FinalProject_group054.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project (change this to your project's title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project was initially set out to assume that developed countries will tend to have more advanced health resources, which leads to a lower infection/death rate and a higher recovery rate during the COVID-19 pandemic. However, to further analyze, we separate the countries into two groups, developed and developing, and determine that there is no correlation between the economic status of a country and its COVID-19 health conditions. Plus, we even noticed that the developed countries have higher infection rate statistically, and both groups share the same death and recovery rates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "* Li-An Wu\n",
    "* Ray Huang\n",
    "* Tony Lin\n",
    "* Steven Kan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the GDP or poverty rate of a country affect its death/infected/recovered rate of the COVID-19?\n",
    " - Is there any negative correlation between the economic status and mortality rate of COVID-19?\n",
    " - Is there any negative correlation between the economic status and infection rate of COVID-19?\n",
    " - Is there any positive correlation between the economic status and recovered rate of COVID-19?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In December 2019, COVID-19 was first discovered in Wuhan, China. It is a highly contagious disease that causes severe complications to peoples’ respiratory systems. Its contagion has been unprecedentedly powerful and since its first appearance, more than a hundred million people have been infected worldwide, resulting in more than 2 million deaths. Many peoples’ lives have thus been affected by the pandemic. In fact, many other lethal viruses have also appeared in our history,  such as SARS virus, Ebola virus, and HIV virus etc… As we researched these past diseases, we found an interesting correlation- most of them appeared to be pervasive in areas of developing countries like Africa and Asia. With COVID-19 however, advanced countries like the United States, UK, France and many more seem to be the most severely affected areas in the world. Thus, we are interested to see if there is a relationship between the severity of COVID-19 and a country’s wealth level.\n",
    "\n",
    "As we searched through the internet for similar projects, we have found a useful COVID-19 project from “Our World in Data”. This project has provided us with a visualized graph with death per millions people of each country put side by side. From this project, we found that many advanced countries such as Belgium, UK, and Germany have the highest death rate and developing countries like Tanzania have significantly lower death rates. \n",
    "\n",
    "In addition, we have also found another research by the british media “mail Online”, it points out that poor countries' death rate of covid may be significantly biased due to multiple reasons. For example, COVID-19 usually causes death in older people, but in poor countries, life expectancy simply just isn’t that long. Moreover, there are also multiple other diseases plaguing these countries, causing deaths and thus impairing the rate of death by COVID-19.\n",
    "\n",
    "Reference:\n",
    "* https://ourworldindata.org/covid-19-project-charts\n",
    "* https://www.dailymail.co.uk/news/article-8697783/Are-people-Africa-protected-Covid-19-POVERTY.html "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since a country’s medical and technological development is highly related to its financial power, we predict that countries with higher GDP and lower poverty rates will have a lower death and infection rate on COVID-19. If a country has a stronger health system and a more stable economy, we think that it has a higher chance of preventing the spread and severity of COVID-19."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Name: Coronavirus death/infected population by country:   \\\n",
    "Link to the dataset: https://www.worldometers.info/coronavirus/#countries  \\\n",
    "Number of observation: 220  \n",
    "Description: This dataset contains COVID related information by country.\n",
    "\n",
    "Dataset Name: GDP by country              \n",
    "Link to the dataset:\n",
    "https://www.imf.org/en/Publications/WEO/weo-database/2020/October/weo-report?c=512,914,612,614,311,213,911,314,193,122,912,313,419,513,316,913,124,339,638,514,218,963,616,223,516,918,748,618,624,522,622,156,626,628,228,924,233,632,636,634,238,662,960,423,935,128,611,321,243,248,469,253,642,643,939,734,644,819,172,132,646,648,915,134,652,174,328,258,656,654,336,263,268,532,944,176,534,536,429,433,178,436,136,343,158,439,916,664,826,542,967,443,917,544,941,446,666,668,672,946,137,546,674,676,548,556,678,181,867,682,684,273,868,921,948,943,686,688,518,728,836,558,138,196,278,692,694,962,142,449,564,565,283,853,288,293,566,964,182,359,453,968,922,714,862,135,716,456,722,942,718,724,576,936,961,813,726,199,733,184,524,361,362,364,732,366,144,146,463,528,923,738,578,537,742,866,369,744,186,925,869,746,926,466,112,111,298,927,846,299,582,487,474,754,698,&s=NGDPD,PPPGDP,&sy=2019&ey=2020&ssm=0&scsm=0&scc=0&ssd=1&ssc=0&sic=0&sort=country&ds=.&br=1\n",
    "\n",
    "Number of observation: 390            \n",
    "Description: This dataset contains information about GDP of each country in US dollar (in Billions)\n",
    "\n",
    "Dataset Name: Poverty rate by country:\n",
    "\n",
    "Link to the dataset: https://worldpopulationreview.com/country-rankings/poverty-rate-by-country\n",
    "Number of observation: 162           \n",
    "Description: This dataset contains the poverty rate among all countries \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import scipy\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe your data cleaning steps here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. drop all non relevent (not needed) columns\n",
    "2. remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_death = pd.read_csv(\"worldometer_coronavirus.csv\")\n",
    "GDP_country = pd.read_csv('WEO_Data.csv', encoding = \"ISO-8859-1\")\n",
    "poverty = pd.read_csv(\"csvData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>World</td>\n",
       "      <td>108416629</td>\n",
       "      <td>2381578.0</td>\n",
       "      <td>80605048.0</td>\n",
       "      <td>13909.0</td>\n",
       "      <td>305.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>USA</td>\n",
       "      <td>28010156</td>\n",
       "      <td>487171.0</td>\n",
       "      <td>17932117.0</td>\n",
       "      <td>84318.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>3.321986e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>India</td>\n",
       "      <td>10880794</td>\n",
       "      <td>155489.0</td>\n",
       "      <td>10589230.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.388346e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>9716298</td>\n",
       "      <td>236397.0</td>\n",
       "      <td>8643693.0</td>\n",
       "      <td>45512.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>2.134898e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Russia</td>\n",
       "      <td>4042837</td>\n",
       "      <td>79194.0</td>\n",
       "      <td>3559142.0</td>\n",
       "      <td>27696.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>1.459731e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "0   World    108416629     2381578.0       80605048.0           13909.0   \n",
       "1     USA     28010156      487171.0       17932117.0           84318.0   \n",
       "2   India     10880794      155489.0       10589230.0            7837.0   \n",
       "3  Brazil      9716298      236397.0        8643693.0           45512.0   \n",
       "4  Russia      4042837       79194.0        3559142.0           27696.0   \n",
       "\n",
       "   Deaths/1M pop    Population  \n",
       "0          305.5           NaN  \n",
       "1         1467.0  3.321986e+08  \n",
       "2          112.0  1.388346e+09  \n",
       "3         1107.0  2.134898e+08  \n",
       "4          543.0  1.459731e+08  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing commas from numbers\n",
    "covid_death = covid_death.replace(',','', regex=True)\n",
    "# Convert string to integer\n",
    "covid_death['Total Cases'] = pd.to_numeric(covid_death['Total Cases'])\n",
    "covid_death['Total Deaths'] = pd.to_numeric(covid_death['Total Deaths'])\n",
    "covid_death['Total Recovered'] = pd.to_numeric(covid_death['Total Recovered'])\n",
    "covid_death['Serious/Critical'] = pd.to_numeric(covid_death['Serious/Critical'])\n",
    "covid_death['TotCases/ 1M pop'] = pd.to_numeric(covid_death['TotCases/ 1M pop'])\n",
    "covid_death['Deaths/1M pop'] = pd.to_numeric(covid_death['Deaths/1M pop'])\n",
    "covid_death['Total Tests'] = pd.to_numeric(covid_death['Total Tests'])\n",
    "covid_death['Tests/1M pop'] = pd.to_numeric(covid_death['Tests/1M pop'])\n",
    "covid_death['Population'] = pd.to_numeric(covid_death['Population'])\n",
    "\n",
    "#Dropping Unused Columns.\n",
    "covid_death = covid_death.drop(columns=['New Cases', 'New Deaths', 'Active Cases','Serious/Critical','Total Tests', 'Tests/1M pop' ])\n",
    "covid_death.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>18.876</td>\n",
       "      <td>19.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>15.276</td>\n",
       "      <td>14.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>169.267</td>\n",
       "      <td>147.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>89.417</td>\n",
       "      <td>62.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>1.662</td>\n",
       "      <td>1.389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country         Units     Scale     2019     2020\n",
       "0          Afghanistan  U.S. dollars  Billions   18.876   19.006\n",
       "1              Albania  U.S. dollars  Billions   15.276   14.034\n",
       "2              Algeria  U.S. dollars  Billions  169.267  147.323\n",
       "3               Angola  U.S. dollars  Billions   89.417   62.724\n",
