Notebooks/Make_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histograms(reg_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": [
    "## District -> top_5_district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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",
    "\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_df.drop(['district'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Season Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 13,
     "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": [
    "## Regression 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treating bathrooms, grade, etc as continuous in this regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "outcome = 'price_100k'\n",
    "predictors = reg_df.drop('price_100k', axis=1)\n",
    "pred_sum = '+'.join(predictors.columns)\n",
    "formula = outcome + '~' + pred_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>   0.714</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.714</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2841.</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>13:16:31</td>     <th>  Log-Likelihood:    </th> <td> -31704.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17094</td>      <th>  AIC:               </th> <td>6.344e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17078</td>      <th>  BIC:               </th> <td>6.356e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    15</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>   25.0568</td> <td>    1.279</td> <td>   19.587</td> <td> 0.000</td> <td>   22.549</td> <td>   27.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>           <td>   -0.1872</td> <td>    0.017</td> <td>  -10.923</td> <td> 0.000</td> <td>   -0.221</td> <td>   -0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>          <td>    0.2461</td> <td>    0.028</td> <td>    8.645</td> <td> 0.000</td> <td>    0.190</td> <td>    0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>        <td>    0.0015</td> <td> 3.01e-05</td> <td>   49.794</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_lot</th>            <td>    0.0596</td> <td>    0.018</td> <td>    3.373</td> <td> 0.001</td> <td>    0.025</td> <td>    0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors</th>             <td>   -0.1605</td> <td>    0.034</td> <td>   -4.675</td> <td> 0.000</td> <td>   -0.228</td> <td>   -0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>         <td>    4.9186</td> <td>    0.171</td> <td>   28.745</td> <td> 0.000</td> <td>    4.583</td> <td>    5.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condition</th>          <td>    0.2966</td> <td>    0.020</td> <td>   14.637</td> <td> 0.000</td> <td>    0.257</td> <td>    0.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade</th>              <td>    0.9434</td> <td>    0.018</td> <td>   52.811</td> <td> 0.000</td> <td>    0.908</td> <td>    0.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_of_last_update</th>  <td>   -0.0165</td> <td>    0.001</td> <td>  -25.836</td> <td> 0.000</td> <td>   -0.018</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>below_ground_ratio</th> <td>   -0.5178</td> <td>    0.051</td> <td>  -10.064</td> <td> 0.000</td> <td>   -0.619</td> <td>   -0.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_seattle</th>         <td>    2.1254</td> <td>    0.037</td> <td>   57.961</td> <td> 0.000</td> <td>    2.054</td> <td>    2.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top_5_district</th>     <td>    1.8324</td> <td>    0.031</td> <td>   59.697</td> <td> 0.000</td> <td>    1.772</td> <td>    1.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Spring</th>             <td>    0.2799</td> <td>    0.033</td> <td>    8.583</td> <td> 0.000</td> <td>    0.216</td> <td>    0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Summer</th>             <td>    0.0181</td> <td>    0.033</td> <td>    0.552</td> <td> 0.581</td> <td>   -0.046</td> <td>    0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Winter</th>             <td>    0.0766</td> <td>    0.038</td> <td>    2.036</td> <td> 0.042</td> <td>    0.003</td> <td>    0.150</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5666.973</td> <th>  Durbin-Watson:     </th> <td>   2.