{
 "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": "2022-11-02T17:10:28.147185Z",
     "iopub.status.busy": "2022-11-02T17:10:28.146696Z",
     "iopub.status.idle": "2022-11-02T17:10:28.632250Z",
     "shell.execute_reply": "2022-11-02T17:10:28.631561Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:28.637569Z",
     "iopub.status.busy": "2022-11-02T17:10:28.636325Z",
     "iopub.status.idle": "2022-11-02T17:10:29.473782Z",
     "shell.execute_reply": "2022-11-02T17:10:29.473063Z"
    }
   },
   "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": "2022-11-02T17:10:29.477705Z",
     "iopub.status.busy": "2022-11-02T17:10:29.477194Z",
     "iopub.status.idle": "2022-11-02T17:10:29.481018Z",
     "shell.execute_reply": "2022-11-02T17:10:29.480457Z"
    }
   },
   "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": "2022-11-02T17:10:29.483971Z",
     "iopub.status.busy": "2022-11-02T17:10:29.483590Z",
     "iopub.status.idle": "2022-11-02T17:10:29.486942Z",
     "shell.execute_reply": "2022-11-02T17:10:29.486390Z"
    }
   },
   "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": "2022-11-02T17:10:29.489744Z",
     "iopub.status.busy": "2022-11-02T17:10:29.489367Z",
     "iopub.status.idle": "2022-11-02T17:10:29.507987Z",
     "shell.execute_reply": "2022-11-02T17:10:29.507364Z"
    }
   },
   "outputs": [],
   "source": [
    "dta = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:29.511454Z",
     "iopub.status.busy": "2022-11-02T17:10:29.511056Z",
     "iopub.status.idle": "2022-11-02T17:10:29.529794Z",
     "shell.execute_reply": "2022-11-02T17:10:29.529242Z"
    }
   },
   "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": "2022-11-02T17:10:29.533676Z",
     "iopub.status.busy": "2022-11-02T17:10:29.533291Z",
     "iopub.status.idle": "2022-11-02T17:10:29.561970Z",
     "shell.execute_reply": "2022-11-02T17:10:29.561397Z"
    }
   },
   "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": "2022-11-02T17:10:29.565173Z",
     "iopub.status.busy": "2022-11-02T17:10:29.564705Z",
     "iopub.status.idle": "2022-11-02T17:10:29.677413Z",
     "shell.execute_reply": "2022-11-02T17:10:29.676589Z"
    }
   },
   "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": "2022-11-02T17:10:29.681228Z",
     "iopub.status.busy": "2022-11-02T17:10:29.680796Z",
     "iopub.status.idle": "2022-11-02T17:10:29.715489Z",
     "shell.execute_reply": "2022-11-02T17:10:29.714797Z"
    }
   },
   "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:                Wed, 02 Nov 2022   Pseudo R-squ.:                  0.1327\n",
      "Time:                        17:10:29   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": "2022-11-02T17:10:29.719073Z",
     "iopub.status.busy": "2022-11-02T17:10:29.718692Z",
     "iopub.status.idle": "2022-11-02T17:10:29.735833Z",
     "shell.execute_reply": "2022-11-02T17:10:29.735137Z"
    }
   },
   "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": "2022-11-02T17:10:29.739333Z",
     "iopub.status.busy": "2022-11-02T17:10:29.738967Z",
     "iopub.status.idle": "2022-11-02T17:10:29.783735Z",
     "shell.execute_reply": "2022-11-02T17:10:29.782909Z"
    }
   },
   "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",
      "===================================================================================\n"
     ]
    }
   ],
   "source": [
    "mfx = affair_mod.get_margeff()\n",
    "print(mfx.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:29.787567Z",
     "iopub.status.busy": "2022-11-02T17:10:29.787161Z",
     "iopub.status.idle": "2022-11-02T17:10:29.795287Z",
     "shell.execute_reply": "2022-11-02T17:10:29.794569Z"
    }
   },
   "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": "2022-11-02T17:10:29.798793Z",
     "iopub.status.busy": "2022-11-02T17:10:29.798329Z",
     "iopub.status.idle": "2022-11-02T17:10:29.805866Z",
     "shell.execute_reply": "2022-11-02T17:10:29.805312Z"
    }
   },
   "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": "2022-11-02T17:10:29.809477Z",
     "iopub.status.busy": "2022-11-02T17:10:29.809006Z",
     "iopub.status.idle": "2022-11-02T17:10:29.839100Z",
     "shell.execute_reply": "2022-11-02T17:10:29.838452Z"
    }
   },
   "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": "2022-11-02T17:10:29.844008Z",
     "iopub.status.busy": "2022-11-02T17:10:29.842667Z",
     "iopub.status.idle": "2022-11-02T17:10:29.872377Z",
     "shell.execute_reply": "2022-11-02T17:10:29.871764Z"
    }
   },
   "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": "2022-11-02T17:10:29.877998Z",
     "iopub.status.busy": "2022-11-02T17:10:29.876372Z",
     "iopub.status.idle": "2022-11-02T17:10:29.886777Z",
     "shell.execute_reply": "2022-11-02T17:10:29.886198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07516159285055479"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affair_mod.fittedvalues[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:29.892074Z",
     "iopub.status.busy": "2022-11-02T17:10:29.890501Z",
     "iopub.status.idle": "2022-11-02T17:10:29.900564Z",
     "shell.execute_reply": "2022-11-02T17:10:29.899992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.518781557212145"
      ]
     },
     "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": "2022-11-02T17:10:29.906068Z",
     "iopub.status.busy": "2022-11-02T17:10:29.904475Z",
     "iopub.status.idle": "2022-11-02T17:10:30.118747Z",
     "shell.execute_reply": "2022-11-02T17:10:30.118004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f37e66a5ab0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": "2022-11-02T17:10:30.123803Z",
     "iopub.status.busy": "2022-11-02T17:10:30.122653Z",
     "iopub.status.idle": "2022-11-02T17:10:30.414803Z",
     "shell.execute_reply": "2022-11-02T17:10:30.414017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f37e45e0e20>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": "2022-11-02T17:10:30.420278Z",
     "iopub.status.busy": "2022-11-02T17:10:30.419098Z",
     "iopub.status.idle": "2022-11-02T17:10:30.425401Z",
     "shell.execute_reply": "2022-11-02T17:10:30.424805Z"
    }
   },
   "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": "2022-11-02T17:10:30.430086Z",
     "iopub.status.busy": "2022-11-02T17:10:30.428945Z",
     "iopub.status.idle": "2022-11-02T17:10:30.435128Z",
     "shell.execute_reply": "2022-11-02T17:10:30.434530Z"
    }
   },
   "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": "2022-11-02T17:10:30.439789Z",
     "iopub.status.busy": "2022-11-02T17:10:30.438675Z",
     "iopub.status.idle": "2022-11-02T17:10:30.444773Z",
     "shell.execute_reply": "2022-11-02T17:10:30.444191Z"
    }
   },
   "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": "2022-11-02T17:10:30.448384Z",
     "iopub.status.busy": "2022-11-02T17:10:30.447934Z",
     "iopub.status.idle": "2022-11-02T17:10:30.460709Z",
     "shell.execute_reply": "2022-11-02T17:10:30.460118Z"
    }
   },
   "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='object')\n"
     ]
    }
   ],
   "source": [
    "dta = sm.datasets.star98.load_pandas().data\n",
    "print(dta.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:30.465335Z",
     "iopub.status.busy": "2022-11-02T17:10:30.464218Z",
     "iopub.status.idle": "2022-11-02T17:10:30.475006Z",
     "shell.execute_reply": "2022-11-02T17:10:30.474421Z"
    }
   },
   "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": "2022-11-02T17:10:30.479599Z",
     "iopub.status.busy": "2022-11-02T17:10:30.478468Z",
     "iopub.status.idle": "2022-11-02T17:10:30.488753Z",
     "shell.execute_reply": "2022-11-02T17:10:30.488154Z"
    }
   },
   "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": "2022-11-02T17:10:30.493392Z",
     "iopub.status.busy": "2022-11-02T17:10:30.492231Z",
     "iopub.status.idle": "2022-11-02T17:10:30.496850Z",
     "shell.execute_reply": "2022-11-02T17:10:30.496277Z"
    }
   },
   "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": "2022-11-02T17:10:30.501577Z",
     "iopub.status.busy": "2022-11-02T17:10:30.500476Z",
     "iopub.status.idle": "2022-11-02T17:10:30.508689Z",
     "shell.execute_reply": "2022-11-02T17:10:30.508116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": "2022-11-02T17:10:30.513206Z",
     "iopub.status.busy": "2022-11-02T17:10:30.512106Z",
     "iopub.status.idle": "2022-11-02T17:10:30.519164Z",
     "shell.execute_reply": "2022-11-02T17:10:30.518602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": "2022-11-02T17:10:30.523587Z",
     "iopub.status.busy": "2022-11-02T17:10:30.522504Z",
     "iopub.status.idle": "2022-11-02T17:10:30.550547Z",
     "shell.execute_reply": "2022-11-02T17:10:30.549823Z"
    }
   },
   "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": "2022-11-02T17:10:30.556063Z",
     "iopub.status.busy": "2022-11-02T17:10:30.554819Z",
     "iopub.status.idle": "2022-11-02T17:10:30.570996Z",
     "shell.execute_reply": "2022-11-02T17:10:30.570343Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Generalized Linear Model Regression Results                   \n",
      "================================================================================\n",
      "Dep. Variable:     ['NABOVE', 'NBELOW']   No. Observations:                  303\n",
      "Model:                              GLM   Df Residuals:                      282\n",
      "Model Family:                  Binomial   Df Model:                           20\n",
      "Link Function:                    Logit   Scale:                          1.0000\n",
      "Method:                            IRLS   Log-Likelihood:                -2998.6\n",
      "Date:                  Wed, 02 Nov 2022   Deviance:                       4078.8\n",
      "Time:                          17:10:30   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": "2022-11-02T17:10:30.576065Z",
     "iopub.status.busy": "2022-11-02T17:10:30.574918Z",
     "iopub.status.idle": "2022-11-02T17:10:30.584994Z",
     "shell.execute_reply": "2022-11-02T17:10:30.584393Z"
    }
   },
   "outputs": [
    {
     "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": "2022-11-02T17:10:30.589702Z",
     "iopub.status.busy": "2022-11-02T17:10:30.588569Z",
     "iopub.status.idle": "2022-11-02T17:10:30.601710Z",
     "shell.execute_reply": "2022-11-02T17:10:30.601097Z"
    }
   },
   "outputs": [
    {
     "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": "2022-11-02T17:10:30.607534Z",
     "iopub.status.busy": "2022-11-02T17:10:30.605880Z",
     "iopub.status.idle": "2022-11-02T17:10:30.612386Z",
     "shell.execute_reply": "2022-11-02T17:10:30.611798Z"
    }
   },
   "outputs": [],
   "source": [
    "exog = glm_mod.model.data.orig_exog  # get the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:10:30.617896Z",
     "iopub.status.busy": "2022-11-02T17:10:30.616221Z",
     "iopub.status.idle": "2022-11-02T17:10:30.628468Z",
     "shell.execute_reply": "2022-11-02T17:10:30.627884Z"
    }
   },
   "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": "2022-11-02T17:10:30.633835Z",
     "iopub.status.busy": "2022-11-02T17:10:30.632243Z",
     "iopub.status.idle": "2022-11-02T17:10:30.643863Z",
     "shell.execute_reply": "2022-11-02T17:10:30.643290Z"
    }
   },
   "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": "2022-11-02T17:10:30.648275Z",
     "iopub.status.busy": "2022-11-02T17:10:30.647173Z",
     "iopub.status.idle": "2022-11-02T17:10:30.655525Z",
     "shell.execute_reply": "2022-11-02T17:10:30.654921Z"
    }
   },
   "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\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": "2022-11-02T17:10:30.660027Z",
     "iopub.status.busy": "2022-11-02T17:10:30.658922Z",
     "iopub.status.idle": "2022-11-02T17:10:30.707380Z",
     "shell.execute_reply": "2022-11-02T17:10:30.706673Z"
    }
   },
   "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": "2022-11-02T17:10:30.712935Z",
     "iopub.status.busy": "2022-11-02T17:10:30.711764Z",
     "iopub.status.idle": "2022-11-02T17:10:30.717965Z",
     "shell.execute_reply": "2022-11-02T17:10:30.717382Z"
    }
   },
   "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": "2022-11-02T17:10:30.722372Z",
     "iopub.status.busy": "2022-11-02T17:10:30.721254Z",
     "iopub.status.idle": "2022-11-02T17:10:30.725808Z",
     "shell.execute_reply": "2022-11-02T17:10:30.725250Z"
    }
   },
   "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": "2022-11-02T17:10:30.730787Z",
     "iopub.status.busy": "2022-11-02T17:10:30.729664Z",
     "iopub.status.idle": "2022-11-02T17:10:30.974367Z",
     "shell.execute_reply": "2022-11-02T17:10:30.973569Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "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": "2022-11-02T17:10:30.978369Z",
     "iopub.status.busy": "2022-11-02T17:10:30.977657Z",
     "iopub.status.idle": "2022-11-02T17:10:31.205062Z",
     "shell.execute_reply": "2022-11-02T17:10:31.204218Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f37e4372530>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6Svfff79OOeWUxu333ntvXXjhhXrvvfe022676fHHH9c333zTrKHhV7/6lV544QUdd9xxjcunbdmyRR999JGefvppffnll+revbut7zR9+nSNGzdOI0aM0AUXXKANGzboD3/4g/bff//GcfqpfJ9k/OpXv9LTTz+tU089VRdccIEOPvhgbdiwQS+88IIefvhhHXjggTrnnHP017/+VRdffLHeeOMNDR8+XPX19fr000/117/+VS+//HLj8A4nHXjggZo4caIeffRRbdy4UUceeaSWLl2qJ554QieccIKOOuqoxm0PPvhgzZw5U7fddpsGDhyonj17Nk6UBwDwOS+nrgcAZLfokm/vvfdeq9/V19dbAwYMsAYMGGDt3LnTsizLqqqqss4991yrqKjIat++vdW7d2/ruOOOs55++unGv7vtttusoUOHWp07d7Z22WUXa99997Vuv/12a/v27Y3bxFqe7fvvv7cuu+wyq1u3blanTp2s8ePHW1999VWrJd8sq2E5stLSUqtDhw7WPvvsY82dOzfme77wwgvWoEGDrPz8fKtv377WjBkzrMcff9ySZK1cubJxOztLvkmyJFmhUMgqLCy09t9/f2vSpEnWu+++G/fvHn30Uevggw+2dtllF6ugoMA64IADrGuvvdb6+uuvm733uHHjrJdfftkaNGiQlZeXZ+27777WU0891er9Nm3aZN1www3WwIEDrQ4dOljdu3e3Dj/8cOvuu+9u3M/RJd/uuuuuVn8fa58+88wz1n777Wfl5eVZJSUl1rPPPht3eT0736elWPt6/fr11qWXXmr17t3b6tChg7X77rtbEydOtNatW9e4zfbt260ZM2ZY+++/v5WXl2d16dLFOvjgg62pU6daNTU1Mfd7VHTJt0RinUM7duywpk6davXr189q37691adPH+uGG25otvShZVnWmjVrrHHjxlkFBQWWJJZ/A4AACVlWCjPBAACAQOnbt69KS0v1t7/9zeuiAACAJhjTDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGYkw7AAAAAACGoqcdAAAAAABDEbQDAAAAAGCodl4XIJMikYi+/vprFRQUKBQKeV0cAAAAAEDAWZalTZs2qVevXsrJsd9vnlVB+9dff60+ffp4XQwAAAAAQJb56quvtPvuu9v+u6wK2gsKCiQ17KzCwkKPSwMAAAAACLra2lr16dOnMR61K6uC9mhKfGFhIUE7AAAAACBjUh2izUR0AAAAAAAYiqAdAAAAAABDEbQDAAAAAGAognYAAAAAAAxF0A4AAAAAgKEI2gEAAAAAMBRBOwAAAAAAhiJoBwAAAADAUATtAAAAAAAYiqAdAAAAAABDEbQDAAAAAGAognYAAAAAAAxF0A4AAAAAgKEI2gEAAAAAMBRBOwAAAAAAhiJoBwAAAADAUATtAAAAAAAYiqAdAAAAAABDEbQDAAAAAGAognYAAAAAAAzVzusCAECQ1UcsLV25QWs3bVPPgnwN7ddVuTkhr4sFAAAAnyBoBwCXlFVUa+qCSlXXbGt8rTicrynjSzS2tNjDkgEAAMAvSI8HABeUVVRr8txlzQJ2SVpTs02T5y5TWUW1RyUDAACAnxC0A4DD6iOWpi6olBXjd9HXpi6oVH0k1hYAAADADwjaAcBhS1duaNXD3pQlqbpmm5au3JC5QgEAAMCXCNoBwGFrN8UP2FPZDgAAANmLoB0AHNazIN/R7QAAAJC9CNoBwGFD+3VVcThf8RZ2C6lhFvmh/bpmslgAAADwIYJ2AHBYbk5IU8aXSFKrwD3685TxJazXDgAAgIQI2gHABWNLizXz7CEqCjdPgS8K52vm2UNYpx0AAABJaed1AQAgqMaWFmtMSZGWrtygtZu2qWdBQ0o8PewAAABIFkE7ALgoNyekYQO6eV0MAAAA+BTp8QAAAAAAGIqgHQAAAAAAQxG0AwAAAABgKIJ2AAAAAAAMRdAOAAAAAIChCNoBAAAAADAUQTsAAAAAAIYiaAcAAAAAwFAE7QAAAAAAGIqgHQAAAAAAQxG0AwAAAABgKIJ2AAAAAAAMRdAOAAAAAIChCNoBAAAAADAUQTsAAAAAAIYiaAcAAAAAwFAE7QAAAAAAGIqgHQAAAAAAQ/k2aL/zzjsVCoV0xRVXeF0UAAAAAABc4cug/b333tMjjzyiQYMGeV0UAAAAAABc47ugffPmzfrZz36mWbNmqUuXLl4XBwAAAAAA1/guaL/kkks0btw4jR49OuG2dXV1qq2tbfYPAAAAAAC/aOd1AeyYN2+eli1bpvfeey+p7adPn66pU6e6XCoAAAAAANzhm572r776Spdffrn+/Oc/Kz8/P6m/ueGGG1RTU9P476uvvnK5lAAAAAAAOCdkWZbldSGS8fzzz+vEE09Ubm5u42v19fUKhULKyclRXV1ds9/FUltbq3A4rJqaGhUWFrpdZAAAAABAlks3DvVNevzRRx+tjz76qNlr559/vvbdd19dd911CQN2AAAAAAD8xjdBe0FBgUpLS5u91qlTJ3Xr1q3V6wAAAAAABIFvxrQDAAAAAJBtfNPTHsubb77pdREAAAAAAHANPe0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwVDuvCwAAAAAATdVHLC1duUFrN21Tz4J8De3XVbk5Ia+LBXiCoB0AAACAMcoqqjV1QaWqa7Y1vlYczteU8SUaW1rsYckAb5AeDwAAAMAIZRXVmjx3WbOAXZLW1GzT5LnLVFZR7VHJAO8QtAMAAADwXH3E0tQFlbJi/C762tQFlaqPxNoCCC6CdgAAAACeW7pyQ6se9qYsSdU127R05YbMFQowAEE7AAAAAM+t3RQ/YE9lOyAoCNoBAAAAeK5nQb6j2wFBQdAOAAAAwHND+3VVcThf8RZ2C6lhFvmh/bpmsliA5wjaAQAAAHguNyekKeNLJKlV4B79ecr4EtZrR9YhaAcAAABghLGlxZp59hAVhZunwBeF8zXz7CGs046s1M7rAgAAAABA1NjSYo0pKdLSlRu0dtM29SxoSImnhx3ZiqAdAAAAgFFyc0IaNqCb18UAjEDQDiShPmLR2gsAAAAg4wjagQTKKqo1dUGlqmt+WBO0OJyvKeNLGFcFAAAAwFVMRAe0oayiWpPnLmsWsEvSmpptmjx3mcoqqj0qGQAAAIBsQNAOxFEfsTR1QaWsGL+LvjZ1QaXqI7G2AAAAAID0EbQDcSxduaFVD3tTlqTqmm1aunJD5goFAAAAIKsQtANxrN0UP2BPZTsAAAAAsIugHYijZ0G+o9sBAAAAgF0E7UAcQ/t1VXE4X/EWdgupYRb5of26ZrJYAAAAALIIQTsQR25OSFPGl0hSq8A9+vOU8SWs1w4AAADANQTtQBvGlhZr5tlDVBRungJfFM7XzLOHsE47AAAAAFe187oAgOnGlhZrTEmRlq7coLWbtqlnQUNKPD3sAAAAANxG0A4kITcnpGEDunldDAAAAABZhvR4AAAAAAAM5ZugfebMmRo0aJAKCwtVWFioYcOG6f/+7/+8LhYAAAAAAK7xTdC+++67684779QHH3yg999/X6NGjdKECRP08ccfe100AAAAAABcEbIsy/K6EKnq2rWr7rrrLl144YVJbV9bW6twOKyamhoVFha6XDoAAAAAQLZLNw715UR09fX1euqpp7RlyxYNGzYs7nZ1dXWqq6tr/Lm2tjYTxQMAAAAAwBG+SY+XpI8++ki77rqr8vLydPHFF+u5555TSUlJ3O2nT5+ucDjc+K9Pnz4ZLC0AAAAAAOnxVXr89u3btWrVKtXU1Ojpp5/WH//4R7311ltxA/dYPe19+vQhPR4AAAAAkBHppsf7KmhvafTo0RowYIAeeeSRpLZnTDsAAAAAIJPSjUN9lR7fUiQSadaTDgAAAABAkPhmIrobbrhBxxxzjPbYYw9t2rRJf/nLX/Tmm2/q5Zdf9rpoAAAAAAC4wjdB+9q1a3Xuueequrpa4XBYgwYN0ssvv6wxY8Z4XTQAAAAAAFzhm6D9scce87oIAAAAAABklG+CdgAAsk19xNLSlRu0dtM29SzI19B+XZWbE/K6WAAAIIMI2gEAMFBZRbWmLqhUdc22xteKw/maMr5EY0uLPSwZAADIJF/PHg8AQBCVVVRr8txlzQJ2SVpTs02T5y5TWUW1RyUDAACZRtAOAIBB6iOWpi6olBXjd9HXpi6oVH0k1hYAACBoCNoBADDI0pUbWvWwN2VJqq7ZpqUrN2SuUAAAwDME7QAAGGTtpvgBeyrbAQAAfyNoBwDAID0L8h3dDgAA+BtBOwAABhnar6uKw/mKt7BbSA2zyA/t1zWTxQIAAB4haAcAwCC5OSFNGV8iSa0C9+jPU8aXsF47AABZgqAdAADDjC0t1syzh6go3DwFviicr5lnD2GddgAAskg7rwsAAABaG1tarDElRVq6coPWbtqmngUNKfH0sAMAkF0I2gEAMFRuTkjDBnTzuhgAAMBDBO1wVH3EolcIAAAAABxC0A7HlFVUa+qCSlXX/LB2cHE4X1PGlzD+EgAAAABSwER0cERZRbUmz13WLGCXpDU12zR57jKVVVR7VDIAAAAA8C+CdqStPmJp6oJKWTF+F31t6oJK1UdibQEAAAAAiIegHWlbunJDqx72pixJ1TXbtHTlhswVCgAAAAACgKAdaVu7KX7Ansp2AAAAAIAGBO1IW8+CfEe3AwAAAAA0IGhH2ob266ricL7iLewWUsMs8kP7dc1ksQAAAADA9wjakbbcnJCmjC+RpFaBe/TnKeNLWK8dAAAAAGwiaIcjxpYWa+bZQ1QUbp4CXxTO18yzh7BOOwAAQJrqI5YWV63X/PLVWly1npV5gCzRzusCIDjGlhZrTEmRlq7coLWbtqlnQUNKPD3sAAAA6SmrqNbUBZXNVuwpDudryvgSOkeAgAtZlpU1TXS1tbUKh8OqqalRYWGh18UBAAAAEiqrqNbkucvUstIe7RYhqxEwW7pxKOnxAAAAgKHqI5amLqhsFbBLanxt6oJKUuWBACNoBwAAAAy1dOWGZinxLVmSqmu2aenKDZkrFICMImgHAAAADLV2U/yAPZXtAPgPQTsAAABgqJ4F+Yk3srEdAP8haAcAAAAMNbRfVxWH8xVvLZ6QGmaRH9qvayaLBSCDCNoBAAAAQ+XmhDRlfIkktQrcoz9PGV/CErtAgBG0AwAAAAYbW1qsmWcPUVG4eQp8UTif5d6ALNDO6wIAAAAAaNvY0mKNKSnS0pUbtHbTNvUsaEiJp4cdCD6CdgAAAMAHcnNCGjagm9fFAJBhpMcDAAAAAGAognYAAAAAAAxF0A4AAAAAgKEI2gEAAAAAMBRBOwAAAAAAhiJoBwAAAADAUATtAAAAAAAYiqAdAAAAAABDtfO6AAAA/6mPWFq6coPWbtqmngX5Gtqvq3JzQl4XCwAAIHAI2gEAtpRVVGvqgkpV12xrfK04nK8p40s0trTYw5IBAAAED+nxAICklVVUa/LcZc0CdklaU7NNk+cuU1lFtUclAwAACCaCdgBAUuojlqYuqJQV43fR16YuqFR9JNYWAAAASAVBOwAgKUtXbmjVw96UJam6ZpuWrtyQuUIBAAAEHEE7ACApazfFD9hT2Q4AAACJEbQDAJLSsyDf0e0AAACQGEE7ACApQ/t1VXE4X/EWdgupYRb5of26ZrJYAAAAgUbQDgBISm5OSFPGl0hSq8A9+vOU8SWs1w4AAOAggnYAQNLGlhZr5tlDVBRungJfFM7XzLOHsE47AACAw9p5XQAAgL+MLS3WmJIiLV25QWs3bVPPgoaUeHrYAQAAnEfQDgCwLTcnpGEDunldDAAAgMAjPR4AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAodp5XQAAAAAAyamPWFq6coPWbtqmngX5Gtqvq3JzQl4XC4CLCNoBAAAAHyirqNbUBZWqrtnW+FpxOF9TxpdobGmxhyUD4CbS4wEAAAAX1EcsLa5ar/nlq7W4ar3qI1bK71VWUa3Jc5c1C9glaU3NNk2eu0xlFdXpFheAoehpBwAAABzmZK94fcTS1AWVihXyW5JCkqYuqNSYkiJS5YEAoqcdAAAAcJDTveJLV25o9V5NWZKqa7Zp6coNqRQXgOEI2gEAAACHJOoVlxp6xe2kyq/dFD9gT2U7AP7im6B9+vTp+tGPfqSCggL17NlTJ5xwgj777DOviwUAAAA0cqNXvGdBvqPbAfAX3wTtb731li655BItWbJEr776qnbs2KGf/OQn2rJli9dFAwAAACS50ys+tF9XFYfzFW+0ekgN4+WH9uua9HsC8A/fTERXVlbW7Oc5c+aoZ8+e+uCDD/TjH//Yo1IBAAAAP3CjVzw3J6Qp40s0ee4yhaRmqffRQH7K+BImoQMCyjc97S3V1NRIkrp2jd+iWFdXp9ra2mb/AMALTi77AwAwl1u94mNLizXz7CEqCjcP9ovC+Zp59hDWaQcCLGRZlu9qjpFIRMcff7w2btyoRYsWxd3ulltu0dSpU1u9XlNTo8LCQjeLCACNnFz2BwBgvujs8VLsXvF0guz6iKWlKzdo7aZt6lnQEPzTww6Yrba2VuFwOOU41JdB++TJk/V///d/WrRokXbfffe429XV1amurq7x59raWvXp04egHUDGRCtuLW+0TlTcAADmosEWQFS6QbtvxrRHXXrppfrb3/6mt99+u82AXZLy8vKUl5eXoZIBQHOJlv0JqWHZnzElRfSSAEDAjC0t1piSInrFAaTNN0G7ZVn6f//v/+m5557Tm2++qX79+nldJABok51lf4YN6Ja5ggEAMiI3J8T9HUDafBO0X3LJJfrLX/6i+fPnq6CgQGvWrJEkhcNh7bLLLh6XDgBac2PZHwAAAGQX38weP3PmTNXU1GjkyJEqLi5u/Pe///u/XhcNAGJyY9kfAAAAZBff9LT7cL48AFkuuuzPmpptMce1h9SwVI/dZX8AAACQPXzT0w4AfpObE9KU8SWS1Gq93ujPU8aXMCkRAAAA4iJoBwAXjS0t1syzh6go3DwFviicz3JvAAAASMg36fEA4Fcs+wMAAIBUEbQDQAaw7A8AeKM+YtFoCsDXCNphGw8/AADgB2UV1Zq6oFLVNT8srVkczteU8SUMTwLgGwTtsIWHHwAA8IOyimpNnrus1eoda2q2afLcZcwrAsA3mIgOSYs+/JoG7NIPD7+yimqPSgYAAPCD+oilqQsqYy63GX1t6oJK1UdYUhiA+QjakRQefgCAWOojlhZXrdf88tVaXLWe5wCMsHTlhladDE1Zkqprtmnpyg2ZKxQApIj0eCTFzsOPybYAIDswZAqmWrspfp0lle0AwEv0tCMpPPwAAE0xZAom61mQ7+h2AOAlgnYkhYcfACCKIVMw3dB+XVUczle8tW1CasgKGdqvayaLBQApIWhHUnj4AQCiGC8M0+XmhDRlfIkktaq7RH+eMr6EJWsB+AJBO5LCww8AEMWQKfjB2NJizTx7iIrCzbMAi8L5LPcGwFeYiA5Jiz78Wk46VMSkQwCQVRgyBb8YW1qsMSVFWrpyg9Zu2qaeBQ1ZgXQyAPATgnbYwsMPABAdMrWmZlvMce0hNTToMmQKJsjNCbGyDQBfI2iHbTz8ACC7RYdMTZ67TCGpWeDOkCkAAJzFmHYAAGAb44UBAMgMetoBAEBKGDIFAID7CNoBAEDKGDIFAIC7bKfHf/XVV/rPf/7T+PPSpUt1xRVX6NFHH3W0YAAAAAAAZDvbQftZZ52lN954Q5K0Zs0ajRkzRkuXLtWNN96oW2+91fECAvBWfcTS4qr1ml++Wour1qs+EmuuaAAAAABusJ0eX1FRoaFDh0qS/vrXv6q0tFTvvPOOXnnlFV188cW6+eabHS8kAG+UVVRr6oJKVddsa3ytOJyvKeNLmGQKAAAAyADbPe07duxQXl6eJOm1117T8ccfL0nad999VV1d7WzpAHimrKJak+cuaxawS9Kamm2aPHeZyiq43gEgG5GBBQCZZbunff/999fDDz+scePG6dVXX9W0adMkSV9//bW6dWMiGiAI6iOWpi6oVKxqmKWGdZinLqjUmJIiZokGgCxCBhYAZJ7tnvYZM2bokUce0ciRI3XmmWfqwAMPlCS98MILjWnzAPxt6coNrXrYm7IkVdds09KVGzJXKACAp8jAAgBv2O5pHzlypNatW6fa2lp16dKl8fVf/OIX6tixo6OFA+CNtZviB+ypbAcA8DcysADAO7Z72iUpNze3WcAuSX379lXPnj0dKRQAb/UsyHd0OwCAv5GBBQDeSaqn/aCDDlIolFyr6bJly9IqEADvDe3XVcXhfK2p2RazVyUkqSicr6H9uma6aIBx6iOWlq7coLWbtqlnQcN1QU+je9jf3iADCwC8k1TQfsIJJ7hcDAAmyc0Jacr4Ek2eu0whqVngHq0aTxlfQkUZWY9JuTKL/e0dMrAAwDshy7KyZp2O2tpahcNh1dTUqLCw0OviAMajggzEF52Uq+VDNNqUNfPsIVwnDmJ/e6s+YmnEjIUJM7AWXTeKBl0AaCHdONT2RHQAssfY0mKNKSkiFRVogUm5Mov97T0ysADAO7Ynoquvr9fdd9+toUOHqqioSF27dm32D0Cw5OaENGxAN00Y3FvDBnSjQgaISbkyjf1thrGlxXrwrIPUpVP7Zq8XhfPJdAAAF9kO2qdOnap77rlHp59+umpqanTVVVfppJNOUk5Ojm655RYXiggAZqiPWFpctV7zy1drcdV61UeyZnQRWmBSrsxif5uhrKJa0178RBu27Gh8rWunDrppHEOmAMBNttPj//znP2vWrFkaN26cbrnlFp155pkaMGCABg0apCVLluiyyy5zo5xAWphtGOlifD+aYlKuzGJ/ey/enALfbdmuS/6yTDNz6GkHALfYDtrXrFmjAw44QJK06667qqamRpJ03HHH6aabbnK2dIADCLaQrniV1TU12zR57jLSQrMQyyJmVhD3t58ak5lTAAC8ZTs9fvfdd1d1dbUkacCAAXrllVckSe+9957y8vKcLR2Qpmiw1XIsZDTYKquo9qhk8ItElVWpobJKqnx2iU7KJf0wCVcUk3I5L2j7u6yiWiNmLNSZs5bo8nnlOnPWEo2YsdDYZxJzCgCAt2wH7SeeeKJef/11SdL/+3//TzfddJP22msvnXvuubrgggscLyCQKoItOIHKKuIZW1qsmWcPUVG4eUo2k3K5Iyj724+NycwpAJiBuXWyl+30+DvvvLPx/6effrr22GMPLV68WHvttZfGjx/vaOGAdNgJtoYN6Ja5gsFXqKyiLSyLmFl+399+TTNnTgHAewz3zG5pr9M+bNgwDRs2zImyAI4i2IITqKwikeiyiMgMP+9vvzYmB3FOAcBPmFsHtoP2P/3pT23+/txzz025MICTCLbgBCqrAJzi18bk6JwCk+cuU0hqdi/045wCgJ/4NUMHzrIdtF9++eXNft6xY4e2bt2qDh06qGPHjgTtMAbBFpxAZRWAU/zcmBydU6Blem4R6bmAq/yaoQNn2Q7av/vuu1avLV++XJMnT9avfvUrRwoFOIFgC06hsgrACd9tqUu4TbHBjcl+n1MA8CO/ZujAWWmPaZekvfbaS3feeafOPvtsffrpp068JeCItoKtm8aVKLxLB80vX21cxcNP6/dmCyqrANJRH7E07cVPEm530zizG5P9PKcA4Ed+ztCBcxwJ2iWpXbt2+vrrr516O8AxsYKt77bUadqLZs7Ayeyg5qKyCiBViVJco7p06pCB0gDwC4Z7QkohaH/hhRea/WxZlqqrq/XAAw9o+PDhjhUMcFLTYKusolqX/OVDI2fgZHZQwBmZzlYhOwaJkOIKIBUM94SUQtB+wgknNPs5FAqpR48eGjVqlH73u985VS7AFSbPwGly2QA/yXS2CtkxSAYprgBSxdw6sB20RyIRN8oBZITJM3CaXDbALzKdrUJ2DJJFiiuAdDC3TnbL8boAQCaZnJ5octkAP0iUrSI1ZKvUR2JtYf7nwd+iKa7SDymtUaS4AkhGdLjnhMG9NWxAN+4XWSSpnvarrroq6Te85557Ui4M4DaT0xNNLhvgB5nOViE7BnaR4goASEVSQfuHH37Y7Odly5Zp586d2meffSRJn3/+uXJzc3XwwQc7X0LAQSanJ5pcNsAPMp2tQnYMUkGKa+qY8BFAtkoqaH/jjTca/3/PPfeooKBATzzxhLp06SJJ+u6773T++efriCOOcKeUgENMnoHT5LIBfpDpbBWyY5Aqlo+0jwkfAWQz22Paf/e732n69OmNAbskdenSRbfddhuzx8MXoumJReHmFemicL7nk0aZXDbAdNFslXjNWiE1VPKdylbJ9OcBpqmPWFpctV7zy1drcdV61+ZviE742HI4SnTCx7KKalc+FwBMYXv2+NraWn377betXv/222+1adMmRwoFuM3k9ESTywaYLNPZKmTHpIdUZ3/LVM83y6EiinsGslnIsixbzaLnnnuu/v73v+t3v/udhg4dKkl699139atf/UpHHHGEnnjiCVcK6oTa2lqFw2HV1NSosLDQ6+IAAFzAOu3ms7vPqKybJd5Sh9Ej4mRm2OKq9Tpz1pKE2z056TCGHAQY91n4XbpxqO2gfevWrbrmmmv0+OOPa8eOHZKkdu3a6cILL9Rdd92lTp062S5EphC0A0B2yHSQR1CZPLsBH5V1s9RHLI2YsTDuygnRSVMXXTfKkWtgfvlqXT6vPOF2958xWBMG907782CeTDYSAW7JeNAetWXLFlVVVUmSBgwYYHSwHkXQDgCAd+wGfFTWzZPpnm962rNbphuJALekG4fanoguqlOnTho0aJAGDRrki4AdAAB4y87a9onGMksNY5ndmvwMsWV6qUMmfMxudu4ZQJAlNRHdSSedpDlz5qiwsFAnnXRSm9s+++yzjhQMAAAEi52Az05lnR7WzMn0UodM+JjdMt1IBJgqqaA9HA4rFAo1/h8AAMAuOwEflXUzRXu+19Rsi5kFEU1XdrLnO7ocasu5DYoyOLcB81Z4I9ONRICpkgraZ8+eHfP/AAAAybIT8CWb7kplPbO86vnOxHKo8QJzJkP0jheNRICJbE9E9/3338uyLHXs2FGS9O9//1vPPfecSkpK9JOf/MSVQjqFiegAIHn0LMEN0cnlpNgBX3RyuegEVIkq60xA5Y2gBbLxvs/xBxbr0bdXMhmih5K9ZwAmy/js8T/5yU900kkn6eKLL9bGjRu1zz77qEOHDlq3bp3uueceTZ482XYhMoWgHQBiaxmgf7dlu6a9GJwKOcySbMBHZd1sQWnYi7dKQSI0HGVO0BqJkH0yHrR3795db731lvbff3/98Y9/1B/+8Ad9+OGHeuaZZ3TzzTfrk08+sV2ITCFoB4DWYlWGYiFQgpOSDfiorMNNiZYUSwbLzWVGUBqJkJ3SjUOTGtPe1NatW1VQUCBJeuWVV3TSSScpJydHhx12mP7973/bLgAAHkTwjp0eJksNgfvUBZUaU1LEOYq05OaEkgp0MjGWGdkr0SoFyWAyxMxI9p4BBJHtoH3gwIF6/vnndeKJJ+rll1/WlVdeKUlau3YtvddACuhFglfaWgc7HpbZgheorMMtTgTcTIYIwG05dv/g5ptv1jXXXKO+fftq6NChGjZsmKSGXveDDjrI8QICQRbt5WzZyr+mZpsmz12msopqj0qGbJBODxM9SwCCIJ2AO6SGRnZmLgfgNttB+ymnnKJVq1bp/fff18svv9z4+tFHH617773X0cIBQdZWL2f0takLKlUfsTs1DpCcdAJvepYABEF0SbFEgy1a/t7N5e0AoCXbQbskFRUVqaCgQK+++qq+//57SdKPfvQj7bvvvo4WDgiyRL2cTdOQATekEnjTs5Q96iOWFlet1/zy1VpctZ4GRARSdN15KXZgHpJ00Y/7qSjc/H5ZFM5nUk4AGWN7TPv69et12mmn6Y033lAoFNLy5cvVv39/XXjhherSpYt+97vfuVFOIHCS7eUkDfkHTNjnrGgPU7x1sFuiZyl7MNcGssnY0mLNPHtIq3O+qMk5f+3Y/Xj+APCM7aD9yiuvVPv27bVq1Srtt99+ja+ffvrpuuqqqwjagSQl28tJGnIDggjnRXuYJs9dppCUMHAvYn9nhXgrCkTn2qB3EUGUaJUCJkME4CXbQfsrr7yil19+Wbvvvnuz1/faay+WfANsSNTLGVJDkEQaMkGEm+L1MBWH83XTuP3UpVMePUse8SKzJNFcGyz5hyAjMAdgKttB+5YtW9SxY8dWr2/YsEF5eXmOFArIBm31cpKG/AOCCPexDrZ5vMossTPXBsGNdxgqBADZxfZEdEcccYT+9Kc/Nf4cCoUUiUT029/+VkcddZSjhWvp7bff1vjx49WrVy+FQiE9//zzrn4e4LZoLycT3MTHhH2ZEe1hmjC4t4YN6EYA4CEvl4Jkrg3zlVVUa8SMhTpz1hJdPq9cZ85aohEzFhq/RCgTGwJA6mz3tP/2t7/V0Ucfrffff1/bt2/Xtddeq48//lgbNmzQO++840YZG23ZskUHHnigLrjgAp100kmufhaQKfRyto0gAtnE68wS5towm1+HCjEnCQCkx3bQXlpaqs8//1wPPPCACgoKtHnzZp100km65JJLVFzs7o33mGOO0THHHOPqZwBeYBxdfAQRyCZep6cz14a5vG7QSZVfGxoAwCS2g3ZJCofDuvHGG5u9tm3bNt1999265pprHCmYE+rq6lRXV9f4c21trYelAZAKgghkk1QzS5wa48xcG4l5NZ7c6wadVPi1oQGA95i7ozlbQfu3336rd999Vx06dNDRRx+t3Nxc7dixQw899JCmT5+unTt3GhW0T58+XVOnTvW6GADSQBCBbJJKZonTqcfJrFmdrbxM8/bjUCE/NjQA8B5DalpLOmhftGiRjjvuONXW1ioUCumQQw7R7NmzdcIJJ6hdu3a65ZZbNHHiRDfLatsNN9ygq666qvHn2tpa9enTx8MSAUgFQQSyhd3MErdSj5lrozWv07z9OFTIjw0NcA49pUiF1/daUyUdtP/mN7/Rscceq1//+td64okn9Lvf/U4nnnii7rjjDp1yyiluljFleXl5LEMHBARBRGxUioLFTmaJ26nHzLXxAxPSvP04VMiPDQ1wRjb3lPJcTp0J91pTJR20f/TRR3rooYdUUlKiW2+9Vffcc49++9vfasKECW6WD0CWSOYhRxDRXDZXioIs2cwSUo8zx4R97cehQn5saED6srmnlOdyeky415oq6aD9u+++U/fu3SVJu+yyizp27KjS0lLXChbL5s2btWLFisafV65cqfLycnXt2lV77LFHRssCwDkmPOT81jKezZWibJBMZsmrlWuSei9Sj9NnSpq334YK+bGhAenJ5p5SnsvpM+VeayJbE9FVVlZqzZqGSoJlWfrss8+0ZcuWZtsMGjTIudK18P777+uoo45q/Dk6Xn3ixImaM2eOa58Ld/ktWIKzTHjImdBoYEc2V4qySVuZJfURS8+Xf53U+5B6nD6T0rz9NlTIbw0NSE+29pTyXHaGSfda09gK2o8++mhZ1g+n43HHHSdJCoVCsixLoVBI9fX1zpawiZEjRzb7fPif34IlOMuEh5wJjQZ2ZWulCD9YunKDNmzZnnC7rp3ak3rsANPSvP02VMhvDQ1IXbb2lPJcdoZp91qTJB20r1y50s1y4L+yqdfZj8ESnOX1Q86ERoNUZGulCD9I9tieOLi3UeeuCVJ5zpLmnT6/NTQgNdnaU2r6c9kv8QX32viSDtr33HNPN8sBZVevs1+DJZP55YbclNcPOa8bDVKVrZUi/CDZYzu6pMjlkvhLOs9Z0ryBxLK1p9Tk57Lf4gvutbHZSo+He7Kt19mvwZKp/HZDjvL6Ied1o0GqsrVShB9kyzngZGOkE89Z0ryBtmVrT6mp92S/xhfca1vL8boASNzrLDX0OtdHgjOe36/BkomiN+SWjSDRG3JZRbVHJUss+pCLdwsOqaHxwa2HnNeNBqmKVooktdp3Qa4U4QfZcA6UVVRrxIyFOnPWEl0+r1xnzlqiETMWpnRPc/I5G03znjC4t4YN6ObrfQy4IdpTWhRu/uwsCucbGySmy8R7st/jC+61zRG0G8BOr3NQ+DVYckJ9xNLiqvWaX75ai6vWp3WzTOeG7GQ5UuX1Q87rRoN0ZGOlyFReXUtBPgecbozMxucs4KWxpcVadN0oPTnpMN1/xmA9OekwLbpulK/vS4mYdk/mvhcspMcbIBt7nU1NI3Kb02nsqQ4zMCmd3suxS35P43MifcyPcyGYxOtrKYgphG7MeZKNz1nAa9k4+aBJ92Tue8FC0G6AbOx19nuwlIq2xhVdPHeZrhy9l/p272TrBp/KDdmJ8U1OB3pePuT8PuFJOpUirwNOvzNlrGDQKsZuzHmSjc9ZAN4w5Z7MfS9YbAft33zzja655hq9/vrrWrt2bat1091cpz2osrXX2e/Bkh3JpLHf+9ryxteSDZzs3pCTTadvqwfLrUDPy4ecSS3jmWJKwOlXrIDhHjd6h7L1OQsge3HfCxbbQft5552nVatW6aabblJxcbFCISoj6crGXueobAmWEvUctZRs4GT3hpxMOaprtumBhct1+ei9W/0uyIGeKS3jmUDAmT5WwHCPG71D2fycBZCduO8Fi+2gfdGiRfr73/+uwYMHu1Cc7JVNvc4tZUOwZHe8ULKBk90bcrLluPe15dqnqKDZeUegFxwEnOljrKB73OodyubnrN8w1wbgDO57wWE7aO/Tp0+rlHg4I1t6nbNRKuOFkg2c7NyQ7ZSjZQBOoBccBJzpY6yge9zsHfL7czYbglnm2gCc5ff7HhrYDtrvu+8+XX/99XrkkUfUt29fF4qU3bKh1zkbJeo5aksygVOyN+RoOZJJ1W8ZgBPoBYedgDMbgoRUMFbQXW72Dvn1OZsNwWyQh2ABXvLrfQ8/sB20n3766dq6dasGDBigjh07qn379s1+v2EDa/0BLbXVc5RIsgFWMjfkaDkunrssqfdsGoDTsxgcyQac322p04gZCwMdJKQi2pBxbGmRHnvny1a/Z6ygM+gd+kE2BLMMwfoBjaUAWkqppx2AffF6juJJp6eurQf+2NJiXTl6b9372ucJ36dpAE7PYnAkk358/IHFuuQvHwY6SEhFrN7OnJAUabKjwh3b6/zD+2lMSZEHJXSHV0EEvUP+CGadOD8YgtUgGzIqANhnO2ifOHGiG+UAskLLnqMv123Vff8Nnp0at5nMA//SUQP15NJ/a01tXcz3iBWAB2kWUnox2k4/vmlciaa9aHaQ4IV4vZ3RaV46dsjV1u312rh1h+597XPNe29VICraBBHeMj2Yder8YAhWdmRUAEiN7aBdaliL/fnnn9cnn3wiSdp///11/PHHKzc319HCAUHUsudon6JdHRu3mewDPzcnpFuO31+T/5smn2wAHoRZSAlAfhAv/dj0IMELiXo7JWnr9vpmrwehok0Q4T2Tg1knz49sH4Llh4wKAN6xHbSvWLFCxx57rFavXq199tlHkjR9+nT16dNHL774ogYMGOB4IYF4MtVj6ubnODVu0+4DP9UA3M/jTAlAWouVfmxykOCVRA0Zsfi9ok0QYQZTg1mnz49sH4JFYymAttgO2i+77DINGDBAS5YsUdeuDTfO9evX6+yzz9Zll12mF1980fFCArFkqsc0E5/jxLjNVB74qQbgfhxnSgCSPFODBC+l2kDh54o2QYQZTA1mnT4/gjQEKxVBaCxl6BngHttB+1tvvdUsYJekbt266c4779Tw4cMdLRwQT6Z6TP3UM5vqAz+ZADwID2ICkOSZGiR4Kd0GCpMr2vFkMogIwj3GLaYGs26cH0EYgpUqvzeWMvQMcJftoD0vL0+bNm1q9frmzZvVoUMHRwoFtCVTPaZ+65l164EflAdxEHoxMsXUIMFLiRoyEjG1ot2WTAURQbnHuCHamFG3M6IrRu+tJ5eu0ppaM4JZt84PPw/BSoefG0v91MEB+FWO3T847rjj9Itf/ELvvvuuLMuSZVlasmSJLr74Yh1//PFulBFoxk6PqR8+xynRB368ak1IDRVhOw/86IO45X6IPojLKqpTL3CG+b0XI9OiPV5F4eb7oyicn5UVsGhDhqS411gsqVx3pnDjntJSkO4xTiurqNaIGQt15qwlunxe+X+X6bR05ei9dP8Zg/XkpMO06LpRnl2Lbp4f0QywCYN7a9iAboEP2KW27zEmN5YmM0nn1AWVqo+k0twJIMp20P773/9eAwYM0LBhw5Sfn6/8/HwNHz5cAwcO1P333+9GGYFmMtVj6reeWacf+EF7EGciAAmasaXFWnTdKD056TAjggSvxWvI6NKxvSR/VbST4XYQ4Zd7TH3E0uKq9ZpfvlqLq9ZnpDzxGjO+qa3Tfa8tV167HM+DWb8GmSbzY2Op3zo4AL+ynR7fuXNnzZ8/XytWrGhc8m2//fbTwIEDHS8cEEumekz92DPr5HjAoI0BJ+U7NX6cdNBN8VJ3X61cE8hxuG6OMfbDPcaL1H0/Dc3K5jHobvHb8AC/dXAAfpXSOu2SNHDgQA0cOFD19fX66KOP9N1336lLly5Olg2IKVPjvvw6vsypB34QH8RUMOGEWA0Zfqto2+HWdzP9HuPVOF0/NGY0FeRz3yt+aiz1YwcH4Ee2g/YrrrhCBxxwgC688ELV19fryCOP1D/+8Q917NhRf/vb3zRy5EgXign8IFM9pn7umXXigR/UBzEVTLjFTxVtu9z4bibfY7zs7Ta9MSOWIJ/7aJtfOzgAv7E9pv3pp5/WgQceKElasGCBvvjiC3366ae68sordeONNzpeQCCWTI378uP4MqcEeQy4CZMceTFOFjCJyfcYL8fpmtyYAbTE3AbItGytP9nuaV+3bp2KiookSS+99JJOO+007b333rrggguYiA4Zlake02ztmfVzpoHpWOIKbvLLmucm32O87O2m5xJ+w9AzZEo2159sB+277babKisrVVxcrLKyMs2cOVOStHXrVuXm5jpeQKAtmUrJ80PqnxsVdR7EzmM9W7jJhAqNnXuRqfcYL3u7TW7MAOLJ1g4OZE62159ClmXZyim45ZZbdN9996m4uFhbt27V559/rry8PD3++OOaNWuWFi9e7FZZ01ZbW6twOKyamhoVFhZ6XRzAMW5X1P3Sc9cWE75DfcTSiBkL46bdRnvQFl03ynf7F96LV6GJnkmZqNCkei8y4fpsWZ4RMxYm7O1281o1oQEGAEwQhPpTunGo7aBdkp555hmtWrVKp556qnbffXdJ0hNPPKHOnTtrwoQJtguRKQTtCCITKuqmM6Xyu7hqvc6ctSThdk9OOsz4zA6/MC0YdIsJFZqg3Yui30eK3dudie+TLecv/INzEl4IQv0p3TjUVnr8jh07NHbsWD388MM6+eSTm/1u4sSJtj8cQHr8tJ6vV0xKp/LjrNB+5nVjTSYrt14vExbEe5EJqft+GJqF7OH1PRXZi/qTzaC9ffv2+te//uVWWQDY5HVF3XSmBRLMCp05XjfWZLpy63WFJqj3IhPG6dKzCRN4fU9FdqP+lMKSb2effbYee+wxN8oCwCavK+qm83LZplhMXuIqSBI11kgNjTVuLRMTrdy2PPeilduyimrHP9PrCk2Q70VeLhFZVlGtETMW6sxZS3T5vHKdOWuJRsxY6Mo5BMTj9T0VoP6UwuzxO3fu1OOPP67XXntNBx98sDp16tTs9/fcc49jhQPQNq8r6qYzLZBgVujM8LLX16vsDq+XCUvnXkRPcmz0bMIUQc2kgX9Qf0ohaK+oqNCQIUMkSZ9//nmz34VCwd1RgIm8rqibzsRGDRPGyQadl401XlRuo0HvMaVFevydLz2p0KR6L2KMbGymDe1BdjOtARzZKdvrT7aD9jfeeMONcgBIAS2PbTO1UcOEcbJ+11bvrJeNNZmu3MYKekMhqem6MJmo0KRyL6InOT56NmESExvAkZ2yuf5kO2gHYJZsb3lsi8mNGolmhSZlOL5EvbNeNtZksnIbL+iNDiu9cHhfjS4pinnuuHF+2bkX0ZPcNno2YRJTG8CRnbJ1VY2Ugvb3339ff/3rX7Vq1Spt37692e+effZZRwoGIHnZ2vKYTODhx0YNUobjS7Z31qvGmkxVbtsKeqOf81LFGv16XOvv6eb5ley9iJ7kttGzCZOY3AAOZAvbQfu8efN07rnn6qc//aleeeUV/eQnP9Hnn3+ub775RieeeKIbZQSQhGxrebQTePipUYOU4fjs9M561ViTqcptqkFvJs6vZO5F9CS3jZ5NmMaPDeBAkNgO2u+44w7de++9uuSSS1RQUKD7779f/fr100UXXaTiYi5YAO5LJfDwQ6MGKcNtsxuoetFYUx+xFN6lgy4Y3lfPla/Whi07Gn/nZOU2laDXpPOre6e8pLbL1p5kejZhIj81gANBYztor6qq0rhx4yRJHTp00JYtWxQKhXTllVdq1KhRmjp1quOFBIAokwIPp5Ey3LZUAtVMNtbEyv7o2qmDThjcS2PijC1PVSrp06acX2UV1brlhcqE23Xp2D6re5Lp2YSJ/NAADgSR7aC9S5cu2rRpkySpd+/eqqio0AEHHKCNGzdq69atjhcQAJoyJfBwAynDbTN5nG+87I/vtmzX7He+dLw3KpX0aRPOr3j7KZZktkmWXyd2pGcTACClELT/+Mc/1quvvqoDDjhAp556qi6//HItXLhQr776qo4++mg3yggAjUwIPNxiclBqAlPH+XqR/ZFK+rTX51eiyfNa2rh1hyONb36f2JGeTQCIza8NsqmwHbQ/8MAD2rat4cF34403qn379vrHP/6hk08+Wb/5zW8cLyAANOV14OEmU4NSU5g6zter7A+76dNunl/JVJwS7adY0m18Y2JHAAgmvzfI2mU7aO/a9YeHeU5Ojq6//npHCwQAbQlyYGtqUGoSE8f5epn9YSd92q3zK9mKUyrfP53GtyDPf4FgyqZeQyex37JPNjbIprROe1VVlWbPnq2qqirdf//96tmzp/7v//5Pe+yxh/bff3+nywgAjYIe2JoYlHqtZYVsTEmRUeN8nc7+sFsBtZM+7fT5ZafiZCcAd6LxLcjzXyB4sq3X0Cnst+yTrQ2ytoP2t956S8ccc4yGDx+ut99+W7fffrt69uypf/7zn3rsscf09NNPu1FOAGgU9MCWyad+4IcKmZPZH5n4vm2dX3YaDOxWnBLtp5bSbXwL8vwXcJ6XvbXZ2GvoBPZbdsrWBlnbQfv111+v2267TVdddZUKCgoaXx81apQeeOABRwsHAPEEPbBl8in/VMicyv7I5PeNdX7ZbTCwW3Fqaz81lWojRcugq/uu6a0FT8pt9vCycTBbew3TxX7LXtnaIGs7aP/oo4/0l7/8pdXrPXv21Lp16xwpFAAkg8A2uPxWIUs3+8Pr75tKg0EqFad4+6lbpw6akMJ69tHA+rXKNXqufLU2bNnR+Luiwjx17theNVt32M6A8EOGB5zhdeNgtvYapov9lr2CPCFxW2wH7Z07d1Z1dbX69evX7PUPP/xQvXv3dqxggFPoLQH8Z8kX631XIUsn+8PLCmiqDQbdOyXXk91yO6eyZGIF1k19U1vX+J3sZEB4HcQhc7xuLJOyt9cwXey37BXkCYnbYjtoP+OMM3TdddfpqaeeUigUUiQS0TvvvKNrrrlG5557rhtlBFJGbwngP2UV1br+mY+S2ta0Clmq2R9eVkBTbjBINoaJsV26WTLxAuumokFXuGN75bfL1ZraxBkQJgRxyBwTemuztdewLcl0trDfslfQJySOx3bQfscdd+iSSy5Rnz59VF9fr5KSEtXX1+uss85inXYYhd4Ss5DxgGQkE4w1FZQKmZcV0FQbDNZtrkvq75LdLlltBdYtWZI2bt2hP184RDk5oYT3n1SDOO5v/mRCb2229hrGk2xnC/stuwV9QuJYbAftHTp00KxZs3TzzTfro48+0ubNm3XQQQdpr732cqN8QEroLTELGQ9Ihp1gTGoYB33wnl1cLVOmeFkBTbXBwKuGhkSBdSzrttRpwuDEQ/hSCeK4v/mXCb212dprGIudzhb2G4I+IXFLOcluGIlENGPGDA0fPlw/+tGP9OCDD+qoo47SaaedRsAO49jpLYG7og/hlscj+hAuq6j2qGQwjd1gbP2W7TryrjcCcQ5FK6BS62xytyug0QaDeO8cUkMQ2rLBINW/S1cqvZ5OB2fR7bi/+ZtX53BL0V7DonDz868onJ81WYGJOlukhs6W+sgPW7DfEB1qNWFw78ZVSoIq6Z7222+/XbfccotGjx6tXXbZRffff7/Wrl2rxx9/3M3yASkxIeUNZDzAnlSuxyANd/Eq3S/VHiuverrs9HrazVCwk/HA/c3/TOqtzbZew5ZSHZqS7fsN2SPpoP1Pf/qTHnroIV100UWSpNdee03jxo3TH//4R+XkJN1hD2SECSlvMGOSn2zmt3G2qVyPQQuOvKqAptpg4EVDQ6LAuiU7QZedIG5xlf9WOEBrJo2NzeZlTNPpbMnm/YbskXTQvmrVKh177LGNP48ePVqhUEhff/21dt99d1cKB6SKCUrMQMZDAy+CZz+Os7UbjEUlGxylexwydRy9qoCm2mCQ6YaGtgLrplI935MN4ri/JeaXhkN6a71HZwvQtqSD9p07dyo/v/mF0r59e+3YscPxQgHpMinlLZvxEPYmePbrygnJBmPxtBUcpXsc/NgIkopUGwwy3dAQL7Du1qmDJgzupTElRWkFXckEcdzf2ua3a4beWm/R2QK0LWRZVlL1opycHB1zzDHKy8trfG3BggUaNWqUOnXq1Pjas88+63wpHVJbW6twOKyamhoVFhZ6XRxkgN8qDUFTH7E0YsbChA/hRdeNCmQDSrzgOfpN3Qieo/s8XtquH/Z5rOs2GU9OOixmpTvd4+DFcYzyS09lspz+Pl7un2y/v7XFy2vGLUG7Fk0UPW+k2J0tfjxvgKh049Ckg/bzzz8/qTecPXu27UJkCkF7duJB6zw7+zRbH8JeBc+Lq9brzFlLEm4XL8A1RdNzrPuuebr6r+X6prYubu97Tkh64MwhOnZQcav3Sec4eNkIErRGx6B9Hyl7729tCULDYUtBPHdNxb5GUGUsaA8CgnYgefEC81QeqNn4EPYqeJ5fvlqXzytPuN39ZwxOat1qU8TruWsqpNZBUrrHwavjGLSeyqB9n6ay8f7WlqA0HEYF+dw1FZ0tCKJ049Ckx7QDyB7xKqHHH1isR99eaXusdDZO8uPVJFVBHWc7trRYD551kC598kNF2ojcW84in+5x8OI4Bm0psaB9n5ay8f7WliBN0GfKuZttQSzzCwCtEbQDaCZer0J1zTY98vbKmH+TTOUl2x7CXgXPQZ7Mp0unvDYD9lizyKd7HLw4jkFbKjFo3ycWp+9vfg7SgtRwaMK5SyYHAEligXUAjdrqVUikaeUFPwTP8arZITVUvJwOnqMzsEc/o+VnSv5dOSGVHrx0j4MTx7E+Ymlx1XrNL1+txVXrVd9Wy4OC1VMpBe/7uK2solojZizUmbOW6PJ55Tpz1hKNmLFQZRXVXhctKV7d+9zg9bkbbURv2XAQzW7zyzkBIH0E7QAaJepVSAYV7wZeBs/R5bCKws17sorC+b4ef5lKD166xyHdv08lAHOzp9JuA4ITgtTz6rYgBGlBajj08txNlJovNWS3ZeIaBuA90uOBLNc0DXP5N5vSfj8q3j+It5Z0UZzURidTYoM4zjbV1H+7x6GlVP8+3lCTRHNAuDXEwas02yAP2XCSKeOnnZDuNWcKL89dE1LzAZiDoB0IELtBX6rrYccSpIq3F8GzGwFV0OYRiPbgTZ67TCHFXmIrXg9euo0Ydv8+nQAsne8ZT6oNCHbEu27c+D5BFLQgLQgNh16eu16n5gMwC0E7EBB2g75kltCKJ8gVby+C50wEVEER7cG75YVKram114OXbiOGnb9PNwBzsqcyEz24ia6boPS8uimIQVoQGg69OncZVgKgKYJ2IADsBn2pTDgXrcr/4sf99MI/qwNZ8fYieA5SSmxmNd9jlmXWuE4nAjCneird7sFN9roJQs+rmwjSzOXFucuwEgBNEbQDPpdK0JfKhHNNA/Nrx+4XuIq3V8Fz0FJi3RYvQPymts6orASnAjAneird7MG1e90Eoee1LekMrQlqkObn5euayvS5y7ASAE0RtAM+l0rQl2zl/NKjBmqv3XZtVdGyW3kxvdJWH7E0552VngTPQUyJdYufshJMCsDc7MF1stHJ7fuE2++f7tCaIAZprDGeHoaVAIjyXdD+4IMP6q677tKaNWt04IEH6g9/+IOGDh3qdbEAz6QS9CVbOR8+sHvaAarplTa7k/E5HTyTEps8P2UlmBSAudmA4FSjk9v3iUy8f6wMkOqabbp47jJdOLyvRpcUJWwoCFKQxlwdzmBYCUxieidMkPkqaP/f//1fXXXVVXr44Yd16KGH6r777tNPf/pTffbZZ+rZs6fXxQM8kUrQl6leQNMrbalMxud08GxSj6zpTMpKSKbiYkoA5mYDghONTm7fJ9x+/2TmCHnsnS/12DtfJtVQEIQgzU9ZMX4Q9GElbiLIdI7pnTBB56ug/Z577tGkSZN0/vnnS5Iefvhhvfjii3r88cd1/fXXJ/0+W7ZsUW5urlvFBDJq/5556rmLpW9q6uIGfbuF87R/zzxt2bKl8fXrju6ry+eVS2pdibckXXf0vtr2/daUy1UfsXTzMx+ofntdzN+HJN38zAc6fM+RnjxAE5Uvli6d2rfaj05w+1gERUFuvSLbEwfkBbn1jh+jpl79eI3u+L9PtKbmh3OnKJynXx+zn8bsX9Rs2yP6FerlSw/V+19u0Leb69Rj1zwd0reh0uhmGVs6ol+h7j1p37jlPqJfYUrlSfX+E+X2fSIT96F3v1iv1d9uTGrbr7/dpose/4fuP2Nwq3OlaZmbni/798zz3fWfzD5Z/e02vfXxVzq0P8Eo3GHnXo22vfrxGl0+r7zVfT6ZexoapPvMD1mmTbkbx/bt29WxY0c9/fTTOuGEExpfnzhxojZu3Kj58+e3+pu6ujrV1f1wodbW1qpPnz6ZKC4AAAAAAI1qampUWFho++9yXCiLK9atW6f6+nrttttuzV7fbbfdtGbNmph/M336dIXD4cZ/BOwAAAAAAD/xVXq8XTfccIOuuuqqxp+jPe1ff/11Si0cQFtapjRGU2CzrQxR736xXufNfi/hdnPO/5En6ZHJlk/6IcX3tau8SeVHc16lPJp+TnstmftPvBTLRFLdp5k4ZvURS6PveTPuEIF47jp1kI4b1EuvfrxGl/13eEw8frsHJdonfvs+pot3XUX3bLalLnOvdtbf/vW1fvXUvxJuF72nIbba2lr16pX6/vFN0N69e3fl5ubqm2++afb6N998o6Ki2DeivLw85eXltXq9U6dO6tSpkyvlRHayMzmH25OijDpgV8feKx1H7t9RvXssTzjB2pH79/Gk0paofE2FJN168hAVFpixb7NR0+tmt26dtejGY/XBv7/L6ORCm+o3KqdD4onXNtXnZu0zpq37T33E0ozXv1SoQ76SPVLp3icydR+69eSDNXnuMklKOnDfo2dX5e/SUTNe/zKp82rt99LHa+t8MyFZvH0Savw991QnJLquQpJmvP6lxh/SP2saSLhXO2uPnl2T2p979OzK/mxDfX19Wn/vm/T4Dh066OCDD9brr7/e+FokEtHrr7+uYcOGeVgyZLvozMQtl6KKzkxcVlHdbNsRMxbqzFlLdPm8cp05a4lGzFjYbJtMq49YWly1XvPLV2tx1XrVR5yZ5iI6Y7WkVhUJE9Ycbqt8TRWH8z2f5T7bxbpujrzrDdV8v10TBvfWsAHdMnIe+WV5Preu6XQlWrKvJSfuE5m6D0VXCigKJz72ITXcV4b262p7n2RiZQSnxNsnRdxTbUl0PdtZCjNb+OVe7RfRVW7i3SWb3tPgHt/0tEvSVVddpYkTJ+qQQw7R0KFDdd9992nLli2Ns8mjbSx74Tw7y9q8WrnGuOXP3F6+w5Qlr+KJV75unTpowuBeGpPEuspwl0nLBvpheT6Tl+SxG3A6dZ/I1H2o6VJtr1au0ePvfJlwiT27+8RvQYaJy9f5qS6UzPVs0lKYpkh0r5akLh3bE2Qmyc1lQ5E838weH/XAAw/orrvu0po1azR48GD9/ve/16GHHprU39bW1iocDqc8a5+fmVyR87PFVet15qwlCbf7888P1TVP/TNua3i0sr/oulEZu+nFC4ain+5kMOR0Jcn094Mz6iOWRsxYaOR1I8WuuHjZg5jJazoVyd4vLz1qoIYP7O74dZjp6zyZ526y+yT6t5k814PIT3WhZK/nZM+hJycd5puhFU4oq6jWxf+9V8fzMBkftvjp+jFRunGo74L2dGRr0G56Rc7P5pevblxfuy2XHjVQD7yxIuF2mXqomhgMJYuHRvZIpzLqZoBm4jnoh2s6WsZEmQom3ndSleg8TLRPokLiWZ0uP9WF7FzPkrLuukpGfcTSwbe9qo1bd8T8fbbul3TRyZG6dONQX6XHwz476dtcdPYln6qYXNtYptLX7IyBM6llPlGq9BWj91bf7h15kAREqmmfmRj2YVrKrx+u6WxMsczNCbW5v9vaJ1FdOrbX9JMOMCagNIHdwMFvdSG713O2XVfJWLpyQ9yAXTLjnuhHie5pcA9Be8D5oSLnZ8mOcR3Wv7seeKMq4ftlaryiH8fAJap0SdK9r33e+JrXPZ9IXyqTCWVqDLxpFRe3rmmne1VMn+fCC/H2Sedd2uv84X116ai9MhZw+aEXLZVGOb/Vhexez1xXrfmxngO0haA94LhpuSvZnqPDBnQzagIrP86saneWZS8n+IMz7E785rfeNCe5cU27lbFgYqaC10zYJyYO+2gp1UY5v9WFUrmeTTiHTOLHeg7QFt8s+YbUcNNyXzLL2pi2/Jkfl++wW5mKVuqmLqg0Zskr2JNoWT5L0k3jfrhusnnpI6evaTtLWaYimqmQySX7TOflPnH7eDshmWyrePd7v9WFUr2eua5+4Md6DtAWgvaA46aVGWNLi7XoulF6ctJhuv+MwXpy0mFadN2oZi3+Jq1Za1ojQjJSqUwFOUjLFonWv572YmVjQOG33jQnOXlNpxMcJcPUdeSzldvH2ynpNMr5rS7kx2e0adiHCBrS4wMuGyf+8UoyY1xNSV+rj1gK79JB5w/vq+fLv9aGLdsbf2fqGLhk1l2NJ4hBWjYZW1qsSMTSL//yYavfNU2LTbZhZ/k3m7S4an3gUkedGtfq5vhfP6RgZ4OmY9fXbarzxXjvdBrl/FgXYpx6+tiHCBKC9izATcssXk9gFavS3LVTe504uLdGlxQZG8gkM8tyPKakPCI19RFL0178JObvmo5Vf+tXRyXVsPPAG1V64I2qQAaLTjQMupWxkKlJAv3O7cngYj0DkuF142e6Ke5+rAuZ0tDvZ+xDBAVBe5bgpgUpfqX5uy079Pg7X+pHhp8T8Spd8did4M8PMyc35UR5/fCdk+35/eDf39lq2AlqsJhuw6Ab43+zeZJAO9zORIj3DEiG142fdiemjMWPdSGvG/qDgH2IICBozyLctLJbUCrNLStdX67bontfW552yqPXabt2g2cnyuv1d06WnZ7fCYN7J92w46fzPpOcCI5a8tuSW15wOxOhrWdAWzK9ukk8TqW4UxcC4EdMRAd4KJMTMgVpZu2mM+RePnpvPZzmBH9ez5xcVlGtETMW6sxZS3T5vHKdOWuJRsxYGPdznSiv19/ZDrs9v00nhrz0qIFt/o2fzvtMcWMCp2yeJDAZmZgMzu6ymZJ5471NmtAVADKJnnbAI5nu5QxypXlMSZEK8ttrcdV6SZaG9e+uw5Jc7sbrDAS7vWtOlNfr72xXKj2/0YadIJ/3bnJ6/K/fltzKNDuNqkP7dU0pvTuVc9zE8d5+THEHgHQRtAMe8GJCpqBWmmM1fjyzbHXSFU0v03ZTCZ6dKK/fUpXTSYsN6nmfCU4GR26k3AdJsgH1q5VrdNVfy1Nq7E32HL9p3H7qXpBndDBMijuAbEN6PJBhXq2J67d1apPhRIq3lz2xqQxZcKK8fux9TjUtNojnfSY1HYoyLMnslXjvw5rJ8SUbUD/+zpcp3++SvRbOG94v7eMNAHAWQTuQYV6NLQ9apdmpxg8ve2JTCZ6dKK9fe5+bjlW//4zBenLSYVp03ag2exiDdt77md/HI7s5B0migFqS4p2iyd7vgngtZHJeGADwEunxQIZ52cvpx3Vq43EqxdvLtN1Ugmcnyju0X1cVFeZpTW1dzN+bnKqcSlpskM57v/PreGS35yBJNATEktRWPNrW/a7lyhQPnjVE0170/7Xgl9UvAMAJBO1AGlJZ49rpXk67ZchEpTkTa3871fjh1DJCqUh1grV0y/tq5Rpt2xmJ+Tu/9rgl4rdgMRPXkFf8Nh45U3OQtNW4dGxpkR5758uE79HyfhcvsL1p3H7q0inPt+eXF/PCAICXCNqBFKXayu9kz26qZXCz0pyp3g8nGz+86olNNQBPp7zxKrtR4Y7tdedJBwSywuuXYNGkHsQgNx4kI9MrLcRrXFq6ckNSQXvT+11bge0lf/lQM88eogmDe6dd5kzz2+oXAOCEkGVZWTMAqLa2VuFwWDU1NSosLPS6ODBcW5XVeJWhaPUgUSt/9O+l2IFaMr0E6ZbBDZksU33E0ogZCxM2fiy6blTSFTevApRUgzS75Y3us7aGFRTb3GdwlknXtUmNB15ZXLVeZ85aknC7Jycd5mqDkN37XaJrPZX7oylMOSYAYEe6cSg97UAMbVVWx5QUpd3Kn27Prok9DZkuk51e6mSDW696YlNN3bZb3kTzAEhmLfWWbUy6rkk/bmDKSgt2s3L8tqyjHaYcEwDIJIJ2oIVEldUrRu/lSGUonTG2JlbIvChTMo0ffuktzESDgR8qu9mcjm3KdW1S44HXTFppwU5jrx+u9VSZdEwAIFMI2oEmkqmszk5iXKGUXGUo1UDNxAqZV2Vqq/GD3sLmkq3Efrluq8slic0vDSxuMeW6NqXxwAReri4RS7KNvUEObE07JgCQCazTDjSRTGV14/c7knovNytDJlbIvCxTtPFjwuDeGjagW2NKvBPruAdJMmtBS9J9r32usorqjJQpKtrA0vL6izawZLo8XjDlujal8cAEJq5tHut+11Kiaz2khgYxPwa2Jh4T07GePeB/BO0witcPlmQroZ13ae9pZcjECplpZbLTW5gtopXdZK6qTDZo0MDSwJRryG7jgdf3bbdF09KLws33S1E439hsnaAHtn48Jl4pq6jWiBkLdeasJbp8XrnOnLVEI2YszIqGUCBISI+HMUxIjU22snr+8H6677XPM76ud5SXa4v7pUz0FsY2trRYV47eS/e+tjzuNplOfyYdu4Ebkyumwk76sQn37UxIZw4Sr3i1lGWm+PGYZBpDxIDgIGiHEUx5sCRbWb101EDtU7Srp5UhEytkJpXJlFTjtng16Vrf7p2S2i5TDRo0sPzAyckVUz2/km08eLVyjRH37UzxanWJdAQ9sPXjMckUJpQEgoWgHZ4z6cFip6fLi8pQy0r4mJIi4ypkplQSTZ+syMseStMaNEwrj9ecmFwx3fMrUePBmJIijZix0Ij7dhC42YAXL7DN5pUasgEZTECwELTDc8k+WJZUrdfwvbq7Xh47vcVut/I3rVR9uW6Lnly6Smtq6xp/b2oaqgm9H6al6zfldWZJphs0EgUHpjeweCHWNZRsA2ckYumSv3yY9vnVVuPB4qr1BAQO8aIBL1uGNWQzMpiAYCFoh+eSfWBc8pdluvPkAzKWeu51b3GsSlVLQU1DdYpJ6fpRJmSWZLJBI5ngwOQGFpMk28D5m/kVjp1f8RrgCAic4UUDnteNhsgMMpiAYGH2eHgu2QfGxu93ZHTpp2SW1XFLvOWvWsqmmbVTNba0WIuuG6UnJx2m+88YrCcnHaZF143yrFJqyqz2mZh92c4ybswGnViyAfCGLfGXpXTq/CIgSJ8XqyawUkP2MGU1CgDOoKcdnkuUGttS0MdJtlWpioU01MRMSNePMqmH0s2MklQyCkzIcDGZkwFwuudXNg9pcGosuBdjjhnnnD3IYAKChaAdnmv6YEkkGyoUiSpV8QQ9DTUokyaZ1kPpVoNGqsFBphpY/Hg+JRMod+3UQeu3bE/4XumeX5kICEw8Rk6OBfeiAc+kRkO4z8QhYgBSQ9AOI0QfLNc/85E2fh8/tTMqyBWKVL9bkNNQ/TZpUlvBRrb0UJocHPjtfIpKJlCeNqFU016szMj55WZAYOIxcnosuBcNeKY1GsJ9ZDABwUDQDmOMLS1WQX57/eyP7ybcNsgVCrvfLShBXjx+mzQpUbCRLSmLpgYHfjufWkomUM7Jka3zK50ebTcCAhOPkRsTSHrRgJctjYZozqQhYgBSQ9COjEpUOTysf7esr1DYGeMfpCAvFhNmWrcj2WAjG1IWTQwO/HY+xZMoULZzfjnRo+1kQGDqMXJjLLjdBjwnhgtkS6MhAAQNQTsyxuSln0waO9nWPmgpSEFeLH6aNMlusBH0lEUTgwM/nU+JJAqUkzm/TOzRNvUYuTXcI9kGFieHC2RDoyEABA1BOzLCTuUw0xWKl/71tX4zv6LZMklej52Mtw+Kw/k640d7qG/3joEL8mIxeVx0S6kEG0FPWTQtOPDT+eSEts6vdHq03WzkNPUYuTncI1EDixuNK0FvNASAoCFoh+tMXvpp+kuVeuTtla1erzZgfCuVKnPHRcdiarDhNSfP43SDRZPOJ6+ze1Lt0XZ7gjiTjlFTbg/3iNfA4uZwgaA3GjrJ6+sVAAja4TpTl3566V/VMQP2puXyenxrtleqTBwXHY+pwYYJnDiPnQgWTTmfTJgZPZVGpkyk05tyjFryariH3ecnwaXzTLheASDH6wIg+EzsgayPWPrN/IqE20UrQ/BGtKIs/VAxjjJt0qRosBGvJCE1VPRMaGDwm2iw2DJ4iQaLZRXVSb2PCeeTU98lXXYbmRL1+EoNjZz1kUTTZ7bNhGMUT3S4R1G4+b4rCue7lpVl5/lZVlGtETMW6sxZS3T5vHKdOWuJRsxYmLFzKohMuV4BgKAdrjOxB3Lpyg3asGV7UttmWzqzabyoKKfC5GDDz5wOFr08nzIV+CbDbiOTnR7fdJl8zY8tLdai60bpyUmH6f4zBuvJSYdp0XWjXCtTss/FL9dtMSa4rI9YWly1XvPLV2tx1fqMnM9uMOl6BQDS4+E6E9Md7QTi2ZjObBq/jO83beK1IHBjNnGvzievZ0ZvmTp907j9dMlfPkwq3TvTGVMmX/OZHLaU7PPzyaWrjFgmL0ip5F5frwDQFEE7XGfi0k/JBuJdO7UnndkQfhnfb3Kw4UduBYtenE9eDhWKF0z94sf99MI/qxM2MnmRMeWXa95NyTw/z/jRHrr3tc/jvkemgksTlxBMh4lD+wBkL4J2ZIRpPZDR3ou2WtEl6bYJpQRbsI1gI3mJJs4ycXhNqrz6Lm0FU4++vVIPnjVEXTp1aLORycSMqWyR6PlZtzOS1Pu4GVy6Ocu9V4J07wHgfwTtyBiTeiCb9l7EG4120Y/76dhBvTJaLiCbJJNKG6Rg0YvvkkwwNe3FSi26blSb92K7GVOmz2Juevlaauv5ubhqfVLv4WZwGcRU8iDdewD4H0E7MsqkHsh4vRfdOnXQtAmlOnaQf9L4AL9JNpXWxOE1qfLiuzgZTCWbMWX6uGbTyxdPvOenCcFlEFPJg3TvAeB/Icuysmbay9raWoXDYdXU1KiwsNDr4iBFTveQ+K3HBf7FudagPmJpxIyFcYPJaJDRtPfXr4FWLJn8LvPLV+vyeeUJt7v/jMGaMLh3Uu/Z1nkcrzEmepZ7Pa7Z9PKlKvq9pNjBpdvfa3HVep05a0nC7Z6cdJgxDffJCtK9B4B30o1D6WmHr7jx8DSp9x/B5ceKn1uNDKn0/po0vCZdmfwubozLjXfPNGlcc6xzV//9fBPK5zSv540xobffLUG69wDwL4J2+EbQZqZF9vDjuetmI0OqqbRBamDL1HfJZDBlyrjmeOfuGT/aw4jyucXL4DLoqeRBuvcA8KccrwsAJCNRD47U0ENSH8ma0R7wCT+eu9FGhpYBTrSRoayiOq33Z1bmzIkGU9IPwVOU08GUCeOa2zp321oWralMjbuuj1haXLVe88tXa3HVekfuAdHgcsLg3ho2oFtGg+Rob39RuPl1WxTON7JhEgD8hJ52+IIpPTiAXX47dzOR4hzkVNpkZXJ+g0ylTnvdGJNMA1kyMtFY5MfhMskglRwA3EHQDl8woQcHSIVX5+72nRH9z+Iv9e8NW7Vn1446Z1hfdWiXOLkqE40MQU+lTcSLgC0TwZTXjTGJzt1EMtVY5MfhMnaQSg4AziM9Hr7gdQ8OkCovzt3pL1Vq35v+T9Ne/ER/WvxvTXvxE+170/9p+kuVCf82U40M2ZpK6/bQg7a4nTqdyVT8WOyck16UT/LncBkAgPfoaYcveN2DEzSmLT1mWnmclOlzd/pLlXrk7ZWtXo9Yanz9hmNL4v59JhsZsi2VNtmhBwV57bVuS50v94eXs5gne05eOXovzXvvK09mWffbcBnAVEGuNwCxELTDF+ym03Izj8+0sZSmlcdpmUwF374zoll/bx2wNzXr7yt19U/2jZsqn+lGhmxKpU02YPvZY+82vubHa8Grxphkz91LR+2lS0ft5ckzgqFePJ+RvqDXG4BYQpZlZU0OVrqL2sN7ydyouZnHF28sZbS6lOm0ZNPK46ZUzku7ldvH/v6Fpr34ScKy3DRuP114RP82yzp57jJJsRsZ0jku2Vxhn1++WpfPK7f1N0G8Ftzk5rnrhMVV63XmrCUJt3ty0mGBbMzi+Yx0ZVO9AcGSbhxK0A7faavSz808vvqIpREzFsbt6Yv2Qi26blRGgijTypMJdgLWVCq3N8+v0J8W/zthOc4dtqdunVDa5jZuVK6zvcKebMDWUhCvBTeZfJ5F73ttZQN07dRBvxm3n4rCuwSqUYvnM9KVjfUGBEe6cSjp8fCdeOm0mViqys9MG0tpWnkyIdlU8FRnl96za8ekypHMdk6nOAd9xuxkJErfjieI14KbTJ4roa3hMvrvz+u3bNeVf/2nJHMaG9LF8xlOyMZ6AxDF7PEIDDs382xk2lhK08pjinRmlz5nWF8lqu/mhBq2S4ZTs40zY3aDtmZXT0a2XQvpaHnuSg2ZDvPLV2tx1XpPz7V4KyfEkolVBTKB5zOcQL0B2YyedgQGN/O2mbZsnmnlMUU6PQkd2uVo0hH9Ys4eHzXpiH5xJ6Fza7w5vSM/iDe7ejL8ci2YNm+BienyTbMB1tRu07S/fawNW3a02i4ovdA8n+EE6g3IZgTtCAxu5m0zbdk808pjinQrt9Hl3Gb9faWadibmhBoC9njLvbkZ2FBhb65l+nb3XfN09V/L9U1tne+vBdMCZJOHZUSzARZXrY8ZsEcFoVGL5zOcQL0B2Yz0eARG9GYerx8ipIbKY7bezNtKzXV66TE/lscUTlRubzi2RJ9OO0Y3jdtP5w7bUzeN20+fTjumzYB98txlrXp+nUrNpcLeWtP07eEDu+uW4/eX5O9rwe3zyC6/DMtItVGrPmIZk/KfCM9nOIF6A7IZQTsCg5t5YvHGUhaF8z3pcTKtPG5LppLtVOW2Q7scXXhEf906oVQXHtG/zZR4twMbJyvsfgpU7PD7tWBigOyXcdSpNGqVVVRrxIyFOnPWEl0+r1xnzlqiETMWJt0wkunriOcznOL3eyWQKtLjESjxxosWBWQGXieYNrOyaeVxS7Jpw23NLu1G5TYT482d+k6mpV47zc/XgonzFvhlWIbdlN90U/69uo54PsMpfr5XAqkiaEfgcDNPLNmlxzLFtPI4zW4lO5OV20wFNul+J5PHJjvJr9eCiQGyn4ZlnPGjPrr3teWtXm/ZqJXu0mleX0cmP59Nm0ARbfPrvRJIFUE7AombOUyRaiU7U5XbTAY2qX4n1ng2n4kBsh8mrYrV691Uy0atdDIaTLmOTHw+Bz2LB4D/MaYdAFyUzrhap9ZJb8t3W+raXNvd6QmiUvlOfhmbnM1MnGjM9HHU8Sbui7py9N5adN2oZkFjOhkNXEexmTaBIgDEQtAOAC4yMW04qqyiWpf85UMlmoPK6wmiTN6HqXB7EjAvJuszNUA2ddKqtnq9pYZ9Nu+9Va1eTyejIWjXkRNMnEARAGIhPR4AXGRi2rCUOGiQGtZ2f+BM78eKm7oPU+F2Gq6Xab6mTjRm4jjqVNPc00n5D9J15BQTJ1AEgFgI2gHARaaOq01UWZWkiCV16dQhQyWKz9R9aJfbk4B5PcmYZGaALJk3jjrVXu90VmIIynXkJLIPAPgF6fEA4CJT04b9VFk1dR/a4XYarklpvpmYi8E0dockpNPrnWrKfxCuI6eRfQDAL+hpBwCXmZg27LfKqon70A6303BJ8/VOKkMS0u31TjWjwe/XkdPIPgDgF74J2m+//Xa9+OKLKi8vV4cOHbRx40aviwQASTMtbdiPlVXT9qEdbmc2+ClzIkhSHZKQTpp70/dIpQHGz9eR05w4DgCQCb5Jj9++fbtOPfVUTZ482euiAEBKTEob9muqrEn70A63Mxv8ljkRBOkOSfByZnu/XkduMHWFAQBoyjc97VOnTpUkzZkzx9uCAEBA+DFVtj5i+bKH0O3MBj9mTiRi+rF2YkgCvd5m4DgAMJ1vgvZU1NXVqa6urvHn2tpaD0sDAObxU2XVy+XM0uV2Gm7Q0nz9cKydGpJg2sz22YrjAMBkvkmPT8X06dMVDocb//Xp08frIgGAcfyQKhsdO9yyZzM6drisotqjkiXP7TTcoKT5+uVYMyQBAJApIcuy3F//JY7rr79eM2bMaHObTz75RPvuu2/jz3PmzNEVV1yR1ER0sXra+/Tpo5qaGhUWFqZcbgCww/Q0X9PVRyyNmLEwbipyNPV70XWjfLFf3T4f/Hy++elYR8uaaEiCCWUFAHirtrZW4XA45TjU0/T4q6++Wuedd16b2/Tv3z/l98/Ly1NeXl7Kfw8A6fJDmq/pgracmdtpuG6+v1sNAtH3fWfFOt8c66ANSQAAmMvToL1Hjx7q0aOHl0UAANekuhwUmmM5MzO41QAV630TMeVY+3EyRwCA//hmIrpVq1Zpw4YNWrVqlerr61VeXi5JGjhwoHbddVdvCwcj+TlFFP6XaDmokBqWgxpTUsR5mQBjh73nVgNUvPdNxKRj7afJHAEA/uSboP3mm2/WE0880fjzQQcdJEl64403NHLkSI9KBVORkhw8fmuECVpKt5eCuJyZn7jVANXW+8Zj6rFm5nGYxm/PTABt803QPmfOHNZoR1JISQ4ePzbCkNLtHMYOe8utBqhE79sSxxp+4kbQnOx7+vGZCaBtvgnagWSQkhw8fm2EIaXbWYwdbi1TPWluNUDZ3T6bjzX8xY2gOdn39OszE0DbCNphDCcqoKQkB4ufG2FI6XYeY4d/kMmeNLcaoJLd/pKjBmjEwB5Ze6zhL24Ezcm+p5+fmQDaluN1AQCp4YE0YsZCnTlriS6fV64zZy3RiBkLVVZRbet9SEkOFjuNMKaJpnRLP6T1RpHmm7ro2OEJg3tr2IBuWbn/ohX4ltdGtAJv976ZSLQBKt6eDqmhwcBuA1Si94165oP/qOb77Vl5rOEviYJmqSForo8kP5ODnff08zMTQNsI2uE5JyugpCQHi98bYaIp3UXh5udbUTifFEWkxI2gIBG3GqDaet+mvqmtc6UxoqX6iKXFVes1v3y1Fletd3QfIju4ETTbeU+/PzMBxEd6PDzldCoXKcnBEoRGGFK64SSvhgC5NadA9H1veaFSa2pjf69MpPUycRec4EbQbOc9g/DMBBAbQTs85XQFlFmmgyUojTAsBwWneNmT5lYD1NjSYhXktdfPHns37jZuzkfCxF1wihtBs533DMozE0BrpMfDU25UQElJDo5oI0y8JFVLNMIgu3jdk+bWnALrttQltZ3TjRFeDDdAcLkx/4Od92QuFSC46GmHp9yqgJKSDCCIgtqT5lVjBCuOwEluZPvZfU+Wx0RQZGpZU78gaIen3KyAkpLsf9FesHhYvgbZJqhDgJx8Ftip6DFxF5zmRtBs9z3puIDfMc9IawTt8FRQK6BwBr1gQGtB7Elz6llgt6Ln9XADBJMbQbPd96TjAn7FPCOxhSzLypqBWrW1tQqHw6qpqVFhYaHXxUETtKghlvnlq3X5vPKE291/xmBNGNzb/QIBBgli6mA6z4J4Fb3oHolV0auPWBoxY2HCHv5F143y/b4FANNF78nxOmz8fE9ONw6lpx1GIJULsdALBsQXxJ60ps+CNTXfa8OW7eq6a57Cu3RQfcSK+0xIdflQsr0AwBxkWMZH0A5jBLECivQEadKtIPaKAm7IzQmp5vvt+u3LnyXd455ORS+Iww0AwI+YZyQ+gnYAxgpKLxjDP4AGyTRepTKeMd2KHtleAOA9MizjI2gHYDS/94IxoYo/kRnhvGQar1JNc3eioke2FwB4K0gZlk4jaAdgPL/2gqUagMBbZEY4L9nGq1TT3KnoAYD/BSXD0g05XhcAAJIR7QWbMLi3hg3o5osbtp0ABGaIBpctj1s0uCyrqPaoZP6VqPFKami8qo9YKae5Ryt60g8Vu6hsr+hJDcdgcdV6zS9frcVV61UfyZqFgwD4TDTDsijcPDOqKJyf1dmJ9LQDgEuYUMVfyIxwh53Gq3TS3P0+lMYtZI4A8Bu/Zli6iaAdAFzChCr+wlIz7rDTeHXcoF5ppblT0WvO9Dk1mDsCQDzMM9IcQTsAuIRxtv5CZoQ77DReOTGekYpeA9MzR8gAAIDkMaYdAFzi1DhbxqNmBpkR7og2XsU7y0NqCNaijVeMZ3SGyXNqMHcEANhDTzsAuCjdcbb0RmUOmRHuSKX3nDT39JmaOWJ6BgAAmIigHQBclmoAYvp41KBhqRn3pNJ4RZp7ekzNHGHuCACwj6AdADLAbgBCb5Q3gjQDuWmTfNF7nlmmZo6YmgEAACYjaAcAA9Eb5Z0gBJemDqug9zxzTM0cMTUDAABMxkR0AGAgeqO8FQ0uJwzurWEDuvkuYGeSL0hmTupnd2JCAAA97QBgJHqjkAqGVaAl0zJHTM0AAACT0dMOAAaiNwqpMHmZL3jHtMwREzMAAMBk9LQDQAsmTOBFbxRSwbAK+IVpGQAAYDKCdgBowqQJvII0kzkyg2EV8BMmJgSA5BC0A8B/mbguOr1RsMPUZb4AAEDqGNMOAEo8gZfUMIFXfSTWFu4ybTwqzBUdViGp1XwIDKsAAMCfCNoBQEzgheBgki8AAIKF9HgAEBN4IVgYVoEopybWNGGCTgDIVgTtACAm8ELw+HmSLwJEZzg1saZJE3QCQDYiaAcABWcCL4Kd4Av6MSZAdIZTE2uaOEEnAGSbkGVZmZ9VySO1tbUKh8OqqalRYWGh18UBYJho5VSKvS666ZVTgp3gC/oxjhcg+uUaNEV9xNKIGQvjztMRbYRcdN2oNht8nHofAMh26cahTEQHAP/l5wm8osFOy8p1tDesrKLao5LBKUE/xiav4OA3Tk2syQSdAGAG0uMBoAk/TuCVKNgJqSHYGVNSZPT3cEoQ08ez4RjbCRD9OlY/U5yaWJMJOgHADATtANCC3ybwItj5QVDTx7PhGBMgOsepiTWTfZ91m+o0v3x1YBrJAMA0BO0A4HMEOw2CPGFWNhxjVnBwjlMTayZ6H0nKCUnTXvyk8ecgNJIBgGkY0w4APkew05A+fssLHwd2PHQ2HONogBivjzakhoDQ9BUcTJCbE9KU8SWS1Gp/Rn+eMr4kYY94W+8T1fKSCsocCwBgEoJ2APCx+oiliGWp8y7t426TDcHOAwtXaE1tXdzf+33CrGwIaJ0KNNHAqYk1471PvMMQhEYyADAN6fEA4FOxxm+3lA3BTllFte597fOktvVr+ng0oJ08d5lCir0kYRCOcTRAbHleF5FynRKnJtZs+T7rNtU1S4lvKQhzLACASQjaAcCH4o3fbinowU50VvVk+Tl9PFsCWj+u4GAypybWbPo+88tXJ/U3fm0kAwDTELQDgM+0tfxXVOeO7fXgmUN02IBugQ52Es2q3pTf08el7Alo/baCQ7bJhjkWAMAkBO0A4DPJBKobt+5QTk4ocMFcS3Z68oKQPi4R0MJ7Ts1ODwBIDhPRAYDPZMPyX8lKtifvytF7BSZ9HPAakwYCQGYRtAOAz5Ca+oNEs6pLDWnxl47aK2NlArKBU7PTAwASIz0eAHzm4D27qGunDtqwZXvM32dTamq2zKoOmChb5lgAAK8RtAOAj0SXeWsrYJeyK1DNllnVARMFeY6F+ohFgwQAIxC0A0AMJlbWklnmLVsDVXr8ADgp2kDatCGwOEvvrwC8F7IsK9Eyv4FRW1urcDismpoaFRYWel0cAIYysbJWH7E0YsbCNmeN79qpvZbcMFod2jFdCQCkKl4DabQJkDH7AOxKNw6lZgcATUQray2D4zU12zR57jKVVVR7Uq5klnnbsGWHPvj3dxkqEWCG+oilxVXrNb98tRZXrVd9JGv6IuCC+oilqQsqY2Y0RV+buqCS8wxARpEeDwD/laiyFlJDZW1MSVHG065Z5g1ozcSsGPhbogZSS1J1zTYtXbkhsGP5AZiHnnYA+C87lbVMY5k3oDlTs2LgbzSQAjARQTsA/JfJlbVE65GH1NDDmA3LvAGkMMMtNJACMBFBOwD8l8mVteh65JJaBe7ZuMwbspvJWTHwNxpIAZiIoB0A/sv0ylp0PfKicPNGg6JwPrMZI6uYnBUDf6OBFICJmIgOAP4rWlmbPHeZQlKz1FtTKmusRw6YnRUD/4s2kLac5LCISQ4BeIR12gGgBWakDqb6iEVjR0DURyyNmLFQa2q2xRzXHlJDgLXoulEcY6SMewYAp6QbhxK0A0AMVNaChYaY4InOHi/FzophyAgAwBQE7TYQtANA9okGdy0fdgR3/kdjDADAD9KNQxnTDgAIrERLg4XUsDTYmJIiMil8iDkeAADZgKAdABBYdpYGGzagW+YKBsfk5oQ4dgCAQGPJNwBAYLE0GAAA8DuCdgBAYLE0GAAA8DuCdgBAYA3t11XF4XzFG+EcUsPEZUP7dc1ksQAAAJJG0A4ACKzcnJCmjC+RpFaBe/TnKeNLmLgMAAAYi6AdABBoY0uLNfPsISoKN0+BLwrns9wbAAAwHrPHAwACj6XBAACAXxG0AwCygklLg9VHLBoQAABAUnwRtH/55ZeaNm2aFi5cqDVr1qhXr146++yzdeONN6pDhw5eFw8AgKSVVVRr6oLKZuvHF4fzNWV8Can6AACgFV8E7Z9++qkikYgeeeQRDRw4UBUVFZo0aZK2bNmiu+++2+viAQCQlLKKak2eu0xWi9fX1GzT5LnLGGMPAABaCVmW1bLu4At33XWXZs6cqS+++CLuNnV1daqrq2v8uba2Vn369FFNTY0KCwszUUwAACQ1pMSPmLGwWQ97UyE1TI636LpRpMoDABAgtbW1CofDKcehvp09vqamRl27tr2u7vTp0xUOhxv/9enTJ0OlAwCguaUrN8QN2CXJklRds01LV27IXKEAAIDxfBm0r1ixQn/4wx900UUXtbndDTfcoJqamsZ/X331VYZKCABAc2s3xQ/YU9kOAABkB0+D9uuvv16hUKjNf59++mmzv1m9erXGjh2rU089VZMmTWrz/fPy8lRYWNjsHwAAXuhZkJ94IxvbAQCA7ODpRHRXX321zjvvvDa36d+/f+P/v/76ax111FE6/PDD9eijj7pcOgAAnDO0X1cVh/O1pmZbq4nopB/GtA/t1/bQLwAAkF08Ddp79OihHj16JLXt6tWrddRRR+nggw/W7NmzlZPjy8x+AECWys0Jacr4Ek2eu0whqVngHp12bsr4EiahAwAAzfgi8l29erVGjhypPfbYQ3fffbe+/fZbrVmzRmvWrPG6aAAAJG1sabFmnj1EReHmKfBF4XyWewMAADH5Yp32V199VStWrNCKFSu0++67N/udT1esAwBkqbGlxRpTUqSlKzdo7aZt6lnQkBJPDzsAAIjFt+u0pyLd9fEAAAAAALAja9dpBwAAAAAg6AjaAQAAAAAwFEE7AAAAAACG8sVEdAAAAMic+ojFZIkAYAiCdgAAADQqq6jW1AWVqq7Z1vhacThfU8aXsCwhAHiA9HgAAABIagjYJ89d1ixgl6Q1Nds0ee4ylVVUe1QyAMheBO0AAABQfcTS1AWVirUWcPS1qQsqVR/JmtWCAcAIBO0AAADQ0pUbWvWwN2VJqq7ZpqUrN2SuUAAAgnYAAABIazfFD9hT2Q4A4AyCdgAAAKhnQb6j2wEAnEHQDgAAAA3t11XF4XzFW9gtpIZZ5If265rJYgFA1iNoBwAAgHJzQpoyvkSSWgXu0Z+njC9hvXYAyDCCdgAAAEiSxpYWa+bZQ1QUbp4CXxTO18yzh7BOOwB4oJ3XBQAAAIA5xpYWa0xJkZau3KC1m7apZ0FDSjw97ADgDYJ2AAAANJObE9KwAd28LgYAQKTHAwAAAABgLIJ2AAAAAAAMRdAOAAAAAIChCNoBAAAAADAUE9EBAAAABquPWMzmD2QxgnYAAADAUGUV1Zq6oFLVNdsaXysO52vK+BKNLS32sGQAMoX0eAAA0lQfsbS4ar3ml6/W4qr1qo9YXhcJQACUVVRr8txlzQJ2SVpTs02T5y5TWUW1RyUDkEn0tAMAkAZ6wQC4oT5iaeqCSsVqArQkhSRNXVCpMSVFpMoDAUdPOwAAKaIXDIBblq7c0Ore0pQlqbpmm5au3JC5QgHwBEE7AAApSNQLJjX0gpEqDyAVazfFD9hT2Q6AfxG0A8goxv4iKOgFA+CmngX5jm4HwL8Y0w4gYxj7iyChFwyAm4b266ricL7W1GyLmdETklQUblj+DUCw0dMOICMY+4ugoRcMgJtyc0KaMr5EUkOA3lT05ynjS5iEDsgCBO0AXMfYXwRRtBcsXnU5pIZMEnrBAKRqbGmxZp49REXh5o1/ReF8zTx7CFlqQJYgPR6A6+yM/R02oFvmCgakIdoLNnnuMoWkZo1S9IIBcMrY0mKNKSnS0pUbtHbTNvUsaGgM5N4CZA+CdgCuY+wvgiraC9ZyroYi5moA4KDcnBCN2kAWI2gH4DrG/iLI6AUDAABuImgH4DpmwEXQ0QsGAADcwkR0AFzHDLgAAABAagjaAWQEM+ACAAAA9pEeDyBjGPsLAAAA2EPQDiCjGPsLAAAAJI/0eAAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGImgHAAAAAMBQBO0AAAAAABiKoB0AAAAAAEMRtAMAAAAAYCiCdgAAAAAADEXQDgAAAACAoQjaAQAAAAAwFEE7AAAAAACGaud1ATLJsixJUm1trcclAQAAAABkg2j8GY1H7cqqoH3Tpk2SpD59+nhcEgAAAABANtm0aZPC4bDtvwtZqYb7PhSJRPT111+roKBAoVDI6+Ikpba2Vn369NFXX32lwsJCr4uDJHHc/Inj5k8cN3/iuPkTx82fOG7+xHHzp1jHzbIsbdq0Sb169VJOjv0R6lnV056Tk6Pdd9/d62KkpLCwkIvVhzhu/sRx8yeOmz9x3PyJ4+ZPHDd/4rj5U8vjlkoPexQT0QEAAAAAYCiCdgAAAAAADEXQbri8vDxNmTJFeXl5XhcFNnDc/Inj5k8cN3/iuPkTx82fOG7+xHHzJzeOW1ZNRAcAAAAAgJ/Q0w4AAAAAgKEI2gEAAAAAMBRBOwAAAAAAhiJoBwAAAADAUATtBnjwwQfVt29f5efn69BDD9XSpUvjbvvxxx/r5JNPVt++fRUKhXTfffdlrqBoxs5xmzVrlo444gh16dJFXbp00ejRo9vcHu6xc9yeffZZHXLIIercubM6deqkwYMH63/+538yWFpE2TluTc2bN0+hUEgnnHCCuwVETHaO25w5cxQKhZr9y8/Pz2BpEWX3etu4caMuueQSFRcXKy8vT3vvvbdeeumlDJUWUXaO28iRI1tdb6FQSOPGjctgiWH3Wrvvvvu0zz77aJdddlGfPn105ZVXatu2bRkqLaLsHLcdO3bo1ltv1YABA5Sfn68DDzxQZWVl9j/UgqfmzZtndejQwXr88cetjz/+2Jo0aZLVuXNn65tvvom5/dKlS61rrrnGevLJJ62ioiLr3nvvzWyBYVmW/eN21llnWQ8++KD14YcfWp988ol13nnnWeFw2PrPf/6T4ZJnN7vH7Y033rCeffZZq7Ky0lqxYoV13333Wbm5uVZZWVmGS57d7B63qJUrV1q9e/e2jjjiCGvChAmZKSwa2T1us2fPtgoLC63q6urGf2vWrMlwqWH3uNXV1VmHHHKIdeyxx1qLFi2yVq5cab355ptWeXl5hkue3ewet/Xr1ze71ioqKqzc3Fxr9uzZmS14FrN7zP785z9beXl51p///Gdr5cqV1ssvv2wVFxdbV155ZYZLnt3sHrdrr73W6tWrl/Xiiy9aVVVV1kMPPWTl5+dby5Yts/W5BO0eGzp0qHXJJZc0/lxfX2/16tXLmj59esK/3XPPPQnaPZLOcbMsy9q5c6dVUFBgPfHEE24VETGke9wsy7IOOugg6ze/+Y0bxUMcqRy3nTt3Wocffrj1xz/+0Zo4cSJBuwfsHrfZs2db4XA4Q6VDPHaP28yZM63+/ftb27dvz1QREUO6z7d7773XKigosDZv3uxWEdGC3WN2ySWXWKNGjWr22lVXXWUNHz7c1XKiObvHrbi42HrggQeavXbSSSdZP/vZz2x9LunxHtq+fbs++OADjR49uvG1nJwcjR49WosXL/awZGiLE8dt69at2rFjh7p27epWMdFCusfNsiy9/vrr+uyzz/TjH//YzaKiiVSP26233qqePXvqwgsvzEQx0UKqx23z5s3ac8891adPH02YMEEff/xxJoqL/0rluL3wwgsaNmyYLrnkEu22224qLS3VHXfcofr6+kwVO+s5US957LHHdMYZZ6hTp05uFRNNpHLMDj/8cH3wwQeNqdhffPGFXnrpJR177LEZKTNSO251dXWthnrtsssuWrRoka3Pbme/uHDKunXrVF9fr912263Z67vttps+/fRTj0qFRJw4btddd5169erV7KKHu1I9bjU1Nerdu7fq6uqUm5urhx56SGPGjHG7uPivVI7bokWL9Nhjj6m8vDwDJUQsqRy3ffbZR48//rgGDRqkmpoa3X333Tr88MP18ccfa/fdd89EsbNeKsftiy++0MKFC/Wzn/1ML730klasWKFf/vKX2rFjh6ZMmZKJYme9dOslS5cuVUVFhR577DG3iogWUjlmZ511ltatW6cRI0bIsizt3LlTF198sX79619noshQasftpz/9qe655x79+Mc/1oABA/T666/r2Weftd2wSU87kGF33nmn5s2bp+eee45JlnygoKBA5eXleu+993T77bfrqquu0ptvvul1sRDHpk2bdM4552jWrFnq3r2718WBDcOGDdO5556rwYMH68gjj9Szzz6rHj166JFHHvG6aGhDJBJRz5499eijj+rggw/W6aefrhtvvFEPP/yw10VDkh577DEdcMABGjp0qNdFQRvefPNN3XHHHXrooYe0bNkyPfvss3rxxRc1bdo0r4uGNtx///3aa6+9tO+++6pDhw669NJLdf755ysnx14YTk+7h7p3767c3Fx98803zV7/5ptvVFRU5FGpkEg6x+3uu+/WnXfeqddee02DBg1ys5hoIdXjlpOTo4EDB0qSBg8erE8++UTTp0/XyJEj3Swu/svucauqqtKXX36p8ePHN74WiUQkSe3atdNnn32mAQMGuFtoOPJ8a9++vQ466CCtWLHCjSIihlSOW3Fxsdq3b6/c3NzG1/bbbz+tWbNG27dvV4cOHVwtM9K73rZs2aJ58+bp1ltvdbOIaCGVY3bTTTfpnHPO0c9//nNJ0gEHHKAtW7boF7/4hW688UbbQSDsS+W49ejRQ88//7y2bdum9evXq1evXrr++uvVv39/W5/N0fVQhw4ddPDBB+v1119vfC0Siej111/XsGHDPCwZ2pLqcfvtb3+radOmqaysTIccckgmioomnLreIpGI6urq3CgiYrB73Pbdd1999NFHKi8vb/x3/PHH66ijjlJ5ebn69OmTyeJnLSeut/r6en300UcqLi52q5hoIZXjNnz4cK1YsaKxcUySPv/8cxUXFxOwZ0g619tTTz2luro6nX322W4XE02kcsy2bt3aKjCPNpZZluVeYdEonWstPz9fvXv31s6dO/XMM89owoQJ9j7c5oR5cNi8efOsvLw8a86cOVZlZaX1i1/8wurcuXPjMjfnnHOOdf311zduX1dXZ3344YfWhx9+aBUXF1vXXHON9eGHH1rLly/36itkJbvH7c4777Q6dOhgPf30082WWNm0aZNXXyEr2T1ud9xxh/XKK69YVVVVVmVlpXX33Xdb7dq1s2bNmuXVV8hKdo9bS8we7w27x23q1KnWyy+/bFVVVVkffPCBdcYZZ1j5+fnWxx9/7NVXyEp2j9uqVausgoIC69JLL7U+++wz629/+5vVs2dP67bbbvPqK2SlVO+TI0aMsE4//fRMFxeW/WM2ZcoUq6CgwHryySetL774wnrllVesAQMGWKeddppXXyEr2T1uS5YssZ555hmrqqrKevvtt61Ro0ZZ/fr1s7777jtbn0vQboA//OEP1h577GF16NDBGjp0qLVkyZLG3x155JHWxIkTG39euXKlJanVvyOPPDLzBc9ydo7bnnvuGfO4TZkyJfMFz3J2jtuNN95oDRw40MrPz7e6dOliDRs2zJo3b54HpYad49YSQbt37By3K664onHb3XbbzTr22GNtr2MLZ9i93v7xj39Yhx56qJWXl2f179/fuv32262dO3dmuNSwe9w+/fRTS5L1yiuvZLikiLJzzHbs2GHdcsst1oABA6z8/HyrT58+1i9/+UvbwR/SZ+e4vfnmm9Z+++1n5eXlWd26dbPOOecca/Xq1bY/M2RZ5FMAAAAAAGAixrQDAAAAAGAognYAAAAAAAxF0A4AAAAAgKEI2gEAAAAAMBRBOwAAAAAAhiJoBwAAAADAUATtAAAAAAAYiqAdAAAAAABDEbQDAGCgkSNH6oorrsjY582ZM0edO3d29TO+/PJLhUIhlZeXu/o5AAAECUE7AAAeOe+88xQKhVr9W7FihZ599llNmzatcdu+ffvqvvvua/b3mQi0Jembb75R+/btNW/evJi/v/DCCzVkyBDXywEAQDYiaAcAwENjx45VdXV1s3/9+vVT165dVVBQ4HXxJEm77babxo0bp8cff7zV77Zs2aK//vWvuvDCCz0oGQAAwUfQDgCAh/Ly8lRUVNTsX25ubrP0+JEjR+rf//63rrzyysbe+DfffFPnn3++ampqGl+75ZZbJEl1dXW65ppr1Lt3b3Xq1EmHHnqo3nzzzWafO2fOHO2xxx7q2LGjTjzxRK1fv77Ncl544YV6/fXXtWrVqmavP/XUU9q5c6d+9rOfqaysTCNGjFDnzp3VrVs3HXfccaqqqor7nrEyBZ5//nmFQqFmr82fP19DhgxRfn6++vfvr6lTp2rnzp2SJMuydMstt2iPPfZQXl6eevXqpcsuu6zN7wIAgJ8QtAMAYLhnn31Wu+++u2699dbG3vjDDz9c9913nwoLCxtfu+aaayRJl156qRYvXqx58+bpX//6l0499VSNHTtWy5cvlyS9++67uvDCC3XppZeqvLxcRx11lG677bY2y3Dsscdqt91205w5c5q9Pnv2bJ100knq3LmztmzZoquuukrvv/++Xn/9deXk5OjEE09UJBJJ+bv//e9/17nnnqvLL79clZWVeuSRRzRnzhzdfvvtkqRnnnlG9957rx555BEtX75czz//vA444ICUPw8AANO087oAAABks7/97W/addddG38+5phj9NRTTzXbpmvXrsrNzVVBQYGKiooaXw+HwwqFQs1eW7VqlWbPnq1Vq1apV69ekqRrrrlGZWVlmj17tu644w7df//9Gjt2rK699lpJ0t57761//OMfKisri1vO3NxcTZw4UXPmzNFNN92kUCikqqoq/f3vf9err74qSTr55JOb/c3jjz+uHj16qLKyUqWlpSntn6lTp+r666/XxIkTJUn9+/fXtGnTdO2112rKlClatWqVioqKNHr0aLVv31577LGHhg4dmtJnAQBgInraAQDw0FFHHaXy8vLGf7///e/Ter+PPvpI9fX12nvvvbXrrrs2/nvrrbcaU9U/+eQTHXrooc3+btiwYQnf+4ILLtDKlSv1xhtvSGroZe/bt69GjRolSVq+fLnOPPNM9e/fX4WFherbt68ktUqpt+Of//ynbr311mbfZdKkSaqurtbWrVt16qmn6vvvv1f//v01adIkPffcc42p8wAABAE97QAAeKhTp04aOHCgY++3efNm5ebm6oMPPlBubm6z3zXt0U/FXnvtpSOOOEKzZ8/WyJEj9ac//UmTJk1qHIM+fvx47bnnnpo1a5Z69eqlSCSi0tJSbd++Peb75eTkyLKsZq/t2LGj1feZOnWqTjrppFZ/n5+frz59+uizzz7Ta6+9pldffVW//OUvddddd+mtt95S+/bt0/q+AACYgKAdAAAf6NChg+rr6xO+dtBBB6m+vl5r167VEUccEfO99ttvP7377rvNXluyZElS5bjwwgs1efJkHX/88Vq9erXOO+88SdL69ev12WefadasWY2fu2jRojbfq0ePHtq0aZO2bNmiTp06SVKrNdyHDBmizz77rM2GjV122UXjx4/X+PHjdckll2jffffVRx99xDJ0AIBAIGgHAMAH+vbtq7fffltnnHGG8vLy1L17d/Xt21ebN2/W66+/rgMPPFAdO3bU3nvvrZ/97Gc699xz9bvf/U4HHXSQvv32W73++usaNGiQxo0bp8suu0zDhw/X3XffrQkTJujll19uczx7U6eeeqouu+wyXXTRRfrJT36iPn36SJK6dOmibt266dFHH1VxcbFWrVql66+/vs33OvTQQ9WxY0f9+te/1mWXXaZ333231UR3N998s4477jjtscceOuWUU5STk6N//vOfqqio0G233aY5c+aovr6+8b3mzp2rXXbZRXvuuWdK+xkAANMwph0AAB+49dZb9eWXX2rAgAHq0aOHJOnwww/XxRdfrNNPP109evTQb3/7W0kNY83PPfdcXX311dpnn310wgkn6L333tMee+whSTrssMM0a9Ys3X///TrwwAP1yiuv6De/+U1S5ejYsaPOOOMMfffdd7rgggsaX8/JydG8efP0wQcfqLS0VFdeeaXuuuuuNt+ra9eumjt3rl566SUdcMABevLJJxuXrYv66U9/qr/97W965ZVX9KMf/UiHHXaY7r333sagvHPnzpo1a5aGDx+uQYMG6bXXXtOCBQvUrVu3pL4PAACmC1ktB5MBAAAAAAAj0NMOAAAAAIChCNoBAAAAADAUQTsAAAAAAIYiaAcAAAAAwFAE7QAAAAAAGIqgHQAAAAAAQxG0AwAAAABgKIJ2AAAAAAAMRdAOAAAAAIChCNoBAAAAADAUQTsAAAAAAIb6/y+QRdQBR4hHAAAAAElFTkSuQmCC\n",
      "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": "2022-11-02T17:10:31.208671Z",
     "iopub.status.busy": "2022-11-02T17:10:31.208402Z",
     "iopub.status.idle": "2022-11-02T17:10:31.218687Z",
     "shell.execute_reply": "2022-11-02T17:10:31.217953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.nonparametric.kde.KDEUnivariate at 0x7f37e43ca110>"
      ]
     },
     "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": "2022-11-02T17:10:31.221752Z",
     "iopub.status.busy": "2022-11-02T17:10:31.221432Z",
     "iopub.status.idle": "2022-11-02T17:10:31.517143Z",
     "shell.execute_reply": "2022-11-02T17:10:31.516360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f37e44160b0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": "2022-11-02T17:10:31.521841Z",
     "iopub.status.busy": "2022-11-02T17:10:31.521294Z",
     "iopub.status.idle": "2022-11-02T17:10:31.706692Z",
     "shell.execute_reply": "2022-11-02T17:10:31.705889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}