Completed all steps for Module 3 Exercise 3 with outputs, plots, and explanations

parent 18306fbb
......@@ -1888,6 +1888,86 @@
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.412727\n",
" Iterations 7\n",
"Optimization terminated successfully.\n",
" Current function value: 0.354560\n",
" Iterations 7\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"\n",
"# Separate data for smokers and non-smokers\n",
"df_smokers = df[df[\"Smoker\"] == \"Yes\"]\n",
"df_nonsmokers = df[df[\"Smoker\"] == \"No\"]\n",
"\n",
"# Logistic regression: Death (1) vs Alive (0)\n",
"model_smokers = sm.Logit(df_smokers[\"Death\"], sm.add_constant(df_smokers[\"Age\"])).fit()\n",
"model_nonsmokers = sm.Logit(df_nonsmokers[\"Death\"], sm.add_constant(df_nonsmokers[\"Age\"])).fit()\n",
"\n",
"# Create age range for prediction\n",
"age_range = np.linspace(df[\"Age\"].min(), df[\"Age\"].max(), 100)\n",
"\n",
"# Predictions + CI for smokers\n",
"X_pred_smokers = sm.add_constant(age_range)\n",
"pred_probs_smokers = model_smokers.predict(X_pred_smokers)\n",
"\n",
"# Standard errors for smokers\n",
"pred_var_smokers = np.diag(X_pred_smokers @ model_smokers.cov_params() @ X_pred_smokers.T)\n",
"pred_se_smokers = np.sqrt(pred_var_smokers)\n",
"ci_lower_smokers = sm.families.links.logit().inverse(np.log(pred_probs_smokers/(1-pred_probs_smokers)) - 1.96*pred_se_smokers)\n",
"ci_upper_smokers = sm.families.links.logit().inverse(np.log(pred_probs_smokers/(1-pred_probs_smokers)) + 1.96*pred_se_smokers)\n",
"\n",
"# Predictions + CI for non-smokers\n",
"X_pred_nonsmokers = sm.add_constant(age_range)\n",
"pred_probs_nonsmokers = model_nonsmokers.predict(X_pred_nonsmokers)\n",
"\n",
"pred_var_nonsmokers = np.diag(X_pred_nonsmokers @ model_nonsmokers.cov_params() @ X_pred_nonsmokers.T)\n",
"pred_se_nonsmokers = np.sqrt(pred_var_nonsmokers)\n",
"ci_lower_nonsmokers = sm.families.links.logit().inverse(np.log(pred_probs_nonsmokers/(1-pred_probs_nonsmokers)) - 1.96*pred_se_nonsmokers)\n",
"ci_upper_nonsmokers = sm.families.links.logit().inverse(np.log(pred_probs_nonsmokers/(1-pred_probs_nonsmokers)) + 1.96*pred_se_nonsmokers)\n",
"\n",
"# Plot\n",
"plt.figure(figsize=(8,6))\n",
"plt.plot(age_range, pred_probs_smokers, color=\"red\", label=\"Smokers\")\n",
"plt.fill_between(age_range, ci_lower_smokers, ci_upper_smokers, color=\"red\", alpha=0.2)\n",
"\n",
"plt.plot(age_range, pred_probs_nonsmokers, color=\"blue\", label=\"Non-smokers\")\n",
"plt.fill_between(age_range, ci_lower_nonsmokers, ci_upper_nonsmokers, color=\"blue\", alpha=0.2)\n",
"\n",
"plt.xlabel(\"Age\")\n",
"plt.ylabel(\"Probability of Death\")\n",
"plt.title(\"Logistic Regression: Death Probability vs Age\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
......
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