{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Influence Measures for GLM Logit\n",
    "\n",
    "\n",
    "Based on draft version for GLMInfluence, which will also apply to discrete Logit, Probit and Poisson, and eventually be extended to cover most models outside of time series analysis.\n",
    "\n",
    "The example for logistic regression was used by Pregibon (1981) \"Logistic Regression diagnostics\" and is based on data by Finney (1947).\n",
    "\n",
    "GLMInfluence includes the basic influence measures but still misses some measures described in Pregibon (1981), for example those related to deviance and effects on confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "from statsmodels.genmod import families\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.stats.tests.test_influence\n",
    "\n",
    "test_module = statsmodels.stats.tests.test_influence.__file__\n",
    "cur_dir = cur_dir = os.path.abspath(os.path.dirname(test_module))\n",
    "\n",
    "file_name = \"binary_constrict.csv\"\n",
    "file_path = os.path.join(cur_dir, \"results\", file_name)\n",
    "df = pd.read_csv(file_path, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:              constrict   No. Observations:                   39\n",
      "Model:                            GLM   Df Residuals:                       36\n",
      "Model Family:                Binomial   Df Model:                            2\n",
      "Link Function:                  Logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -14.614\n",
      "Date:                Thu, 18 Dec 2025   Deviance:                       29.227\n",
      "Time:                        07:37:30   Pearson chi2:                     34.2\n",
      "No. Iterations:                     7   Pseudo R-squ. (CS):             0.4707\n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -2.8754      1.321     -2.177      0.029      -5.464      -0.287\n",
      "log_rate        4.5617      1.838      2.482      0.013       0.959       8.164\n",
      "log_volumne     5.1793      1.865      2.777      0.005       1.524       8.834\n",
      "===============================================================================\n"
     ]
    }
   ],
   "source": [
    "res = GLM(\n",
    "    df[\"constrict\"],\n",
    "    df[[\"const\", \"log_rate\", \"log_volumne\"]],\n",
    "    family=families.Binomial(),\n",
    ").fit(attach_wls=True, atol=1e-10)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the influence measures\n",
    "\n",
    "GLMResults has a `get_influence` method similar to OLSResults, that returns and instance of the GLMInfluence class. This class has methods and (cached) attributes to inspect influence and outlier measures.\n",
    "\n",
    "This measures are based on a one-step approximation to the the results for deleting one observation. One-step approximations are usually accurate for small changes but underestimate the magnitude of large changes. Event though large changes are underestimated, they still show clearly the effect of influential observations\n",
    "\n",
    "In this example observation 4 and 18 have a large standardized residual and large Cook's distance, but not a large leverage. Observation 13 has the largest leverage but only small Cook's distance and not a large studentized residual.\n",
    "\n",
    "Only the two observations 4 and 18 have a large impact on the parameter estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "infl = res.get_influence(observed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dfb_const</th>\n",
       "      <th>dfb_log_rate</th>\n",
