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   "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."
   ]
  },
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   "source": [
    "import os.path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from statsmodels.genmod import families\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
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   "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)"
   ]
  },
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    {
     "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:                Sun, 26 Jul 2026   Deviance:                       29.227\n",
      "Time:                        22:23:04   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."
   ]
  },
  {
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     "shell.execute_reply": "2026-07-26T22:23:04.980514Z"
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   },
   "outputs": [],
   "source": [
    "infl = res.get_influence(observed=False)"
   ]
  },
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       "</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",
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       "  </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",
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      ],
      "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]"
   ]
  },
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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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-07-26T22:23:05.834508Z",
     "iopub.status.busy": "2026-07-26T22:23:05.834166Z",
     "iopub.status.idle": "2026-07-26T22:23:06.587998Z",
     "shell.execute_reply": "2026-07-26T22:23:06.587060Z"
    },
    "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-07-26T22:23:06.592821Z",
     "iopub.status.busy": "2026-07-26T22:23:06.592619Z",
     "iopub.status.idle": "2026-07-26T22:23:07.128059Z",
     "shell.execute_reply": "2026-07-26T22:23:07.126985Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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so/lzpqahXJdNbZ0D7mtRSjKzXJf5c6aOdGkAMCRDCi3e8pa3DFcdAAAAAOyj2ppSli1uzNKV61JK+gQXpV//d9nixtTWlAa4GgCKa0gbcQMAAABQHYuaGrJiybzMLPddAmpmuS4rlszLoqaGKlUGAAduSDMtAAAAAKieRU0NWdg4M2tbtmVzR2dmTNmzJJQZFgCMVkILAAAAgFGstqaUBXOPrHYZADAsLA8FAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBAmVLsAAAAAAAAYK7q6K1nbsi2bOzozY0pd5s+ZmtqaUrXLGjWEFgAAAAAAMAxWr2/N8lXNaW3r7D3WUK7LssWNWdTUUMXKRg/LQwEAAAAAwBCtXt+apSvX9QkskmRTW2eWrlyX1etbq1TZ6CK0AAAAAACAIejqrmT5quZUBjjXc2z5quZ0dQ/Ugr0JLQAAAAAAYAjWtmzrN8Nib5UkrW2dWduybeSKGqWEFgAAAAAAMASbOwYPLA6k3XgmtAAAAAAAgCGYMaVuWNuNZ0ILAAAAAAAYgvlzpqahXJfSIOdLSRrKdZk/Z+pIljUqCS0AAAAAAGAIamtKWba4MUn6BRc9j5ctbkxtzWCxBj2EFgAAAAAAMESLmhqyYsm8zCz3XQJqZrkuK5bMy6KmhipVNrpMqHYBAAAAAAAwFixqasjCxplZ27Itmzs6M2PKniWhzLDYd0ILAAAAAMasru6KzkNgRNXWlLJg7pHVLmPUEloA45Y3rgAAAGPb6vWtWb6qOa1tnb3HGsp1Wba40TItAAUltADGJW9cAQAAxrbV61uzdOW6VJ5xfFNbZ5auXGd9eYCCshE3MO70vHHdO7BI/vuN6+r1rVWqDAAAgOHQ1V3J8lXN/QKLJL3Hlq9qTlf3QC0AqCahBTCueOMKAAAw9q1t2dZvoNreKkla2zqztmXbyBUFwD4RWgDjijeuAAAAY9/mjsH/7juQdgCMHKEFMK544woAADD2zZhSN6ztABg5QgtgXPHGFQAAYOybP2dqGsp1KQ1yvpSkoVyX+XOmjmRZAOwDoQUwrnjjCgAAMPbV1pSybHFjkvT7+6/n8bLFjamtGeyvQwCqRWgBjCveuAIAAIwPi5oasmLJvMws951JP7NclxVL5mVRU0OVKgPg2ZQqlUql2kWMFu3t7SmXy2lra0t9fX21ywGGYPX61ixf1dxnU+6Gcl2WLW70xhUAAGAM6equZG3Ltmzu6MyMKXtm1huoVkxF+14VrR4Yzfanb11osR+EFjC2ePMBAAAAxVC0wYVFqwdGO6HFQSK0gKETFAAAAAB7W72+NUtXrsszOyl7egtGejmvotUDY8H+9K1PGKGaAIxSAAAAAPro6q5k+armfgFBklSyJyhYvqo5Cxtnjsigx6LVA+ORjbiBEdEzSmHvwCJJNrV1ZunKdVm9vrVKlQEAAADVsrZlW7++gr1VkrS2dWZty7ZxWQ+MR0IL4KB7rlEKyZ5RCl3dVqsDAACA8WRzx+ABwYG0G6qi1QPjkdACOOiMUgAAAAAGMmNK3bC2G6qi1QPjkdACOOiMUgAAAAAGMn/O1DSU6zLY7hCl7NkPc/6cqeOynr11dVdy+8+25pp7H8ntP9tqxQrGLBtxAwedUQoAAADAQGprSlm2uDFLV65LKemztHRPcLBsceOIbXpdtHp6rF7fmuWrmvusZNFQrsuyxY1Z1NQworXAwWamBXDQFXmUAgAAAFBdi5oasmLJvMws9x3MOLNclxVL5o14p3zR6lm9vjVLV67rt/T2prbOLF25LqvXt45oPXCwlSqVinlE+6i9vT3lcjltbW2pr6+vdjkwqvT8gk0GHqVQjV/6AAAAQHF0dVeytmVbNnd0ZsaUPYMbR3pGQ9Hq6equ5PTLbhp0r9BS9oQpt/zpq6v6tYLnsj9965aHAkZEzyiFZ05