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Kia TeymourianKia Teymourian
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Distance Matrix example
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‎Code-Example-006-Matplotlib-Visualization-Hist-boxplot.ipynb

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‎Code-Example-007-Normal_Distribution_with_Scipy.ipynb

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"provenance": []
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‎Code-Example-008-t-distribution-scipy-functions.ipynb

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"'1.7.1'"
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"0.9745751901857019"
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"t.cdf(2.76, df=4, loc=0, scale=1)"
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‎Code-Example-008.1-t-distribution-vs.-normal-distribution.ipynb

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‎Code-Example-009-t-test-using-researchpy.ipynb

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"df.info()"
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{
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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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"\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>patient</th>\n",
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" <th>sex</th>\n",
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" <th>agegrp</th>\n",
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" <th>bp_before</th>\n",
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" <th>bp_after</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>0</th>\n",
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" <td>1</td>\n",
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" <td>Male</td>\n",
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" <td>30-45</td>\n",
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" <td>143</td>\n",
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" <td>153</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>Male</td>\n",
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" <td>30-45</td>\n",
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" <td>163</td>\n",
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" <td>170</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
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" <td>Male</td>\n",
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" <td>30-45</td>\n",
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" <td>153</td>\n",
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" <td>168</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>Male</td>\n",
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" <td>30-45</td>\n",
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" <td>153</td>\n",
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" <td>142</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>Male</td>\n",
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" <td>30-45</td>\n",
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" <td>146</td>\n",
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" <td>141</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>115</th>\n",
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" <td>116</td>\n",
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" <td>Female</td>\n",
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" <td>60+</td>\n",
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" <td>152</td>\n",
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" <td>152</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>116</th>\n",
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" <td>117</td>\n",
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" <td>Female</td>\n",
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" <td>60+</td>\n",
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" <td>161</td>\n",
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" <td>152</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>117</th>\n",
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" <td>118</td>\n",
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" <td>Female</td>\n",
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" <td>60+</td>\n",
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" <td>165</td>\n",
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" <td>174</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>118</th>\n",
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" <td>119</td>\n",
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" <td>Female</td>\n",
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" <td>60+</td>\n",
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" <td>149</td>\n",
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" <td>151</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>119</th>\n",
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" <td>120</td>\n",
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" <td>Female</td>\n",
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" <td>60+</td>\n",
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" <td>185</td>\n",
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" <td>163</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>120 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" patient sex agegrp bp_before bp_after\n",
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"0 1 Male 30-45 143 153\n",
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"1 2 Male 30-45 163 170\n",
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"2 3 Male 30-45 153 168\n",
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"3 4 Male 30-45 153 142\n",
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"4 5 Male 30-45 146 141\n",
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".. ... ... ... ... ...\n",
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"115 116 Female 60+ 152 152\n",
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"116 117 Female 60+ 161 152\n",
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"117 118 Female 60+ 165 174\n",
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"118 119 Female 60+ 149 151\n",
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"119 120 Female 60+ 185 163\n",
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"\n",
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"[120 rows x 5 columns]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df"
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]
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},
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‎Code-Example-009.1-Two-Sample-t-test.ipynb

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‎Code-Example-011-Linear-Regression-Residual-Plots.ipynb

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‎Code-Example-011-Linear-Regression.ipynb

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"nbformat": 4,

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