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>33728.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.467</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.225</td>  <th>  Cond. No.          </th> <td>3.16e+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, 3.16e+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:                       0.714\n",
       "Model:                            OLS   Adj. R-squared:                  0.714\n",
       "Method:                 Least Squares   F-statistic:                     2841.\n",
       "Date:                Thu, 09 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        13:16:31   Log-Likelihood:                -31704.\n",
       "No. Observations:               17094   AIC:                         6.344e+04\n",
       "Df Residuals:                   17078   BIC:                         6.356e+04\n",
       "Df Model:                          15                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             25.0568      1.279     19.587      0.000      22.549      27.564\n",
       "bedrooms              -0.1872      0.017    -10.923      0.000      -0.221      -0.154\n",
       "bathrooms              0.2461      0.028      8.645      0.000       0.190       0.302\n",
       "sqft_living            0.0015   3.01e-05     49.794      0.000       0.001       0.002\n",
       "log_lot                0.0596      0.018      3.373      0.001       0.025       0.094\n",
       "floors                -0.1605      0.034     -4.675      0.000      -0.228      -0.093\n",
       "waterfront             4.9186      0.171     28.745      0.000       4.583       5.254\n",
       "condition              0.2966      0.020     14.637      0.000       0.257       0.336\n",
       "grade                  0.9434      0.018     52.811      0.000       0.908       0.978\n",
       "yr_of_last_update     -0.0165      0.001    -25.836      0.000      -0.018      -0.015\n",
       "below_ground_ratio    -0.5178      0.051    -10.064      0.000      -0.619      -0.417\n",
       "is_seattle             2.1254      0.037     57.961      0.000       2.054       2.197\n",
       "top_5_district         1.8324      0.031     59.697      0.000       1.772       1.893\n",
       "Spring                 0.2799      0.033      8.583      0.000       0.216       0.344\n",
       "Summer                 0.0181      0.033      0.552      0.581      -0.046       0.083\n",
       "Winter                 0.0766      0.038      2.036      0.042       0.003       0.150\n",
       "==============================================================================\n",
       "Omnibus:                     5666.973   Durbin-Watson:                   2.004\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            33728.893\n",
       "Skew:                           1.467   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.225   Cond. No.                     3.16e+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, 3.16e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols(formula=formula, data=reg_df).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>   0.714</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.714</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3044.</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>13:16:31</td>     <th>  Log-Likelihood:    </th> <td> -31705.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 17094</td>      <th>  AIC:               </th> <td>6.344e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 17079</td>      <th>  BIC:               </th> <td>6.356e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    14</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>   25.0553</td> <td>    1.279</td> <td>   19.586</td> <td> 0.000</td> <td>   22.548</td> <td>   27.