{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discrete Choice Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fair's Affair data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A survey of women only was conducted in 1974 by *Redbook* asking about extramarital affairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:11.661604Z",
     "iopub.status.busy": "2026-06-27T20:27:11.661405Z",
     "iopub.status.idle": "2026-06-27T20:27:13.172637Z",
     "shell.execute_reply": "2026-06-27T20:27:13.171536Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:13.180742Z",
     "iopub.status.busy": "2026-06-27T20:27:13.180355Z",
     "iopub.status.idle": "2026-06-27T20:27:16.367919Z",
     "shell.execute_reply": "2026-06-27T20:27:16.362815Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from statsmodels.formula.api import logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.370502Z",
     "iopub.status.busy": "2026-06-27T20:27:16.370125Z",
     "iopub.status.idle": "2026-06-27T20:27:16.377557Z",
     "shell.execute_reply": "2026-06-27T20:27:16.376674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fair, Ray. 1978. \"A Theory of Extramarital Affairs,\" `Journal of Political\n",
      "Economy`, February, 45-61.\n",
      "\n",
      "The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.fair.SOURCE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.380452Z",
     "iopub.status.busy": "2026-06-27T20:27:16.380247Z",
     "iopub.status.idle": "2026-06-27T20:27:16.385914Z",
     "shell.execute_reply": "2026-06-27T20:27:16.383185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "\n",
      "    Number of observations: 6366\n",
      "    Number of variables: 9\n",
      "    Variable name definitions:\n",
      "\n",
      "        rate_marriage   : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,\n",
      "                        4 = good, 5 = very good\n",
      "        age             : Age\n",
      "        yrs_married     : No. years married. Interval approximations. See\n",
      "                        original paper for detailed explanation.\n",
      "        children        : No. children\n",
      "        religious       : How relgious, 1 = not, 2 = mildly, 3 = fairly,\n",
      "                        4 = strongly\n",
      "        educ            : Level of education, 9 = grade school, 12 = high\n",
      "                        school, 14 = some college, 16 = college graduate,\n",
      "                        17 = some graduate school, 20 = advanced degree\n",
      "        occupation      : 1 = student, 2 = farming, agriculture; semi-skilled,\n",
      "                        or unskilled worker; 3 = white-colloar; 4 = teacher\n",
      "                        counselor social worker, nurse; artist, writers;\n",
      "                        technician, skilled worker, 5 = managerial,\n",
      "                        administrative, business, 6 = professional with\n",
      "                        advanced degree\n",
      "        occupation_husb : Husband's occupation. Same as occupation.\n",
      "        affairs         : measure of time spent in extramarital affairs\n",
      "\n",
      "    See the original paper for more details.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.fair.NOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.391554Z",
     "iopub.status.busy": "2026-06-27T20:27:16.391353Z",
     "iopub.status.idle": "2026-06-27T20:27:16.412458Z",
     "shell.execute_reply": "2026-06-27T20:27:16.411898Z"
    }
   },
   "outputs": [],
   "source": [
    "dta = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.414825Z",
     "iopub.status.busy": "2026-06-27T20:27:16.414618Z",
     "iopub.status.idle": "2026-06-27T20:27:16.438643Z",
     "shell.execute_reply": "2026-06-27T20:27:16.438106Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
      "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
      "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
      "2            4.0  22.0          2.5       0.0        1.0  16.0         3.0   \n",
      "3            4.0  37.0         16.5       4.0        3.0  16.0         5.0   \n",
      "4            5.0  27.0          9.0       1.0        1.0  14.0         3.0   \n",
      "5            4.0  27.0          9.0       0.0        2.0  14.0         3.0   \n",
      "6            5.0  37.0         23.0       5.5        2.0  12.0         5.0   \n",
      "7            5.0  37.0         23.0       5.5        2.0  12.0         2.0   \n",
      "8            3.0  22.0          2.5       0.0        2.0  12.0         3.0   \n",
      "9            3.0  27.0          6.0       0.0        1.0  16.0         3.0   \n",
      "\n",
      "   occupation_husb   affairs  affair  \n",
      "0              5.0  0.111111     1.0  \n",
      "1              4.0  3.230769     1.0  \n",
      "2              5.0  1.400000     1.0  \n",
      "3              5.0  0.727273     1.0  \n",
      "4              4.0  4.666666     1.0  \n",
      "5              4.0  4.666666     1.0  \n",
      "6              4.0  0.852174     1.0  \n",
      "7              3.0  1.826086     1.0  \n",
      "8              3.0  4.799999     1.0  \n",
      "9              5.0  1.333333     1.0  \n"
     ]
    }
   ],
   "source": [
    "dta[\"affair\"] = (dta[\"affairs\"] > 0).astype(float)\n",
    "print(dta.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.441266Z",
     "iopub.status.busy": "2026-06-27T20:27:16.441056Z",
     "iopub.status.idle": "2026-06-27T20:27:16.488088Z",
     "shell.execute_reply": "2026-06-27T20:27:16.486888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       rate_marriage          age  yrs_married     children    religious  \\\n",
      "count    6366.000000  6366.000000  6366.000000  6366.000000  6366.000000   \n",
      "mean        4.109645    29.082862     9.009425     1.396874     2.426170   \n",
      "std         0.961430     6.847882     7.280120     1.433471     0.878369   \n",
      "min         1.000000    17.500000     0.500000     0.000000     1.000000   \n",
      "25%         4.000000    22.000000     2.500000     0.000000     2.000000   \n",
      "50%         4.000000    27.000000     6.000000     1.000000     2.000000   \n",
      "75%         5.000000    32.000000    16.500000     2.000000     3.000000   \n",
      "max         5.000000    42.000000    23.000000     5.500000     4.000000   \n",
      "\n",
      "              educ   occupation  occupation_husb      affairs       affair  \n",
      "count  6366.000000  6366.000000      6366.000000  6366.000000  6366.000000  \n",
      "mean     14.209865     3.424128         3.850141     0.705374     0.322495  \n",
      "std       2.178003     0.942399         1.346435     2.203374     0.467468  \n",
      "min       9.000000     1.000000         1.000000     0.000000     0.000000  \n",
      "25%      12.000000     3.000000         3.000000     0.000000     0.000000  \n",
      "50%      14.000000     3.000000         4.000000     0.000000     0.000000  \n",
      "75%      16.000000     4.000000         5.000000     0.484848     1.000000  \n",
      "max      20.000000     6.000000         6.000000    57.599991     1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(dta.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.490689Z",
     "iopub.status.busy": "2026-06-27T20:27:16.489862Z",
     "iopub.status.idle": "2026-06-27T20:27:16.520692Z",
     "shell.execute_reply": "2026-06-27T20:27:16.519530Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.545314\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "affair_mod = logit(\n",
    "    \"affair ~ occupation + educ + occupation_husb\"\n",
    "    \"+ rate_marriage + age + yrs_married + children\"\n",
    "    \" + religious\",\n",
    "    dta,\n",
    ").fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.523226Z",
     "iopub.status.busy": "2026-06-27T20:27:16.522410Z",
     "iopub.status.idle": "2026-06-27T20:27:16.549245Z",
     "shell.execute_reply": "2026-06-27T20:27:16.546290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                 affair   No. Observations:                 6366\n",
      "Model:                          Logit   Df Residuals:                     6357\n",
      "Method:                           MLE   Df Model:                            8\n",
      "Date:                Sat, 27 Jun 2026   Pseudo R-squ.:                  0.1327\n",
      "Time:                        20:27:16   Log-Likelihood:                -3471.5\n",
      "converged:                       True   LL-Null:                       -4002.5\n",
      "Covariance Type:            nonrobust   LLR p-value:                5.807e-224\n",
      "===================================================================================\n",
      "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept           3.7257      0.299     12.470      0.000       3.140       4.311\n",
      "occupation          0.1602      0.034      4.717      0.000       0.094       0.227\n",
      "educ               -0.0392      0.015     -2.533      0.011      -0.070      -0.009\n",
      "occupation_husb     0.0124      0.023      0.541      0.589      -0.033       0.057\n",
      "rate_marriage      -0.7161      0.031    -22.784      0.000      -0.778      -0.655\n",
