FinalProject_group086.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Effect of Income on COVID-19 Vaccination Rates In the United States\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After analysing the distribution of vaccine's correlation to county income per capita. We discovered that California, Ohio, and Washington had significant correlations while Arizona, Kansas, and Georgia did not. The overall combined dataset of all 6 states also had no significant correlation between income per capita and vaccine distribution per capita. This led us to conclude that different states were distributing vaccines differently however there seemed to be little evidence of a nationwide effect to bias distribution towards higher income counties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Shushruth Kallutla   \n",
    "- Nicholas Peterzell \n",
    "- Amanda Mark\n",
    "- Taylor Drennen \n",
    "- Carter Nielsen "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does Covid-19 inoculation rate in selected counties in the US correlate with the average income in a county? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the massive rollout of the different Covid-19 vaccines, we wanted to see where these doses were going, and whether there were any factors that affected this distribution. Are current vaccination efforts equitable and reaching the people that need it the most? To that end, we decided to look into how the level of wealth in a county relates to the amount of vaccine being shipped there.  \n",
    "  \n",
    "From one of the sources we looked at (source 1), AstraZeneca, Pfizer, and Moderna, the three manufacturers currently producing the vaccine, are estimated to produce 5.3 billion doses before the end of 2021; knowing where those are going could prove useful.  Furthermore, there are at least 6 other vaccines being worked on at the moment, which could increase these numbers more. We might need to incorporate unexpected data into our analysis at some point in the process if something significant changes, such as an increase in manufacturing rate or the release of a new vaccine. \n",
    "  \n",
    "Another article we read (source 2) explained that West Virginia, one of the states with the lowest rate of poverty, was being much more effective in its distribution of the vaccine than Maryland, which is the state with the second lowest rate of poverty. This is caused by several factors, including West Virginia’s choice to distribute the vaccine through the more widespread independent pharmacies, which were already in a good location to serve the high-risk locations such as nursing homes, as well as already having the information of patients in those locations. Furthermore, Maryland is made up of counties that are very independent, and have seen a large disparity in their inoculation rates. This last point especially informed our choice of using counties as the scale of measurement in our analysis.  \n",
    "  \n",
    "The third article we read (source 3) said that there is a difference in the distribution method in all states, not just the two mentioned before. It also explained that the federal government distributes the vaccines to the states based on total population, and many vaccines sit idle after they arrive while the state government figures out the best way to divide them up.\n",
    "Finally, there is existing data on this realm of study; the CDC has released publicly available datasets on the status of Covid-19 and the progress of vaccinations (source 4). This should prove invaluable to our project, both in the datasets it provides as well as the graphs giving us an easy way to understand important data.  \n",
    "\n",
    "\n",
    "References (include links):\n",
    "1. *Mullard, Asher. “How COVID Vaccines Are Being Divvied up around the World.” Nature News, Nature Publishing Group, 30 Nov.   2020, www.nature.com/articles/d41586-020-03370-6.*\n",
    "2. *Galston, William A. “COVID-19 Vaccinations: Why Are Some States and Localities so Much More Successful?” Brookings, Brookings, 26 Jan. 2021, www.brookings.edu/blog/fixgov/2021/01/25/covid-19-vaccinations-why-are-some-states-and-localities-so-much-more-successful/.*  \n",
    "3. *Huang, Pien, and Audrey Carlsen. “How Is The COVID-19 Vaccination Campaign Going In Your State?” NPR, NPR, 28 Jan. 2021, www.npr.org/sections/health-shots/2021/01/28/960901166/how-is-the-covid-19-vaccination-campaign-going-in-your-state.*  \n",
    "4. *U.S Department of Health and Human Services. “CDC COVID Data Tracker.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, covid.cdc.gov/covid-data-tracker/#datatracker-home.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Historically access to health care and services has been influenced by socioeconomic factors such as income. We believe that Covid 19 Vaccination rate will also be affected by these factors hence we hypothesize that there will be a significant positive effect of income per capita on doses per capita. Our null hypothesis would be that there is no significant effect of income per capita on doses per capita."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name **Personal Income and and Employment by County and Metropolitan Area**\n",
    "- Link to the dataset:<https://apps.bea.gov/iTable/iTable.cfm?reqid=70&step=1&acrdn=6>\n",
    "- Number of observations: 9414\n",
    "- This dataset was reported by U.S Department of Commerce in their Bureau of Econimic Analysis. It includes the names of all the counties in each state, total personal income (in thousands of dollars), population, and per capita personal income( in dollars) of each county  in 2019. This dataset is used to extract the per capita income of all the counties in California. \n",
    "<br />\n",
    "<br />\n",
    "- Dataset Name:**Washington State Vaccine by County**\n",
    "- Link to the dataset: <https://www.doh.wa.gov/Emergencies/COVID19/DataDashboard>\n",
    "- Number of observations: 40 \n",
    "- This dataset was provided by Washington State Department of Health (WA DOH) and it contails name of all the counties in Washington state, and number of vaccine doses given, number of people initiating vaccination, and number of people fully vaccinated in each coounty. We used this dataset to extract number of doses given in Washington State separated by counties.\n",
    "<br />\n",
    "<br />\n",
    "- Dataset Name:**California Vaccine administration by county of residence**\n",
    "- Link to the dataset: <https://covid19.ca.gov/vaccines/#California-vaccines-dashboard>\n",
    "- Number of observations: 58\n",
    "- This dataset can be found on official California statewide websitea and it contains name of all the counties in California and number of vaccine administration by county. We will be using this dataset to see the number of vaccines distributed in each county of California.\n",
    "<br />\n",
    "<br />\n",
    "- Dataset Name:**Kansas Vaccine Allocation and Distribution**\n",
    "- Link to the dataset: <https://www.kansasvaccine.gov/158/Data>\n",
    "- Number of observations: 107\n",
    "- Number of observations: 58\n",
    "- This dataset was reported by Kansas Department of Health and Environment and it contains name of all the counties in Kansas State and number of vaccine distributed by county. This is only the number of first dosage given because at the time data was collected, Kansas has not yet distributed second dose to their poulation.\n",
    "<br />\n",
    "<br />\n",
    "- Dataset Name:**Georgia Vaccine Administered by County**\n",
    "- Link to the dataset: <https://experience.arcgis.com/experience/3d8eea39f5c1443db1743a4cb8948a9c/>\n",
    "- Number of observations: 161\n",
    "- This dataset is given by Georgia Department of Public Health (GA DPH) and it contains name of all the counties in Georgia and number of vaccine administration by county. We will be using this dataset to see the number of vaccines distributed in each county of Georgia.\n",
    "<br />\n",
    "<br />\n",
    "- Dataset Name:**Ohio Vaccine Administered by County**\n",
    "- Link to the dataset: <https://coronavirus.ohio.gov/wps/portal/gov/covid-19/dashboards/covid-19-vaccine/covid-19-vaccination-dashboard>\n",
    "- Number of observations: 87\n",
    "-  This dataset contains data reported to the Ohio Department of Health (ODH) regarding the number of individuals that have started and completed the COVID-19 vaccination seperate by county. We will be using this dataset to see the number of vaccines distributed in each county of Ohio.\n",
    " <br />\n",
    " <br />\n",
    "- DatasetName:**Arizona Vaccine administration by County**\n",
    "- Link to the dataset: <https://www.azdhs.gov/preparedness/epidemiology-disease-control/infectious-disease-epidemiology/covid-19/dashboards/index.php>\n",
    "- Number of observations: 15\n",
    "- This dataset is provided by Arizona Department of Health Services (ADHS). It contains name of all the counties in Arizona and number of vaccine administration by county. We will be using this dataset to see the number of vaccines distributed in each county of Arizona."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stat\n",
    "import numpy as np\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seven datasets were used in total, one containing the population and total income of each US county, and 6 containing the vaccination distribution data of each state. Each dataset was formatted differnetly and thus required different cleaning methods however the end goal was to produce a combined dataset where each row represented a unique county and the columns contained the income per capita and vaccines administered per capita of that county.\n",
    "\n",
    "The census data had to be grouped according to a column as the rows containing income information were split to mulitples of 3 rows. This dataset also had missing values and duplicate counties which needed to be addressed. A function was applied to the dataset to compute the income per capita column of the final dataset.\n",
    "\n",
    "As we were unable to locate a national dataset with per county granularity we opted to use individual state's datasets which did report per county. This made the data cleaning process challenging as each state had a slightly different reporting format. As there were multiple counties with the same name in the US a state column was nesassary to create before preforming the merge to the main dataset which would ensure accurate data points and non duplication.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_raw = pd.read_csv(\"Datasets/IncomeByCounty.csv\")\n",
    "vaccine_count_cal = pd.read_csv(\"Datasets/DosesAdminsteredbyCounty(California).csv\", thousands=',')\n",
    "vaccine_count_wash = pd.read_csv(\"Datasets/Doses(Washington).csv\", thousands=',')\n",
    "vaccine_count_geor = pd.read_csv(\"Datasets/Georgia Doses.csv\", thousands=',')\n",
    "vaccine_count_ohio = pd.read_csv(\"Datasets/Ohio Doses.csv\", thousands=',')\n",
    "vaccine_count_ariz = pd.read_csv(\"Datasets/Arizona Doses.csv\", thousands=',')\n",
    "vaccine_count_kans = pd.read_csv(\"Datasets/Kansas_Vaccine.csv\")\n",
    "\n",
    "group = income_raw.groupby(\"LineCode\")\n",
    "\n",
    "counties = group.get_group(1)[\"GeoName\"].reset_index()\n",
    "del counties[\"index\"]\n",
    "\n",
    "\n",
    "income_data = group.get_group(1)[\"2019\"].reset_index()\n",
    "del income_data[\"index\"]\n",
    "\n",
    "population_data = group.get_group(2)[\"2019\"].reset_index()\n",
    "del population_data[\"index\"]\n",
    "\n",
    "county_data = pd.DataFrame(counties)\n",
    "county_data = county_data.rename(columns={\"GeoName\": \"County\"})\n",
    "\n",
    "county_data[\"Total Income\"] = income_data\n",
    "county_data[\"Population\"] = population_data\n",
    "\n",
    "\n",
    "#Remove NA from Dataset for accurate int datatype\n",
    "county_data = county_data.replace(\"(NA)\", \"1\")\n",
    "#Convert object data to int\n",
    "county_data[\"Total Income\"] = pd.to_numeric(county_data[\"Total Income\"])\n",
    "county_data[\"Population\"] = pd.to_numeric(county_data[\"Population\"])\n",
    "\n",
    "\n",
    "#Calculate Income per Capita\n",
    "percapitaincome = county_data.apply(lambda row: row[\"Total Income\"] / row[\"Population\"], axis = 1).reset_index()\n",
    "del percapitaincome[\"index\"]\n",
    "county_data[\"Income per Capita\"] = percapitaincome\n",
    "county_data.to_csv(\"CleanedIncomePerCapita.csv\")\n",
    "\n",
    "#Seperate County and State\n",
    "count_state_temp = county_data[\"County\"].str.split(\", \", n = 1, expand = True) \n",
    "county_data.drop(columns =[\"County\"], inplace = True) \n",
    "\n",
    "county_data[\"County\"] = count_state_temp[0]\n",
    "county_data[\"State\"] = count_state_temp[1]\n",
    "\n",
    "#filter only california counties\n",
    "cal_income = county_data[(county_data.State == \"CA\")]\n",
    "\n",
    "#filter only washington counties\n",
    "wash_income = county_data[(county_data.State == \"WA\")]\n",
    "\n",
    "#filter only arizona counties\n",
    "ariz_income = county_data[(county_data.State == \"AZ\") | ((county_data.State == \"AZ*\"))]\n",
    "\n",
    "#filter only ohio counties\n",
    "ohio_income = county_data[(county_data.State == \"OH\")]\n",
    "\n",
    "#filter only georgia counties\n",
    "geor_income = county_data[(county_data.State == \"GA\")]\n",
    "\n",
    "#filer only kansas counties\n",
    "kans_income = county_data[(county_data.State == \"KS\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 58 entries, 0 to 57\n",
      "Data columns (total 6 columns):\n",
      "Total Income          58 non-null int64\n",
      "Population            58 non-null int64\n",
      "Income per Capita     58 non-null float64\n",
      "County                58 non-null object\n",
      "State                 58 non-null object\n",
      "Doses Administered    58 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 3.2+ KB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135663560</td>\n",
       "      <td>1671329</td>\n",
       "      <td>81.171068</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>CA</td>\n",
       "      <td>350830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81463</td>\n",
       "      <td>1129</td>\n",
       "      <td>72.155004</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>CA</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1825410</td>\n",
       "      <td>39752</td>\n",
       "      <td>45.919954</td>\n",
       "      <td>Amador</td>\n",
       "      <td>CA</td>\n",
       "      <td>8031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10490209</td>\n",
       "      <td>219186</td>\n",
       "      <td>47.859850</td>\n",
       "      <td>Butte</td>\n",
       "      <td>CA</td>\n",
       "      <td>50346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2347168</td>\n",
       "      <td>45905</td>\n",
       "      <td>51.130988</td>\n",
       "      <td>Calaveras</td>\n",
       "      <td>CA</td>\n",
       "      <td>9462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita     County State  \\\n",
       "0     135663560     1671329          81.171068    Alameda    CA   \n",
       "1         81463        1129          72.155004     Alpine    CA   \n",
       "2       1825410       39752          45.919954     Amador    CA   \n",
       "3      10490209      219186          47.859850      Butte    CA   \n",
       "4       2347168       45905          51.130988  Calaveras    CA   \n",
       "\n",
       "   Doses Administered  \n",
       "0              350830  \n",
       "1                 450  \n",
       "2                8031  \n",
       "3               50346  \n",
       "4                9462  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_cal = cal_income.merge(vaccine_count_cal, \"inner\", \"County\")\n",
    "merged_cal.info()\n",
    "merged_cal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 39 entries, 0 to 38\n",
      "Data columns (total 8 columns):\n",
      "Total Income                     39 non-null int64\n",
      "Population                       39 non-null int64\n",
      "Income per Capita                39 non-null float64\n",
      "County                           39 non-null object\n",
      "State                            39 non-null object\n",
      "Doses Administered               39 non-null int64\n",
      "People Initiating Vaccination    39 non-null int64\n",
      "People Fully Vaccinated          39 non-null int64\n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 2.7+ KB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "      <th>People Initiating Vaccination</th>\n",
       "      <th>People Fully Vaccinated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>867839</td>\n",
       "      <td>19983</td>\n",
       "      <td>43.428865</td>\n",
       "      <td>Adams</td>\n",
