Advanced_Stat_Problem1_ Maria Jasmine_21March.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the needed libraries\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy .stats as stats\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "import math\n",
    "from scipy import stats\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import _get_covariance,anova_lm\n",
    "from statsmodels.graphics.factorplots import interaction_plot\n",
    "from statsmodels.stats.multicomp import (pairwise_tukeyhsd,\n",
    "                                         MultiComparison)\n",
    "import statsmodels.stats.multicomp as mc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the data\n",
    "Salary = pd.read_csv('C:\\\\Users\\\\91861\\\\Documents\\\\Statistics\\\\PCA\\\\Project\\\\SalaryData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>153197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>115945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Education     Occupation  Salary\n",
       "0   Doctorate   Adm-clerical  153197\n",
       "1   Doctorate   Adm-clerical  115945"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a copy of the original data\n",
    "df =  Salary.copy()\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows: 40\n",
      "No of columns: 3\n"
     ]
    }
   ],
   "source": [
    "df.shape\n",
    "print('No of rows:',df.shape[0])\n",
    "print('No of columns:',df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Education   40 non-null     object\n",
      " 1   Occupation  40 non-null     object\n",
      " 2   Salary      40 non-null     int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>40.0</td>\n",
       "      <td>162186.875</td>\n",
       "      <td>64860.407506</td>\n",
       "      <td>50103.0</td>\n",
       "      <td>99897.5</td>\n",
       "      <td>169100.0</td>\n",
       "      <td>214440.75</td>\n",
       "      <td>260151.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count        mean           std      min      25%       50%  \\\n",
       "Salary   40.0  162186.875  64860.407506  50103.0  99897.5  169100.0   \n",
       "\n",
       "              75%       max  \n",
       "Salary  214440.75  260151.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Coeffiecient of Variation explains the variability and the consistency of the data\n",
      "The CV% for the given data set is 39.99 %\n"
     ]
    }
   ],
   "source": [
    "# checking the consitency of the data:\n",
    "Mean = 162186.875\n",
    "StD = 64860.407506\n",
    "Coefficient_Variation =  round((StD / Mean) * 100,2)\n",
    "print('The Coeffiecient of Variation explains the variability and the consistency of the data')\n",
    "print('The CV% for the given data set is',Coefficient_Variation,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking for duplicates\n",
    "duplicate = df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicates in the data: 0\n"
     ]
    }
   ],
   "source": [
    "print('Number of duplicates in the data:', duplicate.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Doctorate    16\n",
       " Bachelors    15\n",
       " HS-grad       9\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no anamolies\n",
    "df['Education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Prof-specialty     13\n",
       " Sales              12\n",
       " Adm-clerical       10\n",
       " Exec-managerial     5\n",
       "Name: Occupation, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no anamolies\n",
    "df['Occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Doctorate</th>\n",
       "      <td>3334832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelors</th>\n",
       "      <td>2477294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HS-grad</th>\n",
       "      <td>675349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Salary\n",
       "Education          \n",
       " Doctorate  3334832\n",
       " Bachelors  2477294\n",
       " HS-grad     675349"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# People who have doctorate earns more\n",
    "Education = pd.DataFrame(df.groupby('Education')['Salary'].sum().sort_values(ascending=False))\n",
    "Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>2196391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>1891253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>1414243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>985588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Salary\n",
       "Occupation               \n",
       " Prof-specialty   2196391\n",
       " Sales            1891253\n",
       " Adm-clerical     1414243\n",
       " Exec-managerial   985588"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# people who are professionals earns more\n",
    "pd.DataFrame(df.groupby('Occupation')['Salary'].sum().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot showing the Salary break-up for different Education')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,7))\n",
    "sns.boxplot(x='Education',y='Salary',data=df)\n",
    "plt.title('Boxplot showing the Salary break-up for different Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot showing the Salary break-up for different Occupations')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,7))\n",
    "sns.boxplot(x='Occupation',y='Salary',data=df)\n",
    "plt.title('Boxplot showing the Salary break-up for different Occupations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**One-Way ANOVA:**\n",
    "\n",
    "1. One Factor and different levels\n",
    "2. One Independent variable\n",
    "\n",
    "**Assumptions for ANOVA**\n",
    "\n",
    "1. All populations under consideration have normal distribution\n",
    "2. All populations under consideration have equal variances.\n",
    "3. The sample is a random sample, i.e. the observations are collected independently of each other "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.1 .State the null and the alternate hypothesis for conducting one-way ANOVA for both Education and Occupation individually.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Hypothesis for One-Way ANOVA on Salary with respect to Education:**\n",
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3\n",
    "- $H_1$: Atleast for one Education level the salary is different from the rest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Hypothesis for One-Way ANOVA on Salary with respect to Occupation:**\n",
