yogeshchappewar/SimpleLinearRegression(delivery_time).ipynb

## SimpleLinearRegression(delivery_time).ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:02.667491Z",
          "start_time": "2023-01-20T04:35:00.203401Z"
        },
        "id": "44VnUqHciDS4",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy import stats\nimport statsmodels.formula.api as smf",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:02.711040Z",
          "start_time": "2023-01-20T04:35:02.670461Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "REFfQXu2iPjp",
        "outputId": "cefe4a53-1227-46f4-e829-85ea67c09629",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt=pd.read_csv('delivery_time.csv')\ndt",
      "execution_count": 2,
      "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\n0           21.00            10\n1           13.50             4\n2           19.75             6\n3           24.00             9\n4           29.00            10\n5           15.35             6\n6           19.00             7\n7            9.50             3\n8           17.90            10\n9           18.75             9\n10          19.83             8\n11          10.75             4\n12          16.68             7\n13          11.50             3\n14          12.03             3\n15          14.88             4\n16          13.75             6\n17          18.11             7\n18           8.00             2\n19          17.83             7\n20          21.50             5"
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:02.728576Z",
          "start_time": "2023-01-20T04:35:02.713877Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Lan0pH2Bid-H",
        "outputId": "4588befd-b8c5-4b61-a909-89778e4ce0d3",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt.describe()",
      "execution_count": 3,
      "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>count</th>\n      <td>21.000000</td>\n      <td>21.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>16.790952</td>\n      <td>6.190476</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>5.074901</td>\n      <td>2.542028</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>8.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>13.500000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>17.830000</td>\n      <td>6.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>19.750000</td>\n      <td>8.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>29.000000</td>\n      <td>10.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Delivery Time  Sorting Time\ncount      21.000000     21.000000\nmean       16.790952      6.190476\nstd         5.074901      2.542028\nmin         8.000000      2.000000\n25%        13.500000      4.000000\n50%        17.830000      6.000000\n75%        19.750000      8.000000\nmax        29.000000     10.000000"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "W9FlEV5dwfZv"
      },
      "cell_type": "markdown",
      "source": "###Correlation"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:02.744849Z",
          "start_time": "2023-01-20T04:35:02.729086Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AiBYAUfPjQNC",
        "outputId": "a8d37b06-9846-4203-9436-aeddf591bf84",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt.corr()",
      "execution_count": 4,
      "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\nDelivery Time       1.000000      0.825997\nSorting Time        0.825997      1.000000"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:03.081719Z",
          "start_time": "2023-01-20T04:35:02.747161Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "8n3fgNSXnTnL",
        "outputId": "5e2e7831-a64f-454e-f612-be409461d75f",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt.hist()",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[<AxesSubplot:title={'center':'Delivery Time'}>,\n        <AxesSubplot:title={'center':'Sorting Time'}>]], dtype=object)"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:03.107293Z",
          "start_time": "2023-01-20T04:35:03.082860Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "N8QwmB73nbUh",
        "outputId": "ee90f626-ec03-4650-86c7-a9a3a4c86da6",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt=dt.rename({'Delivery Time':'delivery_time','Sorting Time':'sorting_time'},axis=1)\ndt",
      "execution_count": 6,
      "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\n0           21.00            10\n1           13.50             4\n2           19.75             6\n3           24.00             9\n4           29.00            10\n5           15.35             6\n6           19.00             7\n7            9.50             3\n8           17.90            10\n9           18.75             9\n10          19.83             8\n11          10.75             4\n12          16.68             7\n13          11.50             3\n14          12.03             3\n15          14.88             4\n16          13.75             6\n17          18.11             7\n18           8.00             2\n19          17.83             7\n20          21.50             5"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:03.237882Z",
          "start_time": "2023-01-20T04:35:03.109553Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Au88V_JeiqGi",
        "outputId": "2e817f1d-fcf5-4258-c28a-b6a8ec23a97f",
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = dt.delivery_time\ny = dt.sorting_time\nplt.scatter(x,y)\nplt.xlabel=(\"delivery_time\")\nplt.ylabel=(\"sorting_time\")",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:03.376920Z",
          "start_time": "2023-01-20T04:35:03.238437Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "mXVS6030pK16",
        "outputId": "322b4779-9c4b-4ebe-e573-2ff22afb5142",
        "trusted": true
      },
      "cell_type": "code",
      "source": "dt.boxplot()",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.023102Z",
          "start_time": "2023-01-20T04:35:03.379029Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "42A2HX_5pLFF",
        "outputId": "c6609c27-9513-4891-f1d1-b7ca0021576e",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.pairplot(dt)",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x14e84cba5e0>"
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 500x500 with 6 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.174199Z",
