Modelling Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "from scipy.stats import linregress\n",
    "from pprint import pprint\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn import linear_model\n",
    "from scipy import stats\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import sklearn.linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "csvpath = \"./final_dataframe.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>69.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Autauga County, Alabama</td>\n",
       "      <td>58786</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>74.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Baldwin County, Alabama</td>\n",
       "      <td>55962</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>53.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Barbour County, Alabama</td>\n",
       "      <td>34186</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bibb County, Alabama</td>\n",
       "      <td>45340</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Blount County, Alabama</td>\n",
       "      <td>48695</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>56037</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Sweetwater County, Wyoming</td>\n",
       "      <td>73008</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sweetwater County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>56039</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>100.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Teton County, Wyoming</td>\n",
       "      <td>83831</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>56041</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Uinta County, Wyoming</td>\n",
       "      <td>58235</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>56043</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>83.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Washakie County, Wyoming</td>\n",
       "      <td>53426</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>56045</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Weston County, Wyoming</td>\n",
       "      <td>52867</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "0      1001                     33                     35   \n",
       "1      1003                     31                     27   \n",
       "2      1005                     42                     24   \n",
       "3      1007                     38                     34   \n",
       "4      1009                     34                     30   \n",
       "...     ...                    ...                    ...   \n",
       "2909  56037                     30                     25   \n",
       "2910  56039                     12                     12   \n",
       "2911  56041                     36                     27   \n",
       "2912  56043                     29                     28   \n",
       "2913  56045                     33                     27   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "0                                        69.0                          45.0   \n",
       "1                                        74.0                          73.0   \n",
       "2                                        53.0                          32.0   \n",
       "3                                        16.0                          49.0   \n",
       "4                                        16.0                          22.0   \n",
       "...                                       ...                           ...   \n",
       "2909                                     90.0                          37.0   \n",
       "2910                                    100.0                         116.0   \n",
       "2911                                     84.0                          44.0   \n",
       "2912                                     83.0                          50.0   \n",
       "2913                                     63.0                          43.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed            County and State  \\\n",
       "0                            90.0           3.6     Autauga County, Alabama   \n",
       "1                            86.0           3.6     Baldwin County, Alabama   \n",
       "2                            81.0           5.2     Barbour County, Alabama   \n",
       "3                            84.0           4.0        Bibb County, Alabama   \n",
       "4                            93.0           3.5      Blount County, Alabama   \n",
       "...                           ...           ...                         ...   \n",
       "2909                         81.0           4.2  Sweetwater County, Wyoming   \n",
       "2910                         88.0           3.0       Teton County, Wyoming   \n",
       "2911                         85.0           4.4       Uinta County, Wyoming   \n",
       "2912                         85.0           4.2    Washakie County, Wyoming   \n",
       "2913                         90.0           3.5      Weston County, Wyoming   \n",
       "\n",
       "      Median Income     State             County  \n",
       "0             58786   Alabama     Autauga County  \n",
       "1             55962   Alabama     Baldwin County  \n",
       "2             34186   Alabama     Barbour County  \n",
       "3             45340   Alabama        Bibb County  \n",
       "4             48695   Alabama      Blount County  \n",
       "...             ...       ...                ...  \n",
       "2909          73008   Wyoming  Sweetwater County  \n",
       "2910          83831   Wyoming       Teton County  \n",
       "2911          58235   Wyoming       Uinta County  \n",
       "2912          53426   Wyoming    Washakie County  \n",
       "2913          52867   Wyoming      Weston County  \n",
       "\n",
       "[2914 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omni_df = pd.read_csv(csvpath)\n",
    "omni_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "omni_df"
   ]
  },
  {
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6001</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>California</td>\n",
       "      <td>Alameda County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>6005</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>76.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>61198</td>\n",
       "      <td>California</td>\n",
       "      <td>Amador County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>6007</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>48443</td>\n",
       "      <td>California</td>\n",
       "      <td>Butte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6009</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>58151</td>\n",
       "      <td>California</td>\n",
       "      <td>Calaveras County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6011</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Colusa County, California</td>\n",
       "      <td>56704</td>\n",
       "      <td>California</td>\n",
       "      <td>Colusa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48499</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Wood County, Texas</td>\n",
       "      <td>48534</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>48501</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yoakum County, Texas</td>\n",
       "      <td>68814</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Yoakum County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>48503</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Young County, Texas</td>\n",
       "      <td>47194</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Young County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>48505</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Zapata County, Texas</td>\n",
       "      <td>31113</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zapata County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>48507</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Zavala County, Texas</td>\n",
       "      <td>30988</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zavala County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "173    6001                     19                     15   \n",
       "174    6005                     30                     25   \n",
       "175    6007                     27                     22   \n",
       "176    6009                     31                     31   \n",
       "177    6011                     41                     28   \n",
       "...     ...                    ...                    ...   \n",
       "2557  48499                     28                     32   \n",
       "2558  48501                     33                     28   \n",
       "2559  48503                     34                     29   \n",
       "2560  48505                     31                     20   \n",
       "2561  48507                     24                     20   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "173                                     100.0                         110.0   \n",
       "174                                      76.0                          67.0   \n",
       "175                                      84.0                          61.0   \n",
       "176                                      60.0                          44.0   \n",
       "177                                      75.0                          32.0   \n",
       "...                                       ...                           ...   \n",
       "2557                                     39.0                          47.0   \n",
       "2558                                     67.0                          35.0   \n",
       "2559                                     60.0                          67.0   \n",
       "2560                                     45.0                           7.0   \n",
       "2561                                     59.0                          17.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed              County and State  \\\n",
       "173                          87.0           3.0    Alameda County, California   \n",
       "174                          88.0           4.0     Amador County, California   \n",
       "175                          85.0           5.0      Butte County, California   \n",
       "176                          90.0           4.0  Calaveras County, California   \n",
       "177                          89.0          12.9     Colusa County, California   \n",
       "...                           ...           ...                           ...   \n",
       "2557                         95.0           4.2            Wood County, Texas   \n",
       "2558                         98.0           2.8          Yoakum County, Texas   \n",
       "2559                         97.0           3.2           Young County, Texas   \n",
       "2560                         90.0           5.6          Zapata County, Texas   \n",
       "2561                         92.0           9.5          Zavala County, Texas   \n",
       "\n",
       "      Median Income        State            County  \n",
       "173           92574   California    Alameda County  \n",
       "174           61198   California     Amador County  \n",
       "175           48443   California      Butte County  \n",
       "176           58151   California  Calaveras County  \n",
       "177           56704   California     Colusa County  \n",
       "...             ...          ...               ...  \n",
       "2557          48534        Texas       Wood County  \n",
       "2558          68814        Texas     Yoakum County  \n",
       "2559          47194        Texas      Young County  \n",
       "2560          31113        Texas     Zapata County  \n",
       "2561          30988        Texas     Zavala County  \n",
       "\n",
       "[354 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df = omni_df.loc[(omni_df[\"State\"]==\" California\") | (omni_df[\"State\"]==\" Texas\") | (omni_df[\"State\"]==\" Florida\") ]\n",
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df.to_csv(\"modelling_data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OMNIBUS MODELS\n",
    "\n",
    "## The omnibus model tests all the proposed predictors together to see which ones would best be part of a \"best fit\" model. For the omnibus model, only data from California, Texas, and Florida will be used \n",
    "\n",
    "## This section considers three models: \n",
    "### 1. The Overall model tests all predictors to see how all proposed predictors will work together. \n",
    "### 2. The Economic-Demographic model tests only economic predictors (e.g. Median Income) and demographic predictors (e.g. high school graduation rate)\n",
    "### 3. The ealth model tests only health policy predictors (e,g. physical inactivity, Access to care)\n",
    "\n",
    "### All three models will be compared to select the predictors for the best fit model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OVERALL MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6001</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>California</td>\n",
       "      <td>Alameda County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>6005</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>76.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>61198</td>\n",
       "      <td>California</td>\n",
       "      <td>Amador County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>6007</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>48443</td>\n",
       "      <td>California</td>\n",
       "      <td>Butte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6009</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>58151</td>\n",
       "      <td>California</td>\n",
       "      <td>Calaveras County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6011</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Colusa County, California</td>\n",
       "      <td>56704</td>\n",
       "      <td>California</td>\n",
       "      <td>Colusa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48499</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Wood County, Texas</td>\n",
       "      <td>48534</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>48501</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yoakum County, Texas</td>\n",
       "      <td>68814</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Yoakum County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>48503</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Young County, Texas</td>\n",
       "      <td>47194</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Young County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>48505</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Zapata County, Texas</td>\n",
       "      <td>31113</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zapata County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>48507</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Zavala County, Texas</td>\n",
       "      <td>30988</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zavala County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "173    6001                     19                     15   \n",
       "174    6005                     30                     25   \n",
       "175    6007                     27                     22   \n",
       "176    6009                     31                     31   \n",
       "177    6011                     41                     28   \n",
       "...     ...                    ...                    ...   \n",
       "2557  48499                     28                     32   \n",
       "2558  48501                     33                     28   \n",
       "2559  48503                     34                     29   \n",
       "2560  48505                     31                     20   \n",
       "2561  48507                     24                     20   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "173                                     100.0                         110.0   \n",
       "174                                      76.0                          67.0   \n",
       "175                                      84.0                          61.0   \n",
       "176                                      60.0                          44.0   \n",
       "177                                      75.0                          32.0   \n",
       "...                                       ...                           ...   \n",
       "2557                                     39.0                          47.0   \n",
       "2558                                     67.0                          35.0   \n",
       "2559                                     60.0                          67.0   \n",
       "2560                                     45.0                           7.0   \n",
       "2561                                     59.0                          17.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed              County and State  \\\n",
       "173                          87.0           3.0    Alameda County, California   \n",
       "174                          88.0           4.0     Amador County, California   \n",
       "175                          85.0           5.0      Butte County, California   \n",
       "176                          90.0           4.0  Calaveras County, California   \n",
       "177                          89.0          12.9     Colusa County, California   \n",
       "...                           ...           ...                           ...   \n",
       "2557                         95.0           4.2            Wood County, Texas   \n",
       "2558                         98.0           2.8          Yoakum County, Texas   \n",
       "2559                         97.0           3.2           Young County, Texas   \n",
       "2560                         90.0           5.6          Zapata County, Texas   \n",
       "2561                         92.0           9.5          Zavala County, Texas   \n",
       "\n",
       "      Median Income        State            County  \n",
       "173           92574   California    Alameda County  \n",
       "174           61198   California     Amador County  \n",
       "175           48443   California      Butte County  \n",