      "age                -0.0605      0.010     -5.885      0.000      -0.081      -0.040\n",
      "yrs_married         0.1100      0.011     10.054      0.000       0.089       0.131\n",
      "children           -0.0042      0.032     -0.134      0.893      -0.066       0.058\n",
      "religious          -0.3752      0.035    -10.792      0.000      -0.443      -0.307\n",
      "===================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(affair_mod.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How well are we predicting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.551819Z",
     "iopub.status.busy": "2026-06-27T20:27:16.551041Z",
     "iopub.status.idle": "2026-06-27T20:27:16.562561Z",
     "shell.execute_reply": "2026-06-27T20:27:16.561417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3882.,  431.],\n",
       "       [1326.,  727.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.pred_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.564570Z",
     "iopub.status.busy": "2026-06-27T20:27:16.564380Z",
     "iopub.status.idle": "2026-06-27T20:27:16.693798Z",
     "shell.execute_reply": "2026-06-27T20:27:16.693233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Logit Marginal Effects       \n",
      "=====================================\n",
      "Dep. Variable:                 affair\n",
      "Method:                          dydx\n",
      "At:                           overall\n",
      "===================================================================================\n",
      "                     dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "occupation          0.0293      0.006      4.744      0.000       0.017       0.041\n",
      "educ               -0.0072      0.003     -2.538      0.011      -0.013      -0.002\n",
      "occupation_husb     0.0023      0.004      0.541      0.589      -0.006       0.010\n",
      "rate_marriage      -0.1308      0.005    -26.891      0.000      -0.140      -0.121\n",
      "age                -0.0110      0.002     -5.937      0.000      -0.015      -0.007\n",
      "yrs_married         0.0201      0.002     10.327      0.000       0.016       0.024\n",
      "children           -0.0008      0.006     -0.134      0.893      -0.012       0.011\n",
      "religious          -0.0685      0.006    -11.119      0.000      -0.081      -0.056\n",
      "==================================================================================="
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mfx = affair_mod.get_margeff()\n",
    "print(mfx.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.698634Z",
     "iopub.status.busy": "2026-06-27T20:27:16.698443Z",
     "iopub.status.idle": "2026-06-27T20:27:16.706418Z",
     "shell.execute_reply": "2026-06-27T20:27:16.705688Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rate_marriage       4.000000\n",
      "age                37.000000\n",
      "yrs_married        23.000000\n",
      "children            3.000000\n",
      "religious           3.000000\n",
      "educ               12.000000\n",
      "occupation          3.000000\n",
      "occupation_husb     4.000000\n",
      "affairs             0.521739\n",
      "affair              1.000000\n",
      "Name: 1000, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "respondent1000 = dta.iloc[1000]\n",
    "print(respondent1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.709631Z",
     "iopub.status.busy": "2026-06-27T20:27:16.709443Z",
     "iopub.status.idle": "2026-06-27T20:27:16.719616Z",
     "shell.execute_reply": "2026-06-27T20:27:16.718894Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3.0, 2: 12.0, 3: 4.0, 4: 4.0, 5: 37.0, 6: 23.0, 7: 3.0, 8: 3.0, 0: 1}\n"
     ]
    }
   ],
   "source": [
    "resp = dict(\n",
    "    zip(\n",
    "        range(1, 9),\n",
    "        respondent1000[\n",
    "            [\n",
    "                \"occupation\",\n",
    "                \"educ\",\n",
    "                \"occupation_husb\",\n",
    "                \"rate_marriage\",\n",
    "                \"age\",\n",
    "                \"yrs_married\",\n",
    "                \"children\",\n",
    "                \"religious\",\n",
    "            ]\n",
    "        ].tolist(),\n",
    "    )\n",
    ")\n",
    "resp.update({0: 1})\n",
    "print(resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.727217Z",
     "iopub.status.busy": "2026-06-27T20:27:16.727029Z",
     "iopub.status.idle": "2026-06-27T20:27:16.851451Z",
     "shell.execute_reply": "2026-06-27T20:27:16.847900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Logit Marginal Effects       \n",
      "=====================================\n",
      "Dep. Variable:                 affair\n",
      "Method:                          dydx\n",
      "At:                           overall\n",
      "===================================================================================\n",
      "                     dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "occupation          0.0400      0.008      4.711      0.000       0.023       0.057\n",
      "educ               -0.0098      0.004     -2.537      0.011      -0.017      -0.002\n",
      "occupation_husb     0.0031      0.006      0.541      0.589      -0.008       0.014\n",
      "rate_marriage      -0.1788      0.008    -22.743      0.000      -0.194      -0.163\n",
      "age                -0.0151      0.003     -5.928      0.000      -0.020      -0.010\n",
      "yrs_married         0.0275      0.003     10.256      0.000       0.022       0.033\n",
      "children           -0.0011      0.008     -0.134      0.893      -0.017       0.014\n",
      "religious          -0.0937      0.009    -10.722      0.000      -0.111      -0.077\n",
      "===================================================================================\n"
     ]
    }
   ],
   "source": [
    "mfx = affair_mod.get_margeff(atexog=resp)\n",
    "print(mfx.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`predict` expects a `DataFrame` since `patsy` is used to select columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.853617Z",
     "iopub.status.busy": "2026-06-27T20:27:16.853403Z",
     "iopub.status.idle": "2026-06-27T20:27:16.900451Z",
     "shell.execute_reply": "2026-06-27T20:27:16.899894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    0.518782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "respondent1000 = dta.iloc[[1000]]\n",
    "affair_mod.predict(respondent1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.903019Z",
     "iopub.status.busy": "2026-06-27T20:27:16.902795Z",
     "iopub.status.idle": "2026-06-27T20:27:16.912433Z",
     "shell.execute_reply": "2026-06-27T20:27:16.911894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.07516159285058754)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.fittedvalues[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.914980Z",
     "iopub.status.busy": "2026-06-27T20:27:16.914626Z",
     "iopub.status.idle": "2026-06-27T20:27:16.927038Z",
     "shell.execute_reply": "2026-06-27T20:27:16.923897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.5187815572121532)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.model.cdf(affair_mod.fittedvalues[1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"correct\" model here is likely the Tobit model. We have an work in progress branch \"tobit-model\" on github, if anyone is interested in censored regression models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Logit vs Probit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:16.929274Z",
     "iopub.status.busy": "2026-06-27T20:27:16.929085Z",
     "iopub.status.idle": "2026-06-27T20:27:17.318065Z",
     "shell.execute_reply": "2026-06-27T20:27:17.316732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa47dd88ad0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "support = np.linspace(-6, 6, 1000)\n",
    "ax.plot(support, stats.logistic.cdf(support), \"r-\", label=\"Logistic\")\n",
    "ax.plot(support, stats.norm.cdf(support), label=\"Probit\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.320159Z",
     "iopub.status.busy": "2026-06-27T20:27:17.319937Z",
     "iopub.status.idle": "2026-06-27T20:27:17.774514Z",
     "shell.execute_reply": "2026-06-27T20:27:17.773911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa479b13e00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "support = np.linspace(-6, 6, 1000)\n",
    "ax.plot(support, stats.logistic.pdf(support), \"r-\", label=\"Logistic\")\n",
    "ax.plot(support, stats.norm.pdf(support), label=\"Probit\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generalized Linear Model Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.779969Z",
     "iopub.status.busy": "2026-06-27T20:27:17.779490Z",
     "iopub.status.idle": "2026-06-27T20:27:17.786232Z",
     "shell.execute_reply": "2026-06-27T20:27:17.785630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Jeff Gill's `Generalized Linear Models: A Unified Approach`\n",
      "\n",
      "http://jgill.wustl.edu/research/books.html\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.star98.SOURCE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.790773Z",
     "iopub.status.busy": "2026-06-27T20:27:17.790571Z",
     "iopub.status.idle": "2026-06-27T20:27:17.795412Z",