       "      <th>dfb_log_volumne</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Case</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.073359</td>\n",
       "      <td>-0.930200</td>\n",
       "      <td>-1.017953</td>\n",
       "      <td>0.429085</td>\n",
       "      <td>3.681352</td>\n",
       "      <td>0.086745</td>\n",
       "      <td>1.134573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.959508</td>\n",
       "      <td>-0.827905</td>\n",
       "      <td>-0.847666</td>\n",
       "      <td>0.328152</td>\n",
       "      <td>3.055542</td>\n",
       "      <td>0.095386</td>\n",
       "      <td>0.992197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.259120</td>\n",
       "      <td>0.202363</td>\n",
       "      <td>-0.004883</td>\n",
       "      <td>0.066770</td>\n",
       "      <td>-1.150095</td>\n",
       "      <td>0.131521</td>\n",
       "      <td>-0.447560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.236747</td>\n",
       "      <td>-0.194984</td>\n",
       "      <td>0.028643</td>\n",
       "      <td>0.065370</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>0.168219</td>\n",
       "      <td>0.442844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.116501</td>\n",
       "      <td>-0.099602</td>\n",
       "      <td>0.132197</td>\n",
       "      <td>0.055382</td>\n",
       "      <td>0.713771</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>0.407609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.128107</td>\n",
       "      <td>0.041039</td>\n",
       "      <td>-0.100410</td>\n",
       "      <td>0.051950</td>\n",
       "      <td>-1.420261</td>\n",
       "      <td>0.071721</td>\n",
       "      <td>-0.394777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.093083</td>\n",
       "      <td>-0.009463</td>\n",
       "      <td>0.177532</td>\n",
       "      <td>0.046492</td>\n",
       "      <td>-0.847046</td>\n",
       "      <td>0.162757</td>\n",
       "      <td>-0.373465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.196119</td>\n",
       "      <td>0.127482</td>\n",
       "      <td>0.035689</td>\n",
       "      <td>0.031168</td>\n",
       "      <td>-1.065576</td>\n",
       "      <td>0.076085</td>\n",
       "      <td>-0.305786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.088174</td>\n",
       "      <td>-0.013657</td>\n",
       "      <td>-0.002161</td>\n",
       "      <td>0.027481</td>\n",
       "      <td>-1.238185</td>\n",
       "      <td>0.051031</td>\n",
       "      <td>-0.287130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.092235</td>\n",
       "      <td>0.170980</td>\n",
       "      <td>0.038080</td>\n",
       "      <td>0.026230</td>\n",
       "      <td>0.661478</td>\n",
       "      <td>0.152431</td>\n",
       "      <td>0.280520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dfb_const  dfb_log_rate  dfb_log_volumne   cooks_d  standard_resid  \\\n",
       "Case                                                                       \n",
       "4      1.073359     -0.930200        -1.017953  0.429085        3.681352   \n",
       "18     0.959508     -0.827905        -0.847666  0.328152        3.055542   \n",
       "19    -0.259120      0.202363        -0.004883  0.066770       -1.150095   \n",
       "29     0.236747     -0.194984         0.028643  0.065370        0.984729   \n",
       "31     0.116501     -0.099602         0.132197  0.055382        0.713771   \n",
       "24    -0.128107      0.041039        -0.100410  0.051950       -1.420261   \n",
