Akubude, Cypran_Master_Thesis_Data_Analytics_2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10df6736",
   "metadata": {},
   "source": [
    "# University Name\n",
    "CBS International Business School\n",
    "<img src=\"https://github.com/CypranA/Master-Thesis-Statistical-Analysis/blob/main/cbs_logo.png\" alt=\"CBS Logo\" width=\"200\"/>\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "## Master Thesis Topic: \n",
    "Leveraging Big Data Analytics for Enhanced Financial Inclusion in Nigeria: A Comprehensive Analysis of Strategies, Impact, And Challenges in Developing Economies.\n",
    "\n",
    "---\n",
    "\n",
    "## Program: \n",
    "Master of Arts in International Business\n",
    "\n",
    "---\n",
    "\n",
    "## Specialization: \n",
    "Digital Transformation Management\n",
    "\n",
    "---\n",
    "\n",
    "## Supervisor: \n",
    "Prof. Dr. Geoffrey Writes\n",
    "\n",
    "---\n",
    "\n",
    "### Student Details:\n",
    "- **Name**: Kosisochukwu Cypran Akubude\n",
    "- **Student Number**: 1195501031\n",
    "- **Date**: April 14, 2024\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6167203c",
   "metadata": {},
   "source": [
    "# TABLE OF CONTENTS\n",
    "1. Summary\n",
    "2. Phase 1: Ask\n",
    "3. Phase 2: Prepare\n",
    "* 3.1. Data Location\n",
    "* 3.2. Data Organization\n",
    "* 3.3. Data Credibility\n",
    "* 3.4. Data License and Privacy\n",
    "4. Phase 3: Process\n",
    "5. Phase 4: Analyze\n",
    "* 5.1. Regression\n",
    "* 5.2. Pearson Correlation Test\n",
    "* 5.3. Test for Multicollinearity\n",
    "* 5.4. Descriptive Statistics\n",
    "* 5.5. Hypothesis Testing\n",
    "6. Phase 5: Share\n",
    "7. Phase 6: Conclusion (Act)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738105db",
   "metadata": {},
   "source": [
    "# 1. Summary\n",
    "\n",
    "The purpose of this study is to analyze the relationship between financial inclusion and big data, including economic data variables. We aim towards analysing the datasets and discuss findings. The following tests will be conducted in this analysis. \n",
    "\n",
    "- Regression\n",
    "- Pearson Correlation Test\n",
    "- Test for Multicollinearity\n",
    "- Descriptive Statistics\n",
    "- Hypothesis Testing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3967588a",
   "metadata": {},
   "source": [
    "# 2. Phase 1: Ask\n",
    "\n",
    "Questions guiding our analysis incude:\n",
    "- What is the expected relationship between financial inclusion and big data?\n",
    "- Do big data sources have a significant impact on increased access to financial services?\n",
    "- Does an economy with a structured data, accessible data source, and data infrastructure help streamline digital financial inclusion?"
   ]
  },
  {
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    }
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   "cell_type": "markdown",
   "id": "8e9516f0",
   "metadata": {},
   "source": [
    "# 3. Phase 2: Prepare\n",
    "**3.1. Data Location:** All data used in this study were obtained from the World Bank DataBank. Data variables for Model 1 were obtained from the Global Findex Database whilst data variables for Model 2 were obtained from the Statistical Performance Indicator Database.\n",
    "\n",
    "**3.2. Data Organization:** The extracted datasets from the Global Findex dataset comprised 1,450 rows whilst the SPI dataset extracted has 625 rows. Each dataset were organized by columns such as country names, series or variable description, and the corresponding years for each country. The downloaded data have already been cleaned and orginized in Excel before being imported for analysis with Python. The data used in this study therefore are organized by the coutnries and data variables in different columns.  \n",
    "\n",
    "**3.3. Data Credibility:** The data used are based on the survey of adults from the age of 15 and above accross all countries. The data were last updated in 2021. Only values for the year 2021 are considered in this analysis due to missing data for the previous year. \n",
    "\n",
    "**3.4. Data License and Privacy:** The metadata from the datasets shows that its an open-source data which justifies the use for this study. The licence can be accessed via this link: https://datacatalog.worldbank.org/public-licenses#cc-by\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4113ad00",
   "metadata": {},
   "source": [
    "# 4. Phase 3: Process\n",
    "\n",
    "Excel has been used to clean and restructure the dataset in a way that is more convenient for analysis. All variables needed for the study have been selected and placed in column sections. The datasets used in this study are named as follow:\n",
    "\n",
    "- **model1:** Contains financial inclusion and big data source variables.\n",
    "\n",
    "- **model2:** Contains financial inclusion and SPI variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6eb531",
   "metadata": {},
   "source": [
    "# 5. Phase 4: Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6a5f6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing relevant packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349c353a",
   "metadata": {},
   "source": [
    "# Importing Model 1 and Model 2 Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d873a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>internet_access</th>\n",
       "      <th>mobile_utility_payment</th>\n",
       "      <th>digital_payment</th>\n",
       "      <th>mobile_phone_ownership</th>\n",
       "      <th>mobile_bill_payment</th>\n",
       "      <th>mobile_money_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>82.91</td>\n",
       "      <td>1.27</td>\n",
       "      <td>33.74</td>\n",
       "      <td>95.18</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Benin</td>\n",
       "      <td>28.85</td>\n",
       "      <td>5.31</td>\n",
       "      <td>43.69</td>\n",
       "      <td>72.39</td>\n",
       "      <td>7.17</td>\n",
       "      <td>36.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>50.40</td>\n",
       "      <td>15.81</td>\n",
       "      <td>51.78</td>\n",
       "      <td>88.43</td>\n",
       "      <td>20.28</td>\n",
       "      <td>36.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>23.76</td>\n",
       "      <td>6.23</td>\n",
       "      <td>33.29</td>\n",
       "      <td>76.13</td>\n",
       "      <td>9.83</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>40.48</td>\n",
       "      <td>8.60</td>\n",
       "      <td>49.85</td>\n",
       "      <td>75.23</td>\n",
       "      <td>11.59</td>\n",
       "      <td>42.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  internet_access  mobile_utility_payment  digital_payment  \\\n",
       "0       Algeria            82.91                    1.27            33.74   \n",
       "1         Benin            28.85                    5.31            43.69   \n",
       "2      Botswana            50.40                   15.81            51.78   \n",
       "3  Burkina Faso            23.76                    6.23            33.29   \n",
       "4      Cameroon            40.48                    8.60            49.85   \n",
       "\n",
       "   mobile_phone_ownership  mobile_bill_payment  mobile_money_account  \n",
       "0                   95.18                 3.57                  0.00  \n",
       "1                   72.39                 7.17                 36.68  \n",
       "2                   88.43                20.28                 36.57  \n",
       "3                   76.13                 9.83                 24.67  \n",
       "4                   75.23                11.59                 42.43  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "    }\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>country</th>\n",
       "      <th>source_data_capacity</th>\n",
       "      <th>spi_data_use</th>\n",
       "      <th>spi_data_services</th>\n",
       "      <th>spi_data_products</th>\n",
       "      <th>spi_data_sources</th>\n",
       "      <th>spi_data_infrastructure</th>\n",
       "      <th>account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Benin</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>69.90</td>\n",
       "      <td>83.63</td>\n",
       "      <td>29.18</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.53</td>\n",
       "      <td>77.80</td>\n",
       "      <td>61.53</td>\n",
       "      <td>40.0</td>\n",
       "      <td>58.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>69.03</td>\n",
       "      <td>81.46</td>\n",
       "      <td>36.66</td>\n",
       "      <td>60.0</td>\n",
       "      <td>36.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.87</td>\n",
       "      <td>82.09</td>\n",
       "      <td>23.34</td>\n",
       "      <td>35.0</td>\n",
       "      <td>51.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chad</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>57.03</td>\n",
       "      <td>75.84</td>\n",
       "      <td>18.02</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  source_data_capacity  spi_data_use  spi_data_services  \\\n",
       "0         Benin                  60.0          80.0              69.90   \n",
       "1      Botswana                  60.0          60.0              67.53   \n",
       "2  Burkina Faso                  60.0          80.0              69.03   \n",
       "3      Cameroon                  20.0          60.0              63.87   \n",
       "4          Chad                  50.0          70.0              57.03   \n",
       "\n",
       "   spi_data_products  spi_data_sources  spi_data_infrastructure  account  \n",
       "0              83.63             29.18                     50.0    48.61  \n",
       "1              77.80             61.53                     40.0    58.76  \n",
       "2              81.46             36.66                     60.0    36.11  \n",
       "3              82.09             23.34                     35.0    51.65  \n",
       "4              75.84             18.02                     30.0    23.65  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing model1 dataframe and indexing country column to analyse on numerical data types\n",
    "model1 = pd.read_csv(\"model1.csv\")\n",
    "model2 = pd.read_csv('model2.csv')\n",
    "display(model1.head())\n",
    "display(model2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b306a625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   country                 40 non-null     object \n",
      " 1   internet_access         40 non-null     float64\n",
      " 2   mobile_utility_payment  40 non-null     float64\n",
      " 3   digital_payment         40 non-null     float64\n",
      " 4   mobile_phone_ownership  40 non-null     float64\n",
      " 5   mobile_bill_payment     40 non-null     float64\n",
      " 6   mobile_money_account    40 non-null     float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 2.3+ KB\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35 entries, 0 to 34\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   country                  35 non-null     object \n",
      " 1   source_data_capacity     35 non-null     float64\n",
      " 2   spi_data_use             35 non-null     float64\n",
      " 3   spi_data_services        35 non-null     float64\n",
      " 4   spi_data_products        35 non-null     float64\n",
      " 5   spi_data_sources         35 non-null     float64\n",
      " 6   spi_data_infrastructure  35 non-null     float64\n",
      " 7   account                  35 non-null     float64\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 2.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking and confirming data types\n",
    "model1.info()\n",
    "print(\"\\n\")\n",
    "model2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205f0b6d",
   "metadata": {},
   "source": [
    "We can confirm from the datasets review that all data variables are numeric whch is essential for statistical analysis. There is only one categorical variable in the dataset, 'country' which contains a list of all countries used in the study for each model. The country column is not used for statistical analysis as it is a non numerical variable. The variable could be used only when creating visual charts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffb49eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of countries for Model 1: 40\n",
      "Number of countries for Model 2: 35\n"
     ]
    }
   ],
   "source": [
    "# Checking number of countries or obervations in each dataset\n",
    "\n",
    "country_count_model_1 = len(model1['country'].unique())\n",
    "country_count_model_2 = len(model2['country'].unique())\n",
    "print(\"Number of countries for Model 1:\", country_count_model_1)\n",
    "print(\"Number of countries for Model 2:\", country_count_model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9dff385",
   "metadata": {},
   "source": [
    "The dataset contain 40 countries and 35 countries for model 1 and 2 respectively. The countries are also referred to as the observations. Therefore, we have 40 observations for model 1 and 35 observations for model 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5362b36",
   "metadata": {},
   "source": [
    "## 5.1. Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6256e26c",
   "metadata": {},
   "source": [
    "### Regression Analysis for Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3624d0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indicating and separating the dependent and independent variables from the model 1 dataframe\n",
    "\n",
    "#Dependent variable definition\n",