       "176           58151   California  Calaveras County  \n",
       "177           56704   California     Colusa County  \n",
       "...             ...          ...               ...  \n",
       "2557          48534        Texas       Wood County  \n",
       "2558          68814        Texas     Yoakum County  \n",
       "2559          47194        Texas      Young County  \n",
       "2560          31113        Texas     Zapata County  \n",
       "2561          30988        Texas     Zavala County  \n",
       "\n",
       "[354 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>92574.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>76.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>61198.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>84.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>48443.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>58151.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>75.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>56704.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>48534.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>67.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>68814.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>47194.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>45.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>59.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>30988.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % With Access to Exercise Opportunities  % Physically Inactive  \\\n",
       "173                                     100.0                   15.0   \n",
       "174                                      76.0                   25.0   \n",
       "175                                      84.0                   22.0   \n",
       "176                                      60.0                   31.0   \n",
       "177                                      75.0                   28.0   \n",
       "...                                       ...                    ...   \n",
       "2557                                     39.0                   32.0   \n",
       "2558                                     67.0                   28.0   \n",
       "2559                                     60.0                   29.0   \n",
       "2560                                     45.0                   20.0   \n",
       "2561                                     59.0                   20.0   \n",
       "\n",
       "      High School Graduation Rate  Median Income  \\\n",
       "173                          87.0        92574.0   \n",
       "174                          88.0        61198.0   \n",
       "175                          85.0        48443.0   \n",
       "176                          90.0        58151.0   \n",
       "177                          89.0        56704.0   \n",
       "...                           ...            ...   \n",
       "2557                         95.0        48534.0   \n",
       "2558                         98.0        68814.0   \n",
       "2559                         97.0        47194.0   \n",
       "2560                         90.0        31113.0   \n",
       "2561                         92.0        30988.0   \n",
       "\n",
       "      Primary Care Physicians Rate  % Unemployed  \n",
       "173                          110.0           3.0  \n",
       "174                           67.0           4.0  \n",
       "175                           61.0           5.0  \n",
       "176                           44.0           4.0  \n",
       "177                           32.0          12.9  \n",
       "...                            ...           ...  \n",
       "2557                          47.0           4.2  \n",
       "2558                          35.0           2.8  \n",
       "2559                          67.0           3.2  \n",
       "2560                           7.0           5.6  \n",
       "2561                          17.0           9.5  \n",
       "\n",
       "[354 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['% With Access to Exercise Opportunities', '% Physically Inactive' ,'High School Graduation Rate',  'Median Income', 'Primary Care Physicians Rate', '% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 14.145343337709765\n",
      "Coefficients: \n",
      " [ 7.09110354e-03  5.26867058e-01  5.41385799e-03 -8.44446062e-07\n",
      " -2.36631532e-02  6.27483900e-01]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     22.687505\n",
       "174     29.308174\n",
       "175     27.882167\n",
       "176     33.269645\n",
       "177     39.326733\n",
       "          ...    \n",
       "2557    33.742135\n",
       "2558    31.638961\n",
       "2559    31.220219\n",
       "2560    27.129015\n",
       "2561    30.751688\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  OLS Regression Results                                  \n",
      "==========================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared (uncentered):                   0.980\n",
      "Model:                               OLS   Adj. R-squared (uncentered):              0.980\n",
      "Method:                    Least Squares   F-statistic:                              2886.\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):                   2.58e-293\n",
      "Time:                           14:26:43   Log-Likelihood:                         -1025.5\n",
      "No. Observations:                    354   AIC:                                      2063.\n",
      "Df Residuals:                        348   BIC:                                      2086.\n",
      "Df Model:                              6                                                  \n",
      "Covariance Type:               nonrobust                                                  \n",
      "===========================================================================================================\n",
      "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "% With Access to Exercise Opportunities     0.0211      0.012      1.731      0.084      -0.003       0.045\n",
      "% Physically Inactive                       0.6381      0.040     15.968      0.000       0.560       0.717\n",
      "High School Graduation Rate                 0.0873      0.018      4.773      0.000       0.051       0.123\n",
      "Median Income                            2.907e-05      2e-05      1.455      0.147   -1.02e-05    6.84e-05\n",
      "Primary Care Physicians Rate               -0.0167      0.011     -1.587      0.113      -0.037       0.004\n",
      "% Unemployed                                0.8521      0.149      5.722      0.000       0.559       1.145\n",
      "==============================================================================\n",
      "Omnibus:                        7.883   Durbin-Watson:                   1.942\n",
      "Prob(Omnibus):                  0.019   Jarque-Bera (JB):                8.571\n",
      "Skew:                           0.267   Prob(JB):                       0.0138\n",
      "Kurtosis:                       3.543   Cond. No.                     3.50e+04\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, 3.5e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Overall Model: Analysi f the overall model suggests a multicollinearity problem. This means that at least some of the predictors are intercorrelated and should not be used as predictors in the same model. The Multiple R is .980; which means thataround 98 percent of the variance in obesity rates in the sample data is explained/predicted by variations in the\n",
    "# 7 predictors in this model. However, the F-ratio is infinitessimally low, which suggests that this model has very low generalizability. \n",
    "# The small coefficient for each predictor in the model is likely a product of chance. The overall model is unsuitable for further analysis; but it does suggest unemployment rate and physical inactivity play a bigger role in obesity rates than the other predictors, "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Economic and Demograhic Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: All Economic/ Demographic Predictors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6001</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>California</td>\n",
       "      <td>Alameda County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>6005</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>76.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>61198</td>\n",
       "      <td>California</td>\n",
       "      <td>Amador County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>6007</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>48443</td>\n",
       "      <td>California</td>\n",
       "      <td>Butte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6009</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>58151</td>\n",
       "      <td>California</td>\n",
       "      <td>Calaveras County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6011</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Colusa County, California</td>\n",
       "      <td>56704</td>\n",
       "      <td>California</td>\n",
       "      <td>Colusa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48499</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Wood County, Texas</td>\n",
       "      <td>48534</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>48501</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yoakum County, Texas</td>\n",
       "      <td>68814</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Yoakum County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>48503</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Young County, Texas</td>\n",
       "      <td>47194</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Young County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>48505</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Zapata County, Texas</td>\n",
       "      <td>31113</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zapata County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>48507</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Zavala County, Texas</td>\n",
       "      <td>30988</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zavala County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "173    6001                     19                     15   \n",
       "174    6005                     30                     25   \n",
       "175    6007                     27                     22   \n",
       "176    6009                     31                     31   \n",
       "177    6011                     41                     28   \n",
       "...     ...                    ...                    ...   \n",
       "2557  48499                     28                     32   \n",
       "2558  48501                     33                     28   \n",
       "2559  48503                     34                     29   \n",
       "2560  48505                     31                     20   \n",
       "2561  48507                     24                     20   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "173                                     100.0                         110.0   \n",
       "174                                      76.0                          67.0   \n",
       "175                                      84.0                          61.0   \n",
       "176                                      60.0                          44.0   \n",
       "177                                      75.0                          32.0   \n",
       "...                                       ...                           ...   \n",
       "2557                                     39.0                          47.0   \n",
       "2558                                     67.0                          35.0   \n",
       "2559                                     60.0                          67.0   \n",
       "2560                                     45.0                           7.0   \n",
       "2561                                     59.0                          17.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed              County and State  \\\n",
       "173                          87.0           3.0    Alameda County, California   \n",
       "174                          88.0           4.0     Amador County, California   \n",
       "175                          85.0           5.0      Butte County, California   \n",
       "176                          90.0           4.0  Calaveras County, California   \n",
       "177                          89.0          12.9     Colusa County, California   \n",
       "...                           ...           ...                           ...   \n",
       "2557                         95.0           4.2            Wood County, Texas   \n",
       "2558                         98.0           2.8          Yoakum County, Texas   \n",
       "2559                         97.0           3.2           Young County, Texas   \n",
       "2560                         90.0           5.6          Zapata County, Texas   \n",
       "2561                         92.0           9.5          Zavala County, Texas   \n",
       "\n",
       "      Median Income        State            County  \n",
       "173           92574   California    Alameda County  \n",
       "174           61198   California     Amador County  \n",
       "175           48443   California      Butte County  \n",
       "176           58151   California  Calaveras County  \n",
       "177           56704   California     Colusa County  \n",
       "...             ...          ...               ...  \n",
       "2557          48534        Texas       Wood County  \n",
       "2558          68814        Texas     Yoakum County  \n",
       "2559          47194        Texas      Young County  \n",
       "2560          31113        Texas     Zapata County  \n",
       "2561          30988        Texas     Zavala County  \n",
       "\n",
       "[354 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>87.0</td>\n",
       "      <td>92574.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>88.0</td>\n",
       "      <td>61198.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>85.0</td>\n",
       "      <td>48443.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>90.0</td>\n",
       "      <td>58151.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>89.0</td>\n",
       "      <td>56704.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>95.0</td>\n",
       "      <td>48534.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>98.0</td>\n",
       "      <td>68814.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>97.0</td>\n",
       "      <td>47194.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>90.0</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>92.0</td>\n",
       "      <td>30988.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      High School Graduation Rate  Median Income  % Unemployed\n",
       "173                          87.0        92574.0           3.0\n",
       "174                          88.0        61198.0           4.0\n",
       "175                          85.0        48443.0           5.0\n",
       "176                          90.0        58151.0           4.0\n",
       "177                          89.0        56704.0          12.9\n",
       "...                           ...            ...           ...\n",
       "2557                         95.0        48534.0           4.2\n",
       "2558                         98.0        68814.0           2.8\n",
       "2559                         97.0        47194.0           3.2\n",
       "2560                         90.0        31113.0           5.6\n",
       "2561                         92.0        30988.0           9.5\n",
       "\n",
       "[354 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['High School Graduation Rate',  'Median Income', '% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 31.780954939908327\n",
      "Coefficients: \n",
      " [ 3.62534257e-02 -1.24641369e-04  5.78210321e-01]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     25.131084\n",
       "174     29.656295\n",
       "175     31.715546\n",
       "176     30.108584\n",
       "177     35.398759\n",
       "          ...    \n",
       "2557    31.604170\n",
       "2558    28.375708\n",
       "2559    31.265486\n",
       "2560    34.403774\n",
       "2561    36.746881\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.155\n",
      "Model:                               OLS   Adj. R-squared:                  0.148\n",
      "Method:                    Least Squares   F-statistic:                     21.41\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           9.33e-13\n",
      "Time:                           14:54:55   Log-Likelihood:                -1080.2\n",
      "No. Observations:                    354   AIC:                             2168.\n",
      "Df Residuals:                        350   BIC:                             2184.\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                          31.7810      3.169     10.029      0.000      25.549      38.013\n",
      "High School Graduation Rate     0.0363      0.030      1.197      0.232      -0.023       0.096\n",
      "Median Income                  -0.0001   2.07e-05     -6.029      0.000      -0.000    -8.4e-05\n",
      "% Unemployed                    0.5782      0.183      3.160      0.002       0.218       0.938\n",
      "==============================================================================\n",
      "Omnibus:                        7.037   Durbin-Watson:                   1.782\n",
      "Prob(Omnibus):                  0.030   Jarque-Bera (JB):                7.149\n",
      "Skew:                           0.326   Prob(JB):                       0.0280\n",
      "Kurtosis:                       2.757   Cond. No.                     6.39e+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, 6.39e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Economic/Demographic Model has a multiple R of 0.155, which is small\n",
    "# Economic/deographix variables account for only 15.5 percent of variabiliy in obesity rates in the dataset. \n",
    "# The model retains the multicollinearity issues of the overall model; but it has a respectable F-ratio of 21.41, this suggests that the model is \n",
