yogeshchappewar/SimpleLinearRegression(Salary_hike).ipynb

## SimpleLinearRegression(Salary_hike).ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:02.843624Z",
          "start_time": "2023-02-06T03:00:01.390859Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tt0aqNbgZMo4",
        "outputId": "4fbc5b0b-f8c0-493e-a141-4f292aa859f8",
        "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": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:02.900678Z",
          "start_time": "2023-02-06T03:00:02.847015Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "3cZtzQTOZPlJ",
        "outputId": "6edb0551-f9a2-4214-957b-cfa77f1ff649",
        "trusted": true
      },
      "cell_type": "code",
      "source": "salary=pd.read_csv('Salary_Data.csv')\nsalary",
      "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>YearsExperience</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.1</td>\n      <td>39343.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.3</td>\n      <td>46205.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.5</td>\n      <td>37731.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.0</td>\n      <td>43525.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.2</td>\n      <td>39891.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2.9</td>\n      <td>56642.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3.0</td>\n      <td>60150.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3.2</td>\n      <td>54445.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3.2</td>\n      <td>64445.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3.7</td>\n      <td>57189.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>3.9</td>\n      <td>63218.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>4.0</td>\n      <td>55794.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4.0</td>\n      <td>56957.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>4.1</td>\n      <td>57081.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>4.5</td>\n      <td>61111.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>4.9</td>\n      <td>67938.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>5.1</td>\n      <td>66029.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>5.3</td>\n      <td>83088.0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>5.9</td>\n      <td>81363.0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>6.0</td>\n      <td>93940.0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>6.8</td>\n      <td>91738.0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>7.1</td>\n      <td>98273.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>7.9</td>\n      <td>101302.0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>8.2</td>\n      <td>113812.0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>8.7</td>\n      <td>109431.0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>9.0</td>\n      <td>105582.0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>9.5</td>\n      <td>116969.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>9.6</td>\n      <td>112635.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>10.3</td>\n      <td>122391.0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>10.5</td>\n      <td>121872.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "    YearsExperience    Salary\n0               1.1   39343.0\n1               1.3   46205.0\n2               1.5   37731.0\n3               2.0   43525.0\n4               2.2   39891.0\n5               2.9   56642.0\n6               3.0   60150.0\n7               3.2   54445.0\n8               3.2   64445.0\n9               3.7   57189.0\n10              3.9   63218.0\n11              4.0   55794.0\n12              4.0   56957.0\n13              4.1   57081.0\n14              4.5   61111.0\n15              4.9   67938.0\n16              5.1   66029.0\n17              5.3   83088.0\n18              5.9   81363.0\n19              6.0   93940.0\n20              6.8   91738.0\n21              7.1   98273.0\n22              7.9  101302.0\n23              8.2  113812.0\n24              8.7  109431.0\n25              9.0  105582.0\n26              9.5  116969.0\n27              9.6  112635.0\n28             10.3  122391.0\n29             10.5  121872.0"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:02.930883Z",
          "start_time": "2023-02-06T03:00:02.903346Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "MbUQgjceaQVr",
        "outputId": "b3babf6e-cc1d-4ea4-cc67-caead231d1eb",
        "trusted": true
      },
      "cell_type": "code",
      "source": "salary.describe()",
      "execution_count": 5,
      "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>YearsExperience</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30.000000</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.313333</td>\n      <td>76003.000000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.837888</td>\n      <td>27414.429785</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.100000</td>\n      <td>37731.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>3.200000</td>\n      <td>56720.750000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.700000</td>\n      <td>65237.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.700000</td>\n      <td>100544.750000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>10.500000</td>\n      <td>122391.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       YearsExperience         Salary\ncount        30.000000      30.000000\nmean          5.313333   76003.000000\nstd           2.837888   27414.429785\nmin           1.100000   37731.000000\n25%           3.200000   56720.750000\n50%           4.700000   65237.000000\n75%           7.700000  100544.750000\nmax          10.500000  122391.000000"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:02.957883Z",
          "start_time": "2023-02-06T03:00:02.933938Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "kNn56r0aaXEP",