       "      <td>WA</td>\n",
       "      <td>3375</td>\n",
       "      <td>2097</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1094225</td>\n",
       "      <td>22582</td>\n",
       "      <td>48.455628</td>\n",
       "      <td>Asotin</td>\n",
       "      <td>WA</td>\n",
       "      <td>4808</td>\n",
       "      <td>2625</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10087552</td>\n",
       "      <td>204390</td>\n",
       "      <td>49.354430</td>\n",
       "      <td>Benton</td>\n",
       "      <td>WA</td>\n",
       "      <td>41142</td>\n",
       "      <td>24261</td>\n",
       "      <td>7685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4342733</td>\n",
       "      <td>77200</td>\n",
       "      <td>56.253018</td>\n",
       "      <td>Chelan</td>\n",
       "      <td>WA</td>\n",
       "      <td>31552</td>\n",
       "      <td>15869</td>\n",
       "      <td>6052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3700741</td>\n",
       "      <td>77331</td>\n",
       "      <td>47.855853</td>\n",
       "      <td>Clallam</td>\n",
       "      <td>WA</td>\n",
       "      <td>30843</td>\n",
       "      <td>20215</td>\n",
       "      <td>10663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita   County State  \\\n",
       "0        867839       19983          43.428865    Adams    WA   \n",
       "1       1094225       22582          48.455628   Asotin    WA   \n",
       "2      10087552      204390          49.354430   Benton    WA   \n",
       "3       4342733       77200          56.253018   Chelan    WA   \n",
       "4       3700741       77331          47.855853  Clallam    WA   \n",
       "\n",
       "   Doses Administered  People Initiating Vaccination  People Fully Vaccinated  \n",
       "0                3375                           2097                      685  \n",
       "1                4808                           2625                      978  \n",
       "2               41142                          24261                     7685  \n",
       "3               31552                          15869                     6052  \n",
       "4               30843                          20215                    10663  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_wash = wash_income.merge(vaccine_count_wash, \"inner\", \"County\")\n",
    "merged_wash = merged_wash.rename(columns = {\"Vaccine Doses Given\": \"Doses Administered\"})\n",
    "merged_wash.info()\n",
    "merged_wash.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15 entries, 0 to 14\n",
      "Data columns (total 6 columns):\n",
      "Total Income          15 non-null int64\n",
      "Population            15 non-null int64\n",
      "Income per Capita     15 non-null float64\n",
      "County                15 non-null object\n",
      "State                 15 non-null object\n",
      "Doses Administered    15 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 840.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2529614</td>\n",
       "      <td>71887</td>\n",
       "      <td>35.188755</td>\n",
       "      <td>Apache</td>\n",
       "      <td>AZ</td>\n",
       "      <td>7555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5259219</td>\n",
       "      <td>125922</td>\n",
       "      <td>41.765688</td>\n",
       "      <td>Cochise</td>\n",
       "      <td>AZ</td>\n",
       "      <td>23144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7057376</td>\n",
       "      <td>143476</td>\n",
       "      <td>49.188547</td>\n",
       "      <td>Coconino</td>\n",
       "      <td>AZ</td>\n",
       "      <td>44455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2226470</td>\n",
       "      <td>54018</td>\n",
       "      <td>41.217187</td>\n",
       "      <td>Gila</td>\n",
       "      <td>AZ</td>\n",
       "      <td>13236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1238695</td>\n",
       "      <td>38837</td>\n",
       "      <td>31.894714</td>\n",
       "      <td>Graham</td>\n",
       "      <td>AZ</td>\n",
       "      <td>5772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita    County State  \\\n",
       "0       2529614       71887          35.188755    Apache    AZ   \n",
       "1       5259219      125922          41.765688   Cochise    AZ   \n",
       "2       7057376      143476          49.188547  Coconino    AZ   \n",
       "3       2226470       54018          41.217187      Gila    AZ   \n",
       "4       1238695       38837          31.894714    Graham    AZ   \n",
       "\n",
       "   Doses Administered  \n",
       "0                7555  \n",
       "1               23144  \n",
       "2               44455  \n",
       "3               13236  \n",
       "4                5772  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ariz = ariz_income.merge(vaccine_count_ariz, \"inner\", \"County\")\n",
    "merged_ariz.info()\n",
    "merged_ariz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 87 entries, 0 to 86\n",
      "Data columns (total 6 columns):\n",
      "Total Income          87 non-null int64\n",
      "Population            87 non-null int64\n",
      "Income per Capita     87 non-null float64\n",
      "County                87 non-null object\n",
      "State                 87 non-null object\n",
      "Doses Administered    87 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 4.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>976876</td>\n",
       "      <td>27698</td>\n",
       "      <td>35.268828</td>\n",
       "      <td>Adams</td>\n",
       "      <td>OH</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4481729</td>\n",
       "      <td>102351</td>\n",
       "      <td>43.787838</td>\n",
       "      <td>Allen</td>\n",
       "      <td>OH</td>\n",
       "      <td>9947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2138133</td>\n",
       "      <td>53484</td>\n",
       "      <td>39.977059</td>\n",
       "      <td>Ashland</td>\n",
       "      <td>OH</td>\n",
       "      <td>3823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3866977</td>\n",
       "      <td>97241</td>\n",
       "      <td>39.766940</td>\n",
       "      <td>Ashtabula</td>\n",
       "      <td>OH</td>\n",
       "      <td>8866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2273150</td>\n",
       "      <td>65327</td>\n",
       "      <td>34.796485</td>\n",
       "      <td>Athens</td>\n",
       "      <td>OH</td>\n",
       "      <td>6062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita     County State  \\\n",
       "0        976876       27698          35.268828      Adams    OH   \n",
       "1       4481729      102351          43.787838      Allen    OH   \n",
       "2       2138133       53484          39.977059    Ashland    OH   \n",
       "3       3866977       97241          39.766940  Ashtabula    OH   \n",
       "4       2273150       65327          34.796485     Athens    OH   \n",
       "\n",
       "   Doses Administered  \n",
       "0                1873  \n",
       "1                9947  \n",
       "2                3823  \n",
       "3                8866  \n",
       "4                6062  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ohio = ohio_income.merge(vaccine_count_ohio, \"inner\", \"County\")\n",
    "merged_ohio.info()\n",
    "merged_ohio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 103 entries, 0 to 102\n",
      "Data columns (total 6 columns):\n",
      "Total Income          103 non-null int64\n",
      "Population            103 non-null int64\n",
      "Income per Capita     103 non-null float64\n",
      "County                103 non-null object\n",
      "State                 103 non-null object\n",
      "Doses Administered    103 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 5.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>533888</td>\n",
       "      <td>12369</td>\n",
       "      <td>43.163392</td>\n",
       "      <td>Allen</td>\n",
       "      <td>KS</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>330401</td>\n",
       "      <td>7858</td>\n",
       "      <td>42.046449</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>KS</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>648297</td>\n",
       "      <td>16073</td>\n",
       "      <td>40.334536</td>\n",
       "      <td>Atchison</td>\n",
       "      <td>KS</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>214662</td>\n",
       "      <td>4427</td>\n",
       "      <td>48.489270</td>\n",
       "      <td>Barber</td>\n",
       "      <td>KS</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1275030</td>\n",
       "      <td>25779</td>\n",
       "      <td>49.460026</td>\n",
       "      <td>Barton</td>\n",
       "      <td>KS</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita    County State  \\\n",
       "0        533888       12369          43.163392     Allen    KS   \n",
       "1        330401        7858          42.046449  Anderson    KS   \n",
       "2        648297       16073          40.334536  Atchison    KS   \n",
       "3        214662        4427          48.489270    Barber    KS   \n",
       "4       1275030       25779          49.460026    Barton    KS   \n",
       "\n",
       "   Doses Administered  \n",
       "0                 200  \n",
       "1                 100  \n",
       "2                 300  \n",
       "3                 100  \n",
       "4                 500  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_kans =kans_income.merge(vaccine_count_kans, \"inner\", \"County\")\n",
    "merged_kans = merged_kans.rename(columns = {\"Number of First Doses Distributed\": \"Doses Administered\"})\n",
    "merged_kans.info()\n",
    "merged_kans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_county(string):\n",
    "    string = string.strip()\n",
    "    string = string.replace(\"County\", \"\")\n",
    "    string = string.strip()\n",
    "    \n",
    "    return string\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 159 entries, 0 to 158\n",
      "Data columns (total 6 columns):\n",
      "Total Income          159 non-null int64\n",
      "Population            159 non-null int64\n",
      "Income per Capita     159 non-null float64\n",
      "County                159 non-null object\n",
      "State                 159 non-null object\n",
      "Doses Administered    159 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 8.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>652960</td>\n",
       "      <td>18386</td>\n",
       "      <td>35.513978</td>\n",
       "      <td>Appling</td>\n",
       "      <td>GA</td>\n",
       "      <td>5964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>257538</td>\n",
       "      <td>8165</td>\n",
       "      <td>31.541702</td>\n",
       "      <td>Atkinson</td>\n",
       "      <td>GA</td>\n",
       "      <td>853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>372801</td>\n",
       "      <td>11164</td>\n",
       "      <td>33.393139</td>\n",
       "      <td>Bacon</td>\n",
       "      <td>GA</td>\n",
       "      <td>5964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125538</td>\n",
       "      <td>3038</td>\n",
       "      <td>41.322581</td>\n",
       "      <td>Baker</td>\n",
       "      <td>GA</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1532017</td>\n",
       "      <td>44890</td>\n",
       "      <td>34.128247</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>GA</td>\n",
       "      <td>3406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita    County State  \\\n",
       "0        652960       18386          35.513978   Appling    GA   \n",
       "1        257538        8165          31.541702  Atkinson    GA   \n",
       "2        372801       11164          33.393139     Bacon    GA   \n",
       "3        125538        3038          41.322581     Baker    GA   \n",
       "4       1532017       44890          34.128247   Baldwin    GA   \n",
       "\n",
       "   Doses Administered  \n",
       "0                5964  \n",
       "1                 853  \n",
       "2                5964  \n",
       "3                 956  \n",
       "4                3406  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine_count_geor[\"County\"] = vaccine_count_geor[\"County\"].apply(standardize_county)\n",
    "\n",
    "merged_geor = geor_income.merge(vaccine_count_geor, \"inner\", \"County\")\n",
    "merged_geor.info()\n",
    "merged_geor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "      <th>People Initiating Vaccination</th>\n",
       "      <th>People Fully Vaccinated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135663560</td>\n",
       "      <td>1671329</td>\n",
       "      <td>81.171068</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>CA</td>\n",
       "      <td>350830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81463</td>\n",
       "      <td>1129</td>\n",
       "      <td>72.155004</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>CA</td>\n",
       "      <td>450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1825410</td>\n",
       "      <td>39752</td>\n",
       "      <td>45.919954</td>\n",
       "      <td>Amador</td>\n",
       "      <td>CA</td>\n",
       "      <td>8031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10490209</td>\n",
       "      <td>219186</td>\n",
       "      <td>47.859850</td>\n",
       "      <td>Butte</td>\n",
       "      <td>CA</td>\n",
       "      <td>50346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2347168</td>\n",
       "      <td>45905</td>\n",
       "      <td>51.130988</td>\n",
       "      <td>Calaveras</td>\n",
       "      <td>CA</td>\n",
       "      <td>9462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Income  Population  Income per Capita     County State  \\\n",
       "0     135663560     1671329          81.171068    Alameda    CA   \n",
       "1         81463        1129          72.155004     Alpine    CA   \n",
       "2       1825410       39752          45.919954     Amador    CA   \n",
       "3      10490209      219186          47.859850      Butte    CA   \n",
       "4       2347168       45905          51.130988  Calaveras    CA   \n",
       "\n",
       "   Doses Administered  People Initiating Vaccination  People Fully Vaccinated  \n",
       "0              350830                            NaN                      NaN  \n",
       "1                 450                            NaN                      NaN  \n",
       "2                8031                            NaN                      NaN  \n",
       "3               50346                            NaN                      NaN  \n",
       "4                9462                            NaN                      NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create merged df of all 6 states\n",
    "all_states = pd.concat([merged_cal, merged_wash, merged_ohio, merged_geor, merged_ariz, merged_kans], sort=False)\n",
    "all_states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create list of states to be analysied\n",
    "states = [\"all\", \"cal\", \"wash\", \"ariz\", \"ohio\", \"geor\", \"kans\"]\n",
    "state_list = [\"cal\", \"wash\", \"ariz\", \"ohio\", \"geor\", \"kans\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first created dictionaries and functions that would aid in our analysis of the data. See function header for description of functionality. We also calculated doses per capita as a metric to control for the effect of population size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define dictionaries for mapping\n",
    "color_dict = {\"all\": \"black\", \"cal\": \"blue\", \"wash\": \"red\", \"geor\": \"pink\", \"ohio\": \"green\", \"ariz\": \"yellow\", \"kans\": \"purple\"}\n",
    "state_name = {\"all\": \"All 6 states\", \"cal\": \"California\", \"wash\": \"Washigton\", \"ariz\": \"Arizona\", \"ohio\": \"Ohio\", \"geor\": \"Georgia\", \"kans\" : \"Kansas\"}\n",
    "dataset_dict = {\"all\": all_states,\"cal\": merged_cal, \"wash\": merged_wash, \"ariz\": merged_ariz, \"ohio\": merged_ohio, \"geor\": merged_geor, \"kans\":merged_kans}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate doses per capita for each state\n",
    "for state in states:\n",
    "    vaccine_per_capita = dataset_dict[state].apply(lambda row: row[\"Doses Administered\"] / row[\"Population\"], axis = 1)\n",
    "    dataset_dict[state][\"Doses per Capita\"] = vaccine_per_capita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_bins = 25\n",
    "def plot_hist(state):\n",
    "    \"\"\"\n",
    "    Function to plot histograms of Income per Capita, Doses Administered and Doses per Capita.\n",
    "    Also provides mean and correlation information\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    \n",
    "    \"\"\"\n",
