{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generalized Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:11.220725Z",
     "iopub.status.busy": "2026-07-29T17:23:11.220482Z",
     "iopub.status.idle": "2026-07-29T17:23:12.401503Z",
     "shell.execute_reply": "2026-07-29T17:23:12.397866Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:12.409724Z",
     "iopub.status.busy": "2026-07-29T17:23:12.409363Z",
     "iopub.status.idle": "2026-07-29T17:23:14.962575Z",
     "shell.execute_reply": "2026-07-29T17:23:14.961427Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Binomial response data\n",
    "\n",
    "### Load Star98 data\n",
    "\n",
    " In this example, we use the Star98 dataset which was taken with permission\n",
    " from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook\n",
    " information can be obtained by typing: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:14.966634Z",
     "iopub.status.busy": "2026-07-29T17:23:14.965408Z",
     "iopub.status.idle": "2026-07-29T17:23:14.977772Z",
     "shell.execute_reply": "2026-07-29T17:23:14.974490Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "\n",
      "    Number of Observations - 303 (counties in California).\n",
      "\n",
      "    Number of Variables - 13 and 8 interaction terms.\n",
      "\n",
      "    Definition of variables names::\n",
      "\n",
      "        NABOVE   - Total number of students above the national median for the\n",
      "                   math section.\n",
      "        NBELOW   - Total number of students below the national median for the\n",
      "                   math section.\n",
      "        LOWINC   - Percentage of low income students\n",
      "        PERASIAN - Percentage of Asian student\n",
      "        PERBLACK - Percentage of black students\n",
      "        PERHISP  - Percentage of Hispanic students\n",
      "        PERMINTE - Percentage of minority teachers\n",
      "        AVYRSEXP - Sum of teachers' years in educational service divided by the\n",
      "                number of teachers.\n",
      "        AVSALK   - Total salary budget including benefits divided by the number\n",
      "                   of full-time teachers (in thousands)\n",
      "        PERSPENK - Per-pupil spending (in thousands)\n",
      "        PTRATIO  - Pupil-teacher ratio.\n",
      "        PCTAF    - Percentage of students taking UC/CSU prep courses\n",
      "        PCTCHRT  - Percentage of charter schools\n",
      "        PCTYRRND - Percentage of year-round schools\n",
      "\n",
      "        The below variables are interaction terms of the variables defined\n",
      "        above.\n",
      "\n",
      "        PERMINTE_AVYRSEXP\n",
      "        PEMINTE_AVSAL\n",
      "        AVYRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO\n",
      "        PERSPEN_PCTAF\n",
      "        PTRATIO_PCTAF\n",
      "        PERMINTE_AVTRSEXP_AVSAL\n",
      "        PERSPEN_PTRATIO_PCTAF\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.star98.NOTE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data and add a constant to the exogenous (independent) variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:14.980594Z",
     "iopub.status.busy": "2026-07-29T17:23:14.979750Z",
     "iopub.status.idle": "2026-07-29T17:23:15.013905Z",
     "shell.execute_reply": "2026-07-29T17:23:15.010322Z"
    }
   },
   "outputs": [],
   "source": [
    "data = sm.datasets.star98.load()\n",
    "data.exog = sm.add_constant(data.exog, prepend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.016591Z",
     "iopub.status.busy": "2026-07-29T17:23:15.016209Z",
     "iopub.status.idle": "2026-07-29T17:23:15.031040Z",
     "shell.execute_reply": "2026-07-29T17:23:15.030214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NABOVE  NBELOW\n",
      "0   452.0   355.0\n",
      "1   144.0    40.0\n",
      "2   337.0   234.0\n",
      "3   395.0   178.0\n",
      "4     8.0    57.0\n"
     ]
    }
   ],
   "source": [
    "print(data.endog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The independent variables include all the other variables described above, as\n",
    " well as the interaction terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.033333Z",
     "iopub.status.busy": "2026-07-29T17:23:15.033054Z",
     "iopub.status.idle": "2026-07-29T17:23:15.062683Z",
     "shell.execute_reply": "2026-07-29T17:23:15.061983Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     LOWINC   PERASIAN   PERBLACK    PERHISP  PERMINTE  AVYRSEXP    AVSALK  \\\n",
      "0  34.39730  23.299300  14.235280  11.411120  15.91837  14.70646  59.15732   \n",
      "1  17.36507  29.328380   8.234897   9.314884  13.63636  16.08324  59.50397   \n",
      "2  32.64324   9.226386  42.406310  13.543720  28.83436  14.59559  60.56992   \n",