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3 = 𝜇4\n",
    "- $H_1$: Atleast for one Occupation type the salary is different from the rest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.2 Perform a one-way ANOVA on Salary with respect to Education. State whether the null hypothesis is accepted or rejected based on the ANOVA results.**\n",
    "\n",
    "**One-Way ANOVA on Salary with respect to Education:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3\n",
    "- $H_1$: Atleast for one Education level the salary is different from the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education\n",
       " Bachelors    165152.933333\n",
       " Doctorate    208427.000000\n",
       " HS-grad       75038.777778\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the mean salaries for all the three different educations are different.\n",
    "df.groupby('Education')['Salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA table on Salary with respect to Education\n",
      "------------------------------------------------------------------\n",
      "             df        sum_sq       mean_sq         F        PR(>F)\n",
      "Education   2.0  1.026955e+11  5.134773e+10  30.95628  1.257709e-08\n",
      "Residual   37.0  6.137256e+10  1.658718e+09       NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "# Checking through Anova:\n",
    "model = ols('Salary ~ Education',df).fit()\n",
    "aov_table1 = anova_lm(model)\n",
    "print('ANOVA table on Salary with respect to Education')\n",
    "print('------------------------------------------------------------------')\n",
    "print(aov_table1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interpretations:\n",
    "pvalue_Edu = aov_table1['PR(>F)'][0]\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \n",
      "We reject the Null hypothesis as pvalue is significantly less than the alpha value\n",
      "  \n",
      "We conclude that the Salary for atleast one of the Education level is different\n"
     ]
    }
   ],
   "source": [
    "if pvalue_Edu < alpha:\n",
    "    print(\"  \")\n",
    "    print('We reject the Null hypothesis as pvalue is significantly less than the alpha value')\n",
    "    print(\"  \")\n",
    "    print('We conclude that the Salary for atleast one of the Education level is different')\n",
    "else:\n",
    "    print('We do not reject the Null hypothesis as pvalue is significantly more than alpha value')\n",
    "    print('We conclude that the Salary for all the Education level is equal/same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x='Education',y='Salary',data=df,ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.3 Perform a one-way ANOVA on Salary with respect to Occupation. State whether the null hypothesis is accepted or rejected based on the ANOVA results.**\n",
    "\n",
    "**One-Way ANOVA on Salary with respect to Occupation:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3 = 𝜇4\n",
    "- $H_1$: Atleast for one Occupation type the salary is different from the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Occupation\n",
       " Adm-clerical       141424.300000\n",
       " Exec-managerial    197117.600000\n",
       " Prof-specialty     168953.153846\n",
       " Sales              157604.416667\n",
       "Name: Salary, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the mean salaries for all the four different occupations are not significantly different.\n",
    "# the salaries of different occupation are almost close to the mean salary.\n",
    "df.groupby('Occupation')['Salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA table on Salary with respect to Occupation\n",
      "------------------------------------------------------------------\n",
      "              df        sum_sq       mean_sq         F    PR(>F)\n",
      "Occupation   3.0  1.125878e+10  3.752928e+09  0.884144  0.458508\n",
      "Residual    36.0  1.528092e+11  4.244701e+09       NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "# Checking through Anova:\n",
    "model = ols('Salary ~ Occupation',df).fit()\n",
    "aov_table2 = anova_lm(model)\n",
    "print('ANOVA table on Salary with respect to Occupation')\n",
    "print('------------------------------------------------------------------')\n",
    "print(aov_table2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interpretations:\n",
    "pvalue_Oc = aov_table2['PR(>F)'][0]\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "We fail to reject the Null hypothesis as pvalue is significantly more than the alpha value\n",
      " \n",
      "We conclude that the Salary for all the Occupation type is not significantly different\n"
     ]
    }
   ],
   "source": [
    "if pvalue_Oc < alpha:\n",
    "    print('We reject the Null hypothesis as pvalue is significantly less than the alpha value')\n",
    "    print('We conclude that the Salary for atleast one of the Education level is different')\n",
    "else:\n",
    "    print(\" \")\n",
    "    print('We fail to reject the Null hypothesis as pvalue is significantly more than the alpha value')\n",
    "    print(\" \")\n",
    "    print('We conclude that the Salary for all the Occupation type is not significantly different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x='Occupation',y='Salary',data=df,ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.4 If the null hypothesis is rejected in either (1.2) or in (1.3), find out which class means are significantly different. Interpret the result.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The salary level with respect to Education is different whereas the salary level with respect to the occupations are equal.\n",
    "- To find which class means of the Independent variable (Education) is significantly different, we use Tukey test (Multi comparison)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library for tukey test (Multi comparison)\n",
    "from statsmodels.stats.multicomp import (pairwise_tukeyhsd,\n",