    "# imrobable and the coefficients produced for the predictors are unlikely to be products of chance. This model predicts that a county with a high school graduation rate of zero, a median income of zero, and virtually no unemployment would, on average have an obesity rate of 31.78%\n",
    "# IF THE OTHER PREDICTORS IN THE MODEL ARE HELD CONSTANT, a county would be expected to report a 0.5782 percent higher obesity rate for every percentage point in its poverty rate,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2: Model without Median Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Theoretically speaking, we would expect a county's median income to be correlated with its unemployment rate ad high school graduation rate. This could account for the multicollinearity model so a second model with median income dropped as a predictor is tested.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      High School Graduation Rate  % Unemployed\n",
       "173                          87.0           3.0\n",
       "174                          88.0           4.0\n",
       "175                          85.0           5.0\n",
       "176                          90.0           4.0\n",
       "177                          89.0          12.9\n",
       "...                           ...           ...\n",
       "2557                         95.0           4.2\n",
       "2558                         98.0           2.8\n",
       "2559                         97.0           3.2\n",
       "2560                         90.0           5.6\n",
       "2561                         92.0           9.5\n",
       "\n",
       "[354 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['High School Graduation Rate', '% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 24.023718859551114\n",
      "Coefficients: \n",
      " [0.         0.03321239 0.91639033]\n",
      "Equation: y = 24.02 + 0.00x1 + 0.03x2\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0],\n",
    "                                                         regr.coef_[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     29.662368\n",
       "174     30.611971\n",
       "175     31.428724\n",
       "176     30.678395\n",
       "177     38.801057\n",
       "          ...    \n",
       "2557    31.027735\n",
       "2558    29.844426\n",
       "2559    30.177770\n",
       "2560    32.144620\n",
       "2561    35.784967\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.067\n",
      "Model:                               OLS   Adj. R-squared:                  0.062\n",
      "Method:                    Least Squares   F-statistic:                     12.67\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           4.86e-06\n",
      "Time:                           15:53:29   Log-Likelihood:                -1097.7\n",
      "No. Observations:                    354   AIC:                             2201.\n",
      "Df Residuals:                        351   BIC:                             2213.\n",
      "Df Model:                              2                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                          24.0237      3.038      7.908      0.000      18.049      29.999\n",
      "High School Graduation Rate     0.0332      0.032      1.046      0.296      -0.029       0.096\n",
      "% Unemployed                    0.9164      0.183      5.015      0.000       0.557       1.276\n",
      "==============================================================================\n",
      "Omnibus:                        4.796   Durbin-Watson:                   1.645\n",
      "Prob(Omnibus):                  0.091   Jarque-Bera (JB):                4.598\n",
      "Skew:                           0.232   Prob(JB):                        0.100\n",
      "Kurtosis:                       2.690   Cond. No.                         953.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- Model 2 solves a few issues from Model 1: \n",
    "    1. It resolves the ulticollinearity problemThe multiple R for Model \n",
    "    2. The removal of median income greatly improves the predictive contributions of the remaining two predictors. \n",
    "\n",
    "However, this also comes with some caveats: \n",
    "    1. The multiple R for Model 2 (0.067) is smaller tha for Model 1. \n",
    "    2. The F-ratio is also smaller; but still significant.Model 2 appears to sacrifice a lot of "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation: y = 18.02 + 0.51x1 + -0.01x2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(10, 10))\n",
    "axes = Axes3D(fig, elev=20, azim=45)\n",
    "axes.scatter(sample_df[\"High School Graduation Rate\"], sample_df[\"% Unemployed\"], sample_df['% Adults with Obesity'], c=sample_df['% Adults with Obesity'], cmap=plt.cm.get_cmap(\"Spectral\"))\n",
    "plt.title(\"Plot 2.1: Best Fit 'Economic/Demographic' model for Selected states\")\n",
    "axes.set_xlabel(\"High Schol Graduation Rate\")\n",
    "axes.set_ylabel(\"% Unemployed\")\n",
    "axes.set_zlabel(\"County Obesity Rate\")\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0],\n",
    "                                                         regr.coef_[1]))\n",
    "plt.savefig(\"Plot 2-1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Health Factors Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: All health factor predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>76.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>84.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>75.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>67.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>45.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>59.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % With Access to Exercise Opportunities  % Physically Inactive  \\\n",
       "173                                     100.0                   15.0   \n",
       "174                                      76.0                   25.0   \n",
       "175                                      84.0                   22.0   \n",
       "176                                      60.0                   31.0   \n",
       "177                                      75.0                   28.0   \n",
       "...                                       ...                    ...   \n",
       "2557                                     39.0                   32.0   \n",
       "2558                                     67.0                   28.0   \n",
       "2559                                     60.0                   29.0   \n",
       "2560                                     45.0                   20.0   \n",
       "2561                                     59.0                   20.0   \n",
       "\n",
       "      Primary Care Physicians Rate  \n",
       "173                          110.0  \n",
       "174                           67.0  \n",
       "175                           61.0  \n",
       "176                           44.0  \n",
       "177                           32.0  \n",
       "...                            ...  \n",
       "2557                          47.0  \n",
       "2558                          35.0  \n",
       "2559                          67.0  \n",
       "2560                           7.0  \n",
       "2561                          17.0  \n",
       "\n",
       "[354 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['% With Access to Exercise Opportunities', '% Physically Inactive' , 'Primary Care Physicians Rate']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 16.86516021279717\n",
      "Coefficients: \n",
      " [ 0.01210807  0.53872704 -0.03122865]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     22.721721\n",
       "174     29.161230\n",
       "175     27.829285\n",
       "176     32.918122\n",
       "177     31.858306\n",
       "          ...    \n",
       "2557    33.108893\n",
       "2558    31.667755\n",
       "2559    31.122409\n",
       "2560    27.965964\n",
       "2561    27.823190\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.378\n",
      "Model:                               OLS   Adj. R-squared:                  0.373\n",
      "Method:                    Least Squares   F-statistic:                     71.02\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           6.78e-36\n",
      "Time:                           15:40:30   Log-Likelihood:                -1025.9\n",
      "No. Observations:                    354   AIC:                             2060.\n",
      "Df Residuals:                        350   BIC:                             2075.\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===========================================================================================================\n",
      "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "const                                      16.8652      1.643     10.262      0.000      13.633      20.097\n",
      "% With Access to Exercise Opportunities     0.0121      0.012      0.984      0.326      -0.012       0.036\n",
      "% Physically Inactive                       0.5387      0.045     11.900      0.000       0.450       0.628\n",
      "Primary Care Physicians Rate               -0.0312      0.010     -3.067      0.002      -0.051      -0.011\n",
      "==============================================================================\n",
      "Omnibus:                        5.632   Durbin-Watson:                   1.896\n",
      "Prob(Omnibus):                  0.060   Jarque-Bera (JB):                5.467\n",
      "Skew:                           0.300   Prob(JB):                       0.0650\n",
      "Kurtosis:                       3.099   Cond. No.                         627.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The all-health factors model has a respectable multiple R of 0.378 and explains 37.8 percent of the variability in obesity rates in the current dataset. \n",
    "# In addition, this model reports a good F-ratio of 71.02 which would almost certainly be statistically sgnificant and not  a product of chance. As for the predictors, access to exercise oppurtunities, while it has a substantial contribution to the model, does not appear to be statistically significant. \n",
    "# It appears that Physical inactivity is the best predictor from this model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BestFit Models\n",
    "\n",
    "## The following best fit models test the best predictors from the previous models in various combinations in order to build a model that best predicts obesity rates across all US counties. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Fit model 1:  Median Income and Rate of Inactivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6001</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>California</td>\n",
       "      <td>Alameda County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>6005</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>76.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>61198</td>\n",
       "      <td>California</td>\n",
       "      <td>Amador County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>6007</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>48443</td>\n",
       "      <td>California</td>\n",
       "      <td>Butte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6009</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>58151</td>\n",
       "      <td>California</td>\n",
       "      <td>Calaveras County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6011</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Colusa County, California</td>\n",
       "      <td>56704</td>\n",
       "      <td>California</td>\n",
       "      <td>Colusa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48499</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Wood County, Texas</td>\n",
       "      <td>48534</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>48501</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yoakum County, Texas</td>\n",
       "      <td>68814</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Yoakum County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>48503</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Young County, Texas</td>\n",
       "      <td>47194</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Young County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>48505</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Zapata County, Texas</td>\n",
       "      <td>31113</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zapata County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>48507</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Zavala County, Texas</td>\n",
       "      <td>30988</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zavala County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "173    6001                     19                     15   \n",
       "174    6005                     30                     25   \n",
       "175    6007                     27                     22   \n",
       "176    6009                     31                     31   \n",
       "177    6011                     41                     28   \n",
       "...     ...                    ...                    ...   \n",
       "2557  48499                     28                     32   \n",
       "2558  48501                     33                     28   \n",
       "2559  48503                     34                     29   \n",
       "2560  48505                     31                     20   \n",
       "2561  48507                     24                     20   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "173                                     100.0                         110.0   \n",
       "174                                      76.0                          67.0   \n",
       "175                                      84.0                          61.0   \n",
       "176                                      60.0                          44.0   \n",
       "177                                      75.0                          32.0   \n",
       "...                                       ...                           ...   \n",
       "2557                                     39.0                          47.0   \n",
       "2558                                     67.0                          35.0   \n",
       "2559                                     60.0                          67.0   \n",
       "2560                                     45.0                           7.0   \n",
       "2561                                     59.0                          17.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed              County and State  \\\n",
       "173                          87.0           3.0    Alameda County, California   \n",
       "174                          88.0           4.0     Amador County, California   \n",
       "175                          85.0           5.0      Butte County, California   \n",
       "176                          90.0           4.0  Calaveras County, California   \n",
       "177                          89.0          12.9     Colusa County, California   \n",
       "...                           ...           ...                           ...   \n",
       "2557                         95.0           4.2            Wood County, Texas   \n",
       "2558                         98.0           2.8          Yoakum County, Texas   \n",
       "2559                         97.0           3.2           Young County, Texas   \n",
       "2560                         90.0           5.6          Zapata County, Texas   \n",
       "2561                         92.0           9.5          Zavala County, Texas   \n",
       "\n",
       "      Median Income        State            County  \n",
       "173           92574   California    Alameda County  \n",
       "174           61198   California     Amador County  \n",
       "175           48443   California      Butte County  \n",
       "176           58151   California  Calaveras County  \n",
       "177           56704   California     Colusa County  \n",
       "...             ...          ...               ...  \n",
       "2557          48534        Texas       Wood County  \n",
       "2558          68814        Texas     Yoakum County  \n",
       "2559          47194        Texas      Young County  \n",
       "2560          31113        Texas     Zapata County  \n",
       "2561          30988        Texas     Zavala County  \n",
       "\n",
       "[354 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Median Income</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>92574.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>61198.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>48443.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>58151.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>56704.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48534.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>68814.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>47194.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>31113.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>30988.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Median Income  % Physically Inactive\n",
       "173         92574.0                   15.0\n",
       "174         61198.0                   25.0\n",
       "175         48443.0                   22.0\n",
       "176         58151.0                   31.0\n",
       "177         56704.0                   28.0\n",
       "...             ...                    ...\n",
       "2557        48534.0                   32.0\n",
       "2558        68814.0                   28.0\n",
       "2559        47194.0                   29.0\n",
       "2560        31113.0                   20.0\n",
       "2561        30988.0                   20.0\n",
       "\n",
       "[354 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['Median Income','% Physically Inactive']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 18.144227123745708\n",
      "Coefficients: \n",
      " [-3.73077877e-05  5.38647337e-01]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     22.770206\n",
       "174     29.327249\n",
       "175     28.187167\n",
       "176     32.672809\n",
       "177     31.110852\n",
