n","Epoch 5/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8819 - loss: 0.4334\n","Epoch 6/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8929 - loss: 0.3934\n","Epoch 7/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8980 - loss: 0.3677\n","Epoch 8/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9025 - loss: 0.3499\n","Epoch 9/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9084 - loss: 0.3294\n","Epoch 10/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9112 - loss: 0.3138\n","Epoch 11/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9118 - loss: 0.3105\n","Epoch 12/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9147 - loss: 0.3025\n","Epoch 13/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9195 - loss: 0.2881\n","Epoch 14/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9206 - loss: 0.2809\n","Epoch 15/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9195 - loss: 0.2836\n","Epoch 16/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9237 - loss: 0.2674\n","Epoch 17/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9241 - loss: 0.2656\n","Epoch 18/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9264 - loss: 0.2599\n","Epoch 19/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9300 - loss: 0.2476\n","Epoch 20/20\n","\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9326 - loss: 0.2402\n"]}]},{"cell_type":"markdown","metadata":{"id":"__m07PF3Xh5A"},"source":["# Apply the Trained Model on MNIST images\n","\n","Once we have finished the model training, we can now apply it to MNIST images."]},{"cell_type":"code","metadata":{"id":"I3ap6GsLXv41","colab":{"base_uri":"https://localhost:8080/","height":827},"executionInfo":{"status":"ok","timestamp":1730172433759,"user_tz":-420,"elapsed":1402,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"020227a2-6819-456d-8b17-44d61e3da060"},"source":["# Sample two MNIST images from the training set\n","test_imgs = x_test[0:2]\n","\n","# Plot images\n","for idx in range(len(test_imgs)):\n"," f, ax = plt.subplots(1)\n"," f.set_figheight(5)\n"," f.set_figwidth(5)\n"," ax.imshow(test_imgs[idx].reshape((img_h, img_w)), cmap=\"gray\")\n"," ax.tick_params(\n"," axis='both',\n"," which='both',\n"," bottom=False, top=False,left=False, right=False,\n"," labelbottom=False, labeltop=False, labelleft=False, labelright=False)"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZQAAAGVCAYAAADZmQcFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKEElEQVR4nO3cvWve5QLH4SelqTgkETNIS11UHBQUpC7OootWBKWC/4X1BUUoiOKf4OagLkUKRVFwUxxUcBBB6FLwBRKRIuSFIqh5zngOh5Mmz+0nbz3XtSZffjddPtwp3HPT6XQ6AYB/6NhBHwCAm4OgAJAQFAASggJAQlAASAgKAAlBASAhKAAkju/ml7a2tiYrKyuThYWFydzc3F6fCYBDZDqdTjY2NianTp2aHDu2/T1kV0FZWVmZ3HnnndnhADh6fvnll8np06e3/fmu/uS1sLCQHQiAo2mnFuwqKP7MBcBOLfCf8gAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgcP+gDcPN48cUXh3a33nrr0O6BBx4Y2j3zzDNDuxHvvPPO0O6rr74a2r3//vtDOyi4oQCQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAIm56XQ63emX1tfXJ0tLS/txHg6JixcvzrzZz1d8b3ZXr14d2j366KMzb37++eehb/H/Z21tbbK4uLjtz91QAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAxPGDPgB7a+TV4MnkaLwcfOXKlaHdZ599NvPmrrvuGvrWk08+ObS7++67h3bPP//8zJu333576Fvw39xQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkPA55RJw5c2Zo9/TTT8cn2d4PP/wwtDt79uzQ7tq1a0O7zc3NmTcnTpwY+tbXX389tHvwwQeHdsvLy0M7KLihAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACa8NHxEnT54c2s3NzQ3tRl4Ofvzxx4e+tbq6OrTbT+fPnx/a3XffffFJbuyTTz7Z1+/Bf3JDASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEl4bPiI+/vjjod0999wztNvY2Jh58/vvvw996yh47rnnhnbz8/PxSeDwckMBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASXhu+yf30008HfYRD56WXXpp5c++99+7BSbb3zTff7OsOCm4oACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASHofkyHriiSeGdm+88cbMmxMnTgx967fffhvavfrqq0O769evD+2g4IYCQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkvDbMkXXmzJmh3ejLwSMuXrw4tPviiy/ik8Dec0MBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASXhvmwF2+fHlo99hjj7UHuYH33ntvaPf666/HJ4HDyw0FgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABIeG2YzMmTJ4d2jzzyyNDulltuGdpdu3Zt5s2bb7459K3Nzc2hHRxFbigAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABIehyRz6dKlod3y8nJ8khv74IMPZt5cvXp1D04CNxc3FAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBIOG1Yf6ns2fPzrx56KGH9uAk2/v888+HdhcuXGgPAkwmEzcUACKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEg4bXhm9zy8vLQ7rXXXpt5Mz8/P/StUd99993QbnNzsz0IMJlM3FAAiAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCE14ZvcufPnx/aPfzww/FJtnf58uWh3YULF9qDAP+IGwoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgMTcdDqd7vRL6+vrk6Wlpf04D7E//vhjaDc/Px+fZHunT58e2q2ursYnAW5kbW1tsri4uO3P3VAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYDE8YM+ANx+++1Duz///DM+yeGxtrY2tBv5Nxl9WXq/XyC/7bbbZt688MIL/UH2wN9//z3z5pVXXhn61vXr14d2u+GGAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJLw2zIH7/vvvD/oIh86HH344tFtdXZ15c8cddwx969y5c0M7Gr/++uvQ7q233opP8m9uKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQMJrwze5Tz/9dGj31FNPxSdhFs8+++xBH2HP/PXXX0O7ra2t+CTb++ijj4Z23377bXyS7X355Zf79q3dckMBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJCYm06n051+aX19fbK0tLQf5+GQePnll2fezM/P78FJevfff//Mm3Pnzu3BSXrvvvvuzJsff/yxP8gNXLp0aWh35cqV+CTMam1tbbK4uLjtz91QAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhNeGAdgVrw0DsC8EBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkNhVUKbT6V6fA4BDbqcW7CooGxsbyWEAOLp2asHcdBfXj62trcnKyspkYWFhMjc3lx0OgMNvOp1ONjY2JqdOnZocO7b9PWRXQQGAnfhPeQASggJAQlAASAgKAAlBASAhKAAkBAWAxL8AQQkgnsDSUxUAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"TIlI5WiVjd4h","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730172446216,"user_tz":-420,"elapsed":857,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"a52c4acf-0daa-4696-a3fb-318c6b8a8dd2"},"source":["# Predict the labels of these images\n","probs = model.predict(test_imgs)\n","\n","# Convert the label back to the original format\n","prob_classes = np.argmax(probs, axis=-1)\n","\n","# Print the probability distribution\n","for idx in range(len(probs)):\n"," for i in range(num_classes):\n"," print(f'{i}: {probs[idx][i]:.4f}')\n"," print(f'Predicted class: {prob_classes[idx]}')\n"," print('')"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 406ms/step\n","0: 0.0002\n","1: 0.0000\n","2: 0.0005\n","3: 0.0042\n","4: 0.0000\n","5: 0.0001\n","6: 0.0000\n","7: 0.9944\n","8: 0.0001\n","9: 0.0006\n","Predicted class: 7\n","\n","0: 0.0063\n","1: 0.0006\n","2: 0.9476\n","3: 0.0125\n","4: 0.0000\n","5: 0.0043\n","6: 0.0246\n","7: 0.0000\n","8: 0.0041\n","9: 0.0000\n","Predicted class: 2\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"uukGYk3JXIgP"},"source":["# Evaluate Performance on Test Set\n","\n","Once you have finished the model training, you then evaluate the classification performance on the test set (i.e., the unseen dataset)."]},{"cell_type":"code","metadata":{"id":"wXZLfaurAblZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1730172510751,"user_tz":-420,"elapsed":1934,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"ec56a97c-baa5-49e4-d75f-c0e631afe911"},"source":["score = model.evaluate(x_test, y_test, verbose=0)\n","print('Test loss:', score[0])\n","print('Test accuracy:', score[1])"],"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Test loss: 0.23876428604125977\n","Test accuracy: 0.9326000213623047\n"]}]},{"cell_type":"markdown","metadata":{"id":"_KRSdi9DkfOk"},"source":["# Analyze the Predictions\n","\n","It's always a good idea to inspect the output and make sure everything looks fine. Here we'll look at some examples our model gets right, and some examples it gets wrong on the test sets.\n","\n","First, we use the trained model to predict the labels of the test sets."]},{"cell_type":"code","metadata":{"id":"wsyT8_egkhm_","executionInfo":{"status":"ok","timestamp":1730172530576,"user_tz":-420,"elapsed":1773,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"93b24947-0c61-4224-fef4-f2afc189c833"},"source":["# We use the trained model to predict the labels of the test set\n","prob_classes = model.predict(x_test)\n","\n","# The output class is the one with the highest probability\n","pred_classes = np.argmax(prob_classes, axis=-1)\n","\n","# Convert the label back to the original format\n","y_test_classes = np.argmax(y_test, axis=-1)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"]}]},{"cell_type":"markdown","metadata":{"id":"m2OeoOjukqYJ"},"source":["Next we determine which samples are correct or incorrect."]},{"cell_type":"code","metadata":{"id":"rtlKYzaVkq__","executionInfo":{"status":"ok","timestamp":1730172533745,"user_tz":-420,"elapsed":429,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}}},"source":["correct_indices = np.where(pred_classes == y_test_classes)[0]\n","incorrect_indices = np.where(pred_classes != y_test_classes)[0]"],"execution_count":19,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bKLdFq6YkrgF"},"source":["Then we plot the images with their corresponding classes. In the incorrect case, we also plot the ground truth classes for comparison."]},{"cell_type":"code","metadata":{"id":"UXjTXE4EksJ_","colab":{"base_uri":"https://localhost:8080/","height":501},"executionInfo":{"status":"ok","timestamp":1730172540087,"user_tz":-420,"elapsed":4737,"user":{"displayName":"Akadej Udomchaiporn","userId":"12826764406638924459"}},"outputId":"70f8de86-5797-4e5c-9b73-5fcd5745c1af"},"source":["print(\"Correct cases\")\n","correct_x_test = x_test[correct_indices]\n","correct_preds = pred_classes[correct_indices]\n","idx = np.random.choice(np.arange(len(correct_x_test)), 8)\n","plot_mnist_data(correct_x_test[idx], correct_preds[idx], 8)\n","\n","print(\"Incorrect cases\")\n","incorrect_x_test = x_test[incorrect_indices]\n","incorrect_preds = pred_classes[incorrect_indices]\n","correct_labels = y_test_classes[incorrect_indices]\n","idx = np.random.choice(np.arange(len(incorrect_x_test)), 8)\n","plot_mnist_data(incorrect_x_test[idx], incorrect_preds[idx], 8)\n","plot_mnist_data(incorrect_x_test[idx], correct_labels[idx], 8)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Correct cases\n"]},{"output_type":"display_data","data":{"text/plain":["
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cases\n"]},{"output_type":"display_data","data":{"text/plain":["
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Play around\n","\n","Now it is your turn! Let's try to change the model architecture and the optimizer to see the effects.\n","\n","For example,\n","* Change the number of fully-connected layers (in this [section](#define-model))\n"," * e.g., 2, 3, 4 layers\n","* Change the number of hidden units (in this [section](#define-model))\n"," * e.g., 10, 128, 256, 512\n","* Change the optimizers (i.e., `optimizer`)\n"," * e.g., [keras.optimizers.RMSprop](https://keras.io/optimizers/#rmsprop), [keras.optimizers.Adadelta](https://keras.io/optimizers/#adadelta), [keras.optimizers.Adam](https://keras.io/optimizers/#adam)\n","* Change the learning rate of the optimizer (i.e., `learning_rate`)\n"," * e.g., 10000, 0.00001, 0.001\n","* Change the number of training epochs (i.e., `epochs`)\n"," * e.g., 1, 10, 20"]},{"cell_type":"code","metadata":{"id":"-P9dZSYrlRL-"},"source":[],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/fall-2024/aicore/aic-501/00030/iris_dataset.csv 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CI TypeLower BoundUpper Bound
0Mean (σ̂1)5.43075.8110
1Mean (σ̂2)5.42465.8171
2Mean (σ̂3)5.39735.8444
3Median (σ̂1)5.62476.1013
4Median (σ̂2)5.61706.1090
5Median (σ̂3)5.58286.1432
\n", + "" + ], + "text/plain": [ + " CI Type Lower Bound Upper Bound\n", + "0 Mean (σ̂1) 5.4307 5.8110\n", + "1 Mean (σ̂2) 5.4246 5.8171\n", + "2 Mean (σ̂3) 5.3973 5.8444\n", + "3 Median (σ̂1) 5.6247 6.1013\n", + "4 Median (σ̂2) 5.6170 6.1090\n", + "5 Median (σ̂3) 5.5828 6.1432" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = len(Data1) # Sample size\n", + "Z = 1.96 # Critical value for 95% confidence level\n", + "\n", + "# Mean and median estimators\n", + "mean_estimator = np.mean(Data1)\n", + "median_estimator = np.median(Data1)\n", + "\n", + "# Standard deviation estimators\n", + "std_dev_estimator = np.std(Data1, ddof=1) # σ̂1\n", + "mad = np.median(np.abs(Data1 - median_estimator))\n", + "mad_estimator = mad * 1.4826 # σ̂2\n", + "q1 = np.percentile(Data1, 25)\n", + "q3 = np.percentile(Data1, 75)\n", + "iqr = q3 - q1\n", + "iqr_estimator = iqr * 0.7413 # σ̂3\n", + "\n", + "# CI for the mean\n", + "def CI_mu_mean(hat_mu, hat_sigma, n, alpha=0.05):\n", + " z_critical = norm.ppf(1 - alpha / 2) # Two-tailed Z-critical value\n", + " margin_error = z_critical * (hat_sigma / np.sqrt(n))\n", + " return hat_mu - margin_error, hat_mu + margin_error\n", + "\n", + "# CI for the median\n", + "def CI_mu_median(hat_mu, hat_sigma, n, alpha=0.05):\n", + " z_critical = norm.ppf(1 - alpha / 2)\n", + " density_at_median = 1 / (np.sqrt(2 * np.pi) * hat_sigma) # f( ̃μ)\n", + " std_error = 1 / (4 * n * density_at_median**2)**0.5 # Standard error for the median\n", + " margin_error = z_critical * std_error\n", + " return hat_mu - margin_error, hat_mu + margin_error\n", + "\n", + "ci_mean_sigma1 = CI_mu_mean(mean_estimator, std_dev_estimator, n)\n", + "ci_mean_sigma2 = CI_mu_mean(mean_estimator, mad_estimator, n)\n", + "ci_mean_sigma3 = CI_mu_mean(mean_estimator, iqr_estimator, n)\n", + "ci_median_sigma1 = CI_mu_median(median_estimator, std_dev_estimator, n)\n", + "ci_median_sigma2 = CI_mu_median(median_estimator, mad_estimator, n)\n", + "ci_median_sigma3 = CI_mu_median(median_estimator, iqr_estimator, n)\n", + "\n", + "# Prepare the results in a table, rounded to 4 decimal places\n", + "result_table = pd.DataFrame({\n", + " 'CI Type': ['Mean (σ̂1)', 'Mean (σ̂2)', 'Mean (σ̂3)', \n", + " 'Median (σ̂1)', 'Median (σ̂2)', 'Median (σ̂3)'],\n", + " 'Lower Bound': [round(ci_mean_sigma1[0], 4), round(ci_mean_sigma2[0], 4), round(ci_mean_sigma3[0], 4),\n", + " round(ci_median_sigma1[0], 4), round(ci_median_sigma2[0], 4), round(ci_median_sigma3[0], 4)],\n", + " 'Upper Bound': [round(ci_mean_sigma1[1], 4), round(ci_mean_sigma2[1], 4), round(ci_mean_sigma3[1], 4),\n", + " round(ci_median_sigma1[1], 4), round(ci_median_sigma2[1], 4), round(ci_median_sigma3[1], 4)]\n", + "})\n", + "\n", + "result_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 4**" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95% Confidence Interval for σ: (np.float64(1.0191), np.float64(1.2924))\n" + ] + } + ], + "source": [ + "# Degrees of freedom\n", + "df = n - 1\n", + "\n", + "# Critical values for chi-squared distribution\n", + "alpha = 0.05\n", + "chi2_lower = chi2.ppf(alpha / 2, df)\n", + "chi2_upper = chi2.ppf(1 - alpha / 2, df)\n", + "\n", + "# Confidence interval for σ²\n", + "lower_bound_variance = (df * (std_dev_estimator ** 2)) / chi2_upper\n", + "upper_bound_variance = (df * (std_dev_estimator ** 2)) / chi2_lower\n", + "\n", + "# Confidence interval for σ (square root of variance bounds)\n", + "lower_bound_sigma = np.sqrt(lower_bound_variance)\n", + "upper_bound_sigma = np.sqrt(upper_bound_variance)\n", + "\n", + "# Print results rounded to 4 decimals\n", + "ci_sigma = (round(lower_bound_sigma, 4), round(upper_bound_sigma, 4))\n", + "print(f\"95% Confidence Interval for σ: {ci_sigma}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 5**" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CI TypeLower BoundUpper Bound
0Mean (σ̂1)3.37947.8623
1Mean (σ̂2)3.30677.9350
2Mean (σ̂3)2.98528.2565
3Median (σ̂1)3.61708.1090
4Median (σ̂2)3.54418.1819
5Median (σ̂3)3.22198.5041
6IQR2.95738.2097
\n", + "
" + ], + "text/plain": [ + " CI Type Lower Bound Upper Bound\n", + "0 Mean (σ̂1) 3.3794 7.8623\n", + "1 Mean (σ̂2) 3.3067 7.9350\n", + "2 Mean (σ̂3) 2.9852 8.2565\n", + "3 Median (σ̂1) 3.6170 8.1090\n", + "4 Median (σ̂2) 3.5441 8.1819\n", + "5 Median (σ̂3) 3.2219 8.5041\n", + "6 IQR 2.9573 8.2097" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = len(Data1) # Sample size\n", + "Z = 1.96 # Critical value for 95% confidence level\n", + "\n", + "# Mean and median estimators\n", + "mean_estimator = np.mean(Data1)\n", + "median_estimator = np.median(Data1)\n", + "\n", + "# Standard deviation estimators\n", + "std_dev_estimator = np.std(Data1, ddof=1) # σ̂1\n", + "mad = np.median(np.abs(Data1 - median_estimator))\n", + "mad_estimator = mad * 1.4826 # σ̂2\n", + "q1 = np.percentile(Data1, 25)\n", + "q3 = np.percentile(Data1, 75)\n", + "iqr = q3 - q1\n", + "iqr_estimator = iqr * 0.7413 # σ̂3\n", + "\n", + "\n", + "def PI_mu_mean(hat_mu, hat_sigma, n, alpha=0.05):\n", + " z_critical = norm.ppf(1 - alpha / 2)\n", + " margin_error = z_critical * np.sqrt(hat_sigma**2 + (hat_sigma**2 / n))\n", + " return hat_mu - margin_error, hat_mu + margin_error\n", + "\n", + "def PI_mu_median(hat_mu, hat_sigma, n, alpha=0.05):\n", + " z_critical = norm.ppf(1 - alpha / 2)\n", + " density_at_median = 1 / (np.sqrt(2 * np.pi) * hat_sigma) # f(μ̃)\n", + " std_error = np.sqrt(1 / (4 * n * density_at_median**2) + hat_sigma**2)\n", + " margin_error = z_critical * std_error\n", + " return hat_mu - margin_error, hat_mu + margin_error\n", + "\n", + "def PI_iqr_nonparametric(q1, q3, iqr, alpha=0.05):\n", + " # Critical Z-value\n", + " z_critical = norm.ppf(1 - alpha / 2)\n", + " # Inverse CDF of the normal distribution at 0.75\n", + " phi_inv_3_4 = norm.ppf(0.75)\n", + " # Delta calculation\n", + " delta = 0.5 * ((z_critical / phi_inv_3_4) - 1)\n", + " # Prediction interval\n", + " lower_bound = q1 - delta * iqr\n", + " upper_bound = q3 + delta * iqr\n", + " return lower_bound, upper_bound\n", + "\n", + "pi_mean_sigma1 = PI_mu_mean(mean_estimator, std_dev_estimator, n)\n", + "pi_mean_sigma2 = PI_mu_mean(mean_estimator, mad_estimator, n)\n", + "pi_mean_sigma3 = PI_mu_mean(mean_estimator, iqr_estimator, n)\n", + "pi_median_sigma1 = PI_mu_median(median_estimator, std_dev_estimator, n)\n", + "pi_median_sigma2 = PI_mu_median(median_estimator, mad_estimator, n)\n", + "pi_median_sigma3 = PI_mu_median(median_estimator, iqr_estimator, n)\n", + "\n", + "pi_iqr = PI_iqr_nonparametric(q1, q3, iqr)\n", + "\n", + "# Prepare the results in a table, rounded to 4 decimal places\n", + "result_table = pd.DataFrame({\n", + " 'CI Type': ['Mean (σ̂1)', 'Mean (σ̂2)', 'Mean (σ̂3)', \n", + " 'Median (σ̂1)', 'Median (σ̂2)', 'Median (σ̂3)',\n", + " 'IQR'],\n", + " 'Lower Bound': [round(pi_mean_sigma1[0], 4), round(pi_mean_sigma2[0], 4), round(pi_mean_sigma3[0], 4),\n", + " round(pi_median_sigma1[0], 4), round(pi_median_sigma2[0], 4), round(pi_median_sigma3[0], 4),\n", + " round(pi_iqr[0], 4)],\n", + " 'Upper Bound': [round(pi_mean_sigma1[1], 4), round(pi_mean_sigma2[1], 4), round(pi_mean_sigma3[1], 4),\n", + " round(pi_median_sigma1[1], 4), round(pi_median_sigma2[1], 4), round(pi_median_sigma3[1], 4),\n", + " round(pi_iqr[1], 4)]\n", + "})\n", + "\n", + "result_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 6**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate basic statistics\n", + "mean_xn = np.mean(Data1)\n", + "std1 = np.std(Data1, ddof=1) # Sample SD\n", + "z_critical = norm.ppf(0.975) # 95% confidence level\n", + "\n", + "# Confidence interval for the mean (μ)\n", + "ci_lower = mean_xn - z_critical * (std1 / np.sqrt(len(Data1)))\n", + "ci_upper = mean_xn + z_critical * (std1 / np.sqrt(len(Data1)))\n", + "\n", + "# Prediction interval for Life Ladder\n", + "pi_lower = mean_xn - z_critical * std1\n", + "pi_upper = mean_xn + z_critical * std1\n", + "\n", + "# Prepare the KDE and prediction distribution\n", + "x_vals = np.linspace(min(Data1), max(Data1), 1000)\n", + "pdf = norm.pdf(x_vals, loc=mean_xn, scale=std1) # Prediction distribution (normal)\n", + "\n", + "# Plot the KDE\n", + "plt.figure(figsize=(12, 7))\n", + "sns.kdeplot(Data1, color=\"blue\", label=\"KDE of Data1\", alpha=0.5)\n", + "\n", + "# Overlay prediction distribution\n", + "plt.plot(x_vals, pdf, color=\"orange\", linestyle=\"--\", label=\"Prediction Distribution\")\n", + "\n", + "# Plot intervals with shaded regions\n", + "plt.hlines(y=0.01, xmin=ci_lower, xmax=ci_upper, color=\"green\", linewidth=2, label=\"Confidence Interval (μ)\")\n", + "plt.hlines(y=0.005, xmin=pi_lower, xmax=pi_upper, color=\"red\", linewidth=2, label=\"Prediction Interval\")\n", + "plt.fill_betweenx([0.01], ci_lower, ci_upper, color=\"green\", alpha=0.2)\n", + "plt.fill_betweenx([0.005], pi_lower, pi_upper, color=\"red\", alpha=0.2)\n", + "\n", + "# Mark mean as a prominent dot for both intervals\n", + "plt.scatter(mean_xn, 0.01, color=\"green\", zorder=5, s=50)\n", + "plt.scatter(mean_xn, 0.005, color=\"red\", zorder=5, s=50)\n", + "\n", + "\n", + "# Add labels, legend, and grid\n", + "plt.title(\"KDE of Life Ladder: Confidence & Prediction Intervals\", fontsize=16)\n", + "plt.ylabel(\"Density\", fontsize=14)\n", + "plt.legend(fontsize=12)\n", + "plt.grid(alpha=0.3)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 7**" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "ALPHA = 0.05\n", + "\n", + "N = len(Data1)\n", + "MU = Data1.mean()\n", + "SIGMA = Data1.std()\n", + "\n", + "np.random.seed(111)\n", + "YSample = norm.rvs(MU, SIGMA, size=N)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def RelativeLogLikelihood(mu, sigma, data):\n", + " n = len(data)\n", + " sigma_hat = np.std(data)\n", + "\n", + " return n * (np.log(sigma_hat / sigma) + 0.5 * (1 - (np.mean(data**2) - 2 * mu * np.mean(data) + mu**2) / sigma**2))\n", + "\n", + "def RelativeLikelihood(mu, sigma, data):\n", + " return np.exp(RelativeLogLikelihood(mu, sigma, data))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "def RelativeLogLikelihood(mu, sigma, data):\n", + " n = len(data)\n", + " sigma_hat = np.std(data)\n", + "\n", + " # Vectorized computation of the log-likelihood\n", + " mean_data = np.mean(data)\n", + " mean_squared_data = np.mean(data**2)\n", + " \n", + " return n * (np.log(sigma_hat / sigma) + 0.5 * (1 - (mean_squared_data - 2 * mu * mean_data + mu**2) / sigma**2))\n", + "\n", + "# Define RelativeLikelihood based on the log-likelihood\n", + "def RelativeLikelihood(mu, sigma, data):\n", + " # Vectorized calculation\n", + " return np.exp(RelativeLogLikelihood(mu, sigma, data))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the MLE of mu and sigma\n", + "Mu_MLE = np.mean(YSample)\n", + "Sigma_MLE = np.std(YSample)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Find confidence intervals for mu and sigma\n", + "S = np.std(YSample, ddof=1)\n", + "\n", + "LowMu, UppMu = t.ppf(ALPHA/2, N-1, Mu_MLE, S/np.sqrt(N)), t.ppf(1 - ALPHA/2, N-1, Mu_MLE, S/np.sqrt(N))\n", + "LowSigma, UppSigma = S * np.sqrt(N-1) / np.sqrt(chi2.ppf(1-ALPHA/2, N-1)), S * np.sqrt(N-1) / np.sqrt(chi2.ppf(ALPHA/2, N-1))\n", + "\n", + "# Find prediction interval for Y\n", + "LowY, UppY = t.ppf(ALPHA/2, N-1, Mu_MLE, np.sqrt(S**2/N + S**2)), t.ppf(1 - ALPHA/2, N-1, Mu_MLE, np.sqrt(S**2/N + S**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "Probabilities = np.array([0.1, 0.5, 0.75, 0.89, 0.95])\n", + "Probabilities = Probabilities[::-1] # We need to write the probabilities in a dicreasing order\n", + "\n", + "Levels = np.exp(-0.5 * chi2.ppf(Probabilities, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "mu_vect = np.linspace(5.3, 6, 100) # Extending the range to cover 5.62\n", + "sigma_vect = np.linspace(0.9, 1.3, 100) # Adjusting to cover 1.139\n", + "\n", + "mu_grid, sigma_grid = np.meshgrid(mu_vect, sigma_vect)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 5))\n", + "\n", + "cnt = ax.contour(mu_grid, sigma_grid, RelativeLikelihood(mu_grid, sigma_grid, YSample), Levels)\n", + "ax.scatter(x=Mu_MLE, y=Sigma_MLE, color='k')\n", + "\n", + "ax.plot([LowMu, LowMu, UppMu, UppMu, LowMu], [LowSigma, UppSigma, UppSigma, LowSigma, LowSigma], color='r', ls='--')\n", + "\n", + "ax.set_title(r\"Confidence regions for $\\mu$ and $\\sigma$\")\n", + "ax.set_xlabel(r\"$\\mu$\")\n", + "ax.set_ylabel(r\"$\\sigma$\")\n", + "\n", + "_, labels = cnt.legend_elements()\n", + "ax.legend(_, Probabilities, loc=\"upper right\", frameon=False)\n", + "\n", + "ax.spines[['right', 'top']].set_visible(False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HD/Laa69hampKr169WLhwIXZ2drq17q4+9/z58wkJCeHvv/9m+vTp1R5/ypQpLFiwgP379+Pq6sqwYcP0rvFGXnnlFR5++GG9Nfd69ep1XbsLFy4wdOhQunfvzpEjR1i8eDFfffUVs2bN0mu3cuVKrK2t2bt3L/PmzeOdd95h8+bNNcpSa4q4TnZ2tgIo2dnZakcRjcjhrYnKM10/V2Y+9J0ycOBABVAWLlxYJ8fetzFOeabr58orA1cpu3fvUfz8/JSIiIg6ObZo+oBqH0OHDtVra2VlVW3bvn376rV1cXGpst2tGjdunDJixAhFURRFq9UqmzdvVszNzZVXXnlF9767u7tSVFSk+8zq1auVoKAgRavV6l4rKipSLC0tlT///FNRFEXx9PRU5s2bp3u/pKREadGihe5ciqIoffv2VSZPnqwoiqLExsYqgLJ58+Yqc27ZskUBlMzMTL3Xrz7GqVOnFEDZuXOn7v309HTF0tJS+e677xRFUZTly5crgBIfH69rs2jRIsXd3b3ar9G0adOUdu3a6b02depUvTzLly9X7O3tde/b2toqK1asqPJ417at5Ofnp4wcOVLvtYSEBAVQ/v77b72vw5o1a3RtMjIyFEtLS2Xt2rWKoijKzJkzlZCQEL3jfPjhh4qfn5/u+dV/7tWd6/XXX7/uz3nRokWKjY2NUlZWpigVX/8+ffroHad79+7K1KlTq7z2utL4b9IJ0UgkVKz+7NPWgXnztwMwYMCAOjl257sDsHO2JCejAKMsJxISEm64p5gQjc2GDRuwsbGhpKQErVbLY489pncbo2PHjnqTBY4cOUJ8fPx1Y28KCws5ffo02dnZJCcnExYWpnvPxMSEbt26XXcbrNLhw4cxNjamb9++tb6OmJgYTExM9M7r7OxMUFAQMTExutesrKxo1aqV7rmnp2e1OxhUHvfqYwI3HesaERHB008/zerVqwkPD+ehhx7SO2d1unXrdtM2157fycnpumusCzExMfTs2VPv+1nv3r3Jzc3l/Pnz+Pr6AtCpUye9z93s61kXpAASooYqCyCvNg5MmTKFgwcP1tneXSamxtw5Kpjflh5i5/pT9BgcWCfHFc1Dbm5ute9du7bZjX5oXLtVS10OtO/fvz+LFy/GzMwMLy+v6wbBWltb6z3Pzc2la9eufP3119cdy9XVtVYZLC0ta/W52rh2/S+NRlNtYVZbb731Fo899hi//fYbf/zxBzNnzmTNmjXcf//9N/zctV/r2jAyMrruemp6e6w2qvp6arXaejsfjWEMkBBNgbZMS+Lxiv2/erTk7bffZsOGDXXaS9PrvjYAxO6/wOWUXEpLS/UGJQrDZW1tXe3DwsKixm2vLRCqa1fbjK1bt8bX17dGM4C6dOlCXFwcbm5utG7dWu9RuSabp6cne/fu1X2mtLSUgwcPVnvMjh07otVq2bp1a5XvV/ZAlZWVVXuM4OBgSktL9c6bkZFBbGws7dq1u+l13ei41270vWfPnpt+rk2bNvz73/9m06ZNjBo1iuXLl+uu5UbXURNXnz8zM5NTp04RHBwMFUVoSkqKXhF07SDqmmQIDg7WjaOqtHPnTmxtbWnRosVt5b9dUgAJUQNpSTkU5pVgZmGCV8v62VfOxduOwC6eKAps/n4/Li4uhIaGysrkolkaM2YMLi4ujBgxgu3bt5OQkEBUVBQvvvgi58+XbzczefJk5syZw88//8zJkyd59tlnb7h4n7+/P+PGjePJJ5/k559/1h3zu+++A8DPzw+NRsOGDRu4dOlSlT1rgYGBjBgxgokTJ7Jjxw6OHDnC448/jre3NyNGjKj19f7rX/8iLi6OKVOmEBsbyzfffMOKFSuqbV9QUMDzzz9PVFQUZ8+eZefOnezfv19XoPj7+5Obm0tkZCTp6enk5+ffcqZ33nmHyMhIjh07xvjx43FxcdGt69OvXz8uXbrEvHnzOH36NIsWLeKPP/7Q+7y/vz/R0dHExsaSnp5eZQ/Rs88+S1JSEi+88AInT57kl19+YebMmURERNRo8+j6JAWQEDVw7mQ6AK4BVqz/9RcyMzPr5Tw97y2/9XU8Kg1XV1fKysrYsmVLvZxLCDVZWVmxbds2fH19GTVqFMHBwTz11FMUFhbq1mJ7+eWXeeKJJxg3bhw9e/bE1tb2prd/Fi9ezIMPPsizzz5L27ZtmThxom5mm7e3N2+//TavvfYa7u7u1W5gvHz5crp27cp9991Hz549URSF33//vdbb3gD4+vry448/8vPPPxMSEsKSJUv4z3/+U217Y2NjMjIyGDt2LG3atOHhhx9myJAhvP322wD06tWLf/3rX4wePRpXV1fmzZt3y5nmzJnD5MmT6dq1KykpKfz666+6XrLg4GA+++wzFi1aREhICPv27btultvEiRMJCgqiW7duuLq6snPnzuvO4e3tze+//86+ffsICQnhX//6F0899RRvvvnmLeetaxqlrm9aNgM5OTnY29uTnZ0tiyIKAH74aA+bV0dj3zGL91dMpXXr1nrrgtSVgtxipgxaTUlRGfmt9/Dftct57rnn+PTTT+v8XEIIYcikB0iIGqjsATqbcQIqBnzWB0sbMzrfHQCAq1lrgPpfC0MIIQyQFEBC3ISiKCTFZgBw9NQBqMPp71XpeW/5YOjCREeMjY05deoU586dq7fzCSGEIZICSIibyEjOJT+niCIll5jY4wDcfffd9Xa+tt29cHC1oizfmI7B5UvRSy+QEELULSmAhLiJpNjy21/5FklQsWBXbdcpqQkjYyO6DGgJgJ9z+Y7Ksi2GEELULSmAhLiJ86fKb3+lFpYPeg4PD6/3c3YZUD4OyCavJdPfnH7DPYaEEELcOlkJWoibuBB/GYBTSeWLgNXn+J9KrTq5Y+dsCRkePDRoMB27+9b7OYUQwpBID5AQN3HhdPmaPysWr+WLL77grrvuqvdzGhkb0bl/eS/Qka11t12BEEKIctIDJMQNFBeWcikpG4Ce/TozyLl3g527011+bP3hBPv+Oo5FUPk2HA8++GCDnV8IIZoz6QES4gYunslEUcDW0QI7Z6sGPXdQV0/MLU2IPXuUhx56SLcCrBBCiNsnBZAQN3AhrnwA9OGMDSxcuJBLly412LlNzU0IDmuBm3X5rbDjx4/LvmDC4I0fP163XxUVe1a99NJLt3XMujjGzURFRaHRaG64l1lToNFo+Pnnn9WOUSekABLiBi7EX6ZUW0JU9E/8+9//rnLzxPrU6U5frEztcbR2Q1EU9u/f36DnF6Imxo8fj0ajQaPRYGZmRuvWrXnnnXcoLS2t93OvW7eOd999t0ZtqytCbuUYtdWrVy+Sk5Oxt7ev8WeuLfZE3ZICSIgbuHgmk4z8c5SVleLq6oq/v3+Dnr9dTx8AnMz9ANizZ0+Dnl+Imho8eDDJycnExcXx8ssv89Zbb/H+++9X2ba4uLjOzuvk5IStra3qx7gZMzMzPDw80Gg09XqeqtTl17s5kQJIiBtIScwiLS8BgLCwsAb/5uXoZo1nS0fcrMpvg+3evbtBzy9ETZmbm+Ph4YGfnx+TJk0iPDyc9evXw1U9Ge+99x5eXl4EBQUBkJSUxMMPP4yDgwNOTk6MGDGCxMR/Zj2WlZURERGBg4MDzs7OvPrqq1y7f/e1t6+KioqYOnUqPj4+mJub07p1a7766isSExN1e/g5Ojqi0WgYP358lcfIzMxk7NixODo6YmVlxZAhQ/Q2P16xYgUODg78+eefBAcHY2NjoysAq3Nt79PNjvHWW2+xcuVKfvnlF13vWlRUVI2+blV9vV9//XXCwsKuyxUSEsI777wDwP79+7nnnntwcXHB3t6evn37cujQoRr+DWh6pAASohqFecVkpuaRllf+jaWqbx4NoV2YN+425StD79mz57ofAMIA5OU17KMOWFpa6vU8REZGEhsby+bNm9mwYQMlJSUMGjQIW1tbtm/fzs6dO3VFQOXnFixYwIoVK1i2bBk7duzg8uXL/PTTTzc879ixY/n222/5+OOPiYmJ4fPPP8fGxgYfHx9+/PFHAGJjY0lOTuajjz6q8hjjx4/nwIEDrF+/nt27d6MoCkOHDqWkpETXJj8/n/nz57N69Wq2bdvGuXPnbnnB0hsd45VXXuHhhx/WFUXJycn06tWrRl+3qr7eY8aMYd++fZw+fVrX5vjx40RHR/PYY48BcOXKFcaNG8eOHTvYs2cPgYGBDB06lCtXrtzSdTUZirhOdna2AijZ2dlqRxEqSjiWqjzT9XPF3tJVAZRNmzapkiN6+1nlqc6LFGMjUwVQ4uLiVMkhVAQN+7hF48aNU0aMGKEoiqJotVpl8+bNirm5ufLKK6/o3nd3d1eKiop0n1m9erUSFBSkaLVa3WtFRUWKpaWl8ueffyqKoiienp7KvHnzdO+XlJQoLVq00J1LURSlb9++yuTJkxVFUZTY2FgFUDZv3lxlzi1btiiAkpmZqff61cc4deqUAig7d+7UvZ+enq5YWloq3333naIoirJ8+XIFUOLj43VtFi1apLi7u1f7Nbr23DU5xtVf11v5ulX19VYURQkJCVHeeecd3fNp06YpYWFh1WYuKytTbG1tlV9//VX3GqD89NNP1X6mKZEeICGqkZyQRUFJDtkF5TO/unfvrkqONl09MTMzY2DLSezfeYRWrVqpkkOIG9mwYQM2NjZYWFgwZMgQRo8ezVtvvaV7v2PHjpiZmemeHzlyhPj4eGxtbbGxscHGxgYnJycKCws5ffo02dnZJCcn6/W8mpiY0K1bt2ozHD58GGNjY/r27Vvr64iJicHExETvvM7OzgQFBRETE6N7zcrKSu/foqenJ2lpabd0rtoc42Zft0rXfr0BxowZwzfffAPlnR98++23jBkzRvd+amoqEydOJDAwEHt7e+zs7MjNzeXcuXO3dF1NhSyEKEQ1UhKzyCxMxsjImDZtAnFwcFAlh7mlKf7tXCmLbk9RurkqgyiFyhp49mFt9O/fn8WLF2NmZoaXlxcmJvo/XqytrfWe5+bm0rVrV77++uvrjlXbzYYtLS1r9bnaMDU11Xuu0Whu+fZ0bY5R06/btV9vgEcffZSpU6dy6NAhCgoKSEpKYvTo0br3x40bR0ZGBh999BF+fn6Ym5vTs2fPZjuIWgogIaqRkpiFl20Qvy7bTWAvdYqfSoFdPTkdnUrcoWR6Dw9SNYtQQRU/zBoba2trWrduXeP2Xbp0Ye3atbi5uWFnZ1dlG09PT/bu3avbfqa0tJSDBw/SpUuXKtt37NgRrVbL1q1bq9y0uLJHpKysrNpcwcHBlJaWsnfvXnr16gVARkYGsbGxtGvXrsbXVxfMzMyuy1qTr1t1WrRoQd++ffn6668pKCjgnnvuwc3NTff+zp07+eyzzxg6dChUDLZOT0+vo6tpfOQWmBDVSE4o3wPMP8iTwMBAVbO06eKJVilj1Xdf8MADD5BXRwNVhVDLmDFjcHFxYcSIEWzfvp2EhASioqJ48cUXOX/+PACTJ09mzpw5/Pzzz5w8eZJnn332hgsJ+vv7M27cOJ588kl+/vln3TG/++47APz8/NBoNGzYsIFLly5Vua5XYGAgI0aMYOLEiezYsYMjR47w+OOP4+3tzYgRI+rxK1L19URHRxMbG0t6ejolJSU1+rrdyJgxY1izZg3ff/+93u0vKq599erVxMTEsHfvXsaMGdOgvWoNTQogIapQWlLGpfPlqy57Bqjb+0PF7vAmJibsi/+DdevWceDAAbUjCXFbrKys2LZtG76+vowaNYrg4GCeeuopCgsLdT0bL7/8Mk888QTjxo2jZ8+e2Nracv/999/wuIsXL+bBBx/k2WefpW3btkycOFH3C4O3tzdvv/02r732Gu7u7jz//PNVHmP58uV07dqV++67j549e6IoCr///vt1t6zq28SJEwkKCqJbt264urqyc+fOGn3dbuTBBx8kIyOD/Pz86xZZ/Oqrr8jMzKRLly488cQTvPjii3o9RM2NRpE5tdfJycnB3t6e7OzsW+5iFM3DxTOZ/Hv4J2w9t4Kx/3qQefPmqR2J2eN+Yun6WSRkHWLOnDlMnTpV7UhCCNFkSQ+QEFVIScgkNfcMqVcSGs3ig61CPHTrATWWTEII0VRJASREFZIT9FeAbgxadXLXbYwqt8CEEOL2SAEkRBWu3gLjjjvuUDsOAC07uuFk2QKACxcuNOjO9EII0dxIASREFZLiU7lccAGAHj16qB0HAEd3G9y9nLEzLx+UePjwYbUjCSFEkyUFkBDX0GoVTsScQEGLk5MzPj4+akfSadnRHRcrH0yMTWo05VUIIUTVZCFEIa6RmZpLSlb5BqhdunRuVCsvt+zoRh/fx+hy10wmTBimdhwhhGiypAdIiGukJGahoMXeyoXOnTurHUePf3s3LExsuHAqW+0oQgjRpEkPkBDXSD2bTTvXvjz20DiemT1A7Th6fIKc0RhpyE7PJ+tSHg6ujX+LBCGEaIykB0iIa6QllfeuuPnYYWxsrHYcPeaWpngGOLD3/Dp69g5j7969akcSQogmSQogIa6RcjYTRVFw87VXO0qV/Nq5kll4kZNxx2U9IGFwFEXhmWeewcnJCY1Gw+HDh+nXrx8vvfTSDT/n7+/PwoULGyxnbURFRaHRaG6431lToNFo+Pnnn9WOcVNSAAlxja17NrM6+hW+WDtf7ShV8gt2xdmyfGba33//rXYcIQBISUnhhRdeoGXLlpibm+Pj48OwYcOIjIys0/Ns3LiRFStWsGHDBpKTk+nQoQPr1q3j3XffrdPzqKFXr14kJydjb1/zX77Gjx9/3Z5eomYaRQG0aNEi/P39sbCwICwsjH379lXbdt26dXTr1g0HBwesra0JDQ1l9erVem0URWHGjBl4enpiaWlJeHg4cXFxDXAloqkrLSkj8WIchaW5GJs1zm3y/Nu54mwlBZBoPBITE+natSt//fUX77//PkePHmXjxo3079+f5557rk7Pdfr0aTw9PenVqxceHh6YmJjg5OSEra1tnZ5HDWZmZnh4eKgy87S4uLjBz6k6RWVr1qxRzMzMlGXLlinHjx9XJk6cqDg4OCipqalVtt+yZYuybt065cSJE0p8fLyycOFCxdjYWNm4caOuzZw5cxR7e3vl559/Vo4cOaIMHz5cCQgIUAoKCmqUKTs7WwGU7OzsOrtO0TQkJ2QqPnbtFUBZtGiR2nGqVFRQojza8T0FUMzMzJTi4mK1IwkDN2TIEMXb21vJzc297r3MzEzd/589e1YZPny4Ym1trdja2ioPPfSQkpKSont/5syZSkhIiLJq1SrFz89PsbOzU0aPHq3k5OQoiqIo48aNUwDdw8/PT1EURenbt68yefJk3XFSU1OV++67T7GwsFD8/f2V//73v4qfn5/y4Ycf6uV66qmnFBcXF8XW1lbp37+/cvjw4RpnURRFKSsrU+bOnau0atVKMTMzU3x8fJRZs2bp3j937pzy0EMPKfb29oqjo6MyfPhwJSEhodqv45YtWxRA9zVbvny5Ym9vr2zcuFFp27atYm1trQwaNEi5ePGiLuPVXw9A2bJlS43OPW7cOGXEiBHKrFmzFE9PT8Xf31+ZNm2a0qNHj+tyderUSXn77bcVRVGUffv2KeHh4Yqzs7NiZ2en3HXXXcrBgwf12gPKTz/9VO11NhaqF0A9evRQnnvuOd3zsrIyxcvLS5k9e3aNj9G5c2flzTffVBRFUbRareLh4aG8//77uvezsrIUc3Nz5dtvv63R8aQAMlxHtiUqliZ2CqDs2rVL7TjVmj5qjWJmbKkAet+0RTNVnNuwj1uQkZGhaDQa5T//+c8N25WVlSmhoaFKnz59lAMHDih79uxRunbtqvTt21fXZubMmYqNjY0yatQo5ejRo8q2bdsUDw8P5fXXX1eUiu/l77zzjtKiRQslOTlZSUtLU5QqCqAhQ4YoISEhyu7du5UDBw4ovXr1UiwtLfUKoPDwcGXYsGHK/v37lVOnTikvv/yy4uzsrGRkZNQoi6Ioyquvvqo4OjoqK1asUOLj45Xt27crS5cuLf8jKy5WgoODlSeffFKJjo5WTpw4oTz22GNKUFCQUlRUVOXXqKoCyNTUVAkPD1f279+vHDx4UAkODlYee+wxRVEU5cqVK8rDDz+sDB48WElOTlaSk5OVoqKiGp173Lhxio2NjfLEE08ox44d0z0AJT4+Xpep8rW4uDhFURQlMjJSWb16tRITE6OcOHFCeeqppxR3d3e9wrCpFECqToMvLi7m4MGDTJs2TfeakZER4eHhNdrtWlEU/vrrL2JjY5k7dy4ACQkJpKSkEB4ermtnb29PWFgYu3fv5pFHHrnuOEVFRRQVFeme5+Tk1MHViaYo5shpCkpz0KChU6dOasepVos2zjhb+pCce4q///6bkJAQtSOJ+vSxTcOe7+Wa3/6Nj49HURTatm17w3aRkZEcPXqUhIQE3erqq1aton379uzfv5/u3bsDoNVqWbFihe6W1hNPPEFkZCTvvfce9vb22NraYmxsjIeHR5XnOXXqFH/88Qf79u3THfOrr74iODhY12bHjh3s27ePtLQ0zM3NAZg/fz4///wzP/zwA88888xNs1y5coWPPvqITz/9lHHjxgHQqlUr+vTpA8DatWvRarV8+eWXultay5cvx8HBgaioKAYOHFijr29JSQlLliyhVatWADz//PO88847ANjY2GBpaUlRUZHe1+O///1vjc5tbW3Nl19+iZmZme6zISEhfPPNN0yfPh2Ar7/+mrCwMFq3bg3A3XffrZfviy++wMHBga1bt3LffffV6JoaC1XHAKWnp1NWVoa7u7ve6+7u7qSkpFT7uezsbGxsbDAzM+Pee+/lk08+4Z577oGKgXiVx6jpMWfPno29vb3u0Zi2PhAN69DB8jE1Xu6+WFs33jV2vFs54WLli7tTC7WjCANX/gv/zcXExODj46P3/bVdu3Y4ODgQExOje83f319vPI+npydpaWk1zhMTE4OJiQldu3bVvda2bVscHBx0z48cOUJubi7Ozs7Y2NjoHgkJCZw+fbpGWWJiYigqKmLAgKrXCjty5Ajx8fHY2trqju/k5ERhYaHeOW7GyspKV/zU9OtR03N37NhRr/gBGDNmDN988w1U/Nl+++23jBkzRvd+amoqEydOJDAwEHt7e+zs7MjNzeXcuXM1vqbGokkuhGhra8vhw4fJzc0lMjKSiIgIWrZsSb9+/Wp1vGnTphEREaF7npOTI0WQgTp+8hgAbdu0VzvKDbUIdOKOFg/iE+TC+PEPqB1H1LcXc9VOUK3AwEA0Gg0nT56sk+OZmprqPddoNGi12jo5dqXc3Fw8PT2Jioq67r2rC6UbZbG0tLzpObp27crXX3993Xuurq41zlpVhpsVnTU9d1W/5D366KNMnTqVQ4cOUVBQQFJSEqNHj9a9P27cODIyMvjoo4/w8/PD3Nycnj17NslB1KoWQC4uLhgbG5Oamqr3empqarXdm1TcJqvsjgsNDSUmJobZs2fTr18/3edSU1Px9PTUO2ZoaGiVxzM3N9d1gwrDpimwxMs2iD69+6gd5Ya8A53RaDQkn8mkrFSLsUmjmNAp6otp4+2NdHJyYtCgQSxatIgXX3zxuh+qWVlZODg4EBwcTFJSEklJSbpfME+cOEFWVhbt2rWrszxt27altLSUgwcP6m6BxcbG6q2t06VLF1JSUjAxMcHf379W5wkMDMTS0pLIyEiefvrp697v0qULa9euxc3NDTs7u9u4ohszMzOjrKyszs7dokUL+vbty9dff01BQQH33HMPbm5uuvd37tzJZ599xtChQwFISkoiPT29jq6mYan6XdPMzIyuXbvqrROh1WqJjIykZ8+eNT6OVqvVjeEJCAjAw8ND75g5OTns3bv3lo4pDE9xYSleJqHc1yaCl1+98aJqanP2tMHC2pSyUi0Xz1ymtLRU7UjCgC1atIiysjJ69OjBjz/+SFxcHDExMXz88ce677vh4eF07NiRMWPGcOjQIfbt28fYsWPp27cv3bp1q7MsQUFBDB48mP/7v/9j7969HDx4kKefflqvxyY8PJyePXsycuRINm3aRGJiIrt27eKNN96o8eKiFhYWTJ06lVdffZVVq1Zx+vRp9uzZw1dffQUVt5JcXFwYMWIE27dvJyEhgaioKF588UXOnz9fZ9fr7+9PdHQ0sbGxpKenU1JSctvnHjNmDGvWrOH777/Xu/1FReG3evVqYmJi2Lt3L2PGjLlpb1hjpfqvjRERESxdupSVK1cSExPDpEmTyMvLY8KECQCMHTtWb5D07Nmz2bx5M2fOnCEmJoYFCxawevVqHn/8cajoHnzppZeYNWsW69ev5+jRo4wdOxYvLy9ZLErc0KXz5YPfLW3MsHGwUDvODWk0GrxbO7Ht7H9pG+rfJFZdFc1Xy5YtOXToEP379+fll1+mQ4cO3HPPPURGRrJ48WKo+Dv7yy+/4OjoyF133UV4eDgtW7Zk7dq1dZ5n+fLleHl50bdvX0aNGsUzzzyj14uh0Wj4/fffueuuu5gwYQJt2rThkUce4ezZs9eNH72R6dOn8/LLLzNjxgyCg4MZPXq0bnyOlZUV27Ztw9fXl1GjRhEcHMxTTz1FYWFhnfYITZw4kaCgILp164arqys7d+687XM/+OCDZGRkkJ+ff93Pza+++orMzEy6dOnCE088wYsvvqj3tW1S1J6GpiiK8sknnyi+vr6KmZmZ0qNHD2XPnj269/r27auMGzdO9/yNN95QWrdurVhYWCiOjo5Kz549lTVr1ugdT6vVKtOnT1fc3d0Vc3NzZcCAAUpsbGyN88g0eMO07dcjyriQD5X3nlindpQa+Xr2dqWtSx8F0JuaK4QQ4uY0Sk2H8BuQnJwc7O3tyc7Ortd7t6JxeXHcG3yy6j/07HgPu6I3qR3nprb+cII3It5lZ9Ia7rvvPn799Ve1IwkhRJOh+i0wIRqLYyeOAuDl5aV2lBppEeiEo6U3AMeOHVM7jhBCNClSAAlR4fS5WABCQ5vGooJerZxwsiwv1hITE7ly5YrakYQQosmQAkiIipmEyRnlC3mF9equdpwasbQxo4WfJ1am5TtHSy+QEELUnBRAQgBxJ+MpKSvESGNC995VrxfVGHm3dsJJboMJIcQta5IrQQtR13Zu3Q+Ak7UnDs4NvO/SbfBq5YSnTRucPGxvafquEEIYOimAhAAO7i/fA6yFW0u1o9wSzwAHOnsOIbCLJ8OHD1M7jhBCNBlyC0wIwNMhgLYud9K9Uy+1o9wSD//yfYtSEjLVjiKEEE2KFEBCAN627bjL73FG3veg2lFuibtfeQF0JbOQhPgkCgsL1Y4khBBNghRAQgBpSeXbYLj52qsd5ZZYWJni6G7NLyfn0jLQlx07dqgdSQghmgQpgITBy8zM5OixI5Rqi3HzaVoFEBW3wSxNy1csl5lgQghRM1IACYO3Yf0ffL1/JhtOfdDkeoCoKIAqp8IfPXpU7ThCCNEkSAEkDN6BPYcAcHfwxcLKVO04t+zqAkh6gIQQomakABIGLzo6GgB/n9ZqR6mVqwug48ePo9Vq1Y4khBCNnhRAwuCdii/fAyy4TbDaUWrFw98BO3NXjDUm5OXlkZiYqHYkIYRo9KQAEgatoKCA5LQkAEK7NJ0tMK5m72KFlY0FDhaeIOOAhBCiRmQlaGHQYmJiUFCwMLGhTXt/tePUikajwcPfgVanuxHevT++vr5qRxJCiEZPCiBh0CoHDTtaeOHm66B2nFrz8Hcg1GMwI+7rRufOndWOI4QQjZ4UQMKgtW/biTDvB7AytcPNx07tOLWm2xIjMUvtKEII0STIGCBh0JysvAjxGEj3tndjbtn0psBXqiyAEuOS2b17NyUlJWpHEkKIRk0KIGHQ0pKyAXBtgitAX83D3wFFUZj34zP06tWLkydPqh1JCCEaNSmAhMHKzc1l3bofyCy42KRvfwG4trDD2MQIB3OZCSaEEDUhBZAwWEeOHGHel6/ze9zHTXIPsKuZmBrj4mWLo6UXACdOnFA7khBCNGpSAAmDVTkDzMnSq8n3AAG4+djjWLEWkBRAQghxY1IACYN1/PhxABwtvZrkJqjXcvWxw9FSCiAhhKgJKYCEwToa/c8aQK4tmlcPUHx8PEVFRWpHEkKIRksKIGGwKntJfD0DmvQU+EpuvvZYmTpgbmpFWVkZcXFxakcSQohGSxZCFAYpMzOTtEupAAQFtVU7Tp1w87FDo9HQxXMIoyPuxMXFRe1IQgjRaEkBJAxSTEwMANamjvi29lQ7Tp1w9rTFyFhDR9eBPDH6MZw8bNSOJIQQjZbcAhMGqW3btjz7wNuEtRjVLGaAARibGOHiXX4tl87nqB1HCCEaNSmAhEFycnKihVVHWjv1aPKrQF/NrYUdWqWM7VG7Wb9+vdpxhBCi0ZJbYMIgKYqi2wbDvRlMga/k5mtPflQ2E6c8i6mpKXl5eZiaNv0B3kIIUdekB0gYpPnzPuBE0j5KtcXNYgp8JTcfO6xNHTE3taSkpITTp0+rHUkIIRolKYCEwbly5QqvvvYKm04vxtrJFDOL5tMR6upjj0ajwdlGtsQQQogbkQJIGJzKndItTezwa+Wldpw6VXk7z87EHaQAEkKIakkBJAxOZVHgaOnZLLbAuJqThw1GxhrszDxACiAhhKiWFEDC4FQWBQ4Wnrg1o/E/XDUVXjZFFUKIG5MCSBgcXQ+QRfPrAaJiIHTlpqgnT56krKxM7UhCCNHoNIoCaNGiRfj7+2NhYUFYWBj79u2rtu3SpUu58847cXR0xNHRkfDw8Ovajx8/Ho1Go/cYPHhwA1yJaAr0boE1ozWAKrn52GNr5szjw5/jxx9/RFEUtSMJIUSjo3oBtHbtWiIiIpg5cyaHDh0iJCSEQYMGkZaWVmX7qKgoHn30UbZs2cLu3bvx8fFh4MCBXLhwQa/d4MGDSU5O1j2+/fbbBroi0ZgVFBSQkJAAFT1AzWkKfKXyPcGM6Nl6GPfeey8mJs1nlpsQQtQV1QugDz74gIkTJzJhwgTatWvHkiVLsLKyYtmyZVW2//rrr3n22WcJDQ2lbdu2fPnll2i1WiIjI/XamZub4+HhoXs4Ojo20BWJxszMzIwN3/9FeMtn8PT2aFZT4CtVrmwt22EIIUT1VC2AiouLOXjwIOHh4f8EMjIiPDyc3bt31+gY+fn5lJSU4OTkpPd6VFQUbm5uBAUFMWnSJDIyMqo9RlFRETk5OXoP0TwZGxvjaO5FS8euuPs6qB2nXlT2ap09k8R3333HTz/9pHYkIYRodFQtgNLT0ykrK8Pd3V3vdXd3d1JSUmp0jKlTp+Ll5aVXRA0ePJhVq1YRGRnJ3Llz2bp1K0OGDKl2MOjs2bOxt7fXPXx8fG7zykRjVtkz0lw2Qb2Ws2f5VPgLmfGMHj2a9957T+1IQgjR6DTp/v85c+awZs0aoqKisLCw0L3+yCOP6P6/Y8eOdOrUiVatWhEVFcWAAQOuO860adOIiIjQPc/JyZEiqJn67LPPiFp3FOMin2a1CerVTEyNcfKw4XJe+UywmJgYtFotRkaq3/EWQohGQ9XviC4uLhgbG5Oamqr3empqKh4eHjf87Pz585kzZw6bNm2iU6dON2zbsmVLXFxciI+Pr/J9c3Nz7Ozs9B6iefr444/5PnIJOUVpzWoT1Gu5trDDztwFUxNT8vPzOXv2rNqRhBCiUVG1ADIzM6Nr1656A5grBzT37Nmz2s/NmzePd999l40bN9KtW7ebnuf8+fNkZGTg6elZZ9lF01NUVKQrgh0tPJvtLTAqCiAjjTGebr5Q0QskhBDiH6r3iUdERLB06VJWrlxJTEwMkyZNIi8vjwkTJgAwduxYpk2bpms/d+5cpk+fzrJly/D39yclJYWUlBRyc3MByM3NZcqUKezZs4fExEQiIyMZMWIErVu3ZtCgQapdp1BfXFwcZWVlmBpZYGXqgIt38y6AAFztvEEKICGEuI7qY4BGjx7NpUuXmDFjBikpKYSGhrJx40bdwOhz587pjV1YvHgxxcXFPPjgg3rHmTlzJm+99RbGxsZER0ezcuVKsrKy8PLyYuDAgbz77ruYm5s3+PWJxqOyCHCw8MDJw6ZZToGvVLnAY+WeYFIACSGEvkbxE+D555/n+eefr/K9qKgoveeJiYk3PJalpSV//vlnneYTzUNz3gT1WpU9QBal5ctDSAEkhBD6GkUBJERDOHnyJFRsgurh1zzXAKrk4m0LgLNJS75ZvYZuYV3UjiSEEI2KFEDCYFT2gjhaeODu17x7gMwtTbF3sYJ06Nm5P/6BbmpHEkKIRkX1QdBCNJQdO3YwMXwOnrZtcG/mPUBcdRtMtsQQQojrSQEkDIaVpRWmuc6YGVvi0cx7gABcK6b5b9+6k9mzZ/PXX3+pHUkIIRoNKYCEwchIzqW0RIuJWflKyc2da8U0/81b/+D111/nl19+UTuSEEI0GjIGSBiEH374ge+//oUr2Y6EdbkTI+PmX/tX3gKzNS4f/yMzwYQQ4h/N/6eAEMDGjRv57uf/kpqXgId/8x//w1WbvZoWO4IUQEIIoUcKIGEQ9GeAGUYBVNkDZFpYfr3nz5/nypUrKqcSQojGQQog0ewpinLVKtCeBjEAGsDa3gIrWzPMTaxxdSm/DVa5FpIQQhg6KYBEs5eWlkZmZiagwd7CzWB6gABcK7bE8GvREuQ2mBBC6EgBJJq9yh/6tmbOmBiZGcwYIADXihWhPZxlV3ghhLiazAITzZ5u/I+lJ3bOlljamKkdqcFUjgPqHzqS9z54gzZt2qgdSQghGgUpgESzd+7cOajYBb657wF2rcpbYEYFdnTq1EntOEII0WhIASSavdmzZ9PBJZw/Vx1u9nuAXUu2wxBCiKrJGCBhEHLTtFia2hrUAGiuWgvockouiz5dxNNPP83p06fVjiWEEKqTAkgYhNSz2QAGNQAawN7FClNzY7RlCl99tZyvvvqKv//+W+1YQgihOimARLMWHR3NwIGD2LBrNYDB3QLTaDS6PcF8vQIAOHHihMqphBBCfVIAiWbt8OHDbN68iQtZsRibGOHsaat2pAZXuSu8m4MPyFR4IYQAKYBEc/fPCtAeuPnaY2xieH/lKwdCO5h7gBRAQggBUgCJ5u7qAsjQbn9VqiyArBQXAGJjY9FqtSqnEkIIdUkBJJo1/T3ADGsAdCW3irWAtFesMDMzo7CwkLNnz6odSwghVCUFkGi2iouLdVO+HaUHiMvJebqVoGUqvBDC0MlCiKLZiouLo6ysDDNjC6xMHQxuDaBKTh42GBlrKCkqY9VX3xDYLgAbGxu1YwkhhKqkB0g0W2lpaTg5OWFv7oFGo8HDQHuArp79Zm3sLMWPEEJIASSas/79+3Nox0nuaxOBjYMF1vYWakdSjWyJIYQQ+mpVAJ05c6bukwhRD1ISszA1NsezpaPaUVRVuRbQ6dgkJk2axLBhw1AURe1YQgihmloVQK1bt6Z///7897//pbCwsO5TCVFHks9kAuAZYJjjfypVrgadk1bM559/zoYNG7h06ZLasYQQQjW1KoAOHTpEp06diIiIwMPDg//7v/9j3759dZ9OiFrSarV06NCB6R+8QGFpHp4BBt4DVHELLDuliICA8i0xZEFEIYQhq1UBFBoaykcffcTFixdZtmwZycnJ9OnThw4dOvDBBx/Ib5ZCdWfPnuX48ePEJB7CzNjC4G+BVe4Kf+l8DsHBwSAFkBDCwN3WIGgTExNGjRrF999/z9y5c4mPj+eVV17Bx8eHsWPHkpycXHdJhbgFlT/c7c3dMNIY42XgBZBLxS2wgtxiWgUEghRAQggDd1sF0IEDB3j22Wfx9PTkgw8+4JVXXuH06dNs3ryZixcvMmLEiLpLKsQtuHoFaCtbM+ycLdWOpCozCxMc3KwB8HLzAymAhBAGrlYLIX7wwQcsX76c2NhYhg4dyqpVqxg6dChGRuX1VEBAACtWrMDf37+u8wpRI1fvAeYZ4IhGo1E7kupcW9iRlZaHi403SAEkhDBwtSqAFi9ezJNPPsn48ePx9PSsso2bmxtfffXV7eYTolau7gEy9PE/lVy9bYk7lIyNsRsAZWVlFBYWYmFhuOsjCSEMV60KoM2bN+Pr66vr8amkKApJSUn4+vpiZmbGuHHj6iqnEDWmKIquAHK08DT4KfCVXCs2Rc3P0JKZmYmDg3xdhBCGq1ZjgFq1akV6evp1r1++fFk3xVYIteTl5REYGIiFqTX2Fu7SA1Th6tWgpfgRQhi6WhVA1a0gm5ubK93pQnU2Njbs2rmbcaEfYGJkavBrAFWS7TCEEOIft3QLLCIiAgCNRsOMGTOwsrLSvVdWVsbevXsJDQ2t+5RC3KK0pGwULVhYm+Lobq12nEahsgDKySjgf5u38P78Ofj7+/P555+rHU0IIRrcLfUA/f333/z9998oisLRo0d1z//++29OnjxJSEgIK1asuOUQixYtwt/fHwsLC8LCwm64qvTSpUu58847cXR0xNHRkfDw8OvaK4rCjBkz8PT0xNLSkvDwcOLi4m45l2iaFEW5agsMmQFWydrOHGt7cwAuXchk06ZNREVFqR1LCCFUcUs9QFu2bAFgwoQJfPTRR9jZ2d12gLVr1xIREcGSJUsICwtj4cKFDBo0iNjYWNzc3K5rHxUVxaOPPkqvXr2wsLBg7ty5DBw4kOPHj+PtXT69d968eXz88cesXLmSgIAApk+fzqBBgzhx4oTcojMA9957L0f/jiHEdiS9AtqoHadRcW1hR172JRwty2dvnj59muLiYszMzNSOJoQQDapWY4CWL19eJ8UPFWsKTZw4kQkTJtCuXTuWLFmClZUVy5Ytq7L9119/zbPPPktoaCht27blyy+/RKvVEhkZCRW//S9cuJA333yTESNG0KlTJ1atWsXFixf5+eef6ySzaNyio6M5n5KIiZHsAn+tyttgFFpia2tLWVmZ9I4KIQxSjXuARo0axYoVK7Czs2PUqFE3bLtu3boaHbO4uJiDBw8ybdo03WtGRkaEh4eze/fuGh0jPz+fkpISnJycAEhISCAlJYXw8HBdG3t7e8LCwti9ezePPPLIdccoKiqiqKhI9zwnRwaJNlXZ2dlcuHABAEdLTxkAfY3KAijjwhWCg4PZt28fMTExtG/fXu1oQgjRoGrcA2Rvb68bS2Fvb3/DR02lp6dTVlaGu7u73uvu7u6kpKTU6BhTp07Fy8tLV/BUfu5Wjjl79my9/D4+PjW+BtG4nDx5EgArU3vMjC3xbu2kdqRGxa1iLaA02RRVCGHgatwDtHz58ir/X01z5sxhzZo1REVF3dbYnmnTpulmuFHRAyRFUNN09RYYMgPseldPhQ/uXV4AnThxQuVUQgjR8Go1BqigoID8/Hzd87Nnz7Jw4UI2bdp0S8dxcXHB2NiY1NRUvddTU1Px8PC44Wfnz5/PnDlz2LRpE506ddK9Xvm5Wzmmubk5dnZ2eg/RNF29BYZ3KyeZAXaNygLockouQW2CcHR0xNLSsDeKFUIYploVQCNGjGDVqlUAZGVl0aNHDxYsWMCIESNYvHhxjY9jZmZG165ddQOYAd2A5p49e1b7uXnz5vHuu++yceNGunXrpvdeQEAAHh4eesfMyclh7969NzymaB7+2QLDA69WMv7nWnbOlphZmKBoFcJC7yIjI6PaCQdCCNGc1aoAOnToEHfeeScAP/zwAx4eHpw9e5ZVq1bx8ccf39KxIiIiWLp0KStXriQmJoZJkyaRl5fHhAkTABg7dqzeIOm5c+cyffp0li1bhr+/PykpKaSkpJCbmwsVizS+9NJLzJo1i/Xr13P06FHGjh2Ll5cXI0eOrM3liiakTZs2eLu2xMnSW8b/VEGj0fwzEPpirvSQCSEMVq02Q83Pz8fW1haATZs2MWrUKIyMjLjjjjs4e/bsLR1r9OjRXLp0iRkzZpCSkkJoaCgbN27UDWI+d+6c3qarixcvpri4mAcffFDvODNnzuStt94C4NVXXyUvL49nnnmGrKws+vTpw8aNG2UNIAMwf/58zOO6kn7hCl6tpACqimsLOy7EXyYt6Z/ZjoqiSDEkhDAoGqW6jb1uoFOnTjz99NPcf//9dOjQgY0bN9KzZ08OHjzIvffeW+MZXI1VTk4O9vb2ZGdny3igJqYwr5jJfctXI1/wv7HYOEjRe60fPtrD5tXR3P1oB5KUXSxevJj/+7//49VXX1U7mhBCNJha3QKbMWMGr7zyCv7+/oSFhenG1mzatInOnTvXdUYhaiQ/P5/z8RkA2LtYSfFTDVfvf2aCFRYWcubMGY4dO6Z2LCGEaFC1KoAefPBBzp07x4EDB9i4caPu9QEDBvDhhx/WZT4hamz27Nm07+7PwYu/ygDoG3DzqSiAknJo164dyFR4IYQBqtUYICqmm187rbxHjx51kUmIWjlx4gTFJcWYGVvJAOgbqBwEnX4hh7ZBvaBiAUmtVqs33k4IIZqzWhVAeXl5zJkzh8jISNLS0tBqtXrvnzlzpq7yCVFjlb0YjpaeMgD6BhzdbTAy1lBaosXZzhNTU1Py8vJISkrCz89P7XhCCNEgalUAPf3002zdupUnnngCT09PmT0iVFdcXKzb1NPRwlN6gG7A2MQIF2870s5lk5VaQJs2bTh+/DgnTpyQAkgIYTBqVQD98ccf/Pbbb/Tu3bvuEwlRC3FxcZSVlWFqZIG1mQNesgv8Dbm2KC+ALp0vHwdUWQANGTJE7WhCCNEgalUAOTo66nZfF6IxuPr2l5uPPWYWtR7eZhBcvcvX8UpLyqZbt26cPXsWR0cpGoUQhqNWIx7fffddZsyYobcfmBBq0hVAFl54ye2vm3Kt2BX+0vkcXn31Vfbu3cuTTz6pdiwhhGgwtfo1ecGCBZw+fRp3d3f8/f0xNTXVe//QoUN1lU+IGgkODqZr8F3YFrTEp42z2nEavcqp8FevBi2EEIakVgWQ7KklGpuHH36Y2J+MOB93WQqgGnD3Le8BSjuXjVarYGSkobi4GI1Gc90vNEII0RzVqgCaOXNm3ScR4jaUlpSRnJAFgE+Qi9pxGj0XbzuMjDUUF5aSlZbH2Kcf4c8//+T333/nnnvuUTueEELUu1qvepaVlcWXX37JtGnTuHz5MlTc+rpw4UJd5hPipnJzczm48xilJWVY2Zrh6G6tdqRGz9jECLeKcUApiVmYmZlRWloqK0ILIQxGrQqg6Oho2rRpw9y5c5k/fz5ZWeW/ea9bt45p06bVdUYhbmjTpk3c0T+UDacW0KKNs6xLVUPufuUFUOrZLNkSQwhhcGpVAEVERDB+/Hji4uKwsPhnw8mhQ4eybdu2uswnxE1V/tC2NXeR8T+3wN3PAYCUs9lSAAkhDE6tCqD9+/fzf//3f9e97u3tTUpKSl3kEqLGjh8/DoCDhaeM/7kFHpU9QIn/9AAdP34cRVFUTiaEEPWvVgWQubk5OTnXT589deoUrq6udZFLiBr7Zw0gT1pID1CNefhX9gBlERQUhEajITMzk7S0NLWjCSFEvatVATR8+HDeeecdSkpKANBoNJw7d46pU6fywAMP1HVGIapVWlpKbGwsAC423ngGOKgdqcmovAWWmZqHESYEBASA3AYTQhiIWhVACxYsIDc3F1dXVwoKCujbty+tW7fG1taW9957r+5TClGNhIQEioqKMNaY0qZtK0xMjdWO1GTYOFhgbW8OFesBDRs2jEcffRRbW1u1owkhRL2r1TpA9vb2bN68mZ07d3LkyBFyc3Pp0qUL4eHhdZ9QiBv4Z/yPB77BbmrHaXI8/B04fSSVlMQsFi5cqHYcIYRoMLdcAGm1WlasWMG6detITExEo9EQEBCAh4cHiqLIFGTRoFq2bMk93R/mSrJWZoDVgodfRQF0NlvtKEII0aBu6RaYoigMHz6cp59+mgsXLtCxY0fat2/P2bNnGT9+PPfff3/9JRWiCp06daKb+wg6ud8jBVAtXL0WEEBZWRlxcXEyE0wI0ezdUg/QihUr2LZtG5GRkfTv31/vvb/++ouRI0eyatUqxo4dW9c5hahSXk4RGcm5ADIDrBYqZ4Klns2mpKQEJycncnNzuXjxIp6enmrHE0KIenNLPUDffvstr7/++nXFD8Ddd9/Na6+9xtdff12X+YSoVmlpKT98/QsFJTk4e9liZWuudqQmp3ImWOrZLExMTGjRogUAx44dUzmZEELUr1sqgKKjoxk8eHC17w8ZMoQjR47URS4hbio+Pp7xz47m22Nv4NVapr/XhmuL8k1RiwrKN0Vt3749SAEkhDAAt1QAXb58GXd392rfd3d3JzMzsy5yCXFTlT+kHS288AuSBThrw9jECFdvO6jYEqNDhw4gBZAQwgDcUgFUVlaGiUn1w4aMjY0pLS2ti1xC3JSuALL0ki0wboNuReiETCmAhBAG45YGQSuKwvjx4zE3r3qsRVFRUV3lEuKmoo9EA+Bk6S0DoG+DZ0tHjmw7S3JCFl1GlhdAx48fR6vVYmRUq7VShRCi0bulAmjcuHE3bSMzwERDOXK4vADycPTD2dNG7ThNlmdLRwAunsnk4dZ3YGZmRl5eHmfPntVtjyGEEM3NLRVAy5cvr78kQtyCgoICEs8lANCpUwdZgPM2eFUWQKcvY2xszLPPPoutrW21Pb1CCNEc1GorDCHUdvLkSbRaLebG1nTo2kbtOE2ah78DGg3kZRdxJbOQDz/8UO1IQghR7+QGv2iSvL29ub/nJLp63Yd/O5kBdjvMLExwqZgJlnxGZnEKIQyDFECiSXJ2csGTrnRwuxs/KYBu29W3wRRFISkpie3bt6sdSwgh6o3cAhNN0sXTlyktLsPSxgzXFnZqx2nyKmeCXTyTSWJiIi1bttQNhr7R0hdCCNFUSQ+QaJI+/+xLUnJP0yLIUQZA14HKmWDJCVn4+flhbW1NcXEx8fHxakcTQoh6IQWQaHKys7N57+NprI+dh1uAtdpxmoWrb4FpNBrZEkMI0exJASSanMofytamjrTv1lLtOM2C3kywywWyIrQQotmTAkg0OZULIDpZeuMXLFtg1AW9mWAJWVIACSGaPdULoEWLFuHv74+FhQVhYWHs27ev2rbHjx/ngQcewN/fH41Gw8KFC69r89Zbb6HRaPQebdu2reerEA1p366DALg5+ODsZat2nGbDM6B8T7CLpy9LASSEaPZULYDWrl1LREQEM2fO5NChQ4SEhDBo0CDS0tKqbJ+fn0/Lli2ZM2cOHh4e1R63ffv2JCcn6x47duyox6sQDe1IxR5gQYHBMgC6Dnm1coKKLTEqC6D4+HgKCwtVTiaEEHVP1fmtH3zwARMnTmTChAkALFmyhN9++41ly5bx2muvXde+e/fudO/eHaDK9yuZmJjcsEASTZeiKMQnnAKgc7cQteM0K1fvCebh4cEbb7xBmzZtUBRF7WhCCFHnVCuAiouLOXjwINOmTdO9ZmRkRHh4OLt3776tY8fFxeHl5YWFhQU9e/Zk9uzZ+Pr6Vtu+qKhIbyf7nJyc2zq/qD+pqank5mcDGnr17a52nGalciZY5WrQs2bNUjmREELUH9VugaWnp1NWVoa7u7ve6+7u7qSkpNT6uGFhYaxYsYKNGzeyePFiEhISuPPOO7ly5Uq1n5k9ezb29va6h4+PT63PL+qXtaUNw9pG0NdvLG07y59TXfLwd0BjpCEvu4js9Hy14wghRL1SfRB0XRsyZAgPPfQQnTp1YtCgQfz+++9kZWXx3XffVfuZadOmkZ2drXskJSU1aGZRc+lJ+XhaB9Et8G4c3WUNoLpkZmGCu689AOdPZZCfn09UVBTr1q1TO5oQQtQ51W6Bubi4YGxsTGpqqt7rqampdTp+x8HBgTZt2txwRVtzc3PMzc3r7Jyi/pyNuQSAX7CrDICuBy0CnUhJzOJ83GUKzdPo378/Li4u3H///fL1FkI0K6r1AJmZmdG1a1ciIyN1r2m1WiIjI+nZs2ednSc3N5fTp0/j6elZZ8cU6lny5SJOZezB2d9C7SjNUos2zgCcj8ugffv2GBkZkZ6eflu3pYUQojFS9RZYREQES5cuZeXKlcTExDBp0iTy8vJ0s8LGjh2rN0i6uLiYw4cPc/jwYYqLi7lw4QKHDx/W69155ZVX2Lp1K4mJiezatYv7778fY2NjHn30UVWuUdSdkpISfvjfUqISl2PvKRt01ocWgeUF0IW4y1haWhIUFATAkSNHVE4mhBB1S9WfIqNHj+bSpUvMmDGDlJQUQkND2bhxo25g9Llz5zAy+qdGu3jxIp07d9Y9nz9/PvPnz6dv375ERUUBcP78eR599FEyMjJwdXWlT58+7NmzB1dXVxWuUNSlo0eOUaYtxdTIgp53d67BJ8StahFYvhZQytksSopKCQkJISYmhiNHjjB48GC14wkhRJ1R/dfo559/nueff77K9yqLmkr+/v43XZNkzZo1dZpPNB5/bSxf0NLdzg9HNxkAXR8c3KyxsjMnP6eI5IQsOnXqxJo1a6QHSAjR7DS7WWCi+dq76wAArfyDZEBuPdFoNLpeoPNxGYSElC82KQWQEKK5kQJINBnHT5TvS9W5c6jaUZq1fwZCX9YVQLGxsbIlhhCiWVH9FpgQNaEoConJcQD07neH2nGatRat/+kB8vK6g6VLl9K+fXtMTOTbhRCi+ZDvaKJJOHnsNAXFV9CgYcDQXmrHadZ0PUCnMgB4+umnVU4khBB1Twog0SQUXDJiTMc5mDoX4Ohir3acZs0zwFFvSwwHVxlwLoRofqQAEk1C4vFLWJs50ie87hbJFFUzszDBw8+e5IQszp/KoFjJY/369eTl5TF58mS14wkhRJ2QQdCiSUg4mgZAQHs3taMYhMoFEc/HXebChQtMnDiRt99++6bLUAghRFMhBZBo9MpKtaz89QMOXFyPnZdMf28IleOAkmLTadeuHcbGxmRmZnL+/Hm1owkhRJ2QAkg0eqePX+BYylYOJf+Gi7et2nEMgm9bFwDOnUzH3Nyctm3bgqwHJIRoRqQAEo3elo27UFCwsXTAy9tL7TgGobIASkvKoSC3WLceUHR0tMrJhBCibkgBJBq9PTv3A9DKr43aUQyGjYMFzp42UNELJCtCCyGaGymARKN37MRRAEJDZQPUhnT1bTApgIQQzY0UQKJRK8gt5nzqaQD69AtTO45BqaoAiouLIz8/X+VkQghx+6QAEo3amaMpZORfAOCO3t3VjmNQfINdATgbcwkPDw/+97//kZqaipWVldrRhBDitslCiKJRO7DjBFqlDBNjU4KCgtSOY1B0A6HPZVOYV8yAAQPUjiSEEHVGeoBEo3blAkzo/DErPliPqamp2nEMip2TJY7u1igKJFXsCyaEEM2FFECi0VIUhYRjaRhpjOjZXwZAq6GyF+hsTDrJyclMmzaNZ555Ru1YQghx26QAEo1WWlIOuVmFmJga4VOxMrFoWH4V44DOxVxCq9UyZ84cvvrqKxkILYRo8qQAEo3W6SMpUPFD2NRchqupQTcTLDYDLy8v3N3d0Wq1Mh1eCNHkSQEkGq3TR1IBaBXqoXYUg1VZAKUkZlFcWErXrl0BOHjwoMrJhBDi9kgBJBqt+MPlPUCtQ9zVjmKw7F2scHCzRtEqnDuZLgWQEKLZkAJINEq5WYWkJGYB0CpEeoDU5N++fBxQwrE0KYCEEM2GFECiUTodXX77y8PfARsHC7XjGLSA9m4AJB6/pCuATpw4QUFBgcrJhBCi9qQAEo1S5e2vVnL7S3UBHcoLoIRjaXh7e+Pm5oatrS2JiYlqRxNCiFqTqTWiUaqcAdZaBkCrzi/YBY0GLqfkkpNRQHR0NG5ubmg0GrWjCSFErUkPkGh0SopKOXviEsj4n0bBwtoMz5aOUNEL5O7uLsWPEKLJkwJINDpnY9IpLdFi62SJm4+d2nHE1bfBjqepHUUIIeqEFECi0am8/dUqRHoaGovKAijx+CXy8/N56KGHaNOmjQyEFkI0WVIAiUbnn/V/5PZXY/FPAZSGubkFW7duJS4ujujoaLWjCSFErUgBJBoVRVF0U+BlAHTj4RngiJmFCYV5JaSezZb1gIQQTZ4UQKJRST2bTV52EabmxvgEyQaojYWxiRF+7a5fEPHQoUMqJxNCiNqRAkg0KpW3vwLau2Fiaqx2HHGVgIoVoROvKoD279+vciohhKgdKYBEoxJ/RBZAbKwCOpb/mZw5mkpYWBgAx44d48qVKyonE0KIWycFkGhUKneAl/E/jU+rTuUF0IX4yzjaueDr64tWq+XAgQNqRxNCiFsmBZBoNLLT80k7l41GAy07SQ9QY2PvYoWLty2KUt4LdNddd9GtWzdKSkrUjiaEELdMtsIQjcapgxcB8AlywcrWXO04ogqtQz1Iv3CF00dSWbVqlazTJIRosqQHSDQapw4mA9Cmq6faUUQ1KrcmiT+cIsWPEKJJU70AWrRoEf7+/lhYWBAWFsa+ffuqbXv8+HEeeOAB/P390Wg0LFy48LaPKRqP2IoeoDZdpABqrFpXDE5POJZGWakWgIKCAlkRWgjR5KhaAK1du5aIiAhmzpzJoUOHCAkJYdCgQaSlVb3fUH5+Pi1btmTOnDl4eFQ9SPZWjykah+z0fFLPlo//ad1ZCqDGyiPAESs7c4oLS0k6lcGTTz6JnZ0d69atUzuaEELcElULoA8++ICJEycyYcIE2rVrx5IlS7CysmLZsmVVtu/evTvvv/8+jzzyCObmVY8RudVjisbh1KHy218t2jhjbSfjfxorIyONbjZY/OEU7O3tKS0tZffu3WpHE0KIW6JaAVRcXMzBgwcJDw//J4yREeHh4bX+ZlrbYxYVFZGTk6P3EA3rlNz+ajIq12g6fSSFO+64A4A9e/aonEoIIW6NagVQeno6ZWVluLvrT3d2d3cnJSWlQY85e/Zs7O3tdQ8fH59anV/U3j8DoL3UjiJuonKNptNHUnUF0JEjR8jPz1c5mRBC1Jzqg6Abg2nTppGdna17JCUlqR3JoGSn55OSmIVGA4GdZQHExs4v2BVjEyOy0/OxNHLA09OT0tJS2RhVCNGkqFYAubi4YGxsTGpqqt7rqamp1Q5wrq9jmpubY2dnp/cQDUc3/ifQGWt7C7XjiJswszDRbYx6dS+Q3AYTQjQlqhVAZmZmdO3alcjISN1rWq2WyMhIevbs2WiOKepfXEUBFCjr/zQZlWO1Yg9c1P3bkgJICNGUqHoLLCIigqVLl7Jy5UpiYmKYNGkSeXl5TJgwAYCxY8cybdo0Xfvi4mIOHz7M4cOHKS4u5sKFCxw+fJj4+PgaH1M0PpUDoINk/E+TEdSt/M/q1MGL9OvXj4cffph7771X7VhCCFFjqm6FMXr0aC5dusSMGTNISUkhNDSUjRs36gYxnzt3DiOjf2q0ixcv0rlzZ93z+fPnM3/+fPr27UtUVFSNjikal5yMfJITZPxPU9OykztGxhoyknMJ8Api7dq1akcSQohbolEURVE7RGOTk5ODvb092dnZMh6onh3YfJql0yJpEejE9G8fVDuOuAXznvyF09GpjJ1+F71HtFU7jhBC3BKZBSZUVTkAWqa/Nz1tKm6DxR64iKIonDp1ShZEFEI0GVIACVWdOlA+/idQFkBscirHAcUeTGbdunUEBQUxadIktWMJIUSNSAEkVJOZlqcb/xMkM8CanFad3DExNSIrLY82fh0BiI6OJjMzU+1oQghxU1IACdWc3HcBKhbWk/V/mh4zCxMCOpZPLsg8V0ZQUBCKorB9+3a1owkhxE1JASRUU1kAtQ3zVjuKqKXKnrvK6fCAbkamEEI0ZlIACVUoikJMRQEU3EMKoKaqcvB67IGL3HXXXQBs3bpV5VRCCHFzUgAJVSSfySQ7PR9Tc2NadZI1mpqqlp3cMbc0ISejgMAWnQA4fPgwWVlZakcTQogbkgJIqKKy9yewsyem5qquxylug6mZsW42WMaZUgIDA9FqtezYsUPtaEIIcUPyk0eoorIAaiu3v5q89r18iN5+juO7knjvvfewsLDQ3Q4TQojGSgog0eDKSrWcOli+AKKM/2n62vf0ASD+SArPfjAOSxsztSMJIcRNyS0w0eASjqVRlF+CjYMFLdo4qx1H3CbXFna4+dqjLVM4uf+C2nGEEKJGpAASDe7EnvMAtO3uhZGRRu04og506FXeC3R8VxK7du3izTffZM+ePWrHEkKIasktMNHgju1KgoqxI6J5aN+zBX+tOcaxXUn8dTqS5cuXU1JSwh133KF2NCGEqJL0AIkGlXO5gLMnLsFVY0dE09emqxcmZsZkpuYR2r4HyHpAQohGTgog0aBO7C7v/fEJcsbexUrtOKKOmFmY0KZiQ1snI38ADhw4wJUrV1ROJoQQVZMCSDSo47vLx/90kNtfzU7HPr4AXDxahL+/P2VlZbIekBCi0ZICSDQYbZmW47tl/E9z1fnuAABOR6dyV5/yfcH+/PNPlVMJIUTVpAASDeZsTDp52UVY2pjRsqNsf9HcOLpZE9DRDYCWbiEAbNy4UeVUQghRNSmARIM5tvMcAMFh3hibyF+95qjrgJYAmFz2xNjYmMuXL3P58mW1YwkhxHXkp5BoMJXT32X8T/PVuX/5AOik4zns3XmAlJQUnJyc1I4lhBDXkQJINAi96e9SADVbLt52+LZ1QdEqFCRbYGQk32KEEI2TfHcSDSJ621kUBXyDXXBwtVY7jqhHXQaUD4Y+GHkGAEVR0Gq1KqcSQgh9UgCJBnFkayIAoX391Y4i6lmXitlgJ/ddIOKll/H29mbLli1qxxJCCD1SAIl6V1RQQsy+8k0yQ/r6qR1H1DN3Pwd82jijLVM4cfg0ycnJMhtMCNHoSAEk6l3M3guUFJXh7GWLd2sZEGsI7rg3EAAHbSsA/vjjD5UTCSGEPimARL07HFV++yukrx8ajez+bgh6DG6NkbEG85wWGBkZcfz4cZKSktSOJYQQOlIAiXpVVqolevtZkPE/BsXO2YoOvXywMLGmtW87kEURhRCNjBRAol6djk4lL7sIKztzWod6qB1HNKA7RwUD4Kwpvx0mBZAQojGRAkjUq8rbXx17+8jqzwamQy8fnDxscDdvC8D//vc/SkpK1I4lhBAgBZCoT1qtwqGKtWAqN8oUhsPI2Ig772+Lq5Uvvq5B/Otf/6KgoEDtWEIIAVIAifp0JjqVzNQ8LKxNZfsLA3XnqGDMLEwZ7BvBkw9Nxs7OTu1IQggBUgCJ+nRg82kAQu7yw9TcRO04QgW2jpb0Gh4EwKZVR9SOI4QQOlIAiXqhLdNy8H/lt7+6D2qtdhyhonvGdEJjpOHIjgSWffYNx44dUzuSEEJIASTqR9zfKeRkFGBlZ05wmLfacYSKXFvYccfQQHaf/46nnhvDokWL1I4khBBSAIn6UXn7q3M/f0xMjdWOI1R279NdCHAMBeD7736grKxM7UhCCAMnBZCoc2WlWg5FJgDQbWArteOIRsC1hR2jx9+PubEVGZfT2bp1m9qRhBAGTgogUedO7DlPblYhNg4WBHXzUjuO+rRlkBQFMd+W/1drmL0fo54No5VrFwA+mv252nGEEAauURRAixYtwt/fHwsLC8LCwti3b98N23///fe0bdsWCwsLOnbsyO+//673/vjx49FoNHqPwYMH1/NViEq71scC0GNIa1n8MG4dLPWH7/rD74+V/3epf/nrBsba3oLxTz8OwP+2buTimctqRxJCGDDVfzqtXbuWiIgIZs6cyaFDhwgJCWHQoEGkpaVV2X7Xrl08+uijPPXUU/z999+MHDmSkSNHXjezZPDgwSQnJ+se3377bQNdkWHLzSrkyLbyvb96DQtSO4664tbB+gch97z+67kXyl83wCLohTfGYWFmTX5JNm8/u4SSYsPsDRNCqE/1AuiDDz5g4sSJTJgwgXbt2rFkyRKsrKxYtmxZle0/+ugjBg8ezJQpUwgODubdd9+lS5cufPrpp3rtzM3N8fDw0D0cHR0b6IoM294/4igr1eIb7IJPG2e146ijJA+KciDyBUCpooFS/vjrxfJ2BsTCwoLhw4cDcDB6D99/uFvtSEIIA6VqAVRcXMzBgwcJDw//J5CREeHh4ezeXfU3xt27d+u1Bxg0aNB17aOionBzcyMoKIhJkyaRkZFRbY6ioiJycnL0HuLWKYrCzl/Kb3/1Hm7AvT8f28Cn9pB38cbtci+UtzMwb70znXWrNtPF8162fn+CqO+Pqx1JCGGAVC2A0tPTKSsrw93dXe91d3d3UlJSqvxMSkrKTdsPHjyYVatWERkZydy5c9m6dStDhgypdurt7Nmzsbe31z18fGTbhto4dzKdC/GXMTEzlsUPRbWCg4O5/4lwRkzqDsCaeTvZtzFe7VhCCAOj+i2w+vDII48wfPhwOnbsyMiRI9mwYQP79+8nKiqqyvbTpk0jOztb90hKSmrwzM3BzorBz537+WNtZ652HPW8mAujfq9BQ2rerhka+lRnwu4LQFFg2fS/2PHzSbUjCSEMiKoFkIuLC8bGxqSmpuq9npqaioeHR5Wf8fDwuKX2AC1btsTFxYX4+Kp/yzQ3N8fOzk7vIW5NQW4xe3+PA6D3CAO+/QVgag1+A8GmBaCpppEGbH3K2xmg4uJixo4dy0sfPEDIQHcUBVbP2sb/vjmqdjQhhIFQtQAyMzOja9euREZG6l7TarVERkbSs2fPKj/Ts2dPvfYAmzdvrrY9wPnz58nIyMDT07MO04ur7VwfS2FeCZ4BDrTtIVtfYGQMd39U8eTaIqjief+F5e0MkJmZGfHx8Vy5coVch1jueaITAN9/sJu183dRVqpVO6IQoplT/RZYREQES5cuZeXKlcTExDBp0iTy8vKYMGECAGPHjmXatGm69pMnT2bjxo0sWLCAkydP8tZbb3HgwAGef/55AHJzc5kyZQp79uwhMTGRyMhIRowYQevWrRk0aJBq19mcacu0bFlbvgxB/9Ed0Giq6/UwMIGjYPgPYHNNQWjbovz1wFFqJWsUnnrqKQCWLVvGqBd6cP/zPQD4a80xPov4k4LcYpUTCiGaM42iKFXN021Qn376Ke+//z4pKSmEhoby8ccfExYWBkC/fv3w9/dnxYoVuvbff/89b775JomJiQQGBjJv3jyGDh0KQEFBASNHjuTvv/8mKysLLy8vBg4cyLvvvnvd4Onq5OTkYG9vT3Z2ttwOq4HDUYksfmUTVnbmzPntMcwtTdWO1Lhoy+DCdshNBhtP8L7TYHt+rnblyhU8PT3Jy8tj+/bt9OnTh0ORZ1g2YwslRWV4tXTkuYWDcfGyVTuqEKIZahQFUGMjBdCt+eBfG4g9cJGBY0N44MUwteOIJuTJJ59k+fLljB8/nuXLlwOQeOISn0X8SXZ6PraOFkyaP5BWIdWP8RNCiNpQ/RaYaNqSTmUQe+AiRsYa+j/cXu04ool5+umnAfjuu+9062/5t3Nl2sqR+AQ5cyWzkA/+tYG9f8SpnFQI0dxIASRuyx/L/gag890BOHnYqB1HNDE9e/akbdu25Ofns2bNGt3rju42TPlyOKH9/Ckt0bJs+hbWLzmAVisd1kKIuiEFkKi1C/GXOfi/MwDc+1QXteOIJkij0fDKK68wa9YsHnroIb33zC1N+b959zBoXAgAv315iBUzt6AtkxliQojbZ6J2ANF0bVh6EIAuAwLwbu2kdhzRRFXOBquKkZGGUS+E4eHvwOpZ29j7RzwajYZxM/tiZCy/vwkhak++g4hauRB/mUORCQDcN7Gr2nFEM9drWBATZ4djZKxhz+9x/Pe97XI7TAhxW6QAErVS2fvTNbyl9P6IOrFhwwZ69uxZ7UbIXe4O4Ml370ZjpGHn+ljWfbK3wTMKIZoPKYDELTt9JIVDkQloNHDv0zL2R9SNn376iT179jBv3rxq23Qf2IrxM/sCsHl1NNt+PNGACYUQzYkUQOKWaLUKaxeU/4bea3iQ9P6IOjNlyhQAfvnlF06erH5j1DvubcPwf3UD4Nt5Ozm+WzYvFkLcOimAxC3Z89spzp64hIW1KSOf7a52HNGMtG3blhEjRqAoCu+///4N2w59qjN33BuItkzhi9f+R+rZrAbLKYRoHqQAEjVWmFfMT5/uA2DoU12wc7ZSO5JoZqZOnQrA6tWruXDhQrXtNBoNT7x5F61DPSjMK2HJq5spKihpwKRCiKZOCiBRY79+cZCcjALcfOy4+5EOascRzVDPnj3p06cPJSUlzJo164ZtTUyNeWZOOHbOllw8ncl/39uO7OwjhKgpKYBEjZw5mkrkt+U7vj/8ci9MzWQzT1E/KgufL774gvj4+Bu2tXex4pk55dPj922MZ+sPMihaCFEzUgCJmyopKmXl21tRtAp3DA2kYx9ftSM1LWVlEBUF335b/t+yMrUTNWp9+/blpZde4vvvv6dVq1Y3bR/Y2ZNRL5Rvwvv9h3s4H5fRACmFEE2d7AZfBdkNXt9Pn+5j44rD2Dlb8tZ3D2Ftb6F2pKZj3TqYPBnOn//ntRYt4KOPYNQoNZM1K4qisOjff3J0xzk8Wzry+qr7MbOQhe6FENWTHiBxQ/GHU9i0+ggAY6bdKcXPrVi3Dh58UL/4Abhwofz1devUStakZGVlUVpaesM2ldtj2Dlbknwmk+8/qHoxRSGEqCQ9QFWQHqByuVmFzBrzI5mpeYQNac2T79596wfJy6uPaI1fWRkEB8PFi9W38faGEyfA2ADHU1lb16jZ8uXLmTJlCv/5z3945plnbto+Zu95Pnr+dxQF/m/ePXS5O6AOwgohmiPpIxZVUhSFlW9HkZmah5uvPY+91qd2B7KxqetozceFC2Bvr3YKddTw964rV66QkZHB9OnTefjhh3FwcLhh++CwFtzzRAibVh1h9axt+LdzxclD/g4KIa4nt8BElSK/OUr09nOYmBrxzOwBWFibqR1JGKBJkyYRFBREWloazz//fI0+M2JSN/zbuZKfU8RX0/9CW6at95xCiKZHboFVwdBvgcXsPc/HL/6BtkzhkSm96D/6Ntb8MdRbYNu2wdChN2/3++9w110NkahxqeEtMIA9e/bQu3dvtFot3333HQ899NBNP3PpfA6zxvxIYV4Jw/6vK/dN7HqbgYUQzY0UQFUw5AIoLSmb2eN+Jj+niDuGBjL+7X5oNBq1YzU9ZWXg719+m6uqf2IaTflssIQEwxwDdIumT5/OrFmzcHJy4tixY3h6et70M3t+j2P5jC1ojDS88sUwWod6NEhWIUTTILfAhE5BbjGfvbyJ/Jwi/Nu78vgbd0rxU1vGxuVT3akodq5W+XzhQil+amjGjBl06dKFy5cv89RTT9Voxec7hgZyx9BAFK3CV2/+RV52YYNkFUI0DVIACQBKS8pY8upmks9kYu9ixaT5AzE1lzHyt2XUKPjhh/LZXldr0aL8dVkHqMZMTU1ZvXo1lpaWtG3b9qbT4is9OrU3bj52XE7JZflbUWi10uEthCgnt8CqYGi3wBRFYfmMLez9Ix5zSxNe/mIYfsGuasdqPsrKYPt2SE4GT0+4807p+aml5OTkGt3+ulpSbDpzJvxCaXEZ9z/fg8HjQ+stnxCi6ZACqAqGVgCt+2Qvf648gpGxhuc+HEyHXj5qRxLipgoKCsjIyKBFixY3bbv9pxj++952jIw1/HvxfbTpcmtFlBCi+ZFbYAbuty8P8efK8pWen3jzLil+RJOQlpZG//79CQ8P5/Llyzdt32dkW+4YGoi2TOHL1yPJychvkJxCiMZLCiADtnHFYdYvOQDAgy/dQa9hQWpHEqJGysrKuHjxIrGxsYwaNYqioqIbttdoNDw2rQ+eLR3JTs9n6euRlJXK+kBCGDIpgAzU5v9G89On+wAY+Vx37nm8k9qRhKgxT09PfvvtN2xtbdm6dStjxoy5aRFkbmnK/80Nx9zShFMHk/l27o4azSYTQjRPUgAZoMhvj/LDwj0A3PdMV4ZM6Kx2JCFuWceOHfnhhx8wNTXlxx9/ZNiwYeTm5t7wM54Bjjz13gA0Gtj+00kivz3WYHmFEI2LFEAGRFEUfv38AN8tKN8pe8iEUO6b2EXtWELU2sCBA/ntt9+wtrZm8+bNDBgwgPT09Bt+JuQuPx6YfAcAP3y4m8NRiQ2UVgjRmEgBZCC0WoU17+9iw9JDUNHzM+LZ7rLQoWjy7rnnHv766y+cnJxISkriypUrN/1M+JiO9Lm/LYoCS6f9jxN7zjdIViFE4yHT4KvQ3KbBF+YVs2z6Fo5sO4tGA6On9Kb/w+3VjiVEnYqJiaG0tJSOHTvWqH1ZqZal0/7H31sSMTU3ZvKnQwnsLNPjhTAU0gPUzGUkX2HeU+s5su0sJmbGPPXeACl+RLMUHBysV/ysWLGCsWPHkpOTU2V7YxMjnv7PADr09qGkqIxPJm8kZq/0BAlhKKQHqArNpQfoyLazrHw7irzsIuycLXl2wSACOripHUuIepednY2fnx/Z2dkEBATw4YcfMnz48Cpv+RYXlvJZxJ/E7LuAkbGGcTP7ccfQQFVyCyEajvQANUMlRaWseX8nn0X8SV52Eb7BLkxbeb8UP8Jg2Nvbs2HDBvz8/EhISGDkyJH069eP/fv3X9fWzMKE5xYOpvvAVmjLyreF+fXzA2jLZJ0gIZoz6QGqQlPuAYrZd4Fv5uwg7Vw2AAMe68j9z/fA1Ez2nhKGJycnhzlz5vDhhx9SWFi+G/xDDz3E7NmzadWqlV5brVZh3cd72fzfaADadPXkyXfvxtHNWpXsQoj6JQVQFZpiAZR+IYdflhxg3x/xANg5WzJ2el869vFVO5oQqjt37hxvvvkmq1evxsjIiNOnT+Pv7w8Vy0NcfWtsz2+n+GbODooKSrG2N2fUC2H0GtYGI2PpMBeiOZECqApNqQBKv5DDnyuPsOOXk2jLFDQa6PtgO0Y+1wNLGzO14wnRqBw5coRt27bxwgsv6F4bNmwYFhYWjBo1iqFDh2Jvb0/q2SyWvh5JUmwGAL5tXXhgchhB3bxk6QghmolGUQAtWrSI999/n5SUFEJCQvjkk0/o0aNHte2///57pk+fTmJiIoGBgcydO5ehQ4fq3lcUhZkzZ7J06VKysrLo3bs3ixcvJjCwZgMbG3sBVFJUyvHd59m+Lobju5Oo/BMMDvNm5HM98G/nqnZEIZqE9PR03N3d0WrLx/uYmprSo0cP7rzzTnr16k1ZsgNbvo6jMK8EKgqhAY91pMvdAZhZmKicXghxO1QvgNauXcvYsWNZsmQJYWFhLFy4kO+//57Y2Fjc3K4ftLtr1y7uuusuZs+ezX333cc333zD3LlzOXToEB06dABg7ty5zJ49m5UrVxIQEMD06dM5evQoJ06cwMLC4qaZGlsBpNUqpJ3LJv5wCsd3J3F8VxJFBaW699vd0YKhT3WWNUyEuEWKonDo0CHWrVvHunXrOHnypN7748eP56MFn7Fh6UG2/3yc2JS92Jq74GTrTtjdHQntG0BgZw+cPGykZ0iIJkb1AigsLIzu3bvz6aefAqDVavHx8eGFF17gtddeu6796NGjycvLY8OGDbrX7rjjDkJDQ1myZAmKouDl5cXLL7/MK6+8AhVTYt3d3VmxYgWPPPLITTM1dAFUVqolL7uQ3KxCrmQWciWzgLSkHNKSskk7m01yYhb5OfobPTq4WdNjUCvuHBWMm499vWcUwhDEx8ezfft23WPSpElEREQAsG/XQcJ6d9O1NdaYYGXqgJWpHXY2TvTpcg9D7hmGm489xpZlRMfuw9XTGRc3Rxyd7LG1s8XS0hILCwusra0xNzdX8UqFEKr24RYXF3Pw4EGmTZume83IyIjw8HB2795d5Wd2796t+4ZUadCgQfz8888AJCQkkJKSQnh4uO59e3t7wsLC2L17d5UFUFFRkd5O0tnZ5TOoqltArbZ2/nKSqO+OU1xUSkmxltLiMkqKS9GW3bwGNTUzxi/YlZad3OjQxw/fIGfdb5x1nVMIQ+Xm5sYDDzzAAw88ABW/kFX++8oryqFPnz6cPXuW8+fPU6aUcqU4nSvF6aTmncH2b3eUZHcAMvLP8+up+dWe547W97Hgk9l06OVLfHw8ffv2xcTEBGNjY0xMTHT/b2RkxLhx43Tf85KTkxk1ahQajQYjIyM0Go3uATBy5Ej+/e9/A5CZmam7jmt7pzQaDQMHDuTVV18FoKCggOHDh1fZFuCuu+7izTff1D0fOHBgtdfWo0cPZs2apXs+bNgwve+vVwsJCeH999/XPX/ooYd033+vFRQUxCeffKJ7/sQTT5CamlplWz8/P5YuXap7PnHiRM6ePVtlW3d3d1avXq17/vzzz3Pq1Kkq29rb2/P999/rnk+ZMoUjR45U2dbCwoL169frnr/xxhtVLsNQadOmTbr/nzVrFtu2bau27fr163V3M95//302b95cbdu1a9fi6OgIwCeffMKvv/5abdtVq1bh4eEBwBdffMEPP/xQbdsvvvhCN5Fg9erVel/Da33yyScEBQXp8nz11VeMHz+exx57rNrP3C5bW9ub98oqKrpw4YICKLt27dJ7fcqUKUqPHj2q/IypqanyzTff6L22aNEixc3NTVEURdm5c6cCKBcvXtRr89BDDykPP/xwlcecOXOmAshDHvKQhzzkIY9m8MjOzr5pDSKj+IBp06bp9SpptVouX76Ms7Nzo7qvn5OTg4+PD0lJSY1ibFJDkeuW6zYUhnrtct1y3XXN1tb2pm1ULYBcXFwwNja+rhszNTVV1w13LQ8Pjxu2r/xvamoqnp6eem1CQ0OrPKa5ufl19+MdHBxqeVX1z87OzqD+sVSS6zYshnrdGPC1y3UbFrWvW9WVvczMzOjatSuRkZG617RaLZGRkfTs2bPKz/Ts2VOvPcDmzZt17QMCAvDw8NBrk5OTw969e6s9phBCCCEMi+q3wCIiIhg3bhzdunWjR48eLFy4kLy8PCZMmADA2LFj8fb2Zvbs2QBMnjyZvn37smDBAu69917WrFnDgQMH+OKLL6BiAN9LL73ErFm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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_vect = np.linspace(4, 16, 100)\n", + "\n", + "ax = sns.kdeplot(YSample, color=\"rebeccapurple\", label=\"KDE\")\n", + "ax.plot(y_vect, t.pdf(y_vect, N-1, Mu_MLE, np.sqrt(S**2/N + S**2)), color=\"k\", ls=\"--\", label=\"Prediction distribution\")\n", + "\n", + "ax.hlines(0.025, LowY, UppY, color='red', label=\"Prediction interval\")\n", + "ax.hlines(0.05, LowMu, UppMu, color='darkorange', label=\"Confidence interval\")\n", + "ax.scatter(Mu_MLE, 0.025, color='red')\n", + "ax.scatter(Mu_MLE, 0.05, color='darkorange')\n", + "\n", + "ax.legend(frameon=False)\n", + "ax.spines[['right', 'top']].set_visible(False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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choices,Generosity,Perceptions of corruption,Positive affect,Negative affect +Afghanistan,2008,3.724,7.350,0.451,50.500,0.718,0.164,0.882,0.414,0.258 +Afghanistan,2009,4.402,7.509,0.552,50.800,0.679,0.187,0.850,0.481,0.237 +Afghanistan,2010,4.758,7.614,0.539,51.100,0.600,0.118,0.707,0.517,0.275 +Afghanistan,2011,3.832,7.581,0.521,51.400,0.496,0.160,0.731,0.480,0.267 +Afghanistan,2012,3.783,7.661,0.521,51.700,0.531,0.234,0.776,0.614,0.268 +Afghanistan,2013,3.572,7.680,0.484,52.000,0.578,0.059,0.823,0.547,0.273 +Afghanistan,2014,3.131,7.671,0.526,52.300,0.509,0.102,0.871,0.492,0.375 +Afghanistan,2015,3.983,7.654,0.529,52.600,0.389,0.078,0.881,0.491,0.339 +Afghanistan,2016,4.220,7.650,0.559,52.925,0.523,0.040,0.793,0.501,0.348 +Afghanistan,2017,2.662,7.648,0.491,53.250,0.427,-0.123,0.954,0.435,0.371 +Afghanistan,2018,2.694,7.631,0.508,53.575,0.374,-0.095,0.928,0.385,0.405 +Afghanistan,2019,2.375,7.640,0.420,53.900,0.394,-0.109,0.924,0.324,0.502 +Afghanistan,2021,2.436,7.325,0.454,54.550,0.394,-0.085,0.946,0.179,0.607 +Afghanistan,2022,1.281,,0.228,54.875,0.368,,0.733,0.206,0.576 +Afghanistan,2023,1.446,,0.368,55.200,0.228,,0.738,0.261,0.460 +Albania,2007,4.634,9.122,0.821,66.760,0.529,-0.013,0.875,0.489,0.246 +Albania,2009,5.485,9.241,0.833,67.320,0.525,-0.162,0.864,0.564,0.279 +Albania,2010,5.269,9.283,0.733,67.600,0.569,-0.176,0.726,0.576,0.300 +Albania,2011,5.867,9.310,0.759,67.880,0.487,-0.209,0.877,0.566,0.257 +Albania,2012,5.510,9.326,0.785,68.160,0.602,-0.173,0.848,0.553,0.271 +Albania,2013,4.551,9.338,0.759,68.440,0.632,-0.131,0.863,0.541,0.338 +Albania,2014,4.814,9.358,0.626,68.720,0.735,-0.029,0.883,0.573,0.335 +Albania,2015,4.607,9.382,0.639,69.000,0.704,-0.085,0.885,0.579,0.350 +Albania,2016,4.511,9.417,0.638,69.025,0.730,-0.021,0.901,0.567,0.322 +Albania,2017,4.640,9.455,0.638,69.050,0.750,-0.033,0.876,0.547,0.334 +Albania,2018,5.004,9.497,0.684,69.075,0.824,0.005,0.899,0.592,0.319 +Albania,2019,4.995,9.522,0.686,69.100,0.777,-0.103,0.914,0.548,0.274 +Albania,2020,5.365,9.494,0.710,69.125,0.754,0.002,0.891,0.563,0.265 +Albania,2021,5.255,9.588,0.702,69.150,0.827,0.039,0.896,0.554,0.254 +Albania,2022,5.212,9.649,0.724,69.175,0.802,-0.070,0.846,0.547,0.255 +Albania,2023,5.445,9.689,0.691,69.200,0.872,0.068,0.855,0.597,0.314 +Algeria,2010,5.464,9.306,,65.500,0.593,-0.212,0.618,, +Algeria,2011,5.317,9.316,0.810,65.600,0.530,-0.188,0.638,0.503,0.255 +Algeria,2012,5.605,9.330,0.839,65.700,0.587,-0.179,0.690,0.540,0.230 +Algeria,2014,6.355,9.355,0.818,65.900,,,,0.558,0.177 +Algeria,2016,5.341,9.383,0.749,66.100,,,,0.565,0.377 +Algeria,2017,5.249,9.377,0.807,66.200,0.437,-0.174,0.700,0.555,0.289 +Algeria,2018,5.043,9.370,0.799,66.300,0.583,-0.153,0.759,0.534,0.293 +Algeria,2019,4.745,9.361,0.803,66.400,0.385,-0.002,0.741,0.544,0.215 +Algeria,2020,5.438,9.291,0.868,66.500,0.574,-0.124,0.724,0.524,0.311 +Algeria,2021,5.217,9.308,0.841,66.600,0.558,-0.116,0.712,0.498,0.258 +Algeria,2022,5.538,9.323,0.783,66.700,0.440,-0.045,0.611,0.583,0.259 +Angola,2011,5.589,8.944,0.723,51.220,0.584,0.050,0.911,0.667,0.361 +Angola,2012,4.360,8.989,0.753,51.840,0.456,-0.141,0.906,0.591,0.305 +Angola,2013,3.937,9.000,0.722,52.460,0.410,-0.109,0.816,0.650,0.371 +Angola,2014,3.795,9.010,0.755,53.080,0.375,-0.173,0.834,0.595,0.368 +Argentina,2006,6.313,9.937,0.938,65.820,0.733,-0.162,0.852,0.748,0.328 +Argentina,2007,6.073,10.013,0.862,65.940,0.653,-0.146,0.881,0.750,0.279 +Argentina,2008,5.961,10.043,0.892,66.060,0.678,-0.137,0.865,0.720,0.318 +Argentina,2009,6.424,9.972,0.919,66.180,0.637,-0.135,0.885,0.762,0.237 +Argentina,2010,6.441,10.066,0.927,66.300,0.730,-0.132,0.855,0.765,0.211 +Argentina,2011,6.776,10.112,0.889,66.420,0.816,-0.180,0.755,0.769,0.232 +Argentina,2012,6.468,10.091,0.902,66.540,0.747,-0.153,0.817,0.744,0.272 +Argentina,2013,6.582,10.103,0.910,66.660,0.737,-0.136,0.823,0.766,0.254 +Argentina,2014,6.671,10.067,0.918,66.780,0.745,-0.170,0.854,0.769,0.238 +Argentina,2015,6.697,10.083,0.926,66.900,0.881,-0.180,0.851,0.768,0.305 +Argentina,2016,6.427,10.051,0.883,66.950,0.848,-0.198,0.851,0.732,0.312 +Argentina,2017,6.039,10.069,0.907,67.000,0.832,-0.192,0.841,0.715,0.292 +Argentina,2018,5.793,10.032,0.900,67.050,0.846,-0.216,0.855,0.732,0.321 +Argentina,2019,6.086,10.002,0.896,67.100,0.817,-0.217,0.830,0.735,0.319 +Argentina,2020,5.901,9.888,0.897,67.150,0.823,-0.131,0.816,0.679,0.342 +Argentina,2021,5.908,9.977,0.882,67.200,0.819,-0.014,0.816,0.685,0.345 +Argentina,2022,6.261,10.019,0.893,67.250,0.825,-0.130,0.810,0.724,0.284 +Argentina,2023,6.393,9.994,0.892,67.300,0.832,-0.129,0.846,0.720,0.301 +Armenia,2006,4.289,9.021,0.682,63.840,0.520,-0.235,0.850,0.453,0.469 +Armenia,2007,4.882,9.157,0.760,64.080,0.605,-0.255,0.817,0.454,0.412 +Armenia,2008,4.652,9.230,0.709,64.320,0.462,-0.219,0.876,0.486,0.385 +Armenia,2009,4.178,9.085,0.680,64.560,0.441,-0.218,0.882,0.479,0.411 +Armenia,2010,4.368,9.113,0.660,64.800,0.459,-0.180,0.891,0.437,0.426 +Armenia,2011,4.260,9.164,0.705,65.040,0.465,-0.230,0.875,0.411,0.459 +Armenia,2012,4.320,9.239,0.676,65.280,0.502,-0.220,0.893,0.470,0.464 +Armenia,2013,4.277,9.276,0.723,65.520,0.504,-0.201,0.900,0.503,0.450 +Armenia,2014,4.453,9.315,0.739,65.760,0.506,-0.225,0.920,0.510,0.404 +Armenia,2015,4.348,9.351,0.723,66.000,0.551,-0.209,0.901,0.527,0.438 +Armenia,2016,4.325,9.357,0.709,66.275,0.611,-0.178,0.921,0.516,0.437 +Armenia,2017,4.288,9.434,0.698,66.550,0.614,-0.155,0.865,0.552,0.437 +Armenia,2018,5.062,9.490,0.814,66.825,0.808,-0.171,0.677,0.535,0.455 +Armenia,2019,5.488,9.569,0.782,67.100,0.844,-0.181,0.583,0.537,0.430 +Armenia,2021,5.301,9.561,0.762,67.650,0.795,-0.159,0.705,0.566,0.478 +Armenia,2022,5.382,9.683,0.811,67.925,0.790,-0.158,0.705,0.531,0.549 +Armenia,2023,5.679,9.730,0.819,68.200,0.819,-0.179,0.681,0.575,0.423 +Australia,2005,7.341,10.662,0.968,69.800,0.935,,0.390,0.770,0.238 +Australia,2007,7.285,10.694,0.965,69.960,0.891,0.342,0.513,0.762,0.215 +Australia,2008,7.254,10.709,0.947,70.040,0.916,0.300,0.431,0.729,0.218 +Australia,2010,7.450,10.714,0.955,70.200,0.932,0.311,0.366,0.762,0.220 +Australia,2011,7.406,10.723,0.967,70.280,0.945,0.364,0.382,0.724,0.195 +Australia,2012,7.196,10.744,0.945,70.360,0.935,0.268,0.368,0.728,0.214 +Australia,2013,7.364,10.752,0.928,70.440,0.933,0.263,0.432,0.770,0.177 +Australia,2014,7.289,10.763,0.924,70.520,0.923,0.313,0.442,0.740,0.245 +Australia,2015,7.309,10.770,0.952,70.600,0.922,0.327,0.357,0.750,0.210 +Australia,2016,7.250,10.781,0.942,70.675,0.922,0.233,0.399,0.736,0.236 +Australia,2017,7.257,10.787,0.950,70.750,0.911,0.312,0.411,0.728,0.225 +Australia,2018,7.177,10.801,0.940,70.825,0.916,0.141,0.405,0.706,0.187 +Australia,2019,7.234,10.807,0.943,70.900,0.918,0.115,0.430,0.727,0.202 +Australia,2020,7.137,10.794,0.937,70.975,0.905,0.202,0.491,0.726,0.205 +Australia,2021,7.112,10.815,0.920,71.050,0.912,0.234,0.454,0.740,0.235 +Australia,2022,7.035,10.840,0.942,71.125,0.854,0.153,0.545,0.711,0.244 +Australia,2023,7.025,10.846,0.896,71.200,0.876,0.187,0.482,0.731,0.248 +Austria,2006,7.122,10.836,0.936,69.500,0.941,0.297,0.490,0.746,0.174 +Austria,2008,7.181,10.881,0.935,69.700,0.879,0.286,0.614,0.716,0.173 +Austria,2010,7.303,10.856,0.914,69.900,0.896,0.125,0.546,0.710,0.156 +Austria,2011,7.471,10.881,0.944,70.000,0.939,0.126,0.703,0.672,0.145 +Austria,2012,7.401,10.884,0.945,70.100,0.920,0.112,0.771,0.712,0.157 +Austria,2013,7.499,10.878,0.950,70.200,0.922,0.163,0.679,0.725,0.163 +Austria,2014,6.950,10.877,0.899,70.300,0.885,0.112,0.567,0.721,0.170 +Austria,2015,7.076,10.876,0.928,70.400,0.900,0.093,0.557,0.748,0.164 +Austria,2016,7.048,10.885,0.926,70.525,0.889,0.074,0.524,0.713,0.197 +Austria,2017,7.294,10.900,0.906,70.650,0.890,0.128,0.518,0.699,0.180 +Austria,2018,7.396,10.919,0.912,70.775,0.904,0.048,0.523,0.695,0.226 +Austria,2019,7.195,10.930,0.964,70.900,0.903,0.054,0.457,0.727,0.205 +Austria,2020,7.213,10.859,0.925,71.025,0.912,0.004,0.464,0.716,0.206 +Austria,2021,7.080,10.899,0.863,71.150,0.795,0.158,0.501,0.722,0.259 +Austria,2022,6.999,10.938,0.876,71.275,0.856,0.137,0.524,0.718,0.226 +Austria,2023,6.636,10.930,0.874,71.400,0.874,0.209,0.529,0.712,0.240 +Azerbaijan,2006,4.728,9.154,0.854,60.580,0.772,-0.239,0.774,0.469,0.276 +Azerbaijan,2007,4.568,9.366,0.753,60.860,0.522,-0.211,0.871,0.474,0.284 +Azerbaijan,2008,4.817,9.447,0.684,61.140,0.601,-0.034,0.715,0.561,0.227 +Azerbaijan,2009,4.574,9.515,0.736,61.420,0.498,-0.091,0.754,0.522,0.234 +Azerbaijan,2010,4.219,9.553,0.687,61.700,0.501,-0.128,0.858,0.516,0.272 +Azerbaijan,2011,4.680,9.541,0.725,61.980,0.537,-0.110,0.795,0.522,0.258 +Azerbaijan,2012,4.911,9.549,0.762,62.260,0.599,-0.146,0.763,0.523,0.266 +Azerbaijan,2013,5.481,9.592,0.770,62.540,0.672,-0.173,0.699,0.516,0.242 +Azerbaijan,2014,5.252,9.607,0.799,62.820,0.733,-0.214,0.654,0.502,0.220 +Azerbaijan,2015,5.147,9.606,0.786,63.100,0.764,-0.203,0.616,0.520,0.206 +Azerbaijan,2016,5.304,9.563,0.777,63.225,0.713,-0.210,0.607,0.509,0.191 +Azerbaijan,2017,5.152,9.555,0.787,63.350,0.731,-0.231,0.653,0.512,0.198 +Azerbaijan,2018,5.168,9.562,0.781,63.475,0.772,-0.237,0.561,0.527,0.191 +Azerbaijan,2019,5.173,9.578,0.887,63.600,0.854,-0.220,0.457,0.577,0.164 +Azerbaijan,2022,4.576,9.619,0.665,63.975,0.800,0.075,0.696,0.533,0.401 +Azerbaijan,2023,5.214,9.637,0.713,64.100,0.829,-0.160,0.627,0.509,0.221 +Bahrain,2009,5.701,10.714,0.904,64.760,0.896,0.031,0.506,0.707,0.422 +Bahrain,2010,5.937,10.728,0.877,65.000,0.862,-0.008,0.715,0.641,0.423 +Bahrain,2011,4.824,10.749,0.908,65.240,0.870,-0.061,0.583,0.506,0.514 +Bahrain,2012,5.027,10.775,0.911,65.480,0.682,,0.438,0.559,0.381 +Bahrain,2013,6.690,10.798,0.884,65.720,0.809,,0.525,0.711,0.306 +Bahrain,2014,6.165,10.802,,65.960,,,,, +Bahrain,2015,6.007,10.788,0.853,66.200,0.850,0.106,,0.653,0.303 +Bahrain,2016,6.170,10.789,0.863,66.125,0.889,0.082,,0.736,0.283 +Bahrain,2017,6.227,10.798,0.876,66.050,0.906,0.128,,0.754,0.290 +Bahrain,2019,7.098,10.815,0.878,65.900,0.907,0.035,,0.711,0.317 +Bahrain,2020,6.173,10.779,0.848,65.825,0.945,0.115,,0.730,0.297 +Bahrain,2023,5.959,10.877,0.817,65.600,0.869,0.155,,0.671,0.336 +Bangladesh,2006,4.319,7.940,0.672,59.120,0.612,0.052,0.786,0.459,0.321 +Bangladesh,2007,4.607,7.997,0.514,59.640,0.605,0.024,0.806,0.484,0.313 +Bangladesh,2008,5.052,8.047,0.467,60.160,0.606,-0.060,0.802,0.545,0.232 +Bangladesh,2009,5.083,8.087,0.528,60.680,0.631,-0.091,0.776,0.506,0.223 +Bangladesh,2010,4.858,8.130,0.549,61.200,0.659,-0.033,0.774,0.496,0.292 +Bangladesh,2011,4.986,8.181,0.606,61.720,0.838,-0.086,0.757,0.501,0.235 +Bangladesh,2012,4.724,8.231,0.582,62.240,0.668,-0.051,0.765,0.537,0.183 +Bangladesh,2013,4.660,8.277,0.530,62.760,0.742,-0.032,0.743,0.492,0.246 +Bangladesh,2014,4.636,8.323,0.577,63.280,0.736,-0.115,0.789,,0.231 +Bangladesh,2015,4.633,8.375,0.601,63.800,0.815,-0.085,0.721,0.543,0.226 +Bangladesh,2016,4.556,8.431,0.649,63.925,0.875,-0.105,0.688,0.437,0.235 +Bangladesh,2017,4.310,8.483,0.713,64.050,0.896,-0.004,0.635,0.436,0.214 +Bangladesh,2018,4.499,8.542,0.706,64.175,0.901,-0.059,0.701,0.433,0.361 +Bangladesh,2019,5.114,8.607,0.673,64.300,0.902,-0.067,0.656,0.433,0.369 +Bangladesh,2020,5.280,8.629,0.739,64.425,0.777,-0.025,0.742,0.485,0.332 +Bangladesh,2021,4.123,8.685,0.485,64.550,0.893,0.089,0.746,0.504,0.448 +Bangladesh,2022,3.408,8.742,0.404,64.675,0.865,-0.058,0.617,0.394,0.448 +Bangladesh,2023,4.114,8.783,0.450,64.800,0.919,0.019,0.756,0.435,0.435 +Belarus,2006,5.658,9.489,0.918,60.060,0.707,-0.252,0.708,0.535,0.269 +Belarus,2007,5.617,9.576,0.858,60.620,0.667,-0.230,0.695,0.502,0.235 +Belarus,2008,5.463,9.677,0.904,61.180,0.640,-0.226,0.696,,0.246 +Belarus,2009,5.564,9.681,0.908,61.740,0.679,-0.209,0.676,0.544,0.223 +Belarus,2010,5.526,9.759,0.918,62.300,0.700,-0.168,0.706,0.532,0.208 +Belarus,2011,5.225,9.813,0.910,62.860,0.656,-0.174,0.672,0.493,0.249 +Belarus,2012,5.749,9.832,0.902,63.420,0.645,-0.223,0.657,0.515,0.181 +Belarus,2013,5.876,9.842,0.923,63.980,0.723,-0.183,0.653,0.545,0.206 +Belarus,2014,5.812,9.858,0.880,64.540,0.647,-0.054,0.682,0.575,0.209 +Belarus,2015,5.719,9.818,0.924,65.100,0.623,-0.097,0.669,0.546,0.184 +Belarus,2016,5.178,9.792,0.927,65.325,0.658,-0.131,0.664,0.503,0.182 +Belarus,2017,5.553,9.818,0.900,65.550,0.621,-0.128,0.654,0.502,0.233 +Belarus,2018,5.234,9.851,0.905,65.775,0.644,-0.181,0.718,0.409,0.236 +Belarus,2019,5.821,9.867,0.917,66.000,0.657,-0.192,0.546,0.559,0.190 +Belgium,2005,7.262,10.744,0.935,68.400,0.924,,0.598,0.677,0.260 +Belgium,2007,7.219,10.791,0.922,68.720,0.901,0.064,0.721,0.744,0.218 +Belgium,2008,7.117,10.788,0.923,68.880,0.887,0.001,0.652,0.709,0.242 +Belgium,2010,6.854,10.778,0.931,69.200,0.807,0.016,0.697,0.793,0.240 +Belgium,2011,7.111,10.782,0.937,69.360,0.880,-0.020,0.711,0.752,0.225 +Belgium,2012,6.935,10.783,0.927,69.520,0.855,-0.056,0.758,0.718,0.238 +Belgium,2013,7.104,10.783,0.909,69.680,0.891,0.011,0.574,0.738,0.217 +Belgium,2014,6.855,10.794,0.944,69.840,0.861,-0.005,0.512,0.744,0.252 +Belgium,2015,6.904,10.809,0.885,70.000,0.869,0.056,0.469,0.747,0.240 +Belgium,2016,6.949,10.816,0.929,70.150,0.866,-0.062,0.497,0.701,0.260 +Belgium,2017,6.928,10.829,0.922,70.300,0.857,0.049,0.543,0.713,0.234 +Belgium,2018,6.892,10.842,0.930,70.450,0.808,-0.130,0.630,0.682,0.250 +Belgium,2019,6.772,10.859,0.884,70.600,0.776,-0.178,0.672,0.699,0.244 +Belgium,2020,6.839,10.799,0.904,70.750,0.767,-0.172,0.634,0.619,0.260 +Belgium,2021,6.882,10.856,0.915,70.900,0.823,0.077,0.523,0.687,0.260 +Belgium,2022,6.857,10.881,0.923,71.050,0.890,0.095,0.483,0.718,0.235 +Belgium,2023,6.944,10.883,0.896,71.200,0.870,0.065,0.522,0.725,0.245 +Belize,2007,6.451,9.192,0.872,64.300,0.705,0.006,0.769,0.732,0.251 +Belize,2014,5.956,9.135,0.757,65.000,0.874,-0.002,0.782,0.735,0.282 +Benin,2006,3.330,7.844,0.445,51.960,0.580,-0.015,0.790,0.521,0.309 +Benin,2008,3.667,7.891,0.382,52.480,0.709,-0.008,0.825,0.574,0.303 +Benin,2011,3.870,7.876,0.477,53.260,0.773,-0.145,0.849,0.574,0.219 +Benin,2012,3.193,7.894,0.523,53.520,0.769,-0.114,0.806,0.563,0.231 +Benin,2013,3.479,7.935,0.577,53.780,0.783,-0.088,0.856,0.646,0.216 +Benin,2014,3.347,7.967,0.506,54.040,0.776,-0.099,0.855,0.558,0.273 +Benin,2015,3.625,7.955,0.434,54.300,0.733,-0.029,0.850,0.555,0.373 +Benin,2016,4.007,7.958,0.493,54.600,0.780,-0.068,0.838,0.578,0.456 +Benin,2017,4.853,7.984,0.436,54.900,0.727,-0.068,0.767,0.598,0.458 +Benin,2018,5.820,8.020,0.504,55.200,0.713,0.000,0.747,0.625,0.468 +Benin,2019,4.976,8.057,0.442,55.500,0.770,-0.018,0.698,0.638,0.441 +Benin,2020,4.408,8.067,0.507,55.800,0.783,-0.086,0.532,0.557,0.305 +Benin,2021,4.493,8.108,0.436,56.100,0.724,-0.016,0.613,0.597,0.435 +Benin,2022,4.217,8.142,0.366,56.400,0.714,-0.033,0.580,0.571,0.444 +Benin,2023,4.420,8.174,0.398,56.700,0.786,-0.073,0.575,0.573,0.428 +Bhutan,2013,5.569,9.097,0.819,62.240,0.810,0.349,0.802,0.664,0.217 +Bhutan,2014,4.939,9.143,0.880,62.420,0.834,0.264,0.650,0.775,0.324 +Bhutan,2015,5.082,9.198,0.848,62.600,0.830,0.273,0.634,0.723,0.312 +Bolivia,2006,5.374,8.671,0.834,60.900,0.770,-0.048,0.794,0.708,0.432 +Bolivia,2007,5.628,8.698,0.796,61.100,0.780,-0.004,0.817,0.746,0.388 +Bolivia,2008,5.298,8.740,0.785,61.300,0.726,-0.096,0.801,0.723,0.392 +Bolivia,2009,6.086,8.756,0.831,61.500,0.779,-0.040,0.763,0.742,0.372 +Bolivia,2010,5.781,8.780,0.807,61.700,0.703,-0.073,0.781,0.720,0.350 +Bolivia,2011,5.779,8.813,0.817,61.900,0.782,-0.043,0.825,0.689,0.361 +Bolivia,2012,6.019,8.847,0.781,62.100,0.862,-0.019,0.840,0.699,0.409 +Bolivia,2013,5.767,8.896,0.803,62.300,0.846,-0.071,0.812,0.721,0.410 +Bolivia,2014,5.865,8.933,0.821,62.500,0.881,0.014,0.832,0.769,0.398 +Bolivia,2015,5.834,8.965,0.829,62.700,0.884,-0.034,0.862,0.749,0.393 +Bolivia,2016,5.770,8.991,0.796,62.850,0.882,-0.051,0.853,0.736,0.376 +Bolivia,2017,5.651,9.017,0.779,63.000,0.884,-0.124,0.819,0.655,0.434 +Bolivia,2018,5.916,9.044,0.827,63.150,0.863,-0.097,0.786,0.705,0.387 +Bolivia,2019,5.674,9.051,0.784,63.300,0.881,-0.090,0.857,0.701,0.419 +Bolivia,2020,5.559,8.946,0.805,63.450,0.877,-0.056,0.868,0.729,0.382 +Bolivia,2021,5.569,8.994,0.798,63.600,0.862,-0.058,0.812,0.721,0.403 +Bolivia,2022,5.929,9.012,0.824,63.750,0.865,-0.083,0.840,0.738,0.426 +Bolivia,2023,5.860,9.025,0.786,63.900,0.832,-0.059,0.877,0.753,0.401 +Bosnia and Herzegovina,2007,4.900,9.191,0.766,67.000,0.342,0.006,0.926,0.570,0.296 +Bosnia and Herzegovina,2009,4.963,9.246,0.735,67.000,0.258,-0.027,0.959,0.507,0.390 +Bosnia and Herzegovina,2010,4.669,9.272,0.773,67.000,0.365,-0.131,0.933,0.465,0.409 +Bosnia and Herzegovina,2011,4.995,9.300,0.725,67.000,0.333,-0.038,0.925,0.551,0.326 +Bosnia and Herzegovina,2012,4.773,9.310,0.779,67.000,0.420,-0.016,0.953,0.469,0.338 +Bosnia and Herzegovina,2013,5.124,9.349,0.767,67.000,0.390,0.039,0.970,0.489,0.315 +Bosnia and Herzegovina,2014,5.249,9.373,0.788,67.000,0.412,0.229,0.976,0.491,0.262 +Bosnia and Herzegovina,2015,5.117,9.428,0.656,67.000,0.631,-0.058,0.960,0.486,0.286 +Bosnia and Herzegovina,2016,5.181,9.473,0.808,67.050,0.633,0.130,0.957,0.566,0.304 +Bosnia and Herzegovina,2017,5.090,9.517,0.775,67.100,0.564,0.087,0.923,0.527,0.271 +Bosnia and Herzegovina,2018,5.887,9.566,0.836,67.150,0.659,0.118,0.913,0.568,0.277 +Bosnia and Herzegovina,2019,6.016,9.606,0.873,67.200,0.722,0.074,0.963,0.545,0.238 +Bosnia and Herzegovina,2020,5.516,9.588,0.899,67.250,0.740,0.132,0.916,0.602,0.325 +Bosnia and Herzegovina,2021,5.749,9.674,0.860,67.300,0.759,0.274,0.921,0.604,0.305 +Bosnia and Herzegovina,2022,5.872,9.723,0.856,67.350,0.743,0.191,0.933,0.543,0.285 +Bosnia and Herzegovina,2023,6.009,9.759,0.879,67.400,0.847,0.241,0.948,0.579,0.249 +Botswana,2006,4.739,9.495,0.883,48.840,0.824,-0.201,0.723,0.643,0.226 +Botswana,2008,5.451,9.543,0.832,49.720,0.858,-0.167,0.806,0.677,0.218 +Botswana,2010,3.553,9.446,0.866,50.600,0.826,-0.141,0.814,0.617,0.172 +Botswana,2011,3.520,9.492,0.860,51.040,0.813,-0.248,0.816,0.647,0.160 +Botswana,2012,4.836,9.471,0.837,51.480,0.799,-0.197,0.814,0.695,0.171 +Botswana,2013,4.128,9.557,0.856,51.920,0.767,-0.148,0.749,0.671,0.244 +Botswana,2014,4.031,9.593,0.859,52.360,0.791,-0.099,0.743,0.626,0.245 +Botswana,2015,3.762,9.524,0.816,52.800,0.857,-0.108,0.860,0.676,0.261 +Botswana,2016,3.499,9.573,0.768,53.075,0.852,-0.246,0.729,0.657,0.252 +Botswana,2017,3.505,9.593,0.768,53.350,0.817,-0.242,0.731,0.612,0.276 +Botswana,2018,3.461,9.613,0.795,53.625,0.818,-0.248,0.807,0.688,0.267 +Botswana,2019,3.471,9.624,0.774,53.900,0.833,-0.233,0.792,0.665,0.273 +Botswana,2022,3.435,9.650,0.750,54.725,0.739,-0.218,0.831,0.623,0.287 +Botswana,2023,3.332,9.673,0.701,55.000,0.741,-0.264,0.814,0.657,0.247 +Brazil,2005,6.637,9.435,0.883,63.100,0.882,,0.745,0.770,0.302 +Brazil,2007,6.321,9.512,0.886,63.420,0.777,-0.022,0.728,0.775,0.299 +Brazil,2008,6.691,9.552,0.878,63.580,0.782,-0.083,0.688,0.718,0.265 +Brazil,2009,7.001,9.541,0.913,63.740,0.767,-0.061,0.723,0.744,0.274 +Brazil,2010,6.837,9.604,0.906,63.900,0.806,-0.059,0.656,0.726,0.250 +Brazil,2011,7.038,9.634,0.916,64.060,0.834,-0.078,0.662,0.698,0.268 +Brazil,2012,6.660,9.644,0.890,64.220,0.849,,0.623,0.685,0.350 +Brazil,2013,7.140,9.665,0.910,64.380,0.785,-0.100,0.707,0.725,0.276 +Brazil,2014,6.981,9.661,0.898,64.540,0.714,-0.121,0.710,0.718,0.274 +Brazil,2015,6.547,9.617,0.907,64.700,0.799,-0.021,0.771,0.687,0.325 +Brazil,2016,6.375,9.575,0.912,64.875,0.807,-0.106,0.781,0.711,0.302 +Brazil,2017,6.333,9.580,0.905,65.050,0.765,-0.181,0.794,0.669,0.308 +Brazil,2018,6.191,9.590,0.882,65.225,0.751,-0.123,0.763,0.677,0.350 +Brazil,2019,6.451,9.595,0.899,65.400,0.830,-0.068,0.762,0.701,0.337 +Brazil,2020,6.110,9.555,0.831,65.575,0.786,-0.061,0.729,0.653,0.389 +Brazil,2021,6.010,9.598,0.814,65.750,0.792,0.086,0.739,0.662,0.407 +Brazil,2022,6.257,9.622,0.866,65.925,0.830,-0.064,0.742,0.681,0.341 +Brazil,2023,6.553,9.635,0.856,66.100,0.870,-0.036,0.733,0.694,0.313 +Bulgaria,2007,3.844,9.746,0.832,64.780,0.566,-0.146,0.976,0.500,0.226 +Bulgaria,2010,3.912,9.807,0.843,65.200,0.545,-0.153,0.941,0.513,0.238 +Bulgaria,2011,3.875,9.834,0.860,65.340,0.664,-0.236,0.948,0.490,0.271 +Bulgaria,2012,4.222,9.848,0.838,65.480,0.641,-0.181,0.938,0.510,0.237 +Bulgaria,2013,3.993,9.848,0.829,65.620,0.603,-0.199,0.962,0.537,0.278 +Bulgaria,2014,4.438,9.863,0.886,65.760,0.576,-0.062,0.955,0.542,0.236 +Bulgaria,2015,4.865,9.903,0.908,65.900,0.637,-0.207,0.941,0.556,0.214 +Bulgaria,2016,4.838,9.940,0.926,66.000,0.700,-0.177,0.936,0.545,0.172 +Bulgaria,2017,5.097,9.974,0.942,66.100,0.689,-0.160,0.911,0.542,0.189 +Bulgaria,2018,5.099,10.008,0.924,66.200,0.724,-0.182,0.952,0.554,0.189 +Bulgaria,2019,5.108,10.055,0.948,66.300,0.822,-0.115,0.943,0.577,0.200 +Bulgaria,2020,5.598,10.020,0.916,66.400,0.818,-0.012,0.901,0.642,0.221 +Bulgaria,2021,5.422,10.102,0.884,66.500,0.841,-0.018,0.891,0.647,0.253 +Bulgaria,2022,5.378,10.197,0.953,66.600,0.741,-0.152,0.942,0.582,0.165 +Bulgaria,2023,5.590,10.273,0.935,66.700,0.754,-0.131,0.948,0.539,0.192 +Burkina Faso,2006,3.801,7.327,0.796,49.440,0.588,0.026,0.798,0.678,0.266 +Burkina Faso,2007,4.017,7.337,0.771,49.880,0.582,-0.062,0.833,0.609,0.281 +Burkina Faso,2008,3.846,7.364,0.727,50.320,0.612,-0.103,0.887,0.538,0.304 +Burkina Faso,2010,4.036,7.416,0.773,51.200,0.587,-0.038,0.767,0.565,0.217 +Burkina Faso,2011,4.785,7.450,0.710,51.640,0.725,-0.107,0.707,0.578,0.205 +Burkina Faso,2012,3.955,7.482,0.744,52.080,0.622,-0.072,0.726,0.487,0.300 +Burkina Faso,2013,3.326,7.509,0.745,52.520,0.741,-0.018,0.765,0.592,0.287 +Burkina Faso,2014,3.481,7.521,0.742,52.960,0.710,-0.006,0.801,0.604,0.256 +Burkina Faso,2015,4.419,7.530,0.705,53.400,0.659,0.001,0.693,0.555,0.359 +Burkina Faso,2016,4.206,7.558,0.764,53.775,0.645,-0.003,0.721,0.590,0.337 +Burkina Faso,2017,4.647,7.590,0.785,54.150,0.614,-0.066,0.727,0.580,0.354 +Burkina Faso,2018,4.927,7.626,0.665,54.525,0.721,-0.016,0.757,0.656,0.343 +Burkina Faso,2019,4.741,7.654,0.683,54.900,0.678,-0.007,0.729,0.656,0.365 +Burkina Faso,2020,4.640,7.647,0.668,55.275,0.750,0.120,0.809,0.605,0.388 +Burkina Faso,2021,4.636,7.687,0.658,55.650,0.644,0.064,0.736,0.620,0.363 +Burkina Faso,2023,4.462,7.693,0.580,56.400,0.715,0.105,0.650,0.639,0.346 +Burundi,2008,3.563,6.700,0.291,49.660,0.260,-0.022,0.860,0.415,0.253 +Burundi,2009,3.792,6.687,0.326,50.280,0.427,-0.021,0.718,0.607,0.164 +Burundi,2011,3.706,6.694,0.422,51.520,0.490,-0.063,0.677,0.572,0.190 +Burundi,2014,2.905,6.723,0.565,53.380,0.431,-0.059,0.808,0.622,0.251 +Burundi,2018,3.775,6.607,0.485,55.200,0.646,-0.027,0.599,0.636,0.363 +Cambodia,2006,3.569,7.746,0.793,57.640,,0.250,0.829,,0.341 +Cambodia,2007,4.156,7.826,0.675,57.980,0.819,0.111,0.879,,0.320 +Cambodia,2008,4.462,7.874,0.619,58.320,0.914,0.041,0.888,0.600,0.335 +Cambodia,2009,4.111,7.860,0.818,58.660,0.937,0.148,0.965,0.691,0.188 +Cambodia,2010,4.141,7.904,0.697,59.000,0.940,0.345,0.896,0.662,0.422 +Cambodia,2011,4.161,7.957,0.716,59.340,0.927,0.413,0.775,0.637,0.308 +Cambodia,2012,3.899,8.013,0.606,59.680,0.956,0.242,0.890,0.713,0.352 +Cambodia,2013,3.674,8.070,0.651,60.020,0.941,0.159,0.812,0.670,0.440 +Cambodia,2014,3.883,8.125,0.693,60.360,0.938,0.234,0.843,0.682,0.482 +Cambodia,2015,4.162,8.179,0.729,60.700,0.956,0.204,0.825,0.731,0.399 +Cambodia,2016,4.461,8.233,0.746,60.900,0.958,0.070,0.840,0.713,0.398 +Cambodia,2017,4.586,8.287,0.765,61.100,0.964,0.082,0.821,0.669,0.408 +Cambodia,2018,5.122,8.347,0.795,61.300,0.958,0.029,,0.723,0.414 +Cambodia,2019,4.998,8.404,0.759,61.500,0.957,0.007,0.828,0.704,0.390 +Cambodia,2020,4.377,8.361,0.724,61.700,0.963,0.047,0.863,0.771,0.390 +Cambodia,2021,4.555,8.379,0.713,61.900,0.965,0.012,0.844,0.759,0.391 +Cambodia,2022,4.250,8.419,0.784,62.100,0.946,0.151,0.860,0.756,0.388 +Cambodia,2023,4.221,8.462,0.738,62.300,0.961,0.070,0.799,0.683,0.394 +Cameroon,2006,3.851,8.090,0.690,47.840,0.653,-0.020,0.907,0.588,0.271 +Cameroon,2007,4.350,8.104,0.717,48.280,0.644,-0.042,0.910,0.630,0.249 +Cameroon,2008,4.292,8.104,0.697,48.720,0.580,-0.079,0.945,0.613,0.312 +Cameroon,2009,4.741,8.101,0.729,49.160,0.698,-0.027,0.925,0.592,0.250 +Cameroon,2010,4.554,8.101,0.759,49.600,0.792,-0.008,0.875,0.594,0.274 +Cameroon,2011,4.434,8.106,0.738,50.040,0.817,-0.038,0.870,0.608,0.272 +Cameroon,2012,4.245,8.123,0.743,50.480,0.766,-0.041,0.898,0.617,0.284 +Cameroon,2013,4.271,8.144,0.760,50.920,0.794,-0.039,0.867,0.640,0.268 +Cameroon,2014,4.240,8.169,0.778,51.360,0.795,-0.080,0.856,0.604,0.216 +Cameroon,2015,5.038,8.193,0.646,51.800,0.791,0.041,0.868,0.624,0.346 +Cameroon,2016,4.816,8.207,0.659,52.475,0.713,-0.012,0.879,0.635,0.367 +Cameroon,2017,5.074,8.214,0.695,53.150,0.767,-0.036,0.844,0.632,0.377 +Cameroon,2018,5.251,8.225,0.677,53.825,0.816,0.028,0.884,0.630,0.356 +Cameroon,2019,4.937,8.231,0.711,54.500,0.712,-0.015,0.817,0.606,0.326 +Cameroon,2020,5.241,8.207,0.720,55.175,0.675,0.042,0.837,0.626,0.386 +Cameroon,2021,4.963,8.216,0.695,55.850,0.715,-0.029,0.849,0.612,0.347 +Cameroon,2022,4.712,8.225,0.629,56.525,0.675,0.022,0.849,0.586,0.362 +Cameroon,2023,4.946,8.238,0.716,57.200,0.739,-0.028,0.855,0.588,0.356 +Canada,2005,7.418,10.707,0.962,70.500,0.957,0.246,0.503,0.783,0.233 +Canada,2007,7.482,10.734,,70.620,0.930,0.244,0.406,0.812,0.257 +Canada,2008,7.486,10.733,0.939,70.680,0.926,0.256,0.370,0.802,0.202 +Canada,2009,7.488,10.692,0.943,70.740,0.915,0.241,0.413,0.793,0.248 +Canada,2010,7.650,10.711,0.954,70.800,0.934,0.225,0.413,0.791,0.233 +Canada,2011,7.426,10.733,0.922,70.860,0.951,0.247,0.433,0.803,0.248 +Canada,2012,7.415,10.739,0.948,70.920,0.918,0.284,0.466,0.776,0.229 +Canada,2013,7.594,10.752,0.936,70.980,0.916,0.310,0.406,0.801,0.263 +Canada,2014,7.304,10.770,0.918,71.040,0.939,0.264,0.442,0.791,0.259 +Canada,2015,7.413,10.769,0.939,71.100,0.931,0.247,0.427,0.792,0.286 +Canada,2016,7.245,10.768,0.924,71.150,0.912,0.205,0.385,0.768,0.237 +Canada,2017,7.415,10.786,0.934,71.200,0.945,0.157,0.362,0.799,0.218 +Canada,2018,7.175,10.799,0.923,71.250,0.946,0.100,0.372,0.773,0.259 +Canada,2019,7.109,10.803,0.925,71.300,0.912,0.105,0.436,0.781,0.285 +Canada,2020,7.025,10.740,0.931,71.350,0.887,0.043,0.434,0.738,0.307 +Canada,2021,6.939,10.783,0.926,71.400,0.898,0.189,0.384,0.763,0.276 +Canada,2022,6.918,10.799,0.929,71.450,0.838,0.220,0.442,0.719,0.287 +Canada,2023,6.841,10.794,0.902,71.500,0.847,0.196,0.468,0.726,0.304 +Central African Republic,2007,4.160,6.946,0.532,41.480,0.663,0.079,0.782,0.567,0.330 +Central African Republic,2010,3.568,7.031,0.483,42.500,0.690,-0.037,0.845,0.478,0.257 +Central African Republic,2011,3.678,7.057,0.387,42.840,0.780,-0.016,0.834,0.502,0.277 +Central African Republic,2016,2.693,6.707,0.290,44.750,0.624,0.033,0.859,0.551,0.494 +Central African Republic,2017,3.476,6.733,0.320,45.300,0.645,0.074,0.890,0.602,0.599 +Chad,2006,3.435,7.369,0.724,47.080,0.306,0.022,0.961,0.571,0.263 +Chad,2007,4.141,7.368,0.479,47.460,0.295,-0.017,0.874,0.598,0.245 +Chad,2008,4.632,7.363,0.571,47.840,0.527,0.057,0.944,0.569,0.225 +Chad,2009,3.639,7.369,0.646,48.220,0.401,0.016,0.931,0.601,0.221 +Chad,2010,3.743,7.462,0.734,48.600,0.505,0.020,0.858,0.560,0.287 +Chad,2011,4.393,7.428,0.819,48.980,0.540,0.025,0.876,0.579,0.289 +Chad,2012,4.033,7.478,0.673,49.360,0.563,-0.039,0.884,0.498,0.316 +Chad,2013,3.508,7.498,0.714,49.740,0.488,-0.051,0.882,0.437,0.314 +Chad,2014,3.460,7.529,0.733,50.120,0.567,-0.075,0.881,0.524,0.329 +Chad,2015,4.323,7.525,0.751,50.500,0.474,-0.034,0.889,0.593,0.358 +Chad,2016,4.029,7.429,0.616,50.875,0.525,0.047,0.820,0.564,0.468 +Chad,2017,4.559,7.365,0.661,51.250,0.615,0.003,0.792,0.584,0.538 +Chad,2018,4.486,7.355,0.577,51.625,0.650,0.020,0.763,0.532,0.544 +Chad,2019,4.251,7.354,0.640,52.000,0.537,0.051,0.832,0.556,0.460 +Chad,2022,4.397,7.253,0.720,53.125,0.679,0.218,0.805,0.588,0.499 +Chad,2023,4.544,7.254,0.609,53.500,0.586,0.138,0.755,0.541,0.467 +Chile,2006,6.063,9.870,0.836,67.780,0.744,0.161,0.634,0.752,0.348 +Chile,2007,5.698,9.910,0.815,67.960,0.662,0.236,0.723,0.708,0.342 +Chile,2008,5.789,9.938,0.804,68.140,0.640,0.076,0.741,0.706,0.330 +Chile,2009,6.494,9.916,0.832,68.320,0.747,0.141,0.734,0.756,0.300 +Chile,2010,6.636,9.963,0.857,68.500,0.786,0.100,0.702,0.760,0.300 +Chile,2011,6.526,10.013,0.819,68.680,0.701,0.104,0.753,0.758,0.317 +Chile,2012,6.599,10.063,0.855,68.860,0.734,0.186,0.782,0.736,0.288 +Chile,2013,6.740,10.086,0.862,69.040,0.737,0.077,0.741,0.791,0.285 +Chile,2014,6.844,10.094,0.862,69.220,0.733,0.209,0.758,0.800,0.276 +Chile,2015,6.533,10.105,0.827,69.400,0.769,0.032,0.812,0.752,0.333 +Chile,2016,6.579,10.110,0.841,69.550,0.652,0.094,0.858,0.792,0.283 +Chile,2017,6.320,10.108,0.880,69.700,0.790,-0.028,0.836,0.765,0.291 +Chile,2018,6.436,10.130,0.890,69.850,0.789,-0.068,0.816,0.755,0.276 +Chile,2019,5.942,10.119,0.869,70.000,0.659,-0.110,0.860,0.741,0.337 +Chile,2020,6.151,10.042,0.888,70.150,0.781,0.026,0.812,0.753,0.336 +Chile,2021,6.436,10.143,0.891,70.300,0.803,-0.052,0.859,0.735,0.221 +Chile,2022,6.415,10.161,0.887,70.450,0.793,-0.014,0.796,0.775,0.253 +Chile,2023,6.230,10.155,0.874,70.600,0.815,-0.027,0.836,0.779,0.263 +China,2006,4.560,8.696,0.747,65.660,,,,0.658,0.170 +China,2007,4.863,8.824,0.811,65.920,,-0.182,,0.664,0.159 +China,2008,4.846,8.911,0.748,66.180,0.853,-0.098,,0.705,0.147 +China,2009,4.454,8.996,0.798,66.440,0.771,-0.166,,0.670,0.162 +China,2010,4.653,9.092,0.768,66.700,0.805,-0.139,,0.658,0.158 +China,2011,5.037,9.178,0.787,66.960,0.824,-0.192,,0.710,0.134 +China,2012,5.095,9.247,0.788,67.220,0.808,-0.190,,0.689,0.159 +China,2013,5.241,9.315,0.778,67.480,0.805,-0.163,,0.717,0.142 +China,2014,5.196,9.380,0.820,67.740,,-0.222,,0.710,0.112 +China,2015,5.304,9.442,0.794,68.000,,-0.250,,0.667,0.171 +China,2016,5.325,9.503,0.742,68.125,,-0.233,,0.683,0.146 +China,2017,5.099,9.564,0.772,68.250,0.878,-0.180,,0.682,0.214 +China,2018,5.131,9.625,0.788,68.375,0.895,-0.164,,0.722,0.190 +China,2019,5.144,9.679,0.822,68.500,0.927,-0.178,,0.760,0.147 +China,2020,5.771,9.699,0.808,68.625,0.891,-0.109,,0.663,0.245 +China,2021,5.863,9.779,0.856,68.750,0.875,0.020,,0.698,0.240 +China,2023,6.145,9.861,0.797,69.000,0.793,-0.032,,0.708,0.210 +Colombia,2006,6.025,9.277,0.910,66.320,0.805,-0.021,0.808,0.776,0.326 +Colombia,2007,6.138,9.330,0.894,66.540,0.786,-0.046,0.860,0.774,0.287 +Colombia,2008,6.168,9.351,0.880,66.760,0.795,-0.047,0.763,0.768,0.307 +Colombia,2009,6.272,9.351,0.886,66.980,0.757,-0.060,0.837,0.786,0.273 +Colombia,2010,6.408,9.383,0.893,67.200,0.816,-0.055,0.815,0.792,0.265 +Colombia,2011,6.464,9.440,0.904,67.420,0.811,-0.079,0.847,0.785,0.286 +Colombia,2012,6.375,9.468,0.914,67.640,0.828,-0.015,0.868,0.829,0.294 +Colombia,2013,6.607,9.508,0.901,67.860,0.841,-0.076,0.898,0.815,0.278 +Colombia,2014,6.449,9.542,0.907,68.080,0.801,-0.096,0.887,0.825,0.278 +Colombia,2015,6.388,9.562,0.890,68.300,0.791,-0.106,0.843,0.803,0.292 +Colombia,2016,6.234,9.572,0.882,68.475,0.835,-0.106,0.898,0.770,0.294 +Colombia,2017,6.157,9.570,0.909,68.650,0.838,-0.163,0.875,0.790,0.299 +Colombia,2018,5.984,9.577,0.871,68.825,0.851,-0.154,0.855,0.775,0.301 +Colombia,2019,6.350,9.590,0.873,69.000,0.822,-0.177,0.854,0.791,0.322 +Colombia,2020,5.709,9.500,0.797,69.175,0.840,-0.091,0.808,0.759,0.340 +Colombia,2021,5.290,9.593,0.793,69.350,0.775,-0.065,0.831,0.752,0.348 +Colombia,2022,5.892,9.658,0.877,69.525,0.799,-0.164,0.863,0.762,0.306 +Colombia,2023,5.904,9.667,0.833,69.700,0.823,-0.142,0.870,0.754,0.285 +Comoros,2009,3.476,7.999,0.629,56.760,0.508,-0.082,0.838,0.626,0.167 +Comoros,2010,3.812,8.015,0.721,57.000,0.529,-0.003,0.741,0.664,0.178 +Comoros,2011,3.838,8.034,0.722,57.240,0.500,-0.084,0.732,0.622,0.173 +Comoros,2012,3.956,8.044,0.719,57.480,0.534,-0.130,0.651,0.616,0.212 +Comoros,2018,3.973,8.100,0.621,58.725,0.560,0.075,0.794,0.688,0.337 +Comoros,2019,4.609,8.099,0.632,58.900,0.538,0.067,0.762,0.665,0.336 +Comoros,2022,3.545,8.085,0.472,59.425,0.481,-0.018,0.732,0.603,0.352 +Comoros,2023,3.588,8.095,0.483,59.600,0.452,0.004,0.704,0.535,0.405 +Congo (Brazzaville),2008,3.820,8.390,0.555,52.240,0.526,-0.125,,0.603,0.298 +Congo (Brazzaville),2011,4.510,8.502,0.637,53.380,0.745,-0.137,0.833,0.601,0.288 +Congo (Brazzaville),2012,3.919,8.569,0.622,53.760,0.773,-0.144,0.800,0.547,0.323 +Congo (Brazzaville),2013,3.955,8.538,0.680,54.140,0.726,-0.107,0.752,0.599,0.291 +Congo (Brazzaville),2014,4.056,8.579,0.686,54.520,0.662,-0.140,0.808,0.558,0.400 +Congo (Brazzaville),2015,4.691,8.519,0.642,54.900,0.850,-0.129,0.841,0.555,0.261 +Congo (Brazzaville),2016,4.119,8.381,0.615,55.225,0.786,-0.091,0.790,0.586,0.304 +Congo (Brazzaville),2017,4.884,8.312,0.655,55.550,0.778,-0.148,0.763,0.574,0.382 +Congo (Brazzaville),2018,5.490,8.239,0.621,55.875,0.699,-0.105,0.738,0.571,0.448 +Congo (Brazzaville),2019,5.213,8.215,0.625,56.200,0.686,-0.059,0.741,0.594,0.405 +Congo (Brazzaville),2020,5.079,8.127,0.597,56.525,0.761,-0.024,0.728,0.572,0.435 +Congo (Brazzaville),2021,4.921,8.082,0.568,56.850,0.738,-0.023,0.733,0.568,0.420 +Congo (Brazzaville),2022,5.805,8.074,0.646,57.175,0.698,0.025,0.760,0.583,0.477 +Congo (Brazzaville),2023,4.954,8.086,0.561,57.500,0.702,-0.051,0.745,0.585,0.409 +Congo (Kinshasa),2009,3.984,6.699,0.733,49.400,0.556,-0.025,0.824,0.487,0.283 +Congo (Kinshasa),2011,4.517,6.769,0.744,50.400,0.631,-0.028,0.856,0.565,0.208 +Congo (Kinshasa),2012,4.639,6.803,0.770,50.900,0.557,-0.037,0.807,0.626,0.230 +Congo (Kinshasa),2013,4.497,6.851,0.830,51.400,0.480,0.009,0.913,0.556,0.187 +Congo (Kinshasa),2014,4.414,6.907,0.822,51.900,0.556,0.006,0.814,0.519,0.305 +Congo (Kinshasa),2015,3.903,6.940,0.767,52.400,0.574,-0.050,0.866,0.538,0.301 +Congo (Kinshasa),2016,4.522,6.929,0.864,52.825,0.637,-0.027,0.875,0.610,0.222 +Congo (Kinshasa),2017,4.311,6.931,0.670,53.250,0.704,0.066,0.809,0.541,0.404 +Congo (Kinshasa),2022,3.207,7.032,0.654,55.375,0.664,0.080,0.836,0.563,0.461 +Congo (Kinshasa),2023,3.383,7.076,0.572,55.800,0.687,0.152,0.837,0.546,0.497 +Costa Rica,2006,7.082,9.607,0.937,68.560,0.882,0.052,0.798,0.815,0.236 +Costa Rica,2007,7.432,9.672,0.918,68.720,0.923,0.089,0.820,0.826,0.240 +Costa Rica,2008,6.851,9.704,0.916,68.880,0.912,0.087,0.816,0.838,0.233 +Costa Rica,2009,7.615,9.682,0.900,69.040,0.886,0.057,0.787,0.840,0.217 +Costa Rica,2010,7.271,9.721,0.915,69.200,0.881,0.038,0.763,0.827,0.221 +Costa Rica,2011,7.229,9.752,0.892,69.360,0.926,-0.042,0.837,0.794,0.269 +Costa Rica,2012,7.272,9.788,0.902,69.520,0.929,0.037,0.794,0.837,0.263 +Costa Rica,2013,7.158,9.801,0.902,69.680,0.898,0.009,0.813,0.809,0.278 +Costa Rica,2014,7.247,9.824,0.914,69.840,0.927,0.000,0.788,0.797,0.290 +Costa Rica,2015,6.854,9.850,0.878,70.000,0.907,-0.068,0.761,0.811,0.286 +Costa Rica,2016,7.136,9.881,0.901,70.000,0.873,-0.042,0.781,0.830,0.281 +Costa Rica,2017,7.225,9.912,0.922,70.000,0.936,-0.086,0.742,0.791,0.275 +Costa Rica,2018,7.141,9.928,0.876,70.000,0.942,-0.117,0.781,0.802,0.326 +Costa Rica,2019,6.998,9.944,0.906,70.000,0.927,-0.156,0.836,0.791,0.303 +Costa Rica,2020,6.338,9.892,0.834,70.000,0.889,-0.139,0.772,0.759,0.350 +Costa Rica,2021,6.408,9.961,0.876,70.000,0.887,-0.029,0.782,0.774,0.318 +Costa Rica,2022,7.077,9.998,0.902,70.000,0.910,-0.049,0.751,0.793,0.272 +Costa Rica,2023,7.384,10.021,0.875,70.000,0.933,-0.067,0.767,0.806,0.282 +Croatia,2007,5.821,10.174,0.910,66.940,0.662,-0.099,0.934,0.550,0.337 +Croatia,2009,5.433,10.120,0.861,67.180,0.549,-0.278,0.958,0.557,0.272 +Croatia,2010,5.596,10.110,0.796,67.300,0.564,-0.244,0.973,0.554,0.259 +Croatia,2011,5.385,10.113,0.790,67.420,0.517,-0.205,0.977,0.552,0.273 +Croatia,2012,6.028,10.092,0.776,67.540,0.542,-0.250,0.924,0.572,0.271 +Croatia,2013,5.885,10.091,0.751,67.660,0.627,-0.211,0.936,0.554,0.285 +Croatia,2014,5.381,10.091,0.646,67.780,0.519,0.125,0.918,0.545,0.286 +Croatia,2015,5.205,10.124,0.768,67.900,0.694,-0.104,0.849,0.570,0.294 +Croatia,2016,5.417,10.166,0.798,68.075,0.672,-0.072,0.884,0.569,0.337 +Croatia,2017,5.343,10.211,0.770,68.250,0.716,-0.112,0.892,0.618,0.316 +Croatia,2018,5.536,10.248,0.910,68.425,0.691,-0.158,0.925,0.512,0.290 +Croatia,2019,5.626,10.287,0.936,68.600,0.739,-0.145,0.932,0.504,0.269 +Croatia,2020,6.508,10.202,0.923,68.775,0.837,-0.071,0.961,0.681,0.286 +Croatia,2021,6.287,10.367,0.918,68.950,0.842,0.001,0.934,0.640,0.274 +Croatia,2022,5.579,10.435,0.910,69.125,0.593,-0.213,0.875,0.573,0.267 +Croatia,2023,5.958,10.462,0.909,69.300,0.573,-0.205,0.810,0.610,0.230 +Cuba,2006,5.418,,0.970,68.000,0.281,,,0.596,0.277 +Cyprus,2006,6.238,10.567,0.878,70.160,0.836,0.012,0.712,0.704,0.253 +Cyprus,2009,6.833,10.559,0.812,70.640,0.775,0.048,0.801,0.668,0.329 +Cyprus,2010,6.387,10.556,0.822,70.800,0.755,0.066,0.833,0.699,0.296 +Cyprus,2011,6.690,10.534,0.844,70.960,0.745,0.173,0.841,0.682,0.272 +Cyprus,2012,6.181,10.484,0.767,71.120,0.725,0.092,0.871,0.687,0.369 +Cyprus,2013,5.439,10.418,0.744,71.280,0.656,0.096,0.867,0.657,0.420 +Cyprus,2014,5.627,10.411,0.770,71.440,0.715,0.054,0.868,0.661,0.397 +Cyprus,2015,5.439,10.451,0.770,71.600,0.628,0.107,0.893,0.660,0.383 +Cyprus,2016,5.795,10.510,0.786,71.800,0.756,-0.036,0.898,0.631,0.336 +Cyprus,2017,6.062,10.556,0.819,72.000,0.812,0.036,0.851,0.670,0.301 +Cyprus,2018,6.276,10.599,0.826,72.200,0.794,-0.031,0.848,0.663,0.298 +Cyprus,2019,6.137,10.639,0.776,72.400,0.740,-0.018,0.865,0.663,0.290 +Cyprus,2020,6.260,10.583,0.806,72.600,0.763,-0.086,0.816,0.671,0.284 +Cyprus,2021,6.269,10.638,0.855,72.800,0.718,-0.038,0.876,0.641,0.275 +Cyprus,2022,5.865,10.683,0.820,73.000,0.698,0.008,0.887,0.659,0.297 +Cyprus,2023,6.071,,0.803,73.200,0.730,,0.840,0.682,0.297 +Czechia,2005,6.439,10.322,0.919,67.100,0.865,,0.901,0.639,0.258 +Czechia,2007,6.500,10.433,0.900,67.340,0.799,-0.069,0.928,0.660,0.277 +Czechia,2010,6.250,10.419,0.934,67.700,0.779,-0.048,0.926,0.648,0.244 +Czechia,2011,6.331,10.434,0.914,67.820,0.787,-0.112,0.950,0.623,0.253 +Czechia,2012,6.334,10.425,0.912,67.940,0.740,-0.160,0.957,0.635,0.257 +Czechia,2013,6.698,10.424,0.888,68.060,0.726,-0.162,0.916,0.656,0.253 +Czechia,2014,6.484,10.445,0.878,68.180,0.800,-0.174,0.897,0.638,0.235 +Czechia,2015,6.608,10.496,0.911,68.300,0.808,-0.152,0.886,0.689,0.206 +Czechia,2016,6.736,10.519,0.931,68.425,0.850,-0.204,0.900,0.710,0.201 +Czechia,2017,6.790,10.567,0.901,68.550,0.832,-0.183,0.867,0.672,0.227 +Czechia,2018,7.034,10.595,0.929,68.675,0.790,-0.299,0.851,0.674,0.178 +Czechia,2020,6.897,10.562,0.964,68.925,0.906,-0.135,0.884,0.748,0.290 +Czechia,2021,6.942,10.615,0.950,69.050,0.891,0.157,0.863,0.716,0.240 +Czechia,2022,6.695,10.637,0.944,69.175,0.908,0.093,0.831,0.743,0.246 +Czechia,2023,6.827,10.639,0.927,69.300,0.906,0.025,0.832,0.750,0.254 +Denmark,2005,8.019,10.849,0.972,68.300,0.971,,0.237,0.777,0.154 +Denmark,2007,7.834,10.889,0.954,68.740,0.932,0.234,0.206,0.778,0.194 +Denmark,2008,7.971,10.878,0.954,68.960,0.970,0.266,0.248,0.759,0.163 +Denmark,2009,7.683,10.822,0.939,69.180,0.949,0.258,0.206,0.782,0.234 +Denmark,2010,7.771,10.836,0.975,69.400,0.944,0.237,0.175,0.796,0.155 +Denmark,2011,7.788,10.845,0.962,69.620,0.935,0.292,0.220,0.778,0.175 +Denmark,2012,7.520,10.844,0.951,69.840,0.933,0.133,0.187,0.783,0.209 +Denmark,2013,7.589,10.849,0.965,70.060,0.920,0.209,0.170,0.826,0.195 +Denmark,2014,7.508,10.860,0.956,70.280,0.942,0.112,0.237,0.780,0.233 +Denmark,2015,7.514,10.876,0.960,70.500,0.941,0.216,0.191,0.801,0.218 +Denmark,2016,7.558,10.900,0.954,70.625,0.948,0.132,0.210,0.786,0.208 +Denmark,2017,7.594,10.922,0.952,70.750,0.955,0.149,0.181,0.779,0.206 +Denmark,2018,7.649,10.936,0.958,70.875,0.935,0.012,0.151,0.773,0.206 +Denmark,2019,7.693,10.948,0.958,71.000,0.963,0.015,0.174,0.797,0.181 +Denmark,2020,7.515,10.924,0.947,71.125,0.938,0.045,0.214,0.753,0.227 +Denmark,2021,7.699,10.968,0.945,71.250,0.933,0.130,0.173,0.792,0.206 +Denmark,2022,7.545,10.997,0.970,71.375,0.930,0.222,0.203,0.787,0.205 +Denmark,2023,7.504,10.996,0.916,71.500,0.923,0.089,0.184,0.757,0.229 +Djibouti,2008,5.009,8.115,0.690,53.640,0.773,0.123,0.576,0.740,0.120 +Djibouti,2009,4.906,8.014,0.901,54.120,0.649,-0.007,0.634,0.630,0.232 +Djibouti,2010,5.006,7.934,,54.600,0.764,-0.072,0.597,, +Djibouti,2011,4.369,8.150,0.633,55.080,0.746,-0.082,0.519,0.543,0.181 +Dominican Republic,2006,5.088,9.306,0.919,65.360,0.858,0.033,0.755,0.733,0.274 +Dominican Republic,2007,5.081,9.365,0.848,65.120,0.886,-0.013,0.772,0.723,0.260 +Dominican Republic,2008,4.842,9.384,0.850,64.880,0.848,-0.050,0.728,0.654,0.329 +Dominican Republic,2009,5.432,9.381,0.878,64.640,0.863,-0.058,0.806,0.709,0.280 +Dominican Republic,2010,4.735,9.448,0.860,64.400,0.824,-0.080,0.780,0.707,0.282 +Dominican Republic,2011,5.397,9.465,0.872,64.160,0.848,0.009,0.788,0.738,0.300 +Dominican Republic,2012,4.753,9.479,0.879,63.920,0.840,-0.067,0.727,0.725,0.297 +Dominican Republic,2013,5.016,9.515,0.878,63.680,0.889,0.016,0.752,0.766,0.295 +Dominican Republic,2014,5.387,9.570,0.891,63.440,0.905,-0.025,0.760,0.772,0.300 +Dominican Republic,2015,5.062,9.625,0.893,63.200,0.856,-0.070,0.755,0.695,0.295 +Dominican Republic,2016,5.239,9.678,0.895,63.400,0.873,-0.085,0.737,0.725,0.278 +Dominican Republic,2017,5.605,9.713,0.894,63.600,0.855,-0.126,0.760,0.710,0.275 +Dominican Republic,2018,5.433,9.769,0.862,63.800,0.867,-0.155,0.762,0.719,0.291 +Dominican Republic,2019,6.004,9.808,0.884,64.000,0.877,-0.127,0.746,0.747,0.264 +Dominican Republic,2020,5.168,9.727,0.806,64.200,0.835,-0.128,0.636,0.724,0.314 +Dominican Republic,2021,6.031,9.832,0.857,64.400,0.859,-0.088,0.677,0.734,0.275 +Dominican Republic,2022,5.518,9.870,0.820,64.600,0.853,-0.086,0.656,0.723,0.306 +Dominican Republic,2023,5.921,9.900,0.860,64.800,0.867,-0.107,0.667,0.730,0.256 +Ecuador,2006,5.024,9.189,0.910,64.440,0.671,-0.097,0.901,0.785,0.357 +Ecuador,2007,4.996,9.194,0.839,64.780,0.670,-0.069,0.830,0.803,0.286 +Ecuador,2008,5.297,9.238,0.829,65.120,0.640,-0.100,0.801,0.811,0.283 +Ecuador,2009,6.022,9.227,0.779,65.460,0.737,-0.114,0.774,0.796,0.256 +Ecuador,2010,5.838,9.245,0.839,65.800,0.723,-0.069,0.806,0.771,0.220 +Ecuador,2011,5.795,9.305,0.818,66.140,0.788,-0.161,0.702,0.806,0.271 +Ecuador,2012,5.961,9.343,0.785,66.480,0.825,-0.089,0.730,0.767,0.333 +Ecuador,2013,6.019,9.376,0.801,66.820,0.787,-0.196,0.646,0.824,0.267 +Ecuador,2014,5.946,9.399,0.831,67.160,0.719,-0.173,0.661,0.841,0.306 +Ecuador,2015,5.964,9.385,0.856,67.500,0.801,-0.120,0.666,0.816,0.323 +Ecuador,2016,6.115,9.358,0.842,67.750,0.846,-0.021,0.774,0.807,0.365 +Ecuador,2017,5.840,9.366,0.849,68.000,0.879,-0.173,0.734,0.793,0.314 +Ecuador,2018,6.128,9.359,0.851,68.250,0.869,-0.105,0.831,0.817,0.328 +Ecuador,2019,5.809,9.341,0.808,68.500,0.830,-0.121,0.839,0.750,0.374 +Ecuador,2020,5.354,9.245,0.804,68.750,0.829,-0.163,0.855,0.755,0.416 +Ecuador,2021,5.435,9.275,0.786,69.000,0.821,-0.086,0.775,0.742,0.403 +Ecuador,2022,5.887,9.293,0.825,69.250,0.759,-0.083,0.866,0.777,0.356 +Ecuador,2023,5.852,9.307,0.782,69.500,0.731,-0.106,0.840,0.763,0.359 +Egypt,2005,5.168,9.042,0.848,61.400,0.817,,,0.689,0.346 +Egypt,2007,5.541,9.138,0.686,61.520,0.609,-0.126,,0.600,0.355 +Egypt,2008,4.632,9.187,0.738,61.580,,-0.093,0.914,0.627,0.301 +Egypt,2009,5.066,9.213,0.744,61.640,0.611,-0.105,0.801,0.549,0.339 +Egypt,2010,4.669,9.243,0.769,61.700,0.486,-0.081,0.826,0.491,0.276 +Egypt,2011,4.174,9.239,0.753,61.760,0.590,-0.157,0.859,0.456,0.353 +Egypt,2012,4.204,9.238,0.737,61.820,0.452,-0.143,0.880,0.458,0.398 +Egypt,2013,3.559,9.236,0.675,61.880,0.474,-0.147,0.913,0.487,0.483 +Egypt,2014,4.885,9.242,0.619,61.940,0.578,-0.132,0.749,0.477,0.327 +Egypt,2015,4.763,9.262,0.730,62.000,0.659,-0.094,0.684,0.554,0.344 +Egypt,2016,4.557,9.284,0.809,62.250,0.656,-0.147,0.818,0.538,0.370 +Egypt,2017,3.929,9.305,0.638,62.500,0.593,-0.158,,0.458,0.414 +Egypt,2018,4.005,9.338,0.759,62.750,0.682,-0.221,,0.407,0.285 +Egypt,2019,4.328,9.374,0.772,63.000,0.774,-0.204,,0.420,0.313 +Egypt,2020,4.472,9.392,0.673,63.250,0.770,-0.119,,0.543,0.442 +Egypt,2021,4.026,9.408,0.717,63.500,0.704,-0.233,0.580,0.387,0.325 +Egypt,2022,4.024,9.456,0.769,63.750,0.733,-0.214,,0.486,0.307 +Egypt,2023,3.881,9.480,0.730,64.000,0.625,-0.210,,0.436,0.352 +El Salvador,2006,5.701,8.885,0.878,65.120,0.683,-0.062,0.807,0.813,0.233 +El Salvador,2007,5.296,8.902,0.717,64.940,0.639,-0.021,0.785,0.833,0.220 +El Salvador,2008,5.191,8.919,0.747,64.760,0.636,-0.084,0.735,0.827,0.232 +El Salvador,2009,6.839,8.894,0.734,64.580,0.671,-0.110,0.648,0.841,0.243 +El Salvador,2010,6.740,8.911,0.757,64.400,0.669,-0.070,0.694,0.823,0.302 +El Salvador,2011,4.741,8.945,0.731,64.220,0.747,-0.133,0.707,0.830,0.336 +El Salvador,2012,5.934,8.968,0.806,64.040,0.683,-0.161,0.786,0.784,0.365 +El Salvador,2013,6.325,8.987,0.827,63.860,0.716,-0.156,0.772,0.801,0.317 +El Salvador,2014,5.857,9.000,0.798,63.680,0.778,-0.201,0.781,0.801,0.330 +El Salvador,2015,6.018,9.020,0.791,63.500,0.733,-0.163,0.805,0.816,0.333 +El Salvador,2016,6.140,9.042,0.794,63.850,0.800,-0.192,0.797,0.742,0.346 +El Salvador,2017,6.339,9.062,0.829,64.200,0.758,-0.179,0.778,0.800,0.268 +El Salvador,2018,6.241,9.084,0.820,64.550,0.863,-0.102,0.801,0.817,0.270 +El Salvador,2019,6.455,9.108,0.764,64.900,0.877,-0.116,0.682,0.826,0.271 +El Salvador,2020,5.462,9.023,0.696,65.250,0.924,-0.132,0.583,0.811,0.329 +El Salvador,2021,6.431,9.126,0.796,65.600,0.915,-0.085,0.663,0.826,0.290 +El Salvador,2022,6.492,9.148,0.772,65.950,0.914,-0.116,0.621,0.823,0.296 +El Salvador,2023,6.482,9.167,0.744,66.300,0.942,-0.104,0.496,0.812,0.318 +Estonia,2006,5.371,10.269,0.910,65.780,0.749,-0.270,0.797,0.589,0.215 +Estonia,2007,5.332,10.346,0.896,66.060,0.712,-0.252,0.743,0.589,0.176 +Estonia,2008,5.452,10.296,0.904,66.340,0.642,-0.223,0.663,0.595,0.218 +Estonia,2009,5.138,10.140,0.874,66.620,0.611,-0.235,0.793,0.591,0.243 +Estonia,2011,5.487,10.240,0.909,67.180,0.735,-0.173,0.687,0.641,0.205 +Estonia,2012,5.364,10.275,0.889,67.460,0.697,-0.197,0.793,0.627,0.199 +Estonia,2013,5.367,10.293,0.901,67.740,0.754,-0.206,0.726,0.651,0.199 +Estonia,2014,5.556,10.325,0.917,68.020,0.773,-0.158,0.652,0.620,0.203 +Estonia,2015,5.629,10.343,0.918,68.300,0.815,-0.169,0.569,0.649,0.183 +Estonia,2016,5.650,10.374,0.938,68.525,0.843,-0.155,0.639,0.657,0.177 +Estonia,2017,5.938,10.429,0.936,68.750,0.862,-0.107,0.668,0.740,0.160 +Estonia,2018,6.091,10.463,0.933,68.975,0.886,-0.147,0.621,0.730,0.163 +Estonia,2019,6.035,10.496,0.934,69.200,0.887,-0.101,0.576,0.738,0.156 +Estonia,2020,6.453,10.488,0.958,69.425,0.954,-0.090,0.398,0.762,0.188 +Estonia,2021,6.554,10.564,0.946,69.650,0.926,0.046,0.441,0.761,0.176 +Estonia,2022,6.357,10.541,0.933,69.875,0.904,0.136,0.390,0.767,0.187 +Estonia,2023,6.430,10.517,0.958,70.100,0.915,0.032,0.334,0.765,0.182 +Eswatini,2011,4.867,8.902,0.837,42.500,0.607,-0.069,0.917,0.756,0.251 +Eswatini,2018,4.212,9.029,0.779,49.300,0.710,-0.182,0.692,0.739,0.252 +Eswatini,2019,4.396,9.048,0.759,50.100,0.597,-0.195,0.724,0.726,0.280 +Eswatini,2022,3.502,9.119,0.712,52.500,0.539,-0.149,0.774,0.661,0.394 +Ethiopia,2012,4.561,7.252,0.659,56.320,0.776,-0.047,,0.556,0.137 +Ethiopia,2013,4.445,7.325,0.602,56.980,0.707,-0.011,0.750,0.570,0.213 +Ethiopia,2014,4.507,7.396,0.640,57.640,0.694,0.076,0.702,0.644,0.303 +Ethiopia,2015,4.573,7.468,0.626,58.300,0.803,0.109,0.567,0.623,0.237 +Ethiopia,2016,4.298,7.531,0.719,58.700,0.744,0.035,0.703,0.627,0.254 +Ethiopia,2017,4.180,7.595,0.734,59.100,0.717,-0.002,0.757,0.514,0.304 +Ethiopia,2018,4.379,7.634,0.740,59.500,0.740,0.036,0.799,0.562,0.272 +Ethiopia,2019,4.100,7.688,0.748,59.900,0.754,0.049,0.732,0.519,0.283 +Ethiopia,2020,4.549,7.720,0.823,60.300,0.769,0.183,0.784,0.615,0.252 +Ethiopia,2022,3.628,7.775,0.740,61.100,0.674,0.357,0.793,0.560,0.335 +Ethiopia,2023,4.093,7.809,0.670,61.500,0.631,0.212,0.800,0.538,0.299 +Finland,2006,7.672,10.745,0.965,68.720,0.969,-0.011,0.132,0.683,0.172 +Finland,2008,7.671,10.796,0.951,69.160,0.934,0.022,0.217,0.691,0.144 +Finland,2010,7.393,10.734,0.935,69.600,0.916,0.085,0.413,0.758,0.202 +Finland,2011,7.354,10.754,0.938,69.820,0.936,0.095,0.320,0.709,0.205 +Finland,2012,7.420,10.735,0.928,70.040,0.921,-0.007,0.361,0.742,0.202 +Finland,2013,7.445,10.722,0.941,70.260,0.919,0.034,0.306,0.752,0.195 +Finland,2014,7.385,10.714,0.952,70.480,0.933,-0.007,0.265,0.766,0.199 +Finland,2015,7.448,10.716,0.948,70.700,0.930,0.105,0.223,0.736,0.191 +Finland,2016,7.660,10.741,0.954,70.775,0.948,-0.033,0.250,0.769,0.182 +Finland,2017,7.788,10.770,0.964,70.850,0.962,-0.008,0.192,0.756,0.176 +Finland,2018,7.858,10.780,0.962,70.925,0.938,-0.133,0.199,0.749,0.182 +Finland,2019,7.780,10.791,0.937,71.000,0.948,-0.058,0.195,0.732,0.181 +Finland,2020,7.889,10.766,0.962,71.075,0.962,-0.123,0.164,0.748,0.193 +Finland,2021,7.794,10.794,0.970,71.150,0.963,-0.039,0.192,0.752,0.175 +Finland,2022,7.729,10.811,0.974,71.225,0.959,0.101,0.190,0.741,0.191 +Finland,2023,7.699,10.808,0.947,71.300,0.943,-0.001,0.185,0.717,0.173 +France,2005,7.093,10.637,0.940,70.700,0.895,,0.688,0.681,0.225 +France,2006,6.583,10.654,0.944,70.800,0.789,0.120,0.699,0.694,0.289 +France,2008,7.008,10.669,0.935,71.000,0.833,-0.037,0.669,0.702,0.281 +France,2009,6.283,10.635,0.918,71.100,0.798,-0.088,0.654,0.691,0.303 +France,2010,6.798,10.649,0.943,71.200,0.850,-0.109,0.623,0.729,0.261 +France,2011,6.959,10.666,0.921,71.300,0.903,-0.108,0.627,0.718,0.281 +France,2012,6.649,10.664,0.937,71.400,0.841,-0.155,0.608,0.705,0.253 +France,2013,6.667,10.665,0.908,71.500,0.878,-0.130,0.699,0.741,0.205 +France,2014,6.467,10.669,0.878,71.600,0.803,-0.124,0.656,0.759,0.216 +France,2015,6.358,10.677,0.896,71.700,0.817,-0.145,0.641,0.740,0.215 +France,2016,6.475,10.685,0.885,71.800,0.787,-0.097,0.623,0.715,0.270 +France,2017,6.635,10.705,0.931,71.900,0.834,-0.129,0.601,0.716,0.242 +France,2018,6.666,10.720,0.921,72.000,0.816,-0.143,0.582,0.705,0.282 +France,2019,6.690,10.735,0.958,72.100,0.827,-0.139,0.568,0.693,0.250 +France,2020,6.714,10.651,0.947,72.200,0.823,-0.176,0.565,0.690,0.231 +France,2021,6.656,10.714,0.915,72.300,0.837,-0.104,0.561,0.685,0.268 +France,2022,6.614,10.737,0.866,72.400,0.798,-0.027,0.533,0.688,0.249 +France,2023,6.557,10.742,0.850,72.500,0.776,0.010,0.558,0.676,0.228 +Gabon,2011,4.255,9.557,0.653,54.460,0.772,-0.213,0.851,0.564,0.264 +Gabon,2012,3.972,9.573,0.736,54.920,0.566,-0.197,0.810,0.504,0.266 +Gabon,2013,3.800,9.593,0.733,55.380,0.682,-0.148,0.780,0.519,0.287 +Gabon,2014,3.918,9.601,0.829,55.840,0.607,-0.201,0.782,0.533,0.293 +Gabon,2015,4.661,9.609,0.756,56.300,0.671,-0.196,0.867,0.600,0.372 +Gabon,2016,4.832,9.601,0.780,56.625,0.699,-0.207,0.817,0.625,0.432 +Gabon,2017,4.782,9.580,0.807,56.950,0.652,-0.231,0.868,0.638,0.446 +Gabon,2018,4.783,9.565,0.785,57.275,0.719,-0.200,0.823,0.614,0.418 +Gabon,2019,4.914,9.580,0.763,57.600,0.736,-0.206,0.846,0.638,0.413 +Gabon,2020,4.887,9.540,0.701,57.925,0.528,-0.194,0.789,0.568,0.416 +Gabon,2021,5.075,9.533,0.754,58.250,0.699,-0.207,0.766,0.620,0.362 +Gabon,2022,5.140,9.543,0.775,58.575,0.699,-0.167,0.803,0.661,0.414 +Gabon,2023,5.104,9.554,0.735,58.900,0.722,-0.160,0.822,0.620,0.424 +Gambia,2017,4.118,7.564,0.697,56.400,0.812,0.111,0.572,0.770,0.277 +Gambia,2018,4.922,7.607,0.685,56.700,0.719,0.440,0.691,0.759,0.379 +Gambia,2019,5.164,7.642,0.694,57.000,0.677,0.409,0.798,0.718,0.401 +Gambia,2022,4.279,7.662,0.588,57.900,0.599,0.360,0.884,0.722,0.438 +Gambia,2023,4.691,7.686,0.651,58.200,0.727,0.430,0.852,0.719,0.291 +Georgia,2006,3.675,8.993,0.647,63.300,0.553,-0.273,0.752,0.353,0.269 +Georgia,2007,3.707,9.117,0.548,63.400,0.464,-0.272,0.697,0.351,0.236 +Georgia,2008,4.156,9.144,0.608,63.500,0.614,-0.230,0.498,0.371,0.262 +Georgia,2009,3.801,9.116,0.544,63.600,0.495,-0.238,0.535,0.391,0.242 +Georgia,2010,4.102,9.184,0.540,63.700,0.558,-0.253,0.460,0.402,0.243 +Georgia,2011,4.203,9.263,0.503,63.800,0.632,-0.260,0.353,0.421,0.247 +Georgia,2012,4.254,9.332,0.533,63.900,0.659,-0.275,0.321,0.443,0.250 +Georgia,2013,4.349,9.371,0.559,64.000,0.722,-0.260,0.349,0.467,0.200 +Georgia,2014,4.288,9.414,0.558,64.100,0.720,-0.239,0.416,0.458,0.204 +Georgia,2015,4.122,9.442,0.517,64.200,0.640,-0.210,0.502,0.448,0.233 +Georgia,2016,4.448,9.470,0.533,64.325,0.606,-0.255,0.561,0.475,0.223 +Georgia,2017,4.451,9.517,0.590,64.450,0.821,-0.250,0.590,0.496,0.210 +Georgia,2018,4.659,9.565,0.617,64.575,0.775,-0.238,0.755,0.479,0.244 +Georgia,2019,4.892,9.615,0.675,64.700,0.811,-0.265,0.647,0.503,0.244 +Georgia,2020,5.123,9.544,0.718,64.825,0.764,-0.225,0.583,0.573,0.295 +Georgia,2021,4.911,9.648,0.671,64.950,0.777,-0.290,0.723,0.510,0.240 +Georgia,2022,5.293,9.743,0.754,65.075,0.821,-0.255,0.655,0.503,0.233 +Georgia,2023,5.351,9.785,0.779,65.200,0.877,-0.268,0.706,0.542,0.231 +Germany,2005,6.620,10.691,0.963,69.900,0.847,,0.781,0.685,0.197 +Germany,2007,6.417,10.760,0.926,69.940,0.801,0.161,0.792,0.647,0.231 +Germany,2008,6.522,10.771,0.923,69.960,0.766,,0.758,0.672,0.220 +Germany,2009,6.641,10.715,0.935,69.980,0.844,0.121,0.690,0.684,0.206 +Germany,2010,6.725,10.758,0.939,70.000,0.843,0.089,0.688,0.698,0.182 +Germany,2011,6.621,10.815,0.947,70.020,0.906,0.027,0.677,0.686,0.165 +Germany,2012,6.702,10.817,0.926,70.040,0.904,0.065,0.679,0.699,0.170 +Germany,2013,6.965,10.819,0.931,70.060,0.894,0.018,0.566,0.693,0.205 +Germany,2014,6.984,10.837,0.938,70.080,0.899,0.082,0.474,0.739,0.188 +Germany,2015,7.037,10.843,0.926,70.100,0.889,0.172,0.412,0.722,0.203 +Germany,2016,6.874,10.857,0.906,70.300,0.871,0.142,0.446,0.709,0.187 +Germany,2017,7.074,10.879,0.892,70.500,0.841,0.139,0.414,0.707,0.196 +Germany,2018,7.118,10.886,0.920,70.700,0.877,0.028,0.496,0.740,0.243 +Germany,2019,7.035,10.894,0.886,70.900,0.885,0.051,0.462,0.712,0.226 +Germany,2020,7.312,10.856,0.905,71.100,0.864,-0.068,0.424,0.698,0.206 +Germany,2021,6.755,10.881,0.868,71.300,0.778,0.074,0.418,0.703,0.251 +Germany,2022,6.608,10.889,0.916,71.500,0.895,0.080,0.417,0.668,0.201 +Germany,2023,6.792,10.878,0.895,71.700,0.845,0.106,0.460,0.706,0.231 +Ghana,2006,4.535,8.067,0.728,52.540,0.849,0.208,0.814,0.637,0.198 +Ghana,2007,5.220,8.084,0.730,52.980,0.891,0.133,0.771,0.658,0.217 +Ghana,2008,4.965,8.145,0.622,53.420,0.838,0.115,0.863,0.674,0.172 +Ghana,2009,4.198,8.167,0.633,53.860,0.757,0.000,0.890,0.714,0.198 +Ghana,2010,4.606,8.219,0.739,54.300,0.891,0.069,0.875,0.693,0.184 +Ghana,2011,5.608,8.326,0.724,54.740,0.852,0.006,0.790,0.658,0.209 +Ghana,2012,5.057,8.390,0.685,55.180,0.679,0.035,0.898,0.709,0.152 +Ghana,2013,4.965,8.436,0.676,55.620,0.794,-0.070,0.880,0.660,0.211 +Ghana,2014,3.860,8.440,0.651,56.060,0.677,-0.004,0.913,0.682,0.280 +Ghana,2015,3.986,8.437,0.687,56.500,0.852,-0.043,0.945,0.675,0.265 +Ghana,2016,4.514,8.447,0.647,56.875,0.751,0.085,0.894,0.659,0.305 +Ghana,2017,5.481,8.503,0.669,57.250,0.783,0.074,0.839,0.715,0.248 +Ghana,2018,5.004,8.542,0.761,57.625,0.817,0.058,0.846,0.716,0.250 +Ghana,2019,4.967,8.584,0.746,58.000,0.787,0.111,0.857,0.645,0.270 +Ghana,2020,5.319,8.569,0.643,58.375,0.824,0.196,0.847,0.675,0.253 +Ghana,2021,4.378,8.601,0.633,58.750,0.730,0.105,0.888,0.588,0.295 +Ghana,2022,4.191,8.613,0.628,59.125,0.786,0.114,0.909,0.620,0.292 +Ghana,2023,4.298,8.610,0.661,59.500,0.834,0.093,0.892,0.638,0.254 +Greece,2005,6.006,10.454,0.837,69.600,0.734,,0.861,0.598,0.264 +Greece,2007,6.647,10.535,0.808,69.760,0.575,-0.196,0.845,0.629,0.222 +Greece,2009,6.039,10.483,0.793,69.920,0.443,-0.298,0.959,0.614,0.254 +Greece,2010,5.840,10.425,0.868,70.000,0.484,-0.308,0.954,0.581,0.292 +Greece,2011,5.372,10.320,0.852,70.080,0.528,-0.321,0.941,0.552,0.323 +Greece,2012,5.096,10.251,0.812,70.160,0.373,-0.310,0.959,0.544,0.352 +Greece,2013,4.720,10.233,0.687,70.240,0.426,-0.277,0.941,0.571,0.482 +Greece,2014,4.756,10.245,0.832,70.320,0.369,-0.293,0.930,0.597,0.385 +Greece,2015,5.623,10.249,0.835,70.400,0.532,-0.277,0.824,0.637,0.277 +Greece,2016,5.303,10.248,0.803,70.525,0.482,-0.265,0.898,0.594,0.336 +Greece,2017,5.148,10.261,0.753,70.650,0.438,-0.295,0.872,0.516,0.333 +Greece,2018,5.409,10.280,0.794,70.775,0.564,-0.340,0.860,0.564,0.255 +Greece,2019,5.952,10.300,0.891,70.900,0.614,-0.293,0.848,0.560,0.236 +Greece,2020,5.788,10.207,0.779,71.025,0.565,-0.246,0.764,0.629,0.322 +Greece,2021,6.104,10.294,0.850,71.150,0.574,-0.161,0.752,0.624,0.311 +Greece,2022,5.900,10.358,0.875,71.275,0.563,-0.318,0.874,0.589,0.183 +Greece,2023,5.796,10.387,0.818,71.400,0.589,-0.223,0.805,0.608,0.311 +Guatemala,2006,5.901,8.850,0.830,58.980,0.663,0.167,0.706,0.789,0.287 +Guatemala,2007,6.330,8.891,0.866,59.260,0.628,0.130,0.810,0.790,0.224 +Guatemala,2008,6.414,8.905,0.866,59.540,0.630,0.200,0.796,0.800,0.234 +Guatemala,2009,6.452,8.890,0.834,59.820,0.643,0.191,0.755,0.814,0.240 +Guatemala,2010,6.290,8.901,0.859,60.100,0.696,0.161,0.795,0.805,0.236 +Guatemala,2011,5.743,8.923,0.768,60.380,0.763,0.003,0.863,0.792,0.289 +Guatemala,2012,5.856,8.935,0.802,60.660,0.865,0.015,0.821,0.808,0.349 +Guatemala,2013,5.985,8.953,0.830,60.940,0.884,0.039,0.817,0.822,0.333 +Guatemala,2014,6.536,8.980,0.834,61.220,0.843,0.102,0.804,0.816,0.305 +Guatemala,2015,6.465,9.003,0.823,61.500,0.869,0.046,0.822,0.826,0.311 +Guatemala,2016,6.359,9.013,0.811,61.700,0.863,0.006,0.812,0.815,0.321 +Guatemala,2017,6.325,9.027,0.826,61.900,0.915,-0.064,0.800,0.819,0.308 +Guatemala,2018,6.627,9.044,0.841,62.100,0.910,-0.016,0.765,0.827,0.262 +Guatemala,2019,6.262,9.068,0.774,62.300,0.901,-0.068,0.773,0.820,0.311 +Guatemala,2022,6.150,9.123,0.806,62.900,0.856,-0.060,0.835,0.835,0.263 +Guatemala,2023,6.421,9.140,0.796,63.100,0.873,-0.049,0.812,0.843,0.303 +Guinea,2011,4.045,7.556,0.598,51.020,0.797,0.036,0.743,0.670,0.260 +Guinea,2012,3.652,7.589,0.542,51.140,0.646,-0.003,0.794,0.657,0.285 +Guinea,2013,3.902,7.604,0.567,51.260,0.693,0.087,0.815,0.606,0.348 +Guinea,2014,3.412,7.615,0.638,51.380,0.684,0.002,0.705,0.643,0.351 +Guinea,2015,3.505,7.627,0.579,51.500,0.666,0.003,0.762,0.658,0.268 +Guinea,2016,3.603,7.704,0.675,51.950,0.726,-0.060,0.803,0.676,0.374 +Guinea,2017,4.874,7.776,0.634,52.400,0.738,0.034,0.750,0.702,0.422 +Guinea,2018,5.252,7.813,0.630,52.850,0.731,0.088,0.778,0.727,0.440 +Guinea,2019,4.768,7.842,0.655,53.300,0.691,0.092,0.756,0.670,0.473 +Guinea,2020,4.972,7.865,0.732,53.750,0.598,0.075,0.790,0.706,0.346 +Guinea,2021,4.945,7.879,0.627,54.200,0.676,0.157,0.784,0.672,0.450 +Guinea,2022,5.317,7.901,0.582,54.650,0.729,0.136,0.770,0.699,0.492 +Guinea,2023,4.827,7.932,0.577,55.100,0.743,0.187,0.791,0.652,0.516 +Guyana,2007,5.993,9.089,0.849,56.240,0.694,0.082,0.836,0.761,0.296 +Haiti,2006,3.754,7.976,0.694,6.720,0.449,0.355,0.854,0.583,0.332 +Haiti,2008,3.846,8.016,0.679,17.360,0.465,0.213,0.812,0.573,0.256 +Haiti,2010,3.766,7.987,0.554,28.000,0.373,0.167,0.848,0.495,0.293 +Haiti,2011,4.845,8.026,0.567,33.320,0.413,0.194,0.682,0.550,0.245 +Haiti,2012,4.413,8.015,0.749,38.640,0.482,0.243,0.717,0.557,0.284 +Haiti,2013,4.622,8.043,0.648,43.960,0.610,0.243,0.669,0.528,0.327 +Haiti,2014,3.889,8.057,0.554,49.280,0.509,0.238,0.708,0.573,0.327 +Haiti,2015,3.570,8.056,0.564,54.600,0.398,0.259,0.777,0.598,0.333 +Haiti,2016,3.352,8.060,0.584,54.900,0.304,0.244,0.839,0.532,0.367 +Haiti,2017,3.824,8.071,0.647,55.200,0.484,0.333,0.647,0.570,0.322 +Haiti,2018,3.615,8.074,0.538,55.500,0.591,0.374,0.720,0.581,0.359 +Honduras,2006,5.397,8.448,0.933,63.100,0.650,0.085,0.844,0.837,0.155 +Honduras,2007,5.097,8.485,0.819,62.900,0.676,0.226,0.826,0.712,0.199 +Honduras,2008,5.420,8.504,0.828,62.700,0.687,0.219,0.863,0.719,0.206 +Honduras,2009,6.033,8.458,0.824,62.500,0.661,0.114,0.857,0.745,0.261 +Honduras,2010,5.866,8.474,0.803,62.300,0.646,0.101,0.820,0.745,0.260 +Honduras,2011,4.961,8.492,0.766,62.100,0.783,0.091,0.884,0.757,0.307 +Honduras,2012,4.602,8.513,0.779,61.900,0.700,-0.007,0.871,0.796,0.294 +Honduras,2013,4.713,8.521,0.792,61.700,0.698,-0.031,0.868,0.795,0.283 +Honduras,2014,5.056,8.533,0.790,61.500,0.696,0.011,0.834,0.794,0.299 +Honduras,2015,4.845,8.553,0.772,61.300,0.534,-0.101,0.848,0.829,0.311 +Honduras,2016,5.648,8.573,0.774,61.725,0.850,0.076,0.793,0.790,0.297 +Honduras,2017,6.020,8.603,0.843,62.150,0.898,0.068,0.783,0.796,0.248 +Honduras,2018,5.908,8.624,0.827,62.575,0.872,0.095,0.804,0.822,0.287 +Honduras,2019,5.930,8.633,0.797,63.000,0.846,0.059,0.815,0.789,0.279 +Honduras,2021,6.114,8.626,0.806,63.850,0.835,0.111,0.847,0.808,0.269 +Honduras,2022,5.932,8.650,0.729,64.275,0.851,0.078,0.834,0.775,0.289 +Honduras,2023,5.861,8.670,0.731,64.700,0.878,0.079,0.784,0.767,0.285 +Hong Kong S.A.R. of China,2006,5.511,10.746,0.812,,0.910,0.150,0.356,0.591,0.236 +Hong Kong S.A.R. of China,2008,5.137,10.816,0.840,,0.922,0.290,0.274,0.575,0.237 +Hong Kong S.A.R. of China,2009,5.397,10.788,0.835,,0.918,0.302,0.272,0.606,0.210 +Hong Kong S.A.R. of China,2010,5.643,10.847,0.857,,0.890,0.326,0.256,0.601,0.183 +Hong Kong S.A.R. of China,2011,5.474,10.887,0.846,,0.894,0.228,0.245,0.582,0.196 +Hong Kong S.A.R. of China,2012,5.484,10.893,0.826,,0.880,0.216,0.380,0.580,0.183 +Hong Kong S.A.R. of China,2014,5.458,10.939,0.834,,0.843,0.218,0.423,0.602,0.243 +Hong Kong S.A.R. of China,2016,5.498,10.970,0.832,,0.800,0.094,0.403,0.569,0.213 +Hong Kong S.A.R. of China,2017,5.362,10.999,0.831,,0.831,0.134,0.416,0.536,0.201 +Hong Kong S.A.R. of China,2019,5.659,10.995,0.856,,0.727,0.062,0.432,0.519,0.358 +Hong Kong S.A.R. of China,2020,5.295,10.931,0.813,,0.705,-0.076,0.380,0.522,0.210 +Hong Kong S.A.R. of China,2021,5.322,11.003,0.821,,0.669,0.021,0.390,0.534,0.224 +Hong Kong S.A.R. of China,2022,5.311,10.976,0.803,,0.697,0.040,0.383,0.549,0.204 +Hungary,2005,5.194,10.103,0.930,65.000,0.697,,0.903,0.578,0.290 +Hungary,2007,4.954,10.147,0.931,65.320,0.538,-0.166,0.895,0.600,0.230 +Hungary,2009,4.895,10.092,0.901,65.640,0.464,-0.131,0.915,0.575,0.228 +Hungary,2010,4.725,10.105,0.896,65.800,0.514,-0.151,0.983,0.574,0.235 +Hungary,2011,4.918,10.127,0.894,65.960,0.631,-0.095,0.940,0.586,0.305 +Hungary,2012,4.683,10.119,0.906,66.120,0.569,-0.142,0.930,0.582,0.315 +Hungary,2013,4.914,10.140,0.877,66.280,0.674,-0.119,0.912,0.647,0.307 +Hungary,2014,5.181,10.184,0.845,66.440,0.494,-0.156,0.855,0.578,0.238 +Hungary,2015,5.344,10.223,0.859,66.600,0.558,-0.204,0.908,0.650,0.245 +Hungary,2016,5.449,10.248,0.900,66.750,0.554,-0.193,0.924,0.590,0.243 +Hungary,2017,6.065,10.292,0.877,66.900,0.661,-0.145,0.886,0.644,0.181 +Hungary,2018,5.936,10.346,0.941,67.050,0.693,-0.249,0.911,0.595,0.201 +Hungary,2019,6.000,10.393,0.947,67.200,0.798,-0.201,0.884,0.653,0.180 +Hungary,2020,6.038,10.349,0.943,67.350,0.771,-0.127,0.836,0.662,0.240 +Hungary,2021,6.227,10.423,0.948,67.500,0.727,-0.046,0.832,0.668,0.192 +Hungary,2022,5.861,10.470,0.937,67.650,0.776,-0.009,0.848,0.628,0.250 +Hungary,2023,5.965,10.473,0.954,67.800,0.755,-0.002,0.847,0.673,0.189 +Iceland,2008,6.888,10.878,0.977,71.200,0.885,0.265,0.708,0.851,0.153 +Iceland,2012,7.591,10.788,0.979,71.600,0.905,0.235,0.759,0.817,0.157 +Iceland,2013,7.501,10.823,0.967,71.700,0.923,0.299,0.713,0.802,0.156 +Iceland,2015,7.498,10.862,0.980,71.900,0.940,0.294,0.639,0.794,0.180 +Iceland,2016,7.510,10.909,0.985,71.925,0.952,0.274,0.719,0.808,0.158 +Iceland,2017,7.476,10.927,0.967,71.950,0.939,0.240,0.727,0.823,0.148 +Iceland,2019,7.533,10.943,0.982,72.000,0.959,,0.699,0.787,0.178 +Iceland,2020,7.575,10.852,0.983,72.025,0.949,0.152,0.644,0.808,0.172 +Iceland,2021,7.565,10.878,0.980,72.050,0.923,0.257,0.664,0.806,0.159 +Iceland,2022,7.449,10.916,0.985,72.075,0.936,0.222,0.692,0.768,0.178 +Iceland,2023,7.562,10.934,0.979,72.100,0.918,0.299,0.697,0.793,0.185 +India,2006,5.348,8.141,0.707,55.860,0.774,,0.855,0.576,0.199 +India,2007,5.027,8.200,0.569,56.220,0.729,-0.056,0.862,0.541,0.253 +India,2008,5.146,8.216,0.684,56.580,0.756,-0.077,0.891,0.573,0.259 +India,2009,4.522,8.278,0.653,56.940,0.679,-0.031,0.895,0.639,0.301 +India,2010,4.989,8.346,0.605,57.300,0.783,0.053,0.863,0.579,0.267 +India,2011,4.635,8.383,0.553,57.660,0.838,-0.043,0.908,0.480,0.232 +India,2012,4.720,8.423,0.511,58.020,0.609,0.062,0.830,0.544,0.295 +India,2013,4.428,8.472,0.553,58.380,0.740,0.079,0.832,0.608,0.330 +India,2014,4.424,8.531,0.621,58.740,0.809,-0.031,0.832,0.651,0.285 +India,2015,4.342,8.596,0.610,59.100,0.777,-0.010,0.776,0.657,0.322 +India,2016,4.179,8.664,0.614,59.400,0.820,0.041,0.765,0.646,0.346 +India,2017,4.046,8.718,0.607,59.700,0.886,-0.046,0.781,0.579,0.318 +India,2018,3.818,8.770,0.638,60.000,0.890,0.080,0.805,0.591,0.357 +India,2019,3.249,8.797,0.561,60.300,0.876,0.108,0.752,0.560,0.466 +India,2020,4.224,8.728,0.616,60.600,0.906,0.068,0.780,0.686,0.383 +India,2021,3.558,8.806,0.570,60.900,0.866,0.052,0.757,0.547,0.429 +India,2022,3.930,8.867,0.608,61.200,0.893,0.085,0.771,0.596,0.432 +India,2023,4.676,8.919,0.633,61.500,0.900,0.121,0.770,0.699,0.389 +Indonesia,2006,4.947,8.839,0.771,60.320,0.713,0.343,0.915,0.715,0.266 +Indonesia,2007,5.101,8.888,0.704,60.540,0.603,0.307,0.960,0.696,0.242 +Indonesia,2008,4.815,8.933,0.675,60.760,0.596,0.160,0.968,0.675,0.239 +Indonesia,2009,5.472,8.966,0.779,60.980,0.784,0.186,0.911,0.768,0.193 +Indonesia,2010,5.457,9.013,0.816,61.200,0.700,0.443,0.954,0.717,0.218 +Indonesia,2011,5.173,9.061,0.825,61.420,0.878,0.433,0.962,0.748,0.273 +Indonesia,2012,5.368,9.107,0.834,61.640,0.770,0.349,0.962,0.764,0.229 +Indonesia,2013,5.292,9.149,0.794,61.860,0.781,0.371,0.973,0.777,0.249 +Indonesia,2014,5.597,9.186,0.905,62.080,0.719,0.403,0.970,0.757,0.242 +Indonesia,2015,5.043,9.222,0.809,62.300,0.779,0.466,0.946,0.796,0.274 +Indonesia,2016,5.136,9.261,0.792,62.425,0.830,0.494,0.890,0.748,0.342 +Indonesia,2017,5.098,9.300,0.796,62.550,0.865,0.482,0.900,0.781,0.319 +Indonesia,2018,5.340,9.341,0.809,62.675,0.879,0.506,0.868,0.796,0.296 +Indonesia,2019,5.347,9.381,0.802,62.800,0.866,0.549,0.861,0.800,0.302 +Indonesia,2020,4.828,9.351,0.751,62.925,0.853,0.529,0.914,0.742,0.351 +Indonesia,2021,5.433,9.381,0.817,63.050,0.885,0.540,0.845,0.799,0.273 +Indonesia,2022,5.585,9.426,0.834,63.175,0.903,0.516,0.862,0.818,0.269 +Indonesia,2023,5.695,9.466,0.781,63.300,0.900,0.590,0.866,0.814,0.289 +Iran,2005,5.308,9.498,0.766,64.300,0.651,,0.636,0.515,0.456 +Iran,2007,5.336,9.595,0.718,64.580,0.533,0.043,0.872,0.553,0.361 +Iran,2008,5.129,9.584,0.633,64.720,0.601,0.040,0.868,0.541,0.345 +Iran,2011,4.768,9.636,0.582,65.140,0.798,0.188,0.665,0.513,0.359 +Iran,2012,4.609,9.585,0.600,65.280,0.764,,0.678,0.529,0.525 +Iran,2013,5.140,9.555,0.664,65.420,0.730,0.202,0.685,0.575,0.552 +Iran,2014,4.682,9.585,0.644,65.560,0.767,0.227,0.640,0.550,0.512 +Iran,2015,4.750,9.548,0.572,65.700,0.780,0.164,0.699,0.548,0.520 +Iran,2016,4.653,9.614,0.566,65.850,0.773,0.176,0.713,0.592,0.526 +Iran,2017,4.717,9.627,0.714,66.000,0.731,0.210,0.715,0.590,0.439 +Iran,2018,4.278,9.591,0.674,66.150,0.603,0.073,0.703,0.482,0.493 +Iran,2019,5.006,9.553,0.698,66.300,0.623,0.128,0.728,0.525,0.449 +Iran,2020,4.865,9.577,0.757,66.450,0.600,0.130,0.710,0.505,0.470 +Iran,2021,4.788,9.616,0.771,66.600,0.609,0.172,0.761,0.518,0.427 +Iran,2022,4.977,9.636,0.800,66.750,0.570,0.209,0.766,0.521,0.466 +Iran,2023,5.004,9.651,0.809,66.900,0.615,0.254,0.764,0.533,0.425 +Iraq,2008,4.590,8.982,0.744,60.940,0.386,-0.061,0.910,0.532,0.448 +Iraq,2009,4.775,8.979,0.862,60.920,0.431,-0.198,0.854,0.504,0.404 +Iraq,2010,5.065,9.009,0.854,60.900,0.419,-0.123,0.859,0.497,0.431 +Iraq,2011,4.725,9.047,0.751,60.880,0.347,-0.068,0.780,0.473,0.557 +Iraq,2012,4.660,9.133,0.730,60.860,0.315,-0.018,0.789,0.410,0.449 +Iraq,2013,4.725,9.159,0.728,60.840,,-0.047,0.710,,0.554 +Iraq,2014,4.542,9.126,0.725,60.820,0.646,0.002,0.726,0.539,0.564 +Iraq,2015,4.493,9.145,0.684,60.800,0.599,0.021,0.762,0.478,0.581 +Iraq,2016,4.413,9.250,0.719,61.275,0.666,-0.050,0.799,0.471,0.570 +Iraq,2017,4.462,9.208,0.695,61.750,0.628,0.001,0.757,0.487,0.591 +Iraq,2018,4.886,9.210,0.764,62.225,0.598,-0.069,0.887,0.552,0.482 +Iraq,2020,4.785,9.088,0.708,63.175,0.700,-0.021,0.849,0.585,0.532 +Iraq,2021,5.094,9.081,0.730,63.650,0.594,0.006,0.901,0.577,0.474 +Iraq,2022,4.928,9.127,0.753,64.125,0.661,0.068,0.855,0.565,0.499 +Iraq,2023,5.475,9.093,0.734,64.600,0.658,-0.017,0.851,0.587,0.469 +Ireland,2006,7.144,10.985,0.967,69.620,0.943,0.235,0.473,0.815,0.209 +Ireland,2008,7.568,10.941,0.983,69.860,0.894,0.315,0.487,0.745,0.148 +Ireland,2009,7.046,10.879,0.959,69.980,0.835,0.308,0.580,0.745,0.233 +Ireland,2010,7.257,10.890,0.973,70.100,0.856,0.341,0.618,0.763,0.201 +Ireland,2011,7.007,10.894,0.977,70.220,0.952,0.376,0.590,0.786,0.190 +Ireland,2012,6.965,10.890,0.962,70.340,0.902,0.295,0.573,0.721,0.237 +Ireland,2013,6.760,10.896,0.955,70.460,0.884,0.325,0.558,0.744,0.245 +Ireland,2014,7.018,10.971,0.968,70.580,0.922,0.257,0.406,0.736,0.229 +Ireland,2015,6.830,11.180,0.953,70.700,0.892,0.226,0.409,0.748,0.225 +Ireland,2016,7.041,11.189,0.958,70.800,0.875,0.169,0.399,0.744,0.211 +Ireland,2017,7.060,11.264,0.943,70.900,0.905,0.210,0.337,0.771,0.213 +Ireland,2018,6.962,11.334,0.938,71.000,0.861,0.138,0.362,0.754,0.213 +Ireland,2019,7.255,11.373,0.944,71.100,0.892,0.067,0.373,0.758,0.223 +Ireland,2020,7.035,11.423,0.960,71.200,0.882,0.000,0.356,0.753,0.246 +Ireland,2021,6.828,11.540,0.850,71.300,0.846,0.131,0.360,0.733,0.245 +Ireland,2022,6.870,11.643,0.906,71.400,0.895,0.138,0.358,0.738,0.234 +Ireland,2023,6.817,11.676,0.921,71.500,0.903,0.182,0.373,0.742,0.245 +Israel,2006,7.173,10.368,0.927,71.080,0.817,,0.905,0.639,0.308 +Israel,2007,6.841,10.408,0.868,71.160,0.683,0.215,0.868,0.642,0.320 +Israel,2008,7.261,10.423,0.859,71.240,0.663,0.134,0.898,0.635,0.349 +Israel,2009,7.353,10.408,0.937,71.320,0.593,0.167,0.923,0.620,0.327 +Israel,2010,7.359,10.444,0.882,71.400,0.561,0.145,0.902,0.628,0.362 +Israel,2011,7.433,10.480,0.893,71.480,0.722,0.136,0.891,0.654,0.384 +Israel,2012,7.111,10.487,0.903,71.560,0.681,0.147,0.862,0.611,0.319 +Israel,2013,7.321,10.512,0.909,71.640,0.739,0.145,0.849,0.649,0.409 +Israel,2014,7.401,10.531,0.889,71.720,0.707,0.088,0.818,0.567,0.271 +Israel,2015,7.079,10.536,0.864,71.800,0.753,0.103,0.789,0.652,0.256 +Israel,2016,7.159,10.560,0.890,71.950,0.772,0.147,0.804,0.602,0.263 +Israel,2017,7.331,10.583,0.916,72.100,0.768,0.138,0.793,0.621,0.276 +Israel,2018,6.927,10.603,0.910,72.250,0.725,0.048,0.770,0.612,0.282 +Israel,2019,7.332,10.625,0.946,72.400,0.834,0.078,0.743,0.598,0.266 +Israel,2020,7.195,10.589,0.959,72.550,0.831,-0.059,0.748,0.564,0.243 +Israel,2021,7.578,10.655,0.917,72.700,0.820,-0.008,0.726,0.558,0.217 +Israel,2022,7.662,10.698,0.954,72.850,0.775,-0.007,0.655,0.583,0.183 +Israel,2023,6.783,10.707,0.952,73.000,0.797,0.146,0.636,0.484,0.516 +Italy,2005,6.854,10.698,0.928,70.600,0.802,,0.944,0.606,0.295 +Italy,2007,6.574,10.722,0.912,70.800,0.684,0.108,0.922,0.650,0.303 +Italy,2008,6.780,10.706,0.880,70.900,0.543,0.044,0.946,0.588,0.268 +Italy,2009,6.334,10.647,0.880,71.000,0.701,0.235,0.890,0.715,0.279 +Italy,2010,6.354,10.661,0.872,71.100,0.738,-0.065,0.921,0.535,0.236 +Italy,2011,6.057,10.666,0.913,71.200,0.568,-0.023,0.933,0.610,0.266 +Italy,2012,5.839,10.633,0.869,71.300,0.570,0.107,0.908,0.651,0.388 +Italy,2013,6.009,10.603,0.916,71.400,0.499,-0.108,0.943,0.702,0.357 +Italy,2014,6.027,10.594,0.898,71.500,0.624,-0.071,0.920,0.659,0.356 +Italy,2015,5.848,10.603,0.909,71.600,0.575,-0.070,0.913,0.646,0.329 +Italy,2016,5.955,10.617,0.927,71.675,0.624,-0.086,0.903,0.632,0.339 +Italy,2017,6.199,10.635,0.920,71.750,0.633,-0.041,0.867,0.613,0.323 +Italy,2018,6.517,10.647,0.913,71.825,0.650,-0.027,0.888,0.598,0.403 +Italy,2019,6.445,10.663,0.838,71.900,0.709,-0.088,0.866,0.569,0.328 +Italy,2020,6.488,10.574,0.890,71.975,0.718,-0.157,0.844,0.614,0.311 +Italy,2021,6.467,10.647,0.886,72.050,0.703,-0.096,0.862,0.634,0.318 +Italy,2022,6.258,10.687,0.869,72.125,0.711,0.026,0.819,0.624,0.298 +Italy,2023,6.245,10.703,0.851,72.200,0.699,-0.053,0.819,0.636,0.293 +Ivory Coast,2009,4.197,8.181,0.667,48.900,0.760,-0.157,0.902,0.555,0.186 +Ivory Coast,2013,3.739,8.285,0.709,51.300,0.739,-0.037,0.691,0.661,0.306 +Ivory Coast,2014,3.570,8.351,0.711,51.900,0.781,-0.087,0.671,0.603,0.291 +Ivory Coast,2015,4.445,8.395,0.704,52.500,0.800,-0.059,0.744,0.614,0.347 +Ivory Coast,2016,4.543,8.438,0.617,53.075,0.769,-0.048,0.757,0.693,0.378 +Ivory Coast,2017,5.038,8.484,0.661,53.650,0.732,-0.116,0.771,0.662,0.357 +Ivory Coast,2018,5.268,8.505,0.621,54.225,0.713,-0.054,0.791,0.659,0.386 +Ivory Coast,2019,5.392,8.543,0.679,54.800,0.736,-0.021,0.799,0.663,0.425 +Ivory Coast,2020,5.257,8.535,0.613,55.375,0.770,0.012,0.777,0.655,0.340 +Ivory Coast,2021,5.056,8.579,0.554,55.950,0.717,-0.004,0.716,0.626,0.345 +Ivory Coast,2022,4.849,8.619,0.536,56.525,0.713,-0.009,0.743,0.629,0.399 +Ivory Coast,2023,5.337,8.656,0.623,57.100,0.753,-0.057,0.676,0.670,0.307 +Jamaica,2006,6.208,9.249,0.909,66.600,0.738,-0.011,0.946,0.753,0.201 +Jamaica,2011,5.374,9.193,0.855,66.600,0.796,-0.071,0.909,0.764,0.237 +Jamaica,2013,5.709,9.182,0.865,66.600,0.793,-0.029,0.931,0.694,0.312 +Jamaica,2014,5.311,9.185,0.874,66.600,0.809,-0.008,0.861,0.708,0.310 +Jamaica,2017,5.890,9.209,0.913,66.600,0.861,-0.138,0.883,0.700,0.243 +Jamaica,2019,6.309,9.234,0.878,66.600,0.891,-0.146,0.885,0.722,0.195 +Jamaica,2020,5.425,9.128,0.870,66.600,0.865,-0.152,0.836,0.712,0.266 +Jamaica,2021,5.814,9.170,0.857,66.600,0.731,-0.084,0.883,0.689,0.308 +Jamaica,2022,5.870,9.212,0.868,66.600,0.874,-0.091,0.910,0.718,0.269 +Japan,2005,6.516,10.552,0.928,72.400,0.868,,0.699,0.686,0.153 +Japan,2007,6.238,10.578,0.938,72.640,0.796,-0.097,0.809,0.683,0.207 +Japan,2008,5.911,10.566,0.887,72.760,0.772,-0.142,0.816,0.705,0.191 +Japan,2009,5.845,10.507,0.888,72.880,0.730,-0.217,0.740,0.713,0.169 +Japan,2010,6.057,10.547,0.902,73.000,0.772,-0.147,0.770,0.779,0.188 +Japan,2011,6.263,10.549,0.917,73.120,0.814,-0.059,0.734,0.714,0.181 +Japan,2012,5.968,10.565,0.905,73.240,0.753,,0.692,0.708,0.171 +Japan,2013,5.959,10.586,0.924,73.360,0.821,-0.154,0.650,0.719,0.175 +Japan,2014,5.923,10.590,0.900,73.480,0.838,-0.146,0.617,0.687,0.189 +Japan,2015,5.880,10.607,0.923,73.600,0.832,-0.162,0.654,0.702,0.176 +Japan,2016,5.955,10.615,0.900,73.725,0.836,-0.069,0.698,0.690,0.192 +Japan,2017,5.911,10.632,0.882,73.850,0.849,-0.213,0.659,0.692,0.176 +Japan,2018,5.794,10.640,0.886,73.975,0.773,-0.268,0.687,0.649,0.185 +Japan,2019,5.908,10.637,0.878,74.100,0.806,-0.261,0.617,0.693,0.194 +Japan,2020,6.118,10.596,0.887,74.225,0.806,-0.266,0.609,0.681,0.186 +Japan,2021,6.091,10.622,0.896,74.350,0.801,-0.213,0.670,0.674,0.189 +Japan,2022,6.178,10.637,0.899,74.475,0.789,-0.237,0.643,0.670,0.165 +Japan,2023,5.910,10.654,0.842,74.600,0.777,-0.222,0.594,0.638,0.178 +Jordan,2005,6.295,9.283,0.920,65.800,,,0.670,0.630,0.240 +Jordan,2007,5.598,9.308,0.841,66.160,0.646,-0.117,0.664,,0.240 +Jordan,2008,4.930,9.354,0.766,66.340,,-0.134,0.709,0.656,0.331 +Jordan,2009,6.000,9.381,0.899,66.520,0.771,-0.083,0.739,0.587,0.265 +Jordan,2010,5.570,9.382,0.918,66.700,0.788,-0.057,,0.564,0.343 +Jordan,2011,5.539,9.383,0.878,66.880,0.760,-0.155,,0.551,0.260 +Jordan,2012,5.132,9.393,0.829,67.060,0.693,-0.175,,0.469,0.345 +Jordan,2013,5.172,9.354,0.840,67.240,0.692,-0.131,,0.597,0.286 +Jordan,2014,5.333,9.269,0.816,67.420,0.729,-0.113,,0.602,0.313 +Jordan,2015,5.405,9.202,0.830,67.600,0.767,-0.051,,0.617,0.305 +Jordan,2016,5.271,9.173,0.820,67.600,0.771,-0.042,,0.598,0.312 +Jordan,2017,4.808,9.173,0.815,67.600,0.766,-0.156,,0.554,0.392 +Jordan,2018,4.639,9.168,0.800,67.600,0.762,-0.189,,, +Jordan,2019,4.453,9.163,0.793,67.600,0.726,-0.168,,, +Jordan,2020,4.094,9.125,0.709,67.600,0.779,-0.154,,, +Jordan,2021,3.909,9.127,0.703,67.600,0.773,-0.148,0.656,0.480,0.429 +Jordan,2022,4.356,9.140,0.774,67.600,0.759,-0.156,0.715,0.521,0.435 +Jordan,2023,4.292,9.151,0.721,67.600,0.754,-0.143,0.651,0.518,0.442 +Kazakhstan,2006,5.476,9.804,0.872,58.000,0.731,-0.280,0.865,0.602,0.185 +Kazakhstan,2007,5.719,9.878,0.861,58.600,0.806,-0.251,0.865,0.557,0.179 +Kazakhstan,2008,5.886,9.892,0.839,59.200,0.727,-0.226,0.899,0.613,0.160 +Kazakhstan,2009,5.383,9.884,0.893,59.800,0.856,-0.255,0.845,0.595,0.129 +Kazakhstan,2010,5.514,9.940,0.904,60.400,0.785,-0.221,0.823,0.655,0.149 +Kazakhstan,2011,5.736,9.997,0.905,61.000,0.878,-0.241,0.802,0.622,0.154 +Kazakhstan,2012,5.759,10.030,0.892,61.600,0.840,-0.177,0.877,0.667,0.184 +Kazakhstan,2013,5.835,10.074,0.889,62.200,0.782,-0.235,0.820,0.629,0.164 +Kazakhstan,2014,5.970,10.101,0.795,62.800,0.799,-0.002,0.805,0.671,0.169 +Kazakhstan,2015,5.950,10.098,0.931,63.400,0.740,-0.043,0.714,0.682,0.174 +Kazakhstan,2016,5.534,10.095,0.928,63.800,0.783,-0.042,0.702,0.641,0.155 +Kazakhstan,2017,5.882,10.121,0.914,64.200,0.745,-0.041,0.755,0.698,0.171 +Kazakhstan,2018,6.008,10.148,0.937,64.600,0.840,-0.104,0.824,0.611,0.162 +Kazakhstan,2019,6.272,10.179,0.951,65.000,0.852,-0.061,0.708,0.711,0.139 +Kazakhstan,2020,6.168,10.141,0.966,65.400,0.872,-0.062,0.661,0.620,0.150 +Kazakhstan,2021,6.260,10.170,0.906,65.800,0.807,0.034,0.782,0.663,0.122 +Kazakhstan,2022,6.006,10.169,0.923,66.200,0.883,0.022,0.720,0.652,0.132 +Kazakhstan,2023,6.299,10.172,0.929,66.600,0.901,0.050,0.812,0.626,0.114 +Kenya,2006,4.223,8.164,0.909,51.420,0.616,-0.034,0.860,0.657,0.198 +Kenya,2007,4.576,8.201,0.841,51.940,0.750,0.040,0.799,0.698,0.162 +Kenya,2008,4.015,8.173,0.827,52.460,0.620,-0.026,0.909,0.733,0.149 +Kenya,2009,4.270,8.176,0.789,52.980,0.584,0.086,0.913,0.679,0.183 +Kenya,2010,4.256,8.226,0.805,53.500,0.635,0.005,0.918,0.758,0.123 +Kenya,2011,4.405,8.249,0.846,54.020,0.709,0.009,0.923,0.706,0.228 +Kenya,2012,4.547,8.269,0.831,54.540,0.628,0.053,0.911,0.667,0.194 +Kenya,2013,3.795,8.282,0.825,55.060,0.708,0.201,0.861,0.729,0.161 +Kenya,2014,4.905,8.308,0.765,55.580,0.819,0.161,0.849,0.779,0.221 +Kenya,2015,4.358,8.334,0.777,56.100,0.793,0.209,0.853,0.673,0.172 +Kenya,2016,4.396,8.353,0.706,56.500,0.749,0.288,0.828,0.730,0.226 +Kenya,2017,4.476,8.369,0.715,56.900,0.853,0.225,0.854,0.754,0.230 +Kenya,2018,4.656,8.404,0.707,57.300,0.821,0.282,0.844,0.747,0.237 +Kenya,2019,4.619,8.434,0.676,57.700,0.818,0.300,0.794,0.728,0.251 +Kenya,2020,4.547,8.411,0.674,58.100,0.702,0.251,0.837,0.738,0.297 +Kenya,2021,4.465,8.465,0.702,58.500,0.678,0.313,0.841,0.745,0.253 +Kenya,2022,4.448,8.493,0.691,58.900,0.706,0.292,0.878,0.725,0.281 +Kenya,2023,4.496,8.523,0.703,59.300,0.736,0.316,0.800,0.756,0.245 +Kosovo,2007,5.104,,0.848,,0.381,,0.894,0.614,0.237 +Kosovo,2008,5.522,8.858,0.884,,,0.094,0.849,0.500,0.318 +Kosovo,2009,5.891,8.899,0.830,,0.506,0.203,0.968,0.528,0.169 +Kosovo,2010,5.177,8.940,0.708,,0.451,0.171,0.967,0.673,0.118 +Kosovo,2011,4.860,8.992,0.759,,0.589,0.004,0.919,0.604,0.124 +Kosovo,2012,5.640,9.000,0.757,,0.636,0.028,0.950,0.562,0.100 +Kosovo,2013,6.126,9.046,0.721,,0.568,0.114,0.935,0.650,0.203 +Kosovo,2014,5.000,9.082,0.706,,0.441,0.010,0.775,0.552,0.206 +Kosovo,2015,5.077,9.153,0.805,,0.561,0.177,0.851,0.685,0.180 +Kosovo,2016,5.759,9.213,0.824,,0.827,0.120,0.941,0.588,0.150 +Kosovo,2017,6.149,9.253,0.792,,0.858,0.112,0.925,0.617,0.186 +Kosovo,2018,6.392,9.283,0.822,,0.890,0.264,0.922,0.642,0.170 +Kosovo,2019,6.425,9.334,0.843,,0.841,0.242,0.920,0.612,0.141 +Kosovo,2020,6.294,9.279,0.792,,0.880,0.302,0.910,0.593,0.201 +Kosovo,2021,6.648,9.383,0.849,,0.840,0.258,0.842,0.578,0.116 +Kosovo,2022,6.160,9.431,0.888,,0.865,0.208,0.846,0.549,0.142 +Kosovo,2023,6.878,9.480,0.807,,0.900,0.285,0.811,0.682,0.140 +Kuwait,2006,6.076,11.233,0.919,68.400,0.769,-0.242,0.328,0.788,0.182 +Kuwait,2009,6.585,11.074,0.926,69.000,0.819,0.000,0.675,0.694,0.252 +Kuwait,2010,6.798,10.998,0.893,69.200,0.703,-0.039,0.486,0.687,0.203 +Kuwait,2011,6.378,11.024,0.882,69.400,0.769,,0.560,0.726,0.177 +Kuwait,2012,6.221,11.012,0.889,69.600,0.934,,,0.794,0.095 +Kuwait,2013,6.480,10.952,0.862,69.800,0.751,,,0.686,0.283 +Kuwait,2014,6.180,10.926,,70.000,,,,, +Kuwait,2015,6.146,10.893,0.823,70.200,0.822,0.077,,0.678,0.324 +Kuwait,2016,5.947,10.887,0.845,70.175,0.841,-0.080,,0.643,0.315 +Kuwait,2017,6.094,10.820,0.853,70.150,0.884,-0.010,,0.649,0.307 +Kuwait,2019,6.106,10.765,0.842,70.100,0.867,-0.106,,0.643,0.303 +Kuwait,2022,6.758,10.803,0.874,70.025,0.969,0.142,,0.738,0.156 +Kuwait,2023,7.130,10.812,0.890,70.000,0.898,0.136,,0.729,0.207 +Kyrgyzstan,2006,4.641,8.185,0.844,59.920,0.678,-0.145,0.879,0.555,0.159 +Kyrgyzstan,2007,4.698,8.258,0.833,60.340,0.684,-0.097,0.929,0.590,0.130 +Kyrgyzstan,2008,4.737,8.329,0.792,60.760,0.719,-0.105,0.923,0.594,0.147 +Kyrgyzstan,2009,5.069,8.345,0.855,61.180,0.699,-0.145,0.896,0.554,0.165 +Kyrgyzstan,2010,4.996,8.329,0.885,61.600,0.720,-0.077,0.926,0.516,0.123 +Kyrgyzstan,2011,4.921,8.374,0.891,62.020,0.748,-0.160,0.932,0.579,0.151 +Kyrgyzstan,2012,5.208,8.357,0.856,62.440,0.703,-0.084,0.892,0.580,0.182 +Kyrgyzstan,2013,5.402,8.441,0.851,62.860,0.755,-0.090,0.900,0.595,0.135 +Kyrgyzstan,2014,5.252,8.460,0.898,63.280,0.736,0.350,0.897,0.617,0.185 +Kyrgyzstan,2015,4.905,8.477,0.857,63.700,0.813,0.194,0.858,0.658,0.173 +Kyrgyzstan,2016,4.857,8.500,0.914,64.225,0.814,0.051,0.917,0.668,0.126 +Kyrgyzstan,2017,5.630,8.526,0.883,64.750,0.859,0.138,0.874,0.640,0.160 +Kyrgyzstan,2018,5.297,8.543,0.898,65.275,0.945,0.262,0.907,0.617,0.203 +Kyrgyzstan,2019,5.685,8.568,0.877,65.800,0.920,-0.008,0.885,0.625,0.207 +Kyrgyzstan,2020,6.250,8.461,0.902,66.325,0.935,0.101,0.931,0.672,0.258 +Kyrgyzstan,2021,5.564,8.504,0.904,66.850,0.918,0.199,0.903,0.660,0.226 +Kyrgyzstan,2022,5.668,8.555,0.927,67.375,0.948,0.230,0.876,0.646,0.204 +Kyrgyzstan,2023,5.910,8.573,0.946,67.900,0.943,0.145,0.899,0.681,0.165 +Laos,2006,5.076,8.234,0.807,55.880,0.925,0.435,0.688,0.790,0.163 +Laos,2007,5.364,8.291,0.790,56.260,0.867,0.474,0.580,0.752,0.136 +Laos,2008,5.044,8.351,0.807,56.640,0.886,0.412,0.637,0.728,0.202 +Laos,2011,4.704,8.538,0.691,57.780,0.882,0.454,0.587,0.746,0.225 +Laos,2012,4.876,8.601,0.693,58.160,,0.227,,0.741,0.387 +Laos,2017,4.623,8.883,0.707,59.900,0.891,0.068,0.592,0.712,0.344 +Laos,2018,4.859,8.929,0.705,60.200,0.907,0.136,0.634,0.711,0.332 +Laos,2019,5.197,8.967,0.729,60.500,0.906,0.055,0.620,0.718,0.306 +Laos,2020,5.284,8.957,0.660,60.800,0.915,0.136,0.748,0.714,0.358 +Laos,2021,4.927,8.968,0.650,61.100,0.927,0.037,0.668,0.702,0.292 +Laos,2022,4.962,8.981,0.659,61.400,0.891,0.091,0.706,0.675,0.334 +Laos,2023,5.486,9.005,0.678,61.700,0.904,0.099,0.724,0.679,0.335 +Latvia,2006,4.710,10.042,0.884,63.100,0.641,-0.236,0.937,0.590,0.234 +Latvia,2007,4.667,10.145,0.836,63.400,0.700,-0.173,0.924,0.594,0.247 +Latvia,2008,5.145,10.123,0.855,63.700,0.630,-0.210,0.926,0.633,0.215 +Latvia,2009,4.669,9.985,0.807,64.000,0.437,-0.187,0.942,0.537,0.242 +Latvia,2011,4.967,10.004,0.836,64.600,0.564,-0.006,0.934,0.563,0.222 +Latvia,2012,5.125,10.085,0.851,64.900,0.564,-0.044,0.895,0.588,0.232 +Latvia,2013,5.070,10.115,0.834,65.200,0.631,-0.079,0.837,0.605,0.227 +Latvia,2014,5.729,10.143,0.881,65.500,0.671,-0.049,0.804,0.605,0.226 +Latvia,2015,5.881,10.190,0.879,65.800,0.656,-0.084,0.808,0.559,0.228 +Latvia,2016,5.940,10.222,0.917,65.900,0.685,-0.163,0.868,0.583,0.231 +Latvia,2017,5.978,10.264,0.895,66.000,0.700,-0.161,0.798,0.565,0.232 +Latvia,2018,5.901,10.311,0.913,66.100,0.608,-0.218,0.799,0.521,0.192 +Latvia,2019,5.970,10.343,0.936,66.200,0.698,-0.200,0.789,0.537,0.212 +Latvia,2020,6.229,10.328,0.928,66.300,0.820,-0.086,0.809,0.674,0.202 +Latvia,2021,6.353,10.376,0.954,66.400,0.815,-0.104,0.840,0.716,0.186 +Latvia,2022,6.055,10.396,0.928,66.500,0.817,0.018,0.844,0.632,0.161 +Latvia,2023,6.296,10.408,0.932,66.600,0.816,0.059,0.800,0.671,0.257 +Lebanon,2005,5.491,9.571,0.796,65.100,0.703,,0.945,0.558,0.292 +Lebanon,2006,4.653,9.570,0.853,65.160,0.670,0.064,0.902,0.501,0.320 +Lebanon,2008,4.595,9.711,0.717,65.280,0.524,0.031,0.927,0.475,0.365 +Lebanon,2009,5.206,9.796,0.736,65.340,0.665,0.067,0.937,0.472,0.401 +Lebanon,2010,5.032,9.864,0.721,65.400,0.678,0.068,0.949,0.457,0.341 +Lebanon,2011,5.188,9.862,0.733,65.460,0.657,-0.002,0.911,0.506,0.320 +Lebanon,2012,4.573,9.862,0.713,65.520,0.621,-0.016,0.856,0.442,0.339 +Lebanon,2013,4.983,9.807,0.708,65.580,0.655,-0.012,0.921,0.446,0.409 +Lebanon,2014,5.233,9.732,0.759,65.640,0.657,-0.017,0.939,0.525,0.267 +Lebanon,2015,5.172,9.717,0.742,65.700,0.597,0.066,0.889,0.524,0.243 +Lebanon,2016,5.271,9.754,0.828,65.775,0.657,0.021,0.853,0.513,0.263 +Lebanon,2017,5.154,9.787,0.777,65.850,0.605,-0.088,0.911,0.469,0.244 +Lebanon,2018,5.167,9.795,0.829,65.925,0.607,-0.081,0.907,0.415,0.271 +Lebanon,2019,4.024,9.752,0.866,66.000,0.447,-0.098,0.890,0.308,0.494 +Lebanon,2020,2.634,9.532,0.547,66.075,0.552,-0.139,0.884,0.352,0.482 +Lebanon,2021,2.179,9.472,0.507,66.150,0.423,-0.164,0.905,0.263,0.569 +Lebanon,2022,2.352,9.458,0.535,66.225,0.450,-0.130,0.883,0.298,0.430 +Lebanon,2023,3.588,9.471,0.686,66.300,0.499,-0.102,0.895,0.373,0.385 +Lesotho,2011,4.898,7.785,0.824,41.520,0.618,-0.093,0.768,0.754,0.170 +Lesotho,2016,3.808,7.897,0.798,42.250,0.729,-0.100,0.743,0.685,0.270 +Lesotho,2017,3.795,7.852,0.769,42.900,0.757,-0.144,0.797,0.706,0.255 +Lesotho,2019,3.512,7.805,0.790,44.200,0.716,-0.127,0.915,0.707,0.273 +Lesotho,2022,3.186,7.733,0.680,46.150,0.709,-0.102,0.815,0.709,0.288 +Liberia,2007,3.701,7.179,0.594,51.860,0.790,0.111,0.776,0.600,0.435 +Liberia,2008,4.221,7.207,0.619,51.940,0.724,-0.038,0.840,0.629,0.261 +Liberia,2010,4.196,7.257,0.827,52.100,0.819,-0.043,0.818,0.549,0.217 +Liberia,2014,4.571,7.386,0.708,52.420,0.590,-0.035,0.869,0.565,0.443 +Liberia,2015,2.702,7.366,0.638,52.500,0.671,-0.067,0.903,0.519,0.388 +Liberia,2016,3.355,7.330,0.643,53.100,0.763,0.028,0.901,0.625,0.509 +Liberia,2017,4.424,7.335,0.685,53.700,0.733,-0.018,0.867,0.674,0.391 +Liberia,2018,4.135,7.328,0.727,54.300,0.766,0.044,0.868,0.664,0.436 +Liberia,2019,5.121,7.283,0.712,54.900,0.706,0.044,0.828,0.645,0.389 +Liberia,2022,4.042,7.287,0.597,56.700,0.732,0.150,0.828,0.637,0.439 +Liberia,2023,4.494,7.309,0.630,57.300,0.720,0.039,0.834,0.608,0.428 +Libya,2012,5.754,10.380,0.855,65.140,0.712,-0.076,0.791,0.633,0.316 +Libya,2015,5.615,9.858,0.868,64.300,0.775,-0.089,,0.652,0.369 +Libya,2016,5.434,9.828,0.876,64.525,0.822,-0.135,,0.645,0.383 +Libya,2017,5.647,10.095,0.823,64.750,0.779,-0.068,0.673,0.643,0.379 +Libya,2018,5.494,10.156,0.824,64.975,0.781,-0.146,0.646,0.635,0.399 +Libya,2019,5.330,10.023,0.827,65.200,0.762,-0.107,0.686,0.629,0.401 +Libya,2022,5.760,9.893,0.813,65.875,0.761,-0.043,0.668,0.627,0.399 +Libya,2023,5.970,,0.748,66.100,0.762,,0.644,0.585,0.372 +Lithuania,2006,5.954,10.042,0.930,63.500,0.567,-0.301,0.967,0.567,0.254 +Lithuania,2007,5.808,10.160,0.941,63.700,0.590,-0.287,0.966,0.523,0.279 +Lithuania,2008,5.554,10.196,0.914,63.900,0.621,-0.265,0.961,0.501,0.276 +Lithuania,2009,5.467,10.046,0.933,64.100,0.496,-0.309,0.979,0.525,0.271 +Lithuania,2010,5.066,10.083,0.882,64.300,0.519,-0.281,0.962,0.463,0.272 +Lithuania,2011,5.432,10.165,0.911,64.500,0.566,-0.154,0.964,0.556,0.275 +Lithuania,2012,5.771,10.216,0.919,64.700,0.503,-0.279,0.957,0.557,0.277 +Lithuania,2013,5.596,10.261,0.913,64.900,0.556,-0.242,0.936,0.540,0.294 +Lithuania,2014,6.126,10.304,0.908,65.100,0.508,-0.269,0.956,0.565,0.287 +Lithuania,2015,5.711,10.334,0.929,65.300,0.641,-0.259,0.924,0.534,0.276 +Lithuania,2016,5.866,10.371,0.938,65.650,0.614,-0.272,0.949,0.553,0.250 +Lithuania,2017,6.273,10.427,0.926,66.000,0.749,-0.179,0.790,0.590,0.195 +Lithuania,2018,6.309,10.476,0.929,66.350,0.699,-0.243,0.852,0.518,0.214 +Lithuania,2019,6.064,10.524,0.918,66.700,0.780,-0.258,0.783,0.568,0.276 +Lithuania,2020,6.391,10.523,0.953,67.050,0.824,-0.129,0.829,0.626,0.202 +Lithuania,2021,6.865,10.579,0.928,67.400,0.707,-0.121,0.878,0.667,0.191 +Lithuania,2022,7.038,10.586,0.937,67.750,0.710,-0.192,0.685,0.471,0.132 +Lithuania,2023,6.553,10.575,0.881,68.100,0.734,-0.232,0.751,0.493,0.201 +Luxembourg,2009,6.958,11.628,0.939,70.300,0.939,0.116,0.432,0.713,0.238 +Luxembourg,2010,7.097,11.647,0.952,70.500,0.908,0.086,0.423,0.718,0.216 +Luxembourg,2011,7.101,11.635,0.934,70.700,0.962,0.097,0.388,0.744,0.200 +Luxembourg,2012,6.964,11.627,0.914,70.900,0.917,0.048,0.403,0.726,0.227 +Luxembourg,2013,7.131,11.636,0.917,71.100,0.790,-0.065,0.301,0.601,0.185 +Luxembourg,2014,6.891,11.638,0.875,71.300,0.938,0.097,0.366,0.760,0.170 +Luxembourg,2015,6.702,11.637,0.934,71.500,0.932,0.044,0.375,0.728,0.193 +Luxembourg,2016,6.967,11.664,0.941,71.525,0.882,0.011,0.356,0.706,0.192 +Luxembourg,2017,7.061,11.653,0.905,71.550,0.903,0.036,0.330,0.726,0.184 +Luxembourg,2018,7.243,11.645,0.902,71.575,0.884,-0.028,0.385,0.715,0.202 +Luxembourg,2019,7.404,11.649,0.912,71.600,0.930,-0.051,0.390,0.742,0.212 +Luxembourg,2022,7.228,11.657,0.878,71.675,0.915,0.023,0.345,0.718,0.218 +Luxembourg,2023,7.016,11.649,0.879,71.700,0.911,0.033,0.343,0.751,0.194 +Madagascar,2006,3.980,7.351,0.711,54.140,,-0.042,,0.563,0.161 +Madagascar,2008,4.640,7.413,0.776,54.620,0.332,-0.103,0.773,0.583,0.215 +Madagascar,2011,4.381,7.309,0.818,55.340,0.546,-0.065,0.897,0.516,0.235 +Madagascar,2012,3.551,7.311,0.673,55.580,0.487,-0.058,0.854,0.588,0.194 +Madagascar,2013,3.816,7.307,0.673,55.820,0.480,-0.022,0.868,0.600,0.241 +Madagascar,2014,3.676,7.314,0.655,56.060,0.529,-0.026,0.791,0.641,0.192 +Madagascar,2015,3.593,7.319,0.647,56.300,0.545,-0.044,0.861,0.674,0.226 +Madagascar,2016,3.663,7.332,0.746,56.550,0.570,-0.072,0.864,0.670,0.204 +Madagascar,2017,4.079,7.345,0.626,56.800,0.570,-0.037,0.847,0.701,0.375 +Madagascar,2018,4.071,7.351,0.666,57.050,0.551,0.000,0.889,0.723,0.362 +Madagascar,2019,4.339,7.369,0.701,57.300,0.550,-0.015,0.720,0.699,0.304 +Madagascar,2022,4.019,7.315,0.642,58.050,0.523,0.070,0.740,0.687,0.345 +Madagascar,2023,4.433,7.333,0.692,58.300,0.504,0.096,0.784,0.718,0.389 +Malawi,2006,3.830,7.015,0.554,45.360,0.767,0.171,0.676,0.609,0.222 +Malawi,2007,4.891,7.078,0.600,46.420,0.910,0.173,0.691,0.691,0.176 +Malawi,2009,5.148,7.174,0.718,48.540,0.879,0.147,0.689,0.694,0.130 +Malawi,2011,3.946,7.231,0.613,50.660,0.733,0.070,0.853,0.620,0.268 +Malawi,2012,4.279,7.221,0.604,51.720,0.637,0.140,0.886,0.717,0.200 +Malawi,2013,4.035,7.246,0.563,52.780,0.752,0.049,0.857,0.699,0.248 +Malawi,2014,4.563,7.273,0.512,53.840,0.786,0.032,0.824,0.653,0.263 +Malawi,2015,3.868,7.273,0.494,54.900,0.801,0.029,0.835,0.602,0.260 +Malawi,2016,3.476,7.270,0.524,55.450,0.810,0.037,0.824,0.584,0.325 +Malawi,2017,3.417,7.283,0.555,56.000,0.848,-0.004,0.735,0.592,0.312 +Malawi,2018,3.335,7.299,0.528,56.550,0.799,0.043,0.766,0.548,0.365 +Malawi,2019,3.869,7.325,0.549,57.100,0.765,-0.027,0.680,0.517,0.348 +Malawi,2021,3.635,7.307,0.558,58.200,0.757,-0.014,0.740,0.551,0.326 +Malawi,2022,3.356,7.291,0.503,58.750,0.744,0.017,0.755,0.536,0.329 +Malawi,2023,3.272,7.279,0.470,59.300,0.738,0.014,0.745,0.520,0.338 +Malaysia,2006,6.012,9.827,0.866,65.080,0.837,0.196,0.740,0.687,0.243 +Malaysia,2007,6.239,9.867,0.871,65.160,0.844,0.085,0.799,0.719,0.162 +Malaysia,2008,5.807,9.893,0.803,65.240,0.780,0.040,0.884,0.728,0.186 +Malaysia,2009,5.385,9.858,0.792,65.320,0.874,-0.013,0.858,0.740,0.164 +Malaysia,2010,5.580,9.912,0.839,65.400,0.769,0.028,0.844,0.752,0.192 +Malaysia,2011,5.786,9.948,0.770,65.480,0.840,-0.021,0.842,0.785,0.155 +Malaysia,2012,5.914,9.985,0.841,65.560,0.848,0.013,0.847,0.744,0.177 +Malaysia,2013,5.770,10.015,0.831,65.640,0.791,0.260,0.755,0.664,0.317 +Malaysia,2014,5.963,10.057,0.863,65.720,0.808,0.235,0.845,0.711,0.261 +Malaysia,2015,6.322,10.092,0.818,65.800,0.675,0.218,0.838,0.733,0.314 +Malaysia,2018,5.339,10.197,0.789,65.725,0.875,0.123,0.894,0.716,0.200 +Malaysia,2019,5.428,10.228,0.842,65.700,0.916,0.119,0.782,0.735,0.176 +Malaysia,2020,6.014,10.159,0.797,65.675,0.878,0.096,0.747,0.721,0.288 +Malaysia,2021,6.010,10.179,0.794,65.650,0.878,0.218,0.769,0.728,0.183 +Malaysia,2022,6.048,10.251,0.815,65.625,0.957,0.208,0.789,0.767,0.191 +Malaysia,2023,5.868,10.283,0.779,65.600,0.965,0.154,0.754,0.761,0.195 +Maldives,2018,5.198,9.893,0.913,69.775,0.855,0.013,,, +Mali,2006,4.014,7.561,0.761,49.940,0.555,-0.075,0.761,0.748,0.209 +Mali,2008,4.115,7.576,0.747,50.620,0.495,-0.015,0.918,0.717,0.164 +Mali,2009,3.977,7.591,0.733,50.960,0.634,0.005,0.819,0.729,0.150 +Mali,2010,3.762,7.610,0.751,51.300,0.749,-0.031,0.811,0.764,0.162 +Mali,2011,4.667,7.609,0.796,51.640,0.823,-0.103,0.726,0.752,0.132 +Mali,2012,4.313,7.572,0.823,51.980,0.704,-0.091,0.787,0.647,0.109 +Mali,2013,3.676,7.565,0.820,52.320,0.665,-0.056,0.755,0.717,0.193 +Mali,2014,3.975,7.602,0.843,52.660,0.652,-0.040,0.658,0.722,0.186 +Mali,2015,4.582,7.631,0.830,53.000,0.634,-0.070,0.800,0.696,0.243 +Mali,2016,4.016,7.655,0.836,53.400,0.696,-0.072,0.862,0.738,0.305 +Mali,2017,4.742,7.675,0.741,53.800,0.753,-0.072,0.863,0.665,0.393 +Mali,2018,4.416,7.690,0.692,54.200,0.737,-0.036,0.793,0.689,0.370 +Mali,2019,4.988,7.705,0.755,54.600,0.670,-0.040,0.846,0.646,0.358 +Mali,2020,4.269,7.661,0.568,55.000,0.645,-0.069,0.895,0.648,0.440 +Mali,2021,4.113,7.659,0.573,55.400,0.673,-0.004,0.902,0.640,0.438 +Mali,2022,4.211,7.665,0.642,55.800,0.818,-0.024,0.746,0.655,0.408 +Mali,2023,4.370,7.673,0.641,56.200,0.776,-0.043,0.790,0.660,0.324 +Malta,2009,6.328,10.353,0.916,70.220,0.803,0.456,,0.626,0.358 +Malta,2010,5.774,10.402,0.908,70.400,0.802,0.278,,0.624,0.375 +Malta,2011,6.155,10.402,0.923,70.580,0.882,0.288,,0.638,0.340 +Malta,2012,5.963,10.433,0.922,70.760,0.861,0.343,,0.639,0.391 +Malta,2013,6.380,10.473,0.942,70.940,0.909,0.400,,0.629,0.370 +Malta,2014,6.452,10.526,0.941,71.120,0.904,0.395,0.670,0.606,0.352 +Malta,2015,6.613,10.594,0.919,71.300,0.912,0.339,0.664,0.641,0.355 +Malta,2016,6.591,10.604,0.930,71.350,0.916,0.339,0.696,0.645,0.355 +Malta,2017,6.676,10.681,0.937,71.400,0.924,0.244,0.690,0.666,0.302 +Malta,2018,6.910,10.705,0.932,71.450,0.927,0.170,0.595,0.666,0.296 +Malta,2019,6.733,10.734,0.922,71.500,0.924,0.077,0.689,0.642,0.356 +Malta,2020,6.157,10.622,0.938,71.550,0.931,-0.005,0.675,0.576,0.411 +Malta,2021,6.444,10.727,0.897,71.600,0.889,0.239,0.753,0.635,0.375 +Malta,2022,6.299,10.784,0.932,71.650,0.838,0.246,0.758,0.671,0.370 +Malta,2023,6.295,,0.912,71.700,0.851,,0.780,0.644,0.361 +Mauritania,2007,4.149,8.528,0.682,56.500,0.573,-0.077,0.586,0.732,0.174 +Mauritania,2008,4.248,8.500,0.670,56.800,0.593,-0.023,0.841,0.747,0.176 +Mauritania,2009,4.500,8.474,0.819,57.100,0.735,0.034,0.848,0.717,0.170 +Mauritania,2010,4.772,8.471,0.857,57.400,0.669,0.050,0.727,0.737,0.129 +Mauritania,2011,4.785,8.482,0.750,57.700,0.567,0.047,0.747,0.729,0.175 +Mauritania,2012,4.673,8.494,0.763,58.000,0.487,-0.026,0.707,0.749,0.164 +Mauritania,2013,4.199,8.506,0.741,58.300,0.603,-0.084,0.676,0.743,0.196 +Mauritania,2014,4.483,8.521,0.853,58.600,0.468,-0.060,0.589,0.743,0.163 +Mauritania,2015,3.923,8.547,0.875,58.900,0.447,0.050,0.715,0.798,0.194 +Mauritania,2016,4.472,8.533,0.785,59.125,0.467,-0.181,0.842,0.710,0.222 +Mauritania,2017,4.678,8.568,0.779,59.350,0.527,-0.161,0.777,0.631,0.272 +Mauritania,2018,4.314,8.588,0.802,59.575,0.467,-0.121,0.711,0.665,0.276 +Mauritania,2019,4.153,8.614,0.798,59.800,0.628,-0.111,0.743,0.686,0.260 +Mauritania,2022,4.724,8.602,0.648,60.475,0.624,-0.016,0.657,0.631,0.389 +Mauritania,2023,4.292,8.620,0.606,60.700,0.540,0.018,0.669,0.708,0.329 +Mauritius,2011,5.477,9.797,0.800,63.520,0.848,0.184,0.847,0.653,0.253 +Mauritius,2014,5.648,9.895,0.785,63.880,0.824,0.168,0.879,0.741,0.222 +Mauritius,2016,5.610,9.968,0.836,63.975,0.819,0.131,0.891,0.706,0.246 +Mauritius,2017,6.174,10.005,0.910,63.950,0.912,0.079,0.818,0.682,0.169 +Mauritius,2018,5.882,10.044,0.909,63.925,0.867,-0.081,0.785,0.710,0.158 +Mauritius,2019,6.241,10.072,0.913,63.900,0.893,-0.061,0.810,0.735,0.149 +Mauritius,2020,6.015,9.914,0.893,63.875,0.843,-0.038,0.772,0.700,0.138 +Mauritius,2021,5.949,9.948,0.887,63.850,0.802,-0.013,0.784,0.666,0.136 +Mauritius,2022,5.741,10.034,0.887,63.825,0.798,-0.042,0.769,0.725,0.168 +Mauritius,2023,5.759,10.083,0.868,63.800,0.797,-0.002,0.769,0.664,0.160 +Mexico,2005,6.581,9.792,0.903,64.400,0.814,,0.764,0.763,0.219 +Mexico,2007,6.525,9.831,0.879,64.680,0.670,-0.101,0.747,0.754,0.248 +Mexico,2008,6.829,9.830,0.876,64.820,0.677,-0.134,0.785,0.774,0.201 +Mexico,2009,6.963,9.764,0.868,64.960,0.682,-0.082,0.764,0.763,0.196 +Mexico,2010,6.802,9.800,0.876,65.100,0.778,-0.055,0.693,0.745,0.215 +Mexico,2011,6.910,9.822,0.824,65.240,0.831,-0.106,0.698,0.700,0.228 +Mexico,2012,7.320,9.844,0.767,65.380,0.788,-0.099,0.633,0.722,0.278 +Mexico,2013,7.443,9.844,0.759,65.520,0.739,-0.171,0.615,0.750,0.223 +Mexico,2014,6.680,9.860,0.782,65.660,0.779,-0.101,0.630,0.760,0.229 +Mexico,2015,6.236,9.880,0.761,65.800,0.719,-0.158,0.708,0.706,0.237 +Mexico,2016,6.824,9.895,0.893,65.800,0.752,-0.160,0.809,0.802,0.220 +Mexico,2017,6.410,9.905,0.800,65.800,0.861,-0.208,0.801,0.775,0.231 +Mexico,2018,6.550,9.917,0.858,65.800,0.816,-0.186,0.809,0.815,0.213 +Mexico,2019,6.432,9.907,0.852,65.800,0.903,-0.148,0.809,0.803,0.252 +Mexico,2020,5.964,9.816,0.779,65.800,0.873,-0.128,0.778,0.745,0.292 +Mexico,2021,5.991,9.857,0.779,65.800,0.837,-0.037,0.745,0.750,0.305 +Mexico,2022,7.038,9.881,0.858,65.800,0.861,-0.123,0.780,0.818,0.205 +Mexico,2023,7.006,9.900,0.868,65.800,0.870,-0.124,0.756,0.809,0.233 +Moldova,2006,5.102,8.922,0.812,59.480,0.554,-0.169,0.926,0.553,0.255 +Moldova,2007,4.775,8.954,0.804,59.860,0.696,-0.190,0.930,0.519,0.306 +Moldova,2008,5.503,9.031,0.872,60.240,0.641,-0.060,0.926,0.565,0.284 +Moldova,2009,5.554,8.970,0.856,60.620,0.551,-0.103,0.925,0.539,0.306 +Moldova,2010,5.590,9.040,0.847,61.000,0.598,-0.093,0.929,0.564,0.278 +Moldova,2011,5.792,9.097,0.869,61.380,0.628,-0.086,0.957,0.553,0.285 +Moldova,2012,5.996,9.091,0.826,61.760,0.602,-0.054,0.955,0.564,0.314 +Moldova,2013,5.756,9.178,0.803,62.140,0.658,-0.073,0.941,0.548,0.261 +Moldova,2014,5.917,9.227,0.805,62.520,0.623,-0.118,0.925,0.547,0.260 +Moldova,2015,6.017,9.232,0.840,62.900,0.595,-0.094,0.943,0.556,0.281 +Moldova,2016,5.578,9.289,0.837,63.300,0.557,-0.052,0.969,0.586,0.275 +Moldova,2017,5.326,9.347,0.831,63.700,0.553,-0.057,0.926,0.563,0.259 +Moldova,2018,5.682,9.404,0.892,64.100,0.824,-0.089,0.929,0.584,0.270 +Moldova,2019,5.803,9.455,0.809,64.500,0.784,-0.097,0.884,0.600,0.262 +Moldova,2020,5.812,9.380,0.874,64.900,0.859,-0.058,0.941,0.698,0.268 +Moldova,2021,5.959,9.518,0.880,65.300,0.833,-0.096,0.875,0.630,0.270 +Moldova,2022,5.687,9.465,0.817,65.700,0.829,-0.084,0.885,0.552,0.276 +Moldova,2023,5.801,9.492,0.842,66.100,0.844,-0.157,0.860,0.578,0.251 +Mongolia,2007,4.609,8.827,0.881,56.540,0.781,0.059,0.918,0.483,0.203 +Mongolia,2008,4.493,8.902,0.920,56.960,0.484,0.062,0.962,0.514,0.173 +Mongolia,2010,4.586,8.925,0.904,57.800,0.631,0.093,0.928,0.559,0.150 +Mongolia,2011,5.031,9.069,0.948,58.220,0.700,0.145,0.931,0.561,0.153 +Mongolia,2012,4.885,9.168,0.919,58.640,0.688,0.100,0.932,0.524,0.181 +Mongolia,2013,4.913,9.260,0.935,59.060,0.748,0.130,0.928,0.549,0.179 +Mongolia,2014,4.825,9.315,0.943,59.480,0.752,0.140,0.909,0.512,0.170 +Mongolia,2015,4.983,9.318,0.906,59.900,0.686,0.167,0.900,0.533,0.208 +Mongolia,2016,5.057,9.311,0.947,60.000,0.760,0.083,0.900,0.555,0.171 +Mongolia,2017,5.334,9.344,0.924,60.100,0.675,0.112,0.865,0.552,0.214 +Mongolia,2018,5.465,9.397,0.942,60.200,0.696,0.048,0.849,0.525,0.192 +Mongolia,2019,5.563,9.430,0.946,60.300,0.711,0.142,0.873,0.562,0.167 +Mongolia,2020,6.011,9.365,0.918,60.400,0.718,0.138,0.843,0.575,0.260 +Mongolia,2021,5.721,9.365,0.927,60.500,0.667,0.215,0.851,0.560,0.202 +Mongolia,2022,5.788,9.397,0.951,60.600,0.717,0.211,0.847,0.550,0.209 +Mongolia,2023,5.580,9.433,0.938,60.700,0.699,0.220,0.871,0.545,0.197 +Montenegro,2007,5.196,9.696,0.832,65.960,0.512,-0.139,0.815,0.536,0.340 +Montenegro,2009,4.801,9.702,0.816,66.120,0.556,-0.107,0.838,0.533,0.423 +Montenegro,2010,5.455,9.727,0.805,66.200,0.552,-0.212,0.757,0.510,0.410 +Montenegro,2011,5.223,9.758,0.818,66.280,0.546,-0.232,0.762,0.510,0.378 +Montenegro,2012,5.219,9.729,0.704,66.360,0.462,-0.198,0.755,0.468,0.379 +Montenegro,2013,5.074,9.763,0.736,66.440,0.502,-0.182,0.693,0.493,0.331 +Montenegro,2014,5.283,9.780,0.863,66.520,0.503,0.091,0.768,0.545,0.368 +Montenegro,2015,5.125,9.813,0.740,66.600,0.583,-0.150,0.781,0.534,0.337 +Montenegro,2016,5.304,9.842,0.866,66.700,0.569,-0.093,0.849,0.547,0.337 +Montenegro,2017,5.615,9.887,0.881,66.800,0.626,-0.089,0.756,0.493,0.350 +Montenegro,2018,5.650,9.937,0.856,66.900,0.626,-0.057,0.769,0.527,0.355 +Montenegro,2019,5.386,9.977,0.832,67.000,0.694,-0.111,0.820,0.547,0.366 +Montenegro,2020,5.722,9.812,0.887,67.100,0.802,0.061,0.845,0.560,0.411 +Montenegro,2022,5.600,10.002,0.875,67.300,0.778,-0.022,0.802,0.485,0.317 +Montenegro,2023,5.813,10.041,0.853,67.400,0.799,-0.066,0.706,0.481,0.318 +Morocco,2010,4.383,8.821,,62.500,0.663,-0.173,0.900,, +Morocco,2011,5.085,8.861,0.833,62.660,0.579,-0.229,0.875,0.687,0.187 +Morocco,2012,4.970,8.877,0.676,62.820,0.757,-0.198,0.845,0.641,0.281 +Morocco,2013,5.142,8.904,0.597,62.980,0.572,-0.221,0.771,0.707,0.239 +Morocco,2015,5.163,8.947,0.606,63.300,0.713,-0.239,0.842,0.596,0.262 +Morocco,2016,5.386,8.940,0.655,63.400,0.817,-0.248,0.717,0.658,0.205 +Morocco,2017,5.312,8.977,0.641,63.500,0.814,-0.227,0.841,0.501,0.323 +Morocco,2018,4.897,8.996,0.554,63.600,0.773,-0.246,0.843,0.575,0.416 +Morocco,2019,5.057,9.014,0.535,63.700,0.757,-0.256,0.757,0.535,0.410 +Morocco,2020,4.803,8.929,0.553,63.800,0.819,-0.238,0.803,0.548,0.256 +Morocco,2021,5.326,8.994,0.505,63.900,0.762,-0.204,0.817,0.554,0.341 +Morocco,2022,4.596,8.995,0.564,64.000,0.795,-0.257,0.802,0.573,0.414 +Morocco,2023,4.487,9.009,0.500,64.100,0.821,-0.094,0.831,0.549,0.415 +Mozambique,2006,4.595,6.792,0.879,44.820,0.684,0.035,0.758,0.602,0.327 +Mozambique,2007,4.833,6.840,0.748,45.240,0.643,0.068,0.854,0.627,0.240 +Mozambique,2008,4.654,6.885,0.756,45.660,0.514,0.000,0.864,0.611,0.280 +Mozambique,2011,4.971,6.996,0.818,46.920,0.639,-0.030,0.719,0.565,0.243 +Mozambique,2015,4.550,7.148,0.666,48.600,0.813,0.083,0.632,0.560,0.340 +Mozambique,2017,4.280,7.160,0.678,49.500,0.823,-0.035,0.682,0.642,0.353 +Mozambique,2018,4.654,7.165,0.738,49.950,0.897,0.043,0.691,0.620,0.397 +Mozambique,2019,4.932,7.159,0.742,50.400,0.870,0.068,0.682,0.588,0.384 +Mozambique,2021,5.178,7.112,0.664,51.300,0.838,0.042,0.627,0.576,0.383 +Mozambique,2022,4.740,7.125,0.711,51.750,0.884,0.043,0.688,0.629,0.351 +Mozambique,2023,5.704,7.147,0.701,52.200,0.867,0.071,0.678,0.625,0.335 +Myanmar,2012,4.439,8.067,0.612,58.160,0.691,0.646,0.695,0.574,0.205 +Myanmar,2013,4.176,8.134,0.757,58.640,0.775,0.691,0.638,0.675,0.217 +Myanmar,2014,4.786,8.205,0.774,59.120,0.870,0.700,0.592,0.713,0.112 +Myanmar,2015,4.224,8.229,0.752,59.600,0.808,0.692,0.633,0.729,0.272 +Myanmar,2016,4.623,8.321,0.793,59.925,0.877,0.680,0.607,0.671,0.302 +Myanmar,2017,4.154,8.369,0.795,60.250,0.886,0.651,0.619,0.617,0.282 +Myanmar,2018,4.411,8.424,0.774,60.575,0.906,0.492,0.647,0.640,0.300 +Myanmar,2019,4.434,8.483,0.763,60.900,0.899,0.560,0.682,0.638,0.286 +Myanmar,2020,4.431,8.507,0.796,61.225,0.825,0.468,0.647,0.700,0.289 +Myanmar,2021,4.314,8.302,0.780,61.550,0.631,0.508,0.671,0.636,0.268 +Myanmar,2022,4.359,8.325,0.746,61.875,0.646,0.600,0.729,0.669,0.353 +Myanmar,2023,4.391,8.347,0.685,62.200,0.695,0.548,0.686,0.634,0.358 +Namibia,2007,4.886,9.073,0.828,51.880,0.781,-0.107,0.839,0.769,0.160 +Namibia,2014,4.574,9.264,0.763,54.260,0.849,-0.191,0.790,0.723,0.239 +Namibia,2017,4.441,9.243,0.828,55.350,0.810,-0.198,0.831,0.697,0.277 +Namibia,2018,4.834,9.237,0.864,55.725,0.754,-0.177,0.846,0.696,0.240 +Namibia,2019,4.436,9.211,0.845,56.100,0.739,-0.182,0.879,0.644,0.256 +Namibia,2020,4.451,9.110,0.741,56.475,0.666,-0.110,0.810,0.652,0.248 +Namibia,2021,4.491,9.128,0.808,56.850,0.659,-0.153,0.829,0.644,0.230 +Namibia,2022,4.949,9.158,0.808,57.225,0.683,-0.124,0.849,0.676,0.261 +Namibia,2023,5.055,9.167,0.852,57.600,0.674,-0.113,0.873,0.635,0.208 +Nepal,2006,4.567,7.734,0.874,59.660,0.689,,0.897,0.583,0.171 +Nepal,2007,4.748,7.761,0.787,59.720,0.413,0.303,0.891,0.502,0.152 +Nepal,2008,4.441,7.814,0.818,59.780,0.618,0.276,0.900,0.589,0.153 +Nepal,2009,4.917,7.853,0.813,59.840,0.616,0.029,0.950,0.484,0.215 +Nepal,2010,4.350,7.895,0.779,59.900,0.519,0.077,0.911,0.538,0.226 +Nepal,2011,3.809,7.924,0.741,59.960,0.525,-0.024,0.935,0.530,0.207 +Nepal,2012,4.233,7.968,0.734,60.020,0.638,0.056,0.883,0.538,0.231 +Nepal,2013,4.605,8.000,0.740,60.080,0.722,0.137,0.877,0.496,0.279 +Nepal,2014,4.975,8.056,0.786,60.140,0.712,0.108,0.841,0.492,0.287 +Nepal,2015,4.812,8.089,0.748,60.200,0.763,0.214,0.824,0.444,0.358 +Nepal,2016,5.100,8.085,0.837,60.475,0.839,0.155,0.817,0.523,0.370 +Nepal,2017,4.737,8.159,0.816,60.750,0.845,0.120,0.770,0.463,0.376 +Nepal,2018,4.910,8.221,0.768,61.025,0.770,0.107,0.742,0.457,0.387 +Nepal,2019,5.449,8.274,0.772,61.300,0.790,0.152,0.712,0.444,0.357 +Nepal,2020,5.982,8.233,0.787,61.575,0.772,0.135,0.812,0.480,0.337 +Nepal,2021,4.622,8.257,0.699,61.850,0.818,0.144,0.770,0.414,0.354 +Nepal,2022,5.474,8.294,0.753,62.125,0.844,0.149,0.760,0.473,0.342 +Nepal,2023,5.389,8.318,0.749,62.400,0.765,0.184,0.792,0.461,0.350 +Netherlands,2005,7.464,10.809,0.947,70.700,0.901,,0.571,0.701,0.233 +Netherlands,2007,7.452,10.876,0.944,70.780,0.896,0.339,0.445,0.718,0.213 +Netherlands,2008,7.631,10.894,0.944,70.820,0.883,0.359,0.419,0.679,0.182 +Netherlands,2010,7.502,10.860,0.957,70.900,0.921,0.344,0.399,0.745,0.206 +Netherlands,2011,7.564,10.870,0.938,70.940,0.925,0.330,0.359,0.770,0.181 +Netherlands,2012,7.471,10.856,0.939,70.980,0.877,0.282,0.434,0.753,0.226 +Netherlands,2013,7.407,10.852,0.925,71.020,0.919,0.299,0.505,0.765,0.235 +Netherlands,2014,7.321,10.863,0.909,71.060,0.910,0.326,0.457,0.776,0.221 +Netherlands,2015,7.324,10.878,0.879,71.100,0.904,0.256,0.412,0.742,0.202 +Netherlands,2016,7.541,10.894,0.926,71.175,0.907,0.233,0.433,0.737,0.215 +Netherlands,2017,7.459,10.917,0.937,71.250,0.920,0.245,0.363,0.729,0.185 +Netherlands,2018,7.463,10.934,0.939,71.325,0.920,0.156,0.371,0.748,0.205 +Netherlands,2019,7.425,10.947,0.941,71.400,0.886,0.207,0.360,0.728,0.231 +Netherlands,2020,7.504,10.902,0.944,71.475,0.935,0.145,0.281,0.691,0.247 +Netherlands,2021,7.314,10.944,0.919,71.550,0.856,0.266,0.397,0.714,0.201 +Netherlands,2022,7.390,10.978,0.929,71.625,0.868,0.223,0.459,0.711,0.198 +Netherlands,2023,7.255,10.977,0.915,71.700,0.847,0.223,0.424,0.693,0.202 +New Zealand,2006,7.305,10.541,0.946,69.720,0.932,0.304,0.224,0.825,0.219 +New Zealand,2007,7.604,10.562,0.967,69.740,0.878,0.272,0.295,0.803,0.238 +New Zealand,2008,7.381,10.541,0.944,69.760,0.893,0.291,0.334,0.784,0.232 +New Zealand,2010,7.224,10.534,0.976,69.800,0.918,0.247,0.321,0.783,0.235 +New Zealand,2011,7.191,10.548,0.954,69.820,0.935,0.278,0.269,0.784,0.210 +New Zealand,2012,7.250,10.565,0.930,69.840,0.902,0.280,0.289,0.786,0.207 +New Zealand,2013,7.280,10.585,0.958,69.860,0.944,0.230,0.312,0.778,0.151 +New Zealand,2014,7.306,10.605,0.942,69.880,0.932,0.341,0.273,0.807,0.199 +New Zealand,2015,7.418,10.622,0.987,69.900,0.942,0.322,0.186,0.795,0.160 +New Zealand,2016,7.226,10.637,0.937,69.975,0.927,0.259,0.278,0.777,0.207 +New Zealand,2017,7.327,10.650,0.955,70.050,0.942,0.287,0.222,0.763,0.172 +New Zealand,2018,7.370,10.667,0.954,70.125,0.949,0.113,0.207,0.785,0.168 +New Zealand,2019,7.205,10.675,0.939,70.200,0.912,0.150,0.234,0.765,0.191 +New Zealand,2020,7.257,10.647,0.952,70.275,0.918,0.116,0.283,0.796,0.209 +New Zealand,2021,7.137,10.693,0.950,70.350,0.910,0.216,0.252,0.747,0.206 +New Zealand,2022,6.975,10.712,0.956,70.425,0.831,0.183,0.281,0.706,0.210 +New Zealand,2023,6.976,10.720,0.933,70.500,0.877,0.181,0.304,0.738,0.229 +Nicaragua,2006,4.460,8.395,0.877,64.300,0.745,0.005,0.844,0.780,0.294 +Nicaragua,2007,4.944,8.431,0.866,64.400,0.836,0.135,0.826,0.787,0.287 +Nicaragua,2008,5.104,8.450,0.857,64.500,0.791,0.070,0.819,0.770,0.289 +Nicaragua,2009,5.353,8.402,0.835,64.600,0.746,0.065,0.794,0.740,0.299 +Nicaragua,2010,5.687,8.431,0.863,64.700,0.792,0.013,0.802,0.749,0.268 +Nicaragua,2011,5.386,8.478,0.800,64.800,0.779,-0.024,0.760,0.747,0.309 +Nicaragua,2012,5.448,8.526,0.894,64.900,0.850,0.012,0.644,0.762,0.255 +Nicaragua,2013,5.772,8.559,0.868,65.000,0.859,0.034,0.636,0.800,0.271 +Nicaragua,2014,6.275,8.591,0.839,65.100,0.817,0.099,0.699,0.782,0.334 +Nicaragua,2015,5.924,8.624,0.827,65.200,0.809,0.073,0.728,0.771,0.346 +Nicaragua,2016,6.013,8.654,0.853,65.275,0.717,0.035,0.731,0.787,0.380 +Nicaragua,2017,6.476,8.685,0.838,65.350,0.922,0.006,0.673,0.793,0.308 +Nicaragua,2018,5.819,8.637,0.854,65.425,0.797,0.004,0.713,0.743,0.408 +Nicaragua,2019,6.113,8.594,0.874,65.500,0.883,0.024,0.622,0.790,0.337 +Nicaragua,2020,6.287,8.562,0.856,65.575,0.818,0.037,0.631,0.775,0.316 +Nicaragua,2021,6.095,8.647,0.848,65.650,0.905,0.020,0.675,0.799,0.293 +Nicaragua,2022,6.392,8.669,0.844,65.725,0.914,-0.004,0.570,0.787,0.339 +Nicaragua,2023,6.362,8.685,0.836,65.800,0.906,-0.008,0.532,0.772,0.340 +Niger,2006,3.737,6.872,0.677,50.140,0.750,0.073,0.755,0.746,0.179 +Niger,2007,4.277,6.867,0.726,50.580,0.584,-0.060,0.748,0.723,0.158 +Niger,2008,4.236,6.905,0.607,51.020,0.649,-0.059,0.749,0.689,0.194 +Niger,2009,4.267,6.887,0.771,51.460,0.880,-0.013,0.483,0.714,0.115 +Niger,2010,4.101,6.932,0.655,51.900,0.817,-0.027,0.529,0.715,0.126 +Niger,2011,4.556,6.918,0.818,52.340,0.780,-0.060,0.549,0.710,0.166 +Niger,2012,3.798,6.980,0.700,52.780,0.734,-0.068,0.777,0.582,0.142 +Niger,2013,3.716,6.994,0.696,53.220,0.825,-0.082,0.711,0.639,0.208 +Niger,2014,4.181,7.020,0.753,53.660,0.688,-0.051,0.605,0.629,0.205 +Niger,2015,3.671,7.025,0.713,54.100,0.728,-0.037,0.703,0.665,0.218 +Niger,2016,4.235,7.042,0.683,54.450,0.702,-0.020,0.814,0.646,0.325 +Niger,2017,4.616,7.053,0.582,54.800,0.684,-0.035,0.778,0.699,0.427 +Niger,2018,5.164,7.084,0.612,55.150,0.791,0.004,0.637,0.759,0.503 +Niger,2019,5.004,7.105,0.677,55.500,0.831,0.021,0.729,0.794,0.304 +Niger,2022,4.501,7.151,0.587,56.550,0.793,0.024,0.740,0.787,0.366 +Niger,2023,4.609,7.181,0.638,56.900,0.767,0.029,,0.747,0.417 +Nigeria,2006,4.710,8.314,0.735,50.220,0.649,0.080,0.871,0.772,0.178 +Nigeria,2007,4.890,8.350,0.718,50.540,0.635,0.132,0.918,0.815,0.141 +Nigeria,2008,4.939,8.389,0.780,50.860,0.584,0.115,0.892,0.755,0.244 +Nigeria,2009,4.980,8.439,0.722,51.180,0.537,0.063,0.913,0.730,0.225 +Nigeria,2010,4.760,8.488,0.824,51.500,0.565,0.062,0.911,0.759,0.190 +Nigeria,2012,5.493,8.526,0.818,52.140,0.652,0.062,0.900,0.782,0.209 +Nigeria,2013,4.818,8.564,0.663,52.460,0.622,0.046,0.905,0.652,0.286 +Nigeria,2015,4.933,8.600,0.812,53.100,0.680,-0.040,0.926,0.715,0.251 +Nigeria,2016,5.220,8.558,0.805,53.425,0.798,0.039,0.905,0.745,0.252 +Nigeria,2017,5.322,8.541,0.733,53.750,0.826,0.120,0.835,0.682,0.236 +Nigeria,2018,5.252,8.535,0.741,54.075,0.790,-0.015,0.866,0.762,0.256 +Nigeria,2019,4.266,8.532,0.735,54.400,0.746,0.019,0.873,0.698,0.229 +Nigeria,2020,5.503,8.490,0.739,54.725,0.713,0.094,0.913,0.737,0.316 +Nigeria,2021,4.479,8.502,0.742,55.050,0.726,0.047,0.912,0.666,0.188 +Nigeria,2022,5.294,8.510,0.785,55.375,0.776,0.172,0.935,0.760,0.253 +Nigeria,2023,4.869,8.514,0.781,55.700,0.720,0.209,0.877,0.721,0.266 +North Macedonia,2007,4.494,9.434,0.811,64.660,0.439,0.073,0.870,0.558,0.251 +North Macedonia,2009,4.428,9.481,0.734,64.820,0.552,-0.049,0.844,0.488,0.370 +North Macedonia,2010,4.180,9.512,0.687,64.900,0.513,-0.065,0.856,0.473,0.314 +North Macedonia,2011,4.898,9.533,0.784,64.980,0.607,-0.094,0.865,0.503,0.363 +North Macedonia,2012,4.640,9.527,0.798,65.060,0.613,-0.091,0.920,0.551,0.422 +North Macedonia,2013,5.186,9.555,0.832,65.140,0.641,0.018,0.861,0.521,0.331 +North Macedonia,2014,5.204,9.589,0.793,65.220,0.645,0.028,0.861,0.583,0.307 +North Macedonia,2015,4.976,9.625,0.766,65.300,0.660,-0.053,0.824,0.551,0.299 +North Macedonia,2016,5.346,9.652,0.871,65.500,0.706,0.073,0.870,0.587,0.292 +North Macedonia,2017,5.234,9.662,0.800,65.700,0.752,-0.065,0.856,0.447,0.299 +North Macedonia,2018,5.240,9.689,0.849,65.900,0.745,-0.048,0.910,0.512,0.298 +North Macedonia,2019,5.015,9.728,0.815,66.100,0.725,0.018,0.923,0.515,0.304 +North Macedonia,2020,5.054,9.666,0.750,66.300,0.787,0.127,0.877,0.542,0.365 +North Macedonia,2021,5.535,9.724,0.809,66.500,0.793,0.188,0.884,0.563,0.303 +North Macedonia,2022,5.167,9.749,0.850,66.700,0.723,0.068,0.937,0.555,0.277 +North Macedonia,2023,5.403,9.776,0.883,66.900,0.738,0.123,0.917,0.517,0.272 +Norway,2006,7.416,11.056,0.959,69.400,0.960,0.101,0.397,0.767,0.197 +Norway,2008,7.632,11.066,0.936,69.800,0.947,0.010,0.503,0.763,0.155 +Norway,2012,7.678,11.041,0.948,70.600,0.947,0.139,0.368,0.798,0.213 +Norway,2014,7.444,11.048,0.941,71.000,0.956,0.173,0.405,0.802,0.194 +Norway,2015,7.603,11.057,0.947,71.200,0.948,0.249,0.299,0.796,0.209 +Norway,2016,7.596,11.060,0.960,71.250,0.954,0.125,0.410,0.809,0.209 +Norway,2017,7.579,11.076,0.950,71.300,0.953,0.228,0.250,0.800,0.203 +Norway,2018,7.444,11.077,0.966,71.350,0.960,0.086,0.268,0.786,0.212 +Norway,2019,7.442,11.082,0.942,71.400,0.954,0.103,0.271,0.782,0.195 +Norway,2020,7.290,11.063,0.956,71.450,0.965,0.068,0.271,0.777,0.216 +Norway,2021,7.362,11.096,0.948,71.500,0.936,0.166,0.263,0.769,0.207 +Norway,2022,7.295,11.119,0.927,71.550,0.939,0.182,0.314,0.759,0.211 +Norway,2023,7.249,11.125,0.952,71.600,0.938,0.219,0.245,0.756,0.228 +Oman,2011,6.853,10.539,,62.340,0.916,0.008,,,0.295 +Pakistan,2005,5.225,8.252,0.591,53.200,0.630,,0.844,,0.237 +Pakistan,2007,5.671,8.314,0.479,53.720,0.396,0.081,0.794,0.583,0.310 +Pakistan,2008,4.414,8.309,0.373,53.980,0.335,0.092,0.848,0.533,0.321 +Pakistan,2009,5.208,8.315,0.522,54.240,0.388,0.069,0.874,0.516,0.349 +Pakistan,2010,5.786,8.308,0.571,54.500,0.364,0.292,0.852,0.527,0.372 +Pakistan,2011,5.267,8.314,0.510,54.760,0.376,0.022,0.857,0.473,0.358 +Pakistan,2012,5.132,8.331,0.542,55.020,0.367,0.157,0.842,0.510,0.332 +Pakistan,2013,5.138,8.359,0.607,55.280,0.448,0.091,0.792,0.474,0.274 +Pakistan,2014,5.436,8.390,0.552,55.540,0.543,0.131,0.677,0.475,0.295 +Pakistan,2015,4.823,8.423,0.562,55.800,0.587,0.076,0.717,0.469,0.329 +Pakistan,2016,5.549,8.465,0.627,56.075,0.634,0.084,0.793,0.503,0.332 +Pakistan,2017,5.831,8.495,0.690,56.350,0.713,0.035,0.714,0.489,0.308 +Pakistan,2018,5.472,8.540,0.685,56.625,0.773,0.058,0.799,0.470,0.377 +Pakistan,2019,4.443,8.548,0.617,56.900,0.685,0.112,0.776,0.489,0.424 +Pakistan,2020,4.624,8.518,0.594,57.175,0.767,0.003,0.833,0.470,0.376 +Pakistan,2021,4.487,8.563,0.608,57.450,0.764,0.007,0.743,0.518,0.307 +Pakistan,2022,4.931,8.604,0.590,57.725,0.745,0.000,0.865,0.469,0.368 +Pakistan,2023,4.549,8.588,0.553,58.000,0.661,0.070,0.889,0.504,0.417 +Panama,2006,6.128,9.806,0.951,66.860,0.882,-0.056,0.912,0.826,0.232 +Panama,2007,6.894,9.901,0.937,67.020,0.640,0.074,0.915,0.789,0.149 +Panama,2008,6.931,9.977,0.922,67.180,0.707,0.051,0.881,0.776,0.150 +Panama,2009,7.034,9.971,0.905,67.340,0.721,0.006,0.889,0.839,0.144 +Panama,2010,7.321,10.010,0.928,67.500,0.755,-0.017,0.880,0.841,0.146 +Panama,2011,7.248,10.099,0.876,67.660,0.829,0.000,0.840,0.853,0.180 +Panama,2012,6.860,10.174,0.897,67.820,0.783,-0.011,0.796,0.838,0.207 +Panama,2013,6.866,10.224,0.896,67.980,0.811,0.010,0.814,0.860,0.226 +Panama,2014,6.631,10.256,0.873,68.140,0.894,-0.007,0.847,0.799,0.254 +Panama,2015,6.606,10.294,0.883,68.300,0.847,-0.016,0.810,0.777,0.264 +Panama,2016,6.118,10.325,0.882,68.400,0.884,-0.111,0.837,0.813,0.244 +Panama,2017,6.568,10.362,0.912,68.500,0.900,-0.178,0.841,0.795,0.242 +Panama,2018,6.281,10.382,0.904,68.600,0.861,-0.139,0.837,0.841,0.223 +Panama,2019,6.086,10.398,0.886,68.700,0.883,-0.208,0.869,0.841,0.244 +Panama,2021,6.553,10.323,0.899,68.900,0.811,-0.152,0.861,0.834,0.218 +Panama,2022,5.979,10.412,0.891,69.000,0.899,-0.128,0.887,0.821,0.259 +Panama,2023,6.543,10.455,0.887,69.100,0.852,-0.147,0.871,0.829,0.257 +Paraguay,2006,4.730,9.154,0.895,64.880,0.691,0.056,0.841,0.752,0.303 +Paraguay,2007,5.272,9.197,0.863,64.960,0.699,0.121,0.930,0.812,0.219 +Paraguay,2008,5.570,9.250,0.889,65.040,0.649,0.046,0.891,0.798,0.259 +Paraguay,2009,5.576,9.237,0.900,65.120,0.718,0.016,0.857,0.803,0.186 +Paraguay,2010,5.841,9.331,0.889,65.200,0.726,0.065,0.780,0.826,0.176 +Paraguay,2011,5.677,9.360,0.869,65.280,0.666,0.179,0.756,0.823,0.190 +Paraguay,2012,5.820,9.339,0.931,65.360,0.748,0.188,0.774,0.849,0.213 +Paraguay,2013,5.936,9.405,0.939,65.440,0.909,0.034,0.903,0.874,0.224 +Paraguay,2014,5.119,9.443,0.959,65.520,0.759,-0.013,0.762,0.876,0.216 +Paraguay,2015,5.560,9.458,0.914,65.600,0.806,-0.019,0.863,0.832,0.219 +Paraguay,2016,5.801,9.485,0.940,65.650,0.854,-0.082,0.756,0.833,0.197 +Paraguay,2017,5.713,9.518,0.902,65.700,0.891,-0.008,0.810,0.820,0.232 +Paraguay,2019,5.653,9.519,0.892,65.800,0.876,0.017,0.882,0.790,0.275 +Paraguay,2020,5.501,9.497,0.907,65.850,0.865,0.054,0.829,0.767,0.269 +Paraguay,2021,5.576,9.523,0.908,65.900,0.888,0.015,0.857,0.806,0.248 +Paraguay,2022,6.138,9.513,0.899,65.950,0.922,-0.014,0.839,0.821,0.238 +Paraguay,2023,6.214,9.549,0.889,66.000,0.902,-0.004,0.835,0.833,0.240 +Peru,2006,4.811,8.979,0.875,66.460,0.668,-0.076,0.895,0.675,0.420 +Peru,2007,5.214,9.054,0.756,66.720,0.638,-0.082,0.931,0.730,0.361 +Peru,2008,5.129,9.134,0.777,66.980,0.638,-0.072,0.896,0.701,0.354 +Peru,2009,5.519,9.138,0.799,67.240,0.638,-0.084,0.880,0.758,0.320 +Peru,2010,5.613,9.210,0.812,67.500,0.757,-0.066,0.881,0.744,0.330 +Peru,2011,5.892,9.263,0.756,67.760,0.773,-0.128,0.824,0.742,0.331 +Peru,2012,5.825,9.313,0.764,68.020,0.703,-0.084,0.867,0.705,0.398 +Peru,2013,5.783,9.361,0.797,68.280,0.703,-0.071,0.870,0.741,0.390 +Peru,2014,5.866,9.374,0.819,68.540,0.722,-0.141,0.878,0.743,0.319 +Peru,2015,5.577,9.394,0.798,68.800,0.802,-0.095,0.884,0.744,0.378 +Peru,2016,5.701,9.419,0.803,68.975,0.830,-0.139,0.866,0.791,0.338 +Peru,2017,5.711,9.429,0.830,69.150,0.827,-0.160,0.895,0.768,0.394 +Peru,2018,5.680,9.449,0.845,69.325,0.830,-0.184,0.906,0.783,0.380 +Peru,2019,5.999,9.452,0.809,69.500,0.815,-0.135,0.874,0.794,0.375 +Peru,2020,4.994,9.323,0.749,69.675,0.806,-0.094,0.912,0.736,0.481 +Peru,2021,5.694,9.436,0.819,69.850,0.812,-0.090,0.880,0.784,0.369 +Peru,2022,5.892,9.453,0.823,70.025,0.764,-0.180,0.884,0.755,0.378 +Peru,2023,5.936,9.459,0.787,70.200,0.757,-0.061,0.919,0.765,0.370 +Philippines,2006,4.670,8.562,0.795,61.360,0.828,0.058,0.841,0.756, +Philippines,2007,5.074,8.606,0.801,61.420,0.852,-0.027,0.880,0.736,0.378 +Philippines,2008,4.589,8.630,0.798,61.480,0.861,0.078,0.817,0.774,0.384 +Philippines,2009,4.880,8.626,0.775,61.540,0.874,-0.001,0.805,0.791,0.311 +Philippines,2010,4.942,8.679,0.805,61.600,0.893,0.028,0.812,0.829,0.294 +Philippines,2011,4.994,8.699,0.789,61.660,0.883,0.068,0.783,0.808,0.358 +Philippines,2012,5.002,8.748,0.813,61.720,0.914,0.048,0.771,0.811,0.351 +Philippines,2013,4.977,8.796,0.846,61.780,0.907,0.016,0.756,0.796,0.332 +Philippines,2014,5.313,8.842,0.813,61.840,0.902,-0.020,0.787,0.787,0.334 +Philippines,2015,5.547,8.887,0.854,61.900,0.912,-0.056,0.755,0.796,0.351 +Philippines,2016,5.431,8.938,0.821,61.925,0.908,-0.076,0.792,0.807,0.290 +Philippines,2017,5.594,8.987,0.851,61.950,0.926,-0.146,0.711,0.753,0.341 +Philippines,2018,5.869,9.032,0.846,61.975,0.918,-0.112,0.726,0.756,0.393 +Philippines,2019,6.268,9.075,0.845,62.000,0.910,-0.087,0.748,0.765,0.341 +Philippines,2020,5.080,8.958,0.781,62.025,0.932,-0.114,0.744,0.793,0.327 +Philippines,2021,5.965,8.999,0.778,62.050,0.905,-0.012,0.721,0.790,0.323 +Philippines,2022,5.995,9.057,0.819,62.075,0.952,-0.155,0.757,0.833,0.301 +Philippines,2023,6.184,9.102,0.796,62.100,0.932,-0.038,0.768,0.800,0.324 +Poland,2005,5.587,9.844,0.922,66.200,0.782,,0.983,0.611,0.282 +Poland,2007,5.886,9.973,0.913,66.560,0.772,-0.053,0.925,0.665,0.238 +Poland,2009,5.772,10.041,0.917,66.920,0.821,0.068,0.898,0.649,0.246 +Poland,2010,5.887,10.073,0.955,67.100,0.795,-0.003,0.905,0.686,0.234 +Poland,2011,5.646,10.122,0.905,67.280,0.868,-0.072,0.908,0.659,0.224 +Poland,2012,5.876,10.137,0.936,67.460,0.811,-0.032,0.888,0.711,0.267 +Poland,2013,5.746,10.146,0.912,67.640,0.776,-0.142,0.916,0.675,0.242 +Poland,2014,5.750,10.184,0.924,67.820,0.875,-0.069,0.898,0.681,0.223 +Poland,2015,6.007,10.228,0.893,68.000,0.793,-0.098,0.810,0.631,0.240 +Poland,2016,6.162,10.258,0.917,68.175,0.871,-0.096,0.848,0.666,0.224 +Poland,2017,6.201,10.308,0.882,68.350,0.831,-0.127,0.639,0.566,0.203 +Poland,2018,6.111,10.365,0.863,68.525,0.870,-0.260,0.720,0.622,0.176 +Poland,2019,6.242,10.409,0.878,68.700,0.883,-0.237,0.696,0.613,0.168 +Poland,2020,6.139,10.390,0.953,68.875,0.767,-0.014,0.787,0.677,0.329 +Poland,2021,5.978,10.461,0.936,69.050,0.732,0.122,0.744,0.700,0.277 +Poland,2022,6.666,10.513,0.886,69.225,0.800,-0.209,0.667,0.594,0.140 +Poland,2023,6.685,10.527,0.890,69.400,0.775,-0.232,0.662,0.556,0.155 +Portugal,2006,5.405,10.359,0.905,68.340,0.882,-0.184,0.880,0.647,0.333 +Portugal,2008,5.717,10.383,0.886,68.820,0.646,-0.223,0.933,0.667,0.309 +Portugal,2010,5.095,10.367,0.864,69.300,0.721,-0.112,0.948,0.681,0.265 +Portugal,2011,5.220,10.352,0.856,69.540,0.875,-0.179,0.962,0.671,0.279 +Portugal,2012,4.994,10.314,0.866,69.780,0.774,-0.103,0.959,0.631,0.370 +Portugal,2013,5.158,10.310,0.867,70.020,0.788,-0.124,0.946,0.665,0.348 +Portugal,2014,5.127,10.324,0.862,70.260,0.847,-0.132,0.941,0.663,0.358 +Portugal,2015,5.081,10.346,0.866,70.500,0.800,-0.169,0.941,0.629,0.371 +Portugal,2016,5.447,10.369,0.905,70.625,0.838,-0.231,0.922,0.659,0.326 +Portugal,2017,5.711,10.406,0.900,70.750,0.905,-0.182,0.881,0.608,0.294 +Portugal,2018,5.920,10.435,0.887,70.875,0.877,-0.267,0.880,0.646,0.318 +Portugal,2019,6.095,10.462,0.876,71.000,0.882,-0.240,0.915,0.675,0.300 +Portugal,2020,5.768,10.374,0.875,71.125,0.913,-0.244,0.867,0.614,0.383 +Portugal,2021,6.183,10.425,0.895,71.250,0.892,-0.211,0.872,0.629,0.284 +Portugal,2022,5.953,10.484,0.862,71.375,0.903,-0.139,0.893,0.638,0.316 +Portugal,2023,5.954,10.504,0.895,71.500,0.847,-0.176,0.889,0.661,0.309 +Qatar,2009,6.418,11.434,0.894,64.360,0.865,0.230,0.184,0.673,0.258 +Qatar,2010,6.850,11.551,,64.700,,0.095,,, +Qatar,2011,6.592,11.625,0.857,65.040,0.905,0.000,,0.661,0.328 +Qatar,2012,6.611,11.617,0.838,65.380,0.924,0.149,,0.683,0.322 +Qatar,2015,6.375,11.532,,66.400,,,,, +Romania,2005,5.049,9.733,0.838,64.500,0.800,,0.957,0.576,0.346 +Romania,2007,5.394,9.901,0.736,64.860,0.686,-0.194,0.949,0.575,0.277 +Romania,2009,5.368,9.958,0.812,65.220,0.606,-0.203,0.967,0.545,0.270 +Romania,2010,4.909,9.924,0.689,65.400,0.566,-0.091,0.974,0.539,0.344 +Romania,2011,5.023,9.973,0.753,65.580,0.650,-0.148,0.964,0.501,0.294 +Romania,2012,5.167,9.997,0.740,65.760,0.645,-0.120,0.959,0.520,0.343 +Romania,2013,5.082,10.003,0.778,65.940,0.655,-0.135,0.952,0.541,0.329 +Romania,2014,5.727,10.047,0.753,66.120,0.754,-0.107,0.958,0.565,0.331 +Romania,2015,5.777,10.083,0.787,66.300,0.796,-0.147,0.962,0.627,0.312 +Romania,2016,5.969,10.117,0.809,66.425,0.822,-0.120,0.949,0.607,0.258 +Romania,2017,6.090,10.201,0.811,66.550,0.839,-0.165,0.926,0.632,0.231 +Romania,2018,6.151,10.266,0.818,66.675,0.845,-0.224,0.921,0.649,0.298 +Romania,2019,6.130,10.309,0.842,66.800,0.848,-0.228,0.954,0.605,0.244 +Romania,2020,6.785,10.277,0.869,66.925,0.863,-0.161,0.918,0.668,0.256 +Romania,2021,6.549,10.341,0.835,67.050,0.871,-0.187,0.928,0.674,0.264 +Romania,2022,6.437,10.396,0.830,67.175,0.836,-0.173,0.941,0.615,0.258 +Romania,2023,6.489,10.431,0.826,67.300,0.849,-0.202,0.905,0.628,0.271 +Russia,2006,4.964,9.988,0.895,58.740,0.643,-0.312,0.935,0.534,0.232 +Russia,2007,5.223,10.071,0.885,59.180,0.593,-0.289,0.933,0.546,0.193 +Russia,2008,5.619,10.122,0.882,59.620,0.643,-0.311,0.924,0.570,0.166 +Russia,2009,5.158,10.041,0.908,60.060,0.617,-0.289,0.954,0.540,0.169 +Russia,2010,5.385,10.084,0.909,60.500,0.613,-0.302,0.937,0.567,0.171 +Russia,2011,5.389,10.126,0.883,60.940,0.626,-0.284,0.935,0.564,0.165 +Russia,2012,5.621,10.163,0.901,61.380,0.609,-0.298,0.938,0.563,0.174 +Russia,2013,5.537,10.179,0.881,61.820,0.661,-0.295,0.934,0.592,0.180 +Russia,2014,6.037,10.168,0.932,62.260,0.744,-0.270,0.869,0.617,0.151 +Russia,2015,5.996,10.146,0.924,62.700,0.685,-0.177,0.913,0.609,0.130 +Russia,2016,5.855,10.146,0.911,63.075,0.714,-0.187,0.925,0.587,0.142 +Russia,2017,5.579,10.163,0.896,63.450,0.731,-0.151,0.862,0.651,0.195 +Russia,2018,5.514,10.191,0.909,63.825,0.729,-0.153,0.865,0.615,0.199 +Russia,2019,5.441,10.213,0.910,64.200,0.715,-0.122,0.848,0.632,0.200 +Russia,2020,5.495,10.188,0.887,64.575,0.714,-0.078,0.823,0.621,0.190 +Russia,2021,5.448,10.247,0.862,64.950,0.671,0.053,0.808,0.590,0.190 +Russia,2022,6.044,10.225,0.920,65.325,0.776,-0.074,0.767,0.614,0.211 +Russia,2023,5.865,10.209,0.854,65.700,0.750,0.056,0.733,0.616,0.190 +Rwanda,2006,4.215,7.087,0.718,53.500,0.915,,0.299,0.701,0.189 +Rwanda,2008,4.363,7.213,0.486,54.700,0.752,0.014,0.286,0.633,0.221 +Rwanda,2009,4.030,7.247,0.559,55.300,0.766,-0.004,0.410,0.658,0.112 +Rwanda,2011,4.097,7.343,0.570,56.500,0.829,-0.042,0.161,0.608,0.154 +Rwanda,2012,3.333,7.401,0.637,57.100,0.835,-0.015,0.081,0.624,0.132 +Rwanda,2013,3.466,7.423,0.750,57.700,0.904,-0.031,0.117,0.728,0.167 +Rwanda,2014,3.596,7.459,0.748,58.300,0.894,-0.026,0.078,0.748,0.134 +Rwanda,2015,3.483,7.520,0.678,58.900,0.908,0.022,0.095,0.692,0.206 +Rwanda,2016,3.333,7.554,0.665,59.225,0.911,0.022,0.159,0.715,0.285 +Rwanda,2017,3.108,7.568,0.517,59.550,0.908,0.048,0.214,0.724,0.358 +Rwanda,2018,3.561,7.625,0.616,59.875,0.924,0.053,0.164,0.765,0.308 +Rwanda,2019,3.268,7.692,0.489,60.200,0.869,0.060,0.168,0.717,0.418 +Saudi Arabia,2005,7.080,10.679,0.868,61.200,,,0.505,0.681,0.243 +Saudi Arabia,2007,7.267,10.646,0.892,61.600,0.622,0.002,,0.718,0.232 +Saudi Arabia,2008,6.811,10.668,0.823,61.800,0.532,-0.024,0.508,0.607,0.202 +Saudi Arabia,2009,6.148,10.610,0.921,62.000,0.639,-0.111,0.445,0.683,0.319 +Saudi Arabia,2010,6.307,10.627,0.880,62.200,0.678,-0.034,,0.645,0.297 +Saudi Arabia,2011,6.700,10.706,0.830,62.400,0.603,-0.144,,0.699,0.240 +Saudi Arabia,2012,6.396,10.737,0.867,62.600,0.560,-0.123,,0.692,0.225 +Saudi Arabia,2013,6.495,10.744,0.827,62.800,0.661,-0.085,,0.691,0.276 +Saudi Arabia,2014,6.278,10.763,0.818,63.000,0.762,-0.077,,0.663,0.313 +Saudi Arabia,2015,6.345,10.790,0.820,63.200,0.820,-0.050,,0.668,0.327 +Saudi Arabia,2016,6.474,10.793,0.890,63.400,0.774,-0.138,,0.725,0.266 +Saudi Arabia,2017,6.294,10.770,0.840,63.600,0.814,-0.138,,0.703,0.306 +Saudi Arabia,2018,6.356,10.773,0.868,63.800,0.855,-0.198,,0.696,0.288 +Saudi Arabia,2019,6.561,10.758,0.912,64.000,0.891,-0.153,,0.674,0.238 +Saudi Arabia,2020,6.560,10.709,0.890,64.200,0.884,-0.117,,0.702,0.251 +Saudi Arabia,2021,6.445,10.749,0.859,64.400,0.902,-0.108,,0.728,0.228 +Saudi Arabia,2022,6.382,10.820,0.900,64.600,,-0.032,,0.677,0.205 +Saudi Arabia,2023,6.953,10.829,0.884,64.800,,0.028,,0.737,0.240 +Senegal,2006,4.417,7.930,0.760,55.500,0.736,-0.059,0.805,0.687,0.225 +Senegal,2007,4.680,7.931,0.718,55.800,0.698,-0.009,0.827,0.718,0.199 +Senegal,2008,4.683,7.941,0.756,56.100,0.612,-0.037,0.879,0.669,0.252 +Senegal,2009,4.335,7.942,0.810,56.400,0.557,-0.044,0.918,0.708,0.228 +Senegal,2010,4.372,7.948,0.760,56.700,0.777,-0.085,0.851,0.670,0.143 +Senegal,2011,3.834,7.934,0.602,57.000,0.641,-0.168,0.870,0.696,0.180 +Senegal,2012,3.669,7.946,0.711,57.300,0.668,-0.042,0.852,0.722,0.214 +Senegal,2013,3.647,7.943,0.823,57.600,0.636,-0.058,0.837,0.694,0.165 +Senegal,2014,4.395,7.976,0.856,57.900,0.692,-0.052,0.700,0.696,0.157 +Senegal,2015,4.617,8.010,0.702,58.200,0.720,-0.117,0.765,0.710,0.208 +Senegal,2016,4.595,8.045,0.839,58.500,0.744,-0.092,0.794,0.781,0.245 +Senegal,2017,4.683,8.089,0.744,58.800,0.687,-0.050,0.825,0.751,0.291 +Senegal,2018,4.769,8.122,0.739,59.100,0.629,-0.080,0.805,0.724,0.247 +Senegal,2019,5.489,8.140,0.688,59.400,0.759,-0.025,0.796,0.768,0.332 +Senegal,2020,4.757,8.127,0.621,59.700,0.797,-0.052,0.855,0.816,0.268 +Senegal,2021,4.903,8.164,0.645,60.000,0.759,0.026,0.821,0.812,0.265 +Senegal,2022,4.907,8.179,0.609,60.300,0.758,0.049,0.854,0.813,0.287 +Senegal,2023,5.093,8.200,0.668,60.600,0.798,0.048,0.836,0.825,0.258 +Serbia,2007,4.750,9.536,0.844,65.280,0.453,-0.171,0.905,0.528,0.334 +Serbia,2009,4.380,9.571,0.770,65.560,0.373,-0.184,0.961,0.466,0.435 +Serbia,2010,4.461,9.583,0.726,65.700,0.463,-0.176,0.965,0.446,0.415 +Serbia,2011,4.815,9.611,0.773,65.840,0.440,-0.191,0.977,0.458,0.410 +Serbia,2012,5.155,9.609,0.819,65.980,0.461,-0.136,0.952,0.447,0.371 +Serbia,2013,5.102,9.642,0.828,66.120,0.533,-0.106,0.908,0.505,0.403 +Serbia,2014,5.113,9.631,0.783,66.260,0.532,0.066,0.912,0.473,0.326 +Serbia,2015,5.318,9.654,0.816,66.400,0.546,-0.068,0.859,0.472,0.303 +Serbia,2016,5.753,9.692,0.895,66.525,0.614,-0.074,0.890,0.492,0.298 +Serbia,2017,5.122,9.718,0.884,66.650,0.685,-0.084,0.851,0.485,0.326 +Serbia,2018,5.936,9.767,0.853,66.775,0.740,-0.106,0.864,0.527,0.296 +Serbia,2019,6.241,9.815,0.903,66.900,0.753,-0.046,0.813,0.474,0.242 +Serbia,2020,6.042,9.813,0.852,67.025,0.843,0.142,0.824,0.579,0.358 +Serbia,2021,6.245,9.895,0.890,67.150,0.850,0.261,0.806,0.568,0.311 +Serbia,2022,6.546,9.928,0.896,67.275,0.782,0.074,0.801,0.514,0.275 +Serbia,2023,6.441,9.961,0.895,67.400,0.807,0.085,0.782,0.526,0.206 +Sierra Leone,2006,3.628,7.122,0.561,46.280,0.679,0.097,0.836,0.535,0.381 +Sierra Leone,2007,3.585,7.177,0.686,46.660,0.720,0.243,0.830,0.635,0.290 +Sierra Leone,2008,2.997,7.205,0.591,47.040,0.716,0.144,0.925,0.588,0.370 +Sierra Leone,2010,4.134,7.245,0.812,47.800,0.726,0.008,0.910,0.497,0.290 +Sierra Leone,2011,4.502,7.277,0.782,48.180,0.770,0.001,0.855,0.495,0.300 +Sierra Leone,2013,4.514,7.557,0.708,48.940,0.720,-0.075,0.856,0.540,0.423 +Sierra Leone,2014,4.500,7.577,0.869,49.320,0.681,0.030,0.786,0.622,0.334 +Sierra Leone,2015,4.909,7.324,0.611,49.700,0.624,0.047,0.825,0.626,0.414 +Sierra Leone,2016,4.733,7.361,0.657,50.500,0.681,0.103,0.863,0.616,0.456 +Sierra Leone,2017,4.090,7.374,0.652,51.300,0.711,0.076,0.848,0.607,0.495 +Sierra Leone,2018,4.306,7.384,0.650,52.100,0.716,0.093,0.856,0.533,0.466 +Sierra Leone,2019,3.447,7.412,0.611,52.900,0.718,0.072,0.874,0.521,0.438 +Sierra Leone,2021,3.714,7.387,0.609,54.500,0.659,0.106,0.851,0.538,0.448 +Sierra Leone,2022,2.560,7.400,0.502,55.300,0.660,0.097,0.862,0.494,0.505 +Sierra Leone,2023,3.467,7.412,0.601,56.100,0.694,0.101,0.866,0.504,0.430 +Singapore,2006,6.463,11.168,0.904,71.580,0.757,0.132,,0.689,0.267 +Singapore,2007,6.834,11.213,0.921,71.760,0.867,0.287,0.064,0.588,0.114 +Singapore,2008,6.642,11.178,0.845,71.940,0.661,0.040,0.066,0.627,0.256 +Singapore,2009,6.145,11.149,0.866,72.120,0.776,-0.081,0.035,0.450,0.208 +Singapore,2010,6.531,11.267,0.864,72.300,0.846,-0.024,0.060,0.527,0.131 +Singapore,2011,6.561,11.306,0.904,72.480,0.822,-0.155,0.099,0.404,0.144 +Singapore,2013,6.533,11.356,0.808,72.840,0.827,0.109,0.242,0.663,0.148 +Singapore,2014,7.062,11.382,0.822,73.020,0.835,0.148,0.133,0.774,0.180 +Singapore,2015,6.620,11.399,0.866,73.200,0.887,0.144,0.099,0.736,0.142 +Singapore,2016,6.033,11.422,0.925,73.300,0.904,0.137,0.047,0.745,0.111 +Singapore,2017,6.378,11.465,0.897,73.400,0.926,0.129,0.162,0.750,0.179 +Singapore,2018,6.375,11.496,0.903,73.500,0.916,-0.073,0.097,0.731,0.107 +Singapore,2019,6.378,11.497,0.925,73.600,0.938,0.020,0.070,0.674,0.138 +Singapore,2021,6.587,11.587,0.876,73.800,0.879,0.060,0.145,0.697,0.160 +Singapore,2022,6.333,11.590,0.852,73.900,0.873,0.088,,0.688,0.209 +Singapore,2023,6.654,,0.916,74.000,0.861,,0.153,0.667,0.190 +Slovakia,2006,5.265,9.989,0.954,65.620,0.542,-0.054,0.946,0.586,0.308 +Slovakia,2010,6.052,10.152,0.920,66.500,0.636,-0.106,0.907,0.603,0.277 +Slovakia,2011,5.945,10.177,0.917,66.720,0.727,0.006,0.907,0.588,0.287 +Slovakia,2012,5.911,10.188,0.926,66.940,0.620,-0.032,0.907,0.585,0.302 +Slovakia,2013,5.937,10.193,0.909,67.160,0.598,-0.055,0.915,0.612,0.277 +Slovakia,2014,6.139,10.219,0.924,67.380,0.635,-0.130,0.914,0.619,0.267 +Slovakia,2015,6.162,10.268,0.943,67.600,0.587,-0.132,0.928,0.632,0.269 +Slovakia,2016,5.993,10.286,0.945,67.825,0.700,-0.065,0.917,0.688,0.232 +Slovakia,2017,6.366,10.314,0.913,68.050,0.714,-0.059,0.920,0.709,0.213 +Slovakia,2018,6.235,10.352,0.922,68.275,0.758,-0.172,0.910,0.670,0.253 +Slovakia,2019,6.243,10.375,0.933,68.500,0.771,-0.133,0.926,0.676,0.252 +Slovakia,2020,6.519,10.340,0.954,68.725,0.762,-0.081,0.901,0.695,0.274 +Slovakia,2021,6.419,10.390,0.951,68.950,0.742,0.042,0.896,0.692,0.241 +Slovakia,2022,6.091,10.409,0.961,69.175,0.732,-0.129,0.841,0.665,0.263 +Slovakia,2023,6.261,10.425,0.941,69.400,0.753,-0.136,0.825,0.647,0.235 +Slovenia,2006,5.811,10.399,0.936,68.560,0.936,0.037,0.708,0.608,0.307 +Slovenia,2009,5.830,10.406,0.919,69.040,0.896,-0.025,0.804,0.583,0.303 +Slovenia,2010,6.083,10.415,0.917,69.200,0.896,0.024,0.845,0.592,0.295 +Slovenia,2011,6.036,10.421,0.931,69.360,0.907,-0.031,0.893,0.587,0.285 +Slovenia,2012,6.063,10.392,0.925,69.520,0.904,-0.025,0.891,0.598,0.284 +Slovenia,2013,5.975,10.381,0.932,69.680,0.890,0.030,0.918,0.625,0.274 +Slovenia,2014,5.678,10.407,0.908,69.840,0.888,0.047,0.909,0.594,0.291 +Slovenia,2015,5.741,10.428,0.901,70.000,0.896,0.002,0.892,0.644,0.261 +Slovenia,2016,5.937,10.459,0.934,70.175,0.904,-0.060,0.838,0.597,0.272 +Slovenia,2017,6.167,10.505,0.928,70.350,0.921,-0.031,0.829,0.582,0.286 +Slovenia,2018,6.249,10.545,0.941,70.525,0.942,-0.125,0.839,0.601,0.275 +Slovenia,2019,6.665,10.572,0.949,70.700,0.945,-0.108,0.785,0.622,0.228 +Slovenia,2020,6.462,10.521,0.953,70.875,0.958,-0.090,0.797,0.575,0.314 +Slovenia,2021,6.761,10.598,0.955,71.050,0.851,0.026,0.754,0.643,0.261 +Slovenia,2022,6.723,10.650,0.942,71.225,0.930,0.099,0.762,0.625,0.242 +Slovenia,2023,6.746,10.664,0.911,71.400,0.931,0.031,0.750,0.615,0.256 +Somalia,2014,5.528,6.830,0.611,47.660,0.874,0.020,0.456,0.689,0.207 +Somalia,2015,5.354,6.937,0.599,48.100,0.968,0.016,0.410,0.764,0.187 +Somalia,2016,4.668,6.981,0.594,48.500,0.917,0.069,0.441,0.773,0.193 +Somaliland region,2009,4.991,,0.880,,0.746,,0.513,0.708,0.112 +Somaliland region,2010,4.657,,0.829,,0.820,,0.471,0.632,0.083 +Somaliland region,2011,4.931,,0.788,,0.858,,0.357,0.691,0.122 +Somaliland region,2012,5.057,,0.786,,0.758,,0.334,0.687,0.152 +South Africa,2006,5.084,9.455,0.913,46.000,0.649,-0.094,,0.724,0.223 +South Africa,2007,5.204,9.497,0.788,46.900,0.690,-0.169,0.859,0.658,0.210 +South Africa,2008,5.346,9.518,0.810,47.800,0.749,-0.106,0.866,0.712,0.206 +South Africa,2009,5.218,9.490,0.877,48.700,0.739,-0.165,0.904,0.656,0.231 +South Africa,2010,4.652,9.508,0.917,49.600,0.739,-0.213,0.791,0.698,0.124 +South Africa,2011,4.931,9.527,0.858,50.500,0.835,-0.166,0.819,0.720,0.230 +South Africa,2012,5.134,9.537,0.907,51.400,0.590,-0.175,0.838,0.711,0.178 +South Africa,2013,3.661,9.548,0.839,52.300,0.714,-0.089,0.800,0.740,0.167 +South Africa,2014,4.828,9.546,0.881,53.200,0.794,-0.128,0.820,0.730,0.243 +South Africa,2015,4.887,9.539,0.898,54.100,0.862,-0.138,0.853,0.717,0.161 +South Africa,2016,4.770,9.536,0.875,54.625,0.774,-0.082,0.813,0.743,0.301 +South Africa,2017,4.514,9.543,0.870,55.150,0.787,-0.141,0.865,0.709,0.268 +South Africa,2018,4.884,9.546,0.841,55.675,0.753,-0.063,0.841,0.736,0.283 +South Africa,2019,5.035,9.536,0.848,56.200,0.738,-0.147,0.820,0.727,0.268 +South Africa,2020,4.947,9.458,0.891,56.725,0.757,-0.030,0.912,0.761,0.294 +South Africa,2021,5.599,9.496,0.922,57.250,0.704,-0.148,0.892,0.784,0.173 +South Africa,2022,5.581,9.508,0.887,57.775,0.713,-0.071,0.908,0.744,0.239 +South Africa,2023,5.076,9.503,0.839,58.300,0.748,-0.109,0.861,0.708,0.255 +South Korea,2006,5.332,10.309,0.775,70.020,0.715,-0.058,0.799,0.545,0.338 +South Korea,2007,5.767,10.360,0.827,70.240,0.656,-0.065,0.803,0.612,0.226 +South Korea,2008,5.390,10.382,0.754,70.460,0.524,-0.108,0.771,0.554,0.239 +South Korea,2009,5.648,10.385,0.811,70.680,0.600,-0.102,0.787,0.596,0.209 +South Korea,2010,6.116,10.446,0.816,70.900,0.677,-0.039,0.752,0.626,0.130 +South Korea,2011,6.947,10.474,0.809,71.120,0.682,-0.054,0.827,0.587,0.168 +South Korea,2012,6.003,10.493,0.775,71.340,0.618,,0.844,0.610,0.206 +South Korea,2013,5.959,10.519,0.797,71.560,0.642,-0.056,0.832,0.589,0.189 +South Korea,2014,5.801,10.544,0.738,71.780,0.623,-0.049,0.834,0.575,0.283 +South Korea,2015,5.780,10.567,0.768,72.000,0.616,-0.041,0.841,0.561,0.244 +South Korea,2016,5.971,10.592,0.811,72.275,0.591,0.020,0.862,0.583,0.233 +South Korea,2017,5.874,10.620,0.807,72.550,0.538,0.008,0.851,0.546,0.235 +South Korea,2018,5.840,10.645,0.798,72.825,0.600,-0.095,0.797,0.579,0.217 +South Korea,2019,5.903,10.663,0.783,73.100,0.706,-0.061,0.718,0.593,0.236 +South Korea,2020,5.793,10.655,0.808,73.375,0.711,-0.112,0.665,0.550,0.247 +South Korea,2021,6.113,10.697,0.811,73.650,0.717,-0.033,0.685,0.566,0.221 +South Korea,2022,5.950,10.725,0.810,73.925,0.723,0.002,0.747,0.585,0.233 +South Korea,2023,6.112,10.742,0.799,74.200,0.762,-0.004,0.714,0.603,0.245 +South Sudan,2014,3.832,,0.545,52.880,0.567,,0.742,0.578,0.428 +South Sudan,2015,4.071,,0.585,53.000,0.512,,0.710,0.553,0.450 +South Sudan,2016,2.888,,0.532,53.175,0.440,,0.785,0.594,0.549 +South Sudan,2017,2.817,,0.557,53.350,0.456,,0.761,0.565,0.517 +Spain,2005,7.153,10.544,0.961,70.400,0.916,,0.777,0.694,0.241 +Spain,2007,6.995,10.585,0.957,70.640,0.782,-0.099,0.784,0.717,0.264 +Spain,2008,7.294,10.577,0.948,70.760,0.834,-0.155,0.683,0.649,0.260 +Spain,2009,6.199,10.530,0.929,70.880,0.749,-0.133,0.798,0.645,0.336 +Spain,2010,6.188,10.527,0.950,71.000,0.796,-0.144,0.840,0.645,0.322 +Spain,2011,6.518,10.516,0.944,71.120,0.819,-0.128,0.846,0.667,0.356 +Spain,2012,6.291,10.485,0.937,71.240,0.755,-0.065,0.844,0.644,0.366 +Spain,2013,6.150,10.474,0.929,71.360,0.759,-0.107,0.916,0.663,0.372 +Spain,2014,6.456,10.491,0.948,71.480,0.738,-0.034,0.854,0.683,0.335 +Spain,2015,6.381,10.529,0.956,71.600,0.732,-0.078,0.822,0.705,0.285 +Spain,2016,6.319,10.558,0.942,71.725,0.768,-0.054,0.819,0.630,0.301 +Spain,2017,6.230,10.585,0.903,71.850,0.756,-0.038,0.791,0.601,0.302 +Spain,2018,6.513,10.604,0.910,71.975,0.722,-0.081,0.777,0.636,0.357 +Spain,2019,6.457,10.616,0.949,72.100,0.778,-0.054,0.730,0.636,0.316 +Spain,2020,6.502,10.491,0.935,72.225,0.783,-0.127,0.730,0.671,0.317 +Spain,2021,6.470,10.544,0.926,72.350,0.782,-0.076,0.729,0.639,0.324 +Spain,2022,6.337,10.592,0.934,72.475,0.781,-0.001,0.673,0.636,0.320 +Spain,2023,6.456,10.609,0.912,72.600,0.779,-0.004,0.675,0.655,0.325 +Sri Lanka,2006,4.345,8.937,0.864,62.280,0.724,0.055,0.838,0.639,0.216 +Sri Lanka,2007,4.415,8.992,0.838,62.760,0.736,0.103,0.847,0.590,0.220 +Sri Lanka,2008,4.431,9.040,0.816,63.240,0.834,0.156,0.861,0.656,0.153 +Sri Lanka,2009,4.212,9.065,0.830,63.720,0.799,0.299,0.690,0.661,0.172 +Sri Lanka,2010,3.977,9.133,0.814,64.200,0.738,0.252,0.769,0.704,0.163 +Sri Lanka,2011,4.181,9.207,0.842,64.680,0.823,0.138,0.760,0.730,0.175 +Sri Lanka,2012,4.225,9.282,0.824,65.160,0.800,0.155,0.823,0.761,0.197 +Sri Lanka,2013,4.365,9.316,0.809,65.640,0.834,0.262,0.842,0.776,0.208 +Sri Lanka,2014,4.268,9.373,0.805,66.120,0.868,0.291,0.791,0.785,0.187 +Sri Lanka,2015,4.612,9.410,0.863,66.600,0.902,0.312,0.859,0.789,0.235 +Sri Lanka,2017,4.331,9.514,0.823,66.800,0.827,0.083,0.844,0.729,0.270 +Sri Lanka,2018,4.435,9.529,0.833,66.900,0.859,0.096,0.856,0.773,0.302 +Sri Lanka,2019,4.213,9.521,0.815,67.000,0.824,0.043,0.863,0.753,0.315 +Sri Lanka,2020,4.778,9.468,0.842,67.100,0.803,-0.050,0.768,0.758,0.285 +Sri Lanka,2021,4.103,9.492,0.812,67.200,0.771,-0.013,0.849,0.733,0.312 +Sri Lanka,2022,3.985,9.409,0.825,67.300,0.740,0.038,0.900,0.715,0.321 +Sri Lanka,2023,3.602,9.364,0.790,67.400,0.754,0.050,0.922,0.709,0.353 +State of Palestine,2006,4.716,8.201,0.818,,0.547,,0.858,0.492,0.431 +State of Palestine,2007,4.151,8.181,0.712,,0.365,-0.083,0.844,0.515,0.412 +State of Palestine,2008,4.386,8.275,0.666,,0.358,-0.075,0.753,0.513,0.403 +State of Palestine,2009,4.470,8.337,0.738,,0.468,-0.091,0.797,0.474,0.466 +State of Palestine,2010,4.703,8.363,0.822,,0.504,-0.121,0.752,0.553,0.381 +State of Palestine,2011,4.751,8.452,0.751,,0.522,-0.131,0.750,0.499,0.388 +State of Palestine,2012,4.647,8.598,0.782,,0.542,-0.163,0.730,0.560,0.379 +State of Palestine,2013,4.844,8.595,0.761,,0.454,-0.163,0.780,0.537,0.365 +State of Palestine,2014,4.722,8.618,0.775,,0.657,-0.163,0.804,0.505,0.380 +State of Palestine,2015,4.695,8.683,0.766,,0.556,-0.173,0.774,0.536,0.369 +State of Palestine,2016,4.907,8.738,0.818,,0.608,-0.151,0.812,0.544,0.378 +State of Palestine,2017,4.628,8.734,0.824,,0.632,-0.186,0.831,0.534,0.416 +State of Palestine,2018,4.554,8.718,0.819,,0.655,-0.163,0.814,0.528,0.419 +State of Palestine,2019,4.483,8.716,0.833,,0.653,-0.135,0.829,0.538,0.400 +State of Palestine,2022,4.908,,0.860,,0.695,,0.836,0.584,0.362 +State of Palestine,2023,4.851,,0.831,,0.708,,0.808,0.580,0.378 +Sudan,2009,4.455,8.457,0.911,57.460,0.710,0.046,0.701,0.688,0.245 +Sudan,2010,4.435,8.465,0.855,57.700,0.648,-0.073,0.737,0.589,0.221 +Sudan,2011,4.314,8.527,0.818,57.940,0.583,-0.053,0.663,0.532,0.249 +Sudan,2012,4.550,8.458,0.813,58.180,0.412,-0.072,0.734,0.511,0.242 +Sudan,2014,4.139,8.471,0.811,58.660,0.390,-0.080,0.794,0.461,0.303 +Suriname,2012,6.269,9.874,0.797,62.840,0.885,-0.088,0.751,0.730,0.250 +Sweden,2005,7.376,10.724,0.951,71.000,0.964,,,0.742,0.151 +Sweden,2007,7.241,10.791,0.917,71.080,0.910,0.141,0.289,0.735,0.177 +Sweden,2008,7.516,10.778,0.923,71.120,0.912,0.120,0.314,0.763,0.134 +Sweden,2009,7.266,10.725,0.903,71.160,0.864,0.216,0.292,0.761,0.151 +Sweden,2010,7.496,10.775,0.970,71.200,0.905,0.136,0.253,0.788,0.200 +Sweden,2011,7.382,10.799,0.921,71.240,0.941,0.156,0.269,0.762,0.179 +Sweden,2012,7.560,10.785,0.929,71.280,0.944,0.127,0.254,0.796,0.170 +Sweden,2013,7.434,10.789,0.916,71.320,0.936,0.154,0.324,0.782,0.184 +Sweden,2014,7.239,10.805,0.933,71.360,0.945,0.197,0.250,0.793,0.208 +Sweden,2015,7.289,10.838,0.929,71.400,0.935,0.206,0.232,0.766,0.191 +Sweden,2016,7.369,10.846,0.912,71.525,0.918,0.141,0.246,0.752,0.201 +Sweden,2017,7.287,10.858,0.914,71.650,0.935,0.165,0.239,0.756,0.175 +Sweden,2018,7.375,10.866,0.931,71.775,0.942,0.072,0.263,0.747,0.161 +Sweden,2019,7.398,10.875,0.934,71.900,0.942,0.085,0.250,0.775,0.202 +Sweden,2020,7.314,10.846,0.936,72.025,0.951,0.084,0.203,0.717,0.222 +Sweden,2021,7.439,10.893,0.932,72.150,0.953,0.172,0.191,0.763,0.190 +Sweden,2022,7.431,10.912,0.949,72.275,0.939,0.232,0.213,0.750,0.163 +Sweden,2023,7.161,10.902,0.927,72.400,0.926,0.147,0.253,0.739,0.194 +Switzerland,2006,7.473,11.056,0.951,71.160,0.919,0.284,0.408,0.742,0.212 +Switzerland,2009,7.525,11.065,0.938,71.340,0.891,0.118,0.342,0.741,0.202 +Switzerland,2012,7.776,11.094,0.947,71.520,0.945,0.131,0.323,0.793,0.176 +Switzerland,2014,7.493,11.111,0.959,71.640,0.949,0.053,0.283,0.788,0.189 +Switzerland,2015,7.572,11.116,0.938,71.700,0.928,0.102,0.210,0.794,0.166 +Switzerland,2016,7.459,11.126,0.928,71.900,0.934,0.081,0.302,0.758,0.206 +Switzerland,2017,7.474,11.130,0.950,72.100,0.925,0.173,0.316,0.734,0.196 +Switzerland,2018,7.509,11.151,0.930,72.300,0.926,0.094,0.301,0.756,0.192 +Switzerland,2019,7.694,11.155,0.949,72.500,0.913,0.029,0.294,0.743,0.171 +Switzerland,2020,7.508,11.124,0.946,72.700,0.917,-0.073,0.280,0.730,0.193 +Switzerland,2021,7.328,11.158,0.934,72.900,0.908,0.024,0.287,0.747,0.183 +Switzerland,2022,6.884,11.170,0.881,73.100,0.848,0.128,0.235,0.710,0.180 +Switzerland,2023,6.969,11.169,0.904,73.300,0.891,0.104,0.247,0.745,0.185 +Syria,2008,5.323,8.658,0.712,68.620,0.661,0.116,0.680,0.562,0.338 +Syria,2009,4.979,8.656,0.842,66.860,0.748,0.076,0.688,0.491,0.292 +Syria,2010,4.465,8.733,0.934,65.100,0.647,0.002,0.743,0.489,0.225 +Syria,2011,4.038,8.735,0.576,63.340,0.530,0.125,0.741,0.521,0.496 +Syria,2012,3.164,8.578,0.588,61.580,0.467,0.310,0.673,0.451,0.705 +Syria,2013,2.688,8.419,0.585,59.820,0.455,0.219,0.663,0.354,0.622 +Syria,2015,3.462,8.492,0.464,56.300,0.448,0.036,0.685,0.363,0.643 +Taiwan Province of China,2006,6.189,10.602,0.882,68.680,0.630,-0.035,0.846,0.683,0.094 +Taiwan Province of China,2008,5.548,10.600,0.830,69.140,0.642,-0.022,0.785,0.682,0.169 +Taiwan Province of China,2010,6.229,10.681,0.831,69.600,0.677,-0.001,0.821,0.738,0.136 +Taiwan Province of China,2011,6.309,10.693,0.863,,0.761,0.030,0.755,0.727,0.112 +Taiwan Province of China,2012,6.126,10.718,0.825,,0.698,0.016,0.803,0.702,0.140 +Taiwan Province of China,2013,6.340,10.724,0.817,,0.690,-0.003,0.841,0.754,0.124 +Taiwan Province of China,2014,6.363,10.749,0.870,,0.693,0.089,0.866,0.767,0.108 +Taiwan Province of China,2015,6.450,10.779,0.885,,0.701,0.017,0.857,0.750,0.129 +Taiwan Province of China,2016,6.513,10.768,0.895,,0.719,-0.049,0.811,0.743,0.108 +Taiwan Province of China,2017,6.359,10.774,0.891,,0.760,-0.070,0.743,0.715,0.114 +Taiwan Province of China,2018,6.467,10.781,0.896,,0.741,-0.179,0.736,0.746,0.093 +Taiwan Province of China,2019,6.537,10.797,0.893,,0.814,-0.131,0.718,0.762,0.093 +Taiwan Province of China,2020,6.751,,0.901,,0.799,,0.711,0.743,0.083 +Taiwan Province of China,2021,6.247,,0.866,,0.818,,0.675,0.667,0.123 +Taiwan Province of China,2022,6.607,,0.883,,0.800,,0.658,0.717,0.095 +Taiwan Province of China,2023,6.655,,0.872,,0.795,,0.641,0.748,0.111 +Tajikistan,2006,4.613,7.591,0.724,60.500,0.702,-0.096,0.768,0.494,0.195 +Tajikistan,2007,4.432,7.648,0.727,60.600,0.818,-0.007,0.659,0.619,0.133 +Tajikistan,2008,5.064,7.705,0.701,60.700,0.816,0.010,0.723,0.488,0.160 +Tajikistan,2009,4.575,7.724,0.676,60.800,0.744,-0.007,0.792,0.475,0.203 +Tajikistan,2010,4.381,7.766,0.759,60.900,0.784,0.054,0.679,0.483,0.192 +Tajikistan,2011,4.263,7.817,0.751,61.000,0.776,-0.127,0.672,0.573,0.166 +Tajikistan,2012,4.497,7.867,0.729,61.100,0.749,-0.081,0.717,0.583,0.198 +Tajikistan,2013,4.967,7.916,0.701,61.200,0.693,0.055,0.764,0.578,0.170 +Tajikistan,2014,4.896,7.958,0.810,61.300,0.853,-0.007,0.698,0.587,0.196 +Tajikistan,2015,5.124,7.993,0.844,61.400,0.847,0.013,0.742,0.633,0.196 +Tajikistan,2016,5.104,8.036,0.857,61.550,0.703,0.001,0.632,0.587,0.220 +Tajikistan,2017,5.829,8.082,0.663,61.700,0.832,0.116,0.718,0.581,0.278 +Tajikistan,2018,5.497,8.133,0.875,61.850,,-0.074,0.578,0.632,0.220 +Tajikistan,2019,5.464,8.182,0.880,62.000,,-0.054,0.490,0.663,0.178 +Tajikistan,2020,5.373,8.203,0.790,62.150,,-0.054,0.550,0.652,0.344 +Tajikistan,2021,5.287,8.271,0.883,62.300,,-0.071,0.499,0.655,0.240 +Tajikistan,2022,5.176,8.328,0.865,62.450,,-0.003,0.397,0.710,0.220 +Tajikistan,2023,5.379,8.371,0.871,62.600,,-0.054,0.482,0.638,0.231 +Tanzania,2006,3.922,7.459,0.783,50.760,0.787,-0.030,0.649,0.725,0.209 +Tanzania,2007,4.318,7.497,0.708,51.420,0.716,-0.016,0.707,0.702,0.220 +Tanzania,2008,4.385,7.525,0.774,52.080,0.562,0.253,0.930,0.740,0.178 +Tanzania,2009,3.408,7.551,0.837,52.740,0.607,0.305,0.903,0.733,0.161 +Tanzania,2010,3.229,7.587,0.813,53.400,0.597,0.135,0.866,0.667,0.146 +Tanzania,2011,4.074,7.632,0.883,54.060,0.736,-0.050,0.816,0.720,0.145 +Tanzania,2012,4.007,7.647,0.832,54.720,0.577,0.209,0.887,0.641,0.195 +Tanzania,2013,3.852,7.683,0.803,55.380,0.654,0.051,0.859,0.707,0.191 +Tanzania,2014,3.483,7.717,0.789,56.040,0.654,0.107,0.878,0.693,0.241 +Tanzania,2015,3.661,7.743,0.790,56.700,0.759,0.145,0.906,0.607,0.192 +Tanzania,2016,2.903,7.775,0.638,57.150,0.775,0.175,0.739,0.649,0.246 +Tanzania,2017,3.347,7.807,0.705,57.600,0.800,0.112,0.654,0.662,0.255 +Tanzania,2018,3.445,7.828,0.675,58.050,0.807,0.150,0.612,0.702,0.221 +Tanzania,2019,3.640,7.855,0.687,58.500,0.850,0.097,0.589,0.679,0.243 +Tanzania,2020,3.786,7.844,0.740,58.950,0.830,0.293,0.521,0.667,0.271 +Tanzania,2021,3.681,7.857,0.619,59.400,0.822,0.110,0.546,0.648,0.246 +Tanzania,2022,3.616,7.872,0.600,59.850,0.856,0.133,0.584,0.707,0.195 +Tanzania,2023,4.042,7.893,0.663,60.300,0.862,0.122,0.609,0.702,0.210 +Thailand,2006,5.885,9.452,0.894,66.380,0.863,0.326,0.935,0.750,0.164 +Thailand,2007,5.784,9.498,0.889,66.560,0.870,0.386,0.898,0.784,0.180 +Thailand,2008,5.636,9.507,0.832,66.740,0.868,0.421,0.933,0.777,0.145 +Thailand,2009,5.476,9.493,0.893,66.920,0.868,0.520,0.904,0.808,0.166 +Thailand,2010,6.217,9.559,0.898,67.100,0.860,0.532,0.917,0.821,0.182 +Thailand,2011,6.664,9.561,0.884,67.280,0.927,0.396,0.923,0.834,0.117 +Thailand,2012,6.300,9.624,0.906,67.460,0.847,0.376,0.909,0.733,0.138 +Thailand,2013,6.231,9.645,0.926,67.640,0.781,0.452,0.925,0.782,0.141 +Thailand,2014,6.985,9.649,0.933,67.820,0.900,0.548,0.920,0.768,0.169 +Thailand,2015,6.202,9.675,0.866,68.000,0.885,0.312,0.914,0.884,0.174 +Thailand,2016,6.074,9.705,0.908,68.075,0.924,0.352,0.878,0.811,0.218 +Thailand,2017,5.939,9.741,0.877,68.150,0.923,0.208,0.884,0.776,0.232 +Thailand,2018,6.012,9.780,0.873,68.225,0.905,0.255,0.907,0.783,0.198 +Thailand,2019,6.022,9.798,0.903,68.300,0.898,0.305,0.877,0.792,0.208 +Thailand,2020,5.885,9.733,0.867,68.375,0.840,0.270,0.918,0.770,0.326 +Thailand,2021,5.638,9.746,0.883,68.450,0.836,0.290,0.943,0.719,0.298 +Thailand,2022,6.007,9.770,0.867,68.525,0.881,0.299,0.868,0.773,0.218 +Thailand,2023,6.282,9.807,0.873,68.600,0.926,0.338,0.889,0.811,0.217 +Togo,2006,3.202,7.342,0.435,50.240,0.628,-0.030,0.850,0.571,0.348 +Togo,2008,2.808,7.312,0.291,51.120,0.287,-0.078,0.932,0.398,0.379 +Togo,2011,2.936,7.406,0.303,52.440,0.584,-0.093,0.832,0.479,0.395 +Togo,2014,2.839,7.509,0.444,53.760,0.663,-0.108,0.795,0.537,0.443 +Togo,2015,3.768,7.540,0.479,54.200,0.772,-0.092,0.733,0.597,0.416 +Togo,2016,3.879,7.569,0.509,54.700,0.730,-0.031,0.815,0.610,0.483 +Togo,2017,4.361,7.587,0.508,55.200,0.717,-0.066,0.726,0.614,0.426 +Togo,2018,4.023,7.613,0.596,55.700,0.612,-0.031,0.809,0.602,0.446 +Togo,2019,4.179,7.637,0.539,56.200,0.617,0.041,0.737,0.606,0.444 +Togo,2021,4.037,7.667,0.603,57.200,0.619,0.037,0.766,0.628,0.417 +Togo,2022,4.239,7.700,0.579,57.700,0.696,0.002,0.713,0.594,0.414 +Togo,2023,4.365,7.725,0.547,58.200,0.665,-0.071,0.685,0.546,0.362 +Trinidad and Tobago,2006,5.832,10.172,0.887,61.780,0.840,0.139,0.917,0.750,0.229 +Trinidad and Tobago,2008,6.696,10.240,0.858,62.540,0.838,0.085,0.959,0.802,0.184 +Trinidad and Tobago,2011,6.519,10.206,0.863,63.680,0.775,0.076,0.900,0.827,0.134 +Trinidad and Tobago,2013,6.168,10.293,0.883,64.440,0.847,0.121,0.948,0.764,0.286 +Trinidad and Tobago,2017,6.192,10.174,0.916,65.700,0.859,0.010,0.911,0.763,0.248 +Tunisia,2009,5.025,9.238,,66.220,0.781,-0.127,0.722,, +Tunisia,2010,5.131,9.257,0.863,66.300,0.624,-0.143,0.732,0.697,0.249 +Tunisia,2011,4.876,9.224,0.715,66.380,0.603,-0.207,0.913,0.513,0.248 +Tunisia,2012,4.464,9.252,0.614,66.460,0.568,-0.184,0.899,0.490,0.327 +Tunisia,2013,5.246,9.265,0.648,66.540,0.536,-0.214,0.886,0.435,0.239 +Tunisia,2014,4.764,9.284,0.680,66.620,0.589,-0.239,0.783,0.424,0.321 +Tunisia,2015,5.132,9.283,0.609,66.700,0.711,-0.233,0.815,0.514,0.320 +Tunisia,2016,4.521,9.283,0.702,66.750,0.614,-0.172,0.811,0.532,0.378 +Tunisia,2017,4.124,9.294,0.717,66.800,0.478,-0.226,0.869,0.367,0.377 +Tunisia,2018,4.741,9.310,0.733,66.850,0.650,-0.199,0.840,0.536,0.365 +Tunisia,2019,4.315,9.316,0.610,66.900,0.659,-0.217,0.889,0.459,0.433 +Tunisia,2020,4.731,9.214,0.719,66.950,0.668,-0.206,0.877,0.519,0.439 +Tunisia,2021,4.499,9.249,0.711,67.000,0.591,-0.206,0.933,0.451,0.336 +Tunisia,2022,4.261,9.267,0.755,67.050,0.474,-0.233,0.908,0.458,0.304 +Tunisia,2023,4.505,9.282,0.702,67.100,0.482,-0.226,0.882,0.461,0.364 +Turkmenistan,2009,6.568,8.955,0.924,59.780,,-0.105,,0.695,0.152 +Turkmenistan,2011,5.792,9.146,0.964,60.420,,0.015,,0.577,0.122 +Turkmenistan,2012,5.464,9.233,0.946,60.740,0.786,-0.126,,0.541,0.117 +Turkmenistan,2013,5.392,9.312,0.846,61.060,0.705,-0.075,,0.552,0.160 +Turkmenistan,2014,5.787,9.392,0.909,61.380,0.805,0.029,,0.614,0.154 +Turkmenistan,2015,5.791,9.437,0.960,61.700,0.701,0.090,,0.633,0.301 +Turkmenistan,2016,5.887,9.479,0.929,61.800,0.749,0.002,,0.560,0.255 +Turkmenistan,2017,5.229,9.525,0.908,61.900,0.720,0.063,,0.488,0.350 +Turkmenistan,2018,4.621,9.569,0.984,62.000,0.858,0.257,,0.567,0.189 +Turkmenistan,2019,5.474,9.615,0.982,62.100,0.892,0.282,,0.494,0.183 +Türkiye,2005,4.719,9.800,0.820,66.100,0.623,,0.877,0.479, +Türkiye,2007,5.623,9.891,0.792,66.420,0.459,-0.183,0.800,0.592,0.395 +Türkiye,2008,5.118,9.887,0.645,66.580,0.415,-0.194,0.785,0.510,0.345 +Türkiye,2009,5.213,9.825,0.755,66.740,0.456,-0.232,0.853,0.454,0.316 +Türkiye,2010,5.490,9.893,0.795,66.900,0.515,-0.192,0.811,0.532,0.327 +Türkiye,2011,5.272,9.986,0.692,67.060,0.446,-0.247,0.649,0.512,0.380 +Türkiye,2012,5.309,10.018,0.739,67.220,0.471,-0.221,0.702,0.506,0.335 +Türkiye,2013,4.888,10.082,0.795,67.380,0.541,-0.235,0.698,0.551,0.392 +Türkiye,2014,5.580,10.111,0.863,67.540,0.649,-0.029,0.764,0.410,0.377 +Türkiye,2015,5.514,10.150,0.851,67.700,0.653,-0.021,0.806,0.391,0.382 +Türkiye,2016,5.326,10.166,0.880,67.875,0.644,-0.070,0.764,0.414,0.390 +Türkiye,2017,5.607,10.225,0.876,68.050,0.644,-0.242,0.671,0.393,0.313 +Türkiye,2018,5.186,10.246,0.847,68.225,0.529,-0.181,0.805,0.379,0.351 +Türkiye,2019,4.872,10.245,0.792,68.400,0.631,-0.141,0.760,0.347,0.368 +Türkiye,2020,4.862,10.257,0.857,68.575,0.510,-0.119,0.774,0.332,0.440 +Türkiye,2021,4.367,10.357,0.736,68.750,0.447,-0.039,0.810,0.297,0.471 +Türkiye,2022,5.093,10.404,0.830,68.925,0.470,-0.195,0.767,0.311,0.390 +Türkiye,2023,5.463,10.429,0.860,69.100,0.523,-0.159,0.748,0.344,0.349 +Uganda,2006,3.734,7.370,0.760,48.740,0.747,-0.046,0.807,0.552,0.254 +Uganda,2007,4.456,7.422,0.845,49.580,0.708,-0.006,0.881,0.670,0.228 +Uganda,2008,4.569,7.476,0.813,50.420,0.578,-0.060,0.848,0.623,0.240 +Uganda,2009,4.612,7.513,0.852,51.260,0.760,-0.043,0.840,0.594,0.296 +Uganda,2010,4.193,7.538,0.830,52.100,0.801,-0.020,0.855,0.628,0.251 +Uganda,2011,4.826,7.599,0.882,52.940,0.733,0.026,0.830,0.618,0.254 +Uganda,2012,4.309,7.608,0.885,53.780,0.649,0.075,0.838,0.709,0.265 +Uganda,2013,3.710,7.614,0.878,54.620,0.763,0.046,0.820,0.647,0.346 +Uganda,2014,3.770,7.634,0.821,55.460,0.834,0.003,0.898,0.635,0.397 +Uganda,2015,4.238,7.654,0.747,56.300,0.758,0.128,0.873,0.679,0.353 +Uganda,2016,4.233,7.667,0.754,56.775,0.739,0.125,0.811,0.665,0.410 +Uganda,2017,4.001,7.663,0.740,57.250,0.772,0.053,0.816,0.689,0.400 +Uganda,2018,4.322,7.690,0.740,57.725,0.729,0.072,0.856,0.687,0.390 +Uganda,2019,4.948,7.719,0.805,58.200,0.704,0.132,0.826,0.689,0.385 +Uganda,2020,4.641,7.714,0.800,58.675,0.687,0.140,0.878,0.705,0.425 +Uganda,2021,4.225,7.717,0.793,59.150,0.711,0.081,0.835,0.699,0.359 +Uganda,2022,4.425,7.733,0.781,59.625,0.720,0.135,0.836,0.708,0.439 +Uganda,2023,4.467,7.759,0.827,60.100,0.848,0.067,0.912,0.726,0.376 +Ukraine,2006,4.804,9.414,0.852,60.920,0.624,-0.265,0.929,0.551,0.249 +Ukraine,2007,5.252,9.499,0.820,61.240,0.494,-0.249,0.968,0.559,0.208 +Ukraine,2008,5.172,9.527,0.860,61.560,0.487,-0.273,0.929,0.545,0.186 +Ukraine,2009,5.166,9.367,0.845,61.880,0.460,-0.249,0.962,0.545,0.189 +Ukraine,2010,5.058,9.411,0.884,62.200,0.484,-0.197,0.954,0.472,0.227 +Ukraine,2011,5.083,9.468,0.859,62.520,0.579,-0.236,0.933,0.539,0.220 +Ukraine,2012,5.030,9.472,0.898,62.840,0.564,-0.231,0.896,0.509,0.193 +Ukraine,2013,4.711,9.474,0.897,63.160,0.569,-0.225,0.937,0.572,0.225 +Ukraine,2014,4.297,9.424,0.877,63.480,0.533,0.078,0.927,0.543,0.249 +Ukraine,2015,3.965,9.325,0.909,63.800,0.431,-0.039,0.952,0.531,0.241 +Ukraine,2016,4.029,9.353,0.885,63.925,0.503,0.005,0.891,0.550,0.220 +Ukraine,2017,4.311,9.381,0.858,64.050,0.599,-0.008,0.937,0.528,0.235 +Ukraine,2018,4.662,9.420,0.901,64.175,0.663,-0.080,0.943,0.550,0.222 +Ukraine,2019,4.702,9.458,0.883,64.300,0.715,-0.087,0.885,0.549,0.201 +Ukraine,2020,5.270,9.426,0.885,64.425,0.784,0.121,0.946,0.629,0.285 +Ukraine,2021,5.311,9.469,0.879,64.550,0.770,0.166,0.922,0.575,0.250 +Ukraine,2022,4.637,9.281,0.863,64.675,0.829,0.408,0.852,0.527,0.390 +Ukraine,2023,4.672,9.423,0.839,64.800,0.772,0.370,0.922,0.490,0.385 +United Arab Emirates,2006,6.734,11.433,0.903,64.860,0.898,-0.043,0.203,0.694,0.275 +United Arab Emirates,2009,6.866,10.952,0.885,65.040,0.849,0.015,0.339,0.718,0.287 +United Arab Emirates,2010,7.097,10.909,0.912,65.100,0.878,0.051,0.355,0.701,0.233 +United Arab Emirates,2011,7.119,10.965,0.881,65.160,0.889,0.063,,0.702,0.216 +United Arab Emirates,2012,7.218,11.001,0.856,65.220,0.920,,,0.719,0.224 +United Arab Emirates,2013,6.621,11.041,0.864,65.280,0.936,,,,0.291 +United Arab Emirates,2014,6.540,11.072,,65.340,,,,, +United Arab Emirates,2015,6.568,11.128,0.824,65.400,0.915,0.192,,0.722,0.296 +United Arab Emirates,2016,6.831,11.174,0.849,65.550,0.949,0.120,,0.739,0.245 +United Arab Emirates,2017,7.039,11.173,0.836,65.700,0.962,0.206,,0.737,0.208 +United Arab Emirates,2018,6.604,11.178,0.851,65.850,0.944,0.043,,0.723,0.302 +United Arab Emirates,2019,6.711,11.181,0.862,66.000,0.911,0.118,,0.730,0.284 +United Arab Emirates,2020,6.458,11.122,0.827,66.150,0.942,0.049,,0.702,0.298 +United Arab Emirates,2021,6.733,11.152,0.826,66.300,0.951,0.150,,0.697,0.217 +United Arab Emirates,2022,6.738,11.216,0.798,66.450,0.932,0.168,,0.715,0.242 +United Arab Emirates,2023,6.728,11.236,0.776,66.600,0.886,0.155,,0.655,0.304 +United Kingdom,2005,6.984,10.661,0.979,69.100,0.922,,0.398,0.779,0.262 +United Kingdom,2007,6.802,10.693,0.970,69.220,0.838,0.331,0.498,0.686,0.241 +United Kingdom,2008,6.986,10.684,0.954,69.280,0.759,0.325,0.548,0.724,0.218 +United Kingdom,2009,6.907,10.630,0.964,69.340,0.816,0.336,0.559,0.739,0.231 +United Kingdom,2010,7.029,10.646,0.955,69.400,0.841,0.397,0.587,0.753,0.176 +United Kingdom,2011,6.869,10.649,0.949,69.460,0.900,0.331,0.438,0.742,0.174 +United Kingdom,2012,6.881,10.656,0.935,69.520,0.889,0.366,0.425,0.739,0.184 +United Kingdom,2013,6.918,10.668,0.937,69.580,0.905,0.341,0.568,0.719,0.252 +United Kingdom,2014,6.758,10.692,0.910,69.640,0.857,0.349,0.484,0.740,0.251 +United Kingdom,2015,6.515,10.707,0.936,69.700,0.833,0.294,0.456,0.740,0.219 +United Kingdom,2016,6.824,10.721,0.954,69.800,0.821,0.244,0.458,0.732,0.230 +United Kingdom,2017,7.103,10.739,0.937,69.900,0.813,0.285,0.419,0.712,0.210 +United Kingdom,2018,7.233,10.750,0.928,70.000,0.838,0.220,0.404,0.736,0.228 +United Kingdom,2019,7.157,10.760,0.943,70.100,0.854,0.264,0.485,0.739,0.251 +United Kingdom,2020,6.798,10.639,0.929,70.200,0.885,0.196,0.490,0.717,0.225 +United Kingdom,2021,6.867,10.713,0.854,70.300,0.815,0.252,0.448,0.684,0.266 +United Kingdom,2022,6.722,10.754,0.863,70.400,0.857,0.308,0.426,0.723,0.270 +United Kingdom,2023,6.658,10.759,0.886,70.500,0.874,0.270,0.490,0.719,0.272 +United States,2006,7.182,10.921,0.965,66.780,0.911,,0.600,0.775,0.261 +United States,2007,7.513,10.931,,66.760,0.872,0.191,0.633,0.756,0.232 +United States,2008,7.280,10.923,0.953,66.740,0.878,0.249,0.668,0.774,0.227 +United States,2009,7.158,10.888,0.912,66.720,0.831,0.195,0.665,0.753,0.262 +United States,2010,7.164,10.906,0.926,66.700,0.828,0.238,0.690,0.776,0.231 +United States,2011,7.115,10.914,0.922,66.680,0.863,0.155,0.697,0.737,0.273 +United States,2012,7.026,10.929,0.903,66.660,0.823,0.208,0.710,0.765,0.260 +United States,2013,7.249,10.941,0.925,66.640,0.792,0.268,0.747,0.776,0.260 +United States,2014,7.151,10.956,0.902,66.620,0.866,0.215,0.702,0.786,0.281 +United States,2015,6.864,10.975,0.904,66.600,0.849,0.213,0.698,0.769,0.275 +United States,2016,6.804,10.985,0.897,66.475,0.758,0.138,0.739,0.737,0.264 +United States,2017,6.992,11.001,0.921,66.350,0.868,0.191,0.681,0.755,0.268 +United States,2018,6.883,11.024,0.904,66.225,0.825,0.110,0.710,0.757,0.292 +United States,2019,6.944,11.042,0.917,66.100,0.836,0.138,0.707,0.755,0.244 +United States,2020,7.028,11.005,0.937,65.975,0.850,0.028,0.678,0.722,0.295 +United States,2021,6.959,11.061,0.920,65.850,0.816,0.188,0.687,0.740,0.277 +United States,2022,6.693,11.078,0.900,65.725,0.736,0.189,0.701,0.712,0.267 +United States,2023,6.521,11.089,0.861,65.600,0.721,0.185,0.722,0.706,0.284 +Uruguay,2006,5.786,9.640,0.912,66.780,0.807,-0.125,0.477,0.701,0.306 +Uruguay,2007,5.694,9.702,0.875,66.860,0.786,-0.178,0.614,0.710,0.274 +Uruguay,2008,5.664,9.769,0.879,66.940,0.808,-0.156,0.597,0.685,0.264 +Uruguay,2009,6.296,9.808,0.924,67.020,0.825,-0.131,0.544,0.722,0.255 +Uruguay,2010,6.062,9.880,0.893,67.100,0.832,-0.171,0.471,0.738,0.231 +Uruguay,2011,6.554,9.928,0.891,67.180,0.851,-0.093,0.556,0.702,0.252 +Uruguay,2012,6.450,9.960,0.865,67.260,0.871,0.054,0.615,0.692,0.214 +Uruguay,2013,6.444,10.002,0.917,67.340,0.888,-0.056,0.586,0.743,0.253 +Uruguay,2014,6.561,10.031,0.902,67.420,0.904,-0.086,0.533,0.788,0.251 +Uruguay,2015,6.628,10.032,0.891,67.500,0.917,-0.045,0.673,0.812,0.300 +Uruguay,2016,6.171,10.045,0.900,67.500,0.886,-0.085,0.676,0.735,0.283 +Uruguay,2017,6.336,10.060,0.914,67.500,0.898,-0.104,0.627,0.742,0.280 +Uruguay,2018,6.372,10.060,0.917,67.500,0.876,-0.109,0.683,0.775,0.275 +Uruguay,2019,6.600,10.067,0.933,67.500,0.903,-0.107,0.599,0.764,0.222 +Uruguay,2020,6.310,10.002,0.921,67.500,0.908,-0.094,0.491,0.721,0.265 +Uruguay,2021,6.502,10.054,0.914,67.500,0.899,-0.054,0.606,0.746,0.263 +Uruguay,2022,6.671,10.103,0.905,67.500,0.878,-0.055,0.631,0.775,0.267 +Uruguay,2023,6.662,10.122,0.908,67.500,0.904,-0.050,0.662,0.753,0.265 +Uzbekistan,2006,5.232,8.256,0.903,61.340,0.784,-0.125,0.609,0.650,0.195 +Uzbekistan,2008,5.311,8.402,0.894,61.820,0.831,-0.033,,0.647,0.187 +Uzbekistan,2009,5.261,8.463,0.905,62.060,,0.003,0.610,0.646,0.159 +Uzbekistan,2010,5.095,8.508,0.903,62.300,,-0.040,0.519,0.665,0.152 +Uzbekistan,2011,5.739,8.554,0.924,62.540,0.934,0.032,0.522,0.663,0.123 +Uzbekistan,2012,6.019,8.608,0.933,62.780,0.914,-0.047,0.463,0.650,0.118 +Uzbekistan,2013,5.940,8.662,0.963,63.020,0.950,-0.043,0.434,0.686,0.130 +Uzbekistan,2014,6.049,8.712,0.952,63.260,0.954,0.052,0.536,0.713,0.106 +Uzbekistan,2015,5.972,8.764,0.968,63.500,0.980,0.366,0.471,0.778,0.103 +Uzbekistan,2016,5.893,8.804,0.945,63.800,0.984,0.199,,0.771,0.147 +Uzbekistan,2017,6.421,8.831,0.942,64.100,0.985,0.114,0.465,0.745,0.203 +Uzbekistan,2018,6.205,8.870,0.921,64.400,0.970,0.308,0.520,0.746,0.209 +Uzbekistan,2019,6.154,8.910,0.915,64.700,0.970,0.295,0.511,0.751,0.220 +Uzbekistan,2020,5.842,8.910,0.850,65.000,0.928,0.190,0.642,0.678,0.279 +Uzbekistan,2021,6.185,8.962,0.896,65.300,0.927,0.183,0.662,0.698,0.233 +Uzbekistan,2022,6.016,8.996,0.879,65.600,0.959,0.306,0.616,0.741,0.225 +Uzbekistan,2023,6.385,9.026,0.909,65.900,0.927,0.247,0.650,0.752,0.202 +Venezuela,2005,7.170,9.316,0.955,65.500,0.838,,0.720,0.803,0.233 +Venezuela,2006,6.525,9.467,0.946,65.460,0.798,-0.037,0.646,0.837,0.178 +Venezuela,2008,6.258,9.719,0.922,65.380,0.678,-0.232,0.776,0.818,0.224 +Venezuela,2009,7.189,9.567,0.945,65.340,0.677,-0.124,0.828,0.792,0.180 +Venezuela,2010,7.478,9.748,0.932,65.300,0.768,-0.163,0.754,0.847,0.130 +Venezuela,2011,6.580,9.859,0.931,65.260,0.766,-0.235,0.772,0.823,0.199 +Venezuela,2012,7.067,9.862,0.932,65.220,0.804,-0.201,0.743,0.844,0.176 +Venezuela,2013,6.553,9.802,0.896,65.180,0.642,-0.230,0.837,0.812,0.238 +Venezuela,2014,6.136,9.366,0.904,65.140,0.570,-0.191,0.827,0.797,0.244 +Venezuela,2015,5.569,8.532,0.911,65.100,0.512,-0.089,0.813,0.837,0.223 +Venezuela,2016,4.041,7.602,0.902,64.925,0.458,-0.060,0.890,0.676,0.392 +Venezuela,2017,5.071,5.943,0.896,64.750,0.636,0.050,0.844,0.697,0.363 +Venezuela,2018,5.006,5.935,0.887,64.575,0.611,0.068,0.828,0.723,0.374 +Venezuela,2019,5.081,5.527,0.888,64.400,0.626,0.124,0.839,0.730,0.351 +Venezuela,2020,4.574,,0.805,64.225,0.612,,0.811,0.689,0.396 +Venezuela,2021,5.108,,0.812,64.050,0.596,,0.824,0.698,0.389 +Venezuela,2022,5.949,,0.899,63.875,0.770,,0.798,0.754,0.292 +Venezuela,2023,5.765,,0.885,63.700,0.757,,0.825,0.758,0.300 +Vietnam,2006,5.294,8.554,0.888,64.180,0.886,-0.006,,0.657,0.204 +Vietnam,2007,5.422,8.613,0.856,64.260,0.918,0.068,0.754,,0.206 +Vietnam,2008,5.480,8.658,0.805,64.340,0.889,0.180,0.789,0.624,0.218 +Vietnam,2009,5.304,8.701,0.815,64.420,0.834,-0.083,0.838,0.481,0.190 +Vietnam,2010,5.296,8.752,0.787,64.500,0.831,-0.027,0.743,0.671,0.216 +Vietnam,2011,5.767,8.804,0.898,64.580,0.818,0.084,0.742,0.494,0.193 +Vietnam,2012,5.535,8.847,0.775,64.660,0.856,-0.131,0.815,0.546,0.221 +Vietnam,2013,5.023,8.890,0.759,64.740,0.920,-0.048,0.771,0.689,0.165 +Vietnam,2014,5.085,8.941,0.792,64.820,,-0.022,,0.634,0.241 +Vietnam,2015,5.076,8.999,0.849,64.900,,0.064,,0.583,0.232 +Vietnam,2016,5.062,9.053,0.876,65.000,0.894,-0.112,0.799,0.487,0.223 +Vietnam,2017,5.175,9.111,,65.100,,,,, +Vietnam,2018,5.296,9.173,0.832,65.200,0.909,-0.063,0.808,0.614,0.191 +Vietnam,2019,5.467,9.235,0.848,65.300,0.952,-0.148,0.788,0.658,0.186 +Vietnam,2020,5.462,9.254,0.765,65.400,0.945,0.063,0.791,0.699,0.286 +Vietnam,2021,5.540,9.271,0.799,65.500,0.897,0.104,0.798,0.651,0.280 +Vietnam,2022,6.267,9.341,0.879,65.600,0.975,-0.182,0.703,0.774,0.108 +Vietnam,2023,6.325,9.392,0.845,65.700,0.956,-0.159,0.655,0.710,0.120 +Yemen,2007,4.477,8.212,0.825,58.720,0.673,0.006,,0.524,0.379 +Yemen,2009,4.809,8.250,0.756,58.640,0.644,-0.056,0.832,0.511,0.374 +Yemen,2010,4.350,8.414,0.727,58.600,0.659,-0.107,0.853,0.514,0.308 +Yemen,2011,3.746,8.264,0.663,58.560,0.638,-0.174,0.754,0.416,0.285 +Yemen,2012,4.061,8.179,0.682,58.520,0.706,-0.172,0.793,0.413,0.263 +Yemen,2013,4.218,8.166,0.694,58.480,0.543,-0.179,0.885,0.478,0.266 +Yemen,2014,3.968,8.159,0.638,58.440,0.664,-0.166,0.885,0.527,0.276 +Yemen,2015,2.983,7.772,0.669,58.400,0.610,-0.138,0.829,0.458,0.321 +Yemen,2016,3.826,7.552,0.775,58.175,0.533,-0.144,,0.401,0.228 +Yemen,2017,3.254,7.243,0.790,57.950,0.595,-0.128,,0.368,0.295 +Yemen,2018,3.058,7.444,0.789,57.725,0.553,-0.127,0.793,0.409,0.315 +Yemen,2019,4.197,7.448,0.870,57.500,0.651,-0.106,0.798,0.481,0.213 +Yemen,2022,3.590,,0.872,56.825,0.607,,0.788,0.460,0.255 +Yemen,2023,3.532,,0.825,56.600,0.583,,0.771,0.447,0.341 +Zambia,2006,4.824,7.834,0.798,46.760,0.721,-0.012,0.785,0.664,0.226 +Zambia,2007,3.998,7.879,0.688,47.420,0.682,-0.073,0.948,0.653,0.246 +Zambia,2008,4.730,7.918,0.624,48.080,0.717,0.051,0.890,0.707,0.206 +Zambia,2009,5.260,7.971,0.782,48.740,0.696,-0.101,0.917,0.693,0.123 +Zambia,2011,4.999,8.054,0.864,50.060,0.663,-0.001,0.882,0.771,0.204 +Zambia,2012,5.013,8.094,0.780,50.720,0.788,0.004,0.806,0.676,0.250 +Zambia,2013,5.244,8.111,0.761,51.380,0.770,-0.108,0.732,0.727,0.308 +Zambia,2014,4.346,8.124,0.706,52.040,0.812,-0.014,0.809,0.639,0.327 +Zambia,2015,4.843,8.121,0.691,52.700,0.759,-0.042,0.871,0.634,0.382 +Zambia,2016,4.348,8.127,0.767,53.125,0.812,0.119,0.771,0.688,0.372 +Zambia,2017,3.933,8.130,0.744,53.550,0.823,0.137,0.740,0.660,0.387 +Zambia,2018,4.041,8.139,0.718,53.975,0.791,0.045,0.811,0.662,0.351 +Zambia,2019,3.307,8.123,0.638,54.400,0.811,0.074,0.832,0.674,0.394 +Zambia,2020,4.838,8.066,0.767,54.825,0.750,0.054,0.810,0.679,0.345 +Zambia,2021,3.082,8.082,0.619,55.250,0.833,0.138,0.824,0.656,0.349 +Zambia,2022,3.728,8.101,0.717,55.675,0.889,-0.009,0.716,0.660,0.309 +Zambia,2023,3.686,8.115,0.664,56.100,0.854,0.092,0.814,0.653,0.359 +Zimbabwe,2006,3.826,7.460,0.822,40.400,0.431,-0.063,0.905,0.669,0.297 +Zimbabwe,2007,3.280,7.413,0.828,41.600,0.456,-0.069,0.946,0.589,0.265 +Zimbabwe,2008,3.174,7.210,0.843,42.800,0.344,-0.077,0.964,0.571,0.250 +Zimbabwe,2009,4.056,7.313,0.806,44.000,0.411,-0.065,0.931,0.660,0.218 +Zimbabwe,2010,4.682,7.495,0.857,45.200,0.665,-0.081,0.828,0.699,0.122 +Zimbabwe,2011,4.846,7.617,0.865,46.400,0.633,-0.077,0.830,0.699,0.211 +Zimbabwe,2012,4.955,7.745,0.896,47.600,0.470,-0.091,0.859,0.613,0.177 +Zimbabwe,2013,4.690,7.755,0.799,48.800,0.576,-0.093,0.831,0.624,0.182 +Zimbabwe,2014,4.184,7.748,0.766,50.000,0.642,-0.062,0.820,0.661,0.239 +Zimbabwe,2015,3.703,7.747,0.736,51.200,0.667,-0.111,0.810,0.639,0.179 +Zimbabwe,2016,3.735,7.735,0.768,51.675,0.733,-0.082,0.724,0.685,0.209 +Zimbabwe,2017,3.638,7.754,0.754,52.150,0.753,-0.084,0.751,0.734,0.224 +Zimbabwe,2018,3.616,7.783,0.775,52.625,0.763,-0.055,0.844,0.658,0.212 +Zimbabwe,2019,2.694,7.698,0.759,53.100,0.632,-0.051,0.831,0.658,0.235 +Zimbabwe,2020,3.160,7.596,0.717,53.575,0.643,0.003,0.789,0.661,0.346 +Zimbabwe,2021,3.155,7.657,0.685,54.050,0.668,-0.079,0.757,0.610,0.242 +Zimbabwe,2022,3.296,7.670,0.666,54.525,0.652,-0.073,0.753,0.641,0.191 +Zimbabwe,2023,3.572,7.679,0.694,55.000,0.735,-0.069,0.757,0.610,0.179 diff --git a/fall-2024/math/mat-206/00030/MAT-206-00030.ipynb b/fall-2024/math/mat-206/00030/MAT-206-00030.ipynb new file mode 100644 index 0000000..b5f0849 --- /dev/null +++ b/fall-2024/math/mat-206/00030/MAT-206-00030.ipynb @@ -0,0 +1,1361 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "import statsmodels.formula.api as smf\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "from scipy.stats import norm, uniform\n", + "from statsmodels.stats import outliers_influence" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "#Significance level\n", + "ALPHA = 0.11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def PlotSimpleRegression(data, variable, ax):\n", + "\n", + " data = data.copy()\n", + " data = data.sort_values(variable).reset_index(drop=True)\n", + "\n", + " # Scatterplot of the observations\n", + " sns.scatterplot(\n", + " data = data,\n", + " x=variable,\n", + " y=\"Life Ladder\",\n", + " ax=ax,\n", + " label=\"Observations\"\n", + " )\n", + "\n", + " # Plot predicted mean\n", + " ax.plot(\n", + " data[variable],\n", + " data[\"mean\"],\n", + " color=\"k\",\n", + " label=\"Prediction\"\n", + " )\n", + "\n", + " # Plot prediction interval\n", + " ax.fill_between(\n", + " data[variable],\n", + " data[\"obs_ci_lower\"],\n", + " data[\"obs_ci_upper\"],\n", + " color=\"rebeccapurple\",\n", + " alpha=0.5,\n", + " label=\"Prediction interval\"\n", + " )\n", + "\n", + " # Plot confidence interval\n", + " ax.fill_between(\n", + " data[variable],\n", + " data[\"mean_ci_lower\"],\n", + " data[\"mean_ci_upper\"],\n", + " color=\"pink\",\n", + " alpha=0.5,\n", + " label=\"Confidence interval\"\n", + " )\n", + "\n", + " ax.legend(frameon=False)\n", + " ax.spines[['right', 'top']].set_visible(False)\n", + "\n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def PlotCompareYHatY(data, ax):\n", + " ax.scatter(data[\"Life Ladder\"], data[\"mean\"], color=\"k\")\n", + "\n", + " ax.errorbar(\n", + " data[\"Life Ladder\"],\n", + " data[\"mean\"],\n", + " yerr=data[\"obs_ci_upper\"] - data[\"mean\"],\n", + " fmt=\"o\",\n", + " color=\"k\"\n", + " )\n", + "\n", + " ax.plot(\n", + " [data[\"Life Ladder\"].min(), data[\"Life Ladder\"].max()]\n", + " , [data[\"Life Ladder\"].min(), data[\"Life Ladder\"].max()]\n", + " , color='r'\n", + " , linestyle='--'\n", + " )\n", + "\n", + " ax.set_xlabel(r\"$Y$\")\n", + " ax.set_ylabel(r\"$\\hat{Y}$\")\n", + " ax.spines[['right', 'top']].set_visible(False)\n", + "\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reading and preprocessing data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "DataWhr2024 = pd.read_csv(\"DataWhr2024.csv\")\n", + "UnM49 = pd.read_csv(\"UnM49.csv\", sep=';')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Hong\"), \"Country name\"] = \"Hong Kong\"\n", + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Somaliland\"), \"Country name\"] = \"Somaliland\"\n", + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Taiwan\"), \"Country name\"] = \"Taiwan\"" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "UnM49 = UnM49[['Country or Area', 'Sub-region Name', 'Region Name']]\n", + "UnM49 = UnM49.rename({'Country or Area':'Country name', 'Sub-region Name':'Subregion', 'Region Name':'Continent'}, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "UnM49.loc[97, \"Country name\"] = \"Bolivia\"\n", + "UnM49.loc[33, \"Country name\"] = \"Congo (Brazzaville)\"\n", + "UnM49.loc[34, \"Country name\"] = \"Congo (Kinshasa)\"\n", + "UnM49.loc[124, \"Country name\"] = \"Hong Kong\"\n", + "UnM49.loc[125, \"Country name\"] = \"Macao\"\n", + "UnM49.loc[126, \"Country name\"] = \"North Korea\"\n", + "UnM49.loc[145, \"Country name\"] = \"Iran\"\n", + "UnM49.loc[46, \"Country name\"] = \"Ivory Coast\"\n", + "UnM49.loc[133, \"Country name\"] = \"Laos\"\n", + "UnM49.loc[129, \"Country name\"] = \"South Korea\"\n", + "UnM49.loc[173, \"Country name\"] = \"Moldova\"\n", + "UnM49.loc[217, \"Country name\"] = \"Netherlands\"\n", + "UnM49.loc[175, \"Country name\"] = \"Russia\"\n", + "UnM49.loc[164, \"Country name\"] = \"Syria\"\n", + "UnM49.loc[26, \"Country name\"] = \"Tanzania\"\n", + "UnM49.loc[116, \"Country name\"] = \"United States\"\n", + "UnM49.loc[193, \"Country name\"] = \"United Kingdom\"\n", + "UnM49.loc[111, \"Country name\"] = \"Venezuela\"\n", + "UnM49.loc[140, \"Country name\"] = \"Vietnam\"" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "_ = pd.DataFrame(\n", + " {\n", + " \"Country name\": [\"Kosovo\", \"Somaliland\", \"Taiwan\"],\n", + " \"Subregion\": [\"Southern Europe\", \"Sub-Saharan Africa\", \"Eastern Asia\"],\n", + " \"Continent\": [\"Europe\", \"Africa\", \"Asia\"],\n", + " }\n", + ")\n", + "\n", + "UnM49 = pd.concat([UnM49, _], axis=0)\n", + "UnM49 = UnM49.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Merging the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Data\n", + "Dat = pd.merge(DataWhr2024, UnM49)\n", + "\n", + "# Data of 2023\n", + "Dat2023 = Dat[Dat['year'] == 2023]\n", + "Dat2023 = Dat2023.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a previous analysis, I found that Afghanistan behaves as a leverage point, while Botswana and Sri Lanka bahave as outliers. Thus, we will not consider these countries in our analyses" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Country nameyearLife LadderLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectSubregionContinent
    0Afghanistan20231.446NaN0.36855.20.228NaN0.7380.2610.460Southern AsiaAsia
    13Botswana20233.3329.6730.70155.00.741-0.2640.8140.6570.247Sub-Saharan AfricaAfrica
    115Sri Lanka20233.6029.3640.79067.40.7540.0500.9220.7090.353Southern AsiaAsia
    \n", + "
    " + ], + "text/plain": [ + " Country name year Life Ladder Log GDP per capita Social support \\\n", + "0 Afghanistan 2023 1.446 NaN 0.368 \n", + "13 Botswana 2023 3.332 9.673 0.701 \n", + "115 Sri Lanka 2023 3.602 9.364 0.790 \n", + "\n", + " Healthy life expectancy at birth Freedom to make life choices \\\n", + "0 55.2 0.228 \n", + "13 55.0 0.741 \n", + "115 67.4 0.754 \n", + "\n", + " Generosity Perceptions of corruption Positive affect Negative affect \\\n", + "0 NaN 0.738 0.261 0.460 \n", + "13 -0.264 0.814 0.657 0.247 \n", + "115 0.050 0.922 0.709 0.353 \n", + "\n", + " Subregion Continent \n", + "0 Southern Asia Asia \n", + "13 Sub-Saharan Africa Africa \n", + "115 Southern Asia Asia " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Dat2023.loc[[0, 13, 115]]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "Dat2023 = Dat2023.drop([0, 13, 115])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "Y = Dat2023[\"Life Ladder\"]\n", + "\n", + "X = Dat2023[[\n", + " 'Log GDP per capita',\n", + " 'Social support',\n", + " 'Healthy life expectancy at birth',\n", + " 'Freedom to make life choices',\n", + " 'Generosity',\n", + " 'Perceptions of corruption',\n", + " 'Positive affect',\n", + " 'Negative affect'\n", + "]]\n", + "\n", + "X = sm.add_constant(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q1" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Life Ladder R-squared: 0.001\n", + "Model: OLS Adj. R-squared: -0.007\n", + "Method: Least Squares F-statistic: 0.07861\n", + "Date: Fri, 29 Nov 2024 Prob (F-statistic): 0.780\n", + "Time: 17:57:08 Log-Likelihood: -187.60\n", + "No. Observations: 127 AIC: 379.2\n", + "Df Residuals: 125 BIC: 384.9\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 5.6752 0.097 58.421 0.000 5.483 5.867\n", + "Generosity 0.1657 0.591 0.280 0.780 -1.004 1.336\n", + "==============================================================================\n", + "Omnibus: 10.908 Durbin-Watson: 1.906\n", + "Prob(Omnibus): 0.004 Jarque-Bera (JB): 6.234\n", + "Skew: -0.367 Prob(JB): 0.0443\n", + "Kurtosis: 2.200 Cond. No. 6.24\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "# Extracting the design matrix and response variable\n", + "XGenerosity = X[[\"const\", \"Generosity\"]].dropna()\n", + "YGenerosity = Y[XGenerosity.index]\n", + "\n", + "# Fit the linear regression model\n", + "Model1 = sm.OLS(YGenerosity, XGenerosity).fit()\n", + "print(Model1.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Ensure that PredictionTable only contains predictions for the same index as XGenerosity\n", + "PredictionTable1 = Model1.get_prediction(XGenerosity).summary_frame(alpha=0.11)\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "# Scatterplot of observations\n", + "sns.scatterplot(\n", + " x=XGenerosity[\"Generosity\"], \n", + " y=YGenerosity, \n", + " ax=ax, \n", + " label=\"Observations\"\n", + ")\n", + "\n", + "# Plot the predicted mean (regression line)\n", + "ax.plot(\n", + " XGenerosity[\"Generosity\"], \n", + " PredictionTable1[\"mean\"], \n", + " color=\"k\", \n", + " label=\"Prediction (Regression Line)\"\n", + ")\n", + "\n", + "# Get the min and max of the x-axis for full range\n", + "x_min, x_max = XGenerosity[\"Generosity\"].min(), XGenerosity[\"Generosity\"].max()\n", + "\n", + "# Create a smoother x-range for the prediction lines and intervals\n", + "x_smooth = np.linspace(x_min, x_max, 300)\n", + "\n", + "# Get the predictions for the smooth x-range\n", + "PredictionSmooth = Model1.get_prediction(sm.add_constant(x_smooth)).summary_frame(alpha=0.11)\n", + "\n", + "# Plot prediction intervals across the full x-range\n", + "ax.fill_between(\n", + " x_smooth, \n", + " PredictionSmooth[\"obs_ci_lower\"], \n", + " PredictionSmooth[\"obs_ci_upper\"], \n", + " color=\"rebeccapurple\", \n", + " alpha=0.5, \n", + " label=\"Prediction Interval (89%)\"\n", + ")\n", + "\n", + "# Plot confidence intervals across the full x-range\n", + "ax.fill_between(\n", + " x_smooth, \n", + " PredictionSmooth[\"mean_ci_lower\"], \n", + " PredictionSmooth[\"mean_ci_upper\"], \n", + " color=\"pink\", \n", + " alpha=0.5, \n", + " label=\"Confidence Interval (89%)\"\n", + ")\n", + "\n", + "# Customize the plot\n", + "ax.set_title(\"Generosity vs. Life Ladder\", fontsize=14)\n", + "ax.set_xlabel(\"Generosity\", fontsize=12)\n", + "ax.set_ylabel(\"Life Ladder\", fontsize=12)\n", + "ax.legend()\n", + "ax.spines[['right', 'top']].set_visible(False)\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q2" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Life Ladder R-squared: 0.271\n", + "Model: OLS Adj. R-squared: 0.266\n", + "Method: Least Squares F-statistic: 49.54\n", + "Date: Fri, 29 Nov 2024 Prob (F-statistic): 9.33e-11\n", + "Time: 17:57:08 Log-Likelihood: -177.57\n", + "No. Observations: 135 AIC: 359.1\n", + "Df Residuals: 133 BIC: 364.9\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "===================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-----------------------------------------------------------------------------------\n", + "const 2.2346 0.496 4.503 0.000 1.253 3.216\n", + "Positive affect 5.2694 0.749 7.039 0.000 3.789 6.750\n", + "==============================================================================\n", + "Omnibus: 6.132 Durbin-Watson: 1.815\n", + "Prob(Omnibus): 0.047 Jarque-Bera (JB): 4.574\n", + "Skew: -0.327 Prob(JB): 0.102\n", + "Kurtosis: 2.379 Cond. No. 13.7\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "# Create the design matrix XPossitive and the response vector YPossitive\n", + "XPossitive = X[['const', 'Positive affect']].dropna() # Drop missing values from XPossitive\n", + "YPossitive = Y.loc[XPossitive.index] # Align Y with XPossitive, matching indices\n", + "\n", + "# Fit the linear regression model (Model 2)\n", + "Model2 = sm.OLS(YPossitive, XPossitive).fit()\n", + "print(Model2.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Ensure that PredictionTable only contains predictions for the same index as XPossitive\n", + "PredictionTable2 = Model2.get_prediction(XPossitive).summary_frame(alpha=0.11)\n", + "\n", + "# Create the plot\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "# Scatterplot of observations\n", + "sns.scatterplot(\n", + " x=XPossitive[\"Positive affect\"], \n", + " y=YPossitive, \n", + " ax=ax, \n", + " label=\"Observations\"\n", + ")\n", + "\n", + "# Plot the predicted mean (regression line)\n", + "ax.plot(\n", + " XPossitive[\"Positive affect\"], \n", + " PredictionTable2[\"mean\"], \n", + " color=\"k\", \n", + " label=\"Prediction (Regression Line)\"\n", + ")\n", + "\n", + "# Get the min and max of the x-axis for full range\n", + "x_min, x_max = XPossitive[\"Positive affect\"].min(), XPossitive[\"Positive affect\"].max()\n", + "\n", + "# Create a smoother x-range for the prediction lines and intervals\n", + "x_smooth = np.linspace(x_min, x_max, 300)\n", + "\n", + "# Get the predictions for the smooth x-range\n", + "PredictionSmooth = Model2.get_prediction(sm.add_constant(x_smooth)).summary_frame(alpha=0.11)\n", + "\n", + "# Plot prediction intervals across the full x-range\n", + "ax.fill_between(\n", + " x_smooth, \n", + " PredictionSmooth[\"obs_ci_lower\"], \n", + " PredictionSmooth[\"obs_ci_upper\"], \n", + " color=\"rebeccapurple\", \n", + " alpha=0.5, \n", + " label=\"Prediction Interval (89%)\"\n", + ")\n", + "\n", + "# Plot confidence intervals across the full x-range\n", + "ax.fill_between(\n", + " x_smooth, \n", + " PredictionSmooth[\"mean_ci_lower\"], \n", + " PredictionSmooth[\"mean_ci_upper\"], \n", + " color=\"pink\", \n", + " alpha=0.5, \n", + " label=\"Confidence Interval (89%)\"\n", + ")\n", + "\n", + "# Customize the plot\n", + "ax.set_title(\"Positive Affect vs. Life Ladder\", fontsize=14)\n", + "ax.set_xlabel(\"Positive Affect\", fontsize=12)\n", + "ax.set_ylabel(\"Life Ladder\", fontsize=12)\n", + "ax.legend()\n", + "ax.spines[['right', 'top']].set_visible(False)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q3" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Log GDP per capita',\n", + " 'Social support',\n", + " 'Healthy life expectancy at birth',\n", + " 'Freedom to make life choices',\n", + " 'Generosity',\n", + " 'Perceptions of corruption',\n", + " 'Positive affect',\n", + " 'Negative affect']" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "['Log GDP per capita', 'Social support', 'Healthy life expectancy at birth', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption', 'Positive affect', 'Negative affect']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Life Ladder R-squared: 0.856\n", + "Model: OLS Adj. R-squared: 0.845\n", + "Method: Least Squares F-statistic: 80.73\n", + "Date: Fri, 29 Nov 2024 Prob (F-statistic): 2.99e-42\n", + "Time: 17:57:08 Log-Likelihood: -59.747\n", + "No. Observations: 118 AIC: 137.5\n", + "Df Residuals: 109 BIC: 162.4\n", + "Df Model: 8 \n", + "Covariance Type: nonrobust \n", + "====================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------------------------\n", + "const -2.5991 0.785 -3.309 0.001 -4.156 -1.042\n", + "Log GDP per capita 0.3141 0.086 3.638 0.000 0.143 0.485\n", + "Social support 3.2510 0.567 5.735 0.000 2.128 4.374\n", + "Healthy life expectancy at birth 0.0102 0.016 0.651 0.516 -0.021 0.041\n", + "Freedom to make life choices 1.3683 0.444 3.082 0.003 0.488 2.248\n", + "Generosity -0.4163 0.253 -1.646 0.103 -0.917 0.085\n", + "Perceptions of corruption -0.8887 0.269 -3.309 0.001 -1.421 -0.356\n", + "Positive affect 1.9932 0.461 4.322 0.000 1.079 2.907\n", + "Negative affect 1.0249 0.599 1.712 0.090 -0.162 2.212\n", + "==============================================================================\n", + "Omnibus: 4.969 Durbin-Watson: 2.170\n", + "Prob(Omnibus): 0.083 Jarque-Bera (JB): 4.505\n", + "Skew: -0.371 Prob(JB): 0.105\n", + "Kurtosis: 3.604 Cond. No. 1.51e+03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 1.51e+03. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n" + ] + } + ], + "source": [ + "# Extract all covariates (independent variables) except the response variable\n", + "XAll = X[['const', 'Log GDP per capita', 'Social support', 'Healthy life expectancy at birth', 'Freedom to make life choices', \n", + " 'Generosity', 'Perceptions of corruption', 'Positive affect', 'Negative affect']].dropna()\n", + "\n", + "# Add a constant column to XAll for the intercept\n", + "XAll = sm.add_constant(XAll)\n", + "\n", + "# Ensure YAll is aligned with XAll\n", + "YAll = Y.loc[XAll.index]\n", + "\n", + "# Fit the linear regression model (Model 3)\n", + "Model3 = sm.OLS(YAll, XAll).fit()\n", + "\n", + "# Display the summary of Model 3\n", + "print(Model3.summary())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "PredictionTable3 = Model3.get_prediction(XAll).summary_frame(alpha=0.11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q4" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = MinMaxScaler()\n", + "\n", + "X[\"Healthy life scaled\"] = scaler.fit_transform(X[[\"Healthy life expectancy at birth\"]])\n", + "\n", + "X[\"Log GDP scaled\"] = scaler.fit_transform(X[[\"Log GDP per capita\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "XAllScaled = X.copy()\n", + "\n", + "XAllScaled = XAllScaled.drop([\"Healthy life expectancy at birth\", \"Log GDP per capita\"], axis=1)\n", + "\n", + "XAllScaled = XAllScaled.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    constLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affect
    count118.0118.000000118.000000118.000000118.000000118.000000118.000000118.000000118.000000
    mean1.09.4956690.79062765.2372880.7963640.0343730.7223470.6546530.293610
    std0.01.1498380.1311705.4926340.1136880.1625900.1735670.1064310.088618
    min1.07.0760000.39800052.2000000.452000-0.2680000.1840000.3440000.114000
    25%1.08.6125000.69575060.7000000.735250-0.0725000.6632500.5782500.229250
    50%1.09.6360000.83750066.1000000.8175000.0220000.7675000.6670000.283000
    75%1.010.4702500.89425069.6500000.8770000.1342500.8445000.7387500.357500
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    " + ], + "text/plain": [ + " const Log GDP per capita Social support \\\n", + "count 118.0 118.000000 118.000000 \n", + "mean 1.0 9.495669 0.790627 \n", + "std 0.0 1.149838 0.131170 \n", + "min 1.0 7.076000 0.398000 \n", + "25% 1.0 8.612500 0.695750 \n", + "50% 1.0 9.636000 0.837500 \n", + "75% 1.0 10.470250 0.894250 \n", + "max 1.0 11.676000 0.979000 \n", + "\n", + " Healthy life expectancy at birth Freedom to make life choices \\\n", + "count 118.000000 118.000000 \n", + "mean 65.237288 0.796364 \n", + "std 5.492634 0.113688 \n", + "min 52.200000 0.452000 \n", + "25% 60.700000 0.735250 \n", + "50% 66.100000 0.817500 \n", + "75% 69.650000 0.877000 \n", + "max 74.600000 0.965000 \n", + "\n", + " Generosity Perceptions of corruption Positive affect Negative affect \n", + "count 118.000000 118.000000 118.000000 118.000000 \n", + "mean 0.034373 0.722347 0.654653 0.293610 \n", + "std 0.162590 0.173567 0.106431 0.088618 \n", + "min -0.268000 0.184000 0.344000 0.114000 \n", + "25% -0.072500 0.663250 0.578250 0.229250 \n", + "50% 0.022000 0.767500 0.667000 0.283000 \n", + "75% 0.134250 0.844500 0.738750 0.357500 \n", + "max 0.590000 0.948000 0.843000 0.516000 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XAll.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    constSocial supportFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectHealthy life scaledLog GDP scaled
    count118.0118.000000118.000000118.000000118.000000118.000000118.000000118.000000118.000000
    mean1.00.7906270.7963640.0343730.7223470.6546530.2936100.5820220.526015
    std0.00.1311700.1136880.1625900.1735670.1064310.0886180.2452070.249965
    min1.00.3980000.452000-0.2680000.1840000.3440000.1140000.0000000.000000
    25%1.00.6957500.735250-0.0725000.6632500.5782500.2292500.3794640.334022
    50%1.00.8375000.8175000.0220000.7675000.6670000.2830000.6205360.556522
    75%1.00.8942500.8770000.1342500.8445000.7387500.3575000.7790180.737880
    max1.00.9790000.9650000.5900000.9480000.8430000.5160001.0000001.000000
    \n", + "
    " + ], + "text/plain": [ + " const Social support Freedom to make life choices Generosity \\\n", + "count 118.0 118.000000 118.000000 118.000000 \n", + "mean 1.0 0.790627 0.796364 0.034373 \n", + "std 0.0 0.131170 0.113688 0.162590 \n", + "min 1.0 0.398000 0.452000 -0.268000 \n", + "25% 1.0 0.695750 0.735250 -0.072500 \n", + "50% 1.0 0.837500 0.817500 0.022000 \n", + "75% 1.0 0.894250 0.877000 0.134250 \n", + "max 1.0 0.979000 0.965000 0.590000 \n", + "\n", + " Perceptions of corruption Positive affect Negative affect \\\n", + "count 118.000000 118.000000 118.000000 \n", + "mean 0.722347 0.654653 0.293610 \n", + "std 0.173567 0.106431 0.088618 \n", + "min 0.184000 0.344000 0.114000 \n", + "25% 0.663250 0.578250 0.229250 \n", + "50% 0.767500 0.667000 0.283000 \n", + "75% 0.844500 0.738750 0.357500 \n", + "max 0.948000 0.843000 0.516000 \n", + "\n", + " Healthy life scaled Log GDP scaled \n", + "count 118.000000 118.000000 \n", + "mean 0.582022 0.526015 \n", + "std 0.245207 0.249965 \n", + "min 0.000000 0.000000 \n", + "25% 0.379464 0.334022 \n", + "50% 0.620536 0.556522 \n", + "75% 0.779018 0.737880 \n", + "max 1.000000 1.000000 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XAllScaled.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(1508.9221), np.float64(43.9715))" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy.linalg import cond\n", + "\n", + "condition_XAll = cond(XAll.values)\n", + "condition_XAllScaled = cond(XAllScaled.values)\n", + "\n", + "condition_XAll.round(4), condition_XAllScaled.round(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q5" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Life Ladder R-squared: 0.841\n", + "Model: OLS Adj. R-squared: 0.834\n", + "Method: Least Squares F-statistic: 119.1\n", + "Date: Fri, 29 Nov 2024 Prob (F-statistic): 2.26e-43\n", + "Time: 17:57:08 Log-Likelihood: -66.306\n", + "No. Observations: 119 AIC: 144.6\n", + "Df Residuals: 113 BIC: 161.3\n", + "Df Model: 5 \n", + "Covariance Type: nonrobust \n", + "================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------------------------\n", + "const 5.6699 0.040 142.681 0.000 5.591 5.749\n", + "Social support 2.8341 0.538 5.266 0.000 1.768 3.900\n", + "Freedom to make life choices 1.3681 0.452 3.027 0.003 0.473 2.264\n", + "Perceptions of corruption -0.7368 0.272 -2.711 0.008 -1.275 -0.198\n", + "Positive affect 1.7803 0.472 3.773 0.000 0.845 2.715\n", + "Log GDP scaled 1.7166 0.289 5.940 0.000 1.144 2.289\n", + "==============================================================================\n", + "Omnibus: 1.443 Durbin-Watson: 2.108\n", + "Prob(Omnibus): 0.486 Jarque-Bera (JB): 0.998\n", + "Skew: -0.194 Prob(JB): 0.607\n", + "Kurtosis: 3.226 Cond. No. 15.6\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "XScaleFewVariables = X.drop([\"Healthy life expectancy at birth\", \"Log GDP per capita\"], axis=1)\n", + "\n", + "XScaleFewVariables = XScaleFewVariables.drop([\"const\", \"Healthy life scaled\", \"Generosity\", \"Negative affect\"], axis=1).dropna()\n", + "\n", + "XScaleFewVariables = XScaleFewVariables - XScaleFewVariables.mean()\n", + "\n", + "XScaleFewVariables = sm.add_constant(XScaleFewVariables)\n", + "\n", + "YScaleFewVariables = Y[XScaleFewVariables.index]\n", + "\n", + "Model4 = sm.OLS(YScaleFewVariables, XScaleFewVariables).fit()\n", + "\n", + "print(Model4.summary())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "PredictionTable4 = Model4.get_prediction(XScaleFewVariables).summary_frame(alpha=0.11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q6" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Assuming PredictionTables and Y variables for each model are already calculated:\n", + "# Replace these with actual data (e.g., YModel1, PredictionTableModel1)\n", + "models = [\n", + " (\"Model 1\", YGenerosity, PredictionTable1),\n", + " (\"Model 2\", YPossitive, PredictionTable2),\n", + " (\"Model 3\", YAll, PredictionTable3),\n", + " (\"Model 4\", YScaleFewVariables, PredictionTable4),\n", + "]\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + "axes = axes.ravel()\n", + "\n", + "for ax, (model_name, Y, PredictionTable) in zip(axes, models):\n", + " # Scatter plot of actual vs predicted values\n", + " ax.scatter(Y, PredictionTable[\"mean\"], color=\"k\")\n", + " \n", + " # Add error bars for prediction interval\n", + " yerr = PredictionTable[\"obs_ci_upper\"] - PredictionTable[\"mean\"]\n", + " ax.errorbar(Y, PredictionTable[\"mean\"], yerr=yerr, fmt=\"o\", color=\"k\")\n", + " \n", + " # Add identity line\n", + " ax.plot(\n", + " [Y.min(), Y.max()],\n", + " [Y.min(), Y.max()],\n", + " color=\"r\",\n", + " linestyle=\"--\",\n", + " )\n", + " \n", + " # Set labels and title\n", + " ax.set_xlabel(r\"$Y$\")\n", + " ax.set_ylabel(r\"$\\hat{Y}$\")\n", + " ax.set_title(model_name)\n", + " ax.spines[[\"right\", \"top\"]].set_visible(False)\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fall-2024/math/mat-206/00030/MAT-206:00030 - 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a/fall-2024/math/mat-206/00020/DataWhr2024.csv b/fall-2024/math/mat-206/00020/DataWhr2024.csv new file mode 100644 index 0000000..f8d857c --- /dev/null +++ b/fall-2024/math/mat-206/00020/DataWhr2024.csv @@ -0,0 +1,2364 @@ +Country name,year,Life Ladder,Log GDP per capita,Social support,Healthy life expectancy at birth,Freedom to make life choices,Generosity,Perceptions of corruption,Positive affect,Negative affect +Afghanistan,2008,3.724,7.350,0.451,50.500,0.718,0.164,0.882,0.414,0.258 +Afghanistan,2009,4.402,7.509,0.552,50.800,0.679,0.187,0.850,0.481,0.237 +Afghanistan,2010,4.758,7.614,0.539,51.100,0.600,0.118,0.707,0.517,0.275 +Afghanistan,2011,3.832,7.581,0.521,51.400,0.496,0.160,0.731,0.480,0.267 +Afghanistan,2012,3.783,7.661,0.521,51.700,0.531,0.234,0.776,0.614,0.268 +Afghanistan,2013,3.572,7.680,0.484,52.000,0.578,0.059,0.823,0.547,0.273 +Afghanistan,2014,3.131,7.671,0.526,52.300,0.509,0.102,0.871,0.492,0.375 +Afghanistan,2015,3.983,7.654,0.529,52.600,0.389,0.078,0.881,0.491,0.339 +Afghanistan,2016,4.220,7.650,0.559,52.925,0.523,0.040,0.793,0.501,0.348 +Afghanistan,2017,2.662,7.648,0.491,53.250,0.427,-0.123,0.954,0.435,0.371 +Afghanistan,2018,2.694,7.631,0.508,53.575,0.374,-0.095,0.928,0.385,0.405 +Afghanistan,2019,2.375,7.640,0.420,53.900,0.394,-0.109,0.924,0.324,0.502 +Afghanistan,2021,2.436,7.325,0.454,54.550,0.394,-0.085,0.946,0.179,0.607 +Afghanistan,2022,1.281,,0.228,54.875,0.368,,0.733,0.206,0.576 +Afghanistan,2023,1.446,,0.368,55.200,0.228,,0.738,0.261,0.460 +Albania,2007,4.634,9.122,0.821,66.760,0.529,-0.013,0.875,0.489,0.246 +Albania,2009,5.485,9.241,0.833,67.320,0.525,-0.162,0.864,0.564,0.279 +Albania,2010,5.269,9.283,0.733,67.600,0.569,-0.176,0.726,0.576,0.300 +Albania,2011,5.867,9.310,0.759,67.880,0.487,-0.209,0.877,0.566,0.257 +Albania,2012,5.510,9.326,0.785,68.160,0.602,-0.173,0.848,0.553,0.271 +Albania,2013,4.551,9.338,0.759,68.440,0.632,-0.131,0.863,0.541,0.338 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+Armenia,2019,5.488,9.569,0.782,67.100,0.844,-0.181,0.583,0.537,0.430 +Armenia,2021,5.301,9.561,0.762,67.650,0.795,-0.159,0.705,0.566,0.478 +Armenia,2022,5.382,9.683,0.811,67.925,0.790,-0.158,0.705,0.531,0.549 +Armenia,2023,5.679,9.730,0.819,68.200,0.819,-0.179,0.681,0.575,0.423 +Australia,2005,7.341,10.662,0.968,69.800,0.935,,0.390,0.770,0.238 +Australia,2007,7.285,10.694,0.965,69.960,0.891,0.342,0.513,0.762,0.215 +Australia,2008,7.254,10.709,0.947,70.040,0.916,0.300,0.431,0.729,0.218 +Australia,2010,7.450,10.714,0.955,70.200,0.932,0.311,0.366,0.762,0.220 +Australia,2011,7.406,10.723,0.967,70.280,0.945,0.364,0.382,0.724,0.195 +Australia,2012,7.196,10.744,0.945,70.360,0.935,0.268,0.368,0.728,0.214 +Australia,2013,7.364,10.752,0.928,70.440,0.933,0.263,0.432,0.770,0.177 +Australia,2014,7.289,10.763,0.924,70.520,0.923,0.313,0.442,0.740,0.245 +Australia,2015,7.309,10.770,0.952,70.600,0.922,0.327,0.357,0.750,0.210 +Australia,2016,7.250,10.781,0.942,70.675,0.922,0.233,0.399,0.736,0.236 +Australia,2017,7.257,10.787,0.950,70.750,0.911,0.312,0.411,0.728,0.225 +Australia,2018,7.177,10.801,0.940,70.825,0.916,0.141,0.405,0.706,0.187 +Australia,2019,7.234,10.807,0.943,70.900,0.918,0.115,0.430,0.727,0.202 +Australia,2020,7.137,10.794,0.937,70.975,0.905,0.202,0.491,0.726,0.205 +Australia,2021,7.112,10.815,0.920,71.050,0.912,0.234,0.454,0.740,0.235 +Australia,2022,7.035,10.840,0.942,71.125,0.854,0.153,0.545,0.711,0.244 +Australia,2023,7.025,10.846,0.896,71.200,0.876,0.187,0.482,0.731,0.248 +Austria,2006,7.122,10.836,0.936,69.500,0.941,0.297,0.490,0.746,0.174 +Austria,2008,7.181,10.881,0.935,69.700,0.879,0.286,0.614,0.716,0.173 +Austria,2010,7.303,10.856,0.914,69.900,0.896,0.125,0.546,0.710,0.156 +Austria,2011,7.471,10.881,0.944,70.000,0.939,0.126,0.703,0.672,0.145 +Austria,2012,7.401,10.884,0.945,70.100,0.920,0.112,0.771,0.712,0.157 +Austria,2013,7.499,10.878,0.950,70.200,0.922,0.163,0.679,0.725,0.163 +Austria,2014,6.950,10.877,0.899,70.300,0.885,0.112,0.567,0.721,0.170 +Austria,2015,7.076,10.876,0.928,70.400,0.900,0.093,0.557,0.748,0.164 +Austria,2016,7.048,10.885,0.926,70.525,0.889,0.074,0.524,0.713,0.197 +Austria,2017,7.294,10.900,0.906,70.650,0.890,0.128,0.518,0.699,0.180 +Austria,2018,7.396,10.919,0.912,70.775,0.904,0.048,0.523,0.695,0.226 +Austria,2019,7.195,10.930,0.964,70.900,0.903,0.054,0.457,0.727,0.205 +Austria,2020,7.213,10.859,0.925,71.025,0.912,0.004,0.464,0.716,0.206 +Austria,2021,7.080,10.899,0.863,71.150,0.795,0.158,0.501,0.722,0.259 +Austria,2022,6.999,10.938,0.876,71.275,0.856,0.137,0.524,0.718,0.226 +Austria,2023,6.636,10.930,0.874,71.400,0.874,0.209,0.529,0.712,0.240 +Azerbaijan,2006,4.728,9.154,0.854,60.580,0.772,-0.239,0.774,0.469,0.276 +Azerbaijan,2007,4.568,9.366,0.753,60.860,0.522,-0.211,0.871,0.474,0.284 +Azerbaijan,2008,4.817,9.447,0.684,61.140,0.601,-0.034,0.715,0.561,0.227 +Azerbaijan,2009,4.574,9.515,0.736,61.420,0.498,-0.091,0.754,0.522,0.234 +Azerbaijan,2010,4.219,9.553,0.687,61.700,0.501,-0.128,0.858,0.516,0.272 +Azerbaijan,2011,4.680,9.541,0.725,61.980,0.537,-0.110,0.795,0.522,0.258 +Azerbaijan,2012,4.911,9.549,0.762,62.260,0.599,-0.146,0.763,0.523,0.266 +Azerbaijan,2013,5.481,9.592,0.770,62.540,0.672,-0.173,0.699,0.516,0.242 +Azerbaijan,2014,5.252,9.607,0.799,62.820,0.733,-0.214,0.654,0.502,0.220 +Azerbaijan,2015,5.147,9.606,0.786,63.100,0.764,-0.203,0.616,0.520,0.206 +Azerbaijan,2016,5.304,9.563,0.777,63.225,0.713,-0.210,0.607,0.509,0.191 +Azerbaijan,2017,5.152,9.555,0.787,63.350,0.731,-0.231,0.653,0.512,0.198 +Azerbaijan,2018,5.168,9.562,0.781,63.475,0.772,-0.237,0.561,0.527,0.191 +Azerbaijan,2019,5.173,9.578,0.887,63.600,0.854,-0.220,0.457,0.577,0.164 +Azerbaijan,2022,4.576,9.619,0.665,63.975,0.800,0.075,0.696,0.533,0.401 +Azerbaijan,2023,5.214,9.637,0.713,64.100,0.829,-0.160,0.627,0.509,0.221 +Bahrain,2009,5.701,10.714,0.904,64.760,0.896,0.031,0.506,0.707,0.422 +Bahrain,2010,5.937,10.728,0.877,65.000,0.862,-0.008,0.715,0.641,0.423 +Bahrain,2011,4.824,10.749,0.908,65.240,0.870,-0.061,0.583,0.506,0.514 +Bahrain,2012,5.027,10.775,0.911,65.480,0.682,,0.438,0.559,0.381 +Bahrain,2013,6.690,10.798,0.884,65.720,0.809,,0.525,0.711,0.306 +Bahrain,2014,6.165,10.802,,65.960,,,,, +Bahrain,2015,6.007,10.788,0.853,66.200,0.850,0.106,,0.653,0.303 +Bahrain,2016,6.170,10.789,0.863,66.125,0.889,0.082,,0.736,0.283 +Bahrain,2017,6.227,10.798,0.876,66.050,0.906,0.128,,0.754,0.290 +Bahrain,2019,7.098,10.815,0.878,65.900,0.907,0.035,,0.711,0.317 +Bahrain,2020,6.173,10.779,0.848,65.825,0.945,0.115,,0.730,0.297 +Bahrain,2023,5.959,10.877,0.817,65.600,0.869,0.155,,0.671,0.336 +Bangladesh,2006,4.319,7.940,0.672,59.120,0.612,0.052,0.786,0.459,0.321 +Bangladesh,2007,4.607,7.997,0.514,59.640,0.605,0.024,0.806,0.484,0.313 +Bangladesh,2008,5.052,8.047,0.467,60.160,0.606,-0.060,0.802,0.545,0.232 +Bangladesh,2009,5.083,8.087,0.528,60.680,0.631,-0.091,0.776,0.506,0.223 +Bangladesh,2010,4.858,8.130,0.549,61.200,0.659,-0.033,0.774,0.496,0.292 +Bangladesh,2011,4.986,8.181,0.606,61.720,0.838,-0.086,0.757,0.501,0.235 +Bangladesh,2012,4.724,8.231,0.582,62.240,0.668,-0.051,0.765,0.537,0.183 +Bangladesh,2013,4.660,8.277,0.530,62.760,0.742,-0.032,0.743,0.492,0.246 +Bangladesh,2014,4.636,8.323,0.577,63.280,0.736,-0.115,0.789,,0.231 +Bangladesh,2015,4.633,8.375,0.601,63.800,0.815,-0.085,0.721,0.543,0.226 +Bangladesh,2016,4.556,8.431,0.649,63.925,0.875,-0.105,0.688,0.437,0.235 +Bangladesh,2017,4.310,8.483,0.713,64.050,0.896,-0.004,0.635,0.436,0.214 +Bangladesh,2018,4.499,8.542,0.706,64.175,0.901,-0.059,0.701,0.433,0.361 +Bangladesh,2019,5.114,8.607,0.673,64.300,0.902,-0.067,0.656,0.433,0.369 +Bangladesh,2020,5.280,8.629,0.739,64.425,0.777,-0.025,0.742,0.485,0.332 +Bangladesh,2021,4.123,8.685,0.485,64.550,0.893,0.089,0.746,0.504,0.448 +Bangladesh,2022,3.408,8.742,0.404,64.675,0.865,-0.058,0.617,0.394,0.448 +Bangladesh,2023,4.114,8.783,0.450,64.800,0.919,0.019,0.756,0.435,0.435 +Belarus,2006,5.658,9.489,0.918,60.060,0.707,-0.252,0.708,0.535,0.269 +Belarus,2007,5.617,9.576,0.858,60.620,0.667,-0.230,0.695,0.502,0.235 +Belarus,2008,5.463,9.677,0.904,61.180,0.640,-0.226,0.696,,0.246 +Belarus,2009,5.564,9.681,0.908,61.740,0.679,-0.209,0.676,0.544,0.223 +Belarus,2010,5.526,9.759,0.918,62.300,0.700,-0.168,0.706,0.532,0.208 +Belarus,2011,5.225,9.813,0.910,62.860,0.656,-0.174,0.672,0.493,0.249 +Belarus,2012,5.749,9.832,0.902,63.420,0.645,-0.223,0.657,0.515,0.181 +Belarus,2013,5.876,9.842,0.923,63.980,0.723,-0.183,0.653,0.545,0.206 +Belarus,2014,5.812,9.858,0.880,64.540,0.647,-0.054,0.682,0.575,0.209 +Belarus,2015,5.719,9.818,0.924,65.100,0.623,-0.097,0.669,0.546,0.184 +Belarus,2016,5.178,9.792,0.927,65.325,0.658,-0.131,0.664,0.503,0.182 +Belarus,2017,5.553,9.818,0.900,65.550,0.621,-0.128,0.654,0.502,0.233 +Belarus,2018,5.234,9.851,0.905,65.775,0.644,-0.181,0.718,0.409,0.236 +Belarus,2019,5.821,9.867,0.917,66.000,0.657,-0.192,0.546,0.559,0.190 +Belgium,2005,7.262,10.744,0.935,68.400,0.924,,0.598,0.677,0.260 +Belgium,2007,7.219,10.791,0.922,68.720,0.901,0.064,0.721,0.744,0.218 +Belgium,2008,7.117,10.788,0.923,68.880,0.887,0.001,0.652,0.709,0.242 +Belgium,2010,6.854,10.778,0.931,69.200,0.807,0.016,0.697,0.793,0.240 +Belgium,2011,7.111,10.782,0.937,69.360,0.880,-0.020,0.711,0.752,0.225 +Belgium,2012,6.935,10.783,0.927,69.520,0.855,-0.056,0.758,0.718,0.238 +Belgium,2013,7.104,10.783,0.909,69.680,0.891,0.011,0.574,0.738,0.217 +Belgium,2014,6.855,10.794,0.944,69.840,0.861,-0.005,0.512,0.744,0.252 +Belgium,2015,6.904,10.809,0.885,70.000,0.869,0.056,0.469,0.747,0.240 +Belgium,2016,6.949,10.816,0.929,70.150,0.866,-0.062,0.497,0.701,0.260 +Belgium,2017,6.928,10.829,0.922,70.300,0.857,0.049,0.543,0.713,0.234 +Belgium,2018,6.892,10.842,0.930,70.450,0.808,-0.130,0.630,0.682,0.250 +Belgium,2019,6.772,10.859,0.884,70.600,0.776,-0.178,0.672,0.699,0.244 +Belgium,2020,6.839,10.799,0.904,70.750,0.767,-0.172,0.634,0.619,0.260 +Belgium,2021,6.882,10.856,0.915,70.900,0.823,0.077,0.523,0.687,0.260 +Belgium,2022,6.857,10.881,0.923,71.050,0.890,0.095,0.483,0.718,0.235 +Belgium,2023,6.944,10.883,0.896,71.200,0.870,0.065,0.522,0.725,0.245 +Belize,2007,6.451,9.192,0.872,64.300,0.705,0.006,0.769,0.732,0.251 +Belize,2014,5.956,9.135,0.757,65.000,0.874,-0.002,0.782,0.735,0.282 +Benin,2006,3.330,7.844,0.445,51.960,0.580,-0.015,0.790,0.521,0.309 +Benin,2008,3.667,7.891,0.382,52.480,0.709,-0.008,0.825,0.574,0.303 +Benin,2011,3.870,7.876,0.477,53.260,0.773,-0.145,0.849,0.574,0.219 +Benin,2012,3.193,7.894,0.523,53.520,0.769,-0.114,0.806,0.563,0.231 +Benin,2013,3.479,7.935,0.577,53.780,0.783,-0.088,0.856,0.646,0.216 +Benin,2014,3.347,7.967,0.506,54.040,0.776,-0.099,0.855,0.558,0.273 +Benin,2015,3.625,7.955,0.434,54.300,0.733,-0.029,0.850,0.555,0.373 +Benin,2016,4.007,7.958,0.493,54.600,0.780,-0.068,0.838,0.578,0.456 +Benin,2017,4.853,7.984,0.436,54.900,0.727,-0.068,0.767,0.598,0.458 +Benin,2018,5.820,8.020,0.504,55.200,0.713,0.000,0.747,0.625,0.468 +Benin,2019,4.976,8.057,0.442,55.500,0.770,-0.018,0.698,0.638,0.441 +Benin,2020,4.408,8.067,0.507,55.800,0.783,-0.086,0.532,0.557,0.305 +Benin,2021,4.493,8.108,0.436,56.100,0.724,-0.016,0.613,0.597,0.435 +Benin,2022,4.217,8.142,0.366,56.400,0.714,-0.033,0.580,0.571,0.444 +Benin,2023,4.420,8.174,0.398,56.700,0.786,-0.073,0.575,0.573,0.428 +Bhutan,2013,5.569,9.097,0.819,62.240,0.810,0.349,0.802,0.664,0.217 +Bhutan,2014,4.939,9.143,0.880,62.420,0.834,0.264,0.650,0.775,0.324 +Bhutan,2015,5.082,9.198,0.848,62.600,0.830,0.273,0.634,0.723,0.312 +Bolivia,2006,5.374,8.671,0.834,60.900,0.770,-0.048,0.794,0.708,0.432 +Bolivia,2007,5.628,8.698,0.796,61.100,0.780,-0.004,0.817,0.746,0.388 +Bolivia,2008,5.298,8.740,0.785,61.300,0.726,-0.096,0.801,0.723,0.392 +Bolivia,2009,6.086,8.756,0.831,61.500,0.779,-0.040,0.763,0.742,0.372 +Bolivia,2010,5.781,8.780,0.807,61.700,0.703,-0.073,0.781,0.720,0.350 +Bolivia,2011,5.779,8.813,0.817,61.900,0.782,-0.043,0.825,0.689,0.361 +Bolivia,2012,6.019,8.847,0.781,62.100,0.862,-0.019,0.840,0.699,0.409 +Bolivia,2013,5.767,8.896,0.803,62.300,0.846,-0.071,0.812,0.721,0.410 +Bolivia,2014,5.865,8.933,0.821,62.500,0.881,0.014,0.832,0.769,0.398 +Bolivia,2015,5.834,8.965,0.829,62.700,0.884,-0.034,0.862,0.749,0.393 +Bolivia,2016,5.770,8.991,0.796,62.850,0.882,-0.051,0.853,0.736,0.376 +Bolivia,2017,5.651,9.017,0.779,63.000,0.884,-0.124,0.819,0.655,0.434 +Bolivia,2018,5.916,9.044,0.827,63.150,0.863,-0.097,0.786,0.705,0.387 +Bolivia,2019,5.674,9.051,0.784,63.300,0.881,-0.090,0.857,0.701,0.419 +Bolivia,2020,5.559,8.946,0.805,63.450,0.877,-0.056,0.868,0.729,0.382 +Bolivia,2021,5.569,8.994,0.798,63.600,0.862,-0.058,0.812,0.721,0.403 +Bolivia,2022,5.929,9.012,0.824,63.750,0.865,-0.083,0.840,0.738,0.426 +Bolivia,2023,5.860,9.025,0.786,63.900,0.832,-0.059,0.877,0.753,0.401 +Bosnia and Herzegovina,2007,4.900,9.191,0.766,67.000,0.342,0.006,0.926,0.570,0.296 +Bosnia and Herzegovina,2009,4.963,9.246,0.735,67.000,0.258,-0.027,0.959,0.507,0.390 +Bosnia and Herzegovina,2010,4.669,9.272,0.773,67.000,0.365,-0.131,0.933,0.465,0.409 +Bosnia and Herzegovina,2011,4.995,9.300,0.725,67.000,0.333,-0.038,0.925,0.551,0.326 +Bosnia and Herzegovina,2012,4.773,9.310,0.779,67.000,0.420,-0.016,0.953,0.469,0.338 +Bosnia and Herzegovina,2013,5.124,9.349,0.767,67.000,0.390,0.039,0.970,0.489,0.315 +Bosnia and Herzegovina,2014,5.249,9.373,0.788,67.000,0.412,0.229,0.976,0.491,0.262 +Bosnia and Herzegovina,2015,5.117,9.428,0.656,67.000,0.631,-0.058,0.960,0.486,0.286 +Bosnia and Herzegovina,2016,5.181,9.473,0.808,67.050,0.633,0.130,0.957,0.566,0.304 +Bosnia and Herzegovina,2017,5.090,9.517,0.775,67.100,0.564,0.087,0.923,0.527,0.271 +Bosnia and