    "                                         MultiComparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tukey test(Multi comparison) for the Independent Variable Education:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "    <th>group1</th>     <th>group2</th>     <th>meandiff</th>    <th>p-adj</th>     <th>lower</th>       <th>upper</th>    <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td> Bachelors</td> <td> Doctorate</td>  <td>43274.0667</td>  <td>0.0146</td>   <td>7541.1439</td>  <td>79006.9894</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td> Bachelors</td>  <td> HS-grad</td>   <td>-90114.1556</td>  <td>0.001</td> <td>-132035.1958</td> <td>-48193.1153</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td> Doctorate</td>  <td> HS-grad</td>  <td>-133388.2222</td>  <td>0.001</td> <td>-174815.0876</td> <td>-91961.3569</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Posthoc test: Tukey test\n",
    "MultiComp = MultiComparison(df['Salary'],df['Education'])\n",
    "post_hoc_res = MultiComp.tukeyhsd()\n",
    "post_hoc_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretations:**\n",
    "1. p-value for all the groups are significantly lesser than the alpha.\n",
    "2. For all the groups we reject the null hypothesis of equality of means.\n",
    "3. The pairs Bachelors & HS-Grad and Doctorate & HS-Grad have very low p-value. Which means the common class among the two pairs HS-Grad is very far from the mean.\n",
    "4. Hence, HS-Grad is the significantly different from the other two class.\n",
    "5. The mean salary of HS-Grad is 75038 whereas the mean salary of the other two class is 165153 and 208427"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing library for multicomp model\n",
    "import statsmodels.stats.multicomp as mc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91861\\anaconda3\\lib\\site-packages\\statsmodels\\sandbox\\stats\\multicomp.py:775: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_yticklabels(np.insert(self.groupsunique.astype(str), 0, ''))\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvPGGxo8fr06dOiktLU2NGzfWwIEDJUmtWrXSBx98oLS0NAUFBal58+bat2+fvLy8VLduXSUmJprL9m5ubpoxY4bGjx+vefPmKT09XS+//LK5tzE0NFSvvvqqLMuSq6urZs6cedOHZy754osv1L1790yXbF1dXTV48GBNmzZNHTp0yNZybuRO7s+rdejQQVu2bFHDhg1vuLzsvr9GjRqlgIAARUdHKzg4WLGxserevbtsNpvKli2b6b0XFBSk9957Tz169Mi0jC1bttyRvgG3w2bd6KYgAHeVVq1aaerUqZke0sjPrve0KHArkpKS1L17dy1btuymTyzfaatXr9by5cs1b948M8zhcCgoKEiffPKJSpcunav1ABKXlQEAdzkPDw+9+uqrmjlzZq6ut2/fvpoxY4bGjBmTafiiRYv0zDPPEAyRZzhzCAAAAIMzhwAAADAIhwAAADAIhwAAADAIhwAAADD4nsM77Ny5ZDmd+f8Zn1KlPHT2bFJel5Hn6MNl9CIDfbiMXmSgD5fRiwyFoQ9FithUsuS1v8uUcHiHOZ1WgQiHkgpMnTmNPlxGLzLQh8voRQb6cBm9yFCY+8BlZQAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABiEQwAAABh5Hg5jYmLUqlWrLMOrVKlifl68eLECAwPVuXNnBQYGKjIyMldqGzlypCIiInJlXQAAAPmBa14XcDM//vijvvzySy1dulTu7u46e/asunXrpqpVq6pq1ap5XR4KsOjolfr44w918uRJPfBAWYWEDFXHjp3yuiwAAPJUvg+HZ86ckWVZSklJkbu7u0qVKqVp06apZMmS15z+/fff11dffaWSJUvK29tbrVq1Uv369TVw4ECVLFlS7u7umj59ukaNGqXY2FjFxcWpUaNGGj9+vCTp3Xff1caNG+Xj4yOHw6H69evn5uYil0RHr1Ro6BilpqZKkk6dOqnQ0DGSREAEANzV8kU4jIuLU2Bg4DXHNW/eXBEREWrWrJlq166tBg0aKDAwUGXKlMky7YYNG7R3716tWrVKKSkpCgoKMpesDx8+rHnz5ql8+fJatWqVqlWrpmnTpslut8vf318//fSTYmJi9L///U+rVq3S+fPn1blz5xzd7vxiwIC+eV1Crtu370fZ7fZMw1JTUzV27Btatiwsj6rKffPnL8rrEgAA+Uy+CIc+Pj6KiorKNOzSPYdubm6aMWOGjh49qm+//VZbtmzR/PnztXDhQtWuXTvTPNu2bZOfn5/c3Nzk5uamNm3amHGlSpVS+fLlJUkBAQH673//q4ULF+r3339XQkKCLly4oF27dqldu3YqWrSovLy81Lx581vellKlPG55nrzi7e0pSXJzyxeHQa66OhheOfxu6selY+B6r+9W9OEyepGBPlxGLzIU5j7k+38FIyMjVaZMGTVq1EgVKlRQ7969NWXKFEVFRens2bOaNm2aJKlVq1YqUqSInE7nNZfj7u5ufl60aJG++uorde/eXY0bN9ahQ4dkWZZsNpssyzLTubreenvOnk2S02ndfMI85u3tqTNnzkuSZs5ckMfV5D4/v1Y6depkluFlyz54V/Xj0jEgZT4m7mb04TJ6kYE+XEYvMhSGPhQpYrvuCa08f1r5ZhwOh95//33Fx8dLyjiz88svv6h69epq3bq1oqKiFBUVpZdfflmNGzfW2rVrZbfblZSUpI0bN8pms2VZ5tatW9WjRw917txZFy9e1IEDB+R0OtWoUSP95z//kd1uV2JiorZs2ZLbm4tcEhIyNNMvDFLGLxAhIUPzqCIAAPKHfH/msFu3bjp37pyeeuopFSmSkWX9/f315JNPZpm2ZcuW+v777xUUFKT77rtPPj4+KlasWJbpnnnmGY0dO1Zz5syRh4eH6tSpo5iYGAUHB2vfvn0KCAhQ6dKlVbFixRzfPuSNSw+d8LQyAACZ2awrr6MWcN9//72OHDmioKAgpaWlqUePHpowYUKufuVNQbysfDejD5fRiwz04TJ6kYE+XEYvMhSGPtzosnK+P3N4Kx555BF99NFHWrBggSzLUpcuXfguRAAAgFtQqMLh/fffr/nz5+d1GQAAAAVWvn8gBQAAALmHcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAACDcAgAAADDNTsTpaSkaM2aNUpMTJRlWWb4s88+m2OFAQAAIPdlKxyOGDFCJ06c0GOPPSabzZbTNQEAACCPZCscHjx4UNHR0XJ1zdbkAAAAKKCydc/hAw88kNN1AAAAIB/I1qnAxx57TE8//bSaNWsmd3d3M5x7DgEAAAqXbIXD5ORkVahQQceOHcvpegAAAJCHshUO33nnHUnSiRMnlJ6ergoVKuRoUQAAAMgb2QqHR48e1Ysvvqi4uDg5nU6VLFlSs2fPVsWKFXO6PgAAAOSibD2QEhoaqoEDB2r37t3au3evXnjhBb311ls5XRsAAAByWbbC4dmzZxUUFGRed+vWTefOncuxogAAAJA3shUOHQ6HEhISzOv4+PicqgcAAAB5KFv3HPbp00c9evSQn5+fbDaboqOj9cwzz+R0bQAAAMhl2QqHPXr0UIUKFbRlyxY5nU69+eabaty4cU7XBgAAgFx2w3D422+/qWLFivrpp5/k6empjh07mnE//fSTatSokeMFAgAAIPfcMBxOnDhRs2fPVkhISJZxNptN69evz7HCAAAAkPtuGA5nz54tSfr888+z/H3lX375JeeqAgAAQJ644dPKCQkJSkhI0KBBg5SYmKiEhAQlJibqjz/+uObZRAAAABRsNzxzOGzYMG3dulWS1KBBg8szubqqffv2OVsZAAAAct0Nw+H8+fMlSa+//rr5+8oAAAAovLL1VTbvvPOOEhISlJKSIsuy5HA4dOzYMTVp0iSn6wMAAEAuylY4nDZtmnk4xcXFRWlpaapUqZJWrlyZrZXExMSoQ4cOqlixoiQpNTVVjz/+uIYNG6bSpUvfctHffPONjhw5omefffaW573af//7X3311VcaPnz4X14WAABAQZetP58XGRmpb775Ru3bt9fatWv1zjvvqFKlSre0Ih8fH0VFRSkqKkpr1qxR6dKl9dJLL91W0fv371dSUtJtzXu1X3/9VWfPnr0jywIAANkXHb1Sfn6tVKdONfn5tVJ0dPZOOiFnZevMoZeXl3x8fPToo4/qwIED6tKli+bOnXvbK7XZbAoJCVGTJk104MABVa1aVbNmzdKKFSvk4uKiJk2aaPjw4XJxcdHChQv1xRdfyMXFRb6+vgoKCtKSJUskSQ8++KA6duyo0aNH6+DBg7LZbBowYIC6dOmiiIgILV++XAkJCfL19VVAQIDGjRunCxcuKD4+XoMGDZK/v7+mTZumCxcuaObMmRo0aJAmTpyoXbt2yeFwqGvXrurXr99tbycAALi26OiVCg0do9TUVEnSqVMnFRo6RpLUsWOnvCztrpetcOjq6qpjx47p0Ucf1Z49e9S0aVNdvHjxL63Yzc1NFSpU0O+//67Y2Fht2LBBy5YtU9GiRRUSEqIlS5bob3/7mz7//HMtW7ZMxYsX18CBA+Xn56eePXtKkrp166aJEyeqZMmSWrVqleLj4xUcHKyqVatKkmJjYxUdHS1XV1eNHz9eL774oho1aqTjx4+rc+fOeuqpp/TSSy9p165deuGFF/TFF19IkpYvXy673a4BAwaoZs2aqlev3l/aVgAActqAAX1zfB1ubq6y29PvyLL27ftRdrs907DU1FSNHfuGli0LuyPryClX9mH+/EV5XM2dl61wOHjwYI0ZM0YzZ87U1KlTFRkZqZYtW/7lldtsNrm7u2vHjh3y9/dX8eLFJWWEvsjISKWmpsrX11eenp6SpIULF0rKuOfwkh07dmjChAmSMs5wtm7dWrt27ZKHh4eqV68uV9eMTRw5cqS2bNmi2bNn69ChQ7pw4UKWerZv366ff/5ZO3bskCRduHBBBw8evKVwWKqUx603Io94e3vmdQn5An24jF5koA+X0YsMBaEPbm7Z+ic936zn6mB45fDc2pa/4lKNBeHYuFXZ6r6vr6+eeOIJlShRQpGRkTp06JBq1ar1l1Zst9t1+PBhVapUSTt37swyPj09Xa6urrLZbGZYbGysCZCXWJaV5bXD4ZAkubu7m+GvvPKK7r33Xvn6+qpjx45atWpVlnU6HA4NHz5c7dq1kyTFx8frnnvuuaXtOns2SU6ndfMJ85i3t6fOnDmf12XkOfpwGb3IQB8uoxcZCkofZs5ckOPruJO98PNrpVOnTmYZXrbsg7myLX/FlX0oCMfGtRQpYrvuCa1sPZASHR2trl27SpJOnz6t559/PtPZu1vldDo1ffp0/f3vf9f//d//qWHDhlq9erVSU1OVnp6uZcuWqWHDhqpXr542bdqk5ORkpaena9iwYdq/f79cXFyUnp5xOrdhw4YKDw+XlBHm1q9fr/r162dZ59atW/XSSy+pTZs22rx5s6SMMHj1ssLCwpSWlqbk5GT16tVLP/zww21vJwAAuLaQkKGZTuJIGSd1QkKG5lFFuCRbZw5nzZqlf//735KkRx55RBEREXrxxRfVqlWrbK8oLi5OgYGBkjLCYbVq1fTBBx9Iyjgz+fPPP6tbt25KT09X06ZN1adPH7m6uqpPnz7q2bOnnE6n2rZtq8aNG6to0aJ67bXXVLp0aQ0ZMkRjx45Vp06d5HA49Pzzz6tGjRo6ePBgpvWHhISoV69eKlasmKpWrapy5copJiZGtWrV0kcffaTJkyfr5Zdf1tGjRxUUFKT09HR17do101+GAQAAd8alh06mT5+i06dP6YEHyiokZCgPo+QDNuvq67LXEBAQkOUybGBgoKKionKssIKKy8oFC324jF5koA+X0YsM9OEyepGhMPThL19W9vLy0pIlS5Seni6Hw6Hw8PDb+vJqAAAA5G/ZCoehoaEKCwtTrVq1VKtWLYWFhenNN9/M6doAAACQy7J1z+HDDz+siIgIJSYmysXFRR4eBefrWgAAAJB92QqHCxZc+5HyO/G3jQEAAJB/ZCscHjp0yPxst9u1e/duNWrUKMeKAgAAQN7IVjh85513Mr2OjY3VG2+8kSMFAQAAIO9k64GUq5UpU0YnTpy407UAAAAgj93yPYeWZWn//v0qVapUjhUFAACAvHHL9xxKUtmyZTVixIgcKQgAAAB557buOQQAAEDhdMNw2LdvX9lstuuOv/T3lgEAAFA43DAc9unTR5L09ddfKykpSd26dZOLi4uioqJ077335kqBAAAAyD03DIft27eXJM2fP19LlixRkSIZDze3bNlSPXr0yPnqAAAAkKuy9VU2586d08WLF83r5ORkJSYm5lhRAAAAyBvZeiAlICBA3bt3V9u2bWVZltasWaPg4OCcrg0AAAC5LFvh8OWXX1aNGjW0Y8cOSdLIkSPVokWLHC0MAAAAue+G4fDkyZN68MEHJUlt2rRRmzZtzLjNmzerefPmOVsdAAAActUN7zkcMmSI+TkkJCTTuClTpuRMRQAAAMgzNwyHlmWZn48fP37dcQAAACgcbhgOr/wC7Ku/DPtGX44NAACAginbZw4BAABQ+N3wgRSn06nExERZliWHw2F+liSHw5ErBQIAACD33DAcHjp0SA0bNjSBsEGDBmYcl5UBAAAKnxuGwwMHDuRWHQAAAMgHsvXn8wAAAHB3IBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAIBwCAADAyPFwuHPnTtWpU0eBgYHq3Lmz/Pz89Omnn972svr27XtL81SpUuW21gUAAHA3cs2NldSsWVOLFi2SJCUlJcnf319NmjRRpUqVcmP1AIBCJDp6paZPn6LTp0/pgQfKKiRkqDp27JTXZQGFRq6EwytdvHhRLi4u8vT0lCT95z//0YIFC5Samiq73a4JEybo8ccf188//6x//etfSk1N1X333afJkydLkuLj4/Xcc8/p2LFjeuSRRzRt2jS5ubkpMjJSn376qZxOp2rUqKE333xTxYoVM+tNSUnR6NGjdfDgQdlsNg0YMEBdunRRRESEli9froSEBPn6+qpy5cqaN2+eXFxcVL58eU2aNCnTcgAAeSc6eqVCQ8coNTVVknTq1EmFho6RJAIicIfkSjjcv3+/AgMD5XQ6dezYMfn5+cnHx0dOp1NLlizRrFmz5OXlpfDwcM2ZM0ezZs3SP//5T/3zn/+Ur6+vPv/8c3366adq2bKlTp48qVmzZqlcuXLq3r27tm3bpnLlyiksLExLlixRsWLF9P7772v+/Pl68cUXTQ3Tp09XyZIltWrVKsXHxys4OFhVq1aVJMXGxio6Olqurq5q3bq1wsLCVKpUKb333nv6/fffVa1atdxoEwDkqgEDbnybjpubq+z29FyqJnv27ftRdrs907DU1FSNHfuGli0Ly5F15lQf5s9fdMeXCdwJeXJZeeDAgZozZ44GDx6sjz/+WBs2bNDhw4e1a9cuFSlSRPHx8Tpz5ox8fX0lSb169ZKUcc9h1apV9dBDD0mSKlasqHPnzikmJkZHjx5V9+7dJUlpaWmqXr16php27NihCRMmSJK8vLzUunVr7dq1Sx4eHqpevbpcXTNa4evrq6eeekpt2rRR+/btbzkYlirlcZtdyn3e3p55XUK+QB8uoxcZ7pY+uLnd/J+A7EyTm64OhlcOz8lac2LZBfU4K6h132mFuQ+5/q738PCQn5+ftm3bpuTkZD355JPq3LmznnjiCVWpUkWLFy9W0aJFZbPZzDwXL15UXFxcRsGul0u22WyyLEsOh0N+fn4aPXq0JCk5OVkOhyPTei3LyvL60jTu7u5m+OjRo3XgwAFt2rRJw4cP1z/+8Q8FBgZme/vOnk2S02ndfMI85u3tqTNnzud1GXmOPlxGLzLcTX2YOXPBDcfnx174+bXSqVMnswwvW/bBm27P7cqpPuS33mZHfjwm8kJh6EORIrbrntDK9a+ycTgc2rVrl6pXr64jR47IZrPp+eefV4MGDfT111/L4XDI09NTZcqU0bfffitJioqK0tSpU6+7zEvznj17VpZlaezYsVmeiG7YsKHCw8MlZdy3uH79etWvXz/TNOnp6WrXrp1KliypwYMHKzAwUD///PMd7gAA4HaFhAzN9Au9lPELfkjI0DyqCCh8cvWeQ5vNpvT0dFWpUkXPPfecihUrpmrVqsnPz082m01NmzbV3r17JUmTJk3S2LFjNWnSJJUsWVITJ07U4cOHr7n8qlWr6h//+IeeeeYZOZ1OVatWTYMGDco0zZAhQzR27Fh16tRJDodDzz//vGrUqKGDBw+aaVxdXfXSSy+pf//+KlasmEqVKqV333035xoDALgllx464WllIOfYrKuvt+Iv4bJywUIfLqMXGejDZfQiA324jF5kKAx9yFeXlQEAAJB/EQ4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABgEA4BAABguOZ1AYVNkSK2vC4h2wpSrTmJPlxGLzLQh8voRQb6cBm9yFDQ+3Cj+m2WZVm5WAsAAADyMS4rAwAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcAgAAwCAcFnB9+/aVv7+/AgMDFRgYqB9//FHbtm1Tp06d1K5dO02ZMsVM+/PPP6tr165q37693njjDaWnp0uSTp48qd69e6tDhw564YUXlJycLEn6888/NWjQIPn5+al37946c+ZMnmxjdnz55ZemB4GBgapbt65CQ0P1+uuvq127dmb4119/Lalw9iIpKUkBAQGKiYmRpBw/Dux2u4YPHy4/Pz8FBQXpt99+y+Utvrar+7B06VIFBASoU6dOev3112W32yVJH330kXx9fc2xsXjxYkmFpw9S1l7k9Pshv/biyj5s2rQp02dFw4YNNXjwYEmF/5j46KOP5O/vL39/f02cOFHS3fk5ca0+3M2fE9dkocByOp1W06ZNrbS0NDMsJSXFatGihXXs2DErLS3N6t+/v7Vx40bLsizL39/f+v777y3LsqzXX3/dWrx4sWVZljVo0CBr1apVlmVZ1kcffWRNnDjRsizLeuutt6zZs2dblmVZy5cvt15++eVc2rK/5tChQ1bbtm2ts2fPWgEBAVZsbGyWaQpbL3744QcrICDAqlGjhnX8+PFcOQ7mzZtnjRkzxrIsy9q1a5cVHBycW5t7XVf34ffff7fatm1rnT9/3nI6ndaIESOsBQsWWJZlWYMHD7a+++67LMsoDH2wrKy9sCwrx98P+bEX1+rDJXFxcVbr1q2tw4cPW5ZVuI+JrVu3Wj169LAuXrxo2e126+mnn7ZWrlx5131OXKsPs2fPvms/J66HcFiA/frrr1bTpk2tvn37Wp06dbIWLVpk7dy503r66afNNMuXL7dGjhxpxcTEWK1btzbDd+/ebfXt29ey2+1WnTp1TMA8efKk1apVK8uyLMvX19c6efKkZVmWlZaWZtWpU8ey2+25uIW3p1evXlZ0dLR14cIF6/HHH7cGDBhgBQQEWFOnTrUcDkeh7MWoUaOs3bt3W76+vtbx48dz5Tjo06ePtXv3brOs1q1bWydOnMiNzb2uq/sQExNjffvtt2b8vHnzrPHjx1uWZVlNmjSxBg8ebAUEBFhvvfWWlZqaWmj6YFlZe5Eb74f82Iur+3Clf/7zn9bcuXPN68J8TBw6dMiEPcvKCDHTp0+/6z4nrteHu/Vz4nq4rFyA/fnnn2rUqJE+/vhjLVy4UEuWLNHJkyfl7e1tpvHx8VFsbKzi4uIyDff29lZsbKzOnTsnDw8Pubq6ZhouKdM8rq6u8vDwUHx8fC5u4a3btm2bUlNT5efnpz/++EMNGzbUhAkTFBYWpj179ig8PLxQ9mL8+PGqV6+eeX31NubEcXCtZZ0+fTpHt/Nmru5DuXLl1KRJE0lSfHy8Fi9erNatWys5OVnVqlXT8OHDtXz5cv3555+aMWNGoemDlLUXufF+yI+9uLoPlxw5ckS7du3S008/LUmF/pioXLmyateuLSlj2//zn//IZrPddZ8T1+pDQEDAXfs5cT2EwwKsTp06mjhxojw9PeXl5aUnn3xS06ZNk81mM9NYliWbzSan03nN4Zf+f6WrX185T5Ei+fuQWbJkiZ599llJ0kMPPaSPP/5YPj4+Kl68uPr27atNmzbdFb243jbeyW2/ep783JPY2Fg988wz6tatmxo0aKB77rlHc+fOVcWKFeXq6qr+/ftr06ZNhboPufF+KCi9kDLuMevVq5fc3Nwk6a45Jn755Rf1799fI0aM0EMPPXTXfk5c2YeHH35YEp8TV8q/leGm9uzZo+3bt5vXlmWpXLlymR6WOHPmjHx8fPTAAw9kGv7HH3/Ix8dHXl5eOn/+vBwOR6bppYzfIv/44w9JUnp6upKTk3X//ffnwpbdHrvdrt27d6tVq1aSpIMHD+qrr74y4y3Lkqur613Ri6u3MSeOgzJlyiguLi7LsvKb3377TT179lRQUJCGDBkiKeNm8vDwcDPNpWOjMPchN94PBaUXkrR+/Xp17NjRvL4bjom9e/eqX79+GjZsmIKCgu7az4mr+yDxOXE1wmEBdv78eU2cOFEXL15UUlKSli9frldffVWHDx/W0aNH5XA4tGrVKjVv3lzlypVTsWLFtHfvXklSVFSUmjdvrqJFi6pevXqKjo6WJEVGRqp58+aSpBYtWigyMlKSFB0drXr16qlo0aJ5sq3ZcfDgQT388MMqUaKEpIw38oQJE5SYmKi0tDQtXbpUbdu2vSt68fe//z3Hj4MWLVooKipKUsYvKsWKFdODDz6Y+xt7A0lJSRowYIBefvll9e/f3wx3d3fXpEmTdPz4cVmWpcWLF6tt27aFtg9S7rwfCkov4uPjlZqaqoceesgMK+zHxKlTpzRkyBBNnjxZ/v7+ku7Oz4lr9YHPiWvIgfsYkYumTJlidejQwWrXrp21cOFCy7Isa9u2bVanTp2sdu3aWePHj7ecTqdlWZb1888/W926dbPat29vvfrqq9bFixcty7KsmJgYq0+fPpafn5/Vv39/KyEhwbIsyzp37pw1ePBgq2PHjlaPHj2y3Myd36xevdp65ZVXMg377LPPLD8/P6tt27bWpEmTzPDC2osrb7rP6eMgNTXVGjFihNWxY0erS5cu1v79+/Ngi6/tUh8WLFhg1ahRw+rcubP578MPP7Qsy7LWrFlj+fv7W+3atbNGjhxZKPtgWZmPiZx+P+TnXlzZhx9//PGaT4sW5mNi3LhxVu3atTO9Fz7//PO77nPiWn2YNWvWXf85cTWbZVlWXgdUAAAA5A9cVgYAAIBBOAQAAIBBOAQAAIBBOAQAAIBBOAQAAIDhmtcFAEBh98MPP+j9999XQkKCLMvSAw88oNdee02VK1e+7jwjR45U5cqVNWDAgFysFAAIhwCQo+x2uwYPHqxPPvlENWrUkJTxpcLPPfec1q9fLxcXlzyuEAAyIxwCQA5KSUnR+fPndeHCBTOsc+fO8vDwkMPh0DvvvKMff/xRycnJsixLb7/9turWrZtpGeHh4Vq6dKnS0tKUmJio5557Tr169VJERITCw8OVkpIiDw8Pubq6ys/PT927d5ckzZgxQwkJCRo1alSubjOAgo1wCAA56L777tPw4cM1cOBAlS5dWo8//rgaNGggf39//fTTT4qLi9PSpUtVpEgRzZkzR3Pnzs0UDpOTk/Xll19qzpw5KlmypH744Qc9++yz6tWrlyTp119/1YYNG+Th4aF169Zp1qxZ6t69u5xOp8LDwzVv3ry82nQABRThEABy2LPPPqvg4GDt3r1bu3fv1ty5czV37lyFh4frlVde0ZIlS3T8+HHt3LlT99xzT6Z577nnHs2aNUubNm3SkSNHdODAgUxnIatUqSIPDw9Jkq+vr8aPH68DBw4oNjZW5cuX16OPPpqr2wqg4ONpZQDIQXv37tW8efPk4eEhX19fjRgxQqtXr5bNZtO6des0ePBgSVLr1q311FNPZZn/9OnT6tKli06cOKG6devqlVdeyTS+RIkS5mcXFxf16NFD4eHhWrZsmXr27Jmj2wagcCIcAkAO8vLy0syZM7Vnzx4z7MyZM0pKStLq1avl6+urXr16qWbNmlq3bp0cDkem+ffv3y8vLy+9+OKLatq0qb755htJyjLdJcHBwVq3bp1++ukntW3bNuc2DEChxWVlAMhBjzzyiD7++GNNmTJFp0+fVrFixeTp6akJEyaoXLlyGjZsmDp16qT09HQ1adJEa9euldPpNPM3adJE4eHh6tChg2w2m+rXry8vLy8dPXr0musrVaqUatasqYoVK6po0aK5tZkAChGbZVlWXhcBALgz4uPj9eSTT2rx4sUqW7ZsXpcDoADisjIAFBJhYWHq2LGjBgwYQDAEcNs4cwgAAACDM4cAAAAwCIcAAAAwCIcAAAAwCIcAAAAwCIcAAAAwCIcAAAAw/h8yOHZM+OLaswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the Tukey test:\n",