      "3  11.90953  13.883090   3.796973  11.443110  11.11111  14.38939  58.33411   \n",
      "4  36.88889  12.187500  76.875000   7.604167  43.58974  13.90568  63.15364   \n",
      "\n",
      "   PERSPENK   PTRATIO     PCTAF  ...   PCTYRRND  PERMINTE_AVYRSEXP  \\\n",
      "0  4.445207  21.71025  57.03276  ...  22.222220         234.102872   \n",
      "1  5.267598  20.44278  64.62264  ...   0.000000         219.316851   \n",
      "2  5.482922  18.95419  53.94191  ...   0.000000         420.854496   \n",
      "3  4.165093  21.63539  49.06103  ...   7.142857         159.882095   \n",
      "4  4.324902  18.77984  52.38095  ...   0.000000         606.144976   \n",
      "\n",
      "   PERMINTE_AVSAL  AVYRSEXP_AVSAL  PERSPEN_PTRATIO  PERSPEN_PCTAF  \\\n",
      "0       941.68811        869.9948         96.50656      253.52242   \n",
      "1       811.41756        957.0166        107.68435      340.40609   \n",
      "2      1746.49488        884.0537        103.92435      295.75929   \n",
      "3       648.15671        839.3923         90.11341      204.34375   \n",
      "4      2752.85075        878.1943         81.22097      226.54248   \n",
      "\n",
      "   PTRATIO_PCTAF  PERMINTE_AVYRSEXP_AVSAL  PERSPEN_PTRATIO_PCTAF  const  \n",
      "0      1238.1955               13848.8985              5504.0352    1.0  \n",
      "1      1321.0664               13050.2233              6958.8468    1.0  \n",
      "2      1022.4252               25491.1232              5605.8777    1.0  \n",
      "3      1061.4545                9326.5797              4421.0568    1.0  \n",
      "4       983.7059               38280.2616              4254.4314    1.0  \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data.exog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.072761Z",
     "iopub.status.busy": "2026-07-29T17:23:15.072529Z",
     "iopub.status.idle": "2026-07-29T17:23:15.113868Z",
     "shell.execute_reply": "2026-07-29T17:23:15.113224Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Generalized Linear Model Regression Results                   \n",
      "================================================================================\n",
      "Dep. Variable:     ['NABOVE', 'NBELOW']   No. Observations:                  303\n",
      "Model:                              GLM   Df Residuals:                      282\n",
      "Model Family:                  Binomial   Df Model:                           20\n",
      "Link Function:                    Logit   Scale:                          1.0000\n",
      "Method:                            IRLS   Log-Likelihood:                -2998.6\n",
      "Date:                  Wed, 29 Jul 2026   Deviance:                       4078.8\n",
      "Time:                          17:23:15   Pearson chi2:                 4.05e+03\n",
      "No. Iterations:                       5   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:              nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "LOWINC                     -0.0168      0.000    -38.749      0.000      -0.018      -0.016\n",
      "PERASIAN                    0.0099      0.001     16.505      0.000       0.009       0.011\n",
      "PERBLACK                   -0.0187      0.001    -25.182      0.000      -0.020      -0.017\n",
      "PERHISP                    -0.0142      0.000    -32.818      0.000      -0.015      -0.013\n",
      "PERMINTE                    0.2545      0.030      8.498      0.000       0.196       0.313\n",
      "AVYRSEXP                    0.2407      0.057      4.212      0.000       0.129       0.353\n",
      "AVSALK                      0.0804      0.014      5.775      0.000       0.053       0.108\n",
      "PERSPENK                   -1.9522      0.317     -6.162      0.000      -2.573      -1.331\n",
      "PTRATIO                    -0.3341      0.061     -5.453      0.000      -0.454      -0.214\n",
      "PCTAF                      -0.1690      0.033     -5.169      0.000      -0.233      -0.105\n",
      "PCTCHRT                     0.0049      0.001      3.921      0.000       0.002       0.007\n",
      "PCTYRRND                   -0.0036      0.000    -15.878      0.000      -0.004      -0.003\n",
      "PERMINTE_AVYRSEXP          -0.0141      0.002     -7.391      0.000      -0.018      -0.010\n",
      "PERMINTE_AVSAL             -0.0040      0.000     -8.450      0.000      -0.005      -0.003\n",
      "AVYRSEXP_AVSAL             -0.0039      0.001     -4.059      0.000      -0.006      -0.002\n",
      "PERSPEN_PTRATIO             0.0917      0.015      6.321      0.000       0.063       0.120\n",
      "PERSPEN_PCTAF               0.0490      0.007      6.574      0.000       0.034       0.064\n",
      "PTRATIO_PCTAF               0.0080      0.001      5.362      0.000       0.005       0.011\n",
      "PERMINTE_AVYRSEXP_AVSAL     0.0002   2.99e-05      7.428      0.000       0.000       0.000\n",
