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‎Testing_tutorial.ipynb

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"colab": {
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"name": "Testing_tutorial.ipynb",
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"provenance": [],
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"authorship_tag": "ABX9TyPtNVbGG+YNd1Y6fmT37q9p",
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"authorship_tag": "ABX9TyMf1BmJ3RBQ2bj7TzVXgfnN",
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"include_colab_link": true
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},
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"kernelspec": {
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{
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"cell_type": "code",
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"metadata": {
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"id": "EjsXAuSdNHtc",
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"outputId": "70959bc1-420f-4c3c-c1b1-8983ed8dd481",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 203
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}
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},
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"id": "EjsXAuSdNHtc",
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"outputId": "70959bc1-420f-4c3c-c1b1-8983ed8dd481"
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},
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"source": [
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"# To read data as dataframe\n",
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 172
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},
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"id": "9ez6DCbeNU3O",
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"outputId": "a39cb14c-e325-4042-f49f-dd975ac9d323",
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"outputId": "b65c7953-59d4-4bd5-b8ec-92df9e2d7f45"
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},
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"source": [
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"model = ols('Defaultee ~ C(Gender) + C(Ethnicity) + C(Gender):C(Ethnicity)', data=data).fit()\n",
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"sm.stats.anova_lm(model, typ=1)"
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],
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"execution_count": 83,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>df</th>\n",
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" <th>sum_sq</th>\n",
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" <th>mean_sq</th>\n",
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" <th>F</th>\n",
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" <th>PR(&gt;F)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>C(Gender)</th>\n",
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" <td>1.0</td>\n",
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" <td>0.000677</td>\n",
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" <td>0.000677</td>\n",
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" <td>0.004523</td>\n",
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" <td>0.946415</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C(Ethnicity)</th>\n",
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" <td>2.0</td>\n",
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" <td>0.007807</td>\n",
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" <td>0.003903</td>\n",
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" <td>0.026083</td>\n",
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" <td>0.974256</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C(Gender):C(Ethnicity)</th>\n",
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" <td>2.0</td>\n",
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" <td>0.069887</td>\n",
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" <td>0.034944</td>\n",
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" <td>0.233504</td>\n",
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" <td>0.791864</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Residual</th>\n",
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" <td>394.0</td>\n",
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" <td>58.961630</td>\n",
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" <td>0.149649</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" df sum_sq mean_sq F PR(>F)\n",
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"C(Gender) 1.0 0.000677 0.000677 0.004523 0.946415\n",
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"C(Ethnicity) 2.0 0.007807 0.003903 0.026083 0.974256\n",
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"C(Gender):C(Ethnicity) 2.0 0.069887 0.034944 0.233504 0.791864\n",
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"Residual 394.0 58.961630 0.149649 NaN NaN"
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]
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},
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"metadata": {},
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"execution_count": 83
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "huuCaEC9OmXt",
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"outputId": "48cf90e8-a45b-4e5f-d635-6265ea7b1397",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 172
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}
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},
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"source": [
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"model = ols('Defaultee ~ C(Gender) + C(Ethnicity) + C(Gender):C(Ethnicity)', data=data).fit()\n",
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"model = ols('Defaultee ~ C(Gender) + C(Ethnicity) + C(Gender)*C(Ethnicity)', data=data).fit()\n",
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"sm.stats.anova_lm(model, typ=2)"
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],
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"execution_count": 69,
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"execution_count": 84,
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"outputs": [
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{
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"output_type": "execute_result",
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]
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},
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"metadata": {},
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"execution_count": 69
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"execution_count": 84
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}
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{
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"cell_type": "code",
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"metadata": {
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"id": "R5NgjCsvO0oj",
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"outputId": "505344f8-5701-4082-d44a-2b8d2bf8b1c2",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 203
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}
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},
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"source": [
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"model= ols('Defaultee ~ C(Gender)*C(Ethnicity)', data=data).fit()\n",
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"sm.stats.anova_lm(model, typ=3)"
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],
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"execution_count": 80,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sum_sq</th>\n",
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" <th>df</th>\n",
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" <th>F</th>\n",
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" <th>PR(&gt;F)</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Intercept</th>\n",
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" <td>1.280000</td>\n",
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" <td>1.0</td>\n",
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" <td>8.553359</td>\n",
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" <td>0.003648</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C(Gender)</th>\n",
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" <td>0.048089</td>\n",
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" <td>1.0</td>\n",
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" <td>0.321346</td>\n",
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" <td>0.571123</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C(Ethnicity)</th>\n",
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" <td>0.042944</td>\n",
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" <td>2.0</td>\n",
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" <td>0.143483</td>\n",
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" <td>0.866381</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C(Gender):C(Ethnicity)</th>\n",
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" <td>0.069887</td>\n",
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" <td>2.0</td>\n",
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" <td>0.233504</td>\n",
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" <td>0.791864</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Residual</th>\n",
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" <td>58.961630</td>\n",
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" <td>394.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" sum_sq df F PR(>F)\n",
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"Intercept 1.280000 1.0 8.553359 0.003648\n",
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"C(Gender) 0.048089 1.0 0.321346 0.571123\n",
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"C(Ethnicity) 0.042944 2.0 0.143483 0.866381\n",
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"C(Gender):C(Ethnicity) 0.069887 2.0 0.233504 0.791864\n",
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"Residual 58.961630 394.0 NaN NaN"
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]
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},
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"metadata": {},
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"execution_count": 80
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}
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]
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},
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"source": [
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"p,dof"
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{
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"source": [
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"expected"

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