    "dep_var_model1 = model1['mobile_money_account']\n",
    "\n",
    "#Independent variable definition\n",
    "indep_var_model1 = model1[['internet_access', 'mobile_utility_payment', \n",
    "                           'digital_payment', 'mobile_phone_ownership', \n",
    "                           'mobile_bill_payment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06d848d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                             \n",
      "================================================================================\n",
      "Dep. Variable:     mobile_money_account   R-squared:                       0.858\n",
      "Model:                              OLS   Adj. R-squared:                  0.837\n",
      "Method:                   Least Squares   F-statistic:                     41.18\n",
      "Date:                  Tue, 23 Apr 2024   Prob (F-statistic):           1.77e-13\n",
      "Time:                          16:59:14   Log-Likelihood:                -135.17\n",
      "No. Observations:                    40   AIC:                             282.3\n",
      "Df Residuals:                        34   BIC:                             292.5\n",
      "Df Model:                             5                                         \n",
      "Covariance Type:              nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "const                     -7.4068      7.749     -0.956      0.346     -23.155       8.341\n",
      "internet_access           -0.5527      0.125     -4.411      0.000      -0.807      -0.298\n",
      "mobile_utility_payment     0.7147      0.415      1.721      0.094      -0.129       1.559\n",
      "digital_payment            0.6621      0.119      5.579      0.000       0.421       0.903\n",
      "mobile_phone_ownership     0.2944      0.161      1.824      0.077      -0.034       0.622\n",
      "mobile_bill_payment        0.0607      0.396      0.153      0.879      -0.744       0.865\n",
      "==============================================================================\n",
      "Omnibus:                        3.825   Durbin-Watson:                   2.053\n",
      "Prob(Omnibus):                  0.148   Jarque-Bera (JB):                2.562\n",
      "Skew:                          -0.484   Prob(JB):                        0.278\n",
      "Kurtosis:                       3.775   Cond. No.                         618.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Adding a constant term to the independent variables for the intercept\n",
    "indep_var_model1 = sm.add_constant(indep_var_model1)\n",
    "\n",
    "# Fitting the OLS regression model\n",
    "ols_model1 = sm.OLS(dep_var_model1, indep_var_model1).fit()\n",
    "\n",
    "# Printing model summary\n",
    "print(ols_model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "055f0559",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a Visual Representation of the Regression Analsis for Model 1\n",
    "\n",
    "# Getting the predicted values from the OLS model\n",
    "predicted_values = ols_model1.predict()\n",
    "\n",
    "# Creating a DataFrame with actual and predicted values\n",
    "results_df = pd.DataFrame({'Actual': dep_var_model1, 'Predicted': predicted_values})\n",
    "\n",
    "# Creating a scatter plot with a regression line using Seaborn\n",
    "sns.lmplot(x='Actual', y='Predicted', data=results_df, line_kws={'color': 'red'})\n",
    "\n",
    "# Setting plot labels and title\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Model 1: Actual vs. Predicted Values')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c10968",
   "metadata": {},
   "source": [
    "### Regression Analysis for Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3dba3b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indicating and separating the dependent and independent variables from the model 2 dataframe\n",
    "\n",
    "# Dependent variable definition for model 2\n",
    "dep_var_model2 = model2['account']\n",
    "\n",
    "# Independent variable definition for model 2\n",
    "indep_var_model2 = model2[['source_data_capacity', 'spi_data_use', \n",
    "                           'spi_data_services', 'spi_data_products', \n",
    "                           'spi_data_sources', 'spi_data_infrastructure']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d75975ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                account   R-squared:                       0.612\n",
      "Model:                            OLS   Adj. R-squared:                  0.529\n",
      "Method:                 Least Squares   F-statistic:                     7.353\n",
      "Date:                Tue, 23 Apr 2024   Prob (F-statistic):           8.64e-05\n",
      "Time:                        16:59:41   Log-Likelihood:                -136.66\n",
      "No. Observations:                  35   AIC:                             287.3\n",
      "Df Residuals:                      28   BIC:                             298.2\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       6.8971     25.628      0.269      0.790     -45.600      59.394\n",
      "source_data_capacity       -0.5816      0.214     -2.720      0.011      -1.020      -0.144\n",
      "spi_data_use               -0.2143      0.243     -0.882      0.385      -0.712       0.284\n",
      "spi_data_services          -0.7636      0.228     -3.343      0.002      -1.231      -0.296\n",
      "spi_data_products           0.7052      0.485      1.455      0.157      -0.288       1.698\n",
      "spi_data_sources            1.1690      0.273      4.276      0.000       0.609       1.729\n",
      "spi_data_infrastructure     0.8336      0.289      2.880      0.008       0.241       1.427\n",
      "==============================================================================\n",
      "Omnibus:                        2.268   Durbin-Watson:                   2.512\n",
      "Prob(Omnibus):                  0.322   Jarque-Bera (JB):                1.155\n",
      "Skew:                          -0.237   Prob(JB):                        0.561\n",
      "Kurtosis:                       3.753   Cond. No.                     1.72e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.72e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Adding a constant term to the independent variables for the intercept\n",
    "indep_var_model2 = sm.add_constant(indep_var_model2)\n",
    "\n",
    "# Fitting the OLS regression model for model 2\n",
    "ols_model2 = sm.OLS(dep_var_model2, indep_var_model2).fit()\n",
    "\n",
    "# Printing model summary for model 2\n",
    "print(ols_model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e074f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TeiorJSUFW7ZsQadOnfDjjz+W+hg+fDgOHDiA48ePw93dHV26dMGmTZvKnQ0o7JOpkUuDBg0QHx+vaDt9+rRxmrCQRqOBq6urInglJiZWOusbgDHoFSZtHTx4sMylEED+gX3ggQfw0ksvQa/X48SJE5V+7sqw5GeoW7duOHHiBI4dO6Z4DFMZ0iUVJnO9//775d6n0+lM/j/t168fzp49i8DAQJN9LMwON/d3pyIDBw5EYGAg1qxZg9jYWNxxxx3o3Lmz8bpGo4GzszOcnJyMbdnZ2Vi/fn2Fj13Y15I/o4XZ74UaN26MRo0a4dixYyZfc5s2bUzWaggNDcXo0aPx6KOP4tSpU8jKyrLkpddoHFHXcosXL67wnpEjR+Ldd9/FqFGjcO7cObRo0QJ79uzBwoUL0adPH/To0QMA0KtXL9x777144YUXkJmZiTZt2uDXX381+UfgrbfeQufOnXHPPffgqaeeQoMGDZCRkYEzZ87gf//7H+Li4ix6HXv27MHAgQMRFhaGmTNn4ujRo4rrzZo1g4+PDwDg/PnzuO222zBq1Khy16k3btyInJwcPPPMMyYrNAUGBmLjxo1YvXo13nzzTSxbtgydO3dGu3btMGPGDNx+++24evUqtm7dilWrVsHb2xvNmzcHAHzwwQfw9vaGm5sboqKiEBgYiMceewwjRozAhAkT8NBDD+H8+fNYunRpqTXvfv36YfPmzZgwYQIefvhhXLhwAa+++irq1KlTpSnDsWPHYsmSJRg2bBjc3d0V65oA8Pjjj8Pd3R2dOnVCnTp1kJiYiEWLFsHX1xd33323Rd9bazD3Z2jy5MlYs2YN+vbti/nz5xuzvs0Zsd1zzz147LHHMH/+fFy9ehX9+vWDTqfDkSNH4OHhYcx/aNGiBT799FN89tlnaNiwIdzc3NCiRQtMnjwZX375Je69915MmTIF0dHRMBgM+Pfff7Fz504899xzaNeunUW/O+XR6XQYPnw43nnnHQghSv1+9+3bF8uWLcOwYcPwxBNP4MaNG3j99dfNelMbFhaGHj16YNGiRfD390dkZCR++OEHk9XOVq1ahQceeAD3338/Ro8ejYiICCQnJ+PPP//E4cOHsWnTJgBAu3bt0K9fP0RHR8Pf3x9//vkn1q9fjw4dOsDDw8Oi116j2TeXjapT8azv8pjKYL1x44Z48sknRZ06dYSzs7OIjIwUL774osjJyVHcl5qaKsaOHSv8/PyEh4eH6Nmzp/jrr79MZq4mJCSIsWPHioiICOHi4iKCg4NFx44dxfz58xX3wIys78KM17I+imeqFj5mRdnQrVq1EiEhISI3N7fMe9q3by+CgoKM95w8eVI88sgjIjAwULi6uor69euL0aNHK75Py5cvF1FRUcLJyUnx2gwGg1i6dKlo2LChcHNzE23atBFxcXEms74XL14sGjRoIHQ6nWjatKn48MMPjd+D4szJ+i6uY8eOAoAYPnx4qWvr1q0T3bp1E6GhocLV1VWEh4eLwYMHi/j4eOM95n5vhSjK+q4IADFx4kST18z5GRJC/n/p2bOncHNzEwEBAWLcuHHi66+/rjDrWwiZGf3mm2+K5s2bC1dXV+Hr6ys6dOgg/ve//xnvOXfunOjVq5fw9vYWABSPcfPmTfHyyy+Lxo0bG7++RYsWYsqUKSIxMdF4nyW/O+U5duyYACCcnJzE5cuXS11fs2aNaNy4sdDpdKJhw4Zi0aJFYvXq1QKASEhIMN5n6ufuypUr4uGHHxYBAQHC19dXjBgxQhw8eNDk7+ixY8fE4MGDRUhIiHBxcRFhYWGie/fuYuXKlcZ7ZsyYIdq0aSP8/f2N/ZkyZYpISkoy+/XWBhohKtjlTkRERHbDNWoiIiIVY6AmIiJSMQZqIiIiFWOgJiIiUjEGaiIiIhVjoCYiIlIxBmrIgxfS09MrPI+ViIioujFQA8jIyICvr6/Jg+SJiIjsiYGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUzK6B+ueff0b//v0RHh4OjUaDr776SnFdCIE5c+YgPDwc7u7u6Nq1K06cOKG4Jzc3F08//TSCgoLg6emJBx98EBcvXqzGV0FERGQ7dg3UmZmZaNmyJVasWGHy+tKlS7Fs2TKsWLECBw4cQFhYGHr27Kk45Wry5MnYsmULPv30U+zZswc3b95Ev379UFBQUF0vg4iIVMBgAA4dAnbskP8aDPbukXVohEoOYdZoNNiyZQsGDhwIQI6mw8PDMXnyZEyfPh2AHD2HhoZiyZIlGD9+PNLS0hAcHIz169djyJAhAIDLly+jXr16+Pbbb3H//feb9dzp6enw9fVFWloafHx8bPL6iIjIduLigMWLgVOnAL0ecHUFGjcGZswAune3d++qRrVr1AkJCUhMTESvXr2MbTqdDl26dMHevXsBAIcOHUJeXp7invDwcDRv3tx4jym5ublIT09XfBARkWOKiwPGjwfi4wEvL6BOHflvfLxsj4uzdw+rRrWBOjExEQAQGhqqaA8NDTVeS0xMhKurK/z9/cu8x5RFixbB19fX+FGvXj0r956IiKqDwSBH0hkZQEQE4O4OaLXy34gI2b54sWNPg6s2UBfSaDSKz4UQpdpKquieF198EWlpacaPCxcuWKWvRERUvY4ckdPdgYFAyT/7Gg0QECCvHzlin/5Zg2oDdVhYGACUGhlfu3bNOMoOCwuDXq9HSkpKmfeYotPp4OPjo/ggIiLHk5Qk16R1OtPX3dzk9aSk6u2XNak2UEdFRSEsLAy7du0ytun1euzevRsdO3YEALRu3RouLi6Ke65cuYLjx48b7yEioporKEgmjuXmmr6ekyOvBwVVb7+sydmeT37z5k2cOXPG+HlCQgKOHj2KgIAA1K9fH5MnT8bChQvRqFEjNGrUCAsXLoSHhweGDRsGAPD19cW4cePw3HPPITAwEAEBAZg2bRpatGiBHj162OtlERFRNYmJkdnd8fFyTbr49LcQQHIyEB0t73NUdg3UBw8eRLdu3YyfT506FQAwatQorF27Fi+88AKys7MxYcIEpKSkoF27dti5cye8vb2NX/Pmm2/C2dkZgwcPRnZ2Nu677z6sXbsWTk5O1f56iIioemm1cgvW+PHApUtyTdrNTY6kk5MBHx95Xava+eOKqWYftT1xHzURkWOryfuoGajBQE1EVBMYDDK7OylJrknHxDj2SLqQXae+iYiIrEWrBVq3tncvrK8GvNcgIiKquRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiISMUYqImIiFSMgZqIiEjFGKiJiIhUjIGaiIhIxRioiYiIVIyBmoiIyBwpKYBeX+1Py0BNRERUHiGAq1eB69flf1cz52p/RiIiIkdRUABcvgxkZ9utCwzUREREpuj1wKVLQF6eXbvBQE1ERFRSZiZw5QpgMNi7JwzURERECsnJQFKSvXthxEBNREQEFCWNpafbuycKDNREREQqSBorCwM1ERHVbipJGisLAzUREdVeWVlyJK2CpLGyMFATEVHtlJ4u16TtUMTEEgzURERU+9y4IT8cAAM1ERHVHirN7C4PAzUREdUOKs7sLg8DNRER1Xy5uTJIqzSzuzwM1EREVLPdvAkkJqo6s7s8DNRERFRzpaTI4ykdGAM1ERHVTNeuAamp9u5FlTFQExFRzWIwyJOvMjPt3ROrYKAmIqKao6BAlgPNybF3T6xGa+8OlCc/Px8vv/wyoqKi4O7ujoYNG2LevHkwFEsIEEJgzpw5CA8Ph7u7O7p27YoTJ07YsddERGQXej3w7781KkgDKg/US5YswcqVK7FixQr8+eefWLp0KV577TW88847xnuWLl2KZcuWYcWKFThw4ADCwsLQs2dPZGRk2LHnRERUrXJygAsXHHL7VUU0Qqi3yGm/fv0QGhqK1atXG9seeugheHh4YP369RBCIDw8HJMnT8b06dMBALm5uQgNDcWSJUswfvx4s54nPT0dvr6+SEtLg4+Pj01eCxER2UhmptwjXR3hLDIS0Ols/zzFqHpE3blzZ/zwww84ffo0AODYsWPYs2cP+vTpAwBISEhAYmIievXqZfwanU6HLl26YO/evWU+bm5uLtLT0xUfRETkgNLTqy9I24mqk8mmT5+OtLQ0NGnSBE5OTigoKMCCBQvw6KOPAgASExMBAKGhoYqvCw0Nxfnz58t83EWLFmHu3Lm26zgREdlecjKQlGTvXticqkfUn332GTZs2ICPP/4Yhw8fxrp16/D6669j3bp1ivs0Go3icyFEqbbiXnzxRaSlpRk/Lly4YJP+ExGRjVy7ViuCNKDyEfXzzz+PGTNmYOjQoQCAFi1a4Pz581i0aBFGjRqFsLAwAHJkXadOHePXXbt2rdQouzidTgddNa8xEBGRFRgMshzozZv27km1UfWIOisrC1qtsotOTk7G7VlRUVEICwvDrl27jNf1ej12796Njh07VmtfiYjIxvLzZWZ3LQrSgMpH1P3798eCBQtQv3593HnnnThy5AiWLVuGsWPHApBT3pMnT8bChQvRqFEjNGrUCAsXLoSHhweGDRtm594TEZHV5OTIpLH8fHv3pNqpOlC/8847eOWVVzBhwgRcu3YN4eHhGD9+PGbNmmW854UXXkB2djYmTJiAlJQUtGvXDjt37oS3t7cde05ERFaTkSGnu2twZnd5VL2PurpwHzURkUolJcnsbrWwwz5qVY+oiYiolhJCHqxRy9ajTWGgJiIidcnPlwdr5ObauyeqwEBNRETqkZMjg3RBgb17ohoM1EREpA7p6cDVq7U2aawsDNRERGR/168DKSn27oUqMVATEZH9GAwyaSwz0949US0GaiIisg8mjZmFgZqIiKpfbq4M0rWw0pilGKiJiKh6ZWbK6e5b5zZQ+RioiYio+qSlySMqmdltNgZqIiKqHmorB+ogGKiJiMi2hJCHamRk2LsnDomBmoiIbCc/Xx5PmZNj7544LAZqIiKyjVp8hrQ1MVATEZH1sRyo1TBQExGRdTFpzKoYqImIyDoMBpk0VpPPkM7KAnS6an1KbbU+GxER1Ux5ecCFCzU3SOflAatXA40aAWfOVOtTM1ATEVHVZGcD//5bc2t2Hz4MDBoELF0K3LgBTJxYrWvvnPomIqLKS02VR1TWxKSx1FRg2TLgs8+U7YcPyzcmkZHV0g2OqImIyHJCyKzumlgOVAjgq6+ABx4oHaTHjgVOnaq2IA1wRE1ERJYqKJCHamRl2bsn1nf2LDBnDrB/v7L9jjtk+6BB1Z5MxkBNRETm0+tlERO93t49sa6cHGDlSuC//5WJY4Xc3YGnnwZGjgRcXOzSNQZqIiIyT1aWHEkXFNi7J9b1yy/A3Lkya7247t2BV14BwsPt069bGKiJiKhiNTFp7OpVYNEi4LvvlO116gAvvwz06GGffpXAQE1ERGUrTBpLT7d3T6ynoAD4+GPgzTeBzMyidicnYPRouf3K09Nu3SuJgZqIiEyriSdf/fEHMHs2cOKEsj0mRiaLNWlil26Vh4GaiIhKy80FLl2qOSdfZWQAy5cDGzcqp+99fYFp04CHHwa06tyxzEBNRERKmZkyacxgsHdPqk4IuQa9cKFcYy9u4EDghReAwEC7dM1cDNRERFQkNVUWMakJ/v1XZnPv2aNsj4qS7e3a2adfFmKgJiIi6fp1ICXF3r2oOr1e7od+/33lfm+dDnjqKWDcOMDV1X79sxADNRFRbSeEnOquCSdf7dsnR8sJCcr2e+4BZs0C6tev1MMaDMDJk8DlU0BguMw9q64lbQZqIqLarKBAJo05emb3jRvA4sXA1q3K9uBgYOZMWbdbo6nUQ+/bB3zwgYz9CQYAOqBxY2DGDFkTxdbUmeJGRES2l5sr13EdOUgbDPLgjN69lUFaqwUeewzYvh3o06dKQXrWLHkOh4cHEBYGeHkB8fHA+PFAXJyVXkc5OKImIqqN0tNlIRNHrjT2119yT/TRo8r25s3l9Hfz5lV6eINBjqQzM4HQUBnrtVpZ/jsiQk5ELF4MdO1q22lwBmoiotpECJnVnZZm755UXmYmsGIFsG6dsu64pycwZQowbJisMlZFJ0/K6W4/v9IDco0GCAiQI+0jR4DWrav8dGVioCYiqi3y8mTSmCNPdX//PTB/vnwdxfXpIxeNQ0Ot9lQpKfJbVlaCuJubvCcpyWpPaRIDNRFRbZCZCSQmOu7JV5cuAa++Cvz4o7K9fn25iHzPPVZ/Sn9/ebKlXi+Dckk5OTKIBwVZ/akVGKiJiGq6GzfkhyPKywPWrgXefRfIzi5qd3EBHn9cZnSZiqJW0KyZrI1y6lTRGnUhIYDkZCA6Wm7VsiVmfRMR1VQGgxyJOmqQPnQIGDQIeP11ZZBu2xb4+mvg2WdtFqQBmSD2xBNy6fvqVdkFg0Eey33pEuDjI2fbbb2fWiOEI6f8WUd6ejp8fX2RlpYGHx8fe3eHiKjq9Hp58lXxylyOIiUFeOMNYNMmZXtAADB9OjBgQKW3W1WGch91JKDTVes+agZqMFATUQ3jqIdqCAFs2QIsXVq6lOmQIcDUqTIF2w6MlclcIhEYrmNlMiIiqqTkZNunIdvCmTPyPOgDB5Ttd9wBzJtn+4XgCmi1clt280gAuup9bgZqIqp1DAa59zUpSWbsVufoyGaEkAup6en27ollsrOBlSuB1atl4lghd3fgmWdkdTEXF/v1TwUsDtTZ2dkQQsDDwwMAcP78eWzZsgXNmjVDr169rN5BIiJriouT1aROnZLLt66u1Vu32Sby8+V6tKPtj969W46WL15UtvfoAbz0EhAebp9+qYzFa9S9evXCoEGD8OSTTyI1NRVNmjSBi4sLkpKSsGzZMjz11FO26qvNcI2aqHaIi5O7eTIygMBAeephbq5Mivb2BlatcsBgnZMjg3R+vr17Yr6rV4EFC4AdO5Tt4eHAyy8D991nn36ZI1Imk1Uniyd7Dh8+jHtubSz/4osvEBoaivPnz+Ojjz7C22+/bfUOEhFZg8EgR9IZGbJOs7u7sm5zRoa8Xl35VwaD3H20