       "          ...    \n",
       "2557    33.570246\n",
       "2558    30.659054\n",
       "2559    32.004296\n",
       "2560    27.756417\n",
       "2561    27.761080\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.368\n",
      "Model:                               OLS   Adj. R-squared:                  0.364\n",
      "Method:                    Least Squares   F-statistic:                     102.1\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           1.14e-35\n",
      "Time:                           17:57:42   Log-Likelihood:                -1028.9\n",
      "No. Observations:                    354   AIC:                             2064.\n",
      "Df Residuals:                        351   BIC:                             2075.\n",
      "Df Model:                              2                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    18.1442      1.994      9.099      0.000      14.223      22.066\n",
      "Median Income         -3.731e-05   1.93e-05     -1.930      0.054   -7.53e-05    7.15e-07\n",
      "% Physically Inactive     0.5386      0.047     11.507      0.000       0.447       0.631\n",
      "==============================================================================\n",
      "Omnibus:                        6.873   Durbin-Watson:                   1.875\n",
      "Prob(Omnibus):                  0.032   Jarque-Bera (JB):                6.889\n",
      "Skew:                           0.342   Prob(JB):                       0.0319\n",
      "Kurtosis:                       3.021   Cond. No.                     4.65e+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, 4.65e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation: y = 18.02 + 0.51x1 + -0.01x2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(10, 10))\n",
    "axes = Axes3D(fig, elev=20, azim=45)\n",
    "axes.scatter(sample_df[\"Median Income\"], sample_df[\"% Physically Inactive\"], sample_df['% Adults with Obesity'], c=sample_df['% Adults with Obesity'], cmap=plt.cm.get_cmap(\"Spectral\"))\n",
    "plt.title(\"Plot 3.1: Best Fit model with Median Income and Primary Care Physician Access for Selected states\")\n",
    "axes.set_xlabel(\"Median Income\")\n",
    "axes.set_ylabel(\"Percent of Physically Inactive Residents\")\n",
    "axes.set_zlabel(\"County Obesity Rate\")\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0], regr.coef_[1]))\n",
    "plt.savefig(\"Plot 3-1.png\")                                                       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Fit model 2: High School Graduation Rate and Access to Primary Care Physicians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6001</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>California</td>\n",
       "      <td>Alameda County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>6005</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>76.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Amador County, California</td>\n",
       "      <td>61198</td>\n",
       "      <td>California</td>\n",
       "      <td>Amador County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>6007</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>84.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Butte County, California</td>\n",
       "      <td>48443</td>\n",
       "      <td>California</td>\n",
       "      <td>Butte County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6009</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>60.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Calaveras County, California</td>\n",
       "      <td>58151</td>\n",
       "      <td>California</td>\n",
       "      <td>Calaveras County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>6011</td>\n",
       "      <td>41</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Colusa County, California</td>\n",
       "      <td>56704</td>\n",
       "      <td>California</td>\n",
       "      <td>Colusa County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>48499</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Wood County, Texas</td>\n",
       "      <td>48534</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Wood County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>48501</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>67.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Yoakum County, Texas</td>\n",
       "      <td>68814</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Yoakum County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>48503</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Young County, Texas</td>\n",
       "      <td>47194</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Young County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>48505</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Zapata County, Texas</td>\n",
       "      <td>31113</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zapata County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>48507</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>59.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Zavala County, Texas</td>\n",
       "      <td>30988</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Zavala County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "173    6001                     19                     15   \n",
       "174    6005                     30                     25   \n",
       "175    6007                     27                     22   \n",
       "176    6009                     31                     31   \n",
       "177    6011                     41                     28   \n",
       "...     ...                    ...                    ...   \n",
       "2557  48499                     28                     32   \n",
       "2558  48501                     33                     28   \n",
       "2559  48503                     34                     29   \n",
       "2560  48505                     31                     20   \n",
       "2561  48507                     24                     20   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "173                                     100.0                         110.0   \n",
       "174                                      76.0                          67.0   \n",
       "175                                      84.0                          61.0   \n",
       "176                                      60.0                          44.0   \n",
       "177                                      75.0                          32.0   \n",
       "...                                       ...                           ...   \n",
       "2557                                     39.0                          47.0   \n",
       "2558                                     67.0                          35.0   \n",
       "2559                                     60.0                          67.0   \n",
       "2560                                     45.0                           7.0   \n",
       "2561                                     59.0                          17.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed              County and State  \\\n",
       "173                          87.0           3.0    Alameda County, California   \n",
       "174                          88.0           4.0     Amador County, California   \n",
       "175                          85.0           5.0      Butte County, California   \n",
       "176                          90.0           4.0  Calaveras County, California   \n",
       "177                          89.0          12.9     Colusa County, California   \n",
       "...                           ...           ...                           ...   \n",
       "2557                         95.0           4.2            Wood County, Texas   \n",
       "2558                         98.0           2.8          Yoakum County, Texas   \n",
       "2559                         97.0           3.2           Young County, Texas   \n",
       "2560                         90.0           5.6          Zapata County, Texas   \n",
       "2561                         92.0           9.5          Zavala County, Texas   \n",
       "\n",
       "      Median Income        State            County  \n",
       "173           92574   California    Alameda County  \n",
       "174           61198   California     Amador County  \n",
       "175           48443   California      Butte County  \n",
       "176           58151   California  Calaveras County  \n",
       "177           56704   California     Colusa County  \n",
       "...             ...          ...               ...  \n",
       "2557          48534        Texas       Wood County  \n",
       "2558          68814        Texas     Yoakum County  \n",
       "2559          47194        Texas      Young County  \n",
       "2560          31113        Texas     Zapata County  \n",
       "2561          30988        Texas     Zavala County  \n",
       "\n",
       "[354 rows x 11 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>110.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>44.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>32.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>47.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>35.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>17.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Primary Care Physicians Rate  High School Graduation Rate\n",
       "173                          110.0                         87.0\n",
       "174                           67.0                         88.0\n",
       "175                           61.0                         85.0\n",
       "176                           44.0                         90.0\n",
       "177                           32.0                         89.0\n",
       "...                            ...                          ...\n",
       "2557                          47.0                         95.0\n",
       "2558                          35.0                         98.0\n",
       "2559                          67.0                         97.0\n",
       "2560                           7.0                         90.0\n",
       "2561                          17.0                         92.0\n",
       "\n",
       "[354 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['Primary Care Physicians Rate','High School Graduation Rate']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 36.877158746888725\n",
      "Coefficients: \n",
      " [-0.06993994 -0.03134912]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     26.456391\n",
       "174     29.432460\n",
       "175     29.946147\n",
       "176     30.978380\n",
       "177     31.849009\n",
       "          ...    \n",
       "2557    30.611815\n",
       "2558    31.357047\n",
       "2559    29.150318\n",
       "2560    33.566158\n",
       "2561    32.804061\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.126\n",
      "Model:                               OLS   Adj. R-squared:                  0.121\n",
      "Method:                    Least Squares   F-statistic:                     25.31\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           5.38e-11\n",
      "Time:                           18:19:07   Log-Likelihood:                -1086.2\n",
      "No. Observations:                    354   AIC:                             2178.\n",
      "Df Residuals:                        351   BIC:                             2190.\n",
      "Df Model:                              2                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "================================================================================================\n",
      "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------\n",
      "const                           36.8772      2.935     12.564      0.000      31.105      42.650\n",
      "Primary Care Physicians Rate    -0.0699      0.010     -7.101      0.000      -0.089      -0.051\n",
      "High School Graduation Rate     -0.0313      0.031     -1.005      0.315      -0.093       0.030\n",
      "==============================================================================\n",
      "Omnibus:                        2.900   Durbin-Watson:                   1.883\n",
      "Prob(Omnibus):                  0.235   Jarque-Bera (JB):                2.735\n",
      "Skew:                           0.152   Prob(JB):                        0.255\n",
      "Kurtosis:                       2.695   Cond. No.                     1.08e+03\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, 1.08e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation: y = 18.02 + 0.51x1 + -0.01x2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(10, 10))\n",
    "axes = Axes3D(fig, elev=10, azim=45)\n",
    "axes.scatter(sample_df[\"Primary Care Physicians Rate\"], sample_df[\"High School Graduation Rate\"], sample_df['% Adults with Obesity'], c=sample_df['% Adults with Obesity'], cmap=plt.cm.get_cmap(\"Spectral\"))\n",
    "plt.title(\"Plot 3.2: Best Fit model with High School Graduation Rate and Primary Care Physician Access for Selected states\")\n",
    "axes.set_xlabel(\"Primary Care Physician Rate\")\n",
    "axes.set_ylabel(\"High School Gradation Rate\")\n",
    "axes.set_zlabel(\"County Obesity Rate\")\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0],\n",
    "                                                         regr.coef_[1]))\n",
    "plt.savefig(\"Plot 3-2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best fit Model 3:  Physical Inactivity and Unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>% Physically Inactive</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>28.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>28.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>29.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % Physically Inactive  % Unemployed\n",
       "173                    15.0           3.0\n",
       "174                    25.0           4.0\n",
       "175                    22.0           5.0\n",
       "176                    31.0           4.0\n",
       "177                    28.0          12.9\n",
       "...                     ...           ...\n",
       "2557                   32.0           4.2\n",
       "2558                   28.0           2.8\n",
       "2559                   29.0           3.2\n",
       "2560                   20.0           5.6\n",
       "2561                   20.0           9.5\n",
       "\n",
       "[354 rows x 2 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['% Physically Inactive','% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 12.726300649964742\n",
      "Coefficients: \n",
      " [0.56245354 0.68160963]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     23.207933\n",
       "174     29.514078\n",
       "175     28.508327\n",
       "176     32.888799\n",
       "177     37.267764\n",
       "          ...    \n",
       "2557    33.587574\n",
       "2558    30.383507\n",
       "2559    31.218604\n",
       "2560    27.792385\n",
       "2561    30.450663\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.398\n",
      "Model:                               OLS   Adj. R-squared:                  0.395\n",
      "Method:                    Least Squares   F-statistic:                     116.1\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           2.00e-39\n",
      "Time:                           18:36:54   Log-Likelihood:                -1020.2\n",
      "No. Observations:                    354   AIC:                             2046.\n",
      "Df Residuals:                        351   BIC:                             2058.\n",
      "Df Model:                              2                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    12.7263      1.211     10.506      0.000      10.344      15.109\n",
      "% Physically Inactive     0.5625      0.040     13.951      0.000       0.483       0.642\n",
      "% Unemployed              0.6816      0.146      4.653      0.000       0.393       0.970\n",
      "==============================================================================\n",
      "Omnibus:                        9.530   Durbin-Watson:                   1.887\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.544\n",
      "Skew:                           0.392   Prob(JB):                      0.00846\n",
      "Kurtosis:                       3.177   Cond. No.                         147.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation: y = 18.02 + 0.51x1 + -0.01x2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(10, 10))\n",
    "axes = Axes3D(fig, elev=10, azim=45)\n",
    "axes.scatter(sample_df['% Physically Inactive'], sample_df['% Unemployed'], sample_df['% Adults with Obesity'], c=sample_df['% Adults with Obesity'], cmap=plt.cm.get_cmap(\"Spectral\"))\n",
    "plt.title(\"Plot 3.3: Best Fit model withPhysical Inactivity Rate and Unemployment Rate for Selected states\")\n",
    "axes.set_xlabel(\"Physical Inactiity Rate\")\n",
    "axes.set_ylabel(\"Unemployment Rate\")\n",
    "axes.set_zlabel(\"County Obesity Rate\")\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0],\n",
    "                                                         regr.coef_[1]))\n",
    "plt.savefig(\"Plot 3-3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPLEX MODELS\n",
    "\n",
    "## More Complex models of 3 or more predictors will be tested for best fit on a sample of selected states. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1: 3 predictors: physical inactivity, education, unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>% Physically Inactive</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>25.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>22.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>31.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>32.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>28.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>29.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>20.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % Physically Inactive  High School Graduation Rate  % Unemployed\n",
       "173                    15.0                         87.0           3.0\n",
       "174                    25.0                         88.0           4.0\n",
