Salary Variance Analysis -- Hypothesis Testing Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1f6ec76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import scipy.stats as stats\n",
    "from statsmodels.formula.api import ols      # For n-way ANOVA\n",
    "from factor_analyzer import FactorAnalyzer # Perform statistical tests before PCA \n",
    "from statsmodels.stats.anova import _get_covariance,anova_lm # For n-way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9edaea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the data\n",
    "df = pd.read_csv(\"SalaryData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee5699dc",
   "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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>175935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>220754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Sales</td>\n",
       "      <td>170769</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\n",
       "2   Doctorate   Adm-clerical  175935\n",
       "3   Doctorate   Adm-clerical  220754\n",
       "4   Doctorate          Sales  170769"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfa65d79",
   "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": 5,
   "id": "9978f374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f83dc3d8",
   "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,
   "id": "9c2eb627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of observations is 120\n",
      "The number of groups is 3\n"
     ]
    }
   ],
   "source": [
    "n=df.shape[0]*df.shape[1] \n",
    "k=df.shape[1] \n",
    "print(\"The total number of observations is\",n )\n",
    "print(\"The number of groups is\",k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2c349f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree of Freedom between groups 2\n",
      "Degree of Freedom within groups 117\n"
     ]
    }
   ],
   "source": [
    "#Degree of freedom between groups\n",
    "dfb=k-1\n",
    "print('Degree of Freedom between groups',dfb)\n",
    "#Degree of freedom within groups\n",
    "dfw=n-k\n",
    "print('Degree of Freedom within groups',dfw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87b47e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Doctorate    16\n",
       " Bachelors    15\n",
       " HS-grad       9\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba77d4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e= df.drop(['Occupation'], axis=1) # creating a new dataframe without the Occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aca42b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(6,6))\n",
    "ax = sns.countplot(x = 'Education',  data = df)\n",
    "ax.set_title(\"Employee count across Education\", fontsize=12, weight='bold', style='italic')\n",
    "ax.bar_label(ax.containers[0], label_type='edge');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2113eeeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,6))\n",
    "ax = sns.countplot(x = 'Education', hue='Occupation', data = df)\n",
    "ax.set_title(\"Breakdown of Employees across Occupation\", fontsize=12, weight='bold', style='italic')\n",
    "ax.bar_label(ax.containers[0], label_type='edge');\n",
    "ax.bar_label(ax.containers[1], label_type='edge');\n",
    "ax.bar_label(ax.containers[2], label_type='edge');\n",
    "ax.bar_label(ax.containers[3], label_type='edge');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61544c9f",
   "metadata": {},
   "source": [
    "## Filtering the stacked data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e1a8533",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df_e[df_e['Education'] == ' Doctorate']\n",
    "b = df_e[df_e['Education'] == ' Bachelors']\n",
    "c = df_e[df_e['Education'] == ' HS-grad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6b91018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,12))\n",
    "plt.subplot(2,2,1) ## Row - Column - Order\n",
    "sns.histplot(a, kde =True, palette= 'Greens') \n",
    "plt.subplot(2,2,2) \n",
    "sns.histplot(b, kde =True, palette ='Oranges')\n",
    "plt.subplot(2,2,3)\n",
    "sns.histplot(c, kde =True, palette='Blues')\n",
    "\n",
    "plt.suptitle(\"Salary distribution by Education\", fontsize =16)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2d9146",
   "metadata": {},
   "source": [
    "## Test for Normality -  Shapiro- Wilk Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba9555ca",
   "metadata": {},
   "source": [
    "### these tests are done for practice/reference only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938bcd5c",
   "metadata": {},
   "source": [
    "### Null Hypothesis : The given observation is normally distributed\n",
    "### Alternative Hypothesis : The given observation is not normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c038bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0675690770149231 0.7050924301147461 0.1783432960510254\n"
     ]
    }
   ],
   "source": [
    "## Post filtering we apply the shapiro- Wilk Test\n",
    "#The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution.\n",
    "#alpha = 0.05\n",
    "t1,p1 = stats.shapiro(a['Salary'])\n",
    "t2,p2 = stats.shapiro(b['Salary'])\n",
    "t3,p3 = stats.shapiro(c['Salary'])\n",
    "print(p1,p2,p3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea658b86",
   "metadata": {},
   "source": [
    "### p-value is higher than alpha =0.05, that means,  we go ahead with null hypothesis as the observations are normally distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f40948f",
   "metadata": {},
   "source": [
    "## Levene's Test\n",
    "#### Computes homogeneity of variance across groups.\n",
    "\n",
    "### The Levene test tests the null hypothesis that all input samples are from populations with equal variances.\n",
    "### alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a49b4a",
   "metadata": {},
   "source": [
    "### Null Hypothesis: Ho -The variances in the given dataset is the same\n",
    "### Alternative Hypothesis: Ha - The variances in the given dataset is different "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "70fd972b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeveneResult(statistic=1.8800921605836554, pvalue=0.16686425699301183)\n"
     ]
    }
   ],
   "source": [
    "LT = stats.levene(a['Salary'], b['Salary'], c['Salary'])\n",
    "print(LT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da199a82",
   "metadata": {},
   "source": [
    "#### Since p-value is greater than 0.05, we shall proceed with null hypothesis that the variances are the same"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17afa1f9",
   "metadata": {},
   "source": [
    "# Formulate the Null and Alternate Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b500a826",
   "metadata": {},
   "source": [
    "𝐻0 : The mean Salary of an individual is same across all levels of Education \n",
    "\n",
    "𝐻1 : The mean Salary of an individual is different for atleast one level of Education"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e2ddaa",
   "metadata": {},
   "source": [
    "# One Way Anova\n",