lnGkqIwAAAJA9SzMtmHtktcvoVYR61rZsGzSwSPYMDG1t68zalm1VrxWGi9ACGDGLmhqysHFm1UcpAAAAAIwGmzsGDywOpB2MBkILYEQVYZQCAHsUYbo7AAAwuBlT6p670X60g9FAaAEAMA6tXt/ab8m+Bkv2AQBAocyfMzUN5bpsauvMQBsT9+xpMX/O1JEuDQ6ammoXAADAyFq9vjVLV67rtzbuprbOLF25LqvXt1apMgAAYG+1NaUsW9yYZE9Asbeex8sWN5oxzZgitAAAGEe6uitZvqp5wFFaPceWr2pOV/dALQAAgJG2qKkhK5bMy8xy3yWgZpbrsmLJPDOlGXMsDwUAMI6sbdnWb4bF3ipJWts6s7Zlmz2IAACgIBY1NWRh40x70jEuCC0AAMaRzR2DBxYH0g4AABgZtTUlA4sYFywPBQAwjsyYUvfcjfajHQAAAAwnoQUAwDgyf87UNJTr+m3i16OUpKG8Z6o5AAAAjDShBQDAOFJbU8qyxY1J0i+46Hm8bHGjtXEBAACoCqEFAMA4s6ipISuWzMvMct8loGaW67JiybwsamqoUmUAMLiu7kpu/9nWXHPvI7n9Z1vT1V2pdkkAwEFgI24AgHFoUVNDFjbOzNqWbdnc0ZkZU/YsCWWGBQBFtHp9a5avak5rW2fvsYZyXZYtbhS2A8AYU6pUKoYm7KP29vaUy+W0tbWlvr6+2uUAAADAmLd6fWuWrlyXZ3Ze9MTsZgkCQPHtT9+65aEAAACAQurqrmT5quZ+gUWS3mPLVzVbKgoAxhChBQAAABSAPRv6W9uyrc+SUM9USdLa1pm1LdtGrigA4KCypwUAAABUmT0bBra5Y/DA4kDaAQDFZ6YFAAAAVFHPng3PnFGwqa0zS1euy+r1rVWqrPpmTKkb1nYAQPEJLQAAAKBK7Nnw7ObPmZqGcl3vptvPVMqeGSnz50wdybIAgINIaAEAAABVYs+GZ1dbU8qyxY1J0i+46Hm8bHFjamsGizUAgNFGaAHDzOZ5AADAvrJnw3Nb1NSQFUvmZWa57xJQM8t1WbFk3rje8wMAxiIbccMwsnkeAACwP+zZsG8WNTVkYePMrG3Zls0dnZkxZc+SUGZYAMDYI7SAYdKzed4z51X0bJ5nBBAAAPBMPXs2bGrrHHBfi1L2zCiwZ8OepaIWzD2y2mUAAAeZ5aFgGNg8DwAAOBD2bAAA6EtoAcPA5nkAAMCBsmcDAMB/szwUDAOb5wGwr7q6K9bjBqAfezYAAOwhtIBhYPM8APbF6vWtWb6quc/svIZyXZYtbjSKFgB7NgAAxPJQMCx6Ns8bbAxUKXs6pWyeBzB+rV7fmqUr1/VbTnBTW2eWrlyX1etbq1QZjH5d3ZXc/rOtuebeR3L7z7baRwwAAEYxMy1gGPRsnrd05bqUkj4bcts8D4Cu7kqWr2rOQN2olez5XbF8VXMWNs70uwL2kxlMAAAwtphpwQExmq0/m+cBMJi1Ldv6zbDYWyVJa1tn1rZsG7miYAwwgwkAAMYeMy3Yb0azDc7meQAMZHPH4IHFgbQDzGACAICxykwL9ovRbM+tZ/O81580KwvmHumPZAAyY0rdczfaj3aAGUwAADBWCS3YZ881mi3ZM5rNUlEA0Nf8OVPTUK7LYDF2KXtmLc6fM3Uky4JRzQwmAAAYm4QW7DOj2QDgwNTWlLJscWOS9Asueh4vW9xodh7sBzOYAABgbBJasM+MZgOAA7eoqSErlszLzHLfDtSZ5bqsWDJv3O8LBfvLDCYAABibbMTNPjOaDQCGZlFTQxY2zszalm3Z3NGZGVP2dKiaYQH7r2cG09KV61JK+ixhagYTAACMXmZasM+MZgOAoautKWXB3CPz+pNmZcHcI3WowhCYwQQAAGOPmRbsM6PZAICDqau7YhYK+80MJgAAGFtKlUql8tzNSJL29vaUy+W0tbWlvr6+2uVUzer1rVm+qrnPptwN5bosW9xoNBsAcEC8vwAAABi79qdvXWixH4QW/81ISABguKxe35qlK9flmW9Ke95ZWOYHAABgdNufvnXLQ3FAetbjLgIBCgCMXl3dlSxf1dwvsEj2LEVZSrJ8VXMWNs70+x0AAGAcEFowqllKAgBGt7Ut2/r8Hn+mSpLWts6sbdlWmAETAAAAHDw11S4ADlTPUhLP7OjY1NaZpSvXZfX61ipVBgDsq80dgwcWB9IOAACA0U1owaj0XEtJJHuWkujqtmULABTZjCl1w9oOAACA0U1owai0P0tJAADFNX/O1DSU6zLYbhWl7Fn6cf6cqSNZFgAAAFUitGBUspQEAIwNtTWlLFvcmCT9gouex8sWN9qEGwAAYJwQWjAqWUoCAMaORU0NWbFkXmaW+/7enlmuy4ol87KoqaFKlQEAADDSJlS7ADgQPUtJbGrrHHBfi1L2dHRYSgIARodFTQ1Z2Dgza1u2ZXNHZ2ZM2fN73AwLAACA8UVowajUs5TE0pXrUkr6BBeWkgCA0am2ppQFc4+sdhm9urorQhQAAIARJrRg1OpZSmL5quY+m3LPLNdl2eJGS0kAAAds9frWfu8xGrzHAAAAOOhKlUploNV1GEB7e3vK5XLa2tpSX19f7XL4NaMgAYDhtHp9a5auXNdvCcqedxf22QAAANg/+9O3bqYFo17RlpIAAEavru5Klq9qHnDPrEr2BBfLVzVnYeNMgyQAAAAOgppqFwAAAEWxtmVbnyWhnqmSpLWtM2tbto1cUQAAAOOI0AIAAH5tc8fggcWBtAMAAGD/CC0AAODXZkypG9Z2AAAA7J9xs6fFY489lh/96EeZMGFCTjrppBx++OHVLgkAgIKZP2dqGsp12dTWOeC+FqUkM8t1mT9n6kiXBgAAMC6M+ZkWO3fuzLve9a7MmjUrCxcuzKte9arMnDkzn/jEJ6pdGgAABVNbU8qyxY1J9gQUe+t5vGxxo024AQAADpIxH1pccskl+cpXvpKampqcddZZ+c3f/M3s2rUry5cvz1//9V9XuzwAAApmUVNDViyZl5nlvktAzSzXZcWSeVnU1FClygCAsaCru5Lbf7Y119z7SG7/2dZ0dQ80vxNg/CpVKpUx+5Pxxz/+cV784hdnypQpueWWW/KSl7wkSXLDDTfknHPOyeTJk7Nx48bU19fv0/O1t7enXC6nra1tn68BAGB06uquZG3Ltmzu6MyMKXuWhDLDAgAYitXrW7N8VXNa2zp7jzWU67JscaOBEcCYtj9962N6psW3vvWtVCqVvO997+sNLJJk4cKFefOb35yOjo5cd911VawQAICiqq0pZcHcI/P6k2ZlwdwjBRYAwJCsXt+apSvX9QkskmRTW2eWrlyX1etbq1QZQLGM6dDi7rvvTpKcc845/c79zu/8Tp82AAAAAHAwdHVXsnxVcwZa7qTn2PJVzZaKAkgyodoFHEwbN25MksydO7ffuec///lJkg0bNgx6/c6dO7Nz587ex+3t7cNcIcB/swzJ6OL7BQAA7Ku1Ldv6zbDYWyVJa1tn1rZsy4K5R45cYQAFNKZDi6eeeipJcthhh/U713PsySefHPT6Sy+9NMuXLz84xQHsxbqmo0vRvl8CFAAAKLbNHYMHFgfSDmAsG9PLQx1yyCFJkl27dvU713Ns4sSJg17/kY98JG1tbb0fzzYrA+BAWdd0dCna92v1+tacftlNeeuX78j7/uXevPXLd+T0y27yugEAgAKZMaVuWNsBjGVjOrSYNm1akqS1tX/HzaOPPtqnzUAmTpyY+vr6Ph8Aw8m6pqNL0b5fRQtQAACAgc2fMzUN5boMNh+6lD2zt+fPmTqSZQEU0pgOLV784hcnSe64445+526//fYkSWNj44jWBLC3/VnXlOor0veraAEKAAAwuNqaUpYt3tMH9czgoufxssWNlnkFyBgPLRYtWpQk+dznPtdniagnnngiX/3qV5Mk55xzTlVqA0isazraFOn7VaQABQAAeG6LmhqyYsm8zCz3XQJqZrkuK5bMs58hwK+N6Y24zznnnDQ2Nuaee+7J2WefnXe+8515+umn87nPfS6tra0577zzcsIJJ1S7TGAcs67p6FKk71eRAhQAAGDfLGpqyMLGmVnbsi2bOzozY8qeJaHMsAD4b2M6tKitrc2VV16Z17zmNbn55ptz880395478cQT8+Uvf7mK1QH897qmm9o6B1zmp5Q9o26sa1oMRfp+FSlAAQAA9l1tTSkL5h5Z7TIACmtMhxZJ0tTUlPvvvz9XXHFF7r333tTW1mbBggW58MILM2nSpGqXB4xzPeuaLl25LqWkT0e4dU376uquVH00UpG+X0UKUAAAAACGS6lSqdihcx+1t7enXC6nra0t9fX11S4HGENWr2/N8lXNffYoaCjXZdniRuuapnhfn6LUs3p9a5auXJdk4ADFurgAAFRDEQYcAVAs+9O3LrTYD0IL4GDyxn5gPR3zz/xlVe2O+aJ8v4oSoADw34ryOwKgGrw/BWAgQouDRGgBMLK6uis5/bKb+vzBs7eeJZBu+dNXj+vOIJ1jAMWhsw4Yz4o64AiA6tufvvWaEaoJAPbb2pZtgwYWyZ4lkVrbOrO2ZdvIFVVAPRv5vf6kWVkw90iBBUCV9HTWPfN316a2zixduS6r17dWqTKAg6+ru5Llq5oH3G+t59jyVc3p6jZ2FoBnJ7QAoLA2dwweWBxIOwA4WHTWAeOdAUcADBehBQCFNWNK3bC2A4CDRWcdMN4ZcATAcBFaAFBY8+dMTUO5LoMtdlTKnnXC58+ZOpJlAUA/OuuA8c6AIwCGi9ACgMKqrSll2eLGJOkXXPQ8Xra40R4OAFSdzjrGoq7uSm7/2dZcc+8juf1nWy1vxrMy4AiA4TKh2gUAwLNZ1NSQFUvmZfmq5j7Lbsws12XZ4sYsamqoYnUAsEdPZ92mts4B97UoZc/vLp11jBar17f2e//V4P0Xz6JnwNHSletSSvr8LDTgCID9UapUKoZK7KP29vaUy+W0tbWlvr6+2uUAjCtd3ZWsbdmWzR2dmTFlT6ePP3gAKJLV61uzdOW6JAN31q1YMk9nL6NCz2v5mZ0FXsvsC4EXAAPZn751ocV+EFoAAADPRmcdo11XdyWnX3bToBvL98wauuVPX20ACYMy4AiAZ9qfvnXLQwEAAAyTRU0NWdg4U2cdo9balm2DBhbJnllErW2dWduyLQvmHjlyhTGq1NaUvD4AOGBCCwAAgGGks47RbHPH4IHFgbQDANhfNdUuAAAAACiGGVPqhrUdAMD+EloAAAAASZL5c6amoVyXwRY0K2XPPi3z50wdybIAgHFEaAEAAAAk2bO82bLFjUnSL7joebxscaN9WgCAg0ZoAQAAAPRa1NSQFUvmZWa57xJQM8t1WbFkXhY1NVSpMgBgPLARNwAAANDHoqaGLGycmbUt27K5ozMzpuxZEsoMCwDgYBNaAAAAAP3U1pSyYO6R1S4DABhnLA8FAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBCEFgAAAAAAQCEILQAAAAAAgEIQWgAAAAAAAIUgtAAAAAAAAApBaAEAAAAAABSC0AIAAAAAACgEoQUAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhTKh2AQAAABw8Xd2VrG3Zls0dnZkxpS7z50xNbU2p2mUBAMCAhBYAAABj1Or1rVm+qjmtbZ29xxrKdVm2uDGLmhqqWBkAAAzM8lAAAABj0Or1rVm6cl2fwCJJNrV1ZunKdVm9vrVKlQEAwOCEFgAAAGNMV3cly1c1pzLAuZ5jy1c1p6t7oBYAAFA9QgsAAIAxZm3Ltn4zLPZWSdLa1pm1LdtGrigAANgHQgsAAIAxZnPH4IHFgbQDAICRIrQAAAAYY2ZMqRvWdgAAMFKEFgAAAGPM/DlT01CuS2mQ86UkDeW6zJ8zdSTLAgCA5yS0AAAAGGNqa0pZtrgxSfoFFz2Ply1uTG3NYLEGAABUh9ACAABgDFrU1JAVS+ZlZrnvElAzy3VZsWReFjU1VKkyAIDh1dVdye0/25pr7n0kt/9sa7q6K9UuiSGYUO0CAAAAODgWNTVkYePMrG3Zls0dnZkxZc+SUGZYAABjxer1rVm+qjmtbZ29xxrKdVm2uNEgjVGqVKlUxE77qL29PeVyOW1tbamvr692OQBQSF3dFZ1jAAAAHHSr17dm6cp1eWYHd89foGaXFsf+9K2baQEADBsjXAAAABgJXd2VLF/V3C+wSJJK9gQXy1c1Z2HjTAPpRhl7WgAAw6JnhMvegUWSbGrrzNKV67J6fWuVKgMAAGCsWduyrd/fn3urJGlt68zalm0jVxTDQmgBAAzZc41wSfaMcLEZGgAAAMNhc8fggcWBtKM4hBYAwJAZ4QIAAMBImjGlbljbURxCCwBgyIxwAQAAYCTNnzM1DeW6DLZbRSl79licP2fqSJbFMBBaAABDZoQLAAAAI6m2ppRlixuTpF9w0fN42eJGm3CPQkILAGDIjHABAABgpC1qasiKJfMys9x3gNzMcl1WLJmXRU0NVaqMoZhQ7QIAgNGvZ4TL0pXrUkr6bMhthAsAAAAHy6KmhixsnJm1LduyuaMzM6bsGTDn78/Rq1SpVCrP3YwkaW9vT7lcTltbW+rr66tdDgAUzur1rVm+qrnPptwN5bosW9xohAsAAACMU/vTt26mBQAwbIxwAQAAAIZCaAEADKvamlIWzD2y2mUAAAAAo5CNuAEAAAAAgEIQWgAAAAAAAIUgtAAAAAAAAApBaAEAAAAAABSC0AIAAAAAACgEoQUAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhCC0AAAAAAIBCEFoAAAAAAACFILQAAAAAAAAKQWgBAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAgTql0AAMDB1NVdydqWbdnc0ZkZU+oyf87U1NaUql0WAAAAMAChBQAwZq1e35rlq5rT2tbZe6yhXJdlixuzqKmhipUBAAAAA7E8FAAwJq1e35qlK9f1CSySZFNbZ5auXJfV61urVBkAAAAwGKEFADDmdHVXsnxVcyoDnOs5tnxVc7q6B2oBAAAAVIvQAgAYc9a2bOs3w2JvlSStbZ1Z27Jt5IoCAAAAnpPQAgAYczZ3DB5YHEg7AAAAYGQILQCAMWfGlLphbQcAAACMDKEFADDmzJ8zNQ3lupQGOV9K0lCuy/w5U0eyLAAAAOA5CC0AgDGntqaUZYsbk6RfcNHzeNnixtTWDBZrAAAAANUgtAAAxqRFTQ1ZsWReZpb7LgE1s1yXFUvmZVFTQ5UqAwAAAAYzodoFAAAcLIuaGrKwcWbWtmzL5o7OzJiyZ0koMywAAACgmIQWAMCYVltTyoK5R1a7DAAAAGAfWB4KAAAAAAAoBKEFAAAAAABQCJaHAuA5VSqVbN26Nbt27crUqVMzceLEapcEAAAAwBgktABgUM3NzVm+fHlWr16d9vb2JEltbW3mzZuX973vfbnggguqXCEAMBo1Nzfnvvvuy8MPP5wdO3Zk1qxZOeOMM/LCF77wOa/dunVrbrnllvzyl7/Mli1bcvjhh+ekk07KmWeemdra2hGoHgCAg0loAcCA1qxZk3POOSc7duxIkkyaNCmHHnpotmzZkrvuuitLlizJHXfckc9//vNVrhQAGC2+8IUv5DOf+Ux+8YtfDHj+3HPPzVe+8pUcddRR/c798pe/zNvf/vbccsst6erq6nd+9uzZ+b//9//mnHPOGe6yYUBd3ZWsbdmWzR2dmTGlLvPnTE1tTanaZQGMGD8HOVhKlUqlUu0iRov29vaUy+W0tbWlvr6+2uUAHFQnnnhiHnzwwRx//PH5x3/8x5xxxhlJktbW1nzwgx/MN77xjSTJHXfckVNPPbWapQIAo8T555+fb3/723nxi1+cl770pTnmmGNSW1ubu+66KzfeeGOS5EUvelHuvvvuTJ48uc+1d9xxRxYsWJBjjjkmp556ao477rhMnjw5v/jFL/Jv//ZvefLJJzNhwoTceOONve9b4GBZvb41y1c1p7Wts/dYQ7kuyxY3ZlFTQxUrAxgZfg6yv/anb11osR+EFsB4sXHjxsyePTtJ8u///u953ete1+f87t27c/zxx+eRRx7JJz/5yXz0ox+tRpkAwCizZs2aHH/88