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bedrooms</th>           <td>   -0.1872</td> <td>    0.017</td> <td>  -10.921</td> <td> 0.000</td> <td>   -0.221</td> <td>   -0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bathrooms</th>          <td>    0.2460</td> <td>    0.028</td> <td>    8.642</td> <td> 0.000</td> <td>    0.190</td> <td>    0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqft_living</th>        <td>    0.0015</td> <td> 3.01e-05</td> <td>   49.792</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>log_lot</th>            <td>    0.0597</td> <td>    0.018</td> <td>    3.378</td> <td> 0.001</td> <td>    0.025</td> <td>    0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>floors</th>             <td>   -0.1605</td> <td>    0.034</td> <td>   -4.675</td> <td> 0.000</td> <td>   -0.228</td> <td>   -0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>waterfront</th>         <td>    4.9174</td> <td>    0.171</td> <td>   28.741</td> <td> 0.000</td> <td>    4.582</td> <td>    5.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condition</th>          <td>    0.2968</td> <td>    0.020</td> <td>   14.650</td> <td> 0.000</td> <td>    0.257</td> <td>    0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>grade</th>              <td>    0.9435</td> <td>    0.018</td> <td>   52.824</td> <td> 0.000</td> <td>    0.908</td> <td>    0.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yr_of_last_update</th>  <td>   -0.0165</td> <td>    0.001</td> <td>  -25.832</td> <td> 0.000</td> <td>   -0.018</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>below_ground_ratio</th> <td>   -0.5177</td> <td>    0.051</td> <td>  -10.062</td> <td> 0.000</td> <td>   -0.618</td> <td>   -0.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_seattle</th>         <td>    2.1255</td> <td>    0.037</td> <td>   57.963</td> <td> 0.000</td> <td>    2.054</td> <td>    2.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top_5_district</th>     <td>    1.8326</td> <td>    0.031</td> <td>   59.709</td> <td> 0.000</td> <td>    1.772</td> <td>    1.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Spring</th>             <td>    0.2698</td> <td>    0.027</td> <td>    9.975</td> <td> 0.000</td> <td>    0.217</td> <td>    0.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Winter</th>             <td>    0.0665</td> <td>    0.033</td> <td>    2.021</td> <td> 0.043</td> <td>    0.002</td> <td>    0.131</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5668.149</td> <th>  Durbin-Watson:     </th> <td>   2.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>33746.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.467</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 9.227</td>  <th>  Cond. No.          </th> <td>3.16e+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, 3.16e+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:                       0.714\n",
       "Model:                            OLS   Adj. R-squared:                  0.714\n",
       "Method:                 Least Squares   F-statistic:                     3044.\n",
       "Date:                Thu, 09 Jul 2020   Prob (F-statistic):               0.00\n",
       "Time:                        13:16:31   Log-Likelihood:                -31705.\n",
       "No. Observations:               17094   AIC:                         6.344e+04\n",
       "Df Residuals:                   17079   BIC:                         6.356e+04\n",
       "Df Model:                          14                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             25.0553      1.279     19.586      0.000      22.548      27.563\n",
       "bedrooms              -0.1872      0.017    -10.921      0.000      -0.221      -0.154\n",
       "bathrooms              0.2460      0.028      8.642      0.000       0.190       0.302\n",
