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Testing equality of variances using Levene Test
{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.stats as stats\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv(\"C:/Users/ouaga/Dropbox/PythonScripts/Stats_tests/Goats_Weights.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Method1</th>\n",
" <th>Method2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>58.88</td>\n",
" <td>58.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>59.37</td>\n",
" <td>63.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>59.60</td>\n",
" <td>56.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>58.54</td>\n",
" <td>55.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>57.73</td>\n",
" <td>58.74</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Method1 Method2\n",
"0 58.88 58.89\n",
"1 59.37 63.62\n",
"2 59.60 56.83\n",
"3 58.54 55.39\n",
"4 57.73 58.74"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing equality of variances with Levene test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{align}\n",
" & {{H}_{0}}:\\,\\,\\sigma _{1}^{2}\\,=\\,\\,\\sigma _{2}^{2} \\\\ \n",
" & {{H}_{1}}:\\,\\,\\sigma _{1}^{2}\\,\\ne \\,\\,\\sigma _{2}^{2} \\\\ \n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> ### Useage: center: default: center='median'\n",
"> 1. ‘median’ : Recommended for skewed (non-normal) distributions>\n",
"> 2. ‘mean’ : Recommended for symmetric, moderate-tailed distributions.\n",
"> 3. ‘trimmed’ : Recommended for heavy-tailed distributions."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The test statistic is: 4.22524\n",
"The p-value is: 0.04529\n"
]
}
],
"source": [
"(test_statistic, p_value) = stats.levene(df['Method1'],df['Method2'],center='mean')\n",
"print(\"The test statistic is: \", round(test_statistic,5))\n",
"print(\"The p-value is: \", round(p_value,5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphically check the variability using boxplots"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"melted_df = pd.melt(df,var_name=\"Feed_type\", value_name=\"Weights\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Feed_type</th>\n",
" <th>Weights</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Method1</td>\n",
" <td>58.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Method1</td>\n",
" <td>59.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Method1</td>\n",
" <td>59.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Method1</td>\n",
" <td>58.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Method1</td>\n",
" <td>57.73</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Feed_type Weights\n",
"0 Method1 58.88\n",
"1 Method1 59.37\n",
"2 Method1 59.60\n",
"3 Method1 58.54\n",
"4 Method1 57.73"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"melted_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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SiaTf7ChSq/UefXhI3rs9CviDpNNI12/eAfyQ9Bss6Gh40lHsN4B/5vHMISWGOSx8RuBK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2aa564931d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context(\"paper\", font_scale=1.5, rc={\"font.size\":16,\"axes.titlesize\":16,\"axes.labelsize\":16}) \n",
"plt.title('Boxplot comparing goats weights by the method of feeding')\n",
"sns.boxplot(x='Feed_type', y='Weights', data=melted_df)\n",
"plt.xlabel('Type of feed')\n",
"plt.ylabel('Weight of goats')\n",
"plt.xticks(rotation=45)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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