2020-LendingClub_OLS_int_rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description:**\n",
    "\n",
    "This is the Jupyter notebook where we implemented a linear regression.\n",
    "\n",
    "**Project Name:** Lending Club\n",
    "\n",
    "**Team:** Silas Mederer, Jonas Bechthold\n",
    "\n",
    "**Date:** 2020-10-02 to 2020-10-08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "df = pd.read_csv(\"data/df_clean.csv\")\n",
    "df.drop(\"Unnamed: 0\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorial var\n",
      "['grade', 'sub_grade', 'purpose', 'addr_state']\n"
     ]
    }
   ],
   "source": [
    "categorical_var = list(df.select_dtypes('object').columns)\n",
    "print(\"categorial var\")\n",
    "print(categorical_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continues var\n",
      "['term_is_36', 'emp_length', 'pub_rec_bankruptcies', 'default', 'loan_amnt', 'int_rate', 'annual_inc', 'dti', 'inq_last_6mths', 'open_acc', 'pub_rec', 'total_acc']\n"
     ]
    }
   ],
   "source": [
    "continuous_var = list(df.select_dtypes('int').columns) + list(df.select_dtypes('float').columns)\n",
    "print(\"continues var\")\n",
    "print(continuous_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "mask = np.triu(df.corr())\n",
    "ax = sns.heatmap(round(df.corr()*100,0)\n",
    "                 ,annot=True\n",
    "                 ,mask=mask\n",
    "                 ,cmap=\"coolwarm\")\n",
    "plt.savefig('plots/correlogram.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                1.078220\n",
       "term_is_36              -1.208320\n",
       "int_rate                 0.237159\n",
       "emp_length               0.247783\n",
       "annual_inc              29.323089\n",
       "dti                     -0.030249\n",
       "inq_last_6mths           3.430048\n",
       "open_acc                 1.045713\n",
       "pub_rec_bankruptcies     4.457929\n",
       "default                  1.936162\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               1.078220\n",
       "term_is_36             -1.208320\n",
       "int_rate                0.237159\n",
       "emp_length              0.247783\n",
       "annual_inc              0.118937\n",
       "dti                    -0.030249\n",
       "inq_last_6mths          3.430048\n",
       "open_acc                1.045713\n",
       "pub_rec_bankruptcies    4.457929\n",
       "default                 1.936162\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"annual_inc\"]=np.log(df.annual_inc)\n",
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.annual_inc.hist(bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"grade\", axis=1, inplace=True)\n",
    "df.drop(\"sub_grade\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.1065\n",
       "1    0.1527\n",
       "2    0.1596\n",
       "3    0.1349\n",
       "4    0.0790\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"int_rate\"]\n",
    "X = pd.get_dummies(df.copy(), drop_first=True)\n",
    "X.drop(\"int_rate\", axis=1, inplace=True)\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, \"['int_rate']\")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "# plot\n",
    "y.plot.hist()\n",
    "y_train.plot.hist()\n",
    "y_test.plot.hist()\n",
    "plt.xlabel([\"int_rate\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lin_reg.predict(X_test)\n",
    "y_pred_train = lin_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores on test data\n",
      "RMSE: 0.031\n",
      "R2:  28.58%\n"
     ]
    }
   ],
   "source": [
    "print(\"Scores on test data\")\n",
    "print(f\"RMSE: {round(mean_squared_error(y_test, y_pred)**0.5,3)}\")\n",
    "print(f\"R2:  {round(r2_score(y_test, y_pred)*100,3)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Curves and graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Learning Curve for LinearRegression'}, xlabel='Training Instances', ylabel='Score'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.model_selection import LearningCurve\n",
    "\n",
    "model = LinearRegression()\n",
    "visualizer = LearningCurve(model, scoring='r2')\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gg8EwpBixYTAYDAaDYUjZI3M2lFJkMhkcxzFrzgaDwbCboLXG8zySyWQfAz7zuz4wA1274cAeKTYymUy5Pt5gMBgMuxdTp06loqKix3Pmd31w9HfthgN7pNgoWTNPnTq1h6fD1vD2229zwAEH7MhhDTm725jNeIee3W3Mu9t4Yfcb83Aer+u6LF26tPwb3p0d8bu+JzPQtRsO7JFioxRii0QiRKPRbT7O9uy7q9jdxmzGO/TsbmPe3cYLu9+Yh/t4+1sm2VG/63s6w3WJafgt7BgMBoPBYNij2CMjGwPh+36fHhqbo3sPj92F3W3M2zJeKeV2N0QzGAwGw85jr4psdHZ2DnpyK3XF3J3Y3ca8reN1XZfOzs4dPBqDwWAwDBV7ze2h7/tYljVgS+7ueJ632yUh7W5j3tbxRiIRstksvu+bCIfBYDDsBuw1kQ2llJmY9iAsyxr0cpjBYDAYdi17jdgw7FkM14xrg8FgMPTFiA2DwWAwECifbKGDQPm7eiiGPRCzrmAwGAx7MUorFq19jg0dK8h7WWJOglGVk5kxdhZSmPtRw47BiI29kCeffJJ58+bR3NzMxRdfzKxZs3b1kAwGwy5i0drnWNWyCCEElrTxApdVLYsAmDnu+F08OsOegpGteyi///3vOfbYY/nQhz7EySefzCOPPFLedvLJJ3PzzTdz++2389e//nWbzzF//nxOO+00TjnlFH7xi1/02b5+/XouueQSzjjjDM466yx+/etfl7fde++9XHDBBZx99tl85StfoVAo9Ng3CALOO+88rrjiim0en8FgGJhA+WzoWNEnB0oIwYaOFWZJxbDD2GsjG4FSLG9Ob3Z7NpslkQ226phT6lJYW+i2d/vtt7Nw4UI2bdpEPp9n/Pjx1NTUcNddd23x+PPnz2f9+vV8/OMf3+JrlyxZwpVXXsmFF17Im2++yeWXX855553X4zV33303F1988RaP1R9BEHDTTTfxq1/9ioaGBi644ALmzJnDvvvuW36NZVlcd911zJw5k3Q6zfnnn8+xxx5LRUUF9913Hw888AB1dXVcffXVPP7443zkIx8p73vfffcxZcoU0unN/xsZDIbto+BlyXtZLNl3Ksh7OQpelkS0cheMzLCnsdeKjeXNaWbc/ugOPeai685l6oiBv5jXXXcdAA8//DArVqzgmmuuGfTxjz9+8CHNpUuXctpppwEwbty4Hs15tNbceeedHH/88cycOXPQx+zOm2++ycSJExk/fjwAZ511FnPnzu0hNkaOHMnIkSMBSKVSTJ48mcbGRioqKgiCgEKhgO/75PP58usANmzYwLx58/j85z/Pvffeu03jMxgMWybqJIg5Cbygr9lhzIkTdQbnS2QwbIm9VmwMNx5++GEeeughlFJcdtllPPbYY3R2dtLa2spHP/pRLrroorJAmTx5Ms888wz5fJ5Vq1Zx+eWX94gKQCg2Jk2ahNaa3/zmN3z5y18ub7v//vv55z//SWdnJytXruTCCy8sb7vooovIZDJ9xnfttddyzDHHlB83NjYyatSo8uOGhgbefPPNzb6/NWvWsGjRIg466CBSqRSf+cxnOPPMM4nFYhx77LE98kZuvfVWvva1r/U7DoPBsOOwpM2oysnlnI0SWmtGVU7uN+JhMGwL5pM0jKisrOTuu+9m4cKFnHXWWZx66qk0NjZyySWXcNFFF/V4bTqd5p577uH999/n85//fA+xsX79ejKZDJ/73OdobGxk2rRpfPGLXyxvv/TSS7n00kv7HcPvfve7QY1Va93nuc15X2QyGa666iq+8Y1vkEqlaG9vZ+7cufzlL3+hoaGBq6++mkcffZRzzz2Xp59+mtraWg444ABeeumlQY3FYNiTKPgBzZkCdckoUdsa8vPNGBsK/bAaJUfMiZerUQyGHYURG8OISZMmAVBfX8+vf/1r/v73v5NKpfD9vkla06dPB2D06NF9+r0sWbKED37wg9x33320t7dz9tln8/rrr3PooYducQyDjWyMGjWKDRs2lB83Njb2WAop4XkeV111Feeccw6nnnoqAC+88ALjxo2jpqYGx3E49dRTef311zn33HP517/+xVNPPcX8+fMpFAqk02muueYa7rzzzi2O3WDYVnb2BN8fgVLcNX8R85Y3lsdy4pQGrjp+xhZzwbYHKSQzxx3PdHUMBS9L1ElsU0RjOFxDw/DFiI1hhCz+oPzyl7/k4IMP5qKLLuLFF1/kmWee6fPagRw0ly5dyv777w9AVVUVZ599Ns8888ygxMZgIxsf+MAHeP/991m9ejUNDQ08/vjjfP/73+/xGq01N9xwA5MnT+bTn/50+fkxY8bwxhtvkMvlSCQS/POf/+SAAw4A4Ktf/Spf/epXAXjppZf45S9/aYSGYcjYVRN8f9w1fxF/XrgGKQRR2yJd8PnzwjUAfPnEbcut2hosaW9TMuhwuoaG4Yv5JAxDZs+ezX333ceFF17Ir3/9ayzL2qpW7EuWLGHGjBnlx3PmzOlXsGwPtm3z7W9/m89+9rOceeaZnHHGGey3334AXH755TQ2NvLaa6/x6KOP8uKLL3Luuedy7rnn8swzz3DQQQdx2mmncfHFF3POOeeglBpUhY3BsKMpTfDpgt9jgr9r/qKdOo6CHzBveSOy102EFIJ5yxsp+FtXGbczGS7X0DC8MZGNXUTvhM7uj4866ij+9re/bXEfgGg0ylNPPdXjud4RhsMPP7yHz8aO4oQTTuCEE07o8/x///d/A2HS6JIlS/rd96qrruKyyy4jmUxu9vhHHnkkRx555I4ZrMHQiy1N8F+YNX2nLQc0Zwo0Zwr9nq8lG24bUzX8KkOG0zU0DG/2WrExpS7FouvO3ez2bDY76Hb03Y9pMBh2D4bTBF+XjFKXjJIu9M3Pqk2E24Yjw+kaGoY3e63YsKQc0BMjk7EGvOs2GAy7N8Npgo/aFidOaSjnbJRQWnPilIZhGx0YTtfQMLwxORsGg2GvpDTBq15l3Ltqgr/q+Bl8aOY4UlEbNwhIRW0+NHMcVx0/Y8s77yKG2zU0DF/22siGwWAwlCbyecsbackWqE10VVLsbCwp+fKJM/nCrOm7VQnpcLqGhuGLERsGg2GvZThO8FHb2q3yHIbjNTQMP4zYMBgMeyWB8ssmVlHb3q0m+OHI7iaSDDsXIzYMBsNehdKKRWufK9pzZ4k5ibI9txQmjc1gGAqM2DAYDHsVi9Y+V248ZkkbL3BZ1RIaUM0cN/jOygaDYfAYGW8wGPYaAuWzoWNFH7t/IQQbOlYQqL4lnAaDYfsxkY29gCeffJJ58+bR3NzMxRdf3KOdu8GwN1HwsuS9bL+NxvJejoKX3ab+IAaDYWD2WrGhtKIz37zZ7bl8Dl9mt+qYFbG6Xbrm++CDD/KLX/yCuro6stksV155Jeeddx4nn3wyJ598Mu3t7fznf/7nNouN+fPnc8stt6CU4qMf/Sif+9zn+rxm/fr1fP3rX6epqQkpJR/72Mf41Kc+BcC9997LAw88gBCCqVOn8s1vfpNkMklHRwff/OY3Wbp0KUIIbr31Vg455JDtuhZ7C6bT5tYRdRLEnARe0LfXUMyJE3VMgqPBMBTstWKjM9/Mn177/pZfuBV8+LCvUhUfMeBrbr/9dhYuXMimTZvI5/OMHz+empoa7rrrrkGdo1Ao8Oc//5mPfvSjfba9++67XHnllVx44YW8+eabXH755Zx33nnl7XfffTcXX3zxVr2nEkEQcNNNN/GrX/2KhoYGLrjgAubMmcO+++7b43WWZXHdddcxc+ZM0uk0559/PsceeywVFRXcd999/PWvfyUWi3H11VfzxBNPcOGFF3LLLbdw3HHHcdddd+G6Lvl8fpvGuDdhOm1uG5a0GVU5uZyzUUJrzajKydvUWt1gMGwZ883ayVx33XUAPPzww6xYsYJrrrlmq/bftGkTDzzwQL9iY9myZZx99tkAjBs3DsdxgPCH9M477+T4449n5sxta1X95ptvMnHiRMaPHw/AWWedxdy5c/uIjZEjRzJy5EgAUqkUkydPprGxkYqKCoIgIJ/PY9s2+XyeESNGkE6neeWVV7j99tsBiEQiRCKRbRrj3sSubke+OzNjbBjZC6tRcsSceLkaxWAwDA07VWwopbjxxhtZsmQJkUiEm2++mYkTJ/Z4TS6X49Of/jS33HILU6ZMGdQ+ewKe5/Gd73yHlStXopTiS1/6EiNHjuT666/Htm0sy+J73/seP/vZz1i2bBk/+clPuPLKK3scY9myZUyaNAmtNb/5zW/48pe/DMD999/PP//5Tzo7O1m5ciUXXnhhj/0uuugiMplMnzFde+21HHPMMQA0NjYyatSo8raGhgbefPPNAd/TmjVrWLRoEQcddBCpVIrPfOYzzJ49m2g0yrHHHsvRRx/NqlWrqK2t5frrr2fx4sXMnDmTG264Yaub4O1NmE6b24cUkpnjjme6Oqbss2EiGoY9nV09/+7Ub9iTTz6J67r84Q9/YMGCBdx+++3cfffd5e1vvfUW3/nOd2hsbBz0PnsKDzzwADU1Ndx66620trbyyU9+kosuuoiZM2dy3XXX8eqrr9Le3s7nP/95li5d2kdorF+/nmw2y+c+9zkaGxuZNm0aX/ziFwG49NJLufTSSzd77t/97ndbHJ/u1fsA6JPR351MJsNVV13FN77xDVKpFO3t7cydO5e5c+dSUVHB1VdfzeOPP86MGTN45513+Na3vsVBBx3EzTffzC9+8Qu+9KUvbXFMeyum0+aOwZK2SQY17DXs6vl3p4qN1157jeOOOw6Agw8+mLfffrvHdtd1+elPf8rXv/71Qe+zp7B06VJee+21crTA931OPvlkHnjgAT772c9SUVFRjlT0x5IlSzjkkEP47W9/S3t7O2effTavv/46hx566BbPPZjIxqhRo9iwYUN5W2NjY3m5pDee53HVVVdxzjnncOqppwLwwgsvMG7cOGprawE49dRTefXVV5k9ezajRo3ioIMOAuD000/nF7/4xRbHvDdjOm0aDIatZVfPvztVbKTTaVKpVPmxZVn4vo9th8M47LDDtnqfgeh9YaZMmYLneUBYbbKjyWVz2KrvpN0fhUIBz/PKk3xpIr7sssvI5/Pcc889PP/888ycOZNPf/rT/O1vf+Puu+/m85//PL7v9xEHb7/9NtOnTyeTyWDbNqeddhr/+Mc/mDZt2hbH8t///d+b3VY6z+TJk3nvvfdYunQpI0eO5LHHHuPWW2/tMw6tNd/+9rcZP348H/vYx8rba2pqeP3112lqaiIWi/Hss8+y//77k0gkGDFiBAsXLmSfffbhmWeeYcKECf2Kn+54nsfy5cu3+N52NK+99tpOP2d/TIn6zN/U0acd+UHjKnn7jQU9XjtcxjxYdrfxwu435t1tvN3ZU284h5qdPf/2ZqeKjVQq1WMSUUptcdDbsk+JAw44gGg0vMtz3bDUrZR8uLVlrYMhnoiTjCcH9dpoNIrjOCST4esvvfRSvvnNb3LFFVeQTqe56KKL+OAHP8jXvvY1/vu//xspJddffz3jx48nCAL+67/+i6997Wvl47333nsceeSR5eOddtpp3HLLLVx77bU77P195zvf4Ytf/CJBEHD++edz4IEHlrddfvnl3HzzzaxevZrHH3+cqVOnctFFFwHwla98hRNOOIEzzjiDT37yk9i2zYwZM/jIRz5CMpnkxhtv5IYbbsDzPMaPH89tt91Wfh+bw3VdPvCBD+zUZNLXXnut3y/kruDgQ7qqUXp32uxejTKcxjwYdrfxwu435uE83kKhsEUx0f133dDFlq7dzp5/e7NTxcahhx7K008/zZlnnsmCBQuYOnXqkOwzGCpidXz4sK9udnsumyOeiG/1MQfLRz7ykR6PI5EI3/ve9/q87g9/+EOf5x599NE+z33/+9/v8aE4/PDDeeSRRwY9nsFwwgkncMIJJ/S7rRQdaWhoYMmSJf2+5qqrruKqq64qPy6Nd8aMGTz88MM7dKw7i13lc2E6bRoMhq1hV8+/O1VsnHLKKTz//PN84hOfQGvNrbfeymOPPUY2m+XjH//4oPfZEUghB/TEsFVm0FEKw97HcPG5MJ02DQbDYNjV8+9OFRtSSm666aYez02ZMqXP6+6///4B9zEYdjW9fS468h4PvLEKX2m+NueAHq/dXPSj4Ads6syQjAZUxiqGdfmlcSo1GHZvdvX8O3x/3QyGAdBaD1h6O5R097nQwJq2DG05Fy/QvNfSCVrzldmhsVZ/0Y9/nzWNnz63iNUtr1AVaSIZCaiMpThq8kEcMMzanA+XCI7BYNi92WvEhpQS13WNO+UeQhAEu+zfsrvPxZq2DE2ZAgKQAnKe4k9vrwYg4/k8vawRW8oeLp/PLG9kTGo5E6qaAIGvBM3ZDC+teBWL4dXm3DiVGgyGHcFeIzZs2yaXy5HNZrEsa4t3xZ7nlStYdhd2tzFvy3i11gRBQBAE25wVvb2UfC468h5tOZfunyRbQmNnnjvmvYMfaCxLUBOLMK46Uf7Mvbp6I587vA267SmAtrzH2rYVTB9zzLBYUjFOpQaDYUexV8VBKyoqiEQigwq/7woPh+1ldxvztoxXCEEkEqGiomIIRtQ/BT9gXXsWN1BAmJR54pQGCr7CC7qcVZXW5P2A1W0ZWnMuadcn6wZsyuRZ1Zah4CsKfoDEJSL7iiwvUGQKaQreji/L3hZKEZz+KDmVGgwGw2DY9bdPO5mtuRveHZdcdrcxD+fx9s5XsLw852UTXHX8DK46fga+CnM0cp7CsQRCazKuQghRjll4QYAXQNbzaUoXcCxBoCWuihCxgh7ncyxJMpoaNm3OjVOpwWDYUexVkQ2DYWso5SukCwUSjksuCPMV7pq/CEtKvjbnAK6aNZ1961JMH1kFOpQYWoMtBUprAg2BBl9BxvXx/AApbJZsSgBdURENVMccxlYPnzbnpQiO6tUXR2nNiVMazBKKwWAYNMPjV81gGGYU/ICnl21gTGoZYyraids+Wc9ifbqap5fZXHHMVH7+wiJeX7ua9lyO1W2atOshhMCWEl8pVK/edQrwNRwyqpo3N8YYkYpTE28i6YTVKEdOPmiXtTnfXGnrVcfPAOjXqXRvJlB+uWOswWDYMkZsGAy9KPgBCze0kbAWM6m6BRAoLYlaiknVLaxoWcT/99QG/GAtHxzl84ERDuvT1fzxzSQCia81Bb+n0hCAlAJPaRCKMRXwxRM+TNS2dqnPxpZKW41TaU+UVixa+xwbOlaQ97LEnASeF0XpQ4ZVybLBMNwwYsNgKNJ94t2UznDkmFaUDktaSygNBzRsJFtoRggLrQURK2BsxUY+NL2Cx5fWk7IdOnrlOUgBEs3sSU0cMX4dFdGA5Y1tjK2ezJjqHeet0f2OezDiZbClrcapNGTR2udY1bIIIQSWtPECl46giUVrnxtWJcsGw3DDiA2Docj35r7NQ2+uIhm1SUUCUpEAT4VKo2TgpdFURn3yvkWgNQU/IGHniDmKQ8dkqE10kPUauOfVKLqUJlpMGJ0zeRMHj+okajtUxWIo7bKqZRGw/d4a/d1xj6qcjNabFwimtHXrCJTPho4VfarZhBBs6FjBdDU8SpYNhuGI+WYY9goGstt2fZ/zf/kUL69eS0deorGoigkOHOngyACtNVqEHh+OBUKARlIZdamK+jiWRgNKC+J2wMhUM1ccVc/9/0oihKDgK8Bnal0Gy5KMSMYYVx2KgB01UfV3x72qZRHCrwQ+2O8+3c3JelMqbTXRjC4KXpa8l+333ynv5Sh4WRLRyl0wMoNh+GPEhmGPZqCcBFB05Fq47tHfs19NOweNVKRdiyXNSZ5eUcPy5ggzRmRQCFRosYHSkHUj1MRcYraPLcPcDEG4TFIZC+go+Jw+VbCmYwT/XNmCUpoRKcnYKsn+o2r72Hxv70Q10B13Rm0kUH6/E+RwLm0djr1Yok4izNEI+nqkxJy4SRY1GAbAiA3DHk1/OQmPLVxNVLzN6NQaWjLNHD5OEShBW94m0IITJzUza0ILOc8i7gSAIOdZpF0LW0qqogWqYh66mM9RSgVVWhB3Atrzms58mv/+6AykVcN7LTkm1cb51/t/HJKJaqA7bl+7mxUypdLW0vUpsStLWwOl+eG8hcOyF4slbUZVTi5HkEporRlVOXxKlg2G4Yj5dhj2WPrPSQjYv/59An8D6bwGFAKwpaYm5pF0AqTQBEqQdm06XQdbKFa1J+jISw4YlSXjSap00aRGABoCFfY4sYSmNu4SBO38c/kjxCNJRlVOpio+a8gmqoHuuG0RGVDIDHVp69ZGKH6/pIXX2/Ww7cVSKk0Oc2NyxJw4ldbYXVaybDDsLhixYdhjWdeRZX17jmTURgoYlVzG2Ip2JlalidoKNxAlrQCAbYFtKZQCJTWWDKiIahJ2wKiKAoGSFAJJe97GDSwkCltrpABfhQmkltDEHZ+WnEXGcxlXbZeTQPubqEZVTt6qiaq/apOB7riTcuSAQmaoSlu3pVtswQ94rTGDHU/2eH44JaxKIZk57nimq2PK/w4LXn/DlL0aDFvAiA3DHkdpopv77gaWN3cihODYCRuYVN2OJcGSYZKnIEzsDP/ehRBh1KIh5RXLXgVSCCypcawwv8H1bZIRH19rbBRagxAarcFXETJelE437B0yviYZJoGOOabPRDXYiMbmqk1mFFvSb07I5NzBLc/s6NLWbekW25wp0O761MX7bhtuCauWtE0yqMGwFRixYdjj+MG8d/jTW6uxJaRdD1BMqu7EVxTtwwUOQFFk9G7LJ0X4upil8ZREE9qNh23kBQlH0ZiOYFsCW3h4WrK8OUrE0UysKhC1fBwrgxtYdBQ0gYoTqK4k0K2ZqErLEBvbX2F92+I+1SYQls32d8dtSZvXGl/bYdd1sGxrSW1dMkpVpP+fpF2dsGowGLYPIzYMuy2B8sm6HaApTt6SHzy9kB/MX4QX+KDzgFX2zPC1QOuwoqQ/kdGdUqRfC4UfCLJe+FWpiARYQiGEZkOnBVqwuDmJ60sOHt2J0gKhFZaEpOOjtUtrtkAyUoFjx7bivXUtQ7Rmc5y+72JGJGWPVvX9lc0OhzvubS2pjdoWhzUkyzkbJUwvFoNh98eIDcNuh9KKt9c8w7LGV8l7GQDikQrWp8fx2MI4J+yzmn2qsjhWQGfBZmlzgrRrEXcUtQkPS2h8LXBET0txTZcAKeVySAG+FrTmnPLrYrZCaUXWtVnSXMkLK2v59GGrqIz6WJbCkV1VKqGo0QTKZ+n6F7do3lWKZPz2tRX8ddFapBBURhW2KNCUDRXQ+JqunIYtlc26gWJde3anlpBuT0ntJ6bVMj4TN71YDIY9DCM2DLsVOc9j7sL7acmsQOuwLFUKSabQgVRvctEHFLYVenf6StCWD0g4PqlIQGXMJ+4UDbiUINClpZHw2N0jHaFJV/j3iKWK+R2CtpzNmxsq+Ne6GtoLkkBbVERcxlYUSEQCAiVwunlvIDQRO04yWj2geVdPq/Q877WkqYg6jKtOUggcCoGDYwW05V3G6kT5zn9zZbOl4z3y2hqCF5t2WAnpYKpLtqek1pLC9GIxGPZAjNgwDCs219ujNHmuafkn+9a8R8QKgGLUQGi0DnCKIqNcXSI1dXGP2riHAIKiMVcoMDRCdy2X9EZrKPgSR2psoalPeKxuj7G4KcnTK+qIOza+CgBNzhdErDCOIYtnVzpMNLWkJBmtRggxYBSie0KlFIKcpyj4xQTT6iSN6WrGVTXhBRovUERta8Cy2dLxcr6iIr79JaRbW12yvSW1pheLwbBnYcSGYViQ8zxeXzmfdH41XtC32uKu+Yv4yzurmLNPM1Ko8n4aQGtEt+iE7rY6Us690BAgsbQi0GCJzQuN0nEF4CmBUoINnQ73vT6WQEfRBGS9sAJFARW2xg0kjhWgRRgDkcU4iUCitQIhNxuF6J1Q6VgSxxIopWnLuYytSrC4eQIAYyo7sITGsSKbLZsdip4nW1tdsiNKaoeji6jBYNg2jNgw7FJKd8yrW16iPr4Bx7KojkUYV23xfvM7NGddGqoPZ+67G4haHpbw8JXElkGP45QiGt2FRveSVl38jwJsAf3ZInTv8CrKB9VkfZuoA46lcd3wBIHWWEIgtCbtWqztiDKmskAqqkFrtNBoJbFsBykGjkL0TqiUQlATi9CUyeMp8AKNtCULmyYwpWE0c2bs0yfy031i3tE9T7ZHvGxLhGJbPDoMBsPwxogNwy6h4Adsynr8YN47/G3xak6c2IwUkkBpmrJ5OvJ5Mp6iI9/C/Qs2oLWLryxm1IU5Go4MsLrNOyWhoYvLF72FhiZM7Ow1X/ag+zYhwBGKnJK05WxyvkVbwcJX3aMqYdVEoCRLm5OkYhqFjRSKmO0RsXwcK0rEjpajEP3drfeXUFlq1NbpeiitsaXk5KmjuOr4/XtMuP1NzLMmjaQ2ESHj9hRksG0lpDu7Ydtd8xfxyNurCZTGseSwcxE1GAxbjxEbhp1KoBQ/ePpNnn9vDe9t7GRjwWJ0hSZqeSgECaeALTwEiqglqI3BpYesICrDCIIfQGtOorGpivnYRYHg+gIhwq6sspegEITLJv3RuwIFKBpzhUsoltDUxD0WrKjEV12TfFhlIojYkrhj8dKaUTjWJg4dkydua9rySepT4znzwNOJR1KA5EfP9H+33l9CpRCCMdUJErZF1vNpzxd47r2N2FL0uMPvb3njL++sIebIHsJIaU3BDzh92uitXpLYmQ3bcl7APS8vo7EzjxdoHCuM8oyrTgwbF1GDwbD1GLFh2Gl4gc91j96HZAMHjvTZt1qyuCnFy2tH0FaQjErliVoegQatBRFbI4VmRMKjJecQc8KqkIzrkPMcOvM+BV+wvDWB6wuOn9RMXaJXOWtRTWwuoFHK8ShFNbQODb3C3QSBFniB5On3asr7WIBjSyKWhW0JZo6qZl17lpfXjqKlkGBMheSofcZxxXEHlEXBHXPf4k9vryZqW/3mPPROqKyJR3ivJc3CjjyeCifdloxLe84t79N7eUMDa9oytOVcAqUZVREjl3dpcjvJ+QFxx+bZ9zZhz1u4VUsSO7Nh2/2LmlnTmsWSAkuAUpqmTB6AhsrYsHIRNRgMg8eIDcNOoeAH/PjpB4hbawGB0pKorTmwoR2ApZviTKjsBMAWCil1mMCpoTLm0Zqz0Qg0Aingt2+NZEpNnmXNcTrdCP/v8NUIJBCUS1ahZ1fWzRGUmqrpLm8NAXga1nVEkAKSEUVbPpxUHUsQsWSxJ4rGDzRjqxKcOWMsFx82uccSSaAUP5j3Dj+Yv4icp3rcqffOeeieUPnrV5bxz/c3oYrJrN0n3dI+vZc31rRlaMoUwsobDTWJKO3ZHI4F00dWIYUg427bksRQN2yD8DPyTnOOiB0up5UQQtCad5k6stK4iBqYcsufWHPTJ3b1MAxbiREbhiGllFPw9LJ1TK15D0eGPUS67sYF+9am+cNbDRw9QZKK+FhSlxM6NeBIqE14NGcjCGDGiDS3npRGSlAKNmUcsp4gbhf9LURXwuiW0BpyXihgYna3qhYBMUdRE/dZ1xEl44YTesySxCMW1fEI7XkPoaE67nDSfqN6RAvK5lyvLudPb68h76k+omF8TbJPzkPUtqhLRpm3bCO+6hJLYfQlnHSbMvnyUsyIlIPn58j5Nm05txzBcSyBJQUZT+Honssf21KVMlQN27rTnCnQ6QVUxyNl0VTCCxQfHF9rllAMht0UIzYMQ0L3yfavi9fhyBwJx8cLSjJAoXTYKTUZCQgQrG6PMKXWR+kwV0JaYcRBA5VRn5asw+TaHHapnJWwfLWhwiNQYYO17vkavXMxeqOBnA/NWYf6hI/SGkv0FCtVUY9nmqvRWDhSE7UlvoJRFXHGViWYvW8D1598YHkSbM3m+Y8n3uCt9W1k3YD3W9MkIza27DIJK4mGsTpBbSJKKmr3cPlszhToLHjYliDrBnhBUF7qsaUk6djUJByWbXieM/ZdRFu2g5zvkLJjPL9yBEpDTSxKoHQYIVAaL9BE7a4rMVBi50Alp0Ppf1HqjVJb7PralnPDJSQpGJOK8/U5BwzJeQ0Gw9BjxIZhh1JaNpi3bA1Ct/PWhoCoHWdcdZScbzMikSPuKCyhCbQg60nWd0TpyNthe3Y7bNlenu1FeFdvSxhflS8LDYovESKcxJ3N3PDq8n/ok7sRKFjWHCMiNLEqhRT9JJdKSEY0Yys12rfIKInSmup4pEc0w/V9Pn7fPF54bzVteUmgZDHvQJD3Amwr7MtSOrwXaPJ+QBUOl/z2uR5Jo1ccM5URqSjvbgqTOssRGg0qUMRsWLz2ada3r2B8dQRBAnIuH2gIu9q+3TiecdWJsOW9FDhS4PTKkO0vsXNXl5x2740yvjrJ2KoEXqCxJJx3wHgSEWfLBzEYDMMSIzYMO4xAKc775VymVr/MaZOzRCzFKVMkK9sS/GXRVA4eGZCK+lCMaEihqYj4bBAOH//ABuJOQKAFUhadQEW4TIIoLWv0vzDSWyCUUBoKAURk2OnVLi7PBAo8FSaA1id8RqT8HiKmvL8KE1VnTWxl+ogMWkdoztczsuowrj/5oPJdvxe4fPOxXzGpciMzD1OkXYslTUn+sbyOAIEQAtuS1CUc2vMentLEHUl9IkpbzsWSskfSqB8o9m+o4skl64Duuktzyr7NHDRyLe+szyOROE6McdVVjK1OsLIlQ8TO0+nFw4ZwQMqRxKLOoBI7t6Ut/I6md2+U7oLHYDDsvhixYdghBEpx7v88xZSql5hSm0EXKzkcqdm3rpNPHfoGFZEAW2iE0CjA9SHqwAcasiCyUKoE0d3yLYpzpK8oL3EMFq0h50o8qSkEFpWxUFRIERp7KTQ1cb+8VCN67SsECK1xpEYgiNmKQ0Z3cuTkNFHbwgtcXlz+JG+seY1J1Vl8Jcl6El8LDh7dAcDfl9fjK4UXCBoq4mGvE19xzsyxvLiyqY9nxsrWDN/9x5uAIB909VixpeCUKc0cPDqNJTVKhz3vc4U0WmsqYjXsU5vElp3UxhXrOjW1iSjn71fL+PHjmL+FxM6hcB3dFkxvFMNgsL56P8H3L9nVwzBsBUZsGLaLgh+wpLGNH85fxPMrVnPKSTl0cdp2ZFi6KgWMqfTLzc2UCv/EHHpGFEoioJcLqBdAoARCaoTcCsGhIe4oBBB3fDwlUSp0ArUsjd2t+qR7+SuETqOSUPwgBKMqk9Q6UFWZYmP7Cl52XZ5Z+hzJSJ6oFUZAlNBURAIE0FGwmT4izVPv1SKFRdSy0Giq4xYnTK7lwx+YyOPvrC33OFnTlmV9R46sHxSvXTgYWypSkQCNxfQRWSBsIOf6EGgfrTUZr5O2XNi0bXJ9Pb866iTacgF1yShvv7GAww6byZVbmLx3tnHXljC9UQyGPQsjNgzbRKAUN/7tVf7nnwtpyUt8JZlQ5RGxFAqBI1XZ4bN73xJLgGX1TebsHlmQ3QRHaLAlisJl6yIbvoZO1yLlKCw0ESusCCmtS4hS0qYAoenjt4GEiBVWz1TGfJQbCoP3m1ezqu09RiQ8wq6zOjxAkdq4RyoSluCeud8mnl05lk8dPokT9tmEJVYTqKUsWb+QI8Yp3m4cTVOmg5acj6t0+RoopThlSjPT6zOkIgEFJWlIurTlI4Ag40miVtj1VqBpyYUVLsdMnkEiEiUR6XkttjR5D2TcVZ90iDsFAhXp127dYDAYtoT55TBsNYvXN/Kdv/6GMRVZLivmKCxuSvLcyircQJCMqs06dpboz+Wzx2PR1eckZumyBXn3rI1iEKDf5FClwuhC0g565HqobkmaZd2hu52vKD5K0Q7HsrCETcHLgrZZuCEgIvMo7ZRLdLsqRXSP5R+twjJdT21g8bpGIrqTiG0xuiLOuGqLmSM2Mq5iNe15Qca1Wbgxzj+W1SGE4NR9mzh4dAeBEvjFfJOYE1ArPNJejKwXAyBiheLA9W0WNVXzyaOPHvjCb4Z+XUzRTK1bxcGj87zw7tI+zfEMhl3Bf562lEsfnrSrh2HYSozYMAyaxvY2vvyne/jAyFZOmOQTKEnWt/A1HDq6nZgdoNBbFBqDpeTm2V1MlA7tKygEIiyTlaqPeFFAMuITsfpGUHS3v5eiHKW+KghQASASVMUjeEG+fM6MXyBdCEhFJIESBEogZdiUzRY9j1Vq4KaQ7FebRhcNwALXZ217FkiTcAJ0VNGSdYjaAYeM6kQKRdzRHDexFUcqlJJkfUl7IYLWUSpjAfmiyMp6MbKeZkO6hoWb9iHva1qzHvGqbava6G3cdfjY9cyo72RcdRIhBF7gsqplEQAzxx2/TecwGAx7J0ZsGLaIF/j86Kn/xRGLmDNZle/6faWwpCLpeEghGFtZIGJplBq4fTsUJ/2BXqfDbZL+fTLCJE9NxNH9bi8lgvaJoHSLmHhBuJxTUh8ZV7CuM0oqKhlVUUFFLEHWbcf18ygdECjFkqZKxlbmsGVA1rdIRXx8FfZlKdlYhMs3Nq258OtVEQsQhEmqmjDPpeAV0FpgS0F13KbgKxJOnnOqN4VGZhYoJfCFpsIKqI7DuOpRpAutNGUlEp9C4NCYrmZx8wQ0gtqEvV0Om92NuzZ1Znhn7cMonep1/QQbOlYwXR2zU5dUTLt5Q2+sr97f47FJGB3eGLFhGJC1re3c8eRdzByZ6elxIcIJ0S7nXvTtSbKl/IqBBMkW9xXh/mKA126uJFYKcIMwF8QNJI1ph2UtCR5fPIKo7fBvh65C6yy1CYuIlSQeqaTg+yzZ2MHT743myHGb2H9Ea1lMJOwAHwiUJu3atGRtNF1vrjNvISQkHEWgBFYxcdYPNF4g0DrDyKSPJbuWdUKPDI3QYEmbmK0IlE8iUo/lHMLTy9biqQhKh+fpXs7afWKGrok6FbVJF/wtTthR26I2AV6Q7VdQ5L0cBS9LIlo58D/SDmBXe38YDIYdgxEbhn7pzBc475fziMu3OXt6pkc79xIDTfT9hRu6N0UbzEpL2ZBL9H19KaF0MKKmv3H4PkgL2nIWr6+r4p+rRhKxLYTwkfiMTGVpy4a9WoSQJCMVLG9OkXE1/7e0moIfML0+Q4eADW6E99rieIHkwFGddI+1CDSBljTEC9TEfQItyXky9BMRoblZXVz3eA+i2/uVQiOET8H3SHesIe3GWN8hidgTsTW05txyOeu/z5rGD+ctLE/MtYkIHe0dxF5u4a3GNvwgbMY2sSbJ6dPG8PWTP7DZCTvqJIg5CbzA7bMt5sSJOjunUmQ4eH8YDIbtx4gNQw8Cpbj5ide458XXOW5SC0eO6wgrMrbyOL2FgNahkVZkKyLgWodJoKWb8B6iQofRicQ2pCdowqUO7du05Bwcy0IKOGb8eo6e0EJd0uuRd6K1oi2XxtNxbClQlsW892r51/oKADKuRczWZNxQREyvz5CMBGRcC6XD3JFO18a2wihIKuLjBRIhwbb0Fi+u1iUzMoEbRBiZbCTvB4yvPZLTZoxl3/oKkhHJj+e/zp8XbsILBI4leWd9GyvbsijS3Y7msbo9x4urmvndgvd57StnEbH7/gxY0mZU5WRWtSxCdLvwWmtGVU7eKUsow8X7wzD8uOfDC7nsT0Zs7k4YsWEo09TZzlUP/oyjxrXynTlbzrvYEt2twoXYSqEBNGctWnM2k2sL5cm/5NXh+qJYndJ/zsZmj1tsvLYxE6cqHqUqFnBAQxuHjdlITdwn5hSXX4pLGYEW+EqgFEyvz/B2o8P0EeuYWNVOZcwnYoUWpzlPknZtFjcluff1UYyvclnb6XDpQY3lJZXWnEMbFrUJn4gVdJXzDvI6OpYi7uTJeTGqo8389Pl3+Ms7azhy/Aam1mVIZ9v54GjJ0qYUL6waQVvOR232OmiWbOzg/Huf4bHPntTva2aMnQXAho4V5L0cMSderkbpjx2dVzHcvD8MBsO2Y8SGAS/wuenxexhT8R6n7LtjjlkqMd0qx0+68hVynqCjEKEm7pc9L8plpoRlpr4SWx1xKfiwrjOGFJL2vEvEEoxM5Ug5YfSgvIRRFByyuGATVon4nLpfI+OrGolbAbZV8gQRtOZtAi04e9pGzpvRiEbgBaEHx9qOCFZRwVTFfBJOgBQShUZptig4SgmtAohIF1cobOlgSY8DRq5lZKKJ9R0K19dELcUHGtrRWvH40rrNHlNpkFLw8spmOvIulbFIn9dIIZk57nimq2MoeFmiTqLfiMZQ5VUM5P3RX28Xg8EwfDFiYy/n3ucW0pa7n0k1W79UMhCbS84cCEHYy6Q5Y2NJzehUjqhddPGkp3iRVugEuiXKuR1AwYP16VBoKKVRaOJRl4Sje1SplMdTrGZx0OQ9gRAeB4xc3SN/RRNamtfGPerjXjkapIsdZC2p2be20CPvRGtQKCQDR496v7tS2W7MCtjgRvEDydjKVoTQBFoVX6PRGibXpLFlDb7a/AkEkPcDljV1cui4zQsTS9oDJoMOVV5Ff94fsPneLoa9m97VKd0xlSq7HpPOvZfyyopGKq/9H9zgfqpiO1ZobAuBgrwP6zocahI+dYmAqF0yy9q68ZU8M0pREK3B8+HJ5SPJeg6BUkgZIFEknaD8ms0hReg+Wp/w+yTKCopluLKncAirdXS5aqbkfiqKjy2xeaGhio3itO5p3a40oMNqluUtUY6Z0MiIRDvV0TSjknlq4i5aa5TWJCMBqUgw4HWypSQRsdi3vmLA1w3ElvIqCv7AY9gSVx0/gw/NHEcqauMGAamozYdmjjON2QyG3QwT2djLyLkuH7zzEc6c+hY/PNPb7ryMHYUQELVgcq23zccoLcEEKnTczHlgW4JkJEJLXuH6AtfXTKwrhM3Min1SuhmH9nPQ8E9kB39TNre8pHQoutryDrUJD61F2W8k0GEOSUfexg8kBzSki1EfgSUhFfWRQtOcdci4Fml34PLWiCU4ckJ9v0sog2Wo8yq6e38Ynw1Dd+758MI+z5mk0eGLERt7EY+88R73/PNBrj62GWeYiIwS27Ls0ofiUogUGlfBpmyCsVVJqhMJljVv4qgJrWTc0PU04YSGZEKAHwiUDqMTdnFppuReKgeIQAwFGvCUxR/emsrRE9OMTTXiKkEiEsH1fXyleX19iv3qsvgKMq6kIhIUoyiaSNwnZgW825IkUH0vqi0gYklSMZsjJ9Tzx09tnxPozsqrMI3ZDIbdGyM29gLe29jO4T/6A9cfv4IP77/1vhS7C4IwAqAF+L7EV9CZb8PzmxmZ1EQsTWvepjkboS0HllRURT3ijiLr2cSdACnD8IinwNmaDrPbSPeISlfViebkfd9HqbBbbZWlcZVPSyHK4qYKXlxdwSGjOvG1oDXnkHAUUaGQQqC0JhdYJJ2A0/drYf579VhSIoTg8PH1/OaSWaxuy7JvfcV2RTRKmLwKg8EwGIzY2MP5+5tv8e6m33LH6bt6JDsHVVxqiFiKSdVZRDlhM/x/0vHR8XCCj9lhdMMSYc7IxnSEiqiPQBG31Tb5i2wNfdJEdFjZorRgUnWOdZ1RmnMRBBpLat7ZlOAfy+qwLUXas4jbGlHsNuspi4glKQSK1qyDBvYf2clLa2oItE1dIsLpM8YwIhVnRCq+Q99H754qJZMxk1dhGC4MlDzaHZNIOnTsVLGhlOLGG29kyZIlRCIRbr75ZiZOnFje/tRTT/HTn/4U27Y5//zz+djHPgbAeeedR0VFmMQ2btw4brvttp057N2S9S0b+eNrP6AyRp9243syYSKm7rcnitKh10dd3A+XXAj7j3hK4NgBCREac8UcgWOpMAdkED4YA9FfLojSXc8LwnOEvWYE7fkwwmLJUGCADju/KsnUuixzVygCbbG0KcnBozqRQmMX8088AnzlUJ8MiFgBllB8+tCVNGZGcOD4WVx1/AwC5Q9Yxgr0sTvfUq7ElvIqTF8Tg2HXs6vn350qNp588klc1+UPf/gDCxYs4Pbbb+fuu+8GwPM8brvtNh588EHi8TgXXnghs2fPprIyLLm7//7BKdO9HaUVj73+ME3pV6mM7erRDA2bTeakp9V3d5TuqgaBYm4HYFkax9IkHNAoAgUFPyx/cQOIAtvaTb2/0lWlwuiFpwRaK9Z1xrC7eYgApCLhck5DsoAlQWtBxhOkLYvxVTlWt8eZu7wegP1HZorHBDdwEFKQjPjF80mqo5LjJsM+9a0sXvcc69qXk3fTxCIpxlRN6dEuvuSXMffdDTR25sm6PkJCVdShPhXbondG77wK09fEsLPpnTRqEka72NXz704VG6+99hrHHXccAAcffDBvv/12edvy5cuZMGECVVVVABx22GG8+uqrjBkzhlwux2c+8xl83+crX/kKBx988M4c9m7DotYmXp33DSL2Dkq4HKaUTa624j2KHokR/RuOhWZhYEeKvpvbeRNe8vfoPQ6ruPSxKRPhzQ1JTtinjZgdRjICFUZVlIaoXeqNook7UJ8I+NLRK2nP27y+vor5K0fzyjrB8RPWMa0+jRSSURUFBAopQ7+NqniWbMHh3cZXCAIPX7koHZAutNKe3YgCPlBsF///zV/Ez55fQnveI+36BEpjCcHYqjgxx95q7wzT18RgGD7s6vl3p4qNdDpNKtXVstqyLHzfx7Zt0ul0OVQDkEwmSafTxGIxLrvsMj760Y/y/vvvc/nll/O3v/0Nu59+Dr3pfjG3hddee2279t9ZdORc/m/935jWUCCyB4uMEr7asvdGKdmyJChKE7/SYV+UyE66sRa9/q7LfzSbMg5Hj++gKhaETqSIsq+GVVwO6i2K4o4mZnucsm8Th47u4NmVNfx58QhOnmIxc2RncfmkeD4BUUuRzrdhWRqBTclz1SfA9V3eev9Z8hti+ErwX8+soCkXRkX8IBRcntasbc9QZYUJqI+8tpSjElki/XXm64YbKB55bQ05v69h+paOsbt877qzu415dxvvzmJPvi47e/7tzU4VG6lUikwmU36slCoPuve2TCZDRUUFkyZNYuLEiQghmDRpEtXV1WzatInRo0dv8XwHHHAA0ei2ld699tprHHbYYdu0787kn8veZFn6AWaM2tUj2TkEGnwlsYTarNoIijO6KM3u3ZZOsr5AoonuwtSBIABPCSpiLnVJr2z4JbVGEPZiiRRLcHtHX2Qx1wQBlbGAQ8d0YEnJ0++N5IXVldx+6rs9jMfC/XVxX4kUgkD7oDUajUcGWd9KQ8VRtLkr8LXAV4qgm928ryASTxB3QmOtidNmbrEMdV17luDFJirifS/0QMfYXb533dndxjycx1soFLb7JnF7OOJ372z1PsMlqXRL125nz7+92akLp4ceeijz588HYMGCBUydOrW8bcqUKaxcuZK2tjZc1+XVV1/lkEMO4cEHH+T2228HoLGxkXQ6zYgRI3bmsIclbekWfvHMdSxe/zvsPXj5u7ezpxQQtVW/Syiqm2toKYygis935gUb0zZSa2J2P5UgO4mwJwnEbM24Co9It/JaKSARUcTsLTRoK25zLE3UDqtO6uMuCafvdSk5nAIE2sUrLqOUtgGsb32XNc2vkPcDPKV65MSobi6mSmsSjk0quuV7lJL/Rn+YviYGw85nV8+/OzWyccopp/D888/ziU98Aq01t956K4899hjZbJaPf/zjXHfddVx22WVorTn//PNpaGjgggsu4Prrr+fCCy9ECMGtt966TSGcPYk/vfIqLdkHh50x11DQ3+TZo/07Xa3sS5Nq9+WKrAsbM1ESdkB1POiyDt8po+9LSRgqDZbcfEIrbD4npfS0JRSjKvJINFcdvYz1aQdB6KAqixU5fY+hy71ZSqW26ztdLGsVUSuCF2iEEOXqnRKNnXk6Ch4VUYdLfvvcoJJFjf+GYVfTn8toib0teXRXz787ddaWUnLTTTf1eG7KlCnlv8+ZM4c5c+b02B6JRPj+97+/U8Y33Fm2oZHH3/ohlbGd62o5rNH9J8OWohupKCQjhXASLkY+SmWvuzK9RZbqXjeD2sz76oEAqyge4k5AQ1KX35cbyDAC1GuX7pEiDWR9h4znEqgWxlaOYUWLwi++SNJ1ndpyLnXJKOOqk4NO9DT+GwbD8GFXz797d4hgN8H1fS745YOcO23BHlvOuq0MVJEier3GLt6tD/ccWg0UfEHE1uUoTg+n0aInh5RdyiFiQSQelu4KqdGIrvwOQCLQ3RaPwkZ1oeIRQEsO9qmrpxBkaMkVcANNxBJUxRw6Cj5T6iuI21Z5DKVGa1+YNX2b/TcMBsPegxEbw5yFa1Yzd/FP+bC5Gdwh7BYlwRoQAlUSDjr0/Agfh8svWnUli5aXYkrLJhq00mXzMEdKQvkhUOhi