Boston_housing_statistical_analysis.ipynb

{"cells":[{"cell_type":"markdown","id":"87d30289-2783-4055-a22c-ac0aa32525f2","metadata":{},"source":["<center>\n","    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\">\n","</center>\n"]},{"cell_type":"markdown","id":"2d849a7d-b72c-408c-975c-a565a30c3e9b","metadata":{},"source":["#### Import the required libraries we need for the lab.\n"]},{"cell_type":"code","execution_count":2,"id":"53d595f3-6e5f-4dab-be47-37eced3f011f","metadata":{},"outputs":[],"source":["import pandas as pd\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import scipy.stats\n","import statsmodels.api as sm\n","from statsmodels.formula.api import ols"]},{"cell_type":"markdown","id":"e4d5a4d2-294e-4b44-bb2b-08c0256b05bb","metadata":{},"source":["#### Read the dataset in the csv file from the URL\n"]},{"cell_type":"code","execution_count":3,"id":"1d3dea52-1fec-48e2-8315-90ea18094055","metadata":{},"outputs":[],"source":["boston_df=pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/boston_housing.csv')\n","boston_df = boston_df.drop(columns = ['Unnamed: 0'])"]},{"cell_type":"markdown","id":"d2f0e97f-eef9-4ca4-a0e4-88e5c990f444","metadata":{},"source":["#### Add your code below following the instructions given in the course to complete the peer graded assignment\n"]},{"cell_type":"markdown","metadata":{},"source":["##### 1. Understanding Dataset"]},{"cell_type":"code","execution_count":3,"id":"03d33cb4-b6b7-44bb-883c-22683d3b77f6","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","      <th>MEDV</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0.0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1.0</td>\n","      <td>296.0</td>\n","      <td>15.3</td>\n","      <td>4.98</td>\n","      <td>24.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2.0</td>\n","      <td>242.0</td>\n","      <td>17.8</td>\n","      <td>9.14</td>\n","      <td>21.6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.02729</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>7.185</td>\n","      <td>61.1</td>\n","      <td>4.9671</td>\n","      <td>2.0</td>\n","      <td>242.0</td>\n","      <td>17.8</td>\n","      <td>4.03</td>\n","      <td>34.7</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.03237</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.998</td>\n","      <td>45.8</td>\n","      <td>6.0622</td>\n","      <td>3.0</td>\n","      <td>222.0</td>\n","      <td>18.7</td>\n","      <td>2.94</td>\n","      <td>33.4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.06905</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>7.147</td>\n","      <td>54.2</td>\n","      <td>6.0622</td>\n","      <td>3.0</td>\n","      <td>222.0</td>\n","      <td>18.7</td>\n","      <td>5.33</td>\n","      <td>36.2</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.02985</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.430</td>\n","      <td>58.7</td>\n","      <td>6.0622</td>\n","      <td>3.0</td>\n","      <td>222.0</td>\n","      <td>18.7</td>\n","      <td>5.21</td>\n","      <td>28.7</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.08829</td>\n","      <td>12.5</td>\n","      <td>7.87</td>\n","      <td>0.0</td>\n","      <td>0.524</td>\n","      <td>6.012</td>\n","      <td>66.6</td>\n","      <td>5.5605</td>\n","      <td>5.0</td>\n","      <td>311.0</td>\n","      <td>15.2</td>\n","      <td>12.43</td>\n","      <td>22.9</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.14455</td>\n","      <td>12.5</td>\n","      <td>7.87</td>\n","      <td>0.0</td>\n","      <td>0.524</td>\n","      <td>6.172</td>\n","      <td>96.1</td>\n","      <td>5.9505</td>\n","      <td>5.0</td>\n","      <td>311.0</td>\n","      <td>15.2</td>\n","      <td>19.15</td>\n","      <td>27.1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.21124</td>\n","      <td>12.5</td>\n","      <td>7.87</td>\n","      <td>0.0</td>\n","      <td>0.524</td>\n","      <td>5.631</td>\n","      <td>100.0</td>\n","      <td>6.0821</td>\n","      <td>5.0</td>\n","      <td>311.0</td>\n","      <td>15.2</td>\n","      <td>29.93</td>\n","      <td>16.5</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.17004</td>\n","      <td>12.5</td>\n","      <td>7.87</td>\n","      <td>0.0</td>\n","      <td>0.524</td>\n","      <td>6.004</td>\n","      <td>85.9</td>\n","      <td>6.5921</td>\n","      <td>5.0</td>\n","      <td>311.0</td>\n","      <td>15.2</td>\n","      <td>17.10</td>\n","      <td>18.9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS  RAD    TAX  \\\n","0  0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900  1.0  296.0   \n","1  0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671  2.0  242.0   \n","2  0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671  2.0  242.0   \n","3  0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622  3.0  222.0   \n","4  0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622  3.0  222.0   \n","5  0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622  3.0  222.0   \n","6  0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605  5.0  311.0   \n","7  0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505  5.0  311.0   \n","8  0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821  5.0  311.0   \n","9  0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921  5.0  311.0   \n","\n","   PTRATIO  LSTAT  MEDV  \n","0     15.3   4.98  24.0  \n","1     17.8   9.14  21.6  \n","2     17.8   4.03  34.7  \n","3     18.7   2.94  33.4  \n","4     18.7   5.33  36.2  \n","5     18.7   5.21  28.7  \n","6     15.2  12.43  22.9  \n","7     15.2  19.15  27.1  \n","8     15.2  29.93  16.5  \n","9     15.2  17.10  18.9  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["boston_df.head(10)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","      <th>MEDV</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>496</th>\n","      <td>0.28960</td>\n","      <td>0.0</td>\n","      <td>9.69</td>\n","      <td>0.0</td>\n","      <td>0.585</td>\n","      <td>5.390</td>\n","      <td>72.9</td>\n","      <td>2.7986</td>\n","      <td>6.0</td>\n","      <td>391.0</td>\n","      <td>19.2</td>\n","      <td>21.14</td>\n","      <td>19.7</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>0.26838</td>\n","      <td>0.0</td>\n","      <td>9.69</td>\n","      <td>0.0</td>\n","      <td>0.585</td>\n","      <td>5.794</td>\n","      <td>70.6</td>\n","      <td>2.8927</td>\n","      <td>6.0</td>\n","      <td>391.0</td>\n","      <td>19.2</td>\n","      <td>14.10</td>\n","      <td>18.3</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>0.23912</td>\n","      <td>0.0</td>\n","      <td>9.69</td>\n","      <td>0.0</td>\n","      <td>0.585</td>\n","      <td>6.019</td>\n","      <td>65.3</td>\n","      <td>2.4091</td>\n","      <td>6.0</td>\n","      <td>391.0</td>\n","      <td>19.2</td>\n","      <td>12.92</td>\n","      <td>21.2</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>0.17783</td>\n","      <td>0.0</td>\n","      <td>9.69</td>\n","      <td>0.0</td>\n","      <td>0.585</td>\n","      <td>5.569</td>\n","      <td>73.5</td>\n","      <td>2.3999</td>\n","      <td>6.0</td>\n","      <td>391.0</td>\n","      <td>19.2</td>\n","      <td>15.10</td>\n","      <td>17.5</td>\n","    </tr>\n","    <tr>\n","      <th>500</th>\n","      <td>0.22438</td>\n","      <td>0.0</td>\n","      <td>9.69</td>\n","      <td>0.0</td>\n","      <td>0.585</td>\n","      <td>6.027</td>\n","      <td>79.7</td>\n","      <td>2.4982</td>\n","      <td>6.0</td>\n","      <td>391.0</td>\n","      <td>19.2</td>\n","      <td>14.33</td>\n","      <td>16.8</td>\n","    </tr>\n","    <tr>\n","      <th>501</th>\n","      <td>0.06263</td>\n","      <td>0.0</td>\n","      <td>11.93</td>\n","      <td>0.0</td>\n","      <td>0.573</td>\n","      <td>6.593</td>\n","      <td>69.1</td>\n","      <td>2.4786</td>\n","      <td>1.0</td>\n","      <td>273.0</td>\n","      <td>21.0</td>\n","      <td>9.67</td>\n","      <td>22.4</td>\n","    </tr>\n","    <tr>\n","      <th>502</th>\n","      <td>0.04527</td>\n","      <td>0.0</td>\n","      <td>11.93</td>\n","      <td>0.0</td>\n","      <td>0.573</td>\n","      <td>6.120</td>\n","      <td>76.7</td>\n","      <td>2.2875</td>\n","      <td>1.0</td>\n","      <td>273.0</td>\n","      <td>21.0</td>\n","      <td>9.08</td>\n","      <td>20.6</td>\n","    </tr>\n","    <tr>\n","      <th>503</th>\n","      <td>0.06076</td>\n","      <td>0.0</td>\n","      <td>11.93</td>\n","      <td>0.0</td>\n","      <td>0.573</td>\n","      <td>6.976</td>\n","      <td>91.0</td>\n","      <td>2.1675</td>\n","      <td>1.0</td>\n","      <td>273.0</td>\n","      <td>21.0</td>\n","      <td>5.64</td>\n","      <td>23.9</td>\n","    </tr>\n","    <tr>\n","      <th>504</th>\n","      <td>0.10959</td>\n","      <td>0.0</td>\n","      <td>11.93</td>\n","      <td>0.0</td>\n","      <td>0.573</td>\n","      <td>6.794</td>\n","      <td>89.3</td>\n","      <td>2.3889</td>\n","      <td>1.0</td>\n","      <td>273.0</td>\n","      <td>21.0</td>\n","      <td>6.48</td>\n","      <td>22.0</td>\n","    </tr>\n","    <tr>\n","      <th>505</th>\n","      <td>0.04741</td>\n","      <td>0.0</td>\n","      <td>11.93</td>\n","      <td>0.0</td>\n","      <td>0.573</td>\n","      <td>6.030</td>\n","      <td>80.8</td>\n","      <td>2.5050</td>\n","      <td>1.0</td>\n","      <td>273.0</td>\n","      <td>21.0</td>\n","      <td>7.88</td>\n","      <td>11.9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n","496  0.28960  0.0   9.69   0.0  0.585  5.390  72.9  2.7986  6.0  391.0   \n","497  0.26838  0.0   9.69   0.0  0.585  5.794  70.6  2.8927  6.0  391.0   \n","498  0.23912  0.0   9.69   0.0  0.585  6.019  65.3  2.4091  6.0  391.0   \n","499  0.17783  0.0   9.69   0.0  0.585  5.569  73.5  2.3999  6.0  391.0   \n","500  0.22438  0.0   9.69   0.0  0.585  6.027  79.7  2.4982  6.0  391.0   \n","501  0.06263  0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n","502  0.04527  0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n","503  0.06076  0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n","504  0.10959  0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n","505  0.04741  0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n","\n","     PTRATIO  LSTAT  MEDV  \n","496     19.2  21.14  19.7  \n","497     19.2  14.10  18.3  \n","498     19.2  12.92  21.2  \n","499     19.2  15.10  17.5  \n","500     19.2  14.33  16.8  \n","501     21.0   9.67  22.4  \n","502     21.0   9.08  20.6  \n","503     21.0   5.64  23.9  \n","504     21.0   6.48  22.0  \n","505     21.0   7.88  11.9  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["boston_df.tail(10)"]},{"cell_type":"markdown","metadata":{},"source":["- The Dataset contains information derived from the U.S. Census Service, about housing prices in Boston MA.\n","- Dataset variables:\n","    - CRIM - per capita crime rate by town\n","    - ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n","    - INDUS - proportion of non-retail business acres per town.\n","    - CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n","    - NOX - nitric oxides concentration (parts per 10 million)\n","    - RM - average number of rooms per dwelling\n","    - AGE - proportion of owner-occupied units built prior to 1940\n","    - DIS - weighted distances to five Boston employment centres\n","    - RAD - index of accessibility to radial highways\n","    - TAX - full-value property-tax rate per $10,000\n","    - PTRATIO - pupil-teacher ratio by town\n","    - LSTAT - % lower status of the population\n","    - MEDV - Median value of owner-occupied homes in $1000's"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 506 entries, 0 to 505\n","Data columns (total 13 columns):\n"," #   Column   Non-Null Count  Dtype  \n","---  ------   --------------  -----  \n"," 0   CRIM     506 non-null    float64\n"," 1   ZN       506 non-null    float64\n"," 2   INDUS    506 non-null    float64\n"," 3   CHAS     506 non-null    float64\n"," 4   NOX      506 non-null    float64\n"," 5   RM       506 non-null    float64\n"," 6   AGE      506 non-null    float64\n"," 7   DIS      506 non-null    float64\n"," 8   RAD      506 non-null    float64\n"," 9   TAX      506 non-null    float64\n"," 10  PTRATIO  506 non-null    float64\n"," 11  LSTAT    506 non-null    float64\n"," 12  MEDV     506 non-null    float64\n","dtypes: float64(13)\n","memory usage: 51.5 KB\n"]}],"source":["boston_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["##### 2. Descriptive Statistics and Visualizations"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","      <th>MEDV</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.613524</td>\n","      <td>11.363636</td>\n","      <td>11.136779</td>\n","      <td>0.069170</td>\n","      <td>0.554695</td>\n","      <td>6.284634</td>\n","      <td>68.574901</td>\n","      <td>3.795043</td>\n","      <td>9.549407</td>\n","      <td>408.237154</td>\n","      <td>18.455534</td>\n","      <td>12.653063</td>\n","      <td>22.532806</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>8.601545</td>\n","      <td>23.322453</td>\n","      <td>6.860353</td>\n","      <td>0.253994</td>\n","      <td>0.115878</td>\n","      <td>0.702617</td>\n","      <td>28.148861</td>\n","      <td>2.105710</td>\n","      <td>8.707259</td>\n","      <td>168.537116</td>\n","      <td>2.164946</td>\n","      <td>7.141062</td>\n","      <td>9.197104</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.006320</td>\n","      <td>0.000000</td>\n","      <td>0.460000</td>\n","      <td>0.000000</td>\n","      <td>0.385000</td>\n","      <td>3.561000</td>\n","      <td>2.900000</td>\n","      <td>1.129600</td>\n","      <td>1.000000</td>\n","      <td>187.000000</td>\n","      <td>12.600000</td>\n","      <td>1.730000</td>\n","      <td>5.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.082045</td>\n","      <td>0.000000</td>\n","      <td>5.190000</td>\n","      <td>0.000000</td>\n","      <td>0.449000</td>\n","      <td>5.885500</td>\n","      <td>45.025000</td>\n","      <td>2.100175</td>\n","      <td>4.000000</td>\n","      <td>279.000000</td>\n","      <td>17.400000</td>\n","      <td>6.950000</td>\n","      <td>17.025000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.256510</td>\n","      <td>0.000000</td>\n","      <td>9.690000</td>\n","      <td>0.000000</td>\n","      <td>0.538000</td>\n","      <td>6.208500</td>\n","      <td>77.500000</td>\n","      <td>3.207450</td>\n","      <td>5.000000</td>\n","      <td>330.000000</td>\n","      <td>19.050000</td>\n","      <td>11.360000</td>\n","      <td>21.200000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.677083</td>\n","      <td>12.500000</td>\n","      <td>18.100000</td>\n","      <td>0.000000</td>\n","      <td>0.624000</td>\n","      <td>6.623500</td>\n","      <td>94.075000</td>\n","      <td>5.188425</td>\n","      <td>24.000000</td>\n","      <td>666.000000</td>\n","      <td>20.200000</td>\n","      <td>16.955000</td>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>88.976200</td>\n","      <td>100.000000</td>\n","      <td>27.740000</td>\n","      <td>1.000000</td>\n","      <td>0.871000</td>\n","      <td>8.780000</td>\n","      <td>100.000000</td>\n","      <td>12.126500</td>\n","      <td>24.000000</td>\n","      <td>711.000000</td>\n","      <td>22.000000</td>\n","      <td>37.970000</td>\n","      <td>50.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n","count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n","mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n","std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n","min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n","25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n","50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n","75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n","max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n","\n","              AGE         DIS         RAD         TAX     PTRATIO       LSTAT  \\\n","count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n","mean    68.574901    3.795043    9.549407  408.237154   18.455534   12.653063   \n","std     28.148861    2.105710    8.707259  168.537116    2.164946    7.141062   \n","min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n","25%     45.025000    2.100175    4.000000  279.000000   17.400000    6.950000   \n","50%     77.500000    3.207450    5.000000  330.000000   19.050000   11.360000   \n","75%     94.075000    5.188425   24.000000  666.000000   20.200000   16.955000   \n","max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n","\n","             MEDV  \n","count  506.000000  \n","mean    22.532806  \n","std      9.197104  \n","min      5.000000  \n","25%     17.025000  \n","50%     21.200000  \n","75%     25.000000  \n","max     50.000000  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["boston_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["2.1 Median value of owner-occupied homes in $1000's (MEDV)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax = sns.boxplot(x = 'MEDV', data = boston_df)\n","plt.title('Values of Median value of owner-occupied homes in $1000s (MEDV)')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["With this plot, it is possible to see the distribution of the MEDV variable. In the boxplot, we can see the median close to 21 (21.2), the 1st quartile close to 17 (17.025), and the 3rd quartile with value of 25. There is one outlier near a value of 5 and some outliers with values greater than 35. Is possible to conclude that most of data is concentrated around the value 20."]},{"cell_type":"markdown","metadata":{},"source":["2.2 Charles River dummy variable (CHAS)"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax = sns.catplot(x = 'CHAS', kind = 'count', data = boston_df)\n","plt.title('Charles River dummy variable (CHAS) counts')\n","plt.grid(axis='y')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["With this plot, it is possible to see that most of the dataset contains the Charles River dummy variable with value of 1 (if tract bounds river). Fewer than 50 values in the dataset are 0 for this variable (not tract bounds river)."