cmkl/fall-2024/math/mat-206/00030/MAT-206-00030.ipynb

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{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country name</th>\n",
" <th>year</th>\n",
" <th>Life Ladder</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" <th>Subregion</th>\n",
" <th>Continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>2023</td>\n",
" <td>1.446</td>\n",
" <td>NaN</td>\n",
" <td>0.368</td>\n",
" <td>55.2</td>\n",
" <td>0.228</td>\n",
" <td>NaN</td>\n",
" <td>0.738</td>\n",
" <td>0.261</td>\n",
" <td>0.460</td>\n",
" <td>Southern Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Botswana</td>\n",
" <td>2023</td>\n",
" <td>3.332</td>\n",
" <td>9.673</td>\n",
" <td>0.701</td>\n",
" <td>55.0</td>\n",
" <td>0.741</td>\n",
" <td>-0.264</td>\n",
" <td>0.814</td>\n",
" <td>0.657</td>\n",
" <td>0.247</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>Sri Lanka</td>\n",
" <td>2023</td>\n",
" <td>3.602</td>\n",
" <td>9.364</td>\n",
" <td>0.790</td>\n",
" <td>67.4</td>\n",
" <td>0.754</td>\n",
" <td>0.050</td>\n",
" <td>0.922</td>\n",
" <td>0.709</td>\n",
" <td>0.353</td>\n",
" <td>Southern Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"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": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>const</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>118.0</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.0</td>\n",
" <td>9.495669</td>\n",
" <td>0.790627</td>\n",
" <td>65.237288</td>\n",
" <td>0.796364</td>\n",
" <td>0.034373</td>\n",
" <td>0.722347</td>\n",
" <td>0.654653</td>\n",
" <td>0.293610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>1.149838</td>\n",
" <td>0.131170</td>\n",
" <td>5.492634</td>\n",
" <td>0.113688</td>\n",
" <td>0.162590</td>\n",
" <td>0.173567</td>\n",
" <td>0.106431</td>\n",
" <td>0.088618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.0</td>\n",
" <td>7.076000</td>\n",
" <td>0.398000</td>\n",
" <td>52.200000</td>\n",
" <td>0.452000</td>\n",
" <td>-0.268000</td>\n",
" <td>0.184000</td>\n",
" <td>0.344000</td>\n",
" <td>0.114000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.0</td>\n",
" <td>8.612500</td>\n",
" <td>0.695750</td>\n",
" <td>60.700000</td>\n",
" <td>0.735250</td>\n",
" <td>-0.072500</td>\n",
" <td>0.663250</td>\n",
" <td>0.578250</td>\n",
" <td>0.229250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.0</td>\n",
" <td>9.636000</td>\n",
" <td>0.837500</td>\n",
" <td>66.100000</td>\n",
" <td>0.817500</td>\n",
" <td>0.022000</td>\n",
" <td>0.767500</td>\n",
" <td>0.667000</td>\n",
" <td>0.283000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.0</td>\n",
" <td>10.470250</td>\n",
" <td>0.894250</td>\n",
" <td>69.650000</td>\n",
" <td>0.877000</td>\n",
" <td>0.134250</td>\n",
" <td>0.844500</td>\n",
" <td>0.738750</td>\n",
" <td>0.357500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.0</td>\n",
" <td>11.676000</td>\n",
" <td>0.979000</td>\n",
" <td>74.600000</td>\n",
" <td>0.965000</td>\n",
" <td>0.590000</td>\n",
" <td>0.948000</td>\n",
" <td>0.843000</td>\n",
" <td>0.516000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>const</th>\n",
" <th>Social support</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" <th>Healthy life scaled</th>\n",
" <th>Log GDP scaled</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>118.0</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" <td>118.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.0</td>\n",
" <td>0.790627</td>\n",
" <td>0.796364</td>\n",
" <td>0.034373</td>\n",
" <td>0.722347</td>\n",
" <td>0.654653</td>\n",
" <td>0.293610</td>\n",
" <td>0.582022</td>\n",
" <td>0.526015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>0.131170</td>\n",
" <td>0.113688</td>\n",
" <td>0.162590</td>\n",
" <td>0.173567</td>\n",
" <td>0.106431</td>\n",
" <td>0.088618</td>\n",
" <td>0.245207</td>\n",
" <td>0.249965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.0</td>\n",
" <td>0.398000</td>\n",
" <td>0.452000</td>\n",
" <td>-0.268000</td>\n",
" <td>0.184000</td>\n",
" <td>0.344000</td>\n",
" <td>0.114000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.0</td>\n",
" <td>0.695750</td>\n",
" <td>0.735250</td>\n",
" <td>-0.072500</td>\n",
" <td>0.663250</td>\n",
" <td>0.578250</td>\n",
" <td>0.229250</td>\n",
" <td>0.379464</td>\n",
" <td>0.334022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.0</td>\n",
" <td>0.837500</td>\n",
" <td>0.817500</td>\n",
" <td>0.022000</td>\n",
" <td>0.767500</td>\n",
" <td>0.667000</td>\n",
" <td>0.283000</td>\n",
" <td>0.620536</td>\n",
" <td>0.556522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.0</td>\n",
" <td>0.894250</td>\n",
" <td>0.877000</td>\n",
" <td>0.134250</td>\n",
" <td>0.844500</td>\n",
" <td>0.738750</td>\n",
" <td>0.357500</td>\n",
" <td>0.779018</td>\n",
" <td>0.737880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.0</td>\n",
" <td>0.979000</td>\n",
" <td>0.965000</td>\n",
" <td>0.590000</td>\n",
" <td>0.948000</td>\n",
" <td>0.843000</td>\n",
" <td>0.516000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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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",
"text/plain": [
"<Figure size 1400x1000 with 4 Axes>"
]
},
"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
}