     "shell.execute_reply": "2026-06-27T20:27:17.794630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "This data is on the California education policy and outcomes (STAR program\n",
      "results for 1998.  The data measured standardized testing by the California\n",
      "Department of Education that required evaluation of 2nd - 11th grade students\n",
      "by the the Stanford 9 test on a variety of subjects.  This dataset is at\n",
      "the level of the unified school district and consists of 303 cases.  The\n",
      "binary response variable represents the number of 9th graders scoring\n",
      "over the national median value on the mathematics exam.\n",
      "\n",
      "The data used in this example is only a subset of the original source.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.star98.DESCRLONG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.798608Z",
     "iopub.status.busy": "2026-06-27T20:27:17.798408Z",
     "iopub.status.idle": "2026-06-27T20:27:17.804433Z",
     "shell.execute_reply": "2026-06-27T20:27:17.803627Z"
    }
   },
   "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": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.807032Z",
     "iopub.status.busy": "2026-06-27T20:27:17.806810Z",
     "iopub.status.idle": "2026-06-27T20:27:17.823665Z",
     "shell.execute_reply": "2026-06-27T20:27:17.822895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n",
      "       'PERMINTE', 'AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF',\n",
      "       'PCTCHRT', 'PCTYRRND', 'PERMINTE_AVYRSEXP', 'PERMINTE_AVSAL',\n",
      "       'AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO', 'PERSPEN_PCTAF', 'PTRATIO_PCTAF',\n",
      "       'PERMINTE_AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO_PCTAF'],\n",
      "      dtype='str')\n"
     ]
    }
   ],
   "source": [
    "dta = sm.datasets.star98.load_pandas().data\n",
    "print(dta.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.825415Z",
     "iopub.status.busy": "2026-06-27T20:27:17.825223Z",
     "iopub.status.idle": "2026-06-27T20:27:17.838384Z",
     "shell.execute_reply": "2026-06-27T20:27:17.837610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   NABOVE  NBELOW    LOWINC   PERASIAN   PERBLACK    PERHISP   PERMINTE\n",
      "0   452.0   355.0  34.39730  23.299300  14.235280  11.411120  15.918370\n",
      "1   144.0    40.0  17.36507  29.328380   8.234897   9.314884  13.636360\n",
      "2   337.0   234.0  32.64324   9.226386  42.406310  13.543720  28.834360\n",
      "3   395.0   178.0  11.90953  13.883090   3.796973  11.443110  11.111110\n",
      "4     8.0    57.0  36.88889  12.187500  76.875000   7.604167  43.589740\n",
      "5  1348.0   899.0  20.93149  28.023510   4.643221  13.808160  15.378490\n",
      "6   477.0   887.0  53.26898   8.447858  19.374830  37.905330  25.525530\n",
      "7   565.0   347.0  15.19009   3.665781   2.649680  13.092070   6.203008\n",
      "8   205.0   320.0  28.21582  10.430420   6.786374  32.334300  13.461540\n",
      "9   469.0   598.0  32.77897  17.178310  12.484930  28.323290  27.259890\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    dta[\n",
    "        [\"NABOVE\", \"NBELOW\", \"LOWINC\", \"PERASIAN\", \"PERBLACK\", \"PERHISP\", \"PERMINTE\"]\n",
    "    ].head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.841320Z",
     "iopub.status.busy": "2026-06-27T20:27:17.841103Z",
     "iopub.status.idle": "2026-06-27T20:27:17.854755Z",
     "shell.execute_reply": "2026-06-27T20:27:17.854015Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   AVYRSEXP    AVSALK  PERSPENK   PTRATIO     PCTAF  PCTCHRT   PCTYRRND\n",
      "0  14.70646  59.15732  4.445207  21.71025  57.03276      0.0  22.222220\n",
      "1  16.08324  59.50397  5.267598  20.44278  64.62264      0.0   0.000000\n",
      "2  14.59559  60.56992  5.482922  18.95419  53.94191      0.0   0.000000\n",
      "3  14.38939  58.33411  4.165093  21.63539  49.06103      0.0   7.142857\n",
      "4  13.90568  63.15364  4.324902  18.77984  52.38095      0.0   0.000000\n",
      "5  14.97755  66.97055  3.916104  24.51914  44.91578      0.0   2.380952\n",
      "6  14.67829  57.62195  4.270903  22.21278  32.28916      0.0  12.121210\n",
      "7  13.66197  63.44740  4.309734  24.59026  30.45267      0.0   0.000000\n",
      "8  16.41760  57.84564  4.527603  21.74138  22.64574      0.0   0.000000\n",
      "9  12.51864  57.80141  4.648917  20.26010  26.07099      0.0   0.000000\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    dta[\n",
    "        [\"AVYRSEXP\", \"AVSALK\", \"PERSPENK\", \"PTRATIO\", \"PCTAF\", \"PCTCHRT\", \"PCTYRRND\"]\n",
    "    ].head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.860772Z",
     "iopub.status.busy": "2026-06-27T20:27:17.860581Z",
     "iopub.status.idle": "2026-06-27T20:27:17.866424Z",
     "shell.execute_reply": "2026-06-27T20:27:17.865660Z"
    }
   },
   "outputs": [],
   "source": [
    "formula = \"NABOVE + NBELOW ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT \"\n",
    "formula += \"+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aside: Binomial distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Toss a six-sided die 5 times, what's the probability of exactly 2 fours?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.868628Z",
     "iopub.status.busy": "2026-06-27T20:27:17.868438Z",
     "iopub.status.idle": "2026-06-27T20:27:17.877570Z",
     "shell.execute_reply": "2026-06-27T20:27:17.876867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.16075102880658423)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.binom(5, 1.0 / 6).pmf(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.886042Z",
     "iopub.status.busy": "2026-06-27T20:27:17.885816Z",
     "iopub.status.idle": "2026-06-27T20:27:17.892651Z",
     "shell.execute_reply": "2026-06-27T20:27:17.891908Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.1607510288065844)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import comb\n",
    "\n",
    "comb(5, 2) * (1 / 6.0) ** 2 * (5 / 6.0) ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.898995Z",
     "iopub.status.busy": "2026-06-27T20:27:17.898692Z",
     "iopub.status.idle": "2026-06-27T20:27:17.952427Z",
     "shell.execute_reply": "2026-06-27T20:27:17.950722Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import glm\n",
    "\n",
    "glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.954977Z",
     "iopub.status.busy": "2026-06-27T20:27:17.954703Z",
     "iopub.status.idle": "2026-06-27T20:27:17.972723Z",
     "shell.execute_reply": "2026-06-27T20:27:17.971171Z"
    }
   },
   "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:                  Sat, 27 Jun 2026   Deviance:                       4078.8\n",
      "Time:                          20:27:17   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",
      "Intercept                    2.9589      1.547      1.913      0.056      -0.073       5.990\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",
      "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                     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",
      "PERMINTE:AVYRSEXP           -0.0141      0.002     -7.391      0.000      -0.018      -0.010\n",
      "AVSALK                       0.0804      0.014      5.775      0.000       0.053       0.108\n",
      "PERMINTE:AVSALK             -0.0040      0.000     -8.450      0.000      -0.005      -0.003\n",
      "AVYRSEXP:AVSALK             -0.0039      0.001     -4.059      0.000      -0.006      -0.002\n",
      "PERMINTE:AVYRSEXP:AVSALK     0.0002   2.99e-05      7.428      0.000       0.000       0.000\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",
      "PERSPENK:PTRATIO             0.0917      0.015      6.321      0.000       0.063       0.120\n",
      "PCTAF                       -0.1690      0.033     -5.169      0.000      -0.233      -0.105\n",
      "PERSPENK: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",
      "PERSPENK:PTRATIO:PCTAF      -0.0022      0.000     -6.445      0.000      -0.003      -0.002\n",
      "============================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(glm_mod.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of trials "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.974944Z",
     "iopub.status.busy": "2026-06-27T20:27:17.974713Z",
     "iopub.status.idle": "2026-06-27T20:27:17.986650Z",
     "shell.execute_reply": "2026-06-27T20:27:17.985908Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4215/1222798614.py:1: Pandas4Warning: Starting with pandas version 4.0 all arguments of sum will be keyword-only.\n",
      "  glm_mod.model.data.orig_endog.sum(1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      807.0\n",
       "1      184.0\n",
       "2      571.0\n",
       "3      573.0\n",
       "4       65.0\n",
       "       ...  \n",
       "298    342.0\n",
       "299    154.0\n",
       "300    595.0\n",
       "301    709.0\n",
       "302    156.0\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_mod.model.data.orig_endog.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:17.992199Z",
     "iopub.status.busy": "2026-06-27T20:27:17.991983Z",
     "iopub.status.idle": "2026-06-27T20:27:18.008035Z",
