{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generalized Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt\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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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:                  Fri, 20 Mar 2026   Deviance:                       4078.8\n",
      "Time:                          11:18:46   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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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\n"
   ]
  },
  {
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "outputs": [],
   "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": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.graphics.api import abline_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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",
    "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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": {
 
 
 
 
    }
   },
   "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:                Fri, 20 Mar 2026   Deviance:                     0.087988\n",
      "Time:                        11:18:46   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(data2.endog, data2.exog, family=sm.families.Gamma(sm.families.links.Log()))\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": {
 
 
 
 
    }
   },
   "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(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and summary (artificial data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "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:                Fri, 20 Mar 2026   Deviance:                   1.0215e-05\n",
      "Time:                        11:18:46   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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