Herzegovina,2018,5.887,9.566,0.836,67.150,0.659,0.118,0.913,0.568,0.277 +Bosnia and Herzegovina,2019,6.016,9.606,0.873,67.200,0.722,0.074,0.963,0.545,0.238 +Bosnia and Herzegovina,2020,5.516,9.588,0.899,67.250,0.740,0.132,0.916,0.602,0.325 +Bosnia and Herzegovina,2021,5.749,9.674,0.860,67.300,0.759,0.274,0.921,0.604,0.305 +Bosnia and Herzegovina,2022,5.872,9.723,0.856,67.350,0.743,0.191,0.933,0.543,0.285 +Bosnia and Herzegovina,2023,6.009,9.759,0.879,67.400,0.847,0.241,0.948,0.579,0.249 +Botswana,2006,4.739,9.495,0.883,48.840,0.824,-0.201,0.723,0.643,0.226 +Botswana,2008,5.451,9.543,0.832,49.720,0.858,-0.167,0.806,0.677,0.218 +Botswana,2010,3.553,9.446,0.866,50.600,0.826,-0.141,0.814,0.617,0.172 +Botswana,2011,3.520,9.492,0.860,51.040,0.813,-0.248,0.816,0.647,0.160 +Botswana,2012,4.836,9.471,0.837,51.480,0.799,-0.197,0.814,0.695,0.171 +Botswana,2013,4.128,9.557,0.856,51.920,0.767,-0.148,0.749,0.671,0.244 +Botswana,2014,4.031,9.593,0.859,52.360,0.791,-0.099,0.743,0.626,0.245 +Botswana,2015,3.762,9.524,0.816,52.800,0.857,-0.108,0.860,0.676,0.261 +Botswana,2016,3.499,9.573,0.768,53.075,0.852,-0.246,0.729,0.657,0.252 +Botswana,2017,3.505,9.593,0.768,53.350,0.817,-0.242,0.731,0.612,0.276 +Botswana,2018,3.461,9.613,0.795,53.625,0.818,-0.248,0.807,0.688,0.267 +Botswana,2019,3.471,9.624,0.774,53.900,0.833,-0.233,0.792,0.665,0.273 +Botswana,2022,3.435,9.650,0.750,54.725,0.739,-0.218,0.831,0.623,0.287 +Botswana,2023,3.332,9.673,0.701,55.000,0.741,-0.264,0.814,0.657,0.247 +Brazil,2005,6.637,9.435,0.883,63.100,0.882,,0.745,0.770,0.302 +Brazil,2007,6.321,9.512,0.886,63.420,0.777,-0.022,0.728,0.775,0.299 +Brazil,2008,6.691,9.552,0.878,63.580,0.782,-0.083,0.688,0.718,0.265 +Brazil,2009,7.001,9.541,0.913,63.740,0.767,-0.061,0.723,0.744,0.274 +Brazil,2010,6.837,9.604,0.906,63.900,0.806,-0.059,0.656,0.726,0.250 +Brazil,2011,7.038,9.634,0.916,64.060,0.834,-0.078,0.662,0.698,0.268 +Brazil,2012,6.660,9.644,0.890,64.220,0.849,,0.623,0.685,0.350 +Brazil,2013,7.140,9.665,0.910,64.380,0.785,-0.100,0.707,0.725,0.276 +Brazil,2014,6.981,9.661,0.898,64.540,0.714,-0.121,0.710,0.718,0.274 +Brazil,2015,6.547,9.617,0.907,64.700,0.799,-0.021,0.771,0.687,0.325 +Brazil,2016,6.375,9.575,0.912,64.875,0.807,-0.106,0.781,0.711,0.302 +Brazil,2017,6.333,9.580,0.905,65.050,0.765,-0.181,0.794,0.669,0.308 +Brazil,2018,6.191,9.590,0.882,65.225,0.751,-0.123,0.763,0.677,0.350 +Brazil,2019,6.451,9.595,0.899,65.400,0.830,-0.068,0.762,0.701,0.337 +Brazil,2020,6.110,9.555,0.831,65.575,0.786,-0.061,0.729,0.653,0.389 +Brazil,2021,6.010,9.598,0.814,65.750,0.792,0.086,0.739,0.662,0.407 +Brazil,2022,6.257,9.622,0.866,65.925,0.830,-0.064,0.742,0.681,0.341 +Brazil,2023,6.553,9.635,0.856,66.100,0.870,-0.036,0.733,0.694,0.313 +Bulgaria,2007,3.844,9.746,0.832,64.780,0.566,-0.146,0.976,0.500,0.226 +Bulgaria,2010,3.912,9.807,0.843,65.200,0.545,-0.153,0.941,0.513,0.238 +Bulgaria,2011,3.875,9.834,0.860,65.340,0.664,-0.236,0.948,0.490,0.271 +Bulgaria,2012,4.222,9.848,0.838,65.480,0.641,-0.181,0.938,0.510,0.237 +Bulgaria,2013,3.993,9.848,0.829,65.620,0.603,-0.199,0.962,0.537,0.278 +Bulgaria,2014,4.438,9.863,0.886,65.760,0.576,-0.062,0.955,0.542,0.236 +Bulgaria,2015,4.865,9.903,0.908,65.900,0.637,-0.207,0.941,0.556,0.214 +Bulgaria,2016,4.838,9.940,0.926,66.000,0.700,-0.177,0.936,0.545,0.172 +Bulgaria,2017,5.097,9.974,0.942,66.100,0.689,-0.160,0.911,0.542,0.189 +Bulgaria,2018,5.099,10.008,0.924,66.200,0.724,-0.182,0.952,0.554,0.189 +Bulgaria,2019,5.108,10.055,0.948,66.300,0.822,-0.115,0.943,0.577,0.200 +Bulgaria,2020,5.598,10.020,0.916,66.400,0.818,-0.012,0.901,0.642,0.221 +Bulgaria,2021,5.422,10.102,0.884,66.500,0.841,-0.018,0.891,0.647,0.253 +Bulgaria,2022,5.378,10.197,0.953,66.600,0.741,-0.152,0.942,0.582,0.165 +Bulgaria,2023,5.590,10.273,0.935,66.700,0.754,-0.131,0.948,0.539,0.192 +Burkina Faso,2006,3.801,7.327,0.796,49.440,0.588,0.026,0.798,0.678,0.266 +Burkina Faso,2007,4.017,7.337,0.771,49.880,0.582,-0.062,0.833,0.609,0.281 +Burkina Faso,2008,3.846,7.364,0.727,50.320,0.612,-0.103,0.887,0.538,0.304 +Burkina Faso,2010,4.036,7.416,0.773,51.200,0.587,-0.038,0.767,0.565,0.217 +Burkina Faso,2011,4.785,7.450,0.710,51.640,0.725,-0.107,0.707,0.578,0.205 +Burkina Faso,2012,3.955,7.482,0.744,52.080,0.622,-0.072,0.726,0.487,0.300 +Burkina Faso,2013,3.326,7.509,0.745,52.520,0.741,-0.018,0.765,0.592,0.287 +Burkina Faso,2014,3.481,7.521,0.742,52.960,0.710,-0.006,0.801,0.604,0.256 +Burkina Faso,2015,4.419,7.530,0.705,53.400,0.659,0.001,0.693,0.555,0.359 +Burkina Faso,2016,4.206,7.558,0.764,53.775,0.645,-0.003,0.721,0.590,0.337 +Burkina Faso,2017,4.647,7.590,0.785,54.150,0.614,-0.066,0.727,0.580,0.354 +Burkina Faso,2018,4.927,7.626,0.665,54.525,0.721,-0.016,0.757,0.656,0.343 +Burkina Faso,2019,4.741,7.654,0.683,54.900,0.678,-0.007,0.729,0.656,0.365 +Burkina Faso,2020,4.640,7.647,0.668,55.275,0.750,0.120,0.809,0.605,0.388 +Burkina Faso,2021,4.636,7.687,0.658,55.650,0.644,0.064,0.736,0.620,0.363 +Burkina Faso,2023,4.462,7.693,0.580,56.400,0.715,0.105,0.650,0.639,0.346 +Burundi,2008,3.563,6.700,0.291,49.660,0.260,-0.022,0.860,0.415,0.253 +Burundi,2009,3.792,6.687,0.326,50.280,0.427,-0.021,0.718,0.607,0.164 +Burundi,2011,3.706,6.694,0.422,51.520,0.490,-0.063,0.677,0.572,0.190 +Burundi,2014,2.905,6.723,0.565,53.380,0.431,-0.059,0.808,0.622,0.251 +Burundi,2018,3.775,6.607,0.485,55.200,0.646,-0.027,0.599,0.636,0.363 +Cambodia,2006,3.569,7.746,0.793,57.640,,0.250,0.829,,0.341 +Cambodia,2007,4.156,7.826,0.675,57.980,0.819,0.111,0.879,,0.320 +Cambodia,2008,4.462,7.874,0.619,58.320,0.914,0.041,0.888,0.600,0.335 +Cambodia,2009,4.111,7.860,0.818,58.660,0.937,0.148,0.965,0.691,0.188 +Cambodia,2010,4.141,7.904,0.697,59.000,0.940,0.345,0.896,0.662,0.422 +Cambodia,2011,4.161,7.957,0.716,59.340,0.927,0.413,0.775,0.637,0.308 +Cambodia,2012,3.899,8.013,0.606,59.680,0.956,0.242,0.890,0.713,0.352 +Cambodia,2013,3.674,8.070,0.651,60.020,0.941,0.159,0.812,0.670,0.440 +Cambodia,2014,3.883,8.125,0.693,60.360,0.938,0.234,0.843,0.682,0.482 +Cambodia,2015,4.162,8.179,0.729,60.700,0.956,0.204,0.825,0.731,0.399 +Cambodia,2016,4.461,8.233,0.746,60.900,0.958,0.070,0.840,0.713,0.398 +Cambodia,2017,4.586,8.287,0.765,61.100,0.964,0.082,0.821,0.669,0.408 +Cambodia,2018,5.122,8.347,0.795,61.300,0.958,0.029,,0.723,0.414 +Cambodia,2019,4.998,8.404,0.759,61.500,0.957,0.007,0.828,0.704,0.390 +Cambodia,2020,4.377,8.361,0.724,61.700,0.963,0.047,0.863,0.771,0.390 +Cambodia,2021,4.555,8.379,0.713,61.900,0.965,0.012,0.844,0.759,0.391 +Cambodia,2022,4.250,8.419,0.784,62.100,0.946,0.151,0.860,0.756,0.388 +Cambodia,2023,4.221,8.462,0.738,62.300,0.961,0.070,0.799,0.683,0.394 +Cameroon,2006,3.851,8.090,0.690,47.840,0.653,-0.020,0.907,0.588,0.271 +Cameroon,2007,4.350,8.104,0.717,48.280,0.644,-0.042,0.910,0.630,0.249 +Cameroon,2008,4.292,8.104,0.697,48.720,0.580,-0.079,0.945,0.613,0.312 +Cameroon,2009,4.741,8.101,0.729,49.160,0.698,-0.027,0.925,0.592,0.250 +Cameroon,2010,4.554,8.101,0.759,49.600,0.792,-0.008,0.875,0.594,0.274 +Cameroon,2011,4.434,8.106,0.738,50.040,0.817,-0.038,0.870,0.608,0.272 +Cameroon,2012,4.245,8.123,0.743,50.480,0.766,-0.041,0.898,0.617,0.284 +Cameroon,2013,4.271,8.144,0.760,50.920,0.794,-0.039,0.867,0.640,0.268 +Cameroon,2014,4.240,8.169,0.778,51.360,0.795,-0.080,0.856,0.604,0.216 +Cameroon,2015,5.038,8.193,0.646,51.800,0.791,0.041,0.868,0.624,0.346 +Cameroon,2016,4.816,8.207,0.659,52.475,0.713,-0.012,0.879,0.635,0.367 +Cameroon,2017,5.074,8.214,0.695,53.150,0.767,-0.036,0.844,0.632,0.377 +Cameroon,2018,5.251,8.225,0.677,53.825,0.816,0.028,0.884,0.630,0.356 +Cameroon,2019,4.937,8.231,0.711,54.500,0.712,-0.015,0.817,0.606,0.326 +Cameroon,2020,5.241,8.207,0.720,55.175,0.675,0.042,0.837,0.626,0.386 +Cameroon,2021,4.963,8.216,0.695,55.850,0.715,-0.029,0.849,0.612,0.347 +Cameroon,2022,4.712,8.225,0.629,56.525,0.675,0.022,0.849,0.586,0.362 +Cameroon,2023,4.946,8.238,0.716,57.200,0.739,-0.028,0.855,0.588,0.356 +Canada,2005,7.418,10.707,0.962,70.500,0.957,0.246,0.503,0.783,0.233 +Canada,2007,7.482,10.734,,70.620,0.930,0.244,0.406,0.812,0.257 +Canada,2008,7.486,10.733,0.939,70.680,0.926,0.256,0.370,0.802,0.202 +Canada,2009,7.488,10.692,0.943,70.740,0.915,0.241,0.413,0.793,0.248 +Canada,2010,7.650,10.711,0.954,70.800,0.934,0.225,0.413,0.791,0.233 +Canada,2011,7.426,10.733,0.922,70.860,0.951,0.247,0.433,0.803,0.248 +Canada,2012,7.415,10.739,0.948,70.920,0.918,0.284,0.466,0.776,0.229 +Canada,2013,7.594,10.752,0.936,70.980,0.916,0.310,0.406,0.801,0.263 +Canada,2014,7.304,10.770,0.918,71.040,0.939,0.264,0.442,0.791,0.259 +Canada,2015,7.413,10.769,0.939,71.100,0.931,0.247,0.427,0.792,0.286 +Canada,2016,7.245,10.768,0.924,71.150,0.912,0.205,0.385,0.768,0.237 +Canada,2017,7.415,10.786,0.934,71.200,0.945,0.157,0.362,0.799,0.218 +Canada,2018,7.175,10.799,0.923,71.250,0.946,0.100,0.372,0.773,0.259 +Canada,2019,7.109,10.803,0.925,71.300,0.912,0.105,0.436,0.781,0.285 +Canada,2020,7.025,10.740,0.931,71.350,0.887,0.043,0.434,0.738,0.307 +Canada,2021,6.939,10.783,0.926,71.400,0.898,0.189,0.384,0.763,0.276 +Canada,2022,6.918,10.799,0.929,71.450,0.838,0.220,0.442,0.719,0.287 +Canada,2023,6.841,10.794,0.902,71.500,0.847,0.196,0.468,0.726,0.304 +Central African Republic,2007,4.160,6.946,0.532,41.480,0.663,0.079,0.782,0.567,0.330 +Central African Republic,2010,3.568,7.031,0.483,42.500,0.690,-0.037,0.845,0.478,0.257 +Central African Republic,2011,3.678,7.057,0.387,42.840,0.780,-0.016,0.834,0.502,0.277 +Central African Republic,2016,2.693,6.707,0.290,44.750,0.624,0.033,0.859,0.551,0.494 +Central African Republic,2017,3.476,6.733,0.320,45.300,0.645,0.074,0.890,0.602,0.599 +Chad,2006,3.435,7.369,0.724,47.080,0.306,0.022,0.961,0.571,0.263 +Chad,2007,4.141,7.368,0.479,47.460,0.295,-0.017,0.874,0.598,0.245 +Chad,2008,4.632,7.363,0.571,47.840,0.527,0.057,0.944,0.569,0.225 +Chad,2009,3.639,7.369,0.646,48.220,0.401,0.016,0.931,0.601,0.221 +Chad,2010,3.743,7.462,0.734,48.600,0.505,0.020,0.858,0.560,0.287 +Chad,2011,4.393,7.428,0.819,48.980,0.540,0.025,0.876,0.579,0.289 +Chad,2012,4.033,7.478,0.673,49.360,0.563,-0.039,0.884,0.498,0.316 +Chad,2013,3.508,7.498,0.714,49.740,0.488,-0.051,0.882,0.437,0.314 +Chad,2014,3.460,7.529,0.733,50.120,0.567,-0.075,0.881,0.524,0.329 +Chad,2015,4.323,7.525,0.751,50.500,0.474,-0.034,0.889,0.593,0.358 +Chad,2016,4.029,7.429,0.616,50.875,0.525,0.047,0.820,0.564,0.468 +Chad,2017,4.559,7.365,0.661,51.250,0.615,0.003,0.792,0.584,0.538 +Chad,2018,4.486,7.355,0.577,51.625,0.650,0.020,0.763,0.532,0.544 +Chad,2019,4.251,7.354,0.640,52.000,0.537,0.051,0.832,0.556,0.460 +Chad,2022,4.397,7.253,0.720,53.125,0.679,0.218,0.805,0.588,0.499 +Chad,2023,4.544,7.254,0.609,53.500,0.586,0.138,0.755,0.541,0.467 +Chile,2006,6.063,9.870,0.836,67.780,0.744,0.161,0.634,0.752,0.348 +Chile,2007,5.698,9.910,0.815,67.960,0.662,0.236,0.723,0.708,0.342 +Chile,2008,5.789,9.938,0.804,68.140,0.640,0.076,0.741,0.706,0.330 +Chile,2009,6.494,9.916,0.832,68.320,0.747,0.141,0.734,0.756,0.300 +Chile,2010,6.636,9.963,0.857,68.500,0.786,0.100,0.702,0.760,0.300 +Chile,2011,6.526,10.013,0.819,68.680,0.701,0.104,0.753,0.758,0.317 +Chile,2012,6.599,10.063,0.855,68.860,0.734,0.186,0.782,0.736,0.288 +Chile,2013,6.740,10.086,0.862,69.040,0.737,0.077,0.741,0.791,0.285 +Chile,2014,6.844,10.094,0.862,69.220,0.733,0.209,0.758,0.800,0.276 +Chile,2015,6.533,10.105,0.827,69.400,0.769,0.032,0.812,0.752,0.333 +Chile,2016,6.579,10.110,0.841,69.550,0.652,0.094,0.858,0.792,0.283 +Chile,2017,6.320,10.108,0.880,69.700,0.790,-0.028,0.836,0.765,0.291 +Chile,2018,6.436,10.130,0.890,69.850,0.789,-0.068,0.816,0.755,0.276 +Chile,2019,5.942,10.119,0.869,70.000,0.659,-0.110,0.860,0.741,0.337 +Chile,2020,6.151,10.042,0.888,70.150,0.781,0.026,0.812,0.753,0.336 +Chile,2021,6.436,10.143,0.891,70.300,0.803,-0.052,0.859,0.735,0.221 +Chile,2022,6.415,10.161,0.887,70.450,0.793,-0.014,0.796,0.775,0.253 +Chile,2023,6.230,10.155,0.874,70.600,0.815,-0.027,0.836,0.779,0.263 +China,2006,4.560,8.696,0.747,65.660,,,,0.658,0.170 +China,2007,4.863,8.824,0.811,65.920,,-0.182,,0.664,0.159 +China,2008,4.846,8.911,0.748,66.180,0.853,-0.098,,0.705,0.147 +China,2009,4.454,8.996,0.798,66.440,0.771,-0.166,,0.670,0.162 +China,2010,4.653,9.092,0.768,66.700,0.805,-0.139,,0.658,0.158 +China,2011,5.037,9.178,0.787,66.960,0.824,-0.192,,0.710,0.134 +China,2012,5.095,9.247,0.788,67.220,0.808,-0.190,,0.689,0.159 +China,2013,5.241,9.315,0.778,67.480,0.805,-0.163,,0.717,0.142 +China,2014,5.196,9.380,0.820,67.740,,-0.222,,0.710,0.112 +China,2015,5.304,9.442,0.794,68.000,,-0.250,,0.667,0.171 +China,2016,5.325,9.503,0.742,68.125,,-0.233,,0.683,0.146 +China,2017,5.099,9.564,0.772,68.250,0.878,-0.180,,0.682,0.214 +China,2018,5.131,9.625,0.788,68.375,0.895,-0.164,,0.722,0.190 +China,2019,5.144,9.679,0.822,68.500,0.927,-0.178,,0.760,0.147 +China,2020,5.771,9.699,0.808,68.625,0.891,-0.109,,0.663,0.245 +China,2021,5.863,9.779,0.856,68.750,0.875,0.020,,0.698,0.240 +China,2023,6.145,9.861,0.797,69.000,0.793,-0.032,,0.708,0.210 +Colombia,2006,6.025,9.277,0.910,66.320,0.805,-0.021,0.808,0.776,0.326 +Colombia,2007,6.138,9.330,0.894,66.540,0.786,-0.046,0.860,0.774,0.287 +Colombia,2008,6.168,9.351,0.880,66.760,0.795,-0.047,0.763,0.768,0.307 +Colombia,2009,6.272,9.351,0.886,66.980,0.757,-0.060,0.837,0.786,0.273 +Colombia,2010,6.408,9.383,0.893,67.200,0.816,-0.055,0.815,0.792,0.265 +Colombia,2011,6.464,9.440,0.904,67.420,0.811,-0.079,0.847,0.785,0.286 +Colombia,2012,6.375,9.468,0.914,67.640,0.828,-0.015,0.868,0.829,0.294 +Colombia,2013,6.607,9.508,0.901,67.860,0.841,-0.076,0.898,0.815,0.278 +Colombia,2014,6.449,9.542,0.907,68.080,0.801,-0.096,0.887,0.825,0.278 +Colombia,2015,6.388,9.562,0.890,68.300,0.791,-0.106,0.843,0.803,0.292 +Colombia,2016,6.234,9.572,0.882,68.475,0.835,-0.106,0.898,0.770,0.294 +Colombia,2017,6.157,9.570,0.909,68.650,0.838,-0.163,0.875,0.790,0.299 +Colombia,2018,5.984,9.577,0.871,68.825,0.851,-0.154,0.855,0.775,0.301 +Colombia,2019,6.350,9.590,0.873,69.000,0.822,-0.177,0.854,0.791,0.322 +Colombia,2020,5.709,9.500,0.797,69.175,0.840,-0.091,0.808,0.759,0.340 +Colombia,2021,5.290,9.593,0.793,69.350,0.775,-0.065,0.831,0.752,0.348 +Colombia,2022,5.892,9.658,0.877,69.525,0.799,-0.164,0.863,0.762,0.306 +Colombia,2023,5.904,9.667,0.833,69.700,0.823,-0.142,0.870,0.754,0.285 +Comoros,2009,3.476,7.999,0.629,56.760,0.508,-0.082,0.838,0.626,0.167 +Comoros,2010,3.812,8.015,0.721,57.000,0.529,-0.003,0.741,0.664,0.178 +Comoros,2011,3.838,8.034,0.722,57.240,0.500,-0.084,0.732,0.622,0.173 +Comoros,2012,3.956,8.044,0.719,57.480,0.534,-0.130,0.651,0.616,0.212 +Comoros,2018,3.973,8.100,0.621,58.725,0.560,0.075,0.794,0.688,0.337 +Comoros,2019,4.609,8.099,0.632,58.900,0.538,0.067,0.762,0.665,0.336 +Comoros,2022,3.545,8.085,0.472,59.425,0.481,-0.018,0.732,0.603,0.352 +Comoros,2023,3.588,8.095,0.483,59.600,0.452,0.004,0.704,0.535,0.405 +Congo (Brazzaville),2008,3.820,8.390,0.555,52.240,0.526,-0.125,,0.603,0.298 +Congo (Brazzaville),2011,4.510,8.502,0.637,53.380,0.745,-0.137,0.833,0.601,0.288 +Congo (Brazzaville),2012,3.919,8.569,0.622,53.760,0.773,-0.144,0.800,0.547,0.323 +Congo (Brazzaville),2013,3.955,8.538,0.680,54.140,0.726,-0.107,0.752,0.599,0.291 +Congo (Brazzaville),2014,4.056,8.579,0.686,54.520,0.662,-0.140,0.808,0.558,0.400 +Congo (Brazzaville),2015,4.691,8.519,0.642,54.900,0.850,-0.129,0.841,0.555,0.261 +Congo (Brazzaville),2016,4.119,8.381,0.615,55.225,0.786,-0.091,0.790,0.586,0.304 +Congo (Brazzaville),2017,4.884,8.312,0.655,55.550,0.778,-0.148,0.763,0.574,0.382 +Congo (Brazzaville),2018,5.490,8.239,0.621,55.875,0.699,-0.105,0.738,0.571,0.448 +Congo (Brazzaville),2019,5.213,8.215,0.625,56.200,0.686,-0.059,0.741,0.594,0.405 +Congo (Brazzaville),2020,5.079,8.127,0.597,56.525,0.761,-0.024,0.728,0.572,0.435 +Congo (Brazzaville),2021,4.921,8.082,0.568,56.850,0.738,-0.023,0.733,0.568,0.420 +Congo (Brazzaville),2022,5.805,8.074,0.646,57.175,0.698,0.025,0.760,0.583,0.477 +Congo (Brazzaville),2023,4.954,8.086,0.561,57.500,0.702,-0.051,0.745,0.585,0.409 +Congo (Kinshasa),2009,3.984,6.699,0.733,49.400,0.556,-0.025,0.824,0.487,0.283 +Congo (Kinshasa),2011,4.517,6.769,0.744,50.400,0.631,-0.028,0.856,0.565,0.208 +Congo (Kinshasa),2012,4.639,6.803,0.770,50.900,0.557,-0.037,0.807,0.626,0.230 +Congo (Kinshasa),2013,4.497,6.851,0.830,51.400,0.480,0.009,0.913,0.556,0.187 +Congo (Kinshasa),2014,4.414,6.907,0.822,51.900,0.556,0.006,0.814,0.519,0.305 +Congo (Kinshasa),2015,3.903,6.940,0.767,52.400,0.574,-0.050,0.866,0.538,0.301 +Congo (Kinshasa),2016,4.522,6.929,0.864,52.825,0.637,-0.027,0.875,0.610,0.222 +Congo (Kinshasa),2017,4.311,6.931,0.670,53.250,0.704,0.066,0.809,0.541,0.404 +Congo (Kinshasa),2022,3.207,7.032,0.654,55.375,0.664,0.080,0.836,0.563,0.461 +Congo (Kinshasa),2023,3.383,7.076,0.572,55.800,0.687,0.152,0.837,0.546,0.497 +Costa Rica,2006,7.082,9.607,0.937,68.560,0.882,0.052,0.798,0.815,0.236 +Costa Rica,2007,7.432,9.672,0.918,68.720,0.923,0.089,0.820,0.826,0.240 +Costa Rica,2008,6.851,9.704,0.916,68.880,0.912,0.087,0.816,0.838,0.233 +Costa Rica,2009,7.615,9.682,0.900,69.040,0.886,0.057,0.787,0.840,0.217 +Costa Rica,2010,7.271,9.721,0.915,69.200,0.881,0.038,0.763,0.827,0.221 +Costa Rica,2011,7.229,9.752,0.892,69.360,0.926,-0.042,0.837,0.794,0.269 +Costa Rica,2012,7.272,9.788,0.902,69.520,0.929,0.037,0.794,0.837,0.263 +Costa Rica,2013,7.158,9.801,0.902,69.680,0.898,0.009,0.813,0.809,0.278 +Costa Rica,2014,7.247,9.824,0.914,69.840,0.927,0.000,0.788,0.797,0.290 +Costa Rica,2015,6.854,9.850,0.878,70.000,0.907,-0.068,0.761,0.811,0.286 +Costa Rica,2016,7.136,9.881,0.901,70.000,0.873,-0.042,0.781,0.830,0.281 +Costa Rica,2017,7.225,9.912,0.922,70.000,0.936,-0.086,0.742,0.791,0.275 +Costa Rica,2018,7.141,9.928,0.876,70.000,0.942,-0.117,0.781,0.802,0.326 +Costa Rica,2019,6.998,9.944,0.906,70.000,0.927,-0.156,0.836,0.791,0.303 +Costa Rica,2020,6.338,9.892,0.834,70.000,0.889,-0.139,0.772,0.759,0.350 +Costa Rica,2021,6.408,9.961,0.876,70.000,0.887,-0.029,0.782,0.774,0.318 +Costa Rica,2022,7.077,9.998,0.902,70.000,0.910,-0.049,0.751,0.793,0.272 +Costa Rica,2023,7.384,10.021,0.875,70.000,0.933,-0.067,0.767,0.806,0.282 +Croatia,2007,5.821,10.174,0.910,66.940,0.662,-0.099,0.934,0.550,0.337 +Croatia,2009,5.433,10.120,0.861,67.180,0.549,-0.278,0.958,0.557,0.272 +Croatia,2010,5.596,10.110,0.796,67.300,0.564,-0.244,0.973,0.554,0.259 +Croatia,2011,5.385,10.113,0.790,67.420,0.517,-0.205,0.977,0.552,0.273 +Croatia,2012,6.028,10.092,0.776,67.540,0.542,-0.250,0.924,0.572,0.271 +Croatia,2013,5.885,10.091,0.751,67.660,0.627,-0.211,0.936,0.554,0.285 +Croatia,2014,5.381,10.091,0.646,67.780,0.519,0.125,0.918,0.545,0.286 +Croatia,2015,5.205,10.124,0.768,67.900,0.694,-0.104,0.849,0.570,0.294 +Croatia,2016,5.417,10.166,0.798,68.075,0.672,-0.072,0.884,0.569,0.337 +Croatia,2017,5.343,10.211,0.770,68.250,0.716,-0.112,0.892,0.618,0.316 +Croatia,2018,5.536,10.248,0.910,68.425,0.691,-0.158,0.925,0.512,0.290 +Croatia,2019,5.626,10.287,0.936,68.600,0.739,-0.145,0.932,0.504,0.269 +Croatia,2020,6.508,10.202,0.923,68.775,0.837,-0.071,0.961,0.681,0.286 +Croatia,2021,6.287,10.367,0.918,68.950,0.842,0.001,0.934,0.640,0.274 +Croatia,2022,5.579,10.435,0.910,69.125,0.593,-0.213,0.875,0.573,0.267 +Croatia,2023,5.958,10.462,0.909,69.300,0.573,-0.205,0.810,0.610,0.230 +Cuba,2006,5.418,,0.970,68.000,0.281,,,0.596,0.277 +Cyprus,2006,6.238,10.567,0.878,70.160,0.836,0.012,0.712,0.704,0.253 +Cyprus,2009,6.833,10.559,0.812,70.640,0.775,0.048,0.801,0.668,0.329 +Cyprus,2010,6.387,10.556,0.822,70.800,0.755,0.066,0.833,0.699,0.296 +Cyprus,2011,6.690,10.534,0.844,70.960,0.745,0.173,0.841,0.682,0.272 +Cyprus,2012,6.181,10.484,0.767,71.120,0.725,0.092,0.871,0.687,0.369 +Cyprus,2013,5.439,10.418,0.744,71.280,0.656,0.096,0.867,0.657,0.420 +Cyprus,2014,5.627,10.411,0.770,71.440,0.715,0.054,0.868,0.661,0.397 +Cyprus,2015,5.439,10.451,0.770,71.600,0.628,0.107,0.893,0.660,0.383 +Cyprus,2016,5.795,10.510,0.786,71.800,0.756,-0.036,0.898,0.631,0.336 +Cyprus,2017,6.062,10.556,0.819,72.000,0.812,0.036,0.851,0.670,0.301 +Cyprus,2018,6.276,10.599,0.826,72.200,0.794,-0.031,0.848,0.663,0.298 +Cyprus,2019,6.137,10.639,0.776,72.400,0.740,-0.018,0.865,0.663,0.290 +Cyprus,2020,6.260,10.583,0.806,72.600,0.763,-0.086,0.816,0.671,0.284 +Cyprus,2021,6.269,10.638,0.855,72.800,0.718,-0.038,0.876,0.641,0.275 +Cyprus,2022,5.865,10.683,0.820,73.000,0.698,0.008,0.887,0.659,0.297 +Cyprus,2023,6.071,,0.803,73.200,0.730,,0.840,0.682,0.297 +Czechia,2005,6.439,10.322,0.919,67.100,0.865,,0.901,0.639,0.258 +Czechia,2007,6.500,10.433,0.900,67.340,0.799,-0.069,0.928,0.660,0.277 +Czechia,2010,6.250,10.419,0.934,67.700,0.779,-0.048,0.926,0.648,0.244 +Czechia,2011,6.331,10.434,0.914,67.820,0.787,-0.112,0.950,0.623,0.253 +Czechia,2012,6.334,10.425,0.912,67.940,0.740,-0.160,0.957,0.635,0.257 +Czechia,2013,6.698,10.424,0.888,68.060,0.726,-0.162,0.916,0.656,0.253 +Czechia,2014,6.484,10.445,0.878,68.180,0.800,-0.174,0.897,0.638,0.235 +Czechia,2015,6.608,10.496,0.911,68.300,0.808,-0.152,0.886,0.689,0.206 +Czechia,2016,6.736,10.519,0.931,68.425,0.850,-0.204,0.900,0.710,0.201 +Czechia,2017,6.790,10.567,0.901,68.550,0.832,-0.183,0.867,0.672,0.227 +Czechia,2018,7.034,10.595,0.929,68.675,0.790,-0.299,0.851,0.674,0.178 +Czechia,2020,6.897,10.562,0.964,68.925,0.906,-0.135,0.884,0.748,0.290 +Czechia,2021,6.942,10.615,0.950,69.050,0.891,0.157,0.863,0.716,0.240 +Czechia,2022,6.695,10.637,0.944,69.175,0.908,0.093,0.831,0.743,0.246 +Czechia,2023,6.827,10.639,0.927,69.300,0.906,0.025,0.832,0.750,0.254 +Denmark,2005,8.019,10.849,0.972,68.300,0.971,,0.237,0.777,0.154 +Denmark,2007,7.834,10.889,0.954,68.740,0.932,0.234,0.206,0.778,0.194 +Denmark,2008,7.971,10.878,0.954,68.960,0.970,0.266,0.248,0.759,0.163 +Denmark,2009,7.683,10.822,0.939,69.180,0.949,0.258,0.206,0.782,0.234 +Denmark,2010,7.771,10.836,0.975,69.400,0.944,0.237,0.175,0.796,0.155 +Denmark,2011,7.788,10.845,0.962,69.620,0.935,0.292,0.220,0.778,0.175 +Denmark,2012,7.520,10.844,0.951,69.840,0.933,0.133,0.187,0.783,0.209 +Denmark,2013,7.589,10.849,0.965,70.060,0.920,0.209,0.170,0.826,0.195 +Denmark,2014,7.508,10.860,0.956,70.280,0.942,0.112,0.237,0.780,0.233 +Denmark,2015,7.514,10.876,0.960,70.500,0.941,0.216,0.191,0.801,0.218 +Denmark,2016,7.558,10.900,0.954,70.625,0.948,0.132,0.210,0.786,0.208 +Denmark,2017,7.594,10.922,0.952,70.750,0.955,0.149,0.181,0.779,0.206 +Denmark,2018,7.649,10.936,0.958,70.875,0.935,0.012,0.151,0.773,0.206 +Denmark,2019,7.693,10.948,0.958,71.000,0.963,0.015,0.174,0.797,0.181 +Denmark,2020,7.515,10.924,0.947,71.125,0.938,0.045,0.214,0.753,0.227 +Denmark,2021,7.699,10.968,0.945,71.250,0.933,0.130,0.173,0.792,0.206 +Denmark,2022,7.545,10.997,0.970,71.375,0.930,0.222,0.203,0.787,0.205 +Denmark,2023,7.504,10.996,0.916,71.500,0.923,0.089,0.184,0.757,0.229 +Djibouti,2008,5.009,8.115,0.690,53.640,0.773,0.123,0.576,0.740,0.120 +Djibouti,2009,4.906,8.014,0.901,54.120,0.649,-0.007,0.634,0.630,0.232 +Djibouti,2010,5.006,7.934,,54.600,0.764,-0.072,0.597,, +Djibouti,2011,4.369,8.150,0.633,55.080,0.746,-0.082,0.519,0.543,0.181 +Dominican Republic,2006,5.088,9.306,0.919,65.360,0.858,0.033,0.755,0.733,0.274 +Dominican Republic,2007,5.081,9.365,0.848,65.120,0.886,-0.013,0.772,0.723,0.260 +Dominican Republic,2008,4.842,9.384,0.850,64.880,0.848,-0.050,0.728,0.654,0.329 +Dominican Republic,2009,5.432,9.381,0.878,64.640,0.863,-0.058,0.806,0.709,0.280 +Dominican Republic,2010,4.735,9.448,0.860,64.400,0.824,-0.080,0.780,0.707,0.282 +Dominican Republic,2011,5.397,9.465,0.872,64.160,0.848,0.009,0.788,0.738,0.300 +Dominican Republic,2012,4.753,9.479,0.879,63.920,0.840,-0.067,0.727,0.725,0.297 +Dominican Republic,2013,5.016,9.515,0.878,63.680,0.889,0.016,0.752,0.766,0.295 +Dominican Republic,2014,5.387,9.570,0.891,63.440,0.905,-0.025,0.760,0.772,0.300 +Dominican Republic,2015,5.062,9.625,0.893,63.200,0.856,-0.070,0.755,0.695,0.295 +Dominican Republic,2016,5.239,9.678,0.895,63.400,0.873,-0.085,0.737,0.725,0.278 +Dominican Republic,2017,5.605,9.713,0.894,63.600,0.855,-0.126,0.760,0.710,0.275 +Dominican Republic,2018,5.433,9.769,0.862,63.800,0.867,-0.155,0.762,0.719,0.291 +Dominican Republic,2019,6.004,9.808,0.884,64.000,0.877,-0.127,0.746,0.747,0.264 +Dominican Republic,2020,5.168,9.727,0.806,64.200,0.835,-0.128,0.636,0.724,0.314 +Dominican Republic,2021,6.031,9.832,0.857,64.400,0.859,-0.088,0.677,0.734,0.275 +Dominican Republic,2022,5.518,9.870,0.820,64.600,0.853,-0.086,0.656,0.723,0.306 +Dominican Republic,2023,5.921,9.900,0.860,64.800,0.867,-0.107,0.667,0.730,0.256 +Ecuador,2006,5.024,9.189,0.910,64.440,0.671,-0.097,0.901,0.785,0.357 +Ecuador,2007,4.996,9.194,0.839,64.780,0.670,-0.069,0.830,0.803,0.286 +Ecuador,2008,5.297,9.238,0.829,65.120,0.640,-0.100,0.801,0.811,0.283 +Ecuador,2009,6.022,9.227,0.779,65.460,0.737,-0.114,0.774,0.796,0.256 +Ecuador,2010,5.838,9.245,0.839,65.800,0.723,-0.069,0.806,0.771,0.220 +Ecuador,2011,5.795,9.305,0.818,66.140,0.788,-0.161,0.702,0.806,0.271 +Ecuador,2012,5.961,9.343,0.785,66.480,0.825,-0.089,0.730,0.767,0.333 +Ecuador,2013,6.019,9.376,0.801,66.820,0.787,-0.196,0.646,0.824,0.267 +Ecuador,2014,5.946,9.399,0.831,67.160,0.719,-0.173,0.661,0.841,0.306 +Ecuador,2015,5.964,9.385,0.856,67.500,0.801,-0.120,0.666,0.816,0.323 +Ecuador,2016,6.115,9.358,0.842,67.750,0.846,-0.021,0.774,0.807,0.365 +Ecuador,2017,5.840,9.366,0.849,68.000,0.879,-0.173,0.734,0.793,0.314 +Ecuador,2018,6.128,9.359,0.851,68.250,0.869,-0.105,0.831,0.817,0.328 +Ecuador,2019,5.809,9.341,0.808,68.500,0.830,-0.121,0.839,0.750,0.374 +Ecuador,2020,5.354,9.245,0.804,68.750,0.829,-0.163,0.855,0.755,0.416 +Ecuador,2021,5.435,9.275,0.786,69.000,0.821,-0.086,0.775,0.742,0.403 +Ecuador,2022,5.887,9.293,0.825,69.250,0.759,-0.083,0.866,0.777,0.356 +Ecuador,2023,5.852,9.307,0.782,69.500,0.731,-0.106,0.840,0.763,0.359 +Egypt,2005,5.168,9.042,0.848,61.400,0.817,,,0.689,0.346 +Egypt,2007,5.541,9.138,0.686,61.520,0.609,-0.126,,0.600,0.355 +Egypt,2008,4.632,9.187,0.738,61.580,,-0.093,0.914,0.627,0.301 +Egypt,2009,5.066,9.213,0.744,61.640,0.611,-0.105,0.801,0.549,0.339 +Egypt,2010,4.669,9.243,0.769,61.700,0.486,-0.081,0.826,0.491,0.276 +Egypt,2011,4.174,9.239,0.753,61.760,0.590,-0.157,0.859,0.456,0.353 +Egypt,2012,4.204,9.238,0.737,61.820,0.452,-0.143,0.880,0.458,0.398 +Egypt,2013,3.559,9.236,0.675,61.880,0.474,-0.147,0.913,0.487,0.483 +Egypt,2014,4.885,9.242,0.619,61.940,0.578,-0.132,0.749,0.477,0.327 +Egypt,2015,4.763,9.262,0.730,62.000,0.659,-0.094,0.684,0.554,0.344 +Egypt,2016,4.557,9.284,0.809,62.250,0.656,-0.147,0.818,0.538,0.370 +Egypt,2017,3.929,9.305,0.638,62.500,0.593,-0.158,,0.458,0.414 +Egypt,2018,4.005,9.338,0.759,62.750,0.682,-0.221,,0.407,0.285 +Egypt,2019,4.328,9.374,0.772,63.000,0.774,-0.204,,0.420,0.313 +Egypt,2020,4.472,9.392,0.673,63.250,0.770,-0.119,,0.543,0.442 +Egypt,2021,4.026,9.408,0.717,63.500,0.704,-0.233,0.580,0.387,0.325 +Egypt,2022,4.024,9.456,0.769,63.750,0.733,-0.214,,0.486,0.307 +Egypt,2023,3.881,9.480,0.730,64.000,0.625,-0.210,,0.436,0.352 +El Salvador,2006,5.701,8.885,0.878,65.120,0.683,-0.062,0.807,0.813,0.233 +El Salvador,2007,5.296,8.902,0.717,64.940,0.639,-0.021,0.785,0.833,0.220 +El Salvador,2008,5.191,8.919,0.747,64.760,0.636,-0.084,0.735,0.827,0.232 +El Salvador,2009,6.839,8.894,0.734,64.580,0.671,-0.110,0.648,0.841,0.243 +El Salvador,2010,6.740,8.911,0.757,64.400,0.669,-0.070,0.694,0.823,0.302 +El Salvador,2011,4.741,8.945,0.731,64.220,0.747,-0.133,0.707,0.830,0.336 +El Salvador,2012,5.934,8.968,0.806,64.040,0.683,-0.161,0.786,0.784,0.365 +El Salvador,2013,6.325,8.987,0.827,63.860,0.716,-0.156,0.772,0.801,0.317 +El Salvador,2014,5.857,9.000,0.798,63.680,0.778,-0.201,0.781,0.801,0.330 +El Salvador,2015,6.018,9.020,0.791,63.500,0.733,-0.163,0.805,0.816,0.333 +El Salvador,2016,6.140,9.042,0.794,63.850,0.800,-0.192,0.797,0.742,0.346 +El Salvador,2017,6.339,9.062,0.829,64.200,0.758,-0.179,0.778,0.800,0.268 +El Salvador,2018,6.241,9.084,0.820,64.550,0.863,-0.102,0.801,0.817,0.270 +El Salvador,2019,6.455,9.108,0.764,64.900,0.877,-0.116,0.682,0.826,0.271 +El Salvador,2020,5.462,9.023,0.696,65.250,0.924,-0.132,0.583,0.811,0.329 +El Salvador,2021,6.431,9.126,0.796,65.600,0.915,-0.085,0.663,0.826,0.290 +El Salvador,2022,6.492,9.148,0.772,65.950,0.914,-0.116,0.621,0.823,0.296 +El Salvador,2023,6.482,9.167,0.744,66.300,0.942,-0.104,0.496,0.812,0.318 +Estonia,2006,5.371,10.269,0.910,65.780,0.749,-0.270,0.797,0.589,0.215 +Estonia,2007,5.332,10.346,0.896,66.060,0.712,-0.252,0.743,0.589,0.176 +Estonia,2008,5.452,10.296,0.904,66.340,0.642,-0.223,0.663,0.595,0.218 +Estonia,2009,5.138,10.140,0.874,66.620,0.611,-0.235,0.793,0.591,0.243 +Estonia,2011,5.487,10.240,0.909,67.180,0.735,-0.173,0.687,0.641,0.205 +Estonia,2012,5.364,10.275,0.889,67.460,0.697,-0.197,0.793,0.627,0.199 +Estonia,2013,5.367,10.293,0.901,67.740,0.754,-0.206,0.726,0.651,0.199 +Estonia,2014,5.556,10.325,0.917,68.020,0.773,-0.158,0.652,0.620,0.203 +Estonia,2015,5.629,10.343,0.918,68.300,0.815,-0.169,0.569,0.649,0.183 +Estonia,2016,5.650,10.374,0.938,68.525,0.843,-0.155,0.639,0.657,0.177 +Estonia,2017,5.938,10.429,0.936,68.750,0.862,-0.107,0.668,0.740,0.160 +Estonia,2018,6.091,10.463,0.933,68.975,0.886,-0.147,0.621,0.730,0.163 +Estonia,2019,6.035,10.496,0.934,69.200,0.887,-0.101,0.576,0.738,0.156 +Estonia,2020,6.453,10.488,0.958,69.425,0.954,-0.090,0.398,0.762,0.188 +Estonia,2021,6.554,10.564,0.946,69.650,0.926,0.046,0.441,0.761,0.176 +Estonia,2022,6.357,10.541,0.933,69.875,0.904,0.136,0.390,0.767,0.187 +Estonia,2023,6.430,10.517,0.958,70.100,0.915,0.032,0.334,0.765,0.182 +Eswatini,2011,4.867,8.902,0.837,42.500,0.607,-0.069,0.917,0.756,0.251 +Eswatini,2018,4.212,9.029,0.779,49.300,0.710,-0.182,0.692,0.739,0.252 +Eswatini,2019,4.396,9.048,0.759,50.100,0.597,-0.195,0.724,0.726,0.280 +Eswatini,2022,3.502,9.119,0.712,52.500,0.539,-0.149,0.774,0.661,0.394 +Ethiopia,2012,4.561,7.252,0.659,56.320,0.776,-0.047,,0.556,0.137 +Ethiopia,2013,4.445,7.325,0.602,56.980,0.707,-0.011,0.750,0.570,0.213 +Ethiopia,2014,4.507,7.396,0.640,57.640,0.694,0.076,0.702,0.644,0.303 +Ethiopia,2015,4.573,7.468,0.626,58.300,0.803,0.109,0.567,0.623,0.237 +Ethiopia,2016,4.298,7.531,0.719,58.700,0.744,0.035,0.703,0.627,0.254 +Ethiopia,2017,4.180,7.595,0.734,59.100,0.717,-0.002,0.757,0.514,0.304 +Ethiopia,2018,4.379,7.634,0.740,59.500,0.740,0.036,0.799,0.562,0.272 +Ethiopia,2019,4.100,7.688,0.748,59.900,0.754,0.049,0.732,0.519,0.283 +Ethiopia,2020,4.549,7.720,0.823,60.300,0.769,0.183,0.784,0.615,0.252 +Ethiopia,2022,3.628,7.775,0.740,61.100,0.674,0.357,0.793,0.560,0.335 +Ethiopia,2023,4.093,7.809,0.670,61.500,0.631,0.212,0.800,0.538,0.299 +Finland,2006,7.672,10.745,0.965,68.720,0.969,-0.011,0.132,0.683,0.172 +Finland,2008,7.671,10.796,0.951,69.160,0.934,0.022,0.217,0.691,0.144 +Finland,2010,7.393,10.734,0.935,69.600,0.916,0.085,0.413,0.758,0.202 +Finland,2011,7.354,10.754,0.938,69.820,0.936,0.095,0.320,0.709,0.205 +Finland,2012,7.420,10.735,0.928,70.040,0.921,-0.007,0.361,0.742,0.202 +Finland,2013,7.445,10.722,0.941,70.260,0.919,0.034,0.306,0.752,0.195 +Finland,2014,7.385,10.714,0.952,70.480,0.933,-0.007,0.265,0.766,0.199 +Finland,2015,7.448,10.716,0.948,70.700,0.930,0.105,0.223,0.736,0.191 +Finland,2016,7.660,10.741,0.954,70.775,0.948,-0.033,0.250,0.769,0.182 +Finland,2017,7.788,10.770,0.964,70.850,0.962,-0.008,0.192,0.756,0.176 +Finland,2018,7.858,10.780,0.962,70.925,0.938,-0.133,0.199,0.749,0.182 +Finland,2019,7.780,10.791,0.937,71.000,0.948,-0.058,0.195,0.732,0.181 +Finland,2020,7.889,10.766,0.962,71.075,0.962,-0.123,0.164,0.748,0.193 +Finland,2021,7.794,10.794,0.970,71.150,0.963,-0.039,0.192,0.752,0.175 +Finland,2022,7.729,10.811,0.974,71.225,0.959,0.101,0.190,0.741,0.191 +Finland,2023,7.699,10.808,0.947,71.300,0.943,-0.001,0.185,0.717,0.173 +France,2005,7.093,10.637,0.940,70.700,0.895,,0.688,0.681,0.225 +France,2006,6.583,10.654,0.944,70.800,0.789,0.120,0.699,0.694,0.289 +France,2008,7.008,10.669,0.935,71.000,0.833,-0.037,0.669,0.702,0.281 +France,2009,6.283,10.635,0.918,71.100,0.798,-0.088,0.654,0.691,0.303 +France,2010,6.798,10.649,0.943,71.200,0.850,-0.109,0.623,0.729,0.261 +France,2011,6.959,10.666,0.921,71.300,0.903,-0.108,0.627,0.718,0.281 +France,2012,6.649,10.664,0.937,71.400,0.841,-0.155,0.608,0.705,0.253 +France,2013,6.667,10.665,0.908,71.500,0.878,-0.130,0.699,0.741,0.205 +France,2014,6.467,10.669,0.878,71.600,0.803,-0.124,0.656,0.759,0.216 +France,2015,6.358,10.677,0.896,71.700,0.817,-0.145,0.641,0.740,0.215 +France,2016,6.475,10.685,0.885,71.800,0.787,-0.097,0.623,0.715,0.270 +France,2017,6.635,10.705,0.931,71.900,0.834,-0.129,0.601,0.716,0.242 +France,2018,6.666,10.720,0.921,72.000,0.816,-0.143,0.582,0.705,0.282 +France,2019,6.690,10.735,0.958,72.100,0.827,-0.139,0.568,0.693,0.250 +France,2020,6.714,10.651,0.947,72.200,0.823,-0.176,0.565,0.690,0.231 +France,2021,6.656,10.714,0.915,72.300,0.837,-0.104,0.561,0.685,0.268 +France,2022,6.614,10.737,0.866,72.400,0.798,-0.027,0.533,0.688,0.249 +France,2023,6.557,10.742,0.850,72.500,0.776,0.010,0.558,0.676,0.228 +Gabon,2011,4.255,9.557,0.653,54.460,0.772,-0.213,0.851,0.564,0.264 +Gabon,2012,3.972,9.573,0.736,54.920,0.566,-0.197,0.810,0.504,0.266 +Gabon,2013,3.800,9.593,0.733,55.380,0.682,-0.148,0.780,0.519,0.287 +Gabon,2014,3.918,9.601,0.829,55.840,0.607,-0.201,0.782,0.533,0.293 +Gabon,2015,4.661,9.609,0.756,56.300,0.671,-0.196,0.867,0.600,0.372 +Gabon,2016,4.832,9.601,0.780,56.625,0.699,-0.207,0.817,0.625,0.432 +Gabon,2017,4.782,9.580,0.807,56.950,0.652,-0.231,0.868,0.638,0.446 +Gabon,2018,4.783,9.565,0.785,57.275,0.719,-0.200,0.823,0.614,0.418 +Gabon,2019,4.914,9.580,0.763,57.600,0.736,-0.206,0.846,0.638,0.413 +Gabon,2020,4.887,9.540,0.701,57.925,0.528,-0.194,0.789,0.568,0.416 +Gabon,2021,5.075,9.533,0.754,58.250,0.699,-0.207,0.766,0.620,0.362 +Gabon,2022,5.140,9.543,0.775,58.575,0.699,-0.167,0.803,0.661,0.414 +Gabon,2023,5.104,9.554,0.735,58.900,0.722,-0.160,0.822,0.620,0.424 +Gambia,2017,4.118,7.564,0.697,56.400,0.812,0.111,0.572,0.770,0.277 +Gambia,2018,4.922,7.607,0.685,56.700,0.719,0.440,0.691,0.759,0.379 +Gambia,2019,5.164,7.642,0.694,57.000,0.677,0.409,0.798,0.718,0.401 +Gambia,2022,4.279,7.662,0.588,57.900,0.599,0.360,0.884,0.722,0.438 +Gambia,2023,4.691,7.686,0.651,58.200,0.727,0.430,0.852,0.719,0.291 +Georgia,2006,3.675,8.993,0.647,63.300,0.553,-0.273,0.752,0.353,0.269 +Georgia,2007,3.707,9.117,0.548,63.400,0.464,-0.272,0.697,0.351,0.236 +Georgia,2008,4.156,9.144,0.608,63.500,0.614,-0.230,0.498,0.371,0.262 +Georgia,2009,3.801,9.116,0.544,63.600,0.495,-0.238,0.535,0.391,0.242 +Georgia,2010,4.102,9.184,0.540,63.700,0.558,-0.253,0.460,0.402,0.243 +Georgia,2011,4.203,9.263,0.503,63.800,0.632,-0.260,0.353,0.421,0.247 +Georgia,2012,4.254,9.332,0.533,63.900,0.659,-0.275,0.321,0.443,0.250 +Georgia,2013,4.349,9.371,0.559,64.000,0.722,-0.260,0.349,0.467,0.200 +Georgia,2014,4.288,9.414,0.558,64.100,0.720,-0.239,0.416,0.458,0.204 +Georgia,2015,4.122,9.442,0.517,64.200,0.640,-0.210,0.502,0.448,0.233 +Georgia,2016,4.448,9.470,0.533,64.325,0.606,-0.255,0.561,0.475,0.223 +Georgia,2017,4.451,9.517,0.590,64.450,0.821,-0.250,0.590,0.496,0.210 +Georgia,2018,4.659,9.565,0.617,64.575,0.775,-0.238,0.755,0.479,0.244 +Georgia,2019,4.892,9.615,0.675,64.700,0.811,-0.265,0.647,0.503,0.244 +Georgia,2020,5.123,9.544,0.718,64.825,0.764,-0.225,0.583,0.573,0.295 +Georgia,2021,4.911,9.648,0.671,64.950,0.777,-0.290,0.723,0.510,0.240 +Georgia,2022,5.293,9.743,0.754,65.075,0.821,-0.255,0.655,0.503,0.233 +Georgia,2023,5.351,9.785,0.779,65.200,0.877,-0.268,0.706,0.542,0.231 +Germany,2005,6.620,10.691,0.963,69.900,0.847,,0.781,0.685,0.197 +Germany,2007,6.417,10.760,0.926,69.940,0.801,0.161,0.792,0.647,0.231 +Germany,2008,6.522,10.771,0.923,69.960,0.766,,0.758,0.672,0.220 +Germany,2009,6.641,10.715,0.935,69.980,0.844,0.121,0.690,0.684,0.206 +Germany,2010,6.725,10.758,0.939,70.000,0.843,0.089,0.688,0.698,0.182 +Germany,2011,6.621,10.815,0.947,70.020,0.906,0.027,0.677,0.686,0.165 +Germany,2012,6.702,10.817,0.926,70.040,0.904,0.065,0.679,0.699,0.170 +Germany,2013,6.965,10.819,0.931,70.060,0.894,0.018,0.566,0.693,0.205 +Germany,2014,6.984,10.837,0.938,70.080,0.899,0.082,0.474,0.739,0.188 +Germany,2015,7.037,10.843,0.926,70.100,0.889,0.172,0.412,0.722,0.203 +Germany,2016,6.874,10.857,0.906,70.300,0.871,0.142,0.446,0.709,0.187 +Germany,2017,7.074,10.879,0.892,70.500,0.841,0.139,0.414,0.707,0.196 +Germany,2018,7.118,10.886,0.920,70.700,0.877,0.028,0.496,0.740,0.243 +Germany,2019,7.035,10.894,0.886,70.900,0.885,0.051,0.462,0.712,0.226 +Germany,2020,7.312,10.856,0.905,71.100,0.864,-0.068,0.424,0.698,0.206 +Germany,2021,6.755,10.881,0.868,71.300,0.778,0.074,0.418,0.703,0.251 +Germany,2022,6.608,10.889,0.916,71.500,0.895,0.080,0.417,0.668,0.201 +Germany,2023,6.792,10.878,0.895,71.700,0.845,0.106,0.460,0.706,0.231 +Ghana,2006,4.535,8.067,0.728,52.540,0.849,0.208,0.814,0.637,0.198 +Ghana,2007,5.220,8.084,0.730,52.980,0.891,0.133,0.771,0.658,0.217 +Ghana,2008,4.965,8.145,0.622,53.420,0.838,0.115,0.863,0.674,0.172 +Ghana,2009,4.198,8.167,0.633,53.860,0.757,0.000,0.890,0.714,0.198 +Ghana,2010,4.606,8.219,0.739,54.300,0.891,0.069,0.875,0.693,0.184 +Ghana,2011,5.608,8.326,0.724,54.740,0.852,0.006,0.790,0.658,0.209 +Ghana,2012,5.057,8.390,0.685,55.180,0.679,0.035,0.898,0.709,0.152 +Ghana,2013,4.965,8.436,0.676,55.620,0.794,-0.070,0.880,0.660,0.211 +Ghana,2014,3.860,8.440,0.651,56.060,0.677,-0.004,0.913,0.682,0.280 +Ghana,2015,3.986,8.437,0.687,56.500,0.852,-0.043,0.945,0.675,0.265 +Ghana,2016,4.514,8.447,0.647,56.875,0.751,0.085,0.894,0.659,0.305 +Ghana,2017,5.481,8.503,0.669,57.250,0.783,0.074,0.839,0.715,0.248 +Ghana,2018,5.004,8.542,0.761,57.625,0.817,0.058,0.846,0.716,0.250 +Ghana,2019,4.967,8.584,0.746,58.000,0.787,0.111,0.857,0.645,0.270 +Ghana,2020,5.319,8.569,0.643,58.375,0.824,0.196,0.847,0.675,0.253 +Ghana,2021,4.378,8.601,0.633,58.750,0.730,0.105,0.888,0.588,0.295 +Ghana,2022,4.191,8.613,0.628,59.125,0.786,0.114,0.909,0.620,0.292 +Ghana,2023,4.298,8.610,0.661,59.500,0.834,0.093,0.892,0.638,0.254 +Greece,2005,6.006,10.454,0.837,69.600,0.734,,0.861,0.598,0.264 +Greece,2007,6.647,10.535,0.808,69.760,0.575,-0.196,0.845,0.629,0.222 +Greece,2009,6.039,10.483,0.793,69.920,0.443,-0.298,0.959,0.614,0.254 +Greece,2010,5.840,10.425,0.868,70.000,0.484,-0.308,0.954,0.581,0.292 +Greece,2011,5.372,10.320,0.852,70.080,0.528,-0.321,0.941,0.552,0.323 +Greece,2012,5.096,10.251,0.812,70.160,0.373,-0.310,0.959,0.544,0.352 +Greece,2013,4.720,10.233,0.687,70.240,0.426,-0.277,0.941,0.571,0.482 +Greece,2014,4.756,10.245,0.832,70.320,0.369,-0.293,0.930,0.597,0.385 +Greece,2015,5.623,10.249,0.835,70.400,0.532,-0.277,0.824,0.637,0.277 +Greece,2016,5.303,10.248,0.803,70.525,0.482,-0.265,0.898,0.594,0.336 +Greece,2017,5.148,10.261,0.753,70.650,0.438,-0.295,0.872,0.516,0.333 +Greece,2018,5.409,10.280,0.794,70.775,0.564,-0.340,0.860,0.564,0.255 +Greece,2019,5.952,10.300,0.891,70.900,0.614,-0.293,0.848,0.560,0.236 +Greece,2020,5.788,10.207,0.779,71.025,0.565,-0.246,0.764,0.629,0.322 +Greece,2021,6.104,10.294,0.850,71.150,0.574,-0.161,0.752,0.624,0.311 +Greece,2022,5.900,10.358,0.875,71.275,0.563,-0.318,0.874,0.589,0.183 +Greece,2023,5.796,10.387,0.818,71.400,0.589,-0.223,0.805,0.608,0.311 +Guatemala,2006,5.901,8.850,0.830,58.980,0.663,0.167,0.706,0.789,0.287 +Guatemala,2007,6.330,8.891,0.866,59.260,0.628,0.130,0.810,0.790,0.224 +Guatemala,2008,6.414,8.905,0.866,59.540,0.630,0.200,0.796,0.800,0.234 +Guatemala,2009,6.452,8.890,0.834,59.820,0.643,0.191,0.755,0.814,0.240 +Guatemala,2010,6.290,8.901,0.859,60.100,0.696,0.161,0.795,0.805,0.236 +Guatemala,2011,5.743,8.923,0.768,60.380,0.763,0.003,0.863,0.792,0.289 +Guatemala,2012,5.856,8.935,0.802,60.660,0.865,0.015,0.821,0.808,0.349 +Guatemala,2013,5.985,8.953,0.830,60.940,0.884,0.039,0.817,0.822,0.333 +Guatemala,2014,6.536,8.980,0.834,61.220,0.843,0.102,0.804,0.816,0.305 +Guatemala,2015,6.465,9.003,0.823,61.500,0.869,0.046,0.822,0.826,0.311 +Guatemala,2016,6.359,9.013,0.811,61.700,0.863,0.006,0.812,0.815,0.321 +Guatemala,2017,6.325,9.027,0.826,61.900,0.915,-0.064,0.800,0.819,0.308 +Guatemala,2018,6.627,9.044,0.841,62.100,0.910,-0.016,0.765,0.827,0.262 +Guatemala,2019,6.262,9.068,0.774,62.300,0.901,-0.068,0.773,0.820,0.311 +Guatemala,2022,6.150,9.123,0.806,62.900,0.856,-0.060,0.835,0.835,0.263 +Guatemala,2023,6.421,9.140,0.796,63.100,0.873,-0.049,0.812,0.843,0.303 +Guinea,2011,4.045,7.556,0.598,51.020,0.797,0.036,0.743,0.670,0.260 +Guinea,2012,3.652,7.589,0.542,51.140,0.646,-0.003,0.794,0.657,0.285 +Guinea,2013,3.902,7.604,0.567,51.260,0.693,0.087,0.815,0.606,0.348 +Guinea,2014,3.412,7.615,0.638,51.380,0.684,0.002,0.705,0.643,0.351 +Guinea,2015,3.505,7.627,0.579,51.500,0.666,0.003,0.762,0.658,0.268 +Guinea,2016,3.603,7.704,0.675,51.950,0.726,-0.060,0.803,0.676,0.374 +Guinea,2017,4.874,7.776,0.634,52.400,0.738,0.034,0.750,0.702,0.422 +Guinea,2018,5.252,7.813,0.630,52.850,0.731,0.088,0.778,0.727,0.440 +Guinea,2019,4.768,7.842,0.655,53.300,0.691,0.092,0.756,0.670,0.473 +Guinea,2020,4.972,7.865,0.732,53.750,0.598,0.075,0.790,0.706,0.346 +Guinea,2021,4.945,7.879,0.627,54.200,0.676,0.157,0.784,0.672,0.450 +Guinea,2022,5.317,7.901,0.582,54.650,0.729,0.136,0.770,0.699,0.492 +Guinea,2023,4.827,7.932,0.577,55.100,0.743,0.187,0.791,0.652,0.516 +Guyana,2007,5.993,9.089,0.849,56.240,0.694,0.082,0.836,0.761,0.296 +Haiti,2006,3.754,7.976,0.694,6.720,0.449,0.355,0.854,0.583,0.332 +Haiti,2008,3.846,8.016,0.679,17.360,0.465,0.213,0.812,0.573,0.256 +Haiti,2010,3.766,7.987,0.554,28.000,0.373,0.167,0.848,0.495,0.293 +Haiti,2011,4.845,8.026,0.567,33.320,0.413,0.194,0.682,0.550,0.245 +Haiti,2012,4.413,8.015,0.749,38.640,0.482,0.243,0.717,0.557,0.284 +Haiti,2013,4.622,8.043,0.648,43.960,0.610,0.243,0.669,0.528,0.327 +Haiti,2014,3.889,8.057,0.554,49.280,0.509,0.238,0.708,0.573,0.327 +Haiti,2015,3.570,8.056,0.564,54.600,0.398,0.259,0.777,0.598,0.333 +Haiti,2016,3.352,8.060,0.584,54.900,0.304,0.244,0.839,0.532,0.367 +Haiti,2017,3.824,8.071,0.647,55.200,0.484,0.333,0.647,0.570,0.322 +Haiti,2018,3.615,8.074,0.538,55.500,0.591,0.374,0.720,0.581,0.359 +Honduras,2006,5.397,8.448,0.933,63.100,0.650,0.085,0.844,0.837,0.155 +Honduras,2007,5.097,8.485,0.819,62.900,0.676,0.226,0.826,0.712,0.199 +Honduras,2008,5.420,8.504,0.828,62.700,0.687,0.219,0.863,0.719,0.206 +Honduras,2009,6.033,8.458,0.824,62.500,0.661,0.114,0.857,0.745,0.261 +Honduras,2010,5.866,8.474,0.803,62.300,0.646,0.101,0.820,0.745,0.260 +Honduras,2011,4.961,8.492,0.766,62.100,0.783,0.091,0.884,0.757,0.307 +Honduras,2012,4.602,8.513,0.779,61.900,0.700,-0.007,0.871,0.796,0.294 +Honduras,2013,4.713,8.521,0.792,61.700,0.698,-0.031,0.868,0.795,0.283 +Honduras,2014,5.056,8.533,0.790,61.500,0.696,0.011,0.834,0.794,0.299 +Honduras,2015,4.845,8.553,0.772,61.300,0.534,-0.101,0.848,0.829,0.311 +Honduras,2016,5.648,8.573,0.774,61.725,0.850,0.076,0.793,0.790,0.297 +Honduras,2017,6.020,8.603,0.843,62.150,0.898,0.068,0.783,0.796,0.248 +Honduras,2018,5.908,8.624,0.827,62.575,0.872,0.095,0.804,0.822,0.287 +Honduras,2019,5.930,8.633,0.797,63.000,0.846,0.059,0.815,0.789,0.279 +Honduras,2021,6.114,8.626,0.806,63.850,0.835,0.111,0.847,0.808,0.269 +Honduras,2022,5.932,8.650,0.729,64.275,0.851,0.078,0.834,0.775,0.289 +Honduras,2023,5.861,8.670,0.731,64.700,0.878,0.079,0.784,0.767,0.285 +Hong Kong S.A.R. of China,2006,5.511,10.746,0.812,,0.910,0.150,0.356,0.591,0.236 +Hong Kong S.A.R. of China,2008,5.137,10.816,0.840,,0.922,0.290,0.274,0.575,0.237 +Hong Kong S.A.R. of China,2009,5.397,10.788,0.835,,0.918,0.302,0.272,0.606,0.210 +Hong Kong S.A.R. of China,2010,5.643,10.847,0.857,,0.890,0.326,0.256,0.601,0.183 +Hong Kong S.A.R. of China,2011,5.474,10.887,0.846,,0.894,0.228,0.245,0.582,0.196 +Hong Kong S.A.R. of China,2012,5.484,10.893,0.826,,0.880,0.216,0.380,0.580,0.183 +Hong Kong S.A.R. of China,2014,5.458,10.939,0.834,,0.843,0.218,0.423,0.602,0.243 +Hong Kong S.A.R. of China,2016,5.498,10.970,0.832,,0.800,0.094,0.403,0.569,0.213 +Hong Kong S.A.R. of China,2017,5.362,10.999,0.831,,0.831,0.134,0.416,0.536,0.201 +Hong Kong S.A.R. of China,2019,5.659,10.995,0.856,,0.727,0.062,0.432,0.519,0.358 +Hong Kong S.A.R. of China,2020,5.295,10.931,0.813,,0.705,-0.076,0.380,0.522,0.210 +Hong Kong S.A.R. of China,2021,5.322,11.003,0.821,,0.669,0.021,0.390,0.534,0.224 +Hong Kong S.A.R. of China,2022,5.311,10.976,0.803,,0.697,0.040,0.383,0.549,0.204 +Hungary,2005,5.194,10.103,0.930,65.000,0.697,,0.903,0.578,0.290 +Hungary,2007,4.954,10.147,0.931,65.320,0.538,-0.166,0.895,0.600,0.230 +Hungary,2009,4.895,10.092,0.901,65.640,0.464,-0.131,0.915,0.575,0.228 +Hungary,2010,4.725,10.105,0.896,65.800,0.514,-0.151,0.983,0.574,0.235 +Hungary,2011,4.918,10.127,0.894,65.960,0.631,-0.095,0.940,0.586,0.305 +Hungary,2012,4.683,10.119,0.906,66.120,0.569,-0.142,0.930,0.582,0.315 +Hungary,2013,4.914,10.140,0.877,66.280,0.674,-0.119,0.912,0.647,0.307 +Hungary,2014,5.181,10.184,0.845,66.440,0.494,-0.156,0.855,0.578,0.238 +Hungary,2015,5.344,10.223,0.859,66.600,0.558,-0.204,0.908,0.650,0.245 +Hungary,2016,5.449,10.248,0.900,66.750,0.554,-0.193,0.924,0.590,0.243 +Hungary,2017,6.065,10.292,0.877,66.900,0.661,-0.145,0.886,0.644,0.181 +Hungary,2018,5.936,10.346,0.941,67.050,0.693,-0.249,0.911,0.595,0.201 +Hungary,2019,6.000,10.393,0.947,67.200,0.798,-0.201,0.884,0.653,0.180 +Hungary,2020,6.038,10.349,0.943,67.350,0.771,-0.127,0.836,0.662,0.240 +Hungary,2021,6.227,10.423,0.948,67.500,0.727,-0.046,0.832,0.668,0.192 +Hungary,2022,5.861,10.470,0.937,67.650,0.776,-0.009,0.848,0.628,0.250 +Hungary,2023,5.965,10.473,0.954,67.800,0.755,-0.002,0.847,0.673,0.189 +Iceland,2008,6.888,10.878,0.977,71.200,0.885,0.265,0.708,0.851,0.153 +Iceland,2012,7.591,10.788,0.979,71.600,0.905,0.235,0.759,0.817,0.157 +Iceland,2013,7.501,10.823,0.967,71.700,0.923,0.299,0.713,0.802,0.156 +Iceland,2015,7.498,10.862,0.980,71.900,0.940,0.294,0.639,0.794,0.180 +Iceland,2016,7.510,10.909,0.985,71.925,0.952,0.274,0.719,0.808,0.158 +Iceland,2017,7.476,10.927,0.967,71.950,0.939,0.240,0.727,0.823,0.148 +Iceland,2019,7.533,10.943,0.982,72.000,0.959,,0.699,0.787,0.178 +Iceland,2020,7.575,10.852,0.983,72.025,0.949,0.152,0.644,0.808,0.172 +Iceland,2021,7.565,10.878,0.980,72.050,0.923,0.257,0.664,0.806,0.159 +Iceland,2022,7.449,10.916,0.985,72.075,0.936,0.222,0.692,0.768,0.178 +Iceland,2023,7.562,10.934,0.979,72.100,0.918,0.299,0.697,0.793,0.185 +India,2006,5.348,8.141,0.707,55.860,0.774,,0.855,0.576,0.199 +India,2007,5.027,8.200,0.569,56.220,0.729,-0.056,0.862,0.541,0.253 +India,2008,5.146,8.216,0.684,56.580,0.756,-0.077,0.891,0.573,0.259 +India,2009,4.522,8.278,0.653,56.940,0.679,-0.031,0.895,0.639,0.301 +India,2010,4.989,8.346,0.605,57.300,0.783,0.053,0.863,0.579,0.267 +India,2011,4.635,8.383,0.553,57.660,0.838,-0.043,0.908,0.480,0.232 +India,2012,4.720,8.423,0.511,58.020,0.609,0.062,0.830,0.544,0.295 +India,2013,4.428,8.472,0.553,58.380,0.740,0.079,0.832,0.608,0.330 +India,2014,4.424,8.531,0.621,58.740,0.809,-0.031,0.832,0.651,0.285 +India,2015,4.342,8.596,0.610,59.100,0.777,-0.010,0.776,0.657,0.322 +India,2016,4.179,8.664,0.614,59.400,0.820,0.041,0.765,0.646,0.346 +India,2017,4.046,8.718,0.607,59.700,0.886,-0.046,0.781,0.579,0.318 +India,2018,3.818,8.770,0.638,60.000,0.890,0.080,0.805,0.591,0.357 +India,2019,3.249,8.797,0.561,60.300,0.876,0.108,0.752,0.560,0.466 +India,2020,4.224,8.728,0.616,60.600,0.906,0.068,0.780,0.686,0.383 +India,2021,3.558,8.806,0.570,60.900,0.866,0.052,0.757,0.547,0.429 +India,2022,3.930,8.867,0.608,61.200,0.893,0.085,0.771,0.596,0.432 +India,2023,4.676,8.919,0.633,61.500,0.900,0.121,0.770,0.699,0.389 +Indonesia,2006,4.947,8.839,0.771,60.320,0.713,0.343,0.915,0.715,0.266 +Indonesia,2007,5.101,8.888,0.704,60.540,0.603,0.307,0.960,0.696,0.242 +Indonesia,2008,4.815,8.933,0.675,60.760,0.596,0.160,0.968,0.675,0.239 +Indonesia,2009,5.472,8.966,0.779,60.980,0.784,0.186,0.911,0.768,0.193 +Indonesia,2010,5.457,9.013,0.816,61.200,0.700,0.443,0.954,0.717,0.218 +Indonesia,2011,5.173,9.061,0.825,61.420,0.878,0.433,0.962,0.748,0.273 +Indonesia,2012,5.368,9.107,0.834,61.640,0.770,0.349,0.962,0.764,0.229 +Indonesia,2013,5.292,9.149,0.794,61.860,0.781,0.371,0.973,0.777,0.249 +Indonesia,2014,5.597,9.186,0.905,62.080,0.719,0.403,0.970,0.757,0.242 +Indonesia,2015,5.043,9.222,0.809,62.300,0.779,0.466,0.946,0.796,0.274 +Indonesia,2016,5.136,9.261,0.792,62.425,0.830,0.494,0.890,0.748,0.342 +Indonesia,2017,5.098,9.300,0.796,62.550,0.865,0.482,0.900,0.781,0.319 +Indonesia,2018,5.340,9.341,0.809,62.675,0.879,0.506,0.868,0.796,0.296 +Indonesia,2019,5.347,9.381,0.802,62.800,0.866,0.549,0.861,0.800,0.302 +Indonesia,2020,4.828,9.351,0.751,62.925,0.853,0.529,0.914,0.742,0.351 +Indonesia,2021,5.433,9.381,0.817,63.050,0.885,0.540,0.845,0.799,0.273 +Indonesia,2022,5.585,9.426,0.834,63.175,0.903,0.516,0.862,0.818,0.269 +Indonesia,2023,5.695,9.466,0.781,63.300,0.900,0.590,0.866,0.814,0.289 +Iran,2005,5.308,9.498,0.766,64.300,0.651,,0.636,0.515,0.456 +Iran,2007,5.336,9.595,0.718,64.580,0.533,0.043,0.872,0.553,0.361 +Iran,2008,5.129,9.584,0.633,64.720,0.601,0.040,0.868,0.541,0.345 +Iran,2011,4.768,9.636,0.582,65.140,0.798,0.188,0.665,0.513,0.359 +Iran,2012,4.609,9.585,0.600,65.280,0.764,,0.678,0.529,0.525 +Iran,2013,5.140,9.555,0.664,65.420,0.730,0.202,0.685,0.575,0.552 +Iran,2014,4.682,9.585,0.644,65.560,0.767,0.227,0.640,0.550,0.512 +Iran,2015,4.750,9.548,0.572,65.700,0.780,0.164,0.699,0.548,0.520 +Iran,2016,4.653,9.614,0.566,65.850,0.773,0.176,0.713,0.592,0.526 +Iran,2017,4.717,9.627,0.714,66.000,0.731,0.210,0.715,0.590,0.439 +Iran,2018,4.278,9.591,0.674,66.150,0.603,0.073,0.703,0.482,0.493 +Iran,2019,5.006,9.553,0.698,66.300,0.623,0.128,0.728,0.525,0.449 +Iran,2020,4.865,9.577,0.757,66.450,0.600,0.130,0.710,0.505,0.470 +Iran,2021,4.788,9.616,0.771,66.600,0.609,0.172,0.761,0.518,0.427 +Iran,2022,4.977,9.636,0.800,66.750,0.570,0.209,0.766,0.521,0.466 +Iran,2023,5.004,9.651,0.809,66.900,0.615,0.254,0.764,0.533,0.425 +Iraq,2008,4.590,8.982,0.744,60.940,0.386,-0.061,0.910,0.532,0.448 +Iraq,2009,4.775,8.979,0.862,60.920,0.431,-0.198,0.854,0.504,0.404 +Iraq,2010,5.065,9.009,0.854,60.900,0.419,-0.123,0.859,0.497,0.431 +Iraq,2011,4.725,9.047,0.751,60.880,0.347,-0.068,0.780,0.473,0.557 +Iraq,2012,4.660,9.133,0.730,60.860,0.315,-0.018,0.789,0.410,0.449 +Iraq,2013,4.725,9.159,0.728,60.840,,-0.047,0.710,,0.554 +Iraq,2014,4.542,9.126,0.725,60.820,0.646,0.002,0.726,0.539,0.564 +Iraq,2015,4.493,9.145,0.684,60.800,0.599,0.021,0.762,0.478,0.581 +Iraq,2016,4.413,9.250,0.719,61.275,0.666,-0.050,0.799,0.471,0.570 +Iraq,2017,4.462,9.208,0.695,61.750,0.628,0.001,0.757,0.487,0.591 +Iraq,2018,4.886,9.210,0.764,62.225,0.598,-0.069,0.887,0.552,0.482 +Iraq,2020,4.785,9.088,0.708,63.175,0.700,-0.021,0.849,0.585,0.532 +Iraq,2021,5.094,9.081,0.730,63.650,0.594,0.006,0.901,0.577,0.474 +Iraq,2022,4.928,9.127,0.753,64.125,0.661,0.068,0.855,0.565,0.499 +Iraq,2023,5.475,9.093,0.734,64.600,0.658,-0.017,0.851,0.587,0.469 +Ireland,2006,7.144,10.985,0.967,69.620,0.943,0.235,0.473,0.815,0.209 +Ireland,2008,7.568,10.941,0.983,69.860,0.894,0.315,0.487,0.745,0.148 +Ireland,2009,7.046,10.879,0.959,69.980,0.835,0.308,0.580,0.745,0.233 +Ireland,2010,7.257,10.890,0.973,70.100,0.856,0.341,0.618,0.763,0.201 +Ireland,2011,7.007,10.894,0.977,70.220,0.952,0.376,0.590,0.786,0.190 +Ireland,2012,6.965,10.890,0.962,70.340,0.902,0.295,0.573,0.721,0.237 +Ireland,2013,6.760,10.896,0.955,70.460,0.884,0.325,0.558,0.744,0.245 +Ireland,2014,7.018,10.971,0.968,70.580,0.922,0.257,0.406,0.736,0.229 +Ireland,2015,6.830,11.180,0.953,70.700,0.892,0.226,0.409,0.748,0.225 +Ireland,2016,7.041,11.189,0.958,70.800,0.875,0.169,0.399,0.744,0.211 +Ireland,2017,7.060,11.264,0.943,70.900,0.905,0.210,0.337,0.771,0.213 +Ireland,2018,6.962,11.334,0.938,71.000,0.861,0.138,0.362,0.754,0.213 +Ireland,2019,7.255,11.373,0.944,71.100,0.892,0.067,0.373,0.758,0.223 +Ireland,2020,7.035,11.423,0.960,71.200,0.882,0.000,0.356,0.753,0.246 +Ireland,2021,6.828,11.540,0.850,71.300,0.846,0.131,0.360,0.733,0.245 +Ireland,2022,6.870,11.643,0.906,71.400,0.895,0.138,0.358,0.738,0.234 +Ireland,2023,6.817,11.676,0.921,71.500,0.903,0.182,0.373,0.742,0.245 +Israel,2006,7.173,10.368,0.927,71.080,0.817,,0.905,0.639,0.308 +Israel,2007,6.841,10.408,0.868,71.160,0.683,0.215,0.868,0.642,0.320 +Israel,2008,7.261,10.423,0.859,71.240,0.663,0.134,0.898,0.635,0.349 +Israel,2009,7.353,10.408,0.937,71.320,0.593,0.167,0.923,0.620,0.327 +Israel,2010,7.359,10.444,0.882,71.400,0.561,0.145,0.902,0.628,0.362 +Israel,2011,7.433,10.480,0.893,71.480,0.722,0.136,0.891,0.654,0.384 +Israel,2012,7.111,10.487,0.903,71.560,0.681,0.147,0.862,0.611,0.319 +Israel,2013,7.321,10.512,0.909,71.640,0.739,0.145,0.849,0.649,0.409 +Israel,2014,7.401,10.531,0.889,71.720,0.707,0.088,0.818,0.567,0.271 +Israel,2015,7.079,10.536,0.864,71.800,0.753,0.103,0.789,0.652,0.256 +Israel,2016,7.159,10.560,0.890,71.950,0.772,0.147,0.804,0.602,0.263 +Israel,2017,7.331,10.583,0.916,72.100,0.768,0.138,0.793,0.621,0.276 +Israel,2018,6.927,10.603,0.910,72.250,0.725,0.048,0.770,0.612,0.282 +Israel,2019,7.332,10.625,0.946,72.400,0.834,0.078,0.743,0.598,0.266 +Israel,2020,7.195,10.589,0.959,72.550,0.831,-0.059,0.748,0.564,0.243 +Israel,2021,7.578,10.655,0.917,72.700,0.820,-0.008,0.726,0.558,0.217 +Israel,2022,7.662,10.698,0.954,72.850,0.775,-0.007,0.655,0.583,0.183 +Israel,2023,6.783,10.707,0.952,73.000,0.797,0.146,0.636,0.484,0.516 +Italy,2005,6.854,10.698,0.928,70.600,0.802,,0.944,0.606,0.295 +Italy,2007,6.574,10.722,0.912,70.800,0.684,0.108,0.922,0.650,0.303 +Italy,2008,6.780,10.706,0.880,70.900,0.543,0.044,0.946,0.588,0.268 +Italy,2009,6.334,10.647,0.880,71.000,0.701,0.235,0.890,0.715,0.279 +Italy,2010,6.354,10.661,0.872,71.100,0.738,-0.065,0.921,0.535,0.236 +Italy,2011,6.057,10.666,0.913,71.200,0.568,-0.023,0.933,0.610,0.266 +Italy,2012,5.839,10.633,0.869,71.300,0.570,0.107,0.908,0.651,0.388 +Italy,2013,6.009,10.603,0.916,71.400,0.499,-0.108,0.943,0.702,0.357 +Italy,2014,6.027,10.594,0.898,71.500,0.624,-0.071,0.920,0.659,0.356 +Italy,2015,5.848,10.603,0.909,71.600,0.575,-0.070,0.913,0.646,0.329 +Italy,2016,5.955,10.617,0.927,71.675,0.624,-0.086,0.903,0.632,0.339 +Italy,2017,6.199,10.635,0.920,71.750,0.633,-0.041,0.867,0.613,0.323 +Italy,2018,6.517,10.647,0.913,71.825,0.650,-0.027,0.888,0.598,0.403 +Italy,2019,6.445,10.663,0.838,71.900,0.709,-0.088,0.866,0.569,0.328 +Italy,2020,6.488,10.574,0.890,71.975,0.718,-0.157,0.844,0.614,0.311 +Italy,2021,6.467,10.647,0.886,72.050,0.703,-0.096,0.862,0.634,0.318 +Italy,2022,6.258,10.687,0.869,72.125,0.711,0.026,0.819,0.624,0.298 +Italy,2023,6.245,10.703,0.851,72.200,0.699,-0.053,0.819,0.636,0.293 +Ivory Coast,2009,4.197,8.181,0.667,48.900,0.760,-0.157,0.902,0.555,0.186 +Ivory Coast,2013,3.739,8.285,0.709,51.300,0.739,-0.037,0.691,0.661,0.306 +Ivory Coast,2014,3.570,8.351,0.711,51.900,0.781,-0.087,0.671,0.603,0.291 +Ivory Coast,2015,4.445,8.395,0.704,52.500,0.800,-0.059,0.744,0.614,0.347 +Ivory Coast,2016,4.543,8.438,0.617,53.075,0.769,-0.048,0.757,0.693,0.378 +Ivory Coast,2017,5.038,8.484,0.661,53.650,0.732,-0.116,0.771,0.662,0.357 +Ivory Coast,2018,5.268,8.505,0.621,54.225,0.713,-0.054,0.791,0.659,0.386 +Ivory Coast,2019,5.392,8.543,0.679,54.800,0.736,-0.021,0.799,0.663,0.425 +Ivory Coast,2020,5.257,8.535,0.613,55.375,0.770,0.012,0.777,0.655,0.340 +Ivory Coast,2021,5.056,8.579,0.554,55.950,0.717,-0.004,0.716,0.626,0.345 +Ivory Coast,2022,4.849,8.619,0.536,56.525,0.713,-0.009,0.743,0.629,0.399 +Ivory Coast,2023,5.337,8.656,0.623,57.100,0.753,-0.057,0.676,0.670,0.307 +Jamaica,2006,6.208,9.249,0.909,66.600,0.738,-0.011,0.946,0.753,0.201 +Jamaica,2011,5.374,9.193,0.855,66.600,0.796,-0.071,0.909,0.764,0.237 +Jamaica,2013,5.709,9.182,0.865,66.600,0.793,-0.029,0.931,0.694,0.312 +Jamaica,2014,5.311,9.185,0.874,66.600,0.809,-0.008,0.861,0.708,0.310 +Jamaica,2017,5.890,9.209,0.913,66.600,0.861,-0.138,0.883,0.700,0.243 +Jamaica,2019,6.309,9.234,0.878,66.600,0.891,-0.146,0.885,0.722,0.195 +Jamaica,2020,5.425,9.128,0.870,66.600,0.865,-0.152,0.836,0.712,0.266 +Jamaica,2021,5.814,9.170,0.857,66.600,0.731,-0.084,0.883,0.689,0.308 +Jamaica,2022,5.870,9.212,0.868,66.600,0.874,-0.091,0.910,0.718,0.269 +Japan,2005,6.516,10.552,0.928,72.400,0.868,,0.699,0.686,0.153 +Japan,2007,6.238,10.578,0.938,72.640,0.796,-0.097,0.809,0.683,0.207 +Japan,2008,5.911,10.566,0.887,72.760,0.772,-0.142,0.816,0.705,0.191 +Japan,2009,5.845,10.507,0.888,72.880,0.730,-0.217,0.740,0.713,0.169 +Japan,2010,6.057,10.547,0.902,73.000,0.772,-0.147,0.770,0.779,0.188 +Japan,2011,6.263,10.549,0.917,73.120,0.814,-0.059,0.734,0.714,0.181 +Japan,2012,5.968,10.565,0.905,73.240,0.753,,0.692,0.708,0.171 +Japan,2013,5.959,10.586,0.924,73.360,0.821,-0.154,0.650,0.719,0.175 +Japan,2014,5.923,10.590,0.900,73.480,0.838,-0.146,0.617,0.687,0.189 +Japan,2015,5.880,10.607,0.923,73.600,0.832,-0.162,0.654,0.702,0.176 +Japan,2016,5.955,10.615,0.900,73.725,0.836,-0.069,0.698,0.690,0.192 +Japan,2017,5.911,10.632,0.882,73.850,0.849,-0.213,0.659,0.692,0.176 +Japan,2018,5.794,10.640,0.886,73.975,0.773,-0.268,0.687,0.649,0.185 +Japan,2019,5.908,10.637,0.878,74.100,0.806,-0.261,0.617,0.693,0.194 +Japan,2020,6.118,10.596,0.887,74.225,0.806,-0.266,0.609,0.681,0.186 +Japan,2021,6.091,10.622,0.896,74.350,0.801,-0.213,0.670,0.674,0.189 +Japan,2022,6.178,10.637,0.899,74.475,0.789,-0.237,0.643,0.670,0.165 +Japan,2023,5.910,10.654,0.842,74.600,0.777,-0.222,0.594,0.638,0.178 +Jordan,2005,6.295,9.283,0.920,65.800,,,0.670,0.630,0.240 +Jordan,2007,5.598,9.308,0.841,66.160,0.646,-0.117,0.664,,0.240 +Jordan,2008,4.930,9.354,0.766,66.340,,-0.134,0.709,0.656,0.331 +Jordan,2009,6.000,9.381,0.899,66.520,0.771,-0.083,0.739,0.587,0.265 +Jordan,2010,5.570,9.382,0.918,66.700,0.788,-0.057,,0.564,0.343 +Jordan,2011,5.539,9.383,0.878,66.880,0.760,-0.155,,0.551,0.260 +Jordan,2012,5.132,9.393,0.829,67.060,0.693,-0.175,,0.469,0.345 +Jordan,2013,5.172,9.354,0.840,67.240,0.692,-0.131,,0.597,0.286 +Jordan,2014,5.333,9.269,0.816,67.420,0.729,-0.113,,0.602,0.313 +Jordan,2015,5.405,9.202,0.830,67.600,0.767,-0.051,,0.617,0.305 +Jordan,2016,5.271,9.173,0.820,67.600,0.771,-0.042,,0.598,0.312 +Jordan,2017,4.808,9.173,0.815,67.600,0.766,-0.156,,0.554,0.392 +Jordan,2018,4.639,9.168,0.800,67.600,0.762,-0.189,,, +Jordan,2019,4.453,9.163,0.793,67.600,0.726,-0.168,,, +Jordan,2020,4.094,9.125,0.709,67.600,0.779,-0.154,,, +Jordan,2021,3.909,9.127,0.703,67.600,0.773,-0.148,0.656,0.480,0.429 +Jordan,2022,4.356,9.140,0.774,67.600,0.759,-0.156,0.715,0.521,0.435 +Jordan,2023,4.292,9.151,0.721,67.600,0.754,-0.143,0.651,0.518,0.442 +Kazakhstan,2006,5.476,9.804,0.872,58.000,0.731,-0.280,0.865,0.602,0.185 +Kazakhstan,2007,5.719,9.878,0.861,58.600,0.806,-0.251,0.865,0.557,0.179 +Kazakhstan,2008,5.886,9.892,0.839,59.200,0.727,-0.226,0.899,0.613,0.160 +Kazakhstan,2009,5.383,9.884,0.893,59.800,0.856,-0.255,0.845,0.595,0.129 +Kazakhstan,2010,5.514,9.940,0.904,60.400,0.785,-0.221,0.823,0.655,0.149 +Kazakhstan,2011,5.736,9.997,0.905,61.000,0.878,-0.241,0.802,0.622,0.154 +Kazakhstan,2012,5.759,10.030,0.892,61.600,0.840,-0.177,0.877,0.667,0.184 +Kazakhstan,2013,5.835,10.074,0.889,62.200,0.782,-0.235,0.820,0.629,0.164 +Kazakhstan,2014,5.970,10.101,0.795,62.800,0.799,-0.002,0.805,0.671,0.169 +Kazakhstan,2015,5.950,10.098,0.931,63.400,0.740,-0.043,0.714,0.682,0.174 +Kazakhstan,2016,5.534,10.095,0.928,63.800,0.783,-0.042,0.702,0.641,0.155 +Kazakhstan,2017,5.882,10.121,0.914,64.200,0.745,-0.041,0.755,0.698,0.171 +Kazakhstan,2018,6.008,10.148,0.937,64.600,0.840,-0.104,0.824,0.611,0.162 +Kazakhstan,2019,6.272,10.179,0.951,65.000,0.852,-0.061,0.708,0.711,0.139 +Kazakhstan,2020,6.168,10.141,0.966,65.400,0.872,-0.062,0.661,0.620,0.150 +Kazakhstan,2021,6.260,10.170,0.906,65.800,0.807,0.034,0.782,0.663,0.122 +Kazakhstan,2022,6.006,10.169,0.923,66.200,0.883,0.022,0.720,0.652,0.132 +Kazakhstan,2023,6.299,10.172,0.929,66.600,0.901,0.050,0.812,0.626,0.114 +Kenya,2006,4.223,8.164,0.909,51.420,0.616,-0.034,0.860,0.657,0.198 +Kenya,2007,4.576,8.201,0.841,51.940,0.750,0.040,0.799,0.698,0.162 +Kenya,2008,4.015,8.173,0.827,52.460,0.620,-0.026,0.909,0.733,0.149 +Kenya,2009,4.270,8.176,0.789,52.980,0.584,0.086,0.913,0.679,0.183 +Kenya,2010,4.256,8.226,0.805,53.500,0.635,0.005,0.918,0.758,0.123 +Kenya,2011,4.405,8.249,0.846,54.020,0.709,0.009,0.923,0.706,0.228 +Kenya,2012,4.547,8.269,0.831,54.540,0.628,0.053,0.911,0.667,0.194 +Kenya,2013,3.795,8.282,0.825,55.060,0.708,0.201,0.861,0.729,0.161 +Kenya,2014,4.905,8.308,0.765,55.580,0.819,0.161,0.849,0.779,0.221 +Kenya,2015,4.358,8.334,0.777,56.100,0.793,0.209,0.853,0.673,0.172 +Kenya,2016,4.396,8.353,0.706,56.500,0.749,0.288,0.828,0.730,0.226 +Kenya,2017,4.476,8.369,0.715,56.900,0.853,0.225,0.854,0.754,0.230 +Kenya,2018,4.656,8.404,0.707,57.300,0.821,0.282,0.844,0.747,0.237 +Kenya,2019,4.619,8.434,0.676,57.700,0.818,0.300,0.794,0.728,0.251 +Kenya,2020,4.547,8.411,0.674,58.100,0.702,0.251,0.837,0.738,0.297 +Kenya,2021,4.465,8.465,0.702,58.500,0.678,0.313,0.841,0.745,0.253 +Kenya,2022,4.448,8.493,0.691,58.900,0.706,0.292,0.878,0.725,0.281 +Kenya,2023,4.496,8.523,0.703,59.300,0.736,0.316,0.800,0.756,0.245 +Kosovo,2007,5.104,,0.848,,0.381,,0.894,0.614,0.237 +Kosovo,2008,5.522,8.858,0.884,,,0.094,0.849,0.500,0.318 +Kosovo,2009,5.891,8.899,0.830,,0.506,0.203,0.968,0.528,0.169 +Kosovo,2010,5.177,8.940,0.708,,0.451,0.171,0.967,0.673,0.118 +Kosovo,2011,4.860,8.992,0.759,,0.589,0.004,0.919,0.604,0.124 +Kosovo,2012,5.640,9.000,0.757,,0.636,0.028,0.950,0.562,0.100 +Kosovo,2013,6.126,9.046,0.721,,0.568,0.114,0.935,0.650,0.203 +Kosovo,2014,5.000,9.082,0.706,,0.441,0.010,0.775,0.552,0.206 +Kosovo,2015,5.077,9.153,0.805,,0.561,0.177,0.851,0.685,0.180 +Kosovo,2016,5.759,9.213,0.824,,0.827,0.120,0.941,0.588,0.150 +Kosovo,2017,6.149,9.253,0.792,,0.858,0.112,0.925,0.617,0.186 +Kosovo,2018,6.392,9.283,0.822,,0.890,0.264,0.922,0.642,0.170 +Kosovo,2019,6.425,9.334,0.843,,0.841,0.242,0.920,0.612,0.141 +Kosovo,2020,6.294,9.279,0.792,,0.880,0.302,0.910,0.593,0.201 +Kosovo,2021,6.648,9.383,0.849,,0.840,0.258,0.842,0.578,0.116 +Kosovo,2022,6.160,9.431,0.888,,0.865,0.208,0.846,0.549,0.142 +Kosovo,2023,6.878,9.480,0.807,,0.900,0.285,0.811,0.682,0.140 +Kuwait,2006,6.076,11.233,0.919,68.400,0.769,-0.242,0.328,0.788,0.182 +Kuwait,2009,6.585,11.074,0.926,69.000,0.819,0.000,0.675,0.694,0.252 +Kuwait,2010,6.798,10.998,0.893,69.200,0.703,-0.039,0.486,0.687,0.203 +Kuwait,2011,6.378,11.024,0.882,69.400,0.769,,0.560,0.726,0.177 +Kuwait,2012,6.221,11.012,0.889,69.600,0.934,,,0.794,0.095 +Kuwait,2013,6.480,10.952,0.862,69.800,0.751,,,0.686,0.283 +Kuwait,2014,6.180,10.926,,70.000,,,,, +Kuwait,2015,6.146,10.893,0.823,70.200,0.822,0.077,,0.678,0.324 +Kuwait,2016,5.947,10.887,0.845,70.175,0.841,-0.080,,0.643,0.315 +Kuwait,2017,6.094,10.820,0.853,70.150,0.884,-0.010,,0.649,0.307 +Kuwait,2019,6.106,10.765,0.842,70.100,0.867,-0.106,,0.643,0.303 +Kuwait,2022,6.758,10.803,0.874,70.025,0.969,0.142,,0.738,0.156 +Kuwait,2023,7.130,10.812,0.890,70.000,0.898,0.136,,0.729,0.207 +Kyrgyzstan,2006,4.641,8.185,0.844,59.920,0.678,-0.145,0.879,0.555,0.159 +Kyrgyzstan,2007,4.698,8.258,0.833,60.340,0.684,-0.097,0.929,0.590,0.130 +Kyrgyzstan,2008,4.737,8.329,0.792,60.760,0.719,-0.105,0.923,0.594,0.147 +Kyrgyzstan,2009,5.069,8.345,0.855,61.180,0.699,-0.145,0.896,0.554,0.165 +Kyrgyzstan,2010,4.996,8.329,0.885,61.600,0.720,-0.077,0.926,0.516,0.123 +Kyrgyzstan,2011,4.921,8.374,0.891,62.020,0.748,-0.160,0.932,0.579,0.151 +Kyrgyzstan,2012,5.208,8.357,0.856,62.440,0.703,-0.084,0.892,0.580,0.182 +Kyrgyzstan,2013,5.402,8.441,0.851,62.860,0.755,-0.090,0.900,0.595,0.135 +Kyrgyzstan,2014,5.252,8.460,0.898,63.280,0.736,0.350,0.897,0.617,0.185 +Kyrgyzstan,2015,4.905,8.477,0.857,63.700,0.813,0.194,0.858,0.658,0.173 +Kyrgyzstan,2016,4.857,8.500,0.914,64.225,0.814,0.051,0.917,0.668,0.126 +Kyrgyzstan,2017,5.630,8.526,0.883,64.750,0.859,0.138,0.874,0.640,0.160 +Kyrgyzstan,2018,5.297,8.543,0.898,65.275,0.945,0.262,0.907,0.617,0.203 +Kyrgyzstan,2019,5.685,8.568,0.877,65.800,0.920,-0.008,0.885,0.625,0.207 +Kyrgyzstan,2020,6.250,8.461,0.902,66.325,0.935,0.101,0.931,0.672,0.258 +Kyrgyzstan,2021,5.564,8.504,0.904,66.850,0.918,0.199,0.903,0.660,0.226 +Kyrgyzstan,2022,5.668,8.555,0.927,67.375,0.948,0.230,0.876,0.646,0.204 +Kyrgyzstan,2023,5.910,8.573,0.946,67.900,0.943,0.145,0.899,0.681,0.165 +Laos,2006,5.076,8.234,0.807,55.880,0.925,0.435,0.688,0.790,0.163 +Laos,2007,5.364,8.291,0.790,56.260,0.867,0.474,0.580,0.752,0.136 +Laos,2008,5.044,8.351,0.807,56.640,0.886,0.412,0.637,0.728,0.202 +Laos,2011,4.704,8.538,0.691,57.780,0.882,0.454,0.587,0.746,0.225 +Laos,2012,4.876,8.601,0.693,58.160,,0.227,,0.741,0.387 +Laos,2017,4.623,8.883,0.707,59.900,0.891,0.068,0.592,0.712,0.344 +Laos,2018,4.859,8.929,0.705,60.200,0.907,0.136,0.634,0.711,0.332 +Laos,2019,5.197,8.967,0.729,60.500,0.906,0.055,0.620,0.718,0.306 +Laos,2020,5.284,8.957,0.660,60.800,0.915,0.136,0.748,0.714,0.358 +Laos,2021,4.927,8.968,0.650,61.100,0.927,0.037,0.668,0.702,0.292 +Laos,2022,4.962,8.981,0.659,61.400,0.891,0.091,0.706,0.675,0.334 +Laos,2023,5.486,9.005,0.678,61.700,0.904,0.099,0.724,0.679,0.335 +Latvia,2006,4.710,10.042,0.884,63.100,0.641,-0.236,0.937,0.590,0.234 +Latvia,2007,4.667,10.145,0.836,63.400,0.700,-0.173,0.924,0.594,0.247 +Latvia,2008,5.145,10.123,0.855,63.700,0.630,-0.210,0.926,0.633,0.215 +Latvia,2009,4.669,9.985,0.807,64.000,0.437,-0.187,0.942,0.537,0.242 +Latvia,2011,4.967,10.004,0.836,64.600,0.564,-0.006,0.934,0.563,0.222 +Latvia,2012,5.125,10.085,0.851,64.900,0.564,-0.044,0.895,0.588,0.232 +Latvia,2013,5.070,10.115,0.834,65.200,0.631,-0.079,0.837,0.605,0.227 +Latvia,2014,5.729,10.143,0.881,65.500,0.671,-0.049,0.804,0.605,0.226 +Latvia,2015,5.881,10.190,0.879,65.800,0.656,-0.084,0.808,0.559,0.228 +Latvia,2016,5.940,10.222,0.917,65.900,0.685,-0.163,0.868,0.583,0.231 +Latvia,2017,5.978,10.264,0.895,66.000,0.700,-0.161,0.798,0.565,0.232 +Latvia,2018,5.901,10.311,0.913,66.100,0.608,-0.218,0.799,0.521,0.192 +Latvia,2019,5.970,10.343,0.936,66.200,0.698,-0.200,0.789,0.537,0.212 +Latvia,2020,6.229,10.328,0.928,66.300,0.820,-0.086,0.809,0.674,0.202 +Latvia,2021,6.353,10.376,0.954,66.400,0.815,-0.104,0.840,0.716,0.186 +Latvia,2022,6.055,10.396,0.928,66.500,0.817,0.018,0.844,0.632,0.161 +Latvia,2023,6.296,10.408,0.932,66.600,0.816,0.059,0.800,0.671,0.257 +Lebanon,2005,5.491,9.571,0.796,65.100,0.703,,0.945,0.558,0.292 +Lebanon,2006,4.653,9.570,0.853,65.160,0.670,0.064,0.902,0.501,0.320 +Lebanon,2008,4.595,9.711,0.717,65.280,0.524,0.031,0.927,0.475,0.365 +Lebanon,2009,5.206,9.796,0.736,65.340,0.665,0.067,0.937,0.472,0.401 +Lebanon,2010,5.032,9.864,0.721,65.400,0.678,0.068,0.949,0.457,0.341 +Lebanon,2011,5.188,9.862,0.733,65.460,0.657,-0.002,0.911,0.506,0.320 +Lebanon,2012,4.573,9.862,0.713,65.520,0.621,-0.016,0.856,0.442,0.339 +Lebanon,2013,4.983,9.807,0.708,65.580,0.655,-0.012,0.921,0.446,0.409 +Lebanon,2014,5.233,9.732,0.759,65.640,0.657,-0.017,0.939,0.525,0.267 +Lebanon,2015,5.172,9.717,0.742,65.700,0.597,0.066,0.889,0.524,0.243 +Lebanon,2016,5.271,9.754,0.828,65.775,0.657,0.021,0.853,0.513,0.263 +Lebanon,2017,5.154,9.787,0.777,65.850,0.605,-0.088,0.911,0.469,0.244 +Lebanon,2018,5.167,9.795,0.829,65.925,0.607,-0.081,0.907,0.415,0.271 +Lebanon,2019,4.024,9.752,0.866,66.000,0.447,-0.098,0.890,0.308,0.494 +Lebanon,2020,2.634,9.532,0.547,66.075,0.552,-0.139,0.884,0.352,0.482 +Lebanon,2021,2.179,9.472,0.507,66.150,0.423,-0.164,0.905,0.263,0.569 +Lebanon,2022,2.352,9.458,0.535,66.225,0.450,-0.130,0.883,0.298,0.430 +Lebanon,2023,3.588,9.471,0.686,66.300,0.499,-0.102,0.895,0.373,0.385 +Lesotho,2011,4.898,7.785,0.824,41.520,0.618,-0.093,0.768,0.754,0.170 +Lesotho,2016,3.808,7.897,0.798,42.250,0.729,-0.100,0.743,0.685,0.270 +Lesotho,2017,3.795,7.852,0.769,42.900,0.757,-0.144,0.797,0.706,0.255 +Lesotho,2019,3.512,7.805,0.790,44.200,0.716,-0.127,0.915,0.707,0.273 +Lesotho,2022,3.186,7.733,0.680,46.150,0.709,-0.102,0.815,0.709,0.288 +Liberia,2007,3.701,7.179,0.594,51.860,0.790,0.111,0.776,0.600,0.435 +Liberia,2008,4.221,7.207,0.619,51.940,0.724,-0.038,0.840,0.629,0.261 +Liberia,2010,4.196,7.257,0.827,52.100,0.819,-0.043,0.818,0.549,0.217 +Liberia,2014,4.571,7.386,0.708,52.420,0.590,-0.035,0.869,0.565,0.443 +Liberia,2015,2.702,7.366,0.638,52.500,0.671,-0.067,0.903,0.519,0.388 +Liberia,2016,3.355,7.330,0.643,53.100,0.763,0.028,0.901,0.625,0.509 +Liberia,2017,4.424,7.335,0.685,53.700,0.733,-0.018,0.867,0.674,0.391 +Liberia,2018,4.135,7.328,0.727,54.300,0.766,0.044,0.868,0.664,0.436 +Liberia,2019,5.121,7.283,0.712,54.900,0.706,0.044,0.828,0.645,0.389 +Liberia,2022,4.042,7.287,0.597,56.700,0.732,0.150,0.828,0.637,0.439 +Liberia,2023,4.494,7.309,0.630,57.300,0.720,0.039,0.834,0.608,0.428 +Libya,2012,5.754,10.380,0.855,65.140,0.712,-0.076,0.791,0.633,0.316 +Libya,2015,5.615,9.858,0.868,64.300,0.775,-0.089,,0.652,0.369 +Libya,2016,5.434,9.828,0.876,64.525,0.822,-0.135,,0.645,0.383 +Libya,2017,5.647,10.095,0.823,64.750,0.779,-0.068,0.673,0.643,0.379 +Libya,2018,5.494,10.156,0.824,64.975,0.781,-0.146,0.646,0.635,0.399 +Libya,2019,5.330,10.023,0.827,65.200,0.762,-0.107,0.686,0.629,0.401 +Libya,2022,5.760,9.893,0.813,65.875,0.761,-0.043,0.668,0.627,0.399 +Libya,2023,5.970,,0.748,66.100,0.762,,0.644,0.585,0.372 +Lithuania,2006,5.954,10.042,0.930,63.500,0.567,-0.301,0.967,0.567,0.254 +Lithuania,2007,5.808,10.160,0.941,63.700,0.590,-0.287,0.966,0.523,0.279 +Lithuania,2008,5.554,10.196,0.914,63.900,0.621,-0.265,0.961,0.501,0.276 +Lithuania,2009,5.467,10.046,0.933,64.100,0.496,-0.309,0.979,0.525,0.271 +Lithuania,2010,5.066,10.083,0.882,64.300,0.519,-0.281,0.962,0.463,0.272 +Lithuania,2011,5.432,10.165,0.911,64.500,0.566,-0.154,0.964,0.556,0.275 +Lithuania,2012,5.771,10.216,0.919,64.700,0.503,-0.279,0.957,0.557,0.277 +Lithuania,2013,5.596,10.261,0.913,64.900,0.556,-0.242,0.936,0.540,0.294 +Lithuania,2014,6.126,10.304,0.908,65.100,0.508,-0.269,0.956,0.565,0.287 +Lithuania,2015,5.711,10.334,0.929,65.300,0.641,-0.259,0.924,0.534,0.276 +Lithuania,2016,5.866,10.371,0.938,65.650,0.614,-0.272,0.949,0.553,0.250 +Lithuania,2017,6.273,10.427,0.926,66.000,0.749,-0.179,0.790,0.590,0.195 +Lithuania,2018,6.309,10.476,0.929,66.350,0.699,-0.243,0.852,0.518,0.214 +Lithuania,2019,6.064,10.524,0.918,66.700,0.780,-0.258,0.783,0.568,0.276 +Lithuania,2020,6.391,10.523,0.953,67.050,0.824,-0.129,0.829,0.626,0.202 +Lithuania,2021,6.865,10.579,0.928,67.400,0.707,-0.121,0.878,0.667,0.191 +Lithuania,2022,7.038,10.586,0.937,67.750,0.710,-0.192,0.685,0.471,0.132 +Lithuania,2023,6.553,10.575,0.881,68.100,0.734,-0.232,0.751,0.493,0.201 +Luxembourg,2009,6.958,11.628,0.939,70.300,0.939,0.116,0.432,0.713,0.238 +Luxembourg,2010,7.097,11.647,0.952,70.500,0.908,0.086,0.423,0.718,0.216 +Luxembourg,2011,7.101,11.635,0.934,70.700,0.962,0.097,0.388,0.744,0.200 +Luxembourg,2012,6.964,11.627,0.914,70.900,0.917,0.048,0.403,0.726,0.227 +Luxembourg,2013,7.131,11.636,0.917,71.100,0.790,-0.065,0.301,0.601,0.185 +Luxembourg,2014,6.891,11.638,0.875,71.300,0.938,0.097,0.366,0.760,0.170 +Luxembourg,2015,6.702,11.637,0.934,71.500,0.932,0.044,0.375,0.728,0.193 +Luxembourg,2016,6.967,11.664,0.941,71.525,0.882,0.011,0.356,0.706,0.192 +Luxembourg,2017,7.061,11.653,0.905,71.550,0.903,0.036,0.330,0.726,0.184 +Luxembourg,2018,7.243,11.645,0.902,71.575,0.884,-0.028,0.385,0.715,0.202 +Luxembourg,2019,7.404,11.649,0.912,71.600,0.930,-0.051,0.390,0.742,0.212 +Luxembourg,2022,7.228,11.657,0.878,71.675,0.915,0.023,0.345,0.718,0.218 +Luxembourg,2023,7.016,11.649,0.879,71.700,0.911,0.033,0.343,0.751,0.194 +Madagascar,2006,3.980,7.351,0.711,54.140,,-0.042,,0.563,0.161 +Madagascar,2008,4.640,7.413,0.776,54.620,0.332,-0.103,0.773,0.583,0.215 +Madagascar,2011,4.381,7.309,0.818,55.340,0.546,-0.065,0.897,0.516,0.235 +Madagascar,2012,3.551,7.311,0.673,55.580,0.487,-0.058,0.854,0.588,0.194 +Madagascar,2013,3.816,7.307,0.673,55.820,0.480,-0.022,0.868,0.600,0.241 +Madagascar,2014,3.676,7.314,0.655,56.060,0.529,-0.026,0.791,0.641,0.192 +Madagascar,2015,3.593,7.319,0.647,56.300,0.545,-0.044,0.861,0.674,0.226 +Madagascar,2016,3.663,7.332,0.746,56.550,0.570,-0.072,0.864,0.670,0.204 +Madagascar,2017,4.079,7.345,0.626,56.800,0.570,-0.037,0.847,0.701,0.375 +Madagascar,2018,4.071,7.351,0.666,57.050,0.551,0.000,0.889,0.723,0.362 +Madagascar,2019,4.339,7.369,0.701,57.300,0.550,-0.015,0.720,0.699,0.304 +Madagascar,2022,4.019,7.315,0.642,58.050,0.523,0.070,0.740,0.687,0.345 +Madagascar,2023,4.433,7.333,0.692,58.300,0.504,0.096,0.784,0.718,0.389 +Malawi,2006,3.830,7.015,0.554,45.360,0.767,0.171,0.676,0.609,0.222 +Malawi,2007,4.891,7.078,0.600,46.420,0.910,0.173,0.691,0.691,0.176 +Malawi,2009,5.148,7.174,0.718,48.540,0.879,0.147,0.689,0.694,0.130 +Malawi,2011,3.946,7.231,0.613,50.660,0.733,0.070,0.853,0.620,0.268 +Malawi,2012,4.279,7.221,0.604,51.720,0.637,0.140,0.886,0.717,0.200 +Malawi,2013,4.035,7.246,0.563,52.780,0.752,0.049,0.857,0.699,0.248 +Malawi,2014,4.563,7.273,0.512,53.840,0.786,0.032,0.824,0.653,0.263 +Malawi,2015,3.868,7.273,0.494,54.900,0.801,0.029,0.835,0.602,0.260 +Malawi,2016,3.476,7.270,0.524,55.450,0.810,0.037,0.824,0.584,0.325 +Malawi,2017,3.417,7.283,0.555,56.000,0.848,-0.004,0.735,0.592,0.312 +Malawi,2018,3.335,7.299,0.528,56.550,0.799,0.043,0.766,0.548,0.365 +Malawi,2019,3.869,7.325,0.549,57.100,0.765,-0.027,0.680,0.517,0.348 +Malawi,2021,3.635,7.307,0.558,58.200,0.757,-0.014,0.740,0.551,0.326 +Malawi,2022,3.356,7.291,0.503,58.750,0.744,0.017,0.755,0.536,0.329 +Malawi,2023,3.272,7.279,0.470,59.300,0.738,0.014,0.745,0.520,0.338 +Malaysia,2006,6.012,9.827,0.866,65.080,0.837,0.196,0.740,0.687,0.243 +Malaysia,2007,6.239,9.867,0.871,65.160,0.844,0.085,0.799,0.719,0.162 +Malaysia,2008,5.807,9.893,0.803,65.240,0.780,0.040,0.884,0.728,0.186 +Malaysia,2009,5.385,9.858,0.792,65.320,0.874,-0.013,0.858,0.740,0.164 +Malaysia,2010,5.580,9.912,0.839,65.400,0.769,0.028,0.844,0.752,0.192 +Malaysia,2011,5.786,9.948,0.770,65.480,0.840,-0.021,0.842,0.785,0.155 +Malaysia,2012,5.914,9.985,0.841,65.560,0.848,0.013,0.847,0.744,0.177 +Malaysia,2013,5.770,10.015,0.831,65.640,0.791,0.260,0.755,0.664,0.317 +Malaysia,2014,5.963,10.057,0.863,65.720,0.808,0.235,0.845,0.711,0.261 +Malaysia,2015,6.322,10.092,0.818,65.800,0.675,0.218,0.838,0.733,0.314 +Malaysia,2018,5.339,10.197,0.789,65.725,0.875,0.123,0.894,0.716,0.200 +Malaysia,2019,5.428,10.228,0.842,65.700,0.916,0.119,0.782,0.735,0.176 +Malaysia,2020,6.014,10.159,0.797,65.675,0.878,0.096,0.747,0.721,0.288 +Malaysia,2021,6.010,10.179,0.794,65.650,0.878,0.218,0.769,0.728,0.183 +Malaysia,2022,6.048,10.251,0.815,65.625,0.957,0.208,0.789,0.767,0.191 +Malaysia,2023,5.868,10.283,0.779,65.600,0.965,0.154,0.754,0.761,0.195 +Maldives,2018,5.198,9.893,0.913,69.775,0.855,0.013,,, +Mali,2006,4.014,7.561,0.761,49.940,0.555,-0.075,0.761,0.748,0.209 +Mali,2008,4.115,7.576,0.747,50.620,0.495,-0.015,0.918,0.717,0.164 +Mali,2009,3.977,7.591,0.733,50.960,0.634,0.005,0.819,0.729,0.150 +Mali,2010,3.762,7.610,0.751,51.300,0.749,-0.031,0.811,0.764,0.162 +Mali,2011,4.667,7.609,0.796,51.640,0.823,-0.103,0.726,0.752,0.132 +Mali,2012,4.313,7.572,0.823,51.980,0.704,-0.091,0.787,0.647,0.109 +Mali,2013,3.676,7.565,0.820,52.320,0.665,-0.056,0.755,0.717,0.193 +Mali,2014,3.975,7.602,0.843,52.660,0.652,-0.040,0.658,0.722,0.186 +Mali,2015,4.582,7.631,0.830,53.000,0.634,-0.070,0.800,0.696,0.243 +Mali,2016,4.016,7.655,0.836,53.400,0.696,-0.072,0.862,0.738,0.305 +Mali,2017,4.742,7.675,0.741,53.800,0.753,-0.072,0.863,0.665,0.393 +Mali,2018,4.416,7.690,0.692,54.200,0.737,-0.036,0.793,0.689,0.370 +Mali,2019,4.988,7.705,0.755,54.600,0.670,-0.040,0.846,0.646,0.358 +Mali,2020,4.269,7.661,0.568,55.000,0.645,-0.069,0.895,0.648,0.440 +Mali,2021,4.113,7.659,0.573,55.400,0.673,-0.004,0.902,0.640,0.438 +Mali,2022,4.211,7.665,0.642,55.800,0.818,-0.024,0.746,0.655,0.408 +Mali,2023,4.370,7.673,0.641,56.200,0.776,-0.043,0.790,0.660,0.324 +Malta,2009,6.328,10.353,0.916,70.220,0.803,0.456,,0.626,0.358 +Malta,2010,5.774,10.402,0.908,70.400,0.802,0.278,,0.624,0.375 +Malta,2011,6.155,10.402,0.923,70.580,0.882,0.288,,0.638,0.340 +Malta,2012,5.963,10.433,0.922,70.760,0.861,0.343,,0.639,0.391 +Malta,2013,6.380,10.473,0.942,70.940,0.909,0.400,,0.629,0.370 +Malta,2014,6.452,10.526,0.941,71.120,0.904,0.395,0.670,0.606,0.352 +Malta,2015,6.613,10.594,0.919,71.300,0.912,0.339,0.664,0.641,0.355 +Malta,2016,6.591,10.604,0.930,71.350,0.916,0.339,0.696,0.645,0.355 +Malta,2017,6.676,10.681,0.937,71.400,0.924,0.244,0.690,0.666,0.302 +Malta,2018,6.910,10.705,0.932,71.450,0.927,0.170,0.595,0.666,0.296 +Malta,2019,6.733,10.734,0.922,71.500,0.924,0.077,0.689,0.642,0.356 +Malta,2020,6.157,10.622,0.938,71.550,0.931,-0.005,0.675,0.576,0.411 +Malta,2021,6.444,10.727,0.897,71.600,0.889,0.239,0.753,0.635,0.375 +Malta,2022,6.299,10.784,0.932,71.650,0.838,0.246,0.758,0.671,0.370 +Malta,2023,6.295,,0.912,71.700,0.851,,0.780,0.644,0.361 +Mauritania,2007,4.149,8.528,0.682,56.500,0.573,-0.077,0.586,0.732,0.174 +Mauritania,2008,4.248,8.500,0.670,56.800,0.593,-0.023,0.841,0.747,0.176 +Mauritania,2009,4.500,8.474,0.819,57.100,0.735,0.034,0.848,0.717,0.170 +Mauritania,2010,4.772,8.471,0.857,57.400,0.669,0.050,0.727,0.737,0.129 +Mauritania,2011,4.785,8.482,0.750,57.700,0.567,0.047,0.747,0.729,0.175 +Mauritania,2012,4.673,8.494,0.763,58.000,0.487,-0.026,0.707,0.749,0.164 +Mauritania,2013,4.199,8.506,0.741,58.300,0.603,-0.084,0.676,0.743,0.196 +Mauritania,2014,4.483,8.521,0.853,58.600,0.468,-0.060,0.589,0.743,0.163 +Mauritania,2015,3.923,8.547,0.875,58.900,0.447,0.050,0.715,0.798,0.194 +Mauritania,2016,4.472,8.533,0.785,59.125,0.467,-0.181,0.842,0.710,0.222 +Mauritania,2017,4.678,8.568,0.779,59.350,0.527,-0.161,0.777,0.631,0.272 +Mauritania,2018,4.314,8.588,0.802,59.575,0.467,-0.121,0.711,0.665,0.276 +Mauritania,2019,4.153,8.614,0.798,59.800,0.628,-0.111,0.743,0.686,0.260 +Mauritania,2022,4.724,8.602,0.648,60.475,0.624,-0.016,0.657,0.631,0.389 +Mauritania,2023,4.292,8.620,0.606,60.700,0.540,0.018,0.669,0.708,0.329 +Mauritius,2011,5.477,9.797,0.800,63.520,0.848,0.184,0.847,0.653,0.253 +Mauritius,2014,5.648,9.895,0.785,63.880,0.824,0.168,0.879,0.741,0.222 +Mauritius,2016,5.610,9.968,0.836,63.975,0.819,0.131,0.891,0.706,0.246 +Mauritius,2017,6.174,10.005,0.910,63.950,0.912,0.079,0.818,0.682,0.169 +Mauritius,2018,5.882,10.044,0.909,63.925,0.867,-0.081,0.785,0.710,0.158 +Mauritius,2019,6.241,10.072,0.913,63.900,0.893,-0.061,0.810,0.735,0.149 +Mauritius,2020,6.015,9.914,0.893,63.875,0.843,-0.038,0.772,0.700,0.138 +Mauritius,2021,5.949,9.948,0.887,63.850,0.802,-0.013,0.784,0.666,0.136 +Mauritius,2022,5.741,10.034,0.887,63.825,0.798,-0.042,0.769,0.725,0.168 +Mauritius,2023,5.759,10.083,0.868,63.800,0.797,-0.002,0.769,0.664,0.160 +Mexico,2005,6.581,9.792,0.903,64.400,0.814,,0.764,0.763,0.219 +Mexico,2007,6.525,9.831,0.879,64.680,0.670,-0.101,0.747,0.754,0.248 +Mexico,2008,6.829,9.830,0.876,64.820,0.677,-0.134,0.785,0.774,0.201 +Mexico,2009,6.963,9.764,0.868,64.960,0.682,-0.082,0.764,0.763,0.196 +Mexico,2010,6.802,9.800,0.876,65.100,0.778,-0.055,0.693,0.745,0.215 +Mexico,2011,6.910,9.822,0.824,65.240,0.831,-0.106,0.698,0.700,0.228 +Mexico,2012,7.320,9.844,0.767,65.380,0.788,-0.099,0.633,0.722,0.278 +Mexico,2013,7.443,9.844,0.759,65.520,0.739,-0.171,0.615,0.750,0.223 +Mexico,2014,6.680,9.860,0.782,65.660,0.779,-0.101,0.630,0.760,0.229 +Mexico,2015,6.236,9.880,0.761,65.800,0.719,-0.158,0.708,0.706,0.237 +Mexico,2016,6.824,9.895,0.893,65.800,0.752,-0.160,0.809,0.802,0.220 +Mexico,2017,6.410,9.905,0.800,65.800,0.861,-0.208,0.801,0.775,0.231 +Mexico,2018,6.550,9.917,0.858,65.800,0.816,-0.186,0.809,0.815,0.213 +Mexico,2019,6.432,9.907,0.852,65.800,0.903,-0.148,0.809,0.803,0.252 +Mexico,2020,5.964,9.816,0.779,65.800,0.873,-0.128,0.778,0.745,0.292 +Mexico,2021,5.991,9.857,0.779,65.800,0.837,-0.037,0.745,0.750,0.305 +Mexico,2022,7.038,9.881,0.858,65.800,0.861,-0.123,0.780,0.818,0.205 +Mexico,2023,7.006,9.900,0.868,65.800,0.870,-0.124,0.756,0.809,0.233 +Moldova,2006,5.102,8.922,0.812,59.480,0.554,-0.169,0.926,0.553,0.255 +Moldova,2007,4.775,8.954,0.804,59.860,0.696,-0.190,0.930,0.519,0.306 +Moldova,2008,5.503,9.031,0.872,60.240,0.641,-0.060,0.926,0.565,0.284 +Moldova,2009,5.554,8.970,0.856,60.620,0.551,-0.103,0.925,0.539,0.306 +Moldova,2010,5.590,9.040,0.847,61.000,0.598,-0.093,0.929,0.564,0.278 +Moldova,2011,5.792,9.097,0.869,61.380,0.628,-0.086,0.957,0.553,0.285 +Moldova,2012,5.996,9.091,0.826,61.760,0.602,-0.054,0.955,0.564,0.314 +Moldova,2013,5.756,9.178,0.803,62.140,0.658,-0.073,0.941,0.548,0.261 +Moldova,2014,5.917,9.227,0.805,62.520,0.623,-0.118,0.925,0.547,0.260 +Moldova,2015,6.017,9.232,0.840,62.900,0.595,-0.094,0.943,0.556,0.281 +Moldova,2016,5.578,9.289,0.837,63.300,0.557,-0.052,0.969,0.586,0.275 +Moldova,2017,5.326,9.347,0.831,63.700,0.553,-0.057,0.926,0.563,0.259 +Moldova,2018,5.682,9.404,0.892,64.100,0.824,-0.089,0.929,0.584,0.270 +Moldova,2019,5.803,9.455,0.809,64.500,0.784,-0.097,0.884,0.600,0.262 +Moldova,2020,5.812,9.380,0.874,64.900,0.859,-0.058,0.941,0.698,0.268 +Moldova,2021,5.959,9.518,0.880,65.300,0.833,-0.096,0.875,0.630,0.270 +Moldova,2022,5.687,9.465,0.817,65.700,0.829,-0.084,0.885,0.552,0.276 +Moldova,2023,5.801,9.492,0.842,66.100,0.844,-0.157,0.860,0.578,0.251 +Mongolia,2007,4.609,8.827,0.881,56.540,0.781,0.059,0.918,0.483,0.203 +Mongolia,2008,4.493,8.902,0.920,56.960,0.484,0.062,0.962,0.514,0.173 +Mongolia,2010,4.586,8.925,0.904,57.800,0.631,0.093,0.928,0.559,0.150 +Mongolia,2011,5.031,9.069,0.948,58.220,0.700,0.145,0.931,0.561,0.153 +Mongolia,2012,4.885,9.168,0.919,58.640,0.688,0.100,0.932,0.524,0.181 +Mongolia,2013,4.913,9.260,0.935,59.060,0.748,0.130,0.928,0.549,0.179 +Mongolia,2014,4.825,9.315,0.943,59.480,0.752,0.140,0.909,0.512,0.170 +Mongolia,2015,4.983,9.318,0.906,59.900,0.686,0.167,0.900,0.533,0.208 +Mongolia,2016,5.057,9.311,0.947,60.000,0.760,0.083,0.900,0.555,0.171 +Mongolia,2017,5.334,9.344,0.924,60.100,0.675,0.112,0.865,0.552,0.214 +Mongolia,2018,5.465,9.397,0.942,60.200,0.696,0.048,0.849,0.525,0.192 +Mongolia,2019,5.563,9.430,0.946,60.300,0.711,0.142,0.873,0.562,0.167 +Mongolia,2020,6.011,9.365,0.918,60.400,0.718,0.138,0.843,0.575,0.260 +Mongolia,2021,5.721,9.365,0.927,60.500,0.667,0.215,0.851,0.560,0.202 +Mongolia,2022,5.788,9.397,0.951,60.600,0.717,0.211,0.847,0.550,0.209 +Mongolia,2023,5.580,9.433,0.938,60.700,0.699,0.220,0.871,0.545,0.197 +Montenegro,2007,5.196,9.696,0.832,65.960,0.512,-0.139,0.815,0.536,0.340 +Montenegro,2009,4.801,9.702,0.816,66.120,0.556,-0.107,0.838,0.533,0.423 +Montenegro,2010,5.455,9.727,0.805,66.200,0.552,-0.212,0.757,0.510,0.410 +Montenegro,2011,5.223,9.758,0.818,66.280,0.546,-0.232,0.762,0.510,0.378 +Montenegro,2012,5.219,9.729,0.704,66.360,0.462,-0.198,0.755,0.468,0.379 +Montenegro,2013,5.074,9.763,0.736,66.440,0.502,-0.182,0.693,0.493,0.331 +Montenegro,2014,5.283,9.780,0.863,66.520,0.503,0.091,0.768,0.545,0.368 +Montenegro,2015,5.125,9.813,0.740,66.600,0.583,-0.150,0.781,0.534,0.337 +Montenegro,2016,5.304,9.842,0.866,66.700,0.569,-0.093,0.849,0.547,0.337 +Montenegro,2017,5.615,9.887,0.881,66.800,0.626,-0.089,0.756,0.493,0.350 +Montenegro,2018,5.650,9.937,0.856,66.900,0.626,-0.057,0.769,0.527,0.355 +Montenegro,2019,5.386,9.977,0.832,67.000,0.694,-0.111,0.820,0.547,0.366 +Montenegro,2020,5.722,9.812,0.887,67.100,0.802,0.061,0.845,0.560,0.411 +Montenegro,2022,5.600,10.002,0.875,67.300,0.778,-0.022,0.802,0.485,0.317 +Montenegro,2023,5.813,10.041,0.853,67.400,0.799,-0.066,0.706,0.481,0.318 +Morocco,2010,4.383,8.821,,62.500,0.663,-0.173,0.900,, +Morocco,2011,5.085,8.861,0.833,62.660,0.579,-0.229,0.875,0.687,0.187 +Morocco,2012,4.970,8.877,0.676,62.820,0.757,-0.198,0.845,0.641,0.281 +Morocco,2013,5.142,8.904,0.597,62.980,0.572,-0.221,0.771,0.707,0.239 +Morocco,2015,5.163,8.947,0.606,63.300,0.713,-0.239,0.842,0.596,0.262 +Morocco,2016,5.386,8.940,0.655,63.400,0.817,-0.248,0.717,0.658,0.205 +Morocco,2017,5.312,8.977,0.641,63.500,0.814,-0.227,0.841,0.501,0.323 +Morocco,2018,4.897,8.996,0.554,63.600,0.773,-0.246,0.843,0.575,0.416 +Morocco,2019,5.057,9.014,0.535,63.700,0.757,-0.256,0.757,0.535,0.410 +Morocco,2020,4.803,8.929,0.553,63.800,0.819,-0.238,0.803,0.548,0.256 +Morocco,2021,5.326,8.994,0.505,63.900,0.762,-0.204,0.817,0.554,0.341 +Morocco,2022,4.596,8.995,0.564,64.000,0.795,-0.257,0.802,0.573,0.414 +Morocco,2023,4.487,9.009,0.500,64.100,0.821,-0.094,0.831,0.549,0.415 +Mozambique,2006,4.595,6.792,0.879,44.820,0.684,0.035,0.758,0.602,0.327 +Mozambique,2007,4.833,6.840,0.748,45.240,0.643,0.068,0.854,0.627,0.240 +Mozambique,2008,4.654,6.885,0.756,45.660,0.514,0.000,0.864,0.611,0.280 +Mozambique,2011,4.971,6.996,0.818,46.920,0.639,-0.030,0.719,0.565,0.243 +Mozambique,2015,4.550,7.148,0.666,48.600,0.813,0.083,0.632,0.560,0.340 +Mozambique,2017,4.280,7.160,0.678,49.500,0.823,-0.035,0.682,0.642,0.353 +Mozambique,2018,4.654,7.165,0.738,49.950,0.897,0.043,0.691,0.620,0.397 +Mozambique,2019,4.932,7.159,0.742,50.400,0.870,0.068,0.682,0.588,0.384 +Mozambique,2021,5.178,7.112,0.664,51.300,0.838,0.042,0.627,0.576,0.383 +Mozambique,2022,4.740,7.125,0.711,51.750,0.884,0.043,0.688,0.629,0.351 +Mozambique,2023,5.704,7.147,0.701,52.200,0.867,0.071,0.678,0.625,0.335 +Myanmar,2012,4.439,8.067,0.612,58.160,0.691,0.646,0.695,0.574,0.205 +Myanmar,2013,4.176,8.134,0.757,58.640,0.775,0.691,0.638,0.675,0.217 +Myanmar,2014,4.786,8.205,0.774,59.120,0.870,0.700,0.592,0.713,0.112 +Myanmar,2015,4.224,8.229,0.752,59.600,0.808,0.692,0.633,0.729,0.272 +Myanmar,2016,4.623,8.321,0.793,59.925,0.877,0.680,0.607,0.671,0.302 +Myanmar,2017,4.154,8.369,0.795,60.250,0.886,0.651,0.619,0.617,0.282 +Myanmar,2018,4.411,8.424,0.774,60.575,0.906,0.492,0.647,0.640,0.300 +Myanmar,2019,4.434,8.483,0.763,60.900,0.899,0.560,0.682,0.638,0.286 +Myanmar,2020,4.431,8.507,0.796,61.225,0.825,0.468,0.647,0.700,0.289 +Myanmar,2021,4.314,8.302,0.780,61.550,0.631,0.508,0.671,0.636,0.268 +Myanmar,2022,4.359,8.325,0.746,61.875,0.646,0.600,0.729,0.669,0.353 +Myanmar,2023,4.391,8.347,0.685,62.200,0.695,0.548,0.686,0.634,0.358 +Namibia,2007,4.886,9.073,0.828,51.880,0.781,-0.107,0.839,0.769,0.160 +Namibia,2014,4.574,9.264,0.763,54.260,0.849,-0.191,0.790,0.723,0.239 +Namibia,2017,4.441,9.243,0.828,55.350,0.810,-0.198,0.831,0.697,0.277 +Namibia,2018,4.834,9.237,0.864,55.725,0.754,-0.177,0.846,0.696,0.240 +Namibia,2019,4.436,9.211,0.845,56.100,0.739,-0.182,0.879,0.644,0.256 +Namibia,2020,4.451,9.110,0.741,56.475,0.666,-0.110,0.810,0.652,0.248 +Namibia,2021,4.491,9.128,0.808,56.850,0.659,-0.153,0.829,0.644,0.230 +Namibia,2022,4.949,9.158,0.808,57.225,0.683,-0.124,0.849,0.676,0.261 +Namibia,2023,5.055,9.167,0.852,57.600,0.674,-0.113,0.873,0.635,0.208 +Nepal,2006,4.567,7.734,0.874,59.660,0.689,,0.897,0.583,0.171 +Nepal,2007,4.748,7.761,0.787,59.720,0.413,0.303,0.891,0.502,0.152 +Nepal,2008,4.441,7.814,0.818,59.780,0.618,0.276,0.900,0.589,0.153 +Nepal,2009,4.917,7.853,0.813,59.840,0.616,0.029,0.950,0.484,0.215 +Nepal,2010,4.350,7.895,0.779,59.900,0.519,0.077,0.911,0.538,0.226 +Nepal,2011,3.809,7.924,0.741,59.960,0.525,-0.024,0.935,0.530,0.207 +Nepal,2012,4.233,7.968,0.734,60.020,0.638,0.056,0.883,0.538,0.231 +Nepal,2013,4.605,8.000,0.740,60.080,0.722,0.137,0.877,0.496,0.279 +Nepal,2014,4.975,8.056,0.786,60.140,0.712,0.108,0.841,0.492,0.287 +Nepal,2015,4.812,8.089,0.748,60.200,0.763,0.214,0.824,0.444,0.358 +Nepal,2016,5.100,8.085,0.837,60.475,0.839,0.155,0.817,0.523,0.370 +Nepal,2017,4.737,8.159,0.816,60.750,0.845,0.120,0.770,0.463,0.376 +Nepal,2018,4.910,8.221,0.768,61.025,0.770,0.107,0.742,0.457,0.387 +Nepal,2019,5.449,8.274,0.772,61.300,0.790,0.152,0.712,0.444,0.357 +Nepal,2020,5.982,8.233,0.787,61.575,0.772,0.135,0.812,0.480,0.337 +Nepal,2021,4.622,8.257,0.699,61.850,0.818,0.144,0.770,0.414,0.354 +Nepal,2022,5.474,8.294,0.753,62.125,0.844,0.149,0.760,0.473,0.342 +Nepal,2023,5.389,8.318,0.749,62.400,0.765,0.184,0.792,0.461,0.350 +Netherlands,2005,7.464,10.809,0.947,70.700,0.901,,0.571,0.701,0.233 +Netherlands,2007,7.452,10.876,0.944,70.780,0.896,0.339,0.445,0.718,0.213 +Netherlands,2008,7.631,10.894,0.944,70.820,0.883,0.359,0.419,0.679,0.182 +Netherlands,2010,7.502,10.860,0.957,70.900,0.921,0.344,0.399,0.745,0.206 +Netherlands,2011,7.564,10.870,0.938,70.940,0.925,0.330,0.359,0.770,0.181 +Netherlands,2012,7.471,10.856,0.939,70.980,0.877,0.282,0.434,0.753,0.226 +Netherlands,2013,7.407,10.852,0.925,71.020,0.919,0.299,0.505,0.765,0.235 +Netherlands,2014,7.321,10.863,0.909,71.060,0.910,0.326,0.457,0.776,0.221 +Netherlands,2015,7.324,10.878,0.879,71.100,0.904,0.256,0.412,0.742,0.202 +Netherlands,2016,7.541,10.894,0.926,71.175,0.907,0.233,0.433,0.737,0.215 +Netherlands,2017,7.459,10.917,0.937,71.250,0.920,0.245,0.363,0.729,0.185 +Netherlands,2018,7.463,10.934,0.939,71.325,0.920,0.156,0.371,0.748,0.205 +Netherlands,2019,7.425,10.947,0.941,71.400,0.886,0.207,0.360,0.728,0.231 +Netherlands,2020,7.504,10.902,0.944,71.475,0.935,0.145,0.281,0.691,0.247 +Netherlands,2021,7.314,10.944,0.919,71.550,0.856,0.266,0.397,0.714,0.201 +Netherlands,2022,7.390,10.978,0.929,71.625,0.868,0.223,0.459,0.711,0.198 +Netherlands,2023,7.255,10.977,0.915,71.700,0.847,0.223,0.424,0.693,0.202 +New Zealand,2006,7.305,10.541,0.946,69.720,0.932,0.304,0.224,0.825,0.219 +New Zealand,2007,7.604,10.562,0.967,69.740,0.878,0.272,0.295,0.803,0.238 +New Zealand,2008,7.381,10.541,0.944,69.760,0.893,0.291,0.334,0.784,0.232 +New Zealand,2010,7.224,10.534,0.976,69.800,0.918,0.247,0.321,0.783,0.235 +New Zealand,2011,7.191,10.548,0.954,69.820,0.935,0.278,0.269,0.784,0.210 +New Zealand,2012,7.250,10.565,0.930,69.840,0.902,0.280,0.289,0.786,0.207 +New Zealand,2013,7.280,10.585,0.958,69.860,0.944,0.230,0.312,0.778,0.151 +New Zealand,2014,7.306,10.605,0.942,69.880,0.932,0.341,0.273,0.807,0.199 +New Zealand,2015,7.418,10.622,0.987,69.900,0.942,0.322,0.186,0.795,0.160 +New Zealand,2016,7.226,10.637,0.937,69.975,0.927,0.259,0.278,0.777,0.207 +New Zealand,2017,7.327,10.650,0.955,70.050,0.942,0.287,0.222,0.763,0.172 +New Zealand,2018,7.370,10.667,0.954,70.125,0.949,0.113,0.207,0.785,0.168 +New Zealand,2019,7.205,10.675,0.939,70.200,0.912,0.150,0.234,0.765,0.191 +New Zealand,2020,7.257,10.647,0.952,70.275,0.918,0.116,0.283,0.796,0.209 +New Zealand,2021,7.137,10.693,0.950,70.350,0.910,0.216,0.252,0.747,0.206 +New Zealand,2022,6.975,10.712,0.956,70.425,0.831,0.183,0.281,0.706,0.210 +New Zealand,2023,6.976,10.720,0.933,70.500,0.877,0.181,0.304,0.738,0.229 +Nicaragua,2006,4.460,8.395,0.877,64.300,0.745,0.005,0.844,0.780,0.294 +Nicaragua,2007,4.944,8.431,0.866,64.400,0.836,0.135,0.826,0.787,0.287 +Nicaragua,2008,5.104,8.450,0.857,64.500,0.791,0.070,0.819,0.770,0.289 +Nicaragua,2009,5.353,8.402,0.835,64.600,0.746,0.065,0.794,0.740,0.299 +Nicaragua,2010,5.687,8.431,0.863,64.700,0.792,0.013,0.802,0.749,0.268 +Nicaragua,2011,5.386,8.478,0.800,64.800,0.779,-0.024,0.760,0.747,0.309 +Nicaragua,2012,5.448,8.526,0.894,64.900,0.850,0.012,0.644,0.762,0.255 +Nicaragua,2013,5.772,8.559,0.868,65.000,0.859,0.034,0.636,0.800,0.271 +Nicaragua,2014,6.275,8.591,0.839,65.100,0.817,0.099,0.699,0.782,0.334 +Nicaragua,2015,5.924,8.624,0.827,65.200,0.809,0.073,0.728,0.771,0.346 +Nicaragua,2016,6.013,8.654,0.853,65.275,0.717,0.035,0.731,0.787,0.380 +Nicaragua,2017,6.476,8.685,0.838,65.350,0.922,0.006,0.673,0.793,0.308 +Nicaragua,2018,5.819,8.637,0.854,65.425,0.797,0.004,0.713,0.743,0.408 +Nicaragua,2019,6.113,8.594,0.874,65.500,0.883,0.024,0.622,0.790,0.337 +Nicaragua,2020,6.287,8.562,0.856,65.575,0.818,0.037,0.631,0.775,0.316 +Nicaragua,2021,6.095,8.647,0.848,65.650,0.905,0.020,0.675,0.799,0.293 +Nicaragua,2022,6.392,8.669,0.844,65.725,0.914,-0.004,0.570,0.787,0.339 +Nicaragua,2023,6.362,8.685,0.836,65.800,0.906,-0.008,0.532,0.772,0.340 +Niger,2006,3.737,6.872,0.677,50.140,0.750,0.073,0.755,0.746,0.179 +Niger,2007,4.277,6.867,0.726,50.580,0.584,-0.060,0.748,0.723,0.158 +Niger,2008,4.236,6.905,0.607,51.020,0.649,-0.059,0.749,0.689,0.194 +Niger,2009,4.267,6.887,0.771,51.460,0.880,-0.013,0.483,0.714,0.115 +Niger,2010,4.101,6.932,0.655,51.900,0.817,-0.027,0.529,0.715,0.126 +Niger,2011,4.556,6.918,0.818,52.340,0.780,-0.060,0.549,0.710,0.166 +Niger,2012,3.798,6.980,0.700,52.780,0.734,-0.068,0.777,0.582,0.142 +Niger,2013,3.716,6.994,0.696,53.220,0.825,-0.082,0.711,0.639,0.208 +Niger,2014,4.181,7.020,0.753,53.660,0.688,-0.051,0.605,0.629,0.205 +Niger,2015,3.671,7.025,0.713,54.100,0.728,-0.037,0.703,0.665,0.218 +Niger,2016,4.235,7.042,0.683,54.450,0.702,-0.020,0.814,0.646,0.325 +Niger,2017,4.616,7.053,0.582,54.800,0.684,-0.035,0.778,0.699,0.427 +Niger,2018,5.164,7.084,0.612,55.150,0.791,0.004,0.637,0.759,0.503 +Niger,2019,5.004,7.105,0.677,55.500,0.831,0.021,0.729,0.794,0.304 +Niger,2022,4.501,7.151,0.587,56.550,0.793,0.024,0.740,0.787,0.366 +Niger,2023,4.609,7.181,0.638,56.900,0.767,0.029,,0.747,0.417 +Nigeria,2006,4.710,8.314,0.735,50.220,0.649,0.080,0.871,0.772,0.178 +Nigeria,2007,4.890,8.350,0.718,50.540,0.635,0.132,0.918,0.815,0.141 +Nigeria,2008,4.939,8.389,0.780,50.860,0.584,0.115,0.892,0.755,0.244 +Nigeria,2009,4.980,8.439,0.722,51.180,0.537,0.063,0.913,0.730,0.225 +Nigeria,2010,4.760,8.488,0.824,51.500,0.565,0.062,0.911,0.759,0.190 +Nigeria,2012,5.493,8.526,0.818,52.140,0.652,0.062,0.900,0.782,0.209 +Nigeria,2013,4.818,8.564,0.663,52.460,0.622,0.046,0.905,0.652,0.286 +Nigeria,2015,4.933,8.600,0.812,53.100,0.680,-0.040,0.926,0.715,0.251 +Nigeria,2016,5.220,8.558,0.805,53.425,0.798,0.039,0.905,0.745,0.252 +Nigeria,2017,5.322,8.541,0.733,53.750,0.826,0.120,0.835,0.682,0.236 +Nigeria,2018,5.252,8.535,0.741,54.075,0.790,-0.015,0.866,0.762,0.256 +Nigeria,2019,4.266,8.532,0.735,54.400,0.746,0.019,0.873,0.698,0.229 +Nigeria,2020,5.503,8.490,0.739,54.725,0.713,0.094,0.913,0.737,0.316 +Nigeria,2021,4.479,8.502,0.742,55.050,0.726,0.047,0.912,0.666,0.188 +Nigeria,2022,5.294,8.510,0.785,55.375,0.776,0.172,0.935,0.760,0.253 +Nigeria,2023,4.869,8.514,0.781,55.700,0.720,0.209,0.877,0.721,0.266 +North Macedonia,2007,4.494,9.434,0.811,64.660,0.439,0.073,0.870,0.558,0.251 +North Macedonia,2009,4.428,9.481,0.734,64.820,0.552,-0.049,0.844,0.488,0.370 +North Macedonia,2010,4.180,9.512,0.687,64.900,0.513,-0.065,0.856,0.473,0.314 +North Macedonia,2011,4.898,9.533,0.784,64.980,0.607,-0.094,0.865,0.503,0.363 +North Macedonia,2012,4.640,9.527,0.798,65.060,0.613,-0.091,0.920,0.551,0.422 +North Macedonia,2013,5.186,9.555,0.832,65.140,0.641,0.018,0.861,0.521,0.331 +North Macedonia,2014,5.204,9.589,0.793,65.220,0.645,0.028,0.861,0.583,0.307 +North Macedonia,2015,4.976,9.625,0.766,65.300,0.660,-0.053,0.824,0.551,0.299 +North Macedonia,2016,5.346,9.652,0.871,65.500,0.706,0.073,0.870,0.587,0.292 +North Macedonia,2017,5.234,9.662,0.800,65.700,0.752,-0.065,0.856,0.447,0.299 +North Macedonia,2018,5.240,9.689,0.849,65.900,0.745,-0.048,0.910,0.512,0.298 +North Macedonia,2019,5.015,9.728,0.815,66.100,0.725,0.018,0.923,0.515,0.304 +North Macedonia,2020,5.054,9.666,0.750,66.300,0.787,0.127,0.877,0.542,0.365 +North Macedonia,2021,5.535,9.724,0.809,66.500,0.793,0.188,0.884,0.563,0.303 +North Macedonia,2022,5.167,9.749,0.850,66.700,0.723,0.068,0.937,0.555,0.277 +North Macedonia,2023,5.403,9.776,0.883,66.900,0.738,0.123,0.917,0.517,0.272 +Norway,2006,7.416,11.056,0.959,69.400,0.960,0.101,0.397,0.767,0.197 +Norway,2008,7.632,11.066,0.936,69.800,0.947,0.010,0.503,0.763,0.155 +Norway,2012,7.678,11.041,0.948,70.600,0.947,0.139,0.368,0.798,0.213 +Norway,2014,7.444,11.048,0.941,71.000,0.956,0.173,0.405,0.802,0.194 +Norway,2015,7.603,11.057,0.947,71.200,0.948,0.249,0.299,0.796,0.209 +Norway,2016,7.596,11.060,0.960,71.250,0.954,0.125,0.410,0.809,0.209 +Norway,2017,7.579,11.076,0.950,71.300,0.953,0.228,0.250,0.800,0.203 +Norway,2018,7.444,11.077,0.966,71.350,0.960,0.086,0.268,0.786,0.212 +Norway,2019,7.442,11.082,0.942,71.400,0.954,0.103,0.271,0.782,0.195 +Norway,2020,7.290,11.063,0.956,71.450,0.965,0.068,0.271,0.777,0.216 +Norway,2021,7.362,11.096,0.948,71.500,0.936,0.166,0.263,0.769,0.207 +Norway,2022,7.295,11.119,0.927,71.550,0.939,0.182,0.314,0.759,0.211 +Norway,2023,7.249,11.125,0.952,71.600,0.938,0.219,0.245,0.756,0.228 +Oman,2011,6.853,10.539,,62.340,0.916,0.008,,,0.295 +Pakistan,2005,5.225,8.252,0.591,53.200,0.630,,0.844,,0.237 +Pakistan,2007,5.671,8.314,0.479,53.720,0.396,0.081,0.794,0.583,0.310 +Pakistan,2008,4.414,8.309,0.373,53.980,0.335,0.092,0.848,0.533,0.321 +Pakistan,2009,5.208,8.315,0.522,54.240,0.388,0.069,0.874,0.516,0.349 +Pakistan,2010,5.786,8.308,0.571,54.500,0.364,0.292,0.852,0.527,0.372 +Pakistan,2011,5.267,8.314,0.510,54.760,0.376,0.022,0.857,0.473,0.358 +Pakistan,2012,5.132,8.331,0.542,55.020,0.367,0.157,0.842,0.510,0.332 +Pakistan,2013,5.138,8.359,0.607,55.280,0.448,0.091,0.792,0.474,0.274 +Pakistan,2014,5.436,8.390,0.552,55.540,0.543,0.131,0.677,0.475,0.295 +Pakistan,2015,4.823,8.423,0.562,55.800,0.587,0.076,0.717,0.469,0.329 +Pakistan,2016,5.549,8.465,0.627,56.075,0.634,0.084,0.793,0.503,0.332 +Pakistan,2017,5.831,8.495,0.690,56.350,0.713,0.035,0.714,0.489,0.308 +Pakistan,2018,5.472,8.540,0.685,56.625,0.773,0.058,0.799,0.470,0.377 +Pakistan,2019,4.443,8.548,0.617,56.900,0.685,0.112,0.776,0.489,0.424 +Pakistan,2020,4.624,8.518,0.594,57.175,0.767,0.003,0.833,0.470,0.376 +Pakistan,2021,4.487,8.563,0.608,57.450,0.764,0.007,0.743,0.518,0.307 +Pakistan,2022,4.931,8.604,0.590,57.725,0.745,0.000,0.865,0.469,0.368 +Pakistan,2023,4.549,8.588,0.553,58.000,0.661,0.070,0.889,0.504,0.417 +Panama,2006,6.128,9.806,0.951,66.860,0.882,-0.056,0.912,0.826,0.232 +Panama,2007,6.894,9.901,0.937,67.020,0.640,0.074,0.915,0.789,0.149 +Panama,2008,6.931,9.977,0.922,67.180,0.707,0.051,0.881,0.776,0.150 +Panama,2009,7.034,9.971,0.905,67.340,0.721,0.006,0.889,0.839,0.144 +Panama,2010,7.321,10.010,0.928,67.500,0.755,-0.017,0.880,0.841,0.146 +Panama,2011,7.248,10.099,0.876,67.660,0.829,0.000,0.840,0.853,0.180 +Panama,2012,6.860,10.174,0.897,67.820,0.783,-0.011,0.796,0.838,0.207 +Panama,2013,6.866,10.224,0.896,67.980,0.811,0.010,0.814,0.860,0.226 +Panama,2014,6.631,10.256,0.873,68.140,0.894,-0.007,0.847,0.799,0.254 +Panama,2015,6.606,10.294,0.883,68.300,0.847,-0.016,0.810,0.777,0.264 +Panama,2016,6.118,10.325,0.882,68.400,0.884,-0.111,0.837,0.813,0.244 +Panama,2017,6.568,10.362,0.912,68.500,0.900,-0.178,0.841,0.795,0.242 +Panama,2018,6.281,10.382,0.904,68.600,0.861,-0.139,0.837,0.841,0.223 +Panama,2019,6.086,10.398,0.886,68.700,0.883,-0.208,0.869,0.841,0.244 +Panama,2021,6.553,10.323,0.899,68.900,0.811,-0.152,0.861,0.834,0.218 +Panama,2022,5.979,10.412,0.891,69.000,0.899,-0.128,0.887,0.821,0.259 +Panama,2023,6.543,10.455,0.887,69.100,0.852,-0.147,0.871,0.829,0.257 +Paraguay,2006,4.730,9.154,0.895,64.880,0.691,0.056,0.841,0.752,0.303 +Paraguay,2007,5.272,9.197,0.863,64.960,0.699,0.121,0.930,0.812,0.219 +Paraguay,2008,5.570,9.250,0.889,65.040,0.649,0.046,0.891,0.798,0.259 +Paraguay,2009,5.576,9.237,0.900,65.120,0.718,0.016,0.857,0.803,0.186 +Paraguay,2010,5.841,9.331,0.889,65.200,0.726,0.065,0.780,0.826,0.176 +Paraguay,2011,5.677,9.360,0.869,65.280,0.666,0.179,0.756,0.823,0.190 +Paraguay,2012,5.820,9.339,0.931,65.360,0.748,0.188,0.774,0.849,0.213 +Paraguay,2013,5.936,9.405,0.939,65.440,0.909,0.034,0.903,0.874,0.224 +Paraguay,2014,5.119,9.443,0.959,65.520,0.759,-0.013,0.762,0.876,0.216 +Paraguay,2015,5.560,9.458,0.914,65.600,0.806,-0.019,0.863,0.832,0.219 +Paraguay,2016,5.801,9.485,0.940,65.650,0.854,-0.082,0.756,0.833,0.197 +Paraguay,2017,5.713,9.518,0.902,65.700,0.891,-0.008,0.810,0.820,0.232 +Paraguay,2019,5.653,9.519,0.892,65.800,0.876,0.017,0.882,0.790,0.275 +Paraguay,2020,5.501,9.497,0.907,65.850,0.865,0.054,0.829,0.767,0.269 +Paraguay,2021,5.576,9.523,0.908,65.900,0.888,0.015,0.857,0.806,0.248 +Paraguay,2022,6.138,9.513,0.899,65.950,0.922,-0.014,0.839,0.821,0.238 +Paraguay,2023,6.214,9.549,0.889,66.000,0.902,-0.004,0.835,0.833,0.240 +Peru,2006,4.811,8.979,0.875,66.460,0.668,-0.076,0.895,0.675,0.420 +Peru,2007,5.214,9.054,0.756,66.720,0.638,-0.082,0.931,0.730,0.361 +Peru,2008,5.129,9.134,0.777,66.980,0.638,-0.072,0.896,0.701,0.354 +Peru,2009,5.519,9.138,0.799,67.240,0.638,-0.084,0.880,0.758,0.320 +Peru,2010,5.613,9.210,0.812,67.500,0.757,-0.066,0.881,0.744,0.330 +Peru,2011,5.892,9.263,0.756,67.760,0.773,-0.128,0.824,0.742,0.331 +Peru,2012,5.825,9.313,0.764,68.020,0.703,-0.084,0.867,0.705,0.398 +Peru,2013,5.783,9.361,0.797,68.280,0.703,-0.071,0.870,0.741,0.390 +Peru,2014,5.866,9.374,0.819,68.540,0.722,-0.141,0.878,0.743,0.319 +Peru,2015,5.577,9.394,0.798,68.800,0.802,-0.095,0.884,0.744,0.378 +Peru,2016,5.701,9.419,0.803,68.975,0.830,-0.139,0.866,0.791,0.338 +Peru,2017,5.711,9.429,0.830,69.150,0.827,-0.160,0.895,0.768,0.394 +Peru,2018,5.680,9.449,0.845,69.325,0.830,-0.184,0.906,0.783,0.380 +Peru,2019,5.999,9.452,0.809,69.500,0.815,-0.135,0.874,0.794,0.375 +Peru,2020,4.994,9.323,0.749,69.675,0.806,-0.094,0.912,0.736,0.481 +Peru,2021,5.694,9.436,0.819,69.850,0.812,-0.090,0.880,0.784,0.369 +Peru,2022,5.892,9.453,0.823,70.025,0.764,-0.180,0.884,0.755,0.378 +Peru,2023,5.936,9.459,0.787,70.200,0.757,-0.061,0.919,0.765,0.370 +Philippines,2006,4.670,8.562,0.795,61.360,0.828,0.058,0.841,0.756, +Philippines,2007,5.074,8.606,0.801,61.420,0.852,-0.027,0.880,0.736,0.378 +Philippines,2008,4.589,8.630,0.798,61.480,0.861,0.078,0.817,0.774,0.384 +Philippines,2009,4.880,8.626,0.775,61.540,0.874,-0.001,0.805,0.791,0.311 +Philippines,2010,4.942,8.679,0.805,61.600,0.893,0.028,0.812,0.829,0.294 +Philippines,2011,4.994,8.699,0.789,61.660,0.883,0.068,0.783,0.808,0.358 +Philippines,2012,5.002,8.748,0.813,61.720,0.914,0.048,0.771,0.811,0.351 +Philippines,2013,4.977,8.796,0.846,61.780,0.907,0.016,0.756,0.796,0.332 +Philippines,2014,5.313,8.842,0.813,61.840,0.902,-0.020,0.787,0.787,0.334 +Philippines,2015,5.547,8.887,0.854,61.900,0.912,-0.056,0.755,0.796,0.351 +Philippines,2016,5.431,8.938,0.821,61.925,0.908,-0.076,0.792,0.807,0.290 +Philippines,2017,5.594,8.987,0.851,61.950,0.926,-0.146,0.711,0.753,0.341 +Philippines,2018,5.869,9.032,0.846,61.975,0.918,-0.112,0.726,0.756,0.393 +Philippines,2019,6.268,9.075,0.845,62.000,0.910,-0.087,0.748,0.765,0.341 +Philippines,2020,5.080,8.958,0.781,62.025,0.932,-0.114,0.744,0.793,0.327 +Philippines,2021,5.965,8.999,0.778,62.050,0.905,-0.012,0.721,0.790,0.323 +Philippines,2022,5.995,9.057,0.819,62.075,0.952,-0.155,0.757,0.833,0.301 +Philippines,2023,6.184,9.102,0.796,62.100,0.932,-0.038,0.768,0.800,0.324 +Poland,2005,5.587,9.844,0.922,66.200,0.782,,0.983,0.611,0.282 +Poland,2007,5.886,9.973,0.913,66.560,0.772,-0.053,0.925,0.665,0.238 +Poland,2009,5.772,10.041,0.917,66.920,0.821,0.068,0.898,0.649,0.246 +Poland,2010,5.887,10.073,0.955,67.100,0.795,-0.003,0.905,0.686,0.234 +Poland,2011,5.646,10.122,0.905,67.280,0.868,-0.072,0.908,0.659,0.224 +Poland,2012,5.876,10.137,0.936,67.460,0.811,-0.032,0.888,0.711,0.267 +Poland,2013,5.746,10.146,0.912,67.640,0.776,-0.142,0.916,0.675,0.242 +Poland,2014,5.750,10.184,0.924,67.820,0.875,-0.069,0.898,0.681,0.223 +Poland,2015,6.007,10.228,0.893,68.000,0.793,-0.098,0.810,0.631,0.240 +Poland,2016,6.162,10.258,0.917,68.175,0.871,-0.096,0.848,0.666,0.224 +Poland,2017,6.201,10.308,0.882,68.350,0.831,-0.127,0.639,0.566,0.203 +Poland,2018,6.111,10.365,0.863,68.525,0.870,-0.260,0.720,0.622,0.176 +Poland,2019,6.242,10.409,0.878,68.700,0.883,-0.237,0.696,0.613,0.168 +Poland,2020,6.139,10.390,0.953,68.875,0.767,-0.014,0.787,0.677,0.329 +Poland,2021,5.978,10.461,0.936,69.050,0.732,0.122,0.744,0.700,0.277 +Poland,2022,6.666,10.513,0.886,69.225,0.800,-0.209,0.667,0.594,0.140 +Poland,2023,6.685,10.527,0.890,69.400,0.775,-0.232,0.662,0.556,0.155 +Portugal,2006,5.405,10.359,0.905,68.340,0.882,-0.184,0.880,0.647,0.333 +Portugal,2008,5.717,10.383,0.886,68.820,0.646,-0.223,0.933,0.667,0.309 +Portugal,2010,5.095,10.367,0.864,69.300,0.721,-0.112,0.948,0.681,0.265 +Portugal,2011,5.220,10.352,0.856,69.540,0.875,-0.179,0.962,0.671,0.279 +Portugal,2012,4.994,10.314,0.866,69.780,0.774,-0.103,0.959,0.631,0.370 +Portugal,2013,5.158,10.310,0.867,70.020,0.788,-0.124,0.946,0.665,0.348 +Portugal,2014,5.127,10.324,0.862,70.260,0.847,-0.132,0.941,0.663,0.358 +Portugal,2015,5.081,10.346,0.866,70.500,0.800,-0.169,0.941,0.629,0.371 +Portugal,2016,5.447,10.369,0.905,70.625,0.838,-0.231,0.922,0.659,0.326 +Portugal,2017,5.711,10.406,0.900,70.750,0.905,-0.182,0.881,0.608,0.294 +Portugal,2018,5.920,10.435,0.887,70.875,0.877,-0.267,0.880,0.646,0.318 +Portugal,2019,6.095,10.462,0.876,71.000,0.882,-0.240,0.915,0.675,0.300 +Portugal,2020,5.768,10.374,0.875,71.125,0.913,-0.244,0.867,0.614,0.383 +Portugal,2021,6.183,10.425,0.895,71.250,0.892,-0.211,0.872,0.629,0.284 +Portugal,2022,5.953,10.484,0.862,71.375,0.903,-0.139,0.893,0.638,0.316 +Portugal,2023,5.954,10.504,0.895,71.500,0.847,-0.176,0.889,0.661,0.309 +Qatar,2009,6.418,11.434,0.894,64.360,0.865,0.230,0.184,0.673,0.258 +Qatar,2010,6.850,11.551,,64.700,,0.095,,, +Qatar,2011,6.592,11.625,0.857,65.040,0.905,0.000,,0.661,0.328 +Qatar,2012,6.611,11.617,0.838,65.380,0.924,0.149,,0.683,0.322 +Qatar,2015,6.375,11.532,,66.400,,,,, +Romania,2005,5.049,9.733,0.838,64.500,0.800,,0.957,0.576,0.346 +Romania,2007,5.394,9.901,0.736,64.860,0.686,-0.194,0.949,0.575,0.277 +Romania,2009,5.368,9.958,0.812,65.220,0.606,-0.203,0.967,0.545,0.270 +Romania,2010,4.909,9.924,0.689,65.400,0.566,-0.091,0.974,0.539,0.344 +Romania,2011,5.023,9.973,0.753,65.580,0.650,-0.148,0.964,0.501,0.294 +Romania,2012,5.167,9.997,0.740,65.760,0.645,-0.120,0.959,0.520,0.343 +Romania,2013,5.082,10.003,0.778,65.940,0.655,-0.135,0.952,0.541,0.329 +Romania,2014,5.727,10.047,0.753,66.120,0.754,-0.107,0.958,0.565,0.331 +Romania,2015,5.777,10.083,0.787,66.300,0.796,-0.147,0.962,0.627,0.312 +Romania,2016,5.969,10.117,0.809,66.425,0.822,-0.120,0.949,0.607,0.258 +Romania,2017,6.090,10.201,0.811,66.550,0.839,-0.165,0.926,0.632,0.231 +Romania,2018,6.151,10.266,0.818,66.675,0.845,-0.224,0.921,0.649,0.298 +Romania,2019,6.130,10.309,0.842,66.800,0.848,-0.228,0.954,0.605,0.244 +Romania,2020,6.785,10.277,0.869,66.925,0.863,-0.161,0.918,0.668,0.256 +Romania,2021,6.549,10.341,0.835,67.050,0.871,-0.187,0.928,0.674,0.264 +Romania,2022,6.437,10.396,0.830,67.175,0.836,-0.173,0.941,0.615,0.258 +Romania,2023,6.489,10.431,0.826,67.300,0.849,-0.202,0.905,0.628,0.271 +Russia,2006,4.964,9.988,0.895,58.740,0.643,-0.312,0.935,0.534,0.232 +Russia,2007,5.223,10.071,0.885,59.180,0.593,-0.289,0.933,0.546,0.193 +Russia,2008,5.619,10.122,0.882,59.620,0.643,-0.311,0.924,0.570,0.166 +Russia,2009,5.158,10.041,0.908,60.060,0.617,-0.289,0.954,0.540,0.169 +Russia,2010,5.385,10.084,0.909,60.500,0.613,-0.302,0.937,0.567,0.171 +Russia,2011,5.389,10.126,0.883,60.940,0.626,-0.284,0.935,0.564,0.165 +Russia,2012,5.621,10.163,0.901,61.380,0.609,-0.298,0.938,0.563,0.174 +Russia,2013,5.537,10.179,0.881,61.820,0.661,-0.295,0.934,0.592,0.180 +Russia,2014,6.037,10.168,0.932,62.260,0.744,-0.270,0.869,0.617,0.151 +Russia,2015,5.996,10.146,0.924,62.700,0.685,-0.177,0.913,0.609,0.130 +Russia,2016,5.855,10.146,0.911,63.075,0.714,-0.187,0.925,0.587,0.142 +Russia,2017,5.579,10.163,0.896,63.450,0.731,-0.151,0.862,0.651,0.195 +Russia,2018,5.514,10.191,0.909,63.825,0.729,-0.153,0.865,0.615,0.199 +Russia,2019,5.441,10.213,0.910,64.200,0.715,-0.122,0.848,0.632,0.200 +Russia,2020,5.495,10.188,0.887,64.575,0.714,-0.078,0.823,0.621,0.190 +Russia,2021,5.448,10.247,0.862,64.950,0.671,0.053,0.808,0.590,0.190 +Russia,2022,6.044,10.225,0.920,65.325,0.776,-0.074,0.767,0.614,0.211 +Russia,2023,5.865,10.209,0.854,65.700,0.750,0.056,0.733,0.616,0.190 +Rwanda,2006,4.215,7.087,0.718,53.500,0.915,,0.299,0.701,0.189 +Rwanda,2008,4.363,7.213,0.486,54.700,0.752,0.014,0.286,0.633,0.221 +Rwanda,2009,4.030,7.247,0.559,55.300,0.766,-0.004,0.410,0.658,0.112 +Rwanda,2011,4.097,7.343,0.570,56.500,0.829,-0.042,0.161,0.608,0.154 +Rwanda,2012,3.333,7.401,0.637,57.100,0.835,-0.015,0.081,0.624,0.132 +Rwanda,2013,3.466,7.423,0.750,57.700,0.904,-0.031,0.117,0.728,0.167 +Rwanda,2014,3.596,7.459,0.748,58.300,0.894,-0.026,0.078,0.748,0.134 +Rwanda,2015,3.483,7.520,0.678,58.900,0.908,0.022,0.095,0.692,0.206 +Rwanda,2016,3.333,7.554,0.665,59.225,0.911,0.022,0.159,0.715,0.285 +Rwanda,2017,3.108,7.568,0.517,59.550,0.908,0.048,0.214,0.724,0.358 +Rwanda,2018,3.561,7.625,0.616,59.875,0.924,0.053,0.164,0.765,0.308 +Rwanda,2019,3.268,7.692,0.489,60.200,0.869,0.060,0.168,0.717,0.418 +Saudi Arabia,2005,7.080,10.679,0.868,61.200,,,0.505,0.681,0.243 +Saudi Arabia,2007,7.267,10.646,0.892,61.600,0.622,0.002,,0.718,0.232 +Saudi Arabia,2008,6.811,10.668,0.823,61.800,0.532,-0.024,0.508,0.607,0.202 +Saudi Arabia,2009,6.148,10.610,0.921,62.000,0.639,-0.111,0.445,0.683,0.319 +Saudi Arabia,2010,6.307,10.627,0.880,62.200,0.678,-0.034,,0.645,0.297 +Saudi Arabia,2011,6.700,10.706,0.830,62.400,0.603,-0.144,,0.699,0.240 +Saudi Arabia,2012,6.396,10.737,0.867,62.600,0.560,-0.123,,0.692,0.225 +Saudi Arabia,2013,6.495,10.744,0.827,62.800,0.661,-0.085,,0.691,0.276 +Saudi Arabia,2014,6.278,10.763,0.818,63.000,0.762,-0.077,,0.663,0.313 +Saudi Arabia,2015,6.345,10.790,0.820,63.200,0.820,-0.050,,0.668,0.327 +Saudi Arabia,2016,6.474,10.793,0.890,63.400,0.774,-0.138,,0.725,0.266 +Saudi Arabia,2017,6.294,10.770,0.840,63.600,0.814,-0.138,,0.703,0.306 +Saudi Arabia,2018,6.356,10.773,0.868,63.800,0.855,-0.198,,0.696,0.288 +Saudi Arabia,2019,6.561,10.758,0.912,64.000,0.891,-0.153,,0.674,0.238 +Saudi Arabia,2020,6.560,10.709,0.890,64.200,0.884,-0.117,,0.702,0.251 +Saudi Arabia,2021,6.445,10.749,0.859,64.400,0.902,-0.108,,0.728,0.228 +Saudi Arabia,2022,6.382,10.820,0.900,64.600,,-0.032,,0.677,0.205 +Saudi Arabia,2023,6.953,10.829,0.884,64.800,,0.028,,0.737,0.240 +Senegal,2006,4.417,7.930,0.760,55.500,0.736,-0.059,0.805,0.687,0.225 +Senegal,2007,4.680,7.931,0.718,55.800,0.698,-0.009,0.827,0.718,0.199 +Senegal,2008,4.683,7.941,0.756,56.100,0.612,-0.037,0.879,0.669,0.252 +Senegal,2009,4.335,7.942,0.810,56.400,0.557,-0.044,0.918,0.708,0.228 +Senegal,2010,4.372,7.948,0.760,56.700,0.777,-0.085,0.851,0.670,0.143 +Senegal,2011,3.834,7.934,0.602,57.000,0.641,-0.168,0.870,0.696,0.180 +Senegal,2012,3.669,7.946,0.711,57.300,0.668,-0.042,0.852,0.722,0.214 +Senegal,2013,3.647,7.943,0.823,57.600,0.636,-0.058,0.837,0.694,0.165 +Senegal,2014,4.395,7.976,0.856,57.900,0.692,-0.052,0.700,0.696,0.157 +Senegal,2015,4.617,8.010,0.702,58.200,0.720,-0.117,0.765,0.710,0.208 +Senegal,2016,4.595,8.045,0.839,58.500,0.744,-0.092,0.794,0.781,0.245 +Senegal,2017,4.683,8.089,0.744,58.800,0.687,-0.050,0.825,0.751,0.291 +Senegal,2018,4.769,8.122,0.739,59.100,0.629,-0.080,0.805,0.724,0.247 +Senegal,2019,5.489,8.140,0.688,59.400,0.759,-0.025,0.796,0.768,0.332 +Senegal,2020,4.757,8.127,0.621,59.700,0.797,-0.052,0.855,0.816,0.268 +Senegal,2021,4.903,8.164,0.645,60.000,0.759,0.026,0.821,0.812,0.265 +Senegal,2022,4.907,8.179,0.609,60.300,0.758,0.049,0.854,0.813,0.287 +Senegal,2023,5.093,8.200,0.668,60.600,0.798,0.048,0.836,0.825,0.258 +Serbia,2007,4.750,9.536,0.844,65.280,0.453,-0.171,0.905,0.528,0.334 +Serbia,2009,4.380,9.571,0.770,65.560,0.373,-0.184,0.961,0.466,0.435 +Serbia,2010,4.461,9.583,0.726,65.700,0.463,-0.176,0.965,0.446,0.415 +Serbia,2011,4.815,9.611,0.773,65.840,0.440,-0.191,0.977,0.458,0.410 +Serbia,2012,5.155,9.609,0.819,65.980,0.461,-0.136,0.952,0.447,0.371 +Serbia,2013,5.102,9.642,0.828,66.120,0.533,-0.106,0.908,0.505,0.403 +Serbia,2014,5.113,9.631,0.783,66.260,0.532,0.066,0.912,0.473,0.326 +Serbia,2015,5.318,9.654,0.816,66.400,0.546,-0.068,0.859,0.472,0.303 +Serbia,2016,5.753,9.692,0.895,66.525,0.614,-0.074,0.890,0.492,0.298 +Serbia,2017,5.122,9.718,0.884,66.650,0.685,-0.084,0.851,0.485,0.326 +Serbia,2018,5.936,9.767,0.853,66.775,0.740,-0.106,0.864,0.527,0.296 +Serbia,2019,6.241,9.815,0.903,66.900,0.753,-0.046,0.813,0.474,0.242 +Serbia,2020,6.042,9.813,0.852,67.025,0.843,0.142,0.824,0.579,0.358 +Serbia,2021,6.245,9.895,0.890,67.150,0.850,0.261,0.806,0.568,0.311 +Serbia,2022,6.546,9.928,0.896,67.275,0.782,0.074,0.801,0.514,0.275 +Serbia,2023,6.441,9.961,0.895,67.400,0.807,0.085,0.782,0.526,0.206 +Sierra Leone,2006,3.628,7.122,0.561,46.280,0.679,0.097,0.836,0.535,0.381 +Sierra Leone,2007,3.585,7.177,0.686,46.660,0.720,0.243,0.830,0.635,0.290 +Sierra Leone,2008,2.997,7.205,0.591,47.040,0.716,0.144,0.925,0.588,0.370 +Sierra Leone,2010,4.134,7.245,0.812,47.800,0.726,0.008,0.910,0.497,0.290 +Sierra Leone,2011,4.502,7.277,0.782,48.180,0.770,0.001,0.855,0.495,0.300 +Sierra Leone,2013,4.514,7.557,0.708,48.940,0.720,-0.075,0.856,0.540,0.423 +Sierra Leone,2014,4.500,7.577,0.869,49.320,0.681,0.030,0.786,0.622,0.334 +Sierra Leone,2015,4.909,7.324,0.611,49.700,0.624,0.047,0.825,0.626,0.414 +Sierra Leone,2016,4.733,7.361,0.657,50.500,0.681,0.103,0.863,0.616,0.456 +Sierra Leone,2017,4.090,7.374,0.652,51.300,0.711,0.076,0.848,0.607,0.495 +Sierra Leone,2018,4.306,7.384,0.650,52.100,0.716,0.093,0.856,0.533,0.466 +Sierra Leone,2019,3.447,7.412,0.611,52.900,0.718,0.072,0.874,0.521,0.438 +Sierra Leone,2021,3.714,7.387,0.609,54.500,0.659,0.106,0.851,0.538,0.448 +Sierra Leone,2022,2.560,7.400,0.502,55.300,0.660,0.097,0.862,0.494,0.505 +Sierra Leone,2023,3.467,7.412,0.601,56.100,0.694,0.101,0.866,0.504,0.430 +Singapore,2006,6.463,11.168,0.904,71.580,0.757,0.132,,0.689,0.267 +Singapore,2007,6.834,11.213,0.921,71.760,0.867,0.287,0.064,0.588,0.114 +Singapore,2008,6.642,11.178,0.845,71.940,0.661,0.040,0.066,0.627,0.256 +Singapore,2009,6.145,11.149,0.866,72.120,0.776,-0.081,0.035,0.450,0.208 +Singapore,2010,6.531,11.267,0.864,72.300,0.846,-0.024,0.060,0.527,0.131 +Singapore,2011,6.561,11.306,0.904,72.480,0.822,-0.155,0.099,0.404,0.144 +Singapore,2013,6.533,11.356,0.808,72.840,0.827,0.109,0.242,0.663,0.148 +Singapore,2014,7.062,11.382,0.822,73.020,0.835,0.148,0.133,0.774,0.180 +Singapore,2015,6.620,11.399,0.866,73.200,0.887,0.144,0.099,0.736,0.142 +Singapore,2016,6.033,11.422,0.925,73.300,0.904,0.137,0.047,0.745,0.111 +Singapore,2017,6.378,11.465,0.897,73.400,0.926,0.129,0.162,0.750,0.179 +Singapore,2018,6.375,11.496,0.903,73.500,0.916,-0.073,0.097,0.731,0.107 +Singapore,2019,6.378,11.497,0.925,73.600,0.938,0.020,0.070,0.674,0.138 +Singapore,2021,6.587,11.587,0.876,73.800,0.879,0.060,0.145,0.697,0.160 +Singapore,2022,6.333,11.590,0.852,73.900,0.873,0.088,,0.688,0.209 +Singapore,2023,6.654,,0.916,74.000,0.861,,0.153,0.667,0.190 +Slovakia,2006,5.265,9.989,0.954,65.620,0.542,-0.054,0.946,0.586,0.308 +Slovakia,2010,6.052,10.152,0.920,66.500,0.636,-0.106,0.907,0.603,0.277 +Slovakia,2011,5.945,10.177,0.917,66.720,0.727,0.006,0.907,0.588,0.287 +Slovakia,2012,5.911,10.188,0.926,66.940,0.620,-0.032,0.907,0.585,0.302 +Slovakia,2013,5.937,10.193,0.909,67.160,0.598,-0.055,0.915,0.612,0.277 +Slovakia,2014,6.139,10.219,0.924,67.380,0.635,-0.130,0.914,0.619,0.267 +Slovakia,2015,6.162,10.268,0.943,67.600,0.587,-0.132,0.928,0.632,0.269 +Slovakia,2016,5.993,10.286,0.945,67.825,0.700,-0.065,0.917,0.688,0.232 +Slovakia,2017,6.366,10.314,0.913,68.050,0.714,-0.059,0.920,0.709,0.213 +Slovakia,2018,6.235,10.352,0.922,68.275,0.758,-0.172,0.910,0.670,0.253 +Slovakia,2019,6.243,10.375,0.933,68.500,0.771,-0.133,0.926,0.676,0.252 +Slovakia,2020,6.519,10.340,0.954,68.725,0.762,-0.081,0.901,0.695,0.274 +Slovakia,2021,6.419,10.390,0.951,68.950,0.742,0.042,0.896,0.692,0.241 +Slovakia,2022,6.091,10.409,0.961,69.175,0.732,-0.129,0.841,0.665,0.263 +Slovakia,2023,6.261,10.425,0.941,69.400,0.753,-0.136,0.825,0.647,0.235 +Slovenia,2006,5.811,10.399,0.936,68.560,0.936,0.037,0.708,0.608,0.307 +Slovenia,2009,5.830,10.406,0.919,69.040,0.896,-0.025,0.804,0.583,0.303 +Slovenia,2010,6.083,10.415,0.917,69.200,0.896,0.024,0.845,0.592,0.295 +Slovenia,2011,6.036,10.421,0.931,69.360,0.907,-0.031,0.893,0.587,0.285 +Slovenia,2012,6.063,10.392,0.925,69.520,0.904,-0.025,0.891,0.598,0.284 +Slovenia,2013,5.975,10.381,0.932,69.680,0.890,0.030,0.918,0.625,0.274 +Slovenia,2014,5.678,10.407,0.908,69.840,0.888,0.047,0.909,0.594,0.291 +Slovenia,2015,5.741,10.428,0.901,70.000,0.896,0.002,0.892,0.644,0.261 +Slovenia,2016,5.937,10.459,0.934,70.175,0.904,-0.060,0.838,0.597,0.272 +Slovenia,2017,6.167,10.505,0.928,70.350,0.921,-0.031,0.829,0.582,0.286 +Slovenia,2018,6.249,10.545,0.941,70.525,0.942,-0.125,0.839,0.601,0.275 +Slovenia,2019,6.665,10.572,0.949,70.700,0.945,-0.108,0.785,0.622,0.228 +Slovenia,2020,6.462,10.521,0.953,70.875,0.958,-0.090,0.797,0.575,0.314 +Slovenia,2021,6.761,10.598,0.955,71.050,0.851,0.026,0.754,0.643,0.261 +Slovenia,2022,6.723,10.650,0.942,71.225,0.930,0.099,0.762,0.625,0.242 +Slovenia,2023,6.746,10.664,0.911,71.400,0.931,0.031,0.750,0.615,0.256 +Somalia,2014,5.528,6.830,0.611,47.660,0.874,0.020,0.456,0.689,0.207 +Somalia,2015,5.354,6.937,0.599,48.100,0.968,0.016,0.410,0.764,0.187 +Somalia,2016,4.668,6.981,0.594,48.500,0.917,0.069,0.441,0.773,0.193 +Somaliland region,2009,4.991,,0.880,,0.746,,0.513,0.708,0.112 +Somaliland region,2010,4.657,,0.829,,0.820,,0.471,0.632,0.083 +Somaliland region,2011,4.931,,0.788,,0.858,,0.357,0.691,0.122 +Somaliland region,2012,5.057,,0.786,,0.758,,0.334,0.687,0.152 +South Africa,2006,5.084,9.455,0.913,46.000,0.649,-0.094,,0.724,0.223 +South Africa,2007,5.204,9.497,0.788,46.900,0.690,-0.169,0.859,0.658,0.210 +South Africa,2008,5.346,9.518,0.810,47.800,0.749,-0.106,0.866,0.712,0.206 +South Africa,2009,5.218,9.490,0.877,48.700,0.739,-0.165,0.904,0.656,0.231 +South Africa,2010,4.652,9.508,0.917,49.600,0.739,-0.213,0.791,0.698,0.124 +South Africa,2011,4.931,9.527,0.858,50.500,0.835,-0.166,0.819,0.720,0.230 +South Africa,2012,5.134,9.537,0.907,51.400,0.590,-0.175,0.838,0.711,0.178 +South Africa,2013,3.661,9.548,0.839,52.300,0.714,-0.089,0.800,0.740,0.167 +South Africa,2014,4.828,9.546,0.881,53.200,0.794,-0.128,0.820,0.730,0.243 +South Africa,2015,4.887,9.539,0.898,54.100,0.862,-0.138,0.853,0.717,0.161 +South Africa,2016,4.770,9.536,0.875,54.625,0.774,-0.082,0.813,0.743,0.301 +South Africa,2017,4.514,9.543,0.870,55.150,0.787,-0.141,0.865,0.709,0.268 +South Africa,2018,4.884,9.546,0.841,55.675,0.753,-0.063,0.841,0.736,0.283 +South Africa,2019,5.035,9.536,0.848,56.200,0.738,-0.147,0.820,0.727,0.268 +South Africa,2020,4.947,9.458,0.891,56.725,0.757,-0.030,0.912,0.761,0.294 +South Africa,2021,5.599,9.496,0.922,57.250,0.704,-0.148,0.892,0.784,0.173 +South Africa,2022,5.581,9.508,0.887,57.775,0.713,-0.071,0.908,0.744,0.239 +South Africa,2023,5.076,9.503,0.839,58.300,0.748,-0.109,0.861,0.708,0.255 +South Korea,2006,5.332,10.309,0.775,70.020,0.715,-0.058,0.799,0.545,0.338 +South Korea,2007,5.767,10.360,0.827,70.240,0.656,-0.065,0.803,0.612,0.226 +South Korea,2008,5.390,10.382,0.754,70.460,0.524,-0.108,0.771,0.554,0.239 +South Korea,2009,5.648,10.385,0.811,70.680,0.600,-0.102,0.787,0.596,0.209 +South Korea,2010,6.116,10.446,0.816,70.900,0.677,-0.039,0.752,0.626,0.130 +South Korea,2011,6.947,10.474,0.809,71.120,0.682,-0.054,0.827,0.587,0.168 +South Korea,2012,6.003,10.493,0.775,71.340,0.618,,0.844,0.610,0.206 +South Korea,2013,5.959,10.519,0.797,71.560,0.642,-0.056,0.832,0.589,0.189 +South Korea,2014,5.801,10.544,0.738,71.780,0.623,-0.049,0.834,0.575,0.283 +South Korea,2015,5.780,10.567,0.768,72.000,0.616,-0.041,0.841,0.561,0.244 +South Korea,2016,5.971,10.592,0.811,72.275,0.591,0.020,0.862,0.583,0.233 +South Korea,2017,5.874,10.620,0.807,72.550,0.538,0.008,0.851,0.546,0.235 +South Korea,2018,5.840,10.645,0.798,72.825,0.600,-0.095,0.797,0.579,0.217 +South Korea,2019,5.903,10.663,0.783,73.100,0.706,-0.061,0.718,0.593,0.236 +South Korea,2020,5.793,10.655,0.808,73.375,0.711,-0.112,0.665,0.550,0.247 +South Korea,2021,6.113,10.697,0.811,73.650,0.717,-0.033,0.685,0.566,0.221 +South Korea,2022,5.950,10.725,0.810,73.925,0.723,0.002,0.747,0.585,0.233 +South Korea,2023,6.112,10.742,0.799,74.200,0.762,-0.004,0.714,0.603,0.245 +South Sudan,2014,3.832,,0.545,52.880,0.567,,0.742,0.578,0.428 +South Sudan,2015,4.071,,0.585,53.000,0.512,,0.710,0.553,0.450 +South Sudan,2016,2.888,,0.532,53.175,0.440,,0.785,0.594,0.549 +South Sudan,2017,2.817,,0.557,53.350,0.456,,0.761,0.565,0.517 +Spain,2005,7.153,10.544,0.961,70.400,0.916,,0.777,0.694,0.241 +Spain,2007,6.995,10.585,0.957,70.640,0.782,-0.099,0.784,0.717,0.264 +Spain,2008,7.294,10.577,0.948,70.760,0.834,-0.155,0.683,0.649,0.260 +Spain,2009,6.199,10.530,0.929,70.880,0.749,-0.133,0.798,0.645,0.336 +Spain,2010,6.188,10.527,0.950,71.000,0.796,-0.144,0.840,0.645,0.322 +Spain,2011,6.518,10.516,0.944,71.120,0.819,-0.128,0.846,0.667,0.356 +Spain,2012,6.291,10.485,0.937,71.240,0.755,-0.065,0.844,0.644,0.366 +Spain,2013,6.150,10.474,0.929,71.360,0.759,-0.107,0.916,0.663,0.372 +Spain,2014,6.456,10.491,0.948,71.480,0.738,-0.034,0.854,0.683,0.335 +Spain,2015,6.381,10.529,0.956,71.600,0.732,-0.078,0.822,0.705,0.285 +Spain,2016,6.319,10.558,0.942,71.725,0.768,-0.054,0.819,0.630,0.301 +Spain,2017,6.230,10.585,0.903,71.850,0.756,-0.038,0.791,0.601,0.302 +Spain,2018,6.513,10.604,0.910,71.975,0.722,-0.081,0.777,0.636,0.357 +Spain,2019,6.457,10.616,0.949,72.100,0.778,-0.054,0.730,0.636,0.316 +Spain,2020,6.502,10.491,0.935,72.225,0.783,-0.127,0.730,0.671,0.317 +Spain,2021,6.470,10.544,0.926,72.350,0.782,-0.076,0.729,0.639,0.324 +Spain,2022,6.337,10.592,0.934,72.475,0.781,-0.001,0.673,0.636,0.320 +Spain,2023,6.456,10.609,0.912,72.600,0.779,-0.004,0.675,0.655,0.325 +Sri Lanka,2006,4.345,8.937,0.864,62.280,0.724,0.055,0.838,0.639,0.216 +Sri Lanka,2007,4.415,8.992,0.838,62.760,0.736,0.103,0.847,0.590,0.220 +Sri Lanka,2008,4.431,9.040,0.816,63.240,0.834,0.156,0.861,0.656,0.153 +Sri Lanka,2009,4.212,9.065,0.830,63.720,0.799,0.299,0.690,0.661,0.172 +Sri Lanka,2010,3.977,9.133,0.814,64.200,0.738,0.252,0.769,0.704,0.163 +Sri Lanka,2011,4.181,9.207,0.842,64.680,0.823,0.138,0.760,0.730,0.175 +Sri Lanka,2012,4.225,9.282,0.824,65.160,0.800,0.155,0.823,0.761,0.197 +Sri Lanka,2013,4.365,9.316,0.809,65.640,0.834,0.262,0.842,0.776,0.208 +Sri Lanka,2014,4.268,9.373,0.805,66.120,0.868,0.291,0.791,0.785,0.187 +Sri Lanka,2015,4.612,9.410,0.863,66.600,0.902,0.312,0.859,0.789,0.235 +Sri Lanka,2017,4.331,9.514,0.823,66.800,0.827,0.083,0.844,0.729,0.270 +Sri Lanka,2018,4.435,9.529,0.833,66.900,0.859,0.096,0.856,0.773,0.302 +Sri Lanka,2019,4.213,9.521,0.815,67.000,0.824,0.043,0.863,0.753,0.315 +Sri Lanka,2020,4.778,9.468,0.842,67.100,0.803,-0.050,0.768,0.758,0.285 +Sri Lanka,2021,4.103,9.492,0.812,67.200,0.771,-0.013,0.849,0.733,0.312 +Sri Lanka,2022,3.985,9.409,0.825,67.300,0.740,0.038,0.900,0.715,0.321 +Sri Lanka,2023,3.602,9.364,0.790,67.400,0.754,0.050,0.922,0.709,0.353 +State of Palestine,2006,4.716,8.201,0.818,,0.547,,0.858,0.492,0.431 +State of Palestine,2007,4.151,8.181,0.712,,0.365,-0.083,0.844,0.515,0.412 +State of Palestine,2008,4.386,8.275,0.666,,0.358,-0.075,0.753,0.513,0.403 +State of Palestine,2009,4.470,8.337,0.738,,0.468,-0.091,0.797,0.474,0.466 +State of Palestine,2010,4.703,8.363,0.822,,0.504,-0.121,0.752,0.553,0.381 +State of Palestine,2011,4.751,8.452,0.751,,0.522,-0.131,0.750,0.499,0.388 +State of Palestine,2012,4.647,8.598,0.782,,0.542,-0.163,0.730,0.560,0.379 +State of Palestine,2013,4.844,8.595,0.761,,0.454,-0.163,0.780,0.537,0.365 +State of Palestine,2014,4.722,8.618,0.775,,0.657,-0.163,0.804,0.505,0.380 +State of Palestine,2015,4.695,8.683,0.766,,0.556,-0.173,0.774,0.536,0.369 +State of Palestine,2016,4.907,8.738,0.818,,0.608,-0.151,0.812,0.544,0.378 +State of Palestine,2017,4.628,8.734,0.824,,0.632,-0.186,0.831,0.534,0.416 +State of Palestine,2018,4.554,8.718,0.819,,0.655,-0.163,0.814,0.528,0.419 +State of Palestine,2019,4.483,8.716,0.833,,0.653,-0.135,0.829,0.538,0.400 +State of Palestine,2022,4.908,,0.860,,0.695,,0.836,0.584,0.362 +State of Palestine,2023,4.851,,0.831,,0.708,,0.808,0.580,0.378 +Sudan,2009,4.455,8.457,0.911,57.460,0.710,0.046,0.701,0.688,0.245 +Sudan,2010,4.435,8.465,0.855,57.700,0.648,-0.073,0.737,0.589,0.221 +Sudan,2011,4.314,8.527,0.818,57.940,0.583,-0.053,0.663,0.532,0.249 +Sudan,2012,4.550,8.458,0.813,58.180,0.412,-0.072,0.734,0.511,0.242 +Sudan,2014,4.139,8.471,0.811,58.660,0.390,-0.080,0.794,0.461,0.303 +Suriname,2012,6.269,9.874,0.797,62.840,0.885,-0.088,0.751,0.730,0.250 +Sweden,2005,7.376,10.724,0.951,71.000,0.964,,,0.742,0.151 +Sweden,2007,7.241,10.791,0.917,71.080,0.910,0.141,0.289,0.735,0.177 +Sweden,2008,7.516,10.778,0.923,71.120,0.912,0.120,0.314,0.763,0.134 +Sweden,2009,7.266,10.725,0.903,71.160,0.864,0.216,0.292,0.761,0.151 +Sweden,2010,7.496,10.775,0.970,71.200,0.905,0.136,0.253,0.788,0.200 +Sweden,2011,7.382,10.799,0.921,71.240,0.941,0.156,0.269,0.762,0.179 +Sweden,2012,7.560,10.785,0.929,71.280,0.944,0.127,0.254,0.796,0.170 +Sweden,2013,7.434,10.789,0.916,71.320,0.936,0.154,0.324,0.782,0.184 +Sweden,2014,7.239,10.805,0.933,71.360,0.945,0.197,0.250,0.793,0.208 +Sweden,2015,7.289,10.838,0.929,71.400,0.935,0.206,0.232,0.766,0.191 +Sweden,2016,7.369,10.846,0.912,71.525,0.918,0.141,0.246,0.752,0.201 +Sweden,2017,7.287,10.858,0.914,71.650,0.935,0.165,0.239,0.756,0.175 +Sweden,2018,7.375,10.866,0.931,71.775,0.942,0.072,0.263,0.747,0.161 +Sweden,2019,7.398,10.875,0.934,71.900,0.942,0.085,0.250,0.775,0.202 +Sweden,2020,7.314,10.846,0.936,72.025,0.951,0.084,0.203,0.717,0.222 +Sweden,2021,7.439,10.893,0.932,72.150,0.953,0.172,0.191,0.763,0.190 +Sweden,2022,7.431,10.912,0.949,72.275,0.939,0.232,0.213,0.750,0.163 +Sweden,2023,7.161,10.902,0.927,72.400,0.926,0.147,0.253,0.739,0.194 +Switzerland,2006,7.473,11.056,0.951,71.160,0.919,0.284,0.408,0.742,0.212 +Switzerland,2009,7.525,11.065,0.938,71.340,0.891,0.118,0.342,0.741,0.202 +Switzerland,2012,7.776,11.094,0.947,71.520,0.945,0.131,0.323,0.793,0.176 +Switzerland,2014,7.493,11.111,0.959,71.640,0.949,0.053,0.283,0.788,0.189 +Switzerland,2015,7.572,11.116,0.938,71.700,0.928,0.102,0.210,0.794,0.166 +Switzerland,2016,7.459,11.126,0.928,71.900,0.934,0.081,0.302,0.758,0.206 +Switzerland,2017,7.474,11.130,0.950,72.100,0.925,0.173,0.316,0.734,0.196 +Switzerland,2018,7.509,11.151,0.930,72.300,0.926,0.094,0.301,0.756,0.192 +Switzerland,2019,7.694,11.155,0.949,72.500,0.913,0.029,0.294,0.743,0.171 +Switzerland,2020,7.508,11.124,0.946,72.700,0.917,-0.073,0.280,0.730,0.193 +Switzerland,2021,7.328,11.158,0.934,72.900,0.908,0.024,0.287,0.747,0.183 +Switzerland,2022,6.884,11.170,0.881,73.100,0.848,0.128,0.235,0.710,0.180 +Switzerland,2023,6.969,11.169,0.904,73.300,0.891,0.104,0.247,0.745,0.185 +Syria,2008,5.323,8.658,0.712,68.620,0.661,0.116,0.680,0.562,0.338 +Syria,2009,4.979,8.656,0.842,66.860,0.748,0.076,0.688,0.491,0.292 +Syria,2010,4.465,8.733,0.934,65.100,0.647,0.002,0.743,0.489,0.225 +Syria,2011,4.038,8.735,0.576,63.340,0.530,0.125,0.741,0.521,0.496 +Syria,2012,3.164,8.578,0.588,61.580,0.467,0.310,0.673,0.451,0.705 +Syria,2013,2.688,8.419,0.585,59.820,0.455,0.219,0.663,0.354,0.622 +Syria,2015,3.462,8.492,0.464,56.300,0.448,0.036,0.685,0.363,0.643 +Taiwan Province of China,2006,6.189,10.602,0.882,68.680,0.630,-0.035,0.846,0.683,0.094 +Taiwan Province of China,2008,5.548,10.600,0.830,69.140,0.642,-0.022,0.785,0.682,0.169 +Taiwan Province of China,2010,6.229,10.681,0.831,69.600,0.677,-0.001,0.821,0.738,0.136 +Taiwan Province of China,2011,6.309,10.693,0.863,,0.761,0.030,0.755,0.727,0.112 +Taiwan Province of China,2012,6.126,10.718,0.825,,0.698,0.016,0.803,0.702,0.140 +Taiwan Province of China,2013,6.340,10.724,0.817,,0.690,-0.003,0.841,0.754,0.124 +Taiwan Province of China,2014,6.363,10.749,0.870,,0.693,0.089,0.866,0.767,0.108 +Taiwan Province of China,2015,6.450,10.779,0.885,,0.701,0.017,0.857,0.750,0.129 +Taiwan Province of China,2016,6.513,10.768,0.895,,0.719,-0.049,0.811,0.743,0.108 +Taiwan Province of China,2017,6.359,10.774,0.891,,0.760,-0.070,0.743,0.715,0.114 +Taiwan Province of China,2018,6.467,10.781,0.896,,0.741,-0.179,0.736,0.746,0.093 +Taiwan Province of China,2019,6.537,10.797,0.893,,0.814,-0.131,0.718,0.762,0.093 +Taiwan Province of China,2020,6.751,,0.901,,0.799,,0.711,0.743,0.083 +Taiwan Province of China,2021,6.247,,0.866,,0.818,,0.675,0.667,0.123 +Taiwan Province of China,2022,6.607,,0.883,,0.800,,0.658,0.717,0.095 +Taiwan Province of China,2023,6.655,,0.872,,0.795,,0.641,0.748,0.111 +Tajikistan,2006,4.613,7.591,0.724,60.500,0.702,-0.096,0.768,0.494,0.195 +Tajikistan,2007,4.432,7.648,0.727,60.600,0.818,-0.007,0.659,0.619,0.133 +Tajikistan,2008,5.064,7.705,0.701,60.700,0.816,0.010,0.723,0.488,0.160 +Tajikistan,2009,4.575,7.724,0.676,60.800,0.744,-0.007,0.792,0.475,0.203 +Tajikistan,2010,4.381,7.766,0.759,60.900,0.784,0.054,0.679,0.483,0.192 +Tajikistan,2011,4.263,7.817,0.751,61.000,0.776,-0.127,0.672,0.573,0.166 +Tajikistan,2012,4.497,7.867,0.729,61.100,0.749,-0.081,0.717,0.583,0.198 +Tajikistan,2013,4.967,7.916,0.701,61.200,0.693,0.055,0.764,0.578,0.170 +Tajikistan,2014,4.896,7.958,0.810,61.300,0.853,-0.007,0.698,0.587,0.196 +Tajikistan,2015,5.124,7.993,0.844,61.400,0.847,0.013,0.742,0.633,0.196 +Tajikistan,2016,5.104,8.036,0.857,61.550,0.703,0.001,0.632,0.587,0.220 +Tajikistan,2017,5.829,8.082,0.663,61.700,0.832,0.116,0.718,0.581,0.278 +Tajikistan,2018,5.497,8.133,0.875,61.850,,-0.074,0.578,0.632,0.220 +Tajikistan,2019,5.464,8.182,0.880,62.000,,-0.054,0.490,0.663,0.178 +Tajikistan,2020,5.373,8.203,0.790,62.150,,-0.054,0.550,0.652,0.344 +Tajikistan,2021,5.287,8.271,0.883,62.300,,-0.071,0.499,0.655,0.240 +Tajikistan,2022,5.176,8.328,0.865,62.450,,-0.003,0.397,0.710,0.220 +Tajikistan,2023,5.379,8.371,0.871,62.600,,-0.054,0.482,0.638,0.231 +Tanzania,2006,3.922,7.459,0.783,50.760,0.787,-0.030,0.649,0.725,0.209 +Tanzania,2007,4.318,7.497,0.708,51.420,0.716,-0.016,0.707,0.702,0.220 +Tanzania,2008,4.385,7.525,0.774,52.080,0.562,0.253,0.930,0.740,0.178 +Tanzania,2009,3.408,7.551,0.837,52.740,0.607,0.305,0.903,0.733,0.161 +Tanzania,2010,3.229,7.587,0.813,53.400,0.597,0.135,0.866,0.667,0.146 +Tanzania,2011,4.074,7.632,0.883,54.060,0.736,-0.050,0.816,0.720,0.145 +Tanzania,2012,4.007,7.647,0.832,54.720,0.577,0.209,0.887,0.641,0.195 +Tanzania,2013,3.852,7.683,0.803,55.380,0.654,0.051,0.859,0.707,0.191 +Tanzania,2014,3.483,7.717,0.789,56.040,0.654,0.107,0.878,0.693,0.241 +Tanzania,2015,3.661,7.743,0.790,56.700,0.759,0.145,0.906,0.607,0.192 +Tanzania,2016,2.903,7.775,0.638,57.150,0.775,0.175,0.739,0.649,0.246 +Tanzania,2017,3.347,7.807,0.705,57.600,0.800,0.112,0.654,0.662,0.255 +Tanzania,2018,3.445,7.828,0.675,58.050,0.807,0.150,0.612,0.702,0.221 +Tanzania,2019,3.640,7.855,0.687,58.500,0.850,0.097,0.589,0.679,0.243 +Tanzania,2020,3.786,7.844,0.740,58.950,0.830,0.293,0.521,0.667,0.271 +Tanzania,2021,3.681,7.857,0.619,59.400,0.822,0.110,0.546,0.648,0.246 +Tanzania,2022,3.616,7.872,0.600,59.850,0.856,0.133,0.584,0.707,0.195 +Tanzania,2023,4.042,7.893,0.663,60.300,0.862,0.122,0.609,0.702,0.210 +Thailand,2006,5.885,9.452,0.894,66.380,0.863,0.326,0.935,0.750,0.164 +Thailand,2007,5.784,9.498,0.889,66.560,0.870,0.386,0.898,0.784,0.180 +Thailand,2008,5.636,9.507,0.832,66.740,0.868,0.421,0.933,0.777,0.145 +Thailand,2009,5.476,9.493,0.893,66.920,0.868,0.520,0.904,0.808,0.166 +Thailand,2010,6.217,9.559,0.898,67.100,0.860,0.532,0.917,0.821,0.182 +Thailand,2011,6.664,9.561,0.884,67.280,0.927,0.396,0.923,0.834,0.117 +Thailand,2012,6.300,9.624,0.906,67.460,0.847,0.376,0.909,0.733,0.138 +Thailand,2013,6.231,9.645,0.926,67.640,0.781,0.452,0.925,0.782,0.141 +Thailand,2014,6.985,9.649,0.933,67.820,0.900,0.548,0.920,0.768,0.169 +Thailand,2015,6.202,9.675,0.866,68.000,0.885,0.312,0.914,0.884,0.174 +Thailand,2016,6.074,9.705,0.908,68.075,0.924,0.352,0.878,0.811,0.218 +Thailand,2017,5.939,9.741,0.877,68.150,0.923,0.208,0.884,0.776,0.232 +Thailand,2018,6.012,9.780,0.873,68.225,0.905,0.255,0.907,0.783,0.198 +Thailand,2019,6.022,9.798,0.903,68.300,0.898,0.305,0.877,0.792,0.208 +Thailand,2020,5.885,9.733,0.867,68.375,0.840,0.270,0.918,0.770,0.326 +Thailand,2021,5.638,9.746,0.883,68.450,0.836,0.290,0.943,0.719,0.298 +Thailand,2022,6.007,9.770,0.867,68.525,0.881,0.299,0.868,0.773,0.218 +Thailand,2023,6.282,9.807,0.873,68.600,0.926,0.338,0.889,0.811,0.217 +Togo,2006,3.202,7.342,0.435,50.240,0.628,-0.030,0.850,0.571,0.348 +Togo,2008,2.808,7.312,0.291,51.120,0.287,-0.078,0.932,0.398,0.379 +Togo,2011,2.936,7.406,0.303,52.440,0.584,-0.093,0.832,0.479,0.395 +Togo,2014,2.839,7.509,0.444,53.760,0.663,-0.108,0.795,0.537,0.443 +Togo,2015,3.768,7.540,0.479,54.200,0.772,-0.092,0.733,0.597,0.416 +Togo,2016,3.879,7.569,0.509,54.700,0.730,-0.031,0.815,0.610,0.483 +Togo,2017,4.361,7.587,0.508,55.200,0.717,-0.066,0.726,0.614,0.426 +Togo,2018,4.023,7.613,0.596,55.700,0.612,-0.031,0.809,0.602,0.446 +Togo,2019,4.179,7.637,0.539,56.200,0.617,0.041,0.737,0.606,0.444 +Togo,2021,4.037,7.667,0.603,57.200,0.619,0.037,0.766,0.628,0.417 +Togo,2022,4.239,7.700,0.579,57.700,0.696,0.002,0.713,0.594,0.414 +Togo,2023,4.365,7.725,0.547,58.200,0.665,-0.071,0.685,0.546,0.362 +Trinidad and Tobago,2006,5.832,10.172,0.887,61.780,0.840,0.139,0.917,0.750,0.229 +Trinidad and Tobago,2008,6.696,10.240,0.858,62.540,0.838,0.085,0.959,0.802,0.184 +Trinidad and Tobago,2011,6.519,10.206,0.863,63.680,0.775,0.076,0.900,0.827,0.134 +Trinidad and Tobago,2013,6.168,10.293,0.883,64.440,0.847,0.121,0.948,0.764,0.286 +Trinidad and Tobago,2017,6.192,10.174,0.916,65.700,0.859,0.010,0.911,0.763,0.248 +Tunisia,2009,5.025,9.238,,66.220,0.781,-0.127,0.722,, +Tunisia,2010,5.131,9.257,0.863,66.300,0.624,-0.143,0.732,0.697,0.249 +Tunisia,2011,4.876,9.224,0.715,66.380,0.603,-0.207,0.913,0.513,0.248 +Tunisia,2012,4.464,9.252,0.614,66.460,0.568,-0.184,0.899,0.490,0.327 +Tunisia,2013,5.246,9.265,0.648,66.540,0.536,-0.214,0.886,0.435,0.239 +Tunisia,2014,4.764,9.284,0.680,66.620,0.589,-0.239,0.783,0.424,0.321 +Tunisia,2015,5.132,9.283,0.609,66.700,0.711,-0.233,0.815,0.514,0.320 +Tunisia,2016,4.521,9.283,0.702,66.750,0.614,-0.172,0.811,0.532,0.378 +Tunisia,2017,4.124,9.294,0.717,66.800,0.478,-0.226,0.869,0.367,0.377 +Tunisia,2018,4.741,9.310,0.733,66.850,0.650,-0.199,0.840,0.536,0.365 +Tunisia,2019,4.315,9.316,0.610,66.900,0.659,-0.217,0.889,0.459,0.433 +Tunisia,2020,4.731,9.214,0.719,66.950,0.668,-0.206,0.877,0.519,0.439 +Tunisia,2021,4.499,9.249,0.711,67.000,0.591,-0.206,0.933,0.451,0.336 +Tunisia,2022,4.261,9.267,0.755,67.050,0.474,-0.233,0.908,0.458,0.304 +Tunisia,2023,4.505,9.282,0.702,67.100,0.482,-0.226,0.882,0.461,0.364 +Turkmenistan,2009,6.568,8.955,0.924,59.780,,-0.105,,0.695,0.152 +Turkmenistan,2011,5.792,9.146,0.964,60.420,,0.015,,0.577,0.122 +Turkmenistan,2012,5.464,9.233,0.946,60.740,0.786,-0.126,,0.541,0.117 +Turkmenistan,2013,5.392,9.312,0.846,61.060,0.705,-0.075,,0.552,0.160 +Turkmenistan,2014,5.787,9.392,0.909,61.380,0.805,0.029,,0.614,0.154 +Turkmenistan,2015,5.791,9.437,0.960,61.700,0.701,0.090,,0.633,0.301 +Turkmenistan,2016,5.887,9.479,0.929,61.800,0.749,0.002,,0.560,0.255 +Turkmenistan,2017,5.229,9.525,0.908,61.900,0.720,0.063,,0.488,0.350 +Turkmenistan,2018,4.621,9.569,0.984,62.000,0.858,0.257,,0.567,0.189 +Turkmenistan,2019,5.474,9.615,0.982,62.100,0.892,0.282,,0.494,0.183 +Türkiye,2005,4.719,9.800,0.820,66.100,0.623,,0.877,0.479, +Türkiye,2007,5.623,9.891,0.792,66.420,0.459,-0.183,0.800,0.592,0.395 +Türkiye,2008,5.118,9.887,0.645,66.580,0.415,-0.194,0.785,0.510,0.345 +Türkiye,2009,5.213,9.825,0.755,66.740,0.456,-0.232,0.853,0.454,0.316 +Türkiye,2010,5.490,9.893,0.795,66.900,0.515,-0.192,0.811,0.532,0.327 +Türkiye,2011,5.272,9.986,0.692,67.060,0.446,-0.247,0.649,0.512,0.380 +Türkiye,2012,5.309,10.018,0.739,67.220,0.471,-0.221,0.702,0.506,0.335 +Türkiye,2013,4.888,10.082,0.795,67.380,0.541,-0.235,0.698,0.551,0.392 +Türkiye,2014,5.580,10.111,0.863,67.540,0.649,-0.029,0.764,0.410,0.377 +Türkiye,2015,5.514,10.150,0.851,67.700,0.653,-0.021,0.806,0.391,0.382 +Türkiye,2016,5.326,10.166,0.880,67.875,0.644,-0.070,0.764,0.414,0.390 +Türkiye,2017,5.607,10.225,0.876,68.050,0.644,-0.242,0.671,0.393,0.313 +Türkiye,2018,5.186,10.246,0.847,68.225,0.529,-0.181,0.805,0.379,0.351 +Türkiye,2019,4.872,10.245,0.792,68.400,0.631,-0.141,0.760,0.347,0.368 +Türkiye,2020,4.862,10.257,0.857,68.575,0.510,-0.119,0.774,0.332,0.440 +Türkiye,2021,4.367,10.357,0.736,68.750,0.447,-0.039,0.810,0.297,0.471 +Türkiye,2022,5.093,10.404,0.830,68.925,0.470,-0.195,0.767,0.311,0.390 +Türkiye,2023,5.463,10.429,0.860,69.100,0.523,-0.159,0.748,0.344,0.349 +Uganda,2006,3.734,7.370,0.760,48.740,0.747,-0.046,0.807,0.552,0.254 +Uganda,2007,4.456,7.422,0.845,49.580,0.708,-0.006,0.881,0.670,0.228 +Uganda,2008,4.569,7.476,0.813,50.420,0.578,-0.060,0.848,0.623,0.240 +Uganda,2009,4.612,7.513,0.852,51.260,0.760,-0.043,0.840,0.594,0.296 +Uganda,2010,4.193,7.538,0.830,52.100,0.801,-0.020,0.855,0.628,0.251 +Uganda,2011,4.826,7.599,0.882,52.940,0.733,0.026,0.830,0.618,0.254 +Uganda,2012,4.309,7.608,0.885,53.780,0.649,0.075,0.838,0.709,0.265 +Uganda,2013,3.710,7.614,0.878,54.620,0.763,0.046,0.820,0.647,0.346 +Uganda,2014,3.770,7.634,0.821,55.460,0.834,0.003,0.898,0.635,0.397 +Uganda,2015,4.238,7.654,0.747,56.300,0.758,0.128,0.873,0.679,0.353 +Uganda,2016,4.233,7.667,0.754,56.775,0.739,0.125,0.811,0.665,0.410 +Uganda,2017,4.001,7.663,0.740,57.250,0.772,0.053,0.816,0.689,0.400 +Uganda,2018,4.322,7.690,0.740,57.725,0.729,0.072,0.856,0.687,0.390 +Uganda,2019,4.948,7.719,0.805,58.200,0.704,0.132,0.826,0.689,0.385 +Uganda,2020,4.641,7.714,0.800,58.675,0.687,0.140,0.878,0.705,0.425 +Uganda,2021,4.225,7.717,0.793,59.150,0.711,0.081,0.835,0.699,0.359 +Uganda,2022,4.425,7.733,0.781,59.625,0.720,0.135,0.836,0.708,0.439 +Uganda,2023,4.467,7.759,0.827,60.100,0.848,0.067,0.912,0.726,0.376 +Ukraine,2006,4.804,9.414,0.852,60.920,0.624,-0.265,0.929,0.551,0.249 +Ukraine,2007,5.252,9.499,0.820,61.240,0.494,-0.249,0.968,0.559,0.208 +Ukraine,2008,5.172,9.527,0.860,61.560,0.487,-0.273,0.929,0.545,0.186 +Ukraine,2009,5.166,9.367,0.845,61.880,0.460,-0.249,0.962,0.545,0.189 +Ukraine,2010,5.058,9.411,0.884,62.200,0.484,-0.197,0.954,0.472,0.227 +Ukraine,2011,5.083,9.468,0.859,62.520,0.579,-0.236,0.933,0.539,0.220 +Ukraine,2012,5.030,9.472,0.898,62.840,0.564,-0.231,0.896,0.509,0.193 +Ukraine,2013,4.711,9.474,0.897,63.160,0.569,-0.225,0.937,0.572,0.225 +Ukraine,2014,4.297,9.424,0.877,63.480,0.533,0.078,0.927,0.543,0.249 +Ukraine,2015,3.965,9.325,0.909,63.800,0.431,-0.039,0.952,0.531,0.241 +Ukraine,2016,4.029,9.353,0.885,63.925,0.503,0.005,0.891,0.550,0.220 +Ukraine,2017,4.311,9.381,0.858,64.050,0.599,-0.008,0.937,0.528,0.235 +Ukraine,2018,4.662,9.420,0.901,64.175,0.663,-0.080,0.943,0.550,0.222 +Ukraine,2019,4.702,9.458,0.883,64.300,0.715,-0.087,0.885,0.549,0.201 +Ukraine,2020,5.270,9.426,0.885,64.425,0.784,0.121,0.946,0.629,0.285 +Ukraine,2021,5.311,9.469,0.879,64.550,0.770,0.166,0.922,0.575,0.250 +Ukraine,2022,4.637,9.281,0.863,64.675,0.829,0.408,0.852,0.527,0.390 +Ukraine,2023,4.672,9.423,0.839,64.800,0.772,0.370,0.922,0.490,0.385 +United Arab Emirates,2006,6.734,11.433,0.903,64.860,0.898,-0.043,0.203,0.694,0.275 +United Arab Emirates,2009,6.866,10.952,0.885,65.040,0.849,0.015,0.339,0.718,0.287 +United Arab Emirates,2010,7.097,10.909,0.912,65.100,0.878,0.051,0.355,0.701,0.233 +United Arab Emirates,2011,7.119,10.965,0.881,65.160,0.889,0.063,,0.702,0.216 +United Arab Emirates,2012,7.218,11.001,0.856,65.220,0.920,,,0.719,0.224 +United Arab Emirates,2013,6.621,11.041,0.864,65.280,0.936,,,,0.291 +United Arab Emirates,2014,6.540,11.072,,65.340,,,,, +United Arab Emirates,2015,6.568,11.128,0.824,65.400,0.915,0.192,,0.722,0.296 +United Arab Emirates,2016,6.831,11.174,0.849,65.550,0.949,0.120,,0.739,0.245 +United Arab Emirates,2017,7.039,11.173,0.836,65.700,0.962,0.206,,0.737,0.208 +United Arab Emirates,2018,6.604,11.178,0.851,65.850,0.944,0.043,,0.723,0.302 +United Arab Emirates,2019,6.711,11.181,0.862,66.000,0.911,0.118,,0.730,0.284 +United Arab Emirates,2020,6.458,11.122,0.827,66.150,0.942,0.049,,0.702,0.298 +United Arab Emirates,2021,6.733,11.152,0.826,66.300,0.951,0.150,,0.697,0.217 +United Arab Emirates,2022,6.738,11.216,0.798,66.450,0.932,0.168,,0.715,0.242 +United Arab Emirates,2023,6.728,11.236,0.776,66.600,0.886,0.155,,0.655,0.304 +United Kingdom,2005,6.984,10.661,0.979,69.100,0.922,,0.398,0.779,0.262 +United Kingdom,2007,6.802,10.693,0.970,69.220,0.838,0.331,0.498,0.686,0.241 +United Kingdom,2008,6.986,10.684,0.954,69.280,0.759,0.325,0.548,0.724,0.218 +United Kingdom,2009,6.907,10.630,0.964,69.340,0.816,0.336,0.559,0.739,0.231 +United Kingdom,2010,7.029,10.646,0.955,69.400,0.841,0.397,0.587,0.753,0.176 +United Kingdom,2011,6.869,10.649,0.949,69.460,0.900,0.331,0.438,0.742,0.174 +United Kingdom,2012,6.881,10.656,0.935,69.520,0.889,0.366,0.425,0.739,0.184 +United Kingdom,2013,6.918,10.668,0.937,69.580,0.905,0.341,0.568,0.719,0.252 +United Kingdom,2014,6.758,10.692,0.910,69.640,0.857,0.349,0.484,0.740,0.251 +United Kingdom,2015,6.515,10.707,0.936,69.700,0.833,0.294,0.456,0.740,0.219 +United Kingdom,2016,6.824,10.721,0.954,69.800,0.821,0.244,0.458,0.732,0.230 +United Kingdom,2017,7.103,10.739,0.937,69.900,0.813,0.285,0.419,0.712,0.210 +United Kingdom,2018,7.233,10.750,0.928,70.000,0.838,0.220,0.404,0.736,0.228 +United Kingdom,2019,7.157,10.760,0.943,70.100,0.854,0.264,0.485,0.739,0.251 +United Kingdom,2020,6.798,10.639,0.929,70.200,0.885,0.196,0.490,0.717,0.225 +United Kingdom,2021,6.867,10.713,0.854,70.300,0.815,0.252,0.448,0.684,0.266 +United Kingdom,2022,6.722,10.754,0.863,70.400,0.857,0.308,0.426,0.723,0.270 +United Kingdom,2023,6.658,10.759,0.886,70.500,0.874,0.270,0.490,0.719,0.272 +United States,2006,7.182,10.921,0.965,66.780,0.911,,0.600,0.775,0.261 +United States,2007,7.513,10.931,,66.760,0.872,0.191,0.633,0.756,0.232 +United States,2008,7.280,10.923,0.953,66.740,0.878,0.249,0.668,0.774,0.227 +United States,2009,7.158,10.888,0.912,66.720,0.831,0.195,0.665,0.753,0.262 +United States,2010,7.164,10.906,0.926,66.700,0.828,0.238,0.690,0.776,0.231 +United States,2011,7.115,10.914,0.922,66.680,0.863,0.155,0.697,0.737,0.273 +United States,2012,7.026,10.929,0.903,66.660,0.823,0.208,0.710,0.765,0.260 +United States,2013,7.249,10.941,0.925,66.640,0.792,0.268,0.747,0.776,0.260 +United States,2014,7.151,10.956,0.902,66.620,0.866,0.215,0.702,0.786,0.281 +United States,2015,6.864,10.975,0.904,66.600,0.849,0.213,0.698,0.769,0.275 +United States,2016,6.804,10.985,0.897,66.475,0.758,0.138,0.739,0.737,0.264 +United States,2017,6.992,11.001,0.921,66.350,0.868,0.191,0.681,0.755,0.268 +United States,2018,6.883,11.024,0.904,66.225,0.825,0.110,0.710,0.757,0.292 +United States,2019,6.944,11.042,0.917,66.100,0.836,0.138,0.707,0.755,0.244 +United States,2020,7.028,11.005,0.937,65.975,0.850,0.028,0.678,0.722,0.295 +United States,2021,6.959,11.061,0.920,65.850,0.816,0.188,0.687,0.740,0.277 +United States,2022,6.693,11.078,0.900,65.725,0.736,0.189,0.701,0.712,0.267 +United States,2023,6.521,11.089,0.861,65.600,0.721,0.185,0.722,0.706,0.284 +Uruguay,2006,5.786,9.640,0.912,66.780,0.807,-0.125,0.477,0.701,0.306 +Uruguay,2007,5.694,9.702,0.875,66.860,0.786,-0.178,0.614,0.710,0.274 +Uruguay,2008,5.664,9.769,0.879,66.940,0.808,-0.156,0.597,0.685,0.264 +Uruguay,2009,6.296,9.808,0.924,67.020,0.825,-0.131,0.544,0.722,0.255 +Uruguay,2010,6.062,9.880,0.893,67.100,0.832,-0.171,0.471,0.738,0.231 +Uruguay,2011,6.554,9.928,0.891,67.180,0.851,-0.093,0.556,0.702,0.252 +Uruguay,2012,6.450,9.960,0.865,67.260,0.871,0.054,0.615,0.692,0.214 +Uruguay,2013,6.444,10.002,0.917,67.340,0.888,-0.056,0.586,0.743,0.253 +Uruguay,2014,6.561,10.031,0.902,67.420,0.904,-0.086,0.533,0.788,0.251 +Uruguay,2015,6.628,10.032,0.891,67.500,0.917,-0.045,0.673,0.812,0.300 +Uruguay,2016,6.171,10.045,0.900,67.500,0.886,-0.085,0.676,0.735,0.283 +Uruguay,2017,6.336,10.060,0.914,67.500,0.898,-0.104,0.627,0.742,0.280 +Uruguay,2018,6.372,10.060,0.917,67.500,0.876,-0.109,0.683,0.775,0.275 +Uruguay,2019,6.600,10.067,0.933,67.500,0.903,-0.107,0.599,0.764,0.222 +Uruguay,2020,6.310,10.002,0.921,67.500,0.908,-0.094,0.491,0.721,0.265 +Uruguay,2021,6.502,10.054,0.914,67.500,0.899,-0.054,0.606,0.746,0.263 +Uruguay,2022,6.671,10.103,0.905,67.500,0.878,-0.055,0.631,0.775,0.267 +Uruguay,2023,6.662,10.122,0.908,67.500,0.904,-0.050,0.662,0.753,0.265 +Uzbekistan,2006,5.232,8.256,0.903,61.340,0.784,-0.125,0.609,0.650,0.195 +Uzbekistan,2008,5.311,8.402,0.894,61.820,0.831,-0.033,,0.647,0.187 +Uzbekistan,2009,5.261,8.463,0.905,62.060,,0.003,0.610,0.646,0.159 +Uzbekistan,2010,5.095,8.508,0.903,62.300,,-0.040,0.519,0.665,0.152 +Uzbekistan,2011,5.739,8.554,0.924,62.540,0.934,0.032,0.522,0.663,0.123 +Uzbekistan,2012,6.019,8.608,0.933,62.780,0.914,-0.047,0.463,0.650,0.118 +Uzbekistan,2013,5.940,8.662,0.963,63.020,0.950,-0.043,0.434,0.686,0.130 +Uzbekistan,2014,6.049,8.712,0.952,63.260,0.954,0.052,0.536,0.713,0.106 +Uzbekistan,2015,5.972,8.764,0.968,63.500,0.980,0.366,0.471,0.778,0.103 +Uzbekistan,2016,5.893,8.804,0.945,63.800,0.984,0.199,,0.771,0.147 +Uzbekistan,2017,6.421,8.831,0.942,64.100,0.985,0.114,0.465,0.745,0.203 +Uzbekistan,2018,6.205,8.870,0.921,64.400,0.970,0.308,0.520,0.746,0.209 +Uzbekistan,2019,6.154,8.910,0.915,64.700,0.970,0.295,0.511,0.751,0.220 +Uzbekistan,2020,5.842,8.910,0.850,65.000,0.928,0.190,0.642,0.678,0.279 +Uzbekistan,2021,6.185,8.962,0.896,65.300,0.927,0.183,0.662,0.698,0.233 +Uzbekistan,2022,6.016,8.996,0.879,65.600,0.959,0.306,0.616,0.741,0.225 +Uzbekistan,2023,6.385,9.026,0.909,65.900,0.927,0.247,0.650,0.752,0.202 +Venezuela,2005,7.170,9.316,0.955,65.500,0.838,,0.720,0.803,0.233 +Venezuela,2006,6.525,9.467,0.946,65.460,0.798,-0.037,0.646,0.837,0.178 +Venezuela,2008,6.258,9.719,0.922,65.380,0.678,-0.232,0.776,0.818,0.224 +Venezuela,2009,7.189,9.567,0.945,65.340,0.677,-0.124,0.828,0.792,0.180 +Venezuela,2010,7.478,9.748,0.932,65.300,0.768,-0.163,0.754,0.847,0.130 +Venezuela,2011,6.580,9.859,0.931,65.260,0.766,-0.235,0.772,0.823,0.199 +Venezuela,2012,7.067,9.862,0.932,65.220,0.804,-0.201,0.743,0.844,0.176 +Venezuela,2013,6.553,9.802,0.896,65.180,0.642,-0.230,0.837,0.812,0.238 +Venezuela,2014,6.136,9.366,0.904,65.140,0.570,-0.191,0.827,0.797,0.244 +Venezuela,2015,5.569,8.532,0.911,65.100,0.512,-0.089,0.813,0.837,0.223 +Venezuela,2016,4.041,7.602,0.902,64.925,0.458,-0.060,0.890,0.676,0.392 +Venezuela,2017,5.071,5.943,0.896,64.750,0.636,0.050,0.844,0.697,0.363 +Venezuela,2018,5.006,5.935,0.887,64.575,0.611,0.068,0.828,0.723,0.374 +Venezuela,2019,5.081,5.527,0.888,64.400,0.626,0.124,0.839,0.730,0.351 +Venezuela,2020,4.574,,0.805,64.225,0.612,,0.811,0.689,0.396 +Venezuela,2021,5.108,,0.812,64.050,0.596,,0.824,0.698,0.389 +Venezuela,2022,5.949,,0.899,63.875,0.770,,0.798,0.754,0.292 +Venezuela,2023,5.765,,0.885,63.700,0.757,,0.825,0.758,0.300 +Vietnam,2006,5.294,8.554,0.888,64.180,0.886,-0.006,,0.657,0.204 +Vietnam,2007,5.422,8.613,0.856,64.260,0.918,0.068,0.754,,0.206 +Vietnam,2008,5.480,8.658,0.805,64.340,0.889,0.180,0.789,0.624,0.218 +Vietnam,2009,5.304,8.701,0.815,64.420,0.834,-0.083,0.838,0.481,0.190 +Vietnam,2010,5.296,8.752,0.787,64.500,0.831,-0.027,0.743,0.671,0.216 +Vietnam,2011,5.767,8.804,0.898,64.580,0.818,0.084,0.742,0.494,0.193 +Vietnam,2012,5.535,8.847,0.775,64.660,0.856,-0.131,0.815,0.546,0.221 +Vietnam,2013,5.023,8.890,0.759,64.740,0.920,-0.048,0.771,0.689,0.165 +Vietnam,2014,5.085,8.941,0.792,64.820,,-0.022,,0.634,0.241 +Vietnam,2015,5.076,8.999,0.849,64.900,,0.064,,0.583,0.232 +Vietnam,2016,5.062,9.053,0.876,65.000,0.894,-0.112,0.799,0.487,0.223 +Vietnam,2017,5.175,9.111,,65.100,,,,, +Vietnam,2018,5.296,9.173,0.832,65.200,0.909,-0.063,0.808,0.614,0.191 +Vietnam,2019,5.467,9.235,0.848,65.300,0.952,-0.148,0.788,0.658,0.186 +Vietnam,2020,5.462,9.254,0.765,65.400,0.945,0.063,0.791,0.699,0.286 +Vietnam,2021,5.540,9.271,0.799,65.500,0.897,0.104,0.798,0.651,0.280 +Vietnam,2022,6.267,9.341,0.879,65.600,0.975,-0.182,0.703,0.774,0.108 +Vietnam,2023,6.325,9.392,0.845,65.700,0.956,-0.159,0.655,0.710,0.120 +Yemen,2007,4.477,8.212,0.825,58.720,0.673,0.006,,0.524,0.379 +Yemen,2009,4.809,8.250,0.756,58.640,0.644,-0.056,0.832,0.511,0.374 +Yemen,2010,4.350,8.414,0.727,58.600,0.659,-0.107,0.853,0.514,0.308 +Yemen,2011,3.746,8.264,0.663,58.560,0.638,-0.174,0.754,0.416,0.285 +Yemen,2012,4.061,8.179,0.682,58.520,0.706,-0.172,0.793,0.413,0.263 +Yemen,2013,4.218,8.166,0.694,58.480,0.543,-0.179,0.885,0.478,0.266 +Yemen,2014,3.968,8.159,0.638,58.440,0.664,-0.166,0.885,0.527,0.276 +Yemen,2015,2.983,7.772,0.669,58.400,0.610,-0.138,0.829,0.458,0.321 +Yemen,2016,3.826,7.552,0.775,58.175,0.533,-0.144,,0.401,0.228 +Yemen,2017,3.254,7.243,0.790,57.950,0.595,-0.128,,0.368,0.295 +Yemen,2018,3.058,7.444,0.789,57.725,0.553,-0.127,0.793,0.409,0.315 +Yemen,2019,4.197,7.448,0.870,57.500,0.651,-0.106,0.798,0.481,0.213 +Yemen,2022,3.590,,0.872,56.825,0.607,,0.788,0.460,0.255 +Yemen,2023,3.532,,0.825,56.600,0.583,,0.771,0.447,0.341 +Zambia,2006,4.824,7.834,0.798,46.760,0.721,-0.012,0.785,0.664,0.226 +Zambia,2007,3.998,7.879,0.688,47.420,0.682,-0.073,0.948,0.653,0.246 +Zambia,2008,4.730,7.918,0.624,48.080,0.717,0.051,0.890,0.707,0.206 +Zambia,2009,5.260,7.971,0.782,48.740,0.696,-0.101,0.917,0.693,0.123 +Zambia,2011,4.999,8.054,0.864,50.060,0.663,-0.001,0.882,0.771,0.204 +Zambia,2012,5.013,8.094,0.780,50.720,0.788,0.004,0.806,0.676,0.250 +Zambia,2013,5.244,8.111,0.761,51.380,0.770,-0.108,0.732,0.727,0.308 +Zambia,2014,4.346,8.124,0.706,52.040,0.812,-0.014,0.809,0.639,0.327 +Zambia,2015,4.843,8.121,0.691,52.700,0.759,-0.042,0.871,0.634,0.382 +Zambia,2016,4.348,8.127,0.767,53.125,0.812,0.119,0.771,0.688,0.372 +Zambia,2017,3.933,8.130,0.744,53.550,0.823,0.137,0.740,0.660,0.387 +Zambia,2018,4.041,8.139,0.718,53.975,0.791,0.045,0.811,0.662,0.351 +Zambia,2019,3.307,8.123,0.638,54.400,0.811,0.074,0.832,0.674,0.394 +Zambia,2020,4.838,8.066,0.767,54.825,0.750,0.054,0.810,0.679,0.345 +Zambia,2021,3.082,8.082,0.619,55.250,0.833,0.138,0.824,0.656,0.349 +Zambia,2022,3.728,8.101,0.717,55.675,0.889,-0.009,0.716,0.660,0.309 +Zambia,2023,3.686,8.115,0.664,56.100,0.854,0.092,0.814,0.653,0.359 +Zimbabwe,2006,3.826,7.460,0.822,40.400,0.431,-0.063,0.905,0.669,0.297 +Zimbabwe,2007,3.280,7.413,0.828,41.600,0.456,-0.069,0.946,0.589,0.265 +Zimbabwe,2008,3.174,7.210,0.843,42.800,0.344,-0.077,0.964,0.571,0.250 +Zimbabwe,2009,4.056,7.313,0.806,44.000,0.411,-0.065,0.931,0.660,0.218 +Zimbabwe,2010,4.682,7.495,0.857,45.200,0.665,-0.081,0.828,0.699,0.122 +Zimbabwe,2011,4.846,7.617,0.865,46.400,0.633,-0.077,0.830,0.699,0.211 +Zimbabwe,2012,4.955,7.745,0.896,47.600,0.470,-0.091,0.859,0.613,0.177 +Zimbabwe,2013,4.690,7.755,0.799,48.800,0.576,-0.093,0.831,0.624,0.182 +Zimbabwe,2014,4.184,7.748,0.766,50.000,0.642,-0.062,0.820,0.661,0.239 +Zimbabwe,2015,3.703,7.747,0.736,51.200,0.667,-0.111,0.810,0.639,0.179 +Zimbabwe,2016,3.735,7.735,0.768,51.675,0.733,-0.082,0.724,0.685,0.209 +Zimbabwe,2017,3.638,7.754,0.754,52.150,0.753,-0.084,0.751,0.734,0.224 +Zimbabwe,2018,3.616,7.783,0.775,52.625,0.763,-0.055,0.844,0.658,0.212 +Zimbabwe,2019,2.694,7.698,0.759,53.100,0.632,-0.051,0.831,0.658,0.235 +Zimbabwe,2020,3.160,7.596,0.717,53.575,0.643,0.003,0.789,0.661,0.346 +Zimbabwe,2021,3.155,7.657,0.685,54.050,0.668,-0.079,0.757,0.610,0.242 +Zimbabwe,2022,3.296,7.670,0.666,54.525,0.652,-0.073,0.753,0.641,0.191 +Zimbabwe,2023,3.572,7.679,0.694,55.000,0.735,-0.069,0.757,0.610,0.179 diff --git a/fall-2024/math/mat-206/00020/MAT-206-00020.ipynb b/fall-2024/math/mat-206/00020/MAT-206-00020.ipynb new file mode 100644 index 0000000..4701e0f --- /dev/null +++ b/fall-2024/math/mat-206/00020/MAT-206-00020.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pingouin as pg\n", + "\n", + "from scipy.stats import bartlett, levene" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reading and preprocessing data" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "DataWhr2024 = pd.read_csv(\"DataWhr2024.csv\")\n", + "UnM49 = pd.read_csv(\"UnM49.csv\", sep=';')" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Hong\"), \"Country name\"] = \"Hong Kong\"\n", + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Somaliland\"), \"Country name\"] = \"Somaliland\"\n", + "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Taiwan\"), \"Country name\"] = \"Taiwan\"" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "UnM49 = UnM49[['Country or Area', 'Sub-region Name', 'Region Name']]\n", + "UnM49 = UnM49.rename({'Country or Area':'Country name', 'Sub-region Name':'Subregion', 'Region Name':'Continent'}, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "UnM49.loc[97, \"Country name\"] = \"Bolivia\"\n", + "UnM49.loc[33, \"Country name\"] = \"Congo (Brazzaville)\"\n", + "UnM49.loc[34, \"Country name\"] = \"Congo (Kinshasa)\"\n", + "UnM49.loc[124, \"Country name\"] = \"Hong Kong\"\n", + "UnM49.loc[125, \"Country name\"] = \"Macao\"\n", + "UnM49.loc[126, \"Country name\"] = \"North Korea\"\n", + "UnM49.loc[145, \"Country name\"] = \"Iran\"\n", + "UnM49.loc[46, \"Country name\"] = \"Ivory Coast\"\n", + "UnM49.loc[133, \"Country name\"] = \"Laos\"\n", + "UnM49.loc[129, \"Country name\"] = \"South Korea\"\n", + "UnM49.loc[173, \"Country name\"] = \"Moldova\"\n", + "UnM49.loc[217, \"Country name\"] = \"Netherlands\"\n", + "UnM49.loc[175, \"Country name\"] = \"Russia\"\n", + "UnM49.loc[164, \"Country name\"] = \"Syria\"\n", + "UnM49.loc[26, \"Country name\"] = \"Tanzania\"\n", + "UnM49.loc[116, \"Country name\"] = \"United States\"\n", + "UnM49.loc[193, \"Country name\"] = \"United Kingdom\"\n", + "UnM49.loc[111, \"Country name\"] = \"Venezuela\"\n", + "UnM49.loc[140, \"Country name\"] = \"Vietnam\"" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [], + "source": [ + "_ = pd.DataFrame(\n", + " {\n", + " \"Country name\": [\"Kosovo\", \"Somaliland\", \"Taiwan\"],\n", + " \"Subregion\": [\"Southern Europe\", \"Sub-Saharan Africa\", \"Eastern Asia\"],\n", + " \"Continent\": [\"Europe\", \"Africa\", \"Asia\"],\n", + " }\n", + ")\n", + "\n", + "UnM49 = pd.concat([UnM49, _], axis=0)\n", + "UnM49 = UnM49.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Merging the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "# Data\n", + "Dat = pd.merge(DataWhr2024, UnM49)\n", + "\n", + "# Data of 2023\n", + "Dat2023 = Dat[Dat['year'] == 2023]\n", + "Dat2023 = Dat2023.reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 1**" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "μSE: 5.678\n", + "\n", + "One-sample t-test result:\n", + " T dof alternative p-val CI95% cohen-d BF10 \\\n", + "T-test -0.075657 8 two-sided 0.941549 [5.02, 6.34] 0.025219 0.322 \n", + "\n", + " power \n", + "T-test 0.050515 \n", + "\n" + ] + } + ], + "source": [ + "# Step 1: Southeast Asia Mean (μSE) and Hypothesis Testing\n", + "Dat2023SEA = Dat2023[Dat2023['Subregion'] == 'South-eastern Asia']['Life Ladder']\n", + "\n", + "mu_se = Dat2023SEA.mean()\n", + "\n", + "t_test_result = pg.ttest(Dat2023SEA, 5.7)\n", + "\n", + "print(f\"μSE: {mu_se:.3f}\\n\")\n", + "print(f\"One-sample t-test result:\\n{t_test_result}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 2**" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "σ²SE: 0.731, σ²LA: 0.194\n", + "\n", + "Levene's test result:\n", + " W pval equal_var\n", + "levene 3.141025 0.088534 True\n", + "\n" + ] + } + ], + "source": [ + "# Step 2: Variance (σ²SE, σ²LA) and Hypothesis Testing\n", + "Dat2023LA = Dat2023[Dat2023['Subregion'] == 'Latin America and the Caribbean']['Life Ladder']\n", + "\n", + "sigma2_se = Dat2023SEA.var(ddof=1)\n", + "sigma2_la = Dat2023LA.var(ddof=1)\n", + "\n", + "f_test_result = pg.homoscedasticity([Dat2023SEA.values, Dat2023LA.values], method='levene')\n", + "\n", + "print(f\"σ²SE: {sigma2_se:.3f}, σ²LA: {sigma2_la:.3f}\\n\")\n", + "print(f\"Levene's test result:\\n{f_test_result}\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 3**" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "μLA: 6.297\n", + "Two-sample t-test result:\n", + " T dof alternative p-val CI95% cohen-d \\\n", + "T-test -2.040107 10.186481 two-sided 0.068122 [-1.29, 0.06] 1.022676 \n", + "\n", + " BF10 power \n", + "T-test 1.597 0.672925 \n", + "\n" + ] + } + ], + "source": [ + "# Step 3: Mean (μLA) and Hypothesis Testing\n", + "mu_LA = Dat2023LA.values.mean()\n", + "t_test_ind_result = pg.ttest(Dat2023SEA, Dat2023LA)\n", + "\n", + "print(f\"μLA: {mu_LA:.3f}\")\n", + "print(f\"Two-sample t-test result:\\n{t_test_ind_result}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(138)" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Dat2023['Continent'].dropna().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 4**" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANOVA Table:\n", + " Source SS DF MS F p-unc np2\n", + "0 Continent 90.218922 4 22.554730 34.218881 1.271847e-19 0.50718\n", + "1 Within 87.664444 133 0.659131 NaN NaN NaN\n", + "\n", + "Post-Hoc Analysis:\n", + "Means by Continent:\n", + "Continent\n", + "Africa 4.485\n", + "Americas 6.336\n", + "Asia 5.433\n", + "Europe 6.454\n", + "Oceania 7.001\n", + "Name: Life Ladder, dtype: float64\n", + "Intercontinental Mean (μ): 5.621\n", + "Intercontinental Variance (τ²): 0.793\n" + ] + } + ], + "source": [ + "import pingouin as pg\n", + "import pandas as pd\n", + "import numpy as np\n", + "'''\n", + "# Define the mapping of sub-regions to continents\n", + "sub_region_to_continent = {\n", + " 'Southern Asia': 'Asia',\n", + " 'South-eastern Asia': 'Asia',\n", + " 'Eastern Asia': 'Asia',\n", + " 'Central Asia': 'Asia',\n", + " 'Southern Europe': 'Europe',\n", + " 'Western Europe': 'Europe',\n", + " 'Eastern Europe': 'Europe',\n", + " 'Northern Europe': 'Europe',\n", + " 'Latin America and the Caribbean': 'America',\n", + " 'Northern America': 'America',\n", + " 'Sub-Saharan Africa': 'Africa',\n", + " 'Northern Africa': 'Africa',\n", + " 'Australia and New Zealand': 'Oceania'\n", + "}\n", + "\n", + "# Map the 'Subregion' values to continents\n", + "Dat2023['Continent'] = Dat2023['Subregion'].map(sub_region_to_continent)\n", + "'''\n", + "# Drop rows with missing values in 'Continent' or 'Life Ladder'\n", + "anova_data = Dat2023[['Continent', 'Life Ladder']].dropna()\n", + "\n", + "# Perform the ANOVA test\n", + "anova_result = pg.anova(data=anova_data, dv='Life Ladder', between='Continent', detailed=True)\n", + "\n", + "# Print the ANOVA table\n", + "print(\"ANOVA Table:\")\n", + "print(anova_result)\n", + "\n", + "# Extract the relevant ANOVA results for sum of squares (SS)\n", + "ss_between = anova_result['SS'].iloc[0] # Sum of Squares between\n", + "ss_within = anova_result['SS'].iloc[1] # Sum of Squares within\n", + "\n", + "# Extract the degrees of freedom (df) for between and within\n", + "df_between = anova_result['DF'].iloc[0] # Degrees of freedom between\n", + "df_within = anova_result['DF'].iloc[1] # Degrees of freedom within\n", + "\n", + "# Extract the mean squares (MS) for between and within\n", + "ms_between = anova_result['MS'].iloc[0] # Mean square between\n", + "ms_within = anova_result['MS'].iloc[1] # Mean square within\n", + "\n", + "# F-statistic\n", + "f_stat = ms_between / ms_within\n", + "\n", + "# Post-hoc analysis if the null hypothesis is rejected\n", + "if anova_result['p-unc'].iloc[0] < 0.05: # If H0 is rejected\n", + " # Group statistics\n", + " continent_means = anova_data.groupby('Continent')['Life Ladder'].mean()\n", + " # Aggregating count, mean, and variance for each continent group\n", + " DatGroup = anova_data.groupby(\"Continent\")[\"Life Ladder\"].agg([\"count\", \"mean\", \"var\"]).reset_index()\n", + "\n", + " # Extract the necessary columns for calculation\n", + " count_values = DatGroup[\"count\"]\n", + " mean_values = DatGroup[\"mean\"]\n", + " var_values = DatGroup[\"var\"]\n", + "\n", + " # Intercontinental mean (μ) calculation\n", + " n_tot = len(anova_data) # Total number of observations\n", + " J = len(DatGroup) # Number of continents/groups\n", + " n_Bar = n_tot / J # Average sample size per group\n", + "\n", + " mu = anova_data['Life Ladder'].mean()\n", + "\n", + " # Intercontinental Variance (τ²)\n", + " tau_squared = (ms_between - ms_within) / n_Bar\n", + "\n", + " # Print results\n", + " print(\"\\nPost-Hoc Analysis:\")\n", + " print(f\"Means by Continent:\\n{continent_means.round(3)}\")\n", + " print(f\"Intercontinental Mean (μ): {mu:.3f}\")\n", + " print(f\"Intercontinental Variance (τ²): {tau_squared:.3f}\")\n", + "else:\n", + " print(\"\\nGlobal Analysis:\")\n", + " global_mean = anova_data['Life Ladder'].mean()\n", + " global_variance = anova_data['Life Ladder'].var(ddof=1)\n", + " print(f\"Global Mean (θ): {global_mean:.3f}\")\n", + " print(f\"Global Variance (σ²): {global_variance:.3f}\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fall-2024/math/mat-206/00020/MAT-206:00020 - Thanawin Pattanaphol.odt 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