       "sqft_living            0.0015   3.01e-05     49.792      0.000       0.001       0.002\n",
       "log_lot                0.0597      0.018      3.378      0.001       0.025       0.094\n",
       "floors                -0.1605      0.034     -4.675      0.000      -0.228      -0.093\n",
       "waterfront             4.9174      0.171     28.741      0.000       4.582       5.253\n",
       "condition              0.2968      0.020     14.650      0.000       0.257       0.337\n",
       "grade                  0.9435      0.018     52.824      0.000       0.908       0.979\n",
       "yr_of_last_update     -0.0165      0.001    -25.832      0.000      -0.018      -0.015\n",
       "below_ground_ratio    -0.5177      0.051    -10.062      0.000      -0.618      -0.417\n",
       "is_seattle             2.1255      0.037     57.963      0.000       2.054       2.197\n",
       "top_5_district         1.8326      0.031     59.709      0.000       1.772       1.893\n",
       "Spring                 0.2698      0.027      9.975      0.000       0.217       0.323\n",
       "Winter                 0.0665      0.033      2.021      0.043       0.002       0.131\n",
       "==============================================================================\n",
       "Omnibus:                     5668.149   Durbin-Watson:                   2.004\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            33746.493\n",
       "Skew:                           1.467   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.227   Cond. No.                     3.16e+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, 3.16e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop non-significant variables\n",
    "outcome = 'price_100k'\n",
    "predictors = reg_df.drop(['price_100k', 'Summer'], axis=1)\n",
    "pred_sum = '+'.join(predictors.columns)\n",
    "formula = outcome + '~' + pred_sum\n",
    "model = ols(formula=formula, data=reg_df).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check Residuals!!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F statistic', 1.0475445683807323), ('p-value', 0.01597415265999172)]"
      ]
     },
     "execution_count": 19,
     "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_df, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression 2 - New Dummy Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating Dummy Variables for Floors, Grade, Condition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sqft_living</th>\n",
       "      <th>log_lot</th>\n",
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       "      <th>below_ground_ratio</th>\n",
       "      <th>price_100k</th>\n",
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       "      <th>...</th>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bedrooms', 'bathrooms', 'sqft_living', 'log_lot', 'floors',\n",
       "       'waterfront', 'condition', 'grade', 'yr_of_last_update',\n",
       "       'below_ground_ratio', 'is_seattle', 'top_5_district', 'Spring',\n",
       "       'Winter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>13:16:32</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:                        13:16:32   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": 24,
     "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": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F statistic', 1.0366048239123289), ('p-value', 0.04858643950896008)]"
      ]
     },
     "execution_count": 26,
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as above but set dummy var reference variable as middle value"
   ]
  },
  {
   "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>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": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.50</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <th>4</th>\n",
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       "      <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": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cond_5</th>\n",
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       "      <td>8.911934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>8.764053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
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       "      <td>4.500</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "      <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": 29,
     "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": 30,
   "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>13:16:35</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:                        13:16:35   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": 30,
     "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('F statistic', 1.0366048239123289), ('p-value', 0.04858643950896008)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xUNCTxk8WkvssTl6w/rplkfMn/luMq1ZSCi1FZaaq46ghPu9TIqD31P0MFxzs3rIW94+vxeRd65qeMoO6l5L+gZ59n2OTtDV5xTqDk5aCyBsft8U9tr8ZWBLcm8jE5lXYNjWbaJ+mm5b/vHllkcNDDq5YlMP5StWoe5+eKTV16nSTuAeOnzEee7jgYOcdl9VAcZ8yqc/vTfras89qtWonsUna6uK/eRFtTqGbfVdc7bjO0MetdT1drmB8rIi7N6xIsjwj3vPu74dz7mIVFy7NYWnBwelyBZP7T7Rcs9MzJUx87UiTaqZcqWLq26dC8y9u64Uk34OgHj5J90k6Q9969p2oVu0GUb2qoNiu662b+qEDwDuu1F8eMWd1pMKm1cuN8f24XrlriO8fX4snDr+GiyHJ4jh4n5J0baVNcf7J/ScCW09X6z13wipx3cS4P6/jSmTDrql2FwaS9tG3nr3pouxV4nhVOgUQ0DzzNchAmjxBU5fJdt8IvlrvE5Mm/nDVH2x5H5xcuj+If/ygTSjMq1oK+0ypXLE6L+VKteka+qqn707YNdXuwkDSPvrW2PfjRWl7AwvqFe9N2o6PFa0TrLobpGnM3w923xY7lNIJgtY2v7CAe6dmsXL7PozuehoAMHnXutSO6eQEt73vamzc8wxWbt+HG+57KlIrB9f4tqO7px+TUxSnloNkg1SMvYi8IiJHRWRWRDIxb7AfL0rdjcrrzdl4/2GzZW2OG6Yeyep51hXVXvKERsqVKu6dmsW9U7OxZ8v6qS7Uiszc8x5VKuoa34nNq1LtwKlDd621uzCQtI80PftNSqnRuPMR06YfL0qdAXWHbADh3r97M4hzXO8Tw+T+E7jzpqK2QCztgqe0iBrPT0O/72Ia8xhGqVxpPKVNfizeE0dxuIARy978umut3YWBpH30bYI2qMCo1yViOlmgAhoJMpP3f/32fchZNBjz4k3m+hPejx8uab/o/vM/PORAKX2fexKO+5S2e8vapv49NrhDYGy6nAZ15qTcsvdJy9grAE+LiALwX5VSD6W030S0s1q1U/i/ZDozXSpXMLrraaPnqhDdU72yPjPVVoURpAF32xsAwMY9z0ROroogVp+bfqRSnce2R2eNbSr8eJ9og27C5y9W0aQ78p3rflW2DRqiUvgWicg1SqnTIvIuAN8A8G+VUt/0bXMPgHsAYMWKFTe9+uqriY/r0oteh27N/iKbN9+aa6nE7DSmDpPero6uBtwfrnBy0qjOjNI/32VxXppi6iSYXP2m6D5FhRVnAfqbr/skYLsN6QwicjhuqDwVz14pdbr+9xsi8nUANwP4pm+bhwA8BADr169P7Zvbi16Hbs2HXj3b1McmK+1mK9V5bX95b2zXpAF3e8y7vxOv9x+GraF/77uW4KU3Llht248sqJoBdh0It6rW21ba/52wUa21U9nWi45ar5LYsxeRJQBySqkf1//9DQCfV0r9T91n1q9frw4dSke004teh27NNgM7uonfw3f7zrseZNrad5I+/gEq3fTsg57yCk6eCV8DSTz7NNQ4PwXgf4vIEQDfBrDPZOjTphf19DqjmGVD76ouXBXGyJAD1BOursRzEMlyPUEQ5y42F8rZqNbapWzrx8LHLJM4jKOU+j6A9KpPItLu6U9xMD2aTs+UtFrvrHr27hfbm/DeuOeZzISZusndG1aENiDLGt7Euo1qrV3KNt0566Vz2Uv0vPTS1PulG4TlEHQ9XQTAhveM4Fsvn02li2NSpH5H0n2x+YWs8VXL/vdRKDh5XLEop5VWDhcc/PjtOcwnSNx7n3xtVGvtULbpnJu0CtlIMz1v7JN4He1IDoVJFHXhJQXgOyfPZ8LQA5eljqVypWmiFGB+OiHx8Q52P/Tq2Zaunm48GwAmHjuCJNMEslDhrHuK9b7OBG569LyxB+J5He1S8YTlEHRhp7xIKsNA2kG5UsVvPVqbZmV6OiHx8SY73eHj3nMsAG5csbTRpdSGjTcsw7HTP255QshKJXmYZ9+LSrss07eN0MJIOznkthLQGcGcSE2Hrkl2ZTFW72VB1cbwxSmKImb8FatB16YC8K2Xz0Y69we/fw4fXnc1Htw6msn2BibPfnqmhM88eoQJ3BQZWGOfporH23xMx7xSDa/EO07O9ejbGacUpDdI2u19T9JjyeJFVq2P4/T1+erBk5h4bBYXL83FWls7BwDphuaMDDnG+cZZVtplmYE19ml2xYzaLnh8rNjw8N0Lup2evUK6PWmy/QzSe5z3/W7SjqdXF2qSy6iTpcI6qCa9EeiecpWC8fuUhXxDL9LzMXvvtCU3BlhMOMHJv++w5FCcdsG6G0RW5ZekffiNV9C1mSa2k6XCQp1J4+k6ccW2qVntZ7KSb+hFUumNE5W0KmhNfVZsKvHC9PA21X3TM6VIA6rzIlhQyri9qRcN6S9cVZProABocV7adVy3p5GL//ugc2Jc1ZBOaLCgVOD3yVZVY6owf+Dj62J/p/uBJBW0PW3sw5KFScq5bUvETWtw8hK5hzkljYOD/3ft5AVQaGp8V3DyuNLJpV7A5jecQc6N7lp0+++EXad+qaj35/I2x/Pi75rq31eYoe/39gvdbpfQNcISNUkSObYJXNPNZsniy1Ey2/wrDX3/UxwuoBjQrro6r1o6nFaq81AKVoNgBDW5pc2l5goGTENvdNfiptXLreLmbshn595jLT9XdUE1zdYFLhtrv6EfLjhWBpvtF8z0tLEPu+CSJHKWatQrroQSAD47bZ745E2KMgxPgMsyyyiOyPlKtakvkQ4F4OFff7/1de81hFHyTlPPn8Km1cutbkCnyxWtOMD/uknoMLn/RGgiuBf7ZHWSnk7Qblq9vKXK0CVqIscb61taL0cPwvWI3ApHQqLgjhSMMjHsmuFCS18inXEe3fV0JOWVawij5Aeq8woHjp/B7i1rG98Z3c8TpRuqziiXK9XGz2RKBGexT1aW6FnPPqjK0GVkyO6xz7svr8SsXKka+45UqvN45LlTDLmQyBx69Sy2Tc1GSrxevDTX5M1ObF6l9fDLlWqkOgjXEEZNBJ8uVzA+VsSz22