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["MEDV_CHAS_groups = boston_df.groupby('CHAS')[['MEDV']].mean().reset_index()\n","ax = sns.barplot(x = 'CHAS', y = 'MEDV', data = MEDV_CHAS_groups)\n","plt.title('Mean of Median value of owner-occupied homes (MEDV) value based on Charles River Variable (CHAS)')\n","plt.grid(axis='y')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["This plot (above), shows mean value of MEDV for each CHAS value (0 or 1)"]},{"cell_type":"markdown","metadata":{},"source":["2.3 Median value of owner-occupied homes in $1000's (MEDV) variable vs the proportion of owner-occupied units built prior to 1940 (AGE) variable (Discretizing the AGE variable into three groups of 35 years and younger, between 35 and 70 years and 70 years and older)."]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":["[]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Creating AGE groups\n","boston_df.loc[(boston_df['AGE'] <= 35), 'AGE_group'] = '35 years and younger'\n","boston_df.loc[(boston_df['AGE'] > 35)&(boston_df['AGE'] < 70), 'AGE_group'] = 'between 35 and 70 years'\n","boston_df.loc[(boston_df['AGE'] >= 70), 'AGE_group'] = '70 years and older'\n","\n","ax = sns.boxplot(x = 'MEDV', y = 'AGE_group', data = boston_df)\n","ax.set_title('MEDV variable for different proportion of owner-occupied units built prior to 1940 (AGE) variable groups')\n","ax.set_xlabel('Mean of Median value of owner-occupied homes (MEDV)')\n","ax.set_ylabel('AGE group')\n","plt.plot()"]},{"cell_type":"markdown","metadata":{},"source":["With this plot, it is possible to see that median and distribution of MEDV values change across AGE groups. MEDV shows the highest median for 35 years and younger group, and the lowest median for 70 years and older group. This may indicate that lower AGES tend to have greater high MEDV values."]},{"cell_type":"markdown","metadata":{},"source":["2.4 Relationship between Nitric oxide concentration (NOX) and proportion of non-retail business acres per town (INDUS)"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAxYAAAHFCAYAAABvi28PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwjklEQVR4nO3dd3hTZfsH8G/apkmapoOUTkoZZQktDmRPERBcIDhAZKsg6k95VRBQhmxRxAG+KqIoiAgCgjLKpgwFhRdEBcou05bSdKVNyvP7AxObZjdtVr+f6/K67Fm5z0POybnPsyRCCAEiIiIiIiIXBHg6ACIiIiIi8n1MLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVOJRZffPEFJBIJ5HI5zp07Z7a+c+fOaNasmcmyOnXqYMiQIca/L126hMmTJ+Pw4cNOBTpkyBDUqVPHqX3cRSKRYPLkyXa3M5Tf2bNnqzwmf7Rs2TK89957VXb8GTNmYM2aNWbLd+zYAYlEgh07dlTZZ9vTtWtXjBw50iwmiUSCffv2mW0/ZMgQhIaGmi3X6XRYuHAh2rRpg/DwcCgUCjRp0gTjxo1Ddna2cbvjx48jJCQEAwYMMDtGTk4OEhIS0KpVK5SWlgIA3njjDdx55524efNmZZwuWVFYWIjJkydb/C76yv1l4sSJqF27NoKCghAREeHpcNzup59+cuj3whZLv4flf2utkUgkeP755136/Ipw9HeS/F9V/5ZXtWHDhuG+++4z/n327FlIJBLMnTvXuKwiv9GdO3c27hMQEACVSoXk5GQ8+uijWLlypcXfV1vX88qVK82eXYQQWL58OTp06IDo6GjI5XLUqlULPXr0wGeffWbcLicnBxERERafiewSTli8eLEAIACIgQMHmq3v1KmTaNq0qcmy3377TWRkZBj/PnDggAAgFi9e7MxHi4yMDPHbb785tY+77Nu3T1y4cMHudobyO3PmTNUH5Yfuv/9+kZSUVGXHVyqVYvDgwWbLc3Nzxb59+0Rubm6VfbYta9asETKZTGRmZhqXbd++3Xgttm/f3myfwYMHC6VSabKsoKBAdOrUSQQGBopRo0aJH3/8UWzbtk1Mnz5dREZGisTERPHXX38Zt583b54AIFauXGlynCeeeELI5XLx559/GpfduHFDREREiM8//7yyTpss+PvvvwUAMWnSJLN1165dE/v27RNardb9gTlozZo1AoCYMGGCSE9PFwcOHPB0SG43evRo4eRPrxlLv4dJSUkW71/lARCjR4926fMrwtHfSfJ/Vf1bXpV+++03ERAQYHLvOnPmjAAg3n77beOyivxGd+rUSdSrV0/s27dP7Nu3T2zZskV8+umn4v777xcARIcOHcSNGzdM9rF1PX/33XcCgNi+fbtx2dixYwUA8fTTT4u1a9eKbdu2icWLF4unnnpKPPDAAyb7T548WSQnJ4vi4mKHy0cIIYKcT0WA++67D8uWLcMrr7yC5s2b29z2jjvuqMhHGBUWFiIkJAT169d36ThVqXXr1p4OgcopLS2FXq+HTCZz+VhhYWEe/TeeMWMG+vTpg4SEBLN19913HzZu3Ih169bhwQcftHmcl19+GTt37sTy5cvx+OOPG5d36dIF/fr1Q8uWLdG3b1/873//Q2BgIP7v//4Pq1evxqhRo4xvN7777jssX74c77zzDho3bmw8Rnh4OAYOHIhZs2ZhyJAhkEgklVcAblZUVAS5XO5V5yCEgFartblNzZo1UbNmTTdFVDG///47AODFF19EdHS0h6OpHJ74vnjz76E1/J20zfCs4y+KioqgUCg8HUalmzVrFlq2bIkWLVo4tL0zv9EAoFAozK6VESNGYPHixRg2bBieeeYZfPvttxWKvaioCO+99x4GDRqETz75xGTdkCFDzGpERo4ciWnTpmHlypUWWy9Y5UwWYnjjvm3bNlGzZk3Ro0cPk/WWaizKvkUpm8GV/c/w9s2QwR05ckR069ZNhIaGitatWxvXlc9wS0tLxfvvvy+aN28u5HK5CA8PF61atRJr1661ey5r164VrVu3FgqFQoSGhop7771X7N2717j+m2++EQDEBx98YLLfm2++KQICAsTmzZuNy2DhDeK+fftE27ZthUwmE3FxcWLcuHHik08+sVhjsXz5ctG6dWsREhIilEql6N69u8O1M5mZmeLpp58WtWrVElKpVMTFxYm+ffuKK1euGLc5d+6cePLJJ0XNmjVFcHCwaNy4sZg7d64oLS01blM2437nnXdEnTp1hFKpFK1btxb79u0z+9z9+/eLBx54QNSoUUPIZDJRr1498X//938m25w4cUL079/f5HM//PBDk20M34lly5aJ8ePHi7i4OKFSqUTXrl1N3p536tTJ4nenbOyzZ88Wb731lqhTp44IDAwUGzZsEEVFRWLMmDGiefPmIiwsTERGRorWrVuLNWvWmMRh6didOnUyibFs1i+E/e+QEEJMmjRJABC///67eOKJJ0RYWJiIjo4WQ4cONXvzYMlvv/0mAIgff/zRYrktX75c3HbbbaJp06ZCr9cb15d/G3L58mURFBRkds2WNWPGDLMailOnTgmlUin69OkjLl++LNRqtejQoYPJd8fg559/FgDE1q1bbZ7Tww8/LGrXrm3xGC1bthR33HGH8e8VK1aIli1birCwMKFQKETdunXF0KFDbR5fiH/f4nz88ceiQYMGIjg4WDRp0kR88803JtsZ7mmbNm0SQ4cOFVFRUQKAKCoqEqWlpWL27NmiUaNGIjg4WNSsWVM89dRTZm9dDfe9Xbt2iVatWgm5XC7i4+PFxIkTTf5NhBAiOztbjBo1SsTHxwupVCrq1q0rxo8fb1bLYIh/4cKFonHjxkIqlYqFCxda/J4a7q/WakQXLVokUlNThUwmE5GRkaJ3797ijz/+MNnG8H05efKk6Nmzp1AqlaJWrVpizJgxDtWAOFJWSUlJVu/9ljgTk7PlumTJEtG4cWOhUChEamqqWLdund1zFML290UI+/fywYMHW/w3NPybffjhh6JDhw6iZs2aIiQkRDRr1kzMnj1blJSUmJVN+d9DZ2ss7F0bhnuXtTIo+z3bunWr6NSpk6hRo4aQy+UiMTFRPPLII6KgoMDkc8v+e5d9nhg5cqRQq9WiRo0aok+fPuLixYtmn+vI7+SpU6fE448/LuLi4kRwcLCIjo4W99xzjzh06JBTsVqyfPly0a1bNxEbGyvkcrlo3LixGDt2rMjPzzfb1t7vo6Fsf/31V9G3b18REREhYmNjhRBC3Lx5U3z00UfG55qIiAjRt29fcerUKZPP+O2338T9999v/H2Ni4sTvXr1slsr5Mz9qri4WLz11lvG6zoqKkoMGTJEXLt2zWS7pKQkcf/994tVq1aJ22+/XchkMjF27Firn2/tt1wIx67lfv36idtuu83kuA888IAAIFasWGFc9uuvvwoA4ocffhBCOP+dK+/KlStCKpWKjz76yGS5rRoLR3+jDWVT/hm6rF69egmJRCLOnj1rXGa4ni0pX2NhqPG29m9jSc+ePUWHDh0c3l4IISqUWBw4cEDMnz/f7CHCXmKRm5trPMbEiRON1T2GC2Hw4MFCKpWKOnXqiJkzZ4qtW7eKTZs2GdeVv5E+9dRTQiKRiBEjRoi1a9eKDRs2iOnTp4v58+fbPI+lS5cKAKJ79+5izZo14ttvvxV33XWXCA4OFrt37zZuN3LkSBEcHGys8tq6dasICAgQEydONDle+RvmsWPHREhIiLjtttvEN998I9auXSt69OghateubXZDnj59upBIJGLYsGFi/fr14vvvvxdt2rQRSqVSHDt2zOZ5ZGZmiri4OBEVFSXeffddsWXLFvHtt9+KYcOGGZupXLt2TSQkJIiaNWuKjz/+WGzcuFE8//zzAoAYNWqU8ViGC6NOnTrivvvuE2vWrBFr1qwRKSkpIjIy0uQheOPGjUIqlYrU1FTxxRdfiG3btonPP/9cPPHEEyZlEB4eLlJSUsSSJUvE5s2bxX/+8x8REBAgJk+ebNzOcPHVqVNHPPnkk+LHH38U33zzjahdu7Zo0KCB8UI8duyYaNeunYiNjTV+bwwJjyH2hIQE0aVLF7Fy5UqxefNmcebMGXHjxg0xZMgQ8dVXX4lt27aJjRs3ildeeUUEBASIL7/80hjHvn37hEKhEL169TIe21D+lhILR79Dhh+QRo0aiTfffFOkpaWJd999V8hkMocekKdOnSoCAwNFXl6eyXJDTN99951Yu3atACAWLVpkXF/+prVs2TIBQCxcuNDqZ/3xxx8CgHj22WdNli9YsEAAEPXr1xdKpdKkaWNZer1ehIaGijFjxtg8J0O8aWlpJsv//PNPAUC8//77Qggh9u7dKyQSiXjiiSfETz/9ZFJlaw8AkZiYaLwGf/jhB3HfffcZy8zAcD9KSEgQzzzzjNiwYYNYuXKl0Ov14plnnhEAxPPPPy82btwoPv74Y1GzZk2RmJgo/v77b+MxOnXqJNRqtYiPjxfvv/++2LRpk3jxxRfNbvhFRUUiNTVVKJVKMXfuXLF582bxxhtviKCgINGrVy+z+BMSEkRqaqpYtmyZ2LZtmzh8+LDYuHGjACCGDx9u/J4a/j0sPfAZksX+/fuLH3/8USxZskTUq1dPhIeHixMnThi3Gzx4sPEBc+7cuWLLli3izTffFBKJREyZMsVueTtSVr/99psYPny4ACA2btxot2mMozE5W6516tQRLVu2FCtWrBA//fST6Ny5swgKCjJ7cLPE1vfFkXt5RkaG6NevnwBgch8zPDS9/PLLYuHChWLjxo1i27ZtYt68eSIqKsrsXuFqYuHIteFoYnHmzBkhl8tFt27dxJo1a8SOHTvE0qVLxVNPPSVycnJMPtdSYlGvXj3xwgsviE2bNonPPvtMREZGii5duph8pqO/k40aNRLJycniq6++Ejt37hSrVq0S//nPf4z3bkdjteStt94S8+bNEz/++KPYsWOH+Pjjj0XdunXNYnXk99FQtklJSWLs2LEiLS3N+LLr6aefFlKpVPznP/8RGzduFMuWLRONGzcWMTExxheG+fn5Qq1WixYtWogVK1aInTt3im+//VaMHDnS7KVBeY7er0pLS8V9990nlEqlmDJlikhLSxOfffaZSEhIELfddpsoLCw0bpuUlCTi4uJEvXr1xOeffy62b98ufvnlF4ufb+u33NFr+eOPPxYAxKVLl4QQQuh0OqFSqYRCoRBPP/20cbvZs2eLoKAgodFohBDOfecsWbJkiQBgVsa2EgtHf6MN/za2EgvDeX/11VfGZc4kFkIIkZycLFQqlXjnnXfEn3/+KW7evGnznGfPni0CAgLsXh9lVTixKC4uFvXq1RMtWrQwBmYvsRDCdh8Lw9scS+20y99Id+3aJYBbbXWdUVpaKuLj40VKSorJG9O8vDwRHR0t2rZta1ym1WrFHXfcIerWrSv++OMPERMTIzp16mSW1Ze/YT7++ONCoVCY1Bro9XrRuHFjkxvy+fPnRVBQkHjhhRdMjpeXlydiY2PFY489ZvNchg0bJqRSqc0bybhx4wQA8fPPP5ssHzVqlJBIJOL48eNCiH8vjJSUFJPz++WXXwQAk7dZ9evXF/Xr1ze+pbOkR48eolatWmb9Ep5//nkhl8vF9evXhRD/XnzlHwBWrFhh/PE1sNYu0xB7/fr1zd7slafX64VOpxPDhw83eTMuhPU+FuUTC2e+Q4YfkDlz5pgc87nnnhNyudzuRd2zZ0/RuHFjqzEZHgTat28vatWqZfw3KX/TmjVrlvGBzpqioiIBQPTs2dNk+c2bN43f3blz59qMt127dqJVq1Y2t9HpdCImJkYMGDDAZPlrr70mgoODRVZWlhBCiLlz5woADtXslAfA6jWYnJxsXGa4pw0aNMhkf0OS89xzz5ksN9TKjB8/3rjM8AaufE3p008/LQICAsS5c+eEEP/+KJR9oybErRs3ALNa0PDwcON1YmCrj0X5B76cnBxjslzW+fPnhUwmMyl/w723fGy9evUSjRo1MvusspwpK8P1UDYxs8bRmJwt15iYGOODhhC33kIGBASImTNn2o3J2vfFmXu5o30sSktLhU6nE0uWLBGBgYEm3wVXEwtHrg1HE4uVK1cKAOLw4cN2P9dSYlH+ezNnzhwBQFy+fFkI4XjZZmVlCQDivffesxqDo7Hac/PmTaHT6cTOnTsFAPG///3PuM6R30dD2b755psmy/ft2ycAiHfeecdk+YULF4RCoRCvvfaaEEKIgwcPCgBmNe+OcPR+ZWi1sWrVKpPtDM9wCxYsMC5LSkoSgYGBxucJe6z9ljt6LWdkZAgAYsmSJUIIIdLT0wUA8dprr4m6desa9+vWrZvJ77Gj3zlrRo0aJRQKhdnvtr3EQgj7v9FC2E8sNmzYIIBbrTMMnE0sfvnlF+NLbgBCpVKJBx54QCxZssTi80haWpoAIDZs2GA1rvIqPNxscHAwpk2bhoMHD2LFihUVPYxFffv2tbvNhg0bAACjR4926tjHjx/HpUuX8NRTTyEg4N/TDw0NRd++fbF//34UFhYCAGQyGVasWIHs7GzceeedEELgm2++QWBgoM3P2L59O7p27YqYmBjjssDAQJN27QCwadMm6PV6DBo0CHq93vifXC5Hp06d7I5CtGHDBnTp0gVNmjSxus22bdtw2223oWXLlibLhwwZAiEEtm3bZrL8/vvvNzm/1NRUADCOAnbixAmcOnUKw4cPh1wut/iZWq0WW7duRZ8+fRASEmJybr169YJWq8X+/ftN9nnooYdM/i7/uY546KGHIJVKzZZ/9913aNeuHUJDQxEUFASpVIpFixbhzz//dPjYZTnzHSobW1mpqanQarW4du2azc+6dOmSQ23RZ8+ejczMTMyfP9+JM7GsfFvxjRs34q+//kJAQAC2bNlic9/o6GhcvHjR5jZBQUEYOHAgvv/+e+Tm5gK41Sfmq6++wsMPPwy1Wg0AuPvuuwEAjz32GFasWGH3uOVZuwYzMjKQmZlpsm35e8727dsBwGyUnZYtW6JJkybYunWryXKVSmX2bzxgwADcvHkTu3btAnDrWlQqlejXr5/JdobPKH/Me+65B5GRkQ6cqWX79u1DUVGR2TkkJibinnvuMfs8iURi1gY4NTXV7jXobFk5w5GYnC3XLl26QKVSGf+OiYlBdHS0yTHL3rP0ej1u/X7/q/z3xdV7ucGhQ4fw0EMPQa1WIzAwEFKpFIMGDUJpaSlOnDjh0DEc4cy1Yc/tt9+O4OBgPPPMM/jyyy9x+vRpp/a3d+93tGxr1KiB+vXr4+2338a7776LQ4cOmbUZdyXW06dPY8CAAYiNjTX+23Tq1AkAjL8ljvw+llX+e7R+/XpIJBIMHDjQ5FxjY2PRvHlz47kmJycjMjISY8eOxccff4w//vjD4fMAHLtfrV+/HhEREXjwwQdNYrn99tsRGxtr9p1OTU1Fw4YNnYqjPEev5fr166NOnTrG36O0tDSkpKRg4MCBOHPmDE6dOoXi4mKkp6fj3nvvNfucij5vXLp0CTVr1qxQf6rK+I0ufx+qiLvvvhsZGRnYuHEjxo8fjzZt2mDr1q0YNGgQHnroIbPPMDx/OPP769I8Fk888QTuvPNOTJgwATqdzpVDGYWEhCAsLMzudn///TcCAwMRGxvr1PENQ2rGxcWZrYuPj8fNmzeRk5NjXJacnIwOHTpAq9XiySeftLifpc+wFFf5ZVevXgVw6x9aKpWa/Pftt98iKyvL5uf8/fffqFWrlt1YrJ2rYX1Zhoc6A0Pn56KiIuNnArD5udnZ2dDr9fjggw/MzqtXr14AYHZu9j7XEZbO8/vvv8djjz2GhIQEfP3119i3bx8OHDiAYcOG2e0Ma42z3yGg4udn6BhqT9u2bdG7d2/MmjXL7LMBoHbt2gCAM2fOWD2GYV1iYqJx2Y0bNzBixAjcfffd+O9//4uNGzdi0aJFVo8hl8sd+jczlP/y5csB3Hp4uHz5MoYOHWrcpmPHjlizZo3xoaJWrVpo1qwZvvnmG7vHB8yvt7LLyn/vy/9b2vs3Lr9/2Yc0a59luC+U/1GKjo5GUFCQ3Zic5ew5hISEmH3XZDKZ3evE2c9xhiMxOVuu5a9FwzEN39uzZ8+a3bd27txpsn35c3X1Xg4A58+fR4cOHXDx4kXMnz8fu3fvxoEDB/DRRx8BcO5eaI8z14Y99evXx5YtWxAdHY3Ro0ejfv36qF+/vsMPUPbujY6WrUQiwdatW9GjRw/MmTMHd955J2rWrIkXX3wReXl5LsWan5+PDh064Oeff8a0adOwY8cOHDhwAN9//71JrI78PpZl6XskhEBMTIzZue7fv994ruHh4di5cyduv/12jB8/Hk2bNkV8fDwmTZrk0LOYI/erq1ev4saNGwgODjaL5cqVK2bfaVfvV4bPdvRa7tq1qzHR2LJlC7p164aUlBTExMRgy5Yt2LNnD4qKiiwmFlX9e2yJvd9oRxgSH8PzG3DrpYBh2Pfy9Ho9AJi9cJVKpejRowemT5+OTZs24cKFC+jcuTPWr19vfGlvYDhfZ+4/FRoVykAikWD27Nno1q2bWQ9zV47piJo1a6K0tBRXrlxx6gtt+EJdvnzZbN2lS5cQEBBg8pbws88+w48//oiWLVviww8/xOOPP45WrVrZ/YwrV66YLS+/LCoqCsCtsYaTkpIcPgeDmjVr2n27pFarrZ5r2Ric+UwANj83MjISgYGBeOqpp6zWKNWtW9epz3WEpe/O119/jbp16+Lbb781WV9cXFzhz3H2O+SKqKgoXL9+3aFtZ86ciWbNmmHGjBlm67p06YKgoCCsWbPGZD6MsgzjVXfr1s247IUXXsD169exZcsWNGnSBKtXr8aYMWPQo0cPiz+e169fd+g7ZahFW7x4MZ599lksXrwY8fHx6N69u8l2Dz/8MB5++GEUFxdj//79mDlzJgYMGIA6deqgTZs2Nj/D1jVY/oel/Hen7L9x+fO8dOmS2TkaHn5sfZZarcbPP/8MIYTJ5127dg16vd7smK6OMmTve+rste/I5zhSVpXN2XK1Jz4+HgcOHDBZ1qhRI5O/y//buHovB25dfwUFBfj+++9NjuHsnE+OcOTaMDxQFBcXm4yuZylJ6tChAzp06IDS0lIcPHgQH3zwAV566SXExMTgiSeecClWZ8o2KSnJ+OLjxIkTWLFiBSZPnoySkhJ8/PHHFY5127ZtuHTpEnbs2GGspQBuvXgpy5Hfx7IsfY8kEgl2795tcUTDsstSUlKwfPlyCCFw5MgRfPHFF5g6dSoUCgXGjRtn83MduV9FRUVBrVZj48aNFo9RttbP0rlUhDPXcteuXbFo0SL88ssv+PnnnzFx4kQAt2p609LScO7cOYSGhlbqaGRRUVH47bffKry/rd9oR/zwww+QSCTo2LGjcVlMTIzV2gTDckuJZFlqtRovvfQSduzYgd9//934AhiA8fnDmfuoyzNv33vvvejWrRumTp2K/Px8u9tX5E20JT179gQALFy40Kn9GjVqhISEBCxbtsykyqegoACrVq1CmzZtjEO+HT16FC+++CIGDRqE3bt3IzU1FY8//rjdbLNLly7YunWrycVbWlpqNkRYjx49EBQUhFOnTqFFixYW/7NXBtu3b8fx48etbtO1a1f88ccfZhfDkiVLIJFI0KVLF5ufUV7Dhg1Rv359fP7551YfzkNCQtClSxccOnQIqampFs/L0ltDe8q+VXSURCJBcHCwyU3qypUrWLt2bYWP78x3yFWNGzd2uLq+cePGGDZsGD744AOcP3/eZF1sbCyGDRuGTZs2WRyq7sSJE5g9ezaaNm2K3r17AwDWrl2Lr7/+Gm+99Zaxud0nn3yCgIAAPP300xZjOH36NG677TaH4h06dCh+/vlnpKenY926dRg8eLDVZoYymQydOnXC7NmzAdxqMmKPtWuwfv36dt8o3nPPPQBuJaZlHThwAH/++Se6du1qsjwvLw8//PCDybJly5YhICDA+CPQtWtX5Ofnm004tGTJEuN6e5y5f7Zp0wYKhcLsHDIzM7Ft2zaHPs8RzpZVZauMci0rODjY7H5V/iGqPGfu5db+DQ33qLIPj0IIfPrpp07F7whHrg3DBHxHjhwx2XfdunVWjxsYGIhWrVoZa1lceQgzqOjvZMOGDTFx4kSkpKRYjMOZWC392wDAf//7X7PPtPf7aMsDDzwAIQQuXrxo8TxTUlIsxta8eXPMmzcPERERDpW5I/erBx54ANnZ2SgtLbUYS/lk2xnWfmuduZa7du0KiUSCN954wyTue++9F9u3b0daWho6duxosXl0RTVu3BjZ2dnGJrwV2d/ab7Q9ixcvxoYNG9C/f39jCwTg3/M11JYZCCHw3XffoU6dOkhOTgZwa4JcazWShuZ8ZWtDABifPxz9XQdcrLEwmD17Nu666y5cu3YNTZs2tblt/fr1oVAosHTpUjRp0gShoaGIj483Oxl7OnTogKeeegrTpk3D1atX8cADD0Amk+HQoUMICQnBCy+8YHG/gIAAzJkzB08++SQeeOABPPvssyguLsbbb7+NGzduYNasWQBuPSQ+9thjqFu3LhYsWIDg4GCsWLECd955J4YOHWpzNsKJEyfihx9+wD333IM333wTISEh+Oijj1BQUGCyXZ06dTB16lRMmDABp0+fxn333YfIyEhcvXoVv/zyC5RKJaZMmWL1c6ZOnYoNGzagY8eOGD9+PFJSUnDjxg1s3LgRY8aMQePGjfHyyy9jyZIluP/++zF16lQkJSXhxx9/xIIFCzBq1KgKtYn86KOP8OCDD6J169Z4+eWXUbt2bZw/fx6bNm3C0qVLAQDz589H+/bt0aFDB4waNQp16tRBXl4eMjIysG7dOrO+HY5ISUnB999/j4ULF+Kuu+5CQECA3eTrgQcewPfff4/nnnsO/fr1w4ULF/DWW28hLi4OJ0+eNDv+jh07sG7dOsTFxUGlUlm8eTr6HaoMnTt3xueff44TJ0449G81efJkLF26FNu3b4dSqTRZ9+677+L48eMYOHAgdu3ahQcffBAymQz79+/H3LlzoVKpsGrVKgQGBiIrKwvPPvss2rZtizFjxhiPkZCQgHnz5mHo0KFYtGgRhg8fblyXnZ2NkydPWr32yuvfvz/GjBmD/v37o7i42KyN/ptvvonMzEx07doVtWrVwo0bNzB//nyTts22REVF4Z577sEbb7wBpVKJBQsW4K+//jI2v7KlUaNGeOaZZ/DBBx8gICAAPXv2xNmzZ/HGG28gMTERL7/8ssn2arUao0aNwvnz59GwYUP89NNP+PTTTzFq1Cjjj8CgQYPw0UcfYfDgwTh79ixSUlKQnp6OGTNmoFevXhar7MtTqVRISkrC2rVr0bVrV9SoUQNRUVFmszADQEREBN544w2MHz8egwYNQv/+/ZGdnY0pU6ZALpdj0qRJdj/PEc6WVWWrjHJ1lTP3csPD4ezZs9GzZ08EBgYiNTUV3bp1Q3BwMPr374/XXnsNWq0WCxcurHCzCVscuTZ69eqFGjVqYPjw4Zg6dSqCgoLwxRdf4MKFCybH+vjjj7Ft2zbcf//9qF27NrRaLT7//HMAqJSyd7Rsjxw5gueffx6PPvooGjRogODgYGzbtg1HjhwxvsGvaKxt27ZFZGQkRo4ciUmTJkEqlWLp0qX43//+Z7atI7+P1rRr1w7PPPMMhg4dioMHD6Jjx45QKpW4fPky0tPTkZKSglGjRmH9+vVYsGABevfujXr16kEIge+//x43btwwqXG2xpH71RNPPIGlS5eiV69e+L//+z+0bNkSUqkUmZmZ2L59Ox5++GH06dPH7mdZYu233JlrOTo6Gs2aNcPmzZvRpUsX48u8e++9F9evX8f169fx7rvvVig+azp37gwhBH7++Wez2nVH2fqNBm69cDD0QS0qKsLp06exZs0arF+/Hp06dTLWvBm8+eabWLduHVq1aoVx48ahQYMGuHLlCj799FMcOHDApA90bm4u6tSpg0cffRT33nsvEhMTkZ+fjx07dmD+/Plo0qQJHnnkEZPj79+/H2q12mJSa5XD3byF6ahQ5Q0YMEAAsDsqlBC3RhswjM0OmM9jYYm1eSzmzZsnmjVrJoKDg0V4eLho06aNQ2OSr1mzxjiGs1KpFF27dhV79uwxrh84cKAICQkxG/LV0Mt+3rx5xmVlz8Fgz549onXr1kImk4nY2Fjx6quvWp3HYs2aNaJLly4iLCxMyGQykZSUJPr16ye2bNli9zwuXLgghg0bJmJjY4VUKhXx8fHiscceE1evXjVuc+7cOTFgwAChVquFVCoVjRo1Em+//bbVeSzKs3R++/btEz179hTh4eFCJpOJ+vXri5dfftlkmzNnzohhw4aJhIQEIZVKRc2aNUXbtm3FtGnTjNuUHzmhfDxlRw+7fv266Nevn4iIiBASicQ4Yomt2IW4NSJSnTp1hEwmE02aNBGffvqpxRFPDh8+LNq1aydCQkIEYH8eC3vfISGsj4Lj6Czsubm5IjQ01GxUKWvlJoQQ48ePFwAsXkslJSXio48+Eq1atRKhoaFCJpOJRo0aiddee804GpMQQjz66KMiJCTEZEjSsnr16iXCwsLE+fPnjcsWLVokpFKpyWgz9hjuG+3atTNbt379etGzZ0+RkJBgHJO+V69eJsP5WoN/RspYsGCBqF+/vpBKpaJx48Zi6dKlJtvZuqcZ5mZo2LChkEqlIioqSgwcONDqPBY7duwQLVq0MM5dM378eKHT6Uy2zc7OFiNHjhRxcXEiKChIJCUliddff93qfAuWbNmyRdxxxx1CJpMJwP48Fp999plITU013iMffvhhs/uatXuvtZGBKlpWzo4K5WhMrparoyMq2fq+COHYvby4uFiMGDFC1KxZ03gfM/ybrVu3zjh/QUJCgnj11VeNI8GUvf9UxjwW9q4NIW6NINO2bVuhVCpFQkKCmDRpkvjss89MYt63b5/o06ePSEpKEjKZTKjVatGpUyfj3AFlP9fSqFDly9LW/dZW2V69elUMGTJENG7cWCiVShEaGipSU1PFvHnzjCMdOhqrJXv37hVt2rQRISEhombNmmLEiBHGeYbKj3Jp7/fR3nXw+eefi1atWgmlUikUCoWoX7++GDRokDh48KAQQoi//vpL9O/fX9SvX18oFAoRHh4uWrZsKb744gu75+HM/Uqn04m5c+cav5OhoaGicePG4tlnnxUnT540bmeYx8JR1n7LhXD8Whbi1vDMAMT06dNNljdo0EAAEEeOHDFZ7ux3rrzS0lJRp04ds1GlHBkVqixrv9Hl5/hQKpWiXr16ol+/fuK7776zOPeTEEKcPHlSDBw40FhmERERonv37mZzShUXF4u5c+eKnj17itq1awuZTCbkcrlo0qSJeO2110R2drbJ9jdv3hRJSUlmI7LZIxGiErqZE1GVeeGFF7B161YcO3bMq2aDLq9Dhw6oXbu23bdy7iCRSDB69Gh8+OGHVf5ZnTt3RlZWlnFWaSIib8X7lWveeecdTJ8+HRcvXvTLmcXL2rp1K7p3745jx46hcePGDu/nch8LIqpaEydOxMWLF7Fq1SpPh2LVrl27cODAAbz11lueDoWIiKhKjB49GuHh4ca+Of5s2rRpGDZsmFNJBcDEgsjrxcTEYOnSpZU63GRly87OxpIlS1CvXj1Ph0JERFQl5HI5vvrqK4ujdvmTnJwcdOrUCdOnT3d6XzaFIiIiIiIil7HGgoiIiIiIXMbEgoiIiIiIXMbEgoiIiIiIXFYpE+QR3bx5E5cuXYJKpfLqIVGJiIjoX0II5OXlIT4+HgEBfN9MrmFiQZXi0qVLSExM9HQYREREVAEXLlxArVq1PB0G+TgmFlQpVCoVgFs3prCwMA9Hc4tOp8PmzZvRvXt3SKVST4dTLbDM3Yvl7X4sc/djmVctjUaDxMRE4+84kSuYWFClMDR/CgsL86rEIiQkBGFhYfwxchOWuXuxvN2PZe5+LHP3YDNmqgxsTEdERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC5jYkFERERERC4L8nQARERUNTJzCpGn1UNTpEO4QopQeRBqRYZ4OiwiIvJTTCyIiPzQuewCjF99FHsyso3L2ierMb1PCpLUSg9GRkRE/opNoYiI/ExmTqFZUgEA6RnZmLD6KDJzCj0UGRER+TMmFkREfiZPqzdLKgzSM7KRp9W7OSIiIqoOmFgQEfkZTZHO5vo8re31REREFcHEgojIz4QppDbXq+S21xMREVUEEwsiIj+jkgehfbLa4rr2yWqo5By3g4iIKh8TCyIiP1MrMgTT+6SYJReGUaE45CwREVUFvrYiIvJDSWolZvVNRZ5WjzytDiq5FCrOY0FERFWIiQURkZ9iEkFERO7EplBEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROQyJhZEREREROSyIE8HQERU3WTmFCJPq4emSIdwhRSh8iDUigzxdFhEREQuYWJBRORG57ILMH71UezJyDYua5+sxvQ+KUhSKz0YGRERkWvYFIqIyE0ycwrNkgoASM/IxoTVR5GZU+ihyIiIiFzHxIKIyE3ytHqzpMIgPSMbeVq9myMiIiKqPEwsiIjcRFOks7k+T2t7PRERkTdjYkFE5CZhCqnN9Sq57fVERETejIkFEZGbqORBaJ+striufbIaKjnH0yAiIt/FxKIamDlzJu6++26oVCpER0ejd+/eOH78uMk2Q4YMgUQiMfmvdevWHoqYyD/VigzB9D4pZsmFYVQoDjlLRES+jK/HqoGdO3di9OjRuPvuu6HX6zFhwgR0794df/zxB5TKf4e3vO+++7B48WLj38HBwZ4Il8ivJamVmNU3FXlaPfK0OqjkUqg4jwUREfkBJhbVwMaNG03+Xrx4MaKjo/Hrr7+iY8eOxuUymQyxsbHuDo+o2mESQURE/ohNoaqh3NxcAECNGjVMlu/YsQPR0dFo2LAhnn76aVy7ds0T4RERERGRD2KNRTUjhMCYMWPQvn17NGvWzLi8Z8+eePTRR5GUlIQzZ87gjTfewD333INff/0VMpnM7DjFxcUoLi42/q3RaAAAOp0OOp13DJlpiMNb4qkOWObuxfJ2P5a5+7HMqxbLlSqTRAghPB0Euc/o0aPx448/Ij09HbVq1bK63eXLl5GUlITly5fjkUceMVs/efJkTJkyxWz5smXLEBLCZh5ERES+oLCwEAMGDEBubi7CwsI8HQ75OCYW1cgLL7yANWvWYNeuXahbt67d7Rs0aIARI0Zg7NixZuss1VgkJiYiKyvLa25MOp0OaWlp6NatG6RSzg/gDixz92J5ux/L3P1Y5lVLo9EgKiqKiQVVCjaFqgaEEHjhhRewevVq7Nixw6GkIjs7GxcuXEBcXJzF9TKZzGITKalU6nU3fm+Myd+xzN2L5e1+LHP3Y5lXDZYpVSZ23q4GRo8eja+//hrLli2DSqXClStXcOXKFRQVFQEA8vPz8corr2Dfvn04e/YsduzYgQcffBBRUVHo06ePh6MnIiIiIl/AGotqYOHChQCAzp07myxfvHgxhgwZgsDAQBw9ehRLlizBjRs3EBcXhy5duuDbb7+FSqXyQMRERERE5GuYWFQD9rrRKBQKbNq0yU3REBEREZE/YlMoIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyGRMLIiIiIiJyWZCnAyAiIu+SmVOIPK0emiIdwhVShMqDUCsyxNNhERGRl2NiQURERueyCzB+9VHsycg2LmufrMb0PilIUis9GBkREXk7NoUiIiIAt2oqyicVAJCekY0Jq48iM6fQQ5EREZEvYGJBREQAgDyt3iypMEjPyEaeVu/miIiIyJcwsSAiIgCApkhnc32e1vZ6IiKq3phYEBERACBMIbW5XiW3vZ6IiKo3JhZERAQAUMmD0D5ZbXFd+2Q1VHKO90FERNYxsSAiIgBArcgQTO+TYpZcGEaF4pCzRERkC18/ERGRUZJaiVl9U5Gn1SNPq4NKLoWK81gQEZEDmFgQEZEJJhFERFQRbApFREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JBREREREQuY2