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Commit daec1aa

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‎.ipynb_checkpoints/Module_1_Basic_Descriptive_Statistics-checkpoint.ipynb‎

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‎.ipynb_checkpoints/Module_2_Data_Visualization_Seaborn-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Module 3 Hypothesis Testing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"import statsmodels.api as sm\n",
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"sleep = sm.datasets.get_rdataset(\"sleep\").data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <td>5</td>\n",
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" <th>5</th>\n",
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" <td>3.4</td>\n",
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" <td>1</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>3.7</td>\n",
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" <td>1</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>0.8</td>\n",
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" <td>1</td>\n",
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" <td>8</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>0.0</td>\n",
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" <td>1</td>\n",
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" <td>9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>2.0</td>\n",
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" <td>1</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>1.9</td>\n",
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" <th>13</th>\n",
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" <td>0.1</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>-0.1</td>\n",
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" <td>2</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>4.4</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>5.5</td>\n",
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" <td>2</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>1.6</td>\n",
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" <td>2</td>\n",
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" <td>8</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>19</th>\n",
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" <td>3.4</td>\n",
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" <td>2</td>\n",
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" <td>10</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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" extra group ID\n",
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"0 0.7 1 1\n",
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"1 -1.6 1 2\n",
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"2 -0.2 1 3\n",
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"3 -1.2 1 4\n",
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"4 -0.1 1 5\n",
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"5 3.4 1 6\n",
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"6 3.7 1 7\n",
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"7 0.8 1 8\n",
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"9 2.0 1 10\n",
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"10 1.9 2 1\n",
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"11 0.8 2 2\n",
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"12 1.1 2 3\n",
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"13 0.1 2 4\n",
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"14 -0.1 2 5\n",
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]
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},
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"execution_count": 22,
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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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"sleep"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 99,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x131209710>"
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]
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},
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"execution_count": 99,
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"gp1 = sleep[sleep.group==1].extra\n",
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"gp2 = sleep[sleep.group==2].extra\n",
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"\n",
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"c = pd.DataFrame({'gp 1':gp1.values,'gp 2':gp2.values})\n",
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"c.plot(kind='box')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 100,
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"metadata": {},
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"outputs": [
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{
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"data": {
250+
"text/plain": [
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"0.07918671421593818"
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]
253+
},
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"execution_count": 100,
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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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"import scipy\n",
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"\n",
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"result = scipy.stats.ttest_ind(gp1,gp2)\n",
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"result.pvalue"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 101,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.00283289019738427"
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]
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},
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"execution_count": 101,
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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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"a1 = gp1.reset_index().extra\n",
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"b1 = gp2.reset_index().extra\n",
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"result = stats.ttest_1samp(a1-b1, 0)\n",
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"result.pvalue"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"metadata": {},
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"outputs": [],
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"source": [
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"chickwts = sm.datasets.get_rdataset(\"chickwts\").data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 98,
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"metadata": {},
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"outputs": [
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{
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"data": {
305+
"text/plain": [
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"8.254541016953191e-07"
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]
308+
},
309+
"execution_count": 98,
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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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"horsebean = chickwts[chickwts.feed=='horsebean'].weight\n",
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"casein = chickwts[chickwts.feed=='casein'].weight\n",
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"\n",
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"result = scipy.stats.ttest_ind(horsebean,casein)\n",
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"result.pvalue"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
350+
"nbformat_minor": 2
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}

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