       "13    -0.093083     -0.009463         0.177532  0.046492       -0.847046   \n",
       "23    -0.196119      0.127482         0.035689  0.031168       -1.065576   \n",
       "33    -0.088174     -0.013657        -0.002161  0.027481       -1.238185   \n",
       "6     -0.092235      0.170980         0.038080  0.026230        0.661478   \n",
       "\n",
       "      hat_diag  dffits_internal  \n",
       "Case                             \n",
       "4     0.086745         1.134573  \n",
       "18    0.095386         0.992197  \n",
       "19    0.131521        -0.447560  \n",
       "29    0.168219         0.442844  \n",
       "31    0.245917         0.407609  \n",
       "24    0.071721        -0.394777  \n",
       "13    0.162757        -0.373465  \n",
       "23    0.076085        -0.305786  \n",
       "33    0.051031        -0.287130  \n",
       "6     0.152431         0.280520  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ_df = infl.summary_frame()\n",
    "summ_df.sort_values(\"cooks_d\", ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_influence()\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"cooks\", threshold=2 * infl.cooks_distance[0].mean())\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AMAxWrGnN4uVr09re2XusqVyXRfObM29WUxUrGz0sDwUAAAAAAEO0Yk1rFi5b3SewSJKN7Z1ZuGx1VqxprVJlo4vQAgAAAAAAhqCru5LFy9emsp1zPccWL1+bru7ttWBbQgsAAAAAABiCVS1t/WZYbKuSpLW9M6ta2kauqFFKaAEAAAAAAEOwafPAgcVzaTeeCS0AAAAAAGAIptbXDWu78UxoAQAAAAAAQzBn5uQ0letSGuB8KUlTuS5zZk4eybJGJaEFAAAAAAAMQW1NKYvmNydJv+Ci5/Gi+c2prRko1qCH0AIAAAAAAIZo3qymLFkwO43lvktANZbrsmTB7Myb1VSlykaXCdUuAAAAAAB2l67uSla1tGXT5s5Mrd+6NIuRzsDuMm9WU+Y2N/q5MwRCCwAAAADGpBVrWrN4+dq0tnf2Hmsq12XR/GYjnoHdpramlOMOm1LtMkYty0MB415XdyW3rHss1932UG5Z91i6uivVLgkAAIAhWrGmNQuXre4TWCTJxvbOLFy2OivWtFapMgB2xEwLYFwz6gYAAGDs6equZPHytdnekLRKtm6Ku3j52sxtbrRkC0DBmGkBjFtG3QAAAIxNq1ra+n3W21YlSWt7Z1a1tI1cUQDsEqEFMC7tbNRNsnXUjaWiAAAARp9NmwcOLJ5LOwBGjtACGJeMugEAABi7ptbXDWs7AEaO0AIYl4y6AQAAGLvmzJycpnJdBtqtopSt+xnOmTl5JMsCYBcILYBxyagbAACAsau2ppRF85uTpF9w0fN40fxmm3ADFJDQAhiXjLoBAAAY2+bNasqSBbPTWO47GK2xXJclC2Zn3qymKlUGwI5MqHYBANXQM+pm4bLVKSV9NuQ26gYAAGBsmDerKXObG7OqpS2bNndmav3WwWk+6xVTV3elUP9XRasHxotSpVKp7LwZPTo6OlIul9Pe3p6GhoZqlwMM0Yo1rVm8fG2fTbmbynVZNL/ZqBsAAAAYIUX7fF60emC0G0y/utBikIQWMHRFG6lQtHoAAABgPFmxpjULl63Oszspez6Zj/RyXkWrB8aCwfSrWx4KGFFFHKlQW1PKcYdNqcq9AQAAYDzr6q5k8fK1/QKCZOtSzqUki5evzdzmxhEZYFi0emA8shE3MGJ6RipsG1gkycb2zixctjor1rRWqTIAAACgGla1tPXrJ9hWJUlre2dWtbSNy3pgPBJaACNiZyMVkq0jFbq6rVgHAAAA48WmzQMHBM+l3VAVrR4Yj4QWwIgwUgEAAAB4tqn1dcPabqiKVg+MR0ILYEQYqQAAAAA825yZk9NUrstAu0OUsnUvzDkzJ4/LemA8EloAI8JIBQAAAODZamtKWTS/OUn6BQU9jxfNbx6xTa+LVs+2uroruWXdY7nutodyy7rHLLHNmDWh2gUA40PPSIWN7Z3b3deilKTRSAUAAAAYd+bNasqSBbOzePnaPktLN5brsmh+c+bNahrX9STJijWt/eppqmI9sDuVKpWKSG4QOjo6Ui6X097enoaGhmqXA6PKijWtWbhsdZL0CS56xiYsWTDbL1oAAAAYp7q6K1nV0pZNmzsztX7rwMZqzGgoWj09/SnP7sTVn8JoMph+daHFIAktYGiMDAAAAADYNV3dlZzwDzf06UfZVs/KFSsvPrmqAQ/szGD61S0PBYyoebOaMre5sRAjFQAAAACKbFVL24CBRbJ1JYvW9s6samnLcYdNGbnCYDcSWgAjrram5BcpAAAAwE5s2jxwYPFc2sFoILQAABjHirJOLwAA0N/U+rphbQejgdACAGCcss8QAAAU25yZk9NUrsvG9s5+G3En/7unxZyZk0e6NNhtaqpdAAAAI2/FmtYsXLa63/q4G9s7s3DZ6qxY01qlygAAgB61NaUsmt+cZGtAsa2ex4vmN5stzZgitAAAGGe6uitZvHztdkdq9RxbvHxturq31wIAABhJ82Y1ZcmC2Wks910CqrFclyULZpslzZhjeSgAgHFmVUtbvxkW26okaW3vzKqWthx32JSRKwwAANiuebOaMre50X50jAtCCwCAcWbT5oEDi+fSDgAA2P1qa0oGFTEuWB4KAGCcmVpft/NGg2gHAAAAw0VoAQAwzsyZOTlN5bp+G/n1KCVpKm+dbg4AAAAjSWgBADDO1NaUsmh+c5L0Cy56Hi+a32x9XAAAAEac0AIAYByaN6spSxbMTmO57xJQjeW6LFkwO/NmNVWpMgDYvq7uSm5Z91iuu+2h3LLusXR1V6pdEgCwG9iIGwBgnJo3qylzmxuzqqUtmzZ3Zmr91iWhzLAAoGhWrGnN4uVr09re2XusqVyXRfObBe0AMMaUKpWKoQmD0NHRkXK5nPb29jQ0NFS7HAAAABjTVqxpzcJlq/PszoueiN0MQQAovsH0q1seCgAAAArCEkh9dXVXsnj52n6BRZLeY4uXrx333ycAGEssDwUAAAAFYAmk/la1tPX5fjxbJUlre2dWtbTluMOmjFxhAMBuY6YFAAAAVFnPEkjP7qDf2N6ZhctWZ8Wa1ipVVl2bNg8cWDyXdgBA8QktAAAAoIosgTSwqfV1w9oOACg+oQUAAABU0WCWQBpv5sycnKZyXe+m289WytYltObMnDySZQEAu5HQAgAAAKrIEkgDq60pZdH85iTpF1z0PF40vzm1NQPFGgDAaCO0gN2gq7uSW9Y9lutueyi3rHtsXE7jBgAAdo0lkHZs3qymLFkwO43lvq+/sVyXJQtmj9tNygFgrJpQ7QJgrFmxpjWLl6/tM727qVyXRfOb/TENAAD007ME0sb2zu3ua1HK1g768bwE0rxZTZnb3JhVLW3ZtLkzU+u3fj/MsACAscdMCxhGK9a0ZuGy1f3Wo93Y3pmFy1ZnxZrWKlUGAAAUlSWQdk1tTSnHHTYlrztmWo47bMq4/34AwFgltIBh0tVdyeLla7c7Mqrn2OLlay0VBQAA9GMJJACArSwPBcNkVUtbvxkW26okaW3vzKqWthx32JSRKwyAwunqrljeAoB+LIEEACC0gGGzafPAgcVzaQfA2GTvIwB2pGcJJACA8cryUDBMptbX7bzRINoBMPbY+wh2n67uSm5Z91iuu+2h3LLuMUtyAgDAKGWmBQyTOTMnp6lcl43tndvd16KUrevRzpk5eaRLA6AAdrb3USlb9z6a29xoGRAYJDOYAABg7DDTAoZJbU0pi+Y3J9na8bStnseL5jfriAIYpwaz9xGw68xgAgCAsUVowXNmCn5/82Y1ZcmC2Wks910CqrFclyULZhvpBzCO2fsIht/OZjAlW2cw+TsVAABGD8tD8ZyYgj+webOaMre5Mata2rJpc2em1m9dEsoMC4Dxzd5HMPwGM4PJxsYAADA6CC0YtJ4p+M8er9YzBd+Mgq1LRflgDMC27H0Ew88MJgAAGHssD8WgmIIPAM+NvY9g+JnBBAAAY4/QgkGxiSgAPHf2PoLh1TODaaCor5StS5iawQQAAKOH5aEYFFPwAWBo7H0Ew6dnBtPCZatTSvrMBjaDCQAARiehBYNiCj4ADJ29j2D49MxgWrx8bZ8ZwY3luiya32wGEwAAjDJCCwbFJqIAABSNGUwAADB2CC0YFFPwAYDdqau7ouOZ58QMJgAAGBuEFgyaKfgAwO6wYk1rv78vmvx9AQAAMK6UKpXK9lb5YQAdHR0pl8tpb29PQ0NDtcupqqKNhCxaPQDArluxpjULl63ut/xkz2/yJQtmCy4AAABGqcH0q5tpwXNWpCn4RmYCwOjV1V3J4uVrt7tfViVbg4vFy9dmbnOjAQkAAABjXE21C4Ch6hmZuW1gkSQb2zuzcNnqrFjTWqXKAIBdsaqlrd/v8W1VkrS2d2ZVS9vIFQUAAEBVCC0Y1XY2MjPZOjKzq9sqaABQVJs2DxxYPJd2AAAAjF5CC0Y1IzMBYPSbWl83rO0AAAAYvYQWjGpGZgLA6Ddn5uQ0lesy0G4VpWzdq2rOzMkjWRYAAABVILRgVDMyEwBGv9qaUhbNb06SfsFFz+NF85ttwg0AADAOCC0Y1YzMBICxYd6spixZMDuN5b4DDRrLdVmyYHbmzWqqUmUAAACMpAnVLgCGomdk5sJlq1NK+mzIbWQmAIwu82Y1ZW5zY1a1tGXT5s5Mrd868MDvcQAAgPGjVKlUKjtvRo+Ojo6Uy+W0t7enoaGh2uXwP1asac3i5Wv7bMrdVK7LovnNRmYCAM9ZV3dFiAIAADBEg+lXN9OCMcHITABguBkUAQAAMPLMtBgkMy0AAMa+FWtas3DZ6jz7D+We4RD22QAAANh1g+lXtxE3AABso6u7ksXL1/YLLJL/3T9r8fK16eo29gcAAGC4CS0AAGAbq1ra+iwJ9WyVJK3tnVnV0jZyRQEAAIwTQgsAANjGps0DBxbPpR0AAAC7TmgBAADbmFpfN6ztAAAA2HXjJrT4+c9/nle/+tV53vOel0mTJmXOnDn5+te/Xu2yAAAomDkzJ6epXNe76fazlZI0lesyZ+bkkSwLAABgXBgXocWNN96YE044ITfffHP+5E/+JAsXLsyjjz6ac845J3//939f7fIAACiQ2ppSFs1vTpJ+wUXP40Xzm1NbM1CsAQAAwHNVqlQqlWoXsTs988wzOfLII/Pggw/mlltuyYtf/OIkyebNm3Pcccfl7rvvztq1a3P44Yfv0vN1dHSkXC6nvb09DQ0Nu7N0AACqaMWa1ixevrbPptxN5bosmt+cebOaqlgZADCadXVXsqqlLZs2d2Zq/dbZmwZDAGPdYPrVJ4xQTVVzww03ZN26dfnzP//z3sAiSerr6/OhD30ob3zjG/PlL3/ZjAsAAPqYN6spc5sbdSoAAMPGoAiAnRvzocWNN96YJDn11FP7nes5dtNNN41kSQAAjBK1NaUcd9iUapcBAIwBK9a0ZuGy1Xn2kicb2zuzcNnqLFkwW3ABkHEQWtx7771Jst3ln573vOdlv/32622zPVu2bMmWLVt6H3d0dAx/kQCMSqZ1AwAAu6Kru5LFy9f2CyySpJKt+2YtXr42c5sbfaYAxr0xH1q0t7cnScrl8nbPNzQ05MEHHxzw+o997GNZvHjxbqkN4Nl0go8eRZvW7b0DAADFtaqlrc9nh2erJGlt78yqljazPIFxb8yHFkP1gQ98IO95z3t6H3d0dGT69OlVrAgYq4rWCc7Aijat23sHAACKbdPmgQOL59IOYCyrqXYBu1vPDIueGRfP1rNr+UAmTpyYhoaGPl8Aw62nE/zZI296OsFXrGmtUmU8286mdSdbp3V3dW+vxfDz3gEAgOKbWl83rO0AxrIxH1r07GWxvX0rHn/88Tz66KPb3e8CYKQUrROcHRvMtO7dzXsHAABGhzkzJ6epXJeBFnAtZets6TkzJ49kWQCFNOZDi1e+8pVJkh/96Ef9zvUc62kDUA1F6gRn54o0rdt7BwAARofamlIWzW9Okn7BRc/jRfOb7UsHkHEQWpxyyik59NBD8/Wvfz233XZb7/HNmzfnwx/+cCZMmJC3vvWtVasPoEid4OxckaZ1e+8AAMDoMW9WU5YsmJ3Gct/PCo3luhHfFw+gyMb8RtwTJkzIlVdemdNOOy0vf/nL86Y3vSkNDQ35zne+k5aWlnzkIx/JEUccUe0ygXGsSJ3g7FzPtO6N7Z3bXZaplK0fOkZiWrf3DgAAjC7zZjVlbnNjVrW0ZdPmzkyt3/rZwQwLgP815kOLJDnppJOycuXKLFq0KFdffXWeeuqpvPCFL8yHP/zhnHPOOdUuDxjnitQJXnRd3ZWq/3HfM6174bLVKSV9/s9Gelq39w4AAIw+tTWlHHfYlGqXAVBYpUqlYnfOQejo6Ei5XE57e3saGhqqXQ4wRqxY05qFy1Yn2X4nuKnCW79Hi5ev7bOHQ1O5LovmN1fle1OUerx3AAAomiIMNgKgWAbTry60GCShBbC7FKUTvIh6Ouaf/Qur2h3zRfkw5r0DUDxF+R0BMNL8bQrA9ggtdiOhBbA76eDor6u7khP+4YY+H3q21bME0sqLTx7X3yvvHYDi0GEHjFdFHWwEQPUNpl99XOxpATBaWNu0v1UtbQMGFsnWJZFa2zuzqqVtXH/vvHcAimGgDruN7Z1ZuGy1DjtgzOrqrmTx8rXb3Wutkq3BxeLlazO3udHgGgB2qKbaBQDAjmzaPHBg8VzaAcDusrMOu2Rrh11Xt8nuwNgzmMFGALAjQgsACm1qfd2wtgOA3UWHHTCeGWwEwHARWgBQaHNmTk5TuS4DTSAvZes64XNmTh7JsgCgHx12wHhmsBEAw0VoAUCh1daUsmh+c5L0Cy56Hi+a32xdXACqTocdMJ4ZbATAcBFaAFB482Y1ZcmC2Wks9+3kaSzX2dAUgMLQYcdY09VdyS3rHst1tz2UW9Y9Zj8WdshgIwCGS6lSqfirYxA6OjpSLpfT3t6ehoaGapcDMK50dVeyqqUtmzZ3Zmr91k4fH3oAKJIVa1qzcNnqJOmzIXfPbythO6PFijWtWbx8bZ99WprKdVk0v9l7mB3y3gFgewbTry60GCShBQAAsCM67BjtesK3Z3cWCN/YVQYbAfBsQovdSGgBAADsjA47Rquu7kpO+Icb+oRu2ypl6xKdKy8+2XsaANhlg+lXnzBCNQEAAIwbtTWlHHfYlGqXAYO2qqVtwMAi2brsWWt7Z1a1tHmPAwC7hY24AQAAgCTJps0DBxbPpR0AwGAJLQAAAIAkydT6umFtBwAwWEILAAAAIEkyZ+bkNJXrMtBuFaVs3Vh+zszJI1kWADCOCC0AAACAJFv3Y1k0vzlJ+gUXPY8XzW+2CTcAsNsILQAAAIBe82Y1ZcmC2Wks910CqrFclyULZmferKYqVQYAjAcTql0AAAAAUCzzZjVlbnNjVrW0ZdPmzkyt37oklBkWAMDuJrQAAAAA+qmtKeW4w6ZUuwwAYJyxPBQAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhCC0AAAAAAIBCEFoAAAAAAACFILQAAAAAAAAKQWgBAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBCEFgAAAAAAQCEILQAAAAAAgEKYUO0CAAAA2L26uitZ1dKWTZs7M7W+LnNmTk5tTanaZQEAQD9CCwAAgDFsxZrWLF6+Nq3tnb3Hmsp1WTS/OfNmNVWxMgAA6M/yUAAAAGPUijWtWbhsdZ/AIkk2tndm4bLVWbGmtUqVAQDA9gktAAAAxqCu7koWL1+bynbO9RxbvHxturq31wIAAKpDaAEAADAGrWpp6zfDYluVJK3tnVnV0jZyRQEAwE4ILQAAAMagTZsHDiyeSzsAABgJQgsAAIAxaGp93bC2AwCAkSC0AAAAGIPmzJycpnJdSgOcLyVpKtdlzszJI1kWAADskNACAABgDKqtKWXR/OYk6Rdc9DxeNL85tTUDxRoAADDyhBYAAABj1LxZTVmyYHYay32XgGos12XJgtmZN6upSpUBAAyfru5Kbln3WK677aHcsu6xdHVXql0SQzCh2gUAAACw+8yb1ZS5zY1Z1dKWTZs7M7V+65JQZlgAAGPBijWtWbx8bVrbO3uPNZXrsmh+swEao1SpUqmInQaho6Mj5XI57e3taWhoqHY5AFBIXd0VnWMAAADsVivWtGbhstV5dgd3z6dPM0uLYzD96mZaAADDyigXAAAAdreu7koWL1/bL7BIkkq2BheLl6/N3OZGg+hGGXtaAADDpmeUy7aBRZJsbO/MwmWrs2JNa5UqAwAAYCxZ1dLW77PntipJWts7s6qlbeSKYlgILQCAYbGzUS7J1lEuNkQDAABgqDZtHjiweC7tKA6hBQAwLIxyAQAAYKRMra8b1nYUh9ACABgWRrkAAAAwUubMnJymcl0G2q2ilK37K86ZOXkky2IYCC0AgGFhlAsAAAAjpbamlEXzm5OkX3DR83jR/GabcI9CQgsAYFgY5QIAAMBImjerKUsWzE5jue/guMZyXZYsmJ15s5qqVBlDMaHaBQAAY0PPKJeFy1anlPTZkNsoFwAAAHaHebOaMre5Mata2rJpc2em1m8dLOez5+hVqlQqlZ03o0dHR0fK5XLa29vT0NBQ7XIAoHBWrGnN4uVr+2zK3VSuy6L5zUa5AAAAwDg0mH51My0AgGFllAsAAADwXAktAIBhV1tTynGHTal2GQAAAMAoYyNuAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBCEFgAAAAAAQCEILQAAAAAAgEIQWgAAAAAAAIUgtAAAAAAAAApBaAEAAAAAABSC0AIAAAAAACgEoQUAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhCC0AAAAAAIBCEFoAAAAAAACFILQAAAAAAAAKQWgBAAAAAAAUwoRqFwAAsLt1dVeyqqUtmzZ3Zmp9XebMnJzamlK1ywIAAACeRWgBAIxpK9a0ZvHytWlt7+w91lSuy6L5zZk3q6mKlQEAAADPZnkoAGDMWrGmNQuXre4TWCTJxvbOLFy2OivWtFapMgAAAGB7hBYAwJjU1V3J4uVrU9nOuZ5ji5evTVf39loAAAAA1SC0AADGpFUtbf1mWGyrkqS1vTOrWtpGrigAAABgh4QWAMCYtGnzwIHFc2kHAAAA7H5CCwBgTJpaXzes7QAAAIDdT2gBAIxJc2ZOTlO5LqUBzpeSNJXrMmfm5JEsCwAAANgBoQUAMCbV1pSyaH5zkvQLLnoeL5rfnNqagWINAAAAYKQJLQCAMWverKYsWTA7jeW+S0A1luuyZMHszJvVVKXKAAAAgO2ZUO0CAAB2p3mzmjK3uTGrWtqyaXNnptZvXRLKDAsAAAAoHqEFADDm1daUctxhU6pdBgAAALATlocCAAAAAAAKQWgBAAAAAAAUgtACAAAAAAAoBKEFADvV2dmZyy+/PC972cuy7777Zo899siUKVNy4okn5gtf+EK6urqqXSIAMIrcfffdueyyy3Laaaelqakpe+65Z8rlcl760pfm0ksvzeOPP77d6772ta/lvPPOy0te8pIceOCBmThxYurr63P00Ufnve99bx588MERfiUAAAy3UqVSqVS7iNGko6Mj5XI57e3taWhoqHY5ALvdY489lpNPPjl33HFHkmTatGlpbGzMgw8+mN/85jdJkle+8pX5wQ9+kL322quapQIAo8C6devy/Oc/v/fxgQcemAMPPDCtra156KGHkiRNTU354Q9/mKOOOqrPtc9//vOzbt26TJw4MU1NTdlvv/3yyCOPZMOGDenu7k59fX2uvfbanHzyySP6mhifurorWdXSlk2bOzO1vi5zZk5ObU2p2mUBjBg/BxmMwfSrTxihmgAYpd7//vfnjjvuyL777ptvf/vbfToBrrnmmrz5zW/OTTfdlE984hNZtGhRFSsFAEaDSqWSqVOn5l3velfOOeecHHroob3nfvrTn+acc87J/fffnzPOOCNr167