        "outputId": "88a4e102-3553-4e8c-e904-14fad48d93e2",
        "trusted": true
      },
      "cell_type": "code",
      "source": "salary.corr()",
      "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>YearsExperience</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>YearsExperience</th>\n      <td>1.000000</td>\n      <td>0.978242</td>\n    </tr>\n    <tr>\n      <th>Salary</th>\n      <td>0.978242</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                 YearsExperience    Salary\nYearsExperience         1.000000  0.978242\nSalary                  0.978242  1.000000"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:03.205387Z",
          "start_time": "2023-02-06T03:00:02.958388Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "F-8jW5zwa_4U",
        "outputId": "36637c4d-f3f7-449e-dc6f-2544e2bee888",
        "trusted": true
      },
      "cell_type": "code",
      "source": "x=salary.YearsExperience\ny=salary.Salary\nplt.scatter(x,y)\nplt.xlabel('YearsExperience')\nplt.ylabel('Salary')",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'Salary')"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAGwCAYAAABrUCsdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABAnUlEQVR4nO3dfVjUdb7/8deAOiDiCBgMs5HRzZqEWWqpZWF5x0nwtNtVu6mkWz8r8ybSytw9rdopSSvrbK52s6dtd62oa802KlnsTjM1XIxNpFYz8haiFRzU5Cb4/P7wMDkCCviFGZjn47rmuuL7fc/Me+acbV59vp/v52MzxhgBAADgjAX5ugEAAIDOgmAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEW6+LqBQFNXV6cDBw4oPDxcNpvN1+0AAIBmMMbo8OHDcrlcCgpqelyKYNXODhw4oLi4OF+3AQAAWmHv3r06++yzmzxPsGpn4eHhko7/H6Znz54+7gYAADRHRUWF4uLiPL/jTSFYtbP6y389e/YkWAEA0MGcbhoPk9cBAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACzCyusAAMBv1dYZ5RaVqfRwpaLDQ3RFfKSCg069+rkvEawAAIBfyi4o1sKsQhW7Kz3HYh0hmp+aoOTEWB921jQuBQIAAL+TXVCsaSu3eoUqSSpxV2rayq3KLij2Ol5bZ7Rp10H9LX+/Nu06qNo6057tejBiBQAA/EptndHCrEI1Fo2MJJukhVmFGp3gVHCQza9GthixAgAAfiW3qKzBSNWJjKRid6Vyi8paPLLV1ghWAADAr5QebjpUnajEfeyUI1vS8ZGt9rwsSLACAAB+JTo8pFl1ZUermz2y1V4IVgAAwK9cER+pWEeImlpUwabjc6gie9ib9XrNHQGzAsEKAAD4leAgm+anJkhSg3BV//f81AQ5ezZvZKu5I2BWIFgBAAC/k5wYqxWTBsrp8A5FTkeIVkwaqOTE2GaPbF0RH9nm/dZjuQUAAOCXkhNjNTrB2eTK6/UjW9NWbpVN8prEfuLIVnuu1O7TEav169crNTVVLpdLNptNb775pudcTU2N5s6dq/79+yssLEwul0u33nqrDhw44PUaVVVVmjlzpnr37q2wsDCNHz9e+/bt86opLy9XWlqaHA6HHA6H0tLSdOjQIa+aPXv2KDU1VWFhYerdu7dmzZql6upqr5pt27YpKSlJoaGh+slPfqKHH35YxvhmATIAAAJBcJBNw86P0n9e+hMNOz+qQUhqzshWe/LpiNXRo0c1YMAA/epXv9KNN97ode7777/X1q1b9dBDD2nAgAEqLy9Xenq6xo8fr3/84x+euvT0dGVlZSkzM1NRUVGaM2eOUlJSlJeXp+DgYEnShAkTtG/fPmVnZ0uS7rjjDqWlpSkrK0uSVFtbq3Hjxumss87Shg0bdPDgQU2ePFnGGD3zzDOSpIqKCo0ePVrXXnuttmzZoh07dmjKlCkKCwvTnDlz2uPrAgAAjTjdyFa7Mn5Cklm9evUpa3Jzc40ks3v3bmOMMYcOHTJdu3Y1mZmZnpr9+/eboKAgk52dbYwxprCw0Egymzdv9tRs2rTJSDJffvmlMcaYd9991wQFBZn9+/d7al599VVjt9uN2+02xhizfPly43A4TGVlpacmIyPDuFwuU1dX12TPlZWVxu12ex579+41kjyvCwAA/J/b7W7W73eHmrzudrtls9nUq1cvSVJeXp5qamo0ZswYT43L5VJiYqI2btwoSdq0aZMcDoeGDBniqRk6dKgcDodXTWJiolwul6dm7NixqqqqUl5enqcmKSlJdrvdq+bAgQP65ptvmuw5IyPDcwnS4XAoLi7ujL8HAADgnzpMsKqsrNSDDz6oCRMmqGfPnpKkkpISdevWTREREV61MTExKikp8dRER0c3eL3o6GivmpiYGK/zERER6tat2ylr6v+ur2nMvHnz5Ha7PY+9e/e25GMDAIAOpEPcFVhTU6Nf/vKXqqur0/Lly09bb4yRzfbjddUT/9nKGvN/E9cbe249u93uNcoFAAA6L78fsaqpqdHNN9+soqIirV271jNaJUlOp1PV1dUqLy/3ek5paalnNMnpdOrbb79t8LrfffedV83Jo07l5eWqqak5ZU1paakkNRjJAgAAgcmvg1V9qNq5c6fee+89RUVFeZ0fNGiQunbtqrVr13qOFRcXq6CgQFdeeaUkadiwYXK73crNzfXUfPrpp3K73V41BQUFKi7+cQfsnJwc2e12DRo0yFOzfv16ryUYcnJy5HK5dO6551r+2QEAQMdjM8Z3CzEdOXJEX331lSTpsssu09KlS3XttdcqMjJSLpdLN954o7Zu3aq3337ba1QoMjJS3bp1kyRNmzZNb7/9tl566SVFRkbqvvvu08GDB72WW/iP//gPHThwQM8995yk48st9OnTx2u5hUsvvVQxMTF6/PHHVVZWpilTpuiGG27wLLfgdrvVt29fXXfddfr1r3+tnTt3asqUKfrtb3/bouUWKioq5HA45Ha7vUbfAACA/2r273eb3594Ch9++KHR8YVSvR6TJ082RUVFjZ6TZD788EPPaxw7dszMmDHDREZGmtDQUJOSkmL27Nnj9T4HDx40EydONOHh4SY8PNxMnDjRlJeXe9Xs3r3bjBs3zoSGhprIyEgzY8YMr6UVjDHm888/N1dffbWx2+3G6XSaBQsWnHKphcY093ZNAADgP5r7++3TEatAxIgVAMAf1dYZ/1hg00819/e7Q9wVCAAA2k52QbEWZhWq2F3pORbrCNH81IR23xKmo/PryesAAKBtZRcUa9rKrV6hSpJK3JWatnKrsguKm3gmGkOwAgAgQNXWGS3MKlRjc4Lqjy3MKlRtHbOGmotgBQBAgMotKmswUnUiI6nYXancorL2a6qDI1gBABCgSg83HapaUweCFQAAASs6PMTSOhCsAAAIWFfERyrWEaKmFlWw6fjdgVfER7ZnWx0awQoAgAAVHGTT/NQESWoQrur/np+awHpWLUCwAgAggCUnxmrFpIFyOrwv9zkdIVoxaSDrWLUQC4QCABDgkhNjNTrBycrrFiBYAQAABQfZNOz8KF+30eFxKRAAAMAijFgBAHACNiPGmSBYAQDwf9iMGGeKS4EAAIjNiGENghUAIOCxGTGsQrACAAQ8NiOGVZhjBQAIeGxG3Dgm8rccwQoAEPDYjLghJvK3DpcCAQABj82IvTGRv/UIVgCAgMdmxD9iIv+ZIVgBACA2I67HRP4zwxwrAAD+D5sRM5H/TBGsAAA4QaBvRsxE/jPDpUAAAODBRP4zQ7ACAAAeTOQ/MwQrAADghYn8rcccKwAA0AAT+VuHYAUAABoV6BP5W4NgBQBAO2L/vc6NYAUAQDth/73Oj8nrAAC0A/bfCwwEKwAA2hj77wUOghUAAG2M/fcCB8EKAIA2xv57gYNgBQBAG2P/vcBBsAIAoI2x/17gIFgBANDG2H8vcBCsAABoB+y/FxhYIBQAgHbC/nudH8EKAIB2xP57nRuXAgEAACxCsAIAALAIwQoAAMAiBCsAAACLMHkdAIAOoLbOcDdhB0CwAgDAz2UXFGthVqHXRs6xjhDNT01g/Ss/w6VAAAD8WHZBsaat3OoVqiSpxF2paSu3Krug2EedoTEEKwAA/FRtndHCrEKZRs7VH1uYVajausYq4AsEKwAA/FRuUVmDkaoTGUnF7krlFpW1X1M4JYIVAAB+qvRw06GqNXVoewQrAAD8VHR4yOmLWlCHtkewAgDAT10RH6lYR4iaWlTBpuN3B14RH9mebeEUCFYAAPip4CCb5qcmSFKDcFX/9/zUBNaz8iMEKwAA/FhyYqxWTBoop8P7cp/TEaIVkwayjpWfYYFQAAD8XHJirEYnOFl5vQMgWAEA0AEEB9k07PwoX7eB0+BSIAAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFuCsQANDp1NYZliaATxCsAACdSnZBsRZmFarY/ePGxLGOEM1PTWAxTbQ5LgUCADqN7IJiTVu51StUSVKJu1LTVm5VdkGxjzpDoCBYAQA6hdo6o4VZhTKNnKs/tjCrULV1jVUA1iBYAQA6hdyisgYjVScykordlcotKmu/phBwCFYAgE6h9HDToao1dUBr+DRYrV+/XqmpqXK5XLLZbHrzzTe9zhtjtGDBArlcLoWGhmrEiBHavn27V01VVZVmzpyp3r17KywsTOPHj9e+ffu8asrLy5WWliaHwyGHw6G0tDQdOnTIq2bPnj1KTU1VWFiYevfurVmzZqm6utqrZtu2bUpKSlJoaKh+8pOf6OGHH5YxDCkDgD+IDg+xtA5oDZ8Gq6NHj2rAgAFatmxZo+eXLFmipUuXatmyZdqyZYucTqdGjx6tw4cPe2rS09O1evVqZWZmasOGDTpy5IhSUlJUW1vrqZkwYYLy8/OVnZ2t7Oxs5efnKy0tzXO+trZW48aN09GjR7VhwwZlZmZq1apVmjNnjqemoqJCo0ePlsvl0pYtW/TMM8/oiSee0NKlS9vgmwEAtNQV8ZGKdYSoqUUVbDp+d+AV8ZHt2RYCjfETkszq1as9f9fV1Rmn02kee+wxz7HKykrjcDjMs88+a4wx5tChQ6Zr164mMzPTU7N//34TFBRksrOzjTHGFBYWGklm8+bNnppNmzYZSebLL780xhjz7rvvmqCgILN//35Pzauvvmrsdrtxu93GGGOWL19uHA6Hqays9NRkZGQYl8tl6urqmv053W63keR5XQCAddZsO2DOnfu2OXfu26bPCY/6Y2u2HfB1i+igmvv77bdzrIqKilRSUqIxY8Z4jtntdiUlJWnjxo2SpLy8PNXU1HjVuFwuJSYmemo2bdokh8OhIUOGeGqGDh0qh8PhVZOYmCiXy+WpGTt2rKqqqpSXl+epSUpKkt1u96o5cOCAvvnmmyY/R1VVlSoqKrweAIC2kZwYqxWTBsrp8L7c53SEaMWkgaxjhTbntwuElpSUSJJiYmK8jsfExGj37t2emm7duikiIqJBTf3zS0pKFB0d3eD1o6OjvWpOfp+IiAh169bNq+bcc89t8D715+Lj4xv9HBkZGVq4cOFpPy8AwBrJibEaneBk5XX4hN8Gq3o2m/f/EIwxDY6d7OSaxuqtqDH/N3H9VP3MmzdPs2fP9vxdUVGhuLi4U/YPADgzwUE2DTs/ytdtIAD57aVAp9Mp6ceRq3qlpaWekSKn06nq6mqVl5efsubbb79t8PrfffedV83J71NeXq6amppT1pSWlkpqOKp2Irvdrp49e3o9AKCzqK0z2rTroP6Wv1+bdh1k8U0EPL8NVvHx8XI6nVq7dq3nWHV1tdatW6crr7xSkjRo0CB17drVq6a4uFgFBQWemmHDhsntdis3N9dT8+mnn8rtdnvVFBQUqLj4x60OcnJyZLfbNWjQIE/N+vXrvZZgyMnJkcvlanCJEAACQXZBsYYv/kC3vLBZ92Tm65YXNmv44g/YNgYBzafB6siRI8rPz1d+fr6k4xPW8/PztWfPHtlsNqWnp2vRokVavXq1CgoKNGXKFHXv3l0TJkyQJDkcDt1+++2aM2eO3n//fX322WeaNGmS+vfvr1GjRkmS+vXrp+TkZE2dOlWbN2/W5s2bNXXqVKWkpKhv376SpDFjxighIUFpaWn67LPP9P777+u+++7T1KlTPSNMEyZMkN1u15QpU1RQUKDVq1dr0aJFmj179mkvTQJAZ8OefEDjbMb4boXLjz76SNdee22D45MnT9ZLL70kY4wWLlyo5557TuXl5RoyZIh+//vfKzEx0VNbWVmp+++/X6+88oqOHTumkSNHavny5V7zmMrKyjRr1iy99dZbkqTx48dr2bJl6tWrl6dmz549uvvuu/XBBx8oNDRUEyZM0BNPPOF1F+C2bds0ffp05ebmKiIiQnfddZd++9vftihYVVRUyOFwyO12c1kQQIdUW2c0fPEHTW4fY9Pxu/A2zL2OCePoNJr7++3TYBWICFYAOrpNuw7qlhc2n7bu1alDmUCOTqO5v99+O8cKAOCf2JMPaBrBCgDQIuzJBzSNYAUAaBH25AOaRrACALRIcJBN81MTJKlBuKr/e35qAhPXEZAIVgCAFmNPPqBxfr+lDQDAP7EnH9AQwQoA0GrsyQd441IgAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEW4KxAA2kltnWFpAqCTI1gBQDvILijWwqxCFbt/3Jg41hGi+akJLKYJdCJcCgSANpZdUKxpK7d6hSpJKnFXatrKrcouKPZRZwCsRrACgDZUW2e0MKtQppFz9ccWZhWqtq6xCgAdDcEKANpQblFZg5GqExlJxe5K5RaVtV9TANoMwQoA2lDp4aZDVWvqAPg3ghUAtKHo8BBL6wD4N4IVALShK+IjFesIUVOLKth0/O7AK+Ij27MtAG2EYAUAbSg4yKb5qQmS1CBc1f89PzWB9ayAToJgBQBtLDkxVismDZTT4X25z+kI0YpJA32+jlVtndGmXQf1t/z92rTrIHcoAmeABUIBoB0kJ8ZqdILT71ZeZ+FSwFo2Ywz/adKOKioq5HA45Ha71bNnT1+3AyCA1S9cevKPQH3U84fRNMBfNPf3m0uBABCAWLgUaBsEKwAIQCxcCrQNghUABCAWLgXaBsEKAAIQC5cCbYNgBQABiIVLgbZBsAKAAMTCpUDbIFgBQIDy94VLgY6IBUIBIID568KlQEdFsAKAABccZNOw86N83QbQKXApEAAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwSKuC1UcffWRxGwAAAB1fq4JVcnKyzj//fD3yyCPau3ev1T0BAAB0SK0KVgcOHNA999yjN954Q/Hx8Ro7dqxef/11VVdXW90fAFiits5o066D+lv+fm3adVC1dcbXLQHohGzGmDP6t0t+fr5efPFFvfrqq6qrq9PEiRN1++23a8CAAVb12KlUVFTI4XDI7XarZ8+evm4HCAjZBcVamFWoYnel51isI0TzUxOUnBjrw84AdBTN/f0+48nrl156qR588EFNnz5dR48e1YsvvqhBgwbp6quv1vbt28/05QHgjGQXFGvayq1eoUqSStyVmrZyq7ILin3UGYDOqNXBqqamRn/96191/fXXq0+fPvr73/+uZcuW6dtvv1VRUZHi4uJ00003WdkrALRIbZ3RwqxCNTYsX39sYVYhlwUBWKZLa540c+ZMvfrqq5KkSZMmacmSJUpMTPScDwsL02OPPaZzzz3XkiYBoDVyi8oajFSdyEgqdlcqt6hMw86Par/GAHRarQpWhYWFeuaZZ3TjjTeqW7dujda4XC59+OGHZ9QcAJyJ0sNNh6rW1AHA6bT4UmBNTY3OOeccDRkypMlQJUldunRRUlLSGTUHAGciOjzE0joAOJ0WB6uuXbtq9erVbdELAFjqivhIxTpCZGvivE3H7w68Ij7S8vdmeQcgMLXqUuDPfvYzvfnmm5o9e7bV/QCAZYKDbJqfmqBpK7fKJnlNYq8PW/NTExQc1FT0ah2WdwACV6uC1QUXXKD//u//1saNGzVo0CCFhYV5nZ81a5YlzQHAmUpOjNWKSQMbBB1nGwWd+uUdTh6fql/eYcWkgYQroBNr1QKh8fHxTb+gzaavv/76jJrqzFggFPCN2jqj3KIylR6uVHT48ct/Vo9U1dYZDV/8QZN3Itp0PNBtmHud5e8NoG019/e7VSNWRUVFrW4MAHwhOMjW5ksqsLwDgDNeeR0AcBzLOwBo1YiVJO3bt09vvfWW9uzZ02Dz5aVLl55xYwDQ0bC8A4BWjVi9//776tu3r5YvX64nn3xSH374of74xz/qxRdfVH5+vmXN/fDDD/qv//ovxcfHKzQ0VOedd54efvhh1dXVeWqMMVqwYIFcLpdCQ0M1YsSIBnsUVlVVaebMmerdu7fCwsI0fvx47du3z6umvLxcaWlpcjgccjgcSktL06FDh7xq9uzZo9TUVIWFhal3796aNWtWg1AJIHD5cnkHAP6hVcFq3rx5mjNnjgoKChQSEqJVq1Zp7969SkpKsnR/wMWLF+vZZ5/VsmXL9MUXX2jJkiV6/PHH9cwzz3hqlixZoqVLl2rZsmXasmWLnE6nRo8ercOHD3tq0tPTtXr1amVmZmrDhg06cuSIUlJSVFtb66mZMGGC8vPzlZ2drezsbOXn5ystLc1zvra2VuPGjdPRo0e1YcMGZWZmatWqVZozZ45lnxdAx1a/vIOkBuGqLZd3AOBHTCv06NHDfPXVV8YYY3r16mUKCgqMMcbk5+ebPn36tOYlGzVu3Dhz2223eR37+c9/biZNmmSMMaaurs44nU7z2GOPec5XVlYah8Nhnn32WWOMMYcOHTJdu3Y1mZmZnpr9+/eboKAgk52dbYwxprCw0Egymzdv9tRs2rTJSDJffvmlMcaYd9991wQFBZn9+/d7al599VVjt9uN2+1u8jNUVlYat9vteezdu9dIOuVzAHRsa7YdMEMXvWf6zH3b8xi66D2zZtsBX7cGoJXcbnezfr9bNWIVFhamqqoqScf3BNy1a5fn3L///e8zT3v/Z/jw4Xr//fe1Y8cOSdI///lPbdiwQddff72k43cnlpSUaMyYMZ7n2O12JSUlaePGjZKkvLw81dTUeNW4XC4lJiZ6ajZt2iSHw6EhQ4Z4aoYOHSqHw+FVk5iYKJfL5akZO3asqqqqlJeX1+RnyMjI8FxedDgciouLO9OvBYCfS06M1Ya51+nVqUP1P7+8VK9OHaoNc69j/SogALRq8vrQoUP1ySefKCEhQePGjdOcOXO0bds2vfHGGxo6dKhlzc2dO1dut1sXXXSRgoODVVtbq0cffVS33HKLJKmkpESSFBMT4/W8mJgY7d6921PTrVs3RURENKipf35JSYmio6MbvH90dLRXzcnvExERoW7dunlqGjNv3jyvFeorKioIV0AAaI/lHQD4n1YFq6VLl+rIkSOSpAULFujIkSN67bXXdMEFF+ipp56yrLnXXntNK1eu1CuvvKKLL75Y+fn5Sk9Pl8vl0uTJkz11Npv3fAVjTINjJzu5prH61tSczG63y263n7IXAADQObQqWJ133nmef+7evbuWL19uWUMnuv/++/Xggw/ql7/8pSSpf//+2r17tzIyMjR58mQ5nU5Jx0eTYmN/HGIvLS31jC45nU5VV1ervLzca9SqtLRUV155pafm22+/bfD+3333ndfrfPrpp17ny8vLVVNT02AkCwAABCa/XiD0+++/V1CQd4vBwcGe5Rbi4+PldDq1du1az/nq6mqtW7fOE5oGDRqkrl27etUUFxeroKDAUzNs2DC53W7l5uZ6aj799FO53W6vmoKCAhUXF3tqcnJyZLfbNWjQIIs/OQAA6IiaPWIVERFx2str9crKylrd0IlSU1P16KOP6pxzztHFF1+szz77TEuXLtVtt90m6filufT0dC1atEgXXnihLrzwQi1atEjdu3fXhAkTJEkOh0O333675syZo6ioKEVGRuq+++5T//79NWrUKElSv379lJycrKlTp+q5556TJN1xxx1KSUlR3759JUljxoxRQkKC0tLS9Pjjj6usrEz33Xefpk6dyp5/AABAUguC1dNPP92GbTTumWee0UMPPaS7775bpaWlcrlcuvPOO/Xb3/7WU/PAAw/o2LFjuvvuu1VeXq4hQ4YoJydH4eHhnpqnnnpKXbp00c0336xjx45p5MiReumllxQcHOypefnllzVr1izP3YPjx4/XsmXLPOeDg4P1zjvv6O6779ZVV12l0NBQTZgwQU888UQ7fBMAAKAjsBljjK+bCCTN3R0bAAD4j+b+frd6r8B6x44dU01NjdcxAgMAAAhErZq8fvToUc2YMUPR0dHq0aOHIiIivB4AAACBqFXB6oEHHtAHH3yg5cuXy2636w9/+IMWLlwol8ulP//5z1b3CAAA0CG06lJgVlaW/vznP2vEiBG67bbbdPXVV+uCCy5Qnz599PLLL2vixIlW9wkAAOD3WjViVVZWpvj4eEnH51PVL68wfPhwrV+/3rruAAAAOpBWBavzzjtP33zzjSQpISFBr7/+uqTjI1m9evWyqjcAAIAOpVXB6le/+pX++c9/Sjq+yXD9XKt7771X999/v6UNAgAAdBSWrGO1Z88e/eMf/9D555+vAQMGWNFXp8U6VgAAdDzN/f1u0YjVp59+qjVr1ngd+/Of/6ykpCTddddd+v3vf6+qqqrWdQwAANDBtShYLViwQJ9//rnn723btun222/XqFGjNG/ePGVlZSkjI8PyJgEAADqCFgWr/Px8jRw50vN3ZmamhgwZohdeeEH33nuvfve733kmsgMAAASaFgWr8vJyxcTEeP5et26dkpOTPX9ffvnl2rt3r3XdAQAAdCAtClYxMTEqKiqSJFVXV2vr1q0aNmyY5/zhw4fVtWtXazsEAADoIFoUrJKTk/Xggw/q448/1rx589S9e3ddffXVnvOff/65zj//fMubBAAA6AhatKXNI488op///OdKSkpSjx499Kc//UndunXznH/xxRc1ZswYy5sEAADoCFq1jpXb7VaPHj0UHBzsdbysrEw9evTwClvwxjpWAAB0PM39/W7VJswOh6PR45GRka15OQAAgE6hVVvaAAAAoCGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFuvi6AQBoTG2dUW5RmUoPVyo6PERXxEcqOMjm67YA4JQIVgD8TnZBsRZmFarYXek5FusI0fzUBCUnxvqwMwA4NS4FAvAr2QXFmrZyq1eokqQSd6Wmrdyq7IJiH3UGAKdHsALgN2rrjBZmFco0cq7+2MKsQtXWNVYBAL5HsALgN3KLyhqMVJ3ISCp2Vyq3qKz9mgKAFiBYAfAbpYebDlWtqQOA9kawAuA3osNDLK0DgPZGsALgN66Ij1SsI0RNLapg0/G7A6+Ij2zPtgCg2QhWAPxGcJBN81MTJKlBuKr/e35qgtd6VrV1Rpt2HdTf8vdr066DTGwH4FOsYwXAryQnxmrFpIEN1rFyNrKOFetdAfA3NmMM/3nXjioqKuRwOOR2u9WzZ09ftwP4rdOtvF6/3tXJ/wKrr1gxaSDhCoBlmvv7zYgVAL8UHGTTsPOjGj13uvWubDq+3tXoBCfb4ABoV8yxAtDhsN4VAH9FsALQ4bDeFQB/xaVAoA2cbn4QzgzrXQHwVwQrwGLcqdb26te7KnFXNjrPyqbjdxGy3hWA9salQMBC9XeqnTz/p8RdqWkrtyq7oNhHnXUurVnvCgDaA8EKsMjp7lSTjt+pxgKW1qhf78rp8L7c53SEsNQCAJ/hUiBgkZbcqdbUMgJomeTEWI1OcDKfDYDfIFgBFvG3O9UCZQL9qda7AoD2RrACLOJPd6oxgR4AfIM5VoBF6u9Ua2pMyKbj4aat71RjAj0A+A7BCrCIP9ypxgR6APAtghVgIV/fqcZWLwDgW8yxAizmyzvV/G0CPQAEGoIV0AZ8daeaP02gB4BAxKVAoBPxlwn0ABCoCFZAJ+LrCfS1dUabdh3U3/L3a9Oug0ySBxBwuBQIdDL1E+hPXsfK2cbrWLF2FgBINmMM/0nZjioqKuRwOOR2u9WzZ09ft4NOrD1XXq9fO+vkf5nUvxt79wHo6Jr7+82IFdBJtdcE+tOtnWXT8bWzRic4O+WWOgBwIuZYAS3AHKKGWDsLAH7EiBXQTMwhahxrZwHAj/x+xGr//v2aNGmSoqKi1L17d1166aXKy8vznDfGaMGCBXK5XAoNDdWIESO0fft2r9eoqqrSzJkz1bt3b4WFhWn8+PHat2+fV015ebnS0tLkcDjkcDiUlpamQ4cOedXs2bNHqampCgsLU+/evTVr1ixVV1e32WeH/2D/vaaxdhYA/Mivg1V5ebmuuuoqde3aVWvWrFFhYaGefPJJ9erVy1OzZMkSLV26VMuWLdOWLVvkdDo1evRoHT582FOTnp6u1atXKzMzUxs2bNCRI0eUkpKi2tpaT82ECROUn5+v7OxsZWdnKz8/X2lpaZ7ztbW1GjdunI4ePaoNGzYoMzNTq1at0pw5c9rlu4DvsP/eqbF2FgD8yK/vCnzwwQf1ySef6OOPP270vDFGLpdL6enpmjt3rqTjo1MxMTFavHix7rzzTrndbp111ln6y1/+ol/84heSpAMHDiguLk7vvvuuxo4dqy+++EIJCQnavHmzhgwZIknavHmzhg0bpi+//FJ9+/bVmjVrlJKSor1798rlckmSMjMzNWXKFJWWljb7Dj/uCux4Nu06qFte2HzaulenDvXJauv+oH5ET5JXAOWuQACdRXN/v/16xOqtt97S4MGDddNNNyk6OlqXXXaZXnjhBc/5oqIilZSUaMyYMZ5jdrtdSUlJ2rhxoyQpLy9PNTU1XjUul0uJiYmemk2bNsnhcHhClSQNHTpUDofDqyYxMdETqiRp7Nixqqqq8ro0ebKqqipVVFR4PdCxMIfo9Hy9+TQA+Au/nrz+9ddfa8WKFZo9e7Z+/etfKzc3V7NmzZLdbtett96qkpISSVJMTIzX82JiYrR7925JUklJibp166aIiIgGNfXPLykpUXR0dIP3j46O9qo5+X0iIiLUrVs3T01jMjIytHDhwhZ+cvgT5hA1jy83nwYAf+HXwaqurk6DBw/WokWLJEmXXXaZtm/frhUrVujWW2/11Nls3v/iNsY0OHayk2saq29NzcnmzZun2bNne/6uqKhQXFzcKXuDf6mfQ1Tirmx0npVNx0dmmEPku82nAcBf+PWlwNjYWCUkJHgd69evn/bs2SNJcjqdktRgxKi0tNQzuuR0OlVdXa3y8vJT1nz77bcN3v+7777zqjn5fcrLy1VTU9NgJOtEdrtdPXv29HqgY/H1/nsAgI7Dr4PVVVddpX/9619ex3bs2KE+ffpIkuLj4+V0OrV27VrP+erqaq1bt05XXnmlJGnQoEHq2rWrV01xcbEKCgo8NcOGDZPb7VZubq6n5tNPP5Xb7faqKSgoUHHxj7fV5+TkyG63a9CgQRZ/cvgb5hABAJrF+LHc3FzTpUsX8+ijj5qdO3eal19+2XTv3t2sXLnSU/PYY48Zh8Nh3njjDbNt2zZzyy23mNjYWFNRUeGpueuuu8zZZ59t3nvvPbN161Zz3XXXmQEDBpgffvjBU5OcnGwuueQSs2nTJrNp0ybTv39/k5KS4jn/ww8/mMTERDNy5EizdetW895775mzzz7bzJgxo0Wfye12G0nG7XafwTcDX/mhts5s/Orf5s3P9pmNX/3b/FBb5+uWAADtoLm/334drIwxJisryyQmJhq73W4uuugi8/zzz3udr6urM/PnzzdOp9PY7XZzzTXXmG3btnnVHDt2zMyYMcNERkaa0NBQk5KSYvbs2eNVc/DgQTNx4kQTHh5uwsPDzcSJE015eblXze7du824ceNMaGioiYyMNDNmzDCVlZUt+jwEKwAAOp7m/n779TpWnRHrWMEKtXXGsrvvrHwtAOismvv77dd3BQJo6Ez2LDw5RJUfrdZ/v8P+hwBgFUas2hkjVh2HP47k1K9wfvL/aJuzwnljgawxrJYOAA0xYgWcgTMZFWorp9uz0KbjexaOTnA2CIBNBbLGnO61AABN8+vlFgBfqA8hJ4/slLgrNW3lVmUXFDfxzLaVW1R2ytEmI6nYXancojKv46cKZC19LQDAqRGsgBOcblRIOj6SU1vX/lfQW7tn4ekCmRXvCQA4jmAFnKC1o0LtobV7Fp5JOAr0/Q8BoKUIVsAJWjsq1B7q9yxsasaTTcfngZ28Z2FrwlFTrwUAODWCFXCC1o4KtYfW7lk4qE+EWjL/nP0PAaD1CFbACVo7KtReWrNnYd7ucrVkShj7HwJA67HcAnCC+lGhaSu3yiZ5TWL3l5Gc5MRYjU5wNnuNreZetrx1WB/9R2KsX6zXBQAdFcEKOEn9qNDJ61g5W7COVVsvLhocZNOw86OaVdvcy5b/kRjb7NcEADSOYAU0oqWjQifyt8VF6y9vlrgrG11GwqbjoZGJ6gBw5phjBTShflToPy/9iYadH9XsUOVvi4u2dtI7AKDlCFaARfx5cdHWTHoHALQclwIBi7RkcVFfzGU6k8ubAIDmIVgBFvHnxUXrtWTSOwCg5bgUCFjEnxcXBQC0D4IVYBF/X1wUAND2CFaARbj7DgBAsAIsxN13ABDYmLwOWIy77wAgcBGsgDbA3XcAEJi4FAgAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWKSLrxtA+6mtM8otKlPp4UpFh4foivhIBQfZfN0WAACdBsEqQGQXFGthVqGK3ZWeY7GOEM1PTVByYqwPOwMAoPPgUmAAyC4o1rSVW71ClSSVuCs1beVWZRcU+6gzAAA6F4JVJ1dbZ7Qwq1CmkXP1xxZmFaq2rrEKAADQEgSrTi63qKzBSNWJjKRid6Vyi8rarykAADopglUnV3q46VDVmjoAANA0glUnFx0eYmkdAABoGsGqk7siPlKxjhA1taiCTcfvDrwiPrI92wIAoFMiWHVywUE2zU9NkKQG4ar+7/mpCaxnBQCABQhWASA5MVYrJg2U0+F9uc/pCNGKSQNZxwoAAIuwQGiASE6M1egEJyuvAwDQhghWASQ4yKZh50f5tAe21QEAdGYEK7QbttUBAHR2zLFCu2BbHQBAICBYoc2xrQ4AIFAQrNDm2FYHABAoCFZoc2yrAwAIFAQrtDm21QEABAqCFdoc2+oAAAIFwQptjm11AACBgmCFdsG2OgCAQMACoWg3bKsDAOjsCFZoV/6wrQ4AAG2FS4EAAAAWIVgBAABYhEuB8Eu1dYa5WACADqdDjVhlZGTIZrMpPT3dc8wYowULFsjlcik0NFQjRozQ9u3bvZ5XVVWlmTNnqnfv3goLC9P48eO1b98+r5ry8nKlpaXJ4XDI4XAoLS1Nhw4d8qrZs2ePUlNTFRYWpt69e2vWrFmqrq5uq48bsLILijV88Qe65YXNuiczX7e8sFnDF3/ARs0AAL/XYYLVli1b9Pzzz+uSSy7xOr5kyRItXbpUy5Yt05YtW+R0OjV69GgdPnzYU5Oenq7Vq1crMzNTGzZs0JEjR5SSkqLa2lpPzYQJE5Sfn6/s7GxlZ2crPz9faWlpnvO1tbUaN26cjh49qg0bNigzM1OrVq3SnDlz2v7DB5DsgmJNW7m1wd6CJe5KTVu5lXAFAPBrNmOM8XUTp3PkyBENHDhQy5cv1yOPPKJLL71UTz/9tIwxcrlcSk9P19y5cyUdH52KiYnR4sWLdeedd8rtduuss87SX/7yF/3iF7+QJB04cEBxcXF69913NXbsWH3xxRdKSEjQ5s2bNWTIEEnS5s2bNWzYMH355Zfq27ev1qxZo5SUFO3du1cul0uSlJmZqSlTpqi0tFQ9e/Zs1mepqKiQw+GQ2+1u9nNOp7NcNqutMxq++IMmN2y26fi6VxvmXtchPx8AoONq7u93hxixmj59usaNG6dRo0Z5HS8qKlJJSYnGjBnjOWa325WUlKSNGzdKkvLy8lRTU+NV43K5lJiY6KnZtGmTHA6HJ1RJ0tChQ+VwOLxqEhMTPaFKksaOHauqqirl5eU12XtVVZUqKiq8HlbqTJfNcovKmgxVkmQkFbsrlVtU1n5NAQDQAn4frDIzM5WXl6eMjIwG50pKSiRJMTExXsdjYmI850pKStStWzdFREScsiY6OrrB60dHR3vVnPw+ERER6tatm6emMRkZGZ55Ww6HQ3Fxcaf7yM3W2S6blR5uOlS1pg4AgPbm18Fq7969uueee/Tyyy8rJCSkyTqbzfuykDGmwbGTnVzTWH1rak42b948ud1uz2Pv3r2n7Ku5auuMFmYVqrHruPXHFmYVqrbO76/0ekSHN/1/49bUAQDQ3vw6WOXl5am0tFSDBg1Sly5d1KVLF61bt06/+93v1KVLF88I0skjRqWlpZ5zTqdT1dXVKi8vP2XNt99+2+D9v/vuO6+ak9+nvLxcNTU1DUayTmS329WzZ0+vhxU642WzK+IjFesIabBRcz2bpFjH8TlkAAD4I78OViNHjtS2bduUn5/veQwePFgTJ05Ufn6+zjvvPDmdTq1du9bznOrqaq1bt05XXnmlJGnQoEHq2rWrV01xcbEKCgo8NcOGDZPb7VZubq6n5tNPP5Xb7faqKSgoUHHxj5fXcnJyZLfbNWjQoDb9HhrTGS+bBQfZND81QZIahKv6v+enJjBxHQDgt/x6gdDw8HAlJiZ6HQsLC1NUVJTneHp6uhYtWqQLL7xQF154oRYtWqTu3btrwoQJkiSHw6Hbb79dc+bMUVRUlCIjI3Xfffepf//+nsnw/fr1U3JysqZOnarnnntOknTHHXcoJSVFffv2lSSNGTNGCQkJSktL0+OPP66ysjLdd999mjp1qmWjUC3RVpfNfH2HYXJirFZMGqiFWYVeI3JOR4jmpyYoOTG23XoBAKCl/DpYNccDDzygY8eO6e6771Z5ebmGDBminJwchYeHe