    "post_hoc_res.plot_simultaneous(ylabel= \"Education\", xlabel= \"Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tukey test(Multi comparison) for the Independent Variable Occupation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "       <th>group1</th>           <th>group2</th>       <th>meandiff</th>    <th>p-adj</th>     <th>lower</th>       <th>upper</th>    <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td> Adm-clerical</td>  <td> Exec-managerial</td>   <td>55693.3</td>   <td>0.4146</td>  <td>-40415.1459</td> <td>151801.7459</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td> Adm-clerical</td>   <td> Prof-specialty</td> <td>27528.8538</td>  <td>0.7252</td>  <td>-46277.4011</td> <td>101335.1088</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td> Adm-clerical</td>       <td> Sales</td>      <td>16180.1167</td>    <td>0.9</td>   <td>-58951.3115</td> <td>91311.5449</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td> Exec-managerial</td>  <td> Prof-specialty</td> <td>-28164.4462</td> <td>0.8263</td> <td>-120502.4542</td> <td>64173.5618</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td> Exec-managerial</td>      <td> Sales</td>      <td>-39513.1833</td> <td>0.6507</td> <td>-132913.8041</td> <td>53887.4374</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td> Prof-specialty</td>      <td> Sales</td>      <td>-11348.7372</td>   <td>0.9</td>   <td>-81592.6398</td> <td>58895.1655</td>   <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Posthoc test: Tukey test\n",
    "MultiComp1 = MultiComparison(df['Salary'],df['Occupation'])\n",
    "post_hoc_res1 = MultiComp1.tukeyhsd()\n",
    "post_hoc_res1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretations:**\n",
    "1. The p-values for all the groups are higher than the alpha.\n",
    "2. We fail to reject the null hypothesis of equality of means.\n",
    "3. All the groups are almost near to the mean salary. \n",
    "4. The classes like Admin clerical and Exec managerial is slightly far from the mean when compared with other groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4oHr16mnkyJGSpA8++EBjx46Vt7e3MjMz9cwzz+jVV1+VJHl4eGjSpEnKzMyUr6+vWrdurV27dsnV1VWNGzfWmTNnbI9TODo6asaMGRo7dqzmzp2rrKwsvfXWW7ZnX0NDQ/X222/LMAw5ODho5syZN33J67IFCxaoR48eOW6lOzg4qH///vriiy/03HPP5Wk5ubmT+/Nqzz33nNavX69mzZrlury8/nwNGzZMXl5eio2NVffu3ZWUlKQePXrIYrGoUqVKOX72fH19NW7cOL3wwgs5lrF+/fo70jfgVliM3B7WAoBC4OHhoSlTpuR4mcjMbvR2NZAfqamp6tGjhxYtWnTTN/zvtGXLlmnx4sWaO3eubVh2drZ8fX319ddfq1y5cne1HkDidj8AAKbg7Oyst99+WzNnzryr6w0MDNSMGTM0YsSIHMPDwsL0r3/9i4CKQsOVVAAAAJgOV1IBAABgOoRUAAAAmA4hFQAAAKZDSAUAAIDp8D2p96h//kmT1Wq+d+LKlnXWqVOphV1GkUG/8o+e5Q/9yh/6lX/0LH+KW7/s7CwqU+b634VMSL1HWa2GKUOqJNPWZVb0K//oWf7Qr/yhX/lHz/KHfl3C7X4AAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIBQAAgOkQUgEAAGA6hFQAAACYDiEVAAAApkNIvQN++OEH+fn5qWvXrvL29tbcuXNvOk9gYKDi4+PvQnUAAABFj0NhF1DUJSUlady4cYqKilKZMmWUlpamwMBAVatWTe3bty/s8gDcAbGxSzV16mQdP56oihUrKTh4sDp39i7ssgDgnkZIvU3//POPMjMzlZ6eLkkqXbq0Pv30U5UsWVKStHz5cn3zzTdKT09XRkaGPv74YzVq1CjHMmbPnq3ly5crOztbLVu21JAhQ5SWlqa3335bJ0+elCQNHDiQ0AsUgtjYpQoNHWH7GU9MPKbQ0BGSRFAFgAJESL1NderUUfv27dWhQwfVrVtXTZs2lbe3tx555BFZrVYtXLhQs2bNkqurqyIjIzV79mzNmjXLNv+6deu0e/duRUZGymKxaMiQIVqyZImsVquqVKmi2bNna8+ePVqyZAkh9R4SFBSYp+kcHR2UkZFVwNXcW+50z3bt+lUZGRk5hqWnp+ujjz7QokURd2w9haU4HGNffRVW2CUAuAWE1Dtg1KhRGjBggDZs2KANGzaoR48emjhxojp16qTp06dr1apVOnjwoLZu3So7u5yPAW/evFm//fab/Pz8JF365Ve5cmX5+/tr0qRJSkpKUtu2bTVw4MB81VS2rPMd2747zc3NpbBLKHSOjnn/0cvPtLjkTvbs6oB65fB7Zd/cK9txI3f6nMM5LP/oWf7Qr0sshmEYhV1EUbZmzRqdP39enTt3tg2LiIhQXFycJk+eLH9/f3Xt2lUNGjTQ2bNnNX/+fIWFhSkwMFBvvPGGVq5cqUqVKunll1+WJJ09e1b29vYqXbq0UlNTtX79eq1evVq//fabYmNjrwm5N3LqVKqsVvPtWjc3F504ca6wyygy6Ff+3emeeXp6KDHx2DXDK1WqrOXLV92x9RQWjrH8oV/5R8/yp7j1y87OcsMLa7zdf5ucnJz02Wef6ciRI5IkwzC0Z88e1a1bV4cOHZLFYtFrr72mpk2b6qefflJ2dnaO+Zs1a6aYmBilpaUpKytLAwcO1IoVK/Ttt99q6tSp8vT01MiRI3X69GmlpqYWxiYCxVpw8GA5OTnlGObk5KTg4MGFVBEAFA/39j2eu6BZs2Z644039NprrykzM1OS1KpVKw0cOFD29vaqW7euPD09ZbFY1LJlS+3YsSPH/B4eHtq7d6969Oih7OxstWrVSr6+vrYXp7y9vWVvb68hQ4bo/vvvL4xNBIq1yy9H8XY/ANxd3O6/R3G7/95Av/KPnuUP/cof+pV/9Cx/ilu/uN0PAACAIoWQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATIeQCgAAANMhpAIAAMB0CKkAAAAwHUIqAAAATMf0IfXIkSNyd3eXj4+PunXrpi5duujll1/W8ePH87WciIgItWrVSuPGjSugSq/Px8cn1/FRUVEKCQmRJH3xxRfavn373SgLAADA1EwfUiWpfPnyiomJUXR0tJYtW6batWtr/Pjx+VrG999/r08++UTvvfdeAVV5fTExMXmedtu2bcrOzi7AagAUdbGxS+Xp6aGGDevK09NDsbFLC7skACgQRSKkXq1p06Y6cOCAJMnDw0ODBg3Ss88+q1OnTmnRokXy8vKSt7e3QkJClJaWpmnTpmnXrl0aNWqU1q5dm2NZqamp6tevn/z8/OTn56eVK1dKkgIDAzV27Fj5+vqqc+fO2rBhgyTp5MmTGjBggPz8/OTv769NmzZJklJSUjRw4EB5enrKx8dHmzdvliTVrl1bkpSUlKSgoCD16NFDbdu21ZQpU3LUER0drd27d2v48OHat2+f2rZtK6vVKkmKj4/Xq6++WkDdBFBUxMYuVWjoCCUmHpNhGEpMPKbQ0BEEVQD3JIfCLiC/MjMztWLFCjVo0MA2rHXr1vr888+1b98+zZo1SxERESpTpoxGjRqladOm6b333lN8fLzeeOMNNW3aNMfyfvrpJ1WpUkWzZ8/Wnj17tGTJErVv317SpQC7ePFi7dmzR3379tWqVas0duxY+fv7q3379kpOTlZAQICio6M1ZcoUPfzww5o+fbr27dunDz/8UM2bN7et5/vvv5eXl5d8fX117tw5tWnTRoGBgbbx3bp106JFi/TGG2+odu3aqlq1quLj49W8eXNFR0fLz8+vYBsLSAoKCrz5RCbn6OigjIyswi6jQOza9asyMjJyDEtPT9dHH32gRYsibmmZ93K/CkJ++/XVV2EFWA1wbysSITU5Odn2bGdGRoaeeOIJvfPOO7bxTz75pKRLt8vbtWunMmXKSJJeeOEFvf/++7kuu2HDhpo0aZKSkpLUtm1bDRw40DauR48ekqS6devKzc1N+/bt06ZNm/TXX3/piy++kCRlZWUpISFB27Zt08SJEyVdunoaHh6eYz1BQUHasmWLvvrqKx04cECZmZm6cOHCDevy9/fXkiVL1KBBA23ZskUfffRRXlplU7asc76mv5vc3FwKu4Qi5W72y9GxSJwSbupe2Y6rXR1Qrxx+O9t8r/aroOSnX5zvLqEP+UO/LikSZ6bLz6TeSMmSJSXJdnv8MsMwlJWV82+8u3bt0vDhwyVJ7u7uGjt2rJYvX67169dr9erV+vrrrxUbGytJsre3t81ntVrl4OAgq9Wqf//733rwwQclXQrQZcuWlYODgywWi236P//8U9WqVbN9/vTTT5WQkCAvLy916NBBmzZtkmEYN9ym5557TpMnT9aKFSvUunVr2zbm1alTqbJab7z8wuLm5qITJ84VdhlFxt3u18yZ39y1dRWUe/kY8/T0UGLisWuGV6pU+Zb33b3cr4KQ337RW46x/Cpu/bKzs9zwwlqRfCb1Rp5++mmtWrVKKSkpki690X/17f369esrJiZGMTExGjt2rL799ltNnTpVnp6eGjlypE6fPq3U1FRJsoXVXbt26ezZs3rsscfUrFkz/fe//5Uk/fHHH/L29taFCxf01FNPadmyZZIuBdS+ffvmCK0bN25UUFCQPD09dfDgQSUlJV0Tqu3t7W0vTpUqVUqtW7fWpEmTuNUPQJIUHDxYTk5OOYY5OTkpOHhwIVUEAAWnSFxJzas6deqof//+CgwMVGZmpurVq6dRo0blOk+3bt309ttvy9vbW/b29hoyZIjuv/9+SVJCQoJ8fX0lSZMnT5a9vb2GDx+uDz/8UN7e3pKk8ePHy9nZWW+++aaGDx+url27ysHBQePHj88RUvv376+hQ4fKyclJFStWlLu7u44cOZKjllatWmnkyJEaN26