    "    f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))\n",
    "    print(\"Income per Capita Centered Around: \" + str(dataset_dict[state][\"Income per Capita\"].mean()) + \" thousand dollars\")\n",
    "    print(\"Doses Administered Centered Around: \" + str(dataset_dict[state][\"Doses Administered\"].mean()) + \" Doses\")\n",
    "    print(\"Doses per Capita Centered Around: \" + str(dataset_dict[state][\"Doses per Capita\"].mean()) + \" Doses\")\n",
    "    print(\"Correlation between Income per Capita and Doses per Capita: \" + str(dataset_dict[state][\"Income per Capita\"].corr(dataset_dict[state][\"Doses per Capita\"])))\n",
    "    ax1.hist(dataset_dict[state][\"Income per Capita\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax1.set_title(\"Income per Capita \" + state_name[state])\n",
    "    ax1.set_xlabel(\"Income per Capita\")\n",
    "    ax2.hist(dataset_dict[state][\"Doses Administered\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax2.set_title(\"Doses Administered \" + state_name[state])\n",
    "    ax2.set_xlabel(\"Doses Administered \")\n",
    "    ax3.hist(dataset_dict[state][\"Doses per Capita\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax3.set_title(\"Doses per Capita \" + state_name[state])\n",
    "    ax3.set_xlabel(\"Doses per Capita\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_ipc_vs_da(state):\n",
    "    \"\"\"\n",
    "    Function to plot scatter plot of Income per Capita vs Doses Administered\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    ax: axes to be plotted on\n",
    "    \"\"\"\n",
    "    if (state != \"all\"):\n",
    "        plt.plot(dataset_dict[state][\"Income per Capita\"], dataset_dict[state][\"Doses Administered\"], color = color_dict[state], marker='o',\n",
    "                 alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "    else:\n",
    "        for state in state_list:\n",
    "            plt.plot(dataset_dict[state][\"Income per Capita\"], dataset_dict[state][\"Doses Administered\"], color = color_dict[state], marker='o',\n",
    "             alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "            plt.legend()\n",
    "    plt.xlabel(\"Income per Capita (in Thousands)\")\n",
    "    plt.ylabel(\"Doses Administered\")\n",
    "    plt.title(\"Income per Capita vs Doses Administered\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter_ipc_vs_dpc(state, ax):\n",
    "    \"\"\"\n",
    "    Function to plot scatter plot of Income per Capita vs Doses per Capita\n",
    "    Must specify axes to plot on\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    ax: axes to plot on\n",
    "    \"\"\"\n",
    "    if (state != \"all\"):\n",
    "        ax.plot(dataset_dict[state][\"Income per Capita\"], dataset_dict[state][\"Doses per Capita\"], color = color_dict[state], marker='o',\n",
    "                 alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "    else:\n",
    "        for state in state_list:\n",
    "            ax.plot(dataset_dict[state][\"Income per Capita\"], dataset_dict[state][\"Doses per Capita\"], color = color_dict[state], marker='o',\n",
    "             alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "        ax.legend()\n",
    "    ax.set_xlabel(\"Income per Capita (in Thousands)\")\n",
    "    ax.set_ylabel(\"Doses per Capita\")\n",
    "    ax.set_title(\"Income per Capita vs Doses per Capita\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_scatter_dpc_vs_da(state):\n",
    "    \"\"\"\n",
    "    Function to plot scatter plot of Doses per Capita vs Doses Administered\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    ax: axes to be plotted on\n",
    "    \"\"\"\n",
    "    if (state != \"all\"):\n",
    "        plt.plot(dataset_dict[state][\"Doses per Capita\"], dataset_dict[state][\"Doses Administered\"], color = color_dict[state], marker='o',\n",
    "                 alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "    else:\n",
    "        for state in state_list:\n",
    "            plt.plot(dataset_dict[state][\"Doses per Capita\"], dataset_dict[state][\"Doses Administered\"], color = color_dict[state], marker='o',\n",
    "             alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "            plt.legend()\n",
    "    plt.xlabel(\"Doses per Capita\")\n",
    "    plt.ylabel(\"Doses Administered\")\n",
    "    plt.title(\"Doses per Capita vs Doses Administered\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_model_state(state):\n",
    "    \"\"\"\n",
    "    Function to use linear regression to model relationship between Incompe per Capita and Doses per Capita\n",
    "    Does not remove outlier\n",
    "    Plots model\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    \n",
    "    \"\"\"\n",
    "    state_lin = dataset_dict[state].rename(columns = {\"Income per Capita\": \"IPC\", \"Doses per Capita\": \"DPC\"})\n",
    "    state_lin.head()\n",
    "    outcome_s, predictors_s = patsy.dmatrices(formula_like = \"DPC ~ IPC\", data = state_lin)\n",
    "    mod_s = sm.OLS(outcome_s, predictors_s)\n",
    "    res_s = mod_s.fit()\n",
    "    print(res_s.summary())\n",
    "    fig, ax = plt.subplots()\n",
    "    plot_scatter_ipc_vs_dpc(state, ax)\n",
    "    xs = np.arange(dataset_dict[state]['Income per Capita'].min(), dataset_dict[state]['Income per Capita'].max())\n",
    "    ys = res_s.params[0] + (res_s.params[1] * xs)\n",
    "    ax.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Linear model of Income per Capita vs Doses per Capita\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier(df):\n",
    "    \"\"\"\n",
    "    Fuction to remove outliers from dataframe\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df: dataframe to remove outliers from\n",
    "    \"\"\"\n",
    "    rem_out = df[(df[\"Income per Capita\"] < INCOME_CAP) & (df[\"Doses per Capita\"] < DOSES_MAX) & (df[\"Doses per Capita\"] > DOSES_MIN)]\n",
    "    return rem_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier(df):\n",
    "    \"\"\"\n",
    "    Function to show outliers\n",
    "    \"\"\"\n",
    "    out = df[(df[\"Income per Capita\"] >= INCOME_CAP) | (df[\"Doses per Capita\"] >= DOSES_MAX) | (df[\"Doses per Capita\"] <= DOSES_MIN)]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ipc_vs_dpc_wo_out(state, ax):\n",
    "    \"\"\"\n",
    "    Function to plot scatter plot of Income per Capita vs Doses per Capita without outliers\n",
    "    Removes outlier\n",
    "    Must specify axes to plot on\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    ax: axes to plot on\n",
    "    \"\"\"\n",
    "    if (state != \"all\"):\n",
    "        state_wo_out = remove_outlier(dataset_dict[state])\n",
    "        ax.plot(state_wo_out[\"Income per Capita\"], state_wo_out[\"Doses per Capita\"], color = color_dict[state], marker='o',\n",
    "                 alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "    else:\n",
    "        for state in state_list:\n",
    "            state_wo_out = remove_outlier(dataset_dict[state])\n",
    "            ax.plot(state_wo_out[\"Income per Capita\"], state_wo_out[\"Doses per Capita\"], color = color_dict[state], marker='o',\n",
    "             alpha = 0.5, linestyle=\"None\", label = state_name[state])\n",
    "        ax.legend()\n",
    "    ax.set_xlabel(\"Income per Capita (in Thousands)\")\n",
    "    ax.set_ylabel(\"Doses per Capita\")\n",
    "    ax.set_title(\"Income per Capita vs Doses per Capita [Outliers Removed]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_model_state_rem_out(state):\n",
    "    \"\"\"\n",
    "    Function to use linear regression to model relationship between Incompe per Capita and Doses per Capita without outliers\n",
    "    Removes Outliers\n",
    "    Plots model\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    \"\"\"\n",
    "    state_df = dataset_dict[state]\n",
    "    state_rem_out = remove_outlier(state_df)\n",
    "    state_lin = state_rem_out.rename(columns = {\"Income per Capita\": \"IPC\", \"Doses per Capita\": \"DPC\"})\n",
    "\n",
    "    outcome_s, predictors_s = patsy.dmatrices(formula_like = \"DPC ~ IPC\", data = state_lin)\n",
    "    mod_s = sm.OLS(outcome_s, predictors_s)\n",
    "    res_s = mod_s.fit()\n",
    "    print(res_s.summary())\n",
    "    print(\"Correlation between Income per Capita and Doses per Capita: \" + str(state_rem_out[\"Income per Capita\"].corr(state_rem_out[\"Doses per Capita\"])))\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    plot_ipc_vs_dpc_wo_out(state, ax)\n",
    "    \n",
    "    xs = np.arange(state_rem_out['Income per Capita'].min(), state_rem_out['Income per Capita'].max())\n",
    "    ys = res_s.params[0] + (res_s.params[1] * xs)\n",
    "    ax.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist_rem_out(state):\n",
    "    \"\"\"\n",
    "    Function to plot histograms of Income per Capita, Doses Administered and Doses per Capita.\n",
    "    Also provides mean and correlation information\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    state: state to be plotted\n",
    "    \n",
    "    \"\"\"\n",
    "    state_df = dataset_dict[state]\n",
    "    state_rem_out = remove_outlier(state_df)\n",
    "    f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16,5))\n",
    "    print(\"Income per Capita Centered Around: \" + str(state_rem_out[\"Income per Capita\"].mean()) + \" thousand dollars\")\n",
    "    print(\"Doses Administered Centered Around: \" + str(state_rem_out[\"Doses Administered\"].mean()) + \" Doses\")\n",
    "    print(\"Doses per Capita Centered Around: \" + str(state_rem_out[\"Doses per Capita\"].mean()) + \" Doses\")\n",
    "    print(\"Correlation between Income per Capita and Doses per Capita: \" + str(state_rem_out[\"Income per Capita\"].corr(state_rem_out[\"Doses per Capita\"])))\n",
    "    ax1.hist(state_rem_out[\"Income per Capita\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax1.set_title(\"Income per Capita \" + state_name[state] + \" [Outliers Removed]\")\n",
    "    ax1.set_xlabel(\"Income per Capita\")\n",
    "    ax2.hist(state_rem_out[\"Doses Administered\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax2.set_title(\"Doses Administered \" + state_name[state] + \" [Outliers Removed]\")\n",
    "    ax2.set_xlabel(\"Doses Administered \")\n",
    "    ax3.hist(state_rem_out[\"Doses per Capita\"], num_bins, facecolor=color_dict[state], alpha=0.7)\n",
    "    ax3.set_title(\"Doses per Capita \" + state_name[state] + \" [Outliers Removed]\")\n",
    "    ax3.set_xlabel(\"Doses per Capita\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explotatory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore our final dataset we  first plotted histograms of our variables to understand distribution of the data. We did this on the entire dataset as well as on a state by state basis. We also printed out the means of each variable and an intial correlation statstic describing the relationship between Income per Capita and Doses per Capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 46.248996195145104 thousand dollars\n",
      "Doses Administered Centered Around: 27943.81561822126 Doses\n",
      "Doses per Capita Centered Around: 0.12887087815254822 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: -0.04498844716303439\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the dataset as a whole there initially seems to be a slight negative correlation between Income per Capita and Doses per Capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 59.397167427549704 thousand dollars\n",
      "Doses Administered Centered Around: 129513.46551724138 Doses\n",
      "Doses per Capita Centered Around: 0.19283967172250172 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: 0.37537476134723663\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of california income per capita\n",
    "plot_hist(\"cal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 50.03624334231999 thousand dollars\n",
      "Doses Administered Centered Around: 34393.02564102564 Doses\n",
      "Doses per Capita Centered Around: 0.19924457483459873 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: 0.09950726897588173\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of washington income per capita\n",
    "plot_hist(\"wash\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 39.19665141654348 thousand dollars\n",
      "Doses Administered Centered Around: 78217.13333333333 Doses\n",
      "Doses per Capita Centered Around: 0.20335400287904776 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: 0.4725884223125636\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of arizona income per capita\n",
    "plot_hist(\"ariz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 44.86432920173931 thousand dollars\n",
      "Doses Administered Centered Around: 12749.6091954023 Doses\n",
      "Doses per Capita Centered Around: 0.09136646885231682 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: 0.23111675933468268\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of ohio income per capita\n",
    "plot_hist(\"ohio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 38.17013016538052 thousand dollars\n",
      "Doses Administered Centered Around: 10589.603773584906 Doses\n",
      "Doses per Capita Centered Around: 0.1767464953958304 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: 0.015564717277815545\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of ohio income per capita\n",
    "plot_hist(\"geor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 52.07903822056869 thousand dollars\n",
      "Doses Administered Centered Around: 609.4174757281553 Doses\n",
      "Doses per Capita Centered Around: 0.013129610180543346 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: -0.3116883243578176\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(\"kans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the histograms we can see that Income per Capita and Doses per Capita seem to be mostly normally distributed with some outliers on the collective level and the state level. Doses Administered is hevily skewed towards the left due to lower vaccination numbers in less populus counties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the data on a state by state basis, the datapoints on each state show a slight positive correlation between Income per Capita and Doses per Capita. The only exception being Kansas but that could be explained by the abundance of datapoints that have a Doses per Capita equal to zero as seen in the histogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better visualize the data, We then plotted scatter plots of the variables in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_ipc_vs_da(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plot_scatter_ipc_vs_dpc(\"all\", ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_dpc_vs_da(\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided the best way to answer our research question would be to perform Linear Regression on our dataset to compute the effect of Income per Capita (Our independent variable) on Doses per Capita (Our dependent variable) and to gauge if this effect was significant or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We performed Linear Regression using patsy and statsmodel.api on the data collectivly and then on a state by state basis. We will be using an alpha value of 0.05 to decide if we should reject out null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.002\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.9309\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):              0.335\n",
      "Time:                        00:18:26   Log-Likelihood:                 398.56\n",
      "No. Observations:                 461   AIC:                            -793.1\n",
      "Df Residuals:                     459   BIC:                            -784.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1439      0.016      8.812      0.000       0.112       0.176\n",
      "IPC           -0.0003      0.000     -0.965      0.335      -0.001       0.000\n",
      "==============================================================================\n",
      "Omnibus:                      141.031   Durbin-Watson:                   1.042\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              469.481\n",
      "Skew:                           1.394   Prob(JB):                    1.13e-102\n",
      "Kurtosis:                       7.082   Cond. No.                         166.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.141\n",
      "Model:                            OLS   Adj. R-squared:                  0.126\n",
      "Method:                 Least Squares   F-statistic:                     9.185\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):            0.00369\n",