2qeeuopdenSRTfffLOOHTumkSNH6qWXXlJwcLCn5uWXX9asWbM8dw+OHz9ey5Yt85wPDg7WO++8o7vvvltXXXWVQkNDNWHCBD3xxBPt92FPUH/ZrMRd2eg8q/qlCVpy2Sy7oLhBoIn1QaBJTozV6ARnp1hCAgAQWDrEOladiZXrWNXfFSjJK1zVx48VkwY2OxDVv9bJ/8/QmtcCAKCz6VTrWKFx9ZfNnA7vy31OR0iLglBnvMMQAABf6PCXAgOdFZfNWnKH4bDzoyzoGgCAzolg1QkEB9nOKPB0xjsMAQDwBS4FgoU5AQCwCMEKLMwJAIBFCFZgYU4AACxCsIIk6+4wBAAgkDF5HR4szAkAwJkhWMHLmd5hCABAIONSIAAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARVh5vZ0ZYyRJFRUVPu4EAAA0V/3vdv3veFMIVu3s8OHDkqS4uDgfdwIAAFrq8OHDcjgcTZ63mdNFL1iqrq5OBw4cUHh4uGy2wNzcuKKiQnFxcdq7d6969uzp63YCDt+/b/H9+xbfv2915O/fGKPDhw/L5XIpKKjpmVSMWLWzoKAgnX322b5uwy/07Nmzw/0PqzPh+/ctvn/f4vv3rY76/Z9qpKoek9cBAAAsQrACAACwCMEK7c5ut2v+/Pmy2+2+biUg8f37Ft+/b/H9+1YgfP9MXgcAALAII1YAAAAWIVgBAABYhGAFAABgEYIVAACARQhWaDcZGRm6/PLLFR4erujoaN1www3617/+5eu2AlJGRoZsNpvS09N93UpA2b9/vyZNmqSoqCh1795dl156qfLy8nzdVkD44Ycf9F//9V+Kj49XaGiozjvvPD388MOqq6vzdWud0vr165WamiqXyyWbzaY333zT67wxRgsWLJDL5VJoaKhGjBih7du3+6ZZixGs0G7WrVun6dOna/PmzVq7dq1++OEHjRkzRkePHvV1awFly5Ytev7553XJJZf4upWAUl5erquuukpdu3bVmjVrVFhYqCeffFK9evXydWsBYfHixXr22We1bNkyffHFF1qyZIkef/xxPfPMM75urVM6evSoBgwYoGXLljV6fsmSJVq6dKmWLVumLVu2yOl0avTo0Z79dDsylluAz3z33XeKjo7WunXrdM011/i6nYBw5MgRDRw4UMuXL9cjjzyiSy+9VE8//bSv2woIDz74oD755BN9/PHHvm4lIKWkpCgmJkb/+7//6zl24403qnv37vrLX/7iw846P5vNptWrV+uGG26QdHy0yuVyKT09XXPnzpUkVVVVKSYmRosXL9add97pw27PHCNW8Bm32y1JioyM9HEngWP69OkaN26cRo0a5etWAs5bb72lwYMH66abblJ0dLQuu+wyvfDCC75uK2AMHz5c77//vnbs2CFJ+uc//6kNGzbo+uuv93FngaeoqEglJSUaM2aM55jdbldSUpI2btzow86swSbM8AljjGbPnq3hw4crMTHR1+0EhMzMTOXl5ekf//iHr1sJSF9//bVWrFih2bNn69e//rVyc3M1a9Ys2e123Xrrrb5ur9ObO3eu3G63LrroIgUHB6u2tlaPPvqobrnlFl+3FnBKSkokSTExMV7HY2JitHv3bl+0ZCmCFXxixowZ+vzzz7VhwwZftxIQ9u7dq3vuuUc5OTkKCQnxdTsBqa6uToMHD9aiRYskSZdddpm2b9+uFStWEKzawWuvvaaVK1fqlVde0cUXX6z8/Hylp6fL5XJp8uTJvm4vINlsNq+/jTENjnVEBCu0u5kzZ+qtt97S+vXrdfbZZ/u6nYCQl5en0tJSDRo0yHOstrZW69ev17Jly1RVVaXg4GAfdtj5xcbGKiEhwetYv379tGrVKh91FFjuv/9+Pfjgg/rlL38pSerfv792796tjIwMglU7czqdko6PXMXGxnqOl5aWNhjF6oiYY4V2Y4zRjBkz9MYbb+iDDz5QfHy8r1sKGCNHjtS2bduUn5/veQwePFgTJ05Ufn4+oaodXHXVVQ2WF9mxY4f69Onjo44Cy/fff6+gIO+fvODgYJZb8IH4+Hg5nU6tXbvWc6y6ulrr1q3TlVde6cPOrMGIFdrN9OnT9corr+hvf/ubwsPDPdfZHQ6HQkNDfdxd5xYeHt5gLltYWJiioqKY49ZO7r33Xl155ZVatGiRbr75ZuXm5ur555/X888/7+vWAkJqaqoeffRRnXPOObr44ov12WefaenSpbrtttt83VqndOTIEX311Veev4uKipSfn6/IyEidc845Sk9P16JFi3ThhRfqwgsv1KJFi9S9e3dNmDDBh11bxADtRFKjjz/+8Y++bi0gJSUlmXvuucfXbQSUrKwsk5iYaOx2u7nooovM888/7+uWAkZFRYW55557zDnnnGNCQkLMeeedZ37zm9+YqqoqX7fWKX344YeN/vt+8uTJxhhj6urqzPz5843T6TR2u91cc801Ztu2bb5t2iKsYwUAAGAR5lgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAGAH/vmm29ks9mUn5/v61YANAPBCkC7McZo1KhRGjt2bINzy5cvl8Ph0J49e9q1p/rg0thj8+bN7dpLY+Li4lRcXMyejkAHwZY2ANrV3r171b9/fy1evFh33nmnpOMbtF5yySV65plnNGXKFEvfr6amRl27dm3y/DfffKP4+Hi99957uvjii73ORUVFnfK5ba26ulrdunXz2fsDaDlGrAC0q7i4OP3P//yP7rvvPhUVFckYo9tvv10jR47UFVdcoeuvv149evRQTEyM0tLS9O9//9vz3OzsbA0fPly9evVSVFSUUlJStGvXLs/5+tGn119/XSNGjFBISIhWrlyp3bt3KzU1VREREQoLC9PFF1+sd99916uvqKgoOZ1Or0fXrl09o2zJycmq/+/QQ4cO6ZxzztFvfvMbSdJHH30km82md955RwMGDFBISIiGDBmibdu2eb3Hxo0bdc011yg0NFRxcXGaNWuWjh496jl/7rnn6pFHHtGUKVPkcDg0derURi8FFhYWnvJ7GjFihGbNmqUHHnhAkZGRcjqdWrBggVcvhw4d0h133KGYmBiFhIQoMTFRb7/9drN7BdAE3+3/DCCQ/ed//qdJSkoyv/vd78xZZ51lvvnmG9O7d28zb94888UXX5itW7ea0aNHm2uvvdbznL/+9a9m1apVZseOHeazzz4zqamppn///qa2ttYYY0xRUZGRZM4991yzatUq8/XXX5v9+/ebcePGmdGjR5vPP//c7Nq1y2RlZZl169Z5Peezzz5rstd9+/aZiIgI8/TTTxtjjPnFL35hBg8ebKqrq40xxnz44YdGkunXr5/Jyckxn3/+uUlJSTHnnnuup+bzzz83PXr0ME899ZTZsWOH+eSTT8xll11mpkyZ4nmfPn36mJ49e5rHH3/c7Ny50+zcubNBfwcOHDjt95SUlGR69uxpFixYYHbs2GH+9Kc/GZvNZnJycowxxtTW1pqhQ4eaiy++2OTk5Hi+k3fffbfZvQJoHMEKgE98++235qyzzjJBQUHmjTfeMA899JAZM2aMV83evXuNJPOvf/2r0dcoLS01ksy2bduMMT+GpPoAVK9///5mwYIFjb5G/XNCQ0NNWFiY1+OHH37w1L3++uvGbrebefPmme7du3v1VB+sMjMzPccOHjxoQkNDzWuvvWaMMSYtLc3ccccdXu/98ccfm6CgIHPs2DFjzPFgdcMNNzTaX32was73lJSUZIYPH+5Vc/nll5u5c+caY4z5+9//boKCgpr8XpvTK4DGdfHRQBmAABcdHa077rhDb775pn72s5/pD3/4gz788EP16NGjQe2uXbv005/+VLt27dJDDz2kzZs369///rfq6uokSXv27PGa3D148GCv58+aNUvTpk1TTk6ORo0apRtvvFGXXHKJV81rr72mfv36eR0LDg72/PNNN92k1atXKyMjQytWrNBPf/rTBn0OGzbM88+RkZHq27evvvjiC0lSXl6evvrqK7388sueGmOM6urqVFRU5Hnvk3s/WV5e3mm/J0kNPl9sbKxKS0slSfn5+Tr77LMb/Qwt6RVAQwQrAD7TpUsXdely/F9DdXV1Sk1N1eLFixvUxcbGSpJSU1MVFxenF154QS6XS3V1dUpMTFR1dbVXfVhYmNff/+///T+NHTtW77zzjnJycpSRkaEnn3xSM2fO9NTExcXpggsuaLLX77//Xnl5eQoODtbOnTub/RltNpvn8915552aNWtWg5pzzjmnyd5P1pzvSVKDSfc2m80TRENDQ0/7Hs3pFUBDBCsAfmHgwIFatWqVzj33XE/YOtHBgwf1xRdf6LnnntPVV18tSdqwYUOzXz8uLk533XWX7rrrLs2bN08vvPCCV7A6nTlz5igoKEhr1qzR9ddfr3Hjxum6667zqtm8ebMneJSXl2vHjh266KKLPJ9v+/btpwxvzXG676k5LrnkEu3bt087duxodNTKql6BQMRdgQD8wvTp01VWVqZbbrlFubm5+vrrr5WTk6PbbrtNtbW1ioiIUFRUlJ5//nl99dVX+uCDDzR79uxmvXZ6err+/ve/q6ioSFu3btUHH3zQ4HLWwYMHVVJS4vWorKyUJL3zzjt68cUX9fLLL2v06NF68MEHNXnyZJWXl3u9xsMPP6z3339fBQUFmjJlinr37q0bbrhBkjR37lxt2rRJ06dPV35+vnbu3Km33nqrReGuOd9TcyQlJemaa67RjTfeqLVr16qoqEhr1qxRdna2pb0CgYhgBcAvuFwuffLJJ6qtrdXYsWOVmJioe+65Rw6HQ0FBQQoKClJmZqby8vKUmJioe++9V48//nizXru2tlbTp09Xv379lJycrL59+2r58uVeNaNGjVJsbKzX480339R3332n22+/XQsWLNDAgQMlSfPnz5fL5dJdd93l9RqPPfaY7rnnHg0aNEjFxcV66623POtQXXLJJVq3bp127typq6++Wpdddpkeeughr8t3VnxPzbVq1SpdfvnluuWWW5SQkKAHHnjAE8ys6hUIRCwQCgBn6KOPPtK1116r8vJy9erVy9ftAPAhRqwAAAAsQrACAACwCJcCAQAALMKIFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgkf8PIF/babT1z6EAAAAASUVORK5CYII=\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:03.426765Z",
          "start_time": "2023-02-06T03:00:03.205387Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "2HwcObM7cHOU",
        "outputId": "8f700fdd-4de2-4b21-9ff5-526ccc28055e",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.distplot(salary['YearsExperience'])",
      "execution_count": 8,
      "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='YearsExperience', ylabel='Density'>"
          },
          "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": "2023-02-06T03:00:03.623671Z",
          "start_time": "2023-02-06T03:00:03.430020Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "-0EHPwHGcfqE",