       "4  Antigua and Barbuda  U.S. dollars  Billions    1.662    1.389"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP_country = GDP_country.drop(columns=['Subject Descriptor', 'Country/Series-specific Notes', 'Estimates Start After'])\n",
    "#Adding units to GDP data to make information clear.\n",
    "GDP_country = GDP_country[GDP_country['Units'] == \"U.S. dollars\"]\n",
    "pd.set_option('display.max_rows', GDP_country.shape[0]+1)\n",
    "GDP_country = GDP_country.reset_index(drop=True)\n",
    "#Coverting strings to integers\n",
    "GDP_country = GDP_country.replace(',','', regex=True)\n",
    "GDP_country['2019'] = pd.to_numeric(GDP_country['2019'])\n",
    "GDP_country['2020'] = pd.to_numeric(GDP_country['2020'])\n",
    "GDP_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing inconsistencies in naming of columns\n",
    "GDP_country = GDP_country.rename(columns={'country': 'Country'})\n",
    "GDP_countries = GDP_country['Country'].unique()\n",
    "GDP_countries.sort()\n",
    "\n",
    "covid_death_countries = covid_death['Country'].unique()\n",
    "covid_death_countries.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the inconsistency in the country names\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Brunei Darussalam'], 'Brunei')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Central African Republic'], 'CAR')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Czech Republic'], 'Czechia')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace([\"Côte d\\'Ivoire\"], 'Ivory Coast')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Democratic Republic of the Congo'], 'DRC')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Hong Kong SAR'], 'Hong Kong')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Islamic Republic of Iran'], 'Iran')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Korea'], 'S. Korea')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Kyrgyz Republic'], 'Kyrgyzstan')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Lao P.D.R.'], 'Laos')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Macao SAR'], 'Macao')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Republic of Congo'], 'Congo')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Slovak Republic'], 'Slovakia')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['St. Kitts and Nevis'], 'Saint Kitts and Nevis')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['St. Lucia'], 'Saint Lucia')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['St. Vincent and the Grenadines'], 'St. Vincent Grenadines')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['São Tomé and Príncipe'], 'Sao Tome and Principe')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['Taiwan Province of China'], 'Taiwan')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['The Bahamas'], 'Bahamas')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['The Gambia'], 'Gambia')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['United Arab Emirates'], 'UAE')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['United Kingdom'], 'UK')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['United States'], 'USA')\n",
    "GDP_country['Country'] = GDP_country['Country'].replace(['West Bank and Gaza'], 'Palestine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Match the corresponding countries \n",
    "combine = pd.merge(covid_death, GDP_country, on='Country')\n",
    "\n",
    "combine = combine.drop(columns = '2019')\n",
    "combine = combine.rename(columns={'2020': '2020 GDP'})\n",
    "\n",
    "combine['Recovery Percentage'] = combine['Total Recovered'] / combine['Total Cases']\n",
    "combine['Death Percentage'] = combine['Total Deaths'] / combine['Total Cases']\n",
    "combine['GDP per Capita'] = combine['2020 GDP'] / combine['Population'] * 1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. add extra columns of infection rates per 100k and 200k \n",
    "\n",
    "#TotCases/ 1M pop\tDeaths/1M pop\n",
    "combine['TotCases_per_100k_pop'] = combine['TotCases/ 1M pop'] / 10\n",
    "combine['Deaths_per_100k_pop'] = combine['Deaths/1M pop'] / 10\n",
    "combine['log_TotCases_per_1M_pop'] = np.log( combine['TotCases/ 1M pop'])\n",
    "combine['sqrt_TotCases_per_1M_pop'] = np.sqrt(combine['TotCases/ 1M pop'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>USA</td>\n",
       "      <td>28010156</td>\n",
       "      <td>487171.0</td>\n",
       "      <td>17932117.0</td>\n",
       "      <td>84318.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>3.321986e+08</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>20807.27</td>\n",
       "      <td>0.640201</td>\n",
       "      <td>0.017393</td>\n",
       "      <td>62635.037833</td>\n",
       "      <td>8431.8</td>\n",
       "      <td>146.7</td>\n",
       "      <td>11.342351</td>\n",
       "      <td>290.375619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>India</td>\n",
       "      <td>10880794</td>\n",
       "      <td>155489.0</td>\n",
       "      <td>10589230.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.388346e+09</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>2592.58</td>\n",
       "      <td>0.973204</td>\n",
       "      <td>0.014290</td>\n",
       "      <td>1867.388044</td>\n",
       "      <td>783.7</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.966611</td>\n",
       "      <td>88.526832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>9716298</td>\n",
       "      <td>236397.0</td>\n",
       "      <td>8643693.0</td>\n",
       "      <td>45512.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>2.134898e+08</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>1363.77</td>\n",
       "      <td>0.889608</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>6387.988182</td>\n",
       "      <td>4551.2</td>\n",
       "      <td>110.7</td>\n",
       "      <td>10.725731</td>\n",
       "      <td>213.335417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Russia</td>\n",
       "      <td>4042837</td>\n",
       "      <td>79194.0</td>\n",
       "      <td>3559142.0</td>\n",
       "      <td>27696.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>1.459731e+08</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>1464.08</td>\n",
       "      <td>0.880358</td>\n",
       "      <td>0.019589</td>\n",
       "      <td>10029.791237</td>\n",
       "      <td>2769.6</td>\n",
       "      <td>54.3</td>\n",
       "      <td>10.229043</td>\n",
       "      <td>166.421153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>UK</td>\n",
       "      <td>3998655</td>\n",
       "      <td>115529.0</td>\n",
       "      <td>2056261.0</td>\n",
       "      <td>58712.0</td>\n",
       "      <td>1696.0</td>\n",
       "      <td>6.810580e+07</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>2638.30</td>\n",
       "      <td>0.514238</td>\n",
       "      <td>0.028892</td>\n",
       "      <td>38738.256103</td>\n",
       "      <td>5871.2</td>\n",
       "      <td>169.6</td>\n",
       "      <td>10.980399</td>\n",
       "      <td>242.305592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "0     USA     28010156      487171.0       17932117.0           84318.0   \n",
       "1   India     10880794      155489.0       10589230.0            7837.0   \n",
       "2  Brazil      9716298      236397.0        8643693.0           45512.0   \n",
       "3  Russia      4042837       79194.0        3559142.0           27696.0   \n",
       "4      UK      3998655      115529.0        2056261.0           58712.0   \n",
       "\n",
       "   Deaths/1M pop    Population         Units     Scale  2020 GDP  \\\n",
       "0         1467.0  3.321986e+08  U.S. dollars  Billions  20807.27   \n",
       "1          112.0  1.388346e+09  U.S. dollars  Billions   2592.58   \n",
       "2         1107.0  2.134898e+08  U.S. dollars  Billions   1363.77   \n",
       "3          543.0  1.459731e+08  U.S. dollars  Billions   1464.08   \n",
       "4         1696.0  6.810580e+07  U.S. dollars  Billions   2638.30   \n",
       "\n",
       "   Recovery Percentage  Death Percentage  GDP per Capita  \\\n",
       "0             0.640201          0.017393    62635.037833   \n",
       "1             0.973204          0.014290     1867.388044   \n",
       "2             0.889608          0.024330     6387.988182   \n",
       "3             0.880358          0.019589    10029.791237   \n",
       "4             0.514238          0.028892    38738.256103   \n",
       "\n",
       "   TotCases_per_100k_pop  Deaths_per_100k_pop  log_TotCases_per_1M_pop  \\\n",
       "0                 8431.8                146.7                11.342351   \n",
       "1                  783.7                 11.2                 8.966611   \n",
       "2                 4551.2                110.7                10.725731   \n",
       "3                 2769.6                 54.3                10.229043   \n",
       "4                 5871.2                169.6                10.980399   \n",
       "\n",
       "   sqrt_TotCases_per_1M_pop  \n",
       "0                290.375619  \n",
       "1                 88.526832  \n",
       "2                213.335417  \n",
       "3                166.421153  \n",
       "4                242.305592  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Include cells that describe the steps in your data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1.840000e+02</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.789856e+05</td>\n",
       "      <td>13524.505682</td>\n",
       "      <td>4.363635e+05</td>\n",
       "      <td>20080.272727</td>\n",
       "      <td>399.169886</td>\n",
       "      <td>4.165806e+07</td>\n",
       "      <td>449.028486</td>\n",
       "      <td>0.828008</td>\n",
       "      <td>0.021446</td>\n",
       "      <td>13416.800734</td>\n",
       "      <td>2008.027273</td>\n",
       "      <td>39.916989</td>\n",
       "      <td>8.482276</td>\n",
       "      <td>111.789248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.366707e+06</td>\n",
       "      <td>46573.355609</td>\n",
       "      <td>1.703491e+06</td>\n",
       "      <td>24531.287276</td>\n",
       "      <td>505.037987</td>\n",
       "      <td>1.509363e+08</td>\n",