      "Time:                        00:18:27   Log-Likelihood:                 72.347\n",
      "No. Observations:                  58   AIC:                            -140.7\n",
      "Df Residuals:                      56   BIC:                            -136.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1203      0.026      4.683      0.000       0.069       0.172\n",
      "IPC            0.0012      0.000      3.031      0.004       0.000       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       54.089   Durbin-Watson:                   2.164\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              290.751\n",
      "Skew:                           2.549   Prob(JB):                     7.32e-64\n",
      "Kurtosis:                      12.712   Cond. No.                         176.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"cal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.053\n",
      "Model:                            OLS   Adj. R-squared:                  0.042\n",
      "Method:                 Least Squares   F-statistic:                     4.796\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):             0.0313\n",
      "Time:                        00:18:27   Log-Likelihood:                 253.92\n",
      "No. Observations:                  87   AIC:                            -503.8\n",
      "Df Residuals:                      85   BIC:                            -498.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0740      0.008      9.160      0.000       0.058       0.090\n",
      "IPC            0.0004      0.000      2.190      0.031    3.58e-05       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        0.935   Durbin-Watson:                   2.037\n",
      "Prob(Omnibus):                  0.627   Jarque-Bera (JB):                0.433\n",
      "Skew:                          -0.068   Prob(JB):                        0.805\n",
      "Kurtosis:                       3.318   Cond. No.                         260.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"ohio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                 -0.017\n",
      "Method:                 Least Squares   F-statistic:                    0.3700\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):              0.547\n",
      "Time:                        00:18:28   Log-Likelihood:                 41.387\n",
      "No. Observations:                  39   AIC:                            -78.77\n",
      "Df Residuals:                      37   BIC:                            -75.45\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1597      0.067      2.401      0.022       0.025       0.294\n",
      "IPC            0.0008      0.001      0.608      0.547      -0.002       0.003\n",
      "==============================================================================\n",
      "Omnibus:                        2.814   Durbin-Watson:                   2.065\n",
      "Prob(Omnibus):                  0.245   Jarque-Bera (JB):                2.362\n",
      "Skew:                           0.599   Prob(JB):                        0.307\n",
      "Kurtosis:                       2.867   Cond. No.                         247.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"wash\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shush\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.223\n",
      "Model:                            OLS   Adj. R-squared:                  0.164\n",
      "Method:                 Least Squares   F-statistic:                     3.738\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):             0.0753\n",
      "Time:                        00:18:28   Log-Likelihood:                 18.201\n",
      "No. Observations:                  15   AIC:                            -32.40\n",
      "Df Residuals:                      13   BIC:                            -30.99\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0624      0.139     -0.449      0.661      -0.362       0.238\n",
      "IPC            0.0068      0.004      1.933      0.075      -0.001       0.014\n",
      "==============================================================================\n",
      "Omnibus:                       11.055   Durbin-Watson:                   1.675\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                7.974\n",
      "Skew:                           1.148   Prob(JB):                       0.0186\n",
      "Kurtosis:                       5.737   Cond. No.                         276.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"ariz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.006\n",
      "Method:                 Least Squares   F-statistic:                   0.03804\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):              0.846\n",
      "Time:                        00:18:28   Log-Likelihood:                 135.57\n",
      "No. Observations:                 159   AIC:                            -267.1\n",
      "Df Residuals:                     157   BIC:                            -261.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1696      0.038      4.514      0.000       0.095       0.244\n",
      "IPC            0.0002      0.001      0.195      0.846      -0.002       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       82.078   Durbin-Watson:                   1.939\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              407.800\n",
      "Skew:                           1.883   Prob(JB):                     2.80e-89\n",
      "Kurtosis:                       9.883   Cond. No.                         179.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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rL/bee29uvNGCxN177728613v4rTTTmPvvfcG4OKLL2a33Xbj6KOPZsGCBfzP/1gQuDPOOIObb74ZMKUwb9489tprL84++2xKZUn3HoEzOmlsgBk79G4R7jh1UC1CZwQRHyOCzPfqdQN+B55++mkOOOCAnPuuvvpqJkyYwJIlS+js7OTQQw/lmGOO4ZFHHuGxxx7j8ccfZ/Xq1cybN4/DDrNQ3Q8//DBPPfUUc+bMoaWlhVtuuYVHH32Unp4eDjjgAA488MBtznPeeedx4YUXAvCRj3yE3//+97zvfe8b0PXEcUXgjF4aG7zir1Y6uqwnEKfEY0Tnnnsu999/P3V1dcyaNYsnnnhia8t9w4YNvPDCC9x///0sWLCAZDLJ9ttvz+GHH86SJUsYP3488+fP3zrX//777+fEE09k7NixAHkr93vuuYdLL72U9vZ21q5dy5577umKwHEcJydlGCPac889ueWWW7ZuX3755axevZrm5mZmzpzJZZddxrHHHtvrmMWLF+ctb9y4cVt/F2Pi6ejo4JOf/CQtLS3svPPOLFy4sGSrrH2MwHGc0UcZxoiOOOIIOjo6+OEPf7g1rb3dxhyOPfZYfvjDH9Ld3Q3A3//+dzZv3sxhhx3GjTfeSCqVYtWqVdx3333Mnz9/m7Lf/va387vf/Y6Ojg7a2tr4wx/+sE2eqNKfMmUKbW1tW3sfpcB7BI7jjD7KMEYkItx666189rOf5dJLL2Xq1KmMGzeOSy65hFNOOYUVK1ZwwAEHoKpMnTqVW2+9lfe///08+OCD7LvvvogIl156KTvssAPPPfdcr7LnzZvHCSecwL777susWbNobm5mwoQJvfJMnDiRs846i7333pvZs2czb968AV/LNtc20tZvNTc3qwemcZzq49lnn2X33XcfbjHKRltbG42NjbS3t3PYYYdxxRVX5B2c7otc90pElqpqc6783iNwHMepAM4++2yeeeYZOjo6OP300wesBAaCKwLHcZwKIFqkNhz4YLHjOE6V44rAcRynynFF4DiOU+W4InAcx6lyXBE4juMUiYjwkY98ZOt2T08PU6dO5b3vfW+/ypk9ezarV68edJ5S4YrAcRynSMaNG8dTTz3Fli1bALjjjjuYPn36MEs1eFwROI4zIhGRAX1yefXsD8cff/xWFxDXX389CxYs2Lpv7dq1nHTSSeyzzz4cfPDBPPHEEwCsWbOGY445hv33359PfOITvXwLXXvttcyfP5/99tuPT3ziE6RSqUHJNxDKqghE5DgReV5ElonIBTn2f1tEHgufv4vI+nLK4ziOM1hOPfVUbrjhBjo6OnjiiSc46KCDtu776le/yv77788TTzzBf/3Xf/HRj34UgIsuuoi3v/3tPProo5xwwgm8/PLLgK0AvvHGG3nggQd47LHHSCaTXHfddUN+TWVbUCYiSeBy4GigFVgiIrep6jNRHlX9bCz/p4D9yyWP4zhOKdhnn31YsWIF119/Pe9+97t77bv//vu3eig94ogjWLNmDRs2bOC+++7j17/+NQDvec97mDTJnN/dddddLF26dKvfoC1btjBt2rQhvBqjnCuL5wPLVHU5gIjcAJwIPJMn/wLgq2WUx3EcpySccMIJfOELX+Dee+9lzZo1W9Nz+W4TkV7fcVSV008/nW984xvlE7YIymkamg68EttuDWnbICKzgDnA3Xn2ny0iLSLSsmrVqpIL6jjOyENVB/RZunTpoM995plncuGFF24NMxlx2GGHbTXt3HvvvUyZMoXx48f3Sv/jH//IunXrADjyyCO5+eabefPNNwEbY3jppZcGLV9/KWePYFv1B/lcnZ4K3KyqOUdJVPUK4Aow76OlEc9xHGdgzJgxg09/+tPbpC9cuJCPfexj7LPPPjQ0NPDzn/8csLGDBQsWcMABB3D44Yczc+ZMAPbYYw++9rWvccwxx5BOp6mtreXyyy9n1qxZQ3o9ZXNDLSKHAAtV9diw/WUAVd2mDyQijwLnqupf+yrX3VA7TnUy2t1Ql5L+uqEup2loCTBXROaISB3W6r8tO5OIvA2YBDxYRlkcx3GcPJRNEahqD3AecDvwLHCTqj4tIotE5IRY1gXADTrSIuQ4juOMEsoaj0BVFwOLs9IuzNpeWE4ZHMcZPahqztk3ToaBtKl9ZbHjOCOC+vp61qxZM6CKrlpQVdasWUN9fX2/jvMIZY7jjAhmzJhBa2srPoW8MPX19cyYMaNfx7gicBxnRFBbW8ucOXOGW4xRiZuGHMdxqhxXBI7jOFWOKwLHcZwqxxWB4zhOleOKwHEcp8pxReA4jlPluCJwHMepclwROI7jVDmuCBzHcaocVwSO4zhVjisCx3GcKscVgeM4TpXjisBxHKfKcUXgOI5T5ZRVEYjIcSLyvIgsE5EL8uT5oIg8IyJPi8gvyymP4ziOsy1li0cgIkngcuBooBVYIiK3qeozsTxzgS8Dh6rqOhGZVi55HMdxnNyUs0cwH1imqstVtQu4ATgxK89ZwOWqug5AVd8sozyO4zhODsqpCKYDr8S2W0NanF2BXUXkARF5SESOy1WQiJwtIi0i0uJh6hzHcUpLORWB5EjLjjpdA8wF3gksAK4SkYnbHKR6hao2q2rz1KlTSy6o4zhONVNORdAK7BzbngG8miPPb1W1W1VfBJ7HFIPjOI4zRJRTESwB5orIHBGpA04FbsvKcyvwLgARmYKZipaXUSbHcRwni7IpAlXtAc4DbgeeBW5S1adFZJGInBCy3Q6sEZFngHuAL6rqmnLJ5DiO42yLqGab7Sub5uZmbWlpGW4xHMdxRhQislRVm3Pt85XFjuM4VY4rAsdxnCrHFYHjOE6V44rAcRynynFF4DiOU+W4InAcx6lyivI+KiJ7AXsA9VGaqv6iXEI5juM4Q0efikBEvor5AtoDWAwcD9wPuCJwHMcZBRTTIzgZ2Bd4VFU/JiLbA1eVVyxn0LS1w+p10NEF9XUwZRI0Ngy3VI7jVCDFjBFsUdU00CMi44E3gV3KK5YzKNraofV16O6BMbX23fq6pTuO42RRTI+gJbiGvhJYCrQBD5dVKmdwrF4HySTUhscbfa9e570Cx3G2oU9FoKqfDD9/JCJ/Asar6hPlFcsZFB1d1hOIU5O0dMdxnCz6NA2JyF3Rb1VdoapPxNOcCqS+DnpSvdN6UpbuOI6TRd4egYjUAw3AFBGZRCbi2HhgpyGQzRkoUybZmABYT6AnBakU7OjR3RzH2ZZCpqFPAJ/BKv1HYukbgcvLKZQzSBobYMYOvWcN7TjVxwccx8lJXkWgqt8Fvisin1LVy4ZQJqcUNDZ4xe84TlEUMg0doap3AytF5APZ+1X112WVzHEcxxkSCg0WHx6+35fj895iCheR40TkeRFZJiIX5Nh/hoisEpHHwudf+ym/4ziOM0gKmYa+Gr4/NpCCRSSJjSUcDbQCS0TkNlV9Jivrjap63kDO4TiO4wyeYqaPThaR74nIIyKyVES+KyKTiyh7PrBMVZerahdwA3DiYAV2HMdxSksxLiZuAFYB/4z5HVoF3FjEcdOBV2LbrSEtm38WkSdE5GYR2TlXQSJytoi0iEjLqlWriji14ziOUyzFKILtVPViVX0xfL4GTCziOMmRplnbvwNmq+o+wJ3Az3MVpKpXqGqzqjZPnepz4R3HcUpJMYrgHhE5VUQS4fNB4A9FHNcKxFv4M4BX4xlUdY2qdobNK4EDixHacRzHKR3FKIJPAL8EusLnBuBzIrJJRDYWOG4JMFdE5ohIHXAqcFs8g4jsGNs8AXi2P8I7juM4g6cYp3NNAylYVXtE5DzgdiAJ/ERVnxaRRUCLqt4GnC8iJwA9wFrgjIGcy3Ecxxk4opptts+RyXwNzaV3qMr7yihXXpqbm7WlpWU4Tj068IA1jlOViMhSVW3Ota+YUJX/Cnwas/E/BhwMPAgcUUohnSEgCliTTPYOWDNjB1cGjlPFFDNG8GlgHvCSqr4L2B+bQuqMNOIBa0TsO5m0dMdxqpZiFEGHqnYAiMgYVX0OeFt5xXLKQkeXuaWO4wFrHKfqKSZUZWsIVXkrcIeIrCNrGqgzQqivM3NQbeyxe8Aax6l6ipk19P7wc6GI3ANMAP5UVqmc8jDaAtb4wLfjlIS8piERmScix8fTVPX/ws+9yyqVUx6igDW1NdDZbd8jdaA4Gvju7uk98N3WPtySOc6Io1CP4L/JPa//GeAKfNbQyGS0BKyJD3xD5nv1utFxfY4zhBQaLJ6sqiuyE1V1GVCM91HHKR8+8O04JaOQIhhbYN+4UgviOP2ivs7GOOL4wLfjDIhCiuBOEfm6iPTyIioiFwF3l1csx+mDKZNsoLu7B1TtO5WydMdx+kWhMYLPA1cBy0TksZC2L9ACeEhJZ3iJBr7js4Z2nOrjA44zAAqFqtwMLBCRXYA9Q/LTqrp8SCRznL4YLQPfjjPMFLOOYDnglb/jOM4opRgXE47jOM4oxhWB4zhOlVNQEYTQlE8NlTCO4zjO0FNQEahqGnhcRGYOkTyO4zjOEFOMaWhH4GkRuUtEbos+xRQuIseJyPMiskxELiiQ72QRURHJGT3HcRzHKR/FuKG+aCAFi0gSuBw4GmgFlojIbar6TFa+JuB84G8DOY9TJtyzp+NUDX32CILH0RVAbfi9BHikiLLnA8tUdbmqdgE3ACfmyHcxcCnQUazQTplxz56OU1X0qQhE5CzgZuDHIWk6FqSmL6YDr8S2W0NavOz9gZ1V9fd9yHC2iLSISMuqVR4ls+x4SEvHqSqKMQ2di7Xu/wagqi+IyLQijpMcabp1p0gC+Da5XV33Pkj1Csz1Nc3NzdpHdmewbNocgtakzaPn2DFQV5vbs6ebkBxnxFPMYHFnMO0AICI1xCr0ArQCO8e2Z9A7xGUTsBdwr4isAA4GbvMB42Gmrd2C1vSkIJmAdBo2tUN7x7aePd2E5DijgmIUwf+JyP8DxorI0cCvgN8VcdwSYK6IzBGROuBUYOtsI1XdoKpTVHW2qs4GHgJOUNWWfl+FUzpWr4OGeuvPpdVMQ6qwpWNbz55uQnKcUUExiuACYBXwJPAJYDHwlb4OUtUe4DzgduBZ4CZVfVpEFonICQMX2SkrHV1mChrfaD2CyDxUP2Zbk48Hh3GcUUExTufSIvJzbIxAgedVtSg7vaouxhRHPO3CPHnfWUyZTpmprzMTT12tfcC2a3O8KlHe+D4PDuM4I45iZg29B/gH8D3g+1h8guMLH+WMWPoT8MWDwzjOqKCYWUP/C7wrxCpGRN4C/AH4YzkFc4aJ/gR88eAwjjMqKEYRvBkpgcBy4M0yyeNUAv0J+OLBYRxnxFOMInhaRBYDN2FjBKdg7iI+AKCqvy6jfI7jOE6ZKUYR1ANvAIeH7VXAdsD7MMXgisBxHGcEU8ysoY8NhSCO4zjO8FBMj8BxCuNuJhxnROOKoFopVeUduZlIJnu7mZixgysDxxkheMziaqSUPoLczYTjjHiKWVD2aREZL8bVIvKIiBwzFMI5ZaKUlbe7mXCcEU8xPYIzVXUjcAwwFfgY8M2ySuWUl1JW3vV15lYijruZcJwRRTGKIIor8G7gp6r6OLljDTgjhXjl3dUN6zfBqnXQ0dl/85C7mXCcEU8ximCpiPwZUwS3hxjD6fKK5ZSVqPJu74ANm6zyjkxE/R0riNxM1NZYHIPamt4DxW3tsGIlPPeifXusAsepOIqZNfRxYD9guaq2i8hkzDzkjFSiynvZy7YksK7GXE+PCd5EV68rfsZPodlHwz2jyKe1Ok5RFNMjUGAP4PywPQ5bbeyMZBobLMbA1EkwscmUAPRvrKCv2UfRoLQqbGizT3sHvDoErqo8eprjFE0xPYIfYKagI4BFwCbgFmBeGeVyhoJi4wnka1mvXmeBazq6MmMO6bSZgaZMtNjHdbX2LYlMoJt1G63McrbO4zOjIPPdn96O41QJxfQIDlLVc4EOAFVdB/iUkNFAMQO9hVrW6zbYGMPmLdDZCVs6bfC5p8fydXZbLyBSAiKQEOt1lHudgU9rdZyiKUYRdItIkhCwXkSmUuRgsYgcJyLPi8gyEbkgx/5zRORJEXlMRO4XkT36Jb0zOPoa6IX8aw5efRO2dFkLP2EVncEAACAASURBVK3Qk7beQBS8rrbGYh93dgNq6emQd9zY8lfIPq3VcYqmGNPQ94DfANNE5OvAyRQRszgoj8uBo4FWzHX1bar6TCzbL1X1RyH/CcC3gOP6dwnOoOgrnkBHl/UE4tQkYc2GWID72L50LIrp2DGZY6PYx+PGQCKRO/RlKZkyyXoukbw9Kevt7Di1vOd1nBFIMd5HrxORpcCR2F/+JFV9toiy5wPLVHU5gIjcAJwIbFUEYaFaxDhCr8OpIPKNIwAgZvIBSOV4dD0pG4hOp60XMZQVskdPc5yi6VMRhNCUL6rq5SLyTuBoEXlNVdf3ceh04JXYditwUI7yzwU+h407HFGs4E6J6GuKZb6WdVMDrN1gNn8RayKkYhbDaLxhxg62PRwVskdPc5yiKGaM4BYgJSJvBa4C5gC/LOK4XKuPt2k2qurlqvoW4EvkMTmJyNki0iIiLatWrSri1E5RFDPFMmpZq5o5aEObVfyTxlsvIZHAHnUYCK6rg5qa3uMNjQ0wezrsNse+vXJ2nIqiGEWQVtUe4APAd1X1s8CORRzXCuwc254BvFog/w3ASbl2qOoVqtqsqs1Tp7qNt2T0x/lcOg0TGmHyBKv8126AqdtZpZ9M2MDwdhNhYqNX+I4zwihmxK5bRBYAH8XCUwLUFsgfsQSYKyJzgJXAqcBp8QwiMldVXwib7wFewBk68g0EZ8/oyTcnX9Uq/WzTEpg7iUpY0eurix2nT4pRBB8DzgG+rqovhor92r4OUtUeETkPuB1IAj9R1adFZBHQoqq3AeeJyFFAN7AOOH2gF+IMgPo6WwPQ3WO2/5pQ2Y8b2ztfIYWRbYcfbrcScSpJFsepYES174k6IlIH7Bo2n1fV7rJKVYDm5mZtaWkZrtOPLt5cA8tXmmknWvWbSsMu02Ha5Ey+FSu3nTkUbc+e3rvM/uQtN5Uki+MMMyKyVFWbc+0rZtbQO4GfAyuwUcGdReR0Vb2vlEI6/WSgJo/4cR2dNtdf1XoEtTUwrtb8AcXpz5z8Ys1NQ0ElyeI4FUwxpqH/BY5R1ecBRGRX4HrgwHIK5hQgMnmkFTq7YGMbrF4PM3fo3ZLPd1xkKtkYZgCNH5dxOqe6bUXZnzn5xfovGgoqSRbHqWCKUQS1kRIAUNW/i0gxg8VOuVi9zpRA+xbz41NbYxXcy69Bw9j8PYPsQd+6YDff0plRBPkqymLn5FfSit5KksVxKphiFEGLiFwNXBO2PwQsLZ9ITp90dFlPIHLmBlbRFYol0NZuvQbVjB+ghnpzGtfVnTEPRRXlQE1PlbSit5JkcZwKphhF8G/AuVg8AgHuw1xTO8NFfZ2ZdeImj6iCz2X/jkxCgpmCUmk7fnwjjGswRdDZbeVOaISVb8C6TRawZtzY/s+2qaQVvZUki+NUKMX4GuoUkWuAa1TVl/VWAlMmWes+mvKpaqaisXW5zTqRSaixATa12zJCFesNSMLGC+rrrIewdoNNKa1N2jrwTZtNYUQLzbxSdZxRR96VxWIsFJHVwHPA8yKySkQuHDrxnJw0NtjAsJCJNzx2jJmJcgWNj3zzj6kzH0GJhK0U7uy24xobrJyXXzOFolieZMIURXuHz7ZxnOGkzLG/C7mY+AxwKDBPVSer6naY07hDReSzJZXC6T/TJsNuu8C07WBsvZlw8plu4r75x9SZR9CapCmBcWMz7iWiWUjReENHp223d9iAss+2cZyhZwjCrhYyDX0UOFpVV0cJqrpcRD4M/Bn4dsmkcAbGYGbydPWYX6A4tTX2ko0dY+MGCbHegQAbN1v4ScdxhpYhCLtaSBHUxpVAhKqu8umjI4xo9szKN2BNW4gUFmIHj60PAWTqbDppKm1KoK7Goo6htq9+zLYLzYYD9x3kVBtDsDCykGmo0FncWDwSUbUKvrs7E6t48xZr7W/eYmMCM3cIvYDgUXTqJJg80X4P9xjBEHSRHafiGIKwq4V6BPuKyMYc6QLUl0wCp7TkazFHi9DatgAhgHwqZT2DKNj8rJmWt72jMlfkDkEX2XEqjiFYGJlXEahqsmRncTKU07RRyNtmtAhN1WYECdYDSKuZferHZOSIv3jptPUWunpgUpOdY7gqXfcd5FQjQ7AwsswRxJ1elNstcqEWc7QILZEwZaBYqwLMVUVDrJMXvXivvgnrN1llO7HRjh1ON87F+A7yMQRnNFLmhZHFRChzSkV/IoINhGi9QJyoxTxlklXkCcmMDyimFMBa/NkhKlXDgrVQdhSEvlTy9pcpk2KyayYucrR2wscQHGdAuCIYSgpV1KWg0KBStAgtmchEjlY1hTSuwfLEK/i2dnMzAcGElLZVyanU8Jliop5KbY0thovHRYbyK1rHGaW4aWgoKbdb5EKDSm3tNggcOamL/A6BmYaiwDQRq9fZDCMN+USAMF4weRjXExTqIvsYguMMCO8RDCV9mTYGS74Wc/sWeG45vLkWOrpNCUQVPALdKVtTkIr1Jjq6bNWxhqhlkT+jnhLKW2qGYJqd44xGytojEJHjgO9iMYuvUtVvZu3/HPCvQA+wCjhTVV8quSCVMoA4FG6Rc8UQfnGlVYgSxgfSwTaUStuYgcTyRrOCot7L+EbrSfSkLO/48ba/Uu5pHI8/4DgDomw9AhFJApcDxwN7AAtEZI+sbI8Czaq6D3AzcGnJBam0AcTGBouXu9sc+y535bnyDRsIlliFn42I2dJFMvb0qPciYq6pJzTazKKJTfD8i/DUMvNUGjm+q4RB2b7GEBzHyUk5ewTzgWWquhxARG4ATgSeiTKo6j2x/A8BHy65FKNxEVJbu03t3LjZtpsaYPr2ua9nU3tsXCCy9Wtmf7SmoKamdzyDXL2XCY2V76ba4w84Tr8ppyKYDrwS227FvJfm4+PAH3PtEJGzgbMBZs6c2T8pRtsAYmTq2bIFEkmrxDe0mX+g7SebGSduroFgJumBlGami0ak0pAU258Os4gi81B2pbpipVX4PcG8FJW1cTNMnjBy76njVDnlVAS5DBGaI43g0bQZODzXflW9ArgCoLm5OWcZeRltAcxXrzNfQcma3i39LV0WT2BCU28T2NgxtpBM6T2QGs0YUjUFUVsLqR7owHyez9zBXF3H6egCNDOoLME7aUenKaBxY8t77U7Vo6qk02lSqRSpVKrX78bGRmprt/WHuWnTJlauXLk1X65jc33S6TS77747b33rW7cpc+PGjdx00039Kiv6vc8++/ChD30o5/Wdc845bN68eZtjr7nmGsaNG1fy+xlRTkXQCuwc254BvJqdSUSOAv4DOFxVO0suxWgbQOzoslZ8fD2ChBZ9TQ4TWHcPICBZ+rO2JvSMOq11n0qZl9HoHr38GjSM7d0jqK+DNevNjBRXKgmBLR0wa6eyXPJIIV8FlatSmDBhAhMmTNimjI6ODp588smiK6z4vp133pl3vOMdOWX7wQ9+QHt7e58VVnbZkydP5uKLL85Z5le/+lUef/zxoirY7LQHH3yQ8ePHb1PmokWL+N73vpdXJs3u0ca48847OfLII7dJX7x4Maeeemre4wpxySWX8O///u/bpK9atYqzzjprQGWecsopeRXB9ddfz8aN27p46+zsHLGKYAkwV0TmACuBU4HT4hlEZH/gx8BxqvpmWaQYbQHM6+tsOmhazaQDwWVEGmrH2HZnlwWS6UlZRZ9M2GzVVA/pVJoUSqqnk1RtDalNm0l1d5NKCKlEglR3F6nuFKlUD6k3XyM1c0eaEjXMagh+hqJFcXU10NXDfY+00ClpUskkqTdeKlhxZbfe8v0Zrr32Wl544YWiKsPs9CuvvDLnH+aqq67i+uuv73eLMJVKcd111+WsYG+55RZOO+20rfn7wze+8Q0uuOCCbdJXrlzJ/Pnz+1VWxMknn5xXEfzHf/wH69ev73eZs2fPzqsIHnjgAe66665+lwnQ3d2dM729vZ01a9YMqMx8zyCZHLjbtHxlJhIDn2dT6F3JJ2t/36/+UjZFoKo9InIecDs2ffQnqvq0iCwCWlT1NuC/gUbgV2KmipdV9YSSCxNs3X/96195/fXX81YEhSqG888/n7FjtzV9LF68mDvvvLPPCipX2YsWLWL//fffpsy7776bCy64IPexPT2kOjrtdzqd2ZdOk9I0//lv5/G5k0/LuJIIawBWvP4au3zwfQO6ff98zHHc/P0fmemnvdNMUwAN9Zz0lS+wbsOGfpc5a9asvIrgF7/4BXfccceAZL3ssstyKoJ//OMf3H333QMqs70992woVaWra2DjIul0Omd6OSqtwZRbjjJhaK+/HJX2UD+nEasIAFR1MbA4K+3C2O+jynn+bBYuXDjgCubMM8/MqQjuv/9+vv3tgQVrO/fcc3Omr1+/niVLlgyozPYNmzILxlKprfGJBzNPOJVOZ0xNE8bZ4HRNEiY0kkxUVgWT2rgJJmeNbbS1k9w08KmtQ1kZVJoiyFdhD6ZMGFylLSIkk8mtn0QiQU1NTV55xo8fz6677trrmPixudKjT67xAYCmpibOPPPMgsfmK3vu3Ll5r+2yyy6jq6trm3JymRFLSVW5mKiOFkyY/ZPAlEGQOymDbBWt32SmppqkTR3d0gVvriM5QFkLVVqDuv5XXoep0zKmv7COZDA+1ftTaRdTESQSCZqamnKWWV9fz4EHHtivyir6HHjggXmv4ZxzzqGtra1fFWEhOcHMTWeddVZRMmbvn5ytrANf+tKXOP/88wuWI5JvQUxujjrqKJ5//vl+HdMXkyZN4uqrry5pmcCAxzIGiyuCIhkprcJ0Om2zhrq6M+sGVEkmEtQka0gmEyQTCZKJJMlkIvxBa0iKWHr0p6upsW2EnbebEjyPJqzczi4zPSVqeMd+B7CubRPJxgaSdXVFVwqTJuVwUxFWK5/2zqOYN/dtJJsaSdSPyV/Wuo0kERK1NVuvZ8LEib3XM4R1JB895YO84+BD7PoVk3XGDr1kzSf3tGnTct7r97znPWzZsqXX8YNl6tSptLS0DLqcbC666KKSl3nooYeWvMzGxkYaGxv7zuiUlKpSBIcccgi1tbXb/uHTSrKzi0QiSbI2VIoiJCc0kayvJ5lKM271BtjYuY07hWOOOYampqZtWz/dKZIb26xCrasloTZQkpw2meS4BpLJZN5BwbfvdwAP3XALydpaOzatduxO25Mc32jneW0VyY2brQKsrSEhCZICDTW1VqHW1po5p6cHBHacNo3uvyyxtIaxVqGPG5sx+XR2ZUw+k8ZnZlf1pGxwOgpi3x31OBJQk+RXl3zbpp1OHA+7zh74w4nFavjQyadkzl9oZfBTLwR/TWEWVUN970VxsHUdyVtmzeYts4J8qrbyeLc5A5eXTOu/rFSiKw9n1CGFpmNVIs3NzVryFtOKlduuNYi2o+mnyWTv6ad9uS4oVObs6YOTZ/U6eGOtVfJ1tZl8qpng82PH2O9UygZ3I9PQpPHWmu9JmSKIutld3aYIOrssWtn4cbDTNGh9w87T1m7B7FPB51AymQlmE00lbd6z73tdqvvV1m6O9BR7Lukwc6phrMkVHTOY5zDcxAMZ9efdc5wciMhSVW3Ota+qegR5KbT6eKAuKopd0ZyrxZfv2LZ2c+nQ1Z2JN7yl0yrzmmSIL4BVhNmt/bZ2qzQb6jPKJPKCunGzTTNNiCmBaBFaZAbq6oa6Ohgjdv506N5EZHmtGBD9XQG+eh2MrbfrVw3KDVvYNnPHTL6RvI5kNLpHcSoSVwRQePXxQF1UFBtWMVfoykTC8mYfG0UJi+bxb0lbK3jzlowiGFcPO001n0CRrImEKYZ4S7J9C7z4qvUW0kGBpAW6wjlEMmaXnjQkU5CoybiY0OCaOuqFTBiEXbet3RTRxjbr4YwdY4vbCq0A7+gypVaTzKyZqAkt53glOZLXkYw29yhOxeKKAAq3GqOWc39dVBTTEs3X4kunt8726XVsKpWp2BMJGKNWCUbU1UJNbTCPjM1f+bW1ZzyHililjkBdMmNOqg+mpUQCxtSYPKm0VdDaaW4pNnfYorb6Meb0rr+0tZt31HWbrEWfDjEaunugIWWD0/la7pGiHVNnH9j2OUWU2hFdue32Uflt7bAZKzu6xpHsHsWpWDwwDRR2XzzQYDLFuETOF7pSyX1s1BqPfPt0hiAztTUwfRpM2y4TcrKQu+tIASUSmQFWNGNyipzKCTY+0NmduW6w8YaapJ1LBDa328DtY8/Bm0WuCo16Q23tNh01GRzoJSRznwvZwssd5Kcvucvl1jxeflODXdOGNnvmQ3WNTtXhPYKIfK3GwZgW+mqJFjIf5Tq2vs4iiSUSVgFHvYZkTJ8XYzqITA41Sav80+mYjV/t9+YtYYpoIqMUunvMlKTYoLOqVVDEWvPLV1ox2Q7rsomUkQb5RYDgSG+7CaZ8Ct274TL5ZPfiVE0pP/ciTJk4+N5BdvkTx4eAQVus/JFi1nJGFK4IiqFcPu7zmY8mNNpsl2zTQ2T6IR2mcIYZP/EpjMWYDurrrPJKpTIVeSJ4Eo0Htic4sxPJKJu0mhxRzwQFlUwEM4BXV/WtCOLKKBqTiGYzxa+hkBlmOGIPxO32Xd02rhE52o16B4OZ1ZM9LlBXa0q3s7vyZzk5IxZXBMNJoeAvkalk7QZ4fQ1MarKKZrumzODo1nn9YeA234yY7MpUxGYKJRNbXVCQxrbrwv4tnZneQDIMNmsYkxBMkXT3hBNoxqSTDi3kvoh6Q2PHWPCcuHKLBsWf/LtVgA31lq8UFe1gSYj1ytJqSjKRgJpwH0sxq2e0uU13RgSuCIabfMFfVG2qpiTMht7WHqZJJixcZER7R5jm2Z3bPJJrZtKGTVaxRrb1yDTT3ZNRMDVJG6CUrkyMYw1KobMn4/k0IiGZgedkEUNPUybBi62ZcYmuYOaa0GhKIJo5pWoznGqS1jqG4Zs+2dZu02l7wkB2TwoIMkbPZLCzekbydFdnxOKKoNKITAMb2kwJJBOZKZpj663ij2IJROaYt87MXzHmmpmUVqtsJ403hRBFOEsrVrERWvcpi4KGZlr/0ZqFuhpIdWSURFqB0DOpH1PkxQblEbWma4OpKBEWyUUrhqNeRl3t8E6fXL3Orq2uNtNjUg1KtkSzekbydFdnxOKKoNKITA/tFkeA2hpLi1woRJ5Ai60koqhim7dk5tpHrX+wCiyRyLiQiCr2pFhCOkwZrQ0DuZ3d1mofUwfJTVZ+d3emt9DUYDGM+2L1OpO/KSZ7d48ppcnB02I0fhCNHcDwmkkiJS01dv0N9da76ss011887rIzxLgiqCTipoconkC0gGxik6U3jevfoKEAGzZbGckwHpBKZZRBTdK+hYxCiGz9CbEZPLlcNoD1JiIzSbSQq9jpjfkWS0FmMV00fqDpjJzDaSbJtt/X1cK4hsKmOccZAbgiqCTipocoGhhkFpANpBKU4P8havErVsGPa8j0LKIxgajyFzJ2+oTA6vWZHkhDfWbVcrQKuG1LpjXfVGQlmG9QdPy4zHqFqPwtwRymatfT+sbwOGDLZb/vyzTnOCMAVwSVRLbpoavbTDpdocLMXh1czOrWtFpA+7gbhsYmUwhRK/+pF6yy1TDwCZmppBtDbyIaaF67wXoJ7R127toaaBhjCiyqHIuZ2ZNvUHTGDpYWXdu4sZlYyHEHbMMxg2g02O/dm6mTg7IqAhE5Dvgu5qLsKlX9Ztb+w4DvAPsAp6rqzeWUp+LJZXoQ2dZTZj4fRbkqxajM+Eyj7JZ4Y4O1/rd0ZAZBa5PmYwi1/ZEcYEFqohk80eB1fxyjRZVRd09mdXVjQ+9KNfvYaDbVcDtgG8n2+/68N05VUTZFICJJ4HLgaKAVWCIit6nqM7FsLwNnAF8olxwVRV+tsWKnDvbHK2UxZUZ5xjfa8VEvJJmw3kRdzJafTpsi2G6CVSYb2zJjDdHMmUIze+KVUWNDRp6+WqalcsDWnxbxaGs9uzdTJw/l9DU0H1imqstVtQu4ATgxnkFVV6jqE9hyptFNMT5qivFPBPl9FHUEd9MrVprLgxXB3UNfZcbPq1glv9dbYfvJ1juIE3k6ra2xXkKkJOLO7wrN7IlXRlEvI5m09ELU12VmDhVznlz0x09QuX0KDQeF3hunqimnaWg68EpsuxU4aCAFicjZwNkAM2fOHLxkw0ExrbFiW6D5BlqF/F3/vmYa5TN5tL5uYxVdwfFcZzdMjE0PjaZQdnUXN4VyoC37Uiy06k+LeDS2nn3VspOHcvYIckWYHlD4ElW9QlWbVbV56tQRusKyr9ZYf1qg+TxvihTf2s7uOeQ6T2OD9Q62dGYqkDFhMVVnkDuaQllXW7gXEzHQln2xvaVC9KdFPBpbz8PlsdWpeMrZI2gFdo5tzwBeLeP5Kpu+WmP9aYHmm73S+obFDoiTLypavp5DdM5NmzNhL2uSMD6MFXR1w/qNGZcX0UK1SeMttGVfFXN2y769wwap68eYQipkhx/IQG28l9XRaWMcUYhNyK+ERmPreTTMenLKQjkVwRJgrojMAVYCpwKnlfF8lU1fpo3+mkzilWIU4GXtBqucx4QVu/EoX9kVYl0t1GcpnZVvZNxZROft6rbvjW02mFxXawPIG9pgfZu5mpjYFOb3FzEDJV4ZbdocnMqNLY9TuWyFl0rZdFjILIbLZ14arT5/SjXrabQNpFc5ZTMNqWoPcB5wO/AscJOqPi0ii0TkBAARmScircApwI9F5OlyyTPs9GXaiFxLrF5vs3K6uotrgba1m/O2DW1mjEulzEnbm+usnFTKWsBxs1NXtwWTiSp5sMpuU7tVmpEjusjE1NllMq/dYMckEqYQJk8Is4fqih/0je7F7Om2SnpCo8nXn4HjYskemB431hRktBK4kHmpFKao0cpoHEivcsq6jkBVFwOLs9IujP1egpmMqoN8rbFcXi3Xb4SxY/tuga5el6nQIwdokWuK9i0Wv7i9o7fZqS78eSNHbpCx20et32QiU45G0cpickVuJeL014Ze7pi8ucpvqLeKfbc5fR8/ktcMlJPROJBe5fjK4kog26tlT8r+aHU1ff+xOrpsCD4VC+4SxRioqcmsAI5XiJEZJnumz/hxmdXH6XSm7Kj8RCIj15gS2NDLbYcvNLsqV+AfpzjKrcCdIcdjFlcC0QyVMXVmb58y0Uwuxcyxqq/L+AaK0OAzKO6lND5TJ/KcmT3TZ6dpphBqazKmqciraCL0AMaNNblKMQOl3LNYcpXf0WmL5fKZNYqZTVXNtLXbPVy1LmPChJE/kF7leI+gEhhMy3jKJBt07ei0ZXlCCA5TaxV+1OLNHvhMJmBWDmdp0UBuW3sm2ExN0noXCbFZQpMn9n8GSr7BxXgZkQfUUjmVyyVj5Lgul1kD3AVDIaKxgdqaEL86BDka12DvxkgfSK9iXBFUAoOZodLYAHNmZAaMIbT4x9qfM1eFW6jSjtJWr4c6TI5ECJCTSvdusRdrQ+/Lx01jQyZPImFTYEtVCWfL+NyL+afYuu27MNH9iRwMbunMLDZ0D6wjGlcElcBg53c3NsDeu5Ym0HtUIUeuqROJjGmltgbGj+//H76YCnaoKuFCvS+3fRcmfn/G1NlH1cyLrgRGNK4IKoVSzFApRRlRhdzYEKaTJkBqrXfRUG/jCP2lmAp2qCrhQr2vyCPqaFpEVkpG4yI7B3BF4GQTj4kA1v1PpyyuQTFmmly9kmIqkKGqZPrqfY3GRWSlYrQusnNcEVQ8Q72CM14hR93/aLsYJZBrLGC7CZmoZn25wi6Up1Tk6zm5C4bC+P0ZtbgiGG4KVfTDEUhkMBVyPjt/e8e2FciERtuOzxCqhErGF5EVxu/PqMQVwXDSV0U/HLNYBtPqK2Tnz/aNNFB32Y7jlBxXBMNJXxV9OQZQizE1ZVfa2S33Qi6mi7Hz+zRNx6kofGVxqRjIitS+fN6XIipXtoz9cRbW3/zFrhQejb7+HWcE44qgFAzUG2NfFX2pXTD0N0xkf/MX67Gz1ArOcZxB4aahUjBQU0dfA7OlnqXRX1PTQExTxQwm+jREx6koXBGUgoHa8oup6Es5S6O/c/XLNbffpyE6TkXhiqAUDKbCHMrpeP1tiZez5e7TEB2nYvAxglIwUoKC9zfqlkfpcpyqoKw9AhE5DvgukASuUtVvZu0fA/wCOBBYA/yLqq4op0xlYSSZOvrbEveWu+OMesqmCEQkCVwOHA20AktE5DZVfSaW7ePAOlV9q4icClwC/Eu5ZCorXmE6jjNCKadpaD6wTFWXq2oXcANwYlaeE4Gfh983A0eKiJRRJsdxHCeLciqC6cArse3WkJYzj6r2ABuAydkFicjZItIiIi2rVq0qk7iO4zjVSTkVQa6WfXYU3mLyoKpXqGqzqjZPnepzzR3HcUpJORVBK7BzbHsG8Gq+PCJSA0wA1pZRJsdxHCeLciqCJcBcEZkjInXAqcBtWXluA04Pv08G7lbVbXoEjuM4TvmQcta7IvJu4DvY9NGfqOrXRWQR0KKqt4lIPXANsD/WEzhVVZf3UeYq4KUSiDcFWF2CcoaCkSQruLzlxuUtL6NV3lmqmtO2XlZFUMmISIuqNg+3HMUwkmQFl7fcuLzlpRrl9ZXFjuM4VY4rAsdxnCqnmhXBFcMtQD8YSbKCy1tuXN7yUnXyVu0YgeM4jmNUc4/AcRzHwRWB4zhO1TPqFYGI7Cwi94jIsyLytIh8OqRvJyJ3iMgL4bsiggeISL2IPCwijwd5Lwrpc0Tkb0HeG8MivYpBRJIi8qiI/D5sV6y8IrJCRJ4UkcdEpCWkVeT7ACAiE0XkZhF5LrzHh1SqvCLytnBfo89GEflMBcv72fA/e0pErg//v0p+dz8dZH1aRD4T0gZ9b0e9IgB6gM+r6u7AwcC5IrIHcAFwl6rOBe4K25VAJ3CEqu4L7AccJyIHYy66vx3kXYe58K4kPg08G9uudHnfpar7xeZfV+r7+Bip0AAACXBJREFUABbT40+quhuwL3afK1JeVX0+3Nf9sDgj7cBvqEB5RWQ6cD7QrKp7YQtfI3f4FffuishewFmYZ+d9gfeKyFxKcW9Vtao+wG+xGAnPAzuGtB2B54dbthyyNgCPAAdhKwdrQvohwO3DLV9MzhnhBTwC+D3mTLCS5V0BTMlKq8j3ARgPvEiY2FHp8mbJeAzwQKXKS8b78XZYbJbfA8dW6rsLnIIF+Iq2/xP491Lc22roEWxFRGZj7iz+Bmyvqq8BhO9pwydZb4KZ5THgTeAO4B/AejVX3ZDbpfdw8h3shUyH7clUtrwK/FlElorI2SGtUt+HXYBVwE+D6e0qERlH5cob51Tg+vC74uRV1ZXA/wAvA69hbvCXUrnv7lPAYSIyWUQagHdjTjsHfW+rRhGISCNwC/AZVd043PIUQlVTal3rGVg3cPdc2YZWqtyIyHuBN1V1aTw5R9aKkDdwqKoeAByPmQoPG26BClADHAD8UFX3BzZTAWaVvgh29ROAXw23LPkItvQTgTnATsA47J3IpiLeXVV9FjNb3QH8CXgcM30PmqpQBCJSiymB61T11yH5DRHZMezfEWt9VxSquh64FxvbmBhcdUNul97DxaHACSKyAotCdwTWQ6hUeVHVV8P3m5j9ej6V+z60Aq2q+rewfTOmGCpV3ojjgUdU9Y2wXYnyHgW8qKqrVLUb+DXwT1T2u3u1qh6gqodhjjpfoAT3dtQrAhER4GrgWVX9VmxX3AX26djYwbAjIlNFZGL4PRZ7WZ8F7sFcdUMFyauqX1bVGao6GzMF3K2qH6JC5RWRcSLSFP3G7NhPUaHvg6q+DrwiIm8LSUcCz1Ch8sZYQMYsBJUp78vAwSLSEOqJ6N5W5LsLICLTwvdM4APYPR78vR3uAZAhGGB5O9a1ewJ4LHzejdmx78I06l3AdsMta5B3H+DRIO9TwIUhfRfgYWAZ1t0eM9yy5pD9ncDvK1neINfj4fM08B8hvSLfhyDbfkBLeCduBSZVuLwNwBpgQiytIuUFLgKeC/+1a4AxlfruBnn/gimrx4EjS3Vv3cWE4zhOlTPqTUOO4zhOYVwROI7jVDmuCBzHcaocVwSO4zhVjisCx3GcKscVQZUjIm3DLcNQISK1IvLN4KXxqeDlNddK0mLKOkdEPhp+nyEiOw2gjM/EylgkIkcVedyxMe+ebSLyfPj9iyDL9/sry1AhIu+U4KE2z/6pIvKnoZTJseXrjjPqCAuERFXTseSLMadce6lqp4hsDxw+kPJV9UexzTOweehFr0ANK1fPxFYJo6oX9uPctwO3h3LuBb6gqpE77TOKLacSUdVVIvKaiByqqg8MtzzVgvcIHGBrS+3emN/760JliojME5G/isVIeFhEmoLf9p+K+fV/VETeFfKeISK3isjvRORFETlPRD4X8jwkItuFfG8RkT8Fx29/EZHdcsi0UESuEZG7Qyv+rNi+L4rIEhF5QjIxG2aL+ev/Aea1dedY/gbMhe+nVLUTQFXfUNWbwv4fikiLxGJAhPQVInJJuO6HReStMdm+ICInA83AdaFVPlZELgyyPSUiV0T3MYsjMBcMPaG8n4WyonNeJCKPhPu7zb3pg53CvX1BRC6NXcuCUN5TInJJLL0t9vtkEflZ+H1KyPu4iNwXu8d/CbI9IiL/FNILvT/HhbT7sdWw0bkOj/VsHpWw4htbNPehfl6zMxiGe6Wcf4b3A7SF73di3hdnYA2EB7FV2XXAcmBeyDce60l+HvhpSNsNW65fj7WOlwFNwNRQ5jkh37cxp39gKyDnht8HYa4psmVbiK2gHAtMwVwG74S5hbgCc26XwNwHHwbMxjygHpyjrH2ARwvch+3CdxLz77RP2F5BZvXxR8msnF6ItcQJ+Zuzywq/rwHel+N8F2FKKdr+GXBy7JyfCr8/Scz1cI5yss99RnheE8LzeAlTiDuFZzQ1PL+7gZPi70D4fTLws/D7SWB6+D0xfDcA9eH3XKClj/enPjy3ueF53RS7h7/DHAACNJJx/TwdeHK4/xvV9PEegRPnYVVtVTOnPIZVrG8DXlPVJQCqulGtFft2rJJDVZ/DKpxdQzn3qOomVV2FVQ6/C+lPArPFPMH+E/ArMXfbP8ZMNrn4rapuUdXVmA+Y+ZgiOAZzxfEIpojmhvwvqepDA7j2D4rII6HMPYE9Yvuuj30fUkRZ7xKLcPUk1vLfM0eeHTH30vmInCMuxZ5Df7hLVTeoagfmjmAWMA+4V83BWg9wHaY8C/EA8LPQE0uGtFrgynBtv6L3fcr1/uyGOXZ7Qa2Wvzar/G+JyPmYook8ab6JKS5niPAxAidOZ+x3Cns/hNxueHOZO3KVk45tp0OZCczn+35FyJR9bg3n/oaq/riXQBZvYnOecpYBM0WkSVU3ZR03B/gC1utZF0wj9XlkKOiTRUTqgR9grfRXRGRhVlkRW/KkR0T3LHoO/SHfc8xH/Jq2yqSq54jIQcB7gMdEZD/gU8AbWISsBNDRx3mzy8+cVPWbIvIHzPfXQyJyVGhU1GP3xxkivEfg9MVzmM15HkAYH6gB7iPYcUVkV2AmFimpT9TiQbwoIqeE40VE9s2T/cQwHjEZMz8swQZKzww9C0RkugSvjAXO2Y55of2ehBi0IrKjiHwYM3dtBjaIDSBnzyT6l9j3gzmK34SZwiBTka4O8p2cIz+YR9m3FpK5xPwNOFxEpohIEvMO+n9h3xsisruIJID3RweIyFtU9W9qA9mrMRPTBKyHmAY+QqankI/ngDki8pawvSCr/CdV9RLMqV40FrIrNvjuDBHeI3AKoqpdIvIvwGVibrG3YK6xfwD8KJgIeoAz1GbiFFv0h4AfishXMHPDDdh4QDYPA3/AFM3FarEEXhWR3YEHw/nagA9jrdBCfAX4GvCMiHRglf+Fqvq4iDyKeSNdjpks4owRkb9hDacFbMvPsHuxBTMdXYmZwVZgiisXfySY1oYCVX1NRL6MmdcEWKyqkbviC7BxllewCrgxpP+3WExcwcZ0Hsee+y1Bid9D/h5YdN4OsShwfxCR1cD9wF5h92fEJhmkMBPWH0P6u7Bn7gwR7n3UqViCWaVNVf9nGGVYgZl5Vpeh7N8A/66qL5S67JFMmKF0oqquG25ZqgU3DTnO8HEB+QfJqxIRmQp8y5XA0OI9AsdxnCrHewSO4zhVjisCx3GcKscVgeM4TpXjisBxHKfKcUXgOI5T5fx/HpOaLJtA7zwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"geor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.097\n",
      "Model:                            OLS   Adj. R-squared:                  0.088\n",
      "Method:                 Least Squares   F-statistic:                     10.87\n",
      "Date:                Thu, 18 Mar 2021   Prob (F-statistic):            0.00135\n",
      "Time:                        00:18:29   Log-Likelihood:                 342.27\n",
      "No. Observations:                 103   AIC:                            -680.5\n",
      "Df Residuals:                     101   BIC:                            -675.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0263      0.004      6.426      0.000       0.018       0.034\n",
      "IPC           -0.0003   7.69e-05     -3.297      0.001      -0.000      -0.000\n",
      "==============================================================================\n",
      "Omnibus:                        0.055   Durbin-Watson:                   1.924\n",
      "Prob(Omnibus):                  0.973   Jarque-Bera (JB):                0.168\n",
      "Skew:                           0.048   Prob(JB):                        0.919\n",
      "Kurtosis:                       2.827   Cond. No.                         252.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state(\"kans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the dataset collectivly showed a statistically insignificant negative relationship between Income per Capita and Doses per Capita. While looking at the data on a state level, California and Ohio showed a positive relationship that was significant enough for us to reject the null hypothesis for these states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better calibrate our model we decided to remove those counties with doses per capita less than zero (To remove the effect of counties that reported no vaccinations), doses per capita greater than 0.5 (To remove the effect of counties with an unusually high number of doses per capita)  and incomes higher than 80 (To remove the effect of very wealthy counties). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declare outlier range\n",
    "INCOME_CAP = 80\n",
    "DOSES_MAX = 0.5\n",
    "DOSES_MIN = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42 entries, 0 to 101\n",
      "Data columns (total 9 columns):\n",
      "Total Income                     42 non-null int64\n",
      "Population                       42 non-null int64\n",
      "Income per Capita                42 non-null float64\n",
      "County                           42 non-null object\n",
      "State                            42 non-null object\n",
      "Doses Administered               42 non-null int64\n",
      "People Initiating Vaccination    2 non-null float64\n",
      "People Fully Vaccinated          2 non-null float64\n",
      "Doses per Capita                 42 non-null float64\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 3.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Income per Capita</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Doses Administered</th>\n",
       "      <th>People Initiating Vaccination</th>\n",
       "      <th>People Fully Vaccinated</th>\n",
       "      <th>Doses per Capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135663560</td>\n",
       "      <td>1671329</td>\n",
       "      <td>81.171068</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>CA</td>\n",
       "      <td>350830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>98423318</td>\n",
       "      <td>1153526</td>\n",
       "      <td>85.323883</td>\n",
       "      <td>Contra Costa</td>\n",
       "      <td>CA</td>\n",
       "      <td>281761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.244261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36684680</td>\n",
       "      <td>258826</td>\n",
       "      <td>141.734911</td>\n",
       "      <td>Marin</td>\n",
       "      <td>CA</td>\n",
       "      <td>73851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.285331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>772750</td>\n",
       "      <td>14444</td>\n",
       "      <td>53.499723</td>\n",
       "      <td>Mono</td>\n",
       "      <td>CA</td>\n",
       "      <td>7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>122892141</td>\n",
       "      <td>881549</td>\n",
       "      <td>139.404776</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>194293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.220400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Total Income  Population  Income per Capita         County State  \\\n",
       "0      135663560     1671329          81.171068        Alameda    CA   \n",
       "6       98423318     1153526          85.323883   Contra Costa    CA   \n",
       "20      36684680      258826         141.734911          Marin    CA   \n",
       "25        772750       14444          53.499723           Mono    CA   \n",
       "37     122892141      881549         139.404776  San Francisco    CA   \n",
       "\n",
       "    Doses Administered  People Initiating Vaccination  \\\n",
       "0               350830                            NaN   \n",
       "6               281761                            NaN   \n",
       "20               73851                            NaN   \n",
       "25                7750                            NaN   \n",
       "37              194293                            NaN   \n",
       "\n",
       "    People Fully Vaccinated  Doses per Capita  \n",
       "0                       NaN          0.209911  \n",
       "6                       NaN          0.244261  \n",
       "20                      NaN          0.285331  \n",
       "25                      NaN          0.536555  \n",
       "37                      NaN          0.220400  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = outlier(all_states)\n",
    "outliers.info()\n",
    "outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plot_ipc_vs_dpc_wo_out(\"all\", ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income per Capita Centered Around: 43.994270234395266 thousand dollars\n",
      "Doses Administered Centered Around: 25390.880668257756 Doses\n",
      "Doses per Capita Centered Around: 0.1324379700033037 Doses\n",
      "Correlation between Income per Capita and Doses per Capita: -0.08442520978551908\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist_rem_out(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                  0.005\n",
      "Method:                 Least Squares   F-statistic:                     2.994\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):             0.0843\n",
      "Time:                        14:02:07   Log-Likelihood:                 412.77\n",
      "No. Observations:                 419   AIC:                            -821.5\n",
      "Df Residuals:                     417   BIC:                            -813.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1669      0.020      8.180      0.000       0.127       0.207\n",
      "IPC           -0.0008      0.000     -1.730      0.084      -0.002       0.000\n",
      "==============================================================================\n",
      "Omnibus:                       53.894   Durbin-Watson:                   1.061\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               71.956\n",
      "Skew:                           0.932   Prob(JB):                     2.37e-16\n",
      "Kurtosis:                       3.807   Cond. No.                         208.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: -0.08442520978551908\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.383\n",
      "Model:                            OLS   Adj. R-squared:                  0.371\n",
      "Method:                 Least Squares   F-statistic:                     30.44\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):           1.29e-06\n",