Y4f8t1LPm5ICXLjgOEE6P1+W/ezdWxmknZ3lGdHbtqk7SNuJxVPfWVlZ8Pb2BgDs3LkTgwYNglarRfv27XH+/Hmrd5CIyBqOHJHT3YGB9q3bDFhh+t1gkFXGHOn86Ph4eYDGyZPK9pgYeYBG48b26ZcDsHhEffvtt+Orr77ChQsXsGPHDuO69LVr1zhtTESqlZQkg2JZs5ZubvK6rROmC6ff4+PlcYl16lh4bGJuLnD+vOME6fR0GYgHD1YGaT8/WbP7448ZpCtgcaCeNWsWpk2bhgYNGqBt27bo0KEDADm6jrFz+jwRUVmCguTINTfX9PXqqNtc5en3mzflVHfx7Gi1EkJOZT/wgAzGxdOhBg0CvvsOeOSRGpBub3uVKniSmJiIK1euoGXLltDe+ibv378fPj4+aNKkidU7aWtMJiOq+QwGuTQaHy+DYsm6zZcuybrN27fbLnYUVsT08pLBuaSsLFmrZPNmE9PvjrQ/+tw5mc3966/K9oYN5ei6bVu7dMsqHCGZDADCwsLg7e2NXbt2IftW/dW7777bIYM0EdUOWq1cA/b2lkE5K6v66zZXavpdCLke7QhBOjdXnhPdv78ySOt08pzor7927CBtJxb/SN64cQP33Xcf7rjjDvTp0wdXbp0J+p///AfPPfec1TtIRGQt3bvLLVjR0UVVNjMz5ecrV9p+a5bF0+8FBXKPsSMUMdm3D3jwQeCdd5T1xe+9F/jmG+DJJ8s+2JnKZXHW95QpU+Di4oJ///0XTZs2NbYPGTIEU6ZMwRtvvGHVDhIRWVP37kDXrvapTBYTI/Omypp+VxybmJsr90erfT36+nW5sL5tm7I9JASYOVOuN1TjARo1kcWBeufOndixYwfq1q2raG/UqBG3ZxGRQ9Bqbb8Fq6znnTFDZndfuiS3hLm5yZF0cnKx6fdsBzhUw2AAPv0UWLZMZsEV0mqBESPkEZReXvbrXw1icaDOzMw0lg8tLikpCbpqXmAnInI0hdPvhfuoU1LkjHB09K191DEpwKXr9u5m+f78U+6JPnZM2d68uUwiu/NO+/SrhrI4UN9777346KOP8OqrrwIANBoNDAYDXnvtNXTr1s3qHSQiqmlMTr+3EtAmXQOup9m7e2W7eVOuQX/0kXK07+Ulj6AcOhRwcrJf/2ooiwP1a6+9hq5du+LgwYPQ6/V44YUXcOLECSQnJ+PXkqn4RERkkmL6vaBATnVnZdm1T2USAti1SxYouXpVea1vXzkVEBJin77VAhYH6mbNmiE+Ph7vv/8+nJyckJmZiUGDBmHixImoU6eOLfpIRFRz5eTIIK3WpLELF2SA/uknZXtkpJz+7tTJLt2qTSpV8KSmYcETIrKL1FSZNa3GP8N6PbB2LfDuu8rjM11cZDbcE09Ue+EPVbBDwROLR9Q///xzudfvvffeSneGiKhWMBjkFHLxbGk1OXhQjpbPnFG2t28v2xs2tE+/aimLA3XXrl1LtWmK7ZErKCioUoeIiGo0vV7ujy5eFEQtkpOB116TNUyLCwwEXnwR6NePe6LtwOJAnZKSovg8Ly8PR44cwSuvvIIFCxZYrWNERDVOpkr3RxsMwJYtwNKlcjq+kEYDDBkiM7p9fe3WvdrO4kDta+J/Vs+ePaHT6TBlyhQcOnTIKh0jIqpRUlLkerTa/P03MGeOnO4urkkTeYBGq1b26BUVY3GgLktwcDBOnTplrYcjIqoZhJDr0Wqr152dDbz3HrBmDZCfX9Tu4SGrio0YAThbLURQFVj8fyE+Pl7xuRACV65cweLFi9GyZUurdYyIyFwGg31qd1coP1+uRxfPmraAwQCcPCkH4/7+QLNmVnpdP/0kK4hduqRs79VL1ufmVlsljUYeu+bjY5dMd4sDdatWraDRaFByV1f79u2xZs0aq3WMiMgccXFF5Tj1elmOs3HjW+U4bXwaVrlyc2UgLD5atcC+fcAHHwAJCXKLtYsLEBUld0V16FDJPiUmAgsWADt3KtsjIoBXXgFYXVLJ3V2uzXt52fWdn8X7qEsevKHVahEcHAw3Nzerdqw6cR81kWOKi5NbejMyZGKyTifj440bcgC0apWdgnVGhgyKldwfvW8fMGuWzD3z85NvPvR6mefl6SkHwxYF6/x8YMMG4K23lNXPnJ2BMWOACRPklDfJd0Q+PvLDxcXevQHAgicAGKiJHJHBIE9QLOvIyEuX5EEX27dX82AoOVnOwVeSwQCMGydnCEJDS7+uq1fljMHq1Wa+rmPHZNT/6y9le+vWMlmsUaNK97XG0GrlqNnXV46iVcasqe+3337b7Ad85plnKt0ZIiJzHTkig1lgYOmtvRqNPELy1Cl5X7UcaSmEHEVXsYjJyZNyutvPz/Tr8vWV10+elIdVlSk9XR5B+emnypG9nx/w/PPAoEEqWci3Izc3+f2w89R2RcwK1G+++aZZD6bRaBioiahaJCXJ6eCycnvc3GQSVhUGt+YrKJBD+EomjRWXkiLXpF1dTV/X6WQMLlHSoogQwLZtwKJFcg2guIceAqZNk+9iaiutVq6L+Pk5TAlUswJ1QkKCrftBRGSRoCAZzHJzTc9W5uTI60FBNu5IFZPGSvL3l0ujer18s2Hq6Vxc5H2lJCTI6ex9+5Ttt98u90rffbdV+uiQ3NzkdIS3t6pHz6ZwkxwROaSYGLlWW9YadXKyXKOOibFhJ27elNPdVqw01qyZzO4ua406LU2+7mbNin1Rbq5MEV+1SnkKl5sbMHEiMHp02UP0mszJqWhblQMnPFcqUF+8eBFbt27Fv//+C32JerXLli2zSseIiMqj1cotWOPHywFtQID8W5yTI4O0j4+8brPBk4lKY9bY96zVyi1Ys2bJxDFf36Js9rQ0uZz6xBPFHnfvXjlaLrEjB126yC1X9epV+iU6JI1GfpN8fGQmew2oTW5x1vcPP/yABx98EFFRUTh16hSaN2+Oc+fOQQiBu+66C3Fxcbbqq80w65vIcVX7PuoyKo1Ze99zhY93/bp84du2Kb8wNBR46SVZvKQGBCmzOTvLdzW+vjWuoprFgbpt27bo3bs35s2bB29vbxw7dgwhISEYPnw4evfujaeeespWfbUZBmoix1ZtlckKCmSlsexsRbPV9z3fYnKELgpkJvebbyozzLVaYORI4Omn5YiytvDwKCpKUkPfmFgcqL29vXH06FHcdttt8Pf3x549e3DnnXfi2LFjGDBgAM6dO2ejrtoOAzURVUivl3PsxdeAYYN9z+U5eVKeB12ilDOio2USmWLhugZzdS0qSlLDRs+mWPwKPT09kZubCwAIDw/H2bNnceeddwIAkqplHwQRUTXLypIjaRNJY1bb91yemzeBt98G1q9X9sHbG3juOWDwYJk4VZPVkMSwyrA4ULdv3x6//vormjVrhr59++K5557DH3/8gc2bN6N9+/a26CMRkf2kp8thcRmTj1Xe91weIYAdO2R97mvXlNf695cL8Tbff2Zn7u5FRUlq6NR2RcyeiLl+K7tx2bJlaNeuHQBgzpw56NmzJz777DNERkZi9erVVu/gpUuXMGLECAQGBsLDwwOtWrVSnHkthMCcOXMQHh4Od3d3dO3aFSdOnLB6P4ioFrpxo8Ka3cX3PZtS7r7n8ly4IFPan31WGaQbNADWrgVef73mBmmtVk5FREbKrHVv71obpAELRtQRERF48MEHMW7cOPTu3RsA4OHhgffee89mnUtJSUGnTp3QrVs3fPfddwgJCcHZs2fh5+dnvGfp0qVYtmwZ1q5dizvuuAPz589Hz549cerUKXh7e9usb0RUc5RKRosugPZaoswOq0Cl9j2XR68HYmOBd9+VUb6Qq6sM3I8/7jAVtSxSWG/b27vGbKuyFrOTyT755BPExsYiLi4OYWFhGDNmDEaPHo3bbrvNZp2bMWMGfv31V/zyyy8mrwshEB4ejsmTJ2P69OkAgNzcXISGhmLJkiUYP368Wc/DZDKi2qvk9i5vlxy0j7yCp/6TZ3amdvGsb1P7nufONTPr+8ABmSx29qyyvWNH2d6ggaUvT900GpkW7+1dq6e2K2Jx1veFCxewZs0arFu3DufPn8e9996L//znP3jooYesftRls2bNcP/99+PixYvYvXs3IiIiMGHCBDz++OMAgH/++Qe33XYbDh8+jJhi5YcGDBgAPz8/rFu3zuTj5ubmGhPiABmo69Wrx0BNVMuUPCYz0CkVnlnXkZYqLN5WVaV91MnJwGuvAZs3K9uDgoAXXwT69nXYIGZyi5mHW9GWqpqeBGcFVTrm8ocffkBsbCy2bNkCV1dXPProo1adCi8M/FOnTsUjjzyC/fv3Y/LkyVi1ahVGjhyJvXv3olOnTrh06RLCw8ONX/fEE0/g/Pnz2LFjh8nHnTNnDubOnVuqnYGaqPYofkxm3XADAguuwrNA7kuu7LYqiyuTGQzAl1/K9ebU1KJ2jQZ49FFgyhSZ5eygir95yc5zRq7OB+FNfDBtpqt9zgl3UFY5j/rLL7/EE088gdTUVBQUFFijXwAAV1dXtGnTBnv37jW2PfPMMzhw4AD27dtnDNSXL19GnTp1jPc8/vjjuHDhArZv327ycTmiJqJDh+RJj/6eetR1ugJXkau4np0tP1asqMK2qvKcPi2nsw8fVrY3aybnyqOjbfCk1WffPuDlWVpcy/KCa5APhLsHcnNlfp63tyxLzmBtnkpvvz937hxmz56NBg0aYMiQIbjrrruwceNGa/YNderUQbMSGRhNmzbFv//+CwAICwsDACQmJiruuXbtGkJDQ8t8XJ1OBx8fH8UHEdUuSUmAc85NRGr+LRWkAbnOnJdXyW1V5cnKktPc//d/yiDt6QnMnAls2uTwQdrg5oEl68JwMqchXOuHQePpAa1W7rSKiJBLDYsXW/UskxrNokCdk5OD9evXo3v37rj99tuxdu1ajBo1CmfOnMGuXbswdOhQq3auU6dOOHXqlKLt9OnTiIyMBABERUUhLCwMu3btMl7X6/XYvXs3OnbsaNW+EFENIgTCnK6jntNl5OWajhaV3lZVnrg4ud783/8qj8W8/37gu++AUaMct9KWi4tcU4+KwpHrdXHkrA8CgrQmi8AEBMjkvSNH7NNVR2P2T8QTTzyBzz//HDk5ORgwYAC++eYb9OrVCxobJjhMmTIFHTt2xMKFCzF48GDs378fH3zwAT744AMAgEajweTJk7Fw4UI0atQIjRo1wsKFC+Hh4YFhw4bZrF9E5MDy84ErV9CibrZ1t1WV58oVWbSk2KACgBxezp4tT7pyRIVZ276+8t9bkpJkBn1Zu8jc3ORMBYtZmsfsQP3bb79h7ty5eOyxxxAQEGDLPhndfffd2LJlC1588UXMmzcPUVFRWL58OYYPH26854UXXkB2djYmTJiAlJQUtGvXDjt37uQeaiIqLStLBs2CAsuPk6yMvDxZ9vOdd+RzF3JxAcaOBZ56Ss4HOxqNRlYL8/c3OQMQFCS3fefmmn55OTnyek2t12JtVkkmc3TcR01UCyQnmxzCWft4SqOjR+W7gBLLd2jTRiaL3X57FR7cTrTaogBdzraq4hn1ERGlZysuXZLL8Nu32/C88BqEgRoM1EQ1WkGBLANaTpUxi7dVlSctDXjjDeDzz5WlR/38gOnTZRKZo+2J1ulk/729zf7GFN+jHhAgp7tzcuT7JR8fYOVKZn2bi4EaDNRENVZOjjz1qnjilq0IAWzdKtOZk5OV1x5+GJg2zcqZaTam0ciI6utb6dOqSlZ9c3WV6/4zZjBIW4KBGgzURDVSWpo8zKI6/sT98w8wZw7w++/K9kaNZHubNrbvg7U4OcnRs5+fVaqGlaqjHsPpbks56D4AIqIyCAFcv66s9GUrOTmycseHH8oF7kJubsDEicDo0WWff6k2Op0c8Vv5pCqtFmjd2moPVyuZFajj4+PNfsBoB9+oT0QO7NbWK2Rn2/65fvlFFgO/VYDJqFs34OWXgbp1bd8Ha/D0lAHaw8PePaEymBWoW7VqBY1GAyFEhfumrVlClIjIbBkZcqrb1n+Drl0DFi0Cvv1W2R4WJgN0jx7qTxbTauX6s5+f44z4azGzAnVCQoLxv48cOYJp06bh+eefR4dbexf27duHN954A0uXLrVNL4mIymIwyOCZnm7b5ykoAD75BHjzTeDmzaJ2Jydg5Ejg6acVRT8qYtVMc3M5O8vg7OvLU6sciMXJZG3btsWcOXPQp08fRfu3336LV155BYcOHbJqB6sDk8mIHFR2ttx6VXx92BaOH5cVxI4fV7a3bCn3RDdtatHD2WzvdllstP5M1cPiZLI//vgDUVFRpdqjoqJw8uRJq3SKiKhCN27ID1vKyADeegvYuFF5goSPD/Dcc8DgwRYPg/ftk3VQMjOLZp71ermFadYsy87ArhDXn2sEiydamjZtivnz5yMnJ8fYlpubi/nz56Ophe8qiYgslpcHXLhg2yAthFyDfuABWQK0eJAeMEAeoDF0qMVB2mCQI+nMTFlf3M1NPoSbm/w8M1Ner9KpUlqtDM5RUbIsGIO0w7N4RL1y5Ur0798f9erVQ8uWLQEAx44dg0ajwbZt26zeQSIio5s35VS3Lc9H/PdfOZ29Z4+yvUEDuSe6CsPdkyfldLefX+kZaI1GLh0nJMj7LD4D29VVPrCPDzcq1zAWB+q2bdsiISEBGzZswF9//QUhBIYMGYJhw4bB04JECiIis1XH3mi9Xh4/uXKlPE2ikKsr8OSTwOOPVzlDOiVFTgiU9TA6ncyJs+gMbA8POYLm398aq1IFTzw8PPDEE09Yuy9ERKXl5ckyoMWDp7X99pscLRfb4QIA6NRJJpFFRlrlafz9ZeKYXm+6KqfZZ2AXlvf08yv7LEmqMSo1P7J+/Xp07twZ4eHhOH/+PADgzTffxNdff23VzhFRLXfzJnD+vO2C9I0b8qCMUaOUQTo4GFi2DFi92mpBGpBbsKKi5MRAyf02hWdgR0WVcwa2q6vsW8OGclGbQbpWsDhQv//++5g6dSoeeOABpKSkGAuc+Pv7Y/ny5dbuHxHVRoVT3Zcv22Y92mCQp1s98ADw1VdF7RoNMGKETBbr29fqW5kKz8D29JRnYGdny65kZ8vPTZ6BXTh6rldPrpNXcMQk1TwW76Nu1qwZFi5ciIEDB8Lb2xvHjh1Dw4YNcfz4cXTt2hVJJs57VTvuoyZSEVuXAf3rLznNfeSIsv3OO2V7NZRBNmsfNYuT0C0Wr1EnJCQgJiamVLtOp0NmOee9EhFV6OZNObS0RRnQzExgxQpg3Trl43t6AlOmAMOGVVtA7NABaNeujMpkbm6ywcuLxUkIQCUCdVRUFI4ePYrIEus23333HZqVubBCRFQOW2d1f/89MH++HKkX17s3MHOmXO+tZlptiS1Y3t4yQFfy7GequSwO1M8//zwmTpyInJwcCCGwf/9+fPLJJ1i0aBH++9//2qKPRFQJDnMOsF4vA6gtEsYuXZIBOi5O2V6vniwDdu+91n9OS2i1cmrbz0/OfxOZYHGgHjNmDPLz8/HCCy8gKysLw4YNQ0REBN566y0MHTrUFn0kIgvFxQGLF8uylHq9TBZu3BiYMQPo3r36+1Pmm4b0dDnVbVmqTMXy8oCPPgLeeUe51u3iAowbBzz1lH1HroV7sFichMxgcTJZcUlJSTAYDAgJCbFmn6odk8moJomLA8aPl2WqAwPlDp7cXLkTydsbWLWqeoO1qTcNTe4w4JXx19A52gYnXh06JJPCTp9WtrdtK9tvu836z2muwvVnb2/79YEcjsVv5bp3747UW+tIQUFBxiCdnp6O7vZ4q05ERgaDDIoZGbLMs7u7HLC5u8vPMzLkdVtW4Cyu8E1DfLzMjapTB/D3yEXy0X8x97l07NtnxSdLSZHnQQ8bpgzS/v7AkiVyhG2vIO3pCdStC9SvzyBNFrN46vunn36CXq8v1Z6Tk4NffvnFKp0iqkmqc634yBE5cg0MNF1LOiBAXj9yBGjd2jZ9KFTyTYNGA3jlp8Lf6ToQInD1qtyi1K5dFb8fQsi90EuWlK69OXiwPOXKz68KT1BJhfuf/f2rXHqUajezA3V8fLzxv0+ePInExETj5wUFBdi+fTsiIiKs2zsiB1fda8VJSfJ5yipY5eYmY1l1lDso/qbBCQUI0F+Fh+GmvFjVAygKnT0rp7P371e233GHPFjjrruq8hIqx8lJvjgWJiErMTtQt2rVChqNBhqNxuQUt7u7O9555x2rdo7IkZW1VhwfL9ttsVYcFCTfDOTmyunuknJy5PWgIOs+rymFbxp8XbIQrE+Ek8hXXK/UARSFsrPl4RmrV8vEsULu7sCkSbIkaHVnUTs7y+Ds68sEMbIqswN1QkIChBBo2LAh9u/fj+DgYOM1V1dXhISEwInvHokAmJ72BYrWii9dkte7drXu3/SYGDlij49XPi8gZ4iTk2XhLRM1i6wuKFCgjtN1+N1MhVNVDqAo6eef5Wj54kVl+333yTXq8PBK97lSdLqiBDEzC5Q4zNY5UgWzA3VhgRNDdWWhEDkwe60Va7VyWn38ePlmICBATnfn5Mgg7eMjr9s8KOTkIMb/ClpG5uHUKVlPpOSbhrQ0+abC7DpJV68CCxcC27cr2+vUkQG6Rw+rdd8slTxeUm1b50j9LP51XbRoEdasWVOqfc2aNViyZIlVOkXk6MxZK9brbbNW3L27nFaPjpZVM69ckf9GR8vZYpsHg+Rk4MIFaAvyLD+AwpSCAmD9enmARvEg7eQk90R/8031BmkvL5m9XbdupYJ0ySx4L6+i5ZCSdVmIgErso27QoAE+/vhjdOzYUdH++++/Y+jQoUgoeZ6rA+A+arK2Q4eAQYPkH2FTa8VZWTJ4bt5su+zrap9eLeMwDbMOoCjLH3/I86BPnFC2x8TIJLImTaz6Esrl4yOnKCqZwW0wyIqlZS1LXLok30xt385pcFKyeHtWYmIi6tSpU6o9ODgYV0rW0SWqpdSwVqzV2n4LllFGBnDtmsnDNMo9gKK8x1u+HNi4UVm1zNcXmDYNePjh6otmVQzQhdS0dY4ci8WBul69evj1118RFRWlaP/1118RXt1JHEQqpZq14iowa0Seny/nsSs4Oa/UARRlEUKeBb1woTyko7iBA4EXXpCRrjp4eRWl0VuBmrbOkWOxOFD/5z//weTJk5GXl2fcpvXDDz/ghRdewHPPPWf1DhI5qsK14sLEoZQU+Tc/Olr9iUNmJTylpsqoYq0E0/PngXnzgD17lO0NG8pp7nbtrPM8FXF3B4KDrV4LXE1b58ixWLxGLYTAjBkz8PbbbxsrlLm5uWH69OmYNWuWTTppa1yjJltytK04FdUK/+DdPHRrmlhqLbqQwWDhNLdeD3z4ocx0K171UKeTh2eMG1c9lb0qmcVtLq5RU2VV+lCOmzdv4s8//4S7uzsaNWoEXVnzOQ6AgZpIqiiYZFxIRcc7krDmvwaTwcTixLHffpOj5ZJJqJ07yySy+vWt+fJK02qLzoGuhjcDxd8EmVoOqZasfHI4VTo9q6ZgoCaSyspWdzHkIiD/GgyZ2cjOBlasKL3mvG+fPOI5M1OW1nZ1lQPk1FQ5SJ03r1iwTkqStbm3blU+SHAw8NJL8t2CmcVDKsXZWXbSz6/ah6/cR02WMmuNetCgQVi7di18fHwwaNCgcu/dvHmzVTpGRNWvZMKTVhTALz8JXgVpAABDGWU/DQY5ks7MVBY3cXOTnxsP4LjbAO0XnwNvvCEfqJBWCwwfDkyeLN8l2Iqra9E50LZ8I1CO7t1lRTpHWg4h+zIrUPv6+kJz64fa19fXph0iIvsxJjzlCIS6psAnPxlaFCWLlVX28+RJOXvt52d665GvL6A9/Rdy/m82PE4fVd7QvLksCVrpkznM4OYm55pt+SbAAtW6dY4cnlmBOjY21uR/E1HNEhMDtLg9G1fjr8I3RG922c+UFLkmbWqZ160gEyNTV6Bf8jo4JxXbZ+3lBUydCgwdartTpjw8ZID28LDN4xNVA4u3ZxFRDVVQAG1SEl4amYZZs+R0ta9vUdZ3WlrZZT/9/eVIW68vtqtJCLTN+AH/ufIqgvITlV/Qt69clA0Jsc1r8fCQKeum9kERORizksliYmKMU98VOXz4cJU7Vd2YTEa1XonKYpZmbxsMchdV4QEcIXmX8J/EV3F3xo+K+0T9+tDMni2zum3B01MGaCvvgSayJ7NG1AMHDjT+d05ODt577z00a9YMHW79xv722284ceIEJkyYYJNOEpGN5OfLAH3zpqLZ0rKfWq0M4nNfyUOPc2sxLvtduImifdZ6uODag4+j7qvjbRNEGaCpBrN4e9Z//vMf1KlTB6+++qqiffbs2bhw4YLJk7XUjiNqqnWEkPumbtywXmWxgweRNX0uPC6eVjSf9G2P3BmzETOooXWepzgGaKoFLA7Uvr6+OHjwIBo1aqRo//vvv9GmTRukpaVZtYPVgYGaapXMTFlHu3gVsKpISQFefx344gtFs947AFfHzEDEkw9C66RcOrO4ellJDNBUi1icTObu7o49e/aUCtR79uyBG39piNQrL09Oc1dwgIbZhAC2bJGFS1JTldeGDIHrc8+hnontnFU69tLDQ+4h498aqkUsDtSTJ0/GU089hUOHDqF9+/YA5Br1mjVrHLbWN1GNV3gsk7UKEZ45I0t/HjigbG/cWO6JLuP8zrKql506JdsV1cuKc3eXAZpZ3FQLVaqE6Oeff4633noLf/75JwCgadOmePbZZzF48GCrd7A6cOqbaiy9HkhMlAWlrSE7WxakXr1aDocLeXgATz8NjBwpy3OaUDIzvOQe7atXZZxfvbrYNLibmwzQ3AdNtRhrfYOBmmqgggKZKJaWZr1R9O7dcsh78aKyvUcPWZ+7gvPojx8HJk2SMdfUzHV2NorqiLfWyQBto5OsiBxJpQqepKam4osvvsA///yDadOmISAgAIcPH0ZoaCgiIiKs3UciMpcQcpo7Odl62dxXrwILFgA7dijbw8OBl18G7rvPrIcpr3oZIAur3Eh3QaImCM0jvavYaaKaw+JAHR8fjx49esDX1xfnzp3Df/7zHwQEBGDLli04f/48PvroI1v0k4pxtPONqZqkp8sfivx86zxefj6wcSOwfDmQlVXU7uwMjB4NTJxo0ZS0yepltxRonJBYEIir7r7wr2+fwzKI1MriP+9Tp07F6NGj8ffffyuyvB944AH8/PPPVu0clRYXJ08AHDRI/q0cNEh+Hhdn756R3eTmAhcuyLVoawXp+HjgkUeAhQuVQTomBti8GXj+eYvXjZs1k9ndqalFs/EGaJHmHIhLLlE4n+aHxk00ZeWhEdVaFo+oDxw4gFWrVpVqj4iIQGJioomvIGspfuh8YGBRDeb4eNm+ahXPs61V8vLkFLc1axekpwNvvgl88olybdvPD5g2DXjooUpP3xRWL5s1C7hyVQttgB/0nv7IynVCcrI8eXLGDM4OEZVk8a+Em5sb0oufI3vLqVOnEBwcbJVOUWkGgzxsPiMDiIiQu1S0WvlvRIRsX7zYesuSpGKFZT/PnbNekBYC2LYNeOAB4OOPlUF60CDgu+/kCLuKUbRDJy1mLQ+AX0wULuUG4VKiEzIzgehomUzON5pEpVk8oh4wYADmzZuHzz//HACg0Wjw77//YsaMGXjooYes3kGSjhyR21oCA02f9xsQIK8fOcJzbmusggI5gi4+d2wN587Jvc979yrbb7tN7pVu27bqz6HVykVqPz/cc7sTvh3APAsic1kcqF9//XX06dMHISEhyM7ORpcuXZCYmIgOHTpgwYIFtugjQf5B0+vldLcpbm5FNS2ohjEYigK0NadMcnOBDz+UaybFy4nqdDJRbMyYslO0zaXVymnzgABFJNZq+YaSyFwWB2ofHx/s2bMHcXFxOHz4MAwGA+666y706NHDFv2jW4KC5N/M3FzTxZlycuT1oKDq7xvZiMEg332lphqPn7SavXvlKPrcOWX7PfcAs2cD9epV7fE1mqIA7eRUtcciquUsKniSn58PNzc3HD16FM2bN7dlv6qVIxQ8MRhkdnd8vFyTLlnV6dIluc63fTunEB1eQUFRgLZ20sH16zKZYds2ZXtIiNwT3atX6bUVS2g0MissMLDMCmUAtxgSWcKiEbWzszMiIyNRYO1391QhrVZmxI4fL4NyQICc7s7JATNmawoh5P/M5GTrrkEDMvh/9hmwbJnMPCyk1QIjRgDPPgt4eZX55WaddnUrQBucXMoNwnFx8r3CqVNyxt3VVZYOnTGDyWREplhcQjQ2NhabNm3Chg0bEBAQYKt+VStHGFEX4h+5GsraxUqKO3lSTmfHxyvbW7SQ09933lnul1d42pW7uxyR63QV/nyWtcXwxg3A25tbDIlMsThQx8TE4MyZM8jLy0NkZCQ8S9TiPXz4sFU7WB0cKVADnDasUbKz5XS0tQ7NKO7mTeDtt4H165VT6F5ewNSpwNChFa4fl3XaVWoq4ObljNkrgtGlnyz3WVEQfv99YOlSLt8QWapS27M0VVnDoipjxqz9WO1Nkl4vH+TmTav3EUIAu3YB8+fLOt3F9esHTJ8uR8AVMBjkSDozU3nalc5NA7eIAJxMDMCCtzW4p49sL77Pv/Dewn3+ly7JJfDLl7nFkMhSFgfqOXPm2KAbROpnlWWH/PyiU61s4cIFGaB/+knZ3qCBnP7u2NHshzp5Uk53+/kVBdYsrRdSXIJRoHFBQGBRYAUq3uf/zz8y+Je1M4FbDIlMM3sskJWVhYkTJyIiIgIhISEYNmwYkvgbRbVE4bRufLycOa5TR/5bWL61wlrrBoMM0NasJlacXi+Hv/36KYO0i4vcE711q0VBGlCedpWvccE1lwgkuYajQOMCQAbWwokBc/b5Gwxy9iE31/Q93GJoPQYDcOiQPPDs0CFWLHR0Zo+oZ8+ejbVr12L48OFwc3PDJ598gqeeegqbNm2yZf+I7K5k+VZT07qLFwNdu5qYBhdCBubkZNskigHAgQOygtiZM8r2Dh3kKDoqqlIPW3ja1Y0CP+S4B0FolC+uZGCtaJ+/h4c8GfPcOdNr1MnJco2ah3JUDRNOax6zA/XmzZuxevVqDB06FAAwYsQIdOrUCQUFBXBiQQOqwSpVvrU6AnRyMvDaa/I0q+ICA+Vf5f79q7QnulkrV3g1DcXBE+6I8AKKP5KpwNq4cdmJYoX3vvAC8NRT3GJoKzy4p2Yy+1fiwoULuOeee4yft23bFs7Ozrh8+bJNOkakFuZM6xZOARuriSUkyIMzbBGkDQbgiy/kARrFg7RGAzz6qDxA48EHq1a4JCAA2qhITH3JHd7eMrBmZcmnzsqSnxcPrIX7/Cu6t0cPGSyio2WS2pUr4KEcVsKDe2ous0fUBQUFcC1R99fZ2Rn5thotEKmEOeVbPVzyEKZNAf5Jt+1fwr//ltPZhw4p25s2lXuiW7as2uPrdDLF+9ZZ8927y8BaOJWakiK/F9HRpadSzb23e3e5TMAthtbFg3tqLrMDtRACo0ePhq7YsCInJwdPPvmkYi/15pLTcEQOLiam7GldXX4m8q+not0dmWhRD4CtYnRWFvDee0BsrHKU7uEhq4qNGFFuyc4KFf4lDwgo9VfeksBq7r3cYmh9PLin5jL7N3vUqFGl2kaMGGHVzhCpUcnyrcH++Qh0SYdrVipupuYjxEtW6bLZiPDHH4FXX5VPXtz99wMzZwJhYVV7fHd3OYou56QsSwIrg7B98OCemsviymQ1kaNVJiM7EAK7v83EyiVpuHIm03QpTWtLTJR7onftUrZHRMhyYV27Vu3xtVr5V9vPr2qPQ6rAg3tqLgZqMFBTOfLzZb3MtDSgoMC8wyms8ZwbNgBvvSWnvAs5OwNjxwITJpgeMlnCw0OOol1cqvQwLGerLsWzvk1l1TNhzzExUIOBmkzIzpYB+uZN659kVZ5jx+Ro+a+/lO1t2si90o0aVe3xrTiK5n5ddeL/l5qHgRoM1HSLEHIokpJSdvksW0lLk0dQfvaZ8o2Bnx/w/PPAoEFVH6paaRQN8BQsteNMR83CQA0G6lrPYJCj55QUeW5zdRIC+N//5BDoxg3ltYceAqZNk3OYVeHkBAQHy7lPK+BaKFH1qsJ+DiIHZ88ADchTKubOBX77Tdl+++2yvU2bqj+Ht7c8KcuK1QNtsV+XI0CisjFQU+1j7wCdmyvnhj/4QJ56UcjNTR6gMXp0uVulzOLkJAO0t7dZt1sSKK29X5drqkTlY6Cm2sPeARoAfv1VjpbPn1e2d+0KvPIKULdu1Z/Dw0PurTazAIqlgdKa+3VZm5qoYlyjBteoazw1BOjr12U03LZN2R4aCrz8MtCzZ9VqcwPy64OC5L4xM1UmKcxaa9Rc6yYyD3/8qeYqKJDzr//8I/+1R5AuKAA2bpQHaBQP0k5Ocor722+BXr2qHqTd3IDISIuCdGUPcTD3AI6Kgqsla91EtRmnvqlmEUIex5SeLv+154TRiRPyAI0//lC2t2wpp7+bNq36c1RiFF2oKklhlhzWURbWpiYyj0ONqBctWgSNRoPJkycb24QQmDNnDsLDw+Hu7o6uXbvixIkT9usk2UdenjxW8p9/gMuXq79QSXE3bwILFgAPP6wM0t7esmjJp59aJ0hXYhRdnEXHd5rQvbuclt68GVi7Vv67fbv5a8rF17pNYW1qIslhRtQHDhzABx98gOjoaEX70qVLsWzZMqxduxZ33HEH5s+fj549e+LUqVPwNjPjlRxYdrYcdt28Wakvt2pJUCGAHTtkkL52TXmtf3851LRG1KnCKLo4aySFVeUAjvJOJRNClr2Mjpb3EdVmDjGivnnzJoYPH44PP/wQ/sX+OAkhsHz5crz00ksYNGgQmjdvjnXr1iErKwsff/yxHXtMNiWErOT177/AhQuVDtL79gHjxgGTJskYOmmS/Hzfvko82IUL8nSOZ59VBukGDeRw8/XXrROkqziKLq4wUN64UXryoTBQNm5su0BprbVuoprOIX4FJk6ciL59+6JHjx6K9oSEBCQmJqJXr17GNp1Ohy5dumDv3r1lPl5ubi7S09MVH+QA8vJk9vQ//wBXr8ohXyXt2ydLap86JXczBQfLf0+dku1mB2u9Xp500Lcv8PPPRe2ursDTT8uqY9Y4WqtwFF2/ftX3WN+ihkBZuNYdHS1TCq5ckf9GR/MACaJCqp/6/vTTT3Ho0CEcPHiw1LXExEQAQGhoqKI9NDQU50vuUy1m0aJFmDt3rnU7SraTmSm3V2VmGpuqMmVtMMhaI5mZcndU4ZSrm5v8/OpVeb1duwoec/9+ueZ89qyyvVMnGe0bNLDgRZbDzU3ui7ZSgC7OGklh1uhD166sTEZUFlUH6gsXLuDZZ5/Fzp074ebmVuZ9mhIpq0KIUm3Fvfjii5g6darx8/T0dNSrV6/qHSbrSk+X87LFq3dBjnY/+ABISEClzoU+eVJ+rZ+f6WxnX195/eRJoHlzEw+QnAwsXQps2aJsDwoCZs4E+vSp+narQoGBMv3aWo9nghoCZVXWuolqOlUH6kOHDuHatWtoXew3uKCgAD///DNWrFiBU6dOAZAj6zp16hjvuXbtWqlRdnE6nQ66slJdyf5u3ixKSS6hcMo6M1MGWldXeVvhlPW8eRUH65QUGeDLGqDqdPI9QkpKiQsGA/Dll3K9OTW1qF2jAYYNAyZPttrBF3BxkaPoqp47bSYGSiL1UnWgvu+++/BHiT2oY8aMQZMmTTB9+nQ0bNgQYWFh2LVrF2JuZbzo9Xrs3r0bS5YssUeXqbIMBhkd09LK3K9jrSlrf38ZB/V6+bUl5ebK64p8rdOn5Z7ow4eVNzdrJvdEl9iNUCU+PrJON+d+iQgqD9Te3t5oXmLu0dPTE4GBgcb2yZMnY+HChWjUqBEaNWqEhQsXwsPDA8OGDbNHl8lS2dkyOGdkVLjvucpT1rc0ayanyk+dUgZ8oCihvHFjeR+ysoB335WZ2/n5RTd6esoM7+HDza6pXSGNRnaIZWyJqBhVB2pzvPDCC8jOzsaECROQkpKCdu3aYefOndxDrWZ6vQzM6eml1p/LU+kp6xK0WrmePWuWHIX7+hbVuE5LA7y85HXtT3HAq6/KAirF9e4t16LLWV6xmKsrUKdO2dVHiKjW4qEc4KEc1aKgoCg4V3Jb1fHjcq+zh4fpKevsbPmxYkX5I+pCZSWlTXroClp/Nx/4/nvlF9StK6N7ly6V6n+ZvL1l0OdUNxGZ4PAjalKxwrrbaWlyCrmK7wktmrI2Q4cOcj3buM3LOw/NDq2H9pV3ZH8LubjISihPPmnd5C6NRq5F+/pa7zGJqMZhoCbr0+tl1ExPt+qJVWZPWVswMNVqb42+jx4tqoBS3N13y73St99utdcBoNqnug0G7lMmclSc+ganvq1CCLmtKjVVzj/bUFX3USukpQFvvAF8/rlyxO/vD7zwAvB//2f9PczVPNUdF1dU0ESvl+8RGjeuvoImRFQ1DNRgoK6SvDwZ7NLSqvW85yofpiEEsHWrjGDJycprjzwCPPecVepplxIUJAuYVJO4OGD8eJkeEBhYNANx44Z8v7BqFYM1kdoxUIOB2mKFa8+pqcq1XEdx9qzc+/z778r2Ro3kNHebNtZ/Tq1WFjDx8rL+Y5fBYJAJ6mWdTnXpktz+vX07p8GJ1Ixr1GQ+vV6uO6enK/cUO4qcHHnSw3//q9wW5uYm08lHj5bz6Nbm4gKEh1f71qsjR+R0d2Cg6X3nAQHy+pEjVatKxvVvIttioKbyCSHnTdPSbL72bFN79shR9L//Ktu7dQNeflluvbIFd3cZpJ2cbPP45SiswlrW+wM3N7l0kJRU+efg+jeR7TFQk2l5eXJq28qZ29Xu6lUZSb79VtkeFga88gpw3322O/DC11duv7LhgRrlCQqSgTM31/Suspwceb2yx2SXtf4dHy/buf5NZB2coCKlzEy5eJmQIIdbjhqkCwqADRvkSVbFg7STEzB2rGzr0cM2QbSwFGjJzd7VLCZGjm5v3Ci9hV0ImUPXuLG8z1IGg3z/k5Eh17/d3eV0t7u7/DwjQ143GKzzWohqM46oSf41LczcNnFilcM5flzuiT5xQtneqpWc/m7SxHbP7eIi90eXcyxrddFq5RT0+PHyvVdAgOxWTo4M0j4+8npl1pOra/2biBioaze9vmh6uyYMfTIygLfeAjZuVL4eHx+53WrwYNtmOamwFGj37nIKunAdOSVFTndHR1dtHbk61r+JSGKgro0Kt1ZlZtq7J9YhBPDdd8DChcD168pr/fvLiFTZhVhzqLwUaPfuQNeu1s3MtvX6NxEVYaCuLQrPe05NrRnT24X+/VdOZ+/Zo2xv0EDuiba4VJmFnJ1lVrcKprrLo9Vadwq6cP27rD3aycly1F6Z9W8iUmKgrulyc2VwzsioGdPbhfR6uR965Ur5Ggu5usrDMx5/3HgeZpWrmJXFjluv7M2W699EpMTKZKiBlckMhqK9z5U8UlLVfvtNjpYTEpTtnTvLJLLISGOTVeuCF+fnBwQH2zWrWw24j5rI9hioUYMCdU6ODM41bfRc6MYNYMkS4Ouvle3BwcDMmcADDygC5759Mm5nZsq46upalD/n6QnMm1eJYF249cqRf06sjJXJiGyLU9+OrvDUqpSUmjl6BmQk2LRJnnKVllbUrtEAw4YBU6bIjOsSX/LBBzJIF9/O7OYmP796VV5v186CoGKnUqBqZ+31byJSYqB2VIV7n1NSHLPutrn++ktOcx85omy/806ZRNaihckvO3lSTnf7+Zne5+vrK6+fPHnrPOqKeHrKama1cD2aiOyLgdrR1LS9z2XJzARWrADWrVNWR/P0BCZPBoYPLzdopqTINelb+WSl6HTyW5iSYkZfAgK4z4iI7IaB2lFkZcmoUlP2PhdTKiv7yvfQLpgPXLmivPGBB4AXX5Rz1xXw95cz1Xq96Z1TubnyerlHTtvhaEoiopIYqNWs8OSqlBTlFqQapHhWdkDOJUzNmg9tbpzypnr1ZFbYvfea/bjNmsns7lOnSpfcFkKuGjRuLO8zydVVrkeXNSQnIqomDNRqlJ8vI0lqquMeimGGwqzsnJt5GI11GJ66Am6i6ChNg5MLtI+PA556yuKCIlqt3II1a5ZMHPP1LTrdKS1NDpKfeKKMRDIvLzmSZuoyEakAt2dBRduzsrNlcL55s/RxRzWMwQCMGwe4Hj+EmblzEJl7WnH9kHNbfNl8DuZ/cluV4qXF+6gDA+UHEZFKcERtb0IUlfasodPbppz6PQUPHXkD/bI3KdrTnAKwLmw6vnMdgOyrGvOzssvQoYPcglVhZTKtVp565elZ+ScjIrIBBmp7ycsryt6uwdPbpQgBfPUVbp+/BE2zlSnXO/0HY0PIc7jp7AedAUjPMDMruwJabQXBnuvRRKRiDNTVLSuraHq7tjl7Vu6J3r8fLsWaz+nuwKrwuTjlcZexzaysbGtQ4dGURETFMVBXh4KCountvDx796b6ZWfLwzNWr1a8/lytOz5wexq7I0fCoC0K3WZlZVtDUJDcI01EpGIM1LaUlSUjTi1IDivTzz/LCmIXLyrbe/TA8Qdewta3wpF5zcKs7KriejQRORAGamsrKChae66No+dCV68CCxcC27cr2+vUAV5+GejRA60BzAssyspOT5fT3Y0bW+F0q7JwPZqIHAy3Z8HK27OyskqPHmuT/Hzg44+B5cuVVdScnIDRo4GJE0uNZG12XnRJnp7yjQLXo4nIgXBETdYTHy+TxU6cULbHxMjp78aNTX5ZhVnZVaXRyL3RXI8mIgfEQE1Vl5EBvPmmHEkXn6Dx9QWefx546CH7jWJdXOQo2sLKZkREasFATZUnBPDtt8CiRcD168prAwcC06fbdxTLrVdEVAMwUFPlnDsHzJsH/Pqrsr1hQzn93a6dPXolaTRAcLA8jJqIyMExUJNl9HqZpr1qlfzvQjodMGECMHasfTOqnZ1lVjenuomohmCgJvPt2ydHy+fOKdvvuUceU1W/vj16VcTDQ65HOznZtx9ERFbEQE0VS0oCliwBtm5VtgcHAy+9BPTurTzw2R4CAmSlMSKiGoaBmspmMACbNgGvvy6rkRTSaoHhw4HJk2UJMXvSauXZ0fbuBxGRjTBQk2l//QXMng0cPapsb95c7om26cZnM7HKGBHVAgzUpJSZCbzzDvDRR8rjN728gKlTgaFD1bEG7OUlR9LFtl4ZDMCRI3KmPihI1lnhziwicnQM1CQJAXz/PTB/PpCYqLzWty8wYwYQEmKfvpUUGCg/iomLAxYvBk6dksnorq6yENqMGUD37nbqJxGRFTBQE3DpEvDqq8CPPyrb69eX09+dO9unXyWVsR4dFweMHy8LpAUGFp3CFR8v21etYrAmIsfFicHaLC8P+PBDOWIuHqRdXOSe6P/9Tz1B2tkZqFevVJA2GORIOiMDiIgA3N1lPHd3l59nZMjrBoOd+k1EVEUcUddWBw/KpLDTp5Xt7dvLUXTDhvbplyk6nYy6zqV/XI8ckdPdgYGld4hpNHLX1qlT8r7Wraupv0REVsRAXdukpMjtVl98oWwPDJS1uR980P57oour4GjKpCS5Jq3Tmf5yNzf5kpOSbNhHIiIbYqCuLYQAtmyRhUtSU4vaNRpgyBCZ0e3ra7fumWRGEZOgIJk4lpsrp7tLysmR11kLhYgcFQN1bXDmjCz9eeCAsr1xY3mwRqtW9uhV2SwoYhITI19GfLycHS8+GSAEkJwMREfL+4iIHBGTyWqy7Gxg2TJgwABlkPbwkPuWNm9WX5B2dZXZ5mZWGtNq5Uvx9pbJ61lZMnEsK0t+7uMjr3M/NRE5Kv75qql275bZ3KtWAfn5Re29eskzpMeMMZmcZVeenjJIW1hprHt3+TKjo2W9litX5L/R0cDKldyaRUSOTSOEEPbuhL2lp6fD19cXaWlp8PHxqdqDZWUBFy9ap2OVcfUqsGABsGOHsj0iAnjlFaBbN/v0qyJ+flUuqMLKZERUE6lsSEWVlp8PbNgAvPWWfLNQyNlZjp4nTJBT3mqj0cgAbYVENq2WW7CIqOZhoK4J4uPl3ueTJ5Xtd90l90rfcYd9+lURrVYeqqHGNxBERCrBQO3I0tNlstinn8oU50J+fsDzzwODBql37tfZWU7Hl7UBmoiIADBQOyYhgG++ARYtKl3JY9AgGaQDAuzTN3OUU2mMiIiU+JfS0Zw7J6ez9+5Vtt92m9wr3batPXplvgoqjRERkRIDtaPQ64EPPpD7kPT6onadDpg4USaMWbitqdr5+wPBwfbuBRGRQ2GgdgT79snR8rlzyvZ77wVmzZKnSqmZFTO7iYhqGwZqNbt+XZ7RuG2bsj0kBHj5ZVm8RE0HaJji5CQzu00V4iYiogoxUKtRQQHw2Wcyozsjo6hdqwVGjACefdbsEpt2pdPJIO3iYu+eEBE5LAZqtTl5Uu6Jjo9XtrdoIQ/QaNbMPv2yFJPGiIisgoFaLW7eBN5+G1i/XtbCLOTlJY+gHDpUTiM7AiaNERFZDQO1vQkB7Nwp63Nfvaq81q+fPPrJUYKeRgOEhsojq4iIyCoYqO3pwgXg1VflSVfFRUbK6e9OnezTr8pg0hgRkU0wUNuDXg+sXQu8+y6Qk1PU7uICPPEEMH68Y5XWZNIYEZHNMFBXtwMH5J7oM2eU7R06yFF0VJRdulVpXl5AWBiTxoiIbISBurokJwOvvQZs3qxsDwyU69D9+6t/T3RJAQHy4GciIrIZBmpbMxhkcH7tNSA1tahdo5GZ3FOmOF7FLiaNERFVGwZqWzp9Wk5zHzqkbG/aVB6s0bKlXbpVJU5O8uQrNzd794SIqFZgoLaFrCzgvfeA2FggP7+o3cNDVhUbMcIxj3jk8ZRERNWOf3Gt7bvvZDC+dEnZfv/9wMyZMvHKETFpjIjILhioreXyZWDSJGDLFmV7RIQ84aprV7t0yyqYNEZEZDcM1NZiMAC7dhV97uwMjBsHPPWUYxcBCQkB/Pzs3QsiolqL85jWUreurDIGAG3aAF99JWt0O2qQ1mjkbACDNBGRXXFEbU2TJsm63G3aON6e6OKY2U1EpBocUVuTszPwf//n2EHa1RWoX59BmohIJTiipiJubnIk7SjHaRIR1QIM1CR5eQF16jj2bAARUQ3EQE2Av7/jnHlNRFTLqHqNetGiRbj77rvh7e2NkJAQDBw4EKdOnVLcI4TAnDlzEB4eDnd3d3Tt2hUnTpywS38NBuDwEeCXX4Djx+XnqlZYs5tBmohItVQdqHfv3o2JEyfit99+w65du5Cfn49evXohMzPTeM/SpUuxbNkyrFixAgcOHEBYWBh69uyJjIyMau1rXBzQuzfw6FB5GNakSXIb9b591doN8xVmdjvagSBERLWMRggh7N0Jc12/fh0hISHYvXs37r33XgghEB4ejsmTJ2P69OkAgNzcXISGhmLJkiUYP368WY+bnp4OX19fpKWlwacSJ0LFxQHjxwMZGUCEfxbqaS5Cr5eHZXl6AvPmyeOmVcPVVQZpFxd794SIiCqg6hF1SWlpaQCAgIAAAEBCQgISExPRq1cv4z06nQ5dunTB3r17q6VPBgOwePGtIB0BuLnLcthubnJWOTMT+OADFU2De3vL7VcM0kREDsFhksmEEJg6dSo6d+6M5s2bAwASExMBAKGhoYp7Q0NDcf78+TIfKzc3F7m5ucbP09PTK92vI0eAU6eAwMBbCdPF5ic0GjmznJAAnDwJ3Oq2/QQFybrdRETkMBxmRD1p0iTEx8fjk08+KXVNU2JLkRCiVFtxixYtgq+vr/GjXr16le5XUhKg18sTIE3R6YC8PCAlpdJPUXVarRzuqzhIGwzy2O4dO+S/qpmBICKyM4cI1E8//TS2bt2KH3/8EXXr1jW2h906MrJwZF3o2rVrpUbZxb344otIS0szfly4cKHSfQsKkku+xQboCrm5cpbZ37/ST1E1hZXGPD3t1IGKFSbiDRoEjB4t/+3dW7YTEdV2qg7UQghMmjQJmzdvRlxcHKKiohTXo6KiEBYWhl3FTq3S6/XYvXs3OnbsWObj6nQ6+Pj4KD4qKyYGaNwYuHEDKJmWJwSQlgZERQHNmlX6KSrP01MGaVdXOzy5eQoT8eLji2queHnJz8ePZ7AmIlJ1oJ44cSI2bNiAjz/+GN7e3khMTERiYiKys7MByCnvyZMnY+HChdiyZQuOHz+O0aNHw8PDA8OGDauWPmq1cjuWtzdw6RKQnSWnbbOzgatXZdB54gl5X7Xy95fT3dX+xOYrmYjnfisRz91dfp6RIa9zGpyIajNVb88qa505NjYWo0ePBiBH3XPnzsWqVauQkpKCdu3a4d133zUmnJmjqtuzADnyW7wYOP9nFoJyLsLFRY6kn3jCDluzHOQM6UOH5DS3l5fp00CzsmTW/ObNQOvW1d8/IiI1UHWgri7WCNSAHPkd3ZeFzL8uwt9fTndX64BWq5Vzxypejy5uxw65Jl2njunvk8EAXLkCrF0L3H9/dfeOiEgdHGZ7liPQaoG7YgDYoyKnszMQHu5Qx1MWT8QzNaLOyZHXg4Kqv29ERGqh3gVMMp+DniFdUSJecrK8HhNjn/4REakBA7Wj8/CQQdrZ8SZHSibiZd1KxMvKkp/7+MjrKs6HIyKyOf4JdGQ+PqrP7K5I9+7AqlVAdLRMHLtyRf4bHQ2sXCmvExHVZkwmg/WSyQDI4eDFi9bpWHkCA+VHDWEwyHKsSUlyTTomxqHffxARWY3jzZfWdoVnSFf1DYXKaLXcgkVEZAoDtSPRamVmt4eHvXtCRETVhIHaUbi4yPVoFZcDJSIi62OgdgQeHrIqiJOTvXtCRETVjIFa7Xx9ZUnQco7tJCKimouBWq00GiA42CFqdhMRke0wUKuRi4tMGtPp7N0TIiKyMwZqtfH2ltuvuImYiIjAQK0enOomIiITGKjVwMVFZnU72KEaRERkewzU9ublBYSFcaqbiIhMYqC2p6AgICDA3r0gIiIVY6C2B61WTnV7etq7J0REpHIM1NXN1VWWAnVxsXdPiIjIATBQVydPTzmS5no0ERGZiYG6ugQEyDVpIiIiCzBQ21oNPT+aiIiqBwO1LTk5yfVo7o8mIqJKYqC2FZ1OBmlnfouJiKjyGEVswctLJo3xaEoiIqoiBmprc3OTJ18RERFZAfcJWRu3XhERkRUxqhAREakYAzUREZGKMVATERGpGAM1ERGRijFQExERqRgDNRERkYoxUBMREakYAzUREZGKMVATERGpGAM1ERGRijFQExERqRgDNRERkYoxUBMREakYAzUREZGKMVATERGpGAM1ERGRijFQExERqZizvTugBkIIAEB6erqde0JERLWJt7c3NBpNufcwUAPIyMgAANSrV8/OPSEiotokLS0NPj4+5d6jEYXDyVrMYDDg8uXLZr2zobKlp6ejXr16uHDhQoU/eFQ5/B7bFr+/tsfvsRJH1GbSarWoW7euvbtRY/j4+PAX0Mb4PbYtfn9tj99j8zGZjIiISMUYqImIiFSMgZqsRqfTYfbs2dDpdPbuSo3F77Ft8ftre/weW47JZERERCrGETUREZGKMVATERGpGAM1ERGRijFQExERqRgDNVlk0aJFuPvuu+Ht7Y2QkBAMHDgQp06dUtwjhMCcOXMQHh4Od3d3dO3aFSdOnLBTjx3bokWLoNFoMHnyZGMbv7/WcenSJYwYMQKBgYHw8PBAq1atcOjQIeN1fp+rJj8/Hy+//DKioqLg7u6Ohg0bYt68eTAYDMZ7+D02kyCywP333y9iY2PF8ePHxdGjR0Xfvn1F/fr1xc2bN433LF68WHh7e4svv/xS/PHHH2LIkCGiTp06Ij093Y49dzz79+8XDRo0ENHR0eLZZ581tvP7W3XJyckiMjJSjB49Wvz+++8iISFBfP/99+LMmTPGe/h9rpr58+eLwMBAsW3bNpGQkCA2bdokvLy8xPLly4338HtsHgZqqpJr164JAGL37t1CCCEMBoMICwsTixcvNt6Tk5MjfH19xcqVK+3VTYeTkZEhGjVqJHbt2iW6dOliDNT8/lrH9OnTRefOncu8zu9z1fXt21eMHTtW0TZo0CAxYsQIIQS/x5bg1DdVSVpaGgAgICAAAJCQkIDExET06tXLeI9Op0OXLl2wd+9eu/TREU2cOBF9+/ZFjx49FO38/lrH1q1b0aZNGzzyyCMICQlBTEwMPvzwQ+N1fp+rrnPnzvjhhx9w+vRpAMCxY8ewZ88e9OnTBwC/x5bgoRxUaUIITJ06FZ07d0bz5s0BAImJiQCA0NBQxb2hoaE4f/58tffREX366ac4dOgQDh48WOoav7/W8c8//+D999/H1KlTMXPmTOzfvx/PPPMMdDodRo4cye+zFUyfPh1paWlo0qQJnJycUFBQgAULFuDRRx8FwJ9lSzBQU6VNmjQJ8fHx2LNnT6lrJY9tE0LwCFEzXLhwAc8++yx27twJNze3Mu/j97dqDAYD2rRpg4ULFwIAYmJicOLECbz//vsYOXKk8T5+nyvvs88+w4YNG/Dxxx/jzjvvxNGjRzF58mSEh4dj1KhRxvv4Pa4Yp76pUp5++mls3boVP/74o+KI0LCwMABF75YLXbt2rdQ7Zyrt0KFDuHbtGlq3bg1nZ2c4Oztj9+7dePvtt+Hs7Gz8HvL7WzV16tRBs2bNFG1NmzbFv//+C4A/x9bw/PPPY8aMGRg6dChatGiBxx57DFOmTMGiRYsA8HtsCQZqsogQApMmTcLmzZsRFxeHqKgoxfWoqCiEhYVh165dxja9Xo/du3ejY8eO1d1dh3Pffffhjz/+wNGjR40fbdq0wfDhw3H06FE0bNiQ318r6NSpU6lthadPn0ZkZCQA/hxbQ1ZWFrRaZYhxcnIybs/i99gC9sxkI8fz1FNPCV9fX/HTTz+JK1euGD+ysrKM9yxevFj4+vqKzZs3iz/++EM8+uij3HJRBcWzvoXg99ca9u/fL5ydncWCBQvE33//LTZu3Cg8PDzEhg0bjPfw+1w1o0aNEhEREcbtWZs3bxZBQUHihRdeMN7D77F5GKjJIgBMfsTGxhrvMRgMYvbs2SIsLEzodDpx7733ij/++MN+nXZwJQM1v7/W8b///U80b95c6HQ60aRJE/HBBx8orvP7XDXp6eni2WefFfXr1xdubm6iYcOG4qWXXhK5ubnGe/g9Ng+PuSQiIlIxrlETERGpGAM1ERGRijFQExERqRgDNRERkYoxUBMREakYAzUREZGKMVATERGpGAM1EZlFo9Hgq6++sulzdO3aFZMnT7bpcxA5GgZqIpXZu3cvnJyc0Lt3b4u/tkGDBli+fLn1O1WB/v37lzo7u9C+ffug0Whw+PDhau4VUc3AQE2kMmvWrMHTTz+NPXv2GE9zUrtx48YhLi7O5DnCa9asQatWrXDXXXfZoWdEjo+BmkhFMjMz8fnnn+Opp55Cv379sHbt2lL3bN26FW3atIGbmxuCgoIwaNAgAHLa+Pz585gyZQo0Go3xTN85c+agVatWisdYvnw5GjRoYPz8wIED6NmzJ4KCguDr64suXbpYNALu168fQkJCSvU3KysLn332GcaNG4cbN27g0UcfRd26deHh4YEWLVrgk08+KfdxTU23+/n5KZ7n0qVLGDJkCPz9/REYGIgBAwbg3Llzxus//fQT2rZtC09PT/j5+aFTp04m31AQqRUDNZGKfPbZZ2jcuDEaN26MESNGIDY2FsXL8X/zzTcYNGgQ+vbtiyNHjuCHH35AmzZtAACbN29G3bp1MW/ePFy5cgVXrlwx+3kzMjIwatQo/PLLL/jtt9/QqFEj9OnTBxkZGWZ9vbOzM0aOHIm1a9cq+rtp0ybo9XoMHz4cOTk5aN26NbZt24bjx4/jiSeewGOPPYbff//d7H6WlJWVhW7dusHLyws///wz9uzZAy8vL/Tu3Rt6vR75+fkYOHAgunTpgvj4eOzbtw9PPPGE8U0MkSNwtncHiKjI6tWrMWLECABA7969cfPmTfzwww/G9d8FCxZg6NChmDt3rvFrWrZsCQAICAiAk5MTvL29ERYWZtHzdu/eXfH5qlWr4O/vj927d6Nfv35mPcbYsWPx2muv4aeffkK3bt0AyGnvQYMGwd/fH/7+/pg2bZrx/qeffhrbt2/Hpk2b0K5dO4v6W+jTTz+FVqvFf//7X2PwjY2NhZ+fH3766Se0adMGaWlp6NevH2677TYAQNOmTSv1XET2whE1kUqcOnUK+/fvx9ChQwHIUeqQIUOwZs0a4z1Hjx7FfffdZ/XnvnbtGp588knccccd8PX1ha+vL27evGnRGnmTJk3QsWNHY3/Pnj2LX375BWPHjgUAFBQUYMGCBYiOjkZgYCC8vLywc+fOKq3DHzp0CGfOnIG3tze8vLzg5eWFgIAA5OTk4OzZswgICMDo0aNx//33o3///njrrbcsmmkgUgOOqIlUYvXq1cjPz0dERISxTQgBFxcXpKSkwN/fH+7u7hY/rlarRcnTbPPy8hSfjx49GtevX8fy5csRGRkJnU6HDh06QK/XW/Rc48aNw6RJk/Duu+8iNjYWkZGRxjcWb7zxBt58800sX74cLVq0gKenJyZPnlzuc2g0mnL7bjAY0Lp1a2zcuLHU1wYHBwOQI+xnnnkG27dvx2effYaXX34Zu3btQvv27S16bUT2whE1kQrk5+fjo48+whtvvIGjR48aP44dO4bIyEhjIIqOjsYPP/xQ5uO4urqioKBA0RYcHIzExERFwDt69Kjinl9++QXPPPMM+vTpgzvvvBM6nQ5JSUkWv47BgwfDyckJH3/8MdatW4cxY8YYp6R/+eUXDBgwACNGjEDLli3RsGFD/P333+U+XnBwsGIE/PfffyMrK8v4+V133YW///4bISEhuP322xUfvr6+xvtiYmLw4osvYu/evWjevDk+/vhji18bkb0wUBOpwLZt25CSkoJx48ahefPmio+HH34Yq1evBgDMnj0bn3zyCWbPno0///wTf/zxB5YuXWp8nAYNGuDnn3/GpUuXjIG2a9euuH79OpYuXYqzZ8/i3XffxXfffad4/ttvvx3r16/Hn3/+id9//x3Dhw+v1Ojdy8sLQ4YMwcyZM3H58mWMHj1a8Ry7du3C3r178eeff2L8+PFITEws9/G6d++OFStW4PDhwzh48CCefPJJuLi4GK8PHz4cQUFBGDBgAH755RckJCRg9+7dePbZZ3Hx4kUkJCTgxRdfxL59+3D+/Hns3LkTp0+f5jo1ORZBRHbXr18/0adPH5PXDh06JACIQ4cOCSGE+PLLL0WrVq2Eq6urCAoKEoMGDTLeu2/fPhEdHS10Op0o/uv9/vvvi3r16glPT08xcuRIsWDBAhEZGWm8fvjwYdGmTRuh0+lEo0aNxKZNm0RkZKR48803jfcAEFu2bKnwtezdu1cAEL169VK037hxQwwYMEB4eXmJkJAQ8fLLL4uRI0eKAQMGGO/p0qWLePbZZ42fX7p0SfTq1Ut4enqKRo0aiW+//Vb4+vqK2NhY4z1XrlwRI0eOFEFBQUKn04mGDRuKxx9/XKSlpYnExEQxcOBAUadOHeHq6ioiIyPFrFmzREFBQYWvg0gtNEKUWAAiIiIi1eDUNxERkYoxUBMREakYAzUREZGKMVATERGpGAM1ERGRijFQExERqRgDNRERkYoxUBMREakYAzUREZGKMVATERGpGAM1ERGRijFQExERqdj/Ax4ZkOkAbHH3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a Visual Representation of the Regression Analsis for Model 2\n",