cGjVqpC5duujnn3+2Pc4AoHjr3PnSeWfq1Mk6fjxRFStWUnDwYNtwALiXWIzc7jkXY4GBgdd90epuyc7O1uTJk1W2bFm9/PLL+Z6f2/33BvqVf/Qsf+hX/tCv/KNn+VPc+pXb7f48XUn95ZdfNGnSJJ05cybHc5RLl/K1JwXF399fZcqU0cyZMwu7FAAAgLsuTyH1ww8/lJ+fnx5//PEct7DvZWFhhfu1IdHR0YW6fgAAgMKUp5Dq4OBwS7ecAQAAgFuRp7f7a9WqpX379hV0LQAAAICkPF5JTUhIkL+/vypXrpzj+zp5JhUAAAAFIU8hdfBgvoMPAAAAd0+ebvc//fTTKlmypLZu3aqNGzfahgEAAAAFIU8hNTo6Wm+++abOnDmjtLQ0vfPOO4qIiCjo2gAAAFBM5el2/7x58/Tdd9+pfPnykqS+ffsqKChIPXr0KNDiAAAAUDzl6Uqq1Wq1BVRJqlChguzs8jQrAAAAkG95SpoPPvig4uLibJ/j4uL0wAMPFFhRAAAAKN7ydLt/xIgRGjBggEaPHi1JKlGihKZNm1aghQEAAKD4ylNIrVWrln744QcdOnRI2dnZql69uhwc8jQrAAAAkG+5Js05c+aob9++Gj16tCwWyzXjhw8fXmCFAQAAoPjKNaS6uLhIksqUKXNXigEAAACkm4TUnj17SpJcXV0VEBCQY9zs2bMLrioAAAAUa7mG1AULFig9PV3z5s3TxYsXbcMzMzO1cOFC9evXr8ALBAAAQPGTa0h1cHDQ/v37lZ6erv3799uG29vbKyQkpMCLAwAAQPGUa0jt3r27unfvrri4OHXo0OFu1QQAAIBiLk/fI9WoUSPNmzdPaWlpMgxDVqtVhw8f1meffVbQ9QEAAKAYylNIHTRokJycnPTHH3/omWee0aZNm9S4ceOCrg0AAADFVJ7+WdRjx45p9uzZat26tXr37q0FCxbor7/+KujaAAAAUEzlKaSWK1dOkvToo49q//79qlChgrKysgq0MAAAABRfebrdX7ZsWc2dO1cNGjTQ1KlT5ezsrPT09IKuDQAAAMVUnq6khoaGytHRUU899ZTc3d31xRdf6N133y3o2gAAAFBM5flK6osvvqj//e9/8vb21rvvviuLxVLQtQEAAKCYylNI3b59uwYPHix7e3tZrVaVKFFCM2bMUO3atQu6PgAAABRDeQqpY8aM0dixY9W6dWtJ0qpVqzRy5EgtXLiwQIsDAABA8ZSnZ1Il2QKqJHl4eOjChQsFUhAAAACQp5D6xBNPKDY21vZ5w4YNeuyxxwqsKAAAABRvebrdv2HDBkVERGjUqFFycHDQqVOnVLJkScXFxclisejnn38u6DoBAABQjOQppIaFhRV0HQAAAIBNnkJqSkrKdYfXq1fvTtYCAAAASMpjSA0ODrb9OTMzUydOnJC7u7siIyMLrDAAAAAUX3kKqatWrcrxOT4+XkuXLi2QggAAAIA8fwXVlZo2barff//9TtcCAAAASMrjldQrA6lhGNq9e7fS09MLrCgAAAAUb/l+JtViscjV1VUfffRRQdUEAACAYi7Pz6SmpqbK2dlZFy9eVGpqqsqWLVvQtQEAAKCYytMzqbGxsfLz85MkHTt2TF5eXte8TAUAAADcKXkKqbNmzdJ//vMfSVK1atUUFRWlqVOnFmhhAAAAKL7yFFKtVqsqVqxo+1ypUiVZrdYCKwoAAADFW55CqqurqxYuXKisrCxlZ2crMjJS5cqVK+jaAAAAUEzlKaSGhoYqIiJCTz75pJ544glFRERo5MiRBV0bAAAAiqk8vd3/6KOPKiwsTFlZWbK3t9fFixd5ux8AAAAFJs9v9/v6+uqBBx7QiRMneLsfAAAABYq3+wEAAGA6vN0PAAAA0+HtfgAAAJgOb/cDAADAdG76dn9SUpLCwsKUnZ2tGjVqqH79+nrjjTdUqVKlu1EfAAAAiqFcr6QmJiaqe/fusre316BBgzRw4ECVLFlS3bt319GjR+9WjQAAAChmcr2S+vnnn+vtt99Wt27dbMOeffZZ1atXT59//rkmTJhQ0PUBAACgGMr1Sur//ve/HAH1Mn9/f/32228FVRMAAACKuVxDqmEYNxzn6Oh4x4sBAAAApJuEVHt7eyUlJV0zPCkpiZAKAACAApNrSO3Zs6eGDRum1NRU27BTp05p6NChCggIyNMKjhw5Ind3d/n4+OT4b/78+bdX+T1iwYIFWrBgQa7TeHh46MiRI3epIgAAgMKX64tTvXr10t9//61WrVqpZs2aysrK0qFDh/TSSy/J398/zyspX768YmJibrvYe1GvXr0KuwQAKPZiY5dq6tTJOn48URUrVlJw8GB17uxd2GUBxdpNvyf1vffeU58+ffTrr79Kkp588klVqFDhjqz8999/V79+/bR06VLZ2dnJ19dXM2bM0MMPP6zQ0FAdOHBA2dnZ6tu3r7y8vHTx4kWNGjVKO3bsUIkSJTRgwAB17tw5xzKPHDmigQMHqnr16vrjjz/0+OOPq2HDhlq8eLHOnDmj6dOnq0aNGlq+fLm++eYbpaenKyMjQx9//LEaNWqkwMBA1a9fXzt27NDp06c1fPhwtWnTRvv379fo0aN1/vx5nT59Wv369VOvXr107tw5DR06VH///bceeughHT9+XNOmTVOlSpU0fvx4bd26VdnZ2fLz81OfPn0UHx+vCRMmyGq1qlatWqpataokKTg4WN9++61iYmJ04cIFlShRQp999pmqV69+R3oNALi+2NilCg0dofT0dElSYuIxhYaOkCSCKlCIbhpSJalChQrq1KnTLa8kOTlZPj4+OYaNHz9e9erV0wsvvKDx48crMzNTvXr1Ut26dTVx4kTVq1dP48aNU2pqqnr27Kknn3xSK1as0Pnz57V8+XKdOnVKffr0UYcOHa55Pnbfvn365JNPVKdOHT377LMqX768wsPDNW3aNIWHhyskJEQLFy7UrFmz5OrqqsjISM2ePVuzZs2SJGVmZio8PFyrVq3SlClT1KZNG3333XcaMGCAmjdvroSEBHXt2lW9evXS9OnTVa1aNc2cOVO7du3SCy+8IEmKiIiQJC1evFgZGRkKCgqSu7u7JOnQoUNavXq1XFxcNHXqVElSamqq4uLiFBYWJicnJ02ZMkXz58/XiBEjbrnvQGEICgos1PU7OjooIyOrUGsoSuiXtGvXr8rIyMgxLD09XR999IEWLYrIMZx+5R89y59b7ddXX4UVQDWFK08h9Xbldrv/9ddfl7+/v5ycnGzfu7pp0yalp6dr0aJFkqTz58/rwIED2rZtm3r06CE7Ozu5ublp2bJl111muXLl9Pjjj0uSKlasqObNm0uSKleurCNHjsjOzk7Tp0/XqlWrdPDgQW3dulV2dv//eG6rVq0kSbVq1VJKSookKSQkROvXr9eXX36p/fv36/z585KkjRs3auLEiZKk+vXr67HHHpMkbd68WXv27NGWLVts27Bv3z7VrFlT1apVk4uLS46anZ2d9dlnn2nZsmU6dOiQ1q9fr7p16+ajyzmVLet8y/MWNDc3l5tPBJui1i9Hx7tyWjF9DUVJce/X1QH1yuHX601x79etoGf5cyv9Kmq/K/Ki0I+ac+fOKS0tTWlpaUpJSZGrq6usVqsmTJigevXqSZJOnjypBx54QIsWLZLFYrHNe/jwYZ05c8Z2tdHd3V2vv/76NVdW7e3tc3xOS0vT888/r65du6pJkyaqXbt2jhe5SpYsKUk51jVo0CDdf//9ateunTp37qzvv//etuzrfVVXdna2hgwZYrsCffr0aZUuXVo7d+6Uk5PTNdMnJiYqMDBQvXv3VuvWrVWuXDnt2bMn7428yqlTqbJab/wVYoXFzc1FJ06cK+wyioyi2K+ZM78p1PUXxZ4VJvoleXp6KDHx2DXDK1WqfM3xTL/yj57lz632q6j22M7OcsMLa7m+3X83jBo1Sr1791ZAQIBGjRolSWrWrJntjffk5GR17dpViYmJatKkiWJjY2UYhk6dOqXevXurevXqiomJUUxMjMaOHZundR46dEgWi0WvvfaamjZtqp9++knZ2dm5zrNx40a9+eab6tChg9atWyfpUhBt3ry5li5dKunSYwYHDhyQxWJRs2bNFBERoczMTKWlpSkgIEA7d+684fJ37dqlRx55RH369FH9+vUVFxd305oAALcvOHjwNRcPnJycFBw8uJAqAiDdpSup13smtUmTJmrUqJESEhI0adIkGYYhf39/xcbG6o033tBHH30kLy8v2xXJhx9+WAEBARozZoy6du0qSRoxYoScnfN/W7tOnTqqW7euPD09ZbFY1LJlS+3YsSPXeYKDgxUQEKCSJUuqTp06qlKliu0lrffff1/e3t56+OGHVa5cOTk5Oalnz546fPiwfH19lZWVJT8/PzVt2lTx8fHXXX6LFi20YMECde7cWYZhqEmTJjpw4EC+tw0AkD+XX47i7X7AXCxGbv+sFG4qJiZGVatWVePGjXXs2DH17t1bcXFxOZ5xLQzc7r830K/8o2f5Q7/yh37lHz3Ln+LWr9xu9xf6M6lFXfXq1TVy5EhZrVbZ2dkpNDS00AMqAABAUUdIvU3169dXVFRUYZcBAABwT+GSHwAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTIaQCAADAdAipAAAAMB1CKgAAAEyHkAoAAADTuSdCqp+fn1577bVcp/Hw8NCRI0fu6Hrzu8wFCxZowYIF+V5PVFSUQkJC8j0fAABAUVXkQ+revXvl6OiovXv3KjExsbDLyVWvXr3Uq1evwi4DuGtiY5fK09NDDRvWlaenh2JjlxZ2SQCAIsKhsAu4XVFRUWrRooVSUlIUERGht956S5KUkpKiIUOG6Pjx46pRo4YuXrxom37NmjVKSUlRcnKyevbsqaNHj2rLli168MEHNXfuXJUsWTLHOi5evKhRo0Zpx44dKlGihAYMGKDOnTvbxmdnZ2v8+PHaunWrsrOz5efnpz59+ig+Pl4TJkyQ1WpVrVq1VLVqVUlScHCwli5dqpkzZ8pisah+/foaPXq0Tp8+rWHDhuncuXNKTk6Wr6+vbXuAoiY2dqlCQ0coPT1dkpSYeEyhoSMkSZ07exdmaQCAIqBIh9TMzEwtXbpUYWFhSklJ0eDBgzVw4EA5ODjoiy++0OOPP645c+Zo27ZtWr58uW2+Xbt2aenSpTpz5ow8PDw0d+5cffDBBwoMDNT69evVoUOHHOsJCwvT+fPntXz5cp06dUp9+vTJMU1ERIQkafHixcrIyFBQUJDc3d0lSYcOHdLq1avl4uKiqVOnSpKSkpL0ySefKCoqShUrVtSQIUO0du1aHT58WF5eXvL19dW5c+fUpk0bBQYGFnQbTSMoqPhsa145OjooIyOrsMu4Jbt2/aqMjIwcw9LT0/XRRx9o0aKIAltvUe5ZYbgT/frqq7A7VA0A/L8iHVLXrFkjNzc31axZU4ZhyM7OTqtXr1bHjh21detWffbZZ5KkJk2a6KGHHrLN16hRIzk7O8vZ2VmS1Lx5c0lSlSpVdPbs2WvWs23bNvXo0UN2dnZyc3PTsmXLcozfvHmz9uzZoy1btkiSzp8/r3379qlmzZqqVq2aXFxcckz/yy+/qFGjRqpYsaIkacKECbZxW7Zs0VdffaUDBw4oMzNTFy5cuKXelC3rfEvz3Q1ubi7XHe7oWKQPxwJTVPtydUC9cnhBb1NR7Vlhud1+3ehn+l5V3Lb3TqBn+UO/LinSZ/JFixYpMTFRHh4ekqTU1FQtXLhQHTt2lMVikWEYtmnt7e1tfy5RokSO5Tg45GzDggULtHDhQklSz5495eDgIIvFYht/+PBhVapUyfY5OztbQ4YMUadOnSRJp0+fVunSpbVz5045OTldU/fVyzt9+rQkafbs2UpISJCXl5c6dOigTZs25diG/Dh1KlVW663NW5Dc3Fx04sS5646bOfObu1yN+eXWL7Pz9PRQYuKxa4ZXqlS5QPd1Ue5ZYbgT/SpO/eb4yj96lj/FrV92dpYbXlgrsi9OnTx5Ups2bdL333+vVatWadWqVYqOjtaWLVuUkJCg5s2bKyYmRpL022+/6e+//87zsnv16qWYmBjFxMSoV69eatKkiWJjY2UYhk6dOqXevXvnuErUrFkzRUREKDMzU2lpaQoICNDOnTtvuPz69etr586dOnHihCTp448/1sqVK7Vx40YFBQXJ09NTBw8eVFJSkqxW6601CChkwcGDr/lLmpOTk4KDBxdSRQCAoqTIXkmNiYlRmzZtVKFCBduwhx56SB4eHgoPD9ebb76pkJAQdenSRdWrV89xuz+/AgICNGbMGHXt2lWSNGLECNujAtKlq62HDx+Wr6+vsrKy5Ofnp6ZNmyo+Pv66y6tQoYI++OADBQUFyWq1qkGDBvLz81OpUqU0dOhQOTk5qWLFinJ3d7/jX5sF3C2XX46