        "outputId": "0ee9ed24-d64e-4e0f-a4ea-938a190932fb",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.distplot(salary['Salary'])\n             ",
      "execution_count": 9,
      "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='Salary', ylabel='Density'>"
          },
          "execution_count": 9,
          "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": "2023-02-06T03:00:03.929780Z",
          "start_time": "2023-02-06T03:00:03.623671Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "v8maqLAQdxK7",
        "outputId": "b2f851ca-3759-44bb-ee72-ae223cf83483",
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.regplot(x=\"YearsExperience\", y=\"Salary\", data=salary);",
      "execution_count": 10,
      "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": "2023-02-06T03:00:03.970472Z",
          "start_time": "2023-02-06T03:00:03.929780Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "UXcOSgXIeWsr",
        "outputId": "5995ebd6-e94e-492a-aad5-28a31ae05d8a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import statsmodels.formula.api as smf\nmodel = smf.ols(\"Salary~YearsExperience\",data = salary).fit()\nmodel.summary()",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.957</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.955</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   622.5</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 06 Feb 2023</td> <th>  Prob (F-statistic):</th> <td>1.14e-20</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>08:30:03</td>     <th>  Log-Likelihood:    </th> <td> -301.44</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   606.9</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   609.7</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> 2.579e+04</td> <td> 2273.053</td> <td>   11.347</td> <td> 0.000</td> <td> 2.11e+04</td> <td> 3.04e+04</td>\n</tr>\n<tr>\n  <th>YearsExperience</th> <td> 9449.9623</td> <td>  378.755</td> <td>   24.950</td> <td> 0.000</td> <td> 8674.119</td> <td> 1.02e+04</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 2.140</td> <th>  Durbin-Watson:     </th> <td>   1.648</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.343</td> <th>  Jarque-Bera (JB):  </th> <td>   1.569</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.363</td> <th>  Prob(JB):          </th> <td>   0.456</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.147</td> <th>  Cond. No.          </th> <td>    13.2</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:                 Salary   R-squared:                       0.957\nModel:                            OLS   Adj. R-squared:                  0.955\nMethod:                 Least Squares   F-statistic:                     622.5\nDate:                Mon, 06 Feb 2023   Prob (F-statistic):           1.14e-20\nTime:                        08:30:03   Log-Likelihood:                -301.44\nNo. Observations:                  30   AIC:                             606.9\nDf Residuals:                      28   BIC:                             609.7\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n===================================================================================\n                      coef    std err          t      P>|t|      [0.025      0.975]\n-----------------------------------------------------------------------------------\nIntercept        2.579e+04   2273.053     11.347      0.000    2.11e+04    3.04e+04\nYearsExperience  9449.9623    378.755     24.950      0.000    8674.119    1.02e+04\n==============================================================================\nOmnibus:                        2.140   Durbin-Watson:                   1.648\nProb(Omnibus):                  0.343   Jarque-Bera (JB):                1.569\nSkew:                           0.363   Prob(JB):                        0.456\nKurtosis:                       2.147   Cond. No.                         13.2\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:03.981518Z",
          "start_time": "2023-02-06T03:00:03.974182Z"
        },
        "id": "C5e-S0TEeymX",
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred=model.params",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.001452Z",
          "start_time": "2023-02-06T03:00:03.981518Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9ppnlBwe9jv",
        "outputId": "1480d769-36f0-4640-c2dc-4798feb8523d",
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(model.tvalues, '\\n', model.pvalues)    ",
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Intercept          11.346940\nYearsExperience    24.950094\ndtype: float64 \n Intercept          5.511950e-12\nYearsExperience    1.143068e-20\ndtype: float64\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.011606Z",
          "start_time": "2023-02-06T03:00:04.001452Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVA3lCnbg3i3",
        "outputId": "0fce1997-193f-454d-ae2a-7408ead0be13",
        "trusted": true
      },
      "cell_type": "code",
      "source": "(model.rsquared,model.rsquared_adj)",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "text/plain": "(0.9569566641435086, 0.9554194021486339)"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.026749Z",
          "start_time": "2023-02-06T03:00:04.011606Z"
        },
        "id": "B_wX_-4DfPre",
        "trusted": true
      },
      "cell_type": "code",
      "source": "newsalary=pd.Series([30,40])",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.042770Z",
          "start_time": "2023-02-06T03:00:04.029725Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "MQydwVJwf630",
        "outputId": "3295d812-fa60-4c02-e148-28d9028ff4a0",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_pred=pd.DataFrame(newsalary,columns=['YearsExperience'])\ndata_pred",
      "execution_count": 16,