       "      <td>1950.741054</td>\n",
       "      <td>0.187563</td>\n",
       "      <td>0.024280</td>\n",
       "      <td>18491.157593</td>\n",
       "      <td>2453.128728</td>\n",
       "      <td>50.503799</td>\n",
       "      <td>2.324829</td>\n",
       "      <td>87.316711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.397500e+04</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.063932</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>258.634519</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.732051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>6.190000e+03</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>4.861000e+03</td>\n",
       "      <td>1132.500000</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>2.419295e+06</td>\n",
       "      <td>10.252000</td>\n",
       "      <td>0.795961</td>\n",
       "      <td>0.011018</td>\n",
       "      <td>1867.388044</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>2.175000</td>\n",
       "      <td>7.032161</td>\n",
       "      <td>33.652453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>5.977700e+04</td>\n",
       "      <td>976.500000</td>\n",
       "      <td>4.490800e+04</td>\n",
       "      <td>9136.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>9.447402e+06</td>\n",
       "      <td>34.624000</td>\n",
       "      <td>0.899102</td>\n",
       "      <td>0.017329</td>\n",
       "      <td>4791.964803</td>\n",
       "      <td>913.600000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>9.119978</td>\n",
       "      <td>95.582425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.634390e+05</td>\n",
       "      <td>5805.500000</td>\n",
       "      <td>1.982050e+05</td>\n",
       "      <td>33945.000000</td>\n",
       "      <td>639.000000</td>\n",
       "      <td>3.084800e+07</td>\n",
       "      <td>241.975000</td>\n",
       "      <td>0.942329</td>\n",
       "      <td>0.025676</td>\n",
       "      <td>16301.398065</td>\n",
       "      <td>3394.500000</td>\n",
       "      <td>63.900000</td>\n",
       "      <td>10.432398</td>\n",
       "      <td>184.237141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.801016e+07</td>\n",
       "      <td>487171.000000</td>\n",
       "      <td>1.793212e+07</td>\n",
       "      <td>107370.000000</td>\n",
       "      <td>2119.000000</td>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>20807.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288660</td>\n",
       "      <td>108527.781644</td>\n",
       "      <td>10737.000000</td>\n",
       "      <td>211.900000</td>\n",
       "      <td>11.584036</td>\n",
       "      <td>327.673618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total Cases   Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "count  1.870000e+02     176.000000     1.840000e+02        187.000000   \n",
       "mean   5.789856e+05   13524.505682     4.363635e+05      20080.272727   \n",
       "std    2.366707e+06   46573.355609     1.703491e+06      24531.287276   \n",
       "min    1.000000e+00       1.000000     1.000000e+00          3.000000   \n",
       "25%    6.190000e+03     142.500000     4.861000e+03       1132.500000   \n",
       "50%    5.977700e+04     976.500000     4.490800e+04       9136.000000   \n",
       "75%    2.634390e+05    5805.500000     1.982050e+05      33945.000000   \n",
       "max    2.801016e+07  487171.000000     1.793212e+07     107370.000000   \n",
       "\n",
       "       Deaths/1M pop    Population      2020 GDP  Recovery Percentage  \\\n",
       "count     176.000000  1.870000e+02    185.000000           184.000000   \n",
       "mean      399.169886  4.165806e+07    449.028486             0.828008   \n",
       "std       505.037987  1.509363e+08   1950.741054             0.187563   \n",
       "min         0.200000  3.397500e+04      0.225000             0.063932   \n",
       "25%        21.750000  2.419295e+06     10.252000             0.795961   \n",
       "50%       119.000000  9.447402e+06     34.624000             0.899102   \n",
       "75%       639.000000  3.084800e+07    241.975000             0.942329   \n",
       "max      2119.000000  1.439324e+09  20807.270000             1.000000   \n",
       "\n",
       "       Death Percentage  GDP per Capita  TotCases_per_100k_pop  \\\n",
       "count        176.000000      185.000000             187.000000   \n",
       "mean           0.021446    13416.800734            2008.027273   \n",
       "std            0.024280    18491.157593            2453.128728   \n",
       "min            0.000485      258.634519               0.300000   \n",
       "25%            0.011018     1867.388044             113.250000   \n",
       "50%            0.017329     4791.964803             913.600000   \n",
       "75%            0.025676    16301.398065            3394.500000   \n",
       "max            0.288660   108527.781644           10737.000000   \n",
       "\n",
       "       Deaths_per_100k_pop  log_TotCases_per_1M_pop  sqrt_TotCases_per_1M_pop  \n",
       "count           176.000000               187.000000                187.000000  \n",
       "mean             39.916989                 8.482276                111.789248  \n",
       "std              50.503799                 2.324829                 87.316711  \n",
       "min               0.020000                 1.098612                  1.732051  \n",
       "25%               2.175000                 7.032161                 33.652453  \n",
       "50%              11.900000                 9.119978                 95.582425  \n",
       "75%              63.900000                10.432398                184.237141  \n",
       "max             211.900000                11.584036                327.673618  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "f1 = combine.hist(column = 'TotCases_per_100k_pop', bins = 10)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted a histogram of the total cases per 100k population to visualize how this variable is distributed.\n",
    "\n",
    "The distribution for this variable doesn't seem to fit any of the common distributions, but the data was highly skewed to the right.\n",
    "\n",
    "We observed that for many countries, the number of cases per 100k people remained on the low end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "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": [
    "# log transformed data\n",
    "f2 = combine.hist(column = 'sqrt_TotCases_per_1M_pop', bins = 10)   \n",
    "f3 = combine.hist(column = 'TotCases_per_100k_pop', bins = 10)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous histogram, we observe that the shape of the distribution is strongly skewed to the right. This hard for us the visualized the detail.\n",
    "\n",
    "Thus, we decide to tranform the value to a smaller scale. In the above graphs, we tried to transform the original data with taking the square root \n",
    "\n",
    "and dividing by 10.\n",
    "\n",
    "We oberseve that taking the square root makes the data more cluster in a way that is convenient for us to visualize.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GDP Data Part 1\n",
    "we perform our analysis of the countries economic status based on the GDP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we here display the central property of the GDP per capita data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       185.000000\n",
       "mean      13416.800734\n",
       "std       18491.157593\n",
       "min         258.634519\n",
       "25%        1867.388044\n",
       "50%        4791.964803\n",
       "75%       16301.398065\n",
       "max      108527.781644\n",
       "Name: GDP per Capita, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['GDP per Capita'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c25d7a050>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='GDP per Capita', data=combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c25da9150>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine.hist(column = 'GDP per Capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the GDP per capita of all the countries. \n",
    "\n",
    "Again, the distribution for this variable doesn't seem to fit any of the common distributions, but the data was highly skewed to the right. \n",
    "\n",
    "We observed that for the vast majority of countries, GDP per capita was low ( < 20000 USD per year)\n",
    "\n",
    "This prompted us to divide the countries based on their GDP per capita into two groups- developing and developed. \n",
    "\n",
    "Note that 20,000 USD per year is just an arbitrary sum that we collectively chose and is not a typical measure for whether a country is categorized as developed or developing. In this case though, countries whose citizens earn, on average, more than 20,000 USD per year is considered developed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GDP Data Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the statistc of GDP per capita and found that most of the data are highly clustered from 0 to 75 percentile. Thus we decide to break up\n",
    "\n",
    "the data into two parts with 75 percentile as the threshold to minimize the imbalance of data.\n",
    "\n",
    "We labeled them as combine_developing and combine_developed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine_developing = combine[combine['GDP per Capita'] < 16301.398065]\n",
    "combine_developed = combine[combine['GDP per Capita'] >= 16301.398065]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c26057390>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developing.hist(column = 'GDP per Capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the GDP per capita of all the developing countries.\n",
    "\n",
    "Again, the distribution for the variables doesn't seem to fit any of the common distributions, but the data was highly skewed to the right. \n",
    "\n",
    "We surprisingly observed that for the vast majority of developing countries, GDP per capita was much lower than we initially expected, (< 5,000 USD per year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c25dda590>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developed.hist(column = 'GDP per Capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the GDP per capita of all the developed countries.\n",