JRDcycORN33303VCoVoqOj0bt3bxw/ftxkGyEEJk+ejPj4eCgUCnTu3BnHjh3zUMRERERE5GuYWFQDO3fuxOjRo7F//36kpaVBr9eje/fuKCgoMG4zZ84cvPvuu/jwww9x4MABxMbGolu3bsjLy/Ng5ERERETkK4I8HQBVvY0bN5r8vXjxYkRHR+PXX39Fx44dIYTAe++9hwkTJuCRRx4BAHz55ZeIiYnBsmXL8Oyzz3oibCIiIiLyIUwsqqHc3FwAQI0aNQAAZ86cwZUrV9C9e3fjNjKZDJ06dcLevXstJhbFxcUoLi42/q3RaAAAOp0OOp2uKsN3mCEOb4mnOmCZuxfL2/1Y5u7HMq9aLFeqTEwsqhkhBMaMGYP27dujWbNmAIArV64AAGJiYky2jYmJwblz5yweZ+bMmZgyZYrZ8s2bNyMkJKSSo3ZNWlqap0Oodljm7sXydj+WufuxzKtGYWGhp0MgP8LEopp5/vnnceTIEaSnp5utk0gkJn8LIcyWGbz++usYM2aM8W+NRoPExER0794dYWFhlRt0Bel0OqSlpaFbt26QSqWeDqdaYJm7V/nyvnSjCHnFeuQV6RCmkCJUFoT4CIWnw/Qr/I67H8scVXptG1ocEFUGJhbVyAsvvIAffvgBu3btQq1atYzLY2NjAdyquYiLizMuv3btmlkthoFMJoNMJjNbLpVKve7G740x+TuWuXtJpVJc0pRg/No/sCcj27i8fbIa0/ukIEmt9GB0/onfcferrmV+LrugSq/t6limVHU4KlQ1IITA888/j++//x7btm1D3bp1TdbXrVsXsbGxJtXMJSUl2LlzJ9q2bevucInISZduFGH86qMmDx4AkJ6RjQmrjyIzh00diHxRZk4hr23yKayxqAZGjx6NZcuWYe3atVCpVMY+FeHh4VAoFJBIJHjppZcwY8YMNGjQAA0aNMCMGTMQEhKCAQMGeDh6IrInr1hv9uBhkJ6RjTyt3s0REVFlyNPy2ibfwsSiGli4cCEAoHPnzibLFy9ejCFDhgAAXnvtNRQVFeG5555DTk4OWrVqhc2bN0OlUrk5WiJyVl6R7VFd8rQc9YXIF2l4bZOPYWJRDQgh7G4jkUgwefJkTJ48ueoDIqJKpVLYbiOtkrMNNZEvCuO1TT6GfSyIiHycShaE9slqi+vaJ6uhkvMdEpEvUsl5bZNvYWJBROTj4iMUmN4nxewBxDByTK1I75pbhogcUysyhNc2+RSmukREfiBJrcSsvqnI0+qRp9VBJZdCJQ/igweRj+O1Tb6EiQURkZ/ggwaRf+K1Tb6CTaGIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlTCyIiIiIiMhlnHmbiHxWZk4h8rR6aIp0CFdIESoP8voZai/mFEKj1aNEp0MNpRz5JaXQFOkQppAiNDgQ0gAJYm2cQ9lzDg2WOPXZrpZXVZX3pRtFyC3SmRy3SKdHToEeYYogRIYEIyZMbraPVqeDNCDQWIaWYqpIzNb2ycwpxI0CLQDgxNU8hCvlNo9l6TgyAFmVWIbuvgZ88ZojIvdhYkFEPulcdgHGrz6KPRnZxmXtk9WY3icFSWqlByOzzhDz35pifDKoBcZZiH9a7xRczC5AgoVzKH/OskCBOS2BC9cLUS8m3KHPrmh5VVV5WzvuW72b4T8rDuNCThHaJ6sxo08Kav/zOeeyC7Bs/zn0b5VksQwNMVUkZlv77DlxBVPW/4k5LYFHFu7F3XWjrB7L2nGm9b51nGkbTjoUT0XKrqquAV+85ojIvdgUioh8TmZOodkDDgCkZ2RjwuqjyMwp9FBk1l0sE/NHA+/EhDWW45+45iiKbwqzc7B2zgAwdf0xm+fsanlVVXlfulFk9bhvrPkdHz15p/Hv8auP4qpGa9xnYOskq2VoiMnZmO3t06FhrEPHsnWciWuOokezBIfiscXd14AvXnNE5H5MLIjI5+Rp9RYfsIFbDzp5Wr2bI7JPUyZmfamwGX+x/qbZOdg6532nr9s8Z1fLq6rKO7dIZ/O4QYEBJn/nFJQY98kvKbUbk7Mx29snv6TUoWM5e5yKlKG7rwFfvOaIyP3YFIqIfI6mSGdzfZ7W9npPKBuz3fiLdEC57hOunLOr5VVV5e1QOZTdXquHEMKhfSsSs7PxWDtWRY7jbBm6+xrwxWuOiNyPiQUR+ZwwhdTmepXc9npPKBuz3fgtrHflnF0tr6oqb2fLIUweBOHgvhWJuSL/LpaOVZHjOFuG7r4GfPGaIyL3Y1MoIvI5KnkQ2ierLa5rn6yGSu5970zCysQcFCixGb8sKMDsHGydc5t6NWyes6vlVVXlHa6Q2jyuvvSmyd+RymDjPqHBgXZjcjZme/uEBgc6dCxnj1ORMnT3NeCL1xwRuR8TCyLyObUiQzC9T4rZg45hhBpvHP4yoUzMo7/+DdN6W49fFiAxOwdr5wwAbz7Q1OY5u1peVVXe8REKq8d9q3cKRi/9zfj3jD4piAmTG/dZuv+czTKsSMz29kk/ccWhY9k7zqbfLzoUjy3uvgZ88ZojIveTCEODVSIXaDQahIeHIzc3F2FhYZ4OBwCg0+nw008/oVevXpBKWU3vDu4uc8OY+nlaHVRyKVQ+MKa+YR4LnV6HyJBb81gY4ndmHos8rQ4hUglO/brb4fJ2tbyqqrwN81iUPa5xHgt5ECKV9uexsBZTRWK2to9hHotTv+5G/bs6IMLBeSzKHscwj0VllaG7rwFPXHO8l1ctb/z9Jt/Fuksi8lnenkRYkhAZggT7m1lV9px1Oh1OVXBfVz+7MsVHKBAfoXB6H8D+PhWJ2do+tSJDEBMqxSkAjWJUdh9yrR2nptMROf8ZVcUXrzkich82hSIiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcxsSAiIiIiIpcFeToAcs7OnTtRUFCANm3aIDIy0tPhEBEREREBYGLhtd5++23k5+djypQpAAAhBHr27InNmzcDAKKjo7F161Y0bdrUk2ESEREREQFgUyiv9c033+C2224z/r1y5Urs2rULu3fvRlZWFlq0aGFMOoiIiIiIPI01Fl7qzJkzSE1NNf79008/oW/fvmjXrh0AYOLEiXj00Uc9FR55kUs3ipBbpIOmSIdwhRRhCiniIxSeDouIiIiqGdZYeCmdTgeZTGb8e9++fWjbtq3x7/j4eGRlZTl8vF27duHBBx9EfHw8JBIJ1qxZY7J+yJAhkEgkJv+1bt3a5fOgqnUuuwCvrvwfes7fjcc/2Y/75u/Gayv/h3PZBZ4OjYiIiKoZJhZeKjk5Gbt27QIAnD9/HidOnECnTp2M6zMzM6FWqx0+XkFBAZo3b44PP/zQ6jb33XcfLl++bPzvp59+qvgJUJW7dKMI41cfxZ6MbJPl6RnZmLD6KC7dKPJQZERERFQdsSmUlxo1ahSef/557N69G/v370ebNm1M+lxs27YNd9xxh8PH69mzJ3r27GlzG5lMhtjY2ArHTO6VW6QzSyoM0jOykVukY5MoIiIichsmFl7q2WefRVBQENavX4+OHTti0qRJJusvXbqEYcOGVepn7tixA9HR0YiIiECnTp0wffp0REdHW9y2uLgYxcXFxr81Gg2AW024dDpdpcZVUYY4vCWeynYjvwiyQGF1fW5BEXQ69yYW/l7m3obl7X4sc/djmVctlitVJokQwvqTCfkliUSC1atXo3fv3sZl3377LUJDQ5GUlIQzZ87gjTfegF6vx6+//mrS18Ng8uTJFkelWrZsGUJCQqoyfCIiIqokhYWFGDBgAHJzcxEWFubpcMjHMbHwUufPn7e4PDw8HOHh4S4d21JiUd7ly5eRlJSE5cuX45FHHjFbb6nGIjExEVlZWV5zY9LpdEhLS0O3bt0glUo9HU6lu5KrxRtrj2Lf6etm69rUq4G3Hk5BbLjcrTH5e5l7G5a3+7HM3Y9lXrU0Gg2ioqKYWFClYFMoL1WnTh1IJBKL62rWrInXXnsNY8aMqbLPj4uLQ1JSEk6ePGlxvUwms1iTIZVKve7G740xVYbEKCmm9G6OCauPIr1MX4v2yWpM6Z2CRLXSY7H5a5l7K5a3+7HM3Y9lXjVYplSZmFh4qUOHDllcfuPGDfzyyy+YPn06QkJCMHLkyCr5/OzsbFy4cAFxcXFVcnyqHElqJeb0a47cIh3ytDqo5FKEcx4LIiIi8gAmFl6qefPmVtd16tQJcXFxmDt3rsOJRX5+PjIyMox/nzlzBocPH0aNGjVQo0YNTJ48GX379kVcXBzOnj2L8ePHIyoqCn369HH5XKhqxUcomEgQERGRxzGx8FFt27bF6dOnHd7+4MGD6NKli/FvQzOqwYMHY+HChTh69CiWLFmCGzduIC4uDl26dMG3334LlUpV6bETERERkf9hYuGjcnJyEBER4fD2nTt3hq1++ps2baqEqIiIiIiouuLM2z6opKQEc+bMQevWrT0dChERERERANZYeC1LQ7wCQG5uLn7//XcEBQVh9+7dbo6KiIiIiMgyJhZeytpcFYmJiejXrx+efPJJjjftAzJzCpGn1UNTpEO4QopQeRBqRXICQSIiIvI/TCy81OLFiz0dArnoXHYBxq8+ij3l5piY3icFSR6cY4JMXcwphKZM8qeSByGByR8REZHTmFj4gKysLJw9exYSiQR16tSBWq32dEhkR2ZOoVlSAQDpGdmYsPooZvVNZc2FF2DyR0REVHnYeduLHTt2DB07dkRMTAxatWqFli1bIjo6Gvfccw+OHz/u6fDIhjyt3iypMEjPyEaeVu/miKi8i3aSv4s5hR6KjIiIyDexxsJLXblyBZ06dULNmjXx7rvvonHjxhBC4I8//sCnn36KDh064Pfff0d0dLSnQyULNEU6m+vztLbXU9XT2En+NFo9EtwcExERkS9jYuGl5s2bh6SkJOzZswdyudy4/L777sOoUaPQvn17zJs3DzNnzvRglGRNmEJqc71Kbns9VT0mf0RERJWLTaG8VFpaGsaOHWuSVBgoFAq8+uqrnNTOi6nkQWifbLkvTPtkNVRy5vSexuSPiIiocjGx8FKnT5/GnXfeaXV9ixYtcPr0aTdGRM6oFRmC6X1SzJILQ8dgdtz2vDA7yV8Ykz8iIiKn8JfTS+Xl5dmcp0KlUiE/P9+NEZGzktRKzOqbijytHnlaHVTyW0OZMqnwDgn/JH8TVh9FuoVRoTjkLBERkXOYWHixvLw8i02hAECj0UAI4eaIyFlMIrxbklqJ2X1ToSmT/IVxHgsiIqIKYWLhpYQQaNiwoc31EonEjRER+aeEyBCO/kRERFQJmFh4qe3bt3s6BCIiIiIihzGx8FKdOnXydAhERERERA7jqFBEREREROQy1lh4qYCAALt9KCQSCfR6vZsioorIzClEnlYPTZEO4QopQjkqFBEREfkpJhZeavXq1VbX7d27Fx988AFHhfJy57ILMH71UeyxMJRpklrpwciIiIiIKh8TCy/18MMPmy3766+/8Prrr2PdunV48skn8dZbb3kgMnJEZk6hWVIBAOkZ2Ziw+ihm9U1lzQURERH5Ffax8AGXLl3C008/jdTUVOj1ehw+fBhffvklateu7enQyIo8rd4sqTBIz8hGnpZN2IiIiMi/MLHwYrm5uRg7diySk5Nx7NgxbN26FevWrUOzZs08HRrZoSnS2Vyfp7W9noiIiMjXsCmUl5ozZw5mz56N2NhYfPPNNxabRpH3ClNIba5XyW2vJyIiIvI1TCy81Lhx46BQKJCcnIwvv/wSX375pcXtvv/+ezdHRo5QyYPQPlmNdAvNodonq6GS89IjIiIi/8KnGy81aNAgu8PNkveqFRmC6X1SMGH1UZPkwjAqFDtuExERkb9hYuGlvvjiC0+HQC5KUisxq28q8rR65Gl1UMmlUHEeCyIiIvJTTCy81COPPGJ3G4lEglWrVrkhGqooJhFERERUXTCx8FLh4eGeDoGIiIiIyGFMLLzU4sWLPR0CEVVQZk4h8rR6aIp0CFdIEcomcEREVA0wsSAiqkTnsgvMZl03dNpPUis9GBkREVHV4gR5RESVJDOn0CypAG7Ntj5h9VFk5hR6KDIiIqKqx8SCiKiS5Gn1ZkmFQXpGNvK0ejdHRERE5D5MLIiIKommSGdzfZ7W9noiIiJfxsSCiKiShCmkNter5LbXExER+TImFkRElUQlD0L7ZLXFde2T1VDJOV4GERH5LyYWRESVpFZkCKb3STFLLgyjQnHIWSIi8md8fUZEVImS1ErM6puKPK0eeVodVHIpVJzHgoiIqgEmFkRElYxJBBERVUdsCkVERERERC5jYkFERERERC5jYkFERERERC5jHwsiABdzCqHR6qEp0iFccauzbQLbyRMRERE5jIkFVXvnsgswfvVR7MnINi4zDA+apFZ6MDIiIiIi38GmUFStXcwpNEsqACA9IxsTVh/FxZxCD0VGRERE5FtYY0HVmkarN0sqDNIzsqHR6pHg5pi8SWZOIfLKNBEL5XwMREREZAUTC6rWNEU6m+vztLbX+zM2ESMiIiJnsCkUVWthCqnN9Sq57fX+KtNOE7FMNhEjIiKicphYULUWJg9C+2S1xXXtk9UIk1fPSr08O03E8rR6N0dERERE3o6JBVVrCZEhmN4nxSy5MDT5qa5DzrKJGBERETmrer6OJSojSa3E7L6p0Gj1yNPqoJJLEVbN57FgEzEiIiJyFhMLItyquajOoz+Vp/qniVi6heZQ7ZPVUFXTJmJERERkHZtCVRO7du3Cgw8+iPj4eEgkEqxZs8ZkvRACkydPRnx8PBQKBTp37oxjx455JljyuFp2moj5+pCzmTmF+POyBj+fzsZflzXsjE5ERFQJ+NqxmigoKEDz5s0xdOhQ9O3b12z9nDlz8O677+KLL75Aw4YNMW3aNHTr1g3Hjx+HSqXyQMTkaUlqJWb1TUVemSZiKj+Yx4LD6BIREVUNJhbVRM+ePdGzZ0+L64QQeO+99zBhwgQ88sgjAIAvv/wSMTExWLZsGZ599ll3hkpexNeTiPIycwpRWFKKF+9pgIn334bQ4EB8uisDS37OxITVRzGrb6rfnTMREZG7MLEgnDlzBleuXEH37t2Ny2QyGTp16oS9e/daTCyKi4tRXFxs/Fuj0QAAdDoddDrvGDHIEIe3xFMdeHOZX7heiCnrj2H/6evGZW3q1cAb9zdFAG7im4MXcaNAi5hQ3+mY7s3l7a9Y5u7HMq9aLFeqTBIhhPB0EOReEokEq1evRu/evQEAe/fuRbt27XDx4kXEx8cbt3vmmWdw7tw5bNq0yewYkydPxpQpU8yWL1u2DCEhfONLRETkCwoLCzFgwADk5uYiLCzM0+GQj2ONBRlJJBKTv4UQZssMXn/9dYwZM8b4t0ajQWJiIrp37+41NyadToe0tDR069YNUqnvvIX2Zd5a5sev5qHvwr1W168a1RZ9F+7FqlFt0SjGvX2KrNWkvPlAUyTWsJ2ke2t5+zOWufuxzKuWocUBUWVgYkGIjY0FAFy5cgVxcXHG5deuXUNMTIzFfWQyGWQymdlyqVTqdTd+b4zJ33lbmReUCBSXWk6SAaCwRODuulGIUMrdGndmTiHeWPcn9mTkAPg3vh0nc6Bf96fDfT68rbyrA5a5+7HMqwbLlCoTh5sl1K1bF7GxsUhLSzMuKykpwc6dO9G2bVsPRkZUOexO+KeQemQY3Tyt3mR0qrLSM7KRp9W7NR4iIiJXsMaimsjPz0dGRobx7zNnzuDw4cOoUaMGateujZdeegkzZsxAgwYN0KBBA8yYMQMhISEYMGCAB6MmqhyOTPjnidGgNEW2O03madmpkoiIfAcTi2ri4MGD6NKli/FvQ/+IwYMH44svvsBrr72GoqIiPPfcc8jJyUGrVq2wefNmzmFBfsEw4d+E1UdNkgtPT/hntyZFziYKRETkO5hYVBOdO3eGrQHAJBIJJk+ejMmTJ7svKCI38sYJ/xypSSEiIvIV/NUiomrD2ya/89aaFCIioopgYkFE5EHeWJNCRERUEUwsiIg8jEkEERH5Aw43S0RERERELmNiQURERERELmNiQURERERELmNiQURERERELmPnbSLyKpk5hcjT6qEp0iFcIUUoR0giIiLyCUwsiMhrnMsuwPjVR7HHwpwOSWqlByMjIiIie9gUioi8QmZOoVlSAQDpGdmYsPooMnMKPRQZEREROYKJBRF5hTyt3iypMEjPyEaeVu/miIiIiMgZTCyIyCtoinQ21+dpba8nIiIiz2IfCyLyCmEKqc31Krnt9WWxAzgREZH7MbEgIq+gkgehfbIa6RaaQ7VPVkMld+x2xQ7gREREnsGmUETkFWpFhmB6nxS0T1abLDckBY7UOLADOBERkeewxoKIvEaSWolZfVORp9UjT6uDSi6FyolmTOwATkRE5DlMLIjIq5RPIjJzCvHnZY1D/SXYAZyIiMhzmFgQkddytr9EZXYAJyIiIuewjwUReaWK9JcwdAC3xJkO4EREROQ8JhZE5JUq0l+iMjqAExERUcXw9R0ReaWK9pdwtQM4ERERVQwTCyLySq70l2ASQURE5H5MLIjIYe6c0bqyJswjIiIi9+AvMxE5xN0zWhv6S0xYfdQkuWB/CSIiIu/ExIKI7LI3QtOsvqlV8qDP/hJERES+g4kFEdnlyRmtmUQQERH5BiYWRH7oqkaLnIISaLR6hCmCEBkSjJgweYWPxxmtiYiIyB4mFkRewJVO0eX3VQYH4r+7TuHrny8Yt2mfrMaMPimoXcG+EJzRmoiIiOxhYkHkYa50ira277TeKTiSmYsjFzUAbjVXGr/6KN557PYK1VxwhCYiIiKyhzNvE3mQvU7RmTmFFdp34pqjeL//HWbLcwpKKhQnZ7QmIiIie/iakciDXOkUbW/fIt1Ns+UaFzpZc4QmIiIisoWJBZEHudIp2u6+FtaHudhkiUkEERERWcOmUEQe5EqnaLv7llvfPlmNSGWw48EREREROYGJBZEHGTpFW2KvU7S9fRXSAJO/Z/RJcWnIWSIiIiJb2BSKyINqRYZgTp8U6AAUlpRCU6RDmEKKkOBASAHE22h6ZOhQPWH1UZPRmgwdqvU3S7Hi2TYIkwchUunaPBZERERE9jCxIPIwHWB1uFl77HWorl+zqqImIiIiMsXEgsiD7A03O6tvqt0O097YofrSjSIU6otws1SHMIUc+f/Uxjg7+R8RERH5DiYWRB7kynCz3uzNH35HkU5gTr/bMa6Ck/8RERGRb2FiQdXSpRtFyC3SGd+ihymkiI9QVPqxI5VShAQHIV+rh0arR5giCJEh//Z3cGW4WW906UYRAGD/6etIG9MFr7tYG0NERES+g4kFVTvnsgus9mlw9S162WNHhQZj6YjWGLvqiNlnzeiTgtpqpUvDzXqTizmF0Gj1KC39t4aloKTUL2tjiIiIyDION0vVyqUbRTb7NBjeuFdE+f4Ss/umYur6YxY/a/zqo7iq0bo03Ky3OJddgNdWHUHP+btNJuXzt9oYIiIiso2JBVUruUU6m2/Rc+08DFtzLrvArL9EdJjM5mflFJQYh4wtn1wYalC8vanQxXLJVNlJ+fylNoaIiIgc4/2vQ4kqUVW8RTfUVLx4TwOT5fnaUtux/NMUyN6Qsd5MUy6ZCpUGGv9fGRyI9slqkzk2DHylNoaIiIgcxxoLqlaq4i26oaZCVe7YofJAK3v8E0uZB+takSFoEheGlnXVaBIX5hNJBWCeqM3a8AcAoE29Gnjlu8OY1tt3a2OIiIjIOXxlSNVKuEJq8y16uJ3EwxLDw7W+9KbJsa9pim1+ljLYduLhC8onattPZqNnS2DyA01RdFOCwmItZvZJQX5Jqc/VxhAREZFzWGNB1Up8hMJmn4aKDDlreLgevfQ3vNW7mfHYY1cdwRsPNLX4WW8+2BQzN/yJizmFFTwT7xBmpfN5j/d3Y/qPfyA8RIFEtdIna2OIiIjIOayxoGonSa3EnH7NkVukM75FD3dhHgvDyE7pGdkYtOgXfPTknRgfGHBrhCSJwMw+KcjV6pGn1SNUHohrmmIM+HQ/svJL8ELXhkio5PNzp4R/Op9PWH3UpGamTb0amNI7BQlMIoiIiKoNJhZUbZSfFC9cIUWTuDCXj1ur3MP1Qx/uAfBvLci8tL/w/eErFvf1hyFXk9RKzO6bCo1Wj9yCIlw9th9TH2qG2pxZm4iIqFphYkHVQlVOigdYH9mpVK+3mlQA/jPkakJkCBIA6HQK/HQMiKukWcyJiIjIdzCxIL93JVeL8WssT1Q3YfVRzOnXvMLNoMqy1HfgYk6hzQ7cYTaGXM3MKUSeVm+sYQn1007P1eU8iYiI/B0TC/J7uVr7k+JVRmJhibU+CIbaEmt9EKq6hsVbVJfzJCIiqg44KhQBACZPngyJRGLyX2xsrKfDqhR5VTApnjMMfRA2/F8HrHi2NTb8XwfM7ptq9cE5s9xs1gaGGpZMHx9JyqC6nCcREVF1wRoLMmratCm2bNli/Dsw0PfnWQBgNnGdQWKkAh89eSekgQH4+XQ2whVShDk5OpSjzXgMfRAckVduNuuy0jOykffPjN2+rrqcJxERUXXBxIKMgoKC/KaWoqxwufmkeImRCiwZ3hIT1/xeoWY4f+cUoqBUYMKaym/GU3426/L8YSQpoPqcJxERUXXBxIKMTp48ifj4eMhkMrRq1QozZsxAvXr1LG5bXFyM4uJi498ajQYAoNPpoNN5xwOhIQ51SCCmPtgEU9cfw77T1wEAHzyRislrj+DgmeuQlamYOXAmC5PW/A9vPZyC2HC5xeNeuF6IAACT1h+zuv+Uh5pVuN+GMlgCWaCwuj5EKvGaMi7PEJcj8fnyeXoLZ8qbKgfL3P1Y5lWL5UqVSSKEsP7LTtXGhg0bUFhYiIYNG+Lq1auYNm0a/vrrLxw7dgxqtfnMypMnT8aUKVPMli9btgwhIRzRh4iIyBcUFhZiwIAByM3NRViY63M7UfXGxIIsKigoQP369fHaa69hzJgxZust1VgkJiYiKyvLa25MOp0OaWlp6NatG6RS034WB89ex5AvDljd98uhd+OupBpmy49fzUPfhXvxxZC7K7S/oy5cLzSpYQFuzWb95gNNkVjDexM3W2Vuia+ep7dwtrzJdSxz92OZVy2NRoOoqCgmFlQp2BSKLFIqlUhJScHJkyctrpfJZJDJZGbLpVKp1934LcUUEapAcanE6j7hSoXF8ygoESgulVR4f1su5hRCU6Yj+LRHbkeRXo+c/H8n3POV+R0c/R7UiwnHtEduN5tY0FfO01t443Xn71jm7scyrxosU6pMTCzIouLiYvz555/o0KGDp0OpEuEK8w7dBu2T1Qi3MpJU2D/LlcGBNvdX2Zj4zhJb8zm0rOvfb5CYRBAREfkHzmNBAIBXXnkFO3fuxJkzZ/Dzzz+jX79+0Gg0GDx4sKdDqxLxEQpM75OC9smm/UcMD/PWOl6HyYPQPlmNV747jGm9re9v62H5qkaLvy5r8MuZ6/jrigaZOYWYteFPq/M5XOR8DkREROQDWGNBAIDMzEz0798fWVlZqFmzJlq3bo39+/cjKSnJ06FVmSS1EnP6NUdukc7YDCfczjwWZWfSfm3lYcx99HYUlJQir0gHlcJ+M57z2QV43ULNxBsPNMWBsznIyi8x2T49Ixsard7hOTCIiIiIPIWJBQEAli9f7ukQPCI+QuH0sLCGmbQ1Wj2u5WlRQylDQIgUhSWlCA4KQG5hCcJDgs32u6rRmiUVwK3k4a31xzC7byqGf3nQbD/O50BERES+gIkFUQUYZtK+dKMIY1cdwe6TWcZ1HRtEYVbfVLOEJaegxOZM02N7Nra4TiVnxzoiIiLyfkwsqNrKLSxBVn4JNFodwhRSRCmDLdY02Nq/fFIBALtOZmHcqiP4oP8dJsfTaPU2j5evLTVb1j5ZjTAnO4J7s8ycQuSVGfkqlCNAERER+Q3/eWIhcoIzNQ3WZOWXmCUVBrtOZiEr37RJlL0Eofx6Q0fwBD958LY18lWSWunByKrOVY0WOQUl0Gj1CFMEITIkGDFhlmd093dMKomI/B8TC6p2nK1psEZjp+9D+b4Rkcpg20Pchkix4f86GDuSh8mD/CapyMwpNEsqgH9HvprVN9XvHjKtddSf0ScFtf00kbKmOiaVRETVEYebJZ+WW1iCU9fyceh8Dk79nY/cwhK7+zhS0+CIMDt9H8r3jYgJk2OGlSFuZ/wzRG2TuDC0rKtGk7gwjyQV5YfCvarRVspx87R6m/1L8uw0E/M1tjrqj199tNLK1RfYSyozOZwyEZHfYI0F+ayKNmdytqbBmqjQYHRsEIVdFpKUjg2iEBVqXutRW63EO4/d/m/zGHkQIpXe0TymKt+wa4oqp8x9hb2O+jkFJV7xb+4O1S2pJCKqzlhjQT7p0o0ijF35P6vNmWzVXDhb02BNeEgwZvVNRccGUSbLOzaIwuy+qVabU8WEydE4Lgwt69ZA47gwr3jArOo37GFWZjI38LeRr+x11Le33p9Ut6SSiKg6Y40F+Zzz2QW4kFOE3VbeghqaM9WOlFlcX5GaBmviIxT4oP8dyMovMfaNiAp1bnQpb1DVb9hV/8xYbq1/icqPRr4CnO+o78+qW1JJRFSdscaCfIrhzXquC29BK1rTYOt49aNDcXvtSNSPDjXZPzOnEH9e1uDn09n467LGa9uTV/Ub9lr/zFhuqX/J9H/6l/gTQ0d9S9onqxGp9K3E0xWGpNISf0wqiYiqM97RyacY3qwPa1fX5nbSoACcuJpndb07ahp8aSQce2/QVfIg/HVZA5ULI1UlqZWY1TcVeVq9scxVfjrkqKGj/vjVR01qaQx9Vryh+Zu7GJLKCRbKwh+TSiKi6oyJBfkUw5vzQxduoF2y2mLznXbJamz4/Qo+3XkSc1oCmdcLUTcm3Gy78JCqa7JkayScBdtP4oWuDU3G9Hflgb0y2BsK99KNIgz/8qDLiVF1eoj05o767ladkkoiouqMiQX5FMOb9c/Tz+D9/ncAgMnDe4cGURjctg5e/OaQcdmU9ccw+9E73fpAZ20knIbRoRjVuQHGrjriUk3GpRtFyC3SGROTMIXU4Yn9LLH1hv3NB5tiwKf7Afw7ROjsvql+M8dGVYoJk1fLRMISJhFERP6PiQX5lLJv1l/85hCGta+LYe3qolh/0/jm/4lP9qOwpBSywFv77Dt93e3De1obCeejgXdiwhrrY/o78sBeVU2sTN+w66AIDsQ1TTEGfLrfZG6P9IxsaLR6JFT4k4iIiMgfsfM2+ZSyk8wVlpTiw20ZGP7lQSz7+RxqqmQY9sUBFJaUmu3n7uE9rY2Eoy8VNkdfshfnpRtFNicbu3SjqGIB/8MwFK4QwIMf7MHwLw9anDCQQ4QSERFReayxIJ9jqe16SHAgHlm4F1n5JQgJDsToLvXRvn4kzh7ag5Uj2yBUHoxrGi2iK7nW4qpG+28ciiBEhtxqQ29teFVXx/TPLdLZTExyi3QuNYky4BChRERE5CwmFuSTyrddv6rRonGsCr+dv4EFT96JRbtP4/0txzGnJdDv431oUTcKz3dJRrGuFImVNCKTvZmqLY2E4+oDu7smGwuzM+9EdZqHgYiIiBzDpwPyC4YmUnsysrBo92nszsg29rEA/u3g/UBKHO6RBrrc38LeTNXvPHa71ZFwbD2wy4ICcC67wGpfCXfVJCTYGSKUHbeJiIioPCYW5Ddqq5XQ6m7i9dW/W1xvmP+iMjpyOzpTtaWRcKw9sE/rk4JnvjyI6DAZZvVNtbhvuEJqMzEJt5N4OCNJrcTsvqnQlEmMwjw8LC4RERF5LyYW5FcKSmx3fi7W37TbQdpav4myXJmpOkmtxMw+KcgvKUVekQ4qhRRBgRI88+VBnLiWjxPX8pFnZf/4CIXNmoTK6F9RVkJkCEd/IiIiIocwsSC/EmanKZAsKMBm/wB7/Sb+/Rzbl4699ZdytXj8k/1W19vqK5GkVmJOv+bILdIZaxLCXZzHgoiIiMhVHG6W/EpUaDA6NIiyuK5dshrXNFpEKi3Ptm2v38RVjda4zDCfhiXtk9VWP8PA1b4S8REKNIkLQ8u6ajSJC6uypCIzpxB/Xtbg59PZ+OuyBpk5hVXyOUREROT7mFiQXwkPCcbsvqlmyUW7ZDWe79IA7ZOjrPavcKTfhEHZ+TTKMtRu2OvDYejEbUn7ZDVUXjDq0rnsAoxddQQ95+/G45/sx33zd2PcqiM4l13g6dCIiIjIC3n+6YWoksVHKPBh/ztw6XoBTvy66595LGRQyoJszmPhbL8JS/NpRCrN+2NYUsvOqEuWOm67U2ZOoc2J+Kx1LvcWuYUlyMq/NYN4mEKKKGUwwkNs1yIRERGRa5hYkE9x9IGxoKQUpRAAgACJBDJpoN3J8SrSb6L8fBrOsDYcrTc8sOdp9TZrb6x1LvcGl24UYeyqI9h9Msu4rGODKMzqm8p+KERERFWIiQX5DEcfGM9lF2D86qM4eCYLc1oCjyzci7vrRmF6nxSr80MA//absDaUq71+ExXhbBJx6UYRcot00BTpEK6QIsyBTtsXcwqh0eqN+6gcGDLWXRPxVbbcwhKz7wgA7DqZhXGrjuCD/new5oKIiKiKMLEgn+DoA+OlG0XGJjxlJ8gzNOGZ06+51QdxQ7+J8RaaJznSb6KqGRKm8iNW2UqYKrIP4L6J+CpbVn6J2XfEYNfJLGTllzCxICIiqiJMLMgnOPrAmFuks9mEJ7dIZ0wsLDWrUkgDMaNPCgpKSpGn1SFMLnW430RZld3Gv2zCVP6crCVMF+30k5jdN9VqzYW9GcK9oXO5JRo7NSneWtNCRETkD7zz6YCoHEcfGB1twlO2WVVU6K2RpLQlpcgv1kMpC8JVjRZjVx1B41gVZvRJcSrWqmjj70zCZKCx009Co9VbnfzOE53LKyMZszePibfWtBAREfkDJhbkExx9YHSkCU/ZZlVRocFYOqI1pq4/ZtZcaOmI1njys/0Yv/oo3nnsdodqLaqqjX9F+jy42k/CnZ3LKysZiwoNRscGUdhloXarY4MoRIWyGRQREVFV4TwW5NVyC0tw6lo+cotK8M3TrfD8PckICQ402absA2O4QmpzfohwhdSkWdXsvqlmSQVw643+W+uPYXbfVLM5LGxxpMlWRVSkz0Nl9JOoFRliMhFfVdVU2ErGcgsdL7PwkGDM6puKjuXmMenYIAqz+6ayfwUREVEVYo0FeS1Lb7HbJ6vxfv878OI3h1BYUmr2wBgfoTA24TlwxnS/6X1SEB+hwKHzOcbl0WEym82FxvZsDMD+HBcGVdXG35AwWevzEG4hiQiz00/C3vC67lLZHa7jIxT4oP8dyMovMda0RIVyHgsiIqKq5h1PFkTlWHuLnZ6RDYlEgrWj2yFAIrH4wJikVmJOv+a4nl+EU7/uxqpRbVEjVGFsUlO2WVW+ttRmHIb1jj6EV1Ub/7IJk6U+D5aaCyXY6Sdhb8hZd6mKZCw8hIkEERGRuzGxIK9k6y327pNZCJBIUD861Or+8REK1FQG4RSAyH9Gi8q8XgiVQorQ4EB0aBCF3SezECoPtHoMAAiVBzo1h4Ujbfwr2knZkDDlFumMb+LD7cxjkaRWYnbfVGjK9JMIc2AeC3dih2siIiL/wMSCvFJlvsUev/oIdp78t/lT18Y1MfWhppi49ndc0xTbbC5UUKx3ag4LQxv/cauOmCQXhiZbhSWleM2FTsrxEQqnR5ZKiAyxOvqTN2CHayIiIv/AxIK8UmW8xb6WVwwA2H/6OgCJcfnWv/7Gsx2LcUftSAQHBOCt3s3wxprfzZoLTeuTAimAUDudoMuz1sYfAJ7/5hBnhS7HXjJWHcuEiIjIFzGxIK9UGW+xb9gYTUghC8SH2zLw4T+fNbtvKsb2bIx8bSlC5YFQyYLQb+FeZOWX2K1RuKrRIqegBBqtHmGKIESG3JpQr/wD8alr+ZwV2gp2uCYiIvJ9TCzIK1XmW+x3H7sdClkwfjufg8/Tz6CwpNSkCVRWfgmGf3nQuH37ZDWGtqtrHBrWVo3C+ewCvF5uduv2yWrM6JOC2mqlybacFdo2drgmIiLybUwsyGu58hb70o0izN30F3pHAWNWHEZxqQTtygxVO3bVEawc2RYT15iPmPTmg00x4NP9JsezVKNwVaM1SyqAWyNXWZpUj52UiYiIyJ8xsSCvVpG32Iahan85fR29y8yTZkgAhrWvi8Pnc5CVX4RpvZshr1iPfG0pwhVSXM4twoBP91ucyK58jUJOQYnNOTByCkpMEgt2UiYiIiJ/xsSC/I5hqFqZhZFk92Rk47nOyeh9ewKkARIMXPQzLuQUAQAWDW5h0iSqvPI1CvYmzSu/np2UiYiIyJ8xsSC/Y68vgywoABKJwPytJ41JBQAcunAD7ZLVFmshLNUo2Js0z9J6dlImIiIif8XEgvyOvb4MuUU6jPz6V6wa2RZ/l5mI7/P0M/h8yN0IkEjM5pmwVKMQqQy2OQeGtUn12EmZiIiI/BETC/I7hr4MP5/+22xdu2Q1jl7MxYcD7sRb6/9A88QIDGlbB8X6m4hQSFE7UoEPHaxRiAmTY0afFIxfbd4B3JlJ9YiIiIj8ARML8juGvgwTvz8M4Jpxebt/hpHNKSjBR9sysDsjC1v+umayb8cGUfig/x2oHx3q0GfVVivxzmO3/zuPhTwIkcpgJhVERERU7TCxIL8UH6HA2/1Skb59C5YOb4VQhQyBARIEBkhQWiMEr648YnG/ikxUFxMmZyJBRERE1R4TC/JbYYpbyUHzxAhIpf/2uzh0PsfmfpYmqsstLEFWfgk0Wh3CFFJEKdlPgoiIiKgsJhZU7Tg7Ud2lG0UYu+qIWYfuWX1TER+hqJIYiYiIiHxNgKcDIHI3Q+duS8oPK2uYbG93uUntdp3MwrhVR5BbaD6RHhEREVF1xMSCqh1D5+7yyYWlYWWzygxHW56hPwYRERERsSkUVVOOTlRnb7I9S/0xiIiIiKoj1liQiQULFqBu3bqQy+W46667sHv3bk+HVGXCQ4JRPzoUt9eORP3oUIudsZ3tj0FERERUXTGxIKNvv/0WL730EiZMmIBDhw6hQ4cO6NmzJ86fP+/p0DzGmf4YRERERNUZEwsyevfddzF8+HCMGDECTZo0wXvvvYfExEQsXLjQ06F5jDP9MYiIiIiqM/axIABASUkJfv31V4wbN85keffu3bF3714PReUdHO2PQURERFSdMbEgAEBWVhZKS0sRExNjsjwmJgZXrlwx2764uBjFxcXGvzUaDQBAp9NBp/OODs2GOCojnhCpBLUjZQBkZsenf1VmmZN9LG/3Y5m7H8u8arFcqTIxsSATEonE5G8hhNkyAJg5cyamTJlitnzz5s0ICQmpsvgqIi0tzdMhVDssc/diebsfy9z9WOZVo7Cw0NMhkB9hYkEAgKioKAQGBprVTly7ds2sFgMAXn/9dYwZM8b4t0ajQWJiIrp3746wsLAqj9cROp0OaWlp6NatG6RSjt7kDixz92J5ux/L3P1Y5lXL0OKAqDIwsSAAQHBwMO666y6kpaWhT58+xuVpaWl4+OGHzbaXyWSQyWRmy6VSqdfd+L0xJn/HMncvlrf7sczdj2VeNVimVJmYWJDRmDFj8NRTT6FFixZo06YNPvnkE5w/fx4jR470dGhERERE5OWYWJDR448/juzsbEydOhWXL19Gs2bN8NNPPyEpKcnToRERERGRl2NiQSaee+45PPfcc54Og4iIiIh8DCfIIyIiIiIilzGxICIiIiIilzGxICIiIiIilzGxICIiIiIil7HzNlUKIQQA75poR6fTobCwEBqNhuN0uwnL3L1Y3u7HMnc/lnnVMvxuG37HiVzBxIIqRV5eHgAgMTHRw5EQERGRs/Ly8hAeHu7pMMjHSQRTVKoEN2/exKVLl6BSqSCRSDwdDoBbb2ESExNx4cIFhIWFeTqcaoFl7l4sb/djmbsfy7xqCSGQl5eH+Ph4BASwhTy5hjUWVCkCAgJQq1YtT4dhUVhYGH+M3Ixl7l4sb/djmbsfy7zqsKaCKgtTUyIiIiIichkTCyIiIiIichkTC/JbMpkMkyZNgkwm83Qo1QbL3L1Y3u7HMnc/ljmR72DnbSIiIiIichlrLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLIiIiIiIyGVMLMinLViwAHXr1oVcLsddd92F3bt3O7Tfnj17EBQUhNtvv71qA/QzzpT3jh07IJFIzP7766+/3Bix73P2O15cXIwJEyYgKSkJMpkM9evXx+eff+6maP2DM2U+ZMgQi9/zpk2bujFi3+fs93zp0qVo3rw5QkJCEBcXh6FDhyI7O9tN0RKRNUwsyGd9++23eOmllzBhwgQcOnQIHTp0QM+ePXH+/Hmb++Xm5mLQoEHo2rWrmyL1DxUt7+PHj+Py5cvG/xo0aOCmiH1fRcr8sccew9atW7Fo0SIcP34c33zzDRo3buzGqH2bs2U+f/58k+/3hQsXUKNGDTz66KNujtx3OVvm6enpGDRoEIYPH45jx47hu+++w4EDBzBixAg3R05EZgSRj2rZsqUYOXKkybLGjRuLcePG2dzv8ccfFxMnThSTJk0SzZs3r8II/Yuz5b19+3YBQOTk5LghOv/kbJlv2LBBhIeHi+zsbHeE55cqel8xWL16tZBIJOLs2bNVEZ5fcrbM3377bVGvXj2TZe+//76oVatWlcVIRI5hjQX5pJKSEvz666/o3r27yfLu3btj7969VvdbvHgxTp06hUmTJlV1iH6louUNAHfccQfi4uLQtWtXbN++vSrD9CsVKfMffvgBLVq0wJw5c5CQkICGDRvilVdeQVFRkTtC9nmufM8NFi1ahHvvvRdJSUlVEaLfqUiZt23bFpmZmfjpp58ghMDVq1excuVK3H///e4ImYhsCPJ0AEQVkZWVhdLSUsTExJgsj4mJwZUrVyzuc/LkSYwbNw67d+9GUBC/+s6oSHnHxcXhk08+wV133YXi4mJ89dVX6Nq1K3bs2IGOHTu6I2yfVpEyP336NNLT0yGXy7F69WpkZWXhueeew/Xr19nPwgEVKfOyLl++jA0bNmDZsmVVFaLfqUiZt23bFkuXLsXjjz8OrVYLvV6Phx56CB988IE7QiYiG/h0RT5NIpGY/C2EMFsGAKWlpRgwYACmTJmChg0buis8v+NoeQNAo0aN0KhRI+Pfbdq0wYULFzB37lwmFk5wpsxv3rwJiUSCpUuXIjw8HADw7rvvol+/fvjoo4+gUCiqPF5/4EyZl/XFF18gIiICvXv3rqLI/JczZf7HH3/gxRdfxJtvvokePXrg8uXLePXVVzFy5EgsWrTIHeESkRVMLMgnRUVFITAw0OyN1rVr18zefAFAXl4eDh48iEOHDuH5558HcOshTAiBoKAgbN68Gffcc49bYvdFzpa3Na1bt8bXX39d2eH5pYqUeVxcHBISEoxJBQA0adIEQghkZmay47wdrnzPhRD4/PPP8dRTTyE4OLgqw/QrFSnzmTNnol27dnj11VcBAKmpqVAqlejQoQOmTZuGuLi4Ko+biCxjHwvyScHBwbjrrruQlpZmsjwtLQ1t27Y12z4sLAxHjx7F4cOHjf+NHDkSjRo1wuHDh9GqVSt3he6TnC1vaw4dOsQffQdVpMzbtWuHS5cuIT8/37jsxIkTCAgIQK1atao0Xn/gyvd8586dyMjIwPDhw6syRL9TkTIvLCxEQIDp40tgYCCAWwkeEXmQp3qNE7lq+fLlQiqVikWLFok//vhDvPTSS0KpVBpHYxk3bpx46qmnrO7PUaGc42x5z5s3T6xevVqcOHFC/P7772LcuHECgFi1apWnTsHnOFvmeXl5olatWqJfv37i2LFjYufOnaJBgwZixIgRnjoFn1PR+8rAgQNFq1at3B2uX3C2zBcvXiyCgoLEggULxKlTp0R6erpo0aKFaNmypadOgYj+waZQ5LMef/xxZGdnY+rUqbh8+TKaNWuGn376yTgay+XLl+3OsUCOc7a8S0pK8Morr+DixYtQKBRo2rQpfvzxR/Tq1ctTp+BznC3z0NBQpKWl4YUXXkCLFi2gVqvx2GOPYdq0aZ46BZ9TkftKbm4uVq1ahfnz53siZJ/nbJkPGTIEeXl5+PDDD/Gf//wHERERuOeeezB79mxPnQIR/UMiBOsNiYiIiIjINexjQURERERELmNiQURERERELmNiQURERERELmNiQURERERELmNiQURERERELmNiQURERERELmNiQURERERELmNiQURERERELmNiQURUzQwZMgQSiQSzZs0yWb5mzRpIJBLj36WlpZg3bx5SU1Mhl8sRERGBnj17Ys+ePcZtFixYgIiICFy4cMHkWM8//zwaNmyIwsLCqj0ZIiLyGkwsiIiqIblcjtmzZyMnJ8fieiEEnnjiCUydOhUvvvgi/vzzT+zcuROJiYno3Lkz1qxZAwAYNWoUWrZsieHDhxv33bZtG/773//iiy++QEhIiDtOh4iIvAATCyKiaujee+9FbGwsZs6caXH9ihUrsHLlSixZsgQjRoxA3bp10bx5c3zyySd46KGHMGLECBQUFEAikWDRokX45Zdf8PHHH0Oj0WDo0KF4+eWX0bZtWzefFREReRITCyKiaigwMBAzZszABx98gMzMTLP1y5YtQ8OGDfHggw+arfvPf/6D7OxspKWlAQASExMxb948vPrqqxg4cCBCQ0Px1ltvVfk5EBGRd2FiQURUTfXp0we33347Jk2aZLbuxIkTaNKkicX9DMtPnDhhXDZ06FA0a9YM69atw+LFiyGTyaomaCIi8lpMLIiIqrHZs2fjyy+/xB9//OH0vmU7ev/vf//Dr7/+ipCQEOzevbsyQyQiIh/BxIKIqBrr2LEjevTogfHjx5ssb9iwodVk488//wQANGjQAABQUlKCQYMGoX///vjvf/+LiRMnmtRmEBFR9cDEgoiomps1axbWrVuHvXv3Gpc98cQTOHnyJNatW2e2/TvvvAO1Wo1u3boBAKZOnYrs7GzMnz8fAwcORI8ePTB06FDcvHnTbedARESex8SCiKiaS0lJwZNPPokPPvjAuOyJJ55Anz59MHjwYCxatAhnz57FkSNH8Oyzz+KHH37AZ599BqVSiYMHD2L27Nn47LPPEBERAQD4+OOP8ddff2HevHkeOiMiIvIEiRBCeDoIIiJynyFDhuDGjRvGuSgA4Ny5c2jUqBGKi4th+FnQ6/WYP38+Fi9ejIyMDMhkMrRp0wYTJ05E+/btUVxcjDvvvBPt2rXDJ598YvIZy5Ytw/Dhw3H48GE0atTInadHREQewsSCiIiIiIhcxqZQRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETkMiYWRERERETksv8H6MshkI5pu+cAAAAASUVORK5CYII=","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax = sns.scatterplot(x = 'NOX', y = 'INDUS', data = boston_df)\n","plt.title('Nitric oxide concentration (NOX) vs proportion of non-retail business acres per town (INDUS)')\n","plt.grid()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["This plot shows that the relationship between NOX and INDUS variables, has a growing trend, which could be aproximated by 45° line for values smaller than 0.6. This indicates a correlation between these variables. For larger NOX values, INDUS values become almost constant, which could indicate industrial areas with higher nitric oxide emissions. This conclusion makes sense, as a high concentration of industries is typically associated with high nitric oxide concentration."]},{"cell_type":"markdown","metadata":{},"source":["2.5 Pupil to teacher ratio variable (PTRATIO)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\luis\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax = sns.histplot(data = boston_df, x = 'PTRATIO')\n","plt.title('pupil-teacher ratio by town (PTRATIO) variable histogram')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["This histogram shows that most of the values for the pupil-to-teacher ratio variable are around 20, representing significantly more observations than other values of the variable."]},{"cell_type":"markdown","metadata":{},"source":["##### 3. Appropriate tests to answer the questions provided."]},{"cell_type":"markdown","metadata":{},"source":["3.1 Is there a significant difference in median value of houses bounded by the Charles river or not?"]},{"cell_type":"markdown","metadata":{},"source":["- State the hypothesis:\n","    - Null Hypothesis ($H_0$): There is no difference in median value of houses bounded by the Charles river or not\n","    - Alternative Hypothesis ($H_a$): There is a difference in median value of houses bounded by the Charles river or not\n","    - $\\alpha$ = 0.05"]},{"cell_type":"markdown","metadata":{},"source":["Checking equality of variance:"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":["LeveneResult(statistic=8.75190489604598, pvalue=0.003238119367639829)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# levene test\n","scipy.stats.levene(boston_df[boston_df['CHAS'] == 0.0]['MEDV'],\n","                   boston_df[boston_df['CHAS'] == 1.0]['MEDV'], center='mean')\n"]},{"cell_type":"markdown","metadata":{},"source":["Since p-value is less than 0.05, the variances are not equal! I will consider both cases: t test with equal and unequal variaces."]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["TtestResult(statistic=-3.996437466090509, pvalue=7.390623170519905e-05, df=504.0)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# t test for equal variances\n","scipy.stats.ttest_ind(boston_df[boston_df['CHAS'] == 0.0]['MEDV'],\n","                   boston_df[boston_df['CHAS'] == 1.0]['MEDV'], equal_var = True)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/plain":["TtestResult(statistic=-3.113291312794837, pvalue=0.003567170098137517, df=36.876408797611994)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# t test for unequal variances\n","scipy.stats.ttest_ind(boston_df[boston_df['CHAS'] == 0.0]['MEDV'],\n","                   boston_df[boston_df['CHAS'] == 1.0]['MEDV'], equal_var = False)"]},{"cell_type":"markdown","metadata":{},"source":["For both cases, p-value is less than $\\alpha$ value of 0.05, so we reject the null hypothesis as there is no difference in median value of houses bounded by the Charles river or not, and we can conclude that is enough proof that there is a statistical difference in median value of houses based on charles river variable (0 or 1)."]},{"cell_type":"markdown","metadata":{},"source":["3.2 Is there a difference in Median values of houses (MEDV) for each proportion of owner occupied units built prior to 1940 (AGE)? "]},{"cell_type":"markdown","metadata":{},"source":["- Considering the age variable into three groups: 35 years and younger, between 35 and 70 years and 70 years and older.\n","- State the hypothesis:\n","    - $H_0$: $\\mu_1$  = $\\mu_2$ =  $\\mu_3$ (The three group means are equal)\n","    - $H_a$: $\\mu_1$ $\\neq$ $\\mu_2$ $\\neq$ $\\mu_3$ (The three group means are different)\n","    - $\\alpha$ = 0.05"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["# Creating AGE groups\n","boston_df.loc[(boston_df['AGE'] <= 35), 'AGE_group'] = '35 years and younger'\n","boston_df.loc[(boston_df['AGE'] > 35)&(boston_df['AGE'] < 70), 'AGE_group'] = 'between 35 and 70 years'\n","boston_df.loc[(boston_df['AGE'] >= 70), 'AGE_group'] = '70 years and older'"]},{"cell_type":"markdown","metadata":{},"source":["Checking equality of variance:"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":["LeveneResult(statistic=2.7806200293748304, pvalue=0.06295337343259205)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# levene test\n","scipy.stats.levene(boston_df[boston_df['AGE_group'] == '35 years and younger']['MEDV'],\n","                   boston_df[boston_df['AGE_group'] == 'between 35 and 70 years']['MEDV'], \n","                   boston_df[boston_df['AGE_group'] == '70 years and older']['MEDV'], \n","                   center='mean')"]},{"cell_type":"markdown","metadata":{},"source":["Since the p-value is greater than 0.05, the variance are not equal, we can assume equality of variance"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["F_Statistic: 36.40764999196599, p-value: 1.7105011022702984e-15\n"]}],"source":["# ANOVA test\n","f_statistic, p_value = scipy.stats.f_oneway(boston_df[boston_df['AGE_group'] == '35 years and younger']['MEDV'],\n","                                            boston_df[boston_df['AGE_group'] == 'between 35 and 70 years']['MEDV'],\n","                                            boston_df[boston_df['AGE_group'] == '70 years and older']['MEDV']\n",")\n","print(\"F_Statistic: {0}, p-value: {1}\".format(f_statistic,p_value))"]},{"cell_type":"markdown","metadata":{},"source":["Since the p-value is less than 0.05, we reject the null hypothesis, and we can conclude that there is enough evidence that, for at least one group of owner occupied units built prior to 1940 (AGE), the mean of median value of houses variable differs. This confirms the differences observed in visualizations of the distributon of MEDV variable for each group."]