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carlthome/f-test.ipynb

Created March 27, 2024 15:03
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F-test.ipynb
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f-test.ipynb
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"metadata": {
"colab": {
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"authorship_tag": "ABX9TyN8ajHLr6msiSzoIcTuHssU",
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},
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"name": "python3",
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/carlthome/8126803fdc1d3b35ccaf3ae7705435c6/f-test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "YdU8K3UyBdOX",
"outputId": "c98961ab-654d-43ad-f534-f2656a168a7f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Degree of freedom 1 Degree of freedom 2 F-statistic p-value\n",
"0 8 29 0.251063 0.023463"
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Degree of freedom 1</th>\n",
" <th>Degree of freedom 2</th>\n",
" <th>F-statistic</th>\n",
" <th>p-value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8</td>\n",
" <td>29</td>\n",
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" document.querySelector('#df-efb548d9-0cdd-4505-8b0a-e578f56829c8 button.colab-df-convert');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
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"summary": "{\n \"name\": \"pd\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"Degree of freedom 1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 8,\n \"max\": 8,\n \"num_unique_values\": 1,\n \"samples\": [\n 8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Degree of freedom 2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 29,\n \"max\": 29,\n \"num_unique_values\": 1,\n \"samples\": [\n 29\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"F-statistic\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.25106326202802354,\n \"max\": 0.25106326202802354,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.25106326202802354\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p-value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.023462662623980567,\n \"max\": 0.023462662623980567,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.023462662623980567\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import scipy.stats as stats\n",
"\n",
"# Create synthetic data for testing.\n",
"group1 = np.random.normal(0.0, 1.0, size=9)\n",
"group2 = np.random.normal(10.0, 2.0, size=30)\n",
"\n",
"# Calculate the F-statistic.\n",
"variance1 = np.var(group1, ddof=1)\n",
"variance2 = np.var(group2, ddof=1)\n",
"f_value = variance1 / variance2\n",
"\n",
"# Calculate the p-value.\n",
"df1 = len(group1) - 1\n",
"df2 = len(group2) - 1\n",
"p_value = stats.f.cdf(f_value, df1, df2)\n",
"\n",
"# Collect results.\n",
"scores = {\n",
" \"Degree of freedom 1\": df1,\n",
" \"Degree of freedom 2\": df2,\n",
" \"F-statistic\": f_value,\n",
" \"p-value\": p_value,\n",
"}\n",
"pd.DataFrame(scores, index=[0])"
]
}
]
}
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