    "\n",
    "Again, the distribution for the variables doesn't seem to fit any of the common distributions, but the data was skewed to the right.\n",
    "\n",
    "We observed that there is a country with a super high GDP, upon investigating, this turned out to be a tax haven for the wealthy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing with death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    176.000000\n",
       "mean       0.021446\n",
       "std        0.024280\n",
       "min        0.000485\n",
       "25%        0.011018\n",
       "50%        0.017329\n",
       "75%        0.025676\n",
       "max        0.288660\n",
       "Name: Death Percentage, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['Death Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c26290ad0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ER+u+IyJ+YZrmdwGfaatjEc1f7fi1iBi/nn1YRMw0Jezk8R9FM1MhNPP8jrsX+O36uOfSXAqB5pLMBRHxs3Xb0dH2t/ek6RjQms7C8Y/Z0cw4dhdwZd32ZWA7sDUiHqL5dMUI8BVgRURsBj4GPApQmjmyvxMRD7W9SdiLj9Fc7niAZtawyX+SbNxfA4vqfh8Aziql7AI+AfxzRDxIE9inzLC/24EPj79JSHPGfEtEfBt4rq3dlcC5EbEVOI/mDzi8VErZTjNp+111n5uAt3VatN58nM1O6pOIWADsLaWM1c9s/+N+LsVIM/IatNQ/xwNfjeYLPa8CfzTk8WiO8wxakpLyGrQkJWVAS1JSBrQkJWVAS1JSBrQkJfX/rwV0P6gpzWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Death Percentage', data=combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2134</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30226897.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>20.948</td>\n",
       "      <td>0.669166</td>\n",
       "      <td>0.28866</td>\n",
       "      <td>693.025156</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.26268</td>\n",
       "      <td>8.42615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "160   Yemen         2134         616.0           1428.0              71.0   \n",
       "\n",
       "     Deaths/1M pop  Population         Units     Scale  2020 GDP  \\\n",
       "160           20.0  30226897.0  U.S. dollars  Billions    20.948   \n",
       "\n",
       "     Recovery Percentage  Death Percentage  GDP per Capita  \\\n",
       "160             0.669166           0.28866      693.025156   \n",
       "\n",
       "     TotCases_per_100k_pop  Deaths_per_100k_pop  log_TotCases_per_1M_pop  \\\n",
       "160                    7.1                  2.0                  4.26268   \n",
       "\n",
       "     sqrt_TotCases_per_1M_pop  \n",
       "160                   8.42615  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_percentage_outlier = combine[combine['Death Percentage'] > 0.25]\n",
    "death_percentage_outlier.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yemen is the country which has the highest death percentage. Since the data is the outlier, we normaly want to exclude it to check the normality of the distribution for the overall countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    175.000000\n",
       "mean       0.019919\n",
       "std        0.013423\n",
       "min        0.000485\n",
       "25%        0.010947\n",
       "50%        0.017265\n",
       "75%        0.025538\n",
       "max        0.086984\n",
       "Name: Death Percentage, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine = combine[combine['Death Percentage'] <= 0.25]\n",
    "combine['Death Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2548fc50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Death Percentage', data=combine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For almost all countries:\n",
    "- The average death percentage is about 2.00%\n",
    "- The median death percentage is about 1.73%\n",
    "- The maximum death percentage is about 8.70%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    128.000000\n",
       "mean       0.020757\n",
       "std        0.014139\n",
       "min        0.000890\n",
       "25%        0.011221\n",
       "50%        0.016889\n",
       "75%        0.026704\n",
       "max        0.086984\n",
       "Name: Death Percentage, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developing = combine_developing[combine_developing['Death Percentage'] <= 0.25]\n",
    "combine_developing['Death Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c26355f90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Death Percentage', data=combine_developing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the developing countries: \n",
    "- The average death percentage is about 2.08%\n",
    "- The median death percentage is about 1.70%\n",
    "- The maximum death percentage is about 8.70%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45.000000\n",
       "mean      0.016477\n",
       "std       0.008580\n",
       "min       0.000485\n",
       "25%       0.009605\n",
       "50%       0.017265\n",
       "75%       0.020931\n",
       "max       0.035861\n",
       "Name: Death Percentage, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developed['Death Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2645c410>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Death Percentage', data=combine_developed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the developed countries: \n",
    "- The average death percentage is about 1.65%\n",
    "- The median death percentage is about 1.73%\n",
    "- The maximum death percentage is about 3.59%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing with Recovery Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    172.000000\n",
       "mean       0.823205\n",
       "std        0.190107\n",
       "min        0.063932\n",
       "25%        0.795961\n",
       "50%        0.889854\n",
       "75%        0.939331\n",
       "max        0.996336\n",
       "Name: Recovery Percentage, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['Recovery Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c266a6dd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Recovery Percentage', data=combine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For almost all countries:\n",
    "- The average recovery percentage is about 82.80%\n",
    "- The median recovery percentage is about 89.91%\n",
    "- The maximum recovery percentage is about 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    128.000000\n",
       "mean       0.829883\n",
       "std        0.162416\n",
       "min        0.075468\n",
       "25%        0.777137\n",
       "50%        0.877776\n",
       "75%        0.939331\n",
       "max        0.993237\n",
       "Name: Recovery Percentage, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developing['Recovery Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c267b4090>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Recovery Percentage', data=combine_developing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For developing countries:\n",
    "- The average recovery percentage is about 83.47%\n",
    "- The median recovery percentage is about 88.04%\n",
    "- The maximum recovery percentage is about 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c267e8f50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developing.hist(column = 'Recovery Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the percentage of recovered patients in the developing countries.\n",
    "\n",
    "The disitribution doesn't fit any of the common distributions, but the data was highly skewed to the left.\n",
    "\n",
    "We observed that the recovery percentage is quite high for most developing countries, contrary to what we initially predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    43.000000\n",
       "mean      0.809926\n",
       "std       0.256441\n",
       "min       0.063932\n",
       "25%       0.833113\n",
       "50%       0.904901\n",
       "75%       0.942338\n",
       "max       0.996336\n",
       "Name: Recovery Percentage, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developed['Recovery Percentage'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2694b990>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0UtU27ypzwbNVTV1I1dTOLFfr66rMlb+++ZeL0xUBDEXEQ6r+/td7I+JuSV+S9IDtnZK+IunsiNgt6W8lfdP2I5I+XU5fKun9tneoCptlIy69VtJ15d9h75N0fbnGbo3+54+GrVc1BTLW9MNYblI1DbDD1R9WvGm0gyJic7nHtjIdMzxl0UqNkrRD0lD5Qd5ySaskLbK9VdX0w/Co+6uq5qB3qVri9NuqljJ8UdVc8N+Vez6sahoEXY7V0DBhuVr0/TMR8fbsWtrF9lkR8Xx5J/EdSb8ZEU9l14UczAFjQnL1J9GXaOy539PVRlcfejlD0k2E7+TGCBgAkjAHDABJCGAASEIAA0ASAhgAkhDAAJDk/wBQ+kg6IoBRngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Recovery Percentage', data=combine_developed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For developed countries:\n",
    "- The average recovery percentage is about 81.00%\n",
    "- The median recovery percentage is about 90.49%\n",
    "- The maximum recovery percentage is about 99.63%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c26975a90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developed.hist(column = 'Recovery Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the recovery percentage of all the developed countries.\n",
    "\n",
    "Again, the distribution for the variables doesn't seem to fit any of the common distributions, but the data was skewed to the left. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "recovery_outlier = combine_developed[combine_developed['Recovery Percentage'] < 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>France</td>\n",
       "      <td>3406685</td>\n",
       "      <td>80803.0</td>\n",
       "      <td>238753.0</td>\n",
       "      <td>52120.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>65362676.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>2551.450</td>\n",
       "      <td>0.070084</td>\n",
       "      <td>0.023719</td>\n",
       "      <td>39035.274504</td>\n",
       "      <td>5212.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>10.861304</td>\n",
       "      <td>228.298051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>733100</td>\n",
       "      <td>21551.0</td>\n",
       "      <td>50362.0</td>\n",
