fatality_cases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CASE STUDY : COVID-19 (Fatality Cases)\n",
    "<HR>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "<br>\n",
    "<div style='text-align:justify'>\n",
    "    In this case study, we explore the COVID-19 data consisting of confirmed, deaths, recovered, and active cases from all around the world. First, we formulate the hypothesis for our study based on that we design our experiment. One-way ANOVA statistical test fit for our case study but, we observed that raw data violate the normality assumption. So, we apply log transformation to reduce the outlier effect. Different methods like the Q-Q plot, Shapiro-Wilk test are used to test the normality of data. After ANOVA analysis, the Tukey HSD method is used for further investigation.\n",
    "</div>\n",
    "\n",
    "Follow notebook [confirmed_cases](confirmed_cases.ipynb) for case study on number of confirmed COVID-19 cases across the different region.  \n",
    "\n",
    "**Content**\n",
    "- Introduction\n",
    "- Hypothesis Formulation\n",
    "- Experimental Design\n",
    "- Analysis and Visualization\n",
    "- Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "<br>\n",
    "<div style=\"text-align: justify\"> \n",
    "    Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It was first identified in December 2019 in Wuhan, China. The WHO declared the outbreak a Public Health Emergency of International Concern in January 2020 and a pandemic in March 2020. \n",
    "It has been observed that the impact of the COVID-19 differs across regions. In this case study, we investigate the statistical significance of COVID-19 across the different regions in the world. Data has been collected on 10th May 2020 for the study from Johns Hopkins University. \n",
    "</div>\n",
    "\n",
    "Data Source: [COVID-19 data Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Formulation\n",
    "<br> \n",
    "\n",
    "   We want to investigate that **is there any \n",
    "statistical difference in the number of COVID-19 fatality cases across the different regions in the world?** If difference exists, we further identify the pair of regions where the difference lies. For our study, we grouped the countries into four different regions based on the continent.   \n",
    "\n",
    "$H_0:$ There is no statistical difference in the mean of COVID-19 fatality cases across the different regions.\n",
    "\n",
    "$H_1:$ Atleast the means of COVID-19 fatality cases of two regions are different from each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimental Design\n",
    "<br>\n",
    "<div style='text-align: justify'>\n",
    "To explore the COVID-19 cases across the different regions and investigate our formulated hypothesis, we use one-way analysis of variance (ANOVA) to determine whether there is any statistically significant difference between the means of COVID-19 cases of two or more regions. One-way ANOVA has been used because we have only one independent variable(i.e., region) and sample data from each region is independent from others. For our study, we consider 20 countries from each region.\n",
    "    \n",
    "- Dependent Variable: number of fatality cases \n",
    "- Independent Variable: region\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from scipy import stats \n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.multicomp import MultiComparison\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.graphics.gofplots import ProbPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('figure', titlesize=18)\n",
    "plt.rc('axes', labelsize=15)\n",
    "plt.rc('axes', titlesize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load preprocessed data**\n",
    "\n",
    "We preprocessed collected data as per the requirement of our case study. Follow [Data Preparation Notebook](data_preparation.ipynb) for details. Preprocessed data has 20 countries from four different regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/covid19_preprocessed_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Australia</td>\n",
       "      <td>6948</td>\n",
       "      <td>97</td>\n",
       "      <td>6167</td>\n",
       "      <td>684</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>OCEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>6656</td>\n",
       "      <td>108</td>\n",
       "      <td>5025</td>\n",
       "      <td>1523</td>\n",
       "      <td>Asia</td>\n",
       "      <td>OCEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>5090</td>\n",
       "      <td>31</td>\n",
       "      <td>1941</td>\n",
       "      <td>3118</td>\n",
       "      <td>Asia</td>\n",
       "      <td>OCEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>4941</td>\n",