TnuuOP6nbvqqqvylre8JZVKJX/zN3+TD3zgA33Ob9q0KRs2bMgpp5zS79rHH388r3zlK/PTn/40CxcuzPXXX3/QPgdYvb41S1euyzM7U3rGFq9YMk+HHTCm+TnIgdifvvWaEaoJgFHqhBNO6HdswoQJmTt3bhWqAQBGszPPPHPAwCJJ3vSmN/UOlPj+97/f7/zMmTMHDCySZPr06bnkkkuSJOvXrx+eYmEAXd2VLF/V3K+jLknvseWrmtPVbXwoMDb5OchIEFoA0M8xxxzT26HQs1TD3h5//PHcd999SZLf/M3fHNHaAICx60UvelGSZPv27Qf8HNOnTx+ucqCftS3b+iyF8kyVJK1tnVnbsm3kigIYQX4OMhJsxA3AgC6//PK88Y1vzAc/+MFs3bo1CxcuzKGHHpr7778/n/zkJ9PW1pYlS5bkVa96VbVLBaCKmpubs2bNmvzyl79MkixdunTQkfR7q1Qque2223LnnXemra0t06dPz6tf/eo0NjYe7JIpsB/+8IdJst+vg8ceeyyXX355kuQP/uAPhr0u6LG5Y/COugNpBzDa+DnISBBaADCg173udbntttvy8Y9/PB/72MfysY99rPfcrFmz8tWvfjUXXXRRFSsEoFqefvrpLFmyJGvWrMnmzZv7nDv33HOfM7R44IEH8gd/8Ae55557+p07//zz89WvftUecuPQnXfemRtuuCE1NTV5xzve8axtP/e5z+XRRx9NZ2dnHn744axevTo7duzIu971rvyv//W/RqhixqMZU+qGtR3AaOPnICNBaAHAgJ566qlcfvnl+d73vpckqaury6GHHpqtW7fmkUceyec///m89KUvzSte8YoqVwrASHv66afzzW9+M0ly4okn5owzzsiXvvSlfbp248aNOfPMM7N58+ZMnjw5v/d7v5fjjz8+Dz30UK6++up861vfyq9+9atcf/31qamxmu148fjjj+etb31ruru784EPfCAve9nLnrX9FVdc0btUZZKUSqV85CMfyZ/92Z/lkEMOOdjlMo7NnzM1DeW6bGrrHHA991KSmeW6zJ8zdaRLAxgRfg4yEvwVAMCAzj333FxxxRVpbGzM7bffnu3bt2fLli3ZvHlz3vve9+bee+/NGWeckebm5mqXCsAImzhxYq688so89thj+fGPf9y7LM+++PM///Ns3rw506ZNy3333Zevfe1r+d//+3/nG9/4Ru6+++4cdthhufHGG7Ny5cqD+BlQJB0dHTn33HPT0tKSs846K5/+9Kef85r3ve99ufTSS/Oxj30sS5YsyZQpU3LppZfmZS97WX784x+PQNWMV7U1pSxbvGf5stIzzvU8Xra4MbU1zzwLMDb4OchIKFUqFVu576P29vaUy+W0tbWZrg6Maddee21e97rXpa6uLj/96U9zzDHH9Gvz+te/Pt/5znfypje9KVdeeWUVqgSgKHbv3t07uv3mm2/O6aefPmC77u7uTJ06NW1tbfnbv/3bvP/97+/XZtmyZfmLv/iLnHrqqbnjjjsOZtkUwJNPPplzzjknt9xySxYsWJDrr78+hx122H4/zxNPPJELLrgg1157bebNm5e77rrLTB0OqtXrW7N8VXOfzWgbynVZtrgxi5oaqlgZwMjwc5D9tT996+NqeagdO3akq6srkydP9gYW4FncfffdSZKXvOQlAwYWyZ49L77zne/krrvuGsnSABjFtmzZkra2tiTJqaeeOmCbnuNr167Nli1bMm3atBGrj5G1d2Bxyimn5LrrrjugwCJJDj/88HzpS1/KMccck3Xr1uX+++/PS17ykmGuGP7boqaGLGycmbUt27K5ozMzpuxZCsXIYmC88HOQg2nMhxY/+MEPsmrVqnznO9/p3ejvRz/6UZqamqpcGUBx7dy5M0myffv2Qdt0dHQkSXbt2jUiNQEwtnR1dT3r8UqlkvXr1+ess84awaoYKXsHFvPmzcv111+fcrk8pOecNWtWJk2alB07duThhx8WWnDQ1daUsmDukdUuA6Bq/BzkYBnTocXu3bttEAtwAHr+yP/xj3+cW2+9Na985Sv7nN+5c2e+9rWvJYkQGIB9Nm3atNTX16e9vT233nrrgMtI3Xbbbb3/3rhx40iWxwjp6OjIOeeck1tvvTUnnXRSbrjhhhx++OFDft4HH3wwO3bsSJLMnDlzyM8HAEB1jOk1kkqlUl7+8pfnE5/4RO6+++6cd9551S4JYFQ477zzcvTRR6e7uzvnnntuPv3pT+eOO+7I/fffn29961v5rd/6rdx3331Jkne/+91VrhaA0aKmpiavf/3rkySXXnpp7r333j7nb7/99nz2s5/tfdwzq4+xo2eGxa233pqXvvSl+d73vpepU6fu07XXXXddWltbBzy3cePGvO1tb0uSHHfccTn55JOHrWYAAEbWmJ5pUVtb27sue7InxADguU2ePDn/+q//mvPOOy+PPfZYPvKRj/RrUyqV8vGPf7y38wkA9sVf/MVf5Lvf/W62bduW+fPnZ9GiRTn++OPz0EMP5frrr8+MGTMyZcqUbN68ObW1tdUul2F24YUX5tZbb02SnHLKKfmrv/qrAdtNmzYtH/rQh/oc++xnP5vvfe97OeWUU3Lcccfl6KOPzu7du/PAAw/kv/7rv7Jz584873nPy5e//GV7GAIAjGJjOrQA4MC9/BWn5B+/e0v+5ev/mAd+cHvaHn80u3fvzpFHHplTTjklF154YebNm1ftMgEYZY4//visWbMmS5YsyX333ZdVq1b1nps7d26+/e1v54wzzkiSHHHEEdUqk4Nk7yW/vvrVrw7abu7cuf1Ci3PPPTc/+clPcscdd+SOO+7od81pp52Wv/3bv81pp502fAUDADDihBYA9LN6fWuWr2pOa1tn8rzTkgWnpaFcl2WLG7OoqaHa5QEwyjU1NeWee+7JXXfdlXvuuSc7duzICSeckN/+7d/Ohg0b0t7eniR58YtfXOVKGW6XXHJJznv967Nh2448tXNXDp14SGZPnZSaZ8yKHyiwes973pP3vOc9Wb9+fR588MFs3LgxpVIpDQ0NecUrXpE5c+aM1KcBAMBBVOjQolKp5Kmnntrn9rW1tZk0adKw3X/nzp3ZuXNn7+OeP54AxrLV61uzdOW6VJ5xfFNbZ5auXJcVS+YJLgAYslKplPnz52f+/Pl9jvfMvJg+fXpOPPHEapTGQTT95Nfkixub01rbmUzec6whdVl27r4PjGhqakpTU9NBrBIAgGoq9EKfjz32WKZMmbLPH7/1W781rPe/9NJLUy6Xez9mz549rM8PUDRd3ZUsX9XcL7BI0nts+armdHUP1AIAhmbHjh35u7/7uyTJO97xDvsSjDE9AyNa2zr7HO8ZGLF6/cCbbAMAML4U+q+AmpqaHHroofv8MXny5GG9/0c+8pG0tbX1fmzYsGFYnx+gaNa2bOvXkbC3SpLWts6sbdk2ckUBMKY8/fTT+e53v5tKpW8A/qtf/Sq///u/n5aWlhx11FH58Ic/XKUKORgMjAAAYF8VenmoGTNm5Mknn6za/SdOnJiJEydW7f4AI21zx+CBxYG0A2Ds+od/+Ic8+OCDSdIngPj7v//7/Pu//3uSZNKkSVm2bFmf655++umce+65mTNnTk455ZTMmjUrjzzySK6//vo88cQTOfTQQ/PNb34zU6dOHblPhoNufwZGLJh75MgVBgBA4RQ6tABgZM2YUjes7QAYu6688sr8x3/8R7/jX//613v/XS6X+4UWEydOzOLFi/Pd7343LS0tfc6ddtppWbFiRU466aSDUjPVY2AEAAD7asyHFjt27EhXV1eS9P53x44dvTM4Jk+ebK1cgF+bP2dqGsp12dTWOeDyDaUkM8t1mT/H6FeA8e7CCy/KnJfMz1M7d+XQiYdk9tRJqSmV+rSpq+sfch9yyCH5zne+kw0bNmTt2rXZsGFDDj/88MyfPz+NjY0jVT4jzMAIgGJrbm7OXXfdlYcffjhPPvlkjjrqqCxYsCALFiw4qNcCDGTMhxbnnHNO1qxZ0+fY/Pnze/99++2357TTThvpsgAKqbamlGWLG7N05bqUkj7BRU831LLFjamtKQ1wNQDjxer1rflsy7