    "**Now, let us go ahead and perform one way ANOVA for 'Education' with respect to 'Salary'.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a3f5b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                df        sum_sq       mean_sq         F        PR(>F)\n",
      "C(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": [
    "formula = 'Salary ~ C(Education)' ## y = f(X)\n",
    "model = ols(formula, df_e).fit()\n",
    "aov_table = anova_lm(model)\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2c85e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x ='Education', y='Salary', data =df_e, ci=None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbe9181e",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Occupation.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8914731a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_o= df.drop(['Education'], axis=1) # creating a new dataframe without the Education column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2701945d",
   "metadata": {},
   "source": [
    "## Filtering the stacked data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58658f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = df_o[df_o['Occupation'] == ' Adm-clerical']\n",
    "e = df_o[df_o['Occupation'] == ' Sales']\n",
    "f = df_o[df_o['Occupation'] == ' Prof-specialty']\n",
    "g = df_o[df_o['Occupation'] == ' Exec-managerial']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3a7700b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(12,12))\n",
    "plt.subplot(2,2,1) ## Row - Column - Order\n",
    "sns.histplot(d, kde =True, palette= 'Greens') \n",
    "plt.subplot(2,2,2) \n",
    "sns.histplot(e, kde =True, palette ='Oranges')\n",
    "plt.subplot(2,2,3)\n",
    "sns.histplot(f, kde =True, palette='Blues')\n",
    "plt.subplot(2,2,4)\n",
    "sns.histplot(g, kde =True, palette='Reds')\n",
    "\n",
    "\n",
    "plt.suptitle(\"Salary distribution by Occupation\", fontsize =16)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31158383",
   "metadata": {},
   "source": [
    "# Formulate the Null and Alternate Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b91489",
   "metadata": {},
   "source": [
    "𝐻0 : The mean Salary of an individual is the same across all levels of Occupation \n",
    "\n",
    "𝐻1 : The mean Salary of an individual is different for atleast one level of Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdc99638",
   "metadata": {},
   "source": [
    "# One Way Anova\n",
    "**Now, let us go ahead and perform one way ANOVA with 'Occupation' with respect to 'Salary'.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "48516ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 df        sum_sq       mean_sq         F    PR(>F)\n",
      "C(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": [
    "formula = 'Salary ~ C(Occupation)' ## y = f(X)\n",
    "model = ols(formula, df_o).fit()\n",
    "aov_table = anova_lm(model)\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d6156905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x ='Occupation', y='Salary', data =df_o, ci=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95aa172f",
   "metadata": {},
   "source": [
    "# Two Way Anova\n",
    "**Let us now perform the Two Way ANOVA. We will now analyse the effect of both Education and Occupation on the 'Salary' variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ac1cd3f1",
   "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(Education)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.026955e+11</td>\n",
       "      <td>5.134773e+10</td>\n",
       "      <td>31.257677</td>\n",
       "      <td>1.981539e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Occupation)</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.519946e+09</td>\n",
       "      <td>1.839982e+09</td>\n",
       "      <td>1.120080</td>\n",
       "      <td>3.545825e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>34.0</td>\n",
       "      <td>5.585261e+10</td>\n",
       "      <td>1.642724e+09</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(Education)    2.0  1.026955e+11  5.134773e+10  31.257677  1.981539e-08\n",
       "C(Occupation)   3.0  5.519946e+09  1.839982e+09   1.120080  3.545825e-01\n",
       "Residual       34.0  5.585261e+10  1.642724e+09        NaN           NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'Salary ~ C(Education) + C(Occupation)'\n",
    "model = ols(formula, df).fit()\n",
    "aov_table = anova_lm(model)\n",
    "(aov_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18b1875",
   "metadata": {},
   "source": [
    "**Let us check whether there is any interaction effect between the treatments.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1be26bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x='Education', y='Salary', hue='Occupation', data=df, ci=None); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a20cdb1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x='Occupation', y='Salary', hue='Education', data=df, ci=None); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a217eab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Method 2 of drawing an interaction plot\n",
    "\n",
    "from statsmodels.graphics.factorplots import interaction_plot\n",
    "interaction_plot(np.array(df['Education']),np.array(df['Occupation']),np.array(df['Salary']));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6e15dd",
   "metadata": {},
   "source": [
    "Still, we can see that there is some sort of interaction between the two treatments. So, we will introduce a new term while performing the Two Way ANOVA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1594a13",
   "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(Education)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.026955e+11</td>\n",
       "      <td>5.134773e+10</td>\n",
       "      <td>72.211958</td>\n",
       "      <td>5.466264e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Occupation)</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.519946e+09</td>\n",
       "      <td>1.839982e+09</td>\n",
       "      <td>2.587626</td>\n",
       "      <td>7.211580e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(Education):C(Occupation)</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.634909e+10</td>\n",
       "      <td>6.058182e+09</td>\n",
       "      <td>8.519815</td>\n",
       "      <td>2.232500e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2.062102e+10</td>\n",
       "      <td>7.110697e+08</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  \\\n",
       "C(Education)                 2.0  1.026955e+11  5.134773e+10  72.211958   \n",
       "C(Occupation)                3.0  5.519946e+09  1.839982e+09   2.587626   \n",
       "C(Education):C(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",
       "C(Education)                5.466264e-12  \n",
       "C(Occupation)               7.211580e-02  \n",
       "C(Education):C(Occupation)  2.232500e-05  \n",
       "Residual                             NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'Salary ~ C(Education) + C(Occupation) + C(Education):C(Occupation)'\n",
    "model = ols(formula, df).fit()\n",
    "aov_table = anova_lm(model)\n",
    "(aov_table)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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