EX_03/ex_03_linregress_1.ipynb

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   "metadata": {
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   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Hello World!\n"
    }
   ],
   "source": [
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We’ll be using Statsmodels since it has some nice characteristics for linear modeling.\n",
    "# # https://www.statsmodels.org/stable/index.html \n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am also using scikit-learn since it provides significantly more useful functionality for machine learning.\n",
    "# https://scikit-learn.org/stable/\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allow plots to appear directly in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Production_Hour_x  Production_Volume_y\n0                 34                  102\n1                 35                  109\n2                 39                  137\n3                 42                  148\n4                 43                  150",
      "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>Production_Hour_x</th>\n      <th>Production_Volume_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>34</td>\n      <td>102</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>35</td>\n      <td>109</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>39</td>\n      <td>137</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>42</td>\n      <td>148</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>43</td>\n      <td>150</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# read data into a DataFrame\n",
    "data = pd.read_csv('plant_prod_hourly.csv')\n",
    "#data = pd.read_csv('advertising.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(6, 2)"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# print the shape of the DataFrame\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 6 observations, and thus 2 variables in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x1d61980cd68>"
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     "metadata": {},
     "execution_count": 12
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kdkmPAVuAeyPioaLVfwRsjoifl9k9gHskPSKpZDE0SYsk9UnqGxgYqPjDmNWbr01rBtUMofxXSV8l+ZZrAOsj4rUK9tsNHC/pQGCFpGMjYk26+hzg9lF2nxcRmyS9CbhX0rqIWDXi+EuAJQC9vb371NYxaxRfm9YMKr6Tl3Qi8HPgK8B1wNOS3lXp/hHxK+ABYH56vCnAB4A7R9lnU/pzC7ACmFvp+czMrLrumquBUyPi3RHxLuA04NrRdpDUnd7BI2ka8F5gXbr6vcC6iOgvs+90SZ2F98CpwJpS25qZWWnVPHjdLyLWFxYi4ul0xMxoDgZultRO8h/KXRFRqGa5kBFdNZIOAW6IiAXATJLunUKct0XE96uI18xs0qsmyT8i6UbglnT5Q8Ajo+0QEU8Aby+z7iMl2jYBC9L3zwBvqyI+MzMbodrp/y4BLiX5xusqkr55MzNrUhUleUltwCMRcSxwTX1DMjOzvFT04DUihoHHJbl+vJnZBFJNd83BwFpJDwM7Co0RcWbuUZmZWS6qSfKfq1sUZmZWF5XUk9+f5KHrW4DVwI0RMVTvwMzMrHaV9MnfTFJHfjXwPl6vL29mZk2uku6aoyPidwHScfIP1zckMzPLSyV38nuKkLmbxsxsYqnkTv5tkn6TvhcwLV0WScn4A+oWnZmZ1aSS6f/axyMQMzPLXzVVKM3MbIJxkjcza2FO8mZmLcxJ3syshTnJm5m1MCd5M7MW5iRvZtbC6prkJe0v6WFJj0taK+lzaftnJb0g6bH0taDM/vMlrZe0QdIV9YzVzKwVVVNqOItdwMkR8Uo66fe/SPpeuu7aiPhSuR3Tyb+/CpwC9AM/lbQyIp6sc8xmE8LwcLB1xyCDQ7vpmNLOjOkdtLWp0WFZk6lrko+IAF5JF/dLX1Hh7nOBDemE3ki6AzgLcJK3SW94OFi/eTsXLu+jf9tOerqmsfS8XubM7HSit73UvU9eUrukx4AtwL0R8VC66hOSnpC0TFJXiV0PBZ4vWu5P28wmva07BvckeID+bTu5cHkfW3cMNjgyazZ1T/IRsTsijgd6gLmSjgWuB44AjgdepHSN+lK3I/v8FiBpkaQ+SX0DAwO5xW1Wq3pem4NDu/ck+IL+bTsZHNqd63ls4hu30TUR8SvgAWB+RGxOk/8wsJSka2akfmBW0XIPsKnEcZdERG9E9HZ3d+cfuFlG9bw2O6a009M1ba+2nq5pdExxPUHbW71H13RLOjB9Pw14L7BO0sFFm50NrCmx+0+BIyUdLqkDWAisrGe8ZhPFjOkdLD2vd0+iL/TJz5je0eDIrNnUe3TNwcDN6UiZNuCuiLhb0i2SjifpftkIfAxA0iHADRGxICKGJH0C+GegHVgWEWvrHK/ZhNDWJubM7GTFxfM8usZGVe/RNU8Aby/R/uEy228CFhQtfxf4bt0CNJvA2tpEd+fURodhTc7feDUza2FO8mZmLcxJ3syshdX7wauZleGyBDYenOTNGsBlCWy8uLvGrAFclsDGi5O8WQO4LIGNFyd5swZwWQIbL07yZg3gsgQ2Xvzg1awBXJbAxouTvFmDuCyBjQd315iZtTAneTOzFqZkGtbWIGkAeK7GwxwEvJRDOM2gVT7LeH6OlyJift4HLbo2J/LfiWNvjIOAdVmvy5ZK8nmQ1BcRvY2OIw+t8lla5XPAxP4sjr0xao3d3TVmZi3MSd7MrIU5ye9rSaMDyFGrfJZW+RwwsT+LY2+MmmJ3n7yZWQvznbyZWQtzkjcza2FO8mZmLWzckrykZZK2SFozov0vJa2XtFbSF4raF0vakK47bbziNDNrJeN5J38TsNc3tiSdBJwFHBcRxwBfStuPBhYCx6T7XCdpzELb8+fPD8Avv2p51YWvTb9qfGU2bkk+IlYBL49ovgi4KiJ2pdtsSdvPAu6IiF0R8SywAZg71jleemmifmvZWp2vTWuURvfJHwX8kaSHJP1I0jvS9kOB54u260/b9iFpkaQ+SX0DAwN1Dtescr42rRk0OslPAbqAdwJ/DdwlSUCpmRNK/soSEUsiojcieru7u+sXqVmVfG1aM2j0pCH9wDcj+UbWw5KGSSqu9QOzirbrATY1ID5rIcPDwdYdg56JySaVRt/Jfws4GUDSUUAHSTnQlcBCSVMlHQ4cCTzcqCBt4hseDtZv3s7Z1/2YeZ//IWdf92PWb97O8HBNz7TMmt54DqG8HfgJMEdSv6Q/B5YBv5MOq7wDOD8Sa4G7gCeB7wOXRMTu8YrVWs/WHYNcuLyP/m07AejftpMLl/exdcdggyMzq69x666JiHPKrDq3zPZXAlfWLyKbTAaHdu9J8AX923YyOOR7Bxvb7Cu+U/U+G686vQ6RVK/R3TVm46JjSjs9XdP2auvpmkbHlDG/fmE2oTnJ26QwY3oHS8/r3ZPoe7qmsfS8XmZM72hwZGb11ejRNWbjoq1NzJnZyYqL53l0jU0qTvI2abS1ie7OqY0Ow2xcubvGzKyFOcmbmbUwJ3kzsxbmJG9m1sKc5M3MWpiTvJlZC3OSNzNrYU7yZmYtzEnezKyFOcmbmbUwJ3kzsxbmJG9m1sLGc2aoZZK2pLNAFdo+K+kFSY+lrwVF6xZL2iBpvaTTxitOy254OBjYvosXtr3KwPZdnlrPrAmMZxXKm4CvAMtHtF8bEV8qbpB0NLAQOAY4BLhP0lGeArB5FeZQLUyxV6jXPmdmp8v5mjXQuN3JR8Qq4OUKNz8LuCMidkXEs8AGYG7dgrOaeQ5Vs+bUDH3yn5D0RNqd05W2HQo8X7RNf9q2D0mLJPVJ6hsYGKh3rFaG51Ddl69NawaZkrykb0g6XVKt/0lcDxwBHA+8CFxdOEWJbUt28EbEkojojYje7u7uGsOxrDyH6r58bVozyJqkrwf+A/BzSVdJemuWg0TE5ojYHRHDwFJe75LpB2YVbdoDbMoYq9WokgeqnkPVrDllevAaEfeRPAz9LeAc4F5Jz5Mk6lsj4rVKjiPp4Ih4MV08GyiMvFkJ3CbpGpIHr0cCD2eJ1WpT6QNVz6Fq1pwyd7dImgF8BPgL4GfA3wMnAPeW2f524CfAHEn9kv4c+IKk1ZKeAE4C/hNARKwF7gKeBL4PXOKRNY1RzQPVwhyqh3a9ge7OqU7wZk0g0528pG8CbwVuAf646G78Tkl9pfaJiHNKNN9Y7hwRcSVwZZb4LD9+oGo2sWUdJ/+ViLi/1IqI6JV0SkSUvKO3iaXwQLU40U/2B6pmE0mm7ppyCb7I57Mc15qPH6iaTWz1+sarO2NbhB+omk1s9UryLlrSQgoPVM1s4mmGb7yamVmd1CvJb6zTcc3MrApZyxq8QdJ/kbQ0XT5S0hmF9RHxgbwCNDOz7LLeyX8N2AX8u3S5H/jvuURkZma5yZrkj4iILwCvAUTETjyixsys6WRN8oOSppGOopF0BMmdvZmZNZGsQyg/Q1JTZpakrwPzSOrYmJlZE8lahfJeSY8C7yTpprksIl7KNTIzM6tZLUMoDwXagQ7gXZI8osbMrMlkrUK5DDgOWAsMp80BfDOnuMzMLAdZ++TfGRFH5xqJmZnlLmt3zU8kOcmbmTW5rHfyN5Mk+l+SDJ0UEBFxXG6RmZlZzbLeyS8DPgzMB/4YOCP9WZakZZK2SFpTYt0nJYWkg4raFkvaIGm9pNMyxmlmNqllvZP/RUSsrHKfm4CvAMuLGyXNAk4BflHUdjSwEDiGZCLv+yQd5XlezcyqkzXJr5N0G/Btir7pGhFlR9dExCpJs0usuhb4FPBPRW1nAXdExC7gWUkbgLkkE4GbmVmFsib5aSTJ/dSitqqHUEo6E3ghIh6X9ip9cyjwYNFyf9pW6hiLgEUAhx12WDWnN6srX5vWDLJ+4/WCWk8s6Q3Ap9n7P4o9q0udtkwsS4AlAL29vZ6RypqGr01rBlm/DPU1SiTdiPhoFYc5AjgcKNzF9wCPSppLcuc+q2jbHmBTlljNzCazrN01dxe93x84myqTcESsBt5UWJa0EeiNiJckrQRuk3QNyYPXI4GHM8ZqZjZpZe2u+UbxsqTbgftG2yfd5kTgIEn9wGci4sYyx18r6S7gSWAIuMQja8zMqpf1Tn6kI4FRnyxFxDljrJ89YvlK4MqaI2sRw8PB1h2DDA7tpmNKOzOmd9DW5nlazGx0Wfvkt5P0ySv9+Uvg8hzjsiLDw8H6zdu5cHkf/dt20tM1jaXn9TJnZqcTvZmNKtM3XiOiMyIOKPp51MguHMvP1h2DexI8QP+2nVy4vI+tOwYbHJmZNbuq7uQlnTDa+oh4tLZwrJTBod17EnxB/7adDA75MYWZja7a7pqrR1kXwMk1xGJldExpp6dr2l6JvqdrGh1T2hsYlZlNBFUl+Yg4qV6BWHkzpnew9LzeffrkZ0zvaHRoZtbksj543Q+4CHhX2vQA8A8R8VpOcVmRtjYxZ2YnKy6e59E1ZlaVrEMorwf2A65Llz+ctv1FHkHZvtraRHfn1EaHYWYTTNYk/46IeFvR8v2SHs8jIDMzy0/WSUN2SzqisCDpdwAP9TAzazJZ7+T/GvihpGdIvhD1ZqDmypRmZpavasfJfwe4DfgWSSmDOSRJfl06wYeZmTWRartrlpDM5/osyTR+vwM85QRvZtacqkryEfFPaaGxN5PMAnU+8It0ku5T6hGgmZlll7V2zc6IuDMiziaZ2entwPdzjczMzGqWKclLminpLyX9mKR//h7g9/IMzMzMalftg9cLgXNIHrh+E/hURPy4HoGZmVntqr2T/wPgKmBWRPxluQQv6ZgSbcskbZG0pqjtv0l6QtJjku6RdEjRusWSNkhaL+m0KuM0MzOqf/B6QUTcExHDY2x6S4m2m4D5I9q+GBHHRcTxJPPG/i2ApKOBhcAx6T7XSXLJRTOzKmX9xutY9qmcFRGrgJdHtP2maHE6SbligLOAOyJiV0Q8C2wA5tYpVjOzlpXXHK8jxdibJCRdCZwH/BoolDI+FHiwaLP+tK3U/ouARQCHHTbqNLNm48rXpjWDet3JVywiPh0Rs4CvA59Im0vV0C35H0dELImI3ojo7e7urleYZlXztWnNoF5JPsvko7cB/z593w/MKlrXA2yqNSgzs8kmc3eNpENJvvm65xhpvzsR8c4Kj3FkRPw8XTwTWJe+XwncJuka4BCSOjkPZ43VzGyyyjoz1OeBPwOe5PUSwwGsGmWf24ETgYMk9QOfARZImgMMA88BHweIiLWS7kqPPwRcEhEuZWwtafYV3xmX82y86vRxOc94yfLnNp5/Bs0SX9Y7+fcDc6opTJbWvBnpxlG2vxK4svrQzMysIGuf/DMk0/+ZmVkTy3on/yrwmKQfAHvu5iPi0lyiMjOzXGRN8ivTl5mZNbFMST4ibpbUARyVNq2PiNfyC8vMzPKQdXTNicDNwEaSLy7NknR+YQilmZk1h6zdNVcDp0bEegBJRwG345ryZmZNJevomv0KCR4gIp7Go23MzJpO1jv5Pkk38npJ4Q8Bj+QTkpmZ5SVrkr8IuAS4lKRPfhVwXV5BmZlZPrKOrtkFXJO+zMysSVU7x+tdEfFBSaspUfo3Io7LLTIzM6tZtXfyl6U/z8g7EDMzy1+1c7y+mL69OCKeK34BF+cfnpmZ1SLrEMpTSrS9r5ZAzMwsf9X2yV9Ecsd+hKQnilZ1Av83z8DMzKx21fbJ3wZ8D/g74Iqi9u0R8XJuUZmZWS6q7ZP/dURsBP4eeLmoP/41Sb9fjwDNzCy7rH3y1wOvFC3vSNvKkrRM0hZJa4ravihpnaQnJK2QdGDRusWSNkhaL+m0jHGamU1qWZO8ImLPOPmIGGbsrp+bgPkj2u4Fjk3H1z8NLAaQdDSwEDgm3ec6Se0ZYzUzm7QyT/8n6VJJ+6Wvy0imBCwrLUP88oi2eyJiKF18EOhJ358F3BERuyLiWWADMDdjrGZmk1bWJP9x4A+AF4B+4PeBRTXG8lGSh7oAhwLPF63rT9v2IWmRpD5JfQMDAzWGYJYfX5vWDDIl+YjYEhELI+JNETEzIv5DRGzJGoSkTwNDwNcLTaVOWyaWJRHRGxG93d3dWUMwy52vTWsGWWeG+