     "shell.execute_reply": "2026-06-27T20:27:18.005440Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4215/1027078210.py:1: Pandas4Warning: Starting with pandas version 4.0 all arguments of sum will be keyword-only.\n",
      "  glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      470.732584\n",
       "1      138.266178\n",
       "2      285.832629\n",
       "3      392.702917\n",
       "4       20.963146\n",
       "          ...    \n",
       "298    111.464708\n",
       "299     61.037884\n",
       "300    235.517446\n",
       "301    290.952508\n",
       "302     53.312851\n",
       "Length: 303, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)"
   ]
  },
  {
   "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\n",
    "on the response variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.011422Z",
     "iopub.status.busy": "2026-06-27T20:27:18.010577Z",
     "iopub.status.idle": "2026-06-27T20:27:18.015785Z",
     "shell.execute_reply": "2026-06-27T20:27:18.014282Z"
    }
   },
   "outputs": [],
   "source": [
    "exog = glm_mod.model.data.orig_exog  # get the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.018300Z",
     "iopub.status.busy": "2026-06-27T20:27:18.018102Z",
     "iopub.status.idle": "2026-06-27T20:27:18.030137Z",
     "shell.execute_reply": "2026-06-27T20:27:18.027370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept                       1.000000\n",
      "LOWINC                         41.409877\n",
      "PERASIAN                        5.896335\n",
      "PERBLACK                        5.636808\n",
      "PERHISP                        34.398080\n",
      "PCTCHRT                         1.175909\n",
      "PCTYRRND                       11.611905\n",
      "PERMINTE                       14.694747\n",
      "AVYRSEXP                       14.253875\n",
      "PERMINTE:AVYRSEXP             209.018700\n",
      "AVSALK                         58.640258\n",
      "PERMINTE:AVSALK               879.979883\n",
      "AVYRSEXP:AVSALK               839.718173\n",
      "PERMINTE:AVYRSEXP:AVSALK    12585.266464\n",
      "PERSPENK                        4.320310\n",
      "PTRATIO                        22.464250\n",
      "PERSPENK:PTRATIO               96.295756\n",
      "PCTAF                          33.630593\n",
      "PERSPENK:PCTAF                147.235740\n",
      "PTRATIO:PCTAF                 747.445536\n",
      "PERSPENK:PTRATIO:PCTAF       3243.607568\n",
      "dtype: float64"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "means25 = exog.mean()\n",
    "print(means25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.031867Z",
     "iopub.status.busy": "2026-06-27T20:27:18.031678Z",
     "iopub.status.idle": "2026-06-27T20:27:18.044482Z",
     "shell.execute_reply": "2026-06-27T20:27:18.041237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept                       1.000000\n",
      "LOWINC                         26.683040\n",
      "PERASIAN                        5.896335\n",
      "PERBLACK                        5.636808\n",
      "PERHISP                        34.398080\n",
      "PCTCHRT                         1.175909\n",
      "PCTYRRND                       11.611905\n",
      "PERMINTE                       14.694747\n",
      "AVYRSEXP                       14.253875\n",
      "PERMINTE:AVYRSEXP             209.018700\n",
      "AVSALK                         58.640258\n",
      "PERMINTE:AVSALK               879.979883\n",
      "AVYRSEXP:AVSALK               839.718173\n",
      "PERMINTE:AVYRSEXP:AVSALK    12585.266464\n",
      "PERSPENK                        4.320310\n",
      "PTRATIO                        22.464250\n",
      "PERSPENK:PTRATIO               96.295756\n",
      "PCTAF                          33.630593\n",
      "PERSPENK:PCTAF                147.235740\n",
      "PTRATIO:PCTAF                 747.445536\n",
      "PERSPENK:PTRATIO:PCTAF       3243.607568\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "means25[\"LOWINC\"] = exog[\"LOWINC\"].quantile(0.25)\n",
    "print(means25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.047360Z",
     "iopub.status.busy": "2026-06-27T20:27:18.046554Z",
     "iopub.status.idle": "2026-06-27T20:27:18.059487Z",
     "shell.execute_reply": "2026-06-27T20:27:18.058262Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept                       1.000000\n",
      "LOWINC                         55.460075\n",
      "PERASIAN                        5.896335\n",
      "PERBLACK                        5.636808\n",
      "PERHISP                        34.398080\n",
      "PCTCHRT                         1.175909\n",
      "PCTYRRND                       11.611905\n",
      "PERMINTE                       14.694747\n",
      "AVYRSEXP                       14.253875\n",
      "PERMINTE:AVYRSEXP             209.018700\n",
      "AVSALK                         58.640258\n",
      "PERMINTE:AVSALK               879.979883\n",
      "AVYRSEXP:AVSALK               839.718173\n",
      "PERMINTE:AVYRSEXP:AVSALK    12585.266464\n",
      "PERSPENK                        4.320310\n",
      "PTRATIO                        22.464250\n",
      "PERSPENK:PTRATIO               96.295756\n",
      "PCTAF                          33.630593\n",
      "PERSPENK:PCTAF                147.235740\n",
      "PTRATIO:PCTAF                 747.445536\n",
      "PERSPENK:PTRATIO:PCTAF       3243.607568\n",
      "dtype: float64"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "means75 = exog.mean()\n",
    "means75[\"LOWINC\"] = exog[\"LOWINC\"].quantile(0.75)\n",
    "print(means75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, `predict` expects a `DataFrame` since `patsy` is used to select columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.061468Z",
     "iopub.status.busy": "2026-06-27T20:27:18.061274Z",
     "iopub.status.idle": "2026-06-27T20:27:18.143923Z",
     "shell.execute_reply": "2026-06-27T20:27:18.143282Z"
    }
   },
   "outputs": [],
   "source": [
    "resp25 = glm_mod.predict(pd.DataFrame(means25).T)\n",
    "resp75 = glm_mod.predict(pd.DataFrame(means75).T)\n",
    "diff = resp75 - resp25"
   ]
  },
  {
   "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": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.149078Z",
     "iopub.status.busy": "2026-06-27T20:27:18.146385Z",
     "iopub.status.idle": "2026-06-27T20:27:18.154066Z",
     "shell.execute_reply": "2026-06-27T20:27:18.152914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.8863%\n"
     ]
    }
   ],
   "source": [
    "print(\"%2.4f%%\" % (diff[0] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.157741Z",
     "iopub.status.busy": "2026-06-27T20:27:18.157551Z",
     "iopub.status.idle": "2026-06-27T20:27:18.163711Z",
     "shell.execute_reply": "2026-06-27T20:27:18.162502Z"
    }
   },
   "outputs": [],
   "source": [
    "nobs = glm_mod.nobs\n",
    "y = glm_mod.model.endog\n",
    "yhat = glm_mod.mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.166177Z",
     "iopub.status.busy": "2026-06-27T20:27:18.165242Z",
     "iopub.status.idle": "2026-06-27T20:27:18.664499Z",
     "shell.execute_reply": "2026-06-27T20:27:18.663913Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.api import abline_plot\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111, ylabel=\"Observed Values\", xlabel=\"Fitted Values\")\n",
    "ax.scatter(yhat, y)\n",
    "y_vs_yhat = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n",
    "fig = abline_plot(model_results=y_vs_yhat, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot fitted values vs Pearson residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson residuals are defined to be\n",
    "\n",
    "$$\\frac{(y - \\mu)}{\\sqrt{(var(\\mu))}}$$\n",
    "\n",
    "where var is typically determined by the family. E.g., binomial variance is $np(1 - p)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:18.670047Z",
     "iopub.status.busy": "2026-06-27T20:27:18.669186Z",
     "iopub.status.idle": "2026-06-27T20:27:19.280305Z",
     "shell.execute_reply": "2026-06-27T20:27:19.279698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa47890c1a0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(\n",
    "    111,\n",
    "    title=\"Residual Dependence Plot\",\n",
    "    xlabel=\"Fitted Values\",\n",
    "    ylabel=\"Pearson Residuals\",\n",
    ")\n",
    "ax.scatter(yhat, stats.zscore(glm_mod.resid_pearson))\n",
    "ax.axis(\"tight\")\n",
    "ax.plot([0.0, 1.0], [0.0, 0.0], \"k-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram of standardized deviance residuals with Kernel Density Estimate overlaid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition of the deviance residuals depends on the family. For the Binomial distribution this is\n",
    "\n",
    "$$r_{dev} = sign\\left(Y-\\mu\\right)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n",
    "\n",
    "They can be used to detect ill-fitting covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:19.288164Z",
     "iopub.status.busy": "2026-06-27T20:27:19.287931Z",
     "iopub.status.idle": "2026-06-27T20:27:19.300945Z",
     "shell.execute_reply": "2026-06-27T20:27:19.299910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.nonparametric.kde.KDEUnivariate at 0x7fa479aa8ad0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid = glm_mod.resid_deviance\n",
