SIMPLE LINEAR REGRESSION ASSIGNMENT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f2d1166",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48e600cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"delivery_time.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1db64d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delivery Time</th>\n",
       "      <th>Sorting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Delivery Time  Sorting Time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f55d5d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery Time  21 non-null     float64\n",
      " 1   Sorting Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 464.0 bytes\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be3285a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Delivery Time', ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Delivery Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07f6594c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sorting Time', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n07jijYOVpA6KZd6YoW5HMf0oMymBlbOyOFbdzOsHK92OYy6SFQgz4I7XNHO8toVLJ6QTE2X/BMPN3DFDmZOTyhsHKzlaZYPoQpn9dJoB986RagbFRpM3xtoewtUNs0cxLDGO53aU2fiIEGYFwgyo6sZ2CioaWDQuzdaYDmPxMdHcMi+HxrZO/rrXlnIJVfYTagbUO4XVREcJi8YNczuKcVhO2mCumJjBzpLTHDhhy5WGooAKhIg8LyLXiYgVFHPBGts62VVSx5zRQ0lKiHU7jhkAyyZnMjIlgRd3l9PU3uV2HHOeAv3Cfwi4FTgiIj8VkckOZjJhanNRDd0e5bJL0t2OYgZITFQUt8zLoa2zmw177FZTqAmoQKjqP1T1M8BcoBh4VUQ2icjnRMT+K2j61NntYduxWiaPTCY9yWZsjSQjUhJYNimT98vrOXjSbjWFkoBvGYnIMOAO4IvALuB+vAXjVUeSmbCyr7yelo5uFlvbQ0S6fGI6mUnxbNhzgo4uj9txTIACbYN4AXgHGAxcr6o3qOqfVPUrQKKTAU142FJUQ0ZiPOMzhrgdxbggJiqKT8zO4nRLJ68VnHI7jglQoFcQj6jqVFX9iapWAIhIPICq5jmWzoSF0toWyupaWTR+GCLidhzjktz0IczPHcp7R6s5cbrV7TgmAIEWiP/Pz7bNfZ0kIstF5JCIFIrIvX72i4g84Nu/V0Tm9tj3ryKyX0TeF5GnRCQhwKwmyGwpqiEuJoo5OaluRzEuWz5tJIPiYvjz7nKbqykEnLNAiMgIEZkHDBKROSIy1/e4Eu/tpnOdGw08CKwApgKrRWRqr8NWABN8j7V4e0shIlnAV4E8VZ0ORAOrzvP3ZoJAU3sXe8vrmTs61RYEMgyKi2bFtBGU1rWyp/S023FMH2L62P8xvA3T2cDPe2xvBL7bx7kLgEJVLQIQkaeBlcCBHsesBB5TVQW2iEiqiJxZtT4Gb2HqxFuMrI9cCMovrqXboywaa43Txmv26FQ2F9Xw8v6TTBuVYiPqg9g5/2ZUdb2qLgPuUNVlPR43qOoLfbx3FlDa43WZb1ufx6hqOXAfUAJUAPWq+oq/DxGRtSKSLyL5VVW2mlUw8aiyrbiWcRlDyEy2O4TGK0qEj88cSUNbl61AF+T6usV0m+9proh8o/ejj/f21xrZ+6aj32NEZCjeq4uxwChgSI8s/3yw6jpVzVPVvIyMjD4imYFUWNnE6ZZOFtiCQKaXMcOGMDM7hXeOVFHX0uF2HHMWfV3bnemTmAgk+XmcSxmQ0+N1Nh++TXS2Yz4CHFPVKlXtBF4AlvTxeSbIbC+uZXBcNFNHJrsdxQSh5dNGAPDy/pMuJzFnc842CFX9je/X/7iA994OTBCRsUA53kbmW3sdswG4x9c+sRDvraQKESkBFonIYKAVuBrIv4AMxiWNbZ0UVDSwZHw6MbZinPEjdXAcl05I581DVVw2oZWs1EFuRzK9BDpQ7j9FJFlEYkXkNRGpPtstnzNUtQu4B3gZKACeUdX9InKniNzpO2wjUAQUAr8F7vKduxV4DtgJ7PPlXHf+vz3jlp0lp/Eo5OXainHm7C6fkMGg2GhesauIoNRXL6YzPqqq3xaRT+K9LXQL8AbwxLlOUtWNeItAz20P93iuwN1nOfcHwA8CzGeCiKqSX1xL7rAhZCZZ47Q5u4TYaK6YmMFL+09SVN3EuHSbmCGYBHrtf2ZCvmuBp1S11qE8JgwUVTdT09zBfLt6MAFYPH4YyQkxvLL/FGqD54JKoAXiLyJyEMgDXhORDKDNuVgmlG0vriUhNorpWSluRzEhIDY6iqsmD6ektoWDJxvdjmN6CHS673uBxXhHNncCzXi7oRrzT1o7ujlwooHZOUOJtcZpE6B5Y4YybEgcrx44hcdjVxHB4nx+gqcAnxaRNcDNwEediWRC2fvl9XR5lLmjU92OYkJIdJRw9ZRMTja08coBa7AOFoH2Ynoc78jmS4H5vofN4mo+ZGdJHZlJ8dZl0Zy3GVmpDBsSxwOvFVpbRJAItBdTHjBV7W/NnENNUzvHa1v42LQRNq23OW/RUcKVkzJ5fmcZrxVU8pGpw92OFPECvcX0PjDCySAm9O0qPY0As21ab3OBZuekkj10EL98/YhdRQSBQAtEOnBARF4WkQ1nHk4GM6HFo8qukjrGZyaSMsiWKTcXJjpKuHvZJewpq+ftI9Vux4l4gd5i+qGTIUzoO17TQl1LJ9fYbQFzkW6am80vXzvCA68d4fIJ6Xa70kWBdnN9CygGYn3Pt+OdBsMYwNs4HRcTxdSRNvbBXJy4mCi+fMV4dhyvI/94ndtxIlqgvZi+hHdupN/4NmUB/+NQJhNiOro8vF9ezwxb/MX0k0/l5TB0cCy/eeuo21EiWqA/zXcDS4EGAFU9AmQ6FcqElgMVDbR3eZgzJtXtKCZMDIqL5vYlufyjoJIjp2x0tVsCLRDtqvrBqh4iEsOHF/8xEWpXSR2pg2PJHTak74ONCdCaxbkkxEax7u0it6NErEALxFsi8l28a0RfAzwL/MW5WCZU1Ld2UljZxJycoURZY6LpR2lD4vh0Xg7/s7uck/U29ZsbAi0Q9wJVeNdm+DLeKbz/3alQJnTsKT2Ngk2tYRzxxcvG0e1RHn3vmNtRIlKgvZg8eBul71LVm1X1tzaq2qgqO0vqGJ02mGGJ8W7HMWEoJ20w180cxZNbS2hs63Q7TsQ5Z4EQrx+KSDVwEDgkIlUi8v2BiWeC2YnTbVQ2tjN3tK37YJzzxUvH0tTexbP5ZW5HiTh9XUF8HW/vpfmqOkxV0/CuHb1URP7V6XAmuO0sqSMmSphh6z4YB83KSWXu6FTWby6m26YCH1B9FYg1wGpV/eAGoKoWAbf59pkI1dHlYU/ZaaaMTGZQXLTbcUyY+/ylYzle08LrByvdjhJR+ioQsar6oQlRVLWK/12G1ESgNw9V0tLRzRxrnDYD4GPTRjAyJcEaqwdYXwWi4wL3ASAiy0XkkIgUisi9fvaLiDzg279XROb22JcqIs+JyEERKRCRxX19nhk4z+8sIzE+hgmZSW5HMREgNjqKNYtz2XS0hoMnG9yOEzH6KhCzRKTBz6MRmHGuE0UkGngQWAFMBVaLyNReh60AJvgea4GHeuy7H3hJVScDs4CCgH9XxlF1zR28frCS2TmpREfZ2AczMFYvyCEhNoo/vFfsdpSIcc4CoarRqprs55Gkqn3dYloAFKpqkW8U9tN8eB3rlcBj6rUFSBWRkSKSDFwO/M6Xo0NVT1/Ib9D0v7/sPUFnt9rtJTOgUgfH8ck52by4q5za5j5vYJh+4OTMallAaY/XZb5tgRwzDu/AvEdFZJeIPCIifudxEJG1IpIvIvlVVVX9l96c1fM7ypgyMpmRKbasqBlYn1+aS3uXh6e2lbgdJSI4WSD83Xvo3UftbMfEAHOBh1R1DtCMdzT3hw9WXaeqeaqal5GRcTF5TQAKKxvZU1bPTXN713pjnDdheBKXTUjnsc3FdHZ73I4T9pwsEGVATo/X2cCJAI8pA8pUdatv+3N4C4Zx2fM7y4mOElbOtgJh3PG5pbmcamhn474Kt6OEPScLxHZggoiMFZE4YBXQe5nSDcAaX2+mRUC9qlao6kmgVEQm+Y67GjjgYFYTgG6P8uLOcq6YmEFGkk2tYdxx5cRMxqYPYf2mYrejhD3HCoSqdgH3AC/j7YH0jKruF5E7ReRO32EbgSKgEPgtcFePt/gK8KSI7AVmAz92KqsJzOajNZxsaONGu71kXBQVJXx20Rh2lpxmX1m923HCWqBrUl8QVd2Itwj03PZwj+eKdzEif+fuBvKczGfOz/M7y0hKiOEjU2zdaeOum/Oyue+VQzy2uZj/umWW23HClq0PaQLS1N7FS++f5OMzR5EQa1NrGHclJ8Ry49ws/rznhHV5dZAVCBOQv++roLWzm5vn2e0lExzWLM6lo8vDn7aX9n2wuSBWIExAnt9ZRu6wwTa1twkaE4cnsXjcMJ7YctxmeXWIFQjTp9LaFrYU1XLT3GzElhU1QeT2JWMoP93KawWn3I4SlqxAmD69sLMcEbhxXrbbUYz5Jx+ZMpxRKQms31zsdpSwZAXCnJPHozy3s5TF44aRlWpTa5jgEhMdxWcWjeG9whoKKxvdjhN2rECYc9peXEtpbSs329WDCVKr5ucQFx3FY5uPux0l7FiBMOf0/M4yhsRFs3z6CLejGOPXsMR4Pj5rJM/vKKOxrdPtOGHFCoQ5q5aOLv62t4JrZ4xkcJyjYyqNuSi3L86luaObF3aWux0lrFiBMGf10vsnae7otttLJujNykllVk4q6zcX47Eur/3GCoQ5q+d3lpGTNoj5uWluRzGmT7cvHkNRVTPvHa12O0rYsAJh/Co/3cqmozXcNDebKFtW1ISA62aOZNiQONZvssbq/mIFwvj14s4yVOGmuXZ7yYSG+JhoVi8YzWsHT1Fa2+J2nLBgBcJ8iKry3I4yFo5NIydtsNtxjAnYrQtHEyXCE1vtKqI/WIEwH7K9uI7imhZrnDYhZ1TqID46dTh/2l5KW2e323FCnhUI8yFPbyshKT6G62aOdDuKMedtzeJcTrd0smF37xWOzfmyAmH+SX1LJ3/bV8ENs0fZ2AcTkhaNS2PS8CT+sKkY75pk5kJZgTD/5MVdZbR3eVi9YLTbUYy5ICLCmiVjOFDRwM6SOrfjhDQrEOYDqsrT20uZkZXC9KwUt+MYc8E+MTuLpIQY6/J6kaxAmA/sLj3NwZONrFqQ43YUYy7KkPgYbpmXw8Z9FVQ2tLkdJ2Q5WiBEZLmIHBKRQhG5189+EZEHfPv3isjcXvujRWSXiPzVyZzG66ltJQyOi+aGWaPcjmLMRfvs4jF0eZQ/bitxO0rIcqxAiEg08CCwApgKrBaRqb0OWwFM8D3WAg/12v81oMCpjOZ/NbZ18pc9FVw/cxRJCbFuxzHmoo1NH8IVEzN4cmsJHV0et+OEJCevIBYAhapapKodwNPAyl7HrAQeU68tQKqIjAQQkWzgOuARBzManw17TtDa2c3qhdY4bcLHHUtyqWps5+X9J92OEpKcLBBZQGmP12W+bYEe8wvg28A5S7+IrBWRfBHJr6qquqjAkeypbSVMHpHErGxrnDbh44qJGYxOG8xjtiTpBXGyQPib4a13p2S/x4jIx4FKVd3R14eo6jpVzVPVvIyMjAvJGfHeL6/n/fIGVi8YjYhNzGfCR1SUsGbxGLYX17H/RL3bcUKOkwWiDOjZHSYb6D208WzHLAVuEJFivLemrhKRJ5yLGtme2lZCfEwUn5jT+wLPmNB3y7wcEmKjeMy6vJ43JwvEdmCCiIwVkThgFbCh1zEbgDW+3kyLgHpVrVDV76hqtqrm+s57XVVvczBrxGpo6+TFXeVcP2sUKYOscdqEn5TBsXxyThb/s7uc0y0dbscJKY4VCFXtAu4BXsbbE+kZVd0vIneKyJ2+wzYCRUAh8FvgLqfyGP+ezS+jpaObO5bkuh3FGMesWZxLe5eHZ/JL+z7YfMDRyXZUdSPeItBz28M9nitwdx/v8SbwpgPxIp7Hozy+uZh5Y4bayGkT1qaMTGbB2DQe33KcL1w6jmhbBCsgNpI6gr11uIrimhbWLB7jdhRjHHf74lxKa1t5/WCl21FChhWICLZ+czEZSfGsmG7Tepvw99Fpw8lKHcRv3y5yO0rIsAIRoY5VN/PmoSo+s3A0cTH2z8CEv9joKD5/6Vi2FdfaLK8Bsm+GCPXY5mJio4VbbeS0iSCr5ueQnBDDurfsKiIQViAiUH1rJ89sL+W6GSPJTEpwO44xA2ZIfAy3LRrDywdOcqy62e04Qc8KRAR6cutxmju6WXv5eLejGDPg7liaS2xUFI+8Y1cRfbECEWHau7p59L1iLpuQztRRyW7HMWbAZSYlcOPcLJ7dUUZ1U7vbcYKaFYgI8z+7yqlqbOfLdvVgItiXLh9HZ7eHR9875naUoGYFIoJ4PMq6t4uYOjKZpZcMczuOMa4Zn5HIiukjWL/pOPUtnW7HCVpWICLI6wcrOVrVzJevGGeztpqId8+yCTS1d/HoJruKOBsrEBFCVXn4raNkpQ7iuhk2MM6YqaOS+ciU4fz+3WM0ttlVhD9WICLEpqM15B+v48tXjCMm2v7ajQH46tWX0NDWxWObbSpwf+ybIgKoKv/96mFGJCfw6fk5fZ9gTISYmZ3KlZMy+N27x2jp6HI7TtCxAhEB3iv0Xj3cvWw88THRbscxJqh85aoJ1DZ38LhdRXyIFYgwp6r89z8OMzIlgU/Z1YMxHzJvzFCumJjBr988Sn2rtUX0ZAUizL1bWM2O43XctewSu3ow5iz+z8cmUd/aaTO99mIFIoypKj9/9TCjUhL4VF6223GMCVrTs1K4ftYofvfuMaoabXT1GVYgwthL759kV8lpvnL1BLt6MKYP37xmIp3dHn71+hG3owQNKxBhqqPLw09fOsik4Ul8Ks/aHozpS276ED49P4c/biuhpKbF7ThBwQpEmHp8y3GO17TwnWsn2/q7xgToq1dPIDpK+NnLB92OEhQcLRAislxEDolIoYjc62e/iMgDvv17RWSub3uOiLwhIgUisl9EvuZkznBT39LJA68d4bIJ6Vw5KdPtOMaEjOHJCdx5xXj+treCLUU1bsdxnWMFQkSigQeBFcBUYLWITO112Apggu+xFnjIt70L+KaqTgEWAXf7OdecxS9fP0JDWyffvXaK21GMCTl3XjGerNRB/HDDfrq6PW7HcZWTVxALgEJVLVLVDuBpYGWvY1YCj6nXFiBVREaqaoWq7gRQ1UagAMhyMGvYOHKqkfWbi7llXjZTRtp6D8acr4TYaP79uikcPNnIU9tK3I7jKicLRBZQ2uN1GR/+ku/zGBHJBeYAW/19iIisFZF8Ecmvqqq62MwhzeNRvvviPobEx/Bvyye7HceYkLV8+giWjB/Gfa8cpq65w+04rnGyQPhrGdXzOUZEEoHnga+raoO/D1HVdaqap6p5GRkZFxw2HDy7o5TtxXV8d8UUhiXGux3HmJAlIvzg+mk0tXfxs5cit8HayQJRBvTsX5kNnAj0GBGJxVscnlTVFxzMGRaqm9r58caDLBibxi02KM6YizZpRBJfuHQsT28v5b3CarfjuMLJArEdmCAiY0UkDlgFbOh1zAZgja830yKgXlUrxLuaze+AAlX9uYMZw8aP/lZAS0cXP/7kdFsMyJh+8o1rJjI2fQj3vrCX5vbIm+3VsQKhql3APcDLeBuZn1HV/SJyp4jc6TtsI1AEFAK/Be7ybV8KfBa4SkR2+x7XOpU11L30/kle3FXOv1wxnksyk9yOY0zYSIiN5mc3zaS0tpX/evmQ23EGXIyTb66qG/EWgZ7bHu7xXIG7/Zz3Lv7bJ0wvpxrauPeFvczISuGeqya4HceYsLNgbBq3Lx7D+s3FXDdzJPNz09yONGBsJHUI83iUbz6zh/ZOD79YNZu4GPvrNMYJ314+mazUQfzrn3ZH1JTg9o0Swn737jHeLazmB9dPZXxGottxjAlbQ+JjuH/VHE7Wt/Fvz+3Fe/Mj/FmBCFE7jtfyny8f5GPThtsyosYMgHljhvLt5ZN4af9J1m8qdjvOgLACEYJOnG7ly4/vJCt1ED+7aab1WjJmgHzx0nFcPTmTH288yN6y027HcZwViBDT2tHNlx7Lp72zm0duzyN1cJzbkYyJGFFRwn23zCI9MY47H9/BqYY2tyM5ygpECPF4lG89t4cDFQ08sHqOdWk1xgVDh8Sxbk0ep1s7+cL67WE9PsIKRIhQVf7jL/v5294K7l0+mWWTbRpvY9wyPSuFB2+dy4ETDXzlqV1hO+urFYgQoKr89O8HWb/5OGsvH8fay8e5HcmYiLdscib/ccM0Xj9Yyfc37A/Lnk2ODpQz/eMX/zjCb94u4rOLxvCdFZOtUdqYIPHZxbmcqG/joTePIsD/u3I6UWG0gqMViCDm8Sg/3ljAI+8e45Z52fzHDdOsOBgTZL79sUmowsNvHaXbo/z4kzPCpkhYgQhSbZ3dfPOZPfxtXwW3Lx7D96+fFjb/6IwJJyLCvy2fRGy08MvXC+no8vCTm2YQHxPtdrSLZgUiCFU1tnPXkzvYXlzH966dwhcvG2tXDsYEMRHhmx+dRHxMFPe9cpiS2hYeum0eGUmhvS6LNVIHmXeOVLHi/nfYW1bPL1fP4UuXj7PiYEyIuOeqCfzq1jm8f6Kelb96l/fL692OdFGsQASJji4PP3vpIGt+v42hg2PZcM+lXD9rlNuxjDHn6eMzR/HcnUtQ4MZfb+I3vraJUGQFIgi8e6Sa5fe/zUNvHmXV/NFsuOdSJo2wQXDGhKrpWSn85SuXsmxyBj/5+0E+9ZvNHKtudjvWebMC4aLi6mbufnInt/1uK90e5dE75vOTG2cwKC70G7eMiXTpifE8fNs8fvHp2Rw51cjHfvE2P9lYEFLThVsjtQsKK5t48I1C/ry7nNjoKL5xzUTWXj6OhFgrDMaEExHhE3OyWDx+GP/50iHWvVPEn/JLufvKS1i9cDSJ8cH9FRzc6cJIZ7eH1woq+dP2Et48XEVCTDSfXzqWtZePIzM5we14xhgHDU9O4P9+ahZfuHQsP/l7AT/aWMADrx3h0/NzuH1JLjlpg92O6JcVCAd1dHnYeqyGVw+cYuO+k1Q3tTM8OZ6vLLuE25fkMiwxtLvAGWPOz9RRyTz+hYXsKqnj9+8V8+imYh559xgLctO4fvYorp0+Iqi+FySc5g/Jy8vT/Px81z6/s9tDQUUD247Vsu1YLZuLamhs6yIhNoorJmbwqbwcrpiYQUx0eDT9/HFridsRTBi6deFotyMMmIr6Vp7NL2PDnhMUVjYRJTAjK4VLJ6SzdHw6M3NSHb8NJSI7VDXP7z4nC4SILAfuB6KBR1T1p732i2//tUALcIeq7gzkXH8GqkDUt3ZSWttCie9RWNlEQUUDR0410eGb1TF32GAWjRvG1VOGc+kl6WHZ8GwFwjghkgrEGarKoVONvPT+Sd49Us2u0tN0exQRGJ+RyIysFMZnDCE3fQi5w7y/9lfhOFeBcKw0iUg08CBwDVAGbBeRDap6oMdhK4AJvsdC4CFgYYDn9pudJXU0tXXR0tFFU3u379cuWtq7qWvpoLa5g5qmDqqb26lubKeh7Z/nf09PjGPKyGQ+d2ku00elsGBsGsOtXcEYEyARYfKIZCaPSObrH5lIY1sn+cfr2Ftaz77y02w+WsOLu8r/6Zy0IXFkJsWTkRRP9tDB/OTGGf2ey8lrlwVAoaoWAYjI08BKoOeX/ErgMfVexmwRkVQRGQnkBnBuv7n1t1to6/zwfO5RAqmD4xg2JI60IXFMGZHMsEviyB46iNFpg8nxPZITYp2IZYyJUEkJsSyblMmySf+77ktLRxfF1S0cr2nmWE0z5XWtVDa2U9nYzuFTjY7kcLJAZAGlPV6X4b1K6OuYrADPBUBE1gJrfS+bROTQRWQ+Ix2o7of36U+WKXDBmMsyBegzwZkr6DPJXRf8PmPOtsPJAuFvAqHeDR5nOyaQc70bVdcB684v2rmJSP7Z7sm5xTIFLhhzWabABWOuSM3kZIEoA3J6vM4GTgR4TFwA5xpjjHGQk/0ttwMTRGSsiMQBq4ANvY7ZAKwRr0VAvapWBHiuMcYYBzl2BaGqXSJyD/Ay3q6qv1fV/SJyp2//w8BGvF1cC/F2c/3cuc51Kqsf/XrLqp9YpsAFYy7LFLhgzBWRmcJqoJwxxpj+Ex5Deo0xxvQ7KxDGGGP8sgLhh4j8l4gcFJG9IvKiiKS6mGW5iBwSkUIRudetHD2JSI6IvCEiBSKyX0S+5namM0QkWkR2ichf3c5yhm8A6HO+f1MFIrI4CDL9q+/v7n0ReUpEBnzov4j8XkQqReT9HtvSRORVETni+3VokORy9TvBX6Ye+74lIioi6f39uVYg/HsVmK6qM4HDwHfcCNFjypEVwFRgtYhMdSNLL13AN1V1CrAIuDtIcgF8DShwO0Qv9wMvqepkYBYu5xORLOCrQJ6qTsfbEWSVC1H+ACzvte1e4DVVnQC85ns90P7Ah3O5/Z3gLxMikoN3SiJHJkazAuGHqr6iqmcmXNqCdxyGGz6YrkRVO4AzU464SlUrzkyqqKqNeL/wstxNBSKSDVwHPOJ2ljNEJBm4HPgdgKp2qOppV0N5xQCDRCQGGIwL44xU9W2gttfmlcB63/P1wCcGMhP4z+X2d8JZ/qwA/hv4NmcZSHyxrED07fPA31367LNNRRI0RCQXmANsdTkKwC/w/rB8eGIt94wDqoBHfbe+HhGRIW4GUtVy4D68/+uswDv+6BU3M/Uw3DcWCt+vmX0c7wY3vxM+ICI3AOWqusepz4jYAiEi//Ddf+39WNnjmO/hvZ3ypFsx/WwLmn7JIpIIPA98XVUbXM7ycaBSVXe4mcOPGGAu8JCqzgGacee2yQd89/VXAmOBUcAQEbnNzUyhIgi+E87kGAx8D/i+k58TsSvKqepHzrVfRG4HPg5cre4NFglkuhJXiEgs3uLwpKq+4HYeYClwg4hcCyQAySLyhKq6/cVXBpSp6pkrrOdwuUAAHwGOqWoVgIi8ACwBnnA1ldcpERmpqhW+mZ0r3Q50RpB8J5wxHm+B3+NdVodsYKeILFDVk/31IRF7BXEuvsWK/g24QVVbXIwSlFOOiPdf5O+AAlX9udt5AFT1O6qaraq5eP+cXg+C4oDvh7VURCb5Nl2NQ9PWn4cSYJGIDPb9XV5N8DTsbwBu9z2/Hfizi1k+EETfCQCo6j5VzVTVXN+/+TJgbn8WB7ACcTa/ApKAV0Vkt4g87EYIX6PYmSlHCoBnBnjKkbNZCnwWuMr357Pb9z93499XgCdFZC8wG/ixm2F8VzPPATuBfXi/BwZ8KgkReQrYDEwSkTIR+QLwU+AaETmCt3dOnytJDlAuV78TzpLJ+c91/0rJGGNMMLIrCGOMMX5ZgTDGGOOXFQhjjDF+WYEwxhjjlxUIY4wxflmBMBFDRL7nm8F0r6+r4sLzPP8OERnV4/UjFztJoYgM69FV+KSIlPd4vUBEHriY9zfmYlg3VxMRfFNs/xy4UlXbfVMjx6lqQCPTfTPrvgZ8S1XzHcr4Q6BJVe9z4v2NOV92BWEixUigWlXbAVS1+kxxEJGrfRPp7fPNux/v214sIt8XkXeB1UAe3gFvu0VkkIi8KSJ5vmObRORHIrJHRLaIyHDf9vG+19tF5P8RkaZAA4vIleJb10JEfigi60XkFV+uG0XkP32ZX/JNfYKIzBORt0Rkh4i87JuuwpgLYgXCRIpXgBwROSwivxaRKwDEu1DOH4BPq+oMvPOT/UuP89pU9VJVfQLIBz6jqrNVtbXX+w8BtqjqLOBt4Eu+7fcD96vqfC5+Hq3xeKczX4l33qQ3fJlbget8ReKXwM2qOg/4PfCji/xME8GsQJiIoKpNwDxgLd7pt/8kIncAk/BOXHfYd+h6vOs3nPGnAD+iAzizit0OINf3fDHwrO/5Hy8kew9/V9VOvNNjRAMv+bbv833eJGA6vukggH/HvbVMTBiI2NlcTeRR1W7gTeBNEdmHdzK43X2c1hzg23f2mOGzG2d+ts7cHvOISM/P8/g+T4D9qur6kqYmPNgVhIkIIjJJRCb02DQbOA4cBHJF5BLf9s8Cb53lbRrxTth2PrYAN/meO72s5yEgw9cgj4jEisg0hz/ThDErECZSJALrReSAb1bVqcAPVbUN+BzwrO+qwgOcbabOPwAPn2mkDvBzvw58Q0S24W0or7+I38M5+ZalvRn4mYjswXt1tMSpzzPhz7q5GuMg38pfraqqIrIKWK2qrq8rbkwgrA3CGGfNA37lW5jnNN71jI0JCXYFYYwxxi9rgzDGGOOXFQhjjDF+WYEwxhjjlxUIY4wxflmBMMYY49f/DxDVz0mq+2L1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Sorting Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cd44f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.rename({\"Delivery Time\":\"delivery_time\",\"Sorting Time\":\"sorting_time\"},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9df12bf5",
   "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>delivery_time</th>\n",
       "      <th>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.75</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.83</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.68</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12.03</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.75</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    delivery_time  sorting_time\n",
       "0           21.00            10\n",
       "1           13.50             4\n",
       "2           19.75             6\n",
       "3           24.00             9\n",
       "4           29.00            10\n",
       "5           15.35             6\n",
       "6           19.00             7\n",
       "7            9.50             3\n",
       "8           17.90            10\n",
       "9           18.75             9\n",
       "10          19.83             8\n",
       "11          10.75             4\n",
       "12          16.68             7\n",
       "13          11.50             3\n",
       "14          12.03             3\n",
       "15          14.88             4\n",
       "16          13.75             6\n",
       "17          18.11             7\n",
       "18           8.00             2\n",
       "19          17.83             7\n",
       "20          21.50             5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b400725",
   "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>delivery_time</th>\n",
       "      <th>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delivery_time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sorting_time</th>\n",
       "      <td>0.825997</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               delivery_time  sorting_time\n",
       "delivery_time       1.000000      0.825997\n",
       "sorting_time        0.825997      1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5212667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sorting_time', ylabel='delivery_time'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55a233irmVsYsinrfUCidyc6sP6j2/EGuhYPPIzT5hLF4monpNJmsFux+1tkS4G63xHR9d2sVI64sESHg8xD0eWjyewn6PAtOBCJS/slnVf2Q+/GaBUXlBNYK/BPwCVUdL/aRR1UfAx4Dpyppofc3ptzqeUOhWmxX8cRLJ9wnlwzvjKULElXA5+H29SvcEtNOvJ7GmzfweoQmn5eg31OWOYLLVUqvpKdV9e75js1xnh8nKXwlbzFcRERWq+opEVkNREsN3JhqqscNhWpx/cFoLMme4SivvTNW0LQOIOT34Pd6+PK/eS+tC6yuqVW5YaGg30vQ5yXgq631FMXMMQSBZmCliHRwYSipHbhinnMF+CJwUFU/n/fSbuA+4LPux2+VHrox1VMvGwrl5g/Gamj9QTKd5QdHz/Lk4GlePHaO/Hzg9wrtQT9tQR+ZrLKipakhkoKIOHMDAS8tgcrOD5RDMX/jDwCfwEkCB7iQGMaBv5rn3NuBjwOvisgr7rFP4ySEr4vIbwLHgV8sKWpjqqzWNxTKzR9MJGqj3bWqMvjOOANDEZ45NMLkdF6JaZOXLauX8cbIJCG/h1DASyKVJZ1V7r21p4pRXx6/1/lvaQ44k8X11HKjmDmGR4BHROS3VfUvLvY+EdmpqgOzzv0uhZPV+S45BGVMLavVDYViyTTj8XTNDBedHksw4JaYvn2+sMR02zWd9PWGed86p8Q0V5V0ejzOKrcqadu6zipGXxqfx0Mw4AwPhfxe/DX+VHApRbfEmPdCIi+r6s1ludgcrCWGqTW5qqRa2FBocjrN+ViyJoaLpqbT7DvsdDH98cmxgteu7W5lZ2+Yuzd109kSqFKE5eH1OMNDwUBtzhPkVKQqqZT7l/FaxtS8am8olOtfNBa7dLvrudYHlPs38UxWOfDWKP1DEb77+pmCBLWiJcA9m7vZ2RtmXVf9lph6PU6bCeePhyZf4y6kK2diqI26N2MaXDarjBe5XWb++oD2oI+zU9M8sucID7KhLMnh6Mgk/UMRnj4Y5ezUhS6mTT4P77/W6WJ685qOuiwxnZkw9nsJBho7EcxW/9P9xiwRqUyWcbd/UbH7H8xs8ei2icjNhzzx0okFJ4ZzU0meHo4yMBjh9ZHCLqY3XrWMvi2r2L5hJS11Vk2U6zcU9DnVQxdbS1DvCxuLUc7/c2+W8VrGGFeuf1F+JU+x5triMej3cHo8fpEz5pZMZ/n+G05ritklpld1hNi52eliumpZsOQYq2UhjefqeWFjKUpZ4LYf+FvgH+bqhKqqP1fOwIxZylSVKXdB2uX0L8pt8RjKayyXSGVZ1T5/G2pV5bW3x+kfirD3cJSp6QtxtDb5+MCmLvp6w/Subq+LUsxcK+rLWV1cjwsbF6KUJ4Z7cXZxeykvSfRrraypN6YBlHv9wcwWj6kMQb+nqPUB75yPz5SYnhpLzBz3eoRtazvZtSXMbetW1GwVTr4mv5dm/6WHhkpRLwsbL1fRiUFVXwd+X0T+b+BDwJeArIh8CXhEVc9VKEZjGt50OsNYbGHDRZeS2+JxvvUBk9Np9h4aYWDoNK++PV7w2obuVnZtCfOBTd10NNd2iWmuhDTktqMu96R3rS9sLJeS5hhE5Cdwnhp+Grf/Ec4GPnuAm8odnDGNLp7McD6eJJ6sXLvrbes655xozmSVl948x4BbYprfxXRFa2Bm3uCalS0Vi60cAj4PzQEfzQFnrqCSanVhY7mVMsdwADiP0/voU6o67b70gojcXoHYjGlI2eyF/ZMvtf6gXGavY9h+3UreGYvz9MEoo7HUzPuCPg/v37CSvt4w76nhEtNcG+qQu7DMs4hx7tjUzcNQMwsbK6Wolc8i4sFJBv+18iHNzVY+m3qXSGUYT6SITWeKLje9XLl1DAIk3e06858MAG7qWUZf7yq2X7eyYIikVvg8ThIIuT2HajVh1aqKrXxW1ayIfBCoWmIwph4l01mmptNMTqcX5ekg33QqwxeefYPRqSSJWa0ymnwefuW9a7inN8yq9toqMc2vHnImjZfOwrJyyq238HetvaHUc0v59WBARP4j8DVgKnfQJp2NKZTJKpOJNBPTi9/qOqvKq2+PMTAY4dnDI0zNmrvweqAj5MfrEX71tqsXNbaLKXZhmSle/noLNFtyRUMpieE33I+/lXdMgcaadTFmgabTGcbjztPBYldxvz3qlJgOHCwsMc3xitPRFITRWIqrO6tbRVPJjezNu9dblKqUctUFb+1pTKPKLUSbSKQqWlk0l4lEir2HRugfijD4TmGJ6cZVbfT1htn9yjucHI3h8QgioIq7c/vi/iAO+DwFW1fWc0vqejDXeotSlFKV1Az8LrBGVe8XkQ3ARlX99oLvbkydqtZGOOlMlpfedLqYfv+NwhLTrtYm7t7cTd+WMGtXOCWmX99/gnB7E6NuB1a/10NHa6Di+zXkNqnJtZuwCePFNdd6i1KUctbf4uzg9pPu1yeBbwCWGMySUY2NcFSV16OTPDkUYc/BKOfjeSWmfg93bOhiV2+YG3uWv+sHcK4lRk/eAqx4KkN3S1NZY/SIzFQONftrf+vKRpe/3mIhSkkM61X1oyLyMQBVjYsNDJolIOU+HUwu8tPBmclpnjoYZWAowrEzM/UeCHDTmuXs6g1zx4YuQoGLV+0spCVGsWyeoHblr7dAPCU/NpRyQlJEQrj7LojIemD60qcYU5+qNXeQSGX43utneHIwwsvHRwu6mK7pbKavN8w9m7vpLrLEtNiWGMXwiDiri+2poC7kNpKSB958tdRzS0kMnwG+A/SIyFeA24FfK/WGxtQqVSWRyjKVTDM1nSaTXZzKoqwqr54c40m3xDSe1021Pejjrk3OvMHGcNuCfiu/WEuMYvi9HlqanHYTVka6dJRSldTvtsW4Dedp9kFVPVOxyIxZBNmsMplME5vOEE9lFrXM9MS5GAMHIwwMRYiMX3j49nmE29atYNeWMNuu6VzUCh4RocnnoSXgo7nJqoeWqlKqknYDXwV2q+rUfO83ppbFkk5FUSy5uMlgPJ5i7+ER+gcjDJ0qLDHdvLqNnZudLqbLLqPUsFRej8x0I232L27vIVObShlK+m/AR4HPisiLOCugv62q715NY0wNyq1IHk8sTvO6nHQmywvHnC6mPzh6tqDEtLutiZ29ThfTNRVadDa7id69t/Zw+4aVNAe8tDT5Kt6R1NSfUoaSngWeFREvcBfwb3H2ZGivUGzGlEU8mWFiOsXU9OI9HagqR6KT9A9GeHo4ylheiWnI72X7dU4X0xt7luOp4Lh9romezyMsC/o5H0/yV3tfZ9WyYMN1BDXlU+p+DCHgZ3CeHG4GHq9EUMYslKqSzGRJprNMp7PEk5lFfToYmZjmqYPO7mdvnb2wq5cAt1zdQd+WMLdfu7Jgq81KERG+fuAETT4PrU0+Z/7A723IrShNeZUyx/A14L04lUl/BexV1cXtEGbMRUynMzNrDRarpXVOPJXhuSNnGBg8zcvHz5N/96tXNLOrN8zdm8N0tZV3UdlcRJwdzJqbvLQEfEQnplke8hdUEzXiVpSmvEpd+fzLqrq4DWGMuYhcRdFkIk0itbjflllVfnTiPP1DEfYdPlNQYros5OfuTd3s7A1zXbi14iWeufYTzXNsXLNUtqI05TVvYhCRu1R1D9AMfGT2N7mqfrNCsRnzLqpKLJlhajrN1CJXFAEcP3uhxDQ6caHE1O8V3rd+BTs3h3nvNZ0VX/zl9zrDQy1NPgK+i99rqWxFacqrmCeGO3H2dP6ZOV5TwBKDqahMVoklndLSxS4vBRiLp3hmOEr/UITh0xMFr/WubqdvS5gd13XRXsES09wQUe7JoNj1BUtlK0pTXkVt7VkLbGvPpSWdyTKVzBBLphe9nTU4/ZFeOHqO/qEIzx89SzpvFXS43Skx7esNV3RIJn+IKOT32qpjsyAV2dpTRH73Uq+r6udLuaExc8m1o8g9GSz2Npi5GIZPT9A/FOGZ4SjjiQudKZsDXu68rou+3jA3XLWsYiWmQb93pjHdpYaIjKmkYoaS2ioehVmSMlllyn0iiCczi15NlBMdT/DUQWeo6Pi5C9U6HnFLTHudEtNKLAQTtzFdKOBUEdm+BaYWzJsYVPUPFyMQszRk3WQwVYXeRPniyQzPHXF2P/vhrBLTa1a2sNPtYrqytfwlpiJCS8BLc5OPloANEZnaU8o6huuALwBhVb1eRH4C+LCq/peKRWcaRiKVYTyRIjZdvSeDTDavxPTICInUheGq5SG/s/tZb5hru8tfYpprWW3JwNSDUtYx/DXwn4BHAVT1xyLyD4AlBvMuqko85QwRVWvOIOets1P0D0V4aijKyGRhient61fStyXM1qs7yl5imksGubbVlgxMvSglMTSr6ouzvrkXb39DU/NSmSwxd76gmsNEAGOxFHsORekfjHAoUlhiev0VTonpndd10RZceInpXM3pblu/wpKBqXulJIYz7q5tuR3cfgE4VZGoTN1IpJzFZtV+KgBIprM8f+wsA4MRnj92rmCjndXLgtyzuZu+3lVc2RG67HvlN6drD/oZjU/zl3tfJ9zexAc2hy/7+sZUUymJ4beAx4BNIvI2cAz4lUudICJfAj4ERFX1evfYZ3A6s464b/u0qv5LiXGbKsktNssNEy3WLmcXkysxfXIwwjOHokzklZi2BLzcubGLnb1hbriyvCWmX9t/goDPQ2vAh4hTZhpLpnnsuWOWGEzdK3Udw78AzwAeYAr4eeBS6xj+DvhL4O9nHf8zVf1cSZGaqsifK4inMiTTtdE38fR4gqeGnC6mJ0fjM8c9AlvXdjolputX0FTGElOPCC1NPlqbfIxMWnM607hKWcewEbgV+BZOF+GPA/sudaKq7hORtZcToCmvvcNRHt13lBOjMXou0h4h6barjqXSJFLZqs4V5Isl0zx7+AwDQ6d55cRYwWvrulpmuph2tgTKet+g30tb0DfTuhqsOZ1pbEWvYxCRfuBmVZ1wv/4M8I0F3vffi8j/AewHPqmqowu8jinB3uEoD+0exO8Vlof8RCcSPLR7kD/IKtvWdRJPZUgks6SztfFUAM7Q1cvHRxkYivDckTNM5z2xdDTnSkxXcW13a1nvG/BdaFI3V18ia05nGlkpcwxrgGTe10lg7QLu+QXgj3Amsf8IZ8vQ35jrjSJyP3A/wJo1axZwK5Pv0X1H8XudZmwKBLweUpk0f7HndT7fdWO1wytw7MwUA0MRBg5GODt54dvO7xXef22uxLSzrCuFAz4PLYH5O5aCNaczja2UxPA/gRdF5J9xfqj/LAvYwU1VI7nPReSvgW9f4r2P4Ux4s3Xr1toYz6hTyXSWt85O0Rb0kcxkyS31bfJ5OD0ev/TJi2Q0lmTPsFNieiQ6WfDaDVcuY2ev08W0NVjSxoOX1OT30hrw0dxUfMfSnB2bui0RmIZUyp7Pfywi/xu4wz3066r6w1JvKCKrVTVX5vqzwGulXsPML796KDc81N0W5OzUdMG2kolUllXtl1++uVDJdJYfHD1L/2CEF98sLDG9YnmQnZvD7OwNc8Xy8sUY8Hloa/LT0uSt+L4JxtSjkn71UtWXgZeLfb+IfBXYAawUkZPAHwA7ROQmnN9Z3wQeKCUGM7diqofuvbWHR/YcIZ7KEPR7SKSypLPKvbf2LHqsQ6fG3S6mI0xO55WYNnn5wEanNcWWK9rLtkDM6xFam3y0Bn00+Sq/37Ix9ax8z+RzUNWPzXH4i5W851KSSGVIpJxEUEz10LZ1nTzIBp546QSnx+OsclfrblvXuSjxnh5LzOx+NrvE9Na1nezaEuZ968pXYppfXhoKWDIwplgVTQymvNKZbMFTwUIWl21b17loiQBgajrNvsNOF9MfnSwsMb22u5W+3jB3beoua4npXOWlxpjiWWKoYbnNa+IpZyezWllcNp9MVjnw1ij9QxG+93phiWlnS4C7N3Wza0uYdV3lKzEtpaLIGHNplhhqTCr/qaCKm9csxNGRSaeL6cEo56YulJgGfB5uX7+CXVtWccvVHWUrMfV7PTNDRZYMjCkfSwxVlspkZ+YJplPZqjeiK9W5qSRPD0cZGIzw+khhiemNVzklpnde10VLU3m+1XLzBm1BX0V2VDPGWGJYdJmszgwN1doq42Il01m+/8YZ+ocivHjsHPlTHVcuD9G3JczOzWFWLQuW5X65Hc9qrZV1Me1FjKlHlhgqLJtVEunaa0JXKlVl8B23xPRQlKnpzMxrrU0+PrCpi77eML2ry1diGvR7aQ36aA348NTYXsgXay/yMFhyMHXPEkMF5MpIY8kM0+naaUIHc28uc6kqpXfOx2daU7xzPjFz3OsRtq3tpM8tMS3XGL+Is95gWchf0/MGufYiuSZ6zQEfsWSaR/cdtcRg6p4lhjJIZ7LEUhkSl1FGuhgKN5fxcXZqmkf2HOFBNhQkh8npNM8eckpMX327sMR0Q3cru7aE+cCmbjqay1di6vd6aAv6aAv6y9r/qFJOjMZYHirc/c3abptGYYlhATJZnZkwjtfAzmXFeuKlE/g8MtMSI9cV9ImXTnDL2g5eevMcA0MRvvfG2YIhrxWtgZnWFNesbClbPLmng3qcSLa226aRWWIoQq5yKJFyPtZLIpjt1Hic9lkN6ATlcHSCX3r0B4zGUjPHm3we7tiwkp29YW5eU74SUwCfx8OykJ/WoK8ung7mYm23TSOzxDCHZDpLIu0MDTn9hOozEcy2uj3E2alp/B5hYjrNWOLCorlY0plMvqlnOX29YbZft7Lgt+HLJSI0B7wzexzUO2u7bRpZ/f8LLYP8tQT1WkI6n+lUhs2r2/jmD8cLViIDrGwN8OEbr+Ce3jCr2stTYprj93poD9b308HFWNtt06iWZGJIZ7Ik3O0r63loaD5ZVV57e4z+oQjPHhphKnmhxFRw2lN8dGsPP3/LlWVdG2CL0Iypb0siMSTTWabT9bu6uFRvj14oMT01Vlhiets1nezcEua2a8pXYpoT8HloD/lrct2BMaZ4DZcYslll2k0EiZTzsVbLR8tpIpHi2cMjPDkYYfCd8YLXNobb6NsS5q6N3Sxr9l/kCgvj9VxobW1PB8Y0hrpPDLnS0UQqQyKdZTqVmf+kBpHOZHnpTaeL6fffOEMqcyEBrmwNsLPXKTFdu6J8JaY5zQFnqKiWWlQYY8qj7hLDzGKyJTIsNJuq8nrU6WK6ZzhaUGIa9Hu4Y0MXu3rD3NizvOyTvbld0NpD/pL3RzbG1I+6SQyZrPL2+fiSeiLId2ZymqcPRukfinDszNTMcQFuWrOcXb1h7tjQVfadynIN7FqDPkJ+ezowZimom8SQzuqSSwqJVIbvve50MT3w1mhBF9OejhC7tqzins3ddJe5xBScRWjtofppUWGMKZ+6SQxLRVaVH58co38wwr4jIzMLzwDagz7u2tTNzt4wm1a1VeS394DPXZVs22Ias2RZYiiTUruWznZyNEb/UISBoQiR8emZ4z6PcNu6FezaEmbbNZ0VG9sPBbwsC/nLutrZGFOf7KdAGRTbtXS28XiKvYdH6B+MMHSqsMR08+o2dm52upguC5W3xDQnV2raFvTR5LNSU2OMwxJDGVyqa+nsxJDOZHnhmNPF9AdHzxaUmHa3Nc2UmK7prFyXzlDAS1vQT4uVmhpj5mCJoQzm6loa9Hs4PR4HnBLTI9FJ+gcjPD0cZSxeWGJ653XO7mc39izHU6Ef1Ln9DlqbfPis1NQYcwmWGMog17U0lLfyN5HK0tnSxBMvHufJoQhvnb2wgYsAN1/dQV9vmPdvWFlwXjnlOpq2B/1lL2M1xjQuSwxlcO+tPfzJk8NExhOkM4qI80P55Gi8YO7g6s7mmaGirramisXj9QhtQT/tQXs6MMaUzhJDGWRVSWWypLNKFkABd5/nZSE/d23qZteWMBu6Wys6pu/3eljW7KfNSk2NMZfBEsNlOH42xsDBCF/ff6JgEllw5g5WtYd49OM3V/y39qDfy/JmKzU1xpSH/SQp0Vg8xTPDTmuK4dMTBa8F3bbTbU0+PB6YSKQrmhSaAz6WN/utq6kxpqwsMRQhlcnywtFz9A9FeP7oWdJ5vSnC7U2gIALtwQvrDeKpDKvaQ2WPJde7aFmz39YeGGMqwhLDRagqhyIT9A86XUzHE+mZ10J+r1NiuiXMT1y1jP3HRnlkzxHiqQxBv8fdJ1q599aessUT9DuN7GwTHGNMpVlimCU6nuApt4vp8XMXSkw9Ardc3cHO3jDvv3ZlwfDNtnWdPMgGnnjpBKfH46xaQEuMuYgILU1Oqwp7OjDGLBZLDEA8meG5IyP0D0X44fHz5O/3ds3KFvp6w9y9uZuVrRcvMd22rvOyE0GOiNAW9LGsAvse7B2O8ui+o5wYjdHT0cwD29fZhvbGmAJLNjFkssqPTpynf8jpYppIXdjwZ3nIz92bu+nrDXNthUtM8+USwvKQvyKT1nuHozy0exC/V1ge8hOdSPDQ7kEeBksOxpgZSy4xvHV2iv6hCE8NRRmZvNDF1O8VfnL9Svp6w9y6tmNRF4aJODujdTRXJiHkPLrvKH6vzJS1Ngd8xJJpHt131BKDMWbGkkgMY7EUTw9HGRiKcChSWGJ6/RXt7OwNs2NjF23BynQxvZTWJh8dLYFF2SrzxGiM5bM6tYb8Xk6Oxi5yhjFmKWrYxJDKZHn+6Dn6B0/z/LFzZPJKTFcvC7Jzs9Oa4sqO8peUFqM54KOjZXEnlXs6molOJAoWwsVTGa7qqFwnV2NM/WmoxKCqDJ92SkyfOVRYYtoSuFBiev2VyyrWxXQ+TX4vnc2BqjS1e2D7Oh7aPUgsmZ5pDZ7KKA9sX7fosRhjaldFE4OIfAn4EBBV1evdY53A14C1wJvAL6nq6OXc5/R4gqeGIvQPRTg5Gp857hHYuraTvt4wt69fQVMVVwi3NDlVRtVcpbxjUzcP48w1nByNcZVVJRlj5iCqOv+7Fnpxke3AJPD3eYnhT4FzqvpZEfkU0KGqvzfftW646Wb91sC+ma9jyTT7Dp+hfyjCKyfOF7x33coW+raEuXtTNysuUWJaablJ5eXN5S87NcaYYojIAVXdWso5FX1iUNV9IrJ21uGPADvczx8H9gLzJgZwSkx/eHyU/qEI3z1yhkT6QolpR7Ofe9x5g2u7Wy879sthCcEYU8+qMccQVtVTAKp6SkSKGsc4MzHNvX/9PGcnkzPHAj4Pt69fwc7eMLeu7cRbA60iWoM+OpoXp8rIGGMqoaYnn0XkfuB+gMCqa2lyk8INV7bT17uKOzd20dpUG/8JzhNCgIDPEoIxpr5V46dqRERWu08Lq4Hoxd6oqo8BjwG0XrVR73vf1ezsDXPF8uqUmM7mEaHVbV3xvSNnrNWEMaYhVOPX293Afe7n9wHfKuaka1a2cN9Prq2JpOARoaM5QE9nMytbm/jekTM8tHuQ6ESioNXE3uGL5jxjjKlZFU0MIvJV4AfARhE5KSK/CXwW2CkiR4Cd7td1IZcQ1nQ209ESmJnTyG81IeJ89HuFR/cdrXLExhhTukpXJX3sIi/dXcn7lptHhGUhP8tC/jn3QrBWE8aYRlIbM7c1yusR2oMXTwg51mrCGNNIrIRmDl6P0NkSoKfDGTKab8e0B7avI5VRYsk0qs5HazVhjKlX9sSQx+txhozag5d+QpjNWk0YYxqJJQbA7/XQHvLTHvQteFOeHZu6LREYYxrCkk4MoYCX9qCflhpZJGeMMbVgyf1E9IjQ0uSjPeRb1L0QjDGmXiyZxOD3emgP+mkL+kqaPzDGmKWm4RNDk9/L8pANFxljTLEa9qdlLWyMY4wx9aihEkNuH4RlIb91OTXGmAWqm5+eR0cm+d2v/YgXj55712siQlvQT09HiK62JksKxhhzGermJ6jXI5ydmuaRPUdmksPshOCzzXGMMeay1dVP0pDfi88jPPHSCVqDPq6yhGCMMWVXd3MMzQEvZyYTdLcFqx2KMcY0pLr5VVsQ/D4P6azS09lS7XCMMaZh1U9iEEikMta11BhjKqxuhpIyWaW7LWhdS40xpsLqJjFsXNXGV++/rdphGGNMw6uboSRjjDGLwxKDMcaYApYYjDHGFLDEYIwxpoAlBmOMMQUsMRhjjClgicEYY0wBUdVqx1AUEZkADlU7jiKsBM5UO4gi1EOc9RAjWJzlZnGW10ZVbSvlhLpZ4AYcUtWt1Q5iPiKy3+Isj3qIESzOcrM4y0tE9pd6jg0lGWOMKWCJwRhjTIF6SgyPVTuAIlmc5VMPMYLFWW4WZ3mVHGfdTD4bY4xZHPX0xGCMMWYR1HRiEJEeEXlGRA6KyKCIPFjtmOYiIkEReVFEfuTG+YfVjulSRMQrIj8UkW9XO5aLEZE3ReRVEXllIVUVi0VElovIP4rIsPt9+r5qxzSbiGx0/x5zf8ZF5BPVjms2EfkP7r+f10TkqyJSk/v3isiDboyDtfT3KCJfEpGoiLyWd6xTRAZE5Ij7saOYa9V0YgDSwCdVdTNwG/BbItJb5ZjmMg3cpao3AjcBHxSRWt484kHgYLWDKMIHVPWmGi8JfAT4jqpuAm6kBv9eVfWQ+/d4E3ALEAP+ubpRFRKRK4HfAbaq6vWAF7i3ulG9m4hcD/xbYBvO/+8PiciG6kY14++AD8469ingaVXdADztfj2vmk4MqnpKVV92P5/A+Ud3ZXWjejd1TLpf+t0/NTl5IyJXAf8K+Jtqx1LvRKQd2A58EUBVk6p6vqpBze9u4A1VfavagczBB4RExAc0A+9UOZ65bAaeV9WYqqaBZ4GfrXJMAKjqPuDcrMMfAR53P38c+NfFXKumE0M+EVkLvAd4ocqhzMkdnnkFiAIDqlqTcQL/HfjPQLbKccxHgX4ROSAi91c7mItYB4wAf+sOzf2NiLRUO6h53At8tdpBzKaqbwOfA44Dp4AxVe2vblRzeg3YLiIrRKQZ+Gmgp8oxXUpYVU+B84s2UNS+yHWRGESkFfgn4BOqOl7teOaiqhn3Uf0qYJv7yFlTRORDQFRVD1Q7liLcrqo3Az+FM4S4vdoBzcEH3Ax8QVXfA0xR5KN6NYhIAPgw8I1qxzKbO/b9EeAa4AqgRUR+tbpRvZuqHgT+BBgAvgP8CGfIu6HUfGIQET9OUviKqn6z2vHMxx1K2Mu7x/pqwe3Ah0XkTeAJ4C4R+XJ1Q5qbqr7jfozijIdvq25EczoJnMx7OvxHnERRq34KeFlVI9UOZA73AMdUdURVU8A3gZ+sckxzUtUvqurNqrodZ+jmSLVjuoSIiKwGcD9GizmpphODiAjO+O1BVf18teO5GBHpEpHl7uchnG/y4aoGNQdV/T9V9SpVXYszpLBHVWvutzIRaRGRttznQB/OI3xNUdXTwAkR2egeuhsYqmJI8/kYNTiM5DoO3CYize6/+7upwYl8ABHpdj+uAX6O2v07BdgN3Od+fh/wrWJOqvUmercDHwdedcfvAT6tqv9SvZDmtBp4XES8OMn266pas6WgdSAM/LPz8wEf8A+q+p3qhnRRvw18xR2mOQr8epXjmZM7Hr4TeKDascxFVV8QkX8EXsYZmvkhtbuy+J9EZAWQAn5LVUerHRCAiHwV2AGsFJGTwB8AnwW+LiK/iZN8f7Goa9nKZ2OMMflqeijJGGPM4rPEYIwxpoAlBmOMMQUsMRhjjClgicEYY0wBSwzGGGMKWGIwBhCRXxORK/K+/ptydvIVkbUi8st5X28VkT8v1/WNKSdbx2CWPHdh4tPAf1TViuz9ICI73Ot/qBLXN6ac7InBNAy3lcb/cjdMek1EPioid7udT191NzJpct/7pog8JCLfxWkVsRVnBfMrIhISkb0istV976SI/LF73edFJOweX+9+/ZKIPCwikxcNzlmBeod7/f8gIjvE3ShJRD4jIo+LSL8b18+JyJ+6MX/H7ReGiNwiIs+6HWefzPXAMabcLDGYRvJB4B1VvdHd7OU7OJuXfFRVb8Bpr/Hv8t6fUNX3q+qXgf3Ar7gb2sRnXbcFpwf/jcA+nI1awNmk5xFVvZX59w74FPCce/0/m+P19Tj7ZHwE+DLwjBtzHPhXbnL4C+AXVPUW4EvAH8/3F2LMQlhiMI3kVeAeEfkTEbkDWIvTsfOw+/rjOBvr5HytyOsmgVzvqwPudQHex4UW1v+wwJhz/rfbVfRVnN3Lcr2hXnXvtxG4Hhhw+4b9Xzgt3o0pu1pvomdM0VT1sIjcgrN5yv8DzLfRy1SRl07phcm4DJX5dzMNoKpZEcm/X9a9nwCDqlpze0qbxmNPDKZhuFVFMXdo6HM4/fzXisi17ls+jrMV41wmgLYSb/k88PPu5/PtT7yQ6+c7BHSJyPvA2adERLZcxvWMuSh7YjCN5Abg/xWRLE5L5H8HLAO+4e4j/BLwPy5y7t8B/0NE4jhDRMX4BPBlEfkk8L+AsUu898dAWkR+5N7rh0XeA3D2kxaRXwD+XESW4fzb/e/AYCnXMaYYVq5qzAK5exzEVVVF5F7gY6r6kWrHZczlsicGYxbuFuAv3R3HzgO/Ud1wjCkPe2IwpoxE5Abgf846PK2q761GPMYshCUGY4wxBawqyRhjTAFLDMYYYwpYYjDGGFPAEoMxxpgClhiMMcYU+P8Bmv+ov3m4HzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=dataset['sorting_time'],y=dataset['delivery_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a795346",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=smf.ols(\"delivery_time~sorting_time\",data=dataset).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee61f9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept       6.582734\n",