       "175                    22.0                         85.0           5.0\n",
       "176                    31.0                         90.0           4.0\n",
       "177                    28.0                         89.0          12.9\n",
       "...                     ...                          ...           ...\n",
       "2557                   32.0                         95.0           4.2\n",
       "2558                   28.0                         98.0           2.8\n",
       "2559                   29.0                         97.0           3.2\n",
       "2560                   20.0                         90.0           5.6\n",
       "2561                   20.0                         92.0           9.5\n",
       "\n",
       "[354 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[[ '% Physically Inactive' ,'High School Graduation Rate','% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 11.189828802214443\n",
      "Coefficients: \n",
      " [0.5612275  0.01698287 0.6939207 ]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     23.167513\n",
       "174     29.490692\n",
       "175     28.449981\n",
       "176     32.892022\n",
       "177     37.367251\n",
       "          ...    \n",
       "2557    33.676948\n",
       "2558    30.511498\n",
       "2559    31.333311\n",
       "2560    27.828793\n",
       "2561    30.569049\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.399\n",
      "Model:                               OLS   Adj. R-squared:                  0.394\n",
      "Method:                    Least Squares   F-statistic:                     77.42\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           1.96e-38\n",
      "Time:                           20:20:38   Log-Likelihood:                -1020.0\n",
      "No. Observations:                    354   AIC:                             2048.\n",
      "Df Residuals:                        350   BIC:                             2063.\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                          11.1898      2.611      4.285      0.000       6.054      16.326\n",
      "% Physically Inactive           0.5612      0.040     13.895      0.000       0.482       0.641\n",
      "High School Graduation Rate     0.0170      0.026      0.664      0.507      -0.033       0.067\n",
      "% Unemployed                    0.6939      0.148      4.696      0.000       0.403       0.985\n",
      "==============================================================================\n",
      "Omnibus:                        9.456   Durbin-Watson:                   1.897\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.437\n",
      "Skew:                           0.385   Prob(JB):                      0.00893\n",
      "Kurtosis:                       3.214   Cond. No.                     1.06e+03\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, 1.06e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>% Physically Inactive</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>15.0</td>\n",
       "      <td>92574.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>25.0</td>\n",
       "      <td>61198.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>22.0</td>\n",
       "      <td>48443.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>31.0</td>\n",
       "      <td>58151.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>28.0</td>\n",
       "      <td>56704.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>32.0</td>\n",
       "      <td>48534.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>28.0</td>\n",
       "      <td>68814.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>29.0</td>\n",
       "      <td>47194.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20.0</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>20.0</td>\n",
       "      <td>30988.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % Physically Inactive  Median Income  % Unemployed\n",
       "173                    15.0        92574.0           3.0\n",
       "174                    25.0        61198.0           4.0\n",
       "175                    22.0        48443.0           5.0\n",
       "176                    31.0        58151.0           4.0\n",
       "177                    28.0        56704.0          12.9\n",
       "...                     ...            ...           ...\n",
       "2557                   32.0        48534.0           4.2\n",
       "2558                   28.0        68814.0           2.8\n",
       "2559                   29.0        47194.0           3.2\n",
       "2560                   20.0        31113.0           5.6\n",
       "2561                   20.0        30988.0           9.5\n",
       "\n",
       "[354 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['% Physically Inactive' ,  'Median Income', '% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 13.852481570078012\n",
      "Coefficients: \n",
      " [ 5.49135434e-01 -1.21893312e-05  6.53340277e-01]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     22.921119\n",
       "174     29.448266\n",
       "175     28.609675\n",
       "176     32.780219\n",
       "177     36.965179\n",
       "          ...    \n",
       "2557    33.577248\n",
       "2558    30.218830\n",
       "2559    31.292835\n",
       "2560    28.114649\n",
       "2561    30.664200\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.399\n",
      "Model:                               OLS   Adj. R-squared:                  0.394\n",
      "Method:                    Least Squares   F-statistic:                     77.39\n",
      "Date:                   Fri, 27 Nov 2020   Prob (F-statistic):           2.02e-38\n",
      "Time:                           20:33:06   Log-Likelihood:                -1020.0\n",
      "No. Observations:                    354   AIC:                             2048.\n",
      "Df Residuals:                        350   BIC:                             2063.\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                    13.8525      2.193      6.316      0.000       9.539      18.166\n",
      "% Physically Inactive     0.5491      0.046     11.996      0.000       0.459       0.639\n",
      "Median Income         -1.219e-05   1.98e-05     -0.616      0.538   -5.11e-05    2.67e-05\n",
      "% Unemployed              0.6533      0.154      4.252      0.000       0.351       0.956\n",
      "==============================================================================\n",
      "Omnibus:                        9.467   Durbin-Watson:                   1.888\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.480\n",
      "Skew:                           0.392   Prob(JB):                      0.00874\n",
      "Kurtosis:                       3.171   Cond. No.                     5.24e+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, 5.24e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>% With Access to Exercise Opportunities</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>76.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>84.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>60.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>75.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>67.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>45.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>59.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % With Access to Exercise Opportunities  % Physically Inactive  \\\n",
       "173                                     100.0                   15.0   \n",
       "174                                      76.0                   25.0   \n",
       "175                                      84.0                   22.0   \n",
       "176                                      60.0                   31.0   \n",
       "177                                      75.0                   28.0   \n",
       "...                                       ...                    ...   \n",
       "2557                                     39.0                   32.0   \n",
       "2558                                     67.0                   28.0   \n",
       "2559                                     60.0                   29.0   \n",
       "2560                                     45.0                   20.0   \n",
       "2561                                     59.0                   20.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed  \n",
       "173                          87.0           3.0  \n",
       "174                          88.0           4.0  \n",
       "175                          85.0           5.0  \n",
       "176                          90.0           4.0  \n",
       "177                          89.0          12.9  \n",
       "...                           ...           ...  \n",
       "2557                         95.0           4.2  \n",
       "2558                         98.0           2.8  \n",
       "2559                         97.0           3.2  \n",
       "2560                         90.0           5.6  \n",
       "2561                         92.0           9.5  \n",
       "\n",
       "[354 rows x 4 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sample_df[['% With Access to Exercise Opportunities', '% Physically Inactive' ,'High School Graduation Rate', '% Unemployed']].astype(float)\n",
    "Y = sample_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 12.20467312643851\n",
      "Coefficients: \n",
      " [-0.0064556   0.55159457  0.0131997   0.69348334]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173     23.061856\n",
       "174     29.439419\n",
       "175     28.386875\n",
       "176     32.878676\n",
       "177     37.285860\n",
       "          ...    \n",
       "2557    33.770533\n",
       "2558    30.452120\n",
       "2559    31.313098\n",
       "2560    28.017543\n",
       "2561    30.658149\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.400\n",
      "Model:                               OLS   Adj. R-squared:                  0.393\n",
      "Method:                    Least Squares   F-statistic:                     58.05\n",
      "Date:                   Sat, 28 Nov 2020   Prob (F-statistic):           1.58e-37\n",
      "Time:                           04:52:40   Log-Likelihood:                -1019.8\n",
      "No. Observations:                    354   AIC:                             2050.\n",
      "Df Residuals:                        349   BIC:                             2069.\n",
      "Df Model:                              4                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===========================================================================================================\n",
      "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "const                                      12.2047      3.122      3.909      0.000       6.064      18.345\n",
      "% With Access to Exercise Opportunities    -0.0065      0.011     -0.594      0.553      -0.028       0.015\n",
      "% Physically Inactive                       0.5516      0.044     12.664      0.000       0.466       0.637\n",
      "High School Graduation Rate                 0.0132      0.026      0.501      0.617      -0.039       0.065\n",
      "% Unemployed                                0.6935      0.148      4.688      0.000       0.403       0.984\n",
      "==============================================================================\n",
      "Omnibus:                        9.524   Durbin-Watson:                   1.906\n",
      "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.537\n",
      "Skew:                           0.392   Prob(JB):                      0.00849\n",
      "Kurtosis:                       3.178   Cond. No.                     1.54e+03\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, 1.54e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying the best model to the full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>FIPS</th>\n",
       "      <th>% Adults with Obesity</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "      <th>County and State</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>69.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Autauga County, Alabama</td>\n",
       "      <td>58786</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>74.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Baldwin County, Alabama</td>\n",
       "      <td>55962</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1005</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>53.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Barbour County, Alabama</td>\n",
       "      <td>34186</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1007</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bibb County, Alabama</td>\n",
       "      <td>45340</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1009</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Blount County, Alabama</td>\n",
       "      <td>48695</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>56037</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Sweetwater County, Wyoming</td>\n",
       "      <td>73008</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sweetwater County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>56039</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>100.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Teton County, Wyoming</td>\n",
       "      <td>83831</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>56041</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Uinta County, Wyoming</td>\n",
       "      <td>58235</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>56043</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>83.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Washakie County, Wyoming</td>\n",
       "      <td>53426</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>56045</td>\n",
       "      <td>33</td>\n",
       "      <td>27</td>\n",
       "      <td>63.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Weston County, Wyoming</td>\n",
       "      <td>52867</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  % Adults with Obesity  % Physically Inactive  \\\n",
       "0      1001                     33                     35   \n",
       "1      1003                     31                     27   \n",
       "2      1005                     42                     24   \n",
       "3      1007                     38                     34   \n",
       "4      1009                     34                     30   \n",
       "...     ...                    ...                    ...   \n",
       "2909  56037                     30                     25   \n",
       "2910  56039                     12                     12   \n",
       "2911  56041                     36                     27   \n",
       "2912  56043                     29                     28   \n",
       "2913  56045                     33                     27   \n",
       "\n",
       "      % With Access to Exercise Opportunities  Primary Care Physicians Rate  \\\n",
       "0                                        69.0                          45.0   \n",
       "1                                        74.0                          73.0   \n",
       "2                                        53.0                          32.0   \n",
       "3                                        16.0                          49.0   \n",
       "4                                        16.0                          22.0   \n",
       "...                                       ...                           ...   \n",
       "2909                                     90.0                          37.0   \n",
       "2910                                    100.0                         116.0   \n",
       "2911                                     84.0                          44.0   \n",
       "2912                                     83.0                          50.0   \n",
       "2913                                     63.0                          43.0   \n",
       "\n",
       "      High School Graduation Rate  % Unemployed            County and State  \\\n",
       "0                            90.0           3.6     Autauga County, Alabama   \n",
       "1                            86.0           3.6     Baldwin County, Alabama   \n",
       "2                            81.0           5.2     Barbour County, Alabama   \n",
       "3                            84.0           4.0        Bibb County, Alabama   \n",
       "4                            93.0           3.5      Blount County, Alabama   \n",
       "...                           ...           ...                         ...   \n",
       "2909                         81.0           4.2  Sweetwater County, Wyoming   \n",
       "2910                         88.0           3.0       Teton County, Wyoming   \n",
       "2911                         85.0           4.4       Uinta County, Wyoming   \n",
       "2912                         85.0           4.2    Washakie County, Wyoming   \n",
       "2913                         90.0           3.5      Weston County, Wyoming   \n",
       "\n",
       "      Median Income     State             County  \n",
       "0             58786   Alabama     Autauga County  \n",
       "1             55962   Alabama     Baldwin County  \n",
       "2             34186   Alabama     Barbour County  \n",
       "3             45340   Alabama        Bibb County  \n",
       "4             48695   Alabama      Blount County  \n",
       "...             ...       ...                ...  \n",
       "2909          73008   Wyoming  Sweetwater County  \n",
       "2910          83831   Wyoming       Teton County  \n",
       "2911          58235   Wyoming       Uinta County  \n",
       "2912          53426   Wyoming    Washakie County  \n",
       "2913          52867   Wyoming      Weston County  \n",
       "\n",
       "[2914 rows x 11 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omni_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>% Physically Inactive</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>25.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>12.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>27.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>28.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>27.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % Physically Inactive  High School Graduation Rate  % Unemployed\n",