       "      <td>8</td>\n",
       "      <td>2070</td>\n",
       "      <td>2863</td>\n",
       "      <td>Asia</td>\n",
       "      <td>OCEA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4403</td>\n",
       "      <td>120</td>\n",
       "      <td>558</td>\n",
       "      <td>3724</td>\n",
       "      <td>Asia</td>\n",
       "      <td>OCEA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Confirmed  Deaths  Recovered  Active Continent Region\n",
       "75    Australia       6948      97       6167     684   Oceania   OCEA\n",
       "76     Malaysia       6656     108       5025    1523      Asia   OCEA\n",
       "77   Kazakhstan       5090      31       1941    3118      Asia   OCEA\n",
       "78      Bahrain       4941       8       2070    2863      Asia   OCEA\n",
       "79  Afghanistan       4403     120        558    3724      Asia   OCEA"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normality Test**\n",
    "\n",
    "Before using statistical test(ANOVA) on our data, we check whether the data is normally distributed across the different regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column=['Deaths'], by=['Region'])\n",
    "plt.title(\"COVID19 Fatality Cases\")\n",
    "plt.suptitle('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raw data does not give a precise and clear picture of data distribution in the quartiles and makes it difficult to interpret accurately. Our raw data is positively skewed. So, we decided to perform the log-transformation on data to reduces the outlier effects and bring the data close to the gaussian distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Log Transformation: To reduces the outlier data effects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['log_deaths'] = np.log(df['Deaths'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the transformed data\n",
    "df.boxplot(column=['log_deaths'], by=['Region'])\n",
    "plt.title(\"Log-Transformation: COVID19 Fatality Cases\")\n",
    "plt.suptitle('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can observe that log transformed data is not normally distributed since there is a clear difference in the spreads around the median. \n",
    "AFRO and OCEA region fatality data is consistence but AMR and EURO regions data is right skewed. \n",
    "\n",
    "Since our sample size is small 20 observations from each group. In this scenario, it is highly recommended to do residual analysis [ref. page_417](https://www.amazon.com/Introduction-Statistical-Analysis-Available-Enhanced/dp/0495017582). So, we estimate the linear model on log_confirmed ~ Region and test the normality conditions on residuals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Residuals Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ols('log_deaths ~ C(Region)', df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression model: log_deaths~Region</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>log_deaths</td>    <th>  R-squared:         </th> <td>   0.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 06 Mar 2021</td> <th>  Prob (F-statistic):</th> <td>6.37e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:36:14</td>     <th>  Log-Likelihood:    </th> <td> -166.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    80</td>      <th>  AIC:               </th> <td>   340.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    76</td>      <th>  BIC:               </th> <td>   349.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    3.7625</td> <td>    0.443</td> <td>    8.491</td> <td> 0.000</td> <td>    2.880</td> <td>    4.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Region)[T.AMR]</th>  <td>    1.7856</td> <td>    0.627</td> <td>    2.850</td> <td> 0.006</td> <td>    0.538</td> <td>    3.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Region)[T.EURO]</th> <td>    3.8995</td> <td>    0.627</td> <td>    6.223</td> <td> 0.000</td> <td>    2.651</td> <td>    5.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(Region)[T.OCEA]</th> <td>    1.7668</td> <td>    0.627</td> <td>    2.819</td> <td> 0.006</td> <td>    0.519</td> <td>    3.015</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.111</td> <th>  Durbin-Watson:     </th> <td>   0.799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.574</td> <th>  Jarque-Bera (JB):  </th> <td>   1.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.266</td> <th>  Prob(JB):          </th> <td>   0.561</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.747</td> <th>  Cond. No.          </th> <td>    4.79</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                   OLS Regression model: log_deaths~Region                    \n",
