Notebooks/Master_Notebook_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Master Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to use our training data set to fit a multiple linear regression model to predict house prices in King County, Washington."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Import Training Data\n",
    "* Check Assumptions of Linear Regression\n",
    "* Create New Metrics\n",
    "* Iterate Through Regression Models (Choose Best in Terms of R-Squared)\n",
    "* Let The User Input Hypothetical Home Data to Predict the Price of Their Home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jamesfay/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>season_sold</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>long_lat</th>\n",
       "      <th>district</th>\n",
       "      <th>is_seattle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7504400290</td>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>12003</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6240</td>\n",
       "      <td>-122.048</td>\n",
       "      <td>1978</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>(-122.04799999999999, 47.623999999999995)</td>\n",
       "      <td>Lake Washington</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6403500570</td>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>7420</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>98059</td>\n",
       "      <td>47.4944</td>\n",
       "      <td>-122.162</td>\n",
       "      <td>1996</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>(-122.162, 47.4944)</td>\n",
       "      <td>Renton</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7934000145</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>6400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5563</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1970</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>(-122.39299999999999, 47.5563)</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203100625</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>21587</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6384</td>\n",
       "      <td>-121.959</td>\n",
       "      <td>1992</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>(-121.959, 47.6384)</td>\n",
       "      <td>Snoqualmie Valley</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2211700160</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>17208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.5659</td>\n",
       "      <td>-122.117</td>\n",
       "      <td>1959</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>(-122.117, 47.5659)</td>\n",
       "      <td>Issaquah</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  bedrooms  bathrooms  sqft_living   log_lot  sqft_lot  floors  \\\n",
       "0  7504400290         5       2.50         3470  9.392912     12003     2.0   \n",
       "1  6403500570         5       2.75         2990  8.911934      7420     2.0   \n",
       "2  7934000145         4       2.75         2900  8.764053      6400     2.0   \n",
       "3   203100625         3       2.50         2620  9.979847     21587     2.0   \n",
       "4  2211700160         3       1.50         1770  9.753130     17208     1.0   \n",
       "\n",
       "   waterfront  condition  grade  zipcode      lat     long  yr_of_last_update  \\\n",
       "0         0.0          3      8    98074  47.6240 -122.048               1978   \n",
       "1         0.0          3      8    98059  47.4944 -122.162               1996   \n",
       "2         0.0          3      7    98136  47.5563 -122.393               1970   \n",
       "3         0.0          3      7    98053  47.6384 -121.959               1992   \n",
       "4         0.0          3      8    98006  47.5659 -122.117               1959   \n",
       "\n",
       "  season_sold  below_ground_ratio  price_100k  \\\n",
       "0      Spring            0.000000       5.990   \n",
       "1      Summer            0.000000       4.985   \n",
       "2      Winter            0.421569       4.500   \n",
       "3      Spring            0.000000       6.720   \n",
       "4      Spring            0.525862       4.500   \n",
       "\n",
       "                                    long_lat           district  is_seattle  \n",
       "0  (-122.04799999999999, 47.623999999999995)    Lake Washington         0.0  \n",
       "1                        (-122.162, 47.4944)             Renton         0.0  \n",
       "2             (-122.39299999999999, 47.5563)            Seattle         1.0  \n",
       "3                        (-121.959, 47.6384)  Snoqualmie Valley         0.0  \n",
       "4                        (-122.117, 47.5659)           Issaquah         0.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pull in Data\n",
    "import pandas as pd\n",
    "import statsmodels.stats.api as sms\n",
    "import statsmodels.formula.api as smf\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('Train_Table.csv')\n",
    "df.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17094, 20)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Linearity Assumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linearity_test(df):\n",
    "    y = df['price_100k']\n",
    "    fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15,15))\n",
    "    for n in range(len(df.columns) - 1):\n",
    "        row = (n-1)//3\n",
    "        col = n%3-1\n",
    "        ax = axes[row][col]\n",
    "        column = df.columns[n]\n",
    "        x = df[column]\n",
    "        ax.scatter(x,y)\n",
    "        ax.set_title(str(column) + ' vs Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create df with only variables we could use in regression.  Create log_lot variable\n",
    "reg_df = df.drop(['zipcode', 'lat', 'long', 'id', 'long_lat', 'sqft_lot'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linearity_test(reg_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grade, Bathrooms, and Sqft_living seem to have linear relationships with price.  Other variables have less linear relationships, so adjustments may have to be made"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_histograms(df):\n",
    "    fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15,15))\n",
    "    for n in range(len(df.columns) - 1):\n",
    "        row = (n-1)//3\n",
    "        col = n%3-1\n",
    "        ax = axes[row][col]\n",
    "        column = df.columns[n]\n",
    "        x = df[column]\n",
    "        ax.hist(x)\n",
    "        ax.set_title(str(column))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAANeCAYAAACLbIreAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde7xfVX3n/9dbouANuUUGCBiUFEVnRJoB1Km1RAFBDfP7eYlDNfqjw8wUb22nCm2nWJUOzq8jSluZoYCCFwKl+pMKiilKbWcKEi6igAwpIAQiRBPwgoLBz++PvQ58czgnfHMu3++5vJ6Px/dxvnvttfdeOydZ2Z+91/6sVBWSJEmSJPXjScNugCRJkiRp9jCIlCRJkiT1zSBSkiRJktQ3g0hJkiRJUt8MIiVJkiRJfTOIlCRJkiT1zSBSTyjJHUleNUX7qiT7TcW+JGkykuyf5LokP07y7m3c9tF+MckfJDmrz+1+kuS5E2mvpLltKq+3xtj3p5J8eDr2rflpwbAboLkryRXAZ6qqr4srSRqw9wFXVNVLoLvIAtZV1R9ty06q6k+3oe4ztqmFkjRgSQpYUlVrh90WzVw+idSMlcSbHJKm03OAG4fdCEmSZhuDSPXrXye5KcmmJJ9MskOSnZN8KcmGVv6lJIsAkpwC/BrwF2341l/07OtVSW5t2/xlkrRt3p7kfyU5LclG4ANJnpTkj5J8L8l9Sc5L8qyRHSV5fZIbk9yf5IokL+hZd0eS309yQ5KfJjk7ye5JvtyGr/1dkp1b3R2SfCbJD9u+rk6y+yD+YCVNXpL3J7m7/du+JcmyJE9tQ7g2tf7r95Osa/W/BvwGj/VRxwPHAu9ry3+7Dcf+QJLPtO9fSfLOUeu/leT/at8fHdLf2vaXSS5p7b4qyfN6tju8ncsDST6R5O+T/NZk/6wkzWxJtk/ysST3tM/Hkmzfs/59Sda3db81kVeFkvz7JGuTbExycZI9W/k3WpVvtb7wzVN4appDDCLVr2OBI4DnAb8C/BHd359P0t3N3wf4GfAXAFX1h8A/AO+sqmdUVe9F1WuBfw28GHhT2++IQ4DbgGcDpwBvb5/fAJ4LPGPkGEl+BTgfeC+wELgU+NskT+nZ3/8NvLq1+XXAl4E/AHZr7R95D2ol8Cxgb2BX4D+285E0wyXZH3gn8K+r6pl0fcodwMl0fdbzWtnKkW2q6jC27KPOBD4L/Le2/LoJNudzwFt62nYAXR95yTj13wL8CbAzsJau3yPJbsBFwEl0fdItwMsm2CZJs8sfAocCB9JdKx1Md91FkiOB3wVeBewH/Pq27jzJYcB/pbsG2wP4HrAKoKpe0aq9uPWFF0zqTDRnGUSqX39RVXdV1Ua6i5y3VNUPq+pvqurBqvpxK++nMzu1qu6vqjuBr9N1kiPuqao/r6rNVfUzuuD1o1V1W1X9hO6CakUb6vpm4JKqWl1VvwD+DHgqW15o/XlV3VtVd9NdMF5VVddV1UPAF4CXtHq/oLtQ26+qHqmqa6rqRxP6k5I0aI8A2wMHJHlyVd1RVf9Md4F0SlVtrKq7gNMH0JYvAAcmeU5bPhb4fOtzxvL5qvpmVW2mC2JH+sOjgBur6vNt3enA96ez4ZJmjGOBD1bVfVW1ge5G01vbujcBn6yqG6vqwbZuIvs/p6qubX3TScBLkyyefNM1XxhEql939Xz/HrBnkqcl+Z9tqOmPgG8AOyXZ7gn21Xsh9CDd08WxjgOwZzte77EXALuPXldVv2zb79VT/96e7z8bY3nk2J8GLgNWteEh/y3Jk5/gPCTNAC35w3uBDwD3JVnVhmbtyeP7ruluy4/pnjquaEUr6ILD8YzXH27R9qoqYN3UtVTSDDbWtc+ePet6+7XR103bvP92k/6HbHn9JG2VQaT6tXfP932Ae4DfA/YHDqmqHYGRIRBpP2sCxxm9zT10Q8F6j72ZLhjcYl17t3Jv4O5tPmjVL6rqT6rqALonma8F3rat+5E0HFX1uar6N3R9QgEfAdbz+L5rq7uZouacD7wlyUvpRkd8fQL7WA8sGllo/dui8atLmkPGuva5p33fom9gyz5uQvtP8nS60VjbfP2k+csgUv06IcmiJLvQvVN4AfBMuqd597fyk0dtcy/de4yTcT7wO0n2TfIM4E+BC9rwrguBo1sCjSfTBbUPAf97Ww+S5DeS/Mv2FPVHdMNbH5lk2yUNQLr5Hg9riSd+TtcvPULXR5yULgnYIuBdT7CrqeizoHs/+znAB+n6q19OYB+XAP8yyTFt+P4JwL+YgrZJmvnOB/4oycL2fvQfA59p6y4E3pHkBUme1tZtq8+1fRzY+s0/pXvd5462fqr6Qs1hBpHq1+eAr9IlvbkN+DDwMbq77D8ArgS+MmqbjwNvaJkRJ/ou0jl0Q02/AdxOd4H4LoCqugX4TeDPWxteB7yuqh6ewHH+BV0Six8BNwN/z2MdtqSZbXvgVLp+4Pt0ibn+gO5doe/R9R1fpetLtuZsuvcq70/y/020Me0do8/TJb743AT38QPgjcB/oxtmdgCwhu5GmaS57cN0/95vAL4NXNvKqKov070j/XW6ZFz/1Lbpu2+oqsuB/wL8Dd2Tzefx2BB86F4NOLf1hW+azIlo7kr3moUkSXNbklcCn6mqWTcsNMmT6N6JPLaqJjI8VtIclG5qs+8A27dRWtJA+CRSkqQZKMkRSXZqw83+gO598yuH3CxJQ5bk3yZ5Srq5rj8C/K0BpAbNIFKSpFGS7NMm2h7r80QJeqbKS4F/5rHh+se0qY8kzW//AdhA1z88AvwngCQ3jtNnHTvMxmpucjirJEmSJKlvPomUJEmSJPVtwbAbMFG77bZbLV68eNjNkDSFrrnmmh9U1cJht2My7Jukuce+SdJMNaz+adYGkYsXL2bNmjXDboakKZTke8Nuw2TZN0lzj32TpJlqWP2Tw1klSZIkSX0ziJQkSZIk9c0gUpIkSZLUN4NISZIkSVLfDCIlSZIkSX2btdlZ57rFJ14yLfu949Sjp2W/kmYW+xBJmhr2p9Lj+SRSkiRJktQ3g0hJkiRJUt8MIiXNSUl2SnJRku8muTnJS5PskmR1klvbz51b3SQ5PcnaJDckOahnPytb/VuTrBzeGUmSJM0MBpGS5qqPA1+pqucDLwZuBk4ELq+qJcDlbRngNcCS9jkeOAMgyS7AycAhwMHAySOBpyRJ0nxlEClpzkmyI/AK4GyAqnq4qu4HlgPntmrnAse078uB86pzJbBTkj2AI4DVVbWxqjYBq4EjB3gqkiRJM45BpKS56LnABuCTSa5LclaSpwO7V9V6gPbz2a3+XsBdPduva2XjlW8hyfFJ1iRZs2HDhqk/G0mSpBnEIFLSXLQAOAg4o6peAvyUx4aujiVjlNVWyrcsqDqzqpZW1dKFCxdOpL2S5okkv5PkxiTfSXJ+kh2S7Jvkqvbu9QVJntLqbt+W17b1i3v2c1IrvyXJEcM6H0nzk0GkpLloHbCuqq5qyxfRBZX3tmGqtJ/39dTfu2f7RcA9WymXpG2WZC/g3cDSqnoRsB2wAvgIcFp7X3sTcFzb5DhgU1XtB5zW6pHkgLbdC+mG2H8iyXaDPBdJ85tBpKQ5p6q+D9yVZP9WtAy4CbgYGMmwuhL4Yvt+MfC2lqX1UOCBNtz1MuDwJDu3hDqHtzJJmqgFwFOTLACeBqwHDqO72QWPf1975D3ui4BlSdLKV1XVQ1V1O7CWLvmXJA3EgmE3QJKmybuAz7ZhYbcB76C7cXZhkuOAO4E3trqXAkfRXYg92OpSVRuTfAi4utX7YFVtHNwpSJpLquruJH9G1//8DPgqcA1wf1VtbtV6371+9L3sqtqc5AFg11Z+Zc+ux31fmy7jNPvss8+Un4+k+csgUtKcVFXXA0vHWLVsjLoFnDDOfs4Bzpna1kmaj9qIhuXAvsD9wF/TTTE02si715N+Xxs4E2Dp0qWPWy9JE+VwVkmSpMF4FXB7VW2oql8AnwdeRjet0MiN/d53rx99L7utfxawEd/XljRkBpGSJEmDcSdwaJKntXcbR97X/jrwhlZn9PvaI+9xvwH4Whs5cTGwomVv3RdYAnxzQOcgSQ5nlSRJGoSquirJRcC1wGbgOrrhppcAq5J8uJWd3TY5G/h0krV0TyBXtP3cmORCugB0M3BCVT0y0JORNK8ZREqSJA1IVZ0MnDyq+DbGyK5aVT/nsQRgo9edApwy5Q2UpD44nFWSJEmS1DeDSEmSJElS3wwiJUmSJEl9M4iUJEmSJPXNIFKSJEmS1DeDSEmSJElS3wwiJUmSJEl9m1QQmWSnJBcl+W6Sm5O8NMkuSVYnubX93LnVTZLTk6xNckOSg3r2s7LVvzXJysmelCRJkiRpekz2SeTHga9U1fOBFwM3AycCl1fVEuDytgzwGmBJ+xwPnAGQZBe6SXcPoZto9+SRwFOSJirJHUm+neT6JGtamTe5JEmSJmnCQWSSHYFXAGcDVNXDVXU/sBw4t1U7FzimfV8OnFedK4GdkuwBHAGsrqqNVbUJWA0cOdF2SVKP36iqA6tqaVv2JpckSdIkTeZJ5HOBDcAnk1yX5KwkTwd2r6r1AO3ns1v9vYC7erZf18rGK3+cJMcnWZNkzYYNGybRdEnzlDe5JEmSJmkyQeQC4CDgjKp6CfBTHrurP5aMUVZbKX98YdWZVbW0qpYuXLhwW9sraX4p4KtJrklyfCublptc3uCSJEnzyYJJbLsOWFdVV7Xli+iCyHuT7FFV69ud/Pt66u/ds/0i4J5W/spR5VdMol2SBPDyqronybOB1Um+u5W6k7rJVVVnAmcCLF26dMybYHPF4hMvmZb93nHq0dOyX0mSNPUm/CSyqr4P3JVk/1a0DLgJuBgYST6xEvhi+34x8LaWwOJQ4IH2JOAy4PAkO7d3jQ5vZZI0YVV1T/t5H/AFunca7203t9iGm1xjlUuSJM1bk83O+i7gs0luAA4E/hQ4FXh1kluBV7dlgEuB24C1wF8Bvw1QVRuBDwFXt88HW5kkTUiSpyd55sh3uptT38GbXJIkSZM2meGsVNX1wNIxVi0bo24BJ4yzn3OAcybTFknqsTvwhSTQ9XOfq6qvJLkauDDJccCdwBtb/UuBo+hucj0IvAO6m1xJRm5ygTe5JEmSJhdEStJMVFW30c1dO7r8h3iTS5IkaVImO5xVkiRJkjSPGERKkiRJkvpmEClJkiRJ6ptBpCRJkiSpbwaRkiRJkqS+GURKkiQNSJKdklyU5LtJbk7y0iS7JFmd5Nb2c+dWN0lOT7I2yQ1JDurZz8pW/9YkK8c/oiRNPYNISZKkwfk48JWqej7dVEQ3AycCl1fVEuDytgzwGmBJ+xwPnAGQZBfgZOAQ4GDg5JHAU5IGwSBSkiRpAJLsCLwCOBugqh6uqvuB5cC5rdq5wDHt+3LgvOpcCeyUZA/gCGB1VW2sqk3AauDIAZ6KpHnOIFKSJGkwngtsAD6Z5LokZyV5OrB7Va0HaD+f3ervBdzVs/26VjZe+RaSHJ9kTZI1GzZsmPqzkTRvGURKkiQNxgLgIOCMqnoJ8FMeG7o6loxRVlsp37Kg6syqWlpVSxcuXDiR9krSmAwiJUmSBmMdsK6qrmrLF9EFlfe2Yaq0n/f11N+7Z/tFwD1bKZekgTCIlCRJGoCq+j5wV5L9W9Ey4CbgYmAkw+pK4Ivt+8XA21qW1kOBB9pw18uAw5Ps3BLqHN7KJGkgFgy7AZI0HZJsB6wB7q6q1ybZF1gF7AJcC7y1qh5Osj1wHvCrwA+BN1fVHW0fJwHHAY8A764qL9IkTda7gM8meQpwG/AOupv6FyY5DrgTeGOreylwFLAWeLDVpao2JvkQcHWr98Gq2ji4U5A03xlESpqr3kOXOn/HtvwR4LSqWpXkf9AFh2e0n5uqar8kK1q9Nyc5AFgBvBDYE/i7JL9SVY8M+kQkzR1VdT2wdIxVy8aoW8AJ4+znHOCcqW2dJPXH4ayS5pwki4CjgbPacoDD6N4/gsen0B9JrX8RsKzVXw6sqqqHqup2uicBBw/mDCRJkmYug0hJc9HHgPcBv2zLuwL3V9XmttybDv/RVPlt/QOtfl8p9CVJkuYbg0hJc0qS1wL3VdU1vcVjVK0nWNdXCv12TOdikyRJ84ZBpKS55uXA65PcQZdI5zC6J5M7JRl5D7w3Hf6jqfLb+mcBG9mGFPrOxSZJkuYTg0hJc0pVnVRVi6pqMV1inK9V1bHA14E3tGqjU+iPpNZ/Q6tfrXxFku1bZtclwDcHdBqSJEkzltlZJc0X7wdWJfkwcB1wdis/G/h0krV0TyBXAFTVjUkupJvDbTNwgplZJUmSDCIlzWFVdQVwRft+G2NkV62qn/PYnGyj150CnDJ9LZQkSZp9HM4qSZIkSeqbQaQkSZIkqW8GkZIkSZKkvhlESpIkSZL6ZhApSZIkSerbpIPIJNsluS7Jl9ryvkmuSnJrkguSPKWVb9+W17b1i3v2cVIrvyXJEZNtkyRJkiRpekzFk8j3ADf3LH8EOK2qlgCbgONa+XHApqraDzit1SPJAXTzsr0QOBL4RJLtpqBdkiRJkqQpNqkgMski4GjgrLYc4DDgolblXOCY9n15W6atX9bqLwdWVdVDVXU7sJYx5nKTJEmSJA3fZJ9Efgx4H/DLtrwrcH9VbW7L64C92ve9gLsA2voHWv1Hy8fYZgtJjk+yJsmaDRs2TLLpkiRJkqRtNeEgMslrgfuq6pre4jGq1hOs29o2WxZWnVlVS6tq6cKFC7epvZIkSZKkyVswiW1fDrw+yVHADsCOdE8md0qyoD1tXATc0+qvA/YG1iVZADwL2NhTPqJ3G0mSJEnSDDLhJ5FVdVJVLaqqxXSJcb5WVccCXwfe0KqtBL7Yvl/clmnrv1ZV1cpXtOyt+wJLgG9OtF2SJEmSpOkzmSeR43k/sCrJh4HrgLNb+dnAp5OspXsCuQKgqm5MciFwE7AZOKGqHpmGdkmSJEmSJmlKgsiqugK4on2/jTGyq1bVz4E3jrP9KcApU9EWSUqyA/ANYHu6fu6iqjq5jXZYBewCXAu8taoeTrI9cB7wq8APgTdX1R1tXyfRTVH0CPDuqrps0OcjSZI0k0zHk0hJGraHgMOq6idJngz8Y5IvA79LN4/tqiT/gy44PIOeeWyTrKCbx/bNo+ax3RP4uyS/4mgJSZpZFp94ybCbIM0rBpGTYIclzUztfeuftMUnt0/RzWP771r5ucAH6ILI5e07dPPY/sXoeWyB29tw/IOBf5r+s5A0VyXZDlgD3F1Vr3WUhKTZZrLzRErSjJRkuyTXA/cBq4F/ZprmsXUOW0nb6D3AzT3LH6EbJbEE2EQXHELPKAngtFaPUaMkjgQ+0QJTSRoIg0hJc1JVPVJVB9JNG3Qw8IKxqrWfk5rH1jlsJfUrySLgaOCsthy6URIXtSrnAse078vbMm39stGjJKrqdmBklIQkDYRBpKQ5rarup0v8dShtHtu2aqx5bHEeW0nT7GPA+4BftuVdcZSEpFnGIFLSnJNkYZKd2venAq+iGzrmPLaShibJa4H7quqa3uIxqjpKQtKMZmIdSXPRHsC57R2hJwEXVtWXktyE89hKGp6XA69PchSwA7Aj3ZPJnZIsaE8bxxolsc5REpJmEoNISXNOVd0AvGSMcuexlTQ0VXUScBJAklcC/7mqjk3y13SjIFYx9iiJf6JnlESSi4HPJfko3fRDjpKQNFAGkZIkScP1fhwlIWkWmRdBpPM5SpKkmaSqrqBL+uUoCUmzjol1JEmSJEl9M4iUJEmSJPVtXgxnlSTNbNP52sEdpx49bfuWJGk+8kmkJEmSJKlvBpGSJEmSpL4ZREqSJEmS+mYQKUmSJEnqm0GkJEmSJKlvBpGSJEmSpL4ZREqSJEmS+mYQKWnOSbJ3kq8nuTnJjUne08p3SbI6ya3t586tPElOT7I2yQ1JDurZ18pW/9YkK4d1TpIkSTOFQaSkuWgz8HtV9QLgUOCEJAcAJwKXV9US4PK2DPAaYEn7HA+cAV3QCZwMHAIcDJw8EnhKkiTNVwaRkuacqlpfVde27z8Gbgb2ApYD57Zq5wLHtO/LgfOqcyWwU5I9gCOA1VW1sao2AauBIwd4KpIkSTOOQaSkOS3JYuAlwFXA7lW1HrpAE3h2q7YXcFfPZuta2Xjlo49xfJI1SdZs2LBhqk9BkiRpRjGIlDRnJXkG8DfAe6vqR1urOkZZbaV8y4KqM6tqaVUtXbhw4cQaK0mSNEsYREqak5I8mS6A/GxVfb4V39uGqdJ+3tfK1wF792y+CLhnK+WSJEnzlkGkpDknSYCzgZur6qM9qy4GRjKsrgS+2FP+tpal9VDggTbc9TLg8CQ7t4Q6h7cySZKkeWvCQaQp9CXNYC8H3gocluT69jkKOBV4dZJbgVe3ZYBLgduAtcBfAb8NUFUbgQ8BV7fPB1uZJEnSvLVgEtuOpNC/NskzgWuSrAbeTpdC/9QkJ9Kl0H8/W6bQP4Quhf4hPSn0l9K9a3RNkotbJkRJ2mZV9Y+M/T4jwLIx6hdwwjj7Ogc4Z+pa95jFJ14yHbuVJEmaVhN+EmkKfUmSJEmafybzJPJRW0uhn2RKUui34xxPNxE4++yzz1Q0fd6Zricfd5x69LTsV5IkSdLMMunEOoNKoQ+m0ZckSZKkYZtUEGkKfUmSpP6YlFDSXDGZ7Kym0JckSerfSFLCFwCHAickOYAuCeHlVbUEuLwtw5ZJCY+nS0pIT1LCQ4CDgZNHAk9JGoTJvBM5kkL/20mub2V/QJcy/8IkxwF3Am9s6y4FjqJLof8g8A7oUugnGUmhD6bQlyRJc1C7eT6SN+LHSXqTEr6yVTsXuIIus/2jSQmBK5OMJCV8JS0pIUDLjn8kcP7ATkbSvDbhIHK2pNCXJEmaaQaRlNCEhJKmy6QT60iSJKl/g0pKaEJCSdPFIFKSJGlATEooaS4wiJQkSRoAkxJKmismk1hHkiRJ/TMpoaQ5wSBSkiRpAExKKGmuMIiUNOckOQd4LXBfVb2ole0CXAAsBu4A3lRVm9rwso/T3e1/EHh7VV3btlkJ/FHb7Yer6txBnockae5afOIl07LfO049elr2K/XynUhJc9Gn6OZM6+Vk3pIkSVPAIFLSnFNV3wBGvx+0nG4Sb9rPY3rKz6vOlcDIZN5H0CbzrqpNwMhk3pIkSfOaQaSk+WKLybyBKZnMW5Ikab7xnUhJ892kJvMGSHI83VBY9tlnn6lrmaaE7x1JkjS1fBIpab6Ytsm8q+rMqlpaVUsXLlw45Q2XJEmaSQwiJc0XTuYtSZI0BRzOKmnOSXI+8EpgtyTr6LKsOpm3JEnSFDCIlDTnVNVbxlnlZN6SJEmT5HBWSZIkSVLfDCIlSZIkSX1zOKskSZIGYrqm3JE0WAaRkiRJ0hwxnYG68+NqhEGkpoQdliRJkjQ/+E6kJEmSJKlvBpGSJEmSpL4ZREqSJEmS+mYQKUmSJEnqm4l1JEmagOlKKGYyMUnSTOeTSEmSJElS33wSKUmSpC1M59RdkmY/g0jNeA4ZkyRJGj6vyTRixgSRSY4EPg5sB5xVVacOuUmSZN8kacayf9JcMZ1Pvg1Qp8eMCCKTbAf8JfBqYB1wdZKLq+qm4bZM0nxm36Rh8GJK/bB/kjRMMyKIBA4G1lbVbQBJVgHLATtCTRuHZKgP9k2aU+z35hT7J6kP9nvTY6YEkXsBd/UsrwMOGV0pyfHA8W3xJ0luGUDbAHYDfjCgY3n8WX78fGS4x58Ggzz+cwZ0nH7N9L5pphj239FhmG/nvNXznYZ+byboPeeZ1jdBH/1TH33TXP57PJfPDeb2+c2Kc5tEvzfV5zeU/mmmBJEZo6weV1B1JnDm9DdnS0nWVNXSQR/X43t8jz90M7pvminm49+R+XbO8+18YVac8xP2T0/UN82Cc5ywuXxuMLfPby6fG8yd85sp80SuA/buWV4E3DOktkjSCPsmSTOV/ZOkoZkpQeTVwJIk+yZ5CrACuHjIbZIk+yZJM5X9k6ShmRHDWatqc5J3ApfRpak+p6puHHKzeg17mJrH9/jz+fhDMwv6ppliPv4dmW/nPN/OF2b4OU9R/zSjz3GS5vK5wdw+v7l8bjBHzi9Vj3u9R5IkSZKkMc2U4aySJEmSpFnAIFKSJEmS1DeDyK1Ick6S+5J8ZwjH3jvJ15PcnOTGJO8Z8PF3SPLNJN9qx/+TQR6/px3bJbkuyZeGcOw7knw7yfVJ1gzh+DsluSjJd9vfg5cO8Nj7t/Me+fwoyXsHdXwN13j9T5JdkqxOcmv7uXMrT5LTk6xNckOSg3r2tbLVvzXJymGdUz9G9zctYclVre0XtOQlJNm+La9t6xf37OOkVn5LkiOGcyb9G6ufmcu/5yS/0/5OfyfJ+e3/ujn/ex5tmP+/DMJYv+dht2mixroWHe/f6Gw0zvn9v+3v5g1JvpBkp2G2caLGOreedf85SSXZbRhtmwoGkVv3KeDIIR17M/B7VfUC4FDghCQHDPD4DwGHVdWLgQOBI5McOsDjj3gPcPMQjjviN6rqwCHN5/Nx4CtV9XzgxQzwz6GqbmnnfSDwq8CDwBcGdXwN3Xj9z4nA5VW1BLi8LQO8BljSPscDZ0B3oQOcTDcB+sHAyTP8Ymd0f/MR4LR2vpuA41r5ccCmqtoPOK3Vo/0ZrQBeSPd/xyeSbDegtk/UWP3MnPw9J9kLeDewtKpeRJeMZgXz4/c82tD+f5luW/k9z1af4vHXouP9G52NPsXjz2818KKq+lfA/wFOGnSjpsinGCOOSLI38GrgzkE3aCoZRG5FVX0D2DikY6+vqmvb9x/TdfB7DfD4VVU/aYtPbp+BZmFKsgg4GjhrkMedCZLsCLwCOBugqh6uqvuH1JxlwD9X1feGdHwN2Fb6n+XAua3aucAx7fty4LzWb1wJ7JRkD+AIYHVVbayqTXQXBsO6MbdVo/ubJAEOAy5qVUaf78ifw0XAslZ/ObCqqh6qqtuBtXRB1Yy0lX5mzv6e6bLSPzXJAuBpwHrm+O95tBn2/8t0Gf17nrXzZ45zLTrev9FZZ6zzq6qvVtXmtngl3Ryos85W4ojTgPcx4Eg3Hp4AACAASURBVOvqqWYQOQu0ITQvAa4a8HG3S3I9cB/dBcJAjw98jO4f2S8HfNwRBXw1yTVJjh/wsZ8LbAA+2YbXnZXk6QNuw4gVwPlDOraGbFT/s3tVrYcu0ASe3artBdzVs9m6VjZe+Uw0ur/ZFbi/50Kmt+2Pnldb/0CrP5vOF8bvZ+bk77mq7gb+jO7u/3q639s1zP3f82gz6f+XKTfW77mqvjrcVk258f6NzkX/D/DlYTdiqiR5PXB3VX1r2G2ZLIPIGS7JM4C/Ad5bVT8a5LGr6pE2nHERcHCSFw3q2EleC9xXVdcM6phjeHlVHUQ3hOuEJK8Y4LEXAAcBZ1TVS4CfMoThKu3doNcDfz3oY2v4tqH/yRhltZXyGWWc/mZrbZ/V59tjW/uZWX3ebYjtcmBfYE/g6XT9+2hz7fc82oz4/2W6jPV7TvKbw22VJiLJH9K9XvHZYbdlKiR5GvCHwB8Puy1TwSByBkvyZLoLuM9W1eeH1Y42zOUKJjg8KV2SluuS/DjJxiQf7mOzlwOvT3IHsAo4LMlnJnL8iaqqe9rP++jeBxzkcKV1wLqep78X0f2nP2ivAa6tqnuHcGwN0Tj9z71t+CLt532tfB2wd8/mi+iGj41XPtM8rr+hezK5UxsOB1u2/dHzauufRTdk6QXA/0zy/TG2mYnG62fm6u/5VcDtVbWhqn4BfB54Gdv+e54t5zuemfL/y3QZ7/c8l4z5bzTJK5OsG6nUkgu9crydJPlyZmgirNau1wLH1tyZ1P55dDc3vtX+v1kEXJvkXwy1VRNkEDlDtfcuzgZurqqPDuH4C0eyYSV5Kl2n/N0J7u59wBVV9Uzg4n42qKqTqmpRVS2mG075taoa2J3EJE9P8syR78DhwMCy9FbV94G7kuzfipYBNw3q+D3egkNZ552t9D8XAyMXHCuBL/aUvy2dQ+mGj60HLgMOT7JzezpweCubaR6i66MW81h/cyzwdeANrc7o8x35c3gD8DW6i4GXAXcAz0myL10Cmm9ORQNHXxxOha30M3P193wncGiSp7W/4yPn2/fvuV3MXgysSJe9dUp/z4Mwg/5/mS5j/Z7nTOKgZrx/o1uoqhdW1RUAST4w+mZ8Vb2mqs4da9thSnIk8H7g9VX14LDbM1Wq6ttV9eyqWtz+v1kHHNT+Tc46C564yvyV5HzglcBu7T/vk6vq7AEd/uXAW4Fvt/cSAf6gqi4d0PH3AM5tGeeeBFxYVROdZuM5dHf3p0X7TyJVNZXvTu4OfKHbNQuAz1XVV6Zw//14F/DZNqT0NuAdgzx4G3bxauA/DPK4mhHG7H+AU4ELkxxHd6H2xrbuUuAougQjD9L+rlbVxiQfAq5u9T5YVUNJVjZB7wdWtdET19ESkbSfn06ylu7J1LF0/dwPgM/RXZBvBk6oqkege5LV897dTDJWP/Mk5uDvuaquSnIRcC3d7+c64EzgEvr7Pa9o+7kxyYWM8XueRYb6/8t02srveVYa61qU8fviWWec8zsJ2B5Y3a7Drqyq/zi0Rk7QkOOI6VdVfvxM24fuDv0jwM+Bn9BdYH24Z/2/p7sg2Uh3Z23PnnUvo7soeaD9fFnPuiuAU4D/BfwM2A94O91/hj8GbqcbAjH0PwM/fvxM34fu4vdve5bX0t30Glm+i26aoo+37z+iS6bya239kcDDwC9aH/WtVv4suiBiPXA38GFgu7bu7a3vOa31Xf/Y+qFftn18ClhM957cyEXeN9q2rwduBEZeE3hBT1vvAP4zcEPr9y4AdqB7d693/z/p7Sv9+PEzOz50w6A/T5fY6IfAX9DdtPkj4Ht0w1LPA57V6o/0IytbP/ID4A979vfU1t9sorup8ft0Q5VH1t9BN5JsvH7uCuC32vcJt8PP/Pw4nFXTqqoOA/4BeGdVPYOuEwMgyWHAfwXeRPfk83u0J5bp5h27BDidLhveR4FLkuzas/u30s1V9ky6Dvl04DXVDZt9GXA9kua6vwd+LcmT2rtBT6Z7kkqS5wLPoAvKrqYLJnehu5n110l2qG6EwZ8CF1TVM6qbGxe6tPmb6W5QvYRuiOZv9Rz3ELqbVs+me2L/GuCeto+399T7dbp3JY9I8it0w8PfCyyke7L3t+1p0Ig30V3w7Qv8K+DtVfXTUft/RrV3tiXNDm1k15fornUW02X0XUV3U+rtwG/QZc59Bl1w2evfAPvTDc394yQvaOUn071n9zy6qXbGfL9xK/1cr8m0Q/OQQaSG6VjgnKq6tqoeohu+8NJ0UwocDdxaVZ+uqs1VdT7dO5mv69n+U1V1Y3VDxDbT3aV/UZKnVjfP3Y0DPRtJA1dVI6MPDqQL2C4D7k7y/Lb8D1X1y6r6TFX9sPUn/51uqNT+Y+0zye50Qdt7q+qn1SXXOo0tJyy/p6r+vO3vZ1tp4gfaPn4GvBm4pKpWV5fw48/oniT0Jv04varuqW446N+285I0+x1Mly3291uf8POq+ke6a6GPVtVt1c3PfRLdO7e9r5z9SVX9rLppIb4FjASBbwJOqW6O1rvobqZP1GTaoXnIIFLDtCfdHTkAWqf1Q7q7c1usa77HlnNxPTpPV7tT/2bgPwLrk1zSLiIlzX1/T/feySva9yvoAshfb8sk+b0kNyd5IMn9dMNVdxtnf8+he6K5Psn9rf7/ZMu52O4ac8vH6603us/7ZVvf26/1Jlh4kO5pgKTZb2/ge/X4d6NHX+98jy4Xw+49ZeP1C3uyZR8z+rppW0ymHZqHDCI1TPfQXawBj2ZB3ZXu/aMt1jX7tHUjtkj5XFWXVdWr6YbGfhf4q2los6SZZySI/LX2/e/pCSKT/Bpdkpw3ATtX1U507xyOzPc3On38XXQZW3erqp3aZ8eqemFPnX5TzvfWG93nhe7C8u7RGz3BfiTNPncB+4x6sgePv97Zh250VT9Ta61ny+lm9tlK3SfqQybTDs1DBpEaps8B70hyYJLt6cbrX1VVd9C9K/QrSf5dkgVJ3gwcQPc+weMk2T3J61sg+hDdi+OzLVuepIn5e7r3eJ5aVevo3sM+ku6m1HV0701vpnt3ekGSPwZ27Nn+XmBxkicBVDdtxVeB/55kx/a+5fOS/Pok23khcHSSZenm4fw9uv7qf/ex7b3ArkmeNck2SBqOb9IFfae2acR2SPJyuvekfyfJvkmewWPvLvaTzflC4KQ2vc4iuqy749minxvDZNqhecggUkNTVZcD/4VuQvP1dC+Gj6RQ/yHdJLO/RzfE9X3Aa6vqB+Ps7kmt7j102RJ/Hfjt6Wy/pJmhqv4P3Y2jf2jLP6JLevO/qpt64TLgy8D/oRui9XO2HAL21+3nD5Nc276/DXgKXcbDTXQTsu8xyXbeAvwm8Od02Q1fB7yuqh7e6obdtt+lu8i7rQ2x3XMybZE0WK0veh1dsq476eYIfDNwDvBp4Bt0meV/ztaDwV5/Qten3U534+vTW6k7Vj/XazLt0DyUKkfISJIkSZL645NISZIkSVLfDCIlSZIkSX0ziJQkSZIk9c0gUpIkSZLUt9Fz1cwau+22Wy1evHjYzZA0ha655pofVNXCYbdjMuybpLnHvknSTDWs/mnWBpGLFy9mzZo1w26GpCmU5HvDbsNk2TdJc499k6SZalj9k8NZJUmSJEl9M4iUJEmSJPXNIFKSJEmS1DeDSEmSJElS3wwiJUmSJEl9m7XZWbfF4hMvmZb93nHq0dOyX0nzw3T1TWD/JEnzlf+3aBB8EilJkiRJ6ptBpCRJkiSpbwaRkiRJkqS+TSqITPI7SW5M8p0k5yfZIcm+Sa5KcmuSC5I8pdXdvi2vbesX9+znpFZ+S5IjJndKkiRJkqTpMuEgMslewLuBpVX1ImA7YAXwEeC0qloCbAKOa5scB2yqqv2A01o9khzQtnshcCTwiSTbTbRdkiRJkqTpM9nhrAuApyZZADwNWA8cBlzU1p8LHNO+L2/LtPXLkqSVr6qqh6rqdmAtcPAk2yVJkiRJmgYTDiKr6m7gz4A76YLHB4BrgPuranOrtg7Yq33fC7irbbu51d+1t3yMbbaQ5Pgka5Ks2bBhw0SbLkmSJEmaoMkMZ92Z7inivsCewNOB14xRtUY2GWfdeOWPL6w6s6qWVtXShQsXbnujJUmSJEmTMpnhrK8Cbq+qDVX1C+DzwMuAndrwVoBFwD3t+zpgb4C2/lnAxt7yMbaRJEmSJM0gkwki7wQOTfK09m7jMuAm4OvAG1qdlcAX2/eL2zJt/deqqlr5ipa9dV9gCfDNSbRLkiRpaJKck+S+JN/pKdslyeqWvX51G9FFOqe3LPU3JDmoZ5uVrf6tSVb2lP9qkm+3bU5v12GSNDCTeSfyKroEOdcC3277OhN4P/C7SdbSvfN4dtvkbGDXVv67wIltPzcCF9IFoF8BTqiqRybaLkmSpCH7FF3G+V4nApe37PWXt2XoXgVa0j7HA2dAF3QCJwOH0CUcPHkk8Gx1ju/ZbvSxJGlaTSo7a1WdXFXPr6oXVdVbW4bV26rq4Krar6reWFUPtbo/b8v7tfW39eznlKp6XlXtX1VfnuxJSZofxrnb/4Ekdye5vn2O6lk35py0SY5sZWuTnNhTPua8t5K0NVX1DbpXdnr1Zqkfnb3+vOpcSfda0B7AEcDqqtpYVZuA1cCRbd2OVfVPbUTXeT37kqSBmOwUH5I0TJ9i7Dvwp1XVge1zKYw/J22bl/Yv6Z4GHAC8pdWF8ee9laRttXtVrQdoP5/dysfLUr+18nVjlD+OWe0lTReDSEmz1jh3+8cz3py0BwNr2yiKh4FVwPL2jtF4895K0lTZ1uz1ZrWXNHQGkZLmone2BBXn9LxDtK13+3dl/HlvJWlb3duGotJ+3tfKx8tSv7XyRWOUS9LAGERKmmvOAJ4HHAisB/57K5+2u/0OGZPUh94s9aOz17+tZWk9FHigDXe9DDg8yc7tZtjhwGVt3Y+THNpGTLytZ1+SNBALnriKJM0eVXXvyPckfwV8qS1ubU7ascp/QJv3tj2NHPduf1WdSZedmqVLl44ZaEqaP5KcD7wS2C3JOrosq6cCFyY5jm6atDe26pcCR9ENsX8QeAdAVW1M8iHg6lbvg1U1Mnz/P9G9E/5U4MvtI0kDYxApaU5JssdI8grg3wIjmVsvBj6X5KPAnjw2J22AJW2e2rvpku/8u6qqJCPz3q5iyycHkjSuqnrLOKuWjVG3gBPG2c85wDljlK8BXjSZNkrSZBhESpq1xrnb/8okB9INPb0D+A/QzUmbZGRO2s30zEmb5J10Q8e2A85p89dCN+/tqiQfBq7jsXlvJUmS5i2DSEmz1jh3+8cN9KrqFOCUMcovpRtSNrr8NrrsrZIkSWpMrCNJkiRJ6ptBpCRJkiSpbwaRkiRJkqS+GURKkiRJkvpmEClJkiRJ6ptBpCRJkiSpbwaRkiRJkqS+GURKkiRJkvpmEClJkiRJ6ptBpCRJkiSpbwuG3QDNDYtPvGTa9n3HqUdP274lSZIkbRufREqSJEmS+mYQKUmSJEnqm0GkJEnSgCT5nSQ3JvlOkvOT7JBk3yRXJbk1yQVJntLqbt+W17b1i3v2c1IrvyXJEcM6H0nzk0GkJEnSACTZC3g3sLSqXgRsB6wAPgKcVlVLgE3AcW2T44BNVbUfcFqrR5ID2nYvBI4EPpFku0Gei6T5bVJBZJKdklyU5LtJbk7y0iS7JFnd7qatTrJzq5skp7e7ZjckOahnPytb/VuTrJzsSUmSJM1QC4CnJlkAPA1YDxwGXNTWnwsc074vb8u09cuSpJWvqqqHqup2YC1w8IDaL0mTfhL5ceArVfV84MXAzcCJwOXtbtrlbRngNcCS9jkeOAMgyS7AycAhdB3gySOBpyRJ0lxRVXcDfwbcSRc8PgBcA9xfVZtbtXXAXu37XsBdbdvNrf6uveVjbPOoJMcnWZNkzYYNG6b+hCTNWxMOIpPsCLwCOBugqh6uqvvZ8q7Z6Ltp51XnSmCnJHsARwCrq2pjVW0CVtMNzZAkSZoz2k3y5cC+wJ7A0+luso9WI5uMs2688i0Lqs6sqqVVtXThwoUTa7QkjWEyTyKfC2wAPpnkuiRnJXk6sHtVrQdoP5/d6o9316yvu2ngHTVJkjSrvQq4vao2VNUvgM8DL6O7sT4yd/ci4J72fR2wN0Bb/yxgY2/5GNtI0rSbTBC5ADgIOKOqXgL8lMeGro5lUnfTwDtqkiRpVrsTODTJ09q7jcuAm4CvA29odVYCX2zfL27LtPVfq6pq5Sta9tZ96V4V+uaAzkGSJhVErgPWVdVVbfkiuqDy3jZMlfbzvp76Y901826aJEma89o100XAtcC36a7DzgTeD/xukrV07zye3TY5G9i1lf8u7WZ9Vd0IXEgXgH4FOKGqHhngqUia5xY8cZWxVdX3k9yVZP+quoXH7qbdRHfX7FQefzftnUlW0SXReaCq1ie5DPjTnmQ6hwMnTbRdkiRJM1VVnUyXULDXbYyRXbWqfg68cZz9nAKcMuUNlKQ+TDiIbN4FfLZNinsb8A66u2oXJjmObtjGSOd3KXAUXRrqB1tdqmpjkg8BV7d6H6yqjZNslyRJkiRpGkwqiKyq64GlY6xaNkbdAk4YZz/nAOdMpi2SJEmSpOk32XkiJUmSJEnziEGkJEmSJKlvBpGSJEmSpL4ZREqSJEmS+mYQKUmSJEnqm0GkJEmSJKlvBpGSJEmSpL4ZREqatZKck+S+JN/pKdslyeokt7afO7fyJDk9ydokNyQ5qGebla3+rUlW9pT/apJvt21OT5LBnqEkSdLMYxApaTb7FHDkqLITgcuraglweVsGeA2wpH2OB86ALugETgYOAQ4GTh4JPFud43u2G30sSZKkeccgUtKsVVXfADaOKl4OnNu+nwsc01N+XnWuBHZKsgdwBLC6qjZW1SZgNXBkW7djVf1TVRVwXs++JEmS5i2DSElzze5VtR6g/Xx2K98LuKun3rpWtrXydWOUP06S45OsSbJmw4YNU3ISkiRJM5VBpKT5Yqz3GWsC5Y8vrDqzqpZW1dKFCxdOoomSJEkzn0GkpLnm3jYUlfbzvla+Dti7p94i4J4nKF80RrkkSdK8ZhApaa65GBjJsLoS+GJP+dtaltZDgQfacNfLgMOT7NwS6hwOXNbW/TjJoS0r69t69iVJE5JkpyQXJflukpuTvHQqs0pL0iAYREqatZKcD/wTsH+SdUmOA04FXp3kVuDVbRngUuA2YC3wV8BvA1TVRuBDwNXt88FWBvCfgLPaNv8MfHkQ5yVpTvs48JWqej7wYuBmpjartCRNuwXDboAkTVRVvWWcVcvGqFvACePs5xzgnDHK1wAvmkwbJWlEkh2BVwBvB6iqh4GHkywHXtmqnQtcAbyfnqzSwJXtKeYere7qkRteSVbTTUF0/qDORdL85pNISZKkwXgusAH4ZJLrkpyV5OlMXVbpLZg5WtJ0MYiUJEkajAXAQcAZVfUS4Kc8NnR1LJPKHm3maEnTxSBSkiRpMNYB66rqqrZ8EV1QOVVZpSVpIAwiJUmSBqCqvg/clWT/VrQMuIkpyio9qPOQJBPrSJIkDc67gM8meQpdxuh30N3Uv7BlmL4TeGOreylwFF2G6AdbXapqY5KRrNKwZVZpSZp2BpGSJEkDUlXXA0vHWDUlWaUlaRAmPZw1yXYtw9iX2vK+Sa5qk99e0O60kWT7try2rV/cs4+TWvktSY6YbJskSZIkSdNjKt6JfA/dRLkjPgKc1ibM3QQc18qPAzZV1X7Aaa0eSQ4AVgAvpJvj6BNJtpuCdkmSJEmSptikgsgki4CjgbPacoDD6LKNQTdh7jHt+/K2TFu/rNVfDqyqqoeq6na6cf8HT6ZdkiRJkqTpMdknkR8D3gf8si3vCtxfVZvbcu/kt49OjNvWP9Dq9zVhLjhpriRJkiQN24SDyCSvBe6rqmt6i8eoWk+wrq8Jc8FJcyVJkiRp2CaTnfXlwOuTHAXsAOxI92RypyQL2tPG3slvRybGXZdkAfAsYCNOmCtJkiRJs8aEn0RW1UlVtaiqFtMlxvlaVR0LfB14Q6s2esLckYl039DqVytf0bK37gssAb450XZJkiRJkqbPdMwT+X5gVZIPA9cBZ7fys4FPJ1lL9wRyBUBV3ZjkQuAmYDNwQlU9Mg3tkiRJkiRN0pQEkVV1BXBF+34bY2RXraqfA28cZ/tTgFOmoi2SJEmSpOkzFfNESpIkSZLmCYNISZIkSVLfDCIlSZIkSX2bjsQ6kiRJktSXxSdeMm37vuPUo6dt3/OZTyIlSZIkSX0ziJQkSZIk9c0gUpIkaYCSbJfkuiRfasv7Jrkqya1JLkjylFa+fVte29Yv7tnHSa38liRHDOdMJM1XBpGSJEmD9R7g5p7ljwCnVdUSYBNwXCs/DthUVfsBp7V6JDkAWAG8EDgS+ESS7QbUdkkyiJQkSRqUJIuAo4Gz2nKAw4CLWpVzgWPa9+VtmbZ+Wau/HFhVVQ9V1e3/P3v3Hm9VXed//PUOvJQ3UNEUMGwkR2sS7Yza2JSpIV5GuqhppmgWNaMzOVmJ1vw0L4VNmTqVDSWJVyQvySippDJOpQgoXhANVBSCBAVRMy3w8/vj+92yOOxzzj6XfTn7vJ+Px36ctb7ru9f+rHX2+Z71Xet7ARYCe9fmCMzMXIk0MzMzq6WLga8Db+b1bYCXImJNXl8CDM7Lg4HFAHn76pz/rfQy73mLpLGSZkuavWLFip4+DjPrw1yJNDMzM6sBSYcDyyNiTjG5TNboYFt771mXEDEhIloiomXQoEGdjtfMrC2eJ9LMzMysNvYDjpB0KLApsCXpyeQASf3z08YhwNKcfwkwFFgiqT+wFbCykF5SfI+ZWdX5SaSZmZlZDUTEmRExJCKGkQbGuTsijgPuAY7M2cYAt+TlqXmdvP3uiIicfkwevXVnYDjwQI0Ow8zMTyLNzMzM6uwMYLKk84GHgMtz+uXAVZIWkp5AHgMQEfMkTQEeB9YAp0TE2tqHbWZ9lSuRZmZmZjUWETOAGXn5acqMrhoRrwNHtfH+C4ALqhehmVnb3JzVzJqSpEWSHpU0V9LsnLa1pOl5Qu/pkgbmdEm6NE/c/YikvQr7GZPzL5A0pq3PMzMzM+srXIk0s2b20YgYEREteX0ccFee0PuuvA5wCKlP0XBgLHAZpEoncDawD+kpwdmliqeZmZlZX+VKpJn1JcWJu1tP6H1lJPeTRkrcATgYmB4RKyNiFTAdGFXroM3MzMwaiSuRZtasArhT0hxJY3Pa9hGxDCD/3C6ntzVxtyf0NjMzM2vFA+uYWbPaLyKWStoOmC7piXbydntCb2ACQEtLywbbzczMzJqJn0SaWVOKiKX553LgZlKfxudzM1Xyz+U5e1sTd3tCbzMzM7NWXIk0s6YjaTNJW5SWgZHAY6w/cXfrCb1PyKO07guszs1d7wBGShqYB9QZmdPMzMzM+iw3ZzWzZrQ9cLMkSOXctRFxu6RZwBRJJwPPsW7+tWnAocBC4DXgJICIWCnpPGBWznduRKys3WGYmZmZNZ4uVyIlDQWuBN4JvAlMiIhL8pD41wPDgEXA0RGxSulq7hLShdprwIkR8WDe1xjgm3nX50fEJMzMuihP3L1HmfQXgQPLpAdwShv7mghM7OkYzczMzHqr7jRnXQOcHhG7AfsCp0jaHc/DZmZmZmZm1rS6XImMiGWlJ4kR8QownzT0vedhMzMzMzMza1I9MrCOpGHAnsBMqjQPW/4cz8VmZmZmZmZWR92uREraHLgROC0iXm4va5m0iudhgzQXW0S0RETLoEGDOh+smZmZmZmZdUu3KpGSNiJVIK+JiJtysudhMzMzMzMza1JdrkTm0VYvB+ZHxEWFTZ6HzczMzMzMrEl150nkfsDxwAGS5ubXocB44GOSFgAfy+uQ5mF7mjQP20+Bf4E0DxtQmodtFp6HzczMzJqQpKGS7pE0X9I8SV/O6VtLmi5pQf45MKdL0qWSFkp6RNJehX2NyfkX5KnSzMxqpsvzREbEbyjfnxE8D5uZmZlZa6Xp0R6UtAUwR9J04ETS9GjjJY0jTY92ButPj7YPaXq0fQrTo7WQxpGYI2lqHuXezKzqemR0VjMzMzNrn6dHM7Nm4UqkmZmZWY3Vano0M7Nq6HJzVquuYeNuq8p+F40/rCr7NTMzs8q0nh4tjVVYPmuZtIqnR5M0FhgLsNNOO3UtWDOzMvwk0szMzKxGajk9mufXNrNqcSXSzMzMrAY8PZqZNQs3ZzUzMzOrjdL0aI9KmpvTziJNhzZF0snAc8BReds04FDS9GivASdBmh5NUml6NPD0aGZWY65EmpmZmdWAp0czs2bh5qxmZmZmZmZWMVcizczMzMzMrGKuRJqZmZmZmVnF3CfSzMzMzMysE/r6nO5+EmlmZmZmZmYVcyXSzMzMzMzMKuZKpJmZmZmZmVXMlUgzMzMzMzOrmCuRZmZmZmZmVjFXIs3MzMzMzKxirkSamZmZmZlZxTxPpFkv0tfnJDIzMzOz+vOTSDMzMzMzM6uYK5FmZmZmZmZWMVcizczMzMzMrGINU4mUNErSk5IWShpX73jMzMBlk5k1LpdPZlYvDVGJlNQP+BFwCLA7cKyk3esblZn1dS6bzKxRuXwys3pqlNFZ9wYWRsTTAJImA6OBx+salTU1j3RqFXDZZGaNyuVTQbX+p4P/r5uV0yiVyMHA4sL6EmCfOsViZlbissmsznzDr01VLZ983s2sPY1SiVSZtNggkzQWGJtXX5X0ZIX73xZ4oYuxtUkX9vQe19PbYq5KvND7Yvb3YgOdifld1QykC3pl2QS97++mynpbzL0tXuiFMevCXl02QQXlUzfKJuid/286o+Lja6CYK9Ub/7d0RqeOr0FirlgnyyaoU/nUKJXIJcDQwvoQYGnrTBExAZjQ2Z1Lmh0RLV0Pr/Z6W8y9LV5wzLXSJ4ouIwAAIABJREFUG2MucNnUimOuvt4WLzjmOumwfOpq2QRNcX7a1czH18zHBj6+RtEQA+sAs4DhknaWtDFwDDC1zjGZmblsMrNG5fLJzOqmIZ5ERsQaSacCdwD9gIkRMa/OYZlZH+eyycwalcsnM6unhqhEAkTENGBalXbfpaYcddbbYu5t8YJjrpXeGPNbXDZtwDFXX2+LFxxzXbh86pZmPr5mPjbw8TUERWwwRoSZmZmZmZlZWY3SJ9LMzMzMzMx6gaapREqaKGm5pMfa2C5Jl0paKOkRSXvVOsYyMXUU8/6SVkuam1//r9YxtopnqKR7JM2XNE/Sl8vkaajzXGHMjXaeN5X0gKSHc8zfKpNnE0nX5/M8U9Kw2kf6ViyVxHuipBWFc/z5esRaL5JGSXoy/77GldneML/PQkwdxfwVSY/nv/O7JNV1CoSO4i3kO1JSSKr7yHeVxCzp6Hye50m6ttYxlomno+/FTrnMfSh/Nw6tR5yFeHrdtUG9dXTOerNKrgl6s0r+HzcDSf1yGXNrvWPpaZIWSXo0XyvNrnc87YqIpngBHwb2Ah5rY/uhwK9I8yrtC8zsBTHvD9xa7zgL8ewA7JWXtwB+D+zeyOe5wpgb7TwL2DwvbwTMBPZtledfgJ/k5WOA6xs83hOBH9b73Nbp/PQDngLeDWwMPFzmO9gwv89OxPxR4B15+Z/r/B3sMN6cbwvgXuB+oKUXnOPhwEPAwLy+XS+IeQLwz3l5d2BRnWPuddcG9X51dM5686uSa4Le/Krk/3EzvICvANc20rVbDx7bImDbesdRyatpnkRGxL3AynayjAaujOR+YICkHWoTXXkVxNxQImJZRDyYl18B5gODW2VrqPNcYcwNJZ+7V/PqRvnVuvPyaGBSXr4BOFBSuYmnq67CePuyvYGFEfF0RPwFmEz6/RU1zO8z6zDmiLgnIl7Lq/eT5qirl0rOMcB5wHeB12sZXBsqifkLwI8iYhVARCyvcYytVRJzAFvm5a0oM69qLfXGa4N6623XJp3RG68JOqMv/D+WNAQ4DPhZvWPp65qmElmBwcDiwvoSekfB8cHcLOFXkt5b72BKcnO7PUl3uYoa9jy3EzM02HnOTTXmAsuB6RHR5nmOiDXAamCb2ka5TgXxAnwqNxe7QdLQMtubVSV/Ew31+6Tzf8cnk57m1EuH8UraExgaEY3S/KmSc/we4D2SfivpfkmjahZdeZXEfA7wWUlLSKOG/mttQuuyhv2fZdXVwTVBr1Xh/+Pe7GLg68Cb9Q6kSgK4U9IcSWPrHUx7+lIlstxd/Ua/O/Mg8K6I2AP4L+CXdY4HAEmbAzcCp0XEy603l3lL3c9zBzE33HmOiLURMYL0dGdvSe9rlaWhznMF8f4PMCwi3g/8mnVP3fqCSn5XDfX7pBPxSPos0AL8Z1Ujal+78Up6G/AD4PSaRdSxSs5xf1KT1v2BY4GfSRpQ5bjaU0nMxwJXRMQQUlPRq/L5b1SN9rdnNdDBNUGvVsH/415L0uHA8oiYU+9Yqmi/iNgLOAQ4RdKH6x1QWxq5YO9pS4Di048h1LmZTUci4uVSs4RIc0FtJGnbesYkaSNSwXtNRNxUJkvDneeOYm7E81wSES8BM4DWTyDeOs+S+pOajdW9+VFb8UbEixHxRl79KfCBGodWT5X8TTTa77Oiv2NJBwHfAI4o/H7roaN4twDeB8yQtIjU921qnQfXqfR7cUtE/DUingGeJFUq66WSmE8GpgBExH3ApkBDlKdtaLj/WVZdFVzHNIV2rh96s/2AI3I5Phk4QNLV9Q2pZ0XE0vxzOXAzqRtBQ+pLlcipwAl5JLZ9gdURsazeQbVH0jtL/aIk7U36fb1Yx3gEXA7Mj4iL2shW0/Ms6Zz2CpBKYm7A8zyo9LRB0tuBg4AnWmWbCozJy0cCd0dEXe6eVxJvqz5GR5D6ofQVs4DhknaWtDFp4JyprfI0zO8z6zDm3Dz0v0kVyHr31SvGO4nU9/EzpY0RsToito2IYRExjPT97BcR9Rz5rpLvxS9JAxiRb2y9B3ha0gzVZ4TjSmJ+DjgQQNJupErkippG2Tm97trAuq7C65heq8Lrh14rIs6MiCG5HD+G9L/ys3UOq8dI2kzSFqVlYCTQsKMk9693AD1F0nWkJj/b5r4YZ5M6FBMRPyH1zTgUWAi8BpxUn0jXqSDmI4F/lrQG+DNwTJ0vLPcDjgceze3tAc4CdoKGPc+VxNxo53kHYJKkfqQK7ZSIuFXSucDsiJhK+id4laSFpCdWx9Qv3Iri/TdJRwBrSPGeWLdoaywi1kg6FbiDNLrlxIiY18C/z0pj/k9gc+AX+R7McxFxRJ3jvZf0fTw/Is5pFW+PkxTA8IhY2Nn3VniO7wBGSnocWAt8LSJeVCfGXMp37D8fEb/ubIxdjPl04KeS/p3ULPTEepanvfHaoN7KnbOIuLy+UfWYstcEuRVSMyj7/7iaHyhpBnB1RGww0I1Sv9NngI1yf39r3/bAzbmM7w9cGxG31zektqm+18pm60jq39lCRtI5wC7NdCfKrC/LTwoUEZ0eNEGpf+aXIuJDHeTbn3TR060RZbtTiezm586gjYu2MnkX0UOVSDOz1lyJ7Lv6UnNWqxNJeylNCvuKpF8oTap+vqT9JS2RdIakPwI/lzRQ0q1KE9OvystDCvvaWdL/5n1Np1VfG0n7SvqdpJeURlvdv8aHa2ZlSPqapBtbpf2XpItz88wLJP2W9DTo3e3sZ0dJUyWtVJog/gs5/WTSkO8flPSqOjHJtqRxkp7K5crjkj5R2LZLLnNWS3pB0vU5/d6c5eH8eZ9uZ/8nSvpNq7SQtEtevkLSTyRNzzH8r6R3FfJ+TNITOYYfUhgMRtLfSLpb0os5vmu0rjnbVaRWF/+TY/x6Tnc5aWZm3eJKpFWVUr+Zm4ErgK2B64BPFLK8M6e/CxhL+k7+PK/vRGpe+sNC/muBOaTK43ms60eGpMHAbcD5eZ9fBW6UNKjnj8zMOulqYFShgtMf+DRwVd5+PKkM2AJ4tp39XEcaDGVHUlP0b0s6MDe3+xJwX0RsHhFndyK2p4B/JA1o9C3gaq3rx3secCcwkDToyn8BRERpxLw98udd34nPK+e4/FnbAnOBa+CtvpA3At/M254iNckrEfAd0vnYjTRIzDk5xuNJfRT/Kcf4XZeTZtWTb4r/Id8MelLSgZLeVrhR9aKkKZK2LrznF5L+mG8S3avCNGOSDs03tl7J+/1qYdsX8o20lfnG2o6FbSHpS5IW5BvyP5Labwff1g2zvO0fJM3K22ZJ+oc29tFP0vfy+58mzedoTcqVSKu2fUntui/NIwzeBDxQ2P4mqb/FGxHx5zyK540R8VqkiYAvAD4CIGkn4O+B/8j57yVNHVHyWWBaREyLiDcjYjowm9TfxczqKA9Wci9wVE4aBbxQGKr9ioiYFxFrIuKv5fahNL/oh4AzIuL1iJhLevp4fDdj+0VELM3lxvXAAtaNiPdX0k2tHfNn/qbNHXXPbRFxbx7l9hukJ6pDSeXX4xFxQz4vFwN/LMS+MCKm5zJxBXARucxsg8tJsyqQtCtwKvD3EbEFcDCwCPg34OOkv8sdgVXAjwpv/RVp1OXtSFOOXVPYdjnwxby/9wF35886gHTz6GhSP8hnSaOVFh1OumbaI+c7uINDKHvDLFd4bwMuJc1ffBFwm6Rycxl/IX/unqSpn47s4DOtF3Ml0qptR+APrQZWKE7svCIiXi+tSHqHpP+W9Kykl0kXnQOUOonvCKyKiD8V3l98YvEu4KjcROslSS+RLjiLI4OaWf1MIlViyD+vKmxbvGH2DewIrMw3mEqepZuTw0s6QdLcQrnxPtY1lf866WnfA5LmSfpcdz6rHW8df6Qph1aSjnfHVtuiuC5pO0mT81OKl0lPfNubUsPlpFl1rAU2AXaXtFFELIqIp4AvAt+IiCX5JtE5wJG5NQYRMTEiXils20PSVnmff8372zIiVkXEgzn9ONLAVg/m951JuvE0rBDP+Ih4KSKeA+4BRnQQf1s3zA4DFkTEVfkm33WkEV//qcw+jgYujojFEbGSVNG1JuVKpFXbMmBwq2YUxTm5Wo/sdDqwK7BPRGwJlJqMKe9roNKwxyU7FZYXA1dFxIDCa7OIGN8jR2Jm3fVL4P1Kk18fzvp33CsZ5W0psLXyEOjZTsAfuhpQ7nv4U9IThG0iYgBpSHUBRMQfI+ILEbEj6WLwx6W+jJ3wJ+Adhc98Z5k8QwvbNyc1NV1KKveK28T6Zeh3SOfu/bnM/CyFPpNseF5dTppVQR5g6zRSRXB5vrmzI6lidnPhps18UoVz+9z8c3xu6voy6cklrLsR9ClSK4Fnc1PTD+b0HSncRM83nl5k/Rtqfywsv0YaTbs9bd0wW++zsrZu3q1306vM+6yJuBJp1XYfqbA8VVJ/SaNpf+LULUj9IF/KTSje6tcUEc+Sml19S9LGkj7E+nfCrgb+SdLBuWDeVGnwnm6NwGhmPSO3OriB1Lf5gXyHvDPvXwz8DvhO/vt+P2ly+2vaf2e7NiNVtFYASDqJ9CSSvH5UoQxZlfOuzevP084gQAUPA++VNELSpuQ+i60cKulDSv3IzwNm5uO9Lb/3k/nJxb+R+pKXbAG8SiozBwNfa7Xf1jG6nDSrkoi4No8O/S5SWXEhqVJ1SKsbN5tGxB9I89mOJs3nuBUwLO+qdBNrVkSMJjV1/SUwJW9fmj8jZU4317ehGzfU2rlhtt5nZW3dvFvvphfr3+i3JuNKpFVVRPwF+CTpQu8l0l3yW4E32njLxcDbgReA+4HW8+N8BtiH1NTrbODKwmctJhXGZ5EuCBeTLqj8PTdrHJOAv2P9pqydcSzpQmspadCus3O/vi6JiMeB75NueD2fY/ttIcvfAzMlvUqamP7LEfFM3nYOaU62lyQd3c5n/B44F/g1qb9luX6V15LKtJXAB0jN1YiIF0j9SMeTnjQMbxXft4C9gNWkCudNrfb7HeCbOcavupw0qw5Ju0o6QNImwOukG+JrgZ8AF+RWD0galG+oQ7oJ9Abpb/sdwLcL+9tY0nGStsr9oV9m3Q2sa4GT8o2pTfL7ZkbEom7E39YNs2nAeyR9Jj8M+DSwO+larrUppHmhh0gaCIzrajzW+DxPpNWcpJnATyLi5/WOxcxqKw+Q9QTwzoh4ud7xNAJJVwBLIuKb9Y7FzLomt4z4GWmU5L+SWk2MJTUrPY30dG9HYDlwfUSclZuuXwMcQLqB9B+kG23DSSMrTyXdOO8HPAn8e6mvoqQvkW4ADcyf9aWIWJK3rTeHbSVljKTvkm5ebUW6oXZhREzI2z4EXALsAiwk3UwrxTGDPE9kbi3xn8AJpErv90gj7HueyCbkSqRVnaSPkAq/F0gF1E+Ad+fRGs2sj5D0NtLIfltGRLUGqOl1XIk0M7Pexs1XrBZ2JfUJWk0aOOdIVyDN+pbcZ+dl4GMU+jq3kffVNl7/2InPO6uNffyqm4fSEJ9nZmZWT34SaWZmZmbW5CT9hHXTLBVdHRFfqnU81ru5EmlmZmZmZmYV61/vALpq2223jWHDhtU7DDPrQXPmzHkhIgbVO47ucNlk1nxcNplZo6pX+dRrK5HDhg1j9uzZ9Q7DzHqQpF4/MbHLJrPm47LJzBpVvconD6xjZmZmZmZmFXMl0szMzMzMzCrmSqSZmZmZmZlVzJVIM2s6kjaV9ICkhyXNk/StnL6zpJmSFki6XtLGOX2TvL4wbx9W2NeZOf1JSQfX54jMzMzMGocrkWbWjN4ADoiIPYARwChJ+wIXAj+IiOHAKuDknP9kYFVE7AL8IOdD0u7AMcB7gVHAjyX1q+mRmJmZmTWYXjs6qzWWYeNuq9q+F40/rGr7tuYUaQLcV/PqRvkVwAHAZ3L6JOAc4DJgdF4GuAH4oSTl9MkR8QbwjKSFwN7AfdU/CjOD6v1/qdf/FkmLgFeAtcCaiGiRtDVwPTAMWAQcHRGrcjl0CXAo8BpwYkQ8mPczBvhm3u35ETGpJ+NstvNuZj3LTyLNrClJ6idpLrAcmA48BbwUEWtyliXA4Lw8GFgMkLevBrYpppd5j5lZV300IkZEREteHwfclVtJ3JXXAQ4BhufXWNJNL3Kl82xgH9KNrbMlDaxh/GbWx7kSaWZNKSLWRsQIYAjpImu3ctnyT7Wxra309UgaK2m2pNkrVqzoashm1neNJrWOIP/8eCH9ykjuBwZI2gE4GJgeESsjYhXpRtmoWgdtZn2XK5Fm1tQi4iVgBrAv6QKs1Ix/CLA0Ly8BhgLk7VsBK4vpZd5T/IwJEdESES2DBg2qxmGYWfMI4E5JcySNzWnbR8QygPxzu5zeVmsIt5Iws7pyn0gzazqSBgF/jYiXJL0dOIg0WM49wJHAZGAMcEt+y9S8fl/efndEhKSpwLWSLgJ2JDUpe6CmB2NmzWa/iFgqaTtguqQn2snb7VYSpGaw7LTTTl2J1XD/ULNy/CTSzJrRDsA9kh4BZpGafd0KnAF8JQ+Qsw1wec5/ObBNTv8KuT9SRMwDpgCPA7cDp0TE2poeiZk1lYhYmn8uB24mNbd/PjdTJf9cnrO31RrCrSTMrK78JNLMmk5EPALsWSb9adIFW+v014Gj2tjXBcAFPR2jmfU9kjYD3hYRr+TlkcC5rGsNMZ4NW0mcKmkyaRCd1RGxTNIdwLcLg+mMBM6s4aGYWR/nSqSZmZlZbWwP3Jxm7qA/cG1E3C5pFjBF0snAc6y7qTWNNL3HQtIUHycBRMRKSeeRWloAnBsRK2t3GGbW17kSaWZmZlYDuTXEHmXSXwQOLJMewClt7GsiMLGnYzQzq4T7RJqZmZmZmVnFXIk0MzMzMzOzirkSaWZmZmZmZhVzJdLMzMzMzMwq5kqkmZmZmZmZVcyVSDMzMzMzM6uYK5FmZmZmZmZWMVcizczMzMzMrGLdqkRKGiDpBklPSJov6YOStpY0XdKC/HNgzitJl0paKOkRSXsV9jMm518gaUx3D8rMzMzMzMyqo7tPIi8Bbo+IvwX2AOYD44C7ImI4cFdeBzgEGJ5fY4HLACRtDZwN7APsDZxdqniamZmZmZlZY+lyJVLSlsCHgcsBIuIvEfESMBqYlLNNAj6el0cDV0ZyPzBA0g7AwcD0iFgZEauA6cCorsZlZmZmZmZm1dOdJ5HvBlYAP5f0kKSfSdoM2D4ilgHkn9vl/IOBxYX3L8lpbaWbmXWapKGS7slN7OdJ+nJOP0fSHyTNza9DC+85Mze1f1LSwYX0UTltoaRx5T7PzMzMrK/p38337gX8a0TMlHQJ65qulqMyadFO+oY7kMaSmsKy0047dS5aM+sr1gCnR8SDkrYA5kianrf9ICK+V8wsaXfgGOC9wI7AryW9J2/+EfAx0s2tWZKmRsTjNTkKMzMzswbVnSeRS4AlETEzr99AqlQ+n5upkn