      "PERSPEN_PTRATIO_PCTAF      -0.0022      0.000     -6.445      0.000      -0.003      -0.002\n",
      "const                       2.9589      1.547      1.913      0.056      -0.073       5.990\n",
      "===========================================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n",
    "res = glm_binom.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantities of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.121361Z",
     "iopub.status.busy": "2026-07-29T17:23:15.118399Z",
     "iopub.status.idle": "2026-07-29T17:23:15.136865Z",
     "shell.execute_reply": "2026-07-29T17:23:15.135147Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of trials: 108418.0\n",
      "Parameters:  LOWINC                    -0.016815\n",
      "PERASIAN                   0.009925\n",
      "PERBLACK                  -0.018724\n",
      "PERHISP                   -0.014239\n",
      "PERMINTE                   0.254487\n",
      "AVYRSEXP                   0.240694\n",
      "AVSALK                     0.080409\n",
      "PERSPENK                  -1.952161\n",
      "PTRATIO                   -0.334086\n",
      "PCTAF                     -0.169022\n",
      "PCTCHRT                    0.004917\n",
      "PCTYRRND                  -0.003580\n",
      "PERMINTE_AVYRSEXP         -0.014077\n",
      "PERMINTE_AVSAL            -0.004005\n",
      "AVYRSEXP_AVSAL            -0.003906\n",
      "PERSPEN_PTRATIO            0.091714\n",
      "PERSPEN_PCTAF              0.048990\n",
      "PTRATIO_PCTAF              0.008041\n",
      "PERMINTE_AVYRSEXP_AVSAL    0.000222\n",
      "PERSPEN_PTRATIO_PCTAF     -0.002249\n",
      "const                      2.958878\n",
      "dtype: float64\n",
      "T-values:  LOWINC                    -38.749083\n",
      "PERASIAN                   16.504736\n",
      "PERBLACK                  -25.182189\n",
      "PERHISP                   -32.817913\n",
      "PERMINTE                    8.498271\n",
      "AVYRSEXP                    4.212479\n",
      "AVSALK                      5.774998\n",
      "PERSPENK                   -6.161911\n",
      "PTRATIO                    -5.453217\n",
      "PCTAF                      -5.168654\n",
      "PCTCHRT                     3.921200\n",
      "PCTYRRND                  -15.878260\n",
      "PERMINTE_AVYRSEXP          -7.390931\n",
      "PERMINTE_AVSAL             -8.449639\n",
      "AVYRSEXP_AVSAL             -4.059162\n",
      "PERSPEN_PTRATIO             6.321099\n",
      "PERSPEN_PCTAF               6.574347\n",
      "PTRATIO_PCTAF               5.362290\n",
      "PERMINTE_AVYRSEXP_AVSAL     7.428064\n",
      "PERSPEN_PTRATIO_PCTAF      -6.445137\n",
      "const                       1.913012\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of trials:\", data.endog.iloc[:, 0].sum())\n",
    "print(\"Parameters: \", res.params)\n",
    "print(\"T-values: \", res.tvalues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.139077Z",
     "iopub.status.busy": "2026-07-29T17:23:15.138858Z",
     "iopub.status.idle": "2026-07-29T17:23:15.153073Z",
     "shell.execute_reply": "2026-07-29T17:23:15.151361Z"
    }
   },
   "outputs": [],
   "source": [
    "means = data.exog.mean(axis=0)\n",
    "means25 = means.copy()\n",
    "means25.iloc[0] = stats.scoreatpercentile(data.exog.iloc[:, 0], 25)\n",
    "means75 = means.copy()\n",
    "means75.iloc[0] = lowinc_75per = stats.scoreatpercentile(data.exog.iloc[:, 0], 75)\n",
    "resp_25 = res.predict(means25)\n",
    "resp_75 = res.predict(means75)\n",
    "diff = resp_75 - resp_25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interquartile first difference for the percentage of low income households in a school district is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.157602Z",
     "iopub.status.busy": "2026-07-29T17:23:15.157358Z",
     "iopub.status.idle": "2026-07-29T17:23:15.165626Z",
     "shell.execute_reply": "2026-07-29T17:23:15.164011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.8753%\n"
     ]
    }
   ],
   "source": [
    "print(\"%2.4f%%\" % (diff.iloc[0] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "\n",
    " We extract information that will be used to draw some interesting plots: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.169952Z",
     "iopub.status.busy": "2026-07-29T17:23:15.169741Z",
     "iopub.status.idle": "2026-07-29T17:23:15.179784Z",
     "shell.execute_reply": "2026-07-29T17:23:15.179216Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4196/1469734208.py:2: Pandas4Warning: Starting with pandas version 4.0 all arguments of sum will be keyword-only.\n",
      "  y = data.endog.iloc[:, 0] / data.endog.sum(1)\n"
     ]
    }
   ],
   "source": [
    "nobs = res.nobs\n",
    "y = data.endog.iloc[:, 0] / data.endog.sum(1)\n",