    "resid_std = stats.zscore(resid)\n",
    "kde_resid = sm.nonparametric.KDEUnivariate(resid_std)\n",
    "kde_resid.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:19.307415Z",
     "iopub.status.busy": "2026-06-27T20:27:19.307187Z",
     "iopub.status.idle": "2026-06-27T20:27:19.785569Z",
     "shell.execute_reply": "2026-06-27T20:27:19.784514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa47890cad0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ro0ZF/vp9+vRRp06dNH78eC1fvlxOp1O///67Jk+erDp16lz2+HvvvVc///yz3nzzzXzP7F9zzTW69dZb9cgjj+jDDz9UYmKiLMvSoUOH9NZbb+mJJ54okiz5OXjwoF599VV169ZNjRo10qefflrobNnuuOMOpaena+jQoXI6nZe8pjlbr1695OfnpyeeeEKpqalKSEjQpEmT8ryVWkFdc801Gj16tCZNmqT3339fiYmJSktL09KlS3Nu51XY91ZQN910kxo3bqznn39eSUlJCg8P16uvvqovvvhCEyZMyDkjfPLkSc2ePVsjR46UJH377bcaP368tm7dqszMTDmdTs2ePVsnTpxQhw4d8n29MmXK6MEHH9THH3+sDz74QGlpaTp+/LjuuusuNW/e/KL9x44dq/379+s///mPMjMzdeTIEY0bN+6ifZs3b67KlSvr5Zdf1tGjR5Wenq4ZM2boxx9/zPVLtby4+l4AAPko4YnRAABXyOl0WvPnz7duuukmq1q1alZISIjl5+dnRUdHWzfeeGOu2Zgty555u1evXlZgYKBVunRpKzIyMmdm602bNlkdO3a0AgICrNDQUGvgwIHW0aNHrYoVK+a6tVG/fv0uuk2QZVnWSy+9ZEnKNXu0ZVnW5MmTrfLly1sBAQFW9erVra+++sp67LHHLpr9+0pf37Is69SpU9bo0aOt4OBgKygoyLrqqqus7du3W02aNLns7N8tWrSwHA6HFRkZedFy99135/qaT5kyxWrSpInlcDiskJAQq3r16tY999xj7d6926UsealRo4bl7++fkyEoKMiKjo62evfubb333nu5bgNV2GzZOnfubEmyevbsmWeGvGb//vTTT63q1atb/v7+VtmyZa3HH3/cWrlypSXJmj59es5+kqzHHnvsoufs2rWr1aFDh1zbMjMzrRdffNGqXbu25XA4rIiICKtHjx7WmjVrXH5vF34t85tJ/euvv7YkWc8991zOtgULFlhdu3a1goKCrODgYCs6Otq64YYbrGXLllmWZc+q/fLLL1tNmza1SpUqZQUHB1v16tWz/vWvf+V8P1lW3rfUysrKsp5++mmrbNmyVkBAgFWrVi1r5syZ1j333HPR7N+WZVmTJk2yIiMjrcDAQKthw4bW0qVL85z9e/Xq1Vbbtm0tf39/KywszBo2bJh14sQJKywsLNef4YWZCvpeAAAF47CsC2a+AAB4lPT0dDmdToWEhFxyv8zMTCUlJcmyrIuGzmZkZMjf3z9nKHBCQoICAgJyznglJSXJ6XTmTISULS0tTSkpKYqMjLxoEjFJSklJUalSpSRJqampSk1NzXPYrquv/2fZZ+yzr71OTEyUn59frpmo4+PjFRQUlJMpMTEx3zOuQUFBeZ7xyz6zl9c13oXJkpeEhISc2bmz98+ehbwgCpIt+88hJCQkz78zF36N/iwtLU3BwcGSpKysLCUmJiosLEyBgYGS7Mny8nres2fPSlKuCeAufN7AwMBLDkUvyHv7s4SEBPn5+eX7mmfOnFFAQMBFn7csS6mpqXm+/z9ncTgcef7ZXO7vavbXXrK/P9LT0y8awp2dIy0trUD7Xvj9c+Gf4aUyXeq9AAAKhlINAAAAAICLuKYaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAFwWYDlAQTqdThw8fVunSpfO8ZQsAAAAAAEXJsiwlJiaqSpUql7zto0eU6sOHDysmJsZ0DAAAAACAjzl48KCqVauW7+c9olSXLl1akv1mIiIiDKcBAAAAAHi7hIQExcTE5PTR/HhEqc4e8h0REUGpBgAAAACUmMtdgsxEZQAAAAAAuIhSDQAAAACAiyjVAAAAAAC4iFINAAAAAICLKNUAAAAAALiIUg0AAAAAgIso1QAAAAAAuIhSDQAAAACAiyjVAAAAAAC4iFINAAAAAICLKNUAAAAAALiIUg0AAAAAgIso1QAAAAAAuIhSDQAAAACAiyjVAAAAAAC4iFINAAAAAICLKNUAAAAAALiIUg0AAAAAgIsCCnvA1q1b9eqrryo2Nlb16tXTo48+qho1auS7/6effqr3338/17bw8HDNmTOn8GkBAAAAAHAjhSrV27dv11VXXaXhw4frL3/5iz777DO1b99eGzduVOXKlfM8JjY2VsePH9cbb7xx/kUDCt3lAQAAAABwO4Vqt88++6yaNGmSc+Z5wIABqlevnl555RW9/PLL+R4XERGhbt26XVFQAAAAAADcTaGuqV64cKEGDhyY83FAQID69++vhQsXXvK4ffv2acCAARo+fLheeuklpaSkuJYWAAAAAAA3UuAz1cnJyTpx4oSqVq2aa3vVqlUVGxub73H+/v4aPHiwrrvuOp05c0YvvfSSPv74Y61Zs0alSpXK85i0tDSlpaXlfJyQkFDQmAAAAAAAlJgCl+r09HRJuqgIh4aG5nwuLw888IBCQ0NzPu7Xr5/q1q2rN998Uw899FCex0yePFmTJk0qaDQAAAAAAIwo8PDv0qVLKyAgQCdPnsy1/eTJkypTpky+x/25UEtShQoV1KpVK23cuDHfYyZOnKj4+Pic5eDBgwWNCQAAAABAiSnwmWp/f381a9ZM69evz7V9zZo1atWqVaFeNC4uTg0aNMj388HBwQoODi7UcwIAAAAAUNIKNVHZ2LFjNX36dO3cuVOStHr1ai1evFhjx47N2eejjz5S//79cz5+9dVXlZqamvPxlClTtG3bNg0fPvxKswMAAAAAYFShbqn117/+VRs3blSLFi1Uv3597dixQ3/72980bNiwnH3279+v5cuX53ycnp6uWrVqqUqVKjp58qSSkpL0/vvvq1evXkX3LgAAHqXmhLkl8jr7X+xXIq8DAAB8l8OyLKuwBx06dEgHDx5U7dq1VbFixVyf279/vw4dOqSrr746Z1taWpq2bdum0NBQ1apVS4GBgYV6vYSEBEVGRio+Pl4RERGFjQsAcDOUagAA4O4K2kMLdaY6W7Vq1VStWrU8P1ezZk3VrFkz17bg4GC1bNnSlZcCAAAAAMBtFeqaagAAAAAAcB6lGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAF1GqAQAAAABwEaUaAAAAAAAXUaoBAAAAAHARpRoAAAAAABdRqgEAAAAAcBGlGgAAAAAAFwWYDgAA8CFnzkg7dqj7njUKyszQ2eBQHQsvq31lqyrLz990OgAAgEKjVAMAio9lSWvWSFOnSgsWSDt2SJI+vGC31IAgra/SUIvqddDMxt10KjSy5LMCAAC4oNCl+uDBg3rzzTcVGxurevXq6d5771V0dHSBjl2zZo3++c9/qmPHjnr44YcLHRYA4CGcTum776TnnpM2bMj9uSpVtNkZqnT/QIWnJatqwnGVTk9RpwOb1enAZk1Y+pFmNu6q1zuP0qHIikbiAwAAFFShSvX+/fvVrl07XX311br++uv15Zdf6qOPPtK6detUrly5Sx6bkJCgm266SampqVcUGADg5tavl/7v/6TVq+2PQ0KkoUOlIUOkbt2ksmU1cMLcnN0dllO1T/2hbnvXaeDWn9Ti6C6N+G2xBm1dqg/bDtKrnUcpNTDEzHsBAAC4DIdlWVZBd77jjju0fv16rVu3Tn5+fkpNTVXdunV122236bnnnrvksaNGjVLt2rW1adMmhYSE6Ouvvy5wyISEBEVGRio+Pl4REREFPg4AUIIyMqR//EN64QX7THXp0tLf/mYvZcvm2rXmn0r1hVr9sV0P/fyZrondKEnaV6ayHuz/iDZWaVDoSPtf7FfoYwAAAKSC99BCzf49b948DRkyRH5+9mEhISEaMGCA5s2bd8nj3nvvPe3evVvPPPNMYV4OAOApjhyxz0I/95xdqEeOtK+f/sc/LirUl7OhakONufE53T70KR0JL6dap49o2tTHdNvaWfY12gAAAG6kwKU6JSVFR48eVfXq1XNtr169uvbu3Zvvcdu2bdPjjz+uqVOnKjAwsECvlZaWpoSEhFwLAMBNrV8vtWsn/fqrFBkpffmlvVSufEVP+2Pd9up95xua0+BqBTkz9cwP7+i5hW/I35lVRMEBAACuXIGvqU5LS5MkhYaG5toeHh6e73XSqampGjlypJ5//nnVr1+/wKEmT56sSZMmFXh/AEDRuNSw7Lx0jN2sd795VuHpKdpVLkZ3DH1KBzaESxsK9zz5SQwO072DHtOGtQ319x/f180bv1fFsyd176AJSgsIKpLXAAAAuBIFPlMdHh4uf39/nT59Otf2kydPKioqKs9jFi5cqB07dmjBggUaNmyYhg0bpnXr1mnlypUaNmyY4uLi8jxu4sSJio+Pz1kOHjxY8HcEACgR3fas1UfTn1Z4eop+qdFcN4x5RQfKXNnZ6Tw5HHq/3WCNG/J3pQYEqdfu1Xp3xrMKzkgr+tcCAAAopAKfqQ4ICFCjRo20efPmXNs3b96s5s2b53lM27ZtNXXq1Fzb9u/fr8DAQN14440KCwvL87jg4GAFBwcXNBoAoIRdvW+D3v72eQVnZWh+/Y66f8B4pQcU7BIfVy2qd5XGDntG78+YpC77N2jKrH/pLzc8riw//2J9XQAAgEsp1ERlN998s6ZNm6ZDhw5JkrZs2aLvv/9eN998c84+06ZN06233ipJqlKlSs4Z6uylUqVKqlq1qoYNG3bRUHIAgPtrenS33vn2OQVnZWhBvat078DHir1QZ1tRo7luGz5Jaf6B6rV7lf6x6E0mLwMAAEYVqlQ/+OCD6tixo1q0aKEePXqoY8eOGjVqlMaMGZOzz9atWzVz5swiDwoAMK9yQpzen/EPhWakaVnNVrpv4GPK9C/woKcisTqmqe4f8KiccuimjfN1z4ppJfr6AAAAf1aon4SCgoI0c+ZMbdq0SQcOHFDdunXVqFGjXPuMGDFCrVu3zvc5Jk6cKH9/huoBgKcJS0vWB19PUsWzp7Q9uob+OnhiiZ2hvtCCBp30TM+79Y/Fb+vRnz/V0dLRmtGsh5EsAADAt7l0eqFFixZq0aJFnp9r3LixGjdunO+xnTt3duUlAQAG+Tuz9N9Z/1KjuP2KC4vSHcOe1tlgs5fwfNJmgConntT/rfpaLyz4r7aXr6EtleoazQQAAHxPoYZ/AwB80wPLP9e1e9cqJSBYdwx9Sn9EVjAdSZL0r663aFHd9grOytSb301WROpZ05EAAICPoVQDAC6p8/6NuvfcdcuP9b1fmyvXN5zoPMvhp4f7PaSDkRVVPf6YXp73KhOXAQCAEkWpBgDkKzrptF6d87L8ZOnzFtdpVuOupiNdJCEkXP83eKLS/APUe9dK3bX6W9ORAACAD6FUAwDy5LCc+s/sV1Q+6Yy2R9fQpB53mY6Ur98r1dWknuMkSY/99JGaHdllOBEAAPAVlGoAQJ7GrfpG18RuVHJgsO4d9JjSAoNNR7qkz1tcpzkNr1GA5dQrc/+j4Mx005EAAIAPoFQDAC5SLy5WDy7/TJL0dM9x2h1d3XCiAnA49ETv/1NcWJTqnzygB3/+zHQiAADgAyjVAIBc/J1Zeun71xSclakf6rTT9Ga9TEcqsDOlIjThuvskSXev/lb69VfDiQAAgLejVAMAcrljzXdqeWSnEoLD9HifeySHw3SkQvmhbgdNb9pTfrKkW2+VkpJMRwIAAF6MUg0AyFH75CE9fG7Y9LPX3qFjpaMNJ3LNP3repcOlo6Xdu6WnnzYdBwAAeDFKNQDA5nTqn9+/ruCsDP1Uq7VHDfu+UGL2WXZJevVVafNmo3kAAID3olQDAGwff6x2f2xVUmCIJl53r8cN+77Q0jrtpKFDpaws6f/+T3I6TUcCAABeiFINAJBOn5Yee0yS9Grn0TocUcFwoCLy6qtSeLg9YdkHH5hOAwAAvBClGgAgPfWUFBenXeVi9GHbgabTFJ1q1aRnn7XXx4+X4uLM5gEAAF6HUg0Avm7jRumNNyRJT/X6izL9A8zmKWr33iu1bGmfjR8/3nQaAADgZSjVAODLnE7pnnvsxxEjtKJGC9OJil5AgPTmm/b6Rx9Ja9cajQMAALwLpRoAfNmXX9rXG4eFSa+8YjpN8bnqKunmm+31v/1NsiyjcQAAgPegVAOAr0pNlR5/3F6fONG+/tibTZ4shYZKv/wiTZtmOg0AAPASlGoA8FVTpkixsVLVqtKDD5pOU/yqVcuZ4Vzjx0spKWbzAAAAr0CpBgBfdPKk9Nxz9vpzz9lncH3BI49IMTHSgQPSv/9tOg0AAPAClGoA8EXPPSfFx0vNm0tjxphOU3JCQ6V//tNenzxZOnbMbB4AAODxKNUA4Gv27JH+9z97/eWXJX9/s3lK2o03Su3bS0lJ58/WAwAAuIhSDQC+5qmnpIwMqU8fqVcv02lKnsMhvfiivf7229LevWbzAAAAjxZgOgAA4PJqTphbJM9TP26/5n/+hfwkXV+5n7YW0fN6nO7dpd69pYUL7V8yfPaZ6UQAAMBDcaYaAHzIg8s/l58szW3QWVsr1jYdx6zJk+3Hzz+XNm0ymwUAAHgsSjUA+IimR3er785f5ZRD/7n6JtNxzGvdWho5UrKs8/frBgAAKCRKNQD4iId+toc4f9ekm3ZHVzecxk0895wUECDNmyctW2Y6DQAA8ECUagDwAa0PbdO1e9cq0+Gn1zqPMh3HfdStK915p70+YYJ91hoAAKAQKNUA4AMeXv6pJOnrZj0VW6aK4TRu5sknpVKlpBUrpNmzTacBAAAehlINAF6uzaGt6hy7WRl+/prSaaTpOO6nShXpb3+z1x9/XMrKMhoHAAB4Fko1AHi5e1d8JUma0bSHDkVWNJzGTY0fL0VFSVu2SNOnm04DAAA8CKUaALxYsyO71H3vOmU5/PTGVcNNx3FfUVHSQw/Z6//4B2erAQBAgVGqAcCLZZ+lntm4qw6UqWw4jZu7/367XG/bxtlqAABQYJRqAPBSDeL2q8+ulXLKof9dNcJ0HPcXGSk9/LC9PmkSZ6sBAECBUKoBwEvds2KaJGleg87aEx1jOI2HuP9+qUwZaft2ado002kAAIAHoFQDgBeqeeoP9d/2syTpf504S11gERHnz1ZzbTUAACgASjUAeKE713wnP1n6oU47batQ23Qcz3LffVLZsvbZ6q++Mp0GAAC4OUo1AHiZcklnNOz3HyRJ77QfYjiNB+JsNQAAKARKNQB4mTEb5iokM10bK9fTqpimpuN4pnvvtc9W79ghffml6TQAAMCNUaoBwIuEZKRqzPq5kqR32w2RHA7DiTxURIT0yCP2OmerAQDAJVCqAcCLDPv9R5VLSdDByIqa36CT6TieLfts9c6d0owZptMAAAA3RakGAC/h58zSHWu+lSS9126wsvz8DSfycKVL27fYkqQXXpAsy2weAADglijVAOAleu1apVqnj+hMSLimN+tpOo53uO8+KTxc2rRJmjfPdBoAAOCGKNUA4CXuXv2NJOnTVv2UHFTKcBovUbas9H//Z68//zxnqwEAwEUo1QDgBVof2qY2h7crzT9An7TubzqOd3noISk4WFqxQlq61HQaAADgZijVAOAFxq22J9L6psm1igsvYziNl6lUSbrzTnv9hRfMZgEAAG6HUg0AHq7G6cPqtWuVJOm9djcYTuOlHn1UCgiQFi+WVq82nQYAALgRSjUAeLhb1s+Vnyz9WLut9kTHmI7jnWrUkG6+2V5//nmzWQAAgFuhVAOABwtNT9HwzYskSR+3GWA4jZebMEFyOKRZs6TffjOdBgAAuAlKNQB4sCFbligiPVl7y1TRslqtTMfxbg0aSMOG2euTJ5vNAgAA3AalGgA8lWXplnVzJEmftO4vy8E/6cXu8cftx6++knbvNpsFAAC4BX4CAwAP1Sl2k+qfPKCzQaX0dbOepuP4hpYtpeuvl5xO6Z//NJ0GAAC4AUo1AHio29bbZ6lnNL1WZ4NDDafxIX//u/348cfSwYNmswAAAOMo1QDggarFH1OP3fatnT5p3d9wGh/TqZPUtauUkSG98orpNAAAwDBKNQB4oJvXz5W/5dSymq20pxy30Spx2ddWv/uudPKk2SwAAMAoSjUAeJiQjFTduHmhJOnjNpylNqJXL/v66uRk6c03TacBAAAGUaoBwMMM2vqTolLP6kBkRS2p3dZ0HN/kcEjjx9vrr78upaSYzQMAAIyhVAOAJ7Es3bZutiTpk9b95PTzNxzIhw0fLtWoIcXFSR99ZDoNAAAwhFINAB6k7R9b1Shuv1ICgjWteW/TcXxbQID08MP2+ssvS1lZZvMAAAAjKNUA4EFGb5wvSZrVqIsSQsINp4Fuv10qV07au1eaMcN0GgAAYAClGgA8RFRKgvptXy5J+rzldYbTQJIUFibde6+9/q9/SZZlNg8AAChxlGoA8BBDf/9RwVkZ2lKhtjZVrm86DrLdc49UqpS0bp20ZInpNAAAoIRRqgHAE1hWztDvz1teZ88+DfdQvrw9DFyS/vlPs1kAAECJo1QDgAfocPB31Tl1SEmBIZrZuJvpOLjQww9Lfn7SwoXSxo2m0wAAgBJEqQYAD5B9lnpm4646GxxqOA0uUquWNGKEvf7SS2azAACAEkWpBgB3d+KErtv5iyTp85Z9DYdBvsaPtx+/+krav99oFAAAUHICTAcAAFzGRx8pOCtTmyvV1e+V6ppO41FqTphbIq+z/8V+UqtWUq9e0qJF0r//Lb3+eom8NgAAMIsz1QDgzixLeucdSdLnLbiNltvLPlv93nvSyZNmswAAgBJBqQYAd7ZkibRrlxKDSmlW466m0+ByevSwz1inpEhvvmk6DQAAKAGUagBwZ2+/LUma2bibkoNKGQ6Dy3I47JnAJWnKFCktzWweAABQ7CjVAOCu4uKkb7+VxARlHmXECKlaNenYMenzz