hqla9d8NMOxzicpk/Ceon7+fmBW0WY9wKYMoZqZTWpZSw3fXfR+f+BsMiRhSfOBy4F3R8SrRatWArdJugY4BDgSeDhjrGZmk1bWUsPfKF6WdDtw32j7pNucCBwkqR/4DMlomqnAvZIAHoyIj0fEWkl3AU+SdONcEhG7s8RqZjaZZb2TH+lI4LDRNoiIc0o03zjK9lcCV9YYl5nZpJa1T347SZ+80p+/JOl2MTOzJpK1u6Yz70DMzCx/1VahPGG09RHxaG3hmJlZnqq9k786/bk/0As8TtJlcxzwEPCH+YVmZma1qrYK5UkRcRLwHHBC+kWP3wPeTlJ6wMzMmkjWsgZvjYjVhYWIWAMcn0tEZmaWm6xDKJ+SdANwK8nomnOBp3KLyszMcpE1yV8AXARcli6vYox68mZmNv6yDqH8V0lfJfmWawDrI+K1XCMzM7OaZf0y1InAzcBGktE1sySdn9aMNzOzJpG1u+Zq4NSIWA8g6SjgduD38grMzMxql3V0zX6FBA8QEU8D++UTkpmZ5SXrnfwjkm4EbkmXPwQ8kk9IZmaWl6xJ/uPAJcClJH3yq4Dr8grKzMzyUXWSl9QGPBIRxwLX5B+SmZnlpeo++YgYBh6XNGr9eDMza7ysD14PBtZK+oGklYXXaDtIWiZpi6Q1RW1/KmmtpGFJvSO2Xyxpg6T1kk7LGKeZ2aSWtU/+cxn2uQn4CrC8qG0N8AHgH4o3lHQ0sBA4hmSO1/skHeUpAM3MqlNtPfn9SR66vgVYDdwYEUOV7BsRqyTNHtH2VHrckZufBdwREbuAZyVtAOYCP6kmXjOzya7a7pqbSerIrwbex+v15fN2KPB80XJ/2rYPSYsk9UnqGxgYyHzC4eFgYPsuXtj2KgPbdzE8HJmPZQb5XZtmtai2u+boiPhdgHSc/MP5hwQkwzJHKpl1I2IJsASgt7c3U2YeHg7Wb97Ohcv76N+2k56uaSw9r5c5MztpaysVitnY8rg2zWpV7Z38niJklXbTZNQPzCpa7gE21etkW3cM7knwAP3bdnLh8j627his1ynNzMZFtUn+bZJ+k762A8cV3kv6TY5xrQQWSpoq6XDgSOr3WwODQ7v3JPiC/m07GRzyc14zm9iq6q6JiPasJ5J0O3AicJCkfuAzwMvA/wS6ge9IeiwiTouItZLuAp4EhoBL6jmypmNKOz1d0/ZK9D1d0+iYkvnjmpk1haxDKKsWEeeUWbWizPZXAlfWL6LXzZjewdLzevfpk58xvWM8Tm9mVjfjluSbWVubmDOzkxUXz2NwaDcdU9qZMb3DD13NbMJzkk+1tYnuzqmNDsPMLFdZyxqYmdkE4CRvZtbCnOTNzFqYk7yZWQtzkjcza2FO8mZmLcxJ3syshTnJm5m1MCd5M7MW5iRvZtbCWr6swfBwsHXHoGvSmNmk1NJJ3jM+mdlk19LdNZ7xycwmu5ZO8p7xycwmu5ZO8oUZn4p5xiczm0zGLclLWiZpi6Q1RW2/LeleST9Pf3YVrVssaYOk9ZJOy3LOwoxPhUTvGZ/MbLIZzwevNwFfAZYXtV0B/CAirpJ0Rbp8uaSjgYXAMcAhwH2Sjqp2nlfP+GRmk9243clHxCqSibuLnQXcnL6/GXh/UfsdEbErIp4FNgBzs5y3MOPToV1voLtzqhO8mU0qje6TnxkRLwKkP9+Uth8KPF+0XX/atg9JiyT1SeobGBioa7Bm1fC1ac2g0Um+nFK321Fqw4hYEhG9EdHb3d1d57DMKudr05qBIkrmzvqcTJoN3B0Rx6bL64ETI+JFSQcDD0TEHEmLASLi79Lt/hn4bET8ZIzjDwDP1RjmQcBLNR6jWbTKZxnPz/FSRMzP+6BF1+ZE/jtx7I1xELAu63XZ6G+8rgTOB65Kf/5TUfttkq4hefB6JPDwWAeLiJpvlyT1RURvrcdpBq3yWVrhcxSuzYn8WRx7Y6SxZ77xGLckL+l24ETgIEn9wGdIkvtdkv4c+AXwpwARsVbSXcCTwBBwSbUja8zMbByTfEScU2bVe8psfyVwZf0iMjNrfc364LWRljQ6gBy1ymdplc8BE/uzOPbGqCn2cX3wamZm48t38mZmLcxJ3syshU3aJC9pf0kPS3pc0lpJn0vbPyvpBUmPpa8FjY61UpLaJf1M0t3pctkCcM2sxOeYMH8njSjEl4cycX9R0jpJT0haIenAonVNEXcayz6xF637pKSQdFBRW9PHLukv0/jWSvpCUXvVsU/aJA/sAk6OiLcBxwPzJb0zXXdtRByfvr7bsAirdxnwVNFyoQDckcAP0uWJYOTngInzd3ITMHJMc8m/hxGF+OYD10lqVB3sm9g37nuBYyPiOOBpYDE0XdxQOnYkzQJOIRmeXWhr+tglnURSv+u4iDgG+FLanin2SZvkI/FKurhf+pqwT6El9QCnAzcUNZcrANe0ynyOCaNRhfhqVSruiLgnIobSxQeBnvR908QNZf/MAa4FPsXe/64nQuwXAVdFxK50my1pe6bYJ22Shz3dAo8BW4B7I+KhdNUn0l9Rl02ULg7gyyQX9HBRW7kCcM3sy+z7OWBi/p0U1FyIrwl8FPhe+r7p45Z0JvBCRDw+YlXTxw4cBfyRpIck/UjSO9L2TLFP6iQfEbsj4niSO5S5ko4FrgeOIOnCeRG4umEBVkjSGcCWiHik0bHUYpTPMeH+TipUcSG+RpL0aZJvnn+90FRis6aJW9IbgE8Df1tqdYm2pok9NQXoAt4J/DVJVQCRMfZJneQLIuJXwAPA/IjYnCb/YWApDfxVrgrzgDMlbQTuAE6WdCuwOS38RvpzS/lDNIWSn2OC/p0UK/f30A/MKtquB9g0zrGNStL5wBnAh+L1L9U0e9xHAIcDj6fXUg/wqKR/Q/PHDkmM30y7lB8m+a32IDLGPmmTvKTuwmgBSdOA9wLrCv8YU2cD+zyxbzYRsTgieiJiNsmDmfsj4lxeLwAHexeAa0rlPsdE/DsZodzfw0pgoaSpkg6nwkJ840XSfOBy4MyIeLVoVVPHHRGrI+JNETE7vZb6gRMi4pc0eeypbwEnA0g6CuggqaCZKfZGV6FspIOBm9On023AXRFxt6RbJB1P8mvQRuBjjQuxZiULwE1AX5gofyeaoIX4ysS9GJgK3Jv0FvBgRHy8meKG0rFHxI2ltp0IsQPLgGXpsMpB4Pz0t6hMsbusgZlZC5u03TVmZpOBk7yZWQtzkjcza2FO8mZmLcxJ3syshTnJm5m1MCf5jCTtVlL2do2kf0y/Sp31WA9IqnomeUkHSrq4aPkQSf87axxlzrFxRJnWE5WWALbGmQzXn6SbJH1sRNv7JZWtQpr1s7QyJ/nsdqZlb48l+cLCx4tXjlP50gOBPf/IImJTRPzJOJy3Zkr4+stuMlx/t5N887nYwrTdKuR/ZPn4P8Bb0rvcH0q6DVitZGKSr0larWQSjJMgKaMg6Y60quKdwLTCgSS9UvT+TyTdlL6fqWTihsfT1x+QfJPyiPSO7ouSZqffkmOUc39E0jclfV/JJBZ7JiSolpLJML6Vfo4HJR2Xtn9W0ieLtluTxjZb0lOSrgMeZe86HIVt35zGdZCkNkn/R9KpWWOcJFr1+rsPeKter/vzBpLyI9+S9J70uKuVVCadOnLnUT7LTZKuT/+snpH07vQYTxW2Sbc7VdJPJD2q5LelN5YKMo1lRdHyKZK+OcrnGleTuaxBLiRNAd4HfD9tmksy0cKzkv4zQET8rqS3AvcoqUVxEfBqRByXJsZHKzjV/wB+FBFnp3dpbySZfOLYtJImkmYXbX9JmXNDUs3x7SQTp6yX9D8joriE6Ug/lFT4+vQbgXXp+88BP4uI90s6GVieHns0c4ALIuLiUisj4jlJnwf+F/AQ8GRE3DPGMSetVr7+ImJ3miw/CPw9cCbwQ+A1ksk23hM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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# visualize the relationship between the features and the response using scatterplots\n",
    "# Create the default pairplot\n",
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "iNTERCEPT :  -45.99999999999994\nCO-EFFICIENT :  [4.5]\n"
    }
   ],
   "source": [
    "### SCIKIT-LEARN ###\n",
    "# create X and y\n",
    "feature_cols = ['Production_Hour_x']\n",
    "X = data[feature_cols]\n",
    "y = data.Production_Volume_y\n",
    "# Initiate and fit\n",
    "lm2 = LinearRegression()\n",
    "lm2.fit(X, y)\n",
    "# print the coefficients\n",
    "print (\"iNTERCEPT : \",lm2.intercept_)\n",
    "print (\"CO-EFFICIENT : \",lm2.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We interpret the Production_Hour_x coefficient (β1)?\n",
    "\n",
    "A 'unit' increase in Production_Hour_x is associated with a 4.5 'units' increase in Production_Volume_y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Intercept           -46.0\nProduction_Hour_x     4.5\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "### STATSMODELS ###\n",
    "# create a fitted model\n",
    "lm1 = smf.ols(formula='Production_Volume_y ~ Production_Hour_x', data=data).fit()\n",
    "# print the coefficients\n",
    "lm1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    516.5\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "### STATSMODELS ###\n",
    "# you have to create a DataFrame since the Statsmodels formula interface expects it\n",
    "X_new = pd.DataFrame({'Production_Hour_x': [125]})\n",