      "Time:                        14:02:08   Log-Likelihood:                 83.104\n",
      "No. Observations:                  51   AIC:                            -162.2\n",
      "Df Residuals:                      49   BIC:                            -158.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0101      0.035     -0.285      0.777      -0.081       0.061\n",
      "IPC            0.0036      0.001      5.517      0.000       0.002       0.005\n",
      "==============================================================================\n",
      "Omnibus:                       12.903   Durbin-Watson:                   1.974\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               14.817\n",
      "Skew:                           0.963   Prob(JB):                     0.000606\n",
      "Kurtosis:                       4.806   Cond. No.                         281.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: 0.6189996138077382\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"cal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.023\n",
      "Model:                            OLS   Adj. R-squared:                 -0.005\n",
      "Method:                 Least Squares   F-statistic:                    0.8255\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):              0.370\n",
      "Time:                        14:02:08   Log-Likelihood:                 38.549\n",
      "No. Observations:                  37   AIC:                            -73.10\n",
      "Df Residuals:                      35   BIC:                            -69.88\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0890      0.122      0.731      0.470      -0.158       0.336\n",
      "IPC            0.0023      0.003      0.909      0.370      -0.003       0.007\n",
      "==============================================================================\n",
      "Omnibus:                        2.225   Durbin-Watson:                   2.134\n",
      "Prob(Omnibus):                  0.329   Jarque-Bera (JB):                2.068\n",
      "Skew:                           0.529   Prob(JB):                        0.356\n",
      "Kurtosis:                       2.528   Cond. No.                         408.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: 0.15179624898831903\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"wash\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.223\n",
      "Model:                            OLS   Adj. R-squared:                  0.164\n",
      "Method:                 Least Squares   F-statistic:                     3.738\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):             0.0753\n",
      "Time:                        14:02:08   Log-Likelihood:                 18.201\n",
      "No. Observations:                  15   AIC:                            -32.40\n",
      "Df Residuals:                      13   BIC:                            -30.99\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0624      0.139     -0.449      0.661      -0.362       0.238\n",
      "IPC            0.0068      0.004      1.933      0.075      -0.001       0.014\n",
      "==============================================================================\n",
      "Omnibus:                       11.055   Durbin-Watson:                   1.675\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                7.974\n",
      "Skew:                           1.148   Prob(JB):                       0.0186\n",
      "Kurtosis:                       5.737   Cond. No.                         276.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: 0.4725884223125636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Taylor\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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cc9mStFd3vCfLZzVXJbXv+k51h20x4f6KCsK9ErdL+hyAmVXfrbyO0C1AyhtpzOxOMys2s+I+fXJOrs455/ZCPpPJPGCQpIGSOhJuEKx1c5OZDTSzAWY2gHAzz9fM7PF4h2YPqOn2ZBLhRjznnHMtUN6qucxsl6TLCXfjFhGu1Foi6dI4/Y4MxQ8kdA9SHeNDZva3fMXqnHMuN63qDvji4mLzNhPn2qadO3dSWVnJ9u2ZOlxuuzp37ky/fv3o0KFDrfGS5ptZca7L9zvgnXOtQmVlJT169GDAgAGkuKCnTTMzNm7cSGVlJQMHDszLOjyZONcqLCL0z/cWoQfxKcDQZo2oqW3fvt0TSRqS2G+//Vi/fn3e1tEaew12ro1ZROhgeRPhoslN8fWi5gyqWXgiSS/f28aTiXMF749A7/jXLmF4r3oSd26veDJxruC9xZ5HrlTbJ453TW369OlI4tVX6zwSqMZxxx3XhBE1DU8mzhW8/oTnYiXazF4+fbUNWUR4gOMF8X/jVAs+/PDDTJgwgUceeaTOtN27wwMbZ82aVWdaofNk4lzBm0JoJ9kEVCUMT2nOoFq4/LQzbd26lRdeeIG77767JpnMmDGDE088kS996Ut84hOfAKB79+4AXHPNNQwfPpzhw4fTt29fzj//fABuueUWhgwZwpAhQ7j11lsBqKio4Oijj+aiiy7imGOOYdKkSXz00UcA3HXXXYwaNYphw4bx+c9/nm3btuX0PvaGJxPnCt5QwtOTexN6MeodX7etq7kaJj/tTI8//jinnHIKH//4x9l333156aWXAJg7dy433ngjS5fWegIH1113HQsWLOCf//wn++23H5dffjnz58/n3nvv5cUXX2TOnDncddddvPzyywC88cYbXHbZZSxZsoRevXrx2GOPATBlyhTmzZvHwoULOfroo7n77rtzeh97w5OJc63CUEJVzT3xvyeSzPLTzvTwww9z9tlnA3D22Wfz8MMPAzB69Oi093eYGeeccw7f+ta3OPbYYykrK+PMM8+kW7dudO/enSlTplBaWgrAwIEDGT58OADHHnssFRUVACxevJiSkhI+8YlP8OCDD7JkyZKc3sfe8PtMnHNtUH9C1VbvhHG5tTNt3LiR559/nsWLFyOJ3bt3I4lTTz2Vbt26pS03bdo0+vXrV1PFlalXkk6dOtUMFxUV1VRznXfeeTz++OMMGzaM++67jxkzZuz1+9hbfmbinGuDGr+d6dFHH+Xcc89l1apVVFRUsHr1agYOHEhZWVnaMn/5y1945pln+OUvf1kzbuLEiTz++ONs27aNDz/8kOnTp1NSUpJx3Vu2bOGggw5i586dPPjgg3v9HnLhycQ51wY1fjvTww8/zJlnnllr3Oc//3keeuihtGV+9rOfsWbNGkaPHs3w4cO55pprGDlyJOeddx6jR49mzJgxfPWrX2XEiBEZ13399dczZswYTjrpJI466qi9fg+58I4enXOtwrJlyzj66KObO4wWLdU2aqyOHv3MxDnnXM48mTjnnMuZJxPnnHM582TinHMuZ55MnHPO5cyTiXPOuZx5MnHOuUYiiS9/+cs1r3ft2kWfPn047bTTGrScAQMGsGHDhpznaUqeTJxzrpF069aNxYsX13Rz8swzz9C3b99mjqppeDJxzrVKkvbq79hjj81pvZMnT+bJJ58Ewl3xU6dOrZn23nvv8bnPfY6hQ4cyduxYFi0KXd5v3LiRSZMmMWLECC655JJa/XM98MADNXfIX3LJJTXPRGlp8ppMJJ0i6TVJyyVdlWG+UZJ2SzqroWWdc64lOfvss3nkkUfYvn07ixYtYsyYMTXTfvCDHzBixAgWLVrED3/4Q84991wArr32WiZMmMDLL7/MZz/7Wd56K/RevGzZMn7/+9/zwgsvsGDBAoqKipqt76365K3XYElFwG3ASYTOb+ZJesLMlqaY70fA3xta1jnnWpqhQ4dSUVHBww8/zKmnnlprWllZWc0zSD75yU+yceNGNm/ezMyZM/njH8OzVD7zmc/Qu3fozfi5555j/vz5jBo1CoCPPvqIAw44oAnfTfby2QX9aGC5ma0AkPQIcAaQnBC+DjwGjNqLss451+J89rOf5corr2TGjBls3LixZnyqvhAl1fqfyMz4yle+wk033ZS/YBtJPqu5+gKrE15XxnE1JPUFzgTuaGjZhGVcLKlcUvn69etzDto51zqY2V79zZ8/P+d1X3DBBVxzzTU1j+mtNnHixJpqqhkzZrD//vvTs2fPWuP/+te/smnTJgA+9alP8eijj7Ju3TogtLmsWrUq5/jyIZ9nJnXTLCSn5VuB75nZ7qSsnE3ZMNLsTuBOCL0GNzxM55xrXP369eOb3/xmnfHTpk3j/PPPZ+jQoXTt2pX7778fCG0pU6dOZeTIkRx//PH07x8e0jV48GBuuOEGJk2aRFVVFR06dOC2227j0EMPbdL3k428dUEvaRwwzcxOjq+vBjCzmxLmWcmexLE/sA24GFhbX9lUvAt659ou74K+fvnsgj6fZybzgEGSBgJvA2cDX0qcwcxqHoos6T7gL2b2uKT29ZV1zjnXcuQtmZjZLkmXE67SKgLuMbMlki6N05PbSeotm69YnXPO5SafZyaY2VPAU0njUiYRMzuvvrLOOZeJmaW8KsqlvpKsMfkd8M65VqFz585s3Lgx7wfNQmRmbNy4kc6dO+dtHXk9M3HOuabSr18/Kisr8VsEUuvcuTP9+vXL2/I9mTjnWoUOHTowcODA+md0eeHVXM4553LmycQ551zOPJk455zLmScT55xzOfNk4pxzLmeeTJxzzuXMk4lzzrmceTJxzjmXM08mzjnncubJxDnnXM48mTjnnMuZJxPnnHM582TinHMuZ55MnHPO5cyTiXPOuZx5MnHOOZczTybOOedy5snEOedczvKaTCSdIuk1ScslXZVi+hmSFklaIKlc0oSEaRWSXqmels84nXPO5SZvz4CXVATcBpwEVALzJD1hZksTZnsOeMLMTNJQ4A/AUQnTTzSzDfmK0TnnXOPI55nJaGC5ma0wsx3AI8AZiTOY2VYzs/iyG2A455wrOPlMJn2B1QmvK+O4WiSdKelV4EnggoRJBjwtab6ki9OtRNLFsYqsfP369Y0UunPOuYbIZzJRinF1zjzMbLqZHQV8Drg+YdJ4MxsJTAYukzQx1UrM7E4zKzaz4j59+jRC2M455xoqn8mkEjgk4XU/YE26mc1sJnC4pP3j6zXx/zpgOqHazDnnXAuUz2QyDxgkaaCkjsDZwBOJM0g6QpLi8EigI7BRUjdJPeL4bsAkYHEeY3XOOZeDvF3NZWa7JF0O/B0oAu4xsyWSLo3T7wA+D5wraSfwEfDFeGXXgcD0mGfaAw+Z2d/yFatzzrncaM/FVIWvuLjYysv9lhTnnMuWpPlmVpzrcvwOeOeccznzZOKccy5nWbWZSBoCDAY6V48zs//NV1DOOecKS73JRNIPgBMIyeQpwn0fZYAnE+ecc0B21VxnAZ8C3jWz84FhQKe8RuWcc66gZJNMPjKzKmCXpJ7AOuCw/IblnHOukGTTZlIuqRdwFzAf2ArMzWdQzjnnCku9ycTMvhYH75D0N6CnmS3Kb1jOOecKSb3VXJKeqx42swozW5Q4zjnnnEt7ZiKpM9AV2F9Sb/b0AtwTOLgJYnPOOVcgMlVzXQJcQUgcLyWM/4DwBEXnnHMOyJBMzOwXwC8kfd3M/qcJY3LOOVdgMlVzfdLMngfeljQlebqZ/TGvkTnnnCsYmaq5jgeeB05PMc0ATybOOeeAzNVcP4j/z2+6cJxzzhWibPrm2g/4ATCBcEZSBlxnZhvzHJtzzrVhiwgVQG8B/YEpwNBmjSiTbLpTeQRYT3gq4llx+Pf5DMo559q2RcBPgU1Av/j/p3F8y5RNMtnXzK43s5Xx7wagV57jcs65NuyPQO/41y5huOU2VWeTTP4h6WxJ7eLfvwFP5jsw55xru94C9kkat08c3zJlk0wuAR4CdsS/R4BvS9oi6YN8Buecc21Tf2Bz0rjNcXzLVG8yMbMeZtbOzNrHv3ZxXA8z69kUQTrnXNsyhdBOsgmoShiuc8tfi5HVM+Al9ZY0WtLE6r8sy50i6TVJyyVdlWL6GZIWSVogqVzShGzLOudc6zUUuJLQTlIZ/19JS76aK5tLg78KfJNwScECYCwwG/hkPeWKCH14nUTYGvMkPWFmSxNmew54wsxM0lDgD8BRWZZ1zrlWbCgtOXkky+bM5JvAKGCVmZ0IjCBcHlyf0cByM1thZtVtLWckzmBmW83M4stuhPtYsirrnHOu5cgmmWw3s+0AkjqZ2avAkVmU6wusTnhdGcfVIulMSa8SrhC7oCFlY/mLYxVZ+fr12eQ455xzjS2bZFIZH9v7OPCMpD8Ba7IopxTjrM4Is+lmdhTwOeD6hpSN5e80s2IzK+7Tp08WYTnnnGts2Ty298w4OE3SPwgXO/8ti2VXAockvO5HhiRkZjMlHS5p/4aWdc4517zSnplIGiVpcuI4M/tnHPxEFsueBwySNFBSR+Bs4ImkdRwhSXF4JNAR2JhNWeeccy1HpjOTnwDnpRi/FLiTeq7mMrNdki4H/g4UAfeY2RJJl8bpdxD6+zpX0k7gI+CLsUE+ZdkGvTPnnHNNRnsupkqaIL1iZinPQCQtNLNheY1sLxQXF1t5eXlzh+GccwVD0nwzK851OZka4LtkmNYt1xU755xrPTIlk2cl3VjdplFN0rWEJzA655xzQOY2k+8AvwWWS1oQxw0DyoGv5jku55xzBSTTY3s/BKZKOgw4Jo5eYmYrmiQy55xzBSOb+0xWAJ5AnHPOpZVVr8HOOedcJp5MnHPO5SxjMomP6V3cVME455wrTBmTiZlVAQsltdxnRTrnnGt29TbAAwcBSyTNBT6sHmlmn81bVM455wpKNsnk2rxH4ZxzbdjTTz9NeXk5V199NUn3iReMtH1z1ZpJOhQYZGbPSuoKFJnZlrxH10DeN5dzrhCNGDGCBQsWsHLlSgYMGNCk626KvrmqV3QR8CjwmziqL+FBWc455zIwM958803uu+8+vve976Wdr6SkBIDS0tKmCq3RZXNp8GXAeOADADN7Azggn0E551whqqqqYuHChfzqV7/ii1/8In379uWII47g/PPP58c//jHvvvtuynKtIZlk02byLzPbUV2PJ6k9aR6h65xzbcmOHTsoLy+ntLSU0tJSXnjhBd5///2085eWlvKFL3yhzviJEydyySWXcPrpp+cx2vzKJpn8U9J/AV0knQR8DfhzfsNyzrmWaeHChTz66KOUlpby4osvsn379qzLpksmBx54IHfccUdjhtnkskkmVwEXAq8AlwBPEXoTds65NmfWrFnccMMNWc/fpUsXxo4dS0lJCaeeemoeI2te2XT0WCXpfuBFQvXWa5bNJWDOOVdAzIyKioqaKquLLrqI0aNH15mvun0jnd69ezNhwgRKSkooKSlh5MiRdOzYMV9htxj1JhNJnwHuAN4EBAyUdImZ/TXfwTnnXL5UVVWxZMmSmuRRWlrK22+/XTO9f//+KZPJ4MGD6d27N5s2bQKgX79+NYmjpKSEwYMH065d2+v2MJtqrp8BJ5rZcgBJhwNPAp5MnHMFY8eOHcyfP79WY3l1Qkhl5syZKce3a9eO66+/np49e1JSUsKhhx5asDcaNqZsksm66kQSrQDW5Ske55xrNO+//z633HJLTWP5Rx99lHXZ2bNns2vXLtq3r3uYvOyyyxozzFYhm2SyRNJTwB8IbSZfAOZJmgJgZn9MV1DSKcAvgCLgt2Z2c9L0c4DqO3m2Av9hZgvjtApgC7Ab2NUYd2g659qWTp06cfPNN7Nz586s5u/cuTNjxoypqbJy2csmmXQG1gLHx9frgX2B0wnJJWUykVQE3AacBFQSEtATZrY0YbaVwPFmtknSZOBOYEzC9BPNbEMD3o9zro1YtWoVM2fOpLS0lH333Zebb765zjxdunShuLiY2bNnp1xGr169GD9+fE3yOPbYY+nUqVO+Q2+Vsrma6/y9XPZoYHn1M+MlPQKcAdQkEzOblTD/HKDfXq7LOdeKVVVVsWzZslqN5atXr66Z3rdvX2666aaUbRclJSU1yeTggw+u1Vg+ZMiQNtlYng/ZnJnsrb7A6oTXldQ+60h2IbUb9Q14WpIBvzGzO1MVknQxcDGEqy+ca7hFhBPst4D+wBRgaCteb8u3c+dOXnrppZrEUVZWxnvvvZd2/rfffpuKigoGDhxYZ9rUqVMZPHgwJSUlDBw40BvL8ySfySTVJ5by/hRJJxKSyYSE0ePNbI2kA4BnJL1qZnUur4hJ5k4IvQbnHrZrWxYBPwV6E06MN8XXV5LfA3tzrbdl+/Wvf82jjz7KnDlz2LZtW4PKlpaWpkwmw4cPZ/jw4Y0UoUsnn8mkEjgk4XU/YE3yTJKGEu6on2xmG6vHm9ma+H+dpOmEarPU1+o5t9f+SDig946veyeMz+dBvbnW27KVl5fz/PPPZzVvp06dajWWH3fccXmOzmWSzU2L3wTuJVxZ9VtgBHCVmT1dT9F5wCBJA4G3gbOBLyUtuz/h2/NlM3s9YXw3oJ2ZbYnDk4Drsn5XzmXtLeo21e0Tx7fG9TaPt956q9b9HWVlZeyzzz515ispKeGee+5JuYx99tmnVmN5cXGxN5a3INmcmVxgZr+QdDLQBzifkFwyJhMz2yXpcuDvhEuD7zGzJZIujdPvAK4B9gNuj/WY1ZcAHwhMj+PaAw+Z2d/25g06l1l/QhVT74Rxm+P41rje/DOzOo3lb71VO0nOmjWLyZMn1ymbeDnuQQcdVKexvKioKO/xu72TTTKpbvs4FbjXzBYqyxYsM3uK0DFk4rg7Eoa/Cnw1RbkVwLBs1uFcbqYQ2iognBlsJhzkL2yl6218u3bt4uWXX67VWL5hQ+Yr+ktLS1Mmk8MOO4wHHniAsWPHcthhh3ljeQHJJpnMl/Q0MBC4WlIPoCq/YTnXVIYSGr0Tr6q6kPy3WzTXehvP7Nmzueaaa5g9ezYffvhhg8qWlZWlHC+Jc845pzHCc00sm2RyITAcWGFm2yTtR6jqcq6VGErzHMSba70NY2YpzxAk8eyzz2a1jI4dOzJ69GhKSkqYOHEi48aNa+wwXTPLJpkYMBg4jdAI3o1wV7xzrhWqrKys1d5x4YUXcsUVV9SZb+TIkXTp0iVlf1c9evSo1Vg+atQoOnf2w0Zrlk0yuZ1QrfVJQjLZAjwGjMpjXM65JmBmvP7665SWltZ0TVJRUVFrnn/+858pk0nHjh0ZN24czz//PAceeGCtxvKhQ4d6Y3kbk00yGWNmIyW9DBD70Wr9T3pxrhXatWsXCxcurNVYvm5d5k7Ay8rK0lZ1/fSnP6V79+4cccQR3ljexmWTTHbGThsNQFIfvAHeuYKya9cuTj/9dMrKyti6dWuDym7cuJEVK1Zw+OGH15k2YsSIxgoxDe9yplBkk0x+CUwHDpB0I3AW8P28RuWc2ys7duxI+YjY9u3b8/bbb2eVSDp06MCoUaNqqqzGjx9Pr1698hBtfbzLmUKSTa/BD0qaD3yKcM/J58xsWd4jc87Va82aNbUay3fu3MnSpUtTzltSUsIrr7xSZ3z37t057rjjapLH6NGj6dKlS75Dz4J3OVNIsulO5XBgpZndJukE4CRJ75jZ+3mOzbk2pP7qHDNj+fLlNYlj5syZrFixos6S1q1bxwEHHFBn/MSJE7n99tvp06cPEyZMqEkew4cPT/k0webXtrqcKXTZ7EGPAcWSjiD0zfVn4CHCHfHOuZylrs7ZvftbLFxotRrL165dW+/SysrKmDJlSp3xkydPZtmyZRx55JEF0ljeerucaY2ySSZVsZ+tKcAvzOx/qq/scs41hrrVOTfdNI+bbhrLli07Gry01157LeX4nj170rNnz72Osum1ni5n2oJsr+aaCpxLeFQvQIf8heRc27B582a6du1Khw51q3N69twnq0TSvn17iouLazWW77vvvnmKuKkVfpczbUk2yeR84FLgRjNbGbuUfyC/YTnX+rz77ru1GssXLlzIjBkzmDixbnVOSUnqhNCtWzfGjRtXkzzGjBlD165dm+YNNIvC6HLGZXc111JJVwIflzQEeM3Mbs5/aM4VLjPjzTffrJU8li9fXme+0tJSJk6sW50zZEg7evXqQfv2neo0lnfo4BUDruXJ5mquE4D7gQrCpcGHSPpKqkfoOtdW7d69m1deeaVW8nj33XfrLVdaWgr8N8nVOe3aXcgrr9xE3759C6Sx3LV12VRz/QyYZGavAUj6OPAwcGw+A3OuUCxYsIATTjiBzZs3N7jsBx98ELsqqVud0y/5qljnWrBskkmH6kQCYGavS/LzbNemfPDBB3zwwQf0S3GEHzRoUFbP82jfvj0jR45k4sSJNY3l++23Xz7Cda7JZZNMyiXdDfwuvj4HmJ+/kJxrfmvXrqWsrKymymrBggVMnTqVBx6oe+1Jt27dGDlyJHPnzq01vmvXrowdO7amvWPs2LF069atqd6Cc00qm2TyH8BlwDcIbSYzCd3Su7zwju2ampmxcuXKWu0dr7/+ep35QvtGaiUlJSxfvrxWY/nIkSO9sdy1GTKz+mcKPQVjZuvzHlEOiouLrby8vLnDyEHindCJN2l5x3aNqaqqisWLF9dKHmvWrMmq7KpVq+jfv+4d2B9++CFdunShXbt2jR2uc3klab6ZFee6nLRnJgqXkPwAuJxwRiJJu4H/MbPrcl2xS8U7tmsKo0aN4qWXXmpwuaOPPpp33nknZTLx6ivX1mX6GXUFMB4YZWb7mdm+wBhgvKRvZbNwSadIek3ScklXpZh+jqRF8W+WpGHZlm2d3iKckSTyju0aasuWLcyaNSvt9E984hP1LqOoqIhRo0bx7W9/m+nTp7N+/XqWLl3KmDFjGjNU51qNTG0m5wInmdmG6hFmtkLSvwNPAz/PtOD4QK3bgJOASmCepCfMLLF/7JXA8fHpjZOBO4ExWZZthbxju72xfv36msbymTNnsmDBAsyMTZs2peyLqqSkhPvvv7/WuC5dutRpLO/evXtTvQXnCl6mZNIhMZFUM7P1WV4aPBpYbmYrACQ9ApwB1CQEM0v8+TiHPR0U1Vu2dfKO7epjZqxatapWe8err76act7Zs2dz8skn1xlfUlJC7969GT9+PCUlJUycOJGRI0emfKiUcy47mZJJpl7msunKtC+wOuF1JaGaLJ0Lgb82tKyki4GLgZR12YXFO7ZLVlVVxdKlS2slj8rKyqzKlpWVpUwmgwYNYsOGDd5Y7lwjypRMhkn6IMV4AZ2zWHaqPiBSXjom6UTCUXNCQ8ua2Z2E6jGKi4vrvzStxfOO7RL9/Oc/58orr2xwuSOPPDLto2YleRclzjWytMnEzIpyXHYlcEjC635AnesvFfqR+C0w2cw2NqSsK3wffvghc+bM4cgjj0x5d/m4cePqXUa7du0YMWJETXvHhAkTUj5p0DmXP/l8Vuc8YFDssv5t4GzgS4kzSOpPqNP5spm93pCyrjBt2LChprG8rKyMl156iV27dvHzn/+cK664os78xcXFdO7cme3bt9eM69SpU63G8nHjxtGjR48mfBfOuWR5Sybx6YyXA38HioB7zGyJpEvj9DuAa4D9gNtjtcMuMytOVzZfsbr8SW4sX7ZsWcr5SktLUyaTjh07cvLJJ7Nz586a5FFcXEynTp3yHLlzriGyugO+UBT+HfCFraqqimXLltVKHqtXr66/IHDAAQfw7rvveluGc00s73fAO9dQmzZtYsiQIQ0uN2jQIEpKSti2bZvfSe5cgfJk4rK2bds25syZw7Zt2zjttNPqTN9vv/045phjWLIkfY1ku3btGDZsWK3G8o997GP5DLvAecefrjB4MnFpvffee7W6YZ8/fz67du1i8ODBKZMJhBsCE5NJp06dGD16dK3G8n32Se4yxqWW2PFnP8INrD/FO/50LZEnE1dj9erVtZLH4sWLU863dOlSNm7cmPLBTpMmTWLVqlU1Zx2jRo2ic+dsbktydXnHn65weDJpw9544w3+8Y9/1CSPVatWZV22rKyMM844o874M888kzPPPLMxw2zD3mJPD0PVvONP1zJ5MmnDrr76ah577LEGlTniiCMoKSnhoIMOylNUbg/v+NMVDk8mrdRHH33Eiy++SFlZGd/97ndT3pdRUlKSMZlIYujQoTWdIU6YMMGTSJPyjj9d4fBk0kps2rSJF154oabKqry8nJ07dwJw4oknMn78+DplSkpKar3u2LEjo0aNqmksP+6449L2b+Wagnf86QqHJ5MC9fbbb9e6OXDx4sWkuwG1tLQ0ZTIZNmwYZ5xxRk0CGT16tDeWtzje8acrDJ5MCsTatWv585//XJM8Vq5cmXXZ0tJSrrqq7sMqi4qKePzxxxsxSudcW+XJJK8a74azhQsXctFFFzWozGGHHUZJSQmTJk3aq3U651y2PJnkTfY3nH300UfMnTuXsrIyiouLUz7Qady4cbRr146qqqqUa5PEkCFDato7SkpK6Nu3b+O+JeecS8OTSd6kv+Hs/ff712ks37EjPLzy3HPPTZlMevTowYgRI5g/fz4AHTp0oLi4uCZxjB8/nt69e9cp55xzTcGTSd7sueFszZotlJauin9v8sor12VsLE/n4osvZu3atTWN5V27ds1H4M4512CeTPKgqqqK++7bSGnpHEpL3+XNNzdlXXblypVUVlamfOrgxRdf3JhhOudco/Fkkgft2rXj+uvLqajI/knDAwYMqKmy6t69ex6jc865xufJpIG2b9/OvHnzKC0tZe3atfziF79IOV9JyaeoqPhd2uUkN5anOhNxzrlC4cmkHps3b2bWrFk1jeVz586taSwvKirixhtvTHkmMWHCBH73u5BM2rdvz7HHHlursTxVj7uFyZ+34ZzzZFLHu+++W+vO8kWLFqW9HHf37t3MmTOHT3/603WmnXTSSUybNo2SkhLGjBnTSp8g6M/bcM4FnkyAZ599loceeojS0lKWL1/eoLKlpaUpk8nAgQP5wQ9+0FghtlD+vA3nXODJBJg7dy733ntv1vP379+/psrqpJNOymNkLZ0/b8M5F+Q1mUg6BfgFUAT81sxuTpp+FHAvMBL4bzP7acK0CmALsBvYZWbF+YozuffcZIMHD67VWN6/vz9PIvDnbTjngrwlE0lFwG3ASUAlME/SE2a2NGG294BvAJ9Ls5gTzWxDvmIMFjFq1N/p2LEdO3ZUUVRUVKexfP/9989vCAXLn7fhnAvyeWYyGlhuZisAJD0CnAHUJBMzWwesk/SZPMaRQWhA7ty5N7fffioDBnRg7NhOdOt2NV7nnw1/3oZzLshnMukLrE54XQmMaUB5A56WZMBvzOzOVDNJuhi4GNiL6qc9DcgXXnhsHLcJb0BuCH/ehnMO2uVx2UoxLnWHVKmNN7ORwGTgMkkTU81kZneaWbGZFffp06eBIb5FqJ5J5A3IzjnXUPlMJpXAIQmv+wFZ9y9iZmvi/3XAdEK1WSPrT6jnT+QNyM4511D5TCbzgEGSBkrqCJwNPJFNQUndJPWoHgYmAYsbP8QphGqtTUBVwvAQYBpwQfy/qPFX7ZxzOVtESzlW5S2ZmNku4HLg78Ay4A9mtkTSpZIuBZD0MUmVwLeB70uqlNQTOBAok7QQmAs8aWZ/a/woqxuQexNOpHoDnyXkvE3UvqvbE4pzriWp7oGiZRyr8nqfiZk9BTyVNO6OhOF3qXvXG8AHwLB8xrZHcgPyNPyubudcy9eyeqDIZzVXgfJGeedcIWhZxypPJnV4o7xzrhC0rGOVJ5M60jXKT2nOoJxzLknLOlZ5MqkjVaO8d6nunGtpWtaxynsNTsnv6nbOFYKWc6zyMxPnnHM582TinHMuZ55MnHPO5czbTArGImp39T6FllJX6pxzfmZSEFpWtwnOOZfMk0lBSOw2oV3C8B+bMyjnnKvhyaQgtKxuE5xzLpknk4LQsrpNcM65ZJ5MCkLL6jbBOeeSeTIpCC2r2wTnnEvmlwYXjJbTbYJzziXzMxPnnHM582TinHMuZ17N1er5nfPOufzzM5NWze+cd841DU8mrZrfOe+caxp5TSaSTpH0mqTlkq5KMf0oSbMl/UvSlQ0p67Lhd84755pG3pKJpCLgNmAyMBiYKmlw0mzvAd8g1L00tKyrl98575xrGvk8MxkNLDezFWa2A3gEOCNxBjNbZ2bzgJ0NLeuy4XfOO+eaRj6TSV9gdcLryjiuUctKulhSuaTy9evX71WgrZffOe+caxr5vDRYKcZZY5c1szuBOwGKi4uzXX4b4nfOO+fyL59nJpXAIQmv+wFrmqCsc865JpbPZDIPGCRpoKSOwNnAE01Q1jnnXBPLWzWXme2SdDnwd6AIuMfMlki6NE6/Q9LHgHKgJ1Al6QpgsJl9kKpsvmJ1zjmXG5m1nmaG4uJiKy8vb+4wnHOuYEiab2bFuS7H74B3zjmXs1Z1ZiJpPbAqz6vZH9iQ53U0Fo+18RVKnOCx5kOhxAnZx3qomfXJdWWtKpk0BUnljXFK2BQ81sZXKHGCx5oPhRInNH2sXs3lnHMuZ55MnHPO5cyTScPd2dwBNIDH2vgKJU7wWPOhUOKEJo7V20ycc87lzM9MnHPO5cyTiXPOuZx5MokkdZY0V9JCSUskXRvH/0TSq5IWSZouqVea8hWSXpG0QFLebsPPEOc0SW/H9S+QdGqa8k32BMsMsf4+Ic4KSQvSlG+SbZq0ziJJL0v6S3y9r6RnJL0R//dOU65JnwyaIs4WtZ/WE2uL21czxNoi99VU62v2fdXM/C+0GwnoHoc7AC8CY4FJQPs4/kfAj9KUrwD2b8Y4pwFX1lO2CHgTOAzoCCwk9IXWpLEmzfMz4Jrm3KZJ6/w28BDwl/j6x8BVcfiqVJ9/U2/XNHG2qP20nlhb3L6aLtakaS1mX021vubeV/3MJLJga3zZIf6ZmT1tZrvi+DmE7vCbTbo4syzepE+wrC9WSQL+DXg4XzE0hKR+wGeA3yaMPgO4Pw7fD3wuRdEm3a6p4mxp+2m1NNs0G03+tNVMsba0fTWNZt1XPZkkiKe4C4B1wDNm9mLSLBcAf