    "yhat = res.mu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot yhat vs y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.196012Z",
     "iopub.status.busy": "2026-07-29T17:23:15.195701Z",
     "iopub.status.idle": "2026-07-29T17:23:15.205600Z",
     "shell.execute_reply": "2026-07-29T17:23:15.201930Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.graphics.api import abline_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.209741Z",
     "iopub.status.busy": "2026-07-29T17:23:15.208909Z",
     "iopub.status.idle": "2026-07-29T17:23:15.787108Z",
     "shell.execute_reply": "2026-07-29T17:23:15.785910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(yhat, y)\n",
    "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
    "abline_plot(model_results=line_fit, ax=ax)\n",
    "\n",
    "\n",
    "ax.set_title(\"Model Fit Plot\")\n",
    "ax.set_ylabel(\"Observed values\")\n",
    "ax.set_xlabel(\"Fitted values\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot yhat vs. Pearson residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:15.793421Z",
     "iopub.status.busy": "2026-07-29T17:23:15.792955Z",
     "iopub.status.idle": "2026-07-29T17:23:16.137545Z",
     "shell.execute_reply": "2026-07-29T17:23:16.133128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Fitted values')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(yhat, res.resid_pearson)\n",
    "ax.hlines(0, 0, 1)\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_title(\"Residual Dependence Plot\")\n",
    "ax.set_ylabel(\"Pearson Residuals\")\n",
    "ax.set_xlabel(\"Fitted values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram of standardized deviance residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:16.139564Z",
     "iopub.status.busy": "2026-07-29T17:23:16.139349Z",
     "iopub.status.idle": "2026-07-29T17:23:16.668320Z",
     "shell.execute_reply": "2026-07-29T17:23:16.666924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "resid = res.resid_deviance.copy()\n",
    "resid_std = stats.zscore(resid)\n",
    "ax.hist(resid_std, bins=25)\n",
    "ax.set_title(\"Histogram of standardized deviance residuals\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QQ Plot of Deviance Residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:16.673351Z",
     "iopub.status.busy": "2026-07-29T17:23:16.670417Z",
     "iopub.status.idle": "2026-07-29T17:23:17.349592Z",
     "shell.execute_reply": "2026-07-29T17:23:17.348676Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels import graphics\n",
    "\n",
    "graphics.gofplots.qqplot(resid, line=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Gamma for proportional count response\n",
    "\n",
    "### Load Scottish Parliament Voting data\n",
    "\n",
    " In the example above, we printed the ``NOTE`` attribute to learn about the\n",
    " Star98 dataset. statsmodels datasets ships with other useful information. For\n",
    " example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:17.355340Z",
     "iopub.status.busy": "2026-07-29T17:23:17.352057Z",
     "iopub.status.idle": "2026-07-29T17:23:17.360409Z",
     "shell.execute_reply": "2026-07-29T17:23:17.358971Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "This data is based on the example in Gill and describes the proportion of\n",
      "voters who voted Yes to grant the Scottish Parliament taxation powers.\n",
      "The data are divided into 32 council districts.  This example's explanatory\n",
      "variables include the amount of council tax collected in pounds sterling as\n",
      "of April 1997 per two adults before adjustments, the female percentage of\n",
      "total claims for unemployment benefits as of January, 1998, the standardized\n",
      "mortality rate (UK is 100), the percentage of labor force participation,\n",
      "regional GDP, the percentage of children aged 5 to 15, and an interaction term\n",
      "between female unemployment and the council tax.\n",
      "\n",
      "The original source files and variable information are included in\n",
      "/scotland/src/\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.scotland.DESCRLONG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Load the data and add a constant to the exogenous variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:17.365276Z",
     "iopub.status.busy": "2026-07-29T17:23:17.365003Z",
     "iopub.status.idle": "2026-07-29T17:23:17.401679Z",
     "shell.execute_reply": "2026-07-29T17:23:17.397863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   COUTAX  UNEMPF    MOR   ACT      GDP   AGE  COUTAX_FEMALEUNEMP  const\n",
      "0   712.0    21.0  105.0  82.4  13566.0  12.3             14952.0    1.0\n",
      "1   643.0    26.5   97.0  80.2  13566.0  15.3             17039.5    1.0\n",
      "2   679.0    28.3  113.0  86.3   9611.0  13.9             19215.7    1.0\n",