       "0                      35.0                         90.0           3.6\n",
       "1                      27.0                         86.0           3.6\n",
       "2                      24.0                         81.0           5.2\n",
       "3                      34.0                         84.0           4.0\n",
       "4                      30.0                         93.0           3.5\n",
       "...                     ...                          ...           ...\n",
       "2909                   25.0                         81.0           4.2\n",
       "2910                   12.0                         88.0           3.0\n",
       "2911                   27.0                         85.0           4.4\n",
       "2912                   28.0                         85.0           4.2\n",
       "2913                   27.0                         90.0           3.5\n",
       "\n",
       "[2914 rows x 3 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = omni_df[[ '% Physically Inactive' ,'High School Graduation Rate','% Unemployed']].astype(float)\n",
    "Y = omni_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 18.01864776885875\n",
      "Coefficients: \n",
      " [ 0.51412861 -0.01129265  0.44094131]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       36.584199\n",
       "1       32.516341\n",
       "2       31.735924\n",
       "3       36.314203\n",
       "4       33.935584\n",
       "          ...    \n",
       "2909    31.809112\n",
       "2910    24.517262\n",
       "2911    32.880386\n",
       "2912    33.306327\n",
       "2913    32.427076\n",
       "Length: 2914, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.342\n",
      "Model:                               OLS   Adj. R-squared:                  0.341\n",
      "Method:                    Least Squares   F-statistic:                     503.9\n",
      "Date:                   Sat, 28 Nov 2020   Prob (F-statistic):          1.05e-263\n",
      "Time:                           05:00:45   Log-Likelihood:                -8451.7\n",
      "No. Observations:                   2914   AIC:                         1.691e+04\n",
      "Df Residuals:                       2910   BIC:                         1.694e+04\n",
      "Df Model:                              3                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "const                          18.0186      1.143     15.766      0.000      15.778      20.260\n",
      "% Physically Inactive           0.5141      0.015     34.820      0.000       0.485       0.543\n",
      "High School Graduation Rate    -0.0113      0.012     -0.958      0.338      -0.034       0.012\n",
      "% Unemployed                    0.4409      0.060      7.342      0.000       0.323       0.559\n",
      "==============================================================================\n",
      "Omnibus:                       35.011   Durbin-Watson:                   1.708\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               55.623\n",
      "Skew:                           0.089   Prob(JB):                     8.35e-13\n",
      "Kurtosis:                       3.653   Cond. No.                     1.31e+03\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, 1.31e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>% With Access to Exercise Opportunities</th>\n",
       "      <th>% Physically Inactive</th>\n",
       "      <th>High School Graduation Rate</th>\n",
       "      <th>Median Income</th>\n",
       "      <th>Primary Care Physicians Rate</th>\n",
       "      <th>% Unemployed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>58786.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>55962.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>34186.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>45340.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>48695.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>90.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>73008.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>100.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>83831.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>84.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>58235.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>83.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>53426.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>63.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>52867.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % With Access to Exercise Opportunities  % Physically Inactive  \\\n",
       "0                                        69.0                   35.0   \n",
       "1                                        74.0                   27.0   \n",
       "2                                        53.0                   24.0   \n",
       "3                                        16.0                   34.0   \n",
       "4                                        16.0                   30.0   \n",
       "...                                       ...                    ...   \n",
       "2909                                     90.0                   25.0   \n",
       "2910                                    100.0                   12.0   \n",
       "2911                                     84.0                   27.0   \n",
       "2912                                     83.0                   28.0   \n",
       "2913                                     63.0                   27.0   \n",
       "\n",
       "      High School Graduation Rate  Median Income  \\\n",
       "0                            90.0        58786.0   \n",
       "1                            86.0        55962.0   \n",
       "2                            81.0        34186.0   \n",
       "3                            84.0        45340.0   \n",
       "4                            93.0        48695.0   \n",
       "...                           ...            ...   \n",
       "2909                         81.0        73008.0   \n",
       "2910                         88.0        83831.0   \n",
       "2911                         85.0        58235.0   \n",
       "2912                         85.0        53426.0   \n",
       "2913                         90.0        52867.0   \n",
       "\n",
       "      Primary Care Physicians Rate  % Unemployed  \n",
       "0                             45.0           3.6  \n",
       "1                             73.0           3.6  \n",
       "2                             32.0           5.2  \n",
       "3                             49.0           4.0  \n",
       "4                             22.0           3.5  \n",
       "...                            ...           ...  \n",
       "2909                          37.0           4.2  \n",
       "2910                         116.0           3.0  \n",
       "2911                          44.0           4.4  \n",
       "2912                          50.0           4.2  \n",
       "2913                          43.0           3.5  \n",
       "\n",
       "[2914 rows x 6 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = omni_df[['% With Access to Exercise Opportunities', '% Physically Inactive' ,'High School Graduation Rate',  'Median Income', 'Primary Care Physicians Rate', '% Unemployed']].astype(float)\n",
    "Y = omni_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>% Physically Inactive</th>\n",
       "      <th>% With Access to Exercise Opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>25.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2910</th>\n",
       "      <td>12.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>27.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2912</th>\n",
       "      <td>28.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2913</th>\n",
       "      <td>27.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      % Physically Inactive  % With Access to Exercise Opportunities\n",
       "0                      35.0                                     69.0\n",
       "1                      27.0                                     74.0\n",
       "2                      24.0                                     53.0\n",
       "3                      34.0                                     16.0\n",
       "4                      30.0                                     16.0\n",
       "...                     ...                                      ...\n",
       "2909                   25.0                                     90.0\n",
       "2910                   12.0                                    100.0\n",
       "2911                   27.0                                     84.0\n",
       "2912                   28.0                                     83.0\n",
       "2913                   27.0                                     63.0\n",
       "\n",
       "[2914 rows x 2 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = omni_df[['% Physically Inactive', \n",
    "               '% With Access to Exercise Opportunities']].astype(float)\n",
    "Y = omni_df['% Adults with Obesity'].astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equation: y = 24.40 + -0.01x1 + 0.43x2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(1, figsize=(10, 10))\n",
    "axes = Axes3D(fig, elev=10, azim=45)\n",
    "axes.scatter(sample_df['% Physically Inactive'], sample_df['% With Access to Exercise Opportunities'], sample_df['% Adults with Obesity'], c=sample_df['% Adults with Obesity'], cmap=plt.cm.get_cmap(\"Spectral\"))\n",
    "plt.title(\"Plot 3.5: Best Fit model with Physical Inactivity Rate and Access to Exercise Oppurtunities\")\n",
    "axes.set_xlabel(\"Physical Inactiity Rate\")\n",
    "axes.set_ylabel(\"Rate With Access to Exercise Opportunities\")\n",
    "axes.set_zlabel(\"County Obesity Rate\")\n",
    "print(\"Equation: y = {:.2f} + {:.2f}x1 + {:.2f}x2\".format(regr.intercept_, regr.coef_[0],\n",
    "                                                         regr.coef_[1]))\n",
    "plt.savefig(\"Plot 3-5.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 24.398582222105713\n",
      "Coefficients: \n",
      " [-9.74494479e-03  4.28506176e-01 -1.53736784e-02 -3.62243783e-05\n",
      " -1.25249378e-02  3.23740021e-01]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       35.812622\n",
       "1       32.148942\n",
       "2       32.965264\n",
       "3       36.559309\n",
       "4       34.761692\n",
       "          ...    \n",
       "2909    31.240540\n",
       "2910    23.694880\n",
       "2911    32.606743\n",
       "2912    33.079300\n",
       "2913    32.650130\n",
       "Length: 2914, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X) \n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                             \n",
      "=================================================================================\n",
      "Dep. Variable:     % Adults with Obesity   R-squared:                       0.358\n",
      "Model:                               OLS   Adj. R-squared:                  0.357\n",
      "Method:                    Least Squares   F-statistic:                     270.2\n",
      "Date:                   Sat, 28 Nov 2020   Prob (F-statistic):          2.52e-275\n",
      "Time:                           10:03:40   Log-Likelihood:                -8415.6\n",
      "No. Observations:                   2914   AIC:                         1.685e+04\n",
      "Df Residuals:                       2907   BIC:                         1.689e+04\n",
      "Df Model:                              6                                         \n",
      "Covariance Type:               nonrobust                                         \n",
      "===========================================================================================================\n",
      "                                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------------------\n",
      "const                                      24.3986      1.380     17.685      0.000      21.693      27.104\n",
      "% With Access to Exercise Opportunities    -0.0097      0.004     -2.205      0.028      -0.018      -0.001\n",
      "% Physically Inactive                       0.4285      0.018     23.886      0.000       0.393       0.464\n",
      "High School Graduation Rate                -0.0154      0.012     -1.292      0.196      -0.039       0.008\n",
      "Median Income                           -3.622e-05   7.86e-06     -4.607      0.000   -5.16e-05   -2.08e-05\n",
      "Primary Care Physicians Rate               -0.0125      0.003     -4.653      0.000      -0.018      -0.007\n",
      "% Unemployed                                0.3237      0.063      5.178      0.000       0.201       0.446\n",
      "==============================================================================\n",
      "Omnibus:                       22.369   Durbin-Watson:                   1.697\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.681\n",
      "Skew:                           0.018   Prob(JB):                     4.86e-08\n",
      "Kurtosis:                       3.525   Cond. No.                     9.17e+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, 9.17e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = df.plot.hist(bins=12, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DBCCD0F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC8EA1080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC8F86AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC906C550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC910F390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC92012E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC9302F60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC94029B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC9402AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC95555C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC96549B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC97476D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC98392B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DC9A1D978>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA5F3F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA4CCC88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA5B96D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA4F4A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA75BDD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA7C6198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA52B518>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA8429E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA8D4748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA716F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCA8FE358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCAA44B70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA34A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA49DD8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA63198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA77518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA8A898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBA9FC18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBAB5F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBACE358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBAE06D8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBAF7A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB0ADD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB25198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB39518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB4D898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB62C18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB77F98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBB90358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBBA46D8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBBB9A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBBCFDD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBBE8198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBBFC518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000012DCBC0F898>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEiCAYAAAD+lrlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXwV1d3/398khC2AEAIEIUQBIcQlQiq1UiuPSxEttSIq8mixWGoVq49dwLZWLM/PYqsWrVut0ipabEsX1IrWFZenFIksKoiChB0EAkgAWb+/P87cMLncO3dm7sy9NzDv12teyZ3lnO9nvmfOnG3OEVUlIiIiIuLoIS/bBkREREREZJYo44+IiIg4yogy/oiIiIijjCjjj4iIiDjKiDL+iIiIiKOMKOOPiIiIOMqIMv6IiIiIo4ycz/hFZJyIzBORPSLyB9v+L4rISyJSJyKbROQvIlKaRVNT4qCln7V/q7W9LCL9smhqSpJpiTvnNhFRETknw+Z5wsEv5Zb99bbt1iya6gon34hIKxF5UEQ2i8h2EXkjS2a6wsE3o+L8ssvy1YAsmutICr9cKiJLRGSHiCwWkYvCtCXnM35gHfC/wNS4/e2BR4ByoAewA/h9Ri3zTjIt64BLgA5AR+AZ4OnMmuaZZFoAEJGeGE3rM2mUTxy1AMeoapG1TcqgXX5x0vMIJp1VWH//J4N2+SGhFlV9yuaTIuA64BPg3SzY6JaEWkTkWOBJ4GagLfBD4I8i0iksQwrCCjgoVPVvACJSDXSz7Z9lP09E7gdmZ9Y6bzho2QZss44JcADolQ0b3ZJMi437gfHAg5m0yw8utDQpkukRkT7AMKCbqn5m7a7JvIXu8eCbbwJPaA5PReCgpRuwzZan/VNEdgI9gU/DsKUplPjdcibwQbaNSAcR2QZ8DvwGuCPL5vhGREYAe1X1+WzbEhArRWSNiPxeRDpm25g0GAisBG63mnreE5Hh2TYqXUSkB+b5fyLbtvhkHrBERIaJSL7VzLMHWBRWhDlf4neDiJwM/Az4erZtSQdVPUZEWmNKLyuzbY8fRKQI89I6L9u2BMBm4AvAAqAYeAB4CvhqNo1Kg27AicBfga7A6ZjS5WJVXZJVy9LjKuBNVV2RbUP8oKoHROQJ4I9AC2AvMEJVd4YVZ5Mv8YtIL2AWcKOqvplte9LFcvbDwBNhtvGFyO3AtKb6ENpR1XpVnaeq+1V1IzAOOE9E2mbbNp/sBvYB/6uqe1V1NvAaTf8lfRXweLaN8Is1+OGXwFlAIfAV4FERqQorziad8VtVvJeBSao6Ldv2BEge0Ao4NtuG+OBs4HsiskFENgDdgT+LyPgs2xUEsfZjyaoV/gmt6SBbiMgZmNrLjGzbkgZVwBtWIeOgqr4D/AcIbTRczmf8IlIgIi2AfCBfRFpY+44FXgUeUNWHs2ulOxy0nCsip1rte22Be4CtQM5Wv5NpwWT8J2IScxVmJMN3MM0kOYmDXwaKSB8RyRORYuA+4HVV3Z5di51x8M0bwCrgFuucMzClzBezZ60zDlpifBP4q6ruyI6F7nHQ8g7w5VgJX0ROBb5MmC9qVc3pDZiIKWnZt4nAbdb/9fYt2/b61DIC+NDSsAl4Hjg52/b60ZLgvFrgnGzb69MvI4EVwE7MsNQngC7Ztjcd3wCVwL8tTYuBb2Tb3jS0tMCMhjs723YGoGUcsAwzLP0T4Pth2iJWpBERERERRwk539QTEREREREsUcYfERERcZSRVsYvIlNF5FMReT8og7JFpCU3ibTkJpGWpk26Jf4/AEMCsCMX+AORllzkD0RacpE/EGlpsqT15a6qviEi5W7P79ixo5aXuz4908S+0nRFpCVjRFpyk0hLjlJTU7NZVUscTwpgiFI58L7D8bGYuSjmlZWVaZD0GP9cYGFZNh7VWoK0IR16jH+uwZZc0BILy26Xl2tzSYtX2+N/h6HFvi8WRyY0B6Ulkb2Z9pk9PmCepsi3Q+/cVdVHVLVaVatLSpxfQrlOpCU3ibTkJpGW3CUa1RMRERFxlBFl/BERERFHGekO55yO+fy7jzVf+ZhgzMoKxxFpyUUiLblJpKUJk+6onpFBGZIDrFDV6mwbERCRltwk0pKbHElaXBE19UREREQcZUQZf0RERMRRRpTxR0RERBxlRBl/RERExFFGlPFHREREHGVEGX9ERETEUUaU8UdEREQcZUQZf0RERMRRRpTxR0RERBxlRBl/RERExFFGlPFHREREHGVEGX9ERETEUUa6s3MOEZGlIrJMRCYEZZSd+++