       "      <td>63086.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>11620726.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>503.416</td>\n",
       "      <td>0.068697</td>\n",
       "      <td>0.029397</td>\n",
       "      <td>43320.529199</td>\n",
       "      <td>6308.6</td>\n",
       "      <td>185.5</td>\n",
       "      <td>11.052254</td>\n",
       "      <td>251.169266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>206801</td>\n",
       "      <td>3846.0</td>\n",
       "      <td>23364.0</td>\n",
       "      <td>41596.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>4971670.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>399.064</td>\n",
       "      <td>0.112978</td>\n",
       "      <td>0.018598</td>\n",
       "      <td>80267.596200</td>\n",
       "      <td>4159.6</td>\n",
       "      <td>77.4</td>\n",
       "      <td>10.635759</td>\n",
       "      <td>203.950975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>32175</td>\n",
       "      <td>218.0</td>\n",
       "      <td>2057.0</td>\n",
       "      <td>26530.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1212770.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>23.246</td>\n",
       "      <td>0.063932</td>\n",
       "      <td>0.006775</td>\n",
       "      <td>19167.690494</td>\n",
       "      <td>2653.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.186031</td>\n",
       "      <td>162.880324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "5     France      3406685       80803.0         238753.0           52120.0   \n",
       "25   Belgium       733100       21551.0          50362.0           63086.0   \n",
       "55   Ireland       206801        3846.0          23364.0           41596.0   \n",
       "104   Cyprus        32175         218.0           2057.0           26530.0   \n",
       "\n",
       "     Deaths/1M pop  Population         Units     Scale  2020 GDP  \\\n",
       "5           1236.0  65362676.0  U.S. dollars  Billions  2551.450   \n",
       "25          1855.0  11620726.0  U.S. dollars  Billions   503.416   \n",
       "55           774.0   4971670.0  U.S. dollars  Billions   399.064   \n",
       "104          180.0   1212770.0  U.S. dollars  Billions    23.246   \n",
       "\n",
       "     Recovery Percentage  Death Percentage  GDP per Capita  \\\n",
       "5               0.070084          0.023719    39035.274504   \n",
       "25              0.068697          0.029397    43320.529199   \n",
       "55              0.112978          0.018598    80267.596200   \n",
       "104             0.063932          0.006775    19167.690494   \n",
       "\n",
       "     TotCases_per_100k_pop  Deaths_per_100k_pop  log_TotCases_per_1M_pop  \\\n",
       "5                   5212.0                123.6                10.861304   \n",
       "25                  6308.6                185.5                11.052254   \n",
       "55                  4159.6                 77.4                10.635759   \n",
       "104                 2653.0                 18.0                10.186031   \n",
       "\n",
       "     sqrt_TotCases_per_1M_pop  \n",
       "5                  228.298051  \n",
       "25                 251.169266  \n",
       "55                 203.950975  \n",
       "104                162.880324  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovery_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observed that the recovery percentage is generally quite high, with 4 countries with super low recovery rates. Those 4 countries are France, Belgium, Ireland, Cyprus. Upon investigating, these countries had a low number of cases which hasn't concluded yet.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Recovery Percentage', data=combine, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Recovery Percentage\")   R-squared:                       0.006\n",
      "Model:                                  OLS   Adj. R-squared:                  0.000\n",
      "Method:                       Least Squares   F-statistic:                     1.014\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):              0.315\n",
      "Time:                              23:31:26   Log-Likelihood:                 42.032\n",
      "No. Observations:                       170   AIC:                            -80.06\n",
      "Df Residuals:                           168   BIC:                            -73.79\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept               0.8347      0.018     46.423      0.000       0.799       0.870\n",
      "Q(\"GDP per Capita\") -7.953e-07    7.9e-07     -1.007      0.315   -2.35e-06    7.64e-07\n",
      "==============================================================================\n",
      "Omnibus:                       92.965   Durbin-Watson:                   2.124\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              339.601\n",
      "Skew:                          -2.251   Prob(JB):                     1.81e-74\n",
      "Kurtosis:                       8.260   Cond. No.                     2.81e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.81e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_1, predictors_1 = patsy.dmatrices('Q(\"Recovery Percentage\") ~ Q(\"GDP per Capita\")', combine)\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted GDP per capita against recovery rate and made a linear model using OLS. Since R^2 is about 0.006, there is no sufficient evidence to support that the correlation between GDP Per Capita and Recovery Rate. This is most likely because most countries have GDP Per Capita lower than 20,000, so this graph is not normally distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Recovery Percentage', data=combine_developing, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Recovery Percentage\")   R-squared:                       0.008\n",
      "Model:                                  OLS   Adj. R-squared:                 -0.000\n",
      "Method:                       Least Squares   F-statistic:                    0.9760\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):              0.325\n",
      "Time:                              23:31:28   Log-Likelihood:                 52.024\n",
      "No. Observations:                       128   AIC:                            -100.0\n",
      "Df Residuals:                           126   BIC:                            -94.34\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept               0.8141      0.021     37.915      0.000       0.772       0.857\n",
      "Q(\"GDP per Capita\")  3.417e-06   3.46e-06      0.988      0.325   -3.43e-06    1.03e-05\n",
      "==============================================================================\n",
      "Omnibus:                       65.984   Durbin-Watson:                   1.939\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              204.603\n",
      "Skew:                          -2.015   Prob(JB):                     3.72e-45\n",
      "Kurtosis:                       7.703   Cond. No.                     9.28e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.28e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_2, predictors_2 = patsy.dmatrices('Q(\"Recovery Percentage\") ~ Q(\"GDP per Capita\")', combine_developing)\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with less than 16,301 GDP Per Capita (75th percentile). We plotted GDP per capita against recovery rate and made a linear model using OLS. Since R^2 is about 0.008, there is no sufficient evidence to support that the correlation between GDP Per Capita and Recovery Rate for developing countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Recovery Percentage', data=combine_developed, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Recovery Percentage\")   R-squared:                       0.016\n",
      "Model:                                  OLS   Adj. R-squared:                 -0.008\n",
      "Method:                       Least Squares   F-statistic:                    0.6873\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):              0.412\n",
      "Time:                              23:31:29   Log-Likelihood:                -1.6342\n",
      "No. Observations:                        43   AIC:                             7.268\n",
      "Df Residuals:                            41   BIC:                             10.79\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept               0.8763      0.089      9.831      0.000       0.696       1.056\n",
      "Q(\"GDP per Capita\") -1.664e-06   2.01e-06     -0.829      0.412   -5.72e-06    2.39e-06\n",
      "==============================================================================\n",
      "Omnibus:                       31.263   Durbin-Watson:                   1.848\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               58.069\n",
      "Skew:                          -2.175   Prob(JB):                     2.46e-13\n",
      "Kurtosis:                       6.672   Cond. No.                     1.01e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.01e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_3, predictors_3 = patsy.dmatrices('Q(\"Recovery Percentage\") ~ Q(\"GDP per Capita\")', combine_developed)\n",
    "mod_3 = sm.OLS(outcome_3, predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with more than 16,301 GDP Per Capita (75th percentile). We plotted GDP per capita against recovery rate and made a linear model using OLS. Since R^2 is about 0.016, there is no sufficient evidence to support that the correlation between GDP Per Capita and Recovery Rate for developed countries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Cases per 100k Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      175.000000\n",
       "mean      2144.123429\n",
       "std       2478.388637\n",
       "min          0.800000\n",
       "25%        142.750000\n",
       "50%       1058.600000\n",
       "75%       3741.400000\n",
       "max      10737.000000\n",
       "Name: TotCases_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine['TotCases_per_100k_pop'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c26945b50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='TotCases_per_100k_pop', data=combine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For almost all countries:\n",