NxIkTe8+/5z3vyVFHHZVjjz02e+yxR+/xe++9N+eee25WrlyZN77xjVm/fn323nvvEX1djC8r1rRm8fK1aW3v7D3WVK7LovnNmTerqYqVAYwMPwfZncy0GCQzLYDxZurUqXnkkUdy2WWX5a/+6q/6nf/ABz6Qj3/84/nDP/zD3HrrrVWoEAAYTTo7O9PV1ZVJkyZt9/zPfvazHH/88UmS6667Lq997Wt36Xk3btyYpqatnSQ/+MEPMm/evOEpGJ5lxZrWLFy2Os/uTOkZW7xkwWwddsCY5ucgz8Vg+tXtaQHADv3ud79Lkhx++OHbPX/EEUckSZ5++ukRqwkAGL3q6uoGDCyS5GUve1nK5XKS5K677trl521sbMzkyZOTJL/97W+HViQMoKu7ksXL1/brqEvSe2zx8rXp6jY+FBib/BxkJAgtANih2bNnJ0luvvnm7Z7/yU9+kiQ59thjR6wmAGDs6urq6h0MMZglntasWZO2trbU1tb2/v0Cw21VS1ufpVCerZKktb0zq1raRq4ogBHk5yAjQWgBwA599KMfzcSJE/PJT34yH/3oR3P//fdny5YtWbduXd73vvdl6dKlmT59ej70oQ9Vu1QARtjGjRuzbNmyvPvd784JJ5yQSZMmpVQqZcaMGTu9tr29PZdeemmOOeaY7LPPPtlnn31y9NFH58Mf/nDvLD/Gp2uuuab3PfDyl798h20rlUo2btyYb37zm5k/f36S5OKLL87MmTN3e52MT5s2D9xR91zaAYw2fg4yEmzEDcAOvfzlL89PfvKTXHLJJfnQhz6UD37wg73namtrc+GFF+YDH/hADjjggCpWCUA1/Nu//dt29zvamXXr1mXu3LlpaWlJqVTKH/zBH2TixIm5884783d/93f5xje+kZtuuin777//bqiaImtra8tFF12UJHn1q1+dY445ZrvtPvWpT/V77x199NG5+uqrc9ZZZ+3uMhnHptbXDWs7gNHGz0FGgpkWAOzU+vXrs3HjxlQqlUydOjWzZ8/O1KlT09XVlauvvjrLly+vdokAVEFDQ0NOOeWUXHzxxbn66qvzqU99aqfXdHd358wzz0xLS0uOOOKIrF27NnfeeWdWr16dDRs25KSTTspdd92VN7/5zbv/BVAoTz/9dM4666w88MAD2X///fPFL35xwLbTpk3L8ccfn2OPPTbTpk1LqVTK2rVrc9VVV+Xhhx8ewaoZb+bMnJymcl3vZrPPVkrSVK7LnJmTR7IsgBHj5yAjQWgBwA5dfvnlOfvss7Nhw4Z873vfy29+85v84he/yG9+85ssX748W7ZsyXnnnZfPfvaz1S4VgBF27rnn5sc//nE+/vGP56yzzsq0adN2es0PfvCD3HHHHUmSf/3Xf82RRx7Ze27q1KlZtmxZJk2alB//+Me54YYbdlvtFEtXV1cWLFiQG264IfX19Vm+fHkOPPDAAdufddZZWblyZW655ZY8+OCDufvuu3P66adn+fLlOfbYY7N58+YRrJ7xpLamlEXzm5OkX4ddz+NF85tTWzNQdx7A6ObnICNBaAHAgB555JHe5aAuv/zyvOY1r+lz/o//+I9z+eWXJ0k+9KEPZcuWLSNeIwCjy80335xk60j5E044od/5Aw88MK94xSuSJF//+tdHtDaqo7u7O+eee26uvvrqTJo0Kddff33+6I/+aFDPcfjhh+eaa65Jc3NzNmzYkCuuuGI3VQvJvFlNWbJgdhrLfZc+aSzXZcmC2Zk3q6lKlQGMDD8H2d3saQHAgG699dbejTBPO+207baZN29ekuTxxx/Pvffem1mzZo1YfQCMPo899liS7HBWxvTp05MkK1euHJGaqJ5KpZK3v/3t+epXv5q999473/ve93a6+fZAamtrc/rpp2ft2rW59dZbh7lS6GverKbMbW7Mqpa2bNrcman1W5dCMbIYGC/8HGR3GvOhxU9+8pN897vfzS9+8YusXr06HR0d+bM/+7MsXbq02qUBFF5HR8eg2nd2du6mSgAYK/bdd98kyUMPPTRgmw0bNiRJfv3rX+eZZ57JhAlj/mPLuPXOd74zV155Zfbaa69897vfzYknnjik53vmmWeSbF1uCna32ppSjjtsSrXLAKgaPwfZXcb88lBf+tKX8slPfjKrVq3a4ZqoAPT3ghe8oPffK1as2G6bH/zgB0m2jm48/PDDR6QuAEavY489NsnW0OJnP/tZv/MbN27sXUKqq6tr0AE6o8cFF1yQJUuWpK6uLtddd11OOeWUIT3fli1b8r3vfS9J8uIXv3g4SgQAoArGfGjxrne9K2vWrElHR0e+/OUvV7scgFHlRS96UV70ohclSd7znvfk+9//fp/z119/fd7znvckSc4888yUy+URrxGA0WX+/PmZMWNGkuStb31rbr/99t5zDz30UP70T/80Tz75ZO+xnmUKGVve97735YorrugNLObOnbvTa66//vp84hOfyAMPPNDv3F133ZU//uM/zrp161JfX5/zzjtvd5QNAMAIGPPzrF/ykpdUuwSAUatUKuXrX/96TjnllGzcuDGvec1rMnXq1Bx00EF58MEHs2nTpiTJC1/4wnz2s5+tcrUAjAZ77rlnvv3tb+e0007Lvffemxe/+MU55JBDstdee+Wee+5Jd3d33vrWt/Yu59rQ0FDdghl2t9xyS/7xH/8xydb/30svvTSXXnrpdtuee+65Offcc5MkjzzySC6++OJcfPHFaWxszLRp01JbW5uHH344Dz74YJJkv/32y7e+9a0d7pkCAECxjfnQAoDnrqu7kvaJB+QT3/iP3Pidr+b2n92Qe++9N7fffnsaGhpywgkn5Mwzz8zChQtTV1dX7XIBGCVmz56d22+/Pf/0T/+U66+/Pvfff3/q6upyyimn5H3ve1+eeOKJLF26NJMmTUp9fX21y2WYbdmypfffmzZt6h0EsT2vetWrev89d+7c/OM//mNuuumm3HXXXbnnnnvS2dmZfffdN694xSty+umn5/zzz8/kyZN3a/0AAOxeQoud2LJlS58/qq2pC4wXK9a0ZvHytWlt/5/Ntfd+ZZrecFq+Ob8582Y1Vbc4AEa9Aw88MJdddlkuu+yyfufe//73J9k6a7pUKo10aexmL3/FK/OzXz+aTZs7M7W+LnNmTk5tzc7/n6dNm5b3vve9ee973zsCVQIAUC1Ci5342Mc+lsWLF1e7DIARtWJNaxYuW53Ks45vbO/MwmWrs2TBbMEFALtFd3d3vvWtbyVJzjjjjOoWw7DrNygiSVO5LosMigAA4H+Mio2499tvv5RKpV3+uvHGG4ft3h/4wAfS3t7e+7Vhw4Zhe26AIurqrmTx8rX9AoskvccWL1+bru7ttQCAofnc5z6X++67L1OmTMlb3/rWapfDMOoZFLFtYJH876CIFWtaq1QZAABFMipmWrzpTW/K5s2bd7l9Y2PjsN174sSJmThx4rA9H0DRrWpp69eZsK1Kktb2zqxqactxh00ZucIAGDN+/OMfp6amJieddFLv8k+dnZ3553/+596loT772c9m3333rWKVDKedDYooZeugiLnNjbu0VBQAAGPXqAgtrrjiimqXADBubNo8cGDxXNoBMHZt2LAhL37xi3sfP/XUU73H99tvv97jb3rTm/r8Tb9y5cosXrw4++yzT2bMmJEJEybknnvuye9+97vsueee+dznPpezzz575F4Iu51BEQAA7KpREVoAMHKm1tcNazsAxq6urq489thj/Y53d3f3Of7sWdPz5s1LS0tL/uu//ivr169PV1dXDjrooJx66qm58MILc/jhh+/22hlZBkUAALCrhBYA9DFn5uQ0leuysb1zu0s4lJI0lusyZ+bkkS4NgALp6q6ktas+1/73g5lav/X3wq4u63Psscfm2GOP3c0VUiQGRQAU1/XXX58f/OAH+cUvfpENGzbk0UcfTW1tbaZPn56TTjopF154YY488sh+133ta1/LjTfemP/+7//Oww8/nMceeyx77rlnZs6cmVNPPTXvfve7c9BBB1XhFQGjXalSqYzpnVRXrlyZK6+8MknyyCOP5Pvf/34OO+ywnHDCCUmSI488snfd3F3R0dGRcrmc9vb2NDQ07JaaAaqtZ6PMJH2Ci56uqCULZmferKYRrwuAYlixpjWLl6/ts9xPU7kui+Y3+/3AdnV1V3LCP9yw00ERKy8+2Z4WACPsVa96Vf7zP/8zEyZMSFNTUw444IA8/vjjuf/++/PMM89kjz32yNKlS/PmN7+5z3XPf/7zs27dukycODFNTU3Zb7/98sgjj2TDhg3p7u5OfX19rr322px88slVemVAkQymX33MhxZLly7Nn//5nw94/pWvfGVuvPHGXX4+oQUwXuiQAmB7eoLtZ3+IEGyzMwZFABTTVVddlcbGxpxwwgnZa6+9eo8/9NBD+cu//Mtcc801qauryz333JPp06f3nv/c5z6Xo446Kscee2z22GOP3uP33ntvzj333KxcuTL7779/1q9fn7333ntEXxNQPEKL3UhoAYwnXd2VrGppy6bNnYNe+gOAsadntPxAGyobLc/OGBQBMLps2bIljY2NeeKJJ7JkyZK84x3v2KXrNm7cmKamrT/Xf/CDH2TevHm7s0xgFBhMv7o9LQAYUG1NKccdNqXaZQBQEKta2gYMLJKto+db2zuzqqXN7w+2a96spsxtbjQoAmCUmDhxYg499NCsXr06v/3tb3f5usbGxkyePDltbW2Dug4gEVoAAAC7aNPmgQOL59KO8cmgCIDRY9OmTbnrrruSJC996Ut3+bo1a9akra0ttbW1mT179u4qDxijaqpdAAAAMDpMra8b1nYAQDE9+uij+f73v59TTz01v//97/PmN785r3jFK3Z4TaVSycaNG/PNb34z8+fPT5JcfPHFmTlz5kiUDIwhQgsAAGCXzJk5OU3lugy0kE8pW/cnmDNz8kiWBQAMg2uvvTalUimlUin7779/XvOa16SjoyP/8i//kmXLlg143ac+9amUSqXU1NSkqakpb3zjG9PQ0JCrr746H/3oR0fwFQBjhdACAADYJbU1pSya35wk/YKLnseL5jfbnwAARqEpU6bk+OOPz3HHHZdDDjkktbW1Wb9+fb7+9a/n7rvvHvC6adOm5fjjj8+xxx6badOmpVQqZe3atbnqqqvy8MMPj+ArAMaKUqVSqVS7iNFkMLucAwDAWLRiTWsWL1/bZ1PupnJdFs1vzrxZTVWsDAAYLq2trfngBz+YL33pS2loaMgdd9yRQw45ZKfX3XvvvbnooouyfPnyTJ8+PXfeeWfq6+tHoGKgyAbTry60GCShBQAAJF3dlaxqacumzZ2ZWr91SSgzLABg7DnttNPyox/9KOedd16+8IUv7NI1XV1dOfroo7N27dp89KMfzd/8zd/s5iqBohtMv7rloQAAgEGrrSnluMOm5HXHTMtxh00RWADAGNWzqfatt966y9fU1tbm9NNPH/R1AInQAgAAAAAYwDPPPJNk6+yJkbgOQGgBAAAAAPRTqVTy7W9/O0ny4he/eJev27JlS773ve8N+jqARGgBAAAAAOPSrbfemg9+8IO5++67+5174IEHcs4552TlypWpra3NhRde2Hvu+uuvzyc+8Yk88MAD/a6766678sd//MdZt25d6uvrc9555+3W1wCMPTbiHiQbcQMAAAAwFtx444056aSTkiRTpkzJwQcfnD333DObNm3K+vXrU6lUMmnSpPzrv/5rzj777N7rli5dmj//8z9PkjQ2NmbatGmpra3Nww8/nAcffDBJst9+++Vb3/pWTjzxxBF/XUDxDKZfXWgxSEILAAAAAMaCxx9/PFctW5Zrrv+P3POrO9PR9mg6f/+7NDQ05IgjjsirXvWqvP3tb89BBx3U57qHHnoo3/jGN3LTTTflrrvuyqZNm9LZ2Zl99903f/AHf5DTTz89559/fiZPnlylVwYUjdBiNxJaAAAAADAWrFjTmsXL16a1vbP3WFO5LovmN2ferKYqVgaMNYPpV7enBQAAAACMMyvWtGbhstV9Aosk2djemYXLVmfFmtYqVQaMd0ILAAAAABhHurorWbx8bba3/ErPscXL16ar2wItwMgTWgAAAADAOLKqpa3fDIttVZK0tndmVUvbyBUF8D+EFgAAAIxrd999dy677LKcdtppaWpqyp577plyuZyXvvSlufTSS/P444/v8nPdeuutmTBhQkqlUkql0m6sGuC527R54MDiubQDGE4Tql0AAAAAVMu6dety5JFH9j4+8MAD86IXvSitra259dZbc+utt+bzn/98fvjDH+aoo47a4XM9/fTTOffcc9PV1bW7ywYYkqn1dcPaDmA4mWkBAADAuFWpVDJ16tRceumlWbduXR566KH8/Oc/z4MPPpiVK1fmkEMOSWtra84444xs2bJlh8/1kY98JL/85S9z5plnjlD1AM/NnJmT01Suy0DzwUpJmsp1mTNz8kiWBZBEaAEAAMA4dtBBB+W+++7Lhz70oRx66KF9zh1//PH5+te/niS577778sMf/nDA57njjjvysY99LMcee2ze+c537taaAYaqtqaURfObk6RfcNHzeNH85tTWWOYOGHlCCwAAAMaturq6TJo0acDzL3vZy1Iul5Mkd91113bbdHV15dxzz02SfPGLX0xNjY/aQPHNm9WUJQtmp7HcdwmoxnJdliyYnXmzmqpUGTDe2dMCAAAABtDV1ZWnn346SbL33ntvt80nPvGJ/OIXv8gHP/jBzJo1KzfeeOMIVgjw3M2b1ZS5zY1Z1dKWTZs7M7V+65JQZlgA1SS0AAAAgAFcc801+d3vfpckefnLX97v/K9+9assXrw4L3jBC/LBD35wpMsDGLLamlKOO2xKtcsA6GXOKgAAAGxHW1tbLrrooiTJq1/96hxzzDF9znd3d+fcc8/NU089lS9+8YuZOHFiFaoEABhbhBYAAADwLE8//XTOOuusPPDAA9l///3zxS9+sV+bT3/607nlllty/vnnb3cWBgAAgye0AAAAgG10dXVlwYIFueGGG1JfX5/ly5fnwAMP7NNm3bp1+eAHP5gDDzww//AP/1ClSgEAxh57WgAAAMD/6Fny6eqrr86kSZNy/fXX54/+6I/6tXv729+e3/3ud/na176WcrlchUoBAMamUqVSqVS7iNGko6Mj5XI57e3taWhoqHY5