},{"cell_type":"markdown","metadata":{},"source":["3.3 Can we conclude that there is no relationship between Nitric oxide concentrations and proportion of non-retail business acres per town?"]},{"cell_type":"markdown","metadata":{},"source":["- State the hypothesis:\n","    - $H_0$: There is no association between Nitric oxide concentrations (NOX) and proportion of non-retail business acres per town (INDUS)\n","    - $H_a$: There is an association between Nitric oxide concentrations (NOX) and proportion of non-retail business acres per town (INDUS)\n","    - $\\alpha$ = 0.05"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["PearsonRResult(statistic=0.763651446920915, pvalue=7.913361061241167e-98)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["# Pearson Correlation test\n","scipy.stats.pearsonr(boston_df['NOX'], boston_df['INDUS'])"]},{"cell_type":"markdown","metadata":{},"source":["Since the p-value is less than 0.05, we reject the Null Hypothesis and conclude that there is an association between nitric oxide concentrations (NOX) and proportion of non-retail business acres per town (INDUS). The pearson product-moment correlation coefficient, indicates a very high correlation between these two variables. "]},{"cell_type":"markdown","metadata":{},"source":["3.4 What is the impact of an additional weighted distance to the five Boston employment centres on the median value of owner occupied homes?"]},{"cell_type":"markdown","metadata":{},"source":["- State the hypothesis:\n","    - $H_0$: $\\beta1$ = 0. There is no additional impact on weighted distances to five Boston employment centres (DIS) on the median  value of owner-occupied homes in $1000's (MEDV)\n","    - $H_a$: $\\beta1$ $\\neq$ 0. There is an additional impact on weighted distances to five Boston employment centres (DIS) on the median  value of owner-occupied homes in $1000's (MEDV)\n","    - $\\alpha$ = 0.05"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":["<table class=\"simpletable\">\n","<caption>OLS Regression Results</caption>\n","<tr>\n","  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.062</td>\n","</tr>\n","<tr>\n","  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.061</td>\n","</tr>\n","<tr>\n","  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   33.58</td>\n","</tr>\n","<tr>\n","  <th>Date:</th>             <td>Mon, 16 Sep 2024</td> <th>  Prob (F-statistic):</th> <td>1.21e-08</td>\n","</tr>\n","<tr>\n","  <th>Time:</th>                 <td>23:35:38</td>     <th>  Log-Likelihood:    </th> <td> -1823.9</td>\n","</tr>\n","<tr>\n","  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3652.</td>\n","</tr>\n","<tr>\n","  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   3660.</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>const</th> <td>   18.3901</td> <td>    0.817</td> <td>   22.499</td> <td> 0.000</td> <td>   16.784</td> <td>   19.996</td>\n","</tr>\n","<tr>\n","  <th>DIS</th>   <td>    1.0916</td> <td>    0.188</td> <td>    5.795</td> <td> 0.000</td> <td>    0.722</td> <td>    1.462</td>\n","</tr>\n","</table>\n","<table class=\"simpletable\">\n","<tr>\n","  <th>Omnibus:</th>       <td>139.779</td> <th>  Durbin-Watson:     </th> <td>   0.570</td>\n","</tr>\n","<tr>\n","  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 305.104</td>\n","</tr>\n","<tr>\n","  <th>Skew:</th>          <td> 1.466</td>  <th>  Prob(JB):          </th> <td>5.59e-67</td>\n","</tr>\n","<tr>\n","  <th>Kurtosis:</th>      <td> 5.424</td>  <th>  Cond. No.          </th> <td>    9.32</td>\n","</tr>\n","</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."],"text/latex":["\\begin{center}\n","\\begin{tabular}{lclc}\n","\\toprule\n","\\textbf{Dep. Variable:}    &       MEDV       & \\textbf{  R-squared:         } &     0.062   \\\\\n","\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.061   \\\\\n","\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     33.58   \\\\\n","\\textbf{Date:}             & Mon, 16 Sep 2024 & \\textbf{  Prob (F-statistic):} &  1.21e-08   \\\\\n","\\textbf{Time:}             &     23:35:38     & \\textbf{  Log-Likelihood:    } &   -1823.9   \\\\\n","\\textbf{No. Observations:} &         506      & \\textbf{  AIC:               } &     3652.   \\\\\n","\\textbf{Df Residuals:}     &         504      & \\textbf{  BIC:               } &     3660.   \\\\\n","\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n","\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\begin{tabular}{lcccccc}\n","               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n","\\midrule\n","\\textbf{const} &      18.3901  &        0.817     &    22.499  &         0.000        &       16.784    &       19.996     \\\\\n","\\textbf{DIS}   &       1.0916  &        0.188     &     5.795  &         0.000        &        0.722    &        1.462     \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\begin{tabular}{lclc}\n","\\textbf{Omnibus:}       & 139.779 & \\textbf{  Durbin-Watson:     } &    0.570  \\\\\n","\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &  305.104  \\\\\n","\\textbf{Skew:}          &   1.466 & \\textbf{  Prob(JB):          } & 5.59e-67  \\\\\n","\\textbf{Kurtosis:}      &   5.424 & \\textbf{  Cond. No.          } &     9.32  \\\\\n","\\bottomrule\n","\\end{tabular}\n","%\\caption{OLS Regression Results}\n","\\end{center}\n","\n","Notes: \\newline\n"," [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."],"text/plain":["<class 'statsmodels.iolib.summary.Summary'>\n","\"\"\"\n","                            OLS Regression Results                            \n","==============================================================================\n","Dep. Variable:                   MEDV   R-squared:                       0.062\n","Model:                            OLS   Adj. R-squared:                  0.061\n","Method:                 Least Squares   F-statistic:                     33.58\n","Date:                Mon, 16 Sep 2024   Prob (F-statistic):           1.21e-08\n","Time:                        23:35:38   Log-Likelihood:                -1823.9\n","No. Observations:                 506   AIC:                             3652.\n","Df Residuals:                     504   BIC:                             3660.\n","Df Model:                           1                                         \n","Covariance Type:            nonrobust                                         \n","==============================================================================\n","                 coef    std err          t      P>|t|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","const         18.3901      0.817     22.499      0.000      16.784      19.996\n","DIS            1.0916      0.188      5.795      0.000       0.722       1.462\n","==============================================================================\n","Omnibus:                      139.779   Durbin-Watson:                   0.570\n","Prob(Omnibus):                  0.000   Jarque-Bera (JB):              305.104\n","Skew:                           1.466   Prob(JB):                     5.59e-67\n","Kurtosis:                       5.424   Cond. No.                         9.32\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n","\"\"\""]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Regression test\n","y = boston_df['MEDV'] # target/dependent variable\n","X = boston_df['DIS'] # the input variables (or independent variables)\n","\n","X = sm.add_constant(X) # add Beta 0 to the model\n","model = sm.OLS(y, X).fit() # Regression fit to build the model\n","predictions = model.predict(X) # model prediction\n","\n","# Statistics\n","model.summary()"]},{"cell_type":"markdown","metadata":{},"source":["Since p-value is less than 0.05, so we reject the null hypothesis and have evidence that there is an impact of an additional weighted distance to the five Boston employment centres on the median value of owner occupied homes. The coefficient 1.0916 indicates a positive impact of the weighted distances to five Boston employment centres (DIS) on the median value of owner occupied homes (MEDV). This means that for each unit increase in DIS, the MEDV value is expected to increase by 1.0916.\n","\n","The R-square value is 0.062. R will be the result of sqrt(0.062), which is a correlation coefficient of approximately 0.24. This indicates a moderate correlation between these two variables."]}],"metadata":{"kernelspec":{"display_name":"base","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"}},"nbformat":4,"nbformat_minor":4}