      "3   801.0    27.1  109.0  80.4   9483.0  13.6             21707.1    1.0\n",
      "4   753.0    22.0  115.0  64.7   9265.0  14.6             16566.0    1.0\n",
      "0    60.3\n",
      "1    52.3\n",
      "2    53.4\n",
      "3    57.0\n",
      "4    68.7\n",
      "Name: YES, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data2 = sm.datasets.scotland.load()\n",
    "data2.exog = sm.add_constant(data2.exog, prepend=False)\n",
    "print(data2.exog.head())\n",
    "print(data2.endog.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Fit and summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:17.404052Z",
     "iopub.status.busy": "2026-07-29T17:23:17.403845Z",
     "iopub.status.idle": "2026-07-29T17:23:17.431573Z",
     "shell.execute_reply": "2026-07-29T17:23:17.430566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                    YES   No. Observations:                   32\n",
      "Model:                            GLM   Df Residuals:                       24\n",
      "Model Family:                   Gamma   Df Model:                            7\n",
      "Link Function:                    Log   Scale:                       0.0035927\n",
      "Method:                          IRLS   Log-Likelihood:                -83.110\n",
      "Date:                Wed, 29 Jul 2026   Deviance:                     0.087988\n",
      "Time:                        17:23:17   Pearson chi2:                   0.0862\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):             0.9797\n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "COUTAX                -0.0024      0.001     -2.466      0.014      -0.004      -0.000\n",
      "UNEMPF                -0.1005      0.031     -3.269      0.001      -0.161      -0.040\n",
      "MOR                    0.0048      0.002      2.946      0.003       0.002       0.008\n",
      "ACT                   -0.0067      0.003     -2.534      0.011      -0.012      -0.002\n",
      "GDP                 8.173e-06   7.19e-06      1.136      0.256   -5.93e-06    2.23e-05\n",
      "AGE                    0.0298      0.015      2.009      0.045       0.001       0.059\n",
      "COUTAX_FEMALEUNEMP     0.0001   4.33e-05      2.724      0.006    3.31e-05       0.000\n",
      "const                  5.6581      0.680      8.318      0.000       4.325       6.991\n",
      "======================================================================================\n"
     ]
    }
   ],
   "source": [
    "glm_gamma = sm.GLM(\n",
    "    data2.endog, data2.exog, family=sm.families.Gamma(sm.families.links.Log())\n",
    ")\n",
    "glm_results = glm_gamma.fit()\n",
    "print(glm_results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GLM: Gaussian distribution with a noncanonical link\n",
    "\n",
    "### Artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:17.437360Z",
     "iopub.status.busy": "2026-07-29T17:23:17.434224Z",
     "iopub.status.idle": "2026-07-29T17:23:17.444810Z",
     "shell.execute_reply": "2026-07-29T17:23:17.444144Z"
    }
   },
   "outputs": [],
   "source": [
    "nobs2 = 100\n",
    "x = np.arange(nobs2)\n",
    "np.random.seed(54321)\n",
    "X = np.column_stack((x, x**2))\n",
    "X = sm.add_constant(X, prepend=False)\n",
    "lny = np.exp(-(0.03 * x + 0.0001 * x**2 - 1.0)) + 0.001 * np.random.rand(nobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and summary (artificial data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T17:23:17.448128Z",
     "iopub.status.busy": "2026-07-29T17:23:17.447352Z",
     "iopub.status.idle": "2026-07-29T17:23:17.475474Z",
     "shell.execute_reply": "2026-07-29T17:23:17.472034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  100\n",
      "Model:                            GLM   Df Residuals:                       97\n",
      "Model Family:                Gaussian   Df Model:                            2\n",
      "Link Function:                    Log   Scale:                      1.0531e-07\n",
      "Method:                          IRLS   Log-Likelihood:                 662.92\n",
      "Date:                Wed, 29 Jul 2026   Deviance:                   1.0215e-05\n",
      "Time:                        17:23:17   Pearson chi2:                 1.02e-05\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):              1.000\n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            -0.0300    5.6e-06  -5361.316      0.000      -0.030      -0.030\n",
      "x2         -9.939e-05   1.05e-07   -951.091      0.000   -9.96e-05   -9.92e-05\n",
      "const          1.0003   5.39e-05   1.86e+04      0.000       1.000       1.000\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.Log()))\n",
    "gauss_log_results = gauss_log.fit()\n",
    "print(gauss_log_results.summary())"
   ]
  }
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