AAAADJNKpZLzzz8/V155Zfbee+9cf/31OfHEE7fbdt999017e3sOOOCAfueeeuqpPP7440nSe/7DH/5wzjvvvN1WOwBAkQ2mX91MCwAAAEjyzne+M1deeWX22muvfPe73x0wsNjWb37zm106/9vf/nY4SgQAGPOEFgAAAIx7F1xwQZYsWZK6urpcd911OeWUU3bY/oknnhjw3I033piTTjopydbZGwAA7DobcQMAADCuve9978sVV1zRG1jMnTu32iUBAIxb9rQYJHtaAAAAjB233HJLXvaylyVJpk6dmsMPP3zAtueee27OPffcnT6nmRYAAH3Z0wIAAAB2we9+39n7702bNmXTpk0Dtn3Vq141EiUBAIxrQgsAAADGpRVrWrP4/3XnkIu/13usqVyXRfObM29W03N+3hNPPNEMCwCA58ieFgAAAIw7K9a0ZuGy1Wlt7+xzfGN7ZxYuW50Va1qrVBkAwPgmtAAAAGBc6equZPHytdneXIieY4uXr01Xt9kSAAAjTWgBAADAuLKqpa3fDIttVZK0tndmVUvbyBUFAEASoQUAAADjzKbNAwcWz6UdAADDR2gBAADAuDK1vm5Y2wEAMHyEFgAAAIwrc2ZOTlO5LqUBzpeSNJXrMmfm5JEsCwCACC0AAAAYZ2prSlk0vzlJ+gUXPY8XzW9Obc1AsQYAALuL0AIAAIBxZ96spixZMDuN5b5LQDWW67JkwezMm9VUpcoAAMa3CdUuAAAAAKph3qymzG1uzKqWtmza3Jmp9VuXhDLDAgCgeoQWAAAAjFu1NaUcd9iUapcBAMD/sDwUAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBCEFgAAAAAAQCEILQAAAAAAgEIQWgAAAAAAAIUgtAAAAAAAAApBaAEAAAAAABSC0AIAAAAAACgEoQUAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhCC0AAAAAAIBCEFoAAAAAAACFILQAAAAAAAAKQWgBAAAAAAAUwoRqFzDaVCqVJElHR0eVKwEAAAAAgOLr6U/v6V/fEaHFIG3evDlJMn369CpXAgAAAAAAo8fmzZtTLpd32KZU2ZVog17d3d15+OGHU19fn1KpVO1yqq6joyPTp0/Phg0b0tDQUO1y4DnzXmYs8D5mrPBeZqzwXmYs8D5mrPBeZqzwXma0qlQq2bx5cw488MDU1Ox41wozLQappqYmBx10ULXLKJyGhgY/KBkTvJcZC7yPGSu8lxkrvJcZC7yPGSu8lxkrvJcZjXY2w6KHjbgBAAAAAIBCEFoAAAAAAACFILRgSCZOnJhFixZl4sSJ1S4FhsR7mbHA+5ixwnuZscJ7mbHA+5ixwnuZscJ7mfHARtwAAAAAAEAhmGkBAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWPCc///nP8+pXvzrPe97zMmnSpMyZMydf//rXq10WDMqMGTNSKpW2+/WOd7yj2uVBH8uWLcvb3/72vOQlL8nEiRNTKpWydOnSAdt3dHTkPe95Tw455JBMnDgxhxxySN7znveko6Nj5IqG7RjMe/mSSy4Z8Od0XV3dyBYO23jooYfyqU99KqeeemoOPvjg7Lnnnv9/e/ceFnWZ/3/8Nc7gUIiIDoInoIKN8iIVDx08byLoGuvlAUU3MQ+51rVpV7vlWiGKe1nWtZ03StfDesC1y0NZarYlx1plLxX1sgJdwVOQJChaqMjn94e/mRgZFKqvM8w+H9fFH9z3/Zl5f8b7ehfz/tz3rZCQEI0ePVq7du1yeQ15GZ6mqfOYnAxPVVlZqSeeeEL333+/QkJCZLVa1alTJ/3617/Whg0bZBhGvWvIyfBETZ3L5GV4M4u7A0Dzk5mZqbi4OLVs2VLjx49XQECANm7cqIkTJ6q4uFhz5851d4hAowUEBGj27Nn12nv16nXzgwGu47nnnlNJSYlsNps6dOigkpKSBsdeuHBBAwcO1L59+xQbG6ukpCQVFBTolVde0c6dO5Wbmys/P7+bGD3wo6bMZbvk5GSFh4c7tVks/G8s3OeNN97Qiy++qDvuuEOxsbFq3769ioqKtHnzZm3evFkZGRlKTEx0jCcvwxM1dR7bkZPhacrLy7Vs2TLdd999GjlypNq2batvv/1WW7Zs0ZgxYzR9+nS9++67jvHkZHiqps5lO/IyvJIBNMHly5eNO+64w7BarcaePXsc7efOnTO6du1qWCwWo7Cw0I0RAo0XFhZmhIWFuTsMoFE++eQTo7i42DAMw1i0aJEhyVi+fLnLsSkpKYYk4+mnn3bZnpKS8n8dLtCgpszlefPmGZKMnTt33rwAgUbYsGGDkZ2dXa89Ozvb8PHxMdq2bWtUV1c72snL8ERNncfkZHiqmpoa4/Lly/Xaz507Z9x9992GJOPgwYOOdnIyPFVT5zJ5Gd6M7aHQJJ999pmOHDmiCRMmqEePHo52f39/Pf/886qpqdHy5cvdGCEAeKchQ4YoLCzshuMMw9DSpUvVqlUrpaSkOPX9+c9/VmBgoP7+97+7XCYP3AyNncuAJxs1apT69+9fr71///4aPHiwzpw5owMHDkgiL8NzNWUeA57MbDa7fKrc399fcXFxkqTDhw9LIifDszVlLgPejrVCaJLMzExJ0tChQ+v12duysrJuZkjAz3Lx4kWtXLlSJ0+eVGBgoB544AF169bN3WEBP1lRUZFOnTqluLi4esvafX19NWDAAL3//vs6fPiwIiMj3RQl0DQ5OTnavXu3zGazoqKiNGTIEFmtVneHBbjk4+Mj6cdtGcjLaI6uncd1kZPRXFRXV+uzzz6TyWTS3XffLYmcjObJ1Vyui7wMb0TRAk1SVFQkSS7/4x0YGCibzeYYAzQHpaWlmjx5slNbfHy8Vq1aJZvN5p6ggJ/henm6bntRURF/iKHZuPZJyA4dOmjlypWKjY11U0SAa8eOHdO//vUvhYSEKDo6WhJ5Gc2Pq3lcFzkZnqqyslKvvvqqamtr9e2332rr1q06fvy45s2b55RrJXIyPFtj5nJd5GV4I7aHQpOcPXtW0tXDi11p3bq1Ywzg6aZMmaLMzEy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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"resid\", threshold=1)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=1, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=2, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
 
 
 
 
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = infl.plot_index(y_var=\"dfbeta\", idx=0, threshold=0.5)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