    "- The average of total cases per 100k population is about 2144\n",
    "- The median of total cases per 100k population  is about 1058\n",
    "- The maximum of total cases per 100k population is about 10737"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c273bd350>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine.hist(column = 'TotCases_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the total cases per 100k populatin. \n",
    "\n",
    "The distribution for the variables doesn't seem to fit any of the common distributions, but the data was highly skewed to the right.\n",
    "\n",
    "We observed that for most countries, there is less than 10000 cases per 100k people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      128.000000\n",
       "mean      1459.460938\n",
       "std       1866.028938\n",
       "min          0.800000\n",
       "25%        115.875000\n",
       "50%        571.400000\n",
       "75%       2326.625000\n",
       "max      10737.000000\n",
       "Name: TotCases_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developing['TotCases_per_100k_pop'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c273f5210>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='TotCases_per_100k_pop', data=combine_developing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For developing countries:\n",
    "- The average of total cases per 100k population is about 1459\n",
    "- The median of total cases per 100k population  is about 571\n",
    "- The maximum of total cases per 100k population is about 10737"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "combine_developing.hist(column = 'TotCases_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we wanted to see if there are distribution differences between developing and developed countries. \n",
    "\n",
    "We plotted a histogram of the total cases per 100k population for developing countries.\n",
    "\n",
    "The data, again, doesn't fit any common distributions and is skewed to the right.\n",
    "\n",
    "We observed that again, most developing countries have a low number of cases per 100k population (<10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       46.000000\n",
       "mean      4088.789130\n",
       "std       2941.385192\n",
       "min          3.900000\n",
       "25%       1315.950000\n",
       "50%       4110.650000\n",
       "75%       6286.400000\n",
       "max      10017.200000\n",
       "Name: TotCases_per_100k_pop, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developed['TotCases_per_100k_pop'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c27595f50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='TotCases_per_100k_pop', data=combine_developed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For developed countries:\n",
    "- The average of total cases per 100k population is about 4088\n",
    "- The median of total cases per 100k population  is about 4110\n",
    "- The maximum of total cases per 100k population is about 10017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c2750ba50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developed.hist(column = 'TotCases_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the total cases per 100k population for developed countries.\n",
    "\n",
    "The data, again, doesn't fit any common distributions, though one can argue that is it nearing a uniform distribution. However, the sample size is too low to say so. The data is skewed to the right.\n",
    "\n",
    "We observed that developed countries' total cases per 100k population was much more varied than previous variables, but still favoring the low end of <10000 cases per 100k population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='TotCases_per_100k_pop', data=combine, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>67441</td>\n",
       "      <td>869.0</td>\n",
       "      <td>58210.0</td>\n",
       "      <td>107370.0</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>628115.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>4.943</td>\n",
       "      <td>0.863125</td>\n",
       "      <td>0.012885</td>\n",
       "      <td>7869.578023</td>\n",
       "      <td>10737.0</td>\n",
       "      <td>138.4</td>\n",
       "      <td>11.584036</td>\n",
       "      <td>327.673618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "91  Montenegro        67441         869.0          58210.0          107370.0   \n",
       "\n",
       "    Deaths/1M pop  Population         Units     Scale  2020 GDP  \\\n",
       "91         1384.0    628115.0  U.S. dollars  Billions     4.943   \n",
       "\n",
       "    Recovery Percentage  Death Percentage  GDP per Capita  \\\n",
       "91             0.863125          0.012885     7869.578023   \n",
       "\n",
       "    TotCases_per_100k_pop  Deaths_per_100k_pop  log_TotCases_per_1M_pop  \\\n",
       "91                10737.0                138.4                11.584036   \n",
       "\n",
       "    sqrt_TotCases_per_1M_pop  \n",
       "91                327.673618  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_cases_outlier = combine[combine['TotCases_per_100k_pop'] == max(combine['TotCases_per_100k_pop'])]\n",
    "total_cases_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Montenegro had the significant high total cases per 100k people among all other countries, and it may cause the correlation and R^2 to be inaccurate between GDP Per Capita and Total Cases Per 100k People. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                OLS Regression Results                                \n",
      "======================================================================================\n",
      "Dep. Variable:     Q(\"TotCases_per_100k_pop\")   R-squared:                       0.227\n",
      "Model:                                    OLS   Adj. R-squared:                  0.223\n",
      "Method:                         Least Squares   F-statistic:                     50.27\n",
      "Date:                        Wed, 17 Mar 2021   Prob (F-statistic):           3.38e-11\n",
      "Time:                                23:31:32   Log-Likelihood:                -1575.1\n",
      "No. Observations:                         173   AIC:                             3154.\n",
      "Df Residuals:                             171   BIC:                             3161.\n",
      "Df Model:                                   1                                         \n",
      "Covariance Type:                    nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept            1296.9322    206.816      6.271      0.000     888.691    1705.174\n",
      "Q(\"GDP per Capita\")     0.0628      0.009      7.090      0.000       0.045       0.080\n",
      "==============================================================================\n",
      "Omnibus:                       36.192   Durbin-Watson:                   1.607\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.119\n",
      "Skew:                           1.097   Prob(JB):                     3.95e-13\n",
      "Kurtosis:                       4.763   Cond. No.                     2.90e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.9e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_4, predictors_4 = patsy.dmatrices('Q(\"TotCases_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine)\n",
    "mod_4 = sm.OLS(outcome_4, predictors_4)\n",
    "res_4 = mod_4.fit()\n",
    "print(res_4.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted GDP per capita against total cases per 100k population and made a linear model using OLS. \n",
    "Since the R^2 is about 0.227, there is no sufficient evidence to support that the correlation between GDP Per Capita and Total Cases Per 100k Population. This is most likely because most countries have GDP Per Capita lower than 16,301, so this graph is not normally distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='TotCases_per_100k_pop', data=combine_developing, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                OLS Regression Results                                \n",
      "======================================================================================\n",
      "Dep. Variable:     Q(\"TotCases_per_100k_pop\")   R-squared:                       0.221\n",
      "Model:                                    OLS   Adj. R-squared:                  0.215\n",
      "Method:                         Least Squares   F-statistic:                     35.69\n",
      "Date:                        Wed, 17 Mar 2021   Prob (F-statistic):           2.21e-08\n",
      "Time:                                23:31:33   Log-Likelihood:                -1129.2\n",
      "No. Observations:                         128   AIC:                             2262.\n",
      "Df Residuals:                             126   BIC:                             2268.\n",
      "Df Model:                                   1                                         \n",
      "Covariance Type:                    nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept             488.3270    218.613      2.234      0.027      55.698     920.956\n",
      "Q(\"GDP per Capita\")     0.2104      0.035      5.974      0.000       0.141       0.280\n",
      "==============================================================================\n",
      "Omnibus:                       62.112   Durbin-Watson:                   1.482\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              242.120\n",
      "Skew:                           1.739   Prob(JB):                     2.66e-53\n",
      "Kurtosis:                       8.771   Cond. No.                     9.28e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.28e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_6, predictors_6 = patsy.dmatrices('Q(\"TotCases_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine_developing)\n",
    "mod_6 = sm.OLS(outcome_6, predictors_6)\n",
    "res_6 = mod_6.fit()\n",