aOnWyjh9PVMWKlRQcPJiXpgAAeWIxbvV+MkytKN7ux7XoV/7Rs/yhX/lDv/KPnuVPcevXPXm7HwAAAPcuQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA03Eo7AJQMOzsLIVdwg2ZuTYzol/5R8/yh37lD/3KP3qWP8WpX7ltq8UwDOMu1gIAAADcFLf7AQAAYDqEVAAAAJgOIRUAAACmQ0gFAACA6RBSAQAAYDqEVAAAAJgOIRUAAACmQ0gFAACA6RBSAQAAYDqEVORLamqqvLy8dOTIEUnSpk2b5O3trU6dOmny5Mm26fbs2SM/Pz89++yz+uCDD5SVlSVJOnbsmF588UU999xzev3115WWliZJOnv2rPr16ydPT0+9+OKLOnHihCQpIyNDQ4YMkaenp3x9ffXnn3/e5S2+PVf3Kzw8XF5eXvL29tb777+vjIwMSdK0adPUrl07+fj4yMfHR/Pnz5dEv95//3116tTJ1peffvpJEsfXla7s2dq1a2298vHxUbNmzdS/f39JHGPSpR506dJFXbp00fjx4yVxDruZ6/WM89iNXa9fnMdugwHk0c6dOw0vLy+jXr16RkJCgnHhwgWjTZs2xt9//21kZmYar7zyirFmzRrDMAyjS5cuxi+//GIYhmG8//77xvz58w3DMIx+/foZ33//vWEYhjFt2jRj/PjxhmEYxqhRo4wvv/zSMAzDWLx4sfHWW28ZhmEYc+fONUaMGGEYhmFs3brV6N69+93a3Nt2db/++usvo2PHjsa5c+cMq9VqDB061Pjmm28MwzCM/v37Gz///PM1yyjO/TIMw/Dy8jKSkpKumZbj65Lr9eyy5ORko3379sbBgwcNw+AY27hxo/HCCy8YFy9eNDIyMoyXXnrJWLp0KeewXFyvZ19++SXnsRu4Xr9+/PFHzmO3gZCKPBs2bJixbds2o127dkZCQoIRHx9vvPTSS7bxixcvNkJCQowjR44Y7du3tw3ftm2bERgYaGRkZBgNGzY0MjMzDcMwjGPHjhkeHh6GYRhGu3btjGPHjhmGYRiZmZlGw4YNjYyMDKN3797Gtm3bbMtq3769cfTo0buxubft6n4dOXLE2LBhg2383LlzjbFjxxqGYRgtWrQw+vfvb3h5eRmjRo0y0tPTi32/zp8/bzRq1MgICgoyvLy8jClTphjZ2dkcX1e4umdXevfdd405c+bYPhf3Y2z//v22QGAYl37hT506lXNYLm7UM85j13e9fs2bN4/z2G3gdj/ybOzYsXrqqadsn5OTk+Xm5mb7XL58eSUlJV0z3M3NTUlJSfrnn3/k7OwsBweHHMOvXpaDg4OcnZ11+vTp6y7r+PHjBbqdd8rV/apSpYpatGghSTp9+rTmz5+v9u3bKy0tTXXr1tWQIUO0ePFinT17VjNmzCj2/Tp58qSaNWumjz/+WBEREdq+fbsiIyM5vq5wdc8uO3TokLZu3aqXXnpJkjjGJNWqVUsNGjSQdKk/y5cvl8Vi4RyWi+v1zMvLi/PYDVyvX61ateI8dhsIqbhlVqtVFovF9tkwDFkslhsOv/z/K139+cp57Ozsrpnn8vCiLCkpSf/617/k7++vpk2bqnTp0pozZ45q1KghBwcHvfLKK1q7dm2x79dDDz2k6dOnq3z58ipVqpQCAwO1du1ajq88CA8PV0BAgBwdHSWJY+wKBw4c0CuvvKKhQ4fqoYce4hyWB1f27NFHH5XEeSw3V/arevXqnMduw72xFSgUFStWtD24LUknTpxQ+fLlrxl+8uRJlS9fXq6urjp37pyys7NzTC9duoJx8uRJSVJWVpbS0tL04IMPqkKFCkpOTr5mWUXVn3/+qZ49e8rX11cDBw6UdOkh+cjISNs0hmHIwcGh2Pdr3759WrFihe3z5b5wfN3cypUr1blzZ9tnjrFLduzYoT59+uidd96Rr68v57A8uLpnEuex3FzdL85jt4eQilv25JNP6uDBgzp8+LCys7P1/fffq3Xr1qpSpYpKliypHTt2SJJiYmLUunVrlShRQk899ZRiY2MlSdHR0WrdurUkqU2bNoqOjpYkxcbG6qmnnlKJEiXUpk0bxcTESJK2b9+ukiVLqnLlynd/Y++A1NRUBQUF6a233tIrr7xiG+7k5KQJEyYoISFBhmFo/vz56tixY7Hvl2EY+vjjj3XmzBllZmYqPDxcHTt25Pi6idOnTys9PV0PPfSQbRjHmJSYmKiBAwdq4sSJ6tKliyTOYTdzvZ5xHrux6/WL89jtsRiGYRR2EShaPDw89J///EdVq1bV5s2b9cknn+jixYtq06aN3n//fVksFu3du1fDhw9Xamqq6tWrp08++USOjo46evSoQkJCdOrUKVWqVEmTJk3SAw88oJSUFIWEhCghIUEuLi6aOHGiqlatqosXL+rDDz/U7t275ejoqDFjxqhevXqF3YJ8udyvuLg4TZw4UTVq1Mgx7q233tKKFSs0depUZWZmqlGjRho1alSx71fVqlU1f/58zZ8/X1lZWerUqZPeffddSeL4usqVPfvtt980ZswYRURE5JimuB9jY8aM0aJFi/Twww/bhvXs2VOPPvoo57AbuF7POnfurKlTp3Ieu44bHWNWq5Xz2C0ipAIAAMB0uN0PAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA03Eo7AIAAHfHzp079dlnnyklJUWGYahixYp67733VKtWrRvOExISolq1aikoKOguVgoAhFQAKBYyMjLUv39/ff3117bvUIyJiVHfvn21cuVK2dvbF3KFAJATIRUAioELFy7o3LlzOn/+vG1Y165d5ezsrOzsbH3yySf69ddflZaWJsMwNGbMGDVu3DjHMiIjIxUeHq7MzEydOXNGffv2VUBAgKKiohQZGakLFy7I2dlZDg4O8vT0VI8ePSRJM2bMUEpKioYNG3ZXtxlA0UZIBYBi4IEHHtCQIUP06quvqly5cmrUqJGaNm2qLl266Pfff1dycrLCw8NlZ2en2bNna86cOTlCalpamr777jvNnj1bZcqU0c6dO/Xyyy8rICBAkvTHH39o1apVcnZ2VlxcnGbNmqUePXrIarUqMjJSc+fOLaxNB1BEEVIBoJh4+eWX1b17d23btk3btm3TnDlzNGfOHEVGRmrQoEFauHChEhISFB8fr9KlS+eYt3Tp0po1a5bWrl2rQ4cOae/evTmuytauXVvOzs6SpHbt2mns2LHau3evkpKSVLVqVVWvXv2ubiuAoo+3+wGgGNixY4fmzp0rZ2dntWvXTkOHDtWyZctksVgUFxen/v37S5Lat2+vXr16XTP/8ePH1a1bNx09elSNGzfWoEGDcoy/7777bH+2t7fXCy+8oMjISC1atEg9e/Ys0G0DcG8ipAJAMeDq6qqZM2dq+/bttmEnTpxQamqqli1bpnbt2ikgIEDu7u6Ki4tTdnZ2jvl3794tV1dXDRgwQC1bttTq1asl6ZrpLuvevbvi4uL0+++/q2PHjgW3YQDuWdzuB4BioFq1apo+fbomT56s48ePq2TJknJxcdHHH3+sKlWq6J133pG3t7eysrLUokUL/fjjj7Jarbb5W7RoocjISD333HOyWCx6+umn5erqqsOHD193fWXLlpW7u7tq1KihEiVK3K3NBHAPsRiGYRR2EQCAe8vp06f1/PPPa/78+apUqVJhlwOgCOJ2PwDgjoqIiFDnzp0VFBREQAVwy7iSCgAAANPhSioAAABMh5AKAAAA0yGkAgAAwHQIqQAAADAdQioAAABMh5AKAAAA0/k/KYjZ/PcjmXUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "post_hoc_res1.plot_simultaneous(ylabel= \"Occupation\", xlabel= \"Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.5 What is the interaction between the two treatments? Analyze the effects of one variable on the other (Education and Occupation) with the help of an interaction plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax= plt.subplots(figsize=(10,6))\n",
    "fig = interaction_plot(x=df['Education'],trace=df['Occupation'],response=df['Salary'],colors=['red','blue','green','black'],\n",
    "                      ylabel='Salary',xlabel='Education',ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the interation plot, we can understand that there is significant interaction between Education and the Occupation with respect to salary.\n",
    "- Below is the interaction anova table to prove the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $H_0$: There is no interaction between Education and Occupation with respect to Salary\n",
    "- $H_1$: There is a significant interaction between Education and Occupation with respect to Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Interaction table on Salary with respect to Education and Occupation\n",
      "-------------------------------------------------------------------------\n",
      "                        df        sum_sq       mean_sq          F  \\\n",
      "Education:Occupation  11.0  1.438019e+11  1.307290e+10  18.384842   \n",
      "Residual              29.0  2.062102e+10  7.110697e+08        NaN   \n",
      "\n",
      "                            PR(>F)  \n",
      "Education:Occupation  3.340466e-10  \n",
      "Residual                       NaN  \n"
     ]
    }
   ],
   "source": [
    "model = ols('Salary ~ Education:Occupation',df).fit()\n",
    "aov_table3 = anova_lm(model)\n",
    "print('ANOVA Interaction table on Salary with respect to Education and Occupation')\n",
    "print('-------------------------------------------------------------------------')\n",
    "print(aov_table3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pvalue_Int = aov_table3['PR(>F)'][0]\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject the Null hypothesis as pvalue is significantly less than the alpha value\n",
      " \n",
      "There is a significant interaction between both the independent variables i.e., Education and Occupation\n"
     ]
    }
   ],
   "source": [
    "if pvalue_Int < alpha:\n",
    "    print('We reject the Null hypothesis as pvalue is significantly less than the alpha value')\n",
    "    print(\" \")\n",