          "start_time": "2023-01-20T04:35:04.026555Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "TFBwZE7Ej3hC",
        "outputId": "80055caa-1a47-4ae0-e68b-5a5f77f79777",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.distplot(dt['delivery_time'])",
      "execution_count": 10,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\yogesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.338482Z",
          "start_time": "2023-01-20T04:35:04.175250Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "SpkgoTMPlNFF",
        "outputId": "5d5b35c9-0150-4f09-953b-ef69e59bc84f",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.distplot(dt['sorting_time'])",
      "execution_count": 11,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\yogesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: 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": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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auKeVXAYMDPHATb184e+uFpzQenX3dcXb0wfj0Q8O4ZMjl9AnwB2PjY4UHYvIarEAEdENMxglpOaUYe+5QhRW1gEAHOQyxEV4Ib6nDzytdLNSS3NzL188PzEKf/8mDUu3pyEq0B0junuLjkVklViAiKjD6g1GHMkswb7zRSirqQcAqB3lGB7pjZE9fOCq4o8Yc5s5OhKn8nT47Fgu/rz5KL7+czwCNLyzRtRe/OlERO1WbzDicGYJ9p4rhK62AQDgqnLAqB4+GBbpBbUjJ+h2FplMhmVT+yNNq8OZ/Ao8+d8UfPx/I6B0sM9FI4k6igWIiNqswWDEod8VH42TI27u5YvYcE+7Xbm5qzkpFVj7YCwmvb0fR7PLsPSb03h5crToWERWhQWIiK5LkiSkaSuw/RctSqr0ABqLz5jevogN84QDi0+Xi/BxwappgzDz30fw7+QsDArzwNSYENGxiKwGCxARXdNlXS2+OaHFhcJKAICbygFj+/ghLpzFR7RbovzxzB964K0fL2DhtpPoE+COqEB30bGIrAILEBG1qq7BgKTTl/HzxWIYpcYF+Ub38MGYXr5QcY6PxXh2XC+kXirH3nOFmP1RCr58ejT3UiNqA/76RkQtXCysxFs7z+NAemP56RvojrnjemF8vwCWHwujkMvw5rRBCPZwQlZxNZ77JBXGpr1GiOiqWICIyMRglPDdL/l4b38GSqvr4eHkiEdGRuDB4eHwcuFaPpbK00WJtQ/GQukgxw9pBXhn1wXRkYgsHgsQEQFo3KV93d507D1fCAAYEuGJZ27piV7+boKTUVv0D9Hg71eeBFv5wzn8dKFIcCIiy8YCRETILKrCO7suIKe0BmpHOaYPDcPUmBCu52Nl7hsSimlxoZAk4NmPjyG/vFZ0JCKLxQJEZOdSskqxYX8GqvQGBGrU+PPYnogO1oiORR308uR+iAp0R1GlHn/efBT1BqPoSEQWiU+BEdkpSZKw91whvj99GQAQHazBPYNDuKKwIJsOZpvtvSZEB+BiYSUOZ5bisY2HMSE60GzvLdr0YWGiI5CN4E86IjskSRK+/SXfVH5u7uWLB4aEsvzYCB9XFe4e3Lgo4r7zRTidpxOciMjy8KcdkZ2RJAnfnNRi/5VJshP7B2J8vwDIZDLBycicooM1GHVlp/hPj+aYVvAmokYsQER2pOnOz4H0YgDA1JhgjO7hIzgVdZbbogMR5uWM2nojNh3K4nwgot9gASKyI7vPFZru/EwZFIwhEV6CE1FnUshluH9IKJyVCuSVNW5pQkSNWICI7MTRrFIkXZnzc8eAQAyNZPmxBx7OStwXFwoZgEOZJTiWXSo6EpFFYAEisgMXCiqx7dglAMBNPX0xsjuHvexJL383jO3jBwD4PDUXl3VcH4iIBYjIxpVU6bH5UDaMEjAwRIOEfv6iI5EAf+jjhx6+rqg3SNh0MBt1DQbRkYiEYgEismH6BiM++jkLNfUGhHo64e7BIZDzaS+7JJfJcN+QULirHVBYWYfPjuVCkrhpKtkvFiAiGyVJErYdu4R8XS1cVQ6YPiwcDgr+J2/PXFUOeGBoGOQy4MSlchzMKBEdiUgY/jQkslEpWaU4cakcchkwfWgYNE6OoiORBQj3dsH4fgEAgG9OanGptFpwIiIxWICIbFBBRS2+OpEHALi1bwAifFwEJyJLMrqHD/oGusNglLD5UDZq9JwPRPaHBYjIxjQYjdhyOAf1Bgk9fF0R35NPfFFzMpkMdw8OgZeLEqXV9fhfSg6MnA9EdkZ4AVq9ejUiIyOhVqsRGxuLffv2XfVcrVaL6dOno3fv3pDL5ZgzZ06r523duhV9+/aFSqVC37598dlnn3VSeiLLs+tMIbTltXBWKnBPHCc9U+uclApMHxoGB7kMZ/IrsO98kehIRF1KaAHasmUL5syZg0WLFuHYsWOIj4/HhAkTkJ3d+q7IdXV18PX1xaJFizBw4MBWz0lOTsa0adMwY8YMHD9+HDNmzMB9992HgwcPdualEFmEvLIa7DlXAACYPCgY7mrO+6GrC/JwwqQBQQCApNP5uFhUKTgRUdeRSQKfgxw2bBgGDx6MNWvWmI5FRUVhypQpWL58+TVfO2bMGAwaNAirVq1qdnzatGnQ6XT49ttvTcduu+02eHp6YvPmzW3KpdPpoNFoUF5eDnd397ZfENmdTQdbL+siNBiNWLM7HdryWkQHuWP6sHDRkcgKSJKET1Mu4VhOGdxUDnj6Dz3gZsHFefqwMNERyIK15+9vYXeA9Ho9UlJSkJCQ0Ox4QkICDhw40OH3TU5ObvGe48ePv+Z71tXVQafTNfsgsjb7zxeZhr7uHBQsOg5ZCZlMhsmDguHnpkJFXQO2HM6Bwcj5QGT7hBWgoqIiGAwG+Ps3X5XW398f+fn5HX7f/Pz8dr/n8uXLodFoTB+hoaEd/v5EIpRW6bHrbOPQ1+39A+GqchCciKyJ0kGO6cPCoHSQ42JRFXamXRYdiajTCZ8ELfvdBE1Jkloc6+z3XLhwIcrLy00fOTk5N/T9ibra1ye1qDdIiPRxwaBQD9FxyAr5uakxNabxzuHuc4U4k8874WTbhBUgHx8fKBSKFndmCgoKWtzBaY+AgIB2v6dKpYK7u3uzDyJrcUarQ5pWB7kMuHNg0A3/AkH2a2CIB4Z38wIA/O/IJZRW6wUnIuo8wgqQUqlEbGwskpKSmh1PSkrCyJEjO/y+I0aMaPGeO3bsuKH3JLJUDUYjvjmpBdC4uJ2/u1pwIrJ2E6MDEeLphJp6AzYfykaD0Sg6ElGnEDpRIDExETNmzEBcXBxGjBiBdevWITs7G7NnzwbQODSVm5uLDz/80PSa1NRUAEBlZSUKCwuRmpoKpVKJvn37AgCeffZZ3HTTTXj11VcxefJkfPHFF/jhhx+wf//+Lr8+os528GIJiqv0cFU5YGxvP9FxyAY4KOR4YGgY3v7xAi6V1uDbk/mYNDBIdCwisxNagKZNm4bi4mIsWbIEWq0W0dHR2L59O8LDGx/f1Wq1LdYEiomJMf1zSkoKNm3ahPDwcGRmZgIARo4ciY8//hiLFy/GCy+8gO7du2PLli0YNmxYl10XUVeo1jfgxzONE59v7esPlaNCcCKyFZ7OStwbF4IPk7OQfLEY4d7OGBDiIToWkVkJXQfIUnEdIGorkesAfX0iDwfSixHgrsbTf+jBFZ/J7L4/lY895wqhdJDj6TE94OOmEh2J6wDRNVnFOkBE1HGlVXocvFgCAJgQHcDyQ51iXJQ/In1coG8w4uPD2WgwcD4Q2Q4WICIrtPNMAQyShO6+Lujp7yY6DtkohVyG++JC4axUIK+8Ft+f6vgabUSWhgWIyMoUVNTiWHYpACChb4DgNGTrNE6OuCc2BADwU3oxzmi5PhDZBhYgIiuzM60AEoCoADeEejmLjkN2oE+AO0Z19wYAfHr0Espr6gUnIrpxLEBEVkRbXoOTueWQARjXt+MLhhK11/h+AQjSqFGtN+CTIzkw8vkZsnIsQERWZPfZQgBAdLAGgRonwWnInjgo5Lh/aON+YRlFVdh9Ze85ImvFAkRkJQoqavFLbjkAcNFDEsLHVYXJVxZF3JlWgIyiKsGJiDqOBYjISuw5WwgJQN9AdwRouOUFiRET5omYUA9IAD45koNqfYPoSEQdwgJEZAWKK+tw/FIZAN79IfHuHBgEbxclymvqse1oLrieLlkjFiAiK7DvQhGMEtDL3xXBnpz7Q2KpHBW4f2gYFHIZTmt1SMkqFR2JqN1YgIgsXGVdA45e+Qvmpl6+gtMQNQr2cMKtUY1PIn59UouSKr3gRETtwwJEZOF+vliMBqOEEE8nRHq7iI5DZDK6pw/CvZ2hbzDi05RLfDSerAoLEJEF0zcY8fPFYgBAfE9fyLjnF1kQuUyGe2NDoXSQI7O4Cj9dKBIdiajNWICILFhKdimq9QZ4uSjRL+jaOxsTieDlosTt/QMBADtOX0a+rlZwIqK2YQEislBGScKBK79Rj+ruzR3fyWLFhXuiT4AbDEYJ/zuSgwYjd40ny8cCRGShLhRUorhKD5WDHIPDPUXHIboqmUyGqTHBcFYqoC2vxa4zhaIjEV0XCxCRhWqa+zM43BMqB4XgNETX5qZ2xORBwQCAPecKkF/OoTCybCxARBaotEqPs/kVAIDhkd6C0xC1Tf9gDfoGusMoAduO8akwsmwsQEQW6GBGCSQAPXxd4eumEh2HqM3uHBgEtaMcl0prcCC9WHQcoqtiASKyMPUGI45klQAAhnfj3R+yLu5OjpgQ3fhUWNLpfC6QSBaLBYjIwpy8VI5qvQEeTo7oE+gmOg5Ru8WFeyLSxwX1BgmfH+NeYWSZWICILMzPGY3DBkMjvfjoO1klmUyGu2KC4SCX4UJhJY5ml4mORNQCCxCRBckpqcal0hoo5DLERXiJjkPUYd6uKoy7slfY9pNaVNY1CE5E1BwLEJEFaXr0fUCwBq4qB8FpiG7MqB4+CNSoUVNvwI5T+aLjEDXDAkRkIarqGnAytxwAJz+TbVDIZbhzYBAAICWrFDkl1YITEf2KBYjIQhzLKUODUUKwhxNCPJ1ExyEyi3BvF8SEekAC8NWJPK4NRBaDBYjIAkiShJQrj77HRXhy13eyKbdFB0Dl0Lg2UEpWqeg4RABYgIgsQm5ZDS7r6uAgl2FAsIfoOERm5aZ2NE2I/v5UPqr1nBBN4rEAEVmApt+K+wW5w0nJfb/I9gzv5g0/NxWq9QYknb4sOg4RCxCRaPUGI45fKgMAxIbz0XeyTQq5DJOuTIg+lFGCvLIawYnI3rEAEQl2Kk+H2nojPJwd0c3XRXQcok7T3dcV/YM1kAB8c1LLFaJJqA4VoIyMDHPnILJbR68Mfw0O8+TKz2TzJkQHwEEuQ0ZRFc5erhAdh+xYhwpQjx49MHbsWHz00Ueora01dyYiu1FapUd6YSUAIDbMU3Aaos7n4azEiO6N61x990s+H4snYTpUgI4fP46YmBg899xzCAgIwBNPPIFDhw6ZOxuRzTuaXQoJQHdfF3i6KEXHIeoSY3r5wclRgYKKOtMdUKKu1qECFB0djZUrVyI3NxcffPAB8vPzMXr0aPTr1w8rV65EYWGhuXMS2RyjJCElu/GHf2w47/6Q/XBSKjC2ty8A4Ie0y9A3GAUnInt0Q5OgHRwcMHXqVHzyySd49dVXkZ6ejnnz5iEkJAQPPfQQtFqtuXIS2ZyMoiqUVddD7ShHvyCN6DhEXWp4N294ODtCV9uAA+lFouOQHbqhAnTkyBE8+eSTCAwMxMqVKzFv3jykp6fjxx9/RG5uLiZPnmyunEQ2p2ntnwEhHnBU8IFMsi8OCjkS+jYujrjnXCGquFs8dbEO/dRduXIl+vfvj5EjRyIvLw8ffvghsrKy8Pe//x2RkZEYNWoU3n33XRw9etTceYlsQl2DAafyGjc+5eRnslcDQjwQpFGjrsGIXWcLRMchO9OhArRmzRpMnz4d2dnZ+Pzzz3HHHXdALm/+VmFhYdiwYcN132v16tWIjIyEWq1GbGws9u3bd83z9+zZg9jYWKjVanTr1g1r165tcc6qVavQu3dvODk5ITQ0FHPnzuXTamRRTufpUG+Q4O2i5ManZLfkMhluiw4EABy8WILSKr3gRGRPOlSAkpKSMH/+fAQEBDQ7LkkSsrOzAQBKpRIPP/zwNd9ny5YtmDNnDhYtWoRjx44hPj4eEyZMML3H72VkZGDixImIj4/HsWPH8Pzzz+OZZ57B1q1bTef897//xYIFC/DSSy8hLS0NGzZswJYtW7Bw4cKOXCpRp2ha+XlgqAc3PiW71sPPFT18XWGQJOw+x7tA1HU6VIC6d++OoqKWk9ZKSkoQGRnZ5vdZuXIlZs6ciVmzZiEqKgqrVq1CaGgo1qxZ0+r5a9euRVhYGFatWoWoqCjMmjULjz32GFasWGE6Jzk5GaNGjcL06dMRERGBhIQEPPDAAzhy5Ej7L5SoE1TWNeBCQePaP4NCPMSGIbIAt0T5AWicF8e7QNRVOlSArrZ8eWVlJdRqdZveQ6/XIyUlBQkJCc2OJyQk4MCBA62+Jjk5ucX548ePx5EjR1BfXw8AGD16NFJSUkzrEl28eBHbt2/H7bffftUsdXV10Ol0zT6IOsvJS2UwSkCwhxN83FSi4xAJF+7tgh5+rjBK4F0g6jIO7Tk5MTERACCTyfDiiy/C2dnZ9DWDwYCDBw9i0KBBbXqvoqIiGAwG+Pv7Nzvu7++P/Pz8Vl+Tn5/f6vkNDQ0oKipCYGAg7r//fhQWFmL06NGQJAkNDQ3405/+hAULFlw1y/Lly/Hyyy+3KTfRjUrNKQMADAr1EJqDyJLc0scPFwoqkZJVijG9/LgwKHW6dhWgY8eOAWi8A3Ty5Ekolb/+C6pUKjFw4EDMmzevXQF+P/9BkqRrzolo7fzfHt+9ezeWLl2K1atXY9iwYbhw4QKeffZZBAYG4oUXXmj1PRcuXGgqdwCg0+kQGhrarusgaoviyjrklNZABmBACNf+IWrSdBfoQkEldp8rwNSYENGRyMa1qwDt2rULAPDoo4/izTffhLu7e4e/sY+PDxQKRYu7PQUFBS3u8jQJCAho9XwHBwd4ezfuLfPCCy9gxowZmDVrFgCgf//+qKqqwv/93/9h0aJFLZ5WAwCVSgWVikMR1PmaJj9393OFm9pRbBgiC8O7QNSVOjQH6IMPPrih8gM03jGKjY1FUlJSs+NJSUkYOXJkq68ZMWJEi/N37NiBuLg4ODo2/mVSXV3douQoFApIknTVuUtEXUGSJKTmNK79w8nPRC1xLhB1pTbfAbrrrruwceNGuLu746677rrmudu2bWvTeyYmJmLGjBmIi4vDiBEjsG7dOmRnZ2P27NkAGoemcnNz8eGHHwIAZs+ejbfffhuJiYl4/PHHkZycjA0bNmDz5s2m95w0aRJWrlyJmJgY0xDYCy+8gDvvvBMKhaKtl0tkdnnltSiqrIODXIa+QTf2CwSRreJdIOoqbS5AGo3GNM9GozHP3IVp06ahuLgYS5YsgVarRXR0NLZv347w8HAAgFarbbYmUGRkJLZv3465c+finXfeQVBQEN566y3cfffdpnMWL14MmUyGxYsXIzc3F76+vpg0aRKWLl1qlsxEHXX8yuTnPoHuUDuyjBO1pvlcoEJMjQkWHYlslEziuFALOp0OGo0G5eXlNzzUR7Zt08HWF+38PaMk4dXvzqCitgEPDgvnHSCia8goqsL6fRfhIJfhL+N7N5svN31YmMBkZOna8/d3h+YA1dTUoLq62vR5VlYWVq1ahR07dnTk7YhsXkZRFSpqG+DkqECvAFfRcYgsWoS3M8K8nNFglJCcXiw6DtmoDhWgyZMnm+bllJWVYejQoXjjjTcwefLkq67iTGTPTl5qnPzcL8gdDq08iUhEv5LJZLippw8A4OeMYtTVGwQnIlvUoZ/ER48eRXx8PADg008/RUBAALKysvDhhx/irbfeMmtAImtnMEr45crO7wP49BdRm/QJdIePqxK19UYczioVHYdsUIcKUHV1Ndzc3AA0PoZ+1113QS6XY/jw4cjKyjJrQCJrl1FUhWq9Ac5KBSJ9XETHIbIKcpkM8T18AQA/XSiCwcjpqmReHSpAPXr0wOeff46cnBx8//33pv25CgoKOGmY6HdO5pYBAKKDNFDIufM7UVsNCvOAq8oB5TX1OHFlEVEic+lQAXrxxRcxb948REREYNiwYRgxYgSAxrtBMTExZg1IZM0MRgmn8ho31+3PrS+I2sVRIcfI7o2r/O87X8TFbMms2rUVRpN77rkHo0ePhlarxcCBA03Hb7nlFkydOtVs4Yis3cXCSlTrDXBROSDCm8NfRO01LNIbu88WIl9Xi/MFlaLjkA3pUAECGvflCggIaHZs6NChNxyIyJaczG2c/Bwd5M7hL6IOcFIqMCTCEz+lF2PvuULRcciGdKgAVVVV4R//+Ad27tyJgoICGI3GZl+/ePGiWcIRWbNmw1/BHP4i6qhRPXyQfLEYF4uqcPJSOYeTySw6VIBmzZqFPXv2YMaMGQgMDDRtkUFEv0ovrERNvQGuKgdE8Okvog7zcFaif7AGxy+VY+OBTLxx38Drv4joOjpUgL799lt88803GDVqlLnzENmMpsUPo4PdIecvCUQ3ZER3Hxy/VI6vTuTh+Yl94O2qEh2JrFyHngLz9PSEl5eXubMQ2YwGoxGntI0FqH+wh9gwRDYg1NMJIZ5O0DcY8fHhHNFxyAZ0qAC98sorePHFF5vtB0ZEv0ovqERtvRFuageEezuLjkNk9WQyGUZ0a3wk/j/JWag3GK/zCqJr69AQ2BtvvIH09HT4+/sjIiICjo6Ozb5+9OhRs4Qjsla/Pv2l4fAXkZn0D9Zg19kC5OtqsePUZdw+IFB0JLJiHSpAU6ZMMXMMItvRYDTitLbx6a9oPv1FZDYOCjmmDw3DWz9ewMYDGSxAdEM6VIBeeuklc+cgshkXC6tQW2+Eq4rDX0Tm9sfh4Vi9Ox2HM0vxS245f8mgDuvQHCAAKCsrw3vvvYeFCxeipKQEQOPQV25urtnCEVmjprV/+gbx6S8ic/N3V2NC/8Y7P/8+kCk2DFm1DhWgEydOoFevXnj11VexYsUKlJWVAQA+++wzLFy40Jz5iKyKUZJMw1/9grgxMFFneGRkOADgi+N5KKnSC05D1qpDBSgxMRGPPPIIzp8/D7VabTo+YcIE7N2712zhiKxNVnE1quoaoHaUo5uPq+g4RDZpcJgn+gdroG8wYvOhbNFxyEp1qAAdPnwYTzzxRIvjwcHByM/Pv+FQRNbqdF7j019RAdz7i6izyGQyPDwyAgCw6WA2DEbuEk/t16ECpFarodPpWhw/e/YsfH19bzgUkTWSpF/3/uoXxImZRJ3pjgGB0Dg5IresBnvPc5NUar8OFaDJkydjyZIlqK+vB9DYxrOzs7FgwQLcfffdZg1IZC3yympRVlMPR4UMPf05/EXUmdSOCtw1OBhA410govbqUAFasWIFCgsL4efnh5qaGtx8883o0aMH3NzcsHTpUnNnJLIKp64Mf/X2d4OjosMPWBJRG/1xWBgA4MczBcgvrxWchqxNh9YBcnd3x/79+7Fr1y6kpKTAaDRi8ODBGDdunLnzEVkNDn8Rda0efm4YGuGFQ5kl+ORIDp65pafoSGRF2l2AjEYjNm7ciG3btiEzMxMymQyRkZEICAiAJEmQcd0TskMFuloUVtZBIZehd4Cb6DhEduOBYaE4lFmCjw9l46mxPfjwAbVZu+7TS5KEO++8E7NmzUJubi769++Pfv36ISsrC4888gimTp3aWTmJLNqpK2v/9PB1hdpRITgNkf2YEB0ID2dH5JXXYs+5AtFxyIq0qwBt3LgRe/fuxc6dO3Hs2DFs3rwZH3/8MY4fP44ffvgBP/74Iz788MPOykpksZrm/3DxQ6KupXZU4O7BIQA4GZrap10FaPPmzXj++ecxduzYFl/7wx/+gAULFuC///2v2cIRWYPSKj3yymohA9AnkAWIqKs9MPTXydB5ZTWC05C1aFcBOnHiBG677barfn3ChAk4fvz4DYcisiZNw18RPi5wVXXouQIiugE9/FwxNNILRgn45EiO6DhkJdpVgEpKSuDv73/Vr/v7+6O0tPSGQxFZEw5/EYnX9Ej8lsM5aDAYBacha9CuAmQwGODgcPXfcBUKBRoaGm44FJG1qKitR3ZxNQCgL4e/iIS5LToAns6O0JbXYs85rgxN19eu+/WSJOGRRx6BSqVq9et1dXVmCUVkLU5rdZAAhHg6wcNZKToOkd1SOShw1+AQbNifgf8duYRboq4+WkEEtLMAPfzww9c956GHHupwGCJrc5qLHxJZjPviQrFhfwZ+SLuM4so6eLu2/ss6EdDOAvTBBx90Vg4iq1NeXY/0wkoAQD8OfxEJ1zvADQNDNDh+qRyfHcvFrPhuoiORBeOGRUQdtPPMZRglwN9dBR83/qZJZAnujQsF0Pg0mCRJgtOQJWMBIuqg737JB8DhLyJLMmlgEFQOcpy7XIkTl8pFxyELxgJE1AHV+gbTkyZ8/J3IcmicHHFbdAAArglE18YCRNQBe84Woq7BCC8XJQLc1aLjENFv3HdlGOzL43morTcITkOWSngBWr16NSIjI6FWqxEbG4t9+/Zd8/w9e/YgNjYWarUa3bp1w9q1a1ucU1ZWhqeeegqBgYFQq9WIiorC9u3bO+sSyA59d+rK8FegO2Qy7j5NZElGdPNGsIcTKmob8P2V/1aJfk9oAdqyZQvmzJmDRYsW4dixY4iPj8eECROQnd36hnYZGRmYOHEi4uPjcezYMTz//PN45plnsHXrVtM5er0et956KzIzM/Hpp5/i7NmzWL9+PYKDg7vqssjG1TUY8GNa467THP4isjxyuQz3xjVukMphMLoaoRsXrVy5EjNnzsSsWbMAAKtWrcL333+PNWvWYPny5S3OX7t2LcLCwrBq1SoAQFRUFI4cOYIVK1bg7rvvBgC8//77KCkpwYEDB+Do6AgACA8P75oLIrtwIL0YFXUN8HNTIcTLWXQcImrFPbEheHPnefx0oRg5JdUI5X+r9DvC7gDp9XqkpKQgISGh2fGEhAQcOHCg1dckJye3OH/8+PE4cuQI6uvrAQBffvklRowYgaeeegr+/v6Ijo7GsmXLYDBwHJjM4/srT3+N7xcAOYe/iCxSiKczRnX3AQB8mnJJcBqyRMIKUFFREQwGQ4vNVf39/ZGf3/qYbX5+fqvnNzQ0oKioCABw8eJFfPrppzAYDNi+fTsWL16MN954A0uXLr1qlrq6Ouh0umYfRK0xGCXsOH0ZAExPmhCRZWoaBvs05RKMRq4JRM0JnwT9+wmkkiRdc1Jpa+f/9rjRaISfnx/WrVuH2NhY3H///Vi0aBHWrFlz1fdcvnw5NBqN6SM0NLSjl0M27nBmCUqq9PBwdsTQSC/RcYjoGsb3C4CbygG5ZTU4nFkiOg5ZGGEFyMfHBwqFosXdnoKCghZ3eZoEBAS0er6DgwO8vb0BAIGBgejVqxcUCoXpnKioKOTn50Ov17f6vgsXLkR5ebnpIyeHk+aodU2LH46L8oejQvjvD0R0DWpHBW4fEAgA2HY0V3AasjTCfoIrlUrExsYiKSmp2fGkpCSMHDmy1deMGDGixfk7duxAXFycacLzqFGjcOHCBRiNRtM5586dQ2BgIJTK1nfrVqlUcHd3b/ZB9HtGo2R6pPa2fhz+IrIGdw1uHAb75qQWNXrOBaVfCf0VNjExEe+99x7ef/99pKWlYe7cucjOzsbs2bMBNN6Z+e3u8rNnz0ZWVhYSExORlpaG999/Hxs2bMC8efNM5/zpT39CcXExnn32WZw7dw7ffPMNli1bhqeeeqrLr49sy4nccmjLa+GsVGB0Tx/RcYioDeLCPRHq5YTKugbsOM01gehXQh+DnzZtGoqLi7FkyRJotVpER0dj+/btpsfWtVptszWBIiMjsX37dsydOxfvvPMOgoKC8NZbb5kegQeA0NBQ7NixA3PnzsWAAQMQHByMZ599FvPnz+/y6yPb0jT8NbaPH9SOiuucTUSWQC6XYWpMCN7aeR7bjuZi8iCuCUeNZBK3y21Bp9NBo9GgvLycw2EEoHGy/dgVu5FZXI1/PRCDSQODAACbDra+aCcRdY7pw8La/ZrMoiqMWbEbchnw88Jb4Mfta2xWe/7+5ixOojY4d7kSmcXVUCrkGNvHT3QcImqHCB8XxIZ7wigBX6TmiY5DFoIFiKgNmoa/4nv6wFUldOSYiDrgrsGNQ19bj14CBz4IYAEiapOmzU/Hc/FDIqt0R/8gKB3kOJNfgdNaLnZLLEBE15VVXIU0rQ4KuQzjolpfo4qILJvG2RG3Xvnvl2sCEcACRHRdTWv/DIv0gpdL62tJEZHlaxoG+yI1Fw0G43XOJlvHAkR0HU3zf7j3F5F1u6mXL7xdlCiq1GPf+SLRcUgwFiCia7isq8XR7DIAQEJfFiAia+aokOPOQY1LWGw9yh3i7R0LENE17Lgy/BUT5oEADdcOIbJ2d1/ZGmPH6csor6kXnIZEYgEiuoZvf+HeX0S2pF+QO3r5u0LfYMT2k1rRcUggFiCiqyiurMPPF4sBABOiAwWnISJzkMlkpg1St3EYzK6xABFdRdLpyzBKjb8xhnk7i45DRGYyNSYYchlwOLMUWcVVouOQICxARFfRNPw1gU9/EdkUf3c1RvXwAcA1gewZCxBRK8qr6/HThcbHZCf05/AXka1pmgz9eWout8awUyxARK34Ie0yGowSevm7oruvq+g4RGRmCf384axUIKu4GkezS0XHIQFYgIhaYXr6i5OfiWySs9LBtLjpVg6D2SUWIKLfqaxrwN7zhQCAif05/4fIVjUNg319PA91DQbBaairsQAR/c6PZwqgbzAi0scFvf3dRMchok4yvJs3AtzV0NU24Me0AtFxqIuxABH9zne/NC6Odlt0AGQymeA0RNRZFHIZpsQ0bpC67RiHwewNCxDRb9ToDdh15srwF+f/ENm8ph3id58tQEmVXnAa6kosQES/sedcAWrqDQjxdEJ0sLvoOETUyXr5u6FfkDvqDRK+PpEnOg51IRYgot/47d5fHP4isg+/bo3BYTB7wgJEdEVdgwE7r0yE5OKHRPbjzoFBUMhlSM0pQ3phpeg41EVYgIiu2H++CJV1DfB3VyEm1EN0HCLqIr5uKtzUs3FrjM85GdpusAARXfHr3l+BkMs5/EVkT6b+ZhjMaOTWGPaABYgIQL3BiKTTlwHAtDosEdmPhL7+cFM5ILesBoczS0THoS7AAkQEIDm9GOU19fBxVWJIhJfoOETUxdSOCky4svL7ZxwGswssQET4dfgroV8AFBz+IrJLU2Mah8G+OaFFbT23xrB1LEBk9wxGCTtONc3/4fAXkb0aFumFYA8nVNQ14Ie0y6LjUCdjASK7dyijBMVVemicHDG8m7foOEQkiFwuw5SYIABcE8gesACR3Wta/XV8P384KvifBJE9axoG23OuEEWVdYLTUGfiT3uyaw0Go2n+z6SBQYLTEJFoPfxcMTBEA4NRwlfHuTWGLWMBIrt2IL0YJVV6eLsoMYLDX0QEYGrTDvEcBrNpLEBk15qGv26LDoADh7+ICI13gx3kMpzMLcf5yxWi41An4U98slv6BiO+uzL8dccADn8RUSNvVxXG9PYFAGzjmkA2iwWI7Nb+C4XQ1TbA102FoZFc/JCIftW0Q/znx7g1hq1iASK79fVxLQDg9v6BXPyQiJr5Qx8/uKkdoC2vxc8Xi0XHoU7AAkR2qbbegB1X9v6aNDBQcBoisjRqR4VpaJzDYLaJBYjs0u6zhaisa0CQRo2YUE/RcYjIAt01uPFpsG9PalGj59YYtoYFiOxS09Nftw8IhJzDX0TUirhwT4R6OaFKb8CO0/mi45CZCS9Aq1evRmRkJNRqNWJjY7Fv375rnr9nzx7ExsZCrVajW7duWLt27VXP/fjjjyGTyTBlyhQzpyZrVq1vwM60AgB8+ouIrk4mk5lWhuaaQLZHaAHasmUL5syZg0WLFuHYsWOIj4/HhAkTkJ2d3er5GRkZmDhxIuLj43Hs2DE8//zzeOaZZ7B169YW52ZlZWHevHmIj4/v7MsgK/PjmQLU1BsQ5uWMASEa0XGIyII1LYq473whCnS1gtOQOQktQCtXrsTMmTMxa9YsREVFYdWqVQgNDcWaNWtaPX/t2rUICwvDqlWrEBUVhVmzZuGxxx7DihUrmp1nMBjwxz/+ES+//DK6devWFZdCVqRpefs7BgRCJuPwFxFdXaSPCwaHecAoAV9yawybIqwA6fV6pKSkICEhodnxhIQEHDhwoNXXJCcntzh//PjxOHLkCOrr603HlixZAl9fX8ycObNNWerq6qDT6Zp9kG0qr6nHrjOFALj3FxG1zdQrawJt5TCYTRFWgIqKimAwGODv79/suL+/P/LzW59slp+f3+r5DQ0NKCoqAgD89NNP2LBhA9avX9/mLMuXL4dGozF9hIaGtvNqyFp8e1ILvcGIPgFuiAp0Fx2HiKzApAGBUCrkSNPq8Etuueg4ZCbCJ0H/fghCkqRrDku0dn7T8YqKCjz44INYv349fHx82pxh4cKFKC8vN33k5OS04wrImnx2ZT2PKVfG9YmIrsfDWYlb+zb+8v1pyiXBachcHER9Yx8fHygUihZ3ewoKClrc5WkSEBDQ6vkODg7w9vbGqVOnkJmZiUmTJpm+bjQaAQAODg44e/Ysunfv3uJ9VSoVVCrVjV4SWbjcshoczCiBTAbcyeEvImqHe+NC8M1JLT5PzcXCiX2gclCIjkQ3SNgdIKVSidjYWCQlJTU7npSUhJEjR7b6mhEjRrQ4f8eOHYiLi4OjoyP69OmDkydPIjU11fRx5513YuzYsUhNTeXQlp37IrXx7s/wSG8EeTgJTkNE1iS+py8C3NUoq643LaNB1k3oEFhiYiLee+89vP/++0hLS8PcuXORnZ2N2bNnA2gcmnrooYdM58+ePRtZWVlITExEWloa3n//fWzYsAHz5s0DAKjVakRHRzf78PDwgJubG6Kjo6FUKoVcJ4knSRI+uzKBcSqHv4ionRRymWll6E+OcJqELRA2BAYA06ZNQ3FxMZYsWQKtVovo6Ghs374d4eHhAACtVttsTaDIyEhs374dc+fOxTvvvIOgoCC89dZbuPvuu0VdAlmJ01odzhdUQukgx239A0THISIrdG9cKFbvTsfec4XIL69FgEYtOhLdAJnUNIuYTHQ6HTQaDcrLy+HuzieFbMHSb05j/b4M3N4/EO/8cbDZ3nfTwdYX7SSizjF9WJjQ73/v2gM4nFmKv97WG0+O6SE0C7XUnr+/hT8FRtTZDEYJX6Q2LmDGp7+I6EbcG9c4l/R/Ry6B9w+sGwsQ2bzk9GIUVNTBw9kRN/fyFR2HiKzY7f0D4axUIKOoCilZpaLj0A1gASKb17T2zx0DAqF04L/yRNRxLioHTOwfCICToa0d/zYgm1ajN+C7X7QA+PQXEZnHfVeGwb45oUW1vkFwGuooFiCyaTtO56NKb0ColxMGh3mKjkNENmBIhCcivJ1RpTfgmxNa0XGog1iAyKZtOdx4i/ruwSHc+Z2IzEImk5kmQ398mMNg1ooFiGxWTkk1DqQXQyYD7okNER2HiGzIvXEhcJDLkJJVirP5FaLjUAewAJHN+t+VTQtH9/BBiKez4DREZEv83NQYF9W4b+XmQ1wPzBqxAJFNMhglfHrlCY2mW9VEROb0wJVFGbcdvYTaeoPgNNReLEBkk366UIS88lponByR0NdfdBwiskHxPXwQ4ukEXW0DJ0NbIRYgsklN63NMGRQEtaNCcBoiskVyuQwPDG28C8RhMOvDAkQ2p7RKjx2nLgPg8BcRda57Y0OgkMtwJKsU5y5zMrQ1YQEim/NFai70BiP6BbkjOlgjOg4R2TA/dzXGRfkB4ObI1oYFiGyKJEnYcqTx6a/7ePeHiLrA9GHhADgZ2tqwAJFNOZWnQ5pWB6WDHJMHBYmOQ0R2IL6HD4I9GidDbz/JydDWggWIbErT5Ofx/QLg4awUnIaI7EHjZOjGO84cBrMeLEBkM6r1Daad3++L48rPRNR17o0LhcOVydBpWp3oONQGLEBkMz4/loeK2gZEeDtjVHcf0XGIyI74u6sxPjoAAPDvA5liw1CbsACRTZAkCR8mZwIAHhweDrmcG58SUdd6ZGQEAODz1FyUVevFhqHrYgEim3AkqxRn8iugdpTj3lg+/UVEXS8u3BN9A91RW2/EFu4Sb/FYgMgmfJicBQCYPDAYGmdHwWmIyB7JZDLTXaD//JwFg1ESG4iuiQWIrF5BRS2++6Xx0dMZI8IFpyEie3bnoCB4OjviUmkNdqZdFh2HroEFiKzelkM5qDdIGBzmwZWfiUgotaMC04Y07g+2kZOhLRoLEFm1BoMRm65sQvjQiAixYYiI0HgnWi4DDqQXc38wC8YCRFbth7TL0JbXwttFiQn9A0THISJCsIcTEvrykXhLxwJEVq1p8vP9Q0OhclAITkNE1OjhK5Ohtx3NRXlNvdgw1CoWILJaFwoqcCC9GHLZr5sREhFZguHdvNDb3w019Qb87wgfibdELEBktTbszwAAjIvyR7CHk+A0RES/kslkeHRUBIDGn1X1BqPYQNQCCxBZpYKKWmxNadz36/9u6iY4DRFRS1NiguHrpoK2vBZfpuaJjkO/wwJEVunfBzKhNxgxOMwDcRFeouMQEbWgdlTgsVGRAIB396ZDkrgwoiVhASKrU1nXgP9cmfz8xM3dBachIrq66cPC4KpywLnLldh9tlB0HPoNFiCyOh8fyoautgHdfFxwa5S/6DhERFelcXLEH4c1Loy4Zk+64DT0WyxAZFXqDUa8f2Xy8+M3deOu70Rk8R4dFQlHhQyHMkpwNLtUdBy6ggWIrMrXJ/KQV14LH1cVpsYEi45DRHRdARo1pgxq/Hn1Lu8CWQwWILIakiTh3T0XAQCPjoqA2pELHxKRdXji5sanVXecvoz0wkrBaQhgASIrsudcIc7kV8BZqcCDXPiQiKxIDz83jIvyhyQB6/deFB2HwAJEVmTtlVvH9w8Jg8bZUXAaIqL2+dOYxrtA247mQlteIzgNsQCRVTicWYKfL5bAQS7DzPhI0XGIiNotNtwLwyK9oDcY8faPF0THsXvCC9Dq1asRGRkJtVqN2NhY7Nu375rn79mzB7GxsVCr1ejWrRvWrl3b7Ovr169HfHw8PD094enpiXHjxuHQoUOdeQnUBd784TwA4N64EG57QURWK/HWXgCAT47kIKekWnAa+ya0AG3ZsgVz5szBokWLcOzYMcTHx2PChAnIzs5u9fyMjAxMnDgR8fHxOHbsGJ5//nk888wz2Lp1q+mc3bt344EHHsCuXbuQnJyMsLAwJCQkIDc3t6sui8zsSGYJ9l8ogoNchifH9BAdh4iow4Z188boHj6oN0i8CySYTBK4NvewYcMwePBgrFmzxnQsKioKU6ZMwfLly1ucP3/+fHz55ZdIS0szHZs9ezaOHz+O5OTkVr+HwWCAp6cn3n77bTz00ENtyqXT6aDRaFBeXg53d/d2XhWZ24wNB7HvfBEeGBqK5XcNEB2nmU0HWy/rRNQ5pl9ZVNCaHc0uxV2rD0Ahl2Fn4s2I8HERHclmtOfvb2F3gPR6PVJSUpCQkNDseEJCAg4cONDqa5KTk1ucP378eBw5cgT19fWtvqa6uhr19fXw8rr6flF1dXXQ6XTNPsgyHM4swb7zvPtDRLZjcJgnxvb2hcEo4a2d50XHsVvCClBRUREMBgP8/ZtvZeDv74/8/PxWX5Ofn9/q+Q0NDSgqKmr1NQsWLEBwcDDGjRt31SzLly+HRqMxfYSGhrbzaqgzSJKEV789AwC4b0goQr2cBSciIjKPuVfmAn2emosLBVwXSAThk6BlsuZbGUiS1OLY9c5v7TgAvPbaa9i8eTO2bdsGtVp91fdcuHAhysvLTR85OTntuQTqJLvOFuBIVilUDnI884eeouMQEZnNgBAP3NrXH0YJeJN3gYQQVoB8fHygUCha3O0pKChocZenSUBAQKvnOzg4wNvbu9nxFStWYNmyZdixYwcGDLj2vBGVSgV3d/dmHySW0Sjh9e/PAQAeGRmBAM3VCywRkTWaO67xLtDXJ/JwNr9CcBr7I6wAKZVKxMbGIikpqdnxpKQkjBw5stXXjBgxosX5O3bsQFxcHBwdf10Y7/XXX8crr7yC7777DnFxceYPT53uqxN5SNPq4KZ2wJ/GdBcdh4jI7PoGuWNi/wBIEvD692dEx7E7QofAEhMT8d577+H9999HWloa5s6di+zsbMyePRtA49DUb5/cmj17NrKyspCYmIi0tDS8//772LBhA+bNm2c657XXXsPixYvx/vvvIyIiAvn5+cjPz0dlJcdYrUVtvQGvfXcWADD75u7wcFYKTkRE1DkSb+0NhVyGH9IKkJxeLDqOXRFagKZNm4ZVq1ZhyZIlGDRoEPbu3Yvt27cjPLxxnyetVttsTaDIyEhs374du3fvxqBBg/DKK6/grbfewt133206Z/Xq1dDr9bjnnnsQGBho+lixYkWXXx91zIb9Gcgtq0GQRo2Zo7nqMxHZrh5+rpg+tPHR/mXb02A0CluZxu4IXQfIUnEdIHEKK+ow5vVdqNIb8Ob9gzB5ULDoSNfEdYCIupYtrAP0e0WVdRjz+m5U1jVg1bRBmBJj2T/3LJlVrANE1Jo3dpxFld6AgaEemDQgSHQcIqJO5+OqMs11fPW7M6jWNwhOZB9YgMhipOaUYcuRxiUIXrg9CnL51ZdDICKyJTNHRyLE0wna8lqs3pUuOo5dYAEii2AwSnjxi18gScBdg4MRF3H1lbuJiGyN2lGBxbf3BQCs23sRmUVVghPZPhYgsggfH87GiUvlcFM5YOGEKNFxiIi63Ph+/ojv6QO9wYhXvj4tOo7NYwEi4Qor6kyPvScm9IKvm0pwIiKirieTyfDSpH5wkMuw80wBvj/V+rZQZB4sQCTcy1+dQnlNPfoGumPG8HDRcYiIhOnh54rHb+oGAHjpi1OoqG19o2+6cSxAJNQPpy/j6xNaKOQyvHbPADgo+K8kEdm3Z2/piXBvZ+TravHGjnOi49gs/m1DwlTU1uOFL34BAMyKj0R0sEZwIiIi8dSOCiyd0h8A8O/kTBzLLhWcyDaxAJEwL391GtryWoR7O2POLb1ExyEishije/rgrphgSBIw73/HUVtvEB3J5rAAkRDfn8rHpymXIJMBK+4dCCelQnQkIiKL8tKkfvBzUyG9sApv7DgrOo7NYQGiLldYUYeF204CAJ64qTuGcM0fIqIWNM6O+MfdjUNh7+3PwOHMEsGJbAsLEHUpo1HCc/87jpIqPfoEuGHurT1FRyIislh/6OOPe2NDIEnAnI9TUV7Dp8LMhQWIutS7ey9i77lCqB3lePP+GKgcOPRFRHQtL07qizAvZ+SW1eD5z06Ce5ibBwsQdZmUrBKsuDKO/fKd/dA7wE1wIiIiy+emdsSb9w+Cg1yGb05oseVwjuhINoEFiLpEQUUtnvzvURiMEqYMCsJ9caGiIxERWY2YME88l9AbAPDSl6dwKq9ccCLrxwJEnU7fYMSfPjqKy7o69PRzxd+n9odMxp3eiYja44mbuuHmXr6oazBi9kcpKKvWi45k1ViAqFNJkoSXvjyFlKxSuKkdsO6hOLiqHETHIiKyOnK5DG/ePwihXk7IKanBnC2pMBg5H6ijWICoU63fdxGbD2VDJgPeeiAGkT4uoiMREVktD2cl1vwxFioHOXafLcQ/vk0THclqsQBRp/nmhBbLtp8BACyaGIWxvf0EJyIisn7RwRq8fu9AAMD6fRn478EswYmsEwsQdYrk9GLM/SQVAPDIyAjMHB0pNhARkQ25c2AQEm9t3ELoxS9OYdfZAsGJrA8LEJnd0exSzPz3YegbjLi1rz9euKMvJz0TEZnZn//QA3fFBMNglPCnj1KQksWVotuDBYjM6pfccjzy/iFU6w0Y1cMb/3ogBgo5yw8RkbnJZDL84+4BGNPbF7X1Rjz6wWGkaXWiY1kNFiAym9ScMkxf/zN0tQ2IDffE+ofioHbkSs9ERJ1F6SDHmj/GIi7cE7raBvzxvYMsQW3EAkRmcTizBA++d9BUfj54dAiclXzcnYioszkpFdjwyBAMCNGgpEqP6et/5kKJbcACRDfsu1+0ePC9g6isa8Dwbl748LGhcFc7io5FRGQ3NE6O+M/MYRgY6oHS6no8sO5n7h5/HSxA1GGSJOH9/Rn403+Poq7BiHFRftj46FC4cKFDIqIu11iChiL2ynDYg+8dxPen8kXHslgsQNQhtfUG/PXTE1jy9WlIEvDg8DCsfTCWc36IiARyVzvio5nDMC7KH3UNRvzpoxSs25vOHeRbwQJE7XaptBrT1v2M/6VcglwGPD+xD16ZHA0HBf91IiISzUmpwNoHB2P6sDAYJWDZ9jNI/OQ4ausNoqNZFI5VULt8c0KLBdtOoKK2AR7OjvjXAzGI7+krOhYREf2Gg0KOpVOi0dvfDUu+Po3PjuUiTavD29Nj0MPPTXQ8i8Bf2alNyqr1SPwkFU9tOoqK2gbEhHngq6dHs/wQEVkomUyGh0dG4D+PDYWPqxJn8isw6V8/YfOhbA6JgQWIrkOSJHxzQotxK/di29FcyGTA02N74JMnRiDUy1l0PCIiuo6RPXyw/dl4jOrhjZp6AxZuO4mH3j+E3LIa0dGEYgGiqzp/uQIPbjiIpzYdRVFlHXr4uWLrn0Zi3vjecOR8HyIiq+HnpsaHjw3DoolRUDnIse98ERJW7sG7e9KhbzCKjicE5wBRC3llNXjzh/P49OglGIwSlA5yzL65O54a2x0qBz7lRURkjRRyGR6/qRv+EOWH+Z+ewJGsUiz/9gy2HMnB/Nv6IKGvv13t28gCRCYXCiqwYX8Gth7NNf1GML6fPxbf3pfDXURENqK7rys+eWIEth69hH98ewYXC6vwxH9SMDjMA3++pSdu7ukLuR3s4cgCZOckSUJyejHW77uIXWcLTceHRXrhr7f1QWy4p8B0RETUGeRyGe6NC0VCvwCs25uODfszcDS7DI9+cBg9/Fwxa3QkpsQE2/TabjKJU8Fb0Ol00Gg0KC8vh7u7u+g4nSKnpBpfHs/DF6m5OHe5EgAgkwEJff0xK74b4sI97epWaEdtOpgtOgKRXZk+LEx0BJtUoKvFur0X8fHhHFTWNQAAvF2UmD4sDFNigtHd11VwwrZpz9/fLECtsNUClFdWg51pl/FFah6OZJWajjs5KnBvXAgeGxWJCB8XgQmtDwsQUddiAepcutp6fHI4Bx/8lNnsKbHoYHdMHhiMOwYGIlDjJDDhtbEA3SBbKUDV+gYczCjB3nOF2HuuEOmFVaavyWTAiG7emDIoGOOjA6Bx4ualHcECRNS1WIC6RoPBiG9/ycfWo5ew73wRDMbGqiCTAdFBGozu6YP4Hj6IjfC0qIdj2vP3t/A5QKtXr8brr78OrVaLfv36YdWqVYiPj7/q+Xv27EFiYiJOnTqFoKAg/PWvf8Xs2bObnbN161a88MILSE9PR/fu3bF06VJMnTq1sy9FKH2DEReLKnE8pwypOeVIzSnDucsVpn9pAUAuAwaFemBCdCAmDQxCgEYtMDEREVkqB4UckwYGYdLAIBRX1mH7L/n4KjUPhzJLcDK3HCdzy7FmdzrUjnLEhXthQIgGA0I06B/igSCN2iqmUAgtQFu2bMGcOXOwevVqjBo1Cu+++y4mTJiA06dPIyysZcvPyMjAxIkT8fjjj+Ojjz7CTz/9hCeffBK+vr64++67AQDJycmYNm0aXnnlFUydOhWfffYZ7rvvPuzfvx/Dhg3r6ks0qwaDEdryWuSW1SC3tAYZRVW4UFCJ8wUVyCquRoOx5c28II0aN/XyxU29fDGquw80zrzTQ0REbeftqsKM4eGYMTwcBbpa7L9QhP3ni7D/QhEKKuoaP79Q9Ov5Lkr09HdFN19XdPNxQXdfV0T4uCBQo7aoSdVCh8CGDRuGwYMHY82aNaZjUVFRmDJlCpYvX97i/Pnz5+PLL79EWlqa6djs2bNx/PhxJCcnAwCmTZsGnU6Hb7/91nTObbfdBk9PT2zevLlNuTp7CEySJNQ1GFFZ14CquoYr/2tAZV09SqrqUVxZh5IqPYqr9KZ/LqyoQ76uFq10HBMXpQLRwRoMCvPAoBAPDArzQIC7dTRxa8UhMKKuxSEwyyFJEs5drkRKVilOXCrDiUvlOPu7kYff83JRIlCjRqDGCX2D3JF4ay+zZrKKITC9Xo+UlBQsWLCg2fGEhAQcOHCg1dckJycjISGh2bHx48djw4YNqK+vh6OjI5KTkzF37twW56xatcqs+TsiJasUj208jKq6hlbv1rSFUiFHoIcawR5OCPd2Rg8/N/Twc0VPP1cEWsltRyIisn4ymQy9A9zQO8DNVExr6w04k1+B9IJKXCyqxMXCKlwsrEJWSRVq640oqdKjpEqPU3m6xj0mzVyA2kNYASoqKoLBYIC/v3+z4/7+/sjPz2/1Nfn5+a2e39DQgKKiIgQGBl71nKu9JwDU1dWhrq7O9Hl5eTmAxiZpTvrqSpSWlTc75qSUw0XpABeVA5yVCng4K+HtooSnixJeLo7wclbC00UFLxdHBGmc4OOqusoCVfWoqKg3a166vuqqCtERiOyKuX8uk/l108jRTeMO9Pz1DowkSSivqUd+eS0uV9Qiv7wWbmpHs/95Nr1fWwa3hE+C/v0dC0mSrnkXo7Xzf3+8ve+5fPlyvPzyyy2Oh4aGXj04ERF1ucdFByCrUFFRAY1Gc81zhBUgHx8fKBSKFndmCgoKWtzBaRIQENDq+Q4ODvD29r7mOVd7TwBYuHAhEhMTTZ8bjUaUlJTA29u704eUdDodQkNDkZOTY9WP3LfGlq8NsO3r47VZL1u+Pl6b9eqq65MkCRUVFQgKCrruucIKkFKpRGxsLJKSkpo9op6UlITJkye3+poRI0bgq6++anZsx44diIuLg6Ojo+mcpKSkZvOAduzYgZEjR141i0qlgkqlanbMw8OjvZd0Q9zd3W3yX3rAtq8NsO3r47VZL1u+Pl6b9eqK67venZ8mQofAEhMTMWPGDMTFxWHEiBFYt24dsrOzTev6LFy4ELm5ufjwww8BND7x9fbbbyMxMRGPP/44kpOTsWHDhmZPdz377LO46aab8Oqrr2Ly5Mn44osv8MMPP2D//v1CrpGIiIgsj9ACNG3aNBQXF2PJkiXQarWIjo7G9u3bER4eDgDQarXIzv71MePIyEhs374dc+fOxTvvvIOgoCC89dZbpjWAAGDkyJH4+OOPsXjxYrzwwgvo3r07tmzZYvVrABEREZH5CJ8E/eSTT+LJJ59s9WsbN25scezmm2/G0aNHr/me99xzD+655x5zxOt0KpUKL730UoshOFtgy9cG2Pb18dqsly1fH6/Nelni9XEvMCIiIrI7ctEBiIiIiLoaCxARERHZHRYgIiIisjssQBYiMzMTM2fORGRkJJycnNC9e3e89NJL0Ov1oqN12OrVqxEZGQm1Wo3Y2Fjs27dPdKQbtnz5cgwZMgRubm7w8/PDlClTcPbsWdGxOsXy5cshk8kwZ84c0VHMJjc3Fw8++CC8vb3h7OyMQYMGISUlRXSsG9bQ0IDFixebfn5069YNS5YsgdFoFB2tQ/bu3YtJkyYhKCgIMpkMn3/+ebOvS5KEv/3tbwgKCoKTkxPGjBmDU6dOiQnbTte6tvr6esyfPx/9+/eHi4sLgoKC8NBDDyEvL09c4Ha43p/bbz3xxBOQyWRC9+lkAbIQZ86cgdFoxLvvvotTp07hn//8J9auXYvnn39edLQO2bJlC+bMmYNFixbh2LFjiI+Px4QJE5ota2CN9uzZg6eeego///wzkpKS0NDQgISEBFRVVYmOZlaHDx/GunXrMGDAANFRzKa0tBSjRo2Co6Mjvv32W5w+fRpvvPFGly962hleffVVrF27Fm+//TbS0tLw2muv4fXXX8e//vUv0dE6pKqqCgMHDsTbb7/d6tdfe+01rFy5Em+//TYOHz6MgIAA3HrrraiosPy9+a51bdXV1Th69CheeOEFHD16FNu2bcO5c+dw5513Ckjaftf7c2vy+eef4+DBg21arblTSWSxXnvtNSkyMlJ0jA4ZOnSoNHv27GbH+vTpIy1YsEBQos5RUFAgAZD27NkjOorZVFRUSD179pSSkpKkm2++WXr22WdFRzKL+fPnS6NHjxYdo1Pcfvvt0mOPPdbs2F133SU9+OCDghKZDwDps88+M31uNBqlgIAA6R//+IfpWG1traTRaKS1a9cKSNhxv7+21hw6dEgCIGVlZXVNKDO52rVdunRJCg4Oln755RcpPDxc+uc//9nl2ZrwDpAFKy8vh5eXl+gY7abX65GSkoKEhIRmxxMSEnDgwAFBqTpHeXk5AFjln9PVPPXUU7j99tsxbtw40VHM6ssvv0RcXBzuvfde+Pn5ISYmBuvXrxcdyyxGjx6NnTt34ty5cwCA48ePY//+/Zg4caLgZOaXkZGB/Pz8Zj9fVCoVbr75Zpv7+QI0/oyRyWQ2cafSaDRixowZ+Mtf/oJ+/fqJjiN+IURqXXp6Ov71r3/hjTfeEB2l3YqKimAwGFpsQOvv799io1prJkkSEhMTMXr0aERHR4uOYxYff/wxUlJScOTIEdFRzO7ixYtYs2YNEhMT8fzzz+PQoUN45plnoFKp8NBDD4mOd0Pmz5+P8vJy9OnTBwqFAgaDAUuXLsUDDzwgOprZNf0Mae3nS1ZWlohInaa2thYLFizA9OnTbWJ/sFdffRUODg545plnREcBwALU6f72t7/h5ZdfvuY5hw8fRlxcnOnzvLw83Hbbbbj33nsxa9aszo7YaWQyWbPPJUlqccyaPf300zhx4oTN7DOXk5ODZ599Fjt27IBarRYdx+yMRiPi4uKwbNkyAEBMTAxOnTqFNWvWWH0B2rJlCz766CNs2rQJ/fr1Q2pqKubMmYOgoCA8/PDDouN1Clv/+VJfX4/7778fRqMRq1evFh3nhqWkpODNN9/E0aNHLebPiQWokz399NO4//77r3lORESE6Z/z8vIwduxY0+aw1sjHxwcKhaLF3Z6CgoIWv7VZqz//+c/48ssvsXfvXoSEhIiOYxYpKSkoKChAbGys6ZjBYMDevXvx9ttvo66uDgqFQmDCGxMYGIi+ffs2OxYVFYWtW7cKSmQ+f/nLX7BgwQLTz5r+/fsjKysLy5cvt7kCFBAQAKDxTlBgYKDpuC39fKmvr8d9992HjIwM/PjjjzZx92ffvn0oKChAWFiY6ZjBYMBzzz2HVatWITMzs8szsQB1Mh8fH/j4+LTp3NzcXIwdOxaxsbH44IMPIJdb5xQtpVKJ2NhYJCUlYerUqabjSUlJmDx5ssBkN06SJPz5z3/GZ599ht27dyMyMlJ0JLO55ZZbcPLkyWbHHn30UfTp0wfz58+36vIDAKNGjWqxZMG5c+dMmy9bs+rq6hY/LxQKhdU+Bn8tkZGRCAgIQFJSEmJiYgA0zjvcs2cPXn31VcHpblxT+Tl//jx27doFb29v0ZHMYsaMGS3mFY4fPx4zZszAo48+KiQTC5CFyMvLw5gxYxAWFoYVK1agsLDQ9LWm33isSWJiImbMmIG4uDjT3azs7GzMnj1bdLQb8tRTT2HTpk344osv4ObmZrrLpdFo4OTkJDjdjXFzc2sxl8nFxQXe3t42Mcdp7ty5GDlyJJYtW4b77rsPhw4dwrp166z2TutvTZo0CUuXLkVYWBj69euHY8eOYeXKlXjsscdER+uQyspKXLhwwfR5RkYGUlNT4eXlhbCwMMyZMwfLli1Dz5490bNnTyxbtgzOzs6YPn26wNRtc61rCwoKwj333IOjR4/i66+/hsFgMP2M8fLyglKpFBW7Ta735/b7Mufo6IiAgAD07t27q6M2Evb8GTXzwQcfSABa/bBW77zzjhQeHi4plUpp8ODBNvGo+NX+jD744APR0TqFLT0GL0mS9NVXX0nR0dGSSqWS+vTpI61bt050JLPQ6XTSs88+K4WFhUlqtVrq1q2btGjRIqmurk50tA7ZtWtXq/+dPfzww5IkNT4K/9JLL0kBAQGSSqWSbrrpJunkyZNiQ7fRta4tIyPjqj9jdu3aJTr6dV3vz+33RD8Gz93giYiIyO5Y5yQTIiIiohvAAkRERER2hwWIiIiI7A4LEBEREdkdFiAiIiKyOyxAREREZHdYgIiIiMjusAARERGR3WEBIiKrFBERgVWrVomOYTE5iKh9uBI0EVm0jRs3Ys6cOSgrK2t2vLCwEC4uLnB2drarHERkHtwMlYgsVn19/VW/5uvr24VJrs5SchBR+3AIjIjM6tNPP0X//v3h5OQEb29vjBs3DlVVVTAajViyZAlCQkKgUqkwaNAgfPfdd6bXZWZmQiaT4ZNPPsGYMWOgVqvx0Ucf4dFHH0V5eTlkMhlkMhn+9re/AWg59CSTyfDee+9h6tSpcHZ2Rs+ePfHll182y/bll1+iZ8+ecHJywtixY/Hvf/8bMpmsxV2d39u9e3e7crz77ru444474OzsjKioKCQnJ+PChQsYM2YMXFxcMGLECKSnpzf7Hl999RViY2OhVqvRrVs3vPzyy2hoaGj3//9E1EbCtmElIpuTl5cnOTg4SCtXrpQyMjKkEydOSO+8845UUVEhrVy5UnJ3d5c2b94snTlzRvrrX/8qOTo6SufOnZMkSTLthB0RESFt3bpVunjxopSVlSWtWrVKcnd3l7RaraTVaqWKigpJklruJA1ACgkJkTZt2iSdP39eeuaZZyRXV1epuLjY9P6Ojo7SvHnzpDNnzkibN2+WgoODJQBSaWnpNa+rrq6uXTmCg4OlLVu2SGfPnpWmTJkiRURESH/4wx+k7777Tjp9+rQ0fPhw6bbbbjO95rvvvpPc3d2ljRs3Sunp6dKOHTukiIgI6W9/+5sZ/lSIqDUsQERkNikpKRIAKTMzs8XXgoKCpKVLlzY7NmTIEOnJJ5+UJOnXArRq1apm53zwwQeSRqNp8X6tFY/FixebPq+srJRkMpn07bffSpIkSfPnz5eio6ObvceiRYvaVIBuJEdycrIEQNqwYYPp2ObNmyW1Wm36PD4+Xlq2bFmz9/3Pf/4jBQYGXjcXEXUM5wARkdkMHDgQt9xyC/r374/x48cjISEB99xzDxQKBfLy8jBq1Khm548aNQrHjx9vdiwuLq7D33/AgAGmf3ZxcYGbmxsKCgoAAGfPnsWQIUOanT906NAOf6+25vD39wcA9O/fv9mx2tpa6HQ6uLu7IyUlBYcPH8bSpUtN5xgMBtTW1qK6upoTrIk6AQsQEZmNQqFAUlISDhw4gB07duBf//oXFi1ahKSkJACN82N+S5KkFsdcXFw6/P0dHR2bfS6TyWA0Gq/6vaROegj2tzmavmdrx5qyGY1GvPzyy7jrrrtavJdare6UjET2jgWIiMxKJpNh1KhRGDVqFF588UWEh4dj586dCAoKwv79+3HTTTeZzj1w4MB178IolUoYDIYbztWnTx9s37692bEjR460+fXmytGawYMH4+zZs+jRo0envD8RtcQCRERmc/DgQezcuRMJCQnw8/PDwYMHUVhYiKioKPzlL3/BSy+9hO7du2PQoEH44IMPkJqaiv/+97/XfM+IiAhUVlZi586dGDhwIJydnTs0JPTEE09g5cqVmD9/PmbOnInU1FRs3LgRQMs7U52ZozUvvvgi7rjjDoSGhuLee++FXC7HiRMncPLkSfz97383y/cgoub4GDwRmY27uzv27t2LiRMnolevXli8eDHeeOMNTJgwAc888wyee+45PPfcc+jfvz++++4702Pp1zJy5EjMnj0b06ZNg6+vL1577bUOZYuMjMSnn36Kbdu2YcCAAVizZg0WLVoEAFCpVNd9vblytGb8+PH4+uuvkZSUhCFDhmD48OFYuXIlwsPDzfY9iKg5rgRNRHZr6dKlWLt2LXJyckRHIaIuxiEwIrIbq1evxpAhQ+Dt7Y2ffvoJr7/+Op5++mnRsYhIAA6BEZHdOH/+PCZPnoy+ffvilVdewXPPPWda0XnChAlwdXVt9WPZsmVigxOR2XEIjIgIQG5uLmpqalr9mpeXF7y8vLo4ERF1JhYgIiIisjscAiMiIiK7wwJEREREdocFiIiIiOwOCxARERHZHRYgIiIisjssQERERGR3WICIiIjI7rAAERERkd35f7swdhpY1NIfAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.603619Z",
          "start_time": "2023-01-20T04:35:04.340832Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "uTyGOxM2lToA",
        "outputId": "bb68c867-c57c-44f6-c2d6-1d2382a59dcb",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.regplot(x='delivery_time', y='sorting_time', data=dt)",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='delivery_time', ylabel='sorting_time'>"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.715706Z",
          "start_time": "2023-01-20T04:35:04.603619Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "orF-iU2_luzB",
        "outputId": "f936d0ff-18df-44d2-9677-7f7af96d509f",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model=smf.ols(\"sorting_time~delivery_time \", data=dt).fit()\nmodel.summary() #build the models",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>      <td>sorting_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>Fri, 20 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>3.98e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>10:05:04</td>     <th>  Log-Likelihood:    </th> <td> -36.839</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   77.68</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   79.77</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>   -0.7567</td> <td>    1.134</td> <td>   -0.667</td> <td> 0.513</td> <td>   -3.130</td> <td>    1.617</td>\n</tr>\n<tr>\n  <th>delivery_time</th> <td>    0.4137</td> <td>    0.065</td> <td>    6.387</td> <td> 0.000</td> <td>    0.278</td> <td>    0.549</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.409</td> <th>  Durbin-Watson:     </th> <td>   1.346</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.494</td> <th>  Jarque-Bera (JB):  </th> <td>   0.371</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.255</td> <th>  Prob(JB):          </th> <td>   0.831</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 3.405</td> <th>  Cond. No.          </th> <td>    62.1</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==============================================================================\nDep. Variable:           sorting_time   R-squared:                       0.682\nModel:                            OLS   Adj. R-squared:                  0.666\nMethod:                 Least Squares   F-statistic:                     40.80\nDate:                Fri, 20 Jan 2023   Prob (F-statistic):           3.98e-06\nTime:                        10:05:04   Log-Likelihood:                -36.839\nNo. Observations:                  21   AIC:                             77.68\nDf Residuals:                      19   BIC:                             79.77\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n=================================================================================\n                    coef    std err          t      P>|t|      [0.025      0.975]\n---------------------------------------------------------------------------------\nIntercept        -0.7567      1.134     -0.667      0.513      -3.130       1.617\ndelivery_time     0.4137      0.065      6.387      0.000       0.278       0.549\n==============================================================================\nOmnibus:                        1.409   Durbin-Watson:                   1.346\nProb(Omnibus):                  0.494   Jarque-Bera (JB):                0.371\nSkew:                           0.255   Prob(JB):                        0.831\nKurtosis:                       3.405   Cond. No.                         62.1\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.732047Z",