8uL+QfWnj/EGBpO+kbiIgJEdESES2DBg3qRuhm1qwiYllEPJiXXyH11W6vdcNoYHJEvBERzwALSf2z9wYWRsTTEfEXYHLOa2ZmZtandbkSGRF/BBZL2jUnHQg8DkwFSiOsjgFuyctTgRPyKK37Aqtzc9c7gJGSBuYBdUbmNDOzbpE0DNgTKN3sOjWPDj2xMICXm9qbmZmZdUJ3mrMC/CtwjaSNgaeBk0gV0ymSTgaeA47KeacBh5Lu8r+W8xIRKyWdB8zK+c6NiJXdjMvM+jhJmwM3AqdFxMuSLgPOIzWXPw/4PvA52m5SX+4mm5vam5mZWZ/XrUpkRMwFWspsOrBM3gBOaWM/E4GJ3YnFzKxE0kakCuQ1EXETQEQ8X9j+U+DWvNpek/qKm9oDEwBaWlrKVjTNzMzMmkV354k0M2sokkSaemh+RFxUSN+hkO0TwGN5eSpwjKRNJO1Mmsv2AVLriOGSds6tLY7Jec3MukVSvzyy/a15fWdJMyUtkHR9LnPI5dL1eYTombmJfmkfZUeVNjOrhe42ZzUzazT7AccDj0qam9POAo6VNILUJHUR8EWAiJgnaQqpT/ca4JSIWAsg6VRSH+1+wMSImFfLAzHrLYaNu63eIfQ2XyYN+rVlXr+QNHr0ZEk/AU4GLss/V0XELpKOyfk+3dao0qWyy8ys2lyJNLOmEhG/oXw/x2ntvOcC4IIy6dPae5+ZWWdJGgIcRipzvpJbTxwAfCZnmQScQ6pEjs7LkEbB/2HO/9ao0sAzkkqjSt9Xo8Mwsz7OzVnNzMzMaudi4OvAm3l9G+CliFiT14sjQb81SnTevjrn9+jRZlZXfhJpZmZmVgOSDgeWR8QcSfuXkstkjQ62tfee4ud55GizKqlWM/5F4w+ryn57mp9EmpmZmdXGfsARkhYBk0nNWC8GBkgq3dgvjgT91ujReftWwEraH1X6LRExISJaIqJl0KBBPX80ZtZn+UmkmZlZF/T1u9DWeRFxJnAmQH4S+dWIOE7SL4AjSRXLMcAt+S1T8/p9efvdERGSpgLXSrqINLBOaVRpM7OacCXSzMzMrL7OACZLOh94iDRNEfnnVXngnJWkEVnbHVXazKwWXIk0MzMzq7GImAHMyMtPk0ZXbZ3ndeCoNt5fdlRpM7NacJ9IMzMzMzMzq5ifRJqZmTWQavW1BPe3NDOznuEnkWZmZmZmZlYxP4k0MzPrI6r5lNPMzPoOP4k0MzMzMzOzirkSaWZmZmZmZhVzJdLMzMzMzMwq5j6R1vCq1YfHoxQ2L0lDgSuBdwJvAhMi4hJJWwPXA8OARcDREbFKkoBLgEOB14ATI+LBvK8xwDfzrs+PiEm1PBYzMzOzRuMnkWbWjNYAp0fEbsC+wCmSdgfGAXdFxHDgrrwOcAgwPL/GApcB5Ern2cA+pInAz5Y0sJYHYmZmZtZoXIk0s6YTEctKTxIj4hVgPjAYGA2UniROAj6el0cDV0ZyPzBA0g7AwcD0iFgZEauA6cCoGh6KmZmZWcNxc1Yza2qShgF7AjOB7SNiGaSKpqTtcrbBwOLC25bktLbSrRfxtBZmZmY9y08izaxpSdocuBE4LSJebi9rmbRoJ73154yVNFvS7BUrVnQtWDMzM7NewpVIM2tKkjYiVSCviYibcvLzuZkq+efynL4EGFp4+xBgaTvp64mICRHREhEtgwYN6tkDMTMzM2swrkSaWdPJo61eDsyPiIsKm6YCY/LyGOCWQvoJSvYFVudmr3cAIyUNzAPqjMxpZmZmZn2W+0SaWTPaDzgeeFTS3Jx2FjAemCLpZOA54Ki8bRppeo+FpCk+TgKIiJWSzgNm5XznRsTK2hyCmZmZWWNyJdLMmk5E/Iby/RkBDiyTP4BT2tjXRGBiz0VnZmZm1ru5OauZmZmZmZlVzJVIMzMzsxqQtKmkByQ9LGmepG/l9J0lzZS0QNL1kjbO6Zvk9YV5+7DCvs7M6U9KOrg+R2RmfZUrkWZmZma18QZwQETsAYwARuXBvC4EfhARw4FVwMk5/8nAqojYBfhBzoek3YFjgPcCo4AfS+pX0yMxsz7NlUgzMzOzGojk1by6UX4FcABwQ06fBHw8L4/O6+TtB+bRp0cDkyPijYh4hjQo2N41OAQzM6AHKpGS+kl6SNKted1NMszMzMzKyNdNc0nz1E4HngJeiog1OcsSYHBeHgwsBsjbVwPbFNPLvMfMrOp64knkl4H5hXU3yTAzMzMrIyLWRsQIYAjp6eFu5bLln+VGmY520tcjaayk2ZJmr1ixoqshm5ltoFtTfEgaAhwGXAB8JTexOAD4TM4yCTgHuIzU9OKcnH4D8MPWTTKAZySVmmTc153YzMzMzBpVRLwkaQawLzBAUv/8tHEIsDRnWwIMBZZI6g9sBawspJcU31P8jAnABICWlpYNKplmnTVs3G1V2e+i8YdVZb9WPd19Enkx8HXgzby+DVVskuE7amZmZtZbSRokaUBefjtwEKk11z3AkTnbGOCWvDw1r5O3353ntZ0KHJO7Cu0MDAceqM1RmJl1oxIp6XBgeUTMKSaXydojTTIg3VGLiJaIaBk0aFCn4jUzMzOrsx2AeyQ9AswCpkfErcAZpBZdC0k32C/P+S8HtsnpXwHGAUTEPGAK8DhwO3BKRKyt6ZGYWZ/Wneas+wFHSDoU2BTYkvRksipNMszMzMx6s4h4BNizTPrTlBldNSJeB45qY18XkLoTmZnVXJefREbEmRExJCKGkQbGuTsijsNNMszMzMzMzJpWtwbWacMZwGRJ5wMPsX6TjKtyk4yVpIonETFPUqlJxhrcJMPMzMzMzKxh9UglMiJmADPysptkmJmZmZmZNamemCfSzKyhSJooabmkxwpp50j6g6S5+XVoYduZkhZKelLSwYX0UTltoaRxtT4OMzMzs0bkSqSZNaMrgFFl0n8QESPyaxqApN1Jzevfm9/zY0n9JPUDfgQcAuwOHJvzmpmZmfVp1egTaWZWVxFxr6RhFWYfDUyOiDeAZ3K/7VKT/IW5iT6SJue8j/dwuGZmZma9ip9EmllfcqqkR3Jz14E5bTCwuJBnSU5rK93MzMysT3Ml0sz6isuAvwFGAMuA7+d0lckb7aRvQNJYSbMlzV6xYkVPxGpmZmbWsFyJNLM+ISKej4i1EfEm8FPWNVldAgwtZB0CLG0nvdy+J0RES0S0DBo0qOeDNzMzM2sgrkSaWZ8gaYfC6ieA0sitU4FjJG0iaWdgOPAAMAsYLmlnSRuTBt+ZWsuYzczMzBqRB9Yxs6Yj6Tpgf2BbSUuAs4H9JY0gNUldBHwRICLmSZpCGjBnDXBKRKzN+zkVuAPoB0yMiHk1PhQzMzOzhuNKpJk1nYg4tkzy5e3kvwC4oEz6NGBaD4ZmZmZm1uu5OauZmZmZmZlVzJVIMzMzMzMzq5grkWZmZmZmZlYxVyLNzMzMzMysYq5EmpmZmZmZWcVciTQzMzOrAUlDJd0jab6keZK+nNO3ljRd0oLP26VzAAAgAElEQVT8c2BOl6RLJS2U9IikvQr7GpPzL5A0pl7HZGZ9kyuRZmZmZrWxBjg9InYD9gVOkbQ7MA64KyKGA3fldYBDgOH5NRa4DFKlkzT/7T7A3sDZpYqnmVktuBJpZmZmVgMRsSwiHszLrwDzgcHAaGBSzjYJ+HheHg1cGcn9wABJOwAHA9MjYmVErAKmA6NqeChm1se5EmlmZmZWY5KGAXsCM4HtI2IZpIomsF3ONhhYXHjbkpzWVrqZWU24EmlmZmZWQ5I2B24ETouIl9vLWiYt2klv/TljJc2WNHvFihVdC9bMrAxXIs3MzMxqRNJGpArkNRFxU05+PjdTJf9cntOXAEMLbx8CLG0nfT0RMSEiWiKiZdCgQT17IGbWp7kSaWZmZlYDkgRcDsyPiIsKm6YCpRFWxwC3FNJPyKO07guszs1d7wBGShqYB9QZmdPMzGqif70DMDMzM+sj9gOOBx6VNDennQWMB6ZIOhl4Djgqb5sGHAosBF4DTgKIiJWSzgNm5XznRsTK2hyCmZkrkWbWhCRNBA4HlkfE+3La1sD1wDBgEXB0RKzKTwYuIV2ovQacWBo9Mc+99s282/MjYhJmZl0UEb+hfH9GgAPL5A/glDb2NRGY2HPRmZlVzs1ZzawZXcGGw917HjYzMzOzHuBKpJk1nYi4F2jdtMvzsJmZmZn1AFcizayvqNo8bB5G38zMzPoSVyLNrK/r1jxs4GH0zczMrG9xJdLM+oqqzMNmZmZm1te4EmlmfYXnYTMzMzPrAV2e4kPSUOBK4J3Am8CEiLjEw+ibWb1Jug7YH9hW0hLSKKueh62BDRt3W71DMDMzswp1Z57INcDpEfGgpC2AOZKmAyeShtEfL2kcaRj9M1h/GP19SMPo71MYRr+F1N9ojqSpeTREM7NOi4hj29jkedjMzMzMuqnLlcjc3Ks00uErkuaTRi4cTXoCAGkY/RmkSuRbw+gD90sqDaO/P3kYfYBcER0FXNfV2MwqUa0nH4vGH1aV/ZqZmZmZNYIe6RMpaRiwJzATD6NvZmZmZmbWtLrTnBUASZsDNwKnRcTLqetj+axl0jo9jD4wAaClpaVsHjMzqx73XTQzM7NuPYmUtBGpAnlNRNyUkz2MvpmZmZmZWZPqciUyj7Z6OTA/Ii4qbPIw+mZmZmZmZk2qO81Z9wOOBx6VNDennYWH0TczMzMzM2ta3Rmd9TeU788IHkbfzMzMzMysKfXI6KxmZmZmZmbWN3R7dFbrXTyyopmZmZmZdYefRJqZmZnVgKSJkpZLeqyQtrWk6ZIW5J8Dc7okXSppoaRHJO1VeM+YnH+BpDHlPsvMrJpciTQzMzOrjSuAUa3SxgF3RcRw4K68DnAIMDy/xgKXQap0AmcD+wB7A2eXKp5mZrXiSqSZmZlZDUTEvUDrEehHA5Py8iTg44X0KyO5HxiQ598+GJgeESsjYhUwnQ0rpmZmVeU+kWZmTcj9n816je3zvNlExDJJ2+X0wcDiQr4lOa2t9A1IGkt6islOO+3Uw2GbWV/mSqSZ9SmSFgGvAGuBNRHRkpuHXQ8MAxYBR0fEKkkCLiHNcfsacGJEPNhTsbiiZ2btKDeNWrSTvmFixARgAkBLS0vZPGZmXeHmrGbWF300IkZEREte71SfJDOzHvR8bqZK/rk8py8BhhbyDQGWtpNuZlYzrkSamXW+T5KZWU+ZCpRGWB0D3FJIPyGP0rovsDo3e70DGClpYB5QZ2ROMzOrGVcizayvCeBOSXNyfyFo1ScJ6KhP0nokjZU0W9LsFStWVDF0M+vNJF0H3AfsKmmJpJOB8cDHJC0APpbXAaYBTwMLgZ8C/wIQESuB84BZ+XVuTjMzqxn3iTSzvma/iFiaB6+YLumJdvJW1PfI/Y7MrBIRcWwbmw4skzeAU9rYz0RgYg+GZmbWKX4SaWZ9SkQszT+XAzeT5lnrbJ8kMzMzsz7LlUgz6zMkbSZpi9IyqS/RY3S+T5KZmZlZn+XmrGbWl2wP3Jxm7qA/cG1E3C5pFjAl9096Djgq559Gmt5jIWmKj5NqH7KZmZlZY3El0qyHVXPuv0XjD6vavvuCiHga2KNM+ot0sk+SmZmZWV/l5qxmZmZmZmZWMVcizczMzMzMrGKuRJqZmZmZmVnFXIk0MzMzMzOzirkSaWZmZmZmZhVzJdLMzMzMzMwq5kqkmZmZmZmZVcyVSDMzMzMzM6uYK5FmZmZmZmZWMVcizczMzMzMrGKuRJqZmZmZmVnF+tc7ADOr3LBxt1Vlv4vGH1aV/ZqZmZlZ8/GTSDMzMzMzM6tYwzyJlDQKuAToB/wsIsbXOaS6qtYTJzPrHJdNZtaoXD6ZWb00xJNISf2AHwGHALsDx0ravb5RmVlf57LJzBqVyyczq6dGeRK5N7AwIp4GkDQZGA08XteozPqIaj757uX9LV02mVmjcvlkZnXTKJXIwcDiwvoSYJ/WmSSNBcbm1VclPVmD2NqzLfBCnWPoLMdcfb0tXqhizLqwU9nfVY0YuqHaZVMjf1ccW9c4tq6peWydKJu2pfHKJqigfOrmdVNVfied/J/QG3T5PDXhuehIm+eqD56LNunCTn+n6lI+NUolUmXSYoOEiAnAhOqHUxlJsyOipd5xdIZjrr7eFi/0zphrpKplUyOfd8fWNY6ta3pBbMPqHUcZHZZP3bluauTfSSPxeaqcz1Vlest5aog+kaS7Z0ML60OApXWKxcysxGWTmTUql09mVjeNUomcBQyXtLOkjYFjgKl1jsnMzGWTmTUql09mVjcN0Zw1ItZIOhW4gzRM9cSImFfnsCrRME1rO8ExV19vixd6Z8xVV4OyqZHPu2PrGsfWNY6tk/p4+dRIfJ4q53NVmV5xnhSxQfceMzMzMzMzs7IapTmrmZmZmZmZ9QKuRJqZmZmZmVnFXInsIkn9JD0k6dZ6x1IJSQMk3SDpCUnzJX2w3jF1RNK/S5on6TFJ10natN4xtSZpoqTlkh4rpG0tabqkBfnnwHrG2FobMf9n/m48IulmSQPqGWNv1sb53UPSfZIelfQ/krYsbDtT0kJJT0o6uJA+KqctlDSu1rFJ+pikOTl9jqQDCu/5QE5fKOlSSeWmGqhabIXtO0l6VdJXC2k9et668Pt8f942L2/fNKfX9ZxJ2kjSpJw+X9KZhfdU47s2VNI9+bPmSfpyTi9bPiq5NMfwiKS9Cvsak/MvkDSmDrEdl2N6RNLvJO1R2FePn7ueJOkb+RgfkTRX0gbz3Lbz3iMa8Zh6QnfOS18l6QeSTius3yHpZ4X170s6S9INFezrrGrF2VtIWpu/e6XXsHbyDiuV85L2V6PUPSLCry68gK8A1wK31juWCuOdBHw+L28MDKh3TB3EOxh4Bnh7Xp8CnFjvuMrE+WFgL+CxQtp3gXF5eRxwYb3jrCDmkUD/vHxho8Xcm15tnN9ZwEfy8ueA8/Ly7sDDwCbAzsBTpAEy+uXld+e/14eB3Wsc257Ajnn5fcAfCu95APggaZ66XwGH1DK2wvYbgV8AX83rPX7eOnnO+gOPAHvk9W2Afo1wzoDPAJPz8juARcCwKn7XdgD2ystbAL/P3/ey5SNwaD4vAvYFZub0rYGn88+BeXlgjWP7h9JnAocUYqvKueupV/6+3Qdskte3Jf9NV/De/vWOvxHPS43iE/C2esdRJq6jgCl5+W3AHOC+wvb7gH0q3NerXfj8fvU+Bz18Pis+B7msfiwv70+D1D38JLILJA0BDgN+1lHeRpDvRH8YuBwgIv4SES/VN6qK9AfeLqk/6aKn4ea/ioh7gZWtkkeTKu3knx+vaVAdKBdzRNwZEWvy6v2k+casC9r4TuwK3JuXpwOfysujSRf2b0TEM8BCYO/8WhgRT0fEX4DJOW/NYouIhyKi9Dc3D9hU0iaSdgC2jIj7Iv1Hu5Ie+I538rwh6eOkCkVxNMoeP2+djGsk8EhEPJzf+2JErG2QcxbAZrk8fTvwF+BlqvddWxYRD+blV4D5pJuDbZWPo4ErI7kfGJDP28HA9IhYGRGr8jGNqmVsEfG7/NmwfvlYlXPXg3YAXoiINwAi4oWIWCppkaQLJT2QX7sASLpC0kWS7gEulHSipB8Wtl2an8Q+LenInP42ST/OT/VulTSttK2BtXdetgWQ1CJpRl4+Jz/FvzPn+aSk7+an+rdL2ijnWyTp20otAWZL2is/rXtK0pdKHy7pa5Jm5aeg38ppw/KT8R8DD7L+/J+N4rekGyoA7wUeA16RNFDSJsBuwKrCE7MTJd2Uz9ECSd/N6eNJ13ZzJV2T0z6bv4tzJf23pH45/VVJ50qaSar8N7X8Pfg/SQ/m1z90/K76cSWyay4Gvg68We9AKvRuYAXwc6UmuD+TtFm9g2pPRPwB+B7wHLAMWB0Rd9Y3qoptHxHLIF2sANvVOZ7O+hzpiYD1nMeAI/LyUay7QBgMLC7kW5LT2kqvZWxFnwIeyhddg3M8dYstl19nAN9qlb9W562tc/YeIPKF44OSvl6Iq67nDLgB+BOpPH0O+F5ErKQG5yw309oTmEnb5WNd/hYqjK3oZNaVj7X8O+2KO4Ghkn6fK3ofKWx7OSL2Bn5IuqYpeQ9wUEScXmZ/OwAfAg4Hxue0T5Kekvwd8Hl6x4V+e+elLX9DengwGrgauCci/g74c04vWRwRHwT+D7gCOJL0ZP1cAEkjgeGkGxAjgA9I+nB+766kGyl7RsSz3TzGHpdvKq6RtBOpMnkf6e/mg0ALqRXGX1q9bQTwadL349OShkbEOODPETEiIo6TtFvOs19EjADWAsfl929GegK3T0T8psqHWGulivRcSTfntOXAxyJiL9I5ubR+4XXMlchOknQ4sDwi5tQ7lk7oT2rydFlE7Em6kGjofg5KfVFGk5r47Ui6g/7Z+kbV/CR9A1gDXFPvWJrM54BTJM0hNZ8r/aMt1y8u2kmvZWwASHovqYnzF0tJDRDbt4AfRMSrrfLXKra24upPusg+Lv/8hKQDaxhXe7HtTbo425FUrp4u6d3Vjk3S5qRmx6dFxMvtZW0jjqrF14nYSvk/SqpEnlFKqlZsPSH/fXwAGEu6kXy9pBPz5usKP4sVv19ExNo2dvnLiHgzIh4Hts9pH8rveTMi/gjc05PHUA0dnJe2/Coi/go8SmrGfHtOf5RUiS6ZWkifGRGvRMQK4HWlsQZG5tdDpCeOf0uqVAI8m5/CN7LS08hSJfK+wvrvyuS/KyJWR8TrwOPAu8rkOZD0+5glaW5ef3fetpb0N9qMShXpERHxiZy2EfBTSY+SumrsXr/wOta/3gH0QvsBR0g6FNgU2FLS1RFR8wqOpEWkfo6/7iDrEmBJRMzM60cDzwL/UcXwuusg4Jlc+CLpJlIhdXVPfYCkAIZHxMKe2mf2vKQdImJZbo71Z0l3RsTIHv6cHqU0YMXhwIG5yZ31kIh4gnThgKT3sO7O9RLWf/I3hHXNtttKr1Vspab7NwMnRMRThZiLzZ3rEds+wJG5edQA4E1Jr5P66JQ9b5L+EfhZROxaxbiWAP8bES/kbdNIN/CupovnLF/cfj4iPtTN2D4D3J4vhJdL+i3p6cFiqvRdy838bgSuiYibcnLr8nF5Tm/rb2EJqQ9QMX1GjWND0vtJXVgOiYgXO4i5YeQK4QxgRr4wLQ1MVCzji8t/amd3bxSW1epnr9LGeVnDuocrrQfyKzV9fVPSXwv/I99k/WvpNwrpxfNVyifgOxHx38Wd5yfif2qVdgXpO3YHPVR25f3+itSNYlKHmTf0O9K12N+RWj0sBk4nNY2fWCZ/8RyspXy9Q8CkiDizzLbX27mp0Yz+HXge2IP0XXy9vuG0z08iOykizoyIIRExDDgGuLseFcjOyHcHF0sqFUBvJzVnamTPAftKeockke5Mza9zTB3K/wjeA5yUk8YAV/eCCuQo0t31IyLitXrH02wkbZd/vg34JvCTvGkqcIxSX8OdSXekHyANjjJc0s6SNiaVNVM33HP1Yst3zW8DzoyI35by52Z+r0jaN/9tngDcUsvYIuIfI2JYLocvBr4dET+knfMWEf/XgxdhG8Ql6TzSjbkxki5Q6nv4EeDxfM42krRM0p9yzHcV9re10qjIf5L0rKTP9GRsedNzwAFKNiM1sXuCKn3X8nfjcmB+RFxU2DSVdRWZMaz77kwFTsjx7UvqwrCMdAE9Uqnf1UBSBfmOWsaWm+/dBBwfEb8v5K/Z32lXSNpV0vBC0gjSDWRITeVKP+/rxsf8BviUUt/I7Vm/wt+Q2jkvi0hPxKDQ/7qH3QF8Lj8FR9Lg0t9sWyotu5T6bnZ4oz0iDqmkAqnUx/OgVsm/Jd1sXhkRa3OT+AGsG6yoUn/NN3IglYVHFsqurSWVe2LZF2wFLIuIN4HjSU+9G5afRPYd/wpck//RbUwaWbZh5H/qyn84RMRMpWGiHyTdHXwImFDHEAGQ1K94V0zSdaR/mttKWsK69usHSTqJdOF2VM0DbUeZmM8GziSNEDo9/Sq4PyK+1OZOrE1tnN/NJZ2Ss9wE/BwgIuZJmkJq5rMGOKX0/ZJ0KumCox8wMSLm0U2diQ04FdgF+A9JpVYLIyNiOfDPpP4+byf1D+t2H9pOxlZWRKwpd94k9Y91A0dVK64TgK8C5wNfIDXHnxYRtyk1CX4n6Q7zn0m/60+SRp0G+BGp2en2pAva2yQ93NHvvJPn7Ed5+THSnf+fR8QjeT89/l0jtdo5HnhUqYkawFmkvnRTJJ3M+uXjNNIIrQuB18g34iJiZa6gz8r5zs0XrrWM7f+RRtr9cS4f10RES1vft27G1pM2B/4r3xBaQzq3Y0mVgE2UBit5G3BsNz7jRtJN3sdIo9zOBFZ3J+gaaOu87AZcrjT9xMx23t9lEXGnUh/A+/J36VXgs6SndFXV+jqrix4ljWZ7bau0zSPihVLluAITgEckPZj7RX4TuDPf/PorcArrbnj0JT8GbpR0FKlpeHstA+ovGmCIWL+69iLdNTuTdAG6inSBsGnedjgwF3iJ1Pzg/a3ed1Be3oR0V3xpfl3MumGv/xf4VF7+EKnJy6F5/SBgbgfx9QO+D7xAmq7j1LyP0lQSM4ALSHe2/ky6YN2RdCd3Jalg/0Jhf1cA5xfW9yc10y0e11dJnbtXA9eXzkfe/jXSoBJLSf2GAtilg2O44v+zd+fxdtT1/cdfbwiL7AQisiQGJaholSUF1NYiyK5CLSjUnwaLplZQW1ewVhTFxi4uVEURUsAKkVItKSAYQWpVtrATEBMhkJAAAUJkC5Dw+f3x+Rzvyc25ycm999z1/Xw8zuOcmfnOnO/MnPu985nvMsAZ5AXOU7Xfh5NB7e/Jphyfb0p/f233yXq9HjgO+GVTmjeQF0TL6v0Ng/1b8suv4fjqqQxslA1k7fqDwPdblBfjySBrCfAo8M2mZX9FtnxYSgYIL12HPP1Hc5lQ874MnN80/XIyaNycHDjiOWDXpuXfB6bV5+7lxz+TtT9bDvbx92t4vurvZtt+3N5m9b4N+ciTlwz2Pg7HFznA003AE3X9MoO8KdW97Po08EClu5sM4g+pcuT5uva4tdJezerXWVdTj3yrNB+o8u6JKkv3rDLohVrnSeBTg318/Bp6LzdnHf7eTQ6B/nKyGeVnlQ9pnk4OhLEN8F1gpnII5u7+nmzWtDvZBntvsgkUZBC5X31+Ezmc/p81Tf/vWvL2AfKZWruThVKrIe3fQ94B3Jy863QBefG3Azmq2ZeVA1O0651kYboz8FryAqzRXPMTwIFkk8HuTTTW5C/JQnhz8uLtKbLWYSsyoPwb5eMGII8L5HM4N4uIVZp3SBpLNhE8nTw3XyVrHbZZh/yYWZfVysCa/xLy2YIvJcuYP1AOH38JWeZMJEfUnFHLjiRrpd4BjCNHWbyAvnk1+QxBACL7lz5X+d0VWBmrNpO8tdZpzvN6kr5HlmsHRcRQr+2x0eOSqtH9P/K5pA8OdoaGm2ol9t9k8DaWHFRltSa1ym5JJwJ/HBGbk2Xf/Ii4nLxZ9cO69nhd02rdr7Oat3c08HnymmYLcmTnRyPiPeRN8bfV9v6pH3fXRggHkcPfNyNiQWTzntPIZikfAL4bEddFtlk/l+zcvG+L9d9NNg96OHIQmy+QBQ5kkNgcNP5j0/SfsfYg8p3ANyJiYeQztqa1SHNORMyJbGr2ErLG89MRsTwibiEHMnhPi/V6cnpELKrj8T9kANvIy79HxB0R8RRZaLbr4oj4VeToc8sj4uqIuL2mbyMvMNsZIhwy6JwbEd+PiBURcQHZL+lt65AfM+vSqgyEvIt+SuQzOJ/pts7e5I2qT0bEU/V33Rg+/q/JgS/uqnLpy8DufeyjsxmrN/FbRl7UrWlZwwZkOTOWvKhzv2Xrtcj+xI/04/b2ixxhcreIOKe/tjvK7Ev+nX89Ip6PiIvoasLdbCXZgmw3SRtExPzoGvSsJ3+4zoocWKvZ+4F/iogbIs2LIfh4ERuaHEQOf83PqLqPvDB6KTl8++ONF9l0a4cW6+/Aqnem7mtKdw2wa3WW3518QPZ45cN496brgdY92aFb/ha0SNM8bweys/YT3fKzLs/dar4D+jR5gdYqL+tSSK6Sb0n7SPq5pCWSlgEfJPsItKP78W7kZSg9W8xsOGlVBgIsiRxWvpXx5HD6rfpJvhT4RlPZ+RjZh7Avf6NPknf5m21BNh9b07KGXcg+ll+IfKi9mY0sOwAPRETzSLmrXadEjib/t+SN8IclzZDU6tquWatrr4bxZBNks3XmIHL4ax5efALZ328BcFpEbNX02qRqvbpbxKrP7Wlsg7rbfSPwUfJhr8+R/Ss/BvyujTuZi1l1WPtWDzFvLjAXAWMlNd+Bn0C2/YdsRrpJ07KXrOX7u+el+7FqV/fHXZxP9tscHxFbkqMfqoe03XU/3o28PNAirZmtXasyENb8t7gAmKAcQbXVsr/uVn6+KCJaPQOtXXPI7gIAKJ/PuBE5EMlvgTHdRot8Xa3TcBc50MxP1DXKtpmNHIuBHWvwm4aW1ykRcX7kI39eSpZzX2ks6mHbaysLX96L9cwcRI4AJ0jaqfrafYbsjP094INVYyZJm0o6vFtw1nAB2Y9yXNUwfo5Vn8X4v2T7+0bT1au7Ta/JhcBHawjrreh6QHNLEbGADFL/UdLGyudyHU/Xg+9vAQ6r4Z9fQt6Na9eFwHGSdpO0CTmKYW9tTtaYLpe0N9lnsmEJ2YzuZS3XzAF6dpX0l5LGSHoX+TDZS/qQH7PRrFUZuDbXkxdt06p83FjSG2vZd4CTa0RVJG1Z/YbWSNIGkjYm/6+OqW02hmf/AfA2SX+qfLzGqcCPIh9E/hQ5wM+plZc3krWO32/eft0E/AzwM0k9XfSZ2fB0DTlS7Efq2uAdZIuvVSgfT7J/jXGxnBz4pjGy60PAxBrhtF1nAZ+QtFddL+7S1HT/IXq+ljFzEDkCnA/8lBz05h5y9NLZZL/Ib5KjC86jBphp4UvAbHJE09vJkcG+1LT8f8mg6Rc9TK/J9ypvt5GjmV5GFpJrGsr6WHKgi0XkQ85PiYhZtez75IAT82u77VwsAhARPyFHnr2KPB5XtbtuCx8iL/ieIIPuxjD9jdrb04BfVXO4VfqhRj6k+q3kw3kfBT4FvLU/+6eYjTKrlYFrWyHyMSpvI5uJ3k8O5vWuWvZj8s7+DEm/Jx9dcGgb+fgeeUF3LDlg2TNUf+7Ixz58kAwmHybL0A81rfsh8nEpD5M39v4mWjwqovq3nwpcpXwmrZmNANXS6x3ktdpSsjz6UYukG5HjSzxCdt95MXlzCXIwHoBHJd3U5vf+J3nNcj7ZhP6/yb7XkONgfLauZT6xjrtko4BWbX5t1jmSDgW+ExGj9SGyZtaPJM0nh6r/2WDnxczMbDRxTaR1jKQXSTqsmmbsSDYh/fFg58vMzMzMzHrPQaT1iaTvSHqyxasx2MwXyKYZN5ODQ3xuMPPbiqQ5PezDuwc7b2Y2NFR/xlblxJODnTczM7OB5uasZmZmZmZm1jbXRJqZmZmZmVnbHESamZmZmZlZ21o9aHlY2HbbbWPixImDnQ0z60c33njjIxExbrDz0Rcum8xGnv4sm+q5yWcBryEf6P5XwN3kY6smko+xemdELK2Hz38DOAx4GjguIm6q7UwBPlub/VI9AqZHLpvMRqbBunYatkHkxIkTmT179mBnw8z6kaT7BjsPfeWyyWzk6eey6RvA5RFxlKQNgU3IZ/1dGRHTJJ0EnAR8mnxG6aR67QOcAewjaSw54vlkMhC9UdLMiFja05e6bDIbmQbr2snNWc3MzMwGgKQtgDcBZ0M+ZD4iHgeOABo1iecCR9bnI4DzIl0LbCVpe+BgYFZEPFaB4yzgkAHcFTMb5RxEmpmZmQ2MlwFLgH+XdLOksyRtCmwXEYsB6v3FlX5HYEHT+gtrXk/zVyFpqqTZkmYvWbKk//fGzEYtB5FmZmZmA2MMsCdwRkTsATxFNl3tiVrMizXMX3VGxJkRMTkiJo8bN6y7m5vZEOMg0szMzGxgLAQWRsR1NX0RGVQ+VM1UqfeHm9KPb1p/J2DRGuabmQ2IYTuwzrqYeNKlHdnu/GmHd2S7ZmZ95XLPbOiJiAclLZD0ioi4GzgAuLNeU4Bp9X5xrTITOFHSDHJgnWURsVjSFcCXJW1d6Q4CTh7Ifektl01mI4NrIs1sRJK0laSLJP1G0l2SXi9prKRZkubW+9aVVpJOlzRP0m2S9mzazpRKP7eG1Dcz64sPAz+QdBuwO/BlMng8UNJc4MCaBrgMuAeYB3wP+BBARDwGfBG4oV6n1jwzswExKmoizWxUGpRh9M3M1iQibiHLlO4OaJE2gBN62M50YHr/5s7MrD2uiTSzEcfD6JuZmZl1joNIMxuJPIy+mZmZWYc4iDSzkcjD6JuZmZl1iINIMxuJPIy+mZmZWYc4iDSzEXThiOsAACAASURBVCciHgQWSHpFzWoMoz+THD4fVh9G/701Suu+1DD6wBXAQZK2rpFcD6p5ZmZmZqOWR2c1s5GqMYz+huQQ+e8jb5xdKOl44H7g6Ep7GXAYOYz+05WWiHhMUmMYffAw+mZmZmYOIs1sZPIw+mZmZmad4easZmZmZmZm1ra1BpGSpkt6WNIdTfPGSpolaW69b13zJel0SfMk3SZpz6Z1plT6uZKmNM3fS9Lttc7pklqNhmhmZmZmZmZDQDs1keew+sO1TwKujIhJwJV0DZ1/KDCpXlOBMyCDTuAUYB9gb+CURuBZaaY2recHeZuZmZmZmQ1Raw0iI+IXQPeBJI4Azq3P5wJHNs0/L9K1wFY1jP7BwKyIeCwilgKzgENq2RYRcU31STqvaVtmZmZmZmY2xPS2T+R2Nfw99f7imr8jsKAp3cKat6b5C1vMb0nSVEmzJc1esmRJL7NuZmZmZmZmvdXfA+u06s8YvZjfUkScGRGTI2LyuHHjeplFMzMzMzMz663eBpEPVVNU6v3hmr8QGN+Ubidg0Vrm79RivpmZmZmZmQ1BvQ0iZwKNEVanABc3zX9vjdK6L7CsmrteARwkaesaUOcg4Ipa9oSkfWtU1vc2bcvMzMxsRJE0v0alv0XS7JrXb6Pem5kNhHYe8XEBcA3wCkkLJR0PTAMOlDQXOLCmAS4D7gHmAd8DPgQQEY8BXwRuqNepNQ/gb4Czap3fAT/pn10zMzMzG5LeHBG7R8Tkmu7PUe/NzDpuzNoSRMSxPSw6oEXaAE7oYTvTgekt5s8GXrO2fJiZmZmNUEcA+9Xnc4GrgU/TNOo9cK2kxqj3+1Gj3gNImkU+Iu2Cgc22mY1W/T2wjpmZmZn1LICfSrpR0tSa11+j3q/Co9qbWaestSbSzMzMzPrNGyNikaQXA7Mk/WYNafs0un1EnAmcCTB58uQeR783M1tXrok0sxHJg1eY2VAUEYvq/WHgx2Sfxv4a9d7MbEA4iDSzkcyDV5jZkCFpU0mbNz6To9XfQT+Nej+Au2Jmo5ybs5rZaOLBK8xsMG0H/DifasYY4PyIuFzSDcCFNQL+/cDRlf4y4DByBPungfdBjnovqTHqPaw66r2ZWcc5iDSzkaoxeEUA362+QasMXlF9kqAfBq8gazCZMGFCf++HmY0QEXEP8LoW8x+ln0a9NzMbCA4izWyk8uAVZmZmZh3gPpFmNiJ58AozMzOzznAQaWYjjgevMDMzM+scN2c1s5HIg1eYmZmZdYiDSDMbcTx4hZmZmVnnuDmrmZmZmZmZtc1BpJmZmZmZmbXNQaSZmZmZmZm1zUGkmZmZmZmZta1PQaSkv5M0R9Idki6QtLGknSVdJ2mupB9K2rDSblTT82r5xKbtnFzz75Z0cN92yczMzMzMzDql10GkpB2BjwCTI+I1wPrAMcBXgK9FxCRgKXB8rXI8sDQidgG+VumQtFut92rgEODbktbvbb7MzMzMzMysc/ranHUM8CJJY4BNgMXA/sBFtfxc4Mj6fERNU8sPUD7E7QhgRkQ8GxH3ks9p27uP+TIzMzMzM7MO6HUQGREPAP9CPrB7MbAMuBF4PCJWVLKFwI71eUdgQa27otJv0zy/xTqrkDRV0mxJs5csWdLbrJuZmZmZmVkv9aU569ZkLeLOwA7ApsChLZJGY5UelvU0f/WZEWdGxOSImDxu3Lh1z7SZmZmZmZn1SV+as74FuDcilkTE88CPgDcAW1XzVoCdgEX1eSEwHqCWbwk81jy/xTpmZmZmI4qk9SXdLOmSmvaghGY2rPQliLwf2FfSJtW38QDgTuDnwFGVZgpwcX2eWdPU8qsiImr+MVVQ7gxMAq7vQ77MzMzMhrKPAnc1TXtQQjMbVvrSJ/I6coCcm4Dba1tnAp8GPiZpHtnn8exa5Wxgm5r/MeCk2s4c4EIyAL0cOCEiVvY2X2ZmZmZDlaSdgMOBs2paeFBCMxtmxqw9Sc8i4hTglG6z76FFQRYRy4Gje9jOacBpfcmLmZmZ2TDwdeBTwOY1vQ1tDkooqXlQwmubttlyUEJJU4GpABMmTOjfvTCzUa2vj/gwMxuS3OfIzIYaSW8FHo6IG5tnt0jaL4MSekBCM+sUB5FmNlK5z5GZDTVvBN4uaT4wg2zG+nU8KKGZDTMOIs1sxHGfIzMbiiLi5IjYKSImkjeproqId+NBCc1smHEQaWYjUaPP0Qs13XafI6C5z9GCpm227HME2e9I0mxJs5csWdKf+2Fmo4MHJTSzYaVPA+uYmQ01zX2OJO3XmN0iab/0OYLsd0SOTs3kyZNbphkpJp50aUe2O3/a4R3ZrtlQFRFXA1fXZw9KaGbDioNIMxtpGn2ODgM2Bragqc9R1Ta26nO00H2OzMzMzNbOzVnNbERxnyMzMzOzznJNpJmNFp8GZkj6EnAzq/Y5+n71OXqMDDyJiDmSGn2OVuA+R2ZmZmaAg0gzG8Hc58jMzMys/7k5q5mZmZmZmbXNQaSZmZmZmZm1zUGkmZmZmZmZtc1BpJmZmZmZmbXNQaSZmZmZmZm1zUGkmZmZmZmZta1PQaSkrSRdJOk3ku6S9HpJYyXNkjS33reutJJ0uqR5km6TtGfTdqZU+rmSpvT8jWZmZmZmZjaY+loT+Q3g8oh4JfA64C7gJODKiJgEXFnTAIcCk+o1FTgDQNJY4BRgH/IZbqc0Ak8zMzMzMzMbWnodREraAngTcDZARDwXEY8DRwDnVrJzgSPr8xHAeZGuBbaStD1wMDArIh6LiKXALOCQ3ubLzMzMzMzMOqcvNZEvA5YA/y7pZklnSdoU2C4iFgPU+4sr/Y7Agqb1F9a8nuavRtJUSbMlzV6yZEkfsm5mZmY2sCRtLOl6SbdKmiPpCzV/Z0nXVbeeH0rasOZvVNPzavnEpm2dXPPvlnTw4OyRmY1WfQkixwB7AmdExB7AU3Q1XW1FLebFGuavPjPizIiYHBGTx40bt675NTMzMxtMzwL7R8TrgN2BQyTtC3wF+Fp1BVoKHF/pjweWRsQuwNcqHZJ2A44BXk223vq2pPUHdE/MbFTrSxC5EFgYEdfV9EVkUPlQNVOl3h9uSj++af2dgEVrmG9mZmY2YlSXnidrcoN6BbA/eR0Fq3cFanQRugg4QJJq/oyIeDYi7gXmkeNKmJkNiDG9XTEiHpS0QNIrIuJu4ADgznpNAabV+8W1ykzgREkzyEF0lkXEYklXAF9uGkznIODk3ubLzGy4mHjSpYOdBTMbYFVjeCOwC/At4HfA4xGxopI0d+v5Q5efiFghaRmwTc2/tmmzPXYFMjPrhF4HkeXDwA+q7f49wPvI2s0LJR0P3A8cXWkvAw4j75Y9XWmJiMckfRG4odKdGhGP9TFfZjaKSdoY+AWwEVnOXRQRp0jaGZgBjAVuAt4TEc9J2gg4D9gLeBR4V0TMr22dTDYpWwl8JCKuGOj9MbORIyJWArtL2gr4MfCqVsnqvU9dgSRNJUfEZ8KECb3Kr5lZK30KIiPiFmByi0UHtEgbwAk9bGc6ML0veTEza9Lod/SkpA2AX0r6CfAxst/RDEnfIYPDM2jqdyTpGLLf0bu69TvaAfiZpF3rItDMrNci4nFJVwP7kiPWj6nayOZuPY0uPwsljQG2BB6jza5AEXEmcCbA5MmTW443YWbWG319TqSZ2ZDjfkdmNhRJGlc1kEh6EfAW8hnbPweOqmTduwJNqc9HAVfVTfmZwDE1euvO5DO4rx+YvTAz63tzVjOzIWkg+x25yZiZtWl74Nwqn9YDLoyISyTdCcyQ9CXgZuoZ3PX+fUnzyBrIYwAiYo6kC8lxKFYAJ7iFhJkNJAeRZjYiDWS/IzcZM7N2RMRtwB4t5t9Di1YOEbGcrrElui87DTitv/PY4IG/zGxN3JzVzEa0iHgcuJqmfke1qFW/I3rT78jMzMxsNHEQaWYjjvsdmZmZmXWOm7Oa2UjkfkdmZmZmHeIg0sxGnOHU78jMzMxsuHFzVjMzMzMzM2ubg0gzMzMzMzNrm4NIMzMzMzMza5uDSDMzMzMzM2ubg0gzMzMzMzNrm4NIMzMzMzMza5uDSDMzMzMzM2ubg0gzMzMzMzNrW5+DSEnrS7pZ0iU1vbOk6yTNlfRDSRvW/I1qel4tn9i0jZNr/t2SDu5rnszMzMzMzKwz+qMm8qPAXU3TXwG+FhGTgKXA8TX/eGBpROwCfK3SIWk34Bjg1cAhwLclrd8P+TIzMzMzM7N+1qcgUtJOwOHAWTUtYH/gokpyLnBkfT6ipqnlB1T6I4AZEfFsRNwLzAP27ku+zMzMzIYaSeMl/VzSXZLmSPpozR8raVa14polaeuaL0mnV2ut2yTt2bStKZV+rqQpg7VPZjY6jenj+l8HPgVsXtPbAI9HxIqaXgjsWJ93BBYARMQKScsq/Y7AtU3bbF5nFZKmAlMBJkyY0Mesm5mZmQ2oFcDHI+ImSZsDN0qaBRwHXBkR0ySdBJwEfBo4FJhUr32AM4B9JI0FTgEmA1HbmRkRSwd8j4aIiSdd2rFtz592eMe2bTZc9bomUtJbgYcj4sbm2S2SxlqWrWmdVWdGnBkRkyNi8rhx49Ypv2Y2evhuv5kNRRGxOCJuqs9PkN2BdmTV1lrdW3GdF+laYCtJ2wMHA7Mi4rEKHGeRXYLMzAZEX5qzvhF4u6T5wAyyGevXyQKuUcO5E7CoPi8ExgPU8i2Bx5rnt1jHzKw3Gnf7XwXsC5xQ/a9PIu/2TwKurGlY9W7/VPJuP013+/chm9mf0gg8zcz6ogYY3AO4DtguIhZDBprAiyvZH1pxlUZrrZ7md/+OqZJmS5q9ZMmS/t4FMxvFet2cNSJOBk4GkLQf8ImIeLek/wSOIgPLKcDFtcrMmr6mll8VESFpJnC+pK8CO5AXcdf3Nl9mZnUR1rgge0JS893+/SrZucDVZJOxP9ztB66V1Ljbvx91tx+gmp0dAlwwYDszSrgpmo0mkjYD/gv424j4fQ4R0Tppi3ltt+KKiDOBMwEmT57cspWXmVlvdOI5kZ8GPiZpHtnn8eyafzawTc3/GFUDEBFzgAuBO4HLgRMiYmUH8mVmo9BA3O03M2uXpA3IAPIHEfGjmv1Q3bii3h+u+T211nIrLjMbVP0SREbE1RHx1vp8T0TsHRG7RMTREfFszV9e07vU8nua1j8tIl4eEa+IiJ/0R57MzLrf7V9T0hbz2r7b7yZjZtaOGpX+bOCuiPhq06JGay1YvRXXe6vf9r7AsroBdgVwkKStq4n9QTXPzGxAdKIm0sxs0A3k3X4P+mVmbXoj8B5gf0m31OswYBpwoKS5wIE1DXAZcA/5+LPvAR8CqCb2XwRuqNepjWb3ZmYDoa+P+DAzG3LauNs/jdXv9p8oaQY5iM6yiFgs6Qrgy02D6RxE9QU3M1tXEfFLWrdwADigRfoATuhhW9OB6f2XOzOz9jmINLORqHG3/3ZJt9S8z5DB44WSjgfuB46uZZcBh5F3+58G3gd5t19S424/+G6/mZkNcR6ozAaCg0gzG3F8t9/MzMysc9wn0szMzMzMzNrmINLMzMzMzMza5iDSzMzMzMzM2uY+kWZmZmZmA6yTA+CYdZprIs3MzMzMzKxtDiLNzMzMzMysbQ4izczMzMzMrG0OIs3MzMzMzKxtDiLNzMzMzMysbQ4izczMzMzMrG0OIs3MzMzMzKxtvQ4iJY2X9HNJd0maI+mjNX+spFmS5tb71jVfkk6XNE/SbZL2bNrWlEo/V9KUvu+WmZmZmZmZdUJfaiJXAB+PiFcB+wInSNoNOAm4MiImAVfWNMChwKR6TQXOgAw6gVOAfYC9gVMagaeZmZnZSCFpuqSHJd3RNM83381s2Ol1EBkRiyPipvr8BHAXsCNwBHBuJTsXOLI+HwGcF+laYCtJ2wMHA7Mi4rGIWArMAg7pbb7MzHyhZmZD1Dmsfo3jm+9mNuz0S59ISROBPYDrgO0iYjFkoAm8uJLtCCxoWm1hzetpfqvvmSpptqTZS5Ys6Y+sm9nIdA6+UDOzISYifgE81m22b76b2bAzpq8bkLQZ8F/A30bE7yX1mLTFvFjD/NVnRpwJnAkwefLklmkG0sSTLu3YtudPO7xj2zYb6SLiF3Vzq9kRwH71+VzgauDTNF2oAddKalyo7UddqAFIalyoXdDh7JvZ6LLKzXdJ/Xrznbw5xoQJE/o522Y2mvUpiJS0ARlA/iAiflSzH5K0fRWE2wMP1/yFwPim1XcCFtX8/brNv7ov+RoJOhWgOji1UcwXamY2nIy4m+9mNnL0OohUVjmeDdwVEV9tWjQTmAJMq/eLm+afKGkG2TxsWV3IXQF8uamZ2EHAyb3Nl5nZOvKFmpkNJt98H+I62fLMbLjqS5/INwLvAfaXdEu9DiODxwMlzQUOrGmAy4B7gHnA94APAVRTsS8CN9Tr1EbzMTOzfvRQXaCxDhdqreabmfWnxs13WP3m+3tr8K99qZvvwBXAQZK2rhvwB9U8M7MB0+uayIj4Ja3v1AMc0CJ9ACf0sK3pwPTe5sXMrA1uJWFmg0rSBWQt4raSFpKDd00DLpR0PHA/cHQlvww4jLz5/jTwPsib75IaN9/BN9/NbBD0eWAdM7OhxhdqZjYURcSxPSzyzXczG1YcRJrZiOMLNWvmgcrMzPrHcOwf6rK6M/rlOZFmZmZmZmY2OjiINDMzMzMzs7Y5iDQzMzMzM7O2uU/kKOO+QWZmZmZm1heuiTQzMzMzM7O2uSbSzMzMzMxGJLfC6wzXRJqZmZmZmVnbHESamZmZmZlZ29yc1czMrBfcRMrMzEYrB5HWLzp1MQW+oDIzMzMzG0ocRJqZmQ0hvilnZmZDnYNIG/LcZMzMzMzMbOhwEGlmZmZmZrYORnslx5AJIiUdAnwDWB84KyKmDXKWzMxcNtmIMtovekYal09mNliGRBApaX3gW8CBwELgBkkzI+LOwc2ZmY1mLpvM2uN+nAPP5ZOZDaYhEUQCewPzIuIeAEkzgCMAF4RmNphcNpkNMtee9sjlk5kNmqESRO4ILGiaXgjs0z2RpKnA1Jp8UtLdbW5/W+CRPuVwaPP+9YK+0t9b7DWfvy4v7WRGeqHTZdNwNtJ/t828ryOQvjKsyyZoo3zqY9m0LsdnXX83Qyn9UMpLp9MPpbysa/qhlJeOpl/HsgkGqXwaKkGkWsyL1WZEnAmcuc4bl2ZHxOTeZGw48P4Nb96/Ia2jZdNwNszP6zrxvo5MI2Bf11o+9aVsWpfjs67HciilH0p56XT6oZSXdU0/lPLS6fTDpWxab7AzUBYC45umdwIWDVJezMwaXDaZ2VDl8snMBs1QCSJvACZJ2lnShsAxwMxBzpOZmcsmMxuqXD6Z2aAZEs1ZI2KFpBOBK8hhqqdHxJx+/IqR3szM+ze8ef+GqAEom4azYXtee8H7OjIN630dYtdO63osh1L6oZSXTqcfSnlZ1/RDKS+dTj8syiZFrNa9x8zMzMzMzKylodKc1czMzMzMzIYBB5FmZmZmZmbWthEVREo6RNLdkuZJOqnF8o0k/bCWXydp4sDnsnfa2LfjJC2RdEu93j8Y+ewtSdMlPSzpjh6WS9Lptf+3SdpzoPPYF23s336SljWdv88NdB57S9J4ST+XdJekOZI+2iLNsD5/tipJ8yXdXr/V2YOdn/7U6m9V0lhJsyTNrfetBzOP/aWHff28pAeayqLDBjOP/aGnMmqwzqukJ9ch7eclfaIX3yFJjzT2SdL2kkLSmXUMbpO0QtKBtXxlne87JP2PpK162O5+ki6R9PdN27lF0j6SzpK0W12P7NBGHo+U9FTt42mSXqjvb/z2rpXU8jEHkn7dbfocSUf1kPYcScu7zTtd0vz6/EFJ721adlz9DSyStG3NWy7pmRbHc2W9P9P0PlHSZEmnV9qNJP2s9uldjfNf6e6r9V7ZLX+tju8KSbuu7bj2pNs2V9Y250s6UdKzkiZUvkPSvzat94k6RyFpYdP8MfUbu73Fd+0n6Q3dzkFP56fxu9mm6dy/0FQOPSlp30rb/Du9pJZdImkHSRc1bbOR7s46J6tdN7fIx9WSDm6a3qby8IikByUtreN0i3Igq1bb+KWk3eu4btt4b+O7/13SK+rzQvX897fG7dU+9MujQdrJ+4gJIiWtD3wLOBTYDThW0m7dkh0PLI2IXYCvAUPncfNr0Oa+AfwwInav11kDmsm+Owc4ZA3LDwUm1WsqcMYA5Kk/ncOa9w/g/5rO36kDkKf+sgL4eES8CtgXOKHF73O4nz9b3Zvrtzrkn2W1js5h9b/Vk4ArI2IScGVNjwTn0Lpc+lpTWXTZAOepE3oqo0bqeSVywIvrgNfXrDcAvwUOBvYEjgbuARoD8TxT5/s1wGPACWvY/FbAW4E9I+K1wFuABRHx/oi4EzgOWGsQCRxJ13XoWPK5l8c1fnvA8p5WjIg39LSshQtYfSDJfYC5ta3vRMR5bWznIVY9njeTv60A9gMWANdHxPyImB0RH6m0ewAb1H79sNs2twJ+SY6sC4Ck19Pi+Lazoz2R9KfdtvlMbXNj4NPAfRFxf0TMBp4F3tEigFgObC7pRTV9IPAw3R50L2kMeTxWO0e1bBWN301EPNp07ldQ5RAwu6Zh9d/pBrWNRRFxVNN3PFPrHgb8LiKmtXGYLgCObcrXo3WM/hz4DjADmFXf/1wb22tbRLwvIu7uz20OiIgYES/yD/uKpumTgZO7pbkCeH19HgM8Qg0uNJRfbe7bccA3BzuvfdzPicAdPSz7LnBs0/TdwPaDned+3L/9gEsGO4/9tJ8XAweOtPPn1yrncz6w7WDno4P7t8rfavPvFdgeuHuw89jBff088InBzleH9/li8gJ4UM4r8GSLeW8jg76bgZ8B23U/H8AHgJ8ALwJeDlwO3Aj8H/DKFtv8DPCl+vyvwNeBxTX9PuDspu8OYDHw+3o/H/gz4HYy4HgBeLqO3R1k8PACGXA8VmluBe6vbUWlWQQ8Ues+V6+n63teqHTP0xWMBbCyvvd54KmmdCvJ67alTdPPAr8BHiefm7mADIzvq+VXAEdV+l8DX648PV/Hb2Ft/4Xa7mm1jWfr9QwZfD9b2/0dsAR4sva1se7l9XtaUcfz8Zr/fL038vrHZDDWWB7A22sf/rnenwZurXMzF/hqfV5BBrLL6zheXPPPr7z/Dri2ll8DPFr5f7CO45U1b0l97/zK35/Xvl0OfLOWLa3zdkl9buzHM7UfjfO3vN4b5/U5YFmlf5r8bdzTlPdnK80DdY7n1DburuO8pL4jgHvJ31Rjmyub8n1bHavngXl1PFfUeWnk9cGmbX0BeFXTuYhK//8qH419CPK38wRwV6Vp7PfK+ryg8rys0iyvY7S00txH/vYbfzcLyb/rm2u/V9a8WyrvXyV/S7fVOivISq5fAv9D/o3PAd5f+74tsClwaR2fO4B31W/hamByfT6DDMDnAF9oKhfm1/G4qfb9lTV/G+Cnlc/v1n6s8f/8iKmJBHZk1Ts1C2teyzQRsYL8AWwzILnrm3b2DeAvqpnCRZLGt1g+nLV7DIaz10u6VdJPJL16sDPTG8om4nuQF0PNRsP5G00C+KmkGyVNHezMDIDtImIxQL2/eJDz02kn1v+S6RohTXcbupVRQ+m8/hLYNyL2IGs8PtW8UPkoj7cBR0bEM+QjAD4cEXsBnwC+3WKbv6arNmhv4B+BLSX9FvgkecHe+O4gA6yPk7WDY2u7S8mL0C2AI8hasYlkDZDIi+0fkjVak8ib3E/Xdm8FTgc2JC9KtycvuO+o+Ssr3WnkBSvkRfQjlYflwEbAf5DBx0NkMCTyQvtqMmBo1FgurnWjjsczwHTgr2r5XmRLmM3oCgZeSwZvF5FBxydrfxrbmQ/8LVnxsHV9/8yaPqjyIvKmRKOp6a+6TgGfAf6z9nUWGZhvVMf59tqvf6njuQnwQfKmwCRJ99Sxe0vT9javfFwP7F/z9qhjfjwwrbb/FjIA2Qi4ENiODKLGAldVfibUekvI2sTzgVfWsbmv3vevtDfVfj5dx3mzOl6303UD4gHyZsW99R5koHcaeZ62IQPWDwMvAb5Y5+dB4N1kIDgvIho1nRtHxOvI4O6FOhYv1LpvJgPGMWRg/DPyN/Mf5N+Oatn9ta1Pkef+udqXvWr+dDLwVn3//9V6IoO05fX5EvK30DgWY4CzyWB6Th3nH5DXNjPq+Cwnz+85wKuBv655D1be/4/8u/rTysuE2tYjZEuAXwFT6m/8j4GP0VVzfwiwKCJeF1krezmr+/vIlkKvBf5M0mublj0SEXuSgWajufwpwC+rDJpZ+VmjkRREqsW87s8vaSfNUNROvv8HmBjZTOFnwLkdz9XAGq7nrl03AS+tAvPfgP8e5PysM0mbAf8F/G1E/L774harjKTzN9q8sf4BHUo2DXzTYGfI+s0ZZC3X7uSF4b+uOfnwsZYyarDtBFxR/cs+SV50NryH/Fv7i4h4tvbjDcB/SrqFrgCtu+uBPSRtSjanfIi8MP4CWZtxvKTj6rsF/BPZbG9j8iL5V2RLqJeTQfc/kxfLC8jfxoNkwHAsGSRsDJxa741axXFkMHcscAMZBO1OBg3NGn3tHicv+Bu1UgLeUd/7EvLm44aVv1fU/Em17p5koPLy2v6LgM9WHhq1Zn9BXqT/gqzNuYq8iD6qtrdB5RcyQIasCRJ5ob4rGaCNIa+7GuaSAdV6wDvru0UGSm+tz78H/qbSfxd4Te3L2PquY4GzyCbXD5GB8MPAa5TjXIjs9vIMGUBvUtt6MRlIQVezz2vIGs4VwKYR8SRZ87WCDGTXr2PyIvKGxNLIJr171T68vI7FGLpq9CCb3+5Q625Qx3w9MrgeQ97o2I2s6V6P/A02at6CrEnftdbfgfxNbAd8CHgdsKekxs2NRozyPBmM3VTzNiIDusb5aaRfTgapm9X0g8CWtc8bsVewlQAAIABJREFUkk25N6rv+VVta0Uduycqj9fXvm5CBrsb0HWtsn69Jtf722tbryN/8/eSv9+/rn2eTp6z6+rYHEHegBkDjAdeVtvdotItJwPRDSpfdwB/J+lW8nzuRFez7NuBt0j6iqQ/jYhlrO6dkm4iaxZfTZ6Xhh/V+42VJ4A3kUE4EXEpeQNpjUZSELmQPCkNO5FNFlqmqTbTW5JV7UPdWvctsi35szX5PbrusowU7ZzfYSsifl+FPJF9kDZopzP2UCFpA/Li7AcR8aMWSUb0+RttImJRvT8M/Jis5RjJHpK0PeSAGuSF3YgUEQ9FxMqIeIH8XzIizm0PZdRQOq//RnZJ+SPyInTjpmV3kBd6O9X0esDj0dVvdffI/p6riIinyeZyf0VegENejG5DXrT+DRlU/Rt5oTyevDBej7ypOY28uL6OvF46hrxmatQa/r6W/Qd5Uf0ceeG9HFgeEQeRF+2bV7rGhfJiMpBt1ggEngf+gawFaTQxvaS2/SjwJfICe0WlW0pevF9F1pK9nQyW/oTsF/dHwN/V/m1EBi7bk/+T/owMlh4kg7eF5MX/Bo1DWO+NGtOnySDhmkr7gcoXZFB+Gl2BY6MZ7yG1vUfIpoKPV/oH6GrG+yQZWK0gg69fRcTOEfEvZG3ub8latPXouiH7fLfj1xjopbH8T8japHkRcXzTsgci4iXkeZhb23yW7Ouoml5JNp1s9Je9pel7Gs2a1wOmVN5fAO6s978gA+DpZE1b4+bACmBFZLvJRu3tmIj4BRnkNGqfTyRrYxvNSiEDp03J4JrK71F0naeGp8la9cY5+TZ5nO+r7W9V+Xg/+ZtsNEVu5A+y+Wfj7+xJ8jf+NLAzeYyfJX+DQf7+5pE32paRNwG2rn3bmKzhfI78Dawg/36WkX93S4E/quXX1/49Wsdso1p/YzKw27cqGG6rbRMRvyWv828H/lHdBmOUtDN5c+SAqly6lFXLlEa8sJJV+wuv0839kRRE3kBW/+9coyYdQ/4BNZtJ/ughf4BX1Q96qFvrvjX+EZa3k+20R5KZwHuV9gWWNZohjQSSXlIFOJL2Jv82H13zWkND5fts4K6I+GoPyUb0+RtNJG0qafPGZ7JJV8tRh0eQ5v8dU8h+YSNSt/8lf84IOLdrKKOG0nndkrzgbeSl2c1kYDlT0g5Vi3qvpKPhDyOxvq6H7f6KbI55TY3+uAD4KHmBuzt5gb1lU/qjKs34Sv8Lshbj5lr2FFnTcxd5UQ5ZM7MeGUjuQDUHlLQ72QyvsWxz8iL4ebKmsLmFSuNCdil5Hp4ka+iWkUHf+mTt0DZk8LCSvGjfkAx+30w2ndyPrLH8bB2bDciL8vXIQPVU8oJ+Zc2n9mkvMhBs7ANkLV13vyYHBGrU/DSCkBvJQAqyZunGWr8RwK1X+9+o2X+CvKkxF/gIeY4ax+m1kg6tv8UDyMCz0fWqUfu4E/CCpPXIWrldan5j4J9G02xJ2rXO5RxgbB2T9cng6KnK1xgy6Lqh8vFjMnB6gQxkGh4nm2dCDs50Jxl4vJIMeKeRAdsj5Plr5af1HRtIemmtfyEZeB8VEf9Fnp+JjX0gA9BGH8ONKt8/a1pO7dPSiGgMmvml2rcXkQHYwvreIGv312/K04rKb6O/aqN569Zk8NVobgz5+xD5+9uAPEf3kDdHNqUrOD++tnc0eay3J38Xb6brN7aE7Ku6Bfn3f3PtX1T+HouIZ6qL0x83Mqsc/fjpiPgPsjl09xHvt6hjtEzSdmRLhrX5BdVCQNKhte9rNhCdyAfqRY7C9Fuyc/Hf17xTgbfX543JtunzyMj/ZYOd537ct38kC4hbgZ/TopP9UH6Ro2ItJv+5LCT/+D4IfLCWixyh9nfknZfJg53nft6/E5vO37XAGwY7z+uwb39CFni3kXcsb6nf64g5f36tcr5fVr/TW+s3+/eDnad+3r9Wf6vbkP/o59b72MHOZwf39fv1N3obGWQN+wGw1lBGDcp5pWuwjcbrY2RTt3vImoh/Bq6utJ+na2Cdg8mLzG3JC/zL6+/wTuBzPXzX0bXvu5CB0jX1/YvJJm3b1ndHLftn8kK3UXN2F12DijRqNm+oY7iSrgFTHidr9G4ib4BGrfd98tplJXlB/TxZ+/IQXQOZLK79bjSBfY7sx9UYTOV5ugZLeZau2q8HKu0TlfaB+vxbsnZyZZVRjRrD5bX9X5E1j40mey/UfjxDBjIP1/c0amG3rTTfbDqeT9b5auT5wToXK8jg8JH6vISuQVs+SVfzyBV09fU7jww6flrHrjFAy5O1XzeSNcCNwYGuo6vv3Jzaj0bf0Afq/YZaZzlZsbBXbaexn1H5f5ysKXuODMp+VHlqDPKyuM771U3H7qr6vpW1/LmaXlbndS5dgxE9QPa1vYusGW78Lhu/m/tq/d+QN6yeaMrfjZV2aW27cXxXkr/Dq2r6ZrJP6zO1z7c2nZfGaz7ZfDgqX0voqgWeVssXkb+bxuBQjaauK+u98bv7WeX9Sbr+BpbX+Xim0j5PDpjzVG1rTr3/to5JY9uvJWvDA5he+7u89uMV5MBQt5L9dH9Rx3tbsixolGc30DWYztVNn8+p435pndfjav58asAc8gZQo6xpDKxzE/kEi7UOrKNa0czMzMxsnSmfuTg5Ih5ZS7pNyGAiJB1Djtp9RIfy9GREbLb2lKuss1lU1xLlswW3j4jVnn3cw7rrkRfgR0fE3Kb588k+kO8haxCfJsewWC7prWSN2UTyZsAHI+K2Wm8PsgZ9EnAZGWjMi4jPStqPbEb8b2Rz3VkR8R5JHyAf2TGWbNr5wajuB8rnNn6DbDr7W+CjEfHrWnZ1pd+fDGx+Xnm6l6zdvbv2a37V7H+VrLXaiAw2/jIihn2rBVs3I6k5q5mZmZkNXXsBt0i6jRxM5eODnJ/uDlc9zJ4cNfNL7axUzx2dRz53dG6LJO8mm+kuI2s6PytpT7Lv4F+TtUDfJZssb1Rdl/6brMkdS7ai+4tu23xJLXspMFXS/mSrtHeSTSfvI0cKRdJYskbq9PqurwKXSmp+QsExZKC7P1nTtWOlH0vWaJ1S6Q4im7juSjZpfhfDpPuN9S/XRJqZmZmZdUDVRE6LiO/U9GFkDeJPyUct/ENT2rvJQXSCDAB3jLpQl/RrciyPRk3kT4EtImJ5LT8beDQiPlXTm5FNQSeRAfGHI2Lvpu+6BvhuRJxTNZGzIuK0WvavwG4RcWhNvw34YkTsXsHqd4D3AtdHDsJlo5BrIs3MzMzMOqf5Ocn3kQOrvBT4uKTHGy9ykKAd6vVArFrTcx+rWtIIIMsOzWmqWe6jZI3iKsuattf8vOaHmj4/02J6s9ruVWT/0G+RIxyfKWmLnnbcRi4HkWZmZmZmndP8iKsJ5CAuC4DTImKrptcmEdEY8GrHxqjtTes1696UcBEZmAJ/GD17G3Igl1WWNW3vAXohIk6PiL3IkXt3JQcNslHGQaSZmZmZWeecIGmn6pv4GXK0ze8BH5S0Tz2mZVNJh9cjlK4hR/D8iKQxkt7B2p/Zej7wPkm7S9qIHBX1uoiYTw7Ms6ukv6ztvYt8+Pwl67ojkv648rwBOfrocrqepWmjiINIMzMzM7POOZ/sw3hPvb4UEbPJx498k+y7OA84DiAingPeUdNLycFrfrSmL4iIK4F/IB8xsph8dMQxtexR4K3kQEaPAp8C3rq20XR7sAUZAC8lm8Q+Sj6r0EYZD6xjZmZmZmZmbXNNpJmZmZmZmbWt40GkpOmSHq5n7rRaLkmnS5on6bZ6bo6ZWUe5bDIzMzPrnYGoiTwHOGQNyw8ln2EziXw2zhkDkCczs3Nw2WRmZma2zjoeREbEL4DH1pDkCOC8SNcCW0navtP5MrPRzWWTmZmZWe+MGewMkA86bX4I68Kat7h7QklTyRoBNt10071e+cpXDkgGzWxg3HjjjY9ExLjBzkdx2WRmwJArm3pl2223jYkTJw52Nsysnw1W+TQUgki1mNdyyNiIOBM4E2Dy5Mkxe/bsTubLzAaYpPsGOw9NXDaZGdC/ZZOkvwPeT5YntwPvA7YHZgBjgZuA90TEc/W8v/OAvchHKbyrnvuHpJOB48ln9H0kIq5Y0/dOnDgRl01mI89gXTsNhdFZFwLjm6Z3AhYNUl7MzBpcNplZv5K0I/ARYHJEvAZYn3yW31eAr0XEJPL5e8fXKscDSyNiF+BrlQ5Ju9V6ryb7dn9b0voDuS9mNroNhSByJvDeGglxX2BZRKzWXMzMbIC5bDKzThgDvEjSGGATson8/sBFtfxc4Mj6fERNU8sPkKSaPyMino2Ie8kH1e89QPk3M+t8c1ZJFwD7AdtKWgicAmwAEBHfAS4DDiMLwKfJZh1mZh3lssnMBlpEPCDpX4D7gWeAnwI3Ao9HxIpK1uh/DU19syNihaRlwDY1/9qmTTev8wfN/bUnTJjQ7/tjZqNXx4PIiDh2LcsDOKHT+TAza+ayycwGmqStyVrEnYHHgf8kHyfUXaP/dU99s9vqs929v3Yvsmxm1tJQaM5qZmZmNhq8Bbg3IpZExPPAj4A3kI8QatzYb+5//Ye+2bV8S/LRRO6zbWaDaiiMzmpmZmY2GtwP7CtpE7I56wHAbODnwFHkCK1TgIsr/cyavqaWXxURIWkmcL6krwI7AJOA6wdyR4aaiSdd2rFtz592eMe2bTZcOYg0MzMzGwARcZ2ki8jHeKwAbiabm14KzJD0pZp3dq1yNvB9SfPIGshjajtzJF0I3FnbOSEiVg7ozpjZqOYg0szMzGyARMQp5EBeze6hxeiqEbEcOLqH7ZwGnNbvGTQza4P7RJqZmZmZmVnbHESamZmZmZlZ2xxEmpmZmZmZWdscRJqZmZmZmVnbHESamZmZmZlZ2xxEmpmZmZmZWdscRJqZmZmZmVnbHESamZmZmZlZ2xxEmpmZmZmZWdscRJqZmZmZmVnbHESamZmZmZlZ2xxEmpmZmZmZWdscRJqZmZmZmVnbHESamZmZmZlZ2xxEmpmZmZmZWds6HkRKOkTS3ZLmSTqpxfIJkn4u6WZJt0k6rNN5MjMDl09mZmZmvdHRIFLS+sC3gEOB3YBjJe3WLdlngQsjYg/gGODbncyTmRm4fDIzMzPrrU7XRO4NzIuIeyLiOWAGcES3NAFsUZ+3BBZ1OE9mZuDyyczMzKxXOh1E7ggsaJpeWPOafR74f5IWApcBH+5pY5KmSpotafaSJUv6O69mNrr0W/nkssnMzMxGk04HkWoxL7pNHwucExE7AYcB35fUMl8RcWZETI6IyePGjevnrJrZKNNv5ZPLJjMzMxtNOh1ELgTGN03vxOrNwY4HLgSIiGuAjYFtO5wvMzOXT2ZmZma90Okg8gZgkqSdJW1IDkwxs1ua+4EDACS9irxIc3swM+s0l09mZmZmvdDRIDIiVgAnAlcAd5GjHM6RdKqkt1eyjwMfkHQrcAFwXER0b1JmZtavXD6ZmZmZ9c6YTn9BRFxGDkjRPO9zTZ/vBN7Y6XyYmXXn8snMzMxs3XW6OauZmZmZmZmNIA4izczMzAaIpK0kXSTpN5LukvR6SWMlzZI0t963rrSSdLqkeZJuk7Rn03amVPq5kqYM3h6Z2WjkINLMzMxs4HwDuDwiXgm8juyTfRJwZURMAq6saYBDgUn1mgqcASBpLHAKsA+wN3BKI/A0MxsIDiLNzMzMBoCkLYA3AWcDRMRzEfE4cARwbiU7FziyPh8BnBfpWmArSdsDBwOzIuKxiFgKzAIOGcBdMbNRzkGkmZmZ2cB4GfmYoH+XdLOksyRtCmwXEYsB6v3FlX5HYEHT+gtrXk/zVyFpqqTZkmYvWeKnE5lZ/3EQaWZmZjYwxgB7AmdExB7AU3Q1XW1FLebFGuavOiPizIiYHBGTx40b15v8mpm15CDSzMzMbGAsBBZGxHU1fREZVD5UzVSp94eb0o9vWn8nYNEa5puZDQgHkWZmZmYDICIeBBZIekXNOgC4E5gJNEZYnQJcXJ9nAu+tUVr3BZZVc9crgIMkbV0D6hxU88zMBsSYwc6AmZmZ2SjyYeAHkjYE7gHeR97Uv1DS8cD9wNGV9jLgMGAe8HSlJSIek/RF4IZKd2pEPDZwu2Bmo52DSDMzM7MBEhG3AJNbLDqgRdoATuhhO9OB6f2bOzOz9rg5q5mZmZmZmbXNQaSZmZmZmZm1zUGkmZmZmZmZtc1BpJmZmZmZmbXNA+uYmZmZmfVg4kmXdmS786cd3pHtmg0E10SamZmZmZlZ2xxEmpmZmZmZWdscRJqZmZmZmVnbHESamZmZmZlZ2zoeREo6RNLdkuZJOqmHNO+UdKekOZLO73SezMzA5ZOZmZlZb3R0dFZJ6wPfAg4EFgI3SJoZEXc2pZkEnAy8MSKWSnpxJ/NkZgYun8zMzMx6q9M1kXsD8yLinoh4DpgBHNEtzQeAb0XEUoCIeLjDeTIzA5dPZmZmZr3S6SByR2BB0/TCmtdsV2BXSb+SdK2kQ3ramKSpkmZLmr1kyZIOZNfMRpF+K59cNpmZmdlo0ukgUi3mRbfpMcAkYD/gWOAsSVu12lhEnBkRkyNi8rhx4/o1o2Y26vRb+eSyyczMzEaTTgeRC4HxTdM7AYtapLk4Ip6PiHuBu8mLNjOzTnL5ZGZmZtYLnQ4ibwAmSdpZ0obAMcDMbmn+G3gzgKRtyeZj93Q4X2ZmLp/MzMzMeqGjQWRErABOBK4A7gIujIg5kk6V9PZKdgXwqKQ7gZ8Dn4yIRzuZLzMzl09mZmZmvdPRR3wARMRlwGXd5n2u6XMAH6uXmdmAcflkZmZmtu463ZzVzMzMzMzMRhAHkWZmZmZmZtY2B5FmZmZmZmbWNgeRZmZmZmZm1jYHkWZmZmZmZtY2B5FmZmZmZmbWNgeRZmZmZmZm1jYHkWZmZmYDSNL6/7+9+4+xrC7vOP7+yAr+QkBYrd1dXIyrcWuaYjdIa9NaQUQwu/5B7ZJSMaEl/mq1mtg1NmpRE9BGtCm1UqAFqgKikQ1irOVHTE1BVrEIrMgWiGyhsi242hpR6tM/7ll6HefH2XHOPefOvF/JzZxz7nfOPM/ce5+Z555zvjfJLUmubtaPSnJTkruSXJ7kwGb7Qc36rub+9WP7eEez/c4kL+8nE0krlU2kJEnSZL0Z2Dm2fg5wblVtAB4Gzmi2nwE8XFXPAc5txpFkI7AV+CXgROCvkxwwodglySZSkiRpUpKsBU4GLmjWA7wUuLIZcjHwqmZ5S7NOc/9xzfgtwGVV9UhV3QPsAo6ZTAaSZBMpSZI0SR8G3g78pFk/HPhuVT3arO8G1jTLa4D7AJr79zbjH9s+y/c8JsmZSXYk2bFnz56lzkPSCmYTKUmSNAFJXgk8WFVfHd88y9Ba4L75vuf/N1SdX1WbqmrT6tWr9zteSZrLqr4DkCRJWiFeDGxOchLwBOCpjI5MHppkVXO0cS1wfzN+N7AO2J1kFXAI8NDY9n3Gv0eSOueRSEmSpAmoqndU1dqqWs9oYpzrqur3gOuBU5phpwNXNcvbm3Wa+6+rqmq2b21mbz0K2AB8ZUJpSJJHIiVJknr2p8BlSd4H3AJc2Gy/ELg0yS5GRyC3AlTV7UmuAO4AHgXeWFX/O/mwJa1UNpGSJEkTVlU3ADc0y3czy+yqVfVD4Hfm+P73A+/vLkJJmpuns0qSJEmSWrOJlCRJkiS1ZhMpSZIkSWqt8yYyyYlJ7kyyK8m2ecadkqSSbOo6JkkC65MkSdJidNpEJjkAOA94BbARODXJxlnGHQz8MXBTl/FI0j7WJ0mSpMXp+kjkMcCuqrq7qn4EXAZsmWXce4EPAD/sOB5J2sf6JEmStAhdN5FrgPvG1nc32x6T5GhgXVVd3XEskjTO+iRJkrQIXTeRmWVbPXZn8jjgXOBtrXaWnJlkR5Ide/bsWaIQJa1QS1afrE2SJGkl6bqJ3A2sG1tfC9w/tn4w8ALghiT3AscC2+eavKKqzq+qTVW1afXq1R2FLGmFWLL6ZG2SJEkrSddN5M3AhiRHJTkQ2Aps33dnVe2tqiOqan1VrQduBDZX1Y6O45Ik65MkSdIidNpEVtWjwJuALwA7gSuq6vYkZyXZ3OXPlqT5WJ8kSZIWZ1XXP6CqrgGumbHtXXOMfUnX8UjSPtYnSZKk/df16aySJEmSpGXEJlKSJEmS1JpNpCRJkiSpNZtISZIkSVJrNpGSJEmSpNZsIiVJkiRJrdlESpIkSZJas4mUJEmSJLVmEylJkiRJas0mUpIkSZLUmk2kJEmSJKk1m0hJkiRJUms2kZIkSZKk1lb1HYAkSdJKkGQdcAnwC8BPgPOr6iNJngZcDqwH7gVeXVUPJwnwEeAk4AfAa6vqa82+Tgf+rNn1+6rq4knmsljrt32u7xAkLQGPREqSJE3Go8Dbqur5wLHAG5NsBLYB11bVBuDaZh3gFcCG5nYm8FGApul8N/Ai4Bjg3UkOm2QiklY2m0hJkqQJqKoH9h1JrKrvAzuBNcAWYN+RxIuBVzXLW4BLauRG4NAkzwReDnyxqh6qqoeBLwInTjAVSSucTaQkSdKEJVkPHA3cBDyjqh6AUaMJPL0Ztga4b+zbdjfb5touSRNhEylJkjRBSZ4CfBp4S1V9b76hs2yrebbP/DlnJtmRZMeePXsWF6wkzcImUpIkaUKSPJ5RA/nxqvpMs/k7zWmqNF8fbLbvBtaNffta4P55tv+Uqjq/qjZV1abVq1cvbSKSVrTOm8gkJya5M8muJNtmuf+tSe5IcmuSa5M8q+uYJAmsT5Imq5lt9UJgZ1V9aOyu7cDpzfLpwFVj21+TkWOBvc3prl8ATkhyWDOhzgnNNkmaiE6byCQHAOcxml1sI3BqMwvZuFuATVX1y8CVwAe6jEmSwPokqRcvBn4feGmSrze3k4CzgZcluQt4WbMOcA1wN7AL+FvgDQBV9RDwXuDm5nZWs02SJqLrz4k8BthVVXcDJLmM0Uxjd+wbUFXXj42/ETit45gkCaxPkiasqv6Z2a9nBDhulvEFvHGOfV0EXLR00UlSe12fzrq/s4edAXy+04gkacT6JEmStAhdH4lsNXsYQJLTgE3Ab825s+RMRh+2y5FHHrkU8UlauZasPlmbJEnSStL1kchWs4clOR54J7C5qh6Za2fOMiZpCS1ZfbI2SZKklaTrJvJmYEOSo5IcCGxlNNPYY5IcDXyM0T9oD86yD0nqgvVJkiRpETptIqvqUeBNjKad3glcUVW3JzkryeZm2AeBpwCfamYp2z7H7iRpyVifJEmSFqfrayKpqmsYTVE9vu1dY8vHdx2DJM3G+iRJkrT/uj6dVZIkSZK0jNhESpIkSZJa6/x0VkmSJEk/bf22z3Wy33vPPrmT/UrjPBIpSZIkSWrNJlKSJEmS1JpNpCRJkiSpNZtISZIkSVJrNpGSJEmSpNZsIiVJkiRJrdlESpIkSZJas4mUJEmSJLVmEylJkiRJas0mUpIkSZLUmk2kJEmSJKk1m0hJkiRJUms2kZIkSZKk1mwiJUmSJEmt2URKkiRJklqziZQkSZIktbaq7wAkSZIkLY312z7X2b7vPfvkzvat6dJ5E5nkROAjwAHABVV19oz7DwIuAX4V+C/gd6vq3q7jkiTrk6RptlAN+3l02YhImn6dns6a5ADgPOAVwEbg1CQbZww7A3i4qp4DnAuc02VMkgTWJ0nTrWUNk6ROdH0k8hhgV1XdDZDkMmALcMfYmC3Ae5rlK4G/SpKqqo5jk7SyWZ8kTbM2NUxaUl0dofY02enTdRO5BrhvbH038KK5xlTVo0n2AocD/zlzZ0nOBM5sVh9JctuSRzxZRzBLnlPGHIZjOeTxvAn+rCWrT9amQVoOOcDyyGM55DDJ2tTWgjVsRm367yR3Tii2+Uzj82HaYp62eMk50xczw/k9P6uPH9p1E5lZts18B7/NmNHGqvOB8wGS7KiqTT9feP0yh2FYDjnA8sgjyY5J/rhZti2qPlmbhmc55ADLI4/lkkPfMcxiwfo0XpuGYhqfD9MW87TFC8Y8jbr+iI/dwLqx9bXA/XONSbIKOAR4qOO4JMn6JGmatalhktSJrpvIm4ENSY5KciCwFdg+Y8x24PRm+RTgOq83kjQB1idJ06xNDZOkTnR6OmtzDdGbgC8wmn76oqq6PclZwI6q2g5cCFyaZBejd/i3ttz9oE7PWCRzGIblkAMsjzwmlkOH9cnHYRiWQw6wPPIwhw7MVcN6DquNwf0uW5i2mKctXjDmqRPfVJckSZIktdX16aySJEmSpGXEJlKSJEmS1Nqgm8gkJya5M8muJNtmuf+gJJc399+UZP3ko5xfixzemuSOJLcmuTZJL5/1spCF8hgbd0qSSjK4KY/b5JDk1c3jcXuST0w6xoW0eD4dmeT6JLc0z6mT+ohzPkkuSvLgXJ+lmJG/bHK8NckLJx1jG9anYbA2Dce016flUpuGKsm65vHf2TyP39x3TG0kOaB5zl7ddyxtJDk0yZVJvtn8rn+t75gWkuRPmufEbUk+meQJfcc002z1IcnTknwxyV3N18P6jHHiqmqQN0YXif8b8GzgQOBfgY0zxrwB+JtmeStwed9xLyKH3wae1Cy/fmg5tM2jGXcw8CXgRmBT33Ev4rHYANwCHNasP73vuBeRw/nA65vljcC9fcc9Sx6/CbwQuG2O+08CPs/oM9COBW7qO+ZFPhbWpwHk0IyzNg0jj0HXp+VQm4Z8A54JvLBZPhj41myv16HdgLcCnwCu7juWlvFeDPxBs3wgcGjfMS0Q7xrgHuCJzfoVwGv7jmuWOH+mPgAfALY1y9uAc/qOc5K3IR+JPAbYVVV3V9WPgMuALTPGbGH0YgG4EjguyWwfvtuXBXOoquur6gfN6o2MPudpaNo8FgDvZfSC+uEkg2upTQ5/CJxXVQ8DVNWDE45xIW1yKOCpzfIhDPAzw6rqS8z/WYu6MqniAAAEPElEQVRbgEtq5Ebg0CTPnEx0rVmfhsHaNBxTX5+WSW0arKp6oKq+1ix/H9jJqIEYrCRrgZOBC/qOpY0kT2XU7FwIUFU/qqrv9htVK6uAJ2b0ecxPYmC1AeasD+N/5y8GXjXRoHo25CZyDXDf2PpufrbYPDamqh4F9gKHTyS6dtrkMO4MRu9yDs2CeSQ5GlhXVUM93aPNY/Fc4LlJvpzkxiQnTiy6dtrk8B7gtCS7gWuAP5pMaEtqf183fbA+DYO1aThWQn2ahto0FZrT+48Gbuo3kgV9GHg78JO+A2np2cAe4O+aU3AvSPLkvoOaT1X9O/AXwLeBB4C9VfWP/UbV2jOq6gEYvUkCPL3neCZqyE3kbO/Yz/w8kjZj+tQ6viSnAZuAD3Ya0eLMm0eSxwHnAm+bWET7r81jsYrRaWMvAU4FLkhyaMdx7Y82OZwK/H1VrWV06tWlzeMzTYb+ugbr01BYm4ZjJdSnob+mp0KSpwCfBt5SVd/rO565JHkl8GBVfbXvWPbDKkanXH60qo4G/ofRaZaD1VxHuAU4CvhF4MnN3xwN3JCL925g3dj6Wn728PZjY5pD4Icw/6kok9YmB5IcD7wT2FxVj0wotv2xUB4HAy8AbkhyL6NrRbYPbAKLts+nq6rqx1V1D3Ano3/chqJNDmcwup6AqvoX4AnAEROJbum0et30zPo0DNam4VgJ9WkaatOgJXk8owby41X1mb7jWcCLgc1N7bgMeGmSf+g3pAXtBnZX1b4jvFcyaiqH7HjgnqraU1U/Bj4D/HrPMbX1nX2ntDdfh3ipQWeG3ETeDGxIclSSAxlNTLF9xpjtwOnN8inAdVU1pHcFF8yhOdXqY4z+QRvqk2/ePKpqb1UdUVXrq2o9o2unNlfVjn7CnVWb59NnGU0kQpIjGJ1CdvdEo5xfmxy+DRwHkOT5jP5J2zPRKH9+24HXNDMhHsvo1JYH+g5qBuvTMFibhmMl1KdpqE2D1VwTfiGws6o+1Hc8C6mqd1TV2qZ2bGVUwwd9hKyq/gO4L8nzmk3HAXf0GFIb3waOTfKk5jlyHKPrZafB+N/504Greoxl8vqa0afNjdHpLt9iNOPbO5ttZzH6JwBGf4A+BewCvgI8u++YF5HDPwHfAb7e3Lb3HfNi8pgx9gYGNgNiy8ciwIcYFdxvAFv7jnkROWwEvsxoZsSvAyf0HfMsOXyS0XUPP2b0rukZwOuA1409Duc1OX5jiM+llo+F9WkAOcwYa23qN49B16flUpuGegN+g9Hpv7eO1ZST+o6rZewvYXpmZ/0VYEfze/4szazOQ74Bfw58E7gNuBQ4qO+YZolxtvpwOHAtcFfz9Wl9xznJW5pfjCRJkiRJCxry6aySJEmSpIGxiZQkSZIktWYTKUmSJElqzSZSkiRJktSaTaQkSZIkqTWbSEmSJElSazaRkiRJkqTW/g8QM5hF8QlYYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histograms(reg_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally distributed variables are not a requirement of Linear Regression, but this helps us understand our data better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check For Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_multicollinearity(df, independent_var_name, cutoff):\n",