02nAQAAxYxSDQDu6rPPpIwMqW1bba1Y23QaFFRgoHT//fb6v/9tXxcPAAC8FqUaANyRZUnvv2+v33GH2SwovLvuksLDpd9/lxYuNJ0GAAAUI0o1ALijtWulLVukkBDpxhtNp0FhRUWd/2XIK68YjQIAAIoXpRoA3NEHH9iPQ4faBQ2e54EHJD8/+77VmzebTgMAAIoJpRoA3E1y8vkJrhj67blq1bJ/KSJJ//mP2SwAAKDYUKoBwN18842UkGCXsq7cm9qjZd9ea+pU6cgRs1kAAECxoFQDgLvJHvo9dqw9fBieq0MHqXNnexb3KVNMpwEAAMWAn9YAwJ3s3SstWSI5HNKtt5pOg6KQfbb6zTelpCSzWQAAQJGjVAOAO/noI/uxVy+penWjUVBEBg6U6tSRTp8+/+cLAAC8RoDpAADgqWpOmFukz+fnzNLPb72lqpLuDWmpOUX8/DDE31/629+k++6zJyz7y1/sbQAAwCtwphoA3ETn2E2qmhinMyHhWlTvKtNxUJTGjpXKlJH27JFmzzadBgAAFCFKNQC4iRGbF0mSvmvcTWkBQYbToEiFhdlnqCXplVfMZgEAAEWKUg0AbiAyJVG9d62QJE1v3stwGhSLe++VAgKk5cul9etNpwEAAEWEUg0AbqD/9p8VnJWpbeVrakvFOqbjoDhUqSKNGGGvv/662SwAAKDIUKoBwA0M+f1HSdLXTXsYToJidf/99uMXX0jHjpnNAgAAigSlGgAMq3nqD7U5vF1ZDj/NatzNdBwUpw4d7CU9XXr7bdNpAABAEaBUA4BhN2xZIklaVquV4sLLGE6DYvfAA/bjm2/a5RoAAHg0SjUAGOSwnBpyrlR/0+Raw2lQIoYNs6+vPnpUmjbNdBoAAHCFKNUAYFC7Q1sVE39MCUGhWsi9qX1DYKD0f/9nr7/2mmRZZvMAAIArQqkGAIOyJyib1/BqpQUGG06DEjNunBQcLK1dK61caToNAAC4ApRqADAkOCNN/bb/LEn6pilDv31K+fLS6NH2+muvmc0CAACuCKUaAAzpvWulSqen6GBkRa2p1th0HJS07AnLvv5aOnTIbBYAAOAySjUAGDJkiz30+5sm3WU5+OfY57RoIXXtKmVlSW+8YToNAABwUYDpAADgi8qfPa0u+zZIkr5t0t1wGlypmhPmunRcnzKd9bZ+0qlX/6eOaW0LdF39/hf7ufRaAACgeHBqBAAMGLh1qfwtp9ZVaaj9ZauajgNDFtXroEMRFVQ2JUGDtv5kOg4AAHABpRoADBh6buj3jGY9DCeBSU4/f33cur8kaey6WdxeCwAAD0SpBoAS1iBuvxof36c0/wDNaXiN6Tgw7KsWvZUcGKxGcfvV8cBvpuMAAIBColQDQAkbeG6Y79LabZUQEm44DUxLCAnXN03sW6qNWT/HcBoAAFBYlGoAKEmWlXPt7MzG3cxmgdv4pLU9+VjvXStVKeGE4TQAAKAwKNUAUIJa/7Fd1RKOKzGolH6o0850HLiJneVralVMUwVYTo3aNN90HAAAUAiUagAoQYO3LpUkLajfqUC3T4Lv+KSVfbZ61KYFCszKMJwGAAAUFKUaAEpIQFam+m3/WZI0s3FXw2ngbhbWv0rHw8qoQtJp9dm5wnQcAABQQJRqACghV+/fqHIpCYoLjdKvNVqYjgM3k+EfqC9aXCdJGrNhnuE0AACgoCjVAFBCBm6zJyib0+gaZfn5G04Dd/R5yz7KdPipw8Hf1SBuv+k4AACgACjVAFACQjJSc4b0zmrE0G/k7VjpaC2o31GSNGb9XMNpAABAQVCqAaAE9Nq1SmEZqYqNqqQNVRqYjgM39tm5Cctu2LJE4WnJhtMAAIDLCSjsAadPn9ann36q2NhY1atXT7fccotCQ0Mvecz27ds1Z84cnThxQvXr19eIESMUHh7ucmgA8DTZQ79nNuoqORyG08CdrajeTDvLVVf9kwc05Pcf9EmbAaYjAQCASyjUmepjx46pdevWmj59uqKiovTOO+/oqquu0tmzZ/M95qWXXtLNN9+skydPKjIyUm+99ZYaN26sP/7444rDA4AniEpJULe96yRJMxt3MxsG7s/h0Ketr5d0bsIyyzIcCAAAXEqhSvVzzz2nkJAQLV68WE8++aSWLFmio0eP6vXXX8/3mMGDB2vt2rWaPHmyJk6cqJ9//lnJycmaOnXqFYcHAE/Qd8evCnRmaUuF2toTHWM6DjzAt02u1dmgUqp38qA6HthsOg4AALiEQpXqmTNnavjw4QoODpYkRUZGasCAAZo5c2a+x9SrVy/XxykpKcrIyFC5cuVciAsAnmfQ1qWSuDc1Cu5scKi+OzeqYfTG+WbDAACASypwqU5LS9PBgwdVq1atXNtr166tXbt2XfLYI0eO6N5779XYsWPVoUMH/fWvf9Wtt956yddKSEjItQCAJ6qcEKerDv4uSZrdqIvhNPAkn7fsK0nqs3OFyiWdMRsGAADkq8ClOiUlRZJUunTpXNsjIiKUnHzp2UmDg4PVsGFD1alTR6VLl9aPP/6ouLi4fPefPHmyIiMjc5aYGIZLAvBMA7YtkyStimmqIxHlDaeBJ9lasbY2Vq6vIGemhv7+g+k4AAAgHwUu1WFhYXI4HDpz5kyu7adPn1ZERMQljy1btqzuvfdePfHEE1qxYoXOnj2rZ555Jt/9J06cqPj4+Jzl4MGDBY0JAG5l0FZ71u/vmKAMLvi8xXWSpFGb5sthOQ2nAQAAeSlwqQ4MDFS9evW0bdu2XNu3bdumxo0bF/gFAwMD1bJlS+3cuTPffYKDgxUREZFrAQBPU/fEATU5vlfpfgH6vkEn03HggWY36qKEoFDVOn1EHWOZsAwAAHdUqInKRo4cqa+++kqnTp2SJMXGxmrOnDkaOXJkzj7z5s3T+PHjcz5euHBhrueIi4vTkiVL1KZNmyvJDQBub+C5s9Q/1W6tM6X45SAKLyUoRN816S5JGr1pgeE0AAAgL4Uq1Y899piqV6+uNm3a6KabblLHjh3VtWtX3XnnnTn7rF69Wu+8807Ox5999platWqlW2+9VSNGjFD9+vXVvHlzPfnkk0X3LgDA3ViWBmy3r6ee3YhZv+G6L1r2kST1ZsIyAADcUkBhdg4LC9PPP/+sxYsX68CBA7rrrrvUtWtXORyOnH2uv/56Va1aNefjTz75RDt37tTq1asVGBiop556Sk2bNi26dwAAbqjJsT2qdfqIUgKCtbhue9Nx4MG2VaitDZUbqNWRHRr2+2JJN5mOBAAA/qRQpVqS/P391adPn3w/3759e7Vvn/sHyPr166t+/fqFTwcAHmrA9p8lST/WaavkoFKG08DTfd6yj1od2aFRGxdITqfkV6iBZgAAoBjxvzIAFDXLUr/tyyVJcxpeYzgMvMGchvaEZTXPHJGWLDEdBwAA/AmlGgCKWIsjOxUTf0xJgSFaUqet6TjwAn+esExvv202DAAAyIVSDQBFrP+5od8/1G2v1MAQw2ngLbInLNO330rHjpkNAwAAclCqAaAIOSwnQ79RLLInLFNmpvTxx6bjAACAcyjVAFCEWv+xXVUSTyghKFQ/1W5jOg68zJctetsrH34oWZbZMAAAQBKlGgCKVPbQ70X1OigtIMhwGnibuQ2vkUJDpe3bpRUrTMcBAACiVANAkfFzZun6Hb9IkuY06mI4DbzR2eBQacQI+4MPPjAbBgAASKJUA0CRaXdoqyqePaX44DAtr9nSdBx4q9tvtx+/+ko6e9ZsFgAAQKkGgKKSPfR7Qf2OyvAPNJwGXuvqq6V69exCPX266TQAAPg8SjUAFAF/Z5b6Zg/9ZtZvFCeH4/zZaoaAAwBgHKUaAIrAVQd+U3RyvE6VitCvNVqYjgNvd8stkp+ftHy5tGOH6TQAAPg0SjUAFIH+25ZJkubX76RM/wDDaeD1qlSRrr/eXv/wQ7NZAADwcZRqALhCAVmZum6nfXujOQ2vNpwGPiN7CPjHH0uZmWazAADgwyjVAHCFOsduUpnURMWFRmlV9Wam48BX9OsnlS8vHT0qff+96TQAAPgsSjUAXKH+2+xZv79v0FlZfv6G08BnBAXZ11ZLTFgGAIBBlGoAuAJBmRnqs+vc0O9GzPqNEjZ2rP04Z4507JjZLAAA+ChKNQBcgc6xGxWRlqRj4WW1plpj03Hga5o0kTp0sK+pnjrVdBoAAHwSpRoArsD12+17U89r0FmWg39SYcBtt9mPn3xiNAYAAL6K+74AgIsCszLU+9zQ7+8bdDacBr6i5oS5uT6OTInSav8ABW/apL63/1fbKtQuktfZ/2K/InkeAAC8HadVAMBFHWM3KzItSXFhUVpbtZHpOPBR8aVK64c67SVJQ37/0XAaAAB8D6UaAFzUd4c99Ht+/U5yMus3DJrRrIckafDWpfJ3ZhlOAwCAb6FUA4ArMjPVZ9dKSfb11IBJP9VqoxOhkSqfdEbX7FtvOg4AAD6FUg0ArvjpJ5VNSdDJUhFaHdPUdBr4uEz/AM1q1FWSNJQh4AAAlChKNQC44uuvJUkL6ndUFkO/4QZmNL1WktR710pFpJ41nAYAAN9BqQaAwsrKkr75RhKzfsN9bKlYR9ujayg4K0PXb19uOg4AAD6DUg0AhbV8uXT8uM6EhGtF9eam0wA2h0PfnDtbPWQLQ8ABACgplGoAKKxzQ78X1rtKmf4BhsMA533XuJuyHH5qf2irqp8+YjoOAAA+gVINAIXhdEozZkhi1m+4n+Oly2l5zZaSOFsNAEBJoVQDQGGsWCEdOSJFRurXGi1NpwEukj1h2ZDff5Qsy3AaAAC8H6UaAArj3NBvDRyo9IBAs1mAPCysd5USg0qpevwxtTu0xXQcAAC8HqUaAArK6TxfqocNM5sFyEdqYEjOrPSDtv5kOA0AAN6PUg0ABbVmjXTokBQeLvXubToNkK+ZjbtJkvptX67ArAyzYQAA8HKUagAoqOyz1AMGSCEhZrMAl7CiejMdDyujMqmJumbfBtNxAADwapRqACgIy2LoNzyG089fsxt1kcQQcAAAihulGgAKYv16af9+KTRUuu4602mAy5rZuKskqdfulQpNTzGcBgAA70WpBoCCyD5L3a+fXawBN7e5Uj3tK1NZoRlp6rVrpek4AAB4LUo1AFwOQ7/hiRyOnAnLGAIOAEDxoVQDwOVs3izt3m1PTnb99abTAAWWXaq77FuvssnxZsMAAOClKNUAcDnZZ6n79rVvpwV4iH1lq2pTpXoKsJy6fvty03EAAPBKlGoAuBTLkqZPt9cZ+g0PNOvchGWDty41GwQAAC9FqQaAS9m6VdqxQwoKkvr3N50GKLTZDa+RUw61/WObqp05ajoOAABeh1INAJcyY4b92KePFBFhNgvgguOly+nXGs0lSQO3LTOcBgAA70OpBoBLYdZveIHse1YP2rrUvqQBAAAUGUo1AORnxw7pt9+kwEBpwADTaQCXLajfSWn+AWpw4oAaxu03HQcAAK9CqQaA/GQP/e7RQypTxmwW4AokhITrxzrtJTFhGQAARY1SDQD5Yeg3vEj2EPABW5fJYTkNpwEAwHtQqgEgL3v2SBs2SP7+0qBBptMAV2xJnXZKCApV1cQ4tT201XQcAAC8BqUaAPKSPfS7e3cpOtpsFqAIpAUEaX6DTpIYAg4AQFGiVANAXhj6DS80s3E3SdL1239RQFam2TAAAHgJSjUAXCg2VlqzRvLzkwYPNp0GKDIrqzdTXGiUyqQmqnPsJtNxAADwCpRqALhQ9tDvLl2kihXNZgGKUJafv75v0FmS1H/bz4bTAADgHSjVAHAhhn7Di81pdI0kqc+uFQrKzDCcBgAAz0epBoA/O3RIWrFCcjikG24wnQYocmuqNdbR8LKKSEvSNfvXm44DAIDHo1QDwJ9984392LmzVKWK2SxAMbAcfprX4GpJDAEHAKAoUKoB4M8Y+g0fMKehPQS81+5VCs5IM5wGAADPRqkGgGxHjkjLl9vrQ4aYzQIUow1VG+iP0uUVnp6ibnvXmY4DAIBHo1QDQLZvv5UsS7rqKikmxnQaoNhYDj/NbWgPAR+wnSHgAABcCUo1AGRj6Dd8SPYs4NfuWa1S6amG0wAA4Lko1QAgScePSz/9ZK8PHWo2C1ACNleqp9ioSgrNSNO1e9aYjgMAgMeiVAOAJH33neR0Sm3bSjVrmk4DFD+HI2cIeH+GgAMA4DJKNQBIDP2GT5rTsIskqfvetQpLSzacBgAAzxRgOgAAFLWaE+YWav+olAStXfyDAiR13VtWsYU8HvBUWyvU0p6yVVXn1B/quXuVZjbpbjoSAAAehzPVAHxer10rFWA5tbVCLcWWqWI6DlByHI6ce1YzBBwAANdQqgH4vOt3/CJJmtvgasNJgJKXfV11173rFZF61nAaAAA8D6UagE+LSD2rzvs3SZK+b9DZcBqg5O0sX1M7y1VXkDNTvXetNB0HAACPQ6kG4NN67l6lIGemtkfX0N5y1UzHAYzIvmd1/20MAQcAoLAo1QB8Wt9zQ7/nNWToN3xX9nXVnWM3KiolwXAaAAA8C6UagM8KT0tWl33rJUnzGPoNH7a3XDVtrVBLgc4s9dm5wnQcAAA8CqUagM+6ds9qBWdlale5GO2Orm46DmBU9tnqftuXG04CAIBnoVQD8FnZ5YGz1MD574NOsZsYAg4AQCFQqgH4pLC0ZHXbu04Ss34DkrS/bFVtrVBLAZZTvZgFHACAAqNUA/BJ3feuVXBWhvaWqaLt5WuajgO4hex7tffb/ovhJAAAeA5KNQCflD3r9/cNOksOh+E0gHvIHrXROXajdOqU2TAAAHgISjUAn1MqPVXd966VxPXUwJ/tLVdN28rXVKAzS5o1y3QcAAA8AqUagM/pum+dQjPSdCCyorZUrGM6DuBWcn7RNH262SAAAHgISjUAn3P9uaHf8xj6DVxk3rnrqrVokXTmjNEsAAB4Ako1AJ8SnJGma/eskcSs30Be9kTHaEd0dSkjgyHgAAAUAKUagE/psn+DwtNTdCiivDZVrm86DuCWvmcIOAAABUapBuBTrt++XBKzfgOXkn1rLS1cKMXHmw0DAICbo1QD8BlBmRnquXuVJIZ+A5eyq3wNqVEjKT1dmj3bdBwAANwapRqAz7h6/waVTk/RkfBy2lClgek4gHsbNsx+ZAg4AACXRKkG4DOyZ/2e36CTLAf//AGXNHy4/bhggZSQYDYLAABujJ8qAfiEwKwM9dq1UtKf7sMLIH9Nm0oNGkhpadKcOabTAADgtijVAHxCp9jNikxL0vGwMlpXtZHpOID7czgYAg4AQAFQqgH4hL7ZQ7/rd5LTz99wGsBDZA8B//57KTHRbBYAANwUpRqA1wvIylSfnSskMes3UCjNm0t169pDwOfONZ0GAAC3RKkG4PU6HPxdZVITdSI0UqtjmpiOA3gOh+P82eqvvzabBQAAN0WpBuD1+m1fLklaWK+jshj6DRROdqmeN09KSjKbBQAAN0SpBuDV/J1Z6r3LHvrNrN+AC1q2lGrXllJSGAIOAEAeKNUAvFr7g1sUnRyvU6UitLJ6M9NxAM/DEHAAAC6JUg3Aq2XP+r2w3lXK9A8wnAbwUNmleu5cKTnZbBYAANwMpRqA1/JzZum6nb9KYtZv4Iq0bi3VrGkX6nnzTKcBAMCtUKoBeK12h7aqQtJpxQeH6dcazU3HATwXQ8ABAMgXpRqA18qe9XtB/Y7K8A80nAbwcMOG2Y9z5tiTlgEAAEmUagBeyt+ZlXM99dyG1xhOA3iBdu2kGjXs22rNn286DQAAboNSDcArtT/4u8onn9HpkNL6pUYL03EAz+dwnD9bPX262SwAALgRSjUArzRg28+SpPn1OzLrN1BUskv17NkMAQcA4BxKNQCv4/+nWb/nNOpiOA3gRTp0kGJipLNnpYULTacBAMAtUKoBeJ1OsZtUNiVBJ0IjtbJ6M9NxAO/BEHAAAC5CqQbgdbJn/Z5fv5Oy/PwNpwG8THapnjVLSkszmwUAADdAqQbgXTIycoZ+M+s3UAyuukqqWlVKTGQIOAAAolQD8DaLFysq9aziwqK0KqaJ6TSA9/HzYwg4AAB/QqkG4F2mTZMkzWvQWU6GfgPFI7tUz5zJEHAAgM+jVAPwHmlp0rffSmLoN1CsOnWSqlSREhKkxYtNpwEAwChu3grAeyxaJMXH61h4Wa2p1th0GsCj1Zww95Kff7pyG409fFgzJvxbD//s+uvsf7Gf6wcDAOAGOFMNwHt89ZUke+i35eCfN6A4zWvQWZLUa9cqBWVmGE4DAIA5hT5TnZKSom+//VaxsbGqV6+eBg8erICASz/N1q1btWzZMmVkZKhdu3a66qqrXA4MAHlKTbWv75Q0u2EXw2EA77e2WmMdCy+rimdPqXPsRi2p0850JAAAjCjUqZzTp0+rffv2evHFF3X06FFNnDhR3bp1U2pqar7HjB49WiNHjtTGjRu1fft2XXfddbrtttuuNDcA5DZ/vn2Ln2rVtKFqA9NpAK9nOfz0ff1Oks7fGx4AAF9UqFL9wgsvKCkpSb/++qtee+01/fLLL9qyZYvefPPNfI+5++679dtvv+mtt97S//73P/3444/6+OOPtZiJTQAUpXOzfmv4cIZ+AyVkbsOrJUm9dq1UYBZDwAEAvqlQP3nOmDFDI0aMUHh4uCSpQoUKGjBggL7++ut8j+nWrVuuj1u1aqWgoCDt27ev8GkBIC/JydKsWfb6yJFmswA+JHsIeGRakjrv32g6DgAARhS4VKenp2vfvn2qV69eru316tXTjh07CvyC33zzjdLT0y95XXVaWpoSEhJyLQCQr++/l5KSpBo1pPbtTacBfMafh4D3Zwg4AMBHFbhUJyUlSZIiIyNzbY+KitLZs2cL9By7du3SuHHjdM8996hZs2b57jd58mRFRkbmLDExMQWNCcAXnZv1WyNGSA6H2SyAj5nHEHAAgI8rcKkOCwuTJMXHx+fafubMmZzh4Jeyf/9+9ezZU927d9drr712yX0nTpyo+Pj4nOXgwYMFjQnA1yQmSrNn2+sM/QZK3NqqjXQ8rAxDwAEAPqvApTooKEi1atXSrl27cm3ftWuXGjS49Ey7sbGx6tatm9q1a6cvvvhC/v7+l9w/ODhYERERuRYAyNN339m306pfX2rd2nQawOc4/fz1fYPsWcB/MZwGAICSV6iJyoYOHapp06blDPc+fvy4Zs+eraFDh+bss3TpUr344os5Hx84cEDdunVT27Zt9eWXX172ntYAUChffGE/jhrF0G/AkHkN7CHgvXetYAg4AMDnOCzLsgq68+nTp3XNNdfIz89P3bt317x581SxYkUtXrxYISEhkqRnnnlGr776qs6cOSNJatCggQ4fPqwHHnggV6Hu1q3bRTOD5