      "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>YearsExperience</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>40</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   YearsExperience\n0               30\n1               40"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.060095Z",
          "start_time": "2023-02-06T03:00:04.042770Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EW_FQhlmgE86",
        "outputId": "c856f4a7-a408-46ae-e6f2-ad2e96efc6fa",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.predict(data_pred)",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "text/plain": "0    309291.069842\n1    403790.693057\ndtype: float64"
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.091893Z",
          "start_time": "2023-02-06T03:00:04.060095Z"
        },
        "id": "jpSbt-iogTa7",
        "trusted": true
      },
      "cell_type": "code",
      "source": "# transformation model log\nimport statsmodels.formula.api as smf\nmodel2 = smf.ols('Salary~np.log(YearsExperience)',data = salary).fit()\nmodel2.summary()",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.854</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.849</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   163.6</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 06 Feb 2023</td> <th>  Prob (F-statistic):</th> <td>3.25e-13</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>08:30:04</td>     <th>  Log-Likelihood:    </th> <td> -319.77</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   643.5</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   646.3</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> 1.493e+04</td> <td> 5156.226</td> <td>    2.895</td> <td> 0.007</td> <td> 4365.921</td> <td> 2.55e+04</td>\n</tr>\n<tr>\n  <th>np.log(YearsExperience)</th> <td> 4.058e+04</td> <td> 3172.453</td> <td>   12.792</td> <td> 0.000</td> <td> 3.41e+04</td> <td> 4.71e+04</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 1.094</td> <th>  Durbin-Watson:     </th> <td>   0.512</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.579</td> <th>  Jarque-Bera (JB):  </th> <td>   0.908</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.156</td> <th>  Prob(JB):          </th> <td>   0.635</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.207</td> <th>  Cond. No.          </th> <td>    5.76</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:                 Salary   R-squared:                       0.854\nModel:                            OLS   Adj. R-squared:                  0.849\nMethod:                 Least Squares   F-statistic:                     163.6\nDate:                Mon, 06 Feb 2023   Prob (F-statistic):           3.25e-13\nTime:                        08:30:04   Log-Likelihood:                -319.77\nNo. Observations:                  30   AIC:                             643.5\nDf Residuals:                      28   BIC:                             646.3\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n===========================================================================================\n                              coef    std err          t      P>|t|      [0.025      0.975]\n-------------------------------------------------------------------------------------------\nIntercept                1.493e+04   5156.226      2.895      0.007    4365.921    2.55e+04\nnp.log(YearsExperience)  4.058e+04   3172.453     12.792      0.000    3.41e+04    4.71e+04\n==============================================================================\nOmnibus:                        1.094   Durbin-Watson:                   0.512\nProb(Omnibus):                  0.579   Jarque-Bera (JB):                0.908\nSkew:                           0.156   Prob(JB):                        0.635\nKurtosis:                       2.207   Cond. No.                         5.76\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.107915Z",
          "start_time": "2023-02-06T03:00:04.091893Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2 = model2.predict(pd.DataFrame(salary['YearsExperience']))\npred2",
      "execution_count": 19,
      "outputs": [
        {
          "data": {
            "text/plain": "0      18795.848339\n1      25575.235192\n2      31382.551905\n3      43057.262306\n4      46925.138875\n5      58136.050079\n6      59511.842441\n7      62130.943929\n8      62130.943929\n9      68022.718504\n10     70159.105863\n11     71186.552842\n12     71186.552842\n13     72188.628149\n14     75966.422577\n15     79422.295729\n16     81045.791737\n17     82606.829882\n18     86959.066704\n19     87641.132977\n20     92720.502137\n21     94472.514696\n22     98805.371390\n23    100317.918684\n24    102719.920751\n25    104095.713112\n26    106289.868435\n27    106714.814600\n28    109571.007247\n29    110351.454145\ndtype: float64"
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.123980Z",
          "start_time": "2023-02-06T03:00:04.107915Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2\nrmse_log = np.sqrt(np.mean((np.array(salary['Salary'])-np.array(pred2))**2))\nrmse_log ",
      "execution_count": 20,
      "outputs": [
        {
          "data": {
            "text/plain": "10302.893706228304"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.148127Z",
          "start_time": "2023-02-06T03:00:04.123980Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred2.corr(salary.Salary)",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "text/plain": "0.9240610817882641"
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.308836Z",
          "start_time": "2023-02-06T03:00:04.148127Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(x=salary['Salary'],y=salary['YearsExperience'],color='green')\nplt.plot(salary['Salary'],pred2,color='blue')\nplt.xlabel('salary')\nplt.ylabel('YearsExperience')",
      "execution_count": 22,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'YearsExperience')"
          },