    "    copy_df = df.drop(independent_var_name, axis=1)\n",
    "    # copy_df = reg_df\n",
    "\n",
    "    # save absolute value of correlation matrix as a data frame\n",
    "    # converts all values to absolute value\n",
    "    # stacks the row:column pairs into a multindex\n",
    "    # reset the index to set the multindex to seperate columns\n",
    "    # sort values. 0 is the column automatically generated by the stacking\n",
    "\n",
    "    pair_df=copy_df.corr().abs().stack().reset_index().sort_values(0, ascending=False)\n",
    "\n",
    "    # zip the variable name columns (Which were only named level_0 and level_1 by default) in a new column named \"pairs\"\n",
    "    pair_df['pairs'] = list(zip(pair_df.level_0, pair_df.level_1))\n",
    "\n",
    "    # set index to pairs\n",
    "    pair_df.set_index(['pairs'], inplace = True)\n",
    "\n",
    "    #drop level columns\n",
    "    pair_df.drop(columns=['level_1', 'level_0'], inplace = True)\n",
    "\n",
    "    # rename correlation column\n",
    "    pair_df.columns = ['correlation_coefficient']\n",
    "\n",
    "    # drop duplicates. \n",
    "    pair_df.drop_duplicates(inplace=True)\n",
    "    return pair_df[(pair_df.correlation_coefficient>cutoff) & (pair_df.correlation_coefficient <1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>correlation_coefficient</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pairs</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(grade, sqft_living)</th>\n",
       "      <td>0.750909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(bathrooms, sqft_living)</th>\n",
       "      <td>0.742246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(grade, bathrooms)</th>\n",
       "      <td>0.650483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          correlation_coefficient\n",
       "pairs                                            \n",
       "(grade, sqft_living)                     0.750909\n",
       "(bathrooms, sqft_living)                 0.742246\n",
       "(grade, bathrooms)                       0.650483"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_multicollinearity(reg_df, 'price_100k', 0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grade, sqft_living, and bathrooms are correlated with eachother, so interpreting the coefficients of these variables will be inaccurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create New Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create top_5_district**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code converts district names into a single binary column.  This new column shows a '1' if the house is in one of the top 5 King County public school districts, and a '0' otherwise.  This metric is more interpretable than having 20 different categorical variables for district (one for each district)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>season_sold</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>district</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1978</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>Lake Washington</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1996</td>\n",
       "      <td>Summer</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>Renton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>Winter</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1992</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>Snoqualmie Valley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1959</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>Issaquah</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  floors  waterfront  condition  \\\n",
       "0         5       2.50         3470  9.392912     2.0         0.0          3   \n",
       "1         5       2.75         2990  8.911934     2.0         0.0          3   \n",
       "2         4       2.75         2900  8.764053     2.0         0.0          3   \n",
       "3         3       2.50         2620  9.979847     2.0         0.0          3   \n",
       "4         3       1.50         1770  9.753130     1.0         0.0          3   \n",
       "\n",
       "   grade  yr_of_last_update season_sold  below_ground_ratio  price_100k  \\\n",
       "0      8               1978      Spring            0.000000       5.990   \n",
       "1      8               1996      Summer            0.000000       4.985   \n",
       "2      7               1970      Winter            0.421569       4.500   \n",
       "3      7               1992      Spring            0.000000       6.720   \n",
       "4      8               1959      Spring            0.525862       4.500   \n",
       "\n",
       "            district  is_seattle  top_5_district  \n",
       "0    Lake Washington         0.0             1.0  \n",
       "1             Renton         0.0             0.0  \n",
       "2            Seattle         1.0             0.0  \n",
       "3  Snoqualmie Valley         0.0             1.0  \n",
       "4           Issaquah         0.0             1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://backgroundchecks.org/top-school-districts-in-washington-2018.html\n",
    "# Rankings based on student performance (math and reading test scores), dropout rates, school funding, and area poverty rates\n",
    "top_5_str = 'Mercer Island|Lake Washington|Snoqualmie Valley|Bellevue|Issaquah'\n",
    "reg_df.loc[reg_df['district'].str.contains(top_5_str),'top_5_district'] = 1\n",
    "reg_df['top_5_district'].fillna(0, inplace=True)\n",
    "reg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop old district variable\n",
    "reg_df.drop(['district'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get Season Dummy Variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the effect that season has on the price of a home, relative to the price of homes sold in the Fall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummy var for season_sold, with Summer as the reference variable\n",
    "seasons = pd.get_dummies(reg_df['season_sold'], drop_first=True)\n",
    "reg_df = pd.concat([reg_df, seasons], axis=1)\n",
    "# reg_df.drop(['Summer'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>Spring</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Winter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  floors  waterfront  condition  \\\n",
       "0         5       2.50         3470  9.392912     2.0         0.0          3   \n",
       "1         5       2.75         2990  8.911934     2.0         0.0          3   \n",
       "2         4       2.75         2900  8.764053     2.0         0.0          3   \n",
       "3         3       2.50         2620  9.979847     2.0         0.0          3   \n",
       "4         3       1.50         1770  9.753130     1.0         0.0          3   \n",
       "\n",
       "   grade  yr_of_last_update  below_ground_ratio  price_100k  is_seattle  \\\n",
       "0      8               1978            0.000000       5.990         0.0   \n",
       "1      8               1996            0.000000       4.985         0.0   \n",
       "2      7               1970            0.421569       4.500         1.0   \n",
       "3      7               1992            0.000000       6.720         0.0   \n",
       "4      8               1959            0.525862       4.500         0.0   \n",
       "\n",
       "   top_5_district  Spring  Summer  Winter  \n",
       "0             1.0       1       0       0  \n",
       "1             0.0       0       1       0  \n",
       "2             0.0       0       0       1  \n",
       "3             1.0       1       0       0  \n",
       "4             1.0       1       0       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df = reg_df.drop(['season_sold'], axis=1)\n",
    "reg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterate Through Regression Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of brevity, early models are not included here.  Early models treated Floors, Grade, and Condition as continuous variables, but R-Squared was improved by treating them as categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check Residual Homoscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check Homoskedasticity\n",
    "def gq_test(data, model):\n",
    "    \n",
    "    #Graph heteroskedasticity\n",
    "    pred_val = model.fittedvalues.copy()\n",
    "#     true_val = data['price_100k'].values.copy()\n",
    "    residual = model.resid\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    graph = ax.scatter(pred_val, residual)\n",
    "    \n",
    "    # Run Goldfeld Quandt test\n",
    "    name = ['F statistic', 'p-value']\n",
    "    test = sms.het_goldfeldquandt(model.resid, model.model.exog)\n",
    "    return list(zip(name, test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression v1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Turn Continuous Variables into Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>Spring</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Winter</th>\n",
       "      <th>floors_1h</th>\n",
       "      <th>floors_2</th>\n",
       "      <th>floors_2h</th>\n",
       "      <th>floors_3</th>\n",
       "      <th>floors_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  condition  grade  \\\n",
       "0         5       2.50         3470  9.392912         0.0          3      8   \n",
       "1         5       2.75         2990  8.911934         0.0          3      8   \n",
       "2         4       2.75         2900  8.764053         0.0          3      7   \n",
       "3         3       2.50         2620  9.979847         0.0          3      7   \n",
       "4         3       1.50         1770  9.753130         0.0          3      8   \n",
       "\n",
       "   yr_of_last_update  below_ground_ratio  price_100k  is_seattle  \\\n",
       "0               1978            0.000000       5.990         0.0   \n",
       "1               1996            0.000000       4.985         0.0   \n",
       "2               1970            0.421569       4.500         1.0   \n",
       "3               1992            0.000000       6.720         0.0   \n",
       "4               1959            0.525862       4.500         0.0   \n",
       "\n",
       "   top_5_district  Spring  Summer  Winter  floors_1h  floors_2  floors_2h  \\\n",
       "0             1.0       1       0       0          0         1          0   \n",
       "1             0.0       0       1       0          0         1          0   \n",
       "2             0.0       0       0       1          0         1          0   \n",
       "3             1.0       1       0       0          0         1          0   \n",
       "4             1.0       1       0       0          0         0          0   \n",
       "\n",
       "   floors_3  floors_3h  \n",
       "0         0          0  \n",
       "1         0          0  \n",
       "2         0          0  \n",
       "3         0          0  \n",
       "4         0          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Floors\n",
    "reg_df2 = reg_df\n",
    "floors = pd.get_dummies(reg_df2['floors'], prefix='floors', drop_first=True)\n",
    "reg_df2 = pd.concat([reg_df2, floors], axis=1)\n",
    "reg_df2.drop('floors', axis=1, inplace=True)\n",
    "\n",
    "#Rename floors so no decimal in column name\n",
    "reg_df2.rename(columns={'floors_1.5':'floors_1h','floors_2.0':'floors_2','floors_2.5':'floors_2h','floors_3.0':'floors_3','floors_3.5':'floors_3h'}, inplace=True)\n",
    "reg_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_4</th>\n",
       "      <th>grade_5</th>\n",
       "      <th>grade_6</th>\n",
       "      <th>grade_7</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  condition  \\\n",
       "0         5       2.50         3470  9.392912         0.0          3   \n",
       "1         5       2.75         2990  8.911934         0.0          3   \n",
       "2         4       2.75         2900  8.764053         0.0          3   \n",
       "3         3       2.50         2620  9.979847         0.0          3   \n",
       "4         3       1.50         1770  9.753130         0.0          3   \n",
       "\n",
       "   yr_of_last_update  below_ground_ratio  price_100k  is_seattle  ...  \\\n",
       "0               1978            0.000000       5.990         0.0  ...   \n",
       "1               1996            0.000000       4.985         0.0  ...   \n",
       "2               1970            0.421569       4.500         1.0  ...   \n",
       "3               1992            0.000000       6.720         0.0  ...   \n",
       "4               1959            0.525862       4.500         0.0  ...   \n",
       "\n",
       "   grade_4  grade_5  grade_6  grade_7  grade_8  grade_9  grade_10  grade_11  \\\n",
       "0        0        0        0        0        1        0         0         0   \n",
       "1        0        0        0        0        1        0         0         0   \n",
       "2        0        0        0        1        0        0         0         0   \n",
       "3        0        0        0        1        0        0         0         0   \n",
       "4        0        0        0        0        1        0         0         0   \n",
       "\n",
       "   grade_12  grade_13  \n",
       "0         0         0  \n",
       "1         0         0  \n",
       "2         0         0  \n",
       "3         0         0  \n",
       "4         0         0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grade - Data in master table ranges from 3-13\n",
    "grade = pd.get_dummies(reg_df2['grade'], prefix='grade', drop_first=True)\n",
    "reg_df2 = pd.concat([reg_df2, grade], axis=1)\n",
    "reg_df2.drop('grade', axis=1, inplace=True)\n",
    "reg_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_3</th>\n",
       "      <th>cond_4</th>\n",
       "      <th>cond_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  yr_of_last_update  \\\n",
       "0         5       2.50         3470  9.392912         0.0               1978   \n",
       "1         5       2.75         2990  8.911934         0.0               1996   \n",
       "2         4       2.75         2900  8.764053         0.0               1970   \n",
       "3         3       2.50         2620  9.979847         0.0               1992   \n",
       "4         3       1.50         1770  9.753130         0.0               1959   \n",
       "\n",
       "   below_ground_ratio  price_100k  is_seattle  top_5_district  ...  grade_8  \\\n",
       "0            0.000000       5.990         0.0             1.0  ...        1   \n",
       "1            0.000000       4.985         0.0             0.0  ...        1   \n",
       "2            0.421569       4.500         1.0             0.0  ...        0   \n",
       "3            0.000000       6.720         0.0             1.0  ...        0   \n",
       "4            0.525862       4.500         0.0             1.0  ...        1   \n",
       "\n",
       "   grade_9  grade_10  grade_11  grade_12  grade_13  cond_2  cond_3  cond_4  \\\n",
       "0        0         0         0         0         0       0       1       0   \n",
       "1        0         0         0         0         0       0       1       0   \n",
       "2        0         0         0         0         0       0       1       0   \n",
       "3        0         0         0         0         0       0       1       0   \n",
       "4        0         0         0         0         0       0       1       0   \n",
       "\n",
       "   cond_5  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Condition \n",
    "condition = pd.get_dummies(reg_df2['condition'], prefix='cond', drop_first=True)\n",