       "sorting_time    1.649020\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d5c6e95b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>delivery_time</td>  <th>  R-squared:         </th> <td>   0.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 26 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:05:30</td>     <th>  Log-Likelihood:    </th> <td> -51.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   106.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   108.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>    6.5827</td> <td>    1.722</td> <td>    3.823</td> <td> 0.001</td> <td>    2.979</td> <td>   10.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sorting_time</th> <td>    1.6490</td> <td>    0.258</td> <td>    6.387</td> <td> 0.000</td> <td>    1.109</td> <td>    2.189</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.649</td> <th>  Durbin-Watson:     </th> <td>   1.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.161</td> <th>  Jarque-Bera (JB):  </th> <td>   2.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.750</td> <th>  Prob(JB):          </th> <td>   0.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.367</td> <th>  Cond. No.          </th> <td>    18.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          delivery_time   R-squared:                       0.682\n",
       "Model:                            OLS   Adj. R-squared:                  0.666\n",
       "Method:                 Least Squares   F-statistic:                     40.80\n",
       "Date:                Sun, 26 Dec 2021   Prob (F-statistic):           3.98e-06\n",
       "Time:                        00:05:30   Log-Likelihood:                -51.357\n",
       "No. Observations:                  21   AIC:                             106.7\n",
       "Df Residuals:                      19   BIC:                             108.8\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept        6.5827      1.722      3.823      0.001       2.979      10.186\n",
       "sorting_time     1.6490      0.258      6.387      0.000       1.109       2.189\n",
       "==============================================================================\n",
       "Omnibus:                        3.649   Durbin-Watson:                   1.248\n",
       "Prob(Omnibus):                  0.161   Jarque-Bera (JB):                2.086\n",
       "Skew:                           0.750   Prob(JB):                        0.352\n",
       "Kurtosis:                       3.367   Cond. No.                         18.3\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c4efc185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.827834"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manual prediction for say sorting time 5\n",
    "delivery_time = (6.582734) + (1.649020)*(5)\n",
    "delivery_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6746f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatic Prediction for say sorting time 5, 8\n",
    "new_data=pd.Series([5,8])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4643255a",
   "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>sorting_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sorting_time\n",
       "0             5\n",
       "1             8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred=pd.DataFrame(new_data,columns=['sorting_time'])\n",
    "data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f379d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14.827833\n",
       "1    19.774893\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dc33678",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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