/n+rqapo3b87o0aMbHdux8EV69epFUVERQ4YMYd26delE1TZsLXv27GHMmDH06NGDNm3acOqppzJr1qyG46+88gp9+/alVatWDB48mJUrV/qNKqta9u7dyyWXXEJ5eTkiwuuvv55OVFnVMmfOHDY+/VM6dOjA6vuuYNM/fsH69ev9RpVVLYsXL6a6upr27dvTvn17zjnnHBYvXuw3qqw/LzFuv/12RISXX37Zd1xh52VOWmpraxERioqKGrZJkyYFbUIjfGf8IpIPPACcD/QDRopIv6AMi9G1a1d++tOf8q1vfavR/tmzZ7PtjSeYOXMmdXV1HHfccYwc6W+y0AMHDgCUEbKW/fv30717d2bPns327duZNGkSl156KbW1tRzYtZ2LL76YSZMmUVdXR3V1NZdddpnnOHJBC8CgQYN48skn6dKli+84ckHL1q1bKaoaQm1tLcd+dypS2JKrr77acxy5oKVr167MmDGDuro6Nm/ezLBhw7j88ss9x5ELWmIsX76cGTNmUFpamm50oeZlbrRs27aN+vp66uvrufXWW4OM/jDSKfGfBixT1U9UdS/wNPD1YMw6xMUXX8xFF11EcXFxo/3PPvssrfoMorKyksLCQm699VbeeOMNli9f7jmOuXPnAuwJW0vr1q2ZOHEi5eXl5OXlceGFF3LcccdRU1PDro/+TWVlJSNGjKBFixZMnDiRhQsX8uGHHzY5LYWFhdx0000MGjSI/Px833Hkgpbzzz+f1n0H0bZtW/KataBN/wt5++23PceRC1qOOeaYhlqYqpKfn8+yZcs8x5ELWmKMGzeOO++8k8LCwrSiIuS8zI2WTCJmbV4fF4pcAgxR1Wus31cCA1V1XNx5YzELFQP0AZYCHTEr23uhK1AI1Fq/uwGtgI+s382Ak4HlwDYP4XYEDgBdVbVlhrTEKLBsXowpQX0OrLIdrwTW4l5Pulrs4XjVZNfyuW3/ycAKYIdL++3xtse7lhOB9z3aHk8yLQCdgA6AlzdyUH5xCj+ZvxJpqQJib+R1wD6H6xPhxy+JtHhNZ/Fa2mN8sRw4CZM3JEtnTnH1Bv7ilJf59IuTDXYtBy3791nHPgPWAPs9hGenh6o6LwycajX2ZBswAnjU9vtK4Dcur025CnyCa/4X+IPt99nWjToZaAn81rqBIz2GOy/TWqzrmgEvA7+1fm8CJsed8zYwOlNa/GqK1xJ3bA1wlp94/Wjx6w+XWk4G6oAvZzKN+fVXCi2tgeuAC3z4OxAtXuJN8LwUAR8Dx1m/a4Fz/MQVpm8S2ZBESzXmZdAZmAG8GOT9i9/SWYFrDdDd9rsbpvSQEVT1FRFZB/wVaAf8GvO2X+MjuIxqEZE8YBqwF4iVKg4CbeNObYu7krKdXNASFDmjRUR6AbOAG1X1TR/B54wWAFXdKSIPYwocqz0GnwtabgemqeqKAKLImJ5EWlS1HlM4ANgoIuOA9SLSVlU/C8OOdNr43wF6i8hxIlIIXA48E4xZrtmkqr1VtRPmBVCAv2p+xrSIiACPYd7sw1U1Vr3bDZxiO6810BP4wGMUuaAlKHJCi4j0wJTQJqnqNJ9R5ISWOPIwzaXNPEaRC1rOBr4nIhtEZAMm4/6ziIz3EU0m8zI3fom1v0tINvhv6rGqGkMxbezLgZ94uG6sh3MLgBbALzBvyha2fROtm1MGvA7c4UPD2Expsc5/GJgDFMXtvxnYDgy3tN0JzMmkFq+akmmxjjW3dKwBzrP+F6/xetXi1R8u/HKsFfcP/YQbpF/canbQci5wKqZ9vy1wH6Zke52PONPW4sZXDlqKgS62bTWmyeawtOgmrrB8ExfH7CRaBmL6DfIsXX8CXgvi/iW9NgyBAd+siZg3oH2bCBwDLAJ2AhswL4b8bNubQksPy/7PgXrbNso6fg6m03A35kVWnm2b09BSm8BvOanHSQtwm3XMvr8+2zb71DLCSl/1mCae54GTs22z3zQWd24tDm382d5S+GUkZgDETmA98ATQJUx7fI/qiYiIiIhomkRTNkREREQcZUQZf0RERMRRRrpz9UwVkU9FJN0PZrKKiHQXkddEZImIfCAiN2bbpoiIiIiwSLfE/wdgiJcLRKRDmnGGwX7g+6paAXwRuN7NXB3paBGRziLSX0ROFZHOfsMJyp7Y9SLSPghbMh1vtmzPJulqdnN9osKddd1LIvKx9de1DZGfcoO0O3dFpBx4TlVPTHDsp6r6v9b//YqLiz8oLy9PK76wqKmp2azWZ84iMhO4X1Vfih1vYlq2qaqrhCYiZcXFxStzWEuDX+IRkTLgl5gx3dsAKS4u7tkUtSSiY8eOmm0tO3bsID8/nxUrVlBZWQnAmjVr2Lhx405VLbJmsmyvqknHzzflNJaIXPCLE670BDBMqRx4P8mxdzHzW8wDtpWVlamqao/xz2kq7OckOz/+nNjmJw5gD2Y8bTlmvpy2QWkJilWrVulZZ52lffv21X79+umUKVMOO+e1116LDZ1cYG0/U2f//XvAgAGu4s+k1hg4f2r/b+AybMN4nbSka789fdnx4JeDbv0SryU+fQelyQ0rVqzQysrKht8nnHCCAgvV+KAUWOqkw2say3Q6c0pjiTY/aSyTmtzoSWfKBleo6iPAIyIyv6SkpCrs+NJgOXAv5ivGmzTBp9LZ1lJQUMDdd99N//792bFjBwMGDODcc8+lX7/DWqUOqqpb+zoGbGYm6aiqf8q2ER78oh78krNs3LgRrAnFVHW9iHRKcUlTTmNHJGGP6jleRJ4RkWcx81/kMrsxc3E/pap/S3A861pKS0vp378/AG3atKGiooK1a9emG2x25oUNhhoReVBEBopIVxHpmg0jQvJLk0dExorIPKB406ZN2TYnY5RP+GfD39j/uUbYJf74Oa1fDzk+X+ihL+v2quo9SU7LKS21tbXMnz+fgQMHJjwuIgsxn+P/QFUPm+/HNs2sNOGH8ipgDGbCrmMJc24TlwToF8rKysI01TedO3dm+/btzQBEpBT4NNF5thpyYUlJyZ5M2hjhTLrDOadj2ln7iMgaERljP66qs+1bOnGFibWwRjGwR0QWWNtQ+zm5pKW+vp7hw4czZcoU2rZtPKGnVfJcqKqnAL8B/pEoDFV9RFWrVXVASYnrfq3AWL16NYMHD6aiooLKykruvffew86xlmyssvnkZ/bjqrpXVR9S1SGqepImGGCQSQL2S3U2/OKGYcOGgXleAL4JzHQ6X83iJjmNiNSKyHtWOpuX+oqmTVoZv6qOVNVSVW2mqt1U9bGgDMskgwYNAqhR1WNVtcrans+2XYnYt28fw4cPZ9SoUVx88cWHHbcynA4AloZmIpJzbayxdvElS5YwZ84cHnjggWTrv9bbfPLz+IMi8lURechqhnPMgIBGVe8gq+Iu/XIQctsv8YwcOZLTTz+dpUuX0q1bNx577DEmTJgAZs3djzETv03OrpWBMdhKZ9VBBhqf5nKB0Dt3mxIiMtaqnuYkqsqYMWOoqKjg5ptvTnjOhg0bGv4XkdMwL/ctycIUkSkDBgwI2tSUlJaWNqyTam8XT9AhmhQRmQKcgJnUKrYOw7CgbU2FB78Ugju/5ArTp09PdugjtxlkttJYRHJCz/hFpC+mffzYJuD8rLcRO/H2228zbdo0TjrpJKqqzOCQO+64g1WrzGqN1157LTNmzADobLUl7wYut4Z4JWOow7GklE/4J7WTL/B0PNk1KdrFixzaxYeq6gn2k6urAy2sucKDXyo9+KWB+FJi7D56LUnGrnHyW0iEksYCRoF/iYhiVsbK2QJgEISa8VuLIozELF48N8y4AiJpW6SINFqYIRsvsUGDBpEqrxg3bhw33HDDFqst2Q3xa8pmFBft4otUtb/V5/IPzPqoMT63Ss9VZLFD1INf9nnwy5FEVtOYS85Q1XXW0NSXRORDVX0jdjCMTvcsvYSB8IdzjgG+oKqTVfXJkOMKgtsdjnXDLIJ8D3B3ZszxjZepG0aHZUQqAmgXH43pKL0JsxZuXa52iFrsyrYBWWJ0tg1Ihaqus/5+CvwdOC3ueM53unsh7Iz/IJCVsdU+6IdZEi0Z1Zgx7z/BrJSVlLA6cJw6Jv3Gqarvpm2Yv3jT7q9Q1XdVdSDwX8AtwI/dxJ2o6ST2N+TOtyDWh21yZCuNeSBPRNpAw5Kn5+FvCdcGcqUTNxlht/HfBLxi9f6vzvE2/mU4fGGoqgeBX4vIXzALu0ekQVDt4taarD0w4/hzfVWhXLcv49ibO7KYWRYAb5mkRAHwR1V9IVvGZIJQM35VfUFETsBUm44Fvh1mfGmyFxcfZanqGmBEdXV1Lj/Ey9yeKCLn5Xh/xQfJRo+IyHnAg8DHQPSpbI6SrTTmgb1BD+HMdUJfiEVVD6rqHFX9a9hxpYuqXpFtGwLCywczh3851XS4F7PO6vmqeo2qXpNtg1LQPdsGZInD0liqZrVkzXG5TCobvej1Eq4fohW4IhpqfYkSmNMDGt82nk4C9RlWAYfG7zcF2gUVkJ/+nVR+jP2f7P6n4ePoe6EcIyMZv4hUicicTMQVNkeSFoup2TYgDaYC74jIeBG5QkQaamz2TMpNhuWnxJUo3ES/bfsOOhpx5JIyjTWFEn08QQwKSDRIIxP3IvCMX0TOEpE/xO3+Jc5DJXOSI0lLMlT1F9m2wS+W7aMwH96dDnwpuxalpDbbBmSDppzGjlTSnaRtiIgsFZFl1ko8yVCgrcPxpOzZs4fNz99Ljx49aNOmDaeeeiqzZs1qOL5r1y6uu+46Vt93Bat+fSkbnkq6EFAq2mZCy5gxYxJqeeqppygqKmrYWrVqhYhQU9N41uQXXniBPn36sPa332b7nL8cbpzpMO1u6VgkIv392JoK3b+vwS+rfj2Cdb//HruXH5rbaueSN6moqKBNmzb069ePf/zj8DnJYlp69erlW4uqLra+E7lBVcelo2XNQ1ez6tcjDktjOxa+SK9evVh1zyVs/PPPWLduXVIta3/7bSZPPnzqGktLcdh+Adj87F2suf9KVv16BGsfGcuOhS82HNtdu4C+ffvSqlUrBg8ezMqVK/1G4/Z5aUSyJqRk/Pd//zelpaWs+vUImnU4luLzvwfA3r17ueSSSyho1xkR4fXXX0+rpOwhL/NNzC9t27blhBNOaPDLnrUfsvHpn9KhQwdW33cFm/7xC9avXx+GCQ34zvhFJB94ADgfMwZ+pCRfp/Ym4Fd+4tm/fz8FbTsye/Zstm/fzqRJk7j00kupra0FYOzYsdTV1dH1mofofuN02p/tfeDQgQMHAMoyoaV79+4JtYwaNYr6+vqG7cEHH+T4449vmOcdQA8e4Prrr2fWrFl0veZBdi6efdjEZlaG1QLzhetY4CEnm0TEV7uzHjzQ4JfuN/2JY77832x65k5qa2tZu3Ytm5+7m3vuuYfPPvuMX/3qV1xxxRUc2Lmt4foDBw5pWbx4sS8tItJORCaLyIciskVEfM19E9PS5YrJdL/pT438Mnv2bLa98QQzZ86k+43TKWjXmZEjRza63q6l6zUPMn369LT9kg5tvziCY6+dStn//IVOw29l25vT2LNhGQd2bWfT3+9g0qRJ1NXVUV1dzWWXXeY5fI/Pi+80BnDLLbdQW1vbSEusMDRo0CA6fu37dOnSxW/wdtzmZY3w8rKJ+eWzzz7jmWeeafDLwc/rKaoaQtE3f8ux352KFLbk+DMuTBq+15dnItIp8Z8GLFPVT6xpV9sDLwOPAsNsU+l+Ffgu8D9+ImndujXHDBpFeXk5eXl5XHjhhRx33HHU1NSwb8sannnmGR555BHyW7VD8vJp3qWX5zjmzp0LsCcTWiZOnJhQSzyPP/44V111FdbYYgD2rv+IXr16cfzxxyP5zWhdcSYzZzaekNL6vcVahW0OcIw1Z3oy/uxHS15hiwa/iOTRqtdpFLTrTE1NDWvWrCGvRWvOP/98RIQLLriA1q1bs3/boVLM3LlzG7QUFhb61fJnYCtwlqoWq2oxPohpMaXHxn559tlnadVnEJWVlUh+M9p96XLeeOMNli9fnlCL5Dfj8ssvT6Zllwe/+KawpAdS0Mz6JQjC/q3r2fXRvynsWMaIESNo0aIFEydOZOHChezbstpT+Amel6c5fL0KO77SGMAF02rpc9vL1i+jZfny5RQWFjJlQ29adKtkU/0+Ln/k30Divh2nzmqL1jTOy1Lp8YXdLyKH/NKyZzWt+w4ir3kr8pq1oE3/C9mzdknQ0TfC92LrInIJMCQ2hE5ErsSsWTsDGK2qo639YzFjrRcAfYClwKnA/ATBdgQ2W/+XYjrDNmLmYllg7S8ATgE+wDisM7ADM01BHmYyr8+t3yviwm2HWVM31slWiFlrdy3QVVVbBqSlN9AS0yyUZ23bLHt6Axssm2NaPsTMb15vnfsZcBJm7d+9Ni3tMc1MKy0tx2Hat/fbtBzEPJTtLZtfAcaraqM5xm1zj5yIWUZvaQIfnIj7LxgLgJOBxZj738fSuR04BlNCfJ9D9/5ky95YvB2AIktzLN5emOUKE2oRkaWq2sc+jwqH/JLIdrs2t1o6YnyyyjrWzDq2HONTaOyXSmATpnS/6lCQnAC0UtWCRFpiJNFi50TMfU2kw665DJOm8jBTRSzFfEtTzKFnCcvetZaW2PWp7lN7Ejwv8U1tDmksmc0kiTuRFntH+cmY52OHTdPHHD6suYsVTvwCOL2Bv8TnZXY9LvwSTwUmHWzG1CJiVcAyIDbnQyItAJ0wz8OHtn0xTW1pfH8KLfvtmnpoWIutAyOAR22/r8TMm3IW8Ie4c5dgSmUAZ2Pmvo8PrwSYb/3fEngTuNB+PeahqwE2Wft/hclcJ2Iyic2YzPNLmIeulx5aKP2EBHHWh6Rlnu3/s4DnbL+vBX5uafk/y14BxgO/xyzm/nNgJyZBl8S0WHb+PqYlZmeclm3AIFt8rwADHPz4L+BHQOeY7ZiX6XjgZZdpoRmmhvRb274xlrb9mAR+Qdw1C63jnW1aHrXHC/zTSUu87da+pLbjYhHqeC2WjzdbvmgJ/NZKcyMTPQuYh/ObMb/YznkeWOPWLyn8tdqNZiAfGAT81NL1GCaDtPv7beB7cffd8T6R5Hlxm8ZS2Jww7ngtccfWYD2TNh8UJAijBlOyT0uPSz+tsfz0feATrEK2dWyRg5aTMfNOfTlu/8eYAsm8uP2FiTSl2tJp6llD4w9SumFK24n4NnC39en9HVhvTjHrpMYWPCnFrOS1CHgHeElVn7Nfj1nirSdwoe0aBS7CVCevAV4DvoCZGGq6FV5fa8uUFiemAsdjSgOVwDfUePABTIm3EvNAPKeqi1R1U0wL8AvgEpuWRHbu86AF4DJMKWi2iGzF1K5ex5Q4Lk0lRkTygGmY0tU4a985mNFPZ2ES5leAR0XEvtD4DzGZUSze32Lm0rfHm8ovjWwXkTovtrvRoqqvALcBf8WU6GuBAzT+fsBu51SMn3rFhphas9SehinMJNPilsswGUBKzap6QFXfsuL6LuZF+yqN/T0Qkyl7uWdenpeYzWn5KYEWJw4b5mv5oALz8ktXjxt+h6mhFAGPY00HIyJdgAWJtIhIL2AWcKOqvhkX3lRMvtglTtN/kmhyJo03WgHmTXYc5uFeCFSm+ZZM9rYXTEn3NaClbf/ZmIe0wLbvWevGuS7pBa3FKb5kWmzHl2BK+2382IkpGcyy4vkiMDeFrQOBdtb/rTAJ/jngzth+r1qAHwB/jzv3H5j59GO/b8I09ThpucCjli9jSljnBe0X23knYDL+9g5++RAzi+tvgPuBCZianmstKfwVqxm3wtQOHf2FqUndiymkfAJ0t/a3xtTG+mbyeXHyk4u4HwXujdvXqMRv7etn3Xe7D94PQ0+SML+HWWrTlRbMfFO1wLUO5/eztNo19fNlX5rihgIfYdo7f5JOWFZ4Y5PsfxiYAxTF7W+GmZfmVst5Z2Da+eITcsJww9LiFF8yLbbjbwBPeLHTylSujcWNqT0sB94DqlPY+gHWixN4BNPMMQhTyv1bimuT+eUrmOaRKuv3qZhZNc+znbMdUwLdhan9/L8EWsRJC7bME1Pbm2/Z/TYwIQi/YNrqT7RsKcOUVJ/34RdHLR7S1ge2MB/BTBjY4C9M+/DlmJJmPvBVTEHi65hmQ8U0JbwFvEiCjDbo58WLn+xxO2mxjje3/LMGM6NmC2xNKh79H3Reth3T7PomcB2mtpHML8da8f7QRbgpfePKviACCXPDvAkV02FYb9tGWccrMQu+78R0oHwj2zanoaWFlVjOzqBNS2z/vxt3bEEaWsZhXso7MKWp78ddPx9TFT4PU1XdBLyAaR8/rLaTxIb5tv/fAUqs/1sD7wXhF0zH9CIrfW3ANOPkO4TVDrMG7YeYl90WTC1uMnBM2P7CZO6zrXT0GeYl823bOcss2/YA6y37PN13Hzb78pMLLbWW3+xbedg+cKs5Ln1vxrxwd8ZrwbwENS7t1YeZrkK/AdGW2xvwF+Bq6//fY5VEMU0a74QYb3ym1QzTxj8dq/PeRRgLMSNMijm802t+EHb60PUipo+mi21fF0y1/KVs+yuI++7D5oz6KWwfuLQh0PsctCbfwzkjjgysj2vuxbS7bgb6Y0YjrAa+p6oLQ4p3vqqemuRYS1Xd7SKMWsxQOMGUmL6kqhtEpAh4S1WrnK4Pg9gQU6/HPISflr+CuO9eybSfwvaBSxsCvc9Ba4oy/ggAxKxAdDzWjJequjHk+E5Q1Y9CCrsVZujgijDCTxH3vzD9JI/H7qGIdMaMzDpXVc8JKB5f/grzvnslLD9lygcpbAj0PgetKZqWuf3harsAACAASURBVIkiIlNF5FMRed+2r4OIvCQiH1t/27sNT1V3qOpCVa0JO9O34gst81HVXdnI9C3sQxfr4oYujggqEr/+8nLfRaS7iLwmIktE5AMRudHa7zudxdkSlp8y4gMnQkjfgWrKiRK/iHTAfKG5Ndu2pEMmdYjImZhOoCdU9URr3y+BOlWdLGaiqfaqmnLWuiPl/icjV/SJyNWq+vs0rs+oDmtKiVJVfdeqYdRgvpkZjY90liKujGhL1wcu48i0nzxrymjG37FjRy0vL89YfF6oqanZrKk+c7aRC1r27NnDsmXLqKysBOD999+nT58+LFq0aAtmyofXHdoFyzAfWZ1dXFzcMdtaktEU/ZKMmpqag6qa7/b8XNOybNkyOnXqxKpVq9izZ0+dqhZbL4ek6SxGrmmxcySlMXCpJ6hebDfbgAED1A89xj/n67p4rr76ai0pKdHKysqGfVu2bNFzzjknNpTvJWwf5jht2daiqrpixYpGWtq1a6eqGtNyFbBVk48SWG5t88rKylzZG6TtbsHFNAvqwS9ha1i1apWeddZZ2rdvX+3Xr59OmTJFVU06w3Rsfuw2nXlJYz3GPxeqthUrVmj37t11+/bt2q5du0Z+SZbOMN+UzEuVxpKRqfQWdBpLRCafHTd6jqo2/tGjR/PCCy802jd58mTOPvtsMBNFvYIZHtXUWYKZB8SJg6raU1WrS0pcF3YiUlBQUMDdd9/NkiVLmDNnDg888ACLFy+OzdF/QFV708TSWX19PcOHD2fKlCm0bet+KQpVfURVq9NNY0GsSLV69WoGDx5MRUUFlZWV3HuvWQZ44sSJACfLoRl4h6YdWRPgqMr4zzzzTDp06NBo38yZM/nmN78Z+/k4pg0zMDK5pFznzp3tCzgUYOY2SkaNiDwoIgO9xJGppeGaKqWlpQ1rKLRp04aKigrWrl0bm5b5M+u0wNNZWOzbt4/hw4czatQoLr74YsCkM8y49Fg/gFM6S4ug0lqyF7LFRlWtsjY38201eY6qjD8RGzdupLTUTIuuqusxn4k3SYYNG8bjjz8O5vPwGmCmw+lXYb4ePGKWkcw1amtrmT9/PgMHDmTjxo1gvmB2TGciMlZE5onIvE2bNmXQ2sNRVcaMGUNFRQU333xzw/5hw4aBGWEC5otfp3SWEyR7IR+tHPUZvxf8PpROi0H4ZeTIkZx++uksXbqUbt268dhjjzFhwgReeuml2CkdMJ9zJ0RV96rqQ6o6xK8NR0rpPwwNR0LzyNtvv820adN49dVXqaqqoqqqiueff54JEyaAWXrxY+BcHNJZLmJ/IVt0ErMc5tRkQ1Nz6YUcBOmuuVsrIu9ZbWPzUl9hcEqUiVbQcXOdX+zNI6mqrakeynjbw2T69OmsX7+effv2sWbNGsaMGUNxcTGvvPIKmFkIz1bVOqcwROSrIhLaEoBu+da3vkWnTp048cQTG/bV1dVx7rnnApyYzlhxtwTpt2w3jwTFoEGDUFUWLVrEggULWLBgAUOHDqW4uBjgI1Xt7SadeSHswkT8C/m73/0umJpvFWbuorsTXRfUCzlXCKLEP9hqG6sOIKyMY2segSZSbXXB8SJS7nSCiEwBbsRMgpUW6b6cj6RO9yOpeSSTZKLm6PBCRlUPYubQPy10Q3KAo6qpJ0XzyIkEVG3NVKnfga2YdUOdGKqqQ1U11XmhcyR1uh+pzSPZJl1/JXsh2wZDAHwD90uNNmkK0rxegX+JiGKWqnsk/gSxrVVZVlaWMJCYU2snX5CmOc5Mnz494f5XXnkFEXlfVc8O1YDMsZVDpctkfC4ip6nq3PgD5RP+mdIXqZrr0vVlfKe7iCTtdHeTxmJ2ORHEyyDWPJKEj8KuGQdx7zNNJgpIsRfySSedRFWVmRPujjvuiOUJ/cSs1FcLfCfouO0FwVzxTboZ/xmqus56KF8SkQ9V9Q37CdbL4BGA6urq7M8PkUOEmBBiC4Y4MRp4SETaDBgwIAwbMkaUxhpzJHS4B02yF/LQoUN58sknFzfVpmq/pNXUo6rrrL+fAn/nKGkfawLsxiwYnRRVfVdVBwL/lRmTvOGl0/1o5kgZWXW0EO+rbPnOd8YvIq2tiZsQkdaYlWY8tY/5HbVzJCT2RPYHqGmTqu5JdZKICGb1KUeyca8z0el+JKQjJ5qytqZue67bn05TT2fg7ybvoAD4o6q+4HxJRIZIOXBcRM4DHsTMHZNVRo4cyeuvv87mzZvp1q0bt99+OxMmTODSSy8F0+m+nQCn0/VSwMiVNtlE5Hrm4pUjTU8yckGn74xfVT8BTgnQFkeSlZBz+cHMIp1E5F5VvdHhnHuBc1S1NtYubr+fYSXORD47ijrdI+LIhUwwLHJZ21E1nPMoYhmQarKpAmBNBmzJKn4evkx+iBc29q/Gm7oWO0eSlmwQZfxHJq0wUzM7MRV4R0RcLaDh9UFLNdwzIj2Oxnt4pL28skm6wzlzlvhvA8L8ViCTidGljjLgK04nqOovRGQmMCww43KMKJPIXdLxTbLaWC40+zaVNNckMv7Nz97F5ysXcnDf55zwt27s6DmENqd8lb2bV1FdXc3y5csBGDBgAPfddx/9+vXLmG1eHb352bsouP9KDu77nPzW7SleOJw2p3y1UXjb3voj29/+I50u+182Pv0TP/F8qKo1qU5S1cXA4urq6l94EmFh90uzR9rTdqDRsn/7RtY+PIai+1uza+8BWhXmM378eG699daGa8Pun/E6WsyuJb/1IS0AZd//K1tfm0rzVf9h3759nHLKKaz60nhX9odR4EilJ5mW+g9eI6/wEloV5rNr7wFQRffvocs3p7D+D07dQdnDyS87l7zJtree4kD9FgradOQfX7yPiy7K3dmunbQ8+uijTJ48mQ0bNjBo0CCmTp1K165dQ7MlrYxfRIZgOgnzgUdV1fVn6F6+omz7xREUn38jUtCMnVtWs236LRR27kmzY7owY8YMvvLQ+6AHeffdf1J11gXs/XRFwnBiv+21ANsD2VZElvrR4gW7ln1bVrPB0tK8Sy8A9m1dz66lb5Nf1CFFSI4ck0qLiLQDbgEu8vsBVzIt+S3bALBt2zZ6/fTFdDO9rPul7oX70YMHWLJkCR06dKDbmPtpbl2XrNSZ5MWWVS1FlYMpqhxM7eQLKJ/wT+rfe5nt//c0hZ17+orGrxYvhZikaaz1MWx+7m46XfxTWhw/gN2fzOOKK66geMzvWP2bUZ7FpJOXpaulyxWTOfjyPbz22mv07t2bG2+8kZEjRzJ7dtrTaCUlnXH8+cADwPlAP2CkiIRS1C4s6YEUNIvFjCDs37qevBZFlJeXYw0pRfLy2L91ffKAknDgwAEwzSNZ0xKj7qWHaX/WaMgz72Q37Zr2cywtPYE7cdbyZ8zUDmfFhxWUlp4/brymRaKXcHzJ3L7lgl/2bVnDrmX/oXjIDZSUlJCfn9/wkvZCOlq81ipT+SUWXv37r9C68r8QkaQz4uaqlgM7tpDXojUte1YjIrTq+QVat27N/m3en38Lz3mZl3vmpGX3srns7nYaF0yrpbCwkFtvvZU33niDY7/zaGj9GumU+E8DllnDOhGRp4GvA4sdr/LJln89yM73XkH376Gwc09a9jz0hfWqKZehe3eDKu2+7P1tP3fuXIA92day88O3kPwCWvb8AuBvtmRLSz1wnapOddBSrqp3AlRX+/9aPZGWg7vNQlNrH7oaEK7e+DV+9atf+dWSVb/s+uj/KGjbiW1vPUXHjt+ltLSUnb2+Rus+ZyQMI9mQ2CC0eMkAnJ4XgP3bP2XP6g8oPv9QE4/bWvj0r3eALPtFCgppVtydXR//h5Y9q9m9bC5bP1e6lhzX4INE/Xz2/bZjrfGZl3ltUkykZc/6pZhpzwyxqSX2bV5Js/aljcJOVnv22nQqDhNKOV8ocgkwRFWvsX5fCQxU1XFx5zVMoAX0AZZa//fGLEW3Bdhv7SsAOmI+QPrI+n9zXNRFQBtgA/a7ZWovxUApZl7t+NUSBKgk8dfF7YGuqtrSh5aTgY3Wvk6Yub1j9MMknkQ6AE6y9m+w7Otn6d4H9Lf+35HguhgVmPV1OwIl1v/tga6Ydv5THbR8gLnPWzA1hKUcfv8TkUyL3S8CtAB2WWGWAccA7ya4Liy/uElfyfTYtZxsXbfe2lpbYX+IuW9uv1ZvD3RX1UKPWg661BEjXk+y56XUur5ZEg2J/BILOx2/2Enlo7oUWjoC3THP/kHMCmfbrXNPARa61NUb+ItTXhayliLgeIwvP8c8Lx0x+cAiFxrifd5DVZ0XDUi1GnuyDfMl5aO231cCv/FwfXtMc8SHmCaHOkzGdSfQwTon4WrxwMPA9xLszwN2YjKc8cAV1jYemA/cErQW4La4rcTa3wV4IoWOWyyHzQT+CfzFZutWzAdWTnHvshLGLswLon1MC2YxlqRa3Nz/JHEm1JLCL10wD+rCDPrFlb5UaQx4DpOp3GKzezHmG4iEdjto2eRVi1c/JdKTyC+YL7avtnTNd+OXWNjp+MWLNictwDmYTLYa89x/AfNirrLOW+NBV9p60tFi/X+95ZNPLZ9sB37rRkOyNOy0pdPUswbzto3RDVjn9mJV3WqJGA8gIl/GNB+9p6lX9CnAlLbiybO2tZi34unW3zXAKDWjWBKRjpatwN9VdbV9p6puwKxrmxQ1QyoHYEpfJ2De/EMwb/22wJ9F5E61mmQSUGH9fRb4GqaJZw2m9PAzJy1p3v9kJPNLrKT5bcykcKH7JQB9MS2/xsxDZU9PgnmpexkNtQYotP12pSUgPzXyi4icgakVzlDVHbZhvZl4XhpIpS3Wd5dESyHwhqrGVv57R0T+g3khLMCUpK9yqSttPWlqQVUfwPQzICInAD/FLD5U6lKDN7y+KWxvmQJM1eo4jBMWApUerp9r+/8azFvsNuBtYELsTYZpPrkckynmA1/FlOq/jlnQ4lRrf1vgPozDajKlBfNmXge8CVyHVeKPOydWUnLSUowpGce21ZiSSJELG+Z51eLm/jvFlULLQEx1ONb89ifgs0ymMbf6XKSxZpgvoW+17DkDU7vq60PLHq9avPoJk+kl1GI75xGs2qhHDfNsWnw/+261OWnBfKeymUMl/FMxNYDz4p+JMNNZQFpaYOakEg5NqX6HV794stfrBXERDsW0Sy0HfuLx2vm2/9/hUBNJa8xbEkybWglmecBtmDa094BvW8dHYKpW9Zg2/eeBLwGzrP1brG0JZsWjY4LWYjk4D1MqfMyy4wXMjJJtYjqsv/FaPnCwdRWpm3raAr8A5gBXxGnZ5qTFzf1Pcl0yLXa/jARWWAl7PfAEpgQzOZN+caPPRRprh2k6242puezHvJQd7U5i031etXj1E3BzMi3W8RbWsbNt+lz5Jeb7dJ99t9pcaBmHeSnvwGTc37cdu9FLektXTzpaMP1fizDPywbMM53v1jd2v7i214/Dgtgwb9X2mBLhvLhj89MI90VMdauLbV8XzFv3pRB0vBv3uxmmajYdW5tuGLYCf7USwUXAM9bv5onsytT9zyG/BKIv03Zn2k/Z1Bemtqaa3jKlIdREm0JULeYtvcL628XaXwQsSCPcpX6OpRFfUqcCLcO0Nf4+AT/BVC2LXWT8odz/HPJLIPoybXem/ZRNfWFqa6rpLVMasjZlg6qWJzl0ELPosV9WisiPgMdVdSOAiHTGLDW42ulCn1yW7ICq7k5xbbq2NheRPFU9aMX3/0RkDfAGJsElJcT7n4yM+iVAfZlOT43IgJ+ypi9kbU01vdkJTUPOzc6pqrtUdUUaQVyGKfHOFpE6EanDdJZ0IMDFPGKoarLx7m5I19ZniVs6UVUfB74P7PVjUAD3PxkZ9UsyfOjLCbvjCdBPOacvIG05oStNLeFpcFHd6A68hulU+AC40do/ETNscoG1DQ2oytQBaB/GtZgMMZGWDsBLmHG0L/mNP2AtV6cZd1rXB6XFzXVB2xq2b7Jpd1hacklfmHlApnXlqm/cRFAK9Lf+b4Pp+e5nZfw/CEhEGfA0ZkTMx5ie+k+tfeVBXYsZA5tIyy85NIR0AnBnNrTEhbMqzXua1vXpaPF6XRC2ZtI3mbY7E1qyrS9TeUAmdDUF33iessH62ON+zHjmelW9y+21HTt21PLyck/xhcmyZcvo1KkTq1atYs+ePXWqWiwipcDrqtrH6dpc02KnpqbmAHBAVZunPJnsa9m7dy8rVqxg//79MXvo3Lkz69atY/369Qc5NA3Gj1X1+aQBkX0tTtTU1OwAylR1m5vzc1zLZk01LYCNSEvmcKXH41umHDO+vC2mxF+LGX86lSTVGcw46XnAvLKyMvVLj/HP+b42EStWrNDu3bvr9u3btV27dkrjj6C2aop7MWDAgKzY7QbLJ+vUpV/dalE1eoLWtG7dOq2pqVFV1c8++0x79+6tH3zwgd52220KrHarIxe0OIEp9S0HrtIQtWRCEx4/GvKiJRFBaLr66qu1pKREKysrG/Zt2bJFMR9hum7mTVdLjLD85MY3rjt3RaQIM078JlX9DDN9ZE+gCvORzt1JXiyPqGq1qlaXlLh+qYZKfX09w4cPZ8qUKbRt29b1dSIyVkTmici8TZvi54DLLKtXr2bw4MFUVFRQWVnJvffeC8DEiRMB+gItRWSBiKRaezfrlJaW0r9/fwDatGlDRUUFa9euzbJVobAKM7fMT4MMtKms+pRtRo8ezQsvvNBo3+TJkwF2qGpv4BVMU+8Rj6uMX0SaYTL9p1T1bwCqulFVD6gZSvg7zLwUOc++ffsYPnw4o0aN4uKLLwagc+fOYD68wmrq+TTRtbn0EisoKODuu+9myZIlzJkzhwceeIDFixum79ioqu1VtUpTNI3kGrW1tcyfP5+BAwfGdnUSkUUiMlVE2ie6JpdeyC44iPk0PyMcievU+tVz5pln0qFD4wWOZs6cCeaLWIDHMR9DHvGkzPjFzC70GLBEVe+x7S+1nfYN3E9PmzVUlTFjxlBRUcHNN9/csH/YsGFghk2BmWphZhbM80S2S8lhZCbxNbHvfve7YNr3m1ytMgllmKmp/1+2DYkwbNy4Ecz0x6jqesy8TYcRVOEiV17Cbkr8Z2CmKf0vq+kg1nzwSxF5T0QWAYOB/wnTUEj/pr399ttMmzaNV199laqqKqqqqnj++eeZMGECmKXkPsZM/BbKknhhOd1PKTnXcKiJkalaZQYeyh1Atar+IeyIIHcymXTIFQ1NqHDhipQZv6q+paqiqidbTQdVqvq8ql6pqidZ+4dZb8ucZtCgQagqixYtYsGCBSxYsIChQ4dSXFwM8JGq9lbVs9X/tMQZx28pOYgSTFAPZbKa2Pr1jZJUaLXKDGYuW9VM39tk+Na3vkWnTp048cQTG/bV1dUB9BaRj0XkpaZSuEiE22beIMiVlxjk4Je7Ee5Jp5ScSyWYZDWxH/3oRwD9MlmrDAKHzLK/iOwQkTfDyiyDzlyO9A7RptjMGwQ5nfHbF95uCiRaPDzROU7/u9Wa7VJykCSriU2bNg1gcVOqVYJjZrkOsxhNDwLILJ2ejaCemSOpQ3TkyJGcfvrpLF26lG7duvHYY49lrJnXCa/PfhBkbZK2VDSVzN4PQTyssVLySSedRFVVFQB33HEH06dPh0Ol5FrgO+naezRgf/i8LFqdiDPPPJPa2tpG+2KZpao+LSJ3YDLL8WlFlCXiO0RFJGmHKNY6tWVlZb7jCyovsJ6NRHykqtXJDvrFvkC610XZwyanS/ypyIUbmC2yUUo+mu93uliZZaGIPMihVb8OI6yhqdnwXTrNiW7sjdKjf3KqxH80OrKpaHaqpaRbQs4GTvfdfixgbV0xTW+PYVZaOgxVfQSzNCLV1dXe5lPJEJ07d2b79u0Z6RA9EnCT1jKdD+RUxn+kEO9EP061VxMjmj5WZlmrqg829cxy2LBh3HXXXYF2iLopQDSVQlJToEk39RypRAn8yMMaPdJTRIYQQGaZqTSSzQ7Rpvwc5LrtOVHi93qTcv2mHkm4vddRDeUQI0eO5PXXX2fz5s1069aN22+/nQkTJnDXXXflYaY+2YL5ziLnyXSHqFsSdZYeaWkvzGbUnMj4vZCoGeVIc3hTI3oRN8Yhs1wMfAGTafr+SDCd+53Lz0s6TaKx/2PacllnLhA19UREpCDgF9sXgM/92hG9ZJ3Jxv1x8/1OrhFl/BGB0hQSvRcCHnVRCfwGs1h2xjnSfBPhn7QyfhEZIiJLRWSZiIT+2fa+urWsvOsbbH720KJfu2sX0LdvX/KatWDw4MGsXLnSb/Bt/WjxWwqL16IH9rHp73ew5qFvsfLOC/l81aLD4vAQjy8tfonXMmfOHM4991w6dOhASUkJI0aMiP+i2AuBaXFzH+O17N28ivWP38TqKZfRvn17zjnnHPv0115ZBxwD/Clbz0uM22+/HRGh8+W+Jwr1/by42RdPvJb92zey8s4LWXXPJQ3btreTNrGlJIi8zO3zmcgvB/d9zpZ/PUh+q3bkNW9Ni+4nJrw2qFqf74xfRPKBB4DzMevWjhSRfm6u9Wt43UsP07y0d8PvA7u2s+nvdzBp0iS63zid6upqLrvsMs/hHjhwAMyUuZ61xPCqKV4LQPNulXT82vfJb516Ghd7ArDHHYSW+PBTEa/l63e/yNixY6mtraXlVQ/Tpk0brr76aq8mBKbFC/FaCoo6UHLRLXS78Wk2b97MsGHDuPzyywFvPre0dCeLz0uMY7/zKDNmzKC0tDTBVanJBb/E6H7Tnyi7eQZlN8/gmDNGugorSQHAV17mh0Ra6l64n4O7d9D1mofofuN02p/97bCiB9Lr3D0NWKaqnwCIyNPA1zEdWCnxmph3Lp5NXvPWNDu2L/u3mtLjro/+TWHHMn5Y0wopgD/t/SJrau7j2G8/TLPi7g3Xxn8yHd8BNHfuXIA9frV4JZEWyW9G2y983Zwgyd/Hyb4RCEpLEH5p2bOaH9bAD2veJK9ZC8aNG8dXvvIVT+ECaWmJ7+jzqyWvRRF5LYoAOP6Wf1K/YAm7ly3zq0WAfwHXYRbezqhfYtS99DC/u/9OrrvuOnYniSP2zNg7SGPHp3+9AwSUxtx0wDppcQrfw2if1vjIyxJ1IKfyUyIt+7asYdey/9DtusfJa94KgOZderkK1+/Hhp4XW2+4UOQSYIiqXmP9vhIYqKrj4s5rmK8DOJHEk4b1Bj7DDHPbb+0rADpi1vfdCnQGlgIlQHNgBaYE1RFThQbzGfxBYC2wDTgZs/5sKtoDXVW1pQctfSx7vGpZhilVbALybVrsnGzt25HE3krMGqF7rd8dgc1AoWXXwQxoKQaWWFri/XIysNE6v5P1fwfgQ+t8pwcqpgXS94s9rFR62gItLdvsWmJUYfwFJr3FcqD4OGLE+6g95svdfsDzwB0etcSTyjfC4X6J2dEBs/bvSZj5nBKls3j7Y8TSmKhqoWWzW79sIfG9ctJTAnTB5Bt2LV2AYzFzBjUDdmGe+Y6YacljpEpvMa1POeVlHtKYHy3FmPxtH9DK+rvO0pPI1mR+6Q18APTQIBdbt2/ACOBR2+8rgd+kuCbhIsCYxHgnJmPYCtRhMpU7MYl0IzDeOnci8KT1/2PAm8Bttu0/mM6zLsDmsLQ4hJVKy72Yybnm2bXEhbEGOMshjo+Bgvj7ajl/fYa0zI9pSeAXuz8etK79suWTJ9ymkXT9Ep/eUuh5GFgTryXu+taY0voFLtJ0vI9GAI9aPlqWjl9caPk0iV+KLLuOs37XAue4sd+2P5bGNnn1S7J7lULPPGBiAi3vYtYvLgB+icnwlllprsQ6J2V6s85b7jedJdLkQ8uPAcVk9oXAV4B6oMKjX5a5TT/pNPWswZS4Y3