S01p6a/HpbuYbUZdm5jVnU1LBPzzF79uzMnj37IFZJkRgYAVBM1113Xf74j/84Dz300IDnTzrppHzta19LU1PTsF4L8GzGfGgxadKkHHrooYOer62tHcFqAIpvUVNDViyZl+WrmvusPT2zXJdli/e9MwqAsWn1+tYsXbmuX8fzprbOLF25LiuWzPO7gn4MjAAopvvvvz8///nP84pXvCIvfelLc/zxx2f37t1pbm7ONddck3vvvTdnnXVW1q9fn5kzZw7btQDPplTZe9c8nlV7e3vK5XLa2tpSX19f7XIADqqu7krWtmzL5o7OzJiyZ+SjjgSA8a2ru5LTL7tp0A2Ve0bL3/Knr/Y7gwGtXt/ab2BEg4ERAFXz4x//OOVyOUcffXS/c/fee29+67d+K08++WSWLVuWT3ziE8N2LTD+7E/f+pifaQHAgamtKWXB3COrXQYABbK2ZduggUWyZ/R8a1tn1rZs8zuEAS1qasjCxpkGRgAUxIte9KJBz5100kk555xz8s1vfjP333//sF4L8GyEFgAAwD7Z3DF4YHEg7RifDIwAGD26urqSJEcdddSIXguMbzXVLgAAABgdZkypG9Z2AEBx/dd//VeuvfbalEqlLFmyZMSuBTDTAgAA2Cfz50xNQ7kum9o6+23Enfz3nhbz50wd6dIAgCF4+umn8+EPfzhJ0tHRkQcffDC33nprJk+enK985Ss57bTTDsq1AAOxEfd+sBE3AADj3er1rVm6cl2S9AkuenYkWLFkng2VAWCUefLJJzNlypQ+x6ZMmZK/+7u/y9ve9rZMmDD4uOehXAuMH/vTty602A9CCwAA2BNcLF/V3GdT7oZyXZYtbhRYAMAotGvXrlx++eVJkm3btuWBBx7INddck6effjpnnHFG/u3f/i1HHHHEsF8LjB9Ci4NEaAEAAHt0dVeytmVbNnd0ZsaUPUtC1daUnvtCAGBUePjhh7Nw4cL85Cc/yR/90R/l7//+70fkWmBsElocJEILAAAAAMaLb3/72zn//PMzZcqUtLe3j9i1wNizP33rNSNUEwAAAAAwisyZMyfJng2229raRuxaYHwTWgAAAAAA/dx1111JkkMPPXS/Vx0ZyrXA+Ca0AAAAAIBx6J/+6Z/yxBNPDHhuzZo1+ehHP5ok+d3f/d2USn33rhrKtQDPxp4W+8GeFgAAAACMFYcddlh2796dl7/85TnmmGMyffr0PPnkk7nnnnvywx/+MEly7LHH5vbbb8/RRx89bNcC48/+9K1PGKGaAAAAAIACufjii3PllVfmtttu63du4sSJedOb3pTLLrssDQ0Nw3otwLMx02I/mGkBAAAAwFjR1V3JHT/bkrU/WJfObZsypfJkJtXV5fjjj88rXvGKlMvlZ72+u7s79913X1paWrJp06ZMnDhxn68FxhczLQAAAACAQa1e35rlq5rT2tb56yPT0lA+JsvObMzZTfs2O6KmpiYnn3xyTj755INXKDDu2IgbAAAAAMaR1etbs3Tlur0Ciz02tXVm6cp1Wb2+tUqVAQgtAAAAIEmyY8eOdHZ2PnfDZ/Hkk09my5Yt2bJlS3bt2jVMlQEMn67uSpavas5A68X3HFu+qjld3VaUB6pDaAEAAMC4dN999+VTn/pUTj/99EybNi2TJ0/OpEmT0tDQkHe84x35yU9+sl/P98QTT+SEE07I9OnTM3369KxZs+YgVQ5w4Na2bOs3w2JvlSStbZ1Z27Jt5IoC2Is9LQAAABiXXve61+WRRx7pfVxXV5ddu3Zl06ZN+Yd/+If8y7/8S775zW/mda973T493wc/+MG0tlpSBSi2zR37NqNsX9sBDDczLQAAABiXTjrppHzsYx/Lbbfdlscffzw7duzI9u3bs3r16jz/+c/Pjh07csEFF2TLli3P+Vw33nhjrrjiilx44YUHv3CAIZgxpW5Y2wEMt1KlUrFA3T5qb29PuVxOW1tb6uvrq10OAAAAB8lPfvKTvOQlL8nTTz+dL3/5y7n44osHbbt9+/a85CUvSUdHRx544IEceeSRSZIbbrghr3nNa0aqZIB90tVdyemX3ZRNbZ0D7mtRSjKzXJdb/vTVqa0pjXR5wBi1P33rZloAAADAM5xwwgl5yUtekiT5xS9+8axtP/axj+XnP/95/u7v/i5Tp04dgeoADlxtTSnLFjcm2RNQ7K3n8bLFjQILoGqEFgAAADCA3bt3J0mOOOKIQdvcdddd+dznPpfXvva1+YM/+IORKg1gSBY1NWTFknmZWe67BNTMcl1WLJmXRU0NVaoMwEbcAAAA0M/69etz3333JUkWLVo0YJtdu3blne98ZyZOnJi///u/H8nyAIZsUVNDFjbOzNqWbdnc0ZkZU+oyf85UMyyAqhNaAAAAwF6efvrp3j0sLrjggrz4xS8esN2ll16aH/3oR/nrv/7rHH/88SNYIcDwqK0pZcHcI6tdBkAflocCAACAX6tUKnnnO9+ZO++8M3PmzMkXvvCFAds1NzfnU5/6VObNm5f3v//9I1skAMAYJrQAAACAX3vPe96TlStXpqGhIddff30OP/zwfm26u7tz8cUXp6urK1/+8pdTW1s78oUCAIxRlocCAACAJH/8x3+cL37xi5kxY0ZuvPHGPP/5zx+w3Ze//OXcfvvtufjii3Psscdmy5YtA7Zrb2/Pli1bMnHixEyZMuVglg4AMGaYaQEAAMC49973vjef//znM23atNx444150YteNGjbH//4x0mSr3zlK5k+fXq/jx5vfOMbM3369LzrXe866PUDAIwVZloAAAAwrr3nPe/J5ZdfniOPPDLf+9730tTU9KztDzvssBx55OAb127dujVJUl9fn0MOOST19fXDWi8AwFhWqlQqlWoXMVq0t7enXC6nra3Nm04AAIAxoCewOOKII3LjjTfm5JNPHvJzlkqlJMkNN9yQ17zmNUN+PgCA0W5/+tbNtAAAAGBc6gksJk+enG984xuZPXv2gPtT1NXV5bDDDqtChQAA44/QAgAAgHHp8ssvT5Js3749ixYtGrTdBRdckJUrV45UWQAA45rQAgAAgHHpyCOPzO7uSrorldSUSplQUxqw3ZQpU/b7eZPkec973pBrBAAYb4QWAAAAjDur17fmBR+8Mq1tnb3HGsp1Wba4MYuaGob03AMtMQUAwL6pqXYBAAAAMJJWr2/N0pXr+gQWSbKprTNLV67L6vWtVaoMAAChBQAAAONGV3cly1c1pzLAuZ5jy1c1p6t7oBYAABxsQgsAAADGjbUt2/rNsNhbJUlrW2fWtmwbuaIAAOgltAAAAGDc2NwxeGBxIO0AABheQgsAAADGjRlT6oa1HQAAw0toAQAAwLgxf87UNJTrUhrkfClJQ7ku8+dMHcmyAAD4NaEFAAAA40ZtTSnLFjcmSb/goufxssWNqa0ZLNYAAOBgEloAAAAwrixqasiKJfMys9x3CaiZ5bqsWDIvi5oaqlQZAAATql0AAAAAjLRFTQ1Z2Dgza1u2ZXNHZ2ZM2bMklBkWAADVJbQAAABgXKqtKWXB3COrXQYAAHuxPBQAAAAAAFAIQgsAAAAAAKAQhBYAAAAAAEAhCC0AAAAAAIBCEFoAAAAAAACFILQAAAAAAAAKQWgBAAAAAAAUgtACAAAAAAAoBKEFAAAAAABQCEILAAAAAACgEIQWAAAAAABAIQgtAAAAAACAQhBaAAAAAAAAhSC0AAAAAAAACkFoAQAAAAAAFILQAgAAAAAAKAShBQAAAAAAUAhCCwAAAAAAoBCEFgAAAAAAQCEILQAAAAAAgEIQWgAAAAAAAIUgtAAAAAAAAApBaAEAAAAAABTChGoXMJpUKpUkSXt7e5UrAQAAAACA0aGnT72nj/3ZCC32Q0dHR5Jk9uzZVa4EAAAAAABGl46OjpTL5WdtU6rsS7RBkqS7uzuPPvpopkyZklKpVO1yqq69vT2zZ8/Ohg0bUl9fX+1y4IB4HTNWeC0zVngtM1Z4LTNWeC0zVngtM1Z4LTNaVSqVdHR05Oijj05NzbPvWmGmxX6oqanJMcccU+0yCqe+vt4PSUY9r2PGCq9lxgqvZcYKr2XGCq9lxgqvZcYKr2VGo+eaYdHDRtwAAAAAAEAhCC0AAAAAAIBCEFpwwCZOnJj/v707j4q6auMA/h0QcdgXUdkEkQRBwwVcEEVNJcRE0BTLDcs3i9clqVdtUcpTmmm8ZmbGm2FhkohL4kLuG+AGqCxirCoKigJGIsNy3z88TE4zIFYyA30/53A6c+9zh+c35+dtmGfuvUuXLoWurq66UyH603gfU2vBe5laC97L1FrwXqbWgvcytRa8l6m14L1M/wQ8iJuIiIiIiIiIiIiIiDQCV1oQEREREREREREREZFGYNGCiIiIiIiIiIiIiIg0AosWRERERERERERERESkEdqoOwFqeW7duoXt27cjPz8fZmZm8PPzg6urq7rTImqyQ4cOYffu3Sr7TExMEBYW1rwJETVRWloa4uLiUFRUhD59+mDatGmNxmdlZWH37t0oKSmBra0tAgICYGVl1UzZEqkmhMDZs2exb98+lJaWYsSIERgzZozK2IiICKSnp6vsa8q/AaKnKSkpCefPn8fVq1dhaGiI/v3747nnnoOWVsPfCztx4gSOHj2KyspKuLi4ICAgAPr6+s2YNZGiuro6HD9+HBcvXkRhYSFMTU0xePBgDBo0SGX8e++9h4qKCpV9AQEB8Pb2fprpEjXq/PnzOHPmDAoKCmBgYPDYebm6uhpxcXFITk6GlpYW+vXrB19f30bncaKnTQiBkydPIiUlBYWFhTAzM8PgwYPh6empFHvr1i18/PHHDT5XaGgobG1tn2a6RE+PIHoCcXFxwsjISACQ/0gkEhEWFqbu1IiabPny5Qr38KM/1tbW6k6PSEl0dLSwt7dXuFcnTZrU6JgVK1YIbW1thTH6+vpi+/btzZQ1kbI1a9aITp06KdyXCxcubDDez8+vwfn6cf8GiJ6WEydOiC5duqi8L93c3ER+fr7SGJlMJiZMmKAUb2dnJzIyMtRwFURC7NixQ1haWqq8l729vUVJSYnSGHNz8wbn5fDw8Oa/CCIhRG5urnB1dVV5X/bp00dcv35daczNmzeFm5ubUryXl5coLy9Xw1UQCZGUlKT0d1/9z8iRI5XuzczMzAbnZADi7NmzaroSor+OKy2oya5fv46JEyfi/v378PHxwdChQ5Gbm4vIyEiEhYWhZ8+eCAwMVHeaRE0WEhICR0dHhTZDQ0M1ZUPUsJSUFOTn58PV1RVubm744YcfGo3fu3cvFi1aBC0tLcyYMQPOzs5ISEjATz/9hJdeegnp6elwcHBopuyJfpeQkIDi4mJ4eHjAysoKu3btatK41atXK33r0cnJ6WmkSPRY2dnZyM/Px8CBA9G7d2/Y2Njg2rVriIqKwoULFxAYGIjz588rjFm6dCm2bdsGExMTzJw5E+bm5ti2bRtSUlIwbtw4pKeno00b/mlGzSstLQ23bt3CsGHD0KNHD1haWiI7OxubN2/GsWPHEBwcjJ9++klpnK2tLRYsWKDUzlUWpC7FxcXIzMzEoEGD0KdPH1hZWeHatWvYunUrkpOT8dJLL+HYsWMKY15++WVcuHABDg4OmDp1KmpqarBp0yacPHkSs2fPfuz7baKnIS8vD4WFhXjuuefg5uaG9u3bIzs7G1u3bsWBAwcwZ84cbNq0SWnckCFDEBAQoNTeuXPn5kib6OlQd9WEWo7Q0FABQLzyyisK7Vu2bBEARO/evdWUGdGTqV9pceTIEXWnQtQkKSkpIjc3VwghxNmzZx/7LfNBgwYJACIiIkKhfd68eQKAeP31159qvkQNSUhIEDdv3hRCCBETE9PklRbV1dXNlSLRY2VnZ4u8vDyV7cbGxgKAyMzMlLdXVFQIPT09oaOjIy5cuCBvl8lkYsCAAQKA2Lx5c3OkTqQgLS1NPic/6syZM0JHR0doaWkpfavX3Nxc9O3bt7lSJGqSW7duiZycHKX269evCxMTEwFAFBUVydsTExPlq91KS0vl7Tdu3BDt27cXEolEZGdnN0fqRAry8/NVzssXL14UWlpaQiqVirq6Onl7/UqLefPmNWOWRM2DG/VRk9WfAfD+++8rtAcFBaFbt27y/faIiOjv1atXL3Tp0qVJsXfv3kVCQgKsra0xc+ZMhb53330XWlpaDZ7pQvS0DRw4EJ06dVJ3GkR/SdeuXWFvb6+yfdiwYQCA8vJyefvhw4dx//59+Pv749lnn5W36+joYPHixQDAeZnUwtXVVeWc7OHhATc3N9TV1eHXX39VQ2ZET8bCwkLlKmJra2v07NkTAFBVVSVvr59z58yZAxMTE3m7paUlZs2aBSEE4uLinm7SRCrY2dmpnJd79uwJa2trVFdXo66uTg2ZETU/rkGmJnnw4AGys7Nha2sLOzs7pf4hQ4bgypUruHjxIqytrdWQIdGTO378OA4ePAgAcHZ2xpgxYxTetBK1RGlpaRBCwNPTU2k7HQsLC7i4uCAtLQ2lpaUwNTVVU5ZET+aHH35ARkYG9PT00KdPH/j4+EBHR0fdaREpuXr1KvT09ODq6ipvu3TpEgBg8ODBSvH12+lcvHixeRIkaoLa2lrcuHED1tbWsLKyUuqvqKjAl19+ifz8fJibm8PLy6vBg7uJ1Ck7OxupqalwcHBQOIz4cfPy8uXLOS+TRklKSsL169fh7e0NbW1tpf6rV6/ik08+wd27d2FjYwMfHx9069ZNDZkS/X1YtKAmuX37Nurq6hT+R/+o+vbi4uLmTIvoL1m6dKnCY0NDQ2zYsAGTJ09WU0ZEf139PNzYfJ2Wlobi4mIWLajFmD59usJjBwcHbNu2Db1791ZTRkTKoqKikJycjLCwMBgYGMjbG5uXjY2NYWhoyPfQpFE+/fRT3LhxA//73/8gkUiU+rOyshASEqLQ5u3tja1bt6JDhw7NlSaRkry8PKxZswa1tbW4fv064uPjIZVK8d133yncy43Ny/xsgzTB+fPn8f3336O6uhq5ubk4ePAgbG1tsX79epXxO3bswI4dO+SPJRIJZs2ahXXr1vHMLGqxeOdSkzx48AAAoKurq7K/Xbt2CnFEms7KygrPP/88HB0dcefOHezbtw8ZGRmYMmUKbG1t4eXlpe4Uif4UztfU2jz77LMYMmSI/FDN2NhY5ObmYtSoUcjKyoKZmZm6UyTC8ePHMWvWLIwcORLvvfeeQl9T5uX79+8/9RyJmmLbtm149913MW3aNLzyyitK/VpaWhg6dCg8PDxgZGSErKwsxMbG4tixYwgICMDJkydVFjqImkNhYSHWrFkjf2xoaIiVK1fC09NTIa6xeZnvlUkTZGZmKtzLHTt2xOeffw5nZ2el2Hbt2sHX1xfPPvssampqkJycjP379+Prr7+GiYkJPvnkk+ZMnehvw6IFNYlUKgUAVFZWquyv/0NLT0+v2XIi+rMmTJiABQsWoG3btvK2Tz/9FG+++SbWrFmDVatWsWhBLRbna2pNVq5cCRcXF4W2VatWYdSoUTh16hQ2btyIt956S03ZET30888/IyAgAAMHDsTOnTuVtm1oyrzMOZk0QVRUFIKDg/Hiiy/im2++URlz9OhRpXl52bJl8PT0REJCAk6dOsX30aQ2Dg4OCA8PR3V1NW7evIm4uDi8+uqr2LdvH7Zt2yaPa2xe5ntl0gTu7u4IDw9HVVUVrl27hp07d2LcuHFYsGABVq9eLY+ztLRETk6O0lZ+8fHxGDNmDNauXYuwsDD5PU/UkvAgbmqSDh06oE2bNsjPz1fZn5eXBwAq9zwl0jSOjo4KBQvg4fLJJUuWAOC+0tSy1Z8r9Lj52tLSsrlSIvrT/vjBGPDwQ4SFCxcC4HxN6hcTE4MXXngBnp6e2LNnj8oPuRqbl2/fvo3ffvuN76FJ7b744gtMmzYNL774IjZv3tzgdiKq5mV7e3sEBwcD4LxM6mVlZYX58+fj7bffxmeffYaMjAz4+PggNjYW8fHx8rjG5mV+tkGawNnZGfPnz8fChQvxxRdf4PLly3Bzc0N4eDguX74sjzM2NlZ5r/r4+MDT0xOVlZX45ZdfmjN1or8NixbUJG3btoWLiwuKioqQkZGh0FdbW4sjR45AIpHAzc1NTRkS/XUymQwAuOcjtWiurq5o06YNTp48Kb+n6129ehVXrlxB165dYWhoqKYMif66qqoqAJyvSb02bNiAoKAgDBs2DLt3727wW4y9evUCABw6dEip7+DBgwoxROoQFhaGOXPmYMqUKfj+++9VHvL6OJyXSRO1adMGPj4+AID09HR5O+dlamkMDAzg7e0NIYTSZ3IN4bxMLR2LFtRk48aNAwAsXrwYNTU18vbPP/8c165dg5eXFywsLNSUHVHTyGQybN++HbW1tQrt9+/fx9y5cwEAffv2VUdqRH8LIyMjDB8+HCUlJVixYoW8va6uTv7t9MDAQHWlR9Rk2dnZSEpKUmq/ceOGfGUc52tSlxUrVmD27Nnw9fXFrl275Hugq+Lt7Q1TU1PEx8fLPwwDgPLycixbtgwA52VSDyEE5syZgw8++ADBwcGIjIxstGCRkJCA3NxcpfbTp08jIiICAOdlUo9Dhw6pPDj77t27+O677wD8vroC+P2zjbVr1+Lq1avy9szMTGzcuBFt2rTB2LFjn27SRCrExcWhvLxcqT0/Px87d+4EoHgv79mzB2VlZUrxEREROHPmDIyMjPDMM888rXSJniqJEEKoOwlqGe7cuQNnZ2eUlJTA1dUVgwcPRk5ODg4cOAAtLS0cOnQIQ4cOVXeaRI2qqKiAoaEhbGxs0KtXL1hbW6OkpARHjx7FnTt3oKuri6SkJH6zhjTKlStX8OWXXwIAbt26hS1btqBbt27w9fUF8HB1xaxZs+TxiYmJGDx4MGprazF06FA4OzsjKSkJqampMDU1RWZmJjp27KiWa6F/tqSkJERHRwN4WJTYs2cP3N3dMWjQIADA4MGDMX78eADA/v374evrC1dXV3Tv3h3m5ua4du0aDh06hKqqKtjZ2SEtLQ0GBgZqux76Z/r+++8xbdo06Ojo4NVXX1XachIAgoODFVYgf/bZZwgNDYWOjg5eeOEFmJubY+/evSgsLET//v2RmJjIw4up2S1fvhzvvPMODA0NERwcrPIefPPNN2FnZwfg4YqMZcuWwcPDA46OjpBKpcjKysKJEycAAH5+foiLi2vWayACgBkzZiA6Ohr9+vWDg4MDdHV1cf36dRw5cgSVlZWwt7dHenq6whZ+EydORExMDExNTTF27FjU1tZi165d+PXXXzF37lyFQ5CJmsvQoUNx/vx59OvXD/b29pBIJMjPz8fx48dRXV2t9J5hxIgRSEpKkt/7MpkM58+fl6/GWL58ORYtWqTOSyL68wTREzh9+rTo3LmzACD/kUql4ptvvlF3akRNIpPJxMSJE0Xbtm0V7mMAwsHBQRw6dEjdKRIpOXDggNL9+uiPn5+f0pioqCihr6+vEGdpaSlOnjyphisgeigiIqLRezkkJEQem52dLYYMGaIybsiQISInJ0eNV0L/ZMuXL2/0PgYgYmJilMaFhoYKiUSiEOfu7i4KCwvVcBVEQoSEhDz2Xk5MTJTH//zzz8LZ2VkpRltbW0ybNk38+uuvarwa+if74YcfhL29vcp7eNiwYSI3N1dpzL1794Svr69SfFBQkKiqqlLDVRAJER4eLjp06KB0X0okEhEYGChKSkoU4tesWaMyXl9fX3z00UdqugqivwdXWtATq6qqwuHDh5Gfnw8zMzOMGDEC5ubm6k6L6ImUlJTg7NmzKCgogK6uLlxcXODh4QEtLe6aR5rn2rVriI2NbbDfwcFB5RL20tJSHDx4ECUlJbC1tcWIESMa3cKE6GlLT0/HgQMHGuzv1auX0qrN3NxcpKamoqioCMbGxnB3d4eTk9NTzpSoYefOncPJkycbjRkzZgwcHR2V2uu/LVlZWQkXFxd4eXlxhQWpzdGjR5GamtpoTFBQEDp16qTQlpKSgsuXL6O0tBSdOnXCwIEDYWlp+RQzJXo8IQTOnTuHrKws3Lt3DxYWFnB3d0eXLl0aHZecnIzk5GRIJBL0798fPXr0aKaMiVSrrq7GmTNn8Msvv6CyshKdOnXCgAEDGpxnZTIZTp8+jezsbMhkMtjZ2cHLy4urkanFY9GCiIiIiIiIiIiIiIg0Ar9STEREREREREREREREGoFFCyIiIiIiIiIiIiIi0ggsWhARERERERERERERkUZg0YKIiIiIiIiIiIiIiDQCixZERERERERERERERKQRWLQgIiIiIiIiIiIiIiKNwKIFERERERERERERERFpBBYtiIiIiIiIiIiIiIhII7BoQUREREREAICKigpER0dj79696k6lxauurkZ0dDR27dql7lSIiIiIiFoUiRBCqDsJIiIiIiL6+xUXFyMjIwOlpaUwMTGBs7MzrKysGozPzs7GM888AycnJ1y+fLkZM219ysrKYGpqio4dO6KoqEjd6RARERERtRht1J0AERERERH9vQ4cOIClS5ciKSkJf/yOkru7Oz744AOMHj1aTdm1fA8ePMDOnTuhp6eHsWPHqjsdIiIiIqJWhUULIiIiIqJWZOnSpfjwww8BADo6OujXrx86dOiAkpISnDlzBufOnYOfnx8WLlyIFStWqDnblqmsrAyTJ0+GtbV1g0WLtm3bYtKkSTAxMWne5IiIiIiIWjgWLYiIiIiIWomIiAh5wWLatGlYvXo12rdvL+8vKyvDokWLsGHDBnzyySewtbVFSEiIutJt1fT09BAdHa3uNIiIiIiIWhwWLYiIiIiIWoHy8nK89dZbAB4WLDZt2qQUY2Jigq+++gpaWlpYv349Fi5ciMmTJ8PMzKzB501JSUFBQQFMTEwwYMAAtGvXrsHYiooKpKSk4Pbt2zAzM4O9vT3s7e0bjBdCIC0tDfn5+ZBIJHBxcYGDg0ODzx0XFwcjIyP51lbp6enIyckBALi4uODcuXOwtbXFoEGDGvyd27dvh0wmg5+fHwwNDRX68vLykJeXh/LycnTs2BG9e/eGVCpViElNTcXp06cBAJWVlQqFCR0dHYwfPx7Aw4O4Y2NjIZVK4e/vrzKXyspKnDt3Drdv34axsTH69u2rcmWGqmtPS0tDTk4O9PT00K9fPxgbGzd4zURERERELYogIiIiIqIW74svvhAAhL6+vrh7926jsRUVFcLExEQAEKtXr5a3//LLLwKAcHJyEsnJyaJ79+4CgPzH2NhYbNy4Uen5amtrxTvvvCOkUqlCPADRu3dvsXfvXqUxW7ZsEXZ2dkrxQ4YMEdnZ2Urxj+aWmpoq3Nzc5GOsrKxEWlqaACA6duwoqqurVV53cnKyACBsbGxEbW2tvP2jjz4Sjo6OSrkYGhqKsLAwhdjQ0FCluPoffX19eVxpaak8nz+qqakR77//vtDT01MYr62tLYKDg8W9e/cavPbMzEzh7u6uME4qlYo1a9aovGYiIiIiopaGKy2IiIiIiFqBw4cPAwBGjx4NU1PTRmP19fUxbtw4REZG4vDhw1iwYIFCf2lpKXx8fFBeXo5hw4bB2NgYZ8+eRWFhIWbOnAkdHR1MmTJFHv/pp5/i448/BgB0794dXbt2hUwmQ05ODlJSUhAbGwtfX195/KpVq/D2228DAKytreHi4oK6ujokJyfj+PHjGDRoEFJSUmBpaamUe2lpKUaNGoWysjJ4eHjA3t4e5ubmcHV1hbu7O86dO4f9+/djzJgxSmPrV59MnToVWlpa8vaVK1fit99+g7OzM7p06QKJRILc3FxcvnwZYWFhqKmpwbJlywAAvXv3hr+/P3bt2gWpVKpwpkVjq1AeNWPGDERFRQF4uELEyckJhYWFOHv2LL799ltkZGTg+PHjaNu2rdK1Dx8+HHfv3kX//v1hYWGBlJQUFBYWYt68eXB1dcVzzz3XpByIiIiIiDSWuqsmRERERET01/Xo0UMAEB9//HGT4tesWSMAiG7dusnb6r/RD0B06dJFYcWDTCYTr732mgAgzMzMxG+//Sbv69u3rwAgvvnmG6Xfk5qaKmJjY+WPL126JLS0tIREIhHr1q0TNTU18r6ysjIxbtw4AUDMmDFD4Xkeza179+4iKytL6XetW7dOABATJkxQ6quurhYWFhYCgLh8+bJCX3h4uLhx44bSmCNHjggDAwMhlUoVVj/cvHlTABDW1tZKY+o1tNLi559/FgCERCIRkZGRCn1Hjx4VBgYGDa6AASB69eolcnJy5H0PHjwQY8aMafC6iYiIiIhaGq0/FjGIiIiIiKjlKSsrA4DHrrKoV3+ORWlpqcr+1atXo2vXrvLHOjo6+O9//wsbGxvcvXsXcXFxCn0AMHz4cKXncXNzQ2BgoPzxV199hbq6Orzxxht44403oK2tLe8zNjbGpk2boKenh5iYGNTU1KjMbf369ejWrZtS++TJk6Grq4vdu3fj7t27Cn179+7F7du3MXDgQDg5OSn0zZ8/H5aWligoKEB8fDxiYmIQHR2NoqIiODo6orKyEsnJySpzeVLfffcdAGDixImYPn26Qp+3tzf+85//AIDKM0nqxz967oeuri6WLFkCAH9bjkRERERE6sTtoYiIiIiIWgE9PT0ADw93bor79+8DeLhV1B+1adNGYdujeu3atYOfnx82bNiA5ORkTJw4EQAQEBCApKQkBAQE4M0338TQoUPRuXNnlb/35MmTAABDQ0Ns27YNQggAUPivhYUFCgoKkJubq1SckEqlGDJkiMrnNjU1xdixYxETE4MtW7YgJCRE3hcZGQng4dZMf3TixAnMnTsXqampKp8XAG7fvt1g35OoLywEBQWp7A8KCsKSJUuQlpYGmUymsEWUubk5evbsqTTG0dERwO+FKyIiIiKiloxFCyIiIiKiVsDa2hpXrlzBlStXmhSflZUlH/dHVlZWCisgHmVnZwcAuHPnjrwtNDQUFRUVWLt2rXz1gIWFBYYNG4bp06dj9OjR8thbt24BAFasWPHYHFV9CG9jYwOJRNLgmODgYMTExGDTpk3yosWdO3ewZ88etGvXDpMmTVKIv3DhAkaOHImqqioYGBjAzc0NFhYW0NXVBQAkJSWhoKCgwVUfT6p+BUj96/hH9e11dXUoKytDhw4d5H3t27dXOaZ+pUttbe3fkiMRERERkTqxaEFERERE1AoMGjQIR44cwb59+1BXV6dw0LQqe/fuBQB4enoq9f1xa6VHlZSUAPh9ZQcAaGtr48MPP8R7772HM2fO4MyZM0hKSsK+ffuwdetWhIaGYtWqVQB+P6zax8cHJiYmjeZYv4XVox53XaNGjYKVlRXOnj2LjIwMuLi4YMuWLZDJZJg8eTKMjY0V4sPDw1FVVYXx48cjKipK6TDt0aNHo6CgoNHf+STqX7dHiz6Pqn99H40lIiIiIvonYdGCiIiIiKgVmDJlCj766CMUFBQgIiICr732WoOxUVFRuHz5MgBg6tSpSv0VFRW4dOmSyq2IEhMTAQDOzs5KfW3btoWXlxe8vLwAAEVFRXBxccHq1avx1ltvoVOnTujRowfy8vIwadIkBAcH/6lrbYy2tjamTJmClStXIjIyEitXrpSfD6Fqa6j61yE0NFSpYFFVVaXynIj6wkn9llZPolu3bsjNzcWpU6cwYsQIpf5Tp04BeLjaxcDA4Imfn4iIiIiopeNB3ERERERErYCTk5P8Q/n58+djx44dKuP279+P119/HQDw8ssvqyxMAMDChQuVthvaunUrEhMToa2tDT8/P3l7enq6yucwNzeXrxa4evWq/HcCwJIlSxpcwSCEkBcT/oz6YkhUVBQuXryIc+fOwcbGRmWRoD6/P16DEAKLFy9GcXGx0pj6YsKdO3cgk8meKDd/f38AD1d41L8m9e7du4f3339fIY6IiIiI6J+GKy2IiIiIiFqJtWvXIiMjA6dPn0ZgYCCGDx+O0aNHo0OHDrhz5w7i4+Oxf/9+AECfPn2wfv16lc8jlUoRHx8PT09PTJkyBUZGRkhISMC3334LAPjXv/6lcNB2//790blzZ/j4+MDOzg7m5uYoKirCjz/+iMLCQpiYmMDV1RUAMHHiRERGRmL//v1wdnbGxIkT4ebmBnNzcxQXFyMvLw9xcXGws7OTH9r9pJydndG/f3+cPn0aM2fOBPBwRYmqraV8fHxw5MgRzJ07F5cuXUKvXr1QXFyMn376CYmJiTAwMEBFRYXCGD09PTg4OCA3NxdTp07F888/D6lUCh0dHYwfP77R3KZPn47PP/8cmZmZ6Nu3L2bPno1u3bqhsLAQERERyM3NhampKRYvXvynrp2IiIiIqKWTiD+zppmIiIiIiDRSZWUlQkNDERERofLwaG1tbQQHByM8PFxp+6Hs7Gw888wzcHJywuuvv47Q0FCl1Rb+/v748ccf5QdVA8CAAQNw+vRplflYWFhg8+bNGDlypLzt/v37+Pe//43IyEiVWyxpaWnhlVdewddff60yt6aswvjqq6/kK0qAh9tAOTk5KcVVVlbi+eefx/Hjx5X6QkJCUFZWhs2bN2PLli0ICgpq8PkBQF9fX17gKCsrg6mpKTp27IiioiKFuPz8fPj7++PixYtKv9PKygqxsbEYMGBAk6+9oqIChoaGMDY2Vnl4ORERERFRS8KVFkRERERErYhUKsWXX36Jd955B3v27EF6ejrKyspgZGQEFxcX+Pn5wc7OTuVYQ0NDTJo0CdbW1pg3bx6GDx+OrVu34urVqzA2Noavry98fX2VxiUlJSErKwvx8fHIzc1FeXk5LC0t0bNnTwQGBioUOICHKxU2btyIRYsWIS4uDllZWaiqqoKlpSUcHBwwZswYWFpaNphbUwQFBeHYsWMQQsDa2lplwaL+9Tp06BBiYmJw7NgxPHjwADY2NvD394eHhwfWrVuHmpoapdds9uzZ6NmzJ3bv3o0bN25AJpMpnInRtm1bTJo0SeVh4/b29khOTkZcXByOHTuGkpISGBkZoX///pgwYQKkUukTXbuOjg4mTZoEfX39Jr02RERERESajCstiIiIiIiIiIiIiIhII/AgbiIiIiIiIiIiIiIi0ggsWhARERERERERERERkUZg0YKIiIiIiIiIiIiIiDQCixZERERERERERERERKQRWLQgIiIiIiIiIiIiIiKNwKIFERERERERERERERFpBBYtiIiIiIiIiIiIiIhII7BoQUREREREREREREREGoFFCyIiIiIiIiIiIiIi0ggsWhARERERERERERERkUZg0YKIiIiIiIiIiIiIiDQCixZERERERERERERERKQRWLQgIiIiIiIiIiIiIiKNwKIFERERERERERERERFphP8DPKavrywgDMoAAAAASUVORK5CYII=",
      "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-07-26T22:23:07.134828Z",
     "iopub.status.busy": "2026-07-26T22:23:07.134417Z",
     "iopub.status.idle": "2026-07-26T22:23:07.682069Z",
     "shell.execute_reply": "2026-07-26T22:23:07.681177Z"
    },
    "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-07-26T22:23:07.686135Z",
     "iopub.status.busy": "2026-07-26T22:23:07.685683Z",
     "iopub.status.idle": "2026-07-26T22:23:08.271777Z",
     "shell.execute_reply": "2026-07-26T22:23:08.271039Z"
    },
    "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": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-07-26T22:23:08.279902Z",
     "iopub.status.busy": "2026-07-26T22:23:08.279678Z",
     "iopub.status.idle": "2026-07-26T22:23:09.045729Z",
     "shell.execute_reply": "2026-07-26T22:23:09.044831Z"
    },
    "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)"
   ]
  }
 ],
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  "kernelspec": {
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