    "print(res_6.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with less than 16,301 GDP Per Capita. We then plotted GDP per capita against total cases per 100k population and made a linear model using OLS. Since the R^2 is about 0.221, this is no sufficient evidence to support that the correlation between GDP Per Capita and Total Cases Per 100k People for developing countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='TotCases_per_100k_pop', data=combine_developed, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                OLS Regression Results                                \n",
      "======================================================================================\n",
      "Dep. Variable:     Q(\"TotCases_per_100k_pop\")   R-squared:                       0.004\n",
      "Model:                                    OLS   Adj. R-squared:                 -0.019\n",
      "Method:                         Least Squares   F-statistic:                    0.1659\n",
      "Date:                        Wed, 17 Mar 2021   Prob (F-statistic):              0.686\n",
      "Time:                                23:31:34   Log-Likelihood:                -432.06\n",
      "No. Observations:                          46   AIC:                             868.1\n",
      "Df Residuals:                              44   BIC:                             871.8\n",
      "Df Model:                                   1                                         \n",
      "Covariance Type:                    nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept            3715.8449   1014.967      3.661      0.001    1670.314    5761.376\n",
      "Q(\"GDP per Capita\")     0.0093      0.023      0.407      0.686      -0.037       0.055\n",
      "==============================================================================\n",
      "Omnibus:                        5.889   Durbin-Watson:                   1.639\n",
      "Prob(Omnibus):                  0.053   Jarque-Bera (JB):                2.283\n",
      "Skew:                           0.156   Prob(JB):                        0.319\n",
      "Kurtosis:                       1.954   Cond. No.                     1.03e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.03e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Q(\"TotCases_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine_developed)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with more than 16,301 GDP Per Capita. We then plotted GDP per capita against total cases per 100k population and made a linear model using OLS. Since the R^2 is about 0.004, this is no sufficient evidence to support that the correlation between GDP Per Capita and Total Cases Per 100k People for developed countries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing with Death per 100k Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c2802d8d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine.hist(column = 'Deaths_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the death per 1 million population.\n",
    "The distribution for the variables doesn't seem to fit any of the common distributions, but the data was \n",
    "highly skewed to the right.\n",
    "We observed that for most countries, there is less than 500 deaths per 1 million peopple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c280d8f50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developing.hist(column = 'Deaths_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we wanted to see if there are distribution differences between developing and developed countries.\n",
    "We plotted a histogram of the deaths per 100k population for developing countries.\n",
    "The data, again, doesn't fit any common distributions and is skewed to the right.\n",
    "We observed that again, most developing countries have a low number of death per 100k people (<200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c280f0d50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine_developed.hist(column = 'Deaths_per_100k_pop', bins = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plotted a histogram of the deaths per 100k population for developed countries.\n",
    "The data, again, doesn't fit any common distributions, though one can argue that is it nearing a uniform distribution. However, the sample size is too low to say so. The data is skewed to the right.\n",
    "We observed that developed countries' deaths per 100k population was much more varied than previous variables, but still favoring the low end of <500 cases per 100k population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Deaths_per_100k_pop', data=combine, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Deaths_per_100k_pop\")   R-squared:                       0.119\n",
      "Model:                                  OLS   Adj. R-squared:                  0.114\n",
      "Method:                       Least Squares   F-statistic:                     23.13\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):           3.31e-06\n",
      "Time:                              23:31:36   Log-Likelihood:                -913.26\n",
      "No. Observations:                       173   AIC:                             1831.\n",
      "Df Residuals:                           171   BIC:                             1837.\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept              27.6661      4.509      6.135      0.000      18.765      36.567\n",
      "Q(\"GDP per Capita\")     0.0009      0.000      4.809      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                       28.087   Durbin-Watson:                   1.416\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               36.517\n",
      "Skew:                           1.106   Prob(JB):                     1.18e-08\n",
      "Kurtosis:                       3.417   Cond. No.                     2.90e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.9e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_5, predictors_5 = patsy.dmatrices('Q(\"Deaths_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine)\n",
    "mod_5 = sm.OLS(outcome_5, predictors_5)\n",
    "res_5 = mod_5.fit()\n",
    "print(res_5.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plotted GDP per capita against the total deaths per 100k population and made a linear model using OLS. Since the R^2 is about 0.119, there is no sufficient evidence to support that the correlation between GDP Per Capita and Total Deaths Per 100k People. This is most likely because most countries have GDP Per Capita lower than 16301, so this graph is not normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Deaths_per_100k_pop', data=combine_developing, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Deaths_per_100k_pop\")   R-squared:                       0.205\n",
      "Model:                                  OLS   Adj. R-squared:                  0.199\n",
      "Method:                       Least Squares   F-statistic:                     32.48\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):           8.09e-08\n",
      "Time:                              23:31:37   Log-Likelihood:                -644.40\n",
      "No. Observations:                       128   AIC:                             1293.\n",
      "Df Residuals:                           126   BIC:                             1298.\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept               9.2294      4.952      1.864      0.065      -0.570      19.029\n",
      "Q(\"GDP per Capita\")     0.0045      0.001      5.699      0.000       0.003       0.006\n",
      "==============================================================================\n",
      "Omnibus:                       20.029   Durbin-Watson:                   1.473\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.945\n",
      "Skew:                           0.968   Prob(JB):                     6.32e-06\n",
      "Kurtosis:                       3.862   Cond. No.                     9.28e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.28e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Q(\"Deaths_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine_developing)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with less than 16301 GDP Per Capita. We plotted GDP per capita against the total deaths per 100k population and made a linear model using OLS. Since the R^2 is about 0.205, there is still no sufficient evidence to support that the correlation between GDP Per Capita and Total Deaths Per 100k People for developing countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='GDP per Capita', y='Deaths_per_100k_pop', data=combine_developed, fit_reg=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Deaths_per_100k_pop\")   R-squared:                       0.000\n",
      "Model:                                  OLS   Adj. R-squared:                 -0.023\n",
      "Method:                       Least Squares   F-statistic:                 0.0004727\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):              0.983\n",
      "Time:                              23:31:39   Log-Likelihood:                -248.87\n",
      "No. Observations:                        45   AIC:                             501.7\n",
      "Df Residuals:                            43   BIC:                             505.4\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept              70.3085     21.390      3.287      0.002      27.171     113.446\n",
      "Q(\"GDP per Capita\") -1.044e-05      0.000     -0.022      0.983      -0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        5.306   Durbin-Watson:                   1.394\n",
      "Prob(Omnibus):                  0.070   Jarque-Bera (JB):                3.777\n",
      "Skew:                           0.562   Prob(JB):                        0.151\n",
      "Kurtosis:                       2.133   Cond. No.                     1.02e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.02e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome8, predictors8 = patsy.dmatrices('Q(\"Deaths_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine_developed)\n",