    "reg_df2 = pd.concat([reg_df2, condition], axis=1)\n",
    "reg_df2.drop('condition', axis=1, inplace=True)\n",
    "reg_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>price_100k</td>    <th>  R-squared:         </th> <td>   0.723</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.722</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1433.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 09 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:27</td>     <th>  Log-Likelihood:    </th> <td> -31441.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17094</td>      <th>  AIC:               </th> <td>6.295e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17062</td>      <th>  BIC:               </th> <td>6.319e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>Intercept</th>          <td>   27.1604</td> <td>    2.044</td> <td>   13.289</td> <td> 0.000</td> <td>   23.154</td> <td>   31.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>           <td>   -0.1211</td> <td>    0.017</td> <td>   -6.992</td> <td> 0.000</td> <td>   -0.155</td> <td>   -0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>          <td>    0.2793</td> <td>    0.028</td> <td>    9.859</td> <td> 0.000</td> <td>    0.224</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>        <td>    0.0014</td> <td>  3.1e-05</td> <td>   43.849</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_lot</th>            <td>    0.0482</td> <td>    0.017</td> <td>    2.754</td> <td> 0.006</td> <td>    0.014</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>         <td>    4.8166</td> <td>    0.169</td> <td>   28.543</td> <td> 0.000</td> <td>    4.486</td> <td>    5.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_of_last_update</th>  <td>   -0.0139</td> <td>    0.001</td> <td>  -20.671</td> <td> 0.000</td> <td>   -0.015</td> <td>   -0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>below_ground_ratio</th> <td>   -0.3800</td> <td>    0.053</td> <td>   -7.231</td> <td> 0.000</td> <td>   -0.483</td> <td>   -0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_seattle</th>         <td>    2.1047</td> <td>    0.037</td> <td>   57.569</td> <td> 0.000</td> <td>    2.033</td> <td>    2.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top_5_district</th>     <td>    1.8094</td> <td>    0.030</td> <td>   59.695</td> <td> 0.000</td> <td>    1.750</td> <td>    1.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Spring</th>             <td>    0.2767</td> <td>    0.032</td> <td>    8.607</td> <td> 0.000</td> <td>    0.214</td> <td>    0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Summer</th>             <td>    0.0262</td> <td>    0.032</td> <td>    0.810</td> <td> 0.418</td> <td>   -0.037</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Winter</th>             <td>    0.0703</td> <td>    0.037</td> <td>    1.895</td> <td> 0.058</td> <td>   -0.002</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_1h</th>          <td>   -0.0031</td> <td>    0.047</td> <td>   -0.067</td> <td> 0.947</td> <td>   -0.095</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_2</th>           <td>   -0.2617</td> <td>    0.039</td> <td>   -6.637</td> <td> 0.000</td> <td>   -0.339</td> <td>   -0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_2h</th>          <td>    0.2624</td> <td>    0.150</td> <td>    1.750</td> <td> 0.080</td> <td>   -0.031</td> <td>    0.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3</th>           <td>   -0.0305</td> <td>    0.087</td> <td>   -0.352</td> <td> 0.725</td> <td>   -0.201</td> <td>    0.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3h</th>          <td>   -0.1524</td> <td>    0.882</td> <td>   -0.173</td> <td> 0.863</td> <td>   -1.881</td> <td>    1.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_4</th>            <td>   -0.5704</td> <td>    1.565</td> <td>   -0.365</td> <td> 0.715</td> <td>   -3.637</td> <td>    2.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_5</th>            <td>   -0.6333</td> <td>    1.529</td> <td>   -0.414</td> <td> 0.679</td> <td>   -3.630</td> <td>    2.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_6</th>            <td>   -0.5888</td> <td>    1.526</td> <td>   -0.386</td> <td> 0.700</td> <td>   -3.579</td> <td>    2.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_7</th>            <td>    0.0190</td> <td>    1.526</td> <td>    0.012</td> <td> 0.990</td> <td>   -2.971</td> <td>    3.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_8</th>            <td>    0.8375</td> <td>    1.526</td> <td>    0.549</td> <td> 0.583</td> <td>   -2.154</td> <td>    3.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_9</th>            <td>    2.0868</td> <td>    1.527</td> <td>    1.367</td> <td> 0.172</td> <td>   -0.906</td> <td>    5.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_10</th>           <td>    3.3062</td> <td>    1.528</td> <td>    2.164</td> <td> 0.030</td> <td>    0.312</td> <td>    6.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_11</th>           <td>    4.6901</td> <td>    1.531</td> <td>    3.064</td> <td> 0.002</td> <td>    1.690</td> <td>    7.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_12</th>           <td>    7.0178</td> <td>    1.544</td> <td>    4.544</td> <td> 0.000</td> <td>    3.991</td> <td>   10.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_13</th>           <td>    9.2811</td> <td>    2.159</td> <td>    4.299</td> <td> 0.000</td> <td>    5.049</td> <td>   13.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_2</th>             <td>   -0.0801</td> <td>    0.346</td> <td>   -0.231</td> <td> 0.817</td> <td>   -0.759</td> <td>    0.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_3</th>             <td>    0.2067</td> <td>    0.321</td> <td>    0.645</td> <td> 0.519</td> <td>   -0.422</td> <td>    0.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_4</th>             <td>    0.4852</td> <td>    0.321</td> <td>    1.513</td> <td> 0.130</td> <td>   -0.143</td> <td>    1.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_5</th>             <td>    0.8998</td> <td>    0.323</td> <td>    2.790</td> <td> 0.005</td> <td>    0.268</td> <td>    1.532</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5653.548</td> <th>  Durbin-Watson:     </th> <td>   2.005</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>35979.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.441</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.497</td>  <th>  Cond. No.          </th> <td>1.28e+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, 1.28e+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:             price_100k   R-squared:                       0.723\n",
       "Model:                            OLS   Adj. R-squared:                  0.722\n",
       "Method:                 Least Squares   F-statistic:                     1433.\n",
       "Date:                Thu, 09 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        17:44:27   Log-Likelihood:                -31441.\n",
       "No. Observations:               17094   AIC:                         6.295e+04\n",
       "Df Residuals:                   17062   BIC:                         6.319e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             27.1604      2.044     13.289      0.000      23.154      31.166\n",
       "bedrooms              -0.1211      0.017     -6.992      0.000      -0.155      -0.087\n",
       "bathrooms              0.2793      0.028      9.859      0.000       0.224       0.335\n",
       "sqft_living            0.0014    3.1e-05     43.849      0.000       0.001       0.001\n",
       "log_lot                0.0482      0.017      2.754      0.006       0.014       0.082\n",
       "waterfront             4.8166      0.169     28.543      0.000       4.486       5.147\n",
       "yr_of_last_update     -0.0139      0.001    -20.671      0.000      -0.015      -0.013\n",
       "below_ground_ratio    -0.3800      0.053     -7.231      0.000      -0.483      -0.277\n",
       "is_seattle             2.1047      0.037     57.569      0.000       2.033       2.176\n",
       "top_5_district         1.8094      0.030     59.695      0.000       1.750       1.869\n",
       "Spring                 0.2767      0.032      8.607      0.000       0.214       0.340\n",
       "Summer                 0.0262      0.032      0.810      0.418      -0.037       0.090\n",
       "Winter                 0.0703      0.037      1.895      0.058      -0.002       0.143\n",
       "floors_1h             -0.0031      0.047     -0.067      0.947      -0.095       0.089\n",
       "floors_2              -0.2617      0.039     -6.637      0.000      -0.339      -0.184\n",
       "floors_2h              0.2624      0.150      1.750      0.080      -0.031       0.556\n",
       "floors_3              -0.0305      0.087     -0.352      0.725      -0.201       0.140\n",
       "floors_3h             -0.1524      0.882     -0.173      0.863      -1.881       1.576\n",
       "grade_4               -0.5704      1.565     -0.365      0.715      -3.637       2.497\n",
       "grade_5               -0.6333      1.529     -0.414      0.679      -3.630       2.364\n",
       "grade_6               -0.5888      1.526     -0.386      0.700      -3.579       2.401\n",
       "grade_7                0.0190      1.526      0.012      0.990      -2.971       3.009\n",
       "grade_8                0.8375      1.526      0.549      0.583      -2.154       3.829\n",
       "grade_9                2.0868      1.527      1.367      0.172      -0.906       5.079\n",
       "grade_10               3.3062      1.528      2.164      0.030       0.312       6.301\n",
       "grade_11               4.6901      1.531      3.064      0.002       1.690       7.690\n",
       "grade_12               7.0178      1.544      4.544      0.000       3.991      10.045\n",
       "grade_13               9.2811      2.159      4.299      0.000       5.049      13.513\n",
       "cond_2                -0.0801      0.346     -0.231      0.817      -0.759       0.599\n",
       "cond_3                 0.2067      0.321      0.645      0.519      -0.422       0.835\n",
       "cond_4                 0.4852      0.321      1.513      0.130      -0.143       1.114\n",
       "cond_5                 0.8998      0.323      2.790      0.005       0.268       1.532\n",
       "==============================================================================\n",
       "Omnibus:                     5653.548   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            35979.009\n",
       "Skew:                           1.441   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.497   Cond. No.                     1.28e+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, 1.28e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome = 'price_100k'\n",
    "predictors = reg_df2.drop('price_100k', axis=1)\n",
    "pred_sum = '+'.join(predictors.columns)\n",
    "formula = outcome + '~' + pred_sum\n",
    "model = ols(formula=formula, data=reg_df2).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check Normality of Residuals**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Normality check for residuals with QQ plot\n",
    "resid1 = model.resid\n",
    "fig = sm.graphics.qqplot(resid1, dist=stats.norm, line='45', fit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates serious issues with normality.  The assumption of normal residuals is not met."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check Heteroscedasticity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F statistic', 1.036604823912329), ('p-value', 0.04858643950891584)]"
      ]
     },
     "execution_count": 24,
     "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": [
    "gq_test(reg_df2, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not a worst-case scenario, but a visual inspection shows that there is some heteroscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the reference variables in the regression's categorical variables were adjusted to more accurately reflect the 'average' house."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>Spring</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Winter</th>\n",
       "      <th>floors_1</th>\n",
       "      <th>floors_1h</th>\n",
       "      <th>floors_2h</th>\n",
       "      <th>floors_3</th>\n",
       "      <th>floors_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  condition  grade  \\\n",
       "0         5       2.50         3470  9.392912         0.0          3      8   \n",
       "1         5       2.75         2990  8.911934         0.0          3      8   \n",
       "2         4       2.75         2900  8.764053         0.0          3      7   \n",
       "3         3       2.50         2620  9.979847         0.0          3      7   \n",
       "4         3       1.50         1770  9.753130         0.0          3      8   \n",
       "\n",
       "   yr_of_last_update  below_ground_ratio  price_100k  is_seattle  \\\n",
       "0               1978            0.000000       5.990         0.0   \n",
       "1               1996            0.000000       4.985         0.0   \n",
       "2               1970            0.421569       4.500         1.0   \n",
       "3               1992            0.000000       6.720         0.0   \n",
       "4               1959            0.525862       4.500         0.0   \n",
       "\n",
       "   top_5_district  Spring  Summer  Winter  floors_1  floors_1h  floors_2h  \\\n",
       "0             1.0       1       0       0         0          0          0   \n",
       "1             0.0       0       1       0         0          0          0   \n",
       "2             0.0       0       0       1         0          0          0   \n",
       "3             1.0       1       0       0         0          0          0   \n",
       "4             1.0       1       0       0         1          0          0   \n",
       "\n",
       "   floors_3  floors_3h  \n",
       "0         0          0  \n",
       "1         0          0  \n",
       "2         0          0  \n",
       "3         0          0  \n",
       "4         0          0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Floors\n",
    "reg_df3 = reg_df\n",
    "floors = pd.get_dummies(reg_df3['floors'], prefix='floors')\n",
    "reg_df3 = pd.concat([reg_df3, floors], axis=1)\n",
    "reg_df3.drop('floors', axis=1, inplace=True)\n",
    "\n",
    "#Drop floor 2 to make it ref var\n",
    "reg_df3.drop('floors_2.0', axis=1, inplace=True)\n",
    "\n",
    "#Rename floors so no decimal in column name\n",
    "reg_df3.rename(columns={'floors_1.0':'floors_1', 'floors_1.5':'floors_1h','floors_2.0':'floors_2','floors_2.5':'floors_2h','floors_3.0':'floors_3','floors_3.5':'floors_3h'}, inplace=True)\n",
    "\n",
    "reg_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>condition</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_3</th>\n",
       "      <th>grade_4</th>\n",
       "      <th>grade_5</th>\n",
       "      <th>grade_6</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  condition  \\\n",
       "0         5       2.50         3470  9.392912         0.0          3   \n",
       "1         5       2.75         2990  8.911934         0.0          3   \n",
       "2         4       2.75         2900  8.764053         0.0          3   \n",
       "3         3       2.50         2620  9.979847         0.0          3   \n",
       "4         3       1.50         1770  9.753130         0.0          3   \n",
       "\n",
       "   yr_of_last_update  below_ground_ratio  price_100k  is_seattle  ...  \\\n",
       "0               1978            0.000000       5.990         0.0  ...   \n",
       "1               1996            0.000000       4.985         0.0  ...   \n",
       "2               1970            0.421569       4.500         1.0  ...   \n",
       "3               1992            0.000000       6.720         0.0  ...   \n",
       "4               1959            0.525862       4.500         0.0  ...   \n",
       "\n",
       "   grade_3  grade_4  grade_5  grade_6  grade_8  grade_9  grade_10  grade_11  \\\n",
       "0        0        0        0        0        1        0         0         0   \n",
       "1        0        0        0        0        1        0         0         0   \n",
       "2        0        0        0        0        0        0         0         0   \n",
       "3        0        0        0        0        0        0         0         0   \n",
       "4        0        0        0        0        1        0         0         0   \n",
       "\n",
       "   grade_12  grade_13  \n",
       "0         0         0  \n",
       "1         0         0  \n",
       "2         0         0  \n",
       "3         0         0  \n",
       "4         0         0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grade - Data in master table ranges from 3-13\n",
    "grade = pd.get_dummies(reg_df3['grade'], prefix='grade')\n",
    "reg_df3 = pd.concat([reg_df3, grade], axis=1)\n",
    "\n",
    "#Drop Grade 7 (average grade) to make ref var\n",
    "reg_df3.drop('grade_7', axis=1, inplace=True)\n",
    "\n",
    "reg_df3.drop('grade', axis=1, inplace=True)\n",
    "reg_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_4</th>\n",
       "      <th>cond_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  yr_of_last_update  \\\n",
       "0         5       2.50         3470  9.392912         0.0               1978   \n",
       "1         5       2.75         2990  8.911934         0.0               1996   \n",
       "2         4       2.75         2900  8.764053         0.0               1970   \n",
       "3         3       2.50         2620  9.979847         0.0               1992   \n",
       "4         3       1.50         1770  9.753130         0.0               1959   \n",