    "# predict for a new observation\n",
    "lm1.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "516.5"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# 125 hour of productions yelds for 516 volume units\n",
    "# manually calculate the prediction to confirm the above calculation :)\n",
    "-45.99999999999994 + 4.5*125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([125.])"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "### SCIKIT-LEARN ###\n",
    "# predict for a new observation\n",
    "lm2.predict([[38]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would predict Production of 532.80 in that market by working 125 hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x1d61bb5af60>"
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Plotting the Least Squares Line \n",
    "# Let’s plot the least squares line for Production versus Volume\n",
    "sns.pairplot(data, x_vars=['Production_Hour_x','Production_Volume_y'], y_vars='Production_Hour_x', size=7, aspect=0.7, kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Intercept            0.126734\nProduction_Hour_x    0.001628\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "### STATSMODELS ###\n",
    "# print the p-values for the model coefficients\n",
    "lm1.pvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis Testing and p-values\n",
    "\n",
    "Generally speaking, you start with a null hypothesis and an alternative hypothesis (that is opposite the null). Then, you check whether the data supports rejecting the null hypothesis or failing to reject the null hypothesis.\n",
    "\n",
    "(Note that “failing to reject” the null is not the same as “accepting” the null hypothesis. The alternative hypothesis may indeed be true, except that you just don’t have enough data to show that.)\n",
    "\n",
    "As it relates to model coefficients, here is the conventional hypothesis test:\n",
    "\n",
    "null hypothesis: There is no relationship between Production and Plant Operating Hours (and thus β1β1 equals zero)\n",
    "alternative hypothesis: There is a relationship between Production and Plant Operating Hours (and thus β1β1 is not equal to zero)\n",
    "\n",
    "A p-value less than 0.05 is one way to decide whether there is likely a relationship between the feature and the response.\n",
    "\n",
    "In this case, the p-value for Production_Hour_x is far less than 0.05, and so we believe that there is a relationship between  Production and Plant Operating Hours.\n",
    "\n",
    "we generally don’t consider the p-value for the intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "StatModel R-Square Value 0.9348473566641846\n"
    }
   ],
   "source": [
    "### STATSMODELS ###\n",