    "mod = sm.OLS(outcome8, predictors8)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with less than 16301 GDP Per Capita. We plotted GDP per capita against the total deaths per 100k population and made a linear model using OLS. Since the R^2 is about 0.205, there is still no sufficient evidence to support that the correlation between GDP Per Capita and Total Deaths Per 100k People for developing countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>TotCases/ 1M pop</th>\n",
       "      <th>Deaths/1M pop</th>\n",
       "      <th>Population</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>2020 GDP</th>\n",
       "      <th>Recovery Percentage</th>\n",
       "      <th>Death Percentage</th>\n",
       "      <th>GDP per Capita</th>\n",
       "      <th>TotCases_per_100k_pop</th>\n",
       "      <th>Deaths_per_100k_pop</th>\n",
       "      <th>log_TotCases_per_1M_pop</th>\n",
       "      <th>sqrt_TotCases_per_1M_pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>3278</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>96483.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>33975.0</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Billions</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.901769</td>\n",
       "      <td>0.021965</td>\n",
       "      <td>41501.103753</td>\n",
       "      <td>9648.3</td>\n",
       "      <td>211.9</td>\n",
       "      <td>11.477122</td>\n",
       "      <td>310.617128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Total Cases  Total Deaths  Total Recovered  TotCases/ 1M pop  \\\n",
       "152  San Marino         3278          72.0           2956.0           96483.0   \n",
       "\n",
       "     Deaths/1M pop  Population         Units     Scale  2020 GDP  \\\n",
       "152         2119.0     33975.0  U.S. dollars  Billions      1.41   \n",
       "\n",
       "     Recovery Percentage  Death Percentage  GDP per Capita  \\\n",
       "152             0.901769          0.021965    41501.103753   \n",
       "\n",
       "     TotCases_per_100k_pop  Deaths_per_100k_pop  log_TotCases_per_1M_pop  \\\n",
       "152                 9648.3                211.9                11.477122   \n",
       "\n",
       "     sqrt_TotCases_per_1M_pop  \n",
       "152                310.617128  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_developed_outlier = combine_developed[combine_developed['Deaths_per_100k_pop'] > 200]\n",
    "combine_developed_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "San Marino had the significant high death rate among other developed countries, and it may cause the correlation and R^2 to be inaccurate between GDP Per Capita and Total Death Rate Per 100k People."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Deaths_per_100k_pop\")   R-squared:                       0.000\n",
      "Model:                                  OLS   Adj. R-squared:                 -0.023\n",
      "Method:                       Least Squares   F-statistic:                 0.0004727\n",
      "Date:                      Wed, 17 Mar 2021   Prob (F-statistic):              0.983\n",
      "Time:                              23:31:39   Log-Likelihood:                -248.87\n",
      "No. Observations:                        45   AIC:                             501.7\n",
      "Df Residuals:                            43   BIC:                             505.4\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept              70.3085     21.390      3.287      0.002      27.171     113.446\n",
      "Q(\"GDP per Capita\") -1.044e-05      0.000     -0.022      0.983      -0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                        5.306   Durbin-Watson:                   1.394\n",
      "Prob(Omnibus):                  0.070   Jarque-Bera (JB):                3.777\n",
      "Skew:                           0.562   Prob(JB):                        0.151\n",
      "Kurtosis:                       2.133   Cond. No.                     1.02e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.02e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Q(\"Deaths_per_100k_pop\") ~ Q(\"GDP per Capita\")', combine_developed)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are focusing on the countries with more than 20,000 GDP Per Capita.We then plotted GDP per capita against the total deaths per 100k population and made a linear model using OLS. Since the R^2 is about 0.205, this is no sufficient evidence to support that the correlation between GDP Per Capita and Total Deaths Per 100k People for developed countries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we plan to fetch our data from official organizations such as the IMF(International Monetary Fund), authorized global database(worldmeters), or some country’s government databases. In theory, we do not expect any bias and privacy issues to rise from the data. However, there are several potential problems that are extremely likely to exist. Many countries' governments may have chosen to intentionally provide modified infection rates, death rates, and other information for political and economic purposes. There are also some poorer countries that cannot afford to have wide-spread covid tests or medical supplies for their people. These issues may cause the data for our project to be inaccurate. To deal with the issue, we would try to have separated analysis on data of the advanced countries and the developing ones. By doing so, we can evaluate each country with countries in similar conditions, which would also allow our analysis to be more intuitive visibly. Therefore, despite the information quality varies from country to country, we can create reasonable analysis surrounding our research question with the limited data we obtained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Conclusion</b>:\n",
    "\n",
    "Our initial hypothesis that GDP of a country will have correlations between death/infected/recovered rate from COVID-19 is not supported by the use of linear regressions and measuring of central tendency. We even splited the countries into developing and developed based on the 75th percentile GDP per capita(16,301 USD), and removed any possible outliers existed in both datasets. \n",
    "\n",
    "- For death rate, both developing and developed countries have the average of 1.73% death percentage. Futhermore, we attempted the linear regression method by taking both developing and developed countries' GDP as the independent variable and the deaths_per_100k as dependent variable, but their R^2 values suggest that there is not sufficient evidence to prove the negative correlation between GDP per capita and the death rates. This result contradicts our initial assumption that the developed countries with the advanced medical treatments will have much lower death percentage than the devloping countries. \n",
    "\n",
    "\n",
    "- For infection rate(total cases per 100k population), the developing countries have 1,459 cases per 100k population and developed countries have 4,110 cases per 100k population. This result surprises us with the fact that developed countries have even higher infection rate than the developing countries. We wonder whether the COVID-19 data provided by those developing countries is reliable and transparent, or the fact that the people living in developing countries have stronger public awareness to danger of COVID-19 than those living in developed countries. Futhermore, we attempted the linear regression method by taking both developing and developed countries' GDP as the independent variable and the infection rate as dependent variable, but their R^2 values suggest that there is not sufficient evidence to prove the negative correlation between GDP per capita and the infection rates. This result contradicts our initial assumption that the developed countries will have much lower infection rate than the devloping countries. \n",
    "\n",
    "\n",
    "- For recovery rate, the developing countries have average of 83% recovery rate and developed countries have the average of 81% recovery percentage. Since both of them have the almost equivalent average recovery rate, the recovery rate might not be depended on the factor of GDP(the poverty rate). Futhermore, we attempted the linear regression method by taking both developing and developed countries' GDP as the independent variable and the recovery percentage as dependent variable, but their R^2 values suggest that there is not sufficient evidence to prove the positive correlation between GDP per capita and the recovery rates. This result contradicts our initial assumption that the developed countries with the advanced medical treatments will have higher recovery rate than the devloping countries. \n",
    "\n",
    "\n",
    "\n",
    "<b>Discussion</b>:\n",
    "  \n",
    "- Limitation in our analysis are based on the GDP from IMF(International Monetary Fund) and COVID-19 data from worldometers official website. Our final result shows that for each country, there is <b>NO</b> correlation between GDP Per Capita(poverty rate) and death/infected/recovered rate from COVID-19. We even discovered that developed countries have higher infection rate than the developing countries. Thus, we question about two possible scenarios:\n",
    "\n",
    "     1. Are the COVID-19 data provided by those developing countries reliable and transparent? \n",
    "     2. Is it possible that the people living in developing countries have stronger public awareness toward the danger of \n",
    "     COVID-19 than those living in developed countries?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We all worked collectively via zoom and everyone contributes to every parts of the project.\n",
    "\n",
    "Li-An Wu: Proposal, Data Cleaning, Hypothesis, Finding dataset, EDA, Ethics and Privacy, Conclusion and Discussion\n",
    "\n",
    "Tony Lin: Proposal, Data Cleaning, Hypothesis, Finding dataset, EDA, Ethics and Privacy, Conclusion and Discussion\n",
    "\n",
    "Ray Hunag: Proposal, Data Cleaning, Hypothesis, Finding dataset, EDA, Ethics and Privacy, Conclusion and Discussion\n",
    "\n",
    "Steven Kan: Proposal, Data Cleaning, Hypothesis, Finding dataset, EDA, Ethics and Privacy, Conclusion and Discussion"
   ]
  }
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