          "start_time": "2023-01-20T04:35:04.715706Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTPnDm6wmjdT",
        "outputId": "199695f9-5a4e-4b1a-9237-058e54f44c93",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.params",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "text/plain": "Intercept       -0.756673\ndelivery_time    0.413744\ndtype: float64"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.748512Z",
          "start_time": "2023-01-20T04:35:04.732304Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TegPNMUqp1HD",
        "outputId": "dcb58801-1bde-48b1-cd92-809deb052b51",
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(model.tvalues,'\\n' ,model.pvalues)",
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Intercept       -0.667290\ndelivery_time    6.387447\ndtype: float64 \n Intercept        0.512611\ndelivery_time    0.000004\ndtype: float64\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.772001Z",
          "start_time": "2023-01-20T04:35:04.748512Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwan7wxzqLQi",
        "outputId": "d35a3fd2-e900-47e8-ac99-f4e847984403",
        "trusted": true
      },
      "cell_type": "code",
      "source": "(model.rsquared,model.rsquared_adj)",
      "execution_count": 16,
      "outputs": [
        {
          "data": {
            "text/plain": "(0.6822714748417231, 0.6655489208860244)"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "dWh9Fcknwr2R"
      },
      "cell_type": "markdown",
      "source": "#Transformation models"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.806345Z",
          "start_time": "2023-01-20T04:35:04.774641Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "CQlTXXB9Z99V",
        "outputId": "e70938e7-434c-4961-a080-bfb76a09fdc6",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2 = smf.ols(\"np.log(sorting_time)~delivery_time\", data=dt).fit() \nmodel2.params\nmodel2.summary()   #build log transformation model to increase r-squared value.",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>    <td>np.log(sorting_time)</td> <th>  R-squared:         </th> <td>   0.695</td>\n</tr>\n<tr>\n  <th>Model:</th>                     <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.679</td>\n</tr>\n<tr>\n  <th>Method:</th>               <td>Least Squares</td>    <th>  F-statistic:       </th> <td>   43.39</td>\n</tr>\n<tr>\n  <th>Date:</th>               <td>Fri, 20 Jan 2023</td>   <th>  Prob (F-statistic):</th> <td>2.64e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                   <td>10:05:04</td>       <th>  Log-Likelihood:    </th> <td>-0.85600</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>        <td>    21</td>        <th>  AIC:               </th> <td>   5.712</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>            <td>    19</td>        <th>  BIC:               </th> <td>   7.801</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>    0.4372</td> <td>    0.204</td> <td>    2.139</td> <td> 0.046</td> <td>    0.009</td> <td>    0.865</td>\n</tr>\n<tr>\n  <th>delivery_time</th> <td>    0.0769</td> <td>    0.012</td> <td>    6.587</td> <td> 0.000</td> <td>    0.052</td> <td>    0.101</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 0.744</td> <th>  Durbin-Watson:     </th> <td>   1.691</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.689</td> <th>  Jarque-Bera (JB):  </th> <td>   0.686</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td>-0.101</td> <th>  Prob(JB):          </th> <td>   0.710</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.138</td> <th>  Cond. No.          </th> <td>    62.1</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================================================================================\nDep. Variable:     np.log(sorting_time)   R-squared:                       0.695\nModel:                              OLS   Adj. R-squared:                  0.679\nMethod:                   Least Squares   F-statistic:                     43.39\nDate:                  Fri, 20 Jan 2023   Prob (F-statistic):           2.64e-06\nTime:                          10:05:04   Log-Likelihood:               -0.85600\nNo. Observations:                    21   AIC:                             5.712\nDf Residuals:                        19   BIC:                             7.801\nDf Model:                             1                                         \nCovariance Type:              nonrobust                                         \n=================================================================================\n                    coef    std err          t      P>|t|      [0.025      0.975]\n---------------------------------------------------------------------------------\nIntercept         0.4372      0.204      2.139      0.046       0.009       0.865\ndelivery_time     0.0769      0.012      6.587      0.000       0.052       0.101\n==============================================================================\nOmnibus:                        0.744   Durbin-Watson:                   1.691\nProb(Omnibus):                  0.689   Jarque-Bera (JB):                0.686\nSkew:                          -0.101   Prob(JB):                        0.710\nKurtosis:                       2.138   Cond. No.                         62.1\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.822427Z",
          "start_time": "2023-01-20T04:35:04.808869Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jy5L4Eypbfyv",
        "outputId": "6b8d4505-cba0-46df-f0e1-dd7d44e695dd",