    "# print the R-squared value for the model\n",
    "print(\"StatModel R-Square Value\",lm1.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "SkLearn R-Square Value 0.9348473566641846\n"
    }
   ],
   "source": [
    "### SCIKIT-LEARN ###\n",
    "# print the R-squared value for the model\n",
    "print(\"SkLearn R-Square Value\",lm2.score(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = smf.ols('Production_Volume_y ~ Production_Hour_x', data=data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "OLS Regression Results                            \n===============================================================================\nDep. Variable:     Production_Volume_y   R-squared:                       0.935\nModel:                             OLS   Adj. R-squared:                  0.919\nMethod:                  Least Squares   F-statistic:                     57.39\nDate:                 Sat, 05 Sep 2020   Prob (F-statistic):            0.00163\nTime:                         08:46:05   Log-Likelihood:                -18.633\nNo. Observations:                    6   AIC:                             41.27\nDf Residuals:                        4   BIC:                             40.85\nDf Model:                            1                                         \nCovariance Type:             nonrobust                                         \n=====================================================================================\n                        coef    std err          t      P>|t|      [0.025      0.975]\n-------------------------------------------------------------------------------------\nIntercept           -46.0000     23.913     -1.924      0.127    -112.392      20.392\nProduction_Hour_x     4.5000      0.594      7.576      0.002       2.851       6.149\n==============================================================================\nOmnibus:                          nan   Durbin-Watson:                   1.250\nProb(Omnibus):                    nan   Jarque-Bera (JB):                0.563\nSkew:                           0.000   Prob(JB):                        0.755\nKurtosis:                       1.500   Cond. No.                         357.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\nC:\\Users\\giljr\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\statsmodels\\stats\\stattools.py:75: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n  \"samples were given.\" % int(n), ValueWarning)\n"
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Thank you for Reading this post o/\n"
    }
   ],
   "source": [
    "# https://medium.com/jungletronics/linear-regressions-the-basics-1a633f351ec2\n",
    "print(\"Thank you for Reading this post o/\")"
   ]
  }
 ]
}