{
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from scipy.stats import norm, t, chi2\n",
"from scipy.stats import iqr, median_abs_deviation"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Significane level\n",
"ALPHA = 0.05"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading and preprocessing data"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"DataWhr2024 = pd.read_csv(\"DataWhr2024.csv\")\n",
"UnM49 = pd.read_csv(\"UnM49.csv\", sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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": 29,
"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": 30,
"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": 31,
"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": 32,
"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": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# Extract the 'Life Ladder' data\n",
"Data1 = Dat2023['Life Ladder']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 1**"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Mean (X̄n) Median (X̃n) Std. Dev (σ̂1) MAD (σ̂2) IQR (σ̂3)\n",
"0 5.6208 5.863 1.1395 1.1764 1.3399\n"
]
}
],
"source": [
"# Estimators for μ\n",
"mean_estimator = np.mean(Data1)\n",
"median_estimator = np.median(Data1)\n",
"\n",
"# Estimators for σ\n",
"# Sample standard deviation (1 degree of freedom)\n",
"std_dev_estimator = np.std(Data1, ddof=1)\n",
"\n",
"# Median Absolute Deviation (MAD), scaled\n",
"mad = np.median(np.abs(Data1 - median_estimator))\n",
"mad_estimator = mad * 1.4826\n",
"\n",
"# Interquartile Range (IQR), scaled\n",
"q1 = np.percentile(Data1, 25)\n",
"q3 = np.percentile(Data1, 75)\n",
"iqr = q3 - q1\n",
"iqr_estimator = iqr * 0.7413\n",
"\n",
"# Create a results table\n",
"results = pd.DataFrame({\n",
" 'Mean (X̄n)': [round(mean_estimator, 4)],\n",
" 'Median (X̃n)': [round(median_estimator, 4)],\n",
" 'Std. Dev (σ̂1)': [round(std_dev_estimator, 4)],\n",
" 'MAD (σ̂2)': [round(mad_estimator, 4)],\n",
" 'IQR (σ̂3)': [round(iqr_estimator, 4)]\n",
"})\n",
"\n",
"# Print the results\n",
"print(results)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 2**"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" MLE for μ MLE for σ\n",
"0 5.6208 1.1353\n"
]
}
],
"source": [
"# MLE for μ (mean)\n",
"mle_mu = np.mean(Data1)\n",
"\n",
"# MLE for σ (standard deviation)\n",
"mle_sigma = np.sqrt(np.mean((Data1 - mle_mu) ** 2))\n",
"\n",
"# Report the results rounded to 4 decimal places\n",
"results = pd.DataFrame({\n",
" 'MLE for μ': [round(mle_mu, 4)],\n",
" 'MLE for σ': [round(mle_sigma, 4)]\n",
"})\n",
"\n",
"# Print the results\n",
"print(results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Question 3**"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" CI Type | \n",
" Lower Bound | \n",
" Upper Bound | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Mean (σ̂1) | \n",
" 5.4307 | \n",
" 5.8110 | \n",
"
\n",
" \n",
" 1 | \n",
" Mean (σ̂2) | \n",
" 5.4246 | \n",
" 5.8171 | \n",
"
\n",
" \n",
" 2 | \n",
" Mean (σ̂3) | \n",
" 5.3973 | \n",
" 5.8444 | \n",
"
\n",
" \n",
" 3 | \n",
" Median (σ̂1) | \n",
" 5.6247 | \n",
" 6.1013 | \n",
"
\n",
" \n",
" 4 | \n",
" Median (σ̂2) | \n",
" 5.6170 | \n",
" 6.1090 | \n",
"
\n",
" \n",
" 5 | \n",
" Median (σ̂3) | \n",
" 5.5828 | \n",
" 6.1432 | \n",
"
\n",
" \n",
"
\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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" CI Type | \n",
" Lower Bound | \n",
" Upper Bound | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Mean (σ̂1) | \n",
" 3.3794 | \n",
" 7.8623 | \n",
"
\n",
" \n",
" 1 | \n",
" Mean (σ̂2) | \n",
" 3.3067 | \n",
" 7.9350 | \n",
"
\n",
" \n",
" 2 | \n",
" Mean (σ̂3) | \n",
" 2.9852 | \n",
" 8.2565 | \n",
"
\n",
" \n",
" 3 | \n",
" Median (σ̂1) | \n",
" 3.6170 | \n",
" 8.1090 | \n",
"
\n",
" \n",
" 4 | \n",
" Median (σ̂2) | \n",
" 3.5441 | \n",
" 8.1819 | \n",
"
\n",
" \n",
" 5 | \n",
" Median (σ̂3) | \n",
" 3.2219 | \n",
" 8.5041 | \n",
"
\n",
" \n",
" 6 | \n",
" IQR | \n",
" 2.9573 | \n",
" 8.2097 | \n",
"
\n",
" \n",
"
\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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