TjUMnbE2oWpxhvbYjIlzFNSe9h2hLbAr9OcGm99Xeqqq62rr0EM1f4BhGpdWlCJrWcA5yKyQj8MhV4x6qSrgY6iMh44HLgH8DxtnPD0nIhh7TEsxX4O6ZEMxv4nqq+aR27yoMJgfkFHPXsBG7gUC0l2fU7ReRhYJOIVKiq07QL8T7qBZyJKZg8FrKWNiR+Xm4HpqlqfA3Tjf1gfBFLY1fYzk1LCyTVcxEwHFM7iuegqs6zzl2DyaxrgF+r6mdWmBtwl972En46c9KyG6uUr6p7gdki8hpwHuaFYcfJL495MdJviaMA+AQ4DvO2WQhU+nnjA3Nt/1+DKU3eBrwNPAscwExqtQGT2e/GvPHHYqpS6zAl/5swVb6+qUoY6WpxCCuVlp2Wjn12LXFhOJb4rXP6YeZ0/w2mhDfB2pcpLdtsWuL9sh2TiX4O/BGrBOYyTnuJPy0t8f530PMJ5uHfF68lSVrZDZyaKo3F+egBTNX/3HT94kLLwSR+WYBplogdO4ApjY53Yf/9cWlsj1e/pLhXifTMsmzckUCLPZ1st44rcLOX9Bazy286S6TJh5azrfRn1/QscKMXv3jS7DfhWQYMBT7CVJV+4uL8sUn2z7f9/w6HqmqtMe1hP8BU27oAdwEzMG1kJdbNvAS4wHLYPuAFzHwoN4SlxSGcVFpiOn5g12Kd0xxogcn4z7P+F6/3NUNa1tq0xPvlfSvu32FKIZtsPmmTKS0JwkqmpyOmZPWDBFrOxdRq8jE1z/swBY0WTmk6zDTmQsu6JH4pjvPZakzNs8hH/Pd51eJ0rxLpwbR3H2f5Jl7LLzBt7XmYfrw/YV5sntJbzC6/vkmkyYeWZphmqpmYl+oZmBdE33TSiKPdYQXsMREtxLSLFXN4KW1+3O+J2NpfMW1eH2Lenq9jqtTDgOnY2iGbiJZaTKnFvpUniKMdZmKsDzGlyC1WgpoMHJNtLZiMRzGlmti2O1s+8aonTssI6z7XWxnK88DJLuIK1Ud+tCQIo5bkbfwZSWNe9MT5ZSSmY3QnprD3BNDFOtYs1/OABM9+JfBvS89i4Bth+sX3qJ4gsdriffEdFAAAIABJREFUD2JGIijwJTVt9EXAW6qadEIrEZmvqonamRGRlqq6O9GxsEhHi4c4XgReBR5X046JiHTBdGqfrarnphuHFWYtPrTkmk9scdcSsm9scYXqo7C1ZCqN2eKrJcoD3IQdiF9yIuNPhoi0AjqrQ2eUiJygqh9l0CxfuNHiIaylqtrH67GgSKWlqfgkRpC+sYWZFR8FpSXbacwWV5QHNA4jEL/k9Fw9qror1U1qCg4Hd1o8sFJEfiQinWM7RKSzNbJntcN1gZBKS1PxSYyAfRMjKz4KUEtW01iMKA84jED8ktMZf0RSLsO0H84WkToRqcP0b3QgvWGiOYmI1IrIeyKyQETmZdselzR1HzV1+10jIt1F5DURWSIiH4jIjdm2yYFA/JKRph4R6QComvGtoV7nN65MEaR9icISkatV9ffphu0lzrCx2kyrVTXZV5/x53uyMezzE1wfio8y5Ruv9oed5tMNCzN6rlRV3xWRNpjvAS5S1YRf/OawHtd+CS3jF5EyzBd1Z2PGfEtxcXHP8vLyUOJLl5qams2a6jNnGx07dtRIS/jU1NRsxox0cMz4Y+mtuLj4slzWoqolIrJKVctSnZ/jftkBlKlqomkFDnv+i4uLe+WwFgX+DExQ1VoRmQncr6ovxc6x6ykuLu6Yq1rA5fPvdviP1w0zNOkyID+2b8CAAeqXHuOf832tG3D5sZcGoMVOj/HPBa4N83HNe5hxzSl1pdIS9r13AvNxzQrMhy41JB43PRYz3HJ5WVmZq3CzoQnzceF7mAnOQklj6epatWqVnnXWWdq3b1/t16+fTpky5bBzXnvtNcWMWtkDrAR+Fm97/PPvRku20pmVxi4H5gDlwCqgrTbWs9za5rlNY14JSr+bZz7MNv6OqvonVT0QYhwRyRmsqlWaxXVRA+QMVe2PmTb3ehE5035QVR8B1qtqz5IS1xWdbLAM+Bpm7HVOUlBQwN13382SJUuYM2cODzzwQLL1Bz7DfIi0R1V/nuB4k3r+VfVpzMdvfwVuUmvaBxsHVbWnqlbneBpzRZgZf42IPCgiA0Wkq4h0DTGuiCMYVV1n/f0UMwfQaQlOqxGRBzNqmHf2qmotpjMuJyktLaV///4AtGnThoqKCtauXZvs9NhY9UQ0ev5DMDVIWouZg6kIM0vn3xKc06Anw7aFQpgZ/1WYau3twIuYecg9Ea0xmhb/EpEaMdPJHoaIjBWReSIyb9OmTSkDy6If8qwON0SkNWYqi0RTe8fSW86jqlekPiv71NbWMn/+fAYOTJjXtcXMEbVbRCoTHL8K85Xpv4BlbtJYFumKmXb7L6p6T5Jz7PlZkye0jF9V96rqQ6o6RFVPUtXEa4lFhMGHTk0jYJpHVLW6CVRdC4C3RGQhMBf4p6q+EH9SLL2lE1FUyDhEfX09w4cPZ8qUKbRt27bRMatGUItp6pmAma2zEZY/RqlqO1VtleNpbD0wEPiKNWR4gYgMtZ9gz8+yY2KwpLvmruP4ahH5qog8JCLPWD3lKXFakzPZ6lPJrvPISdkeKx5gxrMPUjaNNBX2quop1lapqkkXiRWRr2bSsBirV69m8ODBVFRUUFlZyb333nvYOa+//jpAlS1j+ZnXeJLVgIN+Ye3bt4/hw4czatQoLr744sOOWy+COlXdqqrPA81EpGP8efbnP1ADg6cDcL6qnmz1i1VZuhoR05MF+wInnfn4YwzWBMPsRGQKcAJm8qQ11u5hAcQXJgm1NCV27twJ1gvd1jSSqPPtiMKW3jJOrEO0f//+7NixgwEDBnDuuefSr99hy7bWa4DzAYWBqjJmzBgqKiq4+eabE56zYcMGgFPEzAk/HZPeGnVYJ3j+vxam3WmyA/ieiJyvqgk/3orTc20mjQuDMNv4h6rqUFV9WlXfUtW3QowrwmLjxo0AfVM1jQRFDjWPDFXVoalPCx6PHaI5zdtvv820adN49dVXqaqqoqqqiueff56HH36Yhx9+GIAZM2aAWQfjdOBJ4HJrGKGdRs+/H1symLa2YqZ1d0o/DXoyZFOopJvxK8k7ET8XkdO8diLGcLO4cKpzfRBoh2g8mUjIxx9/PMBiN00jRxifi4ivJq0g/ZKiQ7RIRBaKyKwkHaJpp7F0GTRoEKrKokWLWLBgAQsWLGDo0KFce+21XHutKeiOGzcO4ANV7QFsUNX/SxCUb39kiS9gFg5KRlPT40i6Gb/T+OrRmBVibsKs8lOX4x08R1KH6NHIaEx6yxouOkQXqeopGDsP6xCFJpXGWlvDZ+cnOT4a+I2ILBYRzyP6MkwlxiejHc4ZjaUnEwaFTVoZv9P4alV9V1UHAv8F3IJZUNiRZJ24ic4LofScdoeoG7tyqGkkKTEdTp3pTtdmA1t6ywouO0QPAjh1iLrB7XMSMl0xw2qvTHQwwfOfyyxV1YGqWpPshDg9gPd8yk9rhVsfe00LvjN+EWmdany1iAjQw9q6HxZIjpCkQzTRWPGIHMZKbxnHQ4coAFaTwWEdok2Mj1X1QVXdk+yEuOc/l2nuJu3Y9DR50inxd8ZhfLWInIdZFnEiptPkgjTiCpVMd4hGBI8tvQHu+ohS1WrcDCMGTx2ilVYau4/EHaJNiTIRSTqmPcHzn8t0BT62bE5InJ4mj+/hnKr6CXCKwyn3YtbzrI3tqK6uHhP7v3zCP6mdfEHD/35JVn2Khe0GW4dooPPaBFkVj2mKhelF31HCvcA5mAndMkqsQ9SJcePGccMNN3xwhMydBKmHQDZ6/u3Pfg7yMWYu++eBiiTnNOiprq5uyi9sIJhx/E5hr0l5VkSTwm07fxZeTFF6yyyxIZAfAYky/oT+SCdt+C30xMdpT8O2/Wsxi7Qn44hKX2GO458KvCMi40XkChFpEvOTeMXLsFOnc5w6gXKkMy/XmQq8k20jwsDrYIZknfPJzk3DDqchkI2ef9eRZIcuwH+AxxzOadCTGZPCJcy5en4BXIGZve904EthxRURYUtvEZnBcQhkguc/1xll2ZyQOD1NnnTn6hkiIktFZJmITIg/rqpLVHWyqt6gquPSiQtgX91aVt71DTY/excA9R+8xqp7LmnYWrVqhYiwZ8MyP8G3ddISNB9//HEjLQA7l7xJRUUFq349gn79+vGPfyQc6u0GX1r8DuH8+OOPkYJCiioHN+x79NFH6dWrF3mFLWl5/AC6Xf+Ea+Pj7HCtRVWXpArX/tdOi7KTWHnXN1h1zyUUFRXRp0+fhnN31y6gb9++5DVrQYseJ7N/+6eHhdn50p/TrLgbvXr1YvLkyYdpsfoAuls6FolI/1T3wS8b/jghoZa9e/dyySWXUF5ejojE5g86jBdeeIE+ffocpiWGpaUes/br75NpsT//frWcddZZtGjRgqKiIlbdcwlrf/cdAObMmcO5555Lhw4dKCkpYcSIEaxfv95vNBswndWO6Symx3ckf5zQoMXuFzu33347IsLLL7/sNxpXpDOcMx94APPBUz9gpIj0sx0fYvu/nYg8Gvvtt8mi7qWHaV7au+F3UeVgym6e0bA9+OCDHH/88RR27tnoIU/VdHLgwAGAsmRa3OC1ynzyeZc10rJ/x2Y2P3c3dSdeTveb/szmyku5eMTldL/hKVfxBanFK9dff30jLZ+veo8f//jHzJw5k+43TqegXWc2P/srz+F60eI0wsQtHc69lrKbZ1BfX8/SpUuNDbu2s+nvdzBp0iS63zid5l16s2nmnYfZWffSQ3QacTuLFy9m+vTphy1eMmvWLDBru/bGrBgW6mRfibSA6Yh+8skn6dKlS8LrDhw4wPXXX8+sWbNSaWmJg5b45z8dLffffz/19fWU3TyDY7/9WwC2bt3K2LFjqa2tZeXKlbRp04arr77abxT9gL8A/02SdBZE+oJDWuL9ArBv63pmzJhBaWlpEFE5kk6J/zRgmap+oqp7gaeBr9uO32H7/27MW9U3OxfPJq95a1r0SD6Q6PHHH+eqq67C63DuuXPngllJKJmWQEmk5cCOLeS1aE3LntWICK16fgFp1pz927yVYrKh5ZhjjmmkZfeyuYwYMYLKykokvxntvnQ5e1a/z/Llyz2F7VHLHUn2p8Wuj/5/e2cedkdRrfvfygSEEJJ8AUIIIWAgGIaDJAQUkBxlCmg43HuQQYHoxchFBi+oEOERPQ4EQcSD4IRwUBEFj1cZRFBRUAQyQ0IGxgAJM2GKYciwzh+rOl+n07t3d+/u3sNX7/PsZ/dUa623VnV1dXXVqvvoN3QkxxxzDNKnH1vufwKrX3qSxYsXb2Bnn0Hb0nfQMPr168dxxx3H7363YTBat/+KGu4HBolI+Xd4CP369eNzn/scBxxwAL179469ZsaMGYwePdoaUMlcemGjYQYSzyV6/xeKSZMmccwxxzBw4ED69+/P6aefzr333ptX3LPYx93zE8pZKeUrjBV//AEXX3wx/fr1K1tVQxX/dsAzof1l7lgcxqvqBXkVrXtnFa/9/XoGf6j2iLA1r7/IPffcw0knnZRZvguo9W7oUBKXhlCLS79ho+nbtT2rHn0AXbeWVY/ch/TuS9+tdswkvxlcvv3tDe9rRSPDG217wYJsc+Kq5ALw2t3X8cx/nsD++++/vhtk9ctP0Xfrbh/06rcpfQYN4+GHH97Azj4Du8MrjBgxYqMgba3AJQ2WL1/O9tt3z7VM4PI0cAg2zLEel4aGsE6bNo2hQ4fy/M+/wNtPPxR7zT333MNuu8WGP0oDAf6OrbcLJfom4BL1y0033YT07sMRR1Qz5UHyziERkWOAw1T1FLd/IjAh6M8TkWXAZdiHncnY6jVjgCXY69RCbI3LaBjknbH1PF/BIgCCzZbrhzloJbAJ3eO1h2ITMN5yx4UNV2IKdNXCUGAtMFxVN4vjEuI8FXu1Bdid9LN7w5y2xcJDvAzshL3+B6V5KDbDuRdWWz4OvB6RtSfwgtvemo25PpeDS+CXOH9E7Q980gd4DzYEbgEwGsvHJ4EtHLdHsFEfI53sJ7G4TQGCPIzqFezj4fIMXL7n8mVrd3h+SG5cGYhy2hzzyxCs77qfSzPMne+iO9+3wxYZD2be7uFkPeX2hzh54YbRaEBVdbCz+c/AF6NhAmL88grxPqnFA6wVvrmz43ks/xdii6MH2BPzx5sReYNd+n86vUNc+nlRLtgkzoWO9wZc3P3/D+z+3xp76AVlbGuS78kwNsfu77XO1hEu7VonayCWz2OwNY1XxsgIl6+4+2dnrKtngqruGVfOROTVkOztgDkh+eHyVeseCri8hd03a4BBLt1qrLyvcNdsgi10E/VNHOL07aCqyYGetM5q7LV+mEPvCO1PA6aF9i+M/LZyx4cBP3XbG60GjxW8i4HF2Fjh17ECewlWCL8C/Dx0/Swn/xUsxk6srgQes+pxqZUuQ14FnJ7ECuwKYBHWyrjRXXOw4zAeq/gXYpX4XhFZifmah0s9TjE+WQE8gRW4bdw1z0b88lmsK+BFZ8PrwIERudOwIF/LsBETJwDnumPTsnCJyxfn29gyUIPTIndsCPAH4AysRXtVRPZq4JOhfP99PTuBHwLHh/aXANvm9UlKHnPdNX8AzoikWwZMrHVfB3odlz84n5zrfPRn4BchP23EJaGczovzRwqOzzmOq7GHUsBxHNZAODFNHtYoJ+8H/kJ3vRTnv7r1Wdp6IeSzlcAq7GHwUqjsLcUmi+W+Z+umy5PIKeyD3fw7Yq2jB4HdItfsCnwYGBA5fniGTPpPrOJfgbVeVrqMmhPIAL7jMnCLHDxmpeFSRIZjkUr/iVXwbzgeb2Gth88D/z9i12+Bz0dknAls34hfGuUEHOhse9v55HnsgbbeL5Hrd3G8B8ecG4tVQldgrfbzgLF5uETLG92V1+EpOZ0DHOr2b3d5PRW4N8h3rEW2Ftg1471wpJMpwH7AjCJ9EscjxP924MzIdbUq/oDLQ2Euzk/nOT/dQvebe00uUX+EynVdf9TKB8flcsfxE1gleWraPIy7f9KWszg+0fKVxV+hMvYoVq8FdVzQODy36PKxPl2eRCGlR2Cv849jH0bC585wheO3zjlHhc6tr7RjZM4IbZ+CtRAuwWLofB24FPg13U/cWdhr5Crgb8BpwbmMBaoml0YzPOAE9AfOxl4vL8FaKvOxVsdBWAt6L3ftw9gD4tCIrNex1nVNrlm51OMU45O5zheBT4a5Avtrx2VTrBtHsG6CvwLfzJOXabnElbeQb+MeRjMiaR8DvoZV8r/EHlRjHJ/X3f6zWLfCO9F8j7MTW6npVLct2Ci4x53PxzfikwTfPIS9+f0Dq9w/HnBx12zi/LMMezhsiuvyjXB5uxEucf4I3a8b+aMGt0HAYdjEvFmOyzvY2/ClWEPj91nykBr3T71yVotPtHzV8leIy6bYfRP45Xzns3Ad9wwWQmJAFm5ZfpkTpBZsBSJoeY1yjjvL7QevoFNj0s0Nbc8MOWZzJ/MrbNilcBrWX3awK8g/wV6b/gCcTJ23gDgbUvJLnS6B0zeAV0PnTscqoDcdh3PiZGFdQZm55uVUzydu+5bAL66QP+QK9vPARUDvGLlbAtOx1/hX3G+ROzaogPL2y6j9NTjNdb83gdewRsQhofMHYxXhW1hrcGaR+d5IOYvxza7u/00sDPT9ES5Lsf758G9Ukt6Qnxan9VMNf5yFvUFt5I8aMrZyXNY6n9yPVb5bYd0t6s6tDH718jDv/VOLT4wPat1DAZc3sfrqfuzjeFwdt5T0XT356q8SC+zCyP4Al8GXAfMS0j2I9YF1EXma1SowRFoQ2AfHydh6oC+VxTFDXmTmlCCrcq5F2h9JewfWbzwsdGwY1qXwxzLLW1ZOrVrGyvJNo37Ke/+XzTGvH1uVT26fllgg72LjD5N9sNfQtQnplmL9bU+6/2GhjI7N4KTMAjarIiPr5EVmTgmyKudapP0RuUvynCuivGXl1KplrCzfNOqnvPd/2Rzz+rFV+eT95R7OWQ8iMgJYo6obTdwSkf1VNdNsCxHpj40geTLm3C6q+kh+a5uDJE4JaVqGax77I+nvBP4EXKeqL7hj22DxXw5R1YMzyCqkvNXi1Er5ngaN+iYiK7Ofir7/a9hV2f3Tqnxy62qk4heRa4CPAC+q6u6FWdUEeC7VQ0QGY90FR9E9/v4F4GZguqq+2i5c0qBdudTwUy+sm+QpVS01JEjZaFe/NIJGK/4PYh9Vfpolw0RkCDaZ5dUGdDckI5o+LZcibC9bdha/NKKzaB9Ezn1SVa/NU8bK8FFBZbbl/ZI2bYjLzao6otn2NJI2bz1WhI150fC912hXj4iMAm5NUZBHdnV1PTVq1KiG9JWF2bNnvwrsTQwXERkJfAsbw/saIF1dXe9pYS4vY7HSY/0S5dPV1TW6XbkECDh1dXUd265c2tQvj6jqRsFl2pRL3XoMYOjQodqqXMD4aFkzd4MfNrRpQcL5qdjQp5UjR45UVdUdzr1Va2GHc29d/4ser4e4dPWuD4A98WO5APcBxwKfcVxmpeGSF3lkRrjMSvKL4/OjKrjkQdiP9bhodxlbCTyehUvW8pIkJ42OvH4pCkX6FxteuRhYp21YxlQ1Txlreb+oGh+tU2+XuQIXAKr6I7V1Rp/baqvkh1CTkbQM5VBV/ZWq/lBVx6vq+BbnUg9DVXVqh3BBVX8EPKeq72lzLu3kl8ewCWNra5xvJy51EdRjncAFyl16MYrZ9S9pKlYlnJstIleJyL4iMlxEhldmVTnYgE+zjSkIs0XkqmYb0SDayS/vYrN//1njfDtx6XGosuLPHi+5WiQNoToJm1H3VWwyy52VWFQeonw6AQGndkY7+qXWAuTtyKXHoNGlF2/A+vLGiMgyEakZMF9tgYNWxihqcFHVd1X1+6p6uKruoa0/5GtHEvwS5dMcE1MjkUuAgFO1pmVGj/FLJ3HpRCT1a9eFqh5flCEtgCfdt4hYiMhhwL9hsbh13LhxlRmWA4lcYEM+7c4lgIgc1u5cOskvncSl09BQxZ8FInJ5izt/+1onRORyLLTwT+l+tZ1chVFlIIbPR5trUeMIcWpbdJJfOolLJ6Kyih8Le9rKSFoQ+ghV3aBSGT++rRsIG/Bpcy4BjlDVXcaPH19ODJJq0El+6SQuHYcqP+6+XaGuPFiXcO5tEZlQmSXlo9P4QGdw6gQOATqJS8ehyop/SoW68mBpwrkpwBUislBE7nRBq9oZUwjxabYxBWEKtjpUO2MKneOXKXQOl45DZV09qjqnxV/3ao7jV9U5wL4iMgz7uCvYIgptiRg+bcslQMCpnbt6OskvncSlE1Fli7+tISIC7OB+NT8EtwsifDoCjlNbo5P80klcOg1Vjuo5tMVH9QysdUJEDgWuwhZFXl6ZRSWh0/jABpzaFp3kl07i0omossX/3Qp15UFSK/672BqYk1T1FFU9pSqjSsIGfJptTEH4LrY2bjujk/zSSVw6DlVW/FUOHc2DpG6CPtSemt6O6DQ+0BmcOoFDgE7i0nGosuK/pkJdefBywrlrgJkicq6InCAiJ1RlVEnYgE+zjSkI1wAzm21Eg+gkv3QSl45DZRW/ql5Ula6c2GgtzQDO9hOwt4L3Ax+oyqgyEMOn7RHi1LboJL84Lh+nA7h0IvyonpRQ1UWqOl1Vz1DV05ttT6MI82m2LUVBVRc124ZG0Ul+UdWFncKl09BodM7DRWSJiDwmIufVuzavnokTJ/LUpUczYMAABgwYwJgxY9afu/HGG3nve9/LFltswbNX/19WPXJfXjWDa3EJ2y4iW4rI1XmVVISBSX6J8qnWtMxI5BKgkfJVIXqMX9x9Ml1EFovIK80wMAuy1GWdgNwVv4j0Bq4EJgFjgeNFZGxCkm/m1QUw5JBTWblyJStXrmTJkiUALF++nE984hNcdtllvPHGGwz610/x8i2X8uKLL2aSvXbtWrCwzLW4hG3/NgndQs2G4zKSZL9E+bQkUnIJ0FD5Khs90C83Aq8CE1W1q1oLcyFLXdb2aKTFPwF4TFWfcLH2fwkcVYxZ6bBs2TIGDRrEpEmTEBH6v2cfpO8mPP7445nkzJgxA4CUXMar6gUNGV4iHJd3MvilZadT5+DSsuiBfhmlqherass2kkLYnCbXZVVDbG3eHAlF/h04PBijKyInAvtG+79FZCq2UPGeWCC0VVhGh1dLGgssTFA3BuiPfShaDTwDvObO7YoFgOsHvIi1RBbQHXRtKN0jdvYEXnDbW4dsGAzsqKq94riIyDLgMuwj1WSXbgywJIXtAcJ2hLEz8AbwCrDGHevjrh8IPJJBVsBluKpuFsclxOcfjs/W2DJ6S9zpWnwCnXnsDaOWD8K6A111ubjjU4HvOblbu8Npy1dSXjbCNSq3Eb8MdftJ5SyNrbW4pvFJGCOBLepwudPZM9rZMwZ4HJvF25vkvGsk32ulTdK7M3BTUl0WqsdwXDYBVmJLT6bJsyQklcGonUn5ssLJ2UFVExcGbmRsfdy4942eIm4h7B+JyIXu0FRscsdVqvqSi+XxLVWtuTSjiOwLfB97FbsGOBzL9LVYRgzF3l6GA0er6m2htLOCRRZCNgQIbPg08MUELj8GtsAeKAuy2B5nR+T4YOA8rIWxjdP7AnA9cLGqrkgry507BjgsgUvAB+Bh91+XT6Azj70RObV8sF53SFcaLqjqj0Rk23pya9iTlJe5uUblNuiXecA9de6RurYmlMG6Polc/zhwcx0uxzp7dnf2rMYqy7eB3erkXZQLWPfqL4DpGdMG+VBTbxrfBPVYKM2FWF0WfO/LXCeEZNUsgym5XQ9cDNyZekEZVc31w1old4T2pwHT6qTZFWvBDIgcPzyFvlmh7T8AZwCfw564n8Uq/n2A54C9aqQ7E9g+Dxdn+4fz2B61o851BwLnAIfmkZXWL1n51NKZxt7I9bE+iNOVtYzl8VFav2TlGpXbiF+AWWnLWZKtCT6s65PI9YtScpkA7OO2d3P2PJqFR0jWz3KmC/LhkYRrMtdl7roFjdQJecpgko8zleU8Cp2SPsAT2HqV/YAHsSdqrevPwCr9V7EQyEeFzs1JkznADLd9O3ADFgNkEXAvcJ4791vg83GZCrwOPAv8DTgN2CoNl5Dtv81je5JTAk5u+xRgLnBhmFOWgpLGL3n40F0ZZ7Y3IifWBzV0pS5jeX1UJy9zc43KbcQvruwnlrM0tiaUwbo+ibkX63G5ELjfXXsR8Gfgy8CbwPl15N8c81sZbOfMh5W1fJalnEV89XbW8pa1DGbxcVo5qg1U/E75EVif2eMpnDkfGIC9Ho1yBeIsd25uQrpB2GvYaY7ox7FW/nysv/1lYD+3/z6s/yvcypka2p6LvRkcCvwEeAl7ezgZ+N+1uAS2u+3UtkdkTK1xfG5oeybdD6PNgflZZKX1Sx4+gc489kb5JvhgixifpSpjeX2UlJeNcI2Tm9cv2D2TWM7S2JpUBuv5JMotJZfe2Le5N4CB7vhngYfqcJkD/ByYCBzk/p9z2wflzIfTk3yWtpxF+J2RtbxlLYNZfJxWjmqDFX9Gcgsj+wNc4boMmJeQbitH8k2sT38m8L/obhWeDjzmzj0BnJNUoCL7fbGHxw3AS0XbnjJfHsQ+/HWxcSsxUwEq2xdF2JvXB2VyahXfNMsvZfgkUknNjZyrx6UX8P+AP+K6bYEnWslnZdYJVXAr3LgEo+8i1PfujvXBFmNem1LGUle5P+n+h4UyvW5mJ2UOsFmZtpfFqWpfNMsHVZSvZvumWX4pwyfAA0B/t90rdHxL0nePjgBuwkZsPd1KPiuzTqiCW+7hnFkhIiOANRozrldE9lfVexuQ3R/YRlWfrHPdLqpab7hhXLrSbE/QmYpTTtmF8ynbByn0V+ajsnzTLL+U4RMR2URV34k5PhTYVlXnxySrJetIYH9V/VID9hTqs2bUCQm2ZOZWWcXv4eHh4dEaaDRWzzUi8qKILCjKIA8PDw/de/ADAAAS4ElEQVSPctFodM7/wiZTJUJEhrjJB5mQN12jaYtEkXZUzalKfZ3CrQy5IjKkgbTbiMjeIvI+EdmmforibWnmvVim7nb2S8NdPSIyCrhVVXePHB8JfAub4PAaIF1dXe8ZNWpUQ/rKwuzZs1/WOtOc0yKOOzal+i5sLPHSZshqNX2dwq1gf1+gql9322OxceJ9ncxjVfWBlHL2An6AfUwN1rwd4ew7TVXnlGlL1b4tW3en+GU9CvjSPApYEHP8Pmza9mewMa6zRo4cqUVih3NvLUwWOWfPxf1C3HuHjvUGjgPub5asVtPXKdwK9vec0PZtwCS3PQH4RwY587B4M9Hj+wEPlm1L1b4tW3en+GV9ugIyuVbFv9HU7HHjxmmRaOGKv+a09KRzZctqNX2dwq1gf4dv6uj499RjtevY9FjZtlTt27J1d4pfgl+ZC6DPFpGrgOuwaJqMGzeuRHUthY24A9tjMyHnNlFWq+nrFG5Fyt1JRG7GXttHiEh/VV3lzvXNIOd2EbkNG1cetukkbKJR2bZU7duydXeKX4DGonPWw0nA/wG+CmxHfDTPTkUc92VYrJGfNFFWq+nrFG5Fyo3GgQ9ChW+DRahNBVU9U0QmOXlhm65U1d9XYEvVvi1bd6f4BWjw466I3IDF0RiKhQe9UFVrZuz48eN11qxZufVFMeq821g6/chCZInIbE0b0tTDw8OjjdFQi19Vj086LyKHAf+GPdm0B3X1bMQdi374O1VN+zpXiqxW09cp3KrgISJT1eLCN11OGhlV+7ZZutvNL1BiV4+IXA7sgvVlLXOHJ5elr5VQg/sI4EwRmaSqZzVDVqvp6xRuFfIoqru0CDmJMqr2bZN1t41f1qPEL+sbLXzQg0b1xC764JySdZRHYbJaTV+ncCuTB3AAcDYpF7oJpduX7lDIm2H93bdgKzVtWbaMqn1btm4yLlaTIKcf9g3iYLd/AhaE7rNA36pkNDpzNwlvi8iEEuW3Mmpx3wdbvKFZslpNX6dwK0yuiMwIbX8au6G3AC4UkfMyiLoGW98abKnTLbEKexVwbQUyqvZt2bq/BjwgIn8TkdNEJO9kz2uBI4GzRORnwDFYJNN96F56s3QZZY7qmQJ8X0S2wL1u9aA+/ilEuGNDtt5w55olq9X0VamrTH1Fyg0Px5sKHKK2luul2IpW01PK6aWqwYLc41V1b7f9d7E1fMuWMYVqfVu27ieAccDB2OSwr4rIbGzNgt+o6psp5eyhqnuKSB9s5u5wVV0rIj/H4u1XIqO0il9t6vG+YosPB8OWDilLXyuhBvdlGhPCtUpZraavU7gVLLeXiy3TCxt195LT8U8RWZOcdAMsEJFPquq1wIMiMl5VZ4nILtjC56XKqNq3FehWVV0H3AncKSJ9gUnA8cCl2IJRadBLRPphq2b1x96iVmAL0aedD9CwjDJb/IiIADvQ/WW9xyCGe28ReUFdp1yzZLWavk7i5iqW50VkALCLiLytqq9lFLMlMBurrFREhqlqIDPLx79TgO+KyAXY8qT3icgz2KShU6qQUbVvS9a9Qd6r6mrcGsAislkGOT8BFmMhJM4HbhKRJ7CQDb+sSkaZo3oOBa4CHqU7GFGPQA3uI4DRInKaqt7ZDFmtpq9TuInIVap6mts+APgFtnbraBH5jKafmIOqjqpxah1wdAY5rwNTXHfHTti9vkxVX6hCRtW+rUD3sbVOqOpbaYWo6ndE5Fdu+1kR+SnWffRjVZ2RnLo4GWV+WV8EjAof60Gjejbi7o7vCCxqlqxW09cp3NgwdspfgL3d9k5Flqt2+lXt21bR3S6/Mkf19KH7w0pPQy3uy8kW16NoWa2mrxO5DVQXWldVn8Bex3siqvZtq+huC5TZx38NMFNEfknPC9K2EXdsVMFxZI8VUqSsVtPXKdx2FZGHsH7gUSIyWFVfFZFeNFDRiMjngUuArVT15Zjzg4Crgd2xfuxPqep9Yoty/AqLnLsU+Jiz5+PAF0Ii9sTeTuqO8hGRK4BPquqAlOZX7dvSdYvIJcBHgXexrrxPasw3HBE5HBv+2hu4WlWnu+PHAF8B3gtMUNVZ7nhfzI974xZsV9WLUtqU1S+Wzr0ClQKxRQIm476sjxs37rM9JVZPlDsuSJSqLswh671sHNQpl6xW01dkPjVLn4jsEDn0rKquFltY/IOq+puEtBOBKao6JXJ8e6wy2BUYV6Pivw74m6pe7UZ59FfV10TkW8AKVZ0uNvZ/sKqeG0m7BxbCYKcU/MYDZwFHZ6lgqi63Ed0N+TnOL+7bwV2qukZELgaIydfewCPYCMZlwEzgeFVd6PJjHfBD4POhiv8EYLKqHie2cPpCYKLWWTAmr19whjfSl3Y4sAR4DFvZJvH6ntLH73/+l/aHBTn8r5jjvwb+BWuxD405PxB4Etd4i5xbAmzrtrcFlsRc803gG6H9Q7EFTOYANwED3PHe2HeLbYGVzc6vZvsldP5o4PqY4+8H7gjtTwOmRa75KzYvItg/HpsR3Qfowh4cQ8r0S+4+fvdkuxIbyzoWON49ZYPzW4rIdBFZLCKviMgreXW1G+K4i8gid2xQRlmHh7a3FJGrReQhEfmFFLBWZzP1FZlPzdQnIgNF5CIR+ZlrvYXPXZVD3mRguaomTcbZCXgJuFZE5jo/be7ObaOqzwG4/61j0h+LTT7CvZlcgIUA2BtbMe9sd93pWEv5uYwcKi23Ed1VlKtPAbfHHN+O7u4lsFb/dnVk/Rr4J/Ac8DRwqaquKMMvARr5uDsBWzHmCVV9Fxs/Go4TfSPwKvbK0qWqXQ3oajfEcf9XbF3NmzLK+mZo+9vA81g/40zslbFoVKmvyHxqpr5rse6E/waOE5H/FpFN3Ln94hKIyANis1+vBiaLyDz3Owobm/3lOjr7YH3C31fV92EVR6qQDiKyL7BKVReEbBwL3OtsOhnYQUSGY+EArkgjN4Kqy20Yuf2c4JfDQtecD6wBro8TEXOsXn/6BGAtMBwbeXSOiOxEOX5xFuV/Ffp37MNFsH8i8L3Q/hL3PxW35i7wpvtfHToW/b2d4dzSBtJGZbxU4GviRq/Wac7VuD48VHBe5Ny8LLJaTV+R+dRMfTH5dD5wL/baPqdO2omEuhSAPYAXXblcilUwTwPDIumGAUtD+wcCtwVcSOjqAb4DfCm0/1HghhjbjsQq7MCWdeRbHrD0clu0n6N+CR0/Get66V8jXZ6uniuBE0P71wAfK8Mvwa+RUT31nmxPicgXgevUxYd2r3hTgF20xodUEbkT+JNL90Ik3WuqenDo2llhOVnS1pJREMLco3Y8k5QwBluLyNlYfg8UEVHnfRp7Y2sFfUXmUzP1bSIivdSm9KOq3xCRZcA9QKaPbqo6n1DXjIgsxSqJlyPXPS8iz4jIGFVdAnwY+ygINqP0ZCyuz8nA70LyemGtxQ+GxN0PXCkio1X1MfeBcYSq3oY9YIK0K1V1dEoqVZfbMErxs+u+Ohc4SLuXOoxiJrCziOyIDR89DouemYSngQ+Jxdrpj7X0L8e6for2C9CYA5ZhQ6QCjMAWOwhwLNbiuVtEXhWRFdiTbgg2FKoWouledem6sKdgEpJ01ktbJIq048dYdMYB2BqiQwHE4pCkDbbVqvoa8XUR+ooqH7cAHwofUNXrgHOwoX+FQESGi0h4FvAZwPViQ0n3ort7ZTpwiIg8io0uCQd2+yA2+/aJkK0vYZXiDU7W/dhookZQdbkNoyw/B5FS/+i6f34AG/pFLajd6cAd2ESyG1X1YXfd0a5B8H7gNhG5w8m9EsunBdiD41pVfagkvwANDOcUiwz3CNbSWO4MPiEg6a6ZgAU3mikiu2GjgBYB/5GllS0iP1PVE2OOJ7bWReRArP9svtaYpl1Si78md80wfb+GrLFO1uI8slpRX0R3rK8LlF+YXxLkVppnrQoR2RX7sPmAqq4MHT9cK1iFK6Svbj3Q09DomrtHYK8kvYFrVPUboXMXYiN++gB/xDL+biymxCuqGht3RGz1+Cg+BNwFoKqTQ9dusMyYiMxQ1Qlu+xRsYYLfYkOiblE3kSKir5Bl0yIyk7jfEc6nHLL2xVovmWW1mr4svi5IX2F+qSO3VB+1A0TkDKzluwh7GzlLVX/nzs3R7hDPZejOXA/0OGT5IJDlB8zHHgj9sTjY4ZV8HkpINwf4OfZx5SD3/5zbPqiOzrmh7ZnYjEew8KXzy+JaFPeyZbWavkZ83UrcqvZRO/xcngRjzkdhAyjOcvtzS9bdEvVAK//K/MiyRlXXqn0EeVxV34D1kezWJaQbj4WlPR94XVX/Crylqner6t11dPYSkcEi0gUbxjLHRkdUhbzcy5bVavoa8XUelMWtah+1A3qr695Rm4E6EZgkIpcRPzCkSLRKPdCyKDNWz7si0t/dDOuD9IjIliTcDGojI74jIje5/xcy2FlULPNGkYt7BbJaSl+Dvs6DsrhV7aN2wPMispe6OECqulJEPoINVdyjZN2tUg+0LEqL1SMim6jqOzHHh2JjjOenlHMksL+qfqkBW/pjsxmfzCsjo75CuBctqxX1RXQ07Os68kvh1sw8a1WIyAjsTWijVa9EZH9VvbcJNlVaD7QySg3S5uHh4eHReih16cWi4SZhBEupPasZVhNKkDlEVVc0bJyHh4dHm6AtWvwishfwA6zvLryU2mvAaeoWvkgh5wJV/brbHosN8eqL9fsdq6oPFG27h4eHR6uh8opfbE3SCcACTTmZQixA0WeiFbOI7Af8UFX/JaWc9eOHReQ2LLbQ7W7yzeWq+oEsXDw8PDzaEWXHzEBEZoS2P033tOcLxRaJSIPN41rjqno/NjY3D4ar6u1OzgxszLWHh4dHx6OKPv7w0nNTgUNU9SURuRSLPZFmFt3troX+UzZcSu0kIMvU753cbFEBRoSG4EXt9PDw8OhYVFHx9xKRwdjbxQaTKUQk1WQKVT1TRCax8TJuV2q2WChHRfZ7wfqPxt/PIMfDw8OjbVF6H79YWNl1uMkUwAdCkyn+rqp7lWqAh4eHh8cGKL2PX1VHqepOqrqj+w8mdKzD1q1sCCIytVEZRcrx8PDwaHU0bRy/61svYgZdUVOw/VRuDw+PHoGmjuMXkVtV9SMpr92V7j5+xRZ9uVlVF2XUWYgcDw8Pj3ZF6V09dfDpNBeJyLnYYu4CzMBCrQq2Mk3aIaGFyfHw8PBoZ7TLzN1HgN1UdXXkeD/gYVXduUo5Hh4eHu2MKiZwbSki00VksYi84n6L3LFBKcWsA4bHHN+WbGFvi5Lj4eHh0bao4uPujdhSehODET1iCy6fDNyELQZdD58D/iy2eHQwgWskMBpb3i0tipLj4eHh0baoYhz/ElUdk/VczLW9sBg/4QlcM1V1bUZ7CpHj4eHh0a6oouK/E/gTcF0QRtnNlJ2ChW84uFQDPDw8PDw2QBWjeo4FuoC7RWSFiKwA/goMAT5WgX4PDw8PjxCaEZZ5sqreXKlSDw8PD4/1aEbF/5Cq7lmpUg8PDw+P9WjGBC4fGsHDw8OjiWhGxd/6M8Y8PDw8OhjNDtng4eHh4VExfMXv4eHh0cPQjIr/hSbo9PDw8PBwaIsgbR4eHh4excF39Xh4eHj0MFRS8YvI10TkIRGZJyJ3ikhchMzg2t4iMldEbg0d+5VLO09ElorIPHd8Quj4gyKSeilHEblCRFY2xszDw8Oj/VB4V4+ITASmqOqU0LGBqvqG2z4TGKuqp9ZIfzYwHhgYtzqXiHwbeF1V/0NE+gPvquoaEdkWeBAYrqpr6tg4HjgLOFpVB+Th6eHh4dGuqKTFH1T6DptTYyy/iIwAjgSurnFesPg+Nzi5q0KV/KZhuSJyqIjcJyJzROQmERngjvcGLgG+2BgrDw8Pj/ZEZX38IvINEXkG+Djw5RqXXY5VyLUWRTkQeEFVHw3J3VdEHgbmA6e61v9Q4ALgYFXdG5gFnO2SnI6tsftcw6Q8PDw82hCFVfwi8oDre78amBzqez8MQFXPV9XtgeuJWfRERD4CvKiqsxPUHI9r7QdQ1QdUdTdgH2CaiGwK7AeMBe51Np0M7OC+LRwDXNEoXw8PD492RSV9/JHzOwC3qerukeMXAScCa7Bum4HAb1T1E+58H2A5ME5Vl9WQ/RfgC9hSiieo6vGR80cCPwHedodGAk+o6ujsTD08PDzaE1WN6gkvYj4ZWBy9RlWnqeoIVR0FHAfcFVT6DgcDi8OVvojs6B4IwQNlDLAUuB/YX0RGu3P9RWQXVb1NVYep6iinZ5Wv9D08PHoaqlhzF2C6iIzB+u6fAk4FcF0vV6vqESlkHEekmwc4ADhPRFY72aep6stO9hTgBhHZxF17AfBIo0Q8PDw82h1+5q6Hh4dHD4Ofuevh4eHRw+Arfg8PD48eBl/xe3h4ePQw+Irfw8PDo4fBV/weHh4ePQy+4vfw8PDoYfAVv4eHh0cPw/8A1cEkMB5UrqAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist = omni_df.hist(column = 'Median Income', by ='% Adults with Obesity', bins=30)\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}