          "execution_count": 22,
          "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": "2023-02-06T03:00:04.349307Z",
          "start_time": "2023-02-06T03:00:04.308836Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# transformation model sqrt\nimport statsmodels.formula.api as smf\nmodel3 = smf.ols('Salary~np.sqrt(YearsExperience)',data = salary).fit()\nmodel3.summary()",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>         <td>Salary</td>      <th>  R-squared:         </th> <td>   0.931</td>\n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.929</td>\n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   377.8</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Mon, 06 Feb 2023</td> <th>  Prob (F-statistic):</th> <td>8.57e-18</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>08:30:04</td>     <th>  Log-Likelihood:    </th> <td> -308.52</td>\n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   621.0</td>\n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>    28</td>      <th>  BIC:               </th> <td>   623.8</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>-1.606e+04</td> <td> 4921.599</td> <td>   -3.262</td> <td> 0.003</td> <td>-2.61e+04</td> <td>-5974.331</td>\n</tr>\n<tr>\n  <th>np.sqrt(YearsExperience)</th> <td>  4.15e+04</td> <td> 2135.122</td> <td>   19.437</td> <td> 0.000</td> <td> 3.71e+04</td> <td> 4.59e+04</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td> 0.588</td> <th>  Durbin-Watson:     </th> <td>   1.031</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.745</td> <th>  Jarque-Bera (JB):  </th> <td>   0.638</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td> 0.011</td> <th>  Prob(JB):          </th> <td>   0.727</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 2.286</td> <th>  Cond. No.          </th> <td>    9.97</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:                 Salary   R-squared:                       0.931\nModel:                            OLS   Adj. R-squared:                  0.929\nMethod:                 Least Squares   F-statistic:                     377.8\nDate:                Mon, 06 Feb 2023   Prob (F-statistic):           8.57e-18\nTime:                        08:30:04   Log-Likelihood:                -308.52\nNo. Observations:                  30   AIC:                             621.0\nDf Residuals:                      28   BIC:                             623.8\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n============================================================================================\n                               coef    std err          t      P>|t|      [0.025      0.975]\n--------------------------------------------------------------------------------------------\nIntercept                -1.606e+04   4921.599     -3.262      0.003   -2.61e+04   -5974.331\nnp.sqrt(YearsExperience)   4.15e+04   2135.122     19.437      0.000    3.71e+04    4.59e+04\n==============================================================================\nOmnibus:                        0.588   Durbin-Watson:                   1.031\nProb(Omnibus):                  0.745   Jarque-Bera (JB):                0.638\nSkew:                           0.011   Prob(JB):                        0.727\nKurtosis:                       2.286   Cond. No.                         9.97\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\""
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.381310Z",
          "start_time": "2023-02-06T03:00:04.357308Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred3 = model3.predict(pd.DataFrame(salary['YearsExperience']))\npred3",
      "execution_count": 24,
      "outputs": [
        {
          "data": {
            "text/plain": "0      27470.511884\n1      31262.287009\n2      34771.976586\n3      42635.056211\n4      45499.687794\n5      54617.343293\n6      55825.518202\n7      58182.905200\n8      58182.905200\n9      63772.383867\n10     65901.508208\n11     66945.592049\n12     66945.592049\n13     67976.704394\n14     71980.468875\n15     75809.903446\n16     77665.963118\n17     79485.972499\n18     84749.033766\n19     85599.722290\n20     92164.765553\n21     94526.218887\n22    100589.939171\n23    102784.094601\n24    106353.652306\n25    108446.272632\n26    111857.919142\n27    112529.386687\n28    117134.909368\n29    118421.805716\ndtype: float64"
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.396548Z",
          "start_time": "2023-02-06T03:00:04.381310Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred3\nrmse_sqrt = np.sqrt(np.mean((np.array(salary['Salary'])-np.array(pred2))**2))\nrmse_sqrt ",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "text/plain": "10302.893706228304"
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.418658Z",
          "start_time": "2023-02-06T03:00:04.397570Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pred3.corr(salary.Salary)",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "text/plain": "0.9648839072651967"
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-06T03:00:04.579983Z",
          "start_time": "2023-02-06T03:00:04.418658Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(x=salary['Salary'],y=salary['YearsExperience'],color='green')\nplt.plot(salary['Salary'],pred3,color='blue')\nplt.xlabel('salary')\nplt.ylabel('YearsExperience')",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0, 0.5, 'YearsExperience')"
          },
          "execution_count": 27,
          "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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Salary_hike.ipynb",
      "provenance": []
    },
    "gist": {
      "id": "",
      "data": {
        "description": "SimpleLinearRegression(Salary_hike).ipynb",
        "public": true
      }
    },
    "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"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}