5EOgkVjCI+9brGmv5uAx9aRdzdjqBF6gsKXgvZZOMgWfxY3tRCxJdTzCmKoEfqDJ+4NrtGb6mhgMBiM2himduSyf/s1POWNqMxUml25YMyRLMhqkpQk0dOQs2go2gQrXzkalCjTlIjQkC8TsrvLYUtxCC7Asga80tgyN3sLtotxgTmmJFGFFylsbYjyxNMUJ+7ZSFXNQWtOSLbBkUyedOS+sTvE0XqDIuD7rO3LYUhJzJL99dTlfmT3TmHQZ9lq2ZIU+XKpVdjVGbAxDVjY187+v3MmZ0/WwD/nvCAaVnzCM2dFDF6WKm+Kxq+MBqVhAe96mNefgBhKtIefbxB2vR2kvhBbsEavL30J3/48O29cLBHHHBy2YUJVnzuRm0l49WU9w5oyxdOQ9Fm9cQtSx0B54SlHww8+jlAJbCioiDn9dvA7bksaky7Db0T15dG9LFt0VmNuRYcTK5jYu/OVtPLnwDhpSe4fQgG2brLvbe+9JaB0ul5TDFMWlEUdCTcynJu6ztiPKv9ZVsq4jQskzq7RkEijRtZxCKYpRzNEAAi2xi5bmAk3Gs4g7cODodvarXYUUgmeWN/LC+xvxgjDLw7FkcSkmLCXWWlMTjzKuOlHO3Sj4wU6+UgaDYXfCRDaGATnX5eg7/sAVRy/k1P129Wh2PluyHe8273Y9p/a8ipxShEf0ilSUkAKSdsDS5iRPLBvB3BV1nDV1I3Mmt2DJ0AZdyp5JpX4gEEKEZbY6lAuVURcIk0zLJcNCMrayneWtAW+tb6c975N1/a4E02LoSQAVEZuJtUlEsaX9ho4c6zqyTKqtwGAwGPrDiI1dTFu2k+898UO+eFx2t15K2NGU7sRLnhgl3ww0FPywb8ieRH9LSUKEoqrceVZAQUkWb0piS0WgBHlPYonQqAy6RXxKFTgSfC3QWiBlgB9QTBGVKA3JSAB4ZL0oMcujJdNBpqDwA013o/GgaLohgNpkFCEEq9sytOVcAqX54kMvc9J+o0yTNYPB0C972E/27oMX+HzvH/czIr6E/ep39WiGISUHUN3ThEsDEXvrmrDtDmxOaErZtUSiNdhCcdGB68m4FlJoGioKPQSGoNiyXoWPpQybwQRoLK3JBQ5VjiBQqrxT0gkItCQf2KzrVFTHI6xuzfY7Hg2MrU6ypi1DU6YAWlOfjJHzAtNkzWDohy0lkHZnT04mNWJjF/D2mvX89qX/Yt96b49bCthRdPfIKOUcIHZ8MubugBDF5CoBEVtREfNpSLnEI6qcdNW9GqUUBSn3N0GjtCDvSzoKUaqiYMs8aEGgNQiNF3hsStfjBYKWnIffzzgi3axbW7IulhTUxMLcDRic94bBYNg7MWJjJ9KWaeObf/45h4xpZerIXT2a3YOgeIfePZKxp0U1tkR3M6+IhNq4j6cEkl7XpZ/9AgXLW2MsbUoxs6FARVSwtkNRFbOJ2j5SQKAt1nQ0sCa9D0I04wddSybdBUwo+jR+oEg4ksl1FTgWRC2XQuCgtKQlOzjvDYNhONHb1txUp+x4jNjYCfjK5+9v3cvatmUcNm5Xj2b3oZQzUCoB3dtERonuiaOlKIfdzT10c/u4gaA9b3P3yxPI+zaeauKQUZ1hVYl2iDlRpFCsbhvBkpaJvL5mE4HSuEWx0fsMWmsitsSSgrznMza1nOkjskQtj0Lg0JiuZnV6smmyZjAY+mDExhCzob2d3710B9Uxf4/uzjoUWAICBm5WtjewLe890IJO1ybt2sRsTd6HfyyrAw3T6jM4VkBHwaIjP5LFzRNY1Zoh6wYkozYajQ6CHo3YwvJbwehUHFtKTtmvhUk1LdiWg0LiWAFjq5rYf1SVWUIxGAx9MGJjiMh7Ltc/8j/sP3IVNfFdPZrdF2tvVhlFtjaiozVlA7C8J0m74eSvEfx9eT1PvVfLqJTADRymN9QC0FnwkDJcmonZkqgtyfsBgdIopamM2oxMRphcZ+Nrn2n1WZSSSCDQGseSVMei7FOXJVA+ljQ/LQbD1tJfMumekjRqfhGGgPc2tfDogjs4cNQe6DplGFJKJb9bG83pXjqrgLijkDnN4qYkftHm3JaKVCQg7wvyPvjaouArpBB4vqIyavcocY3bFr7SjExGOGW/Fkan2ohaHr6yqIhmyQdxZjRUE6hQbEghcP0cBS9LIlq5Iy+LwWDYzTFiYwfSls1x1A8e5f8d+QZVMSM0DFtPfwZmW0IVk2hL/0eDRLNoU5Inl9ch0Jw8pZnp9RnGVuWJWgrXF6zpjLGytZl3WyYwrjpBXSLC6rYcna6HF2gcSzC+Ks7nDs+TKTQVG7lJpNQ4lk/C8bGl7LE8GHPiRB2THGowGHpixMYOIFCKH8x7i2eWzOXfDmuh0ggNw1aii54iluz53GDcVQuBKNe+ajQSzfutUf767gg0glOmNHHI6A7GVLrEnQABJCIQi+QZmbIYVbWJ51aO5J3GdnwVGoHVJhzGVSf58AFjmDHiX7zfHKMt7+IFKrQvt+LE7QCtNaI4SK01oyonmyUUg8HQB/OrsAO4a/7rtHc8zulTs8ScXT0aw+5EOZIhurWM7/bcQPtpHZa2CnR5+QUEvhIs3FSBryS2VEyvz1AT94nbQdexgbgdEKgCNdEmNnYmqYrHaM+HUY3Ogk9tPMJnj5rA/CXPMb4myVidKIsNAWTyrVhS4gU+MSfOqMrJzBg7a4den0D5FLwsUSdhRIzBsBtjvr3bga98/vH2fSRZRlWd6lsraDBsAbGZv2+OoPtSiQijEKExXKg2fKVZ0pzk78tCW9pUJKAi6pNwgn7FiyU8LCGAAg2pasZXJ8uCQgOWjBNzEniBixSiR6VJTWoUx039BH7g7nAxoLRi0drn2NCxgryXJeYkymJGClPWZTDsbphv7TYSKMUvn72b1a3LsKQq+0DsrV4QhqEl9MYARFihU4qCILoSQ91A0lGwWd6SoCrmY0tF2rXIBxJL9m3spjVIocn5kva8pDGdLwsKKQQt2QJtuYC61CTyno/qdoDSkknEjpGIVu7wqMOitc+xqmURXuBiSRsvcFnVsohFa5/boecxGAw7BxPZ2Ebumv8GUb0Ru/hL3z38bTDsSFRxuQTo49Wii5bk4dOaqljA2dM3ccjoDtKuw+KmJEs3JdinOo/SAkt0CYZAh34c72xMEWhJa9ZlbJXCLnro18Qj/Pa1FTz3ns+ohM3E6k5GJDX71NUzpmrHL5mUx6V8NnSsKOeClBBCsKFjBdPVMWZJxTBkGPfQocF8Y7eCgh/QnCmQitq8vvp9Zo0P0Fp2+TobtWHYwXgK/CBc0hBaY0ndI0LR3VnVsXQ5ylEVD/C1ZM7kZrxAlKMYmmInWcD1Be82J/j7sno00J73eHt9GyOTDvvU2tgyyV8XrUUKwbLCPixrDRDkOW3aJE6ZefCQveeClyXvZfsVFHnPlNYaDLsjRmwMgkAp7pq/iHnLG2nOFEhGHFY2uxw1ThKx1F5tpW0YOjRgAUiNpyDtWTjFfiZQjGrIbn/v9nzMCqiNhy3klZas7YhRHXdJOgE5T9CYibFoU4p/LKtHIxCEFuizJjYytT5DQ0rgBhHWdVSyqGk8q9tytOZdvECzsHEpCJuvnLj/kLSTjzqJcp5Ib0xprcGwe2LExhYo+AG3PfkWTy/bgC0lUdvCDQKac5p0waYuke/jjdDdYMlg2C5E6JkRaElrzqEq6mPZQFFo9JdgGmiwpCbphNmklggjIq25CG058LXgF6+MJx/Y5TwMRwo+MrODmQ1ZhJDFZRuXcVVNtOZcXl9bixACS0DeV/zprdXYUgxJO3lL2oyqnMyqlkU9llJMaa3BsPtivrX9UPADNmRc7pj7Fs+s2Mg/39+EJQXV8QjjqpNIIahPOmzKOsQdl6Sjev7oG6Fh2E5KvhteAGlPkooo9qnJYYlwGaS70NDd/gQq/KMUSKnRCAItUDqMXARKELM0FVFBPhPuE5GK/UfaTB+RQRY/vIHWWEKi0IypbMe2avCD8PW2FERtOaTt5Ev5IGE1Sm7ISmsNhuHGnmJP3hsjNrrRfbnk1fc34iKoiHRZODdlCgCMr04yqdYhZvn0l6ixJa0xGLMmw95Nya5caYnSEo3CDwS+0EQtQIcRDK3D0lfPF9iWxhJg22GuB+jQVRTB6AoXS+iwQVvBZnJdHRNqBSPjy5gxIkt9wqcimqcQOGS9GI4lqYpFaMrkidk+Ni7tnoXSELEsVrdlGFURG7J28lJIZo47nunqGOOzYTDsAZhvbzfumr+IPy9cA0DOVwgpac0WCBRYMlzXbsu5jK1KsKLZY/96SdwOtioxtGTGtC39Lwx7F0JAztMkbB+tRVGgCoTo6l/iKYmNRspw6c5XgBYoJZBCIUUY8yhFPqRQOFKx/4i15H3FxKoOAgVtBUg4ELU8AOJOFeOqQxGxZKOiNS/KXWC9QIVdYgs+1fGhdbGzpG2SQQ2GPQDjs1Gk4AfMW94YNqUKNH5xLVtKiRa6vLbtKU3BV2zMeKzriG61YBCEd6JGaBi2hABqEppkBKKWxrF6VqOEvi4aNxB4gSDvSVa2xnm/Nc66ziir2mJoLcouo0oR+m74NvtUtzKltp1ExCFiSwSCjCvRaFIRxdjqOEIIxlbF2ZStRUgbIQRSdImejoLPT59dvAuvkMFg2F0wYqNIc6ZAc3GZxLEEdrd1DktIauIRpBRYQpCIWAjg8SX1uMWyQuhaN+9Ol410T4zQMAwGKbrM4sKKka4qawFEiiIkYinSnkQjsIq5GaVCkcZMhHWdEXK+pCrmM6Yyx8hkG6NTbSScHBFbEncsPB3HCyJEbUmgPBwrQk1yP5a3TsASYWSv9Fl3LIltCZ58dwMFP9gl18ZgMOw+mGWUInXJKHXJKOmCjxSCioik0y/+oNuSibUplNbM3reBf581nQPveIy8H2PxxiQzG9I9GmghullvGFVh2E5Kn6NSLxSrqDREcaMu9kaJOwEJp1DOzch6AjeQKC2oiflUxX1s0XVMKSBmFyh4ijY3ghAC13eYWDuSE6efTyJSia8EqejT2FKGFua6S/hIKch6Bda2NTGxts7kVBgM28memhwKJrJRJmpbnDilobxcMjLhUJ+MIgVURm0qYw7nHTCeb516EF6gidsWEUtwz2sTWdiYxA268jF8FVYSGJ1h2GEUJ3mr1AulWKmS90GpUH0kbI0UqigkNBURH4VmdEWemoSPIyl/KAMlwugHELN9lFIopfGVYlV7FRWxWixpE7UtTp46CrtY/SLLIkdx6pQmTpuyiLdXP8C8xb9h4Zr5KK12zfUxGHYAxj106BjUrciqVatYsGAB55xzDt/+9rd55513uPHGG/nABz4w1OPbqVx1/AwA5i1vpKVdM6OhilmTRvKJQyYxsiJWLvGrS0Y5YHQ1ixs7aMkVuPuVfYg7AQ2pAnlXcMEB65lanyNqbvQMO4BSlKy3d4slu8pdbanD6pRiyWugBFoIHKnI+xYJJygfSxF6bVD045AidB/tyAtWdVTyVlMdl88Kyp/3r5y4P8+/t5Hn39+EF2gcKZg1sYlJNe34gWTJJkV1zKPgvQPAzHHH76xLYzAYdhMGFdm4/vrrUUoxd+5c3n//fa6//npuueWWoR7bTseSki+fOJM/fuoEbps1jj9+6gS+NucAxtcke3gJRG2L2fuOYnxNjCPGJ5hYHUOKKE3ZKgrKIRHRpgGsYUDUZnJ5Nkfv5bhSS3rHgoitEQL8QLCuM1rMzxDELU3cgbit8FTXOYWAqKWI2GEUZHlLnJ+9Mo67XxnP39+tpynjlvOXIPxePPTpE7nmxP0ZlYpS8AtMrOkoN4YLlKYpW2Bte44NHSsIlL/9F8hgMOxRDOreu1AocN5553HDDTdwzjnn8MEPfhDX7WslvKcQtS1GJJzNmhUprThp32biYhmd+U6OGGuzorWCf60dzep2j7gdENnxPkeGPYhy19Z+2BofllKiqBBhxEIhqYp6pKIKWUomFQIpdM8KKAFCh3cbvpI0Z6MA+CqgKZ3vU9JqSUnEkoyoiFHw01RGFUpL/EBTIIyCtOVdcm7W9C4xGAx9GJTYsCyLJ554gnnz5nH11Vfz5JNPIoegJ8Luwttrn+OlFa/SWfDwlCDpBMyamOUzhye59q+SSO/WnAZDP2xWT3TbEOhiQugW8IKwUZtEkbAD0AJdbAkbsRXd0jXKaA2FQBCxA2K2T94Pfw5yns/PX1jaw4q8VBquNXS4FlnPJmaH+Rm+0kQI/TeEiGJbEbKFjh5GXIHyjTmXwbAFrK/e3+e5PSVpdFDf+ptuuol7772Xb3/724wcOZLHH3+cm2++eajHNiwJlM+LKxbQlssTt10qIj4SjVKCZRtfYXLdgWgdIVAu0jKLKYatZ2tN34QIS2LtCIyqKGBLja8tfCVwip/B7pGSQIGrBLaAmK2ZUpPj/x2+mrc3VvDk8jrGViX6WJGXSsMdSyKFxfLmFDMb2oHQ7EvrsGTcsSyeXfp78l6WmJOgoWISWgg2dqwoP1eyHZfCiHKDYW9hQLGxbt06ACoqKvjiF79Yfu5rX/va0I9sGKK04l/vz0WrjdTGAwQajQyz+oUmUGmOHt9MW86hEHT90BsMW4MslrmWjFu2VEJdSiDVxf4nbiBpzEQYXVFAijAJtEQpz8IWulyuHRRFySGjO7AltBTqacrke1iRdy8Nr4lFmP9+PQBT6tIkHB9PSeoTcfwgj9ISS9p4gcviDS8CkIrVlJ9b1bII6EokLfgBzZkCdcnojruIBsM2cM+HF5qKlCFiQLHxyU9+EiGK4dheCCGYO3fukA1sOLJo7XOsaV2G1rrbWnnX2rgGguB9FqxLMXuyHnQI3GDoTUlcbI1PS5gAKonYCksqLAGeEvhKELFU+XhSgGUVP7MKMn5oBgYwrT7LvQvaiFg2v31tRbmNfKk0/M8L1zCuOoFG8+Tyep5YVkV1DEZVprjiiHV9urR6fgFE+PfSNiEEGzpWsJ9/FD997l3mLW8si40pUZ+DD1FD0rreYDDsOgYUG0899dTOGsewJ1A+GzpWEHVsfO0QId9lrFS8E1VAzM4RtaLkPQu0IhlVxm/DsF3oYjRioM9R6XMYemAIlrUkqI934FiKQAmyWhK1FE63brEifGm3YwiSkYCKSIBjxfnTW6sB+NqcA4CepeFKa6K2oDKeYkJNipTj0p7tYA0JxtckAVA6INB+0TI9wLa6fm7yXo7/eu4N/rywBSkEUdsiXfCZv6mDu+YvGpLW9QaDYdcxqJyN999/n9/85jdks1m01iilWLNmDb/97W+HenzDhoKXJe9li0ZHFQQ6dGqErqoCS4Stvw9oyLC2M0LCDpjkFDD5ooZtRROaxFlyC1EOAbYIG615ATy2aATtOYdDx3QQqHChZXJtPjxmye+8+MGtigW05TRSCnK+Tc6zybguTZkC77V0gtZ8ZfbMcmn4ZUftx0fvnY8bBOWW9IXAoRBEaMu7jNUJBLCuPU+gFGjYkO6kOhZhXHUCIQSOFeeZFe3l/UtIIYa0db3BYNg1DGoa/MpXvkJlZSWLFi1ixowZrFu3jv3222+oxzasiDoJYk64fj22KomUdo87xNJvptaCmqIt9JhKt6eNuWGvoJ9Vx20/lgLb6mvo1ZvSsp5VTBb9zKHreHpFDe9sTOIHELHCCJsfCPKBxA9EWSTbxbwOASzZlKQ1F9BR8HADRc4LeHThSn48/2U68y0Eyidd8Mm4Xg+hoLSkMV2NFwR4gWJNW5amrIsb2LjKKntxrGkLb1hSsfFsSnv9vpeWbKGHz4fBsDdjffX+fqtUdjcGFdnwPI+rrroK3/fZf//9+djHPsb5558/1GMbVljSZlTlZFa1LCLrdRC1JL7qObFowjVypQW1cQ/bKmX37apRG3YFO6ofjtKwtakLgQI3EMxsSHPbqe/iyLB3Sta3CIq9VTTgFddPZDF5tOAJ3t6U4ukVteXx+0HASZOaOXHKu8Qp8OfXHRKRCvapP4y6ZIR0oWcDtsXNE4g4FlFb0J5vww9slraPAQQNqTailsemjOLwfaYzbfQx1CWfJV3oawBWm4iaZFHDLuOeDy8EjHX5jmZQYiMej+O6Lvvssw8LFy7kgx/84FCPa1gydfRR5L0sSxtfDk2UtARUl+Ao9kPJ+5KauFfskyKwpTYN2QxbRX/25IPFscIIR0VUUfBDh42Uo4pLMRovCA/sKYFQms6CzS9eG0fajYTdXYvnnTOlieP2aSHpBECYKJ51O1m+8SVOmTKJ371RQdSW5QhHoGF87ZEcMG48P3juKTSx4ncElrSMJ2p5dLqSfzvmcOKOU0447Rkh0Zw4pcEsoRgMexiDEhsf+tCH+PznP8+dd97Jxz/+cZ599lkaGhqGemzDBqUVi9Y+x4aOFWQKHSgVIK0YTdkYVdGOcgiaYji6JuaVm2UpNH4xFF76SdXF/xgBYuhOd+fQbf1oWBKs4gJJ99yMsIdKWKytlC73RKFYnfLpQ9azuCnJU8vr0AhsSzG9Pk3MDgiKItoNFFHboiOfoTO3kk2d+5D2NAnHZuaoambv28BVx8/AV5p4pLJH1EJpSc6PUh23y1GLHr2IsgVqE1EOGldZft5gMOw5DEpsfPKTn+S8884jlUpx//3389ZbbzFr1qyhHtuwYdHa58reAI2dPoEKEKTRWtKWt6mI+FBsemXLbj/0FEtfZRgSL5XBesXfYNsqPi/NSothYPHZn4V5yVtjcxEQUfTTKC2ZBEqQ8y02dDo0pFxitiLj2bTmHGKOKvpsCJ5b1UDcKlAZDYoeHQIBeIHGVz5ojS1dpo2MkfEiFPyA4yaNKFeQWJJBRS1KCadfmDW9XPr69hsLTNmrwbAHMiix8ZOf/KTPc0uWLOHKK6/c4QMabpRKXoUQrG7N0JR1STo2Ucsl4SjWdUQASNoB0gp//Et3glY3kVH2aCrebToyLJX1lOhhumQw9Ec5ARnIeRC12GI5LIQ5GUKFyaBKw8Z0hPvfGMsnD1pbzOfofhLJ9BFZFjfZbEh7dBQsRiQltqDskRGo0C8j7zsUAgcpBHHH5rn3N3GV39Uptr+oxYlTGvqNWkRtq2weZjAY+mdzSaK7i535Vjcp8DyPZ599loMOOmgoxjPsKJW8ChE2mhJAa84mYQfEnQDH0mzoiNCWtzhiXAeqXBcAoMuRDuhmQ939BMUeFmZJxbAltA6TP1e3xxhTmSfl6M2qDVWMeJSW9xyh0WhGJFw+edBaGlIeLVmnXAFrCZBSYQtF1PKIWhE2pGvZp2YTjuiqGtFoXN+mMV1TzseArgqSkmjoL2ph8jAMhr2XQYmN3hGMf//3f+czn/nMkAxouKB0QLbQgW1FiDkJ0oUcXqBwA0XB0+Q8h0KHw8qOOBOq8oyrcrFlV44GiHJyaIlSp09R7IolNNhSGaFhGBCtIe8J0p5FSy6MpBV8ScoJBoxsFC0usIRCAe15h9acTURC3A6ojkNrzqEm7pFwwiXAQAmOndjBC6sa2JTbl3ebHabUbiDuFBAIOvIOqzvGsaR5Qo9zba6CZGdGLUyzN4Nh+LJN38hMJlPum7I1KKW48cYbWbJkCZFIhJtvvpmJEyeWtz/11FP89Kc/xbZtzj//fD72sY9tcZ8dTSkZdJX7OhsXvxp6a2iwhcCWks6CX+yIopBSMr0ui0LgKUk+sEg4AaDxVZjVL0TPaEa5HXi3pRajNQwD4StIe2GnVcdSVEQCko7abCv6krz1NGRdC1tq8r6kNWcDAgVkvfCzKoBUJCgu8WlcZTO5tpV8oFnZPolFzfuwpGUCcbuA0pqInaI56xMojWOFJlxKa2btM2KXRTC6J3CbZm8GQ//s6vl3UGJjzpw55TVbrTXt7e1cdtllW32yJ598Etd1+cMf/sCCBQu4/fbbufvuu4Fweea2227jwQcfJB6Pc+GFFzJ79mxef/31ze4zFJSSQRU+lozjBS5KKWJOjLhToD2XIxEJ/QXGxlwUgqwnac05rO2IMrayQMwKkCiUtkJR0c+EIIBCEBowdd9uhIehO0qFOUD1SR+BX877Kfll9GfjUlo68QNJa95hdMolGQnQCFpzDgAtOZs6FDE7FMdKSXK+FSY8xwRTajqZPGIEr69tpyVbIBGpYPaUBpRS/PrVFaxvzyGEYFQqypQRlTz73iYeXbiGumRXbsbWJnoGysfTOQLlb1VkovSdFUJsttmbwbC3s6vn30F9o++/vysxRQhBZWUlqVRqq0/22muvcdxxxwFw8MEH8/bbb5e3LV++nAkTJlBVVQXAYYcdxquvvsqCBQs2u8+WOOOMM2hubt6qMea9NBqNVhrRLc1fIEDE6MhlcYpujD27aYattkEghCbnWVjSImF3c0LsPivocJ9SszYpNr/+bjD0ofTR29xnRvd8WQlf9dyh10cSCKMVAkjF7kcIiVKadil4M++R9XwExR8ODRu0ZgNgF78ry4AXgbscm8qYM+i34wV5AuUTqLAJmyVtHCs2qH1L39neCAQxZ+t/p7YW13WJRCJDfp4dxXAeb11dHXfccceAr/nxtY+yqWnTkI+lIjs8r1GJicUeRAcddCew5Wu3s+ff3gwoNh555JEBdz7vvPO26mTpdLqHSLEsC9/3sW2bdDpNRUVFeVsymSSdTg+4z5bwPA/XdQc9Po0m0F2N07TqJibQWPihKyh9f8Sl0GgdpttpLfCVBK1RVth+vu+5QoMlu/v6isEwWEqZnVtwqO3WAqXPYy+QRG1VLJ0ND9T1kRf4nlc+uK8h6/rlJOcSgQo9ZlSv82Zdj6gYXOJzoD0UQTm1WilFoFx8P8ASAwuW3t/Z7ig0BbdQLNwdWrbmd2Y4MFzH63n9W9jvCuoSLs3DWHD0/jfc0rXb2fNvbwbc46WXXgJg1apVrFy5khNOOAHLsnjuuefYd999t1pspFIpMplM+bFSqjzo3tsymQwVFRUD7rMlnnzySaLRwdseB8pn3uLf4AUunZ2dPS6+Y0U4YvKH+Pn8n9OS9dFaUx33SEV8HBmWGHpBaGG+oiXBo4un88kPTiHKXEYmmsq5GSVDr4wncaQmYuntcos0GAZCQ7kPSt6TbMo4pF2bxU1JBJrZk1tIRkKHUAiX9CJWnCMmn8CB3ZYg1rVn+divn+mRj1HwA97e0A4CpjZUE+3WcdANAv5w6QmMqUpQ8IPN5nN0/84BPb53jhXhxOmfHHBJpff+3RnM/juC1157jcMOO2xIz7EjGc7jLRQKW7x7/uJ/nounsztlPMPZsrytV8nrlq7dzp5/ezPgouptt93GbbfdhpSSP//5z9xyyy3cdNNN/OlPfyKb3fp/7EMPPZT58+cDsGDBAqZOnVreNmXKFFauXElbWxuu6/Lqq69yyCGHDLjPjqbU/0T36qSltWZU5WQSkUoOHDOKmkQE25K05WwCXYxcaAiCrsS7g0evoyIiOGxsLQU/FragL/7xlSBi6a4oibHZMAwhocOt5pmV1dz9ygTufmU8896rZVp9lpZcKD6UAoEmYtnUJRPMGH1Uj2PUJftWmziWxLEEjhQ4Vk+1XJuIUh13+OG8hXzs18+U//xw3sKwE2yRUml5f+S9HIXNbOt6bwN/Z01VisEQsqvn30F9Ezdu3Eh1dXX5cTweZ9OmrV8zO+WUU3j++ef5xCc+gdaaW2+9lccee4xsNsvHP/5xrrvuOi677DK01px//vk0NDT0u89QMmNs6Iy6OP06gQqIOfEeme1jq6cQKBdPad7Z0IotXNwAsp5NSzaCLsYvptdn+NfqNUyuytDhRrClV15X1hqidviDWxIgxoHAMCToUkKp4O/v1pHzw2WJ6phHKhLg6zBptEM4JBzJQWPrw/4pfh7H6gohR22r7AoK4AUKx5JUxUKvjv6cQn/+wtKyi2jUtkgX/PL+JbfRUjfl/iITMSdO1Nly2WzpOxtWo+R6fGcNBkPIrp5/ByU2TjzxRD796U9z6qmnorXm//7v/zjjjDO2+mRSSm666aYez02ZMqX89zlz5jBnzpwt7jOUSCGZOe54shtizJw+vU/NfukH7P3md7FEgBTQUXCKWf6lULSgMqpozbs4VhxHZtCEbb6h7xq6Y5SGYQjQQCEQaA0Z1yZqQ65olZ92LdKuRcwp5TsIAiTrO3LsU1fT7yT/77Om8czyRl5a1UTODYhHLI4YX8dxk0fy7HubejiFXnHMVC68/9keIgRCUTJveSNfmDWdqG316KYsur12ayITpe/sdHWM8dkwGDbDrp5/B/WNvP7663niiSd4+eWXEULwmc98