    "\n",
    "# Getting the predicted values from the OLS model for model 2\n",
    "predicted_values_model2 = ols_model2.predict()\n",
    "\n",
    "# Creating a DataFrame with actual and predicted values for model 2\n",
    "results_model2 = pd.DataFrame({'Actual': dep_var_model2, 'Predicted': predicted_values_model2})\n",
    "\n",
    "# Creating a regression plot using Seaborn for model 2\n",
    "sns.lmplot(x='Actual', y='Predicted', data=results_model2, scatter_kws={'color': 'blue'}, line_kws={'color': 'red'})\n",
    "\n",
    "# Setting plot labels and title\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Model 2: Actual vs. Predicted Values')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac80330",
   "metadata": {},
   "source": [
    "## 5.2. Pearson Correlation Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38b3e7f",
   "metadata": {},
   "source": [
    "### Correlation Test for Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1af6afe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        internet_access  mobile_utility_payment  \\\n",
      "internet_access                1.000000                0.275408   \n",
      "mobile_utility_payment         0.275408                1.000000   \n",
      "digital_payment                0.507508                0.736970   \n",
      "mobile_phone_ownership         0.856528                0.346597   \n",
      "mobile_bill_payment            0.389306                0.940515   \n",
      "mobile_money_account           0.092876                0.799250   \n",
      "\n",
      "                        digital_payment  mobile_phone_ownership  \\\n",
      "internet_access                0.507508                0.856528   \n",
      "mobile_utility_payment         0.736970                0.346597   \n",
      "digital_payment                1.000000                0.573997   \n",
      "mobile_phone_ownership         0.573997                1.000000   \n",
      "mobile_bill_payment            0.809292                0.440903   \n",
      "mobile_money_account           0.806038                0.279005   \n",
      "\n",
      "                        mobile_bill_payment  mobile_money_account  \n",
      "internet_access                    0.389306              0.092876  \n",
      "mobile_utility_payment             0.940515              0.799250  \n",
      "digital_payment                    0.809292              0.806038  \n",
      "mobile_phone_ownership             0.440903              0.279005  \n",
      "mobile_bill_payment                1.000000              0.788281  \n",
      "mobile_money_account               0.788281              1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a correlation matrix for model 1\n",
    "correlation_matrix_model1 = model1.corr()\n",
    "\n",
    "# Displaying the correlation matrix\n",
    "print(correlation_matrix_model1)\n",
    "\n",
    "\n",
    "# Plotting the heatmap of correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix_model1, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Model 1: Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea7a0a4b",
   "metadata": {},
   "source": [
    "### Correlation Test for Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c63b0dbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        internet_access  mobile_utility_payment  \\\n",
      "internet_access                1.000000                0.275408   \n",
      "mobile_utility_payment         0.275408                1.000000   \n",
      "digital_payment                0.507508                0.736970   \n",
      "mobile_phone_ownership         0.856528                0.346597   \n",
      "mobile_bill_payment            0.389306                0.940515   \n",
      "mobile_money_account           0.092876                0.799250   \n",
      "\n",
      "                        digital_payment  mobile_phone_ownership  \\\n",
      "internet_access                0.507508                0.856528   \n",
      "mobile_utility_payment         0.736970                0.346597   \n",
      "digital_payment                1.000000                0.573997   \n",
      "mobile_phone_ownership         0.573997                1.000000   \n",
      "mobile_bill_payment            0.809292                0.440903   \n",
      "mobile_money_account           0.806038                0.279005   \n",
      "\n",
      "                        mobile_bill_payment  mobile_money_account  \n",
      "internet_access                    0.389306              0.092876  \n",
      "mobile_utility_payment             0.940515              0.799250  \n",
      "digital_payment                    0.809292              0.806038  \n",
      "mobile_phone_ownership             0.440903              0.279005  \n",
      "mobile_bill_payment                1.000000              0.788281  \n",
      "mobile_money_account               0.788281              1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a correlation matrix for model 2\n",
    "correlation_matrix_model2 = model2.corr()\n",
    "\n",
    "# Displaying the correlation matrix\n",
    "print(correlation_matrix_model1)\n",
    "\n",
    "\n",
    "# Plotting the heatmap of correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix_model2, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Model 2: Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e561e963",
   "metadata": {},
   "source": [
    "## 5.3. Test for Multicollinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6153e775",
   "metadata": {},
   "source": [
    "### Multicollinearity Test for Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e9eb2b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance Inflation Factor (VIF) for Model 1:\n",
      "                 Variable        VIF\n",
      "0                   const  40.483693\n",
      "1         internet_access   3.902965\n",
      "2  mobile_utility_payment   9.475282\n",
      "3         digital_payment   3.495942\n",
      "4  mobile_phone_ownership   4.171238\n",
      "5     mobile_bill_payment  12.216094\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame to store VIF results\n",
    "vif_data_model1 = pd.DataFrame()\n",
    "vif_data_model1[\"Variable\"] = indep_var_model1.columns\n",
    "vif_data_model1[\"VIF\"] = [variance_inflation_factor(indep_var_model1.values, i) for i in range(indep_var_model1.shape[1])]\n",
    "\n",
    "print(\"Variance Inflation Factor (VIF) for Model 1:\")\n",
    "print(vif_data_model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c780b402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix for Model 1:\n",
      "                        internet_access  mobile_utility_payment  \\\n",
      "internet_access                1.000000                0.275408   \n",
      "mobile_utility_payment         0.275408                1.000000   \n",
      "digital_payment                0.507508                0.736970   \n",
      "mobile_phone_ownership         0.856528                0.346597   \n",
      "mobile_bill_payment            0.389306                0.940515   \n",
      "\n",
      "                        digital_payment  mobile_phone_ownership  \\\n",
      "internet_access                0.507508                0.856528   \n",
      "mobile_utility_payment         0.736970                0.346597   \n",
      "digital_payment                1.000000                0.573997   \n",
      "mobile_phone_ownership         0.573997                1.000000   \n",
      "mobile_bill_payment            0.809292                0.440903   \n",
      "\n",
      "                        mobile_bill_payment  \n",
      "internet_access                    0.389306  \n",
      "mobile_utility_payment             0.940515  \n",
      "digital_payment                    0.809292  \n",
      "mobile_phone_ownership             0.440903  \n",
      "mobile_bill_payment                1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating correlation matrix for independent variables in Model 1\n",
    "correlation_matrix_model1 = model1[['internet_access', 'mobile_utility_payment', \n",
    "                                    'digital_payment', 'mobile_phone_ownership', \n",
    "                                    'mobile_bill_payment']].corr()\n",
    "\n",
    "# Displaying correlation matrix for Model 1\n",
    "print(\"Correlation Matrix for Model 1:\")\n",
    "print(correlation_matrix_model1)\n",
    "\n",
    "# Plotting correlation matrix for Model 1\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix_model1, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Model 1: Test for Multicollinearity Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7639fe74",
   "metadata": {},
   "source": [
    "### Multicollinearity Test for Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e1b6fbf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance Inflation Factor (VIF) for Model 2:\n",
      "                  Variable         VIF\n",
      "0                    const  127.526234\n",
      "1     source_data_capacity    2.453183\n",
      "2             spi_data_use    2.039501\n",
      "3        spi_data_services    2.109660\n",
      "4        spi_data_products    2.651508\n",
      "5         spi_data_sources    2.616190\n",
      "6  spi_data_infrastructure    2.604678\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame to store VIF results\n",
    "vif_data_model2 = pd.DataFrame()\n",
    "vif_data_model2[\"Variable\"] = indep_var_model2.columns\n",
    "vif_data_model2[\"VIF\"] = [variance_inflation_factor(indep_var_model2.values, i) for i in range(indep_var_model2.shape[1])]\n",
    "\n",
    "print(\"Variance Inflation Factor (VIF) for Model 2:\")\n",
    "print(vif_data_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "00e7950c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix for Model 2:\n",
      "                         source_data_capacity  spi_data_use  \\\n",
      "source_data_capacity                 1.000000      0.368934   \n",
      "spi_data_use                         0.368934      1.000000   \n",
      "spi_data_services                    0.199737      0.243448   \n",
      "spi_data_products                    0.482582      0.585447   \n",
      "spi_data_sources                     0.605898      0.268184   \n",
      "spi_data_infrastructure              0.654020      0.539743   \n",
      "\n",
      "                         spi_data_services  spi_data_products  \\\n",
      "source_data_capacity              0.199737           0.482582   \n",
      "spi_data_use                      0.243448           0.585447   \n",
      "spi_data_services                 1.000000           0.569445   \n",
      "spi_data_products                 0.569445           1.000000   \n",
      "spi_data_sources                  0.582020           0.546027   \n",
      "spi_data_infrastructure           0.378305           0.439675   \n",
      "\n",
      "                         spi_data_sources  spi_data_infrastructure  \n",
      "source_data_capacity             0.605898                 0.654020  \n",
      "spi_data_use                     0.268184                 0.539743  \n",
      "spi_data_services                0.582020                 0.378305  \n",
      "spi_data_products                0.546027                 0.439675  \n",
      "spi_data_sources                 1.000000                 0.592323  \n",
      "spi_data_infrastructure          0.592323                 1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculatting correlation matrix for independent variables in Model 2\n",
    "correlation_matrix_model2 = model2[['source_data_capacity', 'spi_data_use', \n",
    "                                    'spi_data_services', 'spi_data_products', \n",
    "                                    'spi_data_sources', 'spi_data_infrastructure']].corr()\n",
    "\n",
    "# Displaying correlation matrix for Model 2\n",
    "print(\"Correlation Matrix for Model 2:\")\n",
    "print(correlation_matrix_model2)\n",
    "\n",
    "# Plotting correlation matrix for Model 2\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix_model2, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Model 2: Test for Multicollinearity Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd829793",
   "metadata": {},
   "source": [
    "## 5.4. Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e020942c",
   "metadata": {},
   "source": [
    "### Descriptive Statistics for Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42b1c3c1",
   "metadata": {
    "scrolled": true
   },
   "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>internet_access</th>\n",
       "      <th>mobile_utility_payment</th>\n",
       "      <th>digital_payment</th>\n",
       "      <th>mobile_phone_ownership</th>\n",
       "      <th>mobile_bill_payment</th>\n",
       "      <th>mobile_money_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.359000</td>\n",
       "      <td>9.635500</td>\n",
       "      <td>41.865250</td>\n",
       "      <td>72.711750</td>\n",
       "      <td>13.412500</td>\n",
       "      <td>28.772500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.445526</td>\n",
       "      <td>9.140128</td>\n",
       "      <td>19.434911</td>\n",
       "      <td>15.605675</td>\n",
       "      <td>10.886769</td>\n",
       "      <td>19.105076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>32.160000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.115000</td>\n",
       "      <td>3.142500</td>\n",
       "      <td>26.740000</td>\n",
       "      <td>63.490000</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>9.782500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.055000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>42.985000</td>\n",
       "      <td>75.665000</td>\n",
       "      <td>9.855000</td>\n",
       "      <td>31.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.785000</td>\n",
       "      <td>13.865000</td>\n",
       "      <td>54.182500</td>\n",
       "      <td>83.212500</td>\n",
       "      <td>18.960000</td>\n",
       "      <td>42.477500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.910000</td>\n",
       "      <td>36.060000</td>\n",
       "      <td>80.810000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>68.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internet_access  mobile_utility_payment  digital_payment  \\\n",
       "count        40.000000               40.000000        40.000000   \n",
       "mean         37.359000                9.635500        41.865250   \n",
       "std          19.445526                9.140128        19.434911   \n",
       "min           5.220000                0.000000         4.810000   \n",
       "25%          23.115000                3.142500        26.740000   \n",
       "50%          31.055000                6.225000        42.985000   \n",
       "75%          49.785000               13.865000        54.182500   \n",
       "max          82.910000               36.060000        80.810000   \n",
       "\n",
       "       mobile_phone_ownership  mobile_bill_payment  mobile_money_account  \n",
       "count               40.000000            40.000000             40.000000  \n",
       "mean                72.711750            13.412500             28.772500  \n",
       "std                 15.605675            10.886769             19.105076  \n",
       "min                 32.160000             1.450000              0.000000  \n",
       "25%                 63.490000             4.937500              9.782500  \n",
       "50%                 75.665000             9.855000             31.865000  \n",
       "75%                 83.212500            18.960000             42.477500  \n",
       "max                100.000000            44.910000             68.660000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating descriptive statistics for model 1\n",
    "desc_model1 = model1.describe()\n",
    "desc_model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0dc55e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>mobile_money_account</th>\n",
       "      <th>internet_access</th>\n",
       "      <th>mobile_utility_payment</th>\n",
       "      <th>digital_payment</th>\n",
       "      <th>mobile_phone_ownership</th>\n",
       "      <th>mobile_bill_payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.772500</td>\n",
       "      <td>37.359000</td>\n",
       "      <td>9.635500</td>\n",
       "      <td>41.865250</td>\n",
       "      <td>72.711750</td>\n",
       "      <td>13.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.105076</td>\n",
       "      <td>19.445526</td>\n",
       "      <td>9.140128</td>\n",
       "      <td>19.434911</td>\n",
       "      <td>15.605675</td>\n",
       "      <td>10.886769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>32.160000</td>\n",
       "      <td>1.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.782500</td>\n",
       "      <td>23.115000</td>\n",
       "      <td>3.142500</td>\n",
       "      <td>26.740000</td>\n",
       "      <td>63.490000</td>\n",
       "      <td>4.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.865000</td>\n",
       "      <td>31.055000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>42.985000</td>\n",
       "      <td>75.665000</td>\n",
       "      <td>9.855000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.477500</td>\n",
       "      <td>49.785000</td>\n",
       "      <td>13.865000</td>\n",
       "      <td>54.182500</td>\n",
       "      <td>83.212500</td>\n",
       "      <td>18.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>68.660000</td>\n",
       "      <td>82.910000</td>\n",
       "      <td>36.060000</td>\n",
       "      <td>80.810000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>44.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>0.092837</td>\n",
       "      <td>0.692206</td>\n",
       "      <td>1.338398</td>\n",
       "      <td>0.238878</td>\n",
       "      <td>-0.677023</td>\n",
       "      <td>1.395810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-1.058531</td>\n",
       "      <td>-0.184415</td>\n",
       "      <td>1.173069</td>\n",
       "      <td>-0.652349</td>\n",
       "      <td>0.111974</td>\n",
       "      <td>1.681350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Error</th>\n",
       "      <td>3.020778</td>\n",
       "      <td>3.074608</td>\n",
       "      <td>1.445181</td>\n",
       "      <td>3.072929</td>\n",
       "      <td>2.467474</td>\n",
       "      <td>1.721349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <td>29.380000</td>\n",
       "      <td>5.220000</td>\n",
       "      <td>13.610000</td>\n",
       "      <td>33.740000</td>\n",
       "      <td>82.760000</td>\n",
       "      <td>1.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample Variance</th>\n",
       "      <td>365.003927</td>\n",
       "      <td>378.128496</td>\n",
       "      <td>83.541943</td>\n",
       "      <td>377.715749</td>\n",
       "      <td>243.537097</td>\n",
       "      <td>118.521737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Range</th>\n",
       "      <td>68.660000</td>\n",
       "      <td>77.690000</td>\n",
       "      <td>36.060000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>67.840000</td>\n",
       "      <td>43.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sum</th>\n",
       "      <td>1150.900000</td>\n",
       "      <td>1494.360000</td>\n",
       "      <td>385.420000</td>\n",
       "      <td>1674.610000</td>\n",
       "      <td>2908.470000</td>\n",
       "      <td>536.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mobile_money_account  internet_access  \\\n",
       "count                       40.000000        40.000000   \n",
       "mean                        28.772500        37.359000   \n",
       "std                         19.105076        19.445526   \n",
       "min                          0.000000         5.220000   \n",
       "25%                          9.782500        23.115000   \n",
       "50%                         31.865000        31.055000   \n",
       "75%                         42.477500        49.785000   \n",
       "max                         68.660000        82.910000   \n",
       "skewness                     0.092837         0.692206   \n",
       "kurtosis                    -1.058531        -0.184415   \n",
       "Standard Error               3.020778         3.074608   \n",
       "Mode                        29.380000         5.220000   \n",
       "Sample Variance            365.003927       378.128496   \n",
       "Range                       68.660000        77.690000   \n",
       "Sum                       1150.900000      1494.360000   \n",
       "\n",
       "                 mobile_utility_payment  digital_payment  \\\n",
       "count                         40.000000        40.000000   \n",
       "mean                           9.635500        41.865250   \n",
       "std                            9.140128        19.434911   \n",
       "min                            0.000000         4.810000   \n",
       "25%                            3.142500        26.740000   \n",
       "50%                            6.225000        42.985000   \n",
       "75%                           13.865000        54.182500   \n",
       "max                           36.060000        80.810000   \n",
       "skewness                       1.338398         0.238878   \n",
       "kurtosis                       1.173069        -0.652349   \n",
       "Standard Error                 1.445181         3.072929   \n",
       "Mode                          13.610000        33.740000   \n",
       "Sample Variance               83.541943       377.715749   \n",
       "Range                         36.060000        76.000000   \n",
       "Sum                          385.420000      1674.610000   \n",
       "\n",
       "                 mobile_phone_ownership  mobile_bill_payment  \n",
       "count                         40.000000            40.000000  \n",
       "mean                          72.711750            13.412500  \n",
       "std                           15.605675            10.886769  \n",
       "min                           32.160000             1.450000  \n",
       "25%                           63.490000             4.937500  \n",
       "50%                           75.665000             9.855000  \n",
       "75%                           83.212500            18.960000  \n",
       "max                          100.000000            44.910000  \n",
       "skewness                      -0.677023             1.395810  \n",
       "kurtosis                       0.111974             1.681350  \n",
       "Standard Error                 2.467474             1.721349  \n",
       "Mode                          82.760000             1.450000  \n",
       "Sample Variance              243.537097           118.521737  \n",
       "Range                         67.840000            43.460000  \n",
       "Sum                         2908.470000           536.500000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting only numeric columns for calculating descriptive statistics\n",
    "numeric_cols_model1 = ['mobile_money_account','internet_access', 'mobile_utility_payment', \n",
    "                       'digital_payment', 'mobile_phone_ownership', \n",
    "                       'mobile_bill_payment']\n",
    "\n",
    "desc_model1 = model1[numeric_cols_model1].describe()\n",
    "\n",
    "# Adding skewness and kurtosis\n",
    "desc_model1.loc['skewness'] = model1[numeric_cols_model1].skew()\n",
    "desc_model1.loc['kurtosis'] = model1[numeric_cols_model1].kurt()\n",
    "\n",
    "# Adding additional metrics\n",
    "desc_model1.loc['Standard Error'] = model1[numeric_cols_model1].sem()\n",
    "desc_model1.loc['Mode'] = model1[numeric_cols_model1].mode().iloc[0]  # Get the mode from the first row\n",
    "desc_model1.loc['Sample Variance'] = model1[numeric_cols_model1].var()\n",
    "desc_model1.loc['Range'] = model1[numeric_cols_model1].max() - model1[numeric_cols_model1].min()\n",
    "desc_model1.loc['Sum'] = model1[numeric_cols_model1].sum()\n",
    "\n",
    "# Displaying the summary\n",
    "desc_model1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea1a1a5",
   "metadata": {},
   "source": [
    "### Descriptive Statistics for Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d73e19ee",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_data_capacity</th>\n",
       "      <th>spi_data_use</th>\n",
       "      <th>spi_data_services</th>\n",
       "      <th>spi_data_products</th>\n",
       "      <th>spi_data_sources</th>\n",
       "      <th>spi_data_infrastructure</th>\n",
       "      <th>account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55.142857</td>\n",
       "      <td>76.851429</td>\n",
       "      <td>61.064857</td>\n",
       "      <td>78.389429</td>\n",
       "      <td>32.927143</td>\n",
       "      <td>51.857143</td>\n",
       "      <td>48.731429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.868249</td>\n",
       "      <td>13.529605</td>\n",
       "      <td>14.641475</td>\n",
       "      <td>7.736023</td>\n",
       "      <td>13.621253</td>\n",
       "      <td>12.838689</td>\n",
       "      <td>19.553514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>53.920000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>76.540000</td>\n",
       "      <td>25.590000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>34.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>63.870000</td>\n",
       "      <td>78.960000</td>\n",
       "      <td>29.180000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>49.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>67.200000</td>\n",
       "      <td>82.445000</td>\n",
       "      <td>38.620000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>61.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>86.670000</td>\n",
       "      <td>89.390000</td>\n",
       "      <td>70.280000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>90.530000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source_data_capacity  spi_data_use  spi_data_services  \\\n",
       "count             35.000000     35.000000          35.000000   \n",
       "mean              55.142857     76.851429          61.064857   \n",
       "std               16.868249     13.529605          14.641475   \n",
       "min               20.000000     40.000000          20.600000   \n",
       "25%               45.000000     70.000000          60.300000   \n",
       "50%               60.000000     80.000000          63.870000   \n",
       "75%               65.000000     90.000000          67.200000   \n",
       "max               80.000000    100.000000          86.670000   \n",
       "\n",
       "       spi_data_products  spi_data_sources  spi_data_infrastructure    account  \n",
       "count          35.000000         35.000000                35.000000  35.000000  \n",
       "mean           78.389429         32.927143                51.857143  48.731429  \n",
       "std             7.736023         13.621253                12.838689  19.553514  \n",
       "min            53.920000          9.040000                25.000000   5.830000  \n",