       "\n",
       "   below_ground_ratio  price_100k  is_seattle  top_5_district  ...  grade_8  \\\n",
       "0            0.000000       5.990         0.0             1.0  ...        1   \n",
       "1            0.000000       4.985         0.0             0.0  ...        1   \n",
       "2            0.421569       4.500         1.0             0.0  ...        0   \n",
       "3            0.000000       6.720         0.0             1.0  ...        0   \n",
       "4            0.525862       4.500         0.0             1.0  ...        1   \n",
       "\n",
       "   grade_9  grade_10  grade_11  grade_12  grade_13  cond_1  cond_2  cond_4  \\\n",
       "0        0         0         0         0         0       0       0       0   \n",
       "1        0         0         0         0         0       0       0       0   \n",
       "2        0         0         0         0         0       0       0       0   \n",
       "3        0         0         0         0         0       0       0       0   \n",
       "4        0         0         0         0         0       0       0       0   \n",
       "\n",
       "   cond_5  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Condition \n",
    "condition = pd.get_dummies(reg_df3['condition'], prefix='cond')\n",
    "reg_df3 = pd.concat([reg_df3, condition], axis=1)\n",
    "\n",
    "#Drop Condition 3 (average cond) to make ref var\n",
    "reg_df3.drop('cond_3', axis=1, inplace=True)\n",
    "\n",
    "reg_df3.drop('condition', axis=1, inplace=True)\n",
    "reg_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>price_100k</td>    <th>  R-squared:         </th> <td>   0.723</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.722</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1433.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 09 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:36</td>     <th>  Log-Likelihood:    </th> <td> -31441.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17094</td>      <th>  AIC:               </th> <td>6.295e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17062</td>      <th>  BIC:               </th> <td>6.319e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>Intercept</th>          <td>   27.1244</td> <td>    1.362</td> <td>   19.916</td> <td> 0.000</td> <td>   24.455</td> <td>   29.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>           <td>   -0.1211</td> <td>    0.017</td> <td>   -6.992</td> <td> 0.000</td> <td>   -0.155</td> <td>   -0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>          <td>    0.2793</td> <td>    0.028</td> <td>    9.859</td> <td> 0.000</td> <td>    0.224</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>        <td>    0.0014</td> <td>  3.1e-05</td> <td>   43.849</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_lot</th>            <td>    0.0482</td> <td>    0.017</td> <td>    2.754</td> <td> 0.006</td> <td>    0.014</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>         <td>    4.8166</td> <td>    0.169</td> <td>   28.543</td> <td> 0.000</td> <td>    4.486</td> <td>    5.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_of_last_update</th>  <td>   -0.0139</td> <td>    0.001</td> <td>  -20.671</td> <td> 0.000</td> <td>   -0.015</td> <td>   -0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>below_ground_ratio</th> <td>   -0.3800</td> <td>    0.053</td> <td>   -7.231</td> <td> 0.000</td> <td>   -0.483</td> <td>   -0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_seattle</th>         <td>    2.1047</td> <td>    0.037</td> <td>   57.569</td> <td> 0.000</td> <td>    2.033</td> <td>    2.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top_5_district</th>     <td>    1.8094</td> <td>    0.030</td> <td>   59.695</td> <td> 0.000</td> <td>    1.750</td> <td>    1.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Spring</th>             <td>    0.2767</td> <td>    0.032</td> <td>    8.607</td> <td> 0.000</td> <td>    0.214</td> <td>    0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Summer</th>             <td>    0.0262</td> <td>    0.032</td> <td>    0.810</td> <td> 0.418</td> <td>   -0.037</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Winter</th>             <td>    0.0703</td> <td>    0.037</td> <td>    1.895</td> <td> 0.058</td> <td>   -0.002</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_1</th>           <td>    0.2617</td> <td>    0.039</td> <td>    6.637</td> <td> 0.000</td> <td>    0.184</td> <td>    0.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_1h</th>          <td>    0.2586</td> <td>    0.054</td> <td>    4.822</td> <td> 0.000</td> <td>    0.153</td> <td>    0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_2h</th>          <td>    0.5242</td> <td>    0.147</td> <td>    3.575</td> <td> 0.000</td> <td>    0.237</td> <td>    0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3</th>           <td>    0.2312</td> <td>    0.078</td> <td>    2.966</td> <td> 0.003</td> <td>    0.078</td> <td>    0.384</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3h</th>          <td>    0.1093</td> <td>    0.881</td> <td>    0.124</td> <td> 0.901</td> <td>   -1.617</td> <td>    1.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_3</th>            <td>   -0.0190</td> <td>    1.526</td> <td>   -0.012</td> <td> 0.990</td> <td>   -3.009</td> <td>    2.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_4</th>            <td>   -0.5894</td> <td>    0.352</td> <td>   -1.673</td> <td> 0.094</td> <td>   -1.280</td> <td>    0.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_5</th>            <td>   -0.6524</td> <td>    0.115</td> <td>   -5.693</td> <td> 0.000</td> <td>   -0.877</td> <td>   -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_6</th>            <td>   -0.6079</td> <td>    0.045</td> <td>  -13.593</td> <td> 0.000</td> <td>   -0.696</td> <td>   -0.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_8</th>            <td>    0.8185</td> <td>    0.032</td> <td>   25.328</td> <td> 0.000</td> <td>    0.755</td> <td>    0.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_9</th>            <td>    2.0677</td> <td>    0.049</td> <td>   41.935</td> <td> 0.000</td> <td>    1.971</td> <td>    2.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_10</th>           <td>    3.2872</td> <td>    0.071</td> <td>   46.314</td> <td> 0.000</td> <td>    3.148</td> <td>    3.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_11</th>           <td>    4.6711</td> <td>    0.117</td> <td>   40.093</td> <td> 0.000</td> <td>    4.443</td> <td>    4.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_12</th>           <td>    6.9987</td> <td>    0.235</td> <td>   29.725</td> <td> 0.000</td> <td>    6.537</td> <td>    7.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_13</th>           <td>    9.2620</td> <td>    1.527</td> <td>    6.067</td> <td> 0.000</td> <td>    6.270</td> <td>   12.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_1</th>             <td>   -0.2067</td> <td>    0.321</td> <td>   -0.645</td> <td> 0.519</td> <td>   -0.835</td> <td>    0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_2</th>             <td>   -0.2868</td> <td>    0.138</td> <td>   -2.084</td> <td> 0.037</td> <td>   -0.557</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_4</th>             <td>    0.2784</td> <td>    0.030</td> <td>    9.397</td> <td> 0.000</td> <td>    0.220</td> <td>    0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_5</th>             <td>    0.6931</td> <td>    0.048</td> <td>   14.566</td> <td> 0.000</td> <td>    0.600</td> <td>    0.786</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5653.548</td> <th>  Durbin-Watson:     </th> <td>   2.005</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>35979.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.441</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.497</td>  <th>  Cond. No.          </th> <td>3.83e+05</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, 3.83e+05. 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:             price_100k   R-squared:                       0.723\n",
       "Model:                            OLS   Adj. R-squared:                  0.722\n",
       "Method:                 Least Squares   F-statistic:                     1433.\n",
       "Date:                Thu, 09 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        17:44:36   Log-Likelihood:                -31441.\n",
       "No. Observations:               17094   AIC:                         6.295e+04\n",
       "Df Residuals:                   17062   BIC:                         6.319e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             27.1244      1.362     19.916      0.000      24.455      29.794\n",
       "bedrooms              -0.1211      0.017     -6.992      0.000      -0.155      -0.087\n",
       "bathrooms              0.2793      0.028      9.859      0.000       0.224       0.335\n",
       "sqft_living            0.0014    3.1e-05     43.849      0.000       0.001       0.001\n",
       "log_lot                0.0482      0.017      2.754      0.006       0.014       0.082\n",
       "waterfront             4.8166      0.169     28.543      0.000       4.486       5.147\n",
       "yr_of_last_update     -0.0139      0.001    -20.671      0.000      -0.015      -0.013\n",
       "below_ground_ratio    -0.3800      0.053     -7.231      0.000      -0.483      -0.277\n",
       "is_seattle             2.1047      0.037     57.569      0.000       2.033       2.176\n",
       "top_5_district         1.8094      0.030     59.695      0.000       1.750       1.869\n",
       "Spring                 0.2767      0.032      8.607      0.000       0.214       0.340\n",
       "Summer                 0.0262      0.032      0.810      0.418      -0.037       0.090\n",
       "Winter                 0.0703      0.037      1.895      0.058      -0.002       0.143\n",
       "floors_1               0.2617      0.039      6.637      0.000       0.184       0.339\n",
       "floors_1h              0.2586      0.054      4.822      0.000       0.153       0.364\n",
       "floors_2h              0.5242      0.147      3.575      0.000       0.237       0.812\n",
       "floors_3               0.2312      0.078      2.966      0.003       0.078       0.384\n",
       "floors_3h              0.1093      0.881      0.124      0.901      -1.617       1.836\n",
       "grade_3               -0.0190      1.526     -0.012      0.990      -3.009       2.971\n",
       "grade_4               -0.5894      0.352     -1.673      0.094      -1.280       0.101\n",
       "grade_5               -0.6524      0.115     -5.693      0.000      -0.877      -0.428\n",
       "grade_6               -0.6079      0.045    -13.593      0.000      -0.696      -0.520\n",
       "grade_8                0.8185      0.032     25.328      0.000       0.755       0.882\n",
       "grade_9                2.0677      0.049     41.935      0.000       1.971       2.164\n",
       "grade_10               3.2872      0.071     46.314      0.000       3.148       3.426\n",
       "grade_11               4.6711      0.117     40.093      0.000       4.443       4.899\n",
       "grade_12               6.9987      0.235     29.725      0.000       6.537       7.460\n",
       "grade_13               9.2620      1.527      6.067      0.000       6.270      12.254\n",
       "cond_1                -0.2067      0.321     -0.645      0.519      -0.835       0.422\n",
       "cond_2                -0.2868      0.138     -2.084      0.037      -0.557      -0.017\n",
       "cond_4                 0.2784      0.030      9.397      0.000       0.220       0.337\n",
       "cond_5                 0.6931      0.048     14.566      0.000       0.600       0.786\n",
       "==============================================================================\n",
       "Omnibus:                     5653.548   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            35979.009\n",
       "Skew:                           1.441   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.497   Cond. No.                     3.83e+05\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, 3.83e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome = 'price_100k'\n",
    "predictors = reg_df3.drop('price_100k', axis=1)\n",
    "pred_sum = '+'.join(predictors.columns)\n",
    "formula = outcome + '~' + pred_sum\n",
    "model = ols(formula=formula, data=reg_df3).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Normality check for residuals with QQ plot\n",
    "resid1 = model.resid\n",
    "fig = sm.graphics.qqplot(resid1, dist=stats.norm, line='45', fit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F statistic', 1.0366048239123289), ('p-value', 0.04858643950896008)]"
      ]
     },
     "execution_count": 30,
     "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": [
    "gq_test(reg_df3, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same model as above, but done using a different method.  This allows us to make predictions with confidence intervals at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>Spring</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_4</th>\n",
       "      <th>cond_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  yr_of_last_update  \\\n",
       "0         5       2.50         3470  9.392912         0.0               1978   \n",
       "1         5       2.75         2990  8.911934         0.0               1996   \n",
       "2         4       2.75         2900  8.764053         0.0               1970   \n",
       "\n",
       "   below_ground_ratio  is_seattle  top_5_district  Spring  ...  grade_8  \\\n",
       "0            0.000000         0.0             1.0       1  ...        1   \n",
       "1            0.000000         0.0             0.0       0  ...        1   \n",
       "2            0.421569         1.0             0.0       0  ...        0   \n",
       "\n",
       "   grade_9  grade_10  grade_11  grade_12  grade_13  cond_1  cond_2  cond_4  \\\n",
       "0        0         0         0         0         0       0       0       0   \n",
       "1        0         0         0         0         0       0       0       0   \n",
       "2        0         0         0         0         0       0       0       0   \n",
       "\n",
       "   cond_5  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    5.990\n",
       "1    4.985\n",
       "2    4.500\n",
       "Name: price_100k, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = reg_df3['price_100k']\n",
    "independent_variables = list(predictors.columns)\n",
    "X = reg_df3[independent_variables]\n",
    "display(X.head(3), y.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>price_100k</td>    <th>  R-squared:         </th> <td>   0.723</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.722</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1433.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 09 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:44:40</td>     <th>  Log-Likelihood:    </th> <td> -31441.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17094</td>      <th>  AIC:               </th> <td>6.295e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17062</td>      <th>  BIC:               </th> <td>6.319e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>   27.1244</td> <td>    1.362</td> <td>   19.916</td> <td> 0.000</td> <td>   24.455</td> <td>   29.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>           <td>   -0.1211</td> <td>    0.017</td> <td>   -6.992</td> <td> 0.000</td> <td>   -0.155</td> <td>   -0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>          <td>    0.2793</td> <td>    0.028</td> <td>    9.859</td> <td> 0.000</td> <td>    0.224</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>        <td>    0.0014</td> <td>  3.1e-05</td> <td>   43.849</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_lot</th>            <td>    0.0482</td> <td>    0.017</td> <td>    2.754</td> <td> 0.006</td> <td>    0.014</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>         <td>    4.8166</td> <td>    0.169</td> <td>   28.543</td> <td> 0.000</td> <td>    4.486</td> <td>    5.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_of_last_update</th>  <td>   -0.0139</td> <td>    0.001</td> <td>  -20.671</td> <td> 0.000</td> <td>   -0.015</td> <td>   -0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>below_ground_ratio</th> <td>   -0.3800</td> <td>    0.053</td> <td>   -7.231</td> <td> 0.000</td> <td>   -0.483</td> <td>   -0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_seattle</th>         <td>    2.1047</td> <td>    0.037</td> <td>   57.569</td> <td> 0.000</td> <td>    2.033</td> <td>    2.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top_5_district</th>     <td>    1.8094</td> <td>    0.030</td> <td>   59.695</td> <td> 0.000</td> <td>    1.750</td> <td>    1.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Spring</th>             <td>    0.2767</td> <td>    0.032</td> <td>    8.607</td> <td> 0.000</td> <td>    0.214</td> <td>    0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Summer</th>             <td>    0.0262</td> <td>    0.032</td> <td>    0.810</td> <td> 0.418</td> <td>   -0.037</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Winter</th>             <td>    0.0703</td> <td>    0.037</td> <td>    1.895</td> <td> 0.058</td> <td>   -0.002</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_1</th>           <td>    0.2617</td> <td>    0.039</td> <td>    6.637</td> <td> 0.000</td> <td>    0.184</td> <td>    0.