hpNOOmmHDGC4IoVFIlrZz/PhD9ukkUfymwVPMqN+Ia7vYsluuXoCXBUh5qRY27GGymgbdrExW8liupy/YXI1DENEacku61ms74iSdrtUra8ki5uSHDK6g3CRJYxINGXyeDrBKf1M1j99bgnteY/pI6vwAo1jCdryHmk34P6LZ5Eu+OW8jHXtWZozhX49N3q7jXaPTCitcKxIj8jEQDkf3bGk3e931mDYFoZzvsbuyIBiY+HChcycOZNXXnmF2tpaTj/99PK2V155hcMPP3zIBzhcidoOOT/OgnUxZozIo3SxNbeAiBRsSFczqWoN69pbGVvRZUmudXh36Vihx0bJVtpg2JF4AXQUbNoLDkEAi5uS+EpiS0UqEpB2LZ5cXgfA9BEZKqOKdEHwbmcl6aCOy44N+iSDzlveWI5URGzBmrYMbTmXt9e38erqZk7ab1S590kpx6N759cSvd1Gu0cmXvnXixw+/SgsaRMoxQ+fWci85Y1lsbGtHh4Gg2HXMqDY+P3vf893v/td7rrrrj7bhBDcd999Qzaw4c5d8xfRlnNZ3z4OBOxT1Uki4pP3HToLI1i0aTQfHPNmMTFUUAi6uYjq0KxJQb/+AAbDtqI0uD4UAknMVsTtAm15C4ni1ClNTKvPlMXG4qYkTy6vY957tYxKCbK+jcZi6oiekQeA5kyhR6RiTVuGpkwBQWg61pZze+RjdM/xGKjza3csaeOIeHkJ5K75i7aY82EwGHYPBhQb3/3ud4Gepl5aazKZzDaZeu0plO7yLCkZV5OitTCNpg0+cdvlzfU5Mi441kaOHecRKFGsWOlaZpFSobWkEITlQI6lzWqKYbvQGjZlLF5eU0XetzhoVCdShomhGsGJk1vRQGsugq9FuaU8wJPL6+lwnaJw0KQiTp/Kk+6RCqU1bTm3/Jl1LIFjyT75GFvT+bU3vSMpJXqfw2Aw7B4MKhb59NNPc8cdd5DJZDjzzDM56aSTePjhh4d6bMOW0l1eCSkEjuXwr7UFNmUC8n5AzrdJuxYayHqyWx9YCJQk4wEoYo4RGobtQxP2T/GVIOfb7FeXQyHxlUQjEGjitiLR67OmEUyvz+BIVS6/dizByfuN6jORlyIVSmu8IPwD4c1HTSxSFgWlfAzo6vz6x0+dwB8uPYE/fuoEvnzizEEtgfT+jnWn+zkMBsPuwaDExk9+8hPOOecc/vrXv3LggQfy1FNP8Zvf/GaoxzZs6c9zwFeKjoJfjmAESrKkaJzUmnPodK0wrwNNR8FibXucrGcZjw3DoCh1cO3386JDsVEZVRw2pp2xlT0nYkvq8I/QZYv9ML1IUBFVVMbCBT3QzBxVyWeP3q/fMVx1/Aw+NHMc1XEHSwqkFNQnY4yr7lpu6a/7a6nz69ZEIvr7jg10DoPBMLwZdJbV9OnTmTdvHnPmzCGZTA4rW9mdTfe7vBI510cpjS1lWXA8ubyO19dXkvckHXmb5S0x/rGsjtvmT0IIQWsuSkvONoLDMCClqqXuVuFh9UjXH43AkrpcYt3dBT9iOURsB8eyiDkOlTGHyliEmCPJBzapSJKIJfGU5vU1rez/n3/mvHuewvV7JneWIhUPffpErpw1lf0bqhhfkyyXrA6Uj7G19Pcd29HnMBgMO49Blb7W19fz3e9+l7fffps77riD22+/nTFjxgz12IY1vdej65IxEhELS0oKHriBQiH4x/J6nn6vtpyU5yvZzVCp6251MD0kDHs3ulvlkoBydZOvwyiF1qCUxA0EllTFZRTI+4qOAkSkhR+EvUQsIUhGLOqSk9l3RDUvr2rGKS5vuL7imeWNfOzX83nksjl9xhG1Lb516kFUxRZtUz7GYNmenA+DwTC8GJTY+P73v8+TTz7Jpz71KRKJBOPHj+fKK68c6rENa0p3eZcdtR/LmjrZt76CS3/7HM8sbyRiS7xS3JvQ06At3xVEKhkqja4skIr4aCM0DAOgVNFltvgR6tF7TYQl1JZUKAWjK1068oJFGxNMqCqQiCgyruTJddXUJKJMH5ElbnlkPRthjedjHzyLW+Y93m8i5kurmujIu1TG+jajKn3+vzBr+qA8MLaFnXEOg8GwcxiU2EilUkgpeeihh/j85z9PMpncq6tRAAKluGv+oh4eALMmj0RrzdPLG8NoBX27w0IoPpY2J5hSm0XSf1dYg6GEJTdvZ1+KcJTajUSsgLokHDW+k7WdEd7ZmOT/3q1HIxhTIWnM7UNnrg0vyLO+M+DPi+bTmnVJOFYPB0+AnBuwrKmTQ8fVbXZspXyMoWRnnMNgMAwtgxIbd955Jxs2bGDhwoVcfvnlPPTQQyxevJjrrrtuqMc3bOnPA+Cv76zlzOljcAPNpnQeKWBhYxsFv6+bxr/WVXDW1I1EbGMiath+pAQUaAEFXxJzFHFbM31EhvFV4WexIuoxMuXjyFCZuIGkOVvJK6tGUfAFsV6++fGIxb71Ff2cbesZrAuowWDoSdCru+vuyqASRJ977jnuuOMOotEoqVSKX/3qV+VOcHsjA3kA/GPpehZtbGdlW4alTWkkslcvFM0pU5r44lGriDnGPdSw4xDFUJqgq/KkOu6zX12WuBMwMuWRcAIcSyMl2BaMqWzn349ai6dUj86pSmuOnFDf7xLK1hAotcXOrwaDYc9nUJENWUwcK4VZXdctP7c30ttNsTvvNnXSmQ8rdSwBXjepYUvFmfttYv+RaSpjvhEahh2KEOFnLipCHwylNQlHFYVHQMzumuDDpTtNoAUTqjJUxBQSiRco4hGLIyfU88dPHb/dYzIuoAaDAQYpNk4//XS+9KUv0d7ezr333sujjz7K2WefPdRjG7Zsru+D0pq8H1CbiNKcLaAJq1IEmlP3bWLmyE72qS6ghSZqmTwNw9bTIzl0M0gRRi1qYj6W0ARKYAnRQ9x2bwboWIpxlYoLDprGWTPHs299xXZHNMC4gBoMhi62GJ5YsWIF5557LhdccAGnnXYaGzZs4N/+7d9Yt27dzhjfsGRzHgAFPyDu2IytTuAFis68h9aaLxyxijOmNjG5JldeS7f33sCQYTsYTDCs9LGMO4pAa7K+hR8Iuq9clA3CtMYLJEGQ4tU1LexbX0G64FPwg+0eq3EBNRgMJQac8n784x9z/vnnc/rppyOl5Nprr6W+vp7/+I//YO3atTtrjMOSkptiKmrjBgGpqM2HDxjPAaOqeWdDGwU//GU/dd8m9qvLIkXY6VUKk6dh2Ha6G3v1hyputATEbEVH3qItJwmQ5IKur7tSongczfrOFCMqKnhzXSsf+p+nuODeHZNbYVxADYbtY09JDoUtLKM88sgjPPHEE2zcuJG77rqLX/3qVzQ2NvKjH/2I4447bmeNcViyOQ8A/8k3eWLx2qIvgmL/EWksqbFlmJRnMAyWkqjork17m7+VllV08UHJ6EtpyPuSjGuTcW2kgMZOhxFJj6gdKpZAwcq2JK+uP4A17ZvYmFa05lwilqQpE6G9mHu0rbkV29L51WAw7JkMKDaSySQjR45k5MiRvPnmm5x33nn8/Oc/x7LMj0SJ7h4AgVJs7MwRFO8+U5EwKS9Qgojp7GrYSga1ZKIgIIyWCdElPJSCjGehCO3z//u1scRsTc6TnDSlmQNGdBKxA0YkXM6a+hqdeXADi3dbKpn/3kiaMgV8pZn77obN5lYEyqfgZYk6CSxp91vealxADQYDbEFsdK84qamp2at9NbZEwQ+4+Yk3ePa9jeUf/NAp1KbKd0lsf76dYS9E6S7jrv7QQKBDceHY4WNPQXveoTVnI4DKmE8yEtCcjXLalE0cN7GNuB0QtRWWDPepigqUFoyvzjGhKs3PXp5AxvVpz7nc9uSbfOvUg8rdWpVWLFr7HBs6VpD3skTsBO82JXhiWR3NGZe6ZJegMC6gBoMBtiA2ujsKxmKxIR/M7kigFHc+vZB7XlzG+61pAEqr3L6SLG5KMqYyt+sGaNitGSi/p9RTRwfQ6UlqhEIBXjE3oybuk3AUEs3FH1jP0pYkx4xvJRUJq6gsGf7RGqTUFAKBBPatyXDylE38fdlIlA6jG1WxSHk5ZdHa51jVsgghBJa0eb+5lfbcesan2kkXJvZb3mpcQA2GvZsBxca7777LSSedBEBjY2P571prhBDMnTt36Ec4jAmU4vxfzWPu0g3k/QBF39D3vPdqOG5iK74KcCzjFmrYcSiKgkOGyaD5QJQTkGtioaDwFKRdm6ijOXR0O3VJD6VkuOQidFfH4W5tYqXUTK9P84/ldeQ9WLyxg3teXsYVx0wlags2dKzo0em1Le8iEDSk2ljSMh6lpSlvNRgMPRhQbDzxxBM7axy7JT+Y9w7PvbcRVymEFAjV15Y8GVHkPIvV7TEmVOexZU/BobSpTjFsG6WPjVX01VDosBpFhMnIWkHWtWjLWeU9bAG+EOVE0lLwMmxFH8bklIKGVIGqaICvIiilWdOW5XtPLeTrsyeT97JYMvzp8AKFF6jQtMvyiFoeOT+sMimVt5qIhmF34LI/DQ+TuT2pAqU7A4qNsWPH7qxx7HYU/IAnl27AC4p3h5sRDF0dXvPhSzTlLq9i87sZDFtEijC6URKrFqF4DRTlypRUVBGzXbK+RdaLoBGhEOmnfrYkQDQQdzSz9unkmfdCoRCxJK+ubgYxg5iTwAtcABxL4liSQGkKgUMhcMrHM+WtBoOhhCnG3EaaMwU6Cx4RWxZ/pPtGNSDM2wBNRTTo8kjQ3aLWov8ffoNhMEjRU7SKYsv5Um631gJLQkXEpyYWkHZjFHwbKXSPrsRCdPOAEZD3bSbXZLCkQgPV8QjteZe2XMCoysnlPipSCKpjETSaxnQ1SpeSSE15q8Fg6GJQduWGvtQlo4xIRWnKRMi6Pnm/r2Io2ZRPqslhi76lryXhMRgLaoNhMAjCyJkgjHKUPltSSGKOx4L147GlZP+Rq9ASpNZIqXuIFVF8lHB8KqMKKeOMq05SEbWpS0YZVTkLoFiNkmOfuho8neDFtXW4gWvKWw0GQx+M2NhGSoZF7XkPL1Csasv2ec3JU5o5dEwHjgVeyWujmxcCECbk7cyBG3Y7BitGS68T3R7bUqGUwNeQdy0efSdCIhphVCpKxJZURjNEw8UVSp9KS9qkooqsH2NibR1glSMVABs68uw76limjzmm7LNxirS57Ni+Phu9vTgMBsPeifn2bwelO7fHF61ldVu2xzKKLRXT6zMEShJoQcQKPQ26Jf0bDJv10VAa0q7E9QVxJyBig9OPKu3tIFrKB9LF+mtLgLA0ypd4gSTn2+QDaMtb1MQVEcvCkgKlA0qfylAo+KTdKtwAahM2x08eiRsoPvQ/T9FZ8BiRivXw0ijtV0oG7e3FEXMSjKqczIyxs5DCyGvD8GJXJ4fuqUmh3TFiYzsoGRZ98oOTmXTzw+S8rj4SqUhAKhLga0HWg7htBIahLwNVIr3fEmNRUwVzJjcRsYIeEQ6tQzOvUo5FSbTobq1MSs3+JCCkxrYUsye18dzqBjrdehpSzURtm6CY7FmKbCgVUB2v5ttnXkpHXlMdd7jo/md5/v1NeIHGsQRN6QLtuXC//uzMe3txeIHLqpZFAMwct/2t6w0Gw+6FucXYAXiBZmQyRsyW5ckgrEKR1MQ9krYy5a2GQVOKkNmWZt57Naxtj5W7tGq6/gjCMlXXh6wbio+cJ8j64UZR/COBvGfRno8wpS6NJuCdTeNZ3loHCBQBAokUNo4VRQhJKlZPR15Tl4zy0+eW8Px7G1FKYwlQStOUybOuPce85Y19OsQGyu/hxVFCiNCjI1D+EF9Bg8Ew3DCRjR1AXTLKAaNrsDe205J16ch7BEqitaAi4qOL/gdS9Ax5b86C2rB3UxIVFdGAmriHEIKVrXEitiIV8YnZmogVCtjWvIVGkrB9pNDEnLAUu1RmXUriSEZ8fAUCm6qowrb+//bePE6uqsz/f59zt9p7T2cPSSAkJIRdkISwyah8FVTcQFFxHTfm58xE1K8LKjMOjjozODqjo8OXcVRkQMFBHRQFAogsGQUSEkJWsnbSe9d+7z3n98etqq7q7qwknU44b1/ErrpLnXv6dp3nPsvnkXTl59Ceshgs7iEISyitEMKiGEie2trNTQ88QCYWY333EH6oseosZiEEfcUy3bniKC2Nkp9v0OKop+gXKPl5El7mSE+jwWCYQBhj4zDg2RYXn9jJUMmnPRVj9c5+AuUjpCbUgpita56NqqFhYiqGvVG9NdriPu9cvJ1ZLWW0jjwdoRIUA4EQopYMmvJ87IohW+11Um/Mag2W0KTcED8U5MtR8uaFc5oIVIl0rBWtNUqH7Bgo0p0vY4syGU/RX/DZOVhAaYVFYxmrH2pSrjNKS8NzEg1aHPXEnDieY0S+DIaXG8bYOExUk0V/+8IuLCFwnYDp6RKW0PihJBBgSY0lKk5yVSlRFMbuMDRS1V6xJMxoKRF36sqmbU3CHW4j3xwPax1fR54Dhr0kQgAiCsCUwpDLF0zjI0vn8fC61fhhuWK8WPQXfQTUBLocS+DaknxZo7RuaBXvWIJXnTR5lJaGJW0mZ+bUcjaqaK2ZnJljqlIMR53xSAh9OSR9HgwmZ+MwUU0Wveu6i/jo0pNxpINrRy4MW2ocS2EJ3aCtARUthLrXBoNSECgItSbl6OH28XUCXrIi3mXLxvdhWCROqej+qv6cLUUVKa2xgDefNgvXdhsEuqrS41QEukIt2T6Qp+iHlIOQcqgoh6oWElxyQgd/efHYX9oLpi1lZusCHMslVCGO5TKzdQELpi09gjNnMBgmKuYR4zBSCiKdgU+/ahEv7N6JryQxO8CSw6aEqPxT9CUxR2EZt4ZhBBoI6nRZ9sfIXaohOyGgVMnFDLQgbmsSjs8bF25na+/jTM5cSHPqTHyl6cluIgjzgMu2gQxre2ayrT9Hd66EU4nNeI4kCDWdaY/3nXsSf3nRKbWy15FIIVk4fRnz1flGZ8NgMBhj43AQKsUtK9bw4IaumqjRsrnTSdCOZldDikaoosXAs5VxKxnGxJLgofdbwbS31J8GwTgBZSWxhEIIwVDZIuXC2p3PcPezL/LEtim0JT0umnsm7z9vJi/0v8jqjTsB6C+Uo4RmrZmSiTOtOYEfKprjboO+xr6Euyxpm2RQg8FgjI2XSikI+cr9z/DA+i5sKfFsi2wp4N7VO3nronYcGWkTVOPwgQqxJLgWw1UDBsMIrP1YotVSWFWxOKoeMg0EoSBQGq0FgYpyhZS2yPuSwaJDS0LSky/R4vUQd6aSLQX8fPVOwOL6ZYsAi/9Zu5NyoHBsSUvMY3pzAiEEnm0xUCzTkysxORMzwl0Gg+GAMN8Ih0ioFP/w4GquuvVBvvnwOp7rGmBrX66hQdWvN0wiE2vGklECXaA0ed8mV6pLmjsqozcc69RyMbSgHIpat1c/jJqgyUouh21BORTsGHSQwIzmMhkvS0ssR3Msi2dFFSNSCB7c0EWgNJ+4aCH/9Z5lnDurnUWTm5nRkmxI9Kx2c60Kd/lhuUG4a832R47CjBgMhomM8WwcIresWMPPV2/DD6MsfRR054oAzGhJooGntw8yJdnE1HSBuKVwLU3SCdFKoypKj6L2j8GwdxrUQ4FiEOm4gMYSinIAg2ULCSRdVdkWGSWupZmWKWNJkMhaKM+xAmY3d/Fc9ywAevOlmmZGJubymvlT+fnqbQ3jqPZIsaXep3DXfHW+ydEwTEiORCWKqTzZP8azcQiUgpAHN3QhhcCxov9gWOgoVIpnd/aybSDP95702D0kcO2oN4olwa6IIghhPBuGvRPq4cqSqhhcoGCoCBt7Y+wachgsWgwUHbK+TdLWpL1wuG08UA5sQu0RcxRSiGEFUq0phw6Tkv1IEVm+VY9FleuXLeCKhdNJeTblMCTl2Vw+fypvPm0Wg8Uhiv7o5oMwLNxlMBgMVcyjxyHQk4ueAD3bQgpBc9ylO1dCEAkdbe7LMlDwUTqKl0sBOwZjtCb8yLPB8PuGI0ugIqPuWKv60YBSAimjcmmlI1lykBRDybruJE2xgDmteYLQQgNtCZ8WVyGVphRK8r6kv2Dj2ZBwonPKSre2rC8JdAzP8vEsn5zvctHczgbNjGo590eWzmf3UJHb/7iJRzbt5p7V2+hIObz2xJAZzdYo74YR7jIYDCMxxsYh0JaMngCzlbrC6c1JIMreR2nypRBLiEgnodKQLVISVWjAMYbGuOCHMFiyaI2H+995AhJogc2w/LjSkpwfGQP/5+Q9OJW8jEAJ+os2vXmHmB0igZ2DLqoSeAmUoOBb9BXjxB2J0oKir4g7gkLF83H5gk7ef940QhWMCn94tsWdT2/ml2u2IytJooNFxZ92xhAMMaMlVdvXCHcZDIaxMGGUQ8CzLS6a2xnlahAtBDOakyzobOI9r5jL3PY0cTf6so0aslk19VABSKlNmsYRRlU8Gi3x8JjtQWMJRaCgFAiKvmTHYBTiaIkFuNawSqgtNS2xgOa4T963EEIj5fC9CbClP0mgNKGWgCDh2qQ8i+5cCzMzm/BL93P7E9/jd2v+k9XbVqDq2sfWhw3rWdczkzXdzUhhhLsMBsO+MY8fh0hVnvzBDV305ku0JjwumtvJh86fx9U/eJg92SIDRZ9ASdZ2JzlzyiChFlgVQ8O0Rzkw9H4a1u1tHqWMLOljLSemej1RPxMIdZRnkfUtNIKEE0ahlWouR+XipYiSj7cPxghCQa5kkXAVubLFpv40v9/SwfmzujlzWhFbFOkr2jy6JYYl85w2eQgpbLrzAL2U/BxKh5w642KgMWzYOFbBk9un8JcXL6E1gRHuMkw4TDLoxMF8Mxwi9fHsqpBX9cv4ormdDBR9urIF8r7i/g1tAFzg9tESj3I5hN6/loJh/51xq1Lv1QqL4cW3cvwRHd3ho95oUpWwiRCRZLnWgv6CjSU1dsVYDSrGhlPRKZcC4o6iNVHmdxva+N2mVlJuSK5skfI8pIyMFz8IsR1QaCwJc1qy+CFAQEs8IFRZ+vOSbLEXLQSLpi0bFTaspzXh0ZFOjjJEDAaDoR5jbLxEPNtqaK8Nw16PtGvz+8276S8G/GZDOw9uauG1J+3hwtm9ZLyxn7mrYk0mp+PAkJWKHqdurTtQb8bR9i7Vd2etjqNWCl13EbalmZwqUVaScigRKEId5f7out21rgrFaQIl6S9G1mzBD3nn6VlOn+IzVJIEysG1Qk7t7CfjBvQVPZJOCc9StVGEOuDF7tVYRLLjF83t5OertzWEUqplsMbQMBgM+8M8Wx8Bql6PO6+7iGc/eSU3/tmpLJ7SjBQ2xcCmHEZfziMXxbFe6zHef7mxv+sfaTAcqAFxtO25qldm5PVVvRpCgFMJB+3JR2WuW/tdBksWjtTYctgoVRr6ihZ9BYd57XkcqWrXp3XAzKZBpmQSBCqSLRciSjh1LAUoYrYCKq3rhUAKC0va7BrcSKiCMctgr1g4vWZYGwwGw74wno0jSNXr8ZnLFrNyWy/rdvdyckeWhKtGdX9taDUvRneCPVaTHA8Hx+ulByoqb3VsvdfSXCGi/JNJqRJSCxCaUMlKy/hhj4YAMl5IzCpRDCQZL6S/ZGEBUzIWnh1gWwLHkoRK40iBH2r8UOJKVWkWKLAr1otrxxBC1DQzEl5mr2FDg8Fg2B/G2DiMVLu+Vr+Iq69Tnk2gNJMzFs2xEEuM7cKv9rqoNyxMOGX8qBfDOuKfVQmVhWL/lUlCQ8oBXSmDDbWiHIKtGzVEqqJxMUfxiun9PLBpMhYKV8bJ+Rah0jTFXLpzRVwrMhR25eJs7I1zcaIH1xHEbBvPiZNwm4DRmhljhQ0NhonIS0kONUmgh59xNTaKxSLLly+np6eHZDLJzTffTGtra8M+d9xxB7fffju2bfPhD3+Yiy++GK01y5Yt44QTTgDg9NNP56/+6q/Gc+j7ZGTX19aEixRRv4refImka/NcVz+5siDnO4Tax1KiofU8VF3hNhkvwDYBrnEn0rKIfjjSBocQYAHS2rdBqRQIOVyhggC7WvJa8XpU0UTb82WLE9vyPLVD4GIzpSnFQLGdrqF+Bgo+5UAR6sib4qtOcsEsNvRtYXKqCyltmmMOCddoZhgMLwfGa10e12+RH//4x8ybN4+Pf/zj/OIXv+Db3/42n/3sZ2vb9+zZww9+8APuuusuSqUS11xzDUuWLGHnzp0sXLiQf/3Xfx3P4R4w1T4pVcGjtV2D7MkV6UjFmN6cZGPPEF1DRZSG1V0JOhIFpBupQ0oxvJBoIOMFONbxGzo4WJSKZLstOT5envH0JIn9GDUa0KKxhLeqKjrWPSKIFEL7Cg5pN2TR5DiDQ5Fi7cb+WRSDkCmpfhKOoBi6bB9sYkfuBEphyLO7Z+CHms5UP72qgMZmyZzTjGaGwXCcM17r8rg+P69cuZILLrgAgGXLlvHYY481bH/mmWc444wzcF2XdDrNzJkzWbt2LatXr6arq4trr72WD3zgA2zcuHE8h71PRgoeKa3pK5aRQtBfKLO1L0dPvoxTeQT9zfo2HtjYRjkcbpRVDocXVNcYGg1IGXUufTmFk6ry5NRVJWkNoRL4SqCUGK5AqT9ORwJfUkTdhUuhQ9yWXD5/KkoLnu+ZxYoXT2XFi6fy8Iunsq53Fk9t7a18pmBNZfvDL57Kr9Yv4MTJS0yreIPhOGe81uUj5tn4r//6L2677baG99ra2kin0wAkk0mGhoYatmez2dr26j7ZbJaOjg4++MEP8trXvpannnqK5cuXc9ddd+13DKtWrXpJ17By5cr97rMn77Nldw9uxZgoh4pS2UcIgR9C2Q+iXihCYwEhggc2tbB0VjcxO1pIbWkMjH3xspubSgmrFkBUJILSUUIpREJfDhpd2Va1PDSVChYrZEd/huWnZuhIxBgo5fjhnuF7NKJMWWmGimX6BgZxLYnSmkBpbCkIdYkHHnuSjoQzzhd/YH93E41jbczH2njHm+N1fo7munzEjI23vOUtvOUtb2l472Mf+xi5XA6AXC5HJpNp2J5KpWrbq/uk02lOPPFErEpC29lnn01XVxda61ENoEayaNEiPM/b5z57Y+XKlZx11ln73a8UhMxal68JHimt8Qr9KBWNL1AKuzJOx4ZyEPLqk7roSIY1Ua/9udMNRxa1F12Tvb0/HigNA0VJ2lW4VqVbsNAorQl1FF6qVi3Vi4ChwbNgfmceq2UdA4UEF5735oZ7dPgzNOmiojmTZsdAgb5iGT/UOJagMx1j2blnkXDH19g40L+7icSxNuaJPN5SqfSSHxIPh2roRJ2ffXEgc3c01+Vx9ZGeeeaZPPTQQwCsWLFi1C908eLFrFy5klKpxNDQEBs2bGDevHn88z//c80aW7t2LVOnTt2voTFejOyTIoWgJeaitKYl7uJaEk0krFQMQkIdMLMp31BxMjGu5NikWvr5UqglX9IYljhahkZ1DClX1cTKqiETWTEoikE0OFFRDw11pKJaCKIkZCFshAgYDLezseuxhnu0nnNntrNtIE93rohSUdJoqDQlP+Q7v183HpdrMBwVwq9fu9f/Xk6M17o8rgmiV199NTfccANXX301juPw9a9/HYBbb72VmTNncumll3LttddyzTXXoLXmE5/4BJ7n8cEPfpDly5fz0EMPYVkWX/nKV8Zz2PtlZJ+U+Z0ZThFNUULorn66hooINJ5l4VmatKeO6lPz8UQ10vBSVFdH/n0cbWXR6hg8q07YTYMfSjxL4dggla55MzRRZUq2LNmVjUUN6CoVUUIIdg1u5KNLzwNG9/J5/3kncv4//Q+2JfEr+hvNcZfpzUke3NDFR5bON3oaBsNxzHity0Lrl/pcOPGoupPGI4zS8Llj6Gxs689xxfd+x55cCV9pXKm4ZvEG5rQU8Gw1qspif43HDKOp3cGHEI6qlpNOpCmvhkYsWUkUJcrTEEQJoFA1iARK61o+R9636c65WFIyqyXJjJYo/ppIJrh4/jtIeJlR9+iOgTxvve0hHEvWQijVZOdyGPKTd104rroaE9nFvzeOtTFP5PHu67u7um1t8Rf4Or/XcxxoGOV482AcjnXvSGIK6A8jIwWPPNsi7ti0JDwmpeOUA0VXtsCG3gxT0iVcW415nperwVHvUajvG7I/fBVV8Rzy5x7CfNeHbw7Uo6JULd+zFv4YtU+d6V8LmwBC1BsZ1c+NjA0hokoVW2qkFNhWVAk1rTm6F+uFuUbeo/VN1jy7cUCtiWibwWAwvFSMsXGYGfnkWP9lvidbpDdX4tH8JCDgdSfvaVjkQg3lUCKFqvW9eFnZHDpajKFRxAr2PQ+hEmAduIOuvnnZoSbnhgryZYnnKBx5YJoZQkbDVCOk6Pe2f70RVH/uSGVWoCtnEUAptCulr4oghJwO8EO1X2Guas6RabJmOJY5Eq3kDYcXY2wcJkaqiLYlo5j49csWcNHcTu5etZW+YplQh8StEq+c0Teq5FUKcC1FORAUQo1T1d3Yi7z58YYQNJZzwn4vXAOefWCGhiY6v+LQyo2rXVXLSrBjKEYQwtR0kUCImoFoW2MPvfZexSgJK9c5lodDVxI1SqHAtTRSjnXO4Z8CJegvuhR8yca+FHNacqTcEM+O4VnN+xXmGplzVM3nME3WDMcLx1vI5FjEGBuHiZEqotlSwM9XbwOiL/OBYpG0vZYTW7Oc2JYj4Y7dEyMyODRBpbyxHDLc3fN4tzZolOYOK0JWrqVreRVj2R4HnBiqaVi4DxatoRhG3VKDUKCBfGCTcEKyZYe4EyKFqpWkjiU5X70GWZEFlaLOy1H5oSdvk4mFICIRLwuFJYa9J2HlOKUFpVAwVLIIQ83aPSl+v7WTh7copqYl7196OV0bX9ivMFe1S7FpsmYwGI4URh7wMFBVEa3+XF8G++CGLgKled38QU6fMkjSDYnZYxsaVXtCyqi6wJaVZMHKxuMuk3cMap6AyvW7VpSToFTkDRiZznww6c1jhSQOlJpgloRSILAthS0V/QWLoi/ZnXPoyrrsyTn05m2U3vun1MJjFbEupSAIIe9LevLReUDjWgrXUjUvTNWzEuhIRVRpRcG36MrG+NOuNL9Z34rWATOaLGa3tdORTh7UNVbzOYyhYTAYDjfGs3EY2D1U5NkdfQyVg1pGf0vMZWpTnF2DBbb1D7KjbwNKCxyp9hkWqb5fXdgcqy7HQDcmGB6P1JcEVxdkqpUZKqq6qHoMDjDSctgQIgqTtMQDWhMBaCgr6Mk5/OsT0/FsyJUll5+8h8tP6tnreTTRdVSrTcqhpOBLevIuGpiS9oc9OjTOQyGwcOxWBktFglDx32tns7ZboLTkNfP2MK89S3tC05LYzfpdLlqbDq0Gg+HoYzwbh4Hb/7iJoZLfIIq0bSDPym09rO8e4ob//j3bB/qQQhAqUet7MSa6sQoBGp/2df0bh5mJ4DkZGRIRI17UhyYCFXkZgjB6XS0ZPRLXUZ97UfVMCAGuhEwsZOmsAfqLDr6yeGRLC8WgsbJkrPMJKi3hbUVzPGBqpoQgytvZPuiS8yVKDSeLhpU8jrg9RNLJkfLKnDYlT0fC489O2sWpnf14liITizOj2eXF3jXsKj/PjoE8peokGQzHGSY59NjAeDZeIqUg5JFNu2lJeHTnosWiFCgCrQkCzeRUnIGixZ6crhgLFoVAknSiuouGKoNKKca+chCsI/gIP1GdJQpq6lb1cyNldAMHWmJrhR9GC3y138yRpt5mnN+R5YFNrQRKMlB0GCi6uHZ5TC9UWI2LoRvyUOJ2SFuiRDkUaCx2DFpIFG1Jn7gdydvHrQCwEEIShJJ57Ts4sXUXcadU0dtwmNkSeTO29efZ2d/HV//jAVoS8VrSpyXNM4bh+MUkg05MzLfOS6QnV6InV2J6c5L2pIeUUT+USIBJMikdQ2nJ7mwLjiWwpGDHoEfOl4SV9ulBGD2lK6IEybGYCF6Ho4lSgqAiXlUNQ0ThFY0UilIA5VDg2ONjaMBwvogtIeOFpNwQgebi2b3UGxJaD/9/riwIKm3io+tqzDtRWrJjyB1+jWRPziNbdqJW80IgkFgyBgg8yyfulCtVLYKkG1DwB9nWn6c7X8K2AzKeqiUs37JizfhMjsFgMNRhjI2XSFVHQwAzmpOc1J4hZlskXZukZ+NWku3W9sxk+2A74GAJ2NiT4J41Hfztg7O54Tdz2djjRZ6P/VgV9WJSxysjLy8KN0QhKl9J/Er4pBxCoSwo+oKhsk3c1UfU8zMSWStd1QyVLLJli8vm9nDxnB4ysbAuoROyJUFvQbJj0KupgULUBTjUFuVQUgolQ2WbLX0JxIhZGCxZDBQtdgzGSHjtTMm0k3JVpRdB5BtxLIFn25T9Av3FipfNtyiFTmW8UcKyCakYDIbxxoRRXiJVUaS7V20lVOBYEs+WBKGiJebVhJJCDRv6ZrOuN+CZHbvIli1CJbhsbg9LZ/XRmSrvc6Gs33Qw6prHCtWltT6fRVQW82pibJQroRkoOvQXbCypUQqmZko4ljoshkZ1HAd8Kh2NSSloivksndVH2o3iYYGOslptCY6lKYUWnSkfKhLjmqi015ICS0AQOEgR54nt0yiGu5jXliPphuTKFut6Usxry5FwNHuyJaY3x/As8GwbELh2jHJQqGh4hIQqBCTbBzMoPfxM0ZuPPHHjKUFuMBgMxth4iYRKobSmJ19i50ABIaInzIznMb05gdaabf15+opl4o5FrhQwWHRQwGVzu7loTg9pr7FCZV/VFcdLJUpDhU1dlU2oqClyNqiHVqyAXNmir2AjiMIRtlR4tkaKA3P3qMrn2XV9R+r1K6p5M9aBqIJWqlHKoWROa5GPnLOVzlS5sddKxSiUwGDJIQgFllTErJCWuMK2onGUQoe4k+bVC04nlezkXx91+d3GLEk3JFu2CJREacGZU4fYNhB1DU45FhpNzImTcJvIC0E5KCIlKO2xY7CJZ3e0kEoPj9lIkBsMhqOBMTZeIresWMO9z22nMxWnIxnDDxVSClrjURnjqp19DBZ9WhIeU5sSPLerP1roUMzvyJJw9HBQfy86EAf9tM2w92OiNRmrki0KHt/ezP+80MJlJ/YztyVPJhbS7PlU8xdrIaPKBWTLgqFy1M1UVy9QqQPun1KVQ7dl4/xUK4C0hoGi5H93NjElXWZapkTMCojZUJ9TqRRsHnDRWpLxApJuiLAq6qBiWKhL62GRskZbSNBf8nhmd4rTJpdwLJ8gdHhxoJlrX7mMc+dYZEs+X3twDUNFWZNwf3hzBzHb4sS2LD25AjLp0By3SLhNCCFIes3EHcWU5rnsLnWyeuNONNnhcRsJcsNxhKlCObYwxsZLoCrmVQ2VVNVDIVpsvve2V/LO/3yUchjW9mmOu5RDhSUKNHkBltDYFeGqveluVBdDODCvRm1dm4hWBtGT/IsDMW5/diq2VExOllE6kvy26hb1qpcHIs9ArmyzqbeVzvQgMcsnW7bYlU0ys6WMJcf2CFXzW6zqSVRj35X6PBkpIO5GYZqfr53Eh8/ZSkcKhIgyeYWIJOQBUm4k6BW3I59MqKOy5pGDqB9PxguI2QpLaLSGrQMJtg1kmNncjy19Ml4Pf9yygvPmXsxnLlvMym29dOdKvLBnsHbah7dM4g/bOjhzWhLPifOFV7n0ZDdR9AvEnDiTW+awYNpSFs+Irvrulesoh6GRIDcc95gqlImNMTZeAtVKlLGeFHvzJbb258mV/Ybt05uTaK3ZOVjCtSKVyP1VItbnLQhdF2rZmydEH/1wy75EywIFoZbYUpF2A6ZliqRjwXD4pA5RUdkMNfih5NEXXfpL03BESH/JwpaKV84YIOZEORsj5cgVUWimWqFS215n3NX0SzTkyzbz2vI8tSND3FEknOF2Z1HIR2NJSDqKoZLGktGVCjQzmkq132VNcEwPX3fSDesMR8EFJ+zBs6IKFI0goQJ6s+tYs91h4fRlXHrSZP7r6RcbhM601mS8GKUwxlA5YFLTEk6dvoSSn8dzEsMN1wR84qKFnJfIM+vkhUaC3GAwHFWMsfESqO/oOpLWhMeJ7elR2wUwsyXF4s5tOJZC6cpT916oXxDDsNITgzq9DT3c3AuqHUGPvlOjWokxMiRSCqLKkYSjSLkhr5g+QMwJ91quWr0OKaAlXuaaxTvIlm3W96b4nxdaCZTNht4E8ztyBIAIwZa6QZ1ToLGlJtQCKfSYRpgAQuqMAqAQyMgLUTm+2p9EA45UODKoeDv0XnvXhNV5qAvXRAaUJl5pIKcqxmHCCSgGQ+wa3Mh8dT7XL1tAoDSbeoco+KqiTBvlAsFw/oUlLRJeZsz5cy1pkkENBsNRx5S+vgSqlShqRC1qNTaeibkN25XW5P2Ajd39zGoaorfg0Fe0owWp7vh6sajae5WFOh9I/HB4MddUnvwrvUPGy5uxL3VMGPZgFINhT4sUEHM0zV5AORQESjOvLU8psEd5AuqxROSZSLiadCwk7igWdw5w2YmRJPi/PDGDtXuSBBXDQgAFX+KHojaWaMHXexVM00A5ECQcRc6PhLme251CaY1jaWwZJY1KMTye9mSAFMNN8ho8KhVPVCkQDBQtgorrRCnIliyEqPo3KtL0lbJV3y9SKOcp+XksKVl+ySL+ctkCTunMsGhyMzNakgghTP6FwWA4pjCejZfI/tpzX79sAQq49fH1bO7NUgwUTTGfhBMQaEFfwUUAKS/Ekbqh9Xl1QQ8qjbpeHIjhWpqUG9KS8FFhJHaVDyziTkjc1rWF/Ui7Nvalcqo1+IGgv2gTd0JitqptE4Bra+a15/nAWdvpTPn05G3SXoAldS2fYiyjSQpIuwEC6Cs4LGjP88BGha8k33piFjE7YE5LgTcs2E2gBFPT1bCGINQaex8en2jR11gotvTFCVUUNgkUJMTo8UgJMaJqFM2w1V41bKrXIIDHtjYzq6lI0gsrPU8Eaa/iRxGQsO1aTo/SIa7t4DnD3oi/vHghtiVNC3iDoYJJDj32MMbGS2R/7bktKZFA0Q+wpEAKKAQ22bJFzFEINIOlaP+Eo7Bd1aA4GWoIlCBQku8+NR1bCoqB4INnbSPpBZX8DY0lo/wC4KjHUBQwWJaUAkF7Qo0Z2rEExOyQmB3SHIf+ok3aDRFC41q6weCoD6XYUhO3Q/qwSboBrXFNVy7aXgxs1vUkGSrZxBxFPrBIuQFUymTRGqf6qxGjc1s8C0qB5oENrbxqbg+nTxmiO++QiZXHvE4pgbBRJbRKWUV9TTb2xfnFuklcPLuXM6YMoqvJpFpEreK1QNjDMyOEZErTicO5F5gW8AbD/jDJoRMfY2wcJqrtuUdSCkJ++8IuBkvBsMCXkqzrSXDR7F7itsKSmlAJCn794idqiYSCKPfAloL+ohOJSGmY3lSKkiqh0aOxL6GOOg5wtzGpT1ocdU4NCVuR8RSWNfykPzIp07UhH1gknJAdgzEAkk6ItnRUKioaj9NUwhgySswsBg5JV5IJBNlyJFueckPW9SQ4bfIQfYXo9k5U+op0512UDmlPRK/tuoTUaujKkvCes7ZDJSBjHUBcSimBkMOmhq6cLOdLntudIlCS+ze0ATC/PUc6phgq2thS4tqSuCvQKASSzswsFk6/cMzP2ds9ZjAYDBMdY2wcYXpyJXYPFfFDPaJCpGJM1B7fo8Wtv2hhW5GXwxJRUmPel+wc9MiWo6fZy+b2MLWpVFssq+etGhyayCNSzZMYC02kF1GV3D4YGvJLxgh5SAmeoxko2nhWlNdQTYKsP64UaPK+jUTjKxgs2uwccolZIbOaizjV6anTw4gSNAVNXkDgaq49fQtDZQs/1KAVKVeRLUtyZQspYFDArrLLpv4Yfig5qS1Pc7yAXa2BraM6X/M7cmwbcAm0jR9GXXrHUifVleTcQAt0RRLUsqK57yvYPLKlpWZkaAS/2dDOA5taaY0pFC5LZvVw9rQyc+JxpHCQ1nTOnnMJcm8NcgwGg+EYxRgbh5lSEDa4utuSHpPSMTb3ZaMSTCHRBMxry9FXcOkvRE/qSkeLWtoNGChECYqWBK2jVW5Db5pQSTxLsaAjF3WNrbruxbBXo5pgKRguGaWyWNaX2Aqi11ofnHejaqTU9CnGWLCpfHZv3iHthhWFzxH7CehMB+wYtNk+GOPfVk4jZmsKvoXWms9etJG2hI9j6VrX1+FTSFzbwvdjOJZgUjJP0vXJlmx6Cw4xRyNQPL0rzePbmvFDm6WzejhtchTG2D4YI2aHzGgq1ZRL68flWJD2FH3FqBFa3hekHN3gYYk8F4Kib5NwFfnAZfugy/bBBE9ub2VPziIbiYliVYwtTeTV6i1KErbgma7pvHLOHH74px08snmQodIACffnnDuznTvevQzXNn+eBoPh+MB8mx0mQqW4ZcUaHtzQVTM2qkl8l540med3D9CTL+PZkpgVufsDLSpVG8PLXd6P+mCc0FIiIQMKgcX2oSbW9kwhbhfpSEEmFtTktOupLoRRZUW0aPqhxJKq5ukQI/avhRH2kpRZT1XroxBINOBaCq8udaDmgKh8liU1W/s95rYXRxk0SkPcUliErO1OUwxsikFNd4tHXmzhzCkDtCZCPCvEElGgob+YQWmBY+mKLLyDHw6BFsTtKAdGV0IgJ7fleLarhXjC45RJhUqdSkQ5kLXS3PoKGCEESlXKZFEoouZpUzNlEpZCiaifSSkQ9BZc8r7Lrl0Zeksz8OwEL/Tk6S8WKvklPo4lkYhK+/dI2l5rmJSJ88HzTmLFxt2s2DCAFAJbCsqB4qENXbz1thXc/b5L9v0LMRhehtxw3zzAP9rDMBwkxtg4TNyyYg0/X72tpiJabekNwxUp/+/x9ewcKuLjUggtUq4iCEHp4UUw50sefXEKv9ukiNshed8CbCxZZk5bHM8qMlSyaE+AHtG/o1oGG+jonEJotNa1J2rHUoyFBsohuNbeDQ6loK8IK3e00Jkqk3QUCcdnSjoYVfYJFT0QJXAsVTNSAiUqpaKVkJKEDX2JWqgBqElz/25DO5YQUfMx16cYSNbsSbFtYDLvOG0TIRbNMReoGCIaLKlqXqKmmE8yE/L+s7dQDGza4iV68g5UzJGmeDCcs1IXqqmWzma8gJ6cg9KCdAw29cRZ35vg+e4MQ+U4ubIi42kSboL5na1cdvLkmi7GjoE8BT/gLf/vIXZni5RDTdwWNMfjtKdiJF2Ln153EQB//8BzVENqw4mwgsdf7GawWCYTG243bzAYhjFJoccWxtg4DIyULa9Sben9kaXz+auLFvKxpfPZPpBl854/8Oy2DbhWgVBLCr7FQDFqA/7iQIZMPEZvrsyeXPSM7tkhl8/rZ2q6n6Tj41hhxbAQWHVNN6qeinIg2TXoMTlVYk/eYUq6XNN1GAutIe9L3DGMkWr5bTEQbB9I8NPnJgNRh9N3n7GDjmQQCXKNSP7UKvIa+KFAqShnIVSCkErKq4i23fv8JKQQtTBDbe6k5MFNHazY3EZrAvoKglIY5bIMlCxmtUTNxLrzPhk3UvAMVfQZzXGflBugtKToSwTDVS99BYeWuE/aDfHDYZGv6q8uVFEbe4DOdJkglJTCaLxSWAyU0szvbEHrkNaE5pOXnMGiKe01o8CSMLstzT88uJpSqAiVxpbR9fXkS4DmrUtOJunafPLnK+nNl6IZEWDLqGOwAArlkPXdQ5w5vQ2DwWA41jHGxmFgf7Ll1Zbenm1RKDxNqbwZ106SLfokXEXa9QmU4PGtrfz+xQ5KYRFbWqRcGw0sO6GL0yf3RH01pCKs2AQFX5DyGqsglAZbKJriPut7E6RcHykjjYmR6No/4FYUNyWN3o1qUzEFrO1OElQW4lBF4aBq2GZsh4gi7zvsyjq0JYZVVKOKFc32/iSeHcNXYUVhU1fUOUXUTVcIbGkxWJJYEtK2IFSa1V0JMrE8JT9qepcvWyRdn3wwXH0iiBJro/EJCr5F0gkZKEbqpdVZy/kSWyhcOxpYOZS1K0p7IaFSbB+M4dmahZMGSMUcmmNDNMV6cGSZ+1at5s4/tjOj9RyuX7YQS8qa8TmzOYlE0Fcs44dR2MdzLD50/jxuWbGGJ7Z2R03lKmPxK7/YmC2JuxYntte1azUYDIZjGGNsHAb2J1tebekdqoBdgxsRQjCzJcm63YNA5KVIOj4p16ostpJAKSwZtSN/xbQeEhW9CI2oJHpqFJpAVZQtAUQkfR4KCELBvz01jQ+cvR10ib0VOAgRhUgcO9LDQESlq/UJnaGKPB/1JkW2bDFUtoj8FI1ek6pxsmZ3invXTUIp+PArtjK3tYBrRSJcG3oSfOfJ6YQEtXwFoQW+Uni2RTkM8UMdddGtfGyuHIVkfrWuGYATmgdJuiHdZRelXaTQZGIhQsBQ2aKv4NTG1FOwaUMjhMAWikBJCoFFb8GiLR7gWkFDD5pYpWeNtjStCZ+evINCsGjSbkqhTRAKQi1Juoq408ULXX/glhWRHka98TmjJck0ncAPFY4V/V67hoo8uKEL17JIx2z6Cz5SRN6NKK9DcO7MdhNCMRgMxw3G2DgMVGXLqzkbVUZKSpf8PAU/h0BQ8LM0xRR+5Unas2HR5H6GyiHP7ZlBf9FHKY0tCjTHouBDPQJByhsOe9R7KRBQ8C3SschjsKU/xgktRdyRjhcd9QMph6CJwjlKQ2vcpz6gEoSChK1ZOquXBza14CuLQEm29MU4ZVKuwRNSrVZRSjC7pRDNA5HCZ9IJmNWs2T5kM1gUtf2FiJ7qtY7mUghRCyloDb5Stad+UckF+cXzLViyiaQbki1H47FlpM56zeKdxO1KPxQ1rFeyfTDGD5+dxrtP34orqWRnaHoLbiSwpiEfCNJuY3O8pBOi4jBQdEi7PvmcBCSOlJXfimB6updHNm3nI0vnjzI+67sBtyY8ENSMkUWTW1i1q4+hYkCoNVIIXjGrjTvevWyf95zBYDAcSxhj4zCxP9lypRUbdq8kW+gl1AGhiioVInVLCJVGCptTOwvkwgSit8CeXDGqXqDR1HAqolZSDCtX1koyicSqSqHAFpqmWJkp6fKwcmYdUe8OidKCgi+JOyG7cx5pL4ySLSuVF6riTWmJB7z2pG7+Z30HaU/VJL0bPr9SbhsqQcyOqm76i9GiXAxttg3a5P0w6lMC+Bq00sOdV9HkytEibVeaoAUVy6fawEwDUgp8JekvDlsFgZL05l2UEkzNFKLxV3RK+gs2a7uT9OZtVu9Oc/rkwdqsaiLPjQSSTqWaReuaYQNRl9dsOTJPwhBcR+LZFgmniGsFSBRndP4vKzdbnDf34n0an1MziZoxIoVg8ZRWAqUolEPakh53v+9iXKMQajDsFZMceuxhjI19EKpgdOvuvbA/Sek12x9hW986bNsjKPtRlYiOXOuuZWFbcWa1tbC9f4j/3VVmenOCYhDSlXUYKNq0VKonqoZGlYZqlPoSTuBti3YxNV2O8hHGIDJ1NHnfoq8SZigGUV6EXVmoAyVqwlqhFpw9bZA5LQWSnqI9UcYPLSwZjpIizweSbDmSZY/KYKMFXGnQWmNbEkeApTV2RdI9W45CJ6qy0JeVHlb2rHyAIyWlUKH20gnuVXN7SLoBed+qKIcqErZmh+/xyOYOQPPAxnbQcHJFryRXtnikq52kq7hgVjdCiKipndQEOsoetYRGohksOUgpcS1JwiniWT4gUEhcC3YNPM8zW22uX3YBMLbxaUk5yhixpSTpCf7s5ClGitxgMBx3GGNjDJRW7PHX8eDaZyn6eWJOgsmZOSyYtnS/6o5jSUpXczU0YMsUnq3JlwfQaJRSJL00Sa8ZIQQntLVz2ckn8OCGbqY1J9idLfDY1haWndBDwlFIOdrTUUVW8i/8EOKOxpbhvhW7BBSC4dyGqrjW60/ezYUn9GNJcG1V81oEoaAl7lMIJEpHbdY1giAU2FLXch5CDUMlq5ZQ6loSSwrKQZRwGZ1L42tNU8ymJeHRX/TR5TDyKBCFiYSkZlSkPRulNGXV6MWpVuAoDbZUzO/IoZH0FST92FhWdD7XFsRdAYEgbts8tnUyz+wSnDk9yfO7i/SXFIqQmc1F0p4iZltAnoStkELjK8HTu5pIeg4ntw9EcutWQHU0ed+iGCjW7h5g7e6neGBjC9cvW7RX43N/njCDwWA4njDGxhis2f4Ig+F2MmEGS9r4YZkXe9cAsHD6wcfS8+UsG7u76S+GtUTB1lgc1/IRAuJuGiGiJ/+pTXO4bOFiPrI0UiL90B2P8bsXogV8cecQs1vyBKEg5uoxhbhERbmrM1WKlD5prBapX6SVgp68U/E8KNb1pCgGNnev6eS8GYOkrGGPhVKRQRFoUCrKMwlUFF7xlWSwJEk4UY6IHwr+d0dTTT8j4VgsmNxEd7ZIT66MIogSYEUUPhII5ral2Gbl6C/4+EqhK24at5JUGSpNWzLG9oH88LWOmOcmLyTlhigdGTfNMZ+EEwIKpcpcOqeLP+48ganNyShHBM2333Ihdz69mftf2MmOgQJl1cnk9ADTmpOs3iXZmS1jCcWa7ibWd7fR2dzGK0/I4oqdoIeinjaBxWDRJebISqVNkfvWbgIsPnHRwjH7mZjmagaD4eWEacIwgvqKkXqEEOwa3EioRlec7I/v/eFFdg7pSl5GFKbYnbcphy62dNBoHMtlZusCFkxbCgx7SH507VKaYh4PbOzg24/PYH1PnFI4dk+TYfnwqGpFyEpViW7cp1oimytLWmIBk1MlmrxIQv2yud2V6pJh/QkhqOWIoKPzCinJBxagsYRmsBTJju8YcrlvfRv3re+IxgBMTse5cuEM5nVkWDSlmTOmtTKzKYEnI92NwVKZK0+ZzimdLTiWRdKxSbo2aVfQ5PnYlqIUKgaLPmhNJmYTs0XNkKpGVKSM49kJzp7RxuIpLi1xFUm+V3Q9FnUOcdmJ3bWEzfZkjLZkVPGhVDQv63pm4uvpOJbHgs4005ubsKw4CzrKXHv6Ft62aAPL5k7m6nPejaKNgVKS3rxLoCFbDsiVArrzgqd35Pn3x9eTL+9b6bD6ezaGhsFgOJ4xno0RlPw8RT8/5raiX6Dk50l4mQM/XxDy4IZuZqSamd7UTfV5XCDoLdqcMesVzJ989l7zQoZKIXPa05wkBf35Mn64nbijGRlLqcqR60pyphDUOsIi6jwalRXaV5Gx4dqKXVkHjSTmaM6YMsjCSUMknOF6lNo5dFX9M0qirHZVjVkKiPqarO1O1zwanoQL5nTys/ddTH/B555VW3EtSddAnp3ZAqVAI0VIqDTFUHHpvMn8cXsPWsMFs/Ywu2WIhBOQD2w296fpLpzE6l0DSAGebYMOKIdVo0iQcj12ZpuZnBsk5RaJ2RZKa0pBGaUEHckyrYmtCCFYvWcGF83t5F9+v45/ffR5Boo+fqjZMZjnhW6HP19yJu85exq3PX4fnv0iQRhJmAsCnt/5GI9veIRyUKTJC7GQ9BWdWsO2DT0ZyqFg60Cer/5uFTe+5owDvl8MBoPheMQYGyPwnAQxJ0GewqhtMSeO5xxci++q5kKuNBOAzlQ/nuVTCh029zdxTdMrSHipvR5fX0aZjkmEhGzZwbF87KoRUaes5YcWhcCiyfNriaS15mwV46QafpmUCirN2gR9hWFNh5nNxZq2RbXahMrn1Dq4ViyQ/oJda3hWLUEFiNuST1+6iE+96lQsKbGkpDXh8tjmPfQXhktrlY7KXn+4chMfXjKPJbMnIfUqTunor1TCSBKO5rTJg+zJbWPnYDsv9mdJ2BaB1kgZVcTYUjJY8lnbPYOUu4XTJg9FImFaRbolQqKVxkJxQvMeFnQ2ce25c7jsX+6tlBlLZCWs05Mv87UH1nLb4+v5sxM3E3MUjhRYVHJtdIAfanZnPZpjEHcCQLN9KMa67iSPb20HAY4leWprL6UgNJ4Lg+EwseH/vvFoD8FwCBhjYwSWtJmcmUNPX3fD+1prJmfm7LcqZST1xsKanlk83zujZmwkXJf2VHyfx9dreGQ8RdpVUbfYos3UdImYrWpGRK4s6SvECbQm4/kNJamqIv5VzemwGE6ubIoFDJUsQiWxpMaz9pKEKqDoC8phVNYaKIfVu+P8Zn1bLWQiAMcSdKbjJFwbqyJY4dkWWsNAsXFcVQ2MwZLPA+u7+PG15/PDx1fTl3fJlv2a4JdnW3Sm+pmcmcq2gSi2oyqS6I4VaXL4SlMO4fHt01k2WwJlBgvdgMau+6zpLa2Ug638bs1/8uq5O8nNtNnQk+LhLVHopxQosiWfmFUk6QSEWlAKIo9Os/AJwuhnS0B/0UVjUwrg1v+dHimVugKhNS0xj4FiuaYgazAYDC9XjLExBgumLWXrtu04VomiXyDmxGvVKAfLSMEvpSWFwBsl+DUWpSBkx0Ce1y+aQaA0v9+8i0A5CBGgtWD7YBwpFLaMqiFe6Elx6uQszTEbjagYGBqhwbbqjIe6rFGhI6nyaelSRVUz6gViKYFr6VrvEK2jBm8beuJ8d+UMEo6ujMUGgpqh4dqSKek4M1uStb4wnm1RCkJKgcKSkjAcDtFIAQhNOVTsyRbZkx1kRrPF9OZmVu8cQNX1avWsqKdJc9zlxPY0z+0aaDCKbCmiTrCxONNb57GtdxVah4i6CqKYE6foD1IsZ4m5LqGWxGzFws4BAB7aMolAReMbKEpyvo1rRVUyslJ2rHSUMBuoaumxxLNCPFtT8KMy39a4x/TmBOmYU1OQNRgMhpcrxtgYAykkHc48Tp9/2gHrbOyLgy1zDJXiHx96jluf3MDOgQIgmJqJ8c6z53DutLN4dsfTdA2VoswJYSEl5IJOtvQ005nu45wZkq7BwSi3QkcejXpE7R9q+RyRUBak3JDevI3ngBQV5dJKMki2bPNCb4a4k8BXUVXNSe0ZVu/qpxSESCFwpGSg5LOtP8+ktFd7qu/JlciWA1KezWDRr4R2dK0KxxaCjlSMyZlmNu1O4IdlWhMu3flSbdyl0KEUOpw7M8VA0act4dGdK0Klv0hzPAoFXTS3k8UzFmAJxepiJKImhYVnx0i4Gfrzu5HCJuY4uLakHETXObcty8MvtqOUxnMklrRY35PklI5+QBLqqHOtJRSFwK7laAAUAptAOUxOe8xuS2NLeUAGpcFgMLwcMMbGPrCkfVDJoHs/z8GVOd6yYg3/8vt19OZKlaoYzfbBAl97YDWTMzHOn5GhyeupyJV77M42s65nJo7Msit/Is/3NWOF24hZY0iUj0BT7dAqCUJB1rfozru80JPk/Jn9NMUChIbBksOTO9pY1zMDIXwsIVA6xLML+GG50ilVE2qNrzRBWMR1ZO2pvi3p0ZHy6M655MtRzkMNAc1xh0tPmkzC9ZicmcOLvWuY3hyFHvoKZfwwpKcwidedMpOPLj2Zbz3yPA+s38XqXYq8H5JwbOZPauLiEyMjTgrJqTMuRgvBlj2rCXXk+VAqJFQBMTeFJSMvzPbBPIHSxJ2Appii6EumpOIIKXhocwelUHFye46UoxgsWriWZLDk1JUDKdb3NuFaLtlSyOpd/UzOxLnunLlGN8NgMBgwxsa4Mpbg10hKQchvX9jFQDEqmVQVkatSqCP3/lCJdb0nsGugnbyfI+EmmdqUru173ow2Ht24lVdOd9A6xLX3XXoZKigEHsXAY6gUKXgmXcUftjXzu41ttCVChNbE3Camt6SZlNZ05/pYdsIe5rZG1SKndUQCXg9uaiHtKXK+TTm0qO9qH4WT2ikHWaSIsX2gRCmIwhPtCY8PL51fW5ir4aodAxuBIQJlsamviV35mcxqH228pTybbCkYZcSFSvHb9S3kCwVavH5sS2FJh4TtkHAiI7LeoMmVBQsnd3LqFIv+QhkpJVprHtjYwUOb2mj2QhAer5jezYltWZJugCU9ntudYM3uySycnEFpjR/qqDmeELWcFYPBYHg5Y4yNCUZPrkTXYJ5sKUBpVdPOUJX8gLIfkiv5dGaS7BiQ9Bd9WpMhbQmX0LF4YMNunto2wEmtAs9y8KyQmKNqXWFriZk6OmfO9yiEMUCgtarJlyM84o4kHwjKoSKmQqbqyOC58IQ9nDKpP1IPVYKYE3LJnB4umNVHwbfI+hYbe9P0FFvoyZWYnImxZvsjLOrYSEp2sycn2NKf5ukXW7ls8Vw+fekiEu5wh1YpJAunL+PX65v5zfOb8ZWL0hJQ/Hz1NgA+cdHCBuNtrA6pt6xYwwtdTzCzyWewnESKqHx1UjLAKQ+QirUghGBGS5KpTXFakifxmddehC0Ft6xYw4MbupicieNaFrmyT8bStDZneK47zh+2FuhMC2a1trOpJ8+M1nhl7ALPjjwe9TkrBoPB8HLGGBsTjLakR95XhEohKm3Ho/LTKP+iGCrW7RnCsSUtMZe5bXG+fsUprNjYz0+e6COtFLa0Wd+dYmFnP4XAwrVU1GJeQ6CjxMZ8WdKVTZBwNUpH+RaaSLhrU18GrS1KoSJQYfS0HoQ8vmUPUijOO3uwousRVa20xAOSbrRftmyTdGHx5EEGyztpS3qs2f4IL/auqSzsGaY1a07pDHj9rBKvO39sDYqqPkkpjDW8L4U4oEW8FISs2LiTMzv7qSaoKB1VzPQWXFriNra0KQUlYk6cGS1RArAfarqGinxk6Xw+snQ+u4eK/Oh/N/KfT23kxb4sOwp9TM7E+cRFp3LNmXMItOLa/3xkTFX43nzJVKIYDAYDxtiYkMiKRoNfEayqbwZiSxE1NVOKRZO2cOrkPLv6NuOXypw+LcbmoTTNcZcVWzqQQjOvYxDPivp4CBFJjPcXLB59sYXfb+7gkpP6mN+eI+EpsmWHNd0JHtnSQSlQ+ErVFtGwojw6KaHJxFSlLX1kcCTsKMHSEtVqjSjx85RJeQT+KEVWKQQxxyZX7CZUwZjJt1V9krEMigNZxCNtkyye5aNGCOX6oUIIj3PnvgFL2BXtFMk/PRR5M6p5NRfN7UQB/7N2B5PScTwCYvFk1DPGksxoSVIKwoZ28vW0JjxTiWIwGAwYY2PC0ZMrkYm7TG1KsGuwQDlUNVUuQbTICTTvPmMTs1uz2FIwVPRJOprZrQVi7otoZnLOtB1MTedwpCJbTtGcnMnlC5fy4Tuf5MENQ4RaIgTct66NFZvbmNlsE2qXbEnjqxKBUjhSkXBChkqSQEtsKRgsS/K+jSMjISvXUlhSoZGEWhBWWrJn4g7Tmy2GCj0U/fyYBkWgy3tVZK3XJxnJgSzibUmPpJeiFDo4VtiwzbEkcTfJQMGmI53Ekhb/8ODqWnmyZ1tkSwF3r9pKb67EpHQlRAJ4dmS41HtX9tVO3oRQDAaDwRgb40IpCPdahTJyW1vSoyXuIhBMzSTwQ0U5DFm/Z4iw0pp96cxdzGnNYYlKfxCtiDtRsmVnqh8EdCT68BWEWmJZ4Iid3P3Mo/zhRU2gJLIiEeorBWXJ1gFIOSGuLbFFkTeesoep6RJJR5EtW6zdk+SBTe34oWRDb4IlM/fgWRpbahwr6pTbV7QRQuBaknkdGeJOnHS8jZgTlbKOxBbuXhVZX+oi7tkWy+ZM4YWuTcysk4mvltz++gXNTQ88SlvSY+nsSTy8savhcyBSE90xWKQ9FRu1rd67Yjq4GgwGw74xxsYRJFSqlmhY75qvLkIjty2b2wlas7kvy7a+PK4taa54ORxbMinuMqM5xllTN2FXMj6FoE5lM6AUlmiN7cEPIfI8RBUV2wcKDJU2UPSno4iku2Wlp0igFbmSDyrkY2duY0q6D0tEOSLl0GLnUIwzpgwihUYheMW0PjKxEEmUBxFqgRAaR0rSMacmrjU5MwfXjtVKWetDKVprknLSPvVLDnYRH2m4Xb9sAbesUGztfZImr5ukE+KHLpv6MqzrmYZnC7KlgJ89u5Vdg3lmt6UbzudYEiGi6pJq0meVeu+K6eBqMBgM+8YYG0eQW1asGeWar1ZTAKO2/eujz6OBmc1Jyr6ir1hmd7aIZ0mWnNBBf9EnbpcjRcuKPLgthxdBgWaopBAEKG3h2rKm3ukrRdxRpGOKvoKMVDA1yEqSp9aa95+9hcmpQaxKzaoQ4NkhU9JFtg3EOH9mH/myRcoNCMKoy5sUUA5cEFGPEL8U0BRr4oS2U2olrNX/3zW4sUGRtVDed+LkgS7i+zLqPnHRqZSCU9gzlMOxyvz5nX9isKgajvdsSSGIElzliNySyZn4KFG0vXlXDqS02WAwGF6OGGPjCBFVU4x2zUsh+O0Lu2o/V1Fa1/qGaGBGS5JpOgqjNMddfnTtBXzn9+tYsXEnhdAhoXwSTjBsTISR0dCdn0TK7cWzw1peqa8i0yRbtigGdi3ZVDPcLbY1oZnRnAckUeP3aq0JeLbCtjTNsRBfWZUGbwKBQApBwikTaIkUipZEgnNPWMSCaUuRFZnwainrfHV+gyLryq6VBzSX+1vE92XUVUtkp7dk2DGQZ0/WH2UkRNdgUwpC4s7wn4TSmuvOmVurgOkd0KQ824RIDAaD4SAZV2OjWCyyfPlyenp6SCaT3HzzzbS2to7ar7e3l7e//e3893//N57nHfBxE4l9VVPsyRbQWpCODWtL+KGuGQx+qPBsq7Z4DhTL9Bf82lP+ys2C3uw6isEQZb9AoBQa2DmYZl3vCTR7ZU7pGMBXGkfrSmt4xfbBVH1bFDSR3HbGczilM8QWkcekul1U8hyE0Hh25A0ItUQjax6Vam8WT1pYwqElmWRH/zpsabFw+rKG6z5ciqz17MuoG1kiu6+k04WTm7lgdgePbN4zKmRjSclHls7ngcee5OJXnmNCJAaD4bhhvNblcZU3/PGPf8y8efP40Y9+xBve8Aa+/e1vj9rn4Ycf5r3vfS/d3d0HddxEo7qwjUVHKs6kdKN+hGOJuv8afy31+QGebXHe3IuZ3b6QlsQkYk4ru7NpVnfN4r61JyGEZPXu6azqaqLoSySKlOuTsEMWT87x/rNe5M9O7MYSUQhmRnOCkzub6M/HCLQEBEpHXo3q/0IFBT+SLNdaki9LtFYQtXur+DjAdeIVbRDBrsGNhGr0on64qRp1Y1FN4qxSTTpVWjfsp7Tm4hM7WX7pqdzx7gv5ybsu5I53X8gnLlrY0LW2I+EYQ8NgMBxXjNe6PK7GxsqVK7ngggsAWLZsGY899tjoAUnJrbfeSnNz80EdN9HY18J26UmTufSkyQ3bpBA0xRyaYs5+qy+qYYmL5r+Ti095Jyu7zua5nhNq/VGnNad4rnsGtz87h22DzfjKoaRcLGkTczRnTs3ymnm9ZDyboVLAc7sGeHHApyffVNPiCLWoRlvI+RYrdzTx+xdbAM1AySVbtiIPiIjGE3OTJN2m2hiLfoGSnz/s8zqSfRl1Y5XIXr9sAVcsnE7KsymHISnP5oqF02thkWrIxhgVBoPh5cB4rctHLIzyX//1X9x2220N77W1tZFORxn/yWSSoaGhUcctWbJk1HvZbHa/x43FqlWrDnbYDaxceWA5BXtjSVKztUmwsivHYDkk41qc1ZlkSbIAMGrb62Ym0cAfd4/ef19jme0qVuweRApRm5uM1Fw+J8O5U3ciZYw9+YDBcghaowWc1Jrl4U0thLqyqAq455mZXHOGxnMGkAJKgWDLQIx/f2oWWV+iKhUsJ7XlGMBmoGgxNR2Q8lLosiBbztbGJLFZ/exapNj3ov1S5xhgrhewYs/gKCPttOkZVj39p1H7L0vDeaemGSglaPIsXKvIn/74xwP6rMMx3vHmWBvzsTZeOPbGfKyNt56X+r3+cuZorstHzNh4y1vewlve8paG9z72sY+Ry+UAyOVyZDIHFr9PpVKHdNyiRYvwvENTcFy5ciVnnXXWIR1bzyvO2bvOxt627UuXYyxOPyOqxrh75TqUG6vlG7z/vGmseH4zlrRpykQL8JbeHH2FErb0aYoLBktW1AwtGaOpKcl9m1sp+EUmJUsMlOI8vSOLFpqEoykEikdenMwjWxVtCcUHzjuVJfMG2D5GWevM1gUsnP6KfY77cM1x9frHKpE9nI3QDtd4x5NjbczH2njh2BvzRB5vqVTarzHxUr7Xj2cOZO6O5ro8rgmiZ555Jg899BCLFy9mxYoVB3zDH+pxE4V9VVOMte1gSyirJaLnJfLMOnlhzUgJVdAgqCWF4ITWJPTChh7NYNlCSkFTzKUj5VV6mwSk3Dh9xeiPuTnu0l1pdZ/ybE7pbMIPFW9cNIPll5yK0gqL0WWt1XLX8cDoXBgMBsOhMV7r8rgaG1dffTU33HADV199NY7j8PWvfx2AW2+9lZkzZ3LppZce1HGGRlxLNhgplrRHCWoJIZjZkuD57mbmdzSze6hIf9GnO1fGsQSd6RhXLJzB/6zdgRSC6c1JAPryJTKeTXPcbSj9rOaPzC6fy67BfiZnmkm4R+epw+hcGAwGw8ExXuvyuBob8XicW265ZdT711133aj3fve73+33OMP+GUtQa0bLHGb1tXDv8y/Qm4+8FpaI5LlLfogtBVcsnF4LSyzobGLp7Em88dSZWFYko14NT+xLUOtwhjAMBoPBcPgZr3XZiHpNAA42R+Ng2Jug1uxJPv/++EZsS+IrTczSTMlIWpMJVmzczR3vvrAWlmiOO3zn9+v4q58/Ncqg2J+glsFgMBgMxtg4ioynV2CkoFZ/wacpHuVqnNS6lWmZfmKWTyl02D7URHf2FUxrTjG1KTFmR9Sfr95GECoe2bzngAS1DAaDwfDyxfi5jyJVr0C2FDQs4resWHPA5ygFITsG8gwWy+zJ+5SCcP8HMaxPcUr7Vma3dONaIQqJY4XMbe1lz+DK2vn3ptB5/wu72JM9MEEtg8FgMLx8MZ6No8TByGyPRdUr8sD6Xaza2U8hCJFKcc66LBfNbeIjS0/Dtd29Hh+JjrUzlFtJtf06RBLmzTGPnuwmQrWEnlx5r7Lr2bJP2nMJlBq1rSqodSRDRAaDwWA4NjDGxlFiX71Tql6BA2k+tr0/T3+hjBSwZGYXp7RvplwM+X+//wNL5pzW0BBtJO8/bya3PyHoLwr8UOFYkuaYy/TmRE0BtC2Z3Gs/kfZkjKWzJ/HLNdtHCWotm9vJtx9ZaxJHDQaDwWCMjaPFvpqCjSWzXU/VKwLQVywjhOCCWbtZOGkQISQhNgPFPJt7ngMY1RCtSsJNMae9nVJQqhkbVaMhZscryaSR7Ho1Z6NKVUb9+mULsKUYJailtObnz203iaMGg8FgMMbG0aLaO2Vvi/i+Qg71zcf8UONairlt2ahjqwato86xgdJs799IU/J0OtLJUees1+Go36a1ZnJmDpaMbo+qpsbeFDpHCmoBvPW2h0ziqMFgMBgAY2wcVfa1iO+LqldksOjjWIKkE5J0AgIlkCJqjmYLya7BAoPFPr664rckvaYxwxhj6XCMVAA9EIXOekGtHQP5lxQiMhgMBsPxhTE2jiIHKrM9Msmy3ivSEnPpKwTkfBvPigS5AApByMCAT9GXrNpVJONFKqADxTKfftXi2ufsTYdjLA5UofOlhIgMBoPBcPxhjI0JwN4W8bF0OJbOnsTbz5jNh86fB8D963ayalfA5v4Up3QMkHBsNJCrlMBu6ElRDgXbBvLsGMqzetcgT23t5dKTJjd4OUbqcLzU6znUEJHBYDAYjj+MsTGBqVfndG2LNV0D/H7Tbv7l0edZNKU5WsgFdKYSFNQCRLiJRVNsVu3sohhYbOhJ8fCWDkpBlL8htMaWiv6Cf8STNQ81RGQwGAyG4w9jbExQRupwbOvPRd1XgaFywHNdA/TkSnSkYsxoTlL0Nb94vhURn80jG7eyameeUhgdGyiFEFHiqC0EjiWOeLKm6cRqMBgMhipG8GCCUl9xorSmv1CuSW+VA0VfvowUgv5CGaU1EFV7PLm1jx1DkPMhWw7IlQNCpdFag4CWhFszYMZD5bMaIjKGhsFgMLx8McbGBKWaZAlReasf6to2WwpCFb32VeO2tV2DFMoBjiVxLQshBBpQGppiDjNaUrV9TbKmwWAwGMYDY2xMUKpJlkprHCsKfUCkgdEad3Hs6FfnyOFtCsj7AbNa07QnPZKuRcKxiTsWMdtiYWdzzTtikjUNBoPBMF6YnI0JTH2SZdpzGCz5tMQ8pjcnEP159uSKtMW9WlikHCritoUlBDOak0xrSuCHGtsSbO3L4dkWeT8wyZoGg8FgGFeMsTGBqU+y3D1U4Pb/3cQjm/fQmy8xvzPDyTpDKVDk/ID2pMei6Wk2lR1y5ajsNZIKjwyRRVOa+cE7lpItBSZZ02AwGAzjijE2jgE822JGS4rll57K9UHI7qEit/9xE49s2l3pvOqw9IQOLsyUeDQX36u+RSbmkontvROswWAwGAxHAmNsHGN4tsWdT2+udVqNOzaB0vxy7Q52Ngn+/ppXAUbfwmAwGAwTB2NsHGOM1N+oIoVgZVeOQGmjb2EwGAyGCYWpRjnGqNffGMlgOaxtM/oWBoPBYJgoGGPjGKNef2MkGdcyuhkGg8FgmHAYY+MYo15/ox6lNWd1Jo0nw2AwGAwTDpOzcQyytyZnS5KFozwyg8FgMBhGY4yNY5C9NTlbuXLl0R6awWAwGAyjMMbGMUw1CdRgMBgMhomMydkwGAwGg8FwRDHGhsFgMBgMhiOKMTYMBoPBYDAcUYyxYTAYDAaD4YhijA2DwWAwGAxHFGNsGAwGg8FgOKIYY8NgMBgMBsMR5bjU2dAVKe9yufySzlMqjd3wbCJzrI3ZjPfIc6yN+VgbLxx7Y56o461+Z+sR7Rjq33up3+vHK/uau4mA0BN1ZC+BoaEh1q1bd7SHYTAYDIZDYN68eaTT6Yb3zPf6gTHW3E0EjktjQylFLpfDcRyEEEd7OAaDwWA4ALTW+L5PMplEysYov/le3zf7mruJwHFpbBgMBoPBYJg4TDzzx2AwGAwGw3GFMTYMBoPBYDAcUYyxYTAYDAaD4YhijA2DwWAwGAxHlONSZ2MslFLceOONPP/887iuy0033cSsWbNq23/3u9/xrW99C9u2ueqqq3jrW9/KT3/6U372s58BUV36mjVrePTRR9m6dSt//ud/zgknnADA1VdfzeWXXz6u4wUoFApcd911/M3f/A1z587d6zFbtmzhU5/6FEIITjrpJL7whS8ckWzlQxmz7/t85jOfYfv27ZTLZT784Q9z6aWXsnr16gk5xwBveMMbaqVl06dP5ytf+cqEnuOJfB/fe++93HbbbViWxbx587jxxhsBjtp9fCjjDcPwqN3DhzpmKeVRvY8PlgO578eTp59+mq997Wv84Ac/2Ouc3XHHHdx+++3Yts2HP/xhLr74YorFIsuXL6enp4dkMsnNN99Ma2vrUbuOcUW/TLjvvvv0DTfcoLXW+o9//KP+8z//89q2crmsX/WqV+n+/n5dKpX0m970Jr179+6G42+88UZ9++23a621vuOOO/T3v//9ozZerbV+5pln9Bvf+EZ9/vnn6/Xr1+/zmA996EP6D3/4g9Za68997nP617/+9YQZ85133qlvuukmrbXWvb29+sILL9RaT9w5LhaL+sorrxx1rok8x/VMpPu4UCjoSy+9VOfzea211p/4xCf0/ffff1Tv40MZ79G8hw91zEf7Pj5Y9nffjyff/e539ete9zr9lre8RWs99pzt3r1bv+51r9OlUkkPDg7Wfv73f/93fcstt2ittb733nv1l7/85aN2HePN0TdZx4mVK1dywQUXAHD66aezatWq2rYNGzYwc+ZMmpqacF2Xs846i6eeeqq2/dlnn2X9+vW87W1vA2DVqlU8+OCDvOMd7+Azn/kM2Wx2XMcLkVrct771LebMmbPfY1avXs0rXvEKAJYtW8bvf//7wz7eQx3za17zGv7iL/6i9tqyLGDizvHatWspFAq8973v5V3vehd/+tOfgIk9x1Um2n3sui6333478XgcgCAI8DzvqN7HhzLeo3kPH+qYj/Z9fLDs774fT2bOnMk3v/nN2uux5uyZZ57hjDPOwHVd0uk0M2fOZO3atQ3XsWzZMh577LGjcg1Hg5eNsZHNZkmlUrXXlmURBEFtW73iWjKZbPhi+M53vsNHP/rR2uvFixfzyU9+kh/+8IfMmDGDb33rW+M6XoCzzjqLKVOmHNAxWuuaCE4ymWRoaOiwj/dQx5xMJkmlUmSzWa6//nr+v//v/wMm7hzHYjHe97738f3vf58vfvGL/PVf//WEn+MqE+0+llLS3t4OwA9+8APy+TxLliw5qvfxoYz3aN7Dhzrmo30fHyz7u+/Hk1e/+tXY9nAGwlhztrc1pf79iTS/48HLxthIpVLkcrnaa6VU7YYZuS2Xy9VuiMHBQTZu3Mh5551X237ZZZexaNGi2s/PPffcuI73YI+pj7nmcjkymcxhH+++Pn9/7Ny5k3e9611ceeWVvP71rwcm7hzPnj2bK664AiEEs2fPprm5mT179kz4OZ6o97FSiptvvplHH32Ub37zmwghjup9fCjjhaN3Dx/qmI/2fXywHOp9Px6MNWd7W1Pq359I8zsevGyMjTPPPJMVK1YA8Kc//Yl58+bVts2dO5ctW7bQ399PuVzmqaee4owzzgDgySef5Pzzz2841/ve9z6eeeYZAB577DEWLlw4ruM92GNOOeUUHn/8cQBWrFjB2WeffdjHe6hj7u7u5r3vfS/Lly/nzW9+c+39iTrHd955J3/3d38HQFdXF9lslo6Ojgk9xzBx7+PPf/7zlEolvv3tb9dc/UfzPj6U8R7Ne/hQx3y07+OD5VDv+/FgrDlbvHgxK1eupFQqMTQ0xIYNG5g3bx5nnnkmDz30UG3fs84662gOfVx52ciVV7OZ161bh9aav/3bv+W5554jn8/ztre9rVaNorXmqquu4h3veAcA3/ve97Btm/e85z21c61evZovf/nLOI5De3s7X/7ylxtcfOMx3irXXnstN954Y0M1Sv0xc+fOZdOmTXzuc5/D933mzJnDTTfdVIsrH+0x33TTTfzqV79qyDH4t3/7NzZs2DAh57hcLvPpT3+aHTt2IITgr//6rznzzDMn9BzDxLyPFy1axFVXXcXZZ59d8xC8613v4tJLLz1q9/GhjPfxxx8/avfwoY75wgsvPKr38cGyt++2o8W2bdv4y7/8S+644469ztkdd9zBT37yE7TWfOhDH+LVr341hUKBG264gT179uA4Dl//+tfp6Og4atcxnrxsjA2DwWAwGAxHh5dNGMVgMBgMBsPRwRgbBoPBYDAYjijG2DAYDAaDwXBEMcaGwWAwGAyGI4oxNgwGg8FgMBxRjLFhMOyFbdu2sWjRIq688kre8IY38H/+z//huuuuY9euXYd8zp/+9Kd86lOfAuADH/gAXV1de933lltuaZDNPxBOPvnkhtfZbJYzzjhj1Oc88cQTvPGNb9zreS655BK2bdt2UJ9tMEwE6v9ur7zySl796lfz6U9/mu7ubp599ln+7//9v3s9duvWrXzmM58Zc9uPf/xjfvzjHwOj/872xwMPPMCtt9466jwvJyaGBJvBMEGZNGkS99xzT+313/3d3/HVr36Vb3zjGy/53P/2b/+2z+1PPvkk55577kv6jFQqxWWXXcYvfvEL3vve99bev/vuuxsEqAyG44n6v1utNd/4xje4/vrr+dGPfsSpp5661+N27NjB1q1bx9x29dVXH/J46nu5vJTzHMsYY8NgOAjOPffcmqFxySWXsHjxYtasWcOPfvQjHn74YW677TaUUixcuJAvfOELeJ7H3Xffzb/8y7+QSqWYNm0aiUSidvx//Md/0NHRwRe/+EVWrlyJ4zh85CMfoVwus2rVKj772c/yz//8z8RiMW688Ub6+/uJxWJ87nOf45RTTmHbtm0sX76cfD7PaaedNuaY3/SmN/HVr361ZmyUSiUefPBBbrjhBv7zP/+Te+65h0KhUBMZqhen+ulPf8oTTzxRU5u89tpr+djHPsa5557Ld7/7XX71q18RhiFLly5l+fLlNeEog2GiIITg4x//OEuWLOE//uM/+M1vfsMPfvADbr31Vn72s58hpWTx4sV86Utf4qabbmLbtm188Ytf5DWveQ1///d/j1KKk046ienTpwPw8Y9/HIDPfe5zPPPMM7S0tPC3f/u3TJ06teHvY9u2bbzrXe/iu9/9LrfffjsAU6dOZceOHbXzPPDAA/zjP/4jSilmzJjBl770Jdrb27nkkku44ooreOSRRygUCtx888016ftjFRNGMRgOEN/3ue+++zj99NNr7y1btoz77ruP3t5e7rjjDm6//Xbuuece2tra+P73v09XVxdf+9rX+OEPf8hPfvKThn4JVaoNsn71q19x66238q1vfYvLL7+cRYsWcdNNN3HyySdzww03sHz5cn72s5/x5S9/mU984hMAfPnLX+ZNb3oT99xzD2eeeeaY4z733HNrvVEA7r//fl75yldiWRb3338/P/jBD7j33nu56KKL+OEPf3hAc7FixQpWrVrFnXfeyd13301XVxc///nPD3JGDYbxwXVdZs2aVWtKF4Yh3/nOd7jrrrv46U9/iu/7dHV18dnPfpZFixbxhS98AYDNmzdz2223cfPNN4865znnnMM999zDZZddxt/8zd/s9bNPPPFE3v72t/P2t7+dq666qvZ+T08Pn//85/nWt77Ff//3f3PmmWfypS99qba9ubmZO++8k7e//e185zvfOVxTcdQwng2DYR/s3r2bK6+8Eojaty9evJi/+qu/qm2vehMef/xxtmzZwlvf+lYgMkxOOeUU/vjHP3LGGWfUvuRe//rX84c//KHhM5588kne+ta3IqWko6ODX/ziFw3bc7kcq1at4tOf/nTtvXw+T19fH0888QRf//rXAbjiiiv47Gc/O+oahBC84Q1v4N577+X666/nnnvu4T3veQ+pVIqvf/3r/OIXv2Dz5s08/PDDLFiw4IDm5bHHHuOZZ57hTW96EwDFYpGpU6ce0LEGw9FACEEsFgOirrFnnHEGb37zm7n00ku57rrr6OzsZPPmzQ3HzJ49u6F7a5VYLMYVV1wBwJVXXsk//uM/HvR4nnnmGRYvXlzzmLztbW/ju9/9bm17tRX9SSedxK9//euDPv9EwxgbBsM+GJmzMRLP84DoSem1r31tbbHP5XKEYchjjz1GfUeAsTpV2rbdEH7YsmVLQ5t4pRSu6zaMY9euXTQ3NwPUzi+EaOhAWc+b3vQm3vve93LNNdewefNmXvnKV7Jz506uvfZa3vnOd7Js2TLa29tZs2ZNw3FCiIbx+75fu953v/vdXHfddUDUVXYi9NAwGMaiXC6zadMmenp6au99+9vf5k9/+hMrVqzg/e9/P1/72tdGHVc1TkZS/3emtR7Vch4gCIJ9jkkp1fBaa91wTPW75XgJTZowisFwGDj33HP5zW9+Q09PD1prbrzxRm677TbOOuss/vSnP9HV1YVSil/+8pejjj3nnHP45S9/idaanp4e3vnOd1Iul7EsizAMSafTnHDCCTVj49FHH601Cjz//PNr4Ytf//rXlEqlMcc3depUpkyZwi233FJrLf7ss88ya9Ys3vOe93Dqqady//33E4Zhw3EtLS1s2LABrTVbt27l+eefB+C8887jnnvuIZfLEQQBH/3oR7nvvvsO23waDIcLpRTf/OY3Oe2005g5cyYAvb29XH755cybN4+/+Iu/YMmSJTz//PNYlrVfIwEiz+Jvf/tbAO66665aR+WWlhbWr18PROHKKmOd97TTTuPpp5+uVX395Cc/eckJ4RMZ49kwGA4D8+fP52Mf+xjvfve7UUqxYMECPvjBD+J5Hp/97Gd5z3veQzwe58QTTxx17DXXXMNNN91Uc8t+7nOfI5VKccEFF/CFL3yBm2++mb//+7/nxhtv5Hvf+x6O4/AP//APCCH4/Oc/z/Lly/nJT37CokWLSCaTex3jVVddxSc/+Ul+85vfALBkyRJ+/OMfc/nll6O15pxzzuGFF15oOOb888/nrrvu4jWveQ2zZ8+utcS+5JJLWLt2LW9961sJw5ALLrhgn6W0BsN4Uh/+rP49fuMb32Dt2rUAtLa28ra3vY03v/nNxONxZs+ezVVXXVVrCb98+fJ9VmtlMhnuv/9+/umf/onOzk6+8pWvAPD+97+fT33qU9x1111ceumltf3POeccbrjhhlo4FaC9vZ0vfelLfOxjH8P3faZOnbrP3I9jHdP11WAwGAwGwxHFhFEMBoPBYDAcUYyxYTAYDAaD4YhijA2DwWAwGAxHFGNsGAwGg8FgOKIYY8NgMBgMBsMRxRgbBoPBYDAYjijG2DAYDAaDwXBEMcaGwWAwGAyGI8r/D7SIZG/wwAjRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Residuals for LinearRegression Model'}, xlabel='Predicted Value', ylabel='Residuals'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.regressor import ResidualsPlot\n",
