Module_3_Hypothesis_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3 Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "sleep = sm.datasets.get_rdataset(\"sleep\").data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>extra</th>\n",
       "      <th>group</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    extra  group  ID\n",
       "0     0.7      1   1\n",
       "1    -1.6      1   2\n",
       "2    -0.2      1   3\n",
       "3    -1.2      1   4\n",
       "4    -0.1      1   5\n",
       "5     3.4      1   6\n",
       "6     3.7      1   7\n",
       "7     0.8      1   8\n",
       "8     0.0      1   9\n",
       "9     2.0      1  10\n",
       "10    1.9      2   1\n",
       "11    0.8      2   2\n",
       "12    1.1      2   3\n",
       "13    0.1      2   4\n",
       "14   -0.1      2   5\n",
       "15    4.4      2   6\n",
       "16    5.5      2   7\n",
       "17    1.6      2   8\n",
       "18    4.6      2   9\n",
       "19    3.4      2  10"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131209710>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp1 = sleep[sleep.group==1].extra\n",
    "gp2 = sleep[sleep.group==2].extra\n",
    "\n",
    "c = pd.DataFrame({'gp 1':gp1.values,'gp 2':gp2.values})\n",
    "c.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07918671421593818"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "result = scipy.stats.ttest_ind(gp1,gp2)\n",
    "result.pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00283289019738427"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = gp1.reset_index().extra\n",
    "b1 = gp2.reset_index().extra\n",
    "result = stats.ttest_1samp(a1-b1, 0)\n",
    "result.pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "chickwts = sm.datasets.get_rdataset(\"chickwts\").data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.254541016953191e-07"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horsebean = chickwts[chickwts.feed=='horsebean'].weight\n",
    "casein = chickwts[chickwts.feed=='casein'].weight\n",
    "\n",
    "result = scipy.stats.ttest_ind(horsebean,casein)\n",
    "result.pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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