        "trusted": true
      },
      "cell_type": "code",
      "source": "(model2.rsquared,model2.rsquared_adj)",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "(0.6954434611324224, 0.6794141696130762)"
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.854704Z",
          "start_time": "2023-01-20T04:35:04.823601Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "7xX5S6VhkuZ5",
        "outputId": "fc5e8aee-9564-48b5-d9b6-af132d033e71",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model3 = smf.ols(\"np.sqrt(sorting_time)~delivery_time\", data=dt).fit() \nmodel3.params\nmodel3.summary()     ",
      "execution_count": 19,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>    <td>np.sqrt(sorting_time)</td> <th>  R-squared:         </th> <td>   0.696</td>\n</tr>\n<tr>\n  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.680</td>\n</tr>\n<tr>\n  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   43.46</td>\n</tr>\n<tr>\n  <th>Date:</th>               <td>Fri, 20 Jan 2023</td>    <th>  Prob (F-statistic):</th> <td>2.61e-06</td>\n</tr>\n<tr>\n  <th>Time:</th>                   <td>10:05:04</td>        <th>  Log-Likelihood:    </th> <td> -3.5906</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>        <td>    21</td>         <th>  AIC:               </th> <td>   11.18</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>            <td>    19</td>         <th>  BIC:               </th> <td>   13.27</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>    0.9609</td> <td>    0.233</td> <td>    4.128</td> <td> 0.001</td> <td>    0.474</td> <td>    1.448</td>\n</tr>\n<tr>\n  <th>delivery_time</th> <td>    0.0877</td> <td>    0.013</td> <td>    6.592</td> <td> 0.000</td> <td>    0.060</td> <td>    0.116</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 0.087</td> <th>  Durbin-Watson:     </th> <td>   1.498</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.957</td> <th>  Jarque-Bera (JB):  </th> <td>   0.114</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.099</td> <th>  Prob(JB):          </th> <td>   0.945</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.698</td> <th>  Cond. No.          </th> <td>    62.1</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=================================================================================\nDep. Variable:     np.sqrt(sorting_time)   R-squared:                       0.696\nModel:                               OLS   Adj. R-squared:                  0.680\nMethod:                    Least Squares   F-statistic:                     43.46\nDate:                   Fri, 20 Jan 2023   Prob (F-statistic):           2.61e-06\nTime:                           10:05:04   Log-Likelihood:                -3.5906\nNo. Observations:                     21   AIC:                             11.18\nDf Residuals:                         19   BIC:                             13.27\nDf Model:                              1                                         \nCovariance Type:               nonrobust                                         \n=================================================================================\n                    coef    std err          t      P>|t|      [0.025      0.975]\n---------------------------------------------------------------------------------\nIntercept         0.9609      0.233      4.128      0.001       0.474       1.448\ndelivery_time     0.0877      0.013      6.592      0.000       0.060       0.116\n==============================================================================\nOmnibus:                        0.087   Durbin-Watson:                   1.498\nProb(Omnibus):                  0.957   Jarque-Bera (JB):                0.114\nSkew:                           0.099   Prob(JB):                        0.945\nKurtosis:                       2.698   Cond. No.                         62.1\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.870527Z",
          "start_time": "2023-01-20T04:35:04.856257Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLGDnydZlniv",
        "outputId": "3d4f629a-c5e5-40a3-fd64-b511200e9f6d",
        "trusted": true
      },
      "cell_type": "code",
      "source": "(model3.rsquared,model3.rsquared_adj)",
      "execution_count": 20,
      "outputs": [
        {
          "data": {
            "text/plain": "(0.6958062276308671, 0.6797960290851233)"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "G8vqsjSgw1TB"
      },
      "cell_type": "markdown",
      "source": "#Prediction"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.887065Z",
          "start_time": "2023-01-20T04:35:04.871728Z"
        },
        "id": "y6jboQgOqowz",
        "trusted": true
      },
      "cell_type": "code",
      "source": "newdata=pd.Series([10,5])   #predict the new values",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.904095Z",
          "start_time": "2023-01-20T04:35:04.887065Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "hErEbur6sqZw",
        "outputId": "79693cfc-f5de-4fb1-ab7b-11b45adf61de",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_pred=pd.DataFrame(newdata, columns=['delivery_time'])\ndata_pred",
      "execution_count": 22,
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   delivery_time\n0             10\n1              5"
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-20T04:35:04.920915Z",
          "start_time": "2023-01-20T04:35:04.904095Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pq-JXNhstQC8",
        "outputId": "a05fda5a-0b9a-4503-97a4-5317dda03b40",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model3.predict(data_pred)",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "text/plain": "0    1.837641\n1    1.399287\ndtype: float64"
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "id": "kfsso6DJw_j-"
      },
      "cell_type": "markdown",
      "source": "#Inference"
    },
    {
      "metadata": {
        "id": "YOPaZdhwxLqU"
      },
      "cell_type": "markdown",
      "source": "Hence, np.sqrt transforfation model is suitable accuracy for the data."
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "delivery_time_main (1).ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "SimpleLinearRegression(delivery_time).ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}