    "\n",
    "visualizer = ResidualsPlot(LinearRegression())\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Prediction Error for LinearRegression'}, xlabel='$y$', ylabel='$\\\\hat{y}$'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.regressor import PredictionError\n",
    "\n",
    "visualizer = PredictionError(LinearRegression())\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statsmodel OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>int_rate</td>     <th>  R-squared:         </th>  <td>   0.284</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.282</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   161.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 06 Oct 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:02:08</td>     <th>  Log-Likelihood:    </th>  <td>  59253.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 28904</td>      <th>  AIC:               </th> <td>-1.184e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 28832</td>      <th>  BIC:               </th> <td>-1.178e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    71</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                      <td>    0.1110</td> <td>    0.006</td> <td>   19.029</td> <td> 0.000</td> <td>    0.100</td> <td>    0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>loan_amnt</th>                  <td> 7.886e-07</td> <td> 3.13e-08</td> <td>   25.188</td> <td> 0.000</td> <td> 7.27e-07</td> <td>  8.5e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>term_is_36</th>                 <td>   -0.0304</td> <td>    0.000</td> <td>  -64.991</td> <td> 0.000</td> <td>   -0.031</td> <td>   -0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>emp_length</th>                 <td>   -0.0007</td> <td> 5.43e-05</td> <td>  -12.710</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>annual_inc</th>                 <td>    0.0009</td> <td>    0.000</td> <td>    2.284</td> <td> 0.022</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dti</th>                        <td>    0.0005</td> <td>    3e-05</td> <td>   16.521</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>inq_last_6mths</th>             <td>    0.0044</td> <td>    0.000</td> <td>   35.101</td> <td> 0.000</td> <td>    0.004</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>open_acc</th>                   <td>   -0.0005</td> <td> 4.55e-05</td> <td>  -10.660</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pub_rec_bankruptcies</th>       <td>    0.0127</td> <td>    0.001</td> <td>   13.979</td> <td> 0.000</td> <td>    0.011</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>default</th>                    <td>    0.0113</td> <td>    0.001</td> <td>   21.329</td> <td> 0.000</td> <td>    0.010</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_credit_card</th>        <td>    0.0109</td> <td>    0.001</td> <td>   10.019</td> <td> 0.000</td> <td>    0.009</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_debt_consolidation</th> <td>    0.0147</td> <td>    0.001</td> <td>   14.801</td> <td> 0.000</td> <td>    0.013</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_educational</th>        <td>    0.0186</td> <td>    0.002</td> <td>    8.654</td> <td> 0.000</td> <td>    0.014</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_home_improvement</th>   <td>    0.0060</td> <td>    0.001</td> <td>    5.162</td> <td> 0.000</td> <td>    0.004</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_house</th>              <td>    0.0091</td> <td>    0.002</td> <td>    4.321</td> <td> 0.000</td> <td>    0.005</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_major_purchase</th>     <td>    0.0065</td> <td>    0.001</td> <td>    5.263</td> <td> 0.000</td> <td>    0.004</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_medical</th>            <td>    0.0101</td> <td>    0.002</td> <td>    6.024</td> <td> 0.000</td> <td>    0.007</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_moving</th>             <td>    0.0113</td> <td>    0.002</td> <td>    6.394</td> <td> 0.000</td> <td>    0.008</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_other</th>              <td>    0.0138</td> <td>    0.001</td> <td>   12.547</td> <td> 0.000</td> <td>    0.012</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_renewable_energy</th>   <td>    0.0101</td> <td>    0.004</td> <td>    2.581</td> <td> 0.010</td> <td>    0.002</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_small_business</th>     <td>    0.0167</td> <td>    0.001</td> <td>   12.979</td> <td> 0.000</td> <td>    0.014</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_vacation</th>           <td>    0.0069</td> <td>    0.002</td> <td>    3.234</td> <td> 0.001</td> <td>    0.003</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>purpose_wedding</th>            <td>    0.0141</td> <td>    0.002</td> <td>    9.259</td> <td> 0.000</td> <td>    0.011</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_AL</th>              <td>   -0.0063</td> <td>    0.004</td> <td>   -1.458</td> <td> 0.145</td> <td>   -0.015</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_AR</th>              <td>   -0.0052</td> <td>    0.005</td> <td>   -1.129</td> <td> 0.259</td> <td>   -0.014</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_AZ</th>              <td>   -0.0035</td> <td>    0.004</td> <td>   -0.857</td> <td> 0.392</td> <td>   -0.012</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_CA</th>              <td>   -0.0022</td> <td>    0.004</td> <td>   -0.562</td> <td> 0.574</td> <td>   -0.010</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_CO</th>              <td>   -0.0080</td> <td>    0.004</td> <td>   -1.943</td> <td> 0.052</td> <td>   -0.016</td> <td> 7.11e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_CT</th>              <td>   -0.0045</td> <td>    0.004</td> <td>   -1.081</td> <td> 0.280</td> <td>   -0.013</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_DC</th>              <td>   -0.0023</td> <td>    0.005</td> <td>   -0.480</td> <td> 0.631</td> <td>   -0.011</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_DE</th>              <td>   -0.0004</td> <td>    0.005</td> <td>   -0.086</td> <td> 0.931</td> <td>   -0.011</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_FL</th>              <td>   -0.0040</td> <td>    0.004</td> <td>   -0.994</td> <td> 0.320</td> <td>   -0.012</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_GA</th>              <td>   -0.0052</td> <td>    0.004</td> <td>   -1.289</td> <td> 0.197</td> <td>   -0.013</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_HI</th>              <td>    0.0019</td> <td>    0.005</td> <td>    0.397</td> <td> 0.692</td> <td>   -0.008</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_IA</th>              <td>   -0.0116</td> <td>    0.012</td> <td>   -0.993</td> <td> 0.321</td> <td>   -0.035</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_ID</th>              <td>    0.0006</td> <td>    0.012</td> <td>    0.051</td> <td> 0.959</td> <td>   -0.022</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_IL</th>              <td>   -0.0028</td> <td>    0.004</td> <td>   -0.681</td> <td> 0.496</td> <td>   -0.011</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_IN</th>              <td>   -0.0155</td> <td>    0.011</td> <td>   -1.454</td> <td> 0.146</td> <td>   -0.036</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_KS</th>              <td>   -0.0079</td> <td>    0.004</td> <td>   -1.757</td> <td> 0.079</td> <td>   -0.017</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_KY</th>              <td>   -0.0052</td> <td>    0.004</td> <td>   -1.178</td> <td> 0.239</td> <td>   -0.014</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_LA</th>              <td>   -0.0063</td> <td>    0.004</td> <td>   -1.456</td> <td> 0.145</td> <td>   -0.015</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MA</th>              <td>   -0.0042</td> <td>    0.004</td> <td>   -1.026</td> <td> 0.305</td> <td>   -0.012</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MD</th>              <td>    0.0003</td> <td>    0.004</td> <td>    0.065</td> <td> 0.948</td> <td>   -0.008</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_ME</th>              <td>   -0.0210</td> <td>    0.022</td> <td>   -0.937</td> <td> 0.349</td> <td>   -0.065</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MI</th>              <td>   -0.0056</td> <td>    0.004</td> <td>   -1.357</td> <td> 0.175</td> <td>   -0.014</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MN</th>              <td>   -0.0063</td> <td>    0.004</td> <td>   -1.507</td> <td> 0.132</td> <td>   -0.015</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MO</th>              <td>   -0.0068</td> <td>    0.004</td> <td>   -1.640</td> <td> 0.101</td> <td>   -0.015</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MS</th>              <td>    0.0054</td> <td>    0.010</td> <td>    0.565</td> <td> 0.572</td> <td>   -0.013</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_MT</th>              <td>   -0.0063</td> <td>    0.006</td> <td>   -1.114</td> <td> 0.265</td> <td>   -0.017</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NC</th>              <td>   -0.0058</td> <td>    0.004</td> <td>   -1.402</td> <td> 0.161</td> <td>   -0.014</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NE</th>              <td>    0.0030</td> <td>    0.012</td> <td>    0.243</td> <td> 0.808</td> <td>   -0.021</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NH</th>              <td>   -0.0093</td> <td>    0.005</td> <td>   -1.976</td> <td> 0.048</td> <td>   -0.019</td> <td> -7.7e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NJ</th>              <td>   -0.0015</td> <td>    0.004</td> <td>   -0.385</td> <td> 0.700</td> <td>   -0.009</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NM</th>              <td>   -0.0048</td> <td>    0.005</td> <td>   -1.023</td> <td> 0.306</td> <td>   -0.014</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NV</th>              <td>    0.0013</td> <td>    0.004</td> <td>    0.303</td> <td> 0.762</td> <td>   -0.007</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_NY</th>              <td>   -0.0022</td> <td>    0.004</td> <td>   -0.553</td> <td> 0.580</td> <td>   -0.010</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_OH</th>              <td>   -0.0050</td> <td>    0.004</td> <td>   -1.216</td> <td> 0.224</td> <td>   -0.013</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_OK</th>              <td>   -0.0060</td> <td>    0.004</td> <td>   -1.340</td> <td> 0.180</td> <td>   -0.015</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_OR</th>              <td>   -0.0030</td> <td>    0.004</td> <td>   -0.690</td> <td> 0.490</td> <td>   -0.011</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_PA</th>              <td>   -0.0063</td> <td>    0.004</td> <td>   -1.555</td> <td> 0.120</td> <td>   -0.014</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_RI</th>              <td> 6.083e-06</td> <td>    0.005</td> <td>    0.001</td> <td> 0.999</td> <td>   -0.009</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_SC</th>              <td>   -0.0064</td> <td>    0.004</td> <td>   -1.502</td> <td> 0.133</td> <td>   -0.015</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_SD</th>              <td>   -0.0147</td> <td>    0.006</td> <td>   -2.394</td> <td> 0.017</td> <td>   -0.027</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_TN</th>              <td>    0.0132</td> <td>    0.008</td> <td>    1.650</td> <td> 0.099</td> <td>   -0.002</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_TX</th>              <td>   -0.0033</td> <td>    0.004</td> <td>   -0.825</td> <td> 0.409</td> <td>   -0.011</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_UT</th>              <td>   -0.0034</td> <td>    0.005</td> <td>   -0.759</td> <td> 0.448</td> <td>   -0.012</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_VA</th>              <td>   -0.0028</td> <td>    0.004</td> <td>   -0.689</td> <td> 0.491</td> <td>   -0.011</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_VT</th>              <td>   -0.0092</td> <td>    0.006</td> <td>   -1.471</td> <td> 0.141</td> <td>   -0.021</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_WA</th>              <td>   -0.0011</td> <td>    0.004</td> <td>   -0.274</td> <td> 0.784</td> <td>   -0.009</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_WI</th>              <td>   -0.0069</td> <td>    0.004</td> <td>   -1.610</td> <td> 0.107</td> <td>   -0.015</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_WV</th>              <td>   -0.0074</td> <td>    0.005</td> <td>   -1.540</td> <td> 0.124</td> <td>   -0.017</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>addr_state_WY</th>              <td>    0.0051</td> <td>    0.006</td> <td>    0.912</td> <td> 0.362</td> <td>   -0.006</td> <td>    0.016</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>576.999</td> <th>  Durbin-Watson:     </th> <td>   1.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 387.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.163</td>  <th>  Prob(JB):          </th> <td>8.00e-85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.537</td>  <th>  Cond. No.          </th> <td>2.05e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.05e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               int_rate   R-squared:                       0.284\n",
       "Model:                            OLS   Adj. R-squared:                  0.282\n",
       "Method:                 Least Squares   F-statistic:                     161.0\n",
       "Date:                Tue, 06 Oct 2020   Prob (F-statistic):               0.00\n",
       "Time:                        17:02:08   Log-Likelihood:                 59253.\n",
       "No. Observations:               28904   AIC:                        -1.184e+05\n",
       "Df Residuals:                   28832   BIC:                        -1.178e+05\n",
       "Df Model:                          71                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================================\n",
       "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------\n",
       "const                          0.1110      0.006     19.029      0.000       0.100       0.122\n",
       "loan_amnt                   7.886e-07   3.13e-08     25.188      0.000    7.27e-07     8.5e-07\n",
       "term_is_36                    -0.0304      0.000    -64.991      0.000      -0.031      -0.030\n",
       "emp_length                    -0.0007   5.43e-05    -12.710      0.000      -0.001      -0.001\n",
       "annual_inc                     0.0009      0.000      2.284      0.022       0.000       0.002\n",
       "dti                            0.0005      3e-05     16.521      0.000       0.000       0.001\n",
       "inq_last_6mths                 0.0044      0.000     35.101      0.000       0.004       0.005\n",
       "open_acc                      -0.0005   4.55e-05    -10.660      0.000      -0.001      -0.000\n",
       "pub_rec_bankruptcies           0.0127      0.001     13.979      0.000       0.011       0.014\n",
       "default                        0.0113      0.001     21.329      0.000       0.010       0.012\n",
       "purpose_credit_card            0.0109      0.001     10.019      0.000       0.009       0.013\n",
       "purpose_debt_consolidation     0.0147      0.001     14.801      0.000       0.013       0.017\n",
       "purpose_educational            0.0186      0.002      8.654      0.000       0.014       0.023\n",
       "purpose_home_improvement       0.0060      0.001      5.162      0.000       0.004       0.008\n",
       "purpose_house                  0.0091      0.002      4.321      0.000       0.005       0.013\n",
       "purpose_major_purchase         0.0065      0.001      5.263      0.000       0.004       0.009\n",
       "purpose_medical                0.0101      0.002      6.024      0.000       0.007       0.013\n",
       "purpose_moving                 0.0113      0.002      6.394      0.000       0.008       0.015\n",
       "purpose_other                  0.0138      0.001     12.547      0.000       0.012       0.016\n",
       "purpose_renewable_energy       0.0101      0.004      2.581      0.010       0.002       0.018\n",
       "purpose_small_business         0.0167      0.001     12.979      0.000       0.014       0.019\n",
       "purpose_vacation               0.0069      0.002      3.234      0.001       0.003       0.011\n",
       "purpose_wedding                0.0141      0.002      9.259      0.000       0.011       0.017\n",
       "addr_state_AL                 -0.0063      0.004     -1.458      0.145      -0.015       0.002\n",
       "addr_state_AR                 -0.0052      0.005     -1.129      0.259      -0.014       0.004\n",
       "addr_state_AZ                 -0.0035      0.004     -0.857      0.392      -0.012       0.005\n",
       "addr_state_CA                 -0.0022      0.004     -0.562      0.574      -0.010       0.006\n",
       "addr_state_CO                 -0.0080      0.004     -1.943      0.052      -0.016    7.11e-05\n",
       "addr_state_CT                 -0.0045      0.004     -1.081      0.280      -0.013       0.004\n",
       "addr_state_DC                 -0.0023      0.005     -0.480      0.631      -0.011       0.007\n",
       "addr_state_DE                 -0.0004      0.005     -0.086      0.931      -0.011       0.010\n",
       "addr_state_FL                 -0.0040      0.004     -0.994      0.320      -0.012       0.004\n",
       "addr_state_GA                 -0.0052      0.004     -1.289      0.197      -0.013       0.003\n",
       "addr_state_HI                  0.0019      0.005      0.397      0.692      -0.008       0.011\n",
       "addr_state_IA                 -0.0116      0.012     -0.993      0.321      -0.035       0.011\n",
       "addr_state_ID                  0.0006      0.012      0.051      0.959      -0.022       0.024\n",
       "addr_state_IL                 -0.0028      0.004     -0.681      0.496      -0.011       0.005\n",
       "addr_state_IN                 -0.0155      0.011     -1.454      0.146      -0.036       0.005\n",
       "addr_state_KS                 -0.0079      0.004     -1.757      0.079      -0.017       0.001\n",
       "addr_state_KY                 -0.0052      0.004     -1.178      0.239      -0.014       0.003\n",
       "addr_state_LA                 -0.0063      0.004     -1.456      0.145      -0.015       0.002\n",
       "addr_state_MA                 -0.0042      0.004     -1.026      0.305      -0.012       0.004\n",
       "addr_state_MD                  0.0003      0.004      0.065      0.948      -0.008       0.008\n",
       "addr_state_ME                 -0.0210      0.022     -0.937      0.349      -0.065       0.023\n",
       "addr_state_MI                 -0.0056      0.004     -1.357      0.175      -0.014       0.003\n",
       "addr_state_MN                 -0.0063      0.004     -1.507      0.132      -0.015       0.002\n",
       "addr_state_MO                 -0.0068      0.004     -1.640      0.101      -0.015       0.001\n",
       "addr_state_MS                  0.0054      0.010      0.565      0.572      -0.013       0.024\n",
       "addr_state_MT                 -0.0063      0.006     -1.114      0.265      -0.017       0.005\n",
       "addr_state_NC                 -0.0058      0.004     -1.402      0.161      -0.014       0.002\n",
       "addr_state_NE                  0.0030      0.012      0.243      0.808      -0.021       0.027\n",
       "addr_state_NH                 -0.0093      0.005     -1.976      0.048      -0.019    -7.7e-05\n",
       "addr_state_NJ                 -0.0015      0.004     -0.385      0.700      -0.009       0.006\n",
       "addr_state_NM                 -0.0048      0.005     -1.023      0.306      -0.014       0.004\n",
       "addr_state_NV                  0.0013      0.004      0.303      0.762      -0.007       0.010\n",
       "addr_state_NY                 -0.0022      0.004     -0.553      0.580      -0.010       0.006\n",
       "addr_state_OH                 -0.0050      0.004     -1.216      0.224      -0.013       0.003\n",
       "addr_state_OK                 -0.0060      0.004     -1.340      0.180      -0.015       0.003\n",
       "addr_state_OR                 -0.0030      0.004     -0.690      0.490      -0.011       0.005\n",
       "addr_state_PA                 -0.0063      0.004     -1.555      0.120      -0.014       0.002\n",
       "addr_state_RI               6.083e-06      0.005      0.001      0.999      -0.009       0.009\n",
       "addr_state_SC                 -0.0064      0.004     -1.502      0.133      -0.015       0.002\n",
       "addr_state_SD                 -0.0147      0.006     -2.394      0.017      -0.027      -0.003\n",
       "addr_state_TN                  0.0132      0.008      1.650      0.099      -0.002       0.029\n",
       "addr_state_TX                 -0.0033      0.004     -0.825      0.409      -0.011       0.005\n",
       "addr_state_UT                 -0.0034      0.005     -0.759      0.448      -0.012       0.005\n",
       "addr_state_VA                 -0.0028      0.004     -0.689      0.491      -0.011       0.005\n",
       "addr_state_VT                 -0.0092      0.006     -1.471      0.141      -0.021       0.003\n",
       "addr_state_WA                 -0.0011      0.004     -0.274      0.784      -0.009       0.007\n",
       "addr_state_WI                 -0.0069      0.004     -1.610      0.107      -0.015       0.001\n",
       "addr_state_WV                 -0.0074      0.005     -1.540      0.124      -0.017       0.002\n",
       "addr_state_WY                  0.0051      0.006      0.912      0.362      -0.006       0.016\n",
       "==============================================================================\n",
       "Omnibus:                      576.999   Durbin-Watson:                   1.993\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              387.280\n",
       "Skew:                           0.163   Prob(JB):                     8.00e-85\n",
       "Kurtosis:                       2.537   Cond. No.                     2.05e+06\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.05e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sms\n",
    "\n",
    "# add constant important for this model\n",
    "X_train = sms.add_constant(X_train)\n",
    "\n",
    "model = sms.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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