       "==============================================================================\n",
       "Dep. Variable:             log_deaths   R-squared:                       0.338\n",
       "Model:                            OLS   Adj. R-squared:                  0.312\n",
       "Method:                 Least Squares   F-statistic:                     12.96\n",
       "Date:                Sat, 06 Mar 2021   Prob (F-statistic):           6.37e-07\n",
       "Time:                        00:36:14   Log-Likelihood:                -166.18\n",
       "No. Observations:                  80   AIC:                             340.4\n",
       "Df Residuals:                      76   BIC:                             349.9\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             3.7625      0.443      8.491      0.000       2.880       4.645\n",
       "C(Region)[T.AMR]      1.7856      0.627      2.850      0.006       0.538       3.034\n",
       "C(Region)[T.EURO]     3.8995      0.627      6.223      0.000       2.651       5.148\n",
       "C(Region)[T.OCEA]     1.7668      0.627      2.819      0.006       0.519       3.015\n",
       "==============================================================================\n",
       "Omnibus:                        1.111   Durbin-Watson:                   0.799\n",
       "Prob(Omnibus):                  0.574   Jarque-Bera (JB):                1.154\n",
       "Skew:                           0.266   Prob(JB):                        0.561\n",
       "Kurtosis:                       2.747   Cond. No.                         4.79\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary(title=\"OLS Regression model: log_deaths~Region\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall model F( 3, 76) =  12.959, p_value =  0.0000\n"
     ]
    }
   ],
   "source": [
    "# significance of overall model\n",
    "print(f\"Overall model F({model.df_model: .0f},{model.df_resid: .0f}) = {model.fvalue: .3f}, p_value = {model.f_pvalue: .4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our model is statistically significant at alpha=0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Residuals vs Fitted plot**\n",
    "\n",
    "If there are any nonlinear patterns in the residuals, and thus in the data as well. One of the mathematical assumptions in building an OLS model is that the data can be fit by a line. If this assumption holds and our data can be fit by a linear model, then we should see a relatively flat line when looking at the residuals vs fitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 8.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals vs Fitted plot\n",
    "ax = sns.residplot(x=model.fittedvalues, \n",
    "                   y=model.resid,\n",
    "                   lowess=True,\n",
    "                   scatter_kws={'alpha': 0.5},\n",
    "                   line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})\n",
    "ax.set_title('Residuals vs Fitted')\n",
    "ax.set_xlabel('Fitted values')\n",
    "ax.set_ylabel('Residuals')\n",
    "plt.xlim([3, 8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The residuals in the plot are evenly distributed in the lower\n",
    "and upper boundaries revealing normality condition. We will further prove the normality of residuals using Normal QQ-Plot, Normal PP-Plot and histogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normality Test on residuals: Normal Q-Q Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normality_plot(data, fig):\n",
    "    \"\"\"plot QQ-plot and PP-plot to observe the normality in data\n",
    "    \n",
    "    Note: the normal P-P plot is better at finding deviations from normality in the center \n",
    "    of the distribution, and the normal Q-Q plot is better at finding deviations in the tails.  \n",
    "    Q-Q plots tend to be preferred in research situations. \n",
    "    \"\"\"\n",
    "    PP = ProbPlot(data)\n",
    "\n",
    "    ax1 = fig.add_subplot(1,2,1)\n",
    "    PP.qqplot(line='45', alpha=0.1, lw=1, ax=ax1)\n",
    "    ax1.set_title('Normal Q-Q')\n",
    "    ax1.set_xlabel('Theoretical Quantiles')\n",
    "    ax1.set_ylabel('Standardized Residuals')\n",
    "\n",
    "    ax2 = fig.add_subplot(1,2,2)\n",
    "    ax2.set_title('Normal P-P')\n",
    "    PP.ppplot(line='45', alpha=0.1, lw=1, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_norm_residuals = model.get_influence().resid_studentized_internal\n",