/BD/bchgc+vk7bGVM393bIyTVJN3VP0350oZl+nDudJj1r7E2PfG9VFwJf17HryWOR1S+UR5I46J5ETfjbEo+PFUPHT9oguDzOMip+b9k0iHzH7WtqyWcPOanF7b0a/guX5uDk7G5VQU8BHIZupmfDOKY4nK2OGKh59WzVSzpBkvGK3mt6eqaUiizT/XTUgjs37xA0b3jnHWtavndBevqLl+ZavnfVeYWRIQdDixcZtwOabzb9LrdMi5419mGxQP/NQHdB/M4TL7R7qYRg4w3L8K2Xzybah+uFp1kNHZWcXM47+OcNlytVTDzWPH7Sxd+s8Prt+wL3X75Yxcznbm38XyendEMzaTRLG5SbRc+GcYLic368Zd26su+LEUM+hMTh2ZfPIpdC/6NOGnr/agtOHl/4+Gij86lu3rCN1NG2XUlYaCap3DKsJUQ/0bOevfvL3vbobKCsUaHmeQDU35Js0Et5noKTx503FXHg+JlAj9cURi2VK5ieKSVuV+JiamGeVG4ZNn+in+hZY+9i+v5U55Wx/zdb9RJSw8kJfuLKRShfrA2P37R6udbQA+FhVFeerNtHWqMOk8otB0mb37PtEkx9cbyw/QDJIjmpzQjoJqY+NmFtB6ZnSi0xexsKTg67t7wvNa85aYsE27YoWSFJu4Se8eyDulvaEKWog5BO0U5DP1xwrGL7C0q1NEMD7EIb7t9BfWxMVKoL2iRuHJI+IWRthnU76Qlj77972xp6Jy/YtHp5W4ZCE5JVbPPAulCHKbThV67suH1NpFGJwOUkblrefZSxpEHKG28lcD+rcXoijBNnFF5OgPe/Zxm+/cq5yI+ahPQ7plCH7vs2XHDw9txCixd844qleDaGrNRm7kRaTM+UsHPvscB5vP7wVJYNf9+3OL5++z7G3QlJwMiQA6VqTdW8RizIuAEIbHc8tDiPC5dac2RJ8mJJWhDbGuaw/J4bn++FFsl9H7Nn3J2Q+AjQVKjkoitI2r1lLe68qdjU2kEBgYbefS8ucWWOUYqpwsaGmqbH9ZMMsyeKqmwKqAghwehi8ybjduD4mVSepocLDpYsNn9348gco9TOhO3fPT/9LsPsCc/em3H3qnEoqyTEjJOTptYCNmMHS+VKpM6ZOkaGnKYnCl0uIE4LYp0BDiroMv2sXuVNv7dI7gnPHkCjneornnaqNPSEmPmJKxc1YvP+tgA68iJaA1dwctY3gnMXq00DftJsQWwywP52B7rIgL8Ver+3SO4Jz95PWAyOEFKjXNfAR/nOzCul1Z9f6eRQidBP6uGDJ7H+umVNa3CfzE1qnLDka9D6XHQ1AWHJXJvtsq7WMdGTxr5fYmiE2JAkXBkWj9YdD0Cg/nzb1Gyk4ysAO/cea5JszivV8Jht1DNByVf373s16/H/vLZafNN2aXTY7CY9aeypziGDRBJZo9t3PsoYRIVaZezM524NVLYEffdMN6Sgal6TykWXfN315LGWm0+xg3H2Xlfr9EzM3gvVOYSYyYvgzptqBui+J45G7rh57mI1sM3vptXLA7f/uRuWRU7qlsqVprGE7vF0TyHnLlZbWhEHDT5vV5y919U6PWnsvT2uCSGtzCuFrx48iW2PzsbObwXJGA8cPxO47cHvn4v8BOKORfT3kbf1yivV+cbg806MIrTtwZ9VeqKC1sTY55/mWEEysBScHJYtuaItYU0B8MWto1ZyzTj7DrI8btLWpqOtu5+gZm7tIGmFbRrJ3b6voDWx4/Y12iQNIf3O3ILCyne2KYclzQlQV3+f1D00da09Xa4EqmIuvD0XGPtP26v2GuThgBYTcZumZSG5m4pnLyIfBPAfAeQB/Del1B7T9ml69gCwUjPPkhCSPkkMvpMXbP2Za/HIc6cCDb6uj3wn+taE9dBJcry0+uZ31bMXkTyAPwHwSwBeA/C8iOxVSv3fpPu2RZeRJ4TYM1TX0IcpdxRq37nT5UoklY8A2Poz1+Lxw6XAz3jVQzrvuZ0a97BahCTKmywkd9MI49wM4HtKqe8DgIj8JYCPAOiYsZ/YvAoTjx1BtdujfwjpUT65YQXWX7fMqje91xu1nRgHAItygr8+8nrgtnkR7N6yFgCM4Y4ohjZqjNzGYYxrnLPQiiENNU4RwCnP/1+rv9YxxseKmLxrHYYLTicPS0jP4+QED24dxfrrljXaKZgIkjVe6diZkeqC0k7Qmleq9j2O0ODMhDs20av2mfjakUA5qUveYupLXOOchVYMaXj2QWeoxcUWkXsA3AMAK1asSOGwzfjv+jfc91RkbTEhg8bWm6/F+FgRG/c8E+qdDxcc7LxjjXFWbVykvr+0wh27njzWMrSoOq+w68ljWu8+zF4EGWfbp4dOhKHCSMPYvwbgWs//3w3gtH8jpdRDAB4CagnaFI5r5BM/ey3HERISgqubNxlTXQ+bOD2qRoYclC9WW7xBVd+fKdwRJSyjk2ObZNphuT//E0xUhU3UMFTapBHGeR7Ae0XkehFZDOBXAOxNYb+JuH98LT65If0nCEL6CdfI68ITbnw+yEjFiV/f9r6rtUqe0+WKNtyxafXylq6d/u6WSQmrzD93sdp0zLRCTp0isbFXSs0B+DSA/QC+C+BRpdSxpPtNg/vH1+KVPbfhwa2j3V4KIZlEoSYLjNN2IE78+sDxM9rK92uGC03V8d6K2APHz0QyrLr8nSmv5z92UAzfe8wsKGyikEq7BKXUU0qpf6KUukEp9ftp7DNNxseK9PIJ0VAqV/DwwZON9sOAXduBOD2qTN67e2NxZ1f8YM9tjaeKqIZ15x1r4OSajbWTE+y8Y41xfd5jLxgKv4Dea5/Qk71x4uCGddKYwENIv+GaNbf98KbVyzG5/0RLkzIv42PFRrM1W0zeO4DAxmju53T7C8JV6HmPMXnXukgx87BjZkFhE4We740Tl1/6wt/hpTcudHUNhGQVf5WsrnpUVxkahKkCNUjZIwDu3rAC94+v7UgFrc2a/Mfs9DCTge6NE5dv/NYH8Nnpo1TskIFjyeI8Llwyq2j8LqCuetQ2Pm2aSvlelQgAABFASURBVAUEJzsVapOuADRi9jZTrtLCRi7ZbYVNFAbW2AO10I7rNex68hi7Z5K+x62UjaOPDzLsNp0wbfq/6G4aCmhyyMKmXKVNLxnzMAYmZm9ifKyImc/dige3jjZifEMRBisT0gvkRbD+umWh8yB0131QDNsmSWvj/UdJalaq8/jMo+ZqWNLKQHv2fnR38emZEiYem0WEOcuEZI55pZqKfg69ehYPHzzZFLIR1KZOfefk+ZZYdVDi0d2PKRxqY8jd+ba2GUT/z0LCoWdvwfhYES/9wW345IYVVv0zCMkqbux9eqbUYuiBWtjk2ZfP4konh+GCYzX9STe9Cqi1NLZRp4yPFXF3RLVclguYssjAqnGSEkWFQEjWMA0QcbFVu1y/fZ/WI39w62jkTpWfefRIpL5Wr3RoUlUWSKLGoWcfEw49J51gyeJ8W64zG2Nq6zmbWi1EDbGMjxXxwMfXWf/MfNK2hzH7mHhlWWmNayPEi5MX/P5Ha8VGUb3dtAhKrvq15ZtWL8fjh0tWMX4b/JLHpQXH2BqZ2MEwTkpMz5Q4C5ekhpsofeUfKzhdb/5lQ7FufPe98HoqUmK/bFJXaHTnTUUcOH4mUI+epPAorI1y1LF+vQ6LqjLA+FiRWn2SGm6iNCpv/KiSWqFgkHe+c++xwIZkB46fsZod63arPPTqWe3NwYupjXKWWxNkEcbsU2TH7Wvg5BlDJN0jiTy44OQwMqRX4EzPlLThFJ2WXtcG+OGDJ5vaFW+bmsXKgJ44Jo1+O1slxGF6pqTt7ZMF6NmniD+OT0gvsWzJFcaQiClZq0vS6r4HQZJPd3uvfl5XoRsn+dtOog4y6Qb07FPGbZH64NZRqnVIT6Hzol2P1eTAuOEUv3cb5znXqwLqlc6SvTDIhJ59m6Bah/QaQd65zZzZkSEH42PFQO82Lu6NJwuzW23ohUEmNPZtxNt+watIyFkUtBDSaSY2r2pRzly8NGc09AUnjx231waCRJlJG+b8eG88vdCMzDQ7NyswjNMhvBNwohSNENIJRoZq4/r8c17DBnR7k6S2XmzByePuDSsajdj8oZ4shmnC6IVwEz37LsAQD8kSTl6w4/Y1kTzzIH27zrsdGXIwtHiRNgzT6QEg7SAo3ORO+9o2NZuJn4vGvkvoQjxXOjlU2F6TdJK6pxHFMw/yWCc2rwosuNpx+xqjkeuFMI0N/u901tQ5rKDNIHd/6f/EKqgh/cmSxXksXpSLXLBXcHJ4q7rQ0sI46BvvhlSCPPPhgoMlV+g9cy86L70fvPco6NRLSSt+WUHbZzz86++P1f2P9CcXLs2HjhEMYveW9wFoDi3oFDKnyxXcvWFFYPXth9ddjfvH10Y+vkuQlzvx2BHsevIYyherfWn8s6jOobHPKO6FP/G1I6jO0+CT6LjXkNeIju56OrAKdmnB0falN/Wr96ILXdRCk803q+qCajypZCHEkTZZVOdQjZNhxseKmPzYuoZSwmW44ODBraNNYxSLwwVsvGEZRymSBkEl+7qOwCJ6r7NUrli1ANAVFtmEn7JWgJSULKpz6NlnnLDklf89NzbKdg39z8iQYzSkQR5zWbN9mEF2pZgmDzxpiCJLBUhJyWIxGI19n6FT+VwzXMDKdxaY+O0DnLxg8mPrGonPiceOoLoQHOpzPWb3mjDF7W3w78+Lbt9BieIgslSAlAZZUxnR2PcxQRfbZ6ePBs4eJb3DksWLWuLxpqc5r8ccJI+Mis4DD9q3kxPMLaiW6y0ngPf+1O0QxyBA6eUAEjfUYzO3lHSGYkBYQCf380snN61e3uglr/ttCvSeukk+GNRuIShEFFZoRYJJIr2ksR9wvF/O4SEHSgHlSrVFj11w8om8wV7Cnbw09e1TTeGRHABTuduwYXxeHMJurv6B4EFNy5ycAIImRZf3cyY9uK5IKkofed0wcgHwA82g8EHT5EeBOnsSG11cMegLl3bitxNtIvI5wbwmnq3jzpuKWH/dMkw9f6p5X3nBTy5eFGjQdcYxLk5esPVnrm2Z7erFq2Dx3rCvWJTD+UoVSwsOfvRWFQsLwZ8bHytqDbrXwCYxvFEliFmsPO0X6NkTa2za3drghgE6UTjmjw3b4IYYdH1e3qouaL3dtGYRDxcc7LzjcjdJ003W/9TlPpmYbhRez7qdnrRuZq3u6aBdlaf9Aj170hH8np4b9jlfqWJ4yMGbb81pVSEu3kScu7+0vOEgohp6oCZD1EkUyxer+OLWUa1xHB8rhhp7myeacqWK+544it1b1uLZ7bdojWBeJFDb/shzp4w30SQthKPcHKI+HWSx8rRfoLEnkTAZBm/i1403DxcciCCwLN7dXmfoh5wc3p5XLWGYHIDC4nysFgK26MIPuXpVksnLNMXu3XCPjfdvE27RnbuwWH9c5YtfzWXT+iDKzSSLlaf9Ao09SY0oX2qbkJCC4BM3vxv7Xni9oehwwxumpGQYOQFcWxhkEocLjjb+Pq9UaAx55x1rWrTvTk4wede6yHOKwyY26fajS+7mRWIP6p6eKQXKdtNsfWDKIZBkMGZPukLYTFMXf6w2KIQAADv3HmvxpnXqmRyAL2wdBQCjUTblFIYLDmZ33Nr0mvfJxpsr8N+g3G1tblJhsWpdTDwoZh9VSePH9ndms24TVOPo6VrMXkR2Avh1AG6npN9RSj2VZJ9kMLCNwXq30yk1dm9Zi9kdt1prvBdQ85BdY2SKv2/ThFvKlSqmZ0qNm8KuJ481HcsbeXp7rvWW4/fUlxYcXLg01yKRDPNoTTHx9dctS9VoRombJ4mxZ63ytF9I5NnXjf2bSqk/jvI5evYkjmcfVamh03gD9moU0zrdfIRNoy8bTzfLHm1U5RTVM+2BahzSc9ho0qW+nUtUpcZSQ6LUTfiF6bpNydQoBVQ2nm6UmodO3gTcc6Qz9P5aBsbYs0kaLY4/LSIviMiXRWQkhf2RAWB8rIjdW9Y2JiT5EQB3b1jRZNR0igzd67p2vsDlm4iuLa9brDQ+VmxpMR2HuGoS19B6h4Df98RRY6vhtNEppvIieHDrKB64a11Tq+0keQHSPkI9exH5XwB+OuCt3wXwpwB+DzVRw+8BeADAr2r2cw+AewBgxYoVMZdL+glTh84g79VGqeHdjyng4O7b5mlhx+1rEtUCJPF0TTejThlU3TlaUCpwQEq76faTTq8SauyVUr9osyMR+RKAvzbs5yEADwG1mL3tAslgYJOUCyvQiaJwcbHRdQcdNyzfIHV5Z1DDMlumZ0pWnSzbTZa072ynEJ+kapyrlVKv1//7UQAvJl8SIXpMNwVTgZaXTauXN/5tq+v2H/ef/oe/QaUa3Bbtwa2jRsNj45m6Rk1HUkMbxTvOkvY9C086vUrSBO0ficgoamGcVwD8RuIVERITW2/XO1M1brOvK518oLEfGXJCDb2NZ2q6cSU1tFG94yxNXWI7hfgkMvZKqX+V1kIISYrtFCa/YYij6zb1zjFh65majFfSBGhU7zhLMfIshZR6DQ4cJ31D0JDnINIwDFGVQS62nqluP3kRbJuaDR3+ncYagGyogbxkcZB3r0BjT/oGr5xTUAupOLlm/WVahiGu0bG9SehuXPNKJTa6UW5UYdLUTuP/HVPqaQ+Lqkhf4Q/JtCsEETeOHSUh7N1/LqCxWdzEZJSEaxZj5GynEA8ae9LXtNMwxNl3lJuEd//Xb98XuL84RjfKGhgj7x9o7AnpMHFuEmkbXds1ZEl2SZLBmD0hPUC3EpOMkfcP9OwJ6QG6qXVnjLw/oLEnpEeg0SVJoLEnJCNkqXiJ9B809oRkADb4Iu2GCVpCMkDWipdI/0FjT0gGyGLxEukvGMYhpE1EicGzeIm0G3r2hLSBqA3E2OCLtBsae0LaQNQYPIuXSLthGIeQNhAnBk8dPWknNPaEtIGsxOCp3ScuDOMQ0gayEIPP2uAR0l1o7AlpA1mIwVO7T7wwjENIm+h2DJ7afeKFxp6QPiUreYOkMO+QDgzjENKnZCFvkBTmHdKDxp6QPiULeYOkMO+QHgzjENLHdDtvkBTmHdKDnj0hJLPo8gu9lnfIAjT2hJDM0g95h6zAMA4hJLN0c/Zuv0FjTwjJNL2ed8gKDOMQQsgAQGNPCCEDAI09IYQMADT2hBAyANDYE0LIACBKqc4fVOQMgFcjfuwqAP/QhuWkBdeXDK4vGVleX5bXBvTW+q5TSi2Ps5OuGPs4iMghpdT6bq9DB9eXDK4vGVleX5bXBgzO+hjGIYSQAYDGnhBCBoBeMvYPdXsBIXB9yeD6kpHl9WV5bcCArK9nYvaEEELi00uePSGEkJhkytiLyAdF5ISIfE9Etge8LyLyn+rvvyAiN3Z4fdeKyAER+a6IHBORfxewzQdE5LyIzNb/fK7Da3xFRI7Wj30o4P2unEMRWeU5J7Mi8iMRude3TcfPnYh8WUTeEJEXPa8tE5FviMhL9b9HNJ81Xq9tWtukiByv/+6+LiLDms8ar4M2rm+niJQ8v8MPaT7b1nNnWN+UZ22viMis5rOdOH+B9qRt159SKhN/AOQBvAzgPQAWAzgC4J/5tvkQgL8BIAA2AHiuw2u8GsCN9X+/A8D/C1jjBwD8dRfP4ysArjK839Vz6Pld/z1qmuGunjsAPw/gRgAvel77IwDb6//eDuAPNT+D8Xpt09puBbCo/u8/DFqbzXXQxvXtBPDvLX7/bT13uvX53n8AwOe6eP4C7Um7rr8sefY3A/ieUur7SqlLAP4SwEd823wEwF+oGgcBDIvI1Z1aoFLqdaXUd+r//jGA7wLotd6rXT2HdX4BwMtKqaiFdamjlPomgLO+lz8C4Cv1f38FwHjAR22u19TXppR6Wik1V//vQQDvTvOYUdCcOxvafu4A8/pERAB8HMAjaR/XFoM9acv1lyVjXwRwyvP/19BqSG226QgishLAGIDnAt5+v4gcEZG/EZE1HV0YoAA8LSKHReSegPezcA5/BfovWTfPnctPKaVeB2pfSADvCtgmC+fxV1F7Sgsi7DpoJ5+uh5m+rAlBZOHc/UsAP1RKvaR5v6Pnz2dP2nL9ZcnYS8BrfqmQzTZtR0R+AsDjAO5VSv3I9/Z3UAtPrAPwnwFMd3h5G5VSNwL4ZQD/RkR+3vd+V8+hiCwGcAeAxwLe7va5i0K3z+PvApgD8LBmk7DroF38KYAbAIwCeB21UImfLHyPPwGzV9+x8xdiT7QfC3jNeA6zZOxfA3Ct5//vBnA6xjZtRUQc1H4xDyulnvC/r5T6kVLqzfq/nwLgiMhVnVqfUup0/e83AHwdtcc9L90+h78M4DtKqR/63+j2ufPwQze0Vf/7jYBtunYeReRTAD4M4G5VD+D6sbgO2oJS6odKqXml1AKAL2mO29VrUEQWAdgCYEq3TafOn8aetOX6y5Kxfx7Ae0Xk+rr39ysA9vq22QvgX9cVJRsAnHcfdzpBPc73ZwC+q5T6gmabn65vBxG5GbVz/I8dWt8SEXmH+2/Uknkv+jbr6jmEwaPq5rnzsRfAp+r//hSAvwrYxuZ6TR0R+SCA3wZwh1LqomYbm+ugXevz5n8+qjluV86dh18EcFwp9VrQm506fwZ70p7rr53Z5hjZ6Q+hlpF+GcDv1l/7TQC/Wf+3APiT+vtHAazv8Pr+BWqPSi8AmK3/+ZBvjZ8GcAy17PhBAD/XwfW9p37cI/U1ZOocAhhCzXgv9bzW1XOH2o3ndQBV1LylXwPwTgB/C+Cl+t/L6tteA+Ap0/XagbV9D7VYrXv9/Rf/2nTXQYfW99/r19ULqBmfq7tx7nTrq7/+5+4159m2G+dPZ0/acv2xgpYQQgaALIVxCCGEtAkae0IIGQBo7AkhZACgsSeEkAGAxp4QQgYAGntCCBkAaOwJIWQAoLEnhJAB4P8DTe9ViVf8/wsAAAAASUVORK5CYII=\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following steps in \"Polynomial Regression\" lecture.  Redoing model using OLS()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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>13:16:36</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>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:                        13:16:36   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",
       "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": 34,
     "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find predictions\n",
    "y_predict = fitted_model.predict(Xconst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_columns = independent_variables\n",
    "array_columns.insert(0, 1)\n",
    "array_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "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='<U18')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_values = np.array([array_columns, empty_values])\n",
    "prediction_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ADD ALL POSSIBLE SIGNIFICANT OPTIONS HERE - OTHERS WONT BE ALLOWED & WILL STAY 0\n",
    "# Insignificant - Summer, Winter, floors_3h, grade_3, grade_4, cond_1\n",
    "\n",
    "#Add zipcode for use w/ medians!\n",
    "\n",
    "# Set values for known variables\n",
    "prediction_values[1][find_index('bedrooms', prediction_values)] = 3\n",
    "prediction_values[1][find_index('sqft_living', prediction_values)] = 1000\n",
    "prediction_values[1][find_index('waterfront', prediction_values)] = 1\n",
    "prediction_values[1][find_index('bathrooms', prediction_values)] = 2.5\n",
    "\n",
    "# Set values for known dummy variables\n",
    "prediction_values[1][find_index('grade_8', prediction_values)] = 1\n",
    "prediction_values[1][find_index('cond_4', prediction_values)] = 1\n",
    "prediction_values[1][find_index('floors_1h', prediction_values)] = 1\n",
    "\n",
    "\n",
    "#Set values for vars known from location/date\n",
    "prediction_values[1][find_index('is_seattle', prediction_values)] = 0\n",
    "prediction_values[1][find_index('top_5_district', prediction_values)] = 0\n",
    "prediction_values[1][find_index('Spring', prediction_values)] = 1\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)] = reg_df3['log_lot'].median()\n",
    "prediction_values[1][find_index('below_ground_ratio', prediction_values)] = reg_df3['below_ground_ratio'].median()\n",
    "prediction_values[1][find_index('yr_of_last_update', prediction_values)] = reg_df3['yr_of_last_update'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_input = prediction_values[1].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This house is worth about $814,219\n"
     ]
    }
   ],
   "source": [
    "# Make prediction\n",
    "dollar_prediction = fitted_model.predict(prediction_input)[0] * 100000\n",
    "print('This house is worth about ' + \"${:,.0f}\".format(dollar_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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