ychIUGRkZGKj4/nrDWA8+LipMqVpawsaft26dyomZoT5hoOBvgWP2eWVr5xmyokndZtw57R0jptC3zs/hf7FWMyAABcV9AeWqjTxmXKlNGaNWv0zTff6MCBA5o8ebIGDx58UVnOLtiSNGbMGGVmZrrwFgDgMr7+2i7UrVvnFGoAJS97CPit6+fq+h3LC1WqAQDwdIU6U20KZ6oB5KlLF+nnn6WXXpIeeSRnM2eqgZLX4cBv+uqLiYoPDlPb+z5Thn9ggY7jTDUAwF0VtIcW6ppqAHAbBw7YhdrhkG680XQawOetqdZYcWFR52YB32Q6DgAAJYZSDcAzZd+b+pprpGrVzGYBYA8Br99ZknT9juWG0wAAUHIo1QA80+ef24+jR5vNASDHvIZ2qe69a6UCsphPBQDgGyjVADzPtm3Sxo1SQIA0bJjpNADOWV2tieLCohSVeladYxkCDgDwDZRqAJ4n+97UffpI5cqZzQIgR64h4NsZAg4A8A2UagCexbLOl+pRo8xmAXCR7CHgfXatYAg4AMAnUKoBeJZ166Tdu6VSpaRBg0ynAXABhoADAHwNpRqAZ8meoGzgQCk83GwWABdx+vlrfv1OkhgCDgDwDZRqAJ4jK+v8rbQY+g24rXkNrpbELOAAAN9AqQbgOZYtkw4flqKipOuuM50GQD5WxTRRXGiUyqQmqhNDwAEAXo5SDcBzZE9QNnSoFBxsNguAfDn9/DW/wbkh4Dt+MZwGAIDiRakG4BnS06Wvv7bXR482mwXAZWUPAe+zk1nAAQDejVINwDPMny+dPi1Vrix17Wo6DYDLYAg4AMBXUKoBeIZPP7Ufb7xR8vc3mwXAZTn9/LWgfkdJDAEHAHg3SjUA93fmjDR7tr0+ZozRKAAKbm5DhoADALwfpRqA+5s+XUpLk5o2lVq2NJ0GQAGtjmmaMwS8M0PAAQBeilINwP198on9OGaM5HCYzQKgwLL8/DWvYWdJ0oBtywynAQCgeFCqAbi3ffuk5cvtMs2s34DHmd2oiySp984VCsrMMJwGAICiR6kG4N4++8x+vPZaqVo1s1kAFNq6qo10JLycItKT1XXfOtNxAAAocpRqAO7Lss7P+n3LLWazAHCJ5fDTnEbXSGIIOADAO1GqAbivVaukXbuk0FBpyBDTaQC4KHsIeM/dq1QqPdVwGgAAihalGoD7yj5LfcMNUni42SwAXLa5Uj3FRlVSaEaart2zxnQcAACKFKUagHtKT5e+/NJe597UgGdzODSn4bkh4NsZAg4A8C6UagDu6fvvpVOnpMqVpR49TKcBcIWyh4B337NW4WnJhtMAAFB0KNUA3FP2valHj5YCAsxmAXDFtpevqV3lYhSclaFeu1aajgMAQJGhVANwP6dPS3Pm2OsM/Qa8w5+HgDMLOADAi1CqAbifadPsa6qbN5datDCdBkARyb611jX7NygqJcFwGgAAigalGoD7yZ71m7PUgFfZUy5GWyrUVqAzS312rjAdBwCAIkGpBuBe9uyRfvlF8vOzr6cG4FWyz1YzBBwA4C0o1QDcy2ef2Y89ekhVqpjNAqDIzT53XXXHA78pOum04TQAAFw5SjUA92FZ54d+33KL2SwAisWhqEraULmB/C2n+u74xXQcAACuGKUagPv4+Wd7+Hd4uHTDDabTACgmDAEHAHgTSjUA9/Hhh/bjyJFSWJjZLACKzZyGV8sph9of2iodPGg6DgAAV4RSDcA9JCbat9KSpLFjzWYBUKyOlY7Wmpgm9gfZ3/cAAHgoSjUA9zB9upScLNWvL3XqZDoNgGI2u1EXe+WLL8wGAQDgClGqAbiHDz6wH2+/XXI4zGYBUOzmNuisDD9/ad06accO03EAAHAZpRqAeTt3nr839ZgxptMAKAGnQyO1rFZr+4OpU82GAQDgClCqAZiXPUFZ377cmxrwITMbd7NXpk61b6kHAIAHolQDMCszU/r4Y3udCcoAn7Kobgd7pv+9e6VVq0zHAQDAJZRqAGYtXCgdOSJFR0sDBphOA6AEpQSFSIMH2x8wBBwA4KEo1QDMyp6g7OabpaAgs1kAlLybbrIfv/pKysgwmwUAABdQqgGYc+KENGuWvc7Qb8A39eollS8vxcVJP/xgOg0AAIVGqQZgztSp9pmpNm2k5s1NpwFgQkCANHKkvc4QcACAB6JUAzDDss4P/eYsNeDbsoeAf/utlJRkNgsAAIVEqQZgxoYN0ubNUnCwNGqU6TQATOrQQapTxy7U2ZeEAADgISjVAMzIPks9eLBUtqzRKAAMczik0aPtdYaAAwA8DKUaQMlLTZU+/9xev/12s1kAuIfsUr1ggT2JIQAAHoJSDaDkzZwpnT4txcRIPXqYTgPAHTRsKLVuLWVmStOmmU4DAECBUaoBlLz33rMfb71V8vc3mwWA+8iesIwh4AAAD0KpBlCy9uyRFi+2r6G84w7TaQC4kxtvtP9t+PVXad8+02kAACgQSjWAkvXuu/Zjnz5SzZpGowBwM1WqSNdea69/8YXZLAAAFBClGkDJSU+XPvzQXr/7brNZALinPw8BtyyzWQAAKABKNYCSM2uWdPy4VKmS1L+/6TQA3NGQIfb967dulTZuNJ0GAIDLolQDKDnvvGM/3n67FBhoNgsA9xQZKQ0caK9/8onZLAAAFAClGkDJ2LtXWrTInoTozjtNpwHgzm65xX6cOlXKyDCbBQCAy6BUAygZ2bfR6t1bqlXLbBYA7q1PH6lCBSkuTpo/33QaAAAuiVINoPhlZEgffGCvM0EZgMsJDDw/YdnHH5vNAgDAZVCqARS/2bOlY8ekihWlAQNMpwHgCW691X6cPVs6dcpsFgAALoFSDaD4MUEZgMJq0cJe0tOlL780nQYAgHxRqgEUr337pIUL7XUmKANQGNlnqxkCDgBwY5RqAMXr/fcly5J69ZJq1zadBoAnGT1a8veXVq+Wtm83nQYAgDxRqgEUHyYoA3AlKlaU+va11zlbDQBwU5RqAMVnzhzpyBH71jgDB5pOA8ATZQ8B//RTKSvLbBYAAPJAqQZQfLInKBs7VgoKMpsFgGcaMEAqU0b64w/pxx9NpwEA4CKUagDFY/9+acECe50JygC4KjhYuvFGe50h4AAAN0SpBlA83nrLnqCsZ0+pbl3TaQB4suwh4N98IyUkmM0CAMAFKNUAil5qqvTee/b6PfeYzQLA87VvLzVoIKWkSDNmmE4DAEAulGoARW/6dOnkSSkmRurf33QaAJ7O4eCe1QAAt0WpBlD0/vc/+/Evf5ECAsxmAeAdxoyxy/VPP0n79plOAwBADko1gKK1bp20apUUGCjdcYfpNAC8RbVqUo8e9vqnn5rNAgDAn1CqARSt7LPUw4dLFSuazQLAu/x5CLjTaTYLAADnUKoBFJ1Tp6QvvrDXmaAMQFEbMkSKiJD27rWHgQMA4Aa42BGAak6YWyTPc+fqb/REaqq2Vqil62eekmYVzfMC8F6F/ffn+dqddNPG+fr23n/owQHJBT5u/4v9ChsNAIAC4Uw1gCLhsJy6ecP3kqRPWvWzJxQCgCL2ZfM+kqTrd/yiiNSzhtMAAECpBlBEuuzboJpnjighOEwzG3czHQeAl/qtUl1tK19TwVkZGrR1qek4AABQqgEUjTHr50iSvm7aQylBIYbTAPBaDoe+at5bkjRy8yLDYQAAoFQDKALVzhzVtXvWSpI+bc11iwCK13dNuinNP0BNj+1Rk6O7TccBAPg4SjWAK3bTxvnyk6VlNVtpX9mqpuMA8HJnSkVoQf1OkjhbDQAwj1IN4IoEZ6Zr5OaFkqTPWl1vOA0AX5E9BHzw1qUKzkgznAYA4Mso1QCuSL/tP6tsSoL+KF1eP9RtbzoOAB/xa43mOhhZURFpSbpu56+m4wAAfBilGoDrLEu3nJugbGqrvsry8zccCICvsBx+mtaspySGgAMAzKJUA3BZ68Pb1fLILqX5B+QMxQSAkvJ1s55yyqFOBzar+ukjpuMAAHwUpRqAy8aunSVJmtWom06GRZkNA8DnHIkor2W1WkuSRvzG2WoAgBmUagAuqZRwQn13/CJJ+rDtQMNpAPiqr5r3kiQN/22xArIyDacBAPgiSjUAl4zZMFcBllMrY5pqa8XapuMA8FGL63VQXGiUKp49pR57VpuOAwDwQZRqAIUWkpGq0RvnS+IsNQCzMvwDNe3c2eqbNnxvOA0AwBdRqgEU2uAtS1UmNVEHIytqUd0OpuMA8HFftOgjpxzqsn+DYs4cNR0HAOBjKNUACseyNHadPUHZR637y8lttAAYdiiqUs6EZdmjaAAAKCmUagCF0il2kxqcOKCkwBBNPzfkEgBMm9qqryRp+G+LFJiVYTgNAMCXUKoBFMrta2dKkqY366mEkHDDaQDA9mOddjoSXk7RyfHqs3OF6TgAAB9CqQZQYDVOH9a1e9ZKkj5uM8BwGgA4L8vPX1+16C1JumkjE5YBAEoOpRpAgd22brb8ZOnH2m21r2xV03EAIJcvm/dRlsNPHQ/8pjonD5qOAwDwEZRqAAUSnpasYb8tlsRttAC4p6MR0fqxTjtJ0igmLAMAlBBKNYACGbF5kUqnp2hXuRj9XLOV6TgAkKepLe0Jy4b9/oOCM9IMpwEA+AJKNYDL8nNm6db1syWdO0vtcBhOBAB5W1arlQ5FVFBU6ln127HcdBwAgA+gVAO4rB571qjGmaM6ExKub5p0Nx0HAPLl9PPX5y2vkyTdvGGe4TQAAF9AqQZwWXeu/laS9EWL65QaGGI4DQBc2rTmvZTuF6DWh3eo6dHdpuMAALwcpRrAJbU8vEMdDm1Rul+APuQ2WgA8wImwMprb8GpJ9l0LAAAoTpRqAJeUfZZ6ZuNuOl66nOE0AFAwH5/7JeCAbctUNjnecBoAgDejVAPIV8yZo+q781dJ0rvtB5sNAwCFsLFyfW2qVE/BWRm6cdMC03EAAF6MUg0gX3es+U7+llNLa7XRzvI1TccBgIJzOPRxm/6Szk1YlplpOBAAwFtRqgHkKSolQSN+WyRJervDEMNpAKDw5jTsohOhkaqSeEKaOdN0HACAl6JUA8jTTRu+V2hGmn6vWEcrqjc3HQcACi09IFBftLBvr6X//tdsGACA16JUA7hIcGa6bltvz5j7TvsbJIfDcCIAcM3Uln2V6fCTfvpJ2rzZdBwAgBeiVAO4yOAtS1Q+6Yz+KF1e8xpcbToOALjsaES0FtTvaH8wZYrZMAAAr0SpBpCLw3LqrnO30fqg7UBl+gcYTgQAVyb79lr67DPp1CmzYQAAXqfQpTorK0sLFy7Uu+++q6VLl8qyrAId9+uvv2rKlCnauXNnoUMCKDnd96xV3VOHlBAUqq9a9DEdBwCu2OpqTaTmzaWUFOnDD03HAQB4mUKV6qSkJHXp0kXjxo3T0qVLNXr0aA0YMEAZGRn5HrNs2TK1aNFCDz30kO677z6tXr36ikMDKD53r/5GkvR5q746GxxqOA0AFAGHQ7r3Xnv9v//l9loAgCJVqFL9z3/+U7GxsVq3bp2mTp2qFStW6KefftJ7772X7zEBAQH65JNPtHLlyisOC6B4NT+yU1cd/F0Zfv76MHu4JAB4g5tvlqKjpdhY6ZtvTKcBAHiRQpXqr776SiNHjlTZsmUlSTVq1FD//v311Vdf5XtMp06d1KJFiytLCaBE3H3uWupZjbvqWOlow2kAoAiVKiX99a/2+iuvSAW8fA0AgMspcKnOyMjQrl271KhRo1zbGzVqpK1btxZpqLS0NCUkJORaABSv6qePqO+OXyRJ77a7wXAaACgG99wjBQdLq1dLv/xiOg0AwEsUuFQnJSXJsixFRUXl2l6mTJkiL72TJ09WZGRkzhITE1Okzw/gYn9ZNUP+llNLarfR9gq1TMcBgKJXoYI0Zoy9/sorZrMAALxGgUt1SEiIJCkxMTHX9oSEBIWGFu1kRhMnTlR8fHzOcvDgwSJ9fgC5VUg8qaG/L5YkvXHVcMNpAKAYPfSQ/ThzprRrl9ksAACvUKhSHRMTo3379uXavnfvXtWrV69IQwUHBysiIiLXAqD43LF2poKzMrW6WmOtiWlqOg4AFJ9GjaTrr7evqX71VdNpAABeoFATlQ0cOFDTp09XWlqaJCk+Pl6zZ8/WwIEDc/ZZvXq13n333aJNCaD4nDqlmzfMk8RZagA+4pFH7McPP5ROnjSbBQDg8QpVqp988kmlpKSoZ8+eevbZZ9W9e3dVrFhR999/f84+8+bN06OPPprz8aFDhzRlyhRNmTJFkrRo0SJNmTJFS5YsKaK3AOCKTJmisIxUba1QS0trtzWdBgCKX7duUqtWUkqK9NZbptMAADxcoUp1xYoVtWHDBg0bNkxnzpzR3XffrVWrVql06dI5+7Rv31533313zsdJSUnavn27tm/frnvuuUelS5fW9u3bdeTIkaJ7FwBcc/as9NprkqQ3OwyTHA7DgQCgBDgc0sMP2+tTpkjnRuABAOAKh2W5/40aExISFBkZqfj4eK6vBorSf/4jPfSQ9pWprB53viWnn7/pRABQLPa/2C/3howMqXZt6dAh6YMPpLFjzQQDALitgvbQQp2pBuBF0tJybinzdvuhFGoAviUwUMq+fO2llySn02weAIDHolQDvuqzz6Q//pCqVNE3TXuYTgMAJe/uu6XISGnbNvsWWwAAuIBSDfiirCzpn/+01x96SOkBgWbzAIAJkZHSfffZ688/b99mCwCAQqJUA77oyy+lXbuksmXtMzUA4KseeEAKDZXWrZMWLjSdBgDggSjVgK/JypKee85ef/hh6U+z9wOAz4mOlsaNs9dfeMFsFgCAR6JUA75m+nRp+3apTBnp3ntNpwEA8x5+WAoKkpYtk5YvN50GAOBhKNWAL3E6pWeftdcffFDiFnUAIFWtKt12m73O2WoAQCFRqgFfMmOGtHVr7sl5AADS+PGSn5/0/ffS+vWm0wAAPAilGvAVTqf0j3/Y63/7mxQVZTINALiXOnWkUaPs9cmTzWYBAHgUSjXgK777Tvr9d3vI9wMPmE4DAO5nwgT7ccYM+97VAAAUAKUa8AV/Pkt9//32JGUAgNyaNpUGDbLvV83ZagBAAVGqAV8we7a0aZMUHm4P/QYA5O2JJ+zHqVPtOyUAAHAZlGrA21mWNGmSvX7ffVK5cmbzAIA7a9vWPlvtdErPPGM6DQDAA1CqAW/3zTfShg32WeqHHjKdBgDcX/blMl99JW3ebDYLAMDtUaoBb5aVJT35pL3+4INSdLTZPADgCZo3l0aMsNefespsFgCA26NUA95s6lR7BtsyZaSHHzadBgA8xzPP2PetnjlTWrvWdBoAgBujVAPeKj39/PWA48dLkZFG4wCAR2nUSLr5Zns9e8QPAAB5oFQD3uqDD6R9+6SKFe0JygAAhfPUU5K/vzR/vvTLL6bTAADcFKUa8EYpKdKzz9rrf/+7FBZmNg8AeKI6daTbb7fXOVsNAMgHpRrwRm+8IR0+LFWvLt19t+k0AOC5nnhCCgqSliyRfvzRdBoAgBuiVAPeJiFBmjzZXn/6aSk42GweAPBkf/7l5MSJkmWZzQMAcDuUasDbvPqqdPKkVL++dMstptMAgOd74gn7MprVq6Vp00ynAQC4GUo14E2OH5deftle/8c/pIAAs3kAwBtUrGjfRUGyz1anpZnNAwBwK5RqwJtMmiQlJkqtW0vDh5tOAwDe4+GHpcqV7bsqvPGG6TQAADdCqQa8xY4d0ttv2+svvyz58e0NAEUmLMweASTZd1c4fdpsHgCA2+CnbsBbPPaYlJUlDRggde9uOg0AeJ+xY6UmTexC/cILptMAANwEpRrwBj/9JM2cKfn7S//8p+k0AOCd/P2lf/3LXn/9dWn3brN5AABugVINeDqnU3rkEXv97rulRo3M5gEAb9a3r9S7t5Sebl9nDQDweZRqwNN9+aW0dq1UurT0zDOm0wCAd3M47FsXBgRIs2ZJCxaYTgQAMIxSDXiy1FT79i6SNGGCVKGC2TwA4AsaNZLuv99ef+AB+6w1AMBnUaoBT/b669KBA1K1atLf/mY6DQD4jqeesn+RuWOHNGWK6TQAAIMo1YCnOnpUeu45e/3556XQULN5AMCXREZKkyfb65Mm2f8mAwB8EqUa8FQTJkiJiVK7dtLNN5tOAwC+57bb7H+DExKYtAwAfBilGvBEK1ZIH39sr//3v5If38oAUOL8/KQ337QfP/9cWrTIdCIAgAH8JA54mqws6b777PWxY6UOHczmAQBf1qbN+X+T/+//pJQUs3kAACWOUg14mg8+kNatkyIizl/PBwAw59lnpapVpT17pBdeMJ0GAFDCKNWAJzl1Snr8cXt90iSpYkWzeQAAUunS9t0YJOmf/5S2bjWbBwBQoijVgCd57DHpxAmpSRPpnntMpwEAZLvhBmnAACkjQ7rzTvtSHQCAT6BUA55i+XLpvffs9bfekgIDzeYBAJzncEj/+599ac6KFdJ//mM6EQCghFCqAU+Qni6NG2ev33mndPXVZvMAAC4WEyP9+9/2+hNPSNu3m80DACgRlGrAE7zyin2NXvny9vV6AAD3dPvtUp8+UlqafYcGhoEDgNejVAPubu9e6R//sNdfeUUqW9ZsHgBA/hwO6d137WHgK1eeP3MNAPBalGrAnTmd9nDv1FTp2mulm282nQgAcDkXDgPftMlsHgBAsaJUA+7snXekJUukUqXsdYfDdCIAQEHcfrs9G3h6ujRqlJScbDoRAKCYUKoBdxUbKz36qL0+ebJUp47ZPACAgnM4pA8+kCpXlrZtkx5+2HQiAEAxoVQD7siy7GHfZ8/aM33fd5/pRACAwoqOlj75xF5/6y1p5kyzeQAAxYJSDbij996TFi+WQkLsMx1+fKsCgEfq2fP8qKPbb5cOHjSbBwBQ5PhJHXA3u3dLDz5or7/wglSvntk8AIAr89xzUps20qlT0vDh9nXWAACvQakG3ElGhj3Dd1KS1LWrdP/9phMBAK5UUJA0fboUFSWtWiU99JDpRACAIkSpBtzJs8/aP3BFRtrX4fn7m04EACgKtWpJn31mr//vf9LUqWbzAACKDKUacBe//CI9/7y9/vbbUvXqZvMAAIpWv37Sk0/a63fdJW3ebDYPAKBIUKoBd3DmjD3s2+mUxoyRRo40nQgAUByeflrq3VtKSZEGDpSOHzedCABwhSjVgGmWJY0dK+3fbw8PnDLFdCIAQHHx95e++EKqW1eKjZUGD5ZSU02nAgBcAUo1YNq//y199935iWwiIkwnAgAUp7JlpTlz7InLVqyQ7rzT/gUrAMAjBZgOAPi0X36RHnvMXn/1VfuWK+fUnDDXTCYA8EIl9W/q/hf7FWzHBg2kr7+W+vSxJy2rX1966qniDQcAKBacqQZMOX7cvnY6K0saNUr6y19MJwIAlKQePeyZwCX7Wut33zWbBwDgEko1YEJ6ujRsmPTHH1LDhtI770gOh+lUAICSNm6c9Pjj9vpf/iJ9843ZPACAQqNUAyXNsqR77pF+/tm+fvrbb6XwcNOpAACmPPecfYstp9MeubRkielEAIBCoFQDJW3KFOm99yQ/P+nLL+0z1QAA3+VwSG++KQ0ZYo9kGjhQ+vVX06kAAAVEqQZK0qJF0oMP2uv/+pfUt6/ZPAAA9+Dvb09Y1qOHdPasdN11FGsA8BCUaqCkbNokDR1qT0x2yy3SQw+ZTgQAcCchIdKsWVL37lJiIsUaADwEpRooCfv322elExOlrl2lt99mYjIAwMVCQ+17WGcX6z59pGXLTKcCAFwCpRoobidP2mcbjhyRmjaVvvvOPhsBAEBesov1tdfaQ8F795ZmzjSdCgCQD0o1UJzOnpUGDJB27JCqVZO+/16KijKdCgDg7rKL9cCBUlqaPYnZBx+YTgUAyAOlGiguycl2oV6xwi7S8+fbxRoAgIIoVUqaMUO6/Xb7dlt33GHffsuyTCcDAPwJpRooDqmp0g03SEuXSqVL24W6SRPTqQAAniYgwL4N44QJ9sdPPimNGWP/PwMAcAuUaqCopadLw4dLCxdKYWHSvHlShw6mUwEAPJXDIU2ebN/LOvvWW926SUePmk4GABClGihaycnSoEH2dXAhIdLs2dLVV5tOBQDwBn/5i/0L2zJlpFWrpLZtueUWALiBANMBgKJSc8LcEnmd/S/2y/sTCQlS//7Szz/bE8zMnGnfEgUAgKJy7bV2oR44UNq+XRnXdNGLXW/T++0GF9utGvP9fw8AIIkz1UDROHlS6tHDLtQREfaZhJ49TacCAHijevWk1as1q1EXBTqz9OSS9/X2t88rMiXRdDIA8EmUauBK7d0rde4srV0rRUdLS5bYHwMAUFxKl9b9Ax7VE73/qjT/APXZtVILP7hHXfeuM50MAHwOpRq4EqtWSVddZd+HOiZG+uknqXVr06kAAL7A4dBnra7X0Jtf1p6y1VTx7Cl9PP1pPbvwDZVKZ3ZwACgplGrAVd9+a18zHRdnF+mVK6XGjU2nAgD4mN8r1VW/217Vh20GSJLGbJjHWWsAKEGUaqCwnE7pmWekIUOklBTp+uvtM9RVqphOBgDwUamBIZrUc5xuGvmcDkWUV0z8MX08/Wm9Putfik46bToeAHg1SjVQCKXTkqQbbpAmTbI33HefPct3eLjZYAAASPqlZkv1vuMNvdd2kLIcfhq4bZl+fGec7lz9jQKzMkzHAwCvRKkGCqhB3H5998nD0qxZUnCw9NFH0uuvSwHcmQ4A4D6Sg0rpuR53aeAt/9amSvUUkZ6sJ5Z8oIXv/1W9dq2ULMt0RADwKpRq4HIsS6M3fq+ZnzykOqcOSdWqScuXS7feajoZAAD52lKprgbf8ooe7fuAjoeVUa3TR/TuN8/pm88e0dX7NlCuAaCIUKqBS4hIPaspM/+pFxb8TyGZ6VpSu420fr3Utq3paAAAXJbl8NP05r3U/a639b+rhislIFitD+/QZ9Oe1LTPH1PH2M2mIwKAx6NUA/nounedFrx/j/rvWK4MP3893+123T7saal8edPRAAAolKTgUL3U9VZ1Gfee3m87SGn+gWp/aKu++PJxffHFRHXbs0YOy2k6JgB4JC4GBS4Qnpasx5e8r9GbFkiS9pWprAf7P6KNVRoYTgYAwJWJCy+jZ3vcpbfbD9E9K6fpxk0L1PHAb+p44DftKVtNH7QdqBlNr1VqYIjpqADgMThTDfxJp/0bNf+De3IK9YdtBqjv2P9SqAEAXuV46XJ6utf/qdvd7+qddjcoIShUdU4d0vML39CKN8bq0Z8+VsyZo6ZjAoBH4Ew1ICkqJUGPLf1IozYvlCQdiKyoR6//m1ZVb2Y4GQAAxedIRHm9cO0deq3zKI34bZHGrp2l6vHHdM/K6bpn5XT9Wr251OyMNGSIVKqU6bgA4JYo1fBpDsupYb8t1sSlH6lsSoIk6bOWffVC99uVHMQPDwAA35AUHKoP2w7Sx637q9euVbpp4/e6ev9GdTqwWbr5ZikyUho1SrrxRunqqyV/f9ORAcBtOCzL/e+nkJCQoMjISMXHxysiIsJ0HLipmhPmFmr/BnH79dyCN9Tuj62SpO3RNfREn79qbbUmxREPAACPUjX+uIb9tlgPHvpFio09/4lKlaShQ6URI6TOnYusYBf2/3FX7X+xX4m8DgDPV9AeyjXV8DlRKQl6evHbmvvh/Wr3x1YlBYboue63q/9tr1GoAQA454/ICnrt6tHS3r3SokXS7bdLUVHS0aPS//4nde0qVa1qb58xQ0pIMB0ZAIxg+Dd8RmBWhm5ZP1f3//KFItOSJEnf1++kf/S4S0ciuE0WAAB58vOTeva0lzfflH74QZo2Tfr2W+nYMenDD+0lMFC65hqpb1+pe3epZUuGiQPwCZRqeD/LUp9dKzRh6YeqdfqIJGlb+Zp69to79WvNlmazAQDgSYKC7NLct6/09tvSsmXS3Ln2smuX9OOP9iLZ12F36SJ162aX7BYt7IIOAF6GUg2vdtWBzXr0p0/U5vB2SVJcWJReuuYWfd2sh5x+/PYcAACXBQWdP4P9n//YpXruXPtM9rJlUny8NHu2vUhSmTJSx47SVVfZS7t29nByAPBwlGp4pRaHd+iRZZ/qmtiNkqSUgGC9126w3uowVEnBoWbDAQDgjerVk/72N3vJzJQ2bpSWLLGXn3+WTp+W5s2zl2yNGtkFu317tTicrO3layotMNjQGwAA11Cq4VXqx+3Xwz9/pj67VkqS0v0C9HnL6/S/jiMUF17WcDoAAHxEQIDUtq29PPqoXbLXr5dWrpRWrbIf9+6Vtm2zlw8/1ExJmQ4/7S4Xoy0Va2trxTr2Y4XaSggJN/2OACBflGp4h61b9erslzRw6zL5yVKWw0/fNLlWr109SociK5pOBwCAbwsIkNq3t5dscXF2wV61Slq9WieWr1J0crwanohVwxOxGrplSc6uByIrasu5kr2lYh1tqVBbx8PLSg6HgTcDALlRquHZNm6Unn9emjFDg8/dcn1Og6v1n6tv0p7oGLPZAABA/sqXl/r3txdJbR+bo4pnT6rJsb1qcmyPmh7boybH9qpawnFVjz+m6vHH1HfnrzmHnwiN1NYKtbX13NnsLRVra1+ZKsyZAqDEUarhmVatsst09uQnsm+PNaXjCG2pVNdgMAAA4BKHQ8dKR+tY6Wj9WPf8Ge3IlEQ1Pm4X7SbH9qrx8b2qe/KQopPj1WX/BnXZvyFn3+TAYG0vX/Ncya6jrRVqcZ02gGJHqYbnsCx7spMXX5QWLbK3+flJN94oTZyo//ss1mw+AABQ5OJLldaKGi20okaLnG3BGWlqcCI2p2Q3ObZHDeP2KzQjTa0P71Drwzty9s1y+GlP2Wo512drcbDUqpVUrpyJtwPAC1Gq4f4yMqRp06SXX7aHe0v2tVljxkgTJkj165/bkVINAIAvSAsM1ubK9bW5cv2cbX7OLNU6fViNj+1Vk+N71fhc4Y5Ojlf9kwdU/+QB3bB1qbT0A/uA6tWlNm3spXVr+7FCBTNvCIBHo1TDfcXHS++8I73+unTokL0tNFQaO1Z65BGpZk2j8QAAgPtw+vlrT7kY7SkXo9mNu9obLUsVzp7KVbL7ZR6V9uyRDhywl2+/Pf8k1arlLtlt2kiVKpl5QwA8BqUa7mf3bumNN6T33pMSE+1tFStK998v/eUvUllujQUAAArA4dDx0uV0vHQ5LanTTpLU78V+9i/uN2yQ1q2zb/W1bp20c6f9S/xDh6SZM88/R+XK5wt2duGuUoWZxwHkcFjWuSmT3VhCQoIiIyMVHx+viIgI03FQHDIzpblz7TK9cOH57U2aSA8/LI0eLQVfepKRmhPmFnNIAADgrcLSktX4+F41O7pHTY/tVrOju1Xn5CH56eIflePCovRbxbr6rVJdbalYR79VqqsjpaMvKtr7X+xXItlL6mcg3g98TUF7KGeqYdbRo/YZ6bffPj/E2+GQ+vaV7rtP6tOH3wQDAIBilxQcqjUxTbUmpmnOttD0FDU6vk/Nju5W02N71PTobtU7eVDlk87o2r1rde3etTn7ngiNzCnYv1Wsq98r1bUnWeXnGMDrUapR8jIypPnzpY8/todXZWba26OjpTvukMaNk2rVMpsRAAD4vOSgUlpXrbHWVWucsy0kI1WNj+9Tk2N71OyofUa73okDik6OV9d969V13/rzTzD90dzXZ7dsaf+M48+9tAFvQqlGybAs+9qlTz6RPv9cios7/7lOnaS//lUaNuyyQ7wBAABMSg0M0fqqjbS+aqOcbcGZ6Wp4fJ+anTub3fTYHjWI26/Akyft24Bm3wpUsn/WqVdPatBAatgw9yOXOQIeiVKN4nXggPTVV3aZ/v3389srVpRuukm69VapeXNz+QAAAK5QWkCQNlVpoE1VGuRsC8rM0M4bq52fCG3dOvtnobQ0+/HPPxdlq1RJql3bvsPJhUv16px8ANwUpRpFb/duacYMe1mz5vz24GBp0CC7SPfubd9rGgAAwAulBwRKbdvaS7asLPuEw/bt0o4d9mP2+tGj55dff837SStXtm/7VbmyXcArV5YqV1bPXX/oeHgZHQ8rqxNhUcr052csoCTxHYcrl5UlrVolff+9NHu2tGnT+c85HNI119hnpYcPl8qUMZcTAADAJH9/+5rqWrXsSVn/7MwZ+7ZesbHS/v3nl9hYad8+KTlZOnLEXi7w3gUfJwSF6nRohE6XKq3TpSJ0ulSEzoSU1qnQCJ0pFaFTpSIUHxKuxOBQJQaH6WxQqBKCQ5UWEMTEaoALKNVwzaFD0o8/2kV64ULp1Knzn/P3l7p3l4YOlQYPtn+TCgAAgPxFRUnt29vLhSxLOnnSLtmHD58v1+eWTWu2q8LZUyqfdFoBllMR6cmKSE9WjTNHCxUh3S/gfNEODs1ZTwwqpZTAECnjRyk01F5KlTq/fuHHISH2iMTAQPsxv3U/vzxLvJ8zq0B5nY68jwdKGqUal2dZ9m9Jf/rp/LJ3b+59ypSxh3T37Sv17y+VK2cmKwAAgLdxOOy7pERH5/npQefu6+ywnIpMPasyKYkqk5ygMqkJKpucoKiURJVNSVBUSoLKpiSoTEqCIlKTVDotWaXTkhSeniI/WQpyZqpcSoLKpSTknWPj98X1DnPZe/ldcnHKIafDIcthP0oOOR1+shx28XbKITkcyvTzV5p/oNICgpQWECgtLm//AuDPS0SEvURG5v1Yrpz95xAVZf9SAJCLpXrz5s2KjY1VvXr11LBhw2I7BoYcPy6tXWtfD529HD+eex8/P/sWEX362EW6QweukQYAADDIcvjpTCl7iPe+slULfJzDciosPVWl05LOLcnnH9Pt9ZCMdD3YuZo9DD05WUpJOb9+4cepqfYtU7OXjAx7KSZ+suRnWZJVyANPHHD9Rf39zxfs6GipfPnc6+XLSxUqnF+io/lZ2YsV6k82PT1dI0aM0M8//6yWLVtqzZo1Gj58uN577z058hl64coxKAGWZU+EsWePtHWrtGXL+ZkoLyzQkv2PQNu2Uteu9tK5M7d9AAAA8AKWw09ng0N1NjhUR1Q+3/0efLHflb2Q02mX6+yynZX3MO8WkxZe9qkcsuSw7DJ9ft0phyX5yX50WE45JPlZTvlZlvydWQrOylBwZrqCMzP0xS0t7V8AZC/JyVJiohQfLyUk5P148qS9npVl/8yc18/NeQZ22CX8z0X7UktEBEPbPUihSvWrr76qX375RZs2bVK1atW0ZcsWtW3bVt26ddOYMWOK7BgUgbNn7dJ87Jj9eOCAPcnF3r32sn+//RvFvDgc9r0S27U7v7RoYV8rAwAAALjCz8++G8xlbg0WX6p0yeS5cLK4gkpPl06cOL/ExV38GBd3vnSfOGGf0Mref+vWy79GUFD+hbtixdwfly/P7dYMK1Sp/vTTTzVy5EhVq1ZNktSkSRP17dtXn376ab4F2ZVjfJbTad+7MCXl/G/MstdTUqSkJPs3ZGfOXPx45ox0+vT5Ep2UdPnX8/OTYmKkRo2kpk2lJk3sx0aNpLCw4n2vAAAAgCcKCpKqVLGXgsjKss9wZ5fsvJZjx86vnz1rF/dDh+ylIEJDL30teGSkvWRPKnepJXuf7AnnOGN+WQUu1ZmZmdq2bZvuu+++XNubN2+ut956q8iOkaS0tDSlpaXlfBwfHy9JSkjIZ9IEd5CUZN+D2em0v3GyH/+85LUte0lJKfprTUqVsmferlDBvo9hzZrnlxo17EIdGHjxcVlZ9rAWD+NMSzYdAQAAIEdJ/exaUj8D8X6uQEiIVL26vVxOcnLeZ76zlwu3ZWWdv579aOFmfC8QP7/zM7cHBNjXk184k/uftzkc5xfp4o+zt912m+TmJ1mz/45Y1qUv2C9wqT579qyysrJU5oL7DJcrV05nzpwpsmMkafLkyZo0adJF22NiYgoaF5Jd1PftsxcAAACUqMhXTScoWrwfH+V02mfO09OL9nnXrpXuvbdon7OYJCYmKjIyMt/PF7hUB58bp5+cnPs3R2fPnlVISEiRHSNJEydO1EMPPZTzsdPp1KlTp1SuXDkmN4NPSEhIUExMjA4ePKgIJoSDD+J7AL6Mv//wdXwPwF1YlqXExERVucxQ/wKX6lKlSqlSpUo6cCD31PMHDhxQ7dq1i+wYyS7jwRdcbB8VFVXQqIDXiIiI4D8T+DS+B+DL+PsPX8f3ANzBpc5QZyvUHcv79u2rb775Rk6nU5KUmpqq2bNnq++fZs7bunWrZs2aVahjAAAAAADwRIUq1U899ZQOHTqkoUOH6t1331W/fv0UEBCQa6j2tGnTdMsttxTqGAAAAAAAPFGhSnXNmjW1fv16NWzYUEuXLtU111yjNWvWqFy5cjn7NG7cWIMGDSrUMQByCw4O1tNPP33RZRCAr+B7AL6Mv//wdXwPwNM4rMvNDw4AAAAAAPJUqDPVAAAAAADgPEo1AAAAAAAuolQDAAAAAOCiAt+nGoBZx48f1/r16xUaGqqWLVty30Z4rYMHD2r9+vUqU6aMOnXqpIAA/quC7zh69Kg2btyosLAwtWrVSuHh4aYjAUbMnDlTmZmZGjp0qOkowGUxURng5lJTUzVu3Dj98MMPatq0qU6dOqXdu3fr3Xff5T8aeJ1XXnlFTz75pDp27Ki9e/cqNDRUixYtUpUqVUxHA4pVUlKS7r77bv30009q1qyZjh8/rtjYWH344YcaMGCA6XhAiXr77bd1//33KzAwUGfPnjUdB7gshn8Dbi4tLU09evRQbGys5s+fr9WrV+vBBx/UmDFjdObMGdPxgCKzefNmPfroo/r888/1ww8/aNu2bQoNDdUDDzxgOhpQ7FJSUtSvXz8dOHBA33//vdatW6dx48Zp1KhRSk5ONh0PKDFbtmzRs88+q4ceesh0FKDAKNWAm4uMjNQtt9wif3//nG1Dhw5VSkqKdu7caTAZULQ+//xz1ahRQ4MHD5YkhYSE6K9//atmzpypxMREs+GAYhYdHa3Ro0fLz+/8j2ZDhw5VUlKSdu/ebTAZUHJSUlI0cuRIvfbaa6patarpOECBUaoBD7R48WIFBgaqfv36pqMARea3335Ts2bNcm1r1qyZMjIytGPHDkOpAHMWL16skJAQ1alTx3QUoEQ88MADat++PZe3weMw+wtgwM8//6w//vjjkvsMHTpUgYGBF23//fff9cQTT2jixImKiooqpoRAyYuPj1fdunVzbStXrpwkcakDfM769es1adIkPfXUUwoLCzMdByh206dP148//qiNGzeajgIUGqUaMGDFihVav379JfcZOHDgRaV69+7d6tOnjwYNGqSnn366OCMCJS44OPiiCWmyPw4JCTERCTBi27Zt6tu3r0aNGqUJEyaYjgMUu8zMTN111126/fbbNWfOHEn2L5YyMzP15Zdfql27dozYgFujVAMGjB8/vtDH7N69W926dVO3bt300Ucf5bruDvAGderUuegMRWxsrCSpdu3aBhIBJW/79u269tpr1a9fP7377rtyOBymIwHFzrIsXXfddTp8+LC+++47SdKuXbuUlZWl7777TtHR0ZRquDVuqQV4gL1796pr167q0qWLPvnkk1yTlgHe4rvvvtOQIUO0Z88e1apVS5I0ZswY/f7779qwYYPhdEDx27Fjh7p166a+ffvqvffe45en8GlTpkzRhAkTuKUWPAJnqgE3d+bMGXXv3l3+/v66/vrrNX369JzPXXPNNcyOCa8xaNAg9ejRQ3379tU999yjbdu26auvvtL8+fNNRwOKXVxcnK699lqFhYWpZ8+emjZtWs7nunXrpkqVKhlMBwC4FEo14OaSk5PVsWNHSdLs2bNzfa5u3bqUangNh8OhuXPn6t1339WaNWtU5v/bt2MbhmEYioJcwUsIUKUJtIUn1RraR3XqdMEHAhe+a9mwfQB5XbX3rjHG06vB351zas5ZVVVrra9Z711U8zqttbrv++k14CfOvwEAACDkWQcAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCohoAAABCH/EaEZmrC+HDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111, title=\"Standardized Deviance Residuals\")\n",
    "ax.hist(resid_std, bins=25, density=True)\n",
    "ax.plot(kde_resid.support, kde_resid.density, \"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### QQ-plot of deviance residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:27:19.788748Z",
     "iopub.status.busy": "2026-06-27T20:27:19.788392Z",
     "iopub.status.idle": "2026-06-27T20:27:20.236677Z",
     "shell.execute_reply": "2026-06-27T20:27:20.233470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "fig = sm.graphics.qqplot(resid, line=\"r\", ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.14.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}