01T3ICnJc2XdHEew8wU5+WxmuOeNKe4uTz9srFjBSgB1prZG2mKN9k2jW4F/pPwjONqB5rZOwDx/wEpyjX1dr2VunEmahH7aXQrqWNtcfsqmbdrS9tXU62vWfdVTyYJzGy3mQ0n/KobLWlI9TRJ/w3sAh5MU3y8mY0EJgOXSZrYxHH+GjgcGA68QzglT5bL0y/3SqZtCkwl8y+9Jtumkk4D1pnZ/L0pnmJcXrZrfXG2pP00Q6wtbl/N4vNvMftqjuvL23b1ZJKCmb0PzABOAZD0FeA04ByLFY8pyqyJ/9cB0wmnk00Wp5mtjQfuKuCuNOtvtidYptim7YEpwO8zlGnKbToe+KykCsKp/yclPQCslXRQjPkgwhlWsqbcrunibIn7acpYW+i+mmm7trR9Nd36mndfbaoGo5b+B/QBesXhLkAp4Yt5CrAU6JOhbDegR8LwLMIBvinjPChhnm8Bj6Qo2x5YAQxkT+PbMU29TePrU4B/toRtmmLdJ7Cnsfgn1G7U/HFzb9c0cbao/bSeWFvcvpou1oRt22L21XTra+59NW9PWixABwH3SyoinLH9wcz+Imk50Al4JrTBMcfMLpV0MPBbMzsVOBCYHqe3Bx4ys781cZy/kzSccMpaAVwCkBinpXn6ZZ7iTBtrnHY2SdUGzbhNM7kZ+IOkC4G3gC8kx9oM2zWVX9Gy9tNMftwC99VMWtq+mnJ9kubRjPuqd6finHMuZ95m4pxzLmeeTJxzzuXMk4lzzrmceTJxzjmXM08mzjnncubJxKUlaWv9c7UOkjpIujn2uLpYobfjyXu5rEslnRuHz4uXZjZ0GVckLOM6SZ/OstzJ2tPL7dbYO+wCSf8bY/lVQ2NpKpJOUOytN830PpKa41JmlwW/z8S1ObHTPlm4A7va9YT7YoaY2b8kHQgcvzfLN7M7El6eByymAXcZxzuuLwBGxuVd04B1/51wDwGSZhB6563uovy8bJfTEpnZeknvSBpvZi80dzyuNj8zcfWKvxhnSHpU4ZkZD8YDMpJGSZql8MySuZJ6KDzH5F6F5y28LOnEOO95kh6X9GdJKyVdLunbcZ45kvaN8x0u6W8KndiVSjoqRUzT4o2az8eziYsSpn1X0jyFjgSrn6EyQNIySbcDL5HQpYSkrsBFwNfN7F8AFrr8+EOc/mtJ5Up4JkscXyHpR/F9z5V0REJsV0o6CygGHoxnB10kXRNjWyzpzurtmOSTwEsWewGWdF9cVvU6r5X0Uty+dbZNPQ6O2/YNST9OeC9T4/IWS/pRwvitCcNnSbovDn8hzrtQ0syEbVwaY3tJ0nFxfKb955Q4rozQZUn1uo5POMN6WVKPOOlx4JwGvmfXFJqiewL/K8w/YGv8fwKwmdCPTztgNjCB0B3DCmBUnK8n4Wz3O8C9cdxRhLtxOxN+pS8HehC6WtkMXBrn+zlwRRx+DhgUh8cAz6eIbRqhK4guwP6EnlAPJjzX405Ch3btgL8AE4EBhN5gx6ZY1lDg5QzbYd/4v4jQv9jQ+LoC+O84fC4pntcR5y9OXlYc/h1weor1XUtIbNWv7wPOSljn1+Pw1wh3NqeLO3nd58XPa5/4eawiJNWD42fUJ35+zwOfS9wH4vBZwH1x+BWgbxzuFf93BTrH4UFAeT37T+f4uQ2Kn9cfErbhnwmdGQJ0Z8+zWvoCrzT3d8P/6v75mYnL1lwzq7RQNbSAcHA+EnjHzOYBmNkHFn5NTyAcKDGzVwkHrY/H5fzDzLaY2XrCAebPcfwrwABJ3YHjgP9T6Lr+N4Tqp1T+ZGYfmdkG4B+Ezu4mxb+XCWcgRxEOVgCrzGzOXrz3f5P0UlzmMcDghGkPJ/wfl8WyTpT0oqRXCGcgx6SY5yBgfYZl/DH+n0/4HBriOTPbbGbbCX15HQqMAmaY2fr4+T1ISMCZvADcF88Ii+K4DsBd8b39H7W3U6r95yhgpZm9YSFTPJC0/FskfYOQrKqf1bKOkPxcC+NtJi5b/0oY3k3Yd0Tq7qtTVd2kWk5VwuuquMx2wPsWuq2vT/K6La77JjP7Ta2ApAHAh2mWsxzoL6mHmW1JKjcQuJJw9rUpVvN0ThNDxr6JJHUGbiecLayWNC1pWdU+SjO+WvU2q/4cGiLd55hO4nuqiclCv19jCA+TWqDQ19bXgbXAMMLnuL2e9SYvf89KzW6W9CRwKjBH0qfjD5POhO3jWhg/M3G5eJVQBz8KILaXtAdmEuu1JX0c6A+8ls0CzewDYKWk6k7qJGlYmtnPiO0z+xGqUuYRGp8viGc4SOorKdVDghLXuQ24G/ilpI6x3EGS/p1QdfchsFmhUT75Cq8vJvyfnWLxWwjVerDnYLwhxndWmpCWAUdkirmRvQgcL2l/hU45pwL/jNPWSjpaUjvgzOoCkg43sxctXBywgVBdtg/hTLUK+DJ7zljSeRUYKOnw+Hpq0vJfMbMfAeWEsxgIZ7iLc3mzLj/8zMTtNTPbIemLwP9I6kL4xfhpwq/vO2J1xy7gPAtXSGW76HOAX0v6PqHq5BFC+0iyucCThGR1vYVnPKyRdDQwO65vK/DvhF/DmXwfuAFYKmk7IYFcY2YLJb0MLCG0NyRfRdRJ0ouEH2ZTqes+wrb4iFANdhehSq+CkPxS+SuxmrApmNk7kq4mVBUKeMrM/hQnX0Vod1pNOIh3j+N/Iqm6reM5wudzO/BY/CHwD9KfCVavd7vCUwKflLQBKAOqH552hcKFG7sJ1XHVT448kfCZuxbGew12BSlWEW01s582YwwVhCqrDXlY9nTgPy39Y2LbpHjl2Blmtqm5Y3G1eTWXcy3TVaS/8KBNktQHuMUTScvkZybOOedy5mcmzjnncubJxDnnXM48mTjnnMuZJxPnnHM582TinHMuZ/8f0z0+D87kw/IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"ariz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.053\n",
      "Model:                            OLS   Adj. R-squared:                  0.042\n",
      "Method:                 Least Squares   F-statistic:                     4.796\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):             0.0313\n",
      "Time:                        14:02:08   Log-Likelihood:                 253.92\n",
      "No. Observations:                  87   AIC:                            -503.8\n",
      "Df Residuals:                      85   BIC:                            -498.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0740      0.008      9.160      0.000       0.058       0.090\n",
      "IPC            0.0004      0.000      2.190      0.031    3.58e-05       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        0.935   Durbin-Watson:                   2.037\n",
      "Prob(Omnibus):                  0.627   Jarque-Bera (JB):                0.433\n",
      "Skew:                          -0.068   Prob(JB):                        0.805\n",
      "Kurtosis:                       3.318   Cond. No.                         260.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: 0.23111675933468268\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"ohio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.003\n",
      "Model:                            OLS   Adj. R-squared:                 -0.003\n",
      "Method:                 Least Squares   F-statistic:                    0.5045\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):              0.479\n",
      "Time:                        14:02:09   Log-Likelihood:                 159.20\n",
      "No. Observations:                 156   AIC:                            -314.4\n",
      "Df Residuals:                     154   BIC:                            -308.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.1950      0.036      5.459      0.000       0.124       0.266\n",
      "IPC           -0.0007      0.001     -0.710      0.479      -0.002       0.001\n",
      "==============================================================================\n",
      "Omnibus:                       20.701   Durbin-Watson:                   1.934\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               24.540\n",
      "Skew:                           0.887   Prob(JB):                     4.69e-06\n",
      "Kurtosis:                       3.793   Cond. No.                         196.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: -0.057142461366610185\n"
     ]
    },
    {
     "data": {
      "image/png": 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7ZALDKDxrN0AsBg31IOK+YzGXbhhGxWACwyg8fQNQHxuZVh9z6YZRQaxatYoPf/jD7Ljjjuy3334cdNBB3HHHHWOfOAE+8YlP8NxzzxX0GlExH4ZReJobnRmqIfS6DSVcumEUijz7zVSV97///Zx++un86lduXbBXX32Vu+7KaW2pUSQSCWKxWMb911133YTyzyemYRiFZ9pkSCSc0FB134mESzeMQlAAv9lf/vIXGhsbOfvss7ekzZ49m3PPPZdEIsEXvvAFFi5cyPz58/nxj38MOCHzhS98gT322IM999yTW25xi1UuWrSIww8/nA9/+MPsueeeJJNJzjnnHObNm8dxxx3Hsccey2233QbAYYcdtmU6pE996lMsWLCAefPm8bWvfW3cdRkvpmEYhae9FWZuO7K3t910i5IyCkfYbwbD32s3jPu9e/bZZ9l3333T7vvJT35CZ2cnixcvpr+/n3e84x0cddRRPPbYYzzxxBM8+eSTrF27loULF3LIIYcA8Oijj/LMM88wd+5cbrvtNpYvX87TTz/N6tWrefvb387HPvaxUde57LLLmDJlColEgiOPPJKnnnqK+fPnj6s+48EEhlEc2ltNQBjFo2/AaRZh8uw3+/SnP80DDzxAY2Mjs2fP5qmnntqiFWzatImXXnqJBx54gFNOOYVYLMY222zDoYceyuLFi5k0aRL777//lrESDzzwAB/84Aepq6tj22235fDDD097zVtvvZVrrrmGoaEh3nzzTZ577jkTGIZhGBOiAH6zefPmcfvtt2/5fcUVV7B27VoWLFjArFmz+OEPf8jRRx894py77747Y35tbW1btqMsZLds2TL++7//m8WLFzN58mTOOOOMoo96Nx+GYZQD8R5YvhKeX+a+bYzKxCiA3+yII46gr6+Pq666aktaT497TkcffTRXXXUVg4ODALz44ot0d3dzyCGHcMstt5BIJFizZg33338/+++//6i8Dz74YG6//XaSySSrVq1i0aJFo47ZvHkzbW1tdHZ2smrVKu65555x12W8mIZhGKUmcNDGYiMdtDO3NTPeeCmA30xEuPPOO/n3f/93vv3tbzN9+nTa2tq4/PLL+eAHP8jy5cvZd999UVWmT5/OnXfeyQknnMBDDz3EXnvthYjw7W9/m2233Zbnn39+RN4f+MAHuO+++9hjjz3YddddOeCAA+js7BxxzF577cU+++zDvHnz2HHHHXnHO94x7rqMl4pb03vBggVqCygZVcXylaPNJ8HvOTNKV64y4x//+Advf/vbS12MghGPx2lvb2fdunXsv//+PPjgg2y77bbjyivdvRKRpaq6YCJlNA3DMEpNERy0Rvlz3HHHsXHjRgYGBvjP//zPcQuLQmICwzBKjQ1sNCCt36LcMKe3YZQaG9hoVAgmMAyj1AQO2oZ66B903+bwNsoQM0kZRjlgAxuNCsA0DMMwDCMSJjAMwzAiIiJ89KMf3fJ7aGiI6dOnc9xxx+WUz5w5c1i7du2Ejyk2JjAMwzAi0tbWxjPPPENvby8Af/rTn5gxo3bGypjAMAyjIhGRcX3222+/CV33mGOO4fe//z0AN910E6eccsqWfevXr+f9738/8+fP58ADD+Spp54CYN26dRx11FHss88+nHXWWSPmjrrxxhvZf//92XvvvTnrrLNIJBITKl8hMYFhGIaRAyeffDI333wzfX19PPXUUxxwwAFb9n3ta19jn3324amnnuIb3/gGp512GgAXX3wxBx98MI8//jjHH388r732GuBGZN9yyy08+OCDPPHEE8RiMX75y1+WpF5RsCgpwzCMHJg/fz7Lly/npptu4thjjx2x74EHHtgyo+0RRxzBunXr2LRpE/fffz+//vWvAXjPe97D5MlujM19993H0qVLWbhwIQC9vb1svfXWRaxNbpjAMAzDyJHjjz+eCy64gEWLFrFu3bot6enm5hOREd9hVJXTTz+db37zm4UrbB4xk5RhGBWJqo7rs3Tp0glf+2Mf+xhf/epX2XPPPUekH3LIIVtMSosWLWLatGlMmjRpRPo999zDhg0bADjyyCO57bbbWL16NeB8IK+++uqEy1coTMMwDMPIkZkzZ3LeeeeNSr/ooos488wzmT9/Pq2trfzsZz8DnG/jlFNOYd999+XQQw9l1qxZAOy+++5ceumlHHXUUSSTSRoaGrjiiiuYPXt2UesTFZve3DCMiqDapzfPJ4Wa3txMUoZhGEYkTGAYhmEYkTCBYRhGxVBpJvRSUMh7ZALDMIyKoLm5mXXr1pnQyIKqsm7dOpqbmwuSf0GjpETk3cAPgBhwnap+K8NxC4GHgQ+p6m2FLJNhGJXJzJkzWbFiBWvWrCl1Ucqa5uZmZs6cWZC8CyYwRCQGXAG8C1gBLBaRu1T1uTTHXQ7cW6iyGBVMvAfWbnDrWzc3ulXobN2ImqShoYG5c+eWuhg1TSFNUvsDL6vqK6o6ANwMvC/NcecCtwOrC1gWoxKJ98CKt9ySpU0N7nvFWy7dMIyiU0iBMQN4PfR7hU/bgojMAE4Ars6WkYh8UkSWiMgSU0driLUbIBZzS5aKuO9YzKUbhlF0CikwRk+cAqnequ8DX1LVrPP5quo1qrpAVRdMnz49X+Uzyp2+AaiPjUyrj7l0wzCKTiGd3iuAHUK/ZwJvpByzALjZT8o1DThWRIZU9c4ClsuoFJobnRmqIfSaDiVcumEYRaeQAmMxsIuIzAVWAicDHw4foKpbPFgicgPwOxMWBuD8FAODsGGz0yraWpw5KpGA7UzLNIxSUDCBoapDIvIZXPRTDLheVZ8VkbP9/qx+C6OGCZzdsRhs1QHdvbAxDpM7YOa20aKkLLrKMPJOQcdhqOrdwN0paWkFhaqeUciyGBVE2NkN0BQyTUUVFoHACUdXRRU2hmGkxUZ6G+XHRJ3dFl1lGAXBBIZRfjQ3Oud2mFyc3RZdZRgFwQSGUX5Mm+yc24NDoOq+EwmXHoWJChzDMNJiAsMoP9pbnb+hoR76B913Lv6HiQocwzDSYku0GuVJe+v4HdSBwAlHSW033RzehjFBTGAY1clEBI5hGGkxk5RhGIYRCRMYhmEYRiRMYBiGYRiRMIFhGIZhRMIEhmEYhhEJExiGYRhGJExgGIZhGJEwgWEYhmFEwgSGYRiGEQkTGIZhGEYkTGAYhmEYkbC5pIzqxZZpNYy8YhqGUZ0Ey7QODo1cpjXeU+qSGUbFYhqGUVyK1etPXRc8+F67wbQMwxgnpmEYxaOYvX5bptUw8o4JDKN4hHv9Iu47FnPp+caWaTWMvBPJJCUiewC7A81Bmqr+vFCFMqqUvgGnWYQpVK9/2mSnvQTXGEq4ZVq3m57/axlGjTCmwBCRrwGH4QTG3cAxwAOACQwjN5obnRmqIfTaFarXb8u0GkbeiaJhnATsBTyuqmeKyDbAdYUtllGVFLvXb8u0GkZeieLD6FXVJDAkIpOA1cCOhS2WUZUEvf6GeujudZ/BIacFWLirYZQ9UTSMJSKyFXAtsBSIA48WslBGFRP0+Pv6oanRaRpBtNTMbUujEdgAP8OIxJgCQ1XP8ZtXi8gfgEmq+lRhi2VUNeU0RiII9Y3FRob6lkp4GUYZE8XpfZ+qHgmgqstT0wwjZ4oRLRVVaygn4ZULphUZJSCjD0NEmkVkCjBNRCaLyBT/mQNsX7QSGtVHocdI5DJAMDzAb2AQNna5z9qN5etXsWlPjBKRTcM4CzgfJxweC6VvBq4oYJmMaqfQ0VIrVzmHuvr8W5qGBwim9sKDUF9V2BwHqYM6cfvK1TRVqVqRUfFkFBiq+gPgByJyrqr+sIhlMqqd8Y6RiGKGiffAhi5oiEGsDpJJ6OqB9hZIJEfnGQivnj5AQIAk0NEKdXXl2QgXcwCkYYTIKDBE5AhV/QuwUkROTN2vqr8uaMmM6ibXMRJRndNvrAZNQl/CCYz6eqcxbI5DYyM8v2yksAmE1/PL3Pl1ddDW5CK4VMuzES7mAEjDCJHNJHUo8BfgvWn2KWACwygeUcww8R7YsNkdlxxyGkViwM1blUhAe1t6YdPeCtO2qpxG2KY9MUpENpPU1/z3mcUrjmFkIIoZZu0GlybitIvBQGj4hr/VT4WWTthUUiNs054YJWLMkd4iMlVE/kdEHhORpSLyAxGZGiVzEXm3iLwgIi+LyIVp9r9PRJ4SkSdEZImIHDyeShg1QJTIqr4BaGuBpDpBoerSVaGleeS5qcImPAq9f9B9l6PDO6C9FebMgN3muu9yLadRVUQZ6X0zcD/wAf/7I8AtwDuznSQiMVw01buAFcBiEblLVZ8LHXYfcJeqqojMB24FdsutCkZNEEUDCGz7TQ3O0a06rG309EJjg/tAenOTzT1lGFmJMpfUFFW9RFWX+c+lwFYRztsfeFlVX1HVAZzgeV/4AFWNqwbdQNpwvhHDGE0UDWDaZCdEBgad0GhugsZ66OwAxPk4VL2pKuGONwwjMlE0jL+KyMm43j+42Wt/H+G8GcDrod8rgANSDxKRE4BvAlsD74mQr1GrjKUBhCOeVF1obWur0yrqYxDvdcLGbP6GMS6iCIyzgM8BN/rfdUC3iHwOUFWdlOE8SZM2SoNQ1TuAO0TkEOAS0pi6ROSTwCcBZs2aFaHIRs2SKeIpFnPpc2aUqmSGUfGMaZJS1Q5VrVPVev+p82kdWYQFOI1ih9DvmcAbWa5zP7CTiExLs+8aVV2gqgumTy/DqBWjvAhMU8EIbjNBGUZeiLpE62RgF0Yu0Xr/GKctBnYRkbnASuBk4MMp+e4M/NM7vfcFGoF10YtvVDQTmUAv27kWdmoYBSHKbLWfAM7DaQhPAAcCDwFHZDtPVYdE5DPAvUAMuF5VnxWRs/3+q3GRV6eJyCDQC3wo5AQ3qpmJTCse5VyLeDKMvBNFwzgPWAg8rKqHi8huwMVRMlfVu3HrgIfTrg5tXw5cHr24RtUwkQn0bPI9wygJUcJq+1S1D0BEmlT1eeBthS2WUfWEpxUPiDqB3kTONQxj3ETRMFb4JVrvBP4kIhvI4rw2jEhMZAK9fEy+ZwsQGUbORFmi9QS/eZGI/BXoBP5Q0FIZ1c9E5m6a6LxP1bIsqwk9o8hkW3FvoYgcE05T1f/zm3sWtFRG9dPeClM63UJHqze47ymd0Rq8ic77FPaBiLjvYIGlSsFW3TNKQDYN47+AM9KkPwdcwxhRUoaRlXgPrN/kJgvsbHdawvpN0NoSXWiMtzddDQsQmePfKAHZnN5TVXV5aqKqvgxEmq3WMDJSyl5+odcULwbm+DdKQDYNoyXLvrZ8F8QoI/JtG0+XXyl7+ZW09kUmbNU9owRk0zD+LCKXiciIOaFE5GLcSnxGNZJv23im/ITS9fIrbe2LdNj0J0YJyKZhfB64DnhZRJ7waXsBS4BPFLhcRqkITEWqsCnuGvE6cWtl7zpn/Pml2tpVXQMHpenlV/pIcJv+xCgB2ZZo7QZOEZEdgXk++VlVfaUoJTNKQ9+A6/13dYPUucWHEkm3Vna8J/cGKZPpqX/QGryJUulCz6g4oozDeAUwIVErNDe6aKVAWIDTMCQ2vgicbLZ2a/AMo6KINFutUUNMmwxvrXOLD6m6T1KhvWV8Dul8DLKzwWmGURaYwDBG0t4KkztcQ51Iuka+rQnq6kZqCVEIGvvBoeEw0PbW7KansIAQYGDICYpKHpFtGFVC1skHRaRORJ4pVmGMMmHGNsMD6jrbnbDINQInHB3V3uryq49l1xBSI6riPdDb67ScSh2RbRhVRNYuo6omReRJEZmlqq8Vq1BGiclHBE62kcjBd6qZKfUcBepi0NPn1uUGG5xmGCUkio1hO+BZEXkU6A4SVfX4gpXKKD0TdUhnio6K90Bff/qJ/1LPqY85zSY8XsMGpxlGyYgiMCItlmQYI8gUHTWUgKbG9JpH6jktTW4sSL13wFfiiGzDqCLGXEDJz1C7HGjw24uBxwpcLqPSyTQSOVaXeQ6k1HPq6pzQaG+t3BHZhlFFjCkwROTfgNuAH/ukGbjFlAwjM5mm3+hoyzwlSLpztpk67L8wDKOkRDFJfRrYH3gEQFVfEpGtC1oqozrI5AfJNi4jfE61LHRkGFVCFIHRr6oDwRyEIlKPi18xjOiEx1fU1UEyCf3J7BFYtuaDYZQVUQTG/4nIl4EWEXkXcA7w28IWy6gqUjWFoYQTGGNpCtWw0FEmbAS7UYGM6cMALgTWAE8DZwF3A18pZKGMKiHeA8tXwvPL3BKsyWRuA/CqYaGjdNjyqkaFEmXywaSI/Aznw1DgBVU1k5SRnbBWMTTk5qPq3QDNTdDR6hzZY2kK1bDQUTrM1GZUKGMKDBF5D3A18E/c7D5zReQsVb2n0IUzypQo5pTwuhpJHQ6THRyErh4XLtuWbVFHqnfNh2o2tRlVTRQfxneAw/1a3ojITsDvARMYtUjUyKWgUdwUdz3owUEnNBD33dsHs7cf+3rVOAW6La9qVChRBMbqQFh4XgFWF6g8RjbKwVEa1ZwSNIpDCdd7rhMYGHRGzXp/fi5lL4e654tqNbUZVU8Up/ezInK3iJwhIqfjIqQWi8iJInJigctnBJSLozSYpjxMOnNKMGq7Ttw06SLQ0OD8F8mkO375ymjlL5e654tqWFPcqEmiaBjNwCrgUP97DTAFeC+uv/jrwhTNGEG5OEqjmlOCRvGN1W55V/EmrN5+QKGzI/pAvHKpez6pRlObUfVEiZI6sxgFMcagXByluZhT2lth1znD5qS1G4cXUQpP9zFWw18udS8l1WSSMyqWKCYpoxwolzEJ4zGntLfCnBl+Nb9JI4XFWA1/MB36mg2wsQv6/bG15CSuNpOcUbHYEq2VQjk5SsdrThFg/aZhx3eLX/o1U8MfNJSNDS7KanDIfVr9rLe14iSuRpOcUZGYhlEpVLqjNN7j1udOeC0pkXAht339mZd+DRrK1mbn82ioH54qvZLqPlGiBhoYRoGJMnDvPOCnQBdwHbAPcKGq/rHAZTNSqSRHaarNfWDQfdcJdHXDUBJi4jSMTHUK+y4aG9xH1QnMSrkP+cDGbRhlQhQN42Oquhk4CpgOnAl8q6ClMiqbdDb3DZud/6G3z4XXtjW773hPZlt8ufhtSk2mxagyaWaGUSCiCAzx38cCP1XVJ0NphjGalavcZIOb4u6TTDoTSlc3SJ3zPySSzi8xNAQvv5ZeaFhD6ah0c6RRNURxei8VkT8Cc4H/EJEOIBklcxF5N/ADIAZcp6rfStn/EeBL/mcc+JQXSPnFQhKLR7wHNnRBQ8wJhmTSzR3V1AA9/dDoG/6BQXd8Q4PbTjceI5e5pKr9GVeSOdKoWqIIjI8DewOvqGqPiEzFmaWyIiIx4ArgXcAK3Ojwu1T1udBhy4BDVXWDiBwDXAMckGMdsmOrthWXtRucn6LfC4Q676cYGHRRUeCeQZ0f+S3iBEsw3XnqMxmroYz3OI1mQxc01rsJDe0ZG0ZBiGKSUmB34LP+dxtu9PdY7A+8rKqvqOoAcDPwvhEZq/5dVYNFER4GZkYqdS6EQxJzWYvBGB9d3c58pMnhmWoHBp0AmbWta9DrY9DU6J6HJl0U1HiifoLOQLzHaTQaur49Y8PIO1EExpXAQcAp/ncXTnMYixnA66HfK3xaJj5OhhlwReSTIrJERJasWbMmwqVDWEji+AgvfhR1zidwvom6OrfuRcy/XnXi7nlP3/Bqe/2Dbv+kdhf9NB5n9pYp1HHXjNU5H0lPnz1jwygAUQTGAar6aaAPwGsEUf7Z6RzjaRdeEpHDcQLjS+n2q+o1qrpAVRdMn57jYC2LtMmNeA+8uByefRnWbQQ0t5HF9TF3DuK0iKZG15gnki6fthboaHPaRWODn/p8nM7soDNQ79fdACechhL2jA2jAETxYQx6f4QCiMh0ojm9VwA7hH7PBN5IPUhE5uPGdxyjqusi5Jsb5TRCutwJTDw9fa7nLgLxXjfDbNjHkM3B3N467LMIpjZP1rm0YBxBsHDSwKB7FgkfRRWYkKL6HYLxCTGvVai6MtfH7BkbRgGIomH8D3AHsLWIXAY8AHwjwnmLgV1EZK6INAInA3eFDxCRWbjZbj+qqi/mVPKoWEhidAITT1JdI1xX53rsvf3DJp6x5jWaNtmd09YCUzvdd0JHr67X2uyu0eAd1WFndVTz17TJbqR4bz/U+1c5GEk+pdOesWHkmSiz1f5SRJYCR+LMTO9X1X9EOG9IRD4D3IsLq71eVZ8VkbP9/quBrwJTgStFBGBIVReMuzaZsJDEaAQjq+tjrtcfE9djD5t4osxrVFfnxl+A004md7i0MENes2j2eQ0MOi1hYNCNy9h51tjPrL3VmbwG/ZrhbY1OEIm4vAzDyCtRpgbZCVimqleIyGHAu0TkTVXdONa5qno3cHdK2tWh7U8An8ixzOPmmWee4Z///Cft7e20t7fT1ta2Zbu9vZ2mpia84KpNAhNPazNsjkMCQF03ITDxrFiVearxcAjz1M5h89+UTli1zuXp3Rs0Ngz7HwYG3T6pG/4dNSw2qW4G3PBzUzWHt2EUgCg+jNuBBSKyM87X8FvgV7iR3xXFjTfeyOWXX55xfywWyyhMLr30UvbZZ59R57z66qs88sgjac8JftfXV8ikwIG/JxZzjunuXhhIOA1hxjau8c42r1Em7WPDZsBrK0kfRYUMR0f19A2PAE8mXXqmcRmp2DxLhlE0orRkSW9eOhH4gar+UEQeL3TBCkE8Hs+6P5FIsGnTJjZt2jRq34UXXpj2nAceeIBTTz01a75NTU0jBEnwOeecczjxxNGr3G7atInf/OY3ac8JPi0tLfnXhlJHVk/pHD1iOlsQwYpVTnvY2DXs8G5pciO9O9udeSpgcMgJh2TSaRT1Mf9boa0pfVhsOme7BTUYRtGIGiV1CnAabllWgIYsx5ctYwmMbLS3t487z/7+fvr7+1m3bmQQ2AknnJD2+Ndee43TTz89a54iklajOf744/niF7+Y9pzrr7+elpaWUeeFP41j+XvCQqWre2SE09AQ9PRCrH54vqhNXYCkHwvTn3R5vfyaExqBn6OrxwmecDmyjdiPOn2IYRgTIorAOBM4G7hMVZeJyFzgxsIWqzDsvfferF+/nng8PuLT3d1NPB5nYCCz3TuTwOju7h53eSYihFR1S/nD7LnnnmmPTyaTfPzjHx8z34aGhlECZa+99uK6664LFdw3xn39/O9f/0B3fx9tTc20J6C9udmd19TsPq0ttLV3UDeUSG82am91Du5lK1y0U6zO+TkSCbd+RrzHHZPN2R6s5mcYRkGJEiX1nIhcAOwqInsAL6ROIlgpnH/++Zx//vkZ9w8MDGwRHt3d3XR1dW3ZnjEj/SD1nXfemZNOOimtAAo+GgwqS2EiAiMTbW1tadN7eqKFqg4ODrJx40Y2bty4Ja2xMY0/wDfgF1/xPzz74gtj5tva3EJbayvtbW20t7Yyqa2Nvy36Pze6MyXa6Y+LH+aV1W+5Yyd10L7jbNrXbqats8MJo9Y22ttaaW5sQmrduV3tky4aZUWUKKnDgJ8By3GGgh1E5HRVvb+gJSsBjY2NNDY2Mnly9BHHxx9/PMcff3zG/apKb29vWkEyf/78tOdMnz6dU089dctxXV1dI87t6uqiv78/7bmFEEJp8/QhuN0RBVFPXy89fb2sWe/Mch0dHUhHSLiFop1u+MNvuemu34yZZ11dnRMqHR1bTHK//vWvmTNnzqhjlyxZwoMPPjjKBJfOrNfQUCEWV5tY0ygyUUxS3wGOUtUXAERkV+AmYL9CFqxaEBFaW1tpbY3+B9577735xS9+kfWYRCIxSgDF43F22GGHtMfX19fz8Y9/fJQmFBZGXV1dJIKBbyHSCgwfnRTvGZ9JblSeoWineHc0IZRMJtnc1cXmrq4taZkCAf70pz/x5S9/OVK+jY2NI4TI9773PY466qhRxy1btoxbb701a3BCOEihLnUsSjpy0RhsrW+jyEQRGA2BsABQ1RdFpEK6YNVLLBZj0qRJTJo0KdLx06ZNG+mHSIOqMjAw4ITHW6vpXvkm3evW097YDE+/6EJtgwbMRyed+p7jWb9hgxNCfb109/UR7+km3tNDvLeX7t7etEKlvanZTWyYJtppvEIIoH19F0ztGdVg5qJhDQwMsH79etavX7/ldzqee+65jNFzqQQdh0CAnHfeeZx77rkjD4r30P3iK3z35z91mk9zC+0tLbTvMIO2aVNGC6LNXTS2t42ctM0mXTQKSBSBsUREfgIEXd6PAEsLV6QyodpswxHqIyI0NTXRNJhgaqwJZs6GydMBcefV1bmpOAKTx5ROvvfZzztzUkO9Gz+RTLrjmxu3hLkmBwfpndpJnCTxVWuIv/IqKpIx2un4Q49g59lz6E4OEe/rG9aKNmwk3t1Nd28P8e4e+gdGm+Xau/udIJq1LWw9dbj6BfAL5ZKnqtLd3U13dzerVq1Kf+7aDazdvImv/uC7kfOtr6+nraWV9rZWjjvinVx90aVpx6B861vfQlXHNMcF3xUzdsgoKlHeik8Bn8athyHA/bgpz6uXUtuG8y2scq1PYOro64W60Mp5g0PQ2DJs8ujpg86OkdFPwfiKhvot5a/bbjpt7a20Adv0DsG8jqzRTud/85L09Xh+mSu/NzsNDg7S/eYq4hs309XvtJvG1hYXzvvam9DasqV+hxxyCMlkcoQvKdU3FGwnkyPn1iymX6g7jRDMxtDQEJu6NrvPpk0Zx6B84xvfoCtkuhuLZh/ttttuu/G3v/0t7THXXnstK1euzCiAUtMKMnYoTLV18sqQKFFS/SLyC+AXqprjYhQVSi624VI37vmuDwzPKTWUGF7TIphTKmzyCI4LE4yvmJNh6ZNM50Qxo6SM6m5oaGCr5la2mt7sR4eHyjo4NKJ+H/jAB/jABz4w5iVUlb6wVhOPs9NOO6U9dt68eVxwwQVO81m9lu6+XuK9PcS7u91noN9pRN3do6LURmkt8R7o6ye+8q2x70MGOjo60r4nQQh2LvT19dHX18eGDZkXofrZz37Ggw8+GDlPEdkiRF5++eW0mtudd97JI488MkLQZBs31N7e7oIUSt3JqxEyCgxxXYGvAZ/BaRYiIgngh6r69SKVrzREbdTKoXGPQq6NdNAwhychVB0eSR2YPMYzLcdEpvKYNhmWrRweMBgICNTNkBugOqzh5IiI0NLSQktLC2OtvXLggQdy4IEHugWmUusU/PaCM5FI0NPTs0UITZs2bfjY4D1qqGebqdP48hn/RndfD/HBAeI9PXRrknhf76gAh3g8ztDQ0JZs2rfbJu070tfXlzG0eywyaVeQu4alqnR1ddHV1UVLS0vaY+69916uvvrqtPsy0djYSHtrK/f+5Ocs2Hdflxj63zz09JOjghPGEkKtra3RghRqjGwaxvnAO4CFqroMQER2BK4SkX9X1e8VoXylIWqjVg6NexSi1CesKdUJ9A+4Xnt3j/dL4MZKhE0e45mWI9M5ne2u4R1TU9PhdS9U3dKsQX2ChZSSCi2NxZtPKsIzi8VidHR0OC0gleA9am5i9tt24bLzPudGvjc2jDlr78DAwBbTWqZGGOCSSy5JK3DSmee6u7u3CJh8CoyAbI3xePIcGBhg/cAAjc0pK0f7Z/D444/z/e9/f1zlvPLKK9POurB8+XK+/e1vRxJA4X2VPsFpNoFxGvAuVV0bJKjqKyJyKvBHoHoFRtSGsFSNe66MVZ94jxtpPTA4PJtsXZ1rsFqah01TbS2jF0vKdVqOdOd0tsP6TWNrams3uKVfw+M3BoecI75/YPi+tTS68ua6gt94megzC79HwSqFqm79ljE6Ho2NjUydOpWpU6dmPKalpYWvfOUr0crCyLFDqf6cMJ/73Od466230vqB0gmhvj435XwhhBBAW1PTyAT/DMabZ09PT0bn/+uvv85VV12Vc56xWIzzzjuP73znO6P2DQ0NcdZZZ40SPgcddBALFy7M+VqFIJvAaAgLiwBVXVP1YbVRG8JSNO6pRPGhjFWflauGF0mq8z33QW/q2GOX7OUdz1ojwTlB2V97y123vdVpDsH9XLnKNZ5Bmbu6R1+rPgYNDTB3ZnEdnuk0sqA8uU6AWGYz7kYdO3TOOefklO/Q0BDd3d309vZmPOa0005j4cKFaQe6pht31NXVtUWotTc1D5tSQ8+gVHPIpSORSGQcGBqPx7n++utHpV9yySUVITCydZOrP9A7SkNYiJlSc+m15+JDyVafrp7hFfbANdoxdemFiDyJ98Abq92058FyqsTcmhiT2p1mk0jAxrhbVyOoW/8g1PWNXL0vPCdVsZybqfd9KAH4qdv7k7lPgFgjM+7W19fT2dlJZ2dnxmNOOOGEjJNypkNV6e/vJx6PM6Wx2WmqKf+bY445hq222irjzAnpBFEQpJAppLqYc8hl08iKTTaBsZeIbE6TLkBzmvTaYzwmmaj5Rskjnz6UVJ+o4hrAfDv1060bnkh4IRVz6Y0Nbi2OxvqRdWttdrPhBosvlaphzXTfQ07unCjUe1Qoyih8VURobm6mOfBfTBrduB500EEcdNBBOeWbTCbp7u4ezjeFvffemx/96EdpBU66qXyCMTgDAwPVKTBUNZZpnxGilMu/5suHMqkNNm6GhDjzSlIhmfBjMPLs1E9dN1wE6uuH/SSDQ+4zlICtUhzELU2jxnhsERaRHOZ5IN4DazcOR2K1Ng8LsIn4riplGeFyC18tkPCqq6tLH6Dg2Xnnndl5551zzjfbjNhbb70111577ShtZ4899sj5OoXChnNWMvmyfW+/tTP3DA4ON9wtLYCmX8diIg1junXDw+UPfBipy66CK1t768hefLoG7KXlTiANJV3a9tNHjPoeN8G1xJczkRw2o4nUxip/5TR/VbkJrwiknfnZM2XKFD7xiaKtWD0uTGBUMvmyfbe3wjZT4I01MJiAhjr3u6cv/87Y1HXDBxIuT01CXSPM2s417kFjAE6r6O5162O0t8ALy5zJrLnRRXaFG7C+foj3OUHU3OSu9cpKt2+iQiNoLNtbnX+nDlBxZW1trjqfQ1oyabXxnuJpeQHlJLxqBBuZUskEtu+GeqchNNSPr3cV73HOwrYW2Hqy+16/yTWCiYRrdIPIqURiYuGq0yYP+yyCBj2RhMZGN5XHqvVOIKxY5Y4ZGHRLvqpCm/dhbIoDvjwbNg+PEwEnWOoEEOfEb/Cr/72Rh0kK+gZc49jU6JabDYIEklrWvdq80tzonfwhevudoB4cGtnTj0ebdXjcBM8jjE2+WFBMw6h08mH7ztRT6+nLvzM27ODdFB+OcGry2kK8GwZibj3xoQT0+pln21qc4IjVO5NQ34DzcdTHnJBo8lpPwgsPVdeIJdUdH4QJT4SwCTAYLxH8rgVhAem12p4+N16n2D39MgtHrgVMYFQzUR2C2Zzn+XbGhssEw8ICXMNTF/ODB70vI5l0gqStZeTcVkEvt63Fhd8G8fcibm3xujo/IhwnROpkeLnX8VIj4a9ZSRfR1dTgtNEwxejp2/MoOiYwqpV0DsFlK12oamD/DwRIsXpqqWXqBjZ1wVaTXKTRUMI18LGQmaGhflg7CBzlwrApIhaDyR3DkVMdrU6ABCR9vHCwLni+tKNMGleqkG5tdoKwDEJQ80ZqJyLdXFrF6OlXWjhyFWACo1pJNTOpQm/vsLknHFGS755aJs0mtUztrc4sFe9xUVF1MhwJFRDMXzU45MJqN3UBApOah30qUzpdowxu2pCBQfdJKNTXubTW5vz0eLNpXKMEYi+8tc6FLbc0VUQUz5ike7al7OlXSjhylWBO72ol1SGYztwTiw33uvPhPIfhRjOdAzS1TE2NrjFNqmtcVV1j46f6ZnDICZFZ27kyKU4b6WwHfB2mdDoHffh6qm6djplbw7bThs1ZE+3xBpFAzy9z36lO3bBADKZYj9U54ZV6zyuRTM8W8vf+GGWNaRjVSqqZKZ25J2xnzldPLVuoYzrTVyzmhUYSmtqHQ2g3xp2paayGZ/nK0ddr8Waghvr89XijxPyn+oICn0s4qqiSo3iyPVu/+JVR3ZiGUa0E4atBj7vODzRrCc3oWQg7c7ZQx9QyBSYl1eGGqMlHTTXWu7EOazdkD89Md73WZtdw57PHm6o9pNMWUkNOA59LuHyVHMVjYaw1jwmMakbE+QjWbXINcUvTcPRQPsZUpCNdnH54gsB0pgtluCEaGHQD+gJn9Vgx/Zmu19Hmer27zc1P7zdKY5kqEBvq/RiThsLe82KR7dkaNYGZpKqRsPlkauewSWb65JERO4WIKJk22a2tsTk+vLZGY8OwOSid6au50ZmhBoeGndf1MXfeWDH9xXK4RokkS43aaWspzj0vFhbGWvOYwKhGsg3EG89sqjkjTrtJJv1o6DFWGGttdtFEsTp3TjDCOzCfZTN7FCu0MmpjWc1ROxbGWvOYwKhGCrESYFQC53ZHqBEZHMo+BqKnzzm+BwaHx1wE5hwYdthnmquoGI20NZaOahaIxpiYwKhGSjllwniEVd+A0yZam4cnJcSPyQiWYA3mhsplVtJ8T32dj8ayjNaSMIxcMad3NZIpGimqs3Ws8QbZGI9jNHxOY4ObLrzOm7GCyKnmxuwRSunqkGk8SKkoxzIZRg6YwKhGJjIQb6KN2niEVeo5Ik7TCCKckmOsy5FOwEUJgy025VgmY+JMpINVYRRUYIjIu0XkBRF5WUQuTLN/NxF5SET6ReSCQpal5ggWGso1rHSijdp4hNVY52TTWjIJuK7u/I0ZyFeDYOMYqo8a0xoL5sMQkRhwBfAuYAWwWETuUtXnQoetBz4LvL9Q5agaimX7zofDfDy2/mznZItQyhQR1jfgjpuoHyefq7rZdNzVR40t4lRIDWN/4GVVfUVVB4CbgfeFD1DV1aq6GBgsYDkqn2L2YspxcFY2DSRTr70+lp/Fn/JpRpqobykqE9GIasi8khdqTGssZJTUDOD10O8VwAHjyUhEPgl8EmDWrFkTL1mlUcxeTNTxBsWO9smkgWTqtbe3ujJNNAw2nyHKxQjNnYhGVIFrZJecGtMaCykw0o3W0vFkpKrXANcALFiwYFx5VDTFHFcRdc2HcmlYsgm4fITB5rtBKPQ4hol0LmrMvJIXamz0eyEFxgpgh9DvmcAbBbxe9ZJrozXR3v9YjVopG5Z0dStkr73SGoSJdC5KOeCzUqmxAZ2FFBiLgV1EZC6wEjgZ+HABr1e95NJoFaP3X6qGJVvdCjXlSaU1CBPRiGrMvJI3amj0e8EEhqoOichngHuBGHC9qj4rImf7/VeLyLbAEmASkBSR84HdVXVzocpVkeTSaBWj9z/ehmWimk+pNJtKahAmohFVmjZlFJ2CTg2iqncDd6ekXR3afgtnqjLGImqjVYze/3galvFoPqkCJt7jZoANUwqTSTlP7zERjajStCmj6NhcUtVGMcwK42lYctEO4j2wchVs6HILKbW1DM8pVVfnRoEXqm5jUU4O/6A86YTXeMtSSdqUUXRMYFQbhTArBA14l4/Jn9QG22+dm98gquYTNMjdvdDg1yDv6nbzS0VZerXQvf9yiiQqN+FlVD0mMKqNfJsV4j1uQaTefrdehQIbN7sBdHOzTDmS2nDXSbSR10GDrLjriUACJyg6/ZrfDfXp61bNDv90lJPwMgpLmZhBTWBUE6kv1cxtJv5Srd3g1qmoj/nFkICEwOBg5oYpXcPdP8CWoTnZNJ+gQa6PDS+mFAib8NKrmcparg7/QlBOwssoHGWkSdpstdVCoaYP6RvwS636xn4o4QRITz+s3Zg+/3TTaTQ3OX/EWJMSBlOTtDS5WWqTSbeQkibd+uRd3ZmnrCjGNA3Fmt4jCuU4jYuRf8polmMTGNVCoV6q5kanGKh6YTHgl17F9fzTCaVMDbcy9gy6QYNcVwftLcONclKdEGlvzSwMi9GATmTq+HxTTsLLKBxlNF+VmaSqhUKZJ6ZNdr363n7X+CqAQqzBNZIio00+EzHbhH0wiSRM3cqZs+rqxjY1FWscQblEElkYbG1QRmZQExjVQqFeqvZWmDvTRUmt3eiv1eR8CY0NrmebKpQm2nCnNshPv+jzSLr8WprctVOvW4sNaCbhVSZOUmMCBM+wq9tps63N7t0v4YBKExjVQiF71+2t8La50LQymlDKZ8Md73F/FtVhR3hXj/vjpA7iC65d6w1jGTlJjXESfobtrVDXBz297v1vby1ZR8gERrVQjN51LkIpXw332g2uZ9XT6/wYdeIc4L19MHv7iecfplp65blGi1VLvauJ1GfY1uK06ob6ws2bFgETGNVEoXvXpTD59A04baI+5sZiDCXcdkN9ea0jUU4Nbi7+LNNGypMyDZk2gWHkRrFNPoFvprHBfWC0WSwfjHcMRzk2uLn4s2zwX3lSRo7uMBZWa5QnwVKhXd1u/EVPX2FDR8cbulhGMfJbyCXctoxCNo0QZRoybQLDKD/CgxDbW51JqqfXzy9VoHEP4x3DUY4Nbi5jRWzwX3lSTuN9QphJyig/SuHwG2+UWZmaDiKbDm0NjPKlDCP+TMMwyo9S9NrH26MrU9NBZMq0J2uUJ6ZhGOVHIXvt2SKaxtOjq4bBgmXYkzXKExMYRvlRKDNJoSKarME1agQzSRnlR6HMJOUY0WQYFYRpGLVGuQ0yy0Qheu0TGQxVKffNMAqIaRjVQDBm4fllmdeKCI4rxJoZlcJ4Q0hr/b4ZhscERqWTS2NW6yaZ8UY01fp9MwyPCYxKJ5fGrBwHmRWT8fpGav2+GYbHfBiVTi52+XIdZBaVfPgRxuMbqfT7Zhh5wjSMSicXu3ypBplF9bGMlUep/AiVPjjPMPKECYxik4/GM0wujVkpRvXmq6EvpR/BRkMbBmAmqeJSiIFjuY40LvYgs3xNn13q9QFscJ5hmMAoKoVae6CcG7N8NfTmRzCMkmMmqWJSi9E2+Zo+2/wIhlFyTGAUk1pceyBfDb35EQyj5JhJqpjU4toD+ZzNtZxNb4ZRA5jAKCbVMBX2eLCG3jCqAhMYxcYaT8MwKhTzYRiGYRiRMIFhGIZhRMIEhmEYhhEJExiGYRhGJExgGIZhGJEQVS11GXJCRNYAr+Ypu2nA2jzlVWlY3WuPWq03WN3XArNVdUKDvipOYOQTEVmiqgtKXY5SYHWvvbrXar3B6p6vuptJyjAMw4iECQzDMAwjErUuMK4pdQFKiNW99qjVeoPVPS/UtA/DMAzDiE6taxiGYRhGRExgGIZhGJGoCYEhIjuIyF9F5B8i8qyInOfTp4jIn0TkJf9ddcu3iUiziDwqIk/6ul/s06u+7gEiEhORx0Xkd/53TdRdRJaLyNMi8oSILPFptVL3rUTkNhF53v/vD6qFuovI2/zzDj6bReT8fNW9JgQGMAR8XlXfDhwIfFpEdgcuBO5T1V2A+/zvaqMfOEJV9wL2Bt4tIgdSG3UPOA/4R+h3LdX9cFXdOxSHXyt1/wHwB1XdDdgL9/yrvu6q+oJ/3nsD+wE9wB3kq+6qWnMf4DfAu4AXgO182nbAC6UuW4Hr3Qo8BhxQK3UHZvo/yBHA73xardR9OTAtJa3q6w5MApbhg3pqqe4p9T0KeDCfda8VDWMLIjIH2Ad4BNhGVd8E8N9bl7BoBcObZJ4AVgN/UtWaqTvwfeCLQDKUVit1V+CPIrJURD7p02qh7jsCa4CfelPkdSLSRm3UPczJwE1+Oy91rymBISLtwO3A+aq6udTlKRaqmlCnos4E9heRPUpcpKIgIscBq1V1aanLUiLeoar7AsfgzLCHlLpARaIe2Be4SlX3AbqpQvNTNkSkETge+N985lszAkNEGnDC4peq+mufvEpEtvP7t8P1wKsWVd0ILALeTW3U/R3A8SKyHLgZOEJEbqQ26o6qvuG/V+Ps2PtTG3VfAazwmjTAbTgBUgt1DzgGeExVV/nfeal7TQgMERHgJ8A/VPW7oV13Aaf77dNxvo2qQkSmi8hWfrsFeCfwPDVQd1X9D1WdqapzcOr5X1T1VGqg7iLSJiIdwTbOnv0MNVB3VX0LeF1E3uaTjgSeowbqHuIUhs1RkKe618RIbxE5GPgb8DTDtuwv4/wYtwKzgNeAD6rq+pIUskCIyHzgZ0AM10G4VVW/LiJTqfK6hxGRw4ALVPW4Wqi7iOyI0yrAmWh+paqX1ULdAURkb+A6oBF4BTgT//5T/XVvBV4HdlTVTT4tL8+9JgSGYRiGMXFqwiRlGIZhTBwTGIZhGEYkTGAYhmEYkTCBYRiGYUTCBIZhGIYRCRMYNY6IxEtdhmIhIg0i8i0/Y+czfhbfY8aZ19kicprfPkNEth9HHueH8vi6iLwz4nlHh2YjjYvIC377574sP8q1LMVCRA4LZg3OsH+6iPyhmGUyolNf6gIYRiHwgzVFVcNzSF2Cm3htD1XtF5FtgEPHk7+qXh36eQZuUNwbOZSvHvgYbgQyqvrVHK59L3Cvz2cRbnxJMH35GVHzKUdUdY2IvCki71DVB0tdHmMkpmEYwJae36LQGgK/9I0uIrJQRP4ubk2NR0WkQ9w6Gz8Vt97C4yJyuD/2DBG5U0R+KyLLROQzIvI5f8zDIjLFH7eTiPzBT4z3NxHZLU2ZLhKRX4jIX7xW8G+hfV8QkcUi8pQMr/ExR9zaB1fiZuXdIXR8K/BvwLmq2g+gqqtU9Va//yoRWSKhNUN8+nIRudzX+1ER2TlUtgtE5CRgAfBL38tvEZGv+rI9IyLXBPcxhSNwUzcM+fxu8HkF17xYRB7z93fUvRmD7f29fUlEvh2qyyk+v2dE5PJQejy0fZKI3OC3P+iPfVJE7g/d47/5sj0mIv/i07O9P+/2aQ8AJ4audWhIU3pc/Mh04E7gIznW2SgGpZ6C1z6l/QBx/30YsAk3QWEd8BBwMMMjZRf64ybhNNPPAz/1abvhRo8243rbLwMdwHSf59n+uO/hJn4EN+X4Ln77ANy0Halluwh4EmgBpuFGr26Pm+biGkB8WX8HHALMwY3kPzBNXvOBx7Pchyn+O4abb2u+/70c+P/89mkMT5F+Ea5njz9+QWpefvsXwHvTXO9inPAKft8AnBS65rl++xzguizlTr32Gf55dfrn8SpOcG7vn9F0//z+Arw//A747ZOAG/z208AMv72V/24Fmv32LsCSMd6fZv/cdvHP69bQPfwtboJEgHag3m/PAJ4u9X/DPqM/pmEYYR5V1RXqzDhP4BrgtwFvqupiAFXdrK5XfDCuMURVn8c1TLv6fP6qql2qugbXiPzWpz8NzBE3a/C/AP8rbtr1H+NMRen4jar2qupa4K+4CfSO8p/HcZrEbrgGCeBVVX14HHX/fyLymM9zHrB7aN9Noe+DIuR1uIg8IiJP4zSJeWmO2Q43BXcmggkyl+KeQy7cp6qbVLUPN4fSbGAhsEhV1/jn90uckM3Gg8ANXrOL+bQG4Fpft/9l5H1K9/7sBixT1ZfUSYMbU/L/roh8FieQhnz6apyAM8oM82EYYfpD2wnc+yG4dRVSSWdmSZdPMvQ76fOsAzaqm3J9LFKvrf7a31TVH48okFvrpDtDPi8Ds0SkQ1W7Us6bC1yA06I2eJNMc4YyZJ1LR0SagStxvf7XReSilLwCejOkBwT3LHgOuZDpOWYiXKctZVLVs0XkAOA9wBPi5mc6F1iFW8WuDugb47qp+Q9fVPVbIvJ74FjgYRF5p+98NOPuj1FmmIZhjMXzOJv4QgDvv6gH7sfbmUVkV9ykZi9EyVDdWiTLROSD/nwRkb0yHP4+7y+ZijN7LMY5fD/mNRVEZIaIZF0QRlV7cDMW/4+4tQIQke1E5FScma0b2CTOEZ4aOfWh0PdDabLvwpngYLjBXevLd1KGIv0D2DlbmfPMI8ChIjJNRGK42Uz/z+9bJSJvF5E64ITgBBHZSVUfUeeQX4szbXXiNM4k8FGGNY9MPA/MFZGd/O9TUvJ/WlUvB5bgtBFwmuozE6msURhMwzCyoqoDIvIh4IfipkfvxU2RfiVwtTdNDAFnqIs8ipr1R4CrROQrODPHzTh/RSqPAr/HCaRL1K3x8IaIvB14yF8vDpyK69Vm4yvApcBzItKHExJfVdUnReRx4Fmc/T81OqdJRB7BdbBOYTQ34O5FL85kdS3O/LYcJ+DScQ/epFcMVPVNEfkPnFlPgLtVNZji+kKcH+h1XEPd7tP/S0QC38N9uOdzJXC7F/Z/JbNGF1y3T9xqf78XkbXAA0CwgNf54oIlEjjT2T0+/XDcMzfKDJut1ihbvDknrqr/XcIyLMeZl9YWIO87gC+q6kv5zruS8RFZ71PVDaUuizESM0kZRum4kMzO/ppERKYD3zVhUZ6YhmEYhmFEwjQMwzAMIxImMAzDMIxImMAwDMMwImECwzAMw4iECQzDMAwjEv8/AXLAAuI68YYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"geor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    DPC   R-squared:                       0.100\n",
      "Model:                            OLS   Adj. R-squared:                  0.088\n",
      "Method:                 Least Squares   F-statistic:                     7.905\n",
      "Date:                Tue, 16 Mar 2021   Prob (F-statistic):            0.00637\n",
      "Time:                        14:02:09   Log-Likelihood:                 297.22\n",
      "No. Observations:                  73   AIC:                            -590.4\n",
      "Df Residuals:                      71   BIC:                            -585.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0108      0.003      3.895      0.000       0.005       0.016\n",
      "IPC            0.0002   5.59e-05      2.812      0.006    4.57e-05       0.000\n",
      "==============================================================================\n",
      "Omnibus:                       50.646   Durbin-Watson:                   1.711\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              248.736\n",
      "Skew:                           1.998   Prob(JB):                     9.72e-55\n",
      "Kurtosis:                      11.112   Cond. No.                         283.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Correlation between Income per Capita and Doses per Capita: 0.31652576397426024\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_model_state_rem_out(\"kans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the dataset collectivly showed a statistically insignificant negative relationship between Income per Capita and Doses per Capita. While looking at the data on a state level, California, Ohio and now Kansas showed a positive relationship that was significant enough for us to reject the null hypothesis for these states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One ethics concern is that our reearch result could influence where officials decide to send vaccines in the future, which could be good or bad. To further explain, if this research is made public, it could be positive effect if officials decides to provide more to places that lacks Vaccination, but could also be negative effect if they decides to prioritize counties with higher income.  \n",
    "\n",
    "The income dataset we used was curated by CDC, and COVID 19 vaccine data was curated by Health department of State govenrment. Both of wich are accessible by public. Therefore, there will be no risk of privacy violation since all of our dataset will not contain personally identifying information, and there will also be no issue of consent since the data was provided by government officials and made publicly available; thus, it is unlikely that anyone will look at our study for this information.\n",
    "\n",
    "The concern with dataset provided by the CDC is that they recently admitted to falsely reporting the Covid-19 death figures in 2020 and have previously accepted private donations. These factors could contribute to potential biases in the datasets. Due to the admission of false reports by the CDC, we are aware that this could occur again, and the CDC data is being closely watched by officials and personnel to try to prevent a recurrence.\n",
    "\n",
    "Another concern with dataset is that COVID 19 vaccine data is changing very frequently and at the time of this reasearch, some state has not yet recieved second dose. This could mean that out research does not show full effect of income on COVID 19 vaccines.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After cleaning the data to show only what information we needed for our study, we used the data to refine and add additional columns as needed. We performed data exploration by plotting histograms and scatter plots to visualize any trends and note any possible outliers that could affect our models. To analyze the relationship between Income per Capita and Doses per Capita, we performed a linear regression and used Patsy and statsmodel.api to perform further analysis. For our alpha value, we used a value of 0.05 to determine if we should reject the null hypothesis. Our analysis results showed that the states of California, Washington, and Ohio had statistically significant correlations between the amount of wealth in a county and the number of vaccines distributed to the same county. The analysis of the states of Arizona, Kansas, and Georgia showed that there was no significant correlation. These results could be evidence of unequal distribution of vaccines in some areas of the county. While the overall analysis does not support the hypothesis that the entire country is distributing vaccines unequally, we propose that a possible cause of these unequal vaccine distribution patterns in some states could be a result of many other factors that we did not analyze.\n",
    "\n",
    "For further research on this topic, one could include all of the states for their datasets. Another factor that can be analyzed in the future could be the location of vaccine distribution points within a county or state to determine if the distribution centers are located in wealthier or poorer areas of a population. For instance, it could be hypothesized that in the unequal states, wealthier counties have easier access to transportation and healthcare information necessary to receive a dose. While poorer counties are farther away from distribution points and cannot as easily receive.  To conduct a similar study, one could take the same methods and apply them while studying a different factor, such as race or houselessness. Much more research would be necessary to discern the true pattern and causes of vaccine distribution.\n",
    "\n",
    "It is important to keep the limitations of our study in mind. One limitation of our project is that our datasets were posted by each state, so their most recent updates prior to our data collection could have differed. This is unlikely to result in significant changes in our overall results, yet the data from one state could have been updated at 5 pm at the beginning of the week during which we collected data, while data from another state could have been last updated at 8 pm on Friday of the week that we collected our data. Another limitation is that we only looked at 6 sampled states out of the country, so our data is limited in being representative of the whole national population. We did make sure that the states we chose to be used in our sample were geographically spread across the nation, but even then, the states all different in their distribution plans and it would be much easier to ensure representative data if data were collected from each state in the nation.\n"
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    "# Team Contributions"
   ]
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   "source": [
    "**Shushruth Kallutla**\n",
    "- Sourced California and Washington Data \n",
    "- Worked on data cleaning\n",
    "- Wrote analysis functions.\n",
    "\n",
    "**Nicholas Peterzell**\n",
    "- Sourced Arizona and Georgia data\n",
    "- Assisted in data cleaning and analysis\n",
    "- Worked on presentation slides\n",
    "- Brought group questions to meetings with the professor\n",
    "\n",
    "**Amanda Mark**\n",
    "- Helped search potential datasets\n",
    "- Helped edit writeup for extra info or to fix typos or wording\n",
    "- Worked on the Conclusion & Discussion portion of the writeup\n",
    "- Worked on the presentation slides and filmed the presentation video\n",
    "\n",
    "**Taylor Drennen** \n",
    "- Sourced Kansas Data\n",
    "- Worked on data cleaning\n",
    "- Worked on dataset, ethics and plivacy, and part of background portion of writeup\n",
    "\n",
    "**Carter Nielsen** \n",
    "- Sourced and cleaned census income data and Ohio data\n",
    "- Worked on setup, interpretations and writeup"
   ]
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