    "normality_plot(model_norm_residuals, plt.figure(figsize=(10,5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: All of the residuals lying on or close to the red line in both plots so we conclude that residuals is approximately normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normality Test on residuals: Histogram**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=model.resid, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ANOVA Table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(Region)</th>\n",
       "      <td>3.0</td>\n",
       "      <td>152.664211</td>\n",
       "      <td>50.888070</td>\n",
       "      <td>12.95904</td>\n",
       "      <td>6.366125e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>76.0</td>\n",
       "      <td>298.439799</td>\n",
       "      <td>3.926839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             df      sum_sq    mean_sq         F        PR(>F)\n",
       "C(Region)   3.0  152.664211  50.888070  12.95904  6.366125e-07\n",
       "Residual   76.0  298.439799   3.926839       NaN           NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_table = sm.stats.anova_lm(model, typ= 1)\n",
    "anova_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Difference in number of fatality cases among the regions is significant at alpha=0.05 so, we reject the null hypothesis in favour of alternative. However, we don’t know where the difference between regions is yet. So, we will do Post Hoc test to investigate further where the difference lies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Post Hoc Test: Tukey HSD**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will examine where differences in the number of fatality cases among the regions lies using the Tukey HSD test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "====================================================\n",
      "group1 group2 meandiff p-adj   lower   upper  reject\n",
      "----------------------------------------------------\n",
      "  AFRO    AMR   1.7856 0.0282  0.1395  3.4318   True\n",
      "  AFRO   EURO   3.8995  0.001  2.2533  5.5456   True\n",
      "  AFRO   OCEA   1.7668 0.0306  0.1206  3.4129   True\n",
      "   AMR   EURO   2.1138 0.0063  0.4677  3.7599   True\n",
      "   AMR   OCEA  -0.0189    0.9  -1.665  1.6272  False\n",
      "  EURO   OCEA  -2.1327 0.0058 -3.7788 -0.4866   True\n",
      "----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mc = MultiComparison(df['log_deaths'],df['Region'])\n",
    "results = mc.tukeyhsd()\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The mean COVID-19 cases for AFRO region is statistically different from other regions AMR, EURO, and OCEA at 0.05% significance level. Also, mean COVID-19 fatality cases for AMR-EURO and EURO-OCEA regions is statistically different at 0.05% significance level. But, at 95% confidence level AMR-OCEA pair of region is not significantly different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pairwise_confint(confint, meandiffs, pairs):\n",
    "    \"\"\"plot pairwise confidence internval for the groups\"\"\"\n",
    "    for lower, upper, mean, pair in zip(confint[:,0],\n",
    "                                        confint[:,1], \n",
    "                                        meandiffs,\n",
    "                                        pairs):\n",
    "        plt.hlines(pair, lower, upper)\n",
    "        plt.plot(mean, pair, \"-o\")\n",
    "    plt.axvline(x=0, color = 'r', linestyle = 'dashed', label = \"red line\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lst = np.unique(mc.groups)\n",
    "labels = [str(g1+'-'+g2) for i,g1 in enumerate(lst) for j,g2 in enumerate(lst) if i<j ]\n",
    "\n",
    "plot_pairwise_confint(results.confint, results.meandiffs, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion \n",
    "<br>\n",
    "\n",
    "   Statistical analysis has been conducted to establish the statistical significance of COVID-19 cases across the different regions. Raw data was not normally distributed. So, we decided to apply log transformation to reduce the outlier effect. Further, we did residual analysis using linear model fatality_cases~Region, residuals normality has been tested. Then, we analyze the ANOVA result and observed that there is a statistically significant difference in means of COVID-19 cases across the different regions. For further investigation where the difference exists, we used the Tukey HSD test and observed that the **mean of COVID-19 fatality cases for the AFRO region is statistically different from other regions AMR, EURO, and OCEA at 0.05% significance level. It might be because of data gap governments in african countries don’t register deaths properly**. Also, mean COVID-19 fatality cases for AMR-EURO and EURO-OCEA regions is statistically different at 0.05% significance level. At 95% confidence level AMR-OCEA pair of region is not significantly different.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}