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_1h</th>          <td>    0.2586</td> <td>    0.054</td> <td>    4.822</td> <td> 0.000</td> <td>    0.153</td> <td>    0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_2h</th>          <td>    0.5242</td> <td>    0.147</td> <td>    3.575</td> <td> 0.000</td> <td>    0.237</td> <td>    0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3</th>           <td>    0.2312</td> <td>    0.078</td> <td>    2.966</td> <td> 0.003</td> <td>    0.078</td> <td>    0.384</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors_3h</th>          <td>    0.1093</td> <td>    0.881</td> <td>    0.124</td> <td> 0.901</td> <td>   -1.617</td> <td>    1.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_3</th>            <td>   -0.0190</td> <td>    1.526</td> <td>   -0.012</td> <td> 0.990</td> <td>   -3.009</td> <td>    2.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_4</th>            <td>   -0.5894</td> <td>    0.352</td> <td>   -1.673</td> <td> 0.094</td> <td>   -1.280</td> <td>    0.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_5</th>            <td>   -0.6524</td> <td>    0.115</td> <td>   -5.693</td> <td> 0.000</td> <td>   -0.877</td> <td>   -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_6</th>            <td>   -0.6079</td> <td>    0.045</td> <td>  -13.593</td> <td> 0.000</td> <td>   -0.696</td> <td>   -0.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_8</th>            <td>    0.8185</td> <td>    0.032</td> <td>   25.328</td> <td> 0.000</td> <td>    0.755</td> <td>    0.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_9</th>            <td>    2.0677</td> <td>    0.049</td> <td>   41.935</td> <td> 0.000</td> <td>    1.971</td> <td>    2.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_10</th>           <td>    3.2872</td> <td>    0.071</td> <td>   46.314</td> <td> 0.000</td> <td>    3.148</td> <td>    3.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_11</th>           <td>    4.6711</td> <td>    0.117</td> <td>   40.093</td> <td> 0.000</td> <td>    4.443</td> <td>    4.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_12</th>           <td>    6.9987</td> <td>    0.235</td> <td>   29.725</td> <td> 0.000</td> <td>    6.537</td> <td>    7.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade_13</th>           <td>    9.2620</td> <td>    1.527</td> <td>    6.067</td> <td> 0.000</td> <td>    6.270</td> <td>   12.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_1</th>             <td>   -0.2067</td> <td>    0.321</td> <td>   -0.645</td> <td> 0.519</td> <td>   -0.835</td> <td>    0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_2</th>             <td>   -0.2868</td> <td>    0.138</td> <td>   -2.084</td> <td> 0.037</td> <td>   -0.557</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_4</th>             <td>    0.2784</td> <td>    0.030</td> <td>    9.397</td> <td> 0.000</td> <td>    0.220</td> <td>    0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cond_5</th>             <td>    0.6931</td> <td>    0.048</td> <td>   14.566</td> <td> 0.000</td> <td>    0.600</td> <td>    0.786</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5653.548</td> <th>  Durbin-Watson:     </th> <td>   2.005</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>35979.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.441</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.497</td>  <th>  Cond. No.          </th> <td>3.83e+05</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, 3.83e+05. 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:             price_100k   R-squared:                       0.723\n",
       "Model:                            OLS   Adj. R-squared:                  0.722\n",
       "Method:                 Least Squares   F-statistic:                     1433.\n",
       "Date:                Thu, 09 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        17:44:40   Log-Likelihood:                -31441.\n",
       "No. Observations:               17094   AIC:                         6.295e+04\n",
       "Df Residuals:                   17062   BIC:                         6.319e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "const                 27.1244      1.362     19.916      0.000      24.455      29.794\n",
       "bedrooms              -0.1211      0.017     -6.992      0.000      -0.155      -0.087\n",
       "bathrooms              0.2793      0.028      9.859      0.000       0.224       0.335\n",
       "sqft_living            0.0014    3.1e-05     43.849      0.000       0.001       0.001\n",
       "log_lot                0.0482      0.017      2.754      0.006       0.014       0.082\n",
       "waterfront             4.8166      0.169     28.543      0.000       4.486       5.147\n",
       "yr_of_last_update     -0.0139      0.001    -20.671      0.000      -0.015      -0.013\n",
       "below_ground_ratio    -0.3800      0.053     -7.231      0.000      -0.483      -0.277\n",
       "is_seattle             2.1047      0.037     57.569      0.000       2.033       2.176\n",
       "top_5_district         1.8094      0.030     59.695      0.000       1.750       1.869\n",
       "Spring                 0.2767      0.032      8.607      0.000       0.214       0.340\n",
       "Summer                 0.0262      0.032      0.810      0.418      -0.037       0.090\n",
       "Winter                 0.0703      0.037      1.895      0.058      -0.002       0.143\n",
       "floors_1               0.2617      0.039      6.637      0.000       0.184       0.339\n",
       "floors_1h              0.2586      0.054      4.822      0.000       0.153       0.364\n",
       "floors_2h              0.5242      0.147      3.575      0.000       0.237       0.812\n",
       "floors_3               0.2312      0.078      2.966      0.003       0.078       0.384\n",
       "floors_3h              0.1093      0.881      0.124      0.901      -1.617       1.836\n",
       "grade_3               -0.0190      1.526     -0.012      0.990      -3.009       2.971\n",
       "grade_4               -0.5894      0.352     -1.673      0.094      -1.280       0.101\n",
       "grade_5               -0.6524      0.115     -5.693      0.000      -0.877      -0.428\n",
       "grade_6               -0.6079      0.045    -13.593      0.000      -0.696      -0.520\n",
       "grade_8                0.8185      0.032     25.328      0.000       0.755       0.882\n",
       "grade_9                2.0677      0.049     41.935      0.000       1.971       2.164\n",
       "grade_10               3.2872      0.071     46.314      0.000       3.148       3.426\n",
       "grade_11               4.6711      0.117     40.093      0.000       4.443       4.899\n",
       "grade_12               6.9987      0.235     29.725      0.000       6.537       7.460\n",
       "grade_13               9.2620      1.527      6.067      0.000       6.270      12.254\n",
       "cond_1                -0.2067      0.321     -0.645      0.519      -0.835       0.422\n",
       "cond_2                -0.2868      0.138     -2.084      0.037      -0.557      -0.017\n",
       "cond_4                 0.2784      0.030      9.397      0.000       0.220       0.337\n",
       "cond_5                 0.6931      0.048     14.566      0.000       0.600       0.786\n",
       "==============================================================================\n",
       "Omnibus:                     5653.548   Durbin-Watson:                   2.005\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            35979.009\n",
       "Skew:                           1.441   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.497   Cond. No.                     3.83e+05\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, 3.83e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "Xconst = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(y, Xconst, hasconst= True)\n",
    "\n",
    "fitted_model = model.fit()\n",
    "\n",
    "fitted_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find predictions\n",
    "y_predict = fitted_model.predict(Xconst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>yr_of_last_update</th>\n",
       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
       "      <th>is_seattle</th>\n",
       "      <th>top_5_district</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_8</th>\n",
       "      <th>grade_9</th>\n",
       "      <th>grade_10</th>\n",
       "      <th>grade_11</th>\n",
       "      <th>grade_12</th>\n",
       "      <th>grade_13</th>\n",
       "      <th>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_4</th>\n",
       "      <th>cond_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3470</td>\n",
       "      <td>9.392912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2990</td>\n",
       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2900</td>\n",
       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.421569</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2620</td>\n",
       "      <td>9.979847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1770</td>\n",
       "      <td>9.753130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.525862</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living   log_lot  waterfront  yr_of_last_update  \\\n",
       "0         5       2.50         3470  9.392912         0.0               1978   \n",
       "1         5       2.75         2990  8.911934         0.0               1996   \n",
       "2         4       2.75         2900  8.764053         0.0               1970   \n",
       "3         3       2.50         2620  9.979847         0.0               1992   \n",
       "4         3       1.50         1770  9.753130         0.0               1959   \n",
       "\n",
       "   below_ground_ratio  price_100k  is_seattle  top_5_district  ...  grade_8  \\\n",
       "0            0.000000       5.990         0.0             1.0  ...        1   \n",
       "1            0.000000       4.985         0.0             0.0  ...        1   \n",
       "2            0.421569       4.500         1.0             0.0  ...        0   \n",
       "3            0.000000       6.720         0.0             1.0  ...        0   \n",
       "4            0.525862       4.500         0.0             1.0  ...        1   \n",
       "\n",
       "   grade_9  grade_10  grade_11  grade_12  grade_13  cond_1  cond_2  cond_4  \\\n",
       "0        0         0         0         0         0       0       0       0   \n",
       "1        0         0         0         0         0       0       0       0   \n",
       "2        0         0         0         0         0       0       0       0   \n",
       "3        0         0         0         0         0       0       0       0   \n",
       "4        0         0         0         0         0       0       0       0   \n",
       "\n",
       "   cond_5  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ex: 5 provided columns are Grade, Condition, Sqft_living, Waterfront, Bedroom, Bathroom\n",
    "#Know date, so if spring, factor that in\n",
    "#Know location, so factor in school district and is_seattle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1,\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'sqft_living',\n",
       " 'log_lot',\n",
       " 'waterfront',\n",
       " 'yr_of_last_update',\n",
       " 'below_ground_ratio',\n",
       " 'is_seattle',\n",
       " 'top_5_district',\n",
       " 'Spring',\n",
       " 'Summer',\n",
       " 'Winter',\n",
       " 'floors_1',\n",
       " 'floors_1h',\n",
       " 'floors_2h',\n",
       " 'floors_3',\n",
       " 'floors_3h',\n",
       " 'grade_3',\n",
       " 'grade_4',\n",
       " 'grade_5',\n",
       " 'grade_6',\n",
       " 'grade_8',\n",
       " 'grade_9',\n",
       " 'grade_10',\n",
       " 'grade_11',\n",
       " 'grade_12',\n",
       " 'grade_13',\n",
       " 'cond_1',\n",
       " 'cond_2',\n",
       " 'cond_4',\n",
       " 'cond_5']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_columns = independent_variables\n",
    "array_columns.insert(0, 1)\n",
    "array_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we create a list of empty values.  At the end the user will fill in their data.  Data not filled in is calculated as the median value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List of Empty Values\n",
    "empty_values = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "len(empty_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1', 'bedrooms', 'bathrooms', 'sqft_living', 'log_lot',\n",
       "        'waterfront', 'yr_of_last_update', 'below_ground_ratio',\n",
       "        'is_seattle', 'top_5_district', 'Spring', 'Summer', 'Winter',\n",
       "        'floors_1', 'floors_1h', 'floors_2h', 'floors_3', 'floors_3h',\n",
       "        'grade_3', 'grade_4', 'grade_5', 'grade_6', 'grade_8', 'grade_9',\n",
       "        'grade_10', 'grade_11', 'grade_12', 'grade_13', 'cond_1',\n",
       "        'cond_2', 'cond_4', 'cond_5'],\n",
       "       ['1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',\n",
       "        '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0',\n",
       "        '0', '0', '0', '0', '0', '0']], dtype='<U21')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_values = np.array([array_columns, empty_values])\n",
    "prediction_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to find index based on column name\n",
    "def find_index(col_name, array_2d):\n",
    "    x = 0\n",
    "    for i in array_2d[0]:\n",
    "        if i == col_name:\n",
    "            return x\n",
    "        else:\n",
    "            x += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create User Input Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_home_stats(df, col_names, fit_model, seattle=0, top_dist=0, spring=0):\n",
    "    #Known variables given as inputs\n",
    "        \n",
    "    empty_values = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "    prediction_values = np.array([col_names, empty_values])\n",
    "    \n",
    "    #Floors\n",
    "    floor_num = input(\"Number of floors ('1' for one floor, '1h' for 1.5 floors): \")\n",
    "    floor_str = 'floors_' + str(floor_num)\n",
    "    prediction_values[1][find_index(floor_str, prediction_values)] = 1\n",
    "    \n",
    "    #Bedrooms\n",
    "    bed_num = input(\"Number of bedrooms (whole number): \")\n",
    "    prediction_values[1][find_index('bedrooms', prediction_values)] = bed_num\n",
    "    \n",
    "    #Bathrooms\n",
    "    bath_num = input(\"Number of bathrooms: \")\n",
    "    prediction_values[1][find_index('bathrooms', prediction_values)] = bath_num\n",
    "    \n",
    "    #Waterfront\n",
    "    wf = input(\"Is your house on the waterfront? (1 for yes, 0 for no): \")\n",
    "    prediction_values[1][find_index('waterfront', prediction_values)] = wf\n",
    "    \n",
    "    #Square feet\n",
    "    sqft = input(\"Total square feet: \")\n",
    "    prediction_values[1][find_index('sqft_living', prediction_values)] = sqft\n",
    "    \n",
    "    #Grade\n",
    "    grade_num = input(\"House grade(whole number 3-13): \")\n",
    "    grade_str = 'grade_' + str(grade_num)\n",
    "    prediction_values[1][find_index(grade_str, prediction_values)] = 1\n",
    "    \n",
    "    #Condition\n",
    "    cond_num = input(\"House condition (whole number 1-5): \")\n",
    "    cond_str = 'cond_' + str(cond_num)\n",
    "    prediction_values[1][find_index(cond_str, prediction_values)] = 1\n",
    "    \n",
    "    \n",
    "    #Set values for vars known from location/date\n",
    "    prediction_values[1][find_index('is_seattle', prediction_values)] = seattle\n",
    "    prediction_values[1][find_index('top_5_district', prediction_values)] = top_dist\n",
    "    prediction_values[1][find_index('Spring', prediction_values)] = spring\n",
    "    \n",
    "    # Set values for unknown variables as median - log_lot, below_ground_ratio, yr_of_last_update\n",
    "    prediction_values[1][find_index('log_lot', prediction_values)] = df['log_lot'].median()\n",
    "    prediction_values[1][find_index('below_ground_ratio', prediction_values)] = df['below_ground_ratio'].median()\n",
    "#     prediction_values[1][find_index('yr_of_last_update', prediction_values)] = df['yr_of_last_update'].median()\n",
    "    prediction_values[1][find_index('yr_of_last_update', prediction_values)] = 1905\n",
    "    \n",
    "    \n",
    "    #Get values only in array\n",
    "    prediction_input = prediction_values[1].astype(float)\n",
    "    \n",
    "    #Calculate intervals\n",
    "    intervals = fit_model.get_prediction(prediction_input)\n",
    "    bounds = intervals.summary_frame(alpha=0.05)\n",
    "    \n",
    "    #Format and print\n",
    "    # Shows the bounds for true average value of a home of this type\n",
    "    lower_bound = bounds['mean_ci_lower'][0] * 100000\n",
    "    upper_bound = bounds['mean_ci_upper'][0] * 100000\n",
    "    print('Your house is worth between ' + \"${:,.0f}\".format(lower_bound) + ' and ' \"${:,.0f}\".format(upper_bound) + '!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Predicted Price for Our Home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call this function and input different parameters to see the home's predicted value.  Our product assumes we know the user's location and the date, so is_seattle, is_top_5_dist, and Spring are treated as known variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_home_stats(reg_df3, array_columns, fitted_model, seattle=0, top_dist=0, spring=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With additional time we would:\n",
    "* Fix normality issues in model.\n",
    "* Fix heteroskedasticity issues in model.\n",
    "* Fix multicollinearity issues in model\n",
    "* Test effectiveness of model using test data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "317.975px",
    "width": "322.975px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}