    "    print('There is a significant interaction between both the independent variables i.e., Education and Occupation')\n",
    "else:\n",
    "    print('We fail to reject the Null hypothesis as pvalue is not less than the alpha value')\n",
    "    print('There is no significant interaction between both the independent variables i.e., Education and Occupation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.6 Perform a two-way ANOVA based on the Education and Occupation (along with their interaction Education*Occupation) with the variable ‘Salary’. State the null and alternative hypotheses and state your results. How will you interpret this result?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Two-Way ANOVA:**\n",
    "1. Two factors with multiple levels\n",
    "2. Two or more independent variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Bachelors</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>170711.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>193201.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>105787.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>192300.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Doctorate</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>166457.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>212781.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>247772.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>193916.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">HS-grad</th>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>78759.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>95534.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>50822.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Salary\n",
       "Education  Occupation                 \n",
       " Bachelors  Adm-clerical     170711.00\n",
       "            Exec-managerial  193201.75\n",
       "            Prof-specialty   105787.75\n",
       "            Sales            192300.75\n",
       " Doctorate  Adm-clerical     166457.75\n",
       "            Exec-managerial  212781.00\n",
       "            Prof-specialty   247772.83\n",
       "            Sales            193916.60\n",
       " HS-grad    Adm-clerical      78759.67\n",
       "            Prof-specialty    95534.33\n",
       "            Sales             50822.33"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparison of mean across different Education levels and Occupation types\n",
    "Mean_Edu_Occ = pd.DataFrame(df.groupby(['Education','Occupation'])['Salary'].mean())\n",
    "round(Mean_Edu_Occ,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='Salary'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x='Education',y='Salary',hue='Occupation',data=df,ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis for Two-Way ANOVA**\n",
    "\n",
    "1. Factor: Education Effects\n",
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3\n",
    "- $H_1$: Atleast for one Education level the salary is different from the rest\n",
    "2. Factor: Occupation Effects\n",
    "- $H_0$: 𝜇1 = 𝜇2 = 𝜇3 = 𝜇4\n",
    "- $H_1$: Atleast for one Occupation type the salary is different from the rest\n",
    "3. Interaction Effects\n",
    "- $H_0$: There is no interaction between Education and Occupation with respect to Salary\n",
    "- $H_1$: There is a significant interaction between Education and Occupation with respect to Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two-Way Anova Table\n",
      "-------------------------------------------------------------------------\n",
      "                        df        sum_sq       mean_sq          F  \\\n",
      "Education              2.0  1.026955e+11  5.134773e+10  72.211958   \n",
      "Occupation             3.0  5.519946e+09  1.839982e+09   2.587626   \n",
      "Education:Occupation   6.0  3.634909e+10  6.058182e+09   8.519815   \n",
      "Residual              29.0  2.062102e+10  7.110697e+08        NaN   \n",
      "\n",
      "                            PR(>F)  \n",
      "Education             5.466264e-12  \n",
      "Occupation            7.211580e-02  \n",
      "Education:Occupation  2.232500e-05  \n",
      "Residual                       NaN  \n"
     ]
    }
   ],
   "source": [
    "model = ols('Salary ~ Education + Occupation + Education:Occupation',df).fit()\n",
    "aov_table4 = anova_lm(model)\n",
    "print('Two-Way Anova Table')\n",
    "print('-------------------------------------------------------------------------')\n",
    "print(aov_table4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equality of means for the factor Education is rejected\n",
    "aov_table4['PR(>F)'][0] < alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # equality of means for the factor Occupation is not rejected\n",
    "aov_table4['PR(>F)'][1] < alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there is an interaction effect\n",
    "aov_table4['PR(>F)'][2] < alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretations for Two-Way ANOVA Table:**\n",
    "1. The pvalue for Education is significantly less than alpha. So, we reject the null hypothesis of equality of means. We conclude that there is a cause and effect relationship between Education and Salary.\n",
    "\n",
    "2. The pvalue for Education is not less than alpha. So, we fail to reject the null hypothesis of equality of means. We conclude that there is no cause and effect relationship between Occupation and Salary.\n",
    "\n",
    "3. The pvalue for the interaction effect is less than the alpha. So, the equality of means at each combination of Education and Occupation is also rejected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sns.pointplot(x='Occupation',y='Salary',hue='Education',data=df,ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.7 Explain the business implications of performing ANOVA for this particular case study.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Education is the significant cause for the Salary to either increase or decrease.\n",
    "2. There is no significant cause relationship between the occupation types and the Salary. \n",
    "3. On studying the interaction effect, though Occupation itself is not a significant cause but when it interacts with Education, it shows a cause effect relationship with Salary.\n",
    "4. When Education along with Occupation is high, there is an increase in the Salary."
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}