       "25%            76.540000         25.590000                42.500000  34.560000  \n",
       "50%            78.960000         29.180000                50.000000  49.490000  \n",
       "75%            82.445000         38.620000                60.000000  61.690000  \n",
       "max            89.390000         70.280000                80.000000  90.530000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating descriptive statistics for model 2\n",
    "desc_model2 = model2.describe()\n",
    "\n",
    "# Displaying the summary\n",
    "desc_model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e988d03e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>source_data_capacity</th>\n",
       "      <th>spi_data_use</th>\n",
       "      <th>spi_data_services</th>\n",
       "      <th>spi_data_products</th>\n",
       "      <th>spi_data_sources</th>\n",
       "      <th>spi_data_infrastructure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.731429</td>\n",
       "      <td>55.142857</td>\n",
       "      <td>76.851429</td>\n",
       "      <td>61.064857</td>\n",
       "      <td>78.389429</td>\n",
       "      <td>32.927143</td>\n",
       "      <td>51.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.553514</td>\n",
       "      <td>16.868249</td>\n",
       "      <td>13.529605</td>\n",
       "      <td>14.641475</td>\n",
       "      <td>7.736023</td>\n",
       "      <td>13.621253</td>\n",
       "      <td>12.838689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.830000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>20.600000</td>\n",
       "      <td>53.920000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.560000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>76.540000</td>\n",
       "      <td>25.590000</td>\n",
       "      <td>42.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.490000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>63.870000</td>\n",
       "      <td>78.960000</td>\n",
       "      <td>29.180000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.690000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>67.200000</td>\n",
       "      <td>82.445000</td>\n",
       "      <td>38.620000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.530000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>86.670000</td>\n",
       "      <td>89.390000</td>\n",
       "      <td>70.280000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>-0.048651</td>\n",
       "      <td>-0.377096</td>\n",
       "      <td>-0.616829</td>\n",
       "      <td>-1.168967</td>\n",
       "      <td>-1.191027</td>\n",
       "      <td>0.971202</td>\n",
       "      <td>0.102107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.076453</td>\n",
       "      <td>-0.434600</td>\n",
       "      <td>0.417437</td>\n",
       "      <td>1.996276</td>\n",
       "      <td>1.960332</td>\n",
       "      <td>1.244482</td>\n",
       "      <td>-0.361374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Error</th>\n",
       "      <td>3.305147</td>\n",
       "      <td>2.851254</td>\n",
       "      <td>2.286921</td>\n",
       "      <td>2.474861</td>\n",
       "      <td>1.307627</td>\n",
       "      <td>2.302412</td>\n",
       "      <td>2.170135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mode</th>\n",
       "      <td>5.830000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>57.030000</td>\n",
       "      <td>53.920000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample Variance</th>\n",
       "      <td>382.339924</td>\n",
       "      <td>284.537815</td>\n",
       "      <td>183.050218</td>\n",
       "      <td>214.372796</td>\n",
       "      <td>59.846053</td>\n",
       "      <td>185.538533</td>\n",
       "      <td>164.831933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Range</th>\n",
       "      <td>84.700000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.070000</td>\n",
       "      <td>35.470000</td>\n",
       "      <td>61.240000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sum</th>\n",
       "      <td>1705.600000</td>\n",
       "      <td>1930.000000</td>\n",
       "      <td>2689.800000</td>\n",
       "      <td>2137.270000</td>\n",
       "      <td>2743.630000</td>\n",
       "      <td>1152.450000</td>\n",
       "      <td>1815.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     account  source_data_capacity  spi_data_use  \\\n",
       "count              35.000000             35.000000     35.000000   \n",
       "mean               48.731429             55.142857     76.851429   \n",
       "std                19.553514             16.868249     13.529605   \n",
       "min                 5.830000             20.000000     40.000000   \n",
       "25%                34.560000             45.000000     70.000000   \n",
       "50%                49.490000             60.000000     80.000000   \n",
       "75%                61.690000             65.000000     90.000000   \n",
       "max                90.530000             80.000000    100.000000   \n",
       "skewness           -0.048651             -0.377096     -0.616829   \n",
       "kurtosis           -0.076453             -0.434600      0.417437   \n",
       "Standard Error      3.305147              2.851254      2.286921   \n",
       "Mode                5.830000             60.000000     80.000000   \n",
       "Sample Variance   382.339924            284.537815    183.050218   \n",
       "Range              84.700000             60.000000     60.000000   \n",
       "Sum              1705.600000           1930.000000   2689.800000   \n",
       "\n",
       "                 spi_data_services  spi_data_products  spi_data_sources  \\\n",
       "count                    35.000000          35.000000         35.000000   \n",
       "mean                     61.064857          78.389429         32.927143   \n",
       "std                      14.641475           7.736023         13.621253   \n",
       "min                      20.600000          53.920000          9.040000   \n",
       "25%                      60.300000          76.540000         25.590000   \n",
       "50%                      63.870000          78.960000         29.180000   \n",
       "75%                      67.200000          82.445000         38.620000   \n",
       "max                      86.670000          89.390000         70.280000   \n",
       "skewness                 -1.168967          -1.191027          0.971202   \n",
       "kurtosis                  1.996276           1.960332          1.244482   \n",
       "Standard Error            2.474861           1.307627          2.302412   \n",
       "Mode                     57.030000          53.920000          9.040000   \n",
       "Sample Variance         214.372796          59.846053        185.538533   \n",
       "Range                    66.070000          35.470000         61.240000   \n",
       "Sum                    2137.270000        2743.630000       1152.450000   \n",
       "\n",
       "                 spi_data_infrastructure  \n",
       "count                          35.000000  \n",
       "mean                           51.857143  \n",
       "std                            12.838689  \n",
       "min                            25.000000  \n",
       "25%                            42.500000  \n",
       "50%                            50.000000  \n",
       "75%                            60.000000  \n",
       "max                            80.000000  \n",
       "skewness                        0.102107  \n",
       "kurtosis                       -0.361374  \n",
       "Standard Error                  2.170135  \n",
       "Mode                           40.000000  \n",
       "Sample Variance               164.831933  \n",
       "Range                          55.000000  \n",
       "Sum                          1815.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting only numeric columns for calculating descriptive statistics\n",
    "numeric_cols_model2 = ['account','source_data_capacity', 'spi_data_use', \n",
    "                'spi_data_services', 'spi_data_products', \n",
    "                'spi_data_sources', 'spi_data_infrastructure']\n",
    "\n",
    "desc_model2 = model2[numeric_cols_model2].describe()\n",
    "\n",
    "# Adding skewness and kurtosis\n",
    "desc_model2.loc['skewness'] = model2[numeric_cols_model2].skew()\n",
    "desc_model2.loc['kurtosis'] = model2[numeric_cols_model2].kurt()\n",
    "\n",
    "# Adding additional metrics\n",
    "desc_model2.loc['Standard Error'] = model2[numeric_cols_model2].sem()\n",
    "desc_model2.loc['Mode'] = model2[numeric_cols_model2].mode().iloc[0]  # Get the mode from the first row\n",
    "desc_model2.loc['Sample Variance'] = model2[numeric_cols_model2].var()\n",
    "desc_model2.loc['Range'] = model2[numeric_cols_model2].max() - model2[numeric_cols_model2].min()\n",
    "desc_model2.loc['Sum'] = model2[numeric_cols_model2].sum()\n",
    "\n",
    "# Displaying the summary\n",
    "desc_model2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1d6499",
   "metadata": {},
   "source": [
    "### Plotting Skewness for Model 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "60594ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Skewness data for Model 1\n",
    "skewness_model1 = [0.09, 0.69, 1.34, 0.24, -0.68, 1.40]\n",
    "\n",
    "# Variable names for Model 2\n",
    "variables_model1 = ['MMA', 'IA', 'MUP', 'DP', 'MPO', 'MBP']\n",
    "\n",
    "# Plotting skewness for Model 2\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=skewness_model1, y=variables_model1, palette=\"viridis\")\n",
    "plt.title('Skewness for Model 1 Variables')\n",
    "plt.xlabel('Skewness')\n",
    "plt.ylabel('Variables')\n",
    "\n",
    "# Adding data points to the bars\n",
    "for i in range(len(skewness_model1)):\n",
    "    ax.text(skewness_model1[i], i, f'{skewness_model1[i]:.2f}', ha='left', va='center', color='black')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e1f7b6ba",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OD+cIDg6Wl5fXZR9x/ucx5tf+/fvVsmVLGWO0atWqXPX+lQcffFDvv/++ZsyYoWrVquno0aN67bXXHK/PnTtX6enpmjNnjtMs3JYtW/LcX37/bmbOnCl3d3ctWLBAnp6eTsfLy+XeYynvLxaki+fWpZnPRx99NM82UVFRki4+KfGpp57SU089pZSUFC1btkzPPfec2rdvr4MHD8rb2ztf4wJw4yJYAcCfHD58OM9v3ZOSkiQ5B5BL+vbtKx8fH/Xs2VPp6en64IMP5OrqqqZNm6pMmTLauXOnHnvssb88dtu2bfXpp58qPDxcnTp1kiRVq1ZNlStX1vDhw5WZmemY2SqohIQELVy4UFWrVlVgYGC++vj4+Khjx466cOGCOnfurB07duS6xC0kJET9+vXT1q1bNWHCBMfjtfPLx8dHjRs31pw5czRu3DjHbFxOTo4++ugjR+AsLM8//7w+//zzy/5DW5LjUraPPvpIt956q2P7pk2blJSU5JhViY2Nlaenpz7++GOnyxPXrVun/fv3OwWrhIQEvfLKKypbtqzjH/NWHThwQC1btlR2drZWrVqV6+8mPxo3bqzatWtr2rRpqlatmgICApzGciko2e12xzZjjN5//31LtdtsNrm5ucnV1dWx7dy5c/rwww/zbL9jxw5t3brV6XLA//znP/Lz81NMTEyefby9vdWqVStt3rxZdevWlYeHR75qK1OmjLp06aLff/9dTz75pPbt28fj4gH8JYIVAPxJ+/btValSJd1+++2qUaOGcnJytGXLFr322mvy9fXVoEGD8uzXpUsXeXt7q0uXLjp37pw++eQT+fr66q233lLfvn118uRJdenSReXLl9exY8e0detWHTt2TBMnTnTso02bNnrnnXd0/PhxTZgwwWn7tGnTFBgY6PSo9YIYPXq0li5dqri4OD3xxBOqXr26zp8/r3379mnhwoWaNGmSKlWqpAEDBsjLy0tNmzZVWFiYkpOTlZiYqICAAEfIaNy4sRISElS3bl0FBgYqKSlJH374oZo0aXJV3+wnJiYqPj5erVq10tNPPy0PDw+988472r59uz755JOrnqHKS69evdSrV68rtqlevboeeughvfXWW3JxcVHHjh21b98+vfDCCwoPD9fgwYMlXZwRefrpp/XSSy+pf//+6tq1qw4ePKiRI0fmunTtySef1OzZs9W8eXMNHjxYdevWVU5Ojg4cOKAlS5ZoyJAhaty4cb7HcfToUbVq1UqHDx/WlClTdPToUaf7kipVqpTv2asHHnhATz31lH7++WcNHDjQ6VLT+Ph4eXh46N5779Wzzz6r8+fPa+LEiTp16lS+a81Lp06dNH78ePXs2VMPPfSQTpw4oXHjxjkFuD+qUKGC7rjjDo0cOVJhYWH66KOPtHTpUo0ZM+aK59wbb7yhZs2a6bbbbtPf//53RUZG6vTp0/rll180f/58rVixQpJ0++23q3bt2mrYsKHKlSun/fv3a8KECYqIiNDNN99saawAbhDF+ugMACiBZs2aZXr27Gluvvlm4+vra9zd3U3lypVN7969zc6dO53a/vFx65esXLnS+Pr6mg4dOpizZ88aY4xZvXq16dSpkwkKCjLu7u6mYsWKplOnTuazzz5z6nvq1Cnj4uJifHx8nB6B/fHHHxtJ5u67785Vb141GJP3U9iOHTtmnnjiCRMVFWXc3d1NUFCQadCggRk2bJg5c+aMMcaYDz74wLRq1cqEhIQYDw8PU6FCBdOtWzfz448/Ovbzz3/+0zRs2NAEBgYau91uqlSpYgYPHmyOHz9+xff2ck8FNMaYNWvWmNatWxsfHx/j5eVlYmNjzfz5853aXHqK3Z+frHc1x/ujvB63np2dbcaMGWOqVatm3N3dTXBwsOnVq5c5ePCgU7ucnByTmJhowsPDjYeHh6lbt66ZP39+nu//mTNnzPPPP2+qV69uPDw8HI8BHzx4sElOTna0y89TAVeuXOl4cmVey4gRI/7y/bnk2LFjxsPDw0gyGzduzPX6/PnzTXR0tPH09DQVK1Y0zzzzjFm0aJGRZFauXOlo16JFC1OrVq08j5HX+zF16lRTvXp1xzmUmJhopkyZYiSZvXv3OtpdOsc///xzU6tWLePh4WEiIyPN+PHjnfaX11MBL21/4IEHTMWKFY27u7spV66ciYuLMy+99JKjzWuvvWbi4uJMcHCw8fDwMJUrVzYPPvig2bdvX/7eRAA3PJsxl/kVPgAAAABAvvBUQAAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARPxD8Jzk5OTp06JD8/PwK9QcpAQAAAJQuxhidPn1aFSpUkIvLleekCFZ/cujQIYWHhxd3GQAAAABKiIMHD6pSpUpXbEOw+hM/Pz9JF988f3//Yq4GAAAAQHFJS0tTeHi4IyNcCcHqTy5d/ufv70+wAgAAAJCvW4R4eAUAAAAAWESwAgAAAACLCFYAAAAAYBH3WAEAcA10in3KUv+vNowvpEoAAEWBGSsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAoBgZY7Trt/Va/sN7WrzxTW3Y+ZlOnz3+l/1mz56tmjVrym63q2bNmvriiy+cXh85cqRsNpvTEhoaWlTDAIAbHsEKAIBi9Ovh77Tv8A+qFdlKTWv3lN3dWxt/mqOs7AuX7bN+/Xp1795dvXv31tatW9W7d29169ZN3377rVO7WrVq6fDhw45l27ZtRT0cALhhlZhgtW7dOrm6uqpDhw65Xrtw4YLGjh2r6OhoeXt7Kzg4WE2bNtW0adOUmZnpaJecnKzHH39cVapUkd1uV3h4uG6//XYtX778Wg4FAIB8McZoX/IPqlqxkUKDbpafd7DqVm2v7JwsHTr+02X7TZgwQfHx8Ro6dKhq1KihoUOHqk2bNpowYYJTOzc3N4WGhjqWcuXKFfGIAODGVWKC1dSpU/X4449r7dq1OnDggGP7hQsX1L59e7366qt66KGHtG7dOm3cuFGPPvqo3nrrLe3YsUOStG/fPjVo0EArVqzQ2LFjtW3bNi1evFitWrXSo48+WlzDAgDgss5lpCoj86yCAyIc21xd3BTkV1Gnzhy6bL/169erXbt2Ttvat2+vdevWOW3bvXu3KlSooKioKPXo0UO//vpr4Q4AAODgVtwFSFJ6ero+/fRTbdq0ScnJyZo+fbqGDx8u6eK3ct98842+++471a9f39GnSpUq6tq1qy5cuHipxCOPPCKbzaaNGzfKx8fH0a5WrVp64IEHru2AAADIh4zMs5Iku7u303a7u7fOXTh92X7JyckKCQlx2hYSEqLk5GTHeuPGjTVjxgxVq1ZNR44c0UsvvaS4uDjt2LFDZcuWLcRRAACkEhKsZs2aperVq6t69erq1auXHn/8cb3wwguy2Wz6+OOP1bZtW6dQdYm7u7vc3d118uRJLV68WC+//LJTqLqkTJkylz12RkaGMjIyHOtpaWmFMiYAAP7s9+NJ2r73f5enN6ze+ar3ZbPZnNaNMU7bOnbs6PhznTp11KRJE1WtWlUffPCBnnrqqas+LgAgbyUiWE2ZMkW9evWSJHXo0EFnzpzR8uXL1bZtW+3evVstW7a8Yv9ffvlFxhjVqFGjwMdOTEzUqFGjrqZsAAAKJCSwqsr4hjnWc3KyJF2cufL08HVsz8g8m2sW649CQ0OdZqck6ejRo7lmsf7Ix8dHderU0e7du6+2fADAFRT7PVY///yzNm7cqB49eki6eKNt9+7dNXXqVEm5v4HLizFGUu5v7/Jj6NChSk1NdSwHDx4s8D4AAMgPN1cP+XiWcSy+XmVld/fW8dT9jjY5Odk6efp3BfpWuOx+mjRpoqVLlzptW7JkieLi4i7bJyMjQ0lJSQoLC7tsGwDA1Sv2GaspU6YoKytLFStWdGwzxsjd3V2nTp1StWrVlJSUdMV93HzzzbLZbEpKSlLnzp0LdHy73S673X41pQMAYInNZlNkaIz2HNokH89A+XiW0S+HNsrVxU0Vgv93FcbWPYs1dKhdiYmJkqRBgwapefPmGjNmjO688059+eWXWrZsmdauXevo8/TTT+v2229X5cqVdfToUb300ktKS0tT3759r/k4AeBGUKwzVllZWZoxY4Zee+01bdmyxbFs3bpVERER+vjjj9WzZ08tW7ZMmzdvzrN/enq6goKC1L59e7399ttKT0/P1S4lJeUajAYAgIKrEtZQkaH1tWPfcv13+3+UceGMGtW4W26uHo425zJO6/Dhw471uLg4zZw5U9OmTVPdunU1ffp0zZo1S40bN3a0+e2333TvvfeqevXquvvuu+Xh4aENGzYoIiJCAIDCZzOXrqMrBnPnzlX37t119OhRBQQEOL02bNgwLVy4UBs2bFB8fLy2b9+uF198Uc2aNZOfn5++++47jRkzRlOmTFG9evW0d+9excXFKSgoSKNHj1bdunWVlZWlpUuXauLEiX8563VJWlqaAgIClJqaKn9//6IYNgDgBtQp1toDI77aML6QKgEA5FdBskGxXgo4ZcoUtW3bNleokqR77rlHr7zyinbs2KGlS5fq9ddf17vvvqunn35a3t7euuWWW/TEE0+odu3akqSoqCj98MMPevnllzVkyBAdPnxY5cqVU4MGDTRx4sRrPTQAAAAAN5BinbEqiZixAgAUBWasAKD0KUg2KPanAgIAAABAaUewAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgkVtxFwAAwI3gqw3ji7sEAEARYsYKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABa5FXcBAICr06776OIuAdfQklnDi7sEAMAVMGMFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACL3Iq7AABA4TLG6Ncdq/Tbr98rK/OcAoIqqUZMJ/kGlL9snyO/7dTepDU6d+akcnKy5e1XVhHV4lQhMtrRJicnW7/uWKXDB37UhfNnZPf0U1hkPVWp2Vw2G9/TAQBubAQrALjO7PtprfbvWq9ajTrLx6+sft35jb5fPUNNOz4uN3d7nn3cPbxU5Zbm8vYPlouLq44f+lk7N82Vh6ePgkNvcuz3tz3fqVaju+QbUE5pJw9px6a5cne3q3K1JtdyiAAAlDjF/hXj0aNHNXDgQFWuXFl2u12hoaFq37691q9fL0mKjIyUzWaTzWaTl5eXIiMj1a1bN61YsSLP/c2ePVstW7ZUQECAfH19VbduXY0ePVonT568lsMCgGJhjNGB3RsUdcttCqlUU74BIard6C7lZGcq+cCPl+0XVD5K5SvdIl//cvL2DVLlak3kGxCilGP7HW1ST/ymchWrq1yFavLyCVRIeC2VDamqtFOHrsXQAAAo0Yo9WN1zzz3aunWrPvjgA+3atUvz5s1Ty5YtnYLQ6NGjdfjwYf3888+aMWOGypQpo7Zt2+rll1922tewYcPUvXt33XrrrVq0aJG2b9+u1157TVu3btWHH354rYcGANfcufRTunD+jMr+/yyTJLm4uimwXIRSjh/M1z6MMTpx5Felnz6uwHKRju1lgivr5JG9Sj99XJJ0OiVZKccPqGxYtUIdAwAApVGxXgqYkpKitWvXatWqVWrRooUkKSIiQo0aNXJq5+fnp9DQUElS5cqV1bx5c4WFhWn48OHq0qWLqlevro0bN+qVV17RhAkTNGjQIEffyMhIxcfHKyUl5ZqNCwCKy4XzZyRJHp4+Tts9PH11Pj3lin0zL5zXmgWvKSc7Szabi2rEdFLZ0KqO1yNrNFNW5nmtW/Rv2Ww2GWN0U53WCqtcp9DHAQBAaVOswcrX11e+vr6aO3euYmNjZbfnfe1/XgYNGqQXX3xRX375pZ599ll9/PHH8vX11SOPPJJn+zJlyuS5PSMjQxkZGY71tLS0Ao0BAIrT4f0/Kun7+Y71es3ukyTZZHNuaIxk+9O2P3Fz91Bs/MPKzrqgE0d/1a6tX8vLN1BB5aMkSUcObtfh/T+qTuw98vEvr9Mpydq1ZZHsXv6qEFmvUMcFAEBpU6zBys3NTdOnT9eAAQM0adIkxcTEqEWLFurRo4fq1q17xb5BQUEqX7689u3bJ0navXu3qlSpInd39wLVkJiYqFGjRl3tEACgWJWrUF0BQRUd6zk52ZKkjPNnZPfyc2y/kJEuD7tPrv5/ZLO5yNuvrCTJLzBM6WnHtS9pjSNY7dq6RFE1min0/2eo/MqE6PzZFO1NWkOwAgDc8ErEPVaHDh3SvHnz1L59e61atUoxMTGaPn36X/Y1xsj2/9/A/vHPBTF06FClpqY6loMH83cPAgCUBG7udnn7lXUsPv7l5OHpq5NH9jja5GRn6dSx/SoTHF7AvRtHULu4n8xcs142m+3ibBgAADe4Yg9WkuTp6an4+HgNHz5c69atU79+/TRixIgr9jlx4oSOHTumqKiL36RWq1ZNe/bsUWZmZoGObbfb5e/v77QAQGlls9lU+eZY7U1ao6O/JelM6hHt2DRXLq7uCq38vysBtn87R7t/XOpY35v0jU4k79HZMyeVnnZM+39ep8P7tio04n99gitU196kNTp2aJfOpZ/S0d+StH/XepWvWOOajhEAgJKoRP6OVc2aNTV37twrtnnjjTfk4uKizp07S5J69uypN998U++8847TwysuSUlJuex9VgBwPYms0Uw52VlK+mGBsi6cl3/ZimrQorfTb1idP5vqNPuUnZWppB8WKONcmlxc3eXjF6zaje9RaOXajjY16v9Ne7av0E8/LNCFjHTZPf1UqUpDVanZ4pqODwCAkqhYg9WJEyfUtWtXPfDAA6pbt678/Pz03XffaezYsbrzzjsd7U6fPq3k5GRlZmZq7969+uijjzR58mQlJibqppsuPlK4cePGevbZZzVkyBD9/vvvuuuuu1ShQgX98ssvmjRpkpo1a5Zn4AKA643NZlPV2q1UtXary7Zp2Op+p/Wb6rTRTXXaXHG/bu52Va/fUdXrdyyUOgEAuJ4U+1MBGzdurNdff91xGV94eLgGDBig5557ztFu+PDhGj58uDw8PBQaGqrY2FgtX75crVo5/6NhzJgxatCggd5++21NmjRJOTk5qlq1qrp06aK+ffte6+EBAAAAuEHYjOGu4z9KS0tTQECAUlNTud8KQInWrvvo4i4B19CSWcOLuwQAuOEUJBuUiIdXAAAAAEBpRrACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGCRW3EXAAC4OktmDS/uEgAAwP9jxgoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFrkVdwEA0OSJF4u7BKDEW//mC8VdAgDgCpixAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARW7FXQAAXI4xRr9tXKmjO75X1vlz8g2tpKgWCfIuW/6K/bIyzung+uU6uWensjLOy+5fRhHNOigwspok6ffvvtHJPTt17tRxubi5yy80XJWbtpNXYPC1GBYAALgOEawAlFiHflir5M3rVbXtXfIMLKvfN61W0pcfqF6vJ+TqYc+zT052lpLmfiB3Lx9V69hdHr4ByjiTKlf3/7VP+32fQuo2lm/5ijI5OTq4YZmSvvxA0fc9Lld3j2s1PAAAcB0p1ksB+/XrJ5vNJpvNJnd3d4WEhCg+Pl5Tp05VTk6Oo53NZtPcuXNz9X/yySfVsmVLx/rRo0c1cOBAVa5cWXa7XaGhoWrfvr3Wr19/DUYDoDAZY5S8Zb0q3NpcQTfVlHfZEFWNv1s5mZk6vuvHy/Y7tnOzss6fU7VOPeVXIUJ2/zLyrxAhn3Khjja33NlH5W+pL++y5eVTLlRV296lC6dTlX700LUYGgAAuA4V+4xVhw4dNG3aNGVnZ+vIkSNavHixBg0apM8//1zz5s2Tm1v+S7znnnuUmZmpDz74QFWqVNGRI0e0fPlynTx5sghHAKAoZKSdUubZMypT+SbHNhdXN/lXjNTpwwcVUvvWPPud2vuT/MLCtW/1Ap369Se5efkouFodVWhwm2wueX+XlJ1xXpLk5ulV+AMBAAA3hGIPVpdmliSpYsWKiomJUWxsrNq0aaPp06erf//++dpPSkqK1q5dq1WrVqlFixaSpIiICDVq1KjIagdQdDLPnpEkuXv5OG139/ZRxumUy/Y7n3pKGb/tVXD1uqp+R2+dTzmhfasXyJgcVWrUKld7Y4z2r10sv7DK8i4bUqhjAAAAN45iD1Z5ad26taKjozVnzpx8BytfX1/5+vpq7ty5io2Nld2e9/0Xf5aRkaGMjAzHelpa2lXVDMCa4z9v1a8r5zvWa9x+38U/2GzODY0k/Wnbnxq4e/moSqs7ZHNxkW/5CspMT9OhH/6bZ7Dat/orpR8/olpdHrQ8BgAAcOMqkcFKkmrUqKEff7z8fRR/5ubmpunTp2vAgAGaNGmSYmJi1KJFC/Xo0UN169a9bL/ExESNGjWqMEoGYEFgVA3VDankWM/JzpZ0cebKw8fPsT3zXLrcvX0vux93b1/ZXFydLvvzDCynzLNnlJOdJRfX/33s7V39lU7t/Uk1735Qdt+AwhwOAAC4wZTY37Eyxsj252+q/8I999yjQ4cOad68eWrfvr1WrVqlmJgYTZ8+/bJ9hg4dqtTUVMdy8OBBi5UDuBquHnZ5linrWLyCysnd21epB35xtMnJzlLa7/vkFxZ+2f34hVXW+dSTMuZ/D8A5n3JC7j5+jlBljNHeVQt0cs9O3XLX/fIMCCy6gQEAgBtCiQ1WSUlJioqKkiT5+fkpNTU1V5uUlBQFBDh/y+zp6an4+HgNHz5c69atU79+/TRixIjLHsdut8vf399pAVD8bDabQus10e/frdHJPTt19sQR7Vn2hVzc3RVc7X+z0L8sma0D65Y61kPqNFLW+bPa980inTt1XKf2/qxD332j0Dr/u99y3+oFOv7zj7q5fRe5unvoQvppXUg/rZyszGs6RgAAcP0okZcCrlixQtu2bdPgwYMlXbwscNOmTerbt6+jjTFG33//vTp27HjFfdWsWTPPR7UDKPkqxDRTTlam9q5aoKyM8/INqahb7uzj9BtWGWdSne7DsvsF6JY7+2j/msX68ZN35OHjp9DoWFVocJujzZFtmyRJO+dMczpelbZ3qfwt9Yt4VAAA4HpU7MEqIyNDycnJTo9bT0xMVEJCgvr06SNJevrpp9W3b1/VqFFD7dq107lz5/Tee+9pz549evTRRyVJJ06cUNeuXfXAAw+obt268vPz03fffaexY8fqzjvvLM4hArhKNptN4Y1bK7xx68u2qXX3A7m2+YVVVu1uD122T+zjowulPgAAgEuKPVgtXrxYYWFhcnNzU2BgoKKjo/Xmm2+qb9++cvn/m8+7desmY4zGjRunYcOGydPTU/Xr19eaNWsUEREh6eJTARs3bqzXX39de/bsUWZmpsLDwzVgwAA999xzxTlEAAAAANc5mzHGFHcRJUlaWpoCAgKUmprK/VbANdLkiReLuwSgxFv/5gvFXQIA3HAKkg1K7MMrAAAAAKC0IFgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALDIrbgLAID1b75Q3CUAAABYwowVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsKHKw++OADffXVV471Z599VmXKlFFcXJz2799fqMUBAAAAQGlQ4GD1yiuvyMvLS5K0fv16/fvf/9bYsWMVHByswYMHF3qBAAAAAFDSuRW0w8GDB3XTTTdJkubOnasuXbrooYceUtOmTdWyZcvCrg9XUO+lkcVdAgDgGtny/MjiLgEAcAUFnrHy9fXViRMnJElLlixR27ZtJUmenp46d+5c4VYHAAAAAKVAgWes4uPj1b9/f9WvX1+7du1Sp06dJEk7duxQZGRkYdcHAAAAACVegWes3n77bTVp0kTHjh3T7NmzVbZsWUnS999/r3vvvbfQCwQAAACAkq7AM1ZlypTRv//971zbR40aVSgFAQAAAEBpc1W/Y7VmzRr16tVLcXFx+v333yVJH374odauXVuoxQEAAABAaVDgYDV79my1b99eXl5e+uGHH5SRkSFJOn36tF555ZVCLxAAAAAASroCB6uXXnpJkyZN0vvvvy93d3fH9ri4OP3www+FWhwAAAAAlAYFDlY///yzmjdvnmu7v7+/UlJSCqMmAAAAAChVChyswsLC9Msvv+TavnbtWlWpUqVQigIAAACA0qTAwWrgwIEaNGiQvv32W9lsNh06dEgff/yxnn76aT3yyCNFUSMAAAAAlGgFftz6s88+q9TUVLVq1Urnz59X8+bNZbfb9fTTT+uxxx4rihoBAAAAoEQrcLCSpJdfflnDhg3Tzp07lZOTo5o1a8rX17ewawMAAACAUuGqgpUkeXt7q2HDhoVZCwAAAACUSvkKVnfffXe+dzhnzpyrLgYAAAAASqN8BauAgICirgMAAAAASq18Batp06YVdR0AAAAAUGpd9T1WR48e1c8//yybzaZq1aqpfPnyhVkXAAAAAJQaBf4dq7S0NPXu3VsVK1ZUixYt1Lx5c1WsWFG9evVSampqUdQIAAAAACVagYNV//799e2332rBggVKSUlRamqqFixYoO+++04DBgwoihoBAAAAoEQr8KWAX331lb7++ms1a9bMsa19+/Z6//331aFDh0ItDgAAAABKgwIHq7Jly+b5lMCAgAAFBgYWSlG4eik7knTiu+917tAhZZ89p2qPDJRXWOgV+5w/clTJK1bp7KFDykxJVYWO7VUuLtapzc7XJigzJfelnmUbNVSl2zsV6hgAAACA0qbAwer555/XU089pRkzZigsLEySlJycrGeeeUYvvPBCoReIgsnJvCCfyuEqU6umfvtyfj77ZMojsIwCatXUoUVf59mm2sMDZHKMY/380aP6dfqHKlO7VqHUDQAAAJRm+brHqn79+oqJiVFMTIwmTZqkDRs2KCIiQjfddJNuuukmVa5cWevWrdO7775b4AL69esnm80mm80md3d3hYSEKD4+XlOnTlVOTo6j3ebNm5WQkKDy5cvL09NTkZGR6t69u44fP+5oM3v2bDVu3FgBAQHy8/NTrVq1NGTIkALXVJoF1YtWaKsW8qtaJd99vCtVVIUO7RRYt7Zsbq55tnHz8ZG7n69jSft5lzyCAuUTGVFYpQMAAAClVr5mrDp37lykRXTo0EHTpk1Tdna2jhw5osWLF2vQoEH6/PPPNW/ePJ08eVJt27bV7bffrq+//lplypTR3r17NW/ePJ09e1aStGzZMvXo0UOvvPKK7rjjDtlsNu3cuVPLly8v0tpvRDlZ2Tq19UeVi2sim81W3OUAAAAAxS5fwWrEiBFFWoTdbldo6MX7gCpWrKiYmBjFxsaqTZs2mj59uoKDg5WWlqbJkyfLze1iyVFRUWrdurVjHwsWLFCzZs30zDPPOLZVq1atyEPhjSgt6Sdlnz+voPr1irsUAAAAoEQo8OPWr5XWrVsrOjpac+bMUWhoqLKysvTFF1/IGJNn+9DQUO3YsUPbt28v0HEyMjKUlpbmtJQWp7b+qG0vvuJYzuzbf02Oe+KHzfK/+Wa5+/tdk+MBAAAAJV2Bg1V2drbGjRunRo0aKTQ0VEFBQU5LYapRo4b27dun2NhYPffcc+rZs6eCg4PVsWNH/etf/9KRI0ccbR9//HHdeuutqlOnjiIjI9WjRw9NnTpVGRkZVzxGYmKiAgICHEt4eHihjqEo+deormqPPOxYvCtWKPJjXkhJ0Zk9vyqoQf0iPxYAAABQWhQ4WI0aNUrjx49Xt27dlJqaqqeeekp33323XFxcNHLkyEItzhjjuIfn5ZdfVnJysiZNmqSaNWtq0qRJqlGjhrZt2yZJ8vHx0VdffaVffvlFzz//vHx9fTVkyBA1atTIcR9WXoYOHarU1FTHcvDgwUIdQ1FytdtlLxvkWFzc3Yv8mCd/2CI3Hx/5V6tW5McCAAAASosCB6uPP/5Y77//vp5++mm5ubnp3nvv1eTJkzV8+HBt2LChUItLSkpSVFSUY71s2bLq2rWrXnvtNSUlJalChQoaN26cU5+qVauqf//+mjx5sn744Qft3LlTs2bNuuwx7Ha7/P39nZbSLOvsOZ07nKzzx45Jks4fP65zh5OVefqMo82Bz7/Q4SXLHOs5Wdk6dzhZ5w4ny2RnKzMtTecOJyvjxEmnfZsco5M/bFFg/WjZXEvsVaQAAADANVfg37FKTk5WnTp1JEm+vr5KTb34o7EJCQmF+jtWK1as0LZt2zR48OA8X/fw8FDVqlWVnp5+2X1ERkbK29v7im2uN2k//ayDX3zpWD/w6WxJUkirFgpt3VKSdCE1VXL539P8sk6f1q53/veo/GP/Xa9j/10vn8gI3fRgP8f2M7/+qszUVJWN4TJAAAAA4I8KHKwqVaqkw4cPq3Llyrrpppu0ZMkSxcTEaNOmTbLb7VdVREZGhpKTk50et56YmKiEhAT16dNHCxYs0MyZM9WjRw9Vq1ZNxhjNnz9fCxcu1LRp0yRJI0eO1NmzZ/W3v/1NERERSklJ0ZtvvqnMzEzFx8dfVV2lUVBMPQXF1Ltimz+GJUnyCCyj6Bf/+smPfjdVzVc7AAAA4EZT4GB11113afny5WrcuLEGDRqke++9V1OmTNGBAwcuO7v0VxYvXqywsDC5ubkpMDBQ0dHRevPNN9W3b1+5uLioZs2a8vb21pAhQ3Tw4EHZ7XbdfPPNmjx5snr37i1JatGihd5++2316dNHR44cUWBgoOrXr68lS5aoevXqV1UXAAAAAOSHzVzu+eX5tGHDBq1bt0433XST7rjjjsKqq9ikpaUpICBAqampJf5+q3ovjSzuEgAA18iW50cWdwkAcMMpSDYo8IzVn8XGxio2NtbqbgAAAACg1MpXsJo3b546duwod3d3zZs374ptr4dZKwAAAAAoiHwFq86dOys5OVnly5dX586dL9vOZrMpOzu7sGoDAAAAgFIhX8EqJycnzz8DAAAAAAr4A8GZmZlq1aqVdu3aVVT1AAAAAECpU6Bg5e7uru3bt8tms/11YwAAAAC4QRQoWElSnz59NGXKlKKoBQAAAABKpQI/bv3ChQuaPHmyli5dqoYNG8rHx8fp9fHjxxdacQAAAABQGhQ4WG3fvl0xMTGSlOteKy4RBAAAAHAjKnCwWrlyZVHUAQAAAAClVoHvsQIAAAAAOCvwjJUkbdq0SZ999pkOHDigCxcuOL02Z86cQikMAAAAAEqLAs9YzZw5U02bNtXOnTv1xRdfKDMzUzt37tSKFSsUEBBQFDUCAAAAQIlW4GD1yiuv6PXXX9eCBQvk4eGhN954Q0lJSerWrZsqV65cFDUCAAAAQIlW4GC1Z88ederUSZJkt9uVnp4um82mwYMH67333iv0AgEAAACgpCtwsAoKCtLp06clSRUrVtT27dslSSkpKTp79mzhVgcAAAAApUC+g9WWLVskSbfddpuWLl0qSerWrZsGDRqkAQMG6N5771WbNm2KpEgAAAAAKMny/VTAmJgY1a9fX507d9a9994rSRo6dKjc3d21du1a3X333XrhhReKrFAAAAAAKKnyPWP13//+VzExMRo3bpyqVq2qXr16afXq1Xr22Wc1b948jR8/XoGBgUVZKwAAAACUSDZjjClIh3PnzunTTz/VtGnTtGbNGkVGRuqBBx5Q3759ValSpaKq85pJS0tTQECAUlNT5e/vX9zlAAAAACgmBckGBX54hZeXl/r27atVq1Zp165duvfee/Xuu+8qKipKf/vb3666aAAAAAAorQo8Y/VnZ86c0ccff6znnntOKSkpys7OLqzaigUzVgAAAACkgmWDfD+84s9Wr16tqVOnavbs2XJ1dVW3bt304IMPXu3uAAAAAKDUKlCwOnjwoKZPn67p06dr7969iouL01tvvaVu3brJx8enqGoEAAAAgBIt38EqPj5eK1euVLly5dSnTx898MADql69elHWBgAAAAClQr6DlZeXl2bPnq2EhAS5uroWZU0AAAAAUKrkO1jNmzevKOsAAAAAgFKrwI9bBwAAAAA4I1gBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAogL9QDCuvbhpzxd3CQCAEmDd/S8VdwkAgCtgxgoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABa5FXcBuDonvv9JR1ZtVvr+w8o6c051Rz4on8qhV+xz9vdjOjh3tdL3JSvjRKoie8QrrF0jpzbZ5zJ04IvVOrn5Z2WmnZVP5RBF9Wwn36gKRTkcAAAAoFRjxqqUysnIlN/NlVS5S6v897mQKXu5QFXu0kruAT55ttkz/Sul7tyrm/vfqejRA1SmVhXtHPcfZZxKK6zSAQAAgOtOiQhW/fr1k81mk81mk7u7u0JCQhQfH6+pU6cqJyfH0S4yMtLRztvbW7Vr19a7777reD07O1uJiYmqUaOGvLy8FBQUpNjYWE2bNq04hlWkysXVUfgdtymgZlS++/hGVVBktzYKblxLLm65JyuzL2TqxPc/KaJra/lXryyvkCCFd24ue3CAjqz8oTDLBwAAAK4rJSJYSVKHDh10+PBh7du3T4sWLVKrVq00aNAgJSQkKCsry9Fu9OjROnz4sH788Ud17txZDz/8sGbNmiVJGjlypCZMmKAXX3xRO3fu1MqVKzVgwACdOnWquIZVumTnSDlGLu7OocvFw12ndx8spqIAAACAkq/E3GNlt9sVGnrxHqGKFSsqJiZGsbGxatOmjaZPn67+/ftLkvz8/BztXnrpJX366aeaO3euunfvrvnz5+uRRx5R165dHfuNjo6+9oMppVy97PKtWlG/zV8rr7BguQf46Pi3O3Tm19/lWT6ouMsDAAAASqwSM2OVl9atWys6Olpz5sy5bBtPT09lZmZKkkJDQ7VixQodO3Ys38fIyMhQWlqa01LSHFu/Xd/+faxjSdt1oMiOdfOAO2WM9P2QN7XhoVd1eNkmBTeuLZtLiT5VAAAAgGJVYmasLqdGjRr68ccfc23PysrSRx99pG3btunvf/+7JGn8+PHq0qWLQkNDVatWLcXFxenOO+9Ux44dL7v/xMREjRo1qsjqLwxB9W6Wb5X+jnWPQL8iO5Zn+UDV/mdvZWdcUPa5DHmU8dOuiXNkLxdQZMcEAAAASrsSPw1hjJHNZnOs/+Mf/5Cvr6+8vLz06KOP6plnntHAgQMlSTVr1tT27du1YcMG3X///Tpy5Ihuv/12x2WEeRk6dKhSU1Mdy8GDJe9eIlcvu7xCghyLq4d70R/T7iGPMn7KSj+nlO2/KqhetSI/JgAAAFBalfgZq6SkJEVF/e/Jd88884z69esnb29vhYWFOYUuSXJxcdGtt96qW2+9VYMHD9ZHH32k3r17a9iwYU77ucRut8tutxf5OApb5plzunAyVRdSzkiSziWflCS5B/jKI8BXkrT7/XnyCPRTxP8/kj0nK1vnDh1z/Dkj5bTSDyTLxe4hr5CL91ClbN8jYySv0LI6f/Sk9n+6XF6hZVWuGfeqAQAAAJdTooPVihUrtG3bNg0ePNixLTg4WDfddFO+91GzZk1JUnp6eqHXV5xObdmlPVMXONZ3T/pCklTpjtsU3rm5JOnCyVTZXP4XPC+knNaPI6c41g8v3qDDizfIv3pl1fpHb0lS1tkMHZi9UhdOnZabj6eCGtRQ5btbysXN9VoMCwAAACiVSkywysjIUHJysrKzs3XkyBEtXrxYiYmJSkhIUJ8+ffK1jy5duqhp06aKi4tTaGio9u7dq6FDh6patWqqUaNGEY/g2irfLFrl/2IW6VJYusQzuIyaTB12xT7BjWoquFFNy/UBAAAAN5ISE6wWL16ssLAwubm5KTAwUNHR0XrzzTfVt29fueTziXTt27fXJ598osTERKWmpio0NFStW7fWyJEj5ZbHD+ICAAAAQGGwGWNMcRdRkqSlpSkgIECpqany9/cv7nIUN+354i4BAFACrLv/peIuAQBuOAXJBiX+qYAAAAAAUNIRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWORW3AXgytbd/1JxlwAAAADgLzBjBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACL3Iq7AAAA8NceXz6ouEsAgGvmrTZvFHcJBcaMFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAACix3nnnHUVFRcnT01MNGjTQmjVrrtj+7bff1i233CIvLy9Vr15dM2bMcHq9ZcuWstlsuZZOnTpZqtPNUm8AAAAAKCKzZs3Sk08+qXfeeUdNmzbVu+++q44dO2rnzp2qXLlyrvYTJ07U0KFD9f777+vWW2/Vxo0bNWDAAAUGBur222+XJM2ZM0cXLlxw9Dlx4oSio6PVtWtXS7UW64xVv379HAnR3d1dISEhio+P19SpU5WTk+Not3nzZiUkJKh8+fLy9PRUZGSkunfvruPHjzvtb/bs2WrZsqUCAgLk6+urunXravTo0Tp58uS1HhoAAAAAi8aPH68HH3xQ/fv31y233KIJEyYoPDxcEydOzLP9hx9+qIEDB6p79+6qUqWKevTooQcffFBjxoxxtAkKClJoaKhjWbp0qby9vUt3sJKkDh066PDhw9q3b58WLVqkVq1aadCgQUpISFBWVpaOHj2qtm3bKjg4WF9//bWSkpI0depUhYWF6ezZs479DBs2TN27d9ett96qRYsWafv27Xrttde0detWffjhh8U4QgAAAAAFdeHCBX3//fdq166d0/Z27dpp3bp1efbJyMiQp6en0zYvLy9t3LhRmZmZefaZMmWKevToIR8fH0v1FvulgHa7XaGhoZKkihUrKiYmRrGxsWrTpo2mT5+u4OBgpaWlafLkyXJzu1huVFSUWrdu7djHxo0b9corr2jChAkaNGiQY3tkZKTi4+OVkpJyTccEAAAAwJrjx48rOztbISEhTttDQkKUnJycZ5/27dtr8uTJ6ty5s2JiYvT9999r6tSpyszM1PHjxxUWFubUfuPGjdq+fbumTJliud5in7HKS+vWrRUdHa05c+YoNDRUWVlZ+uKLL2SMybP9xx9/LF9fXz3yyCN5vl6mTJnLHisjI0NpaWlOCwAAAICSwWazOa0bY3Jtu+SFF15Qx44dFRsbK3d3d915553q16+fJMnV1TVX+ylTpqh27dpq1KiR5TpLZLCSpBo1amjfvn2KjY3Vc889p549eyo4OFgdO3bUv/71Lx05csTRdvfu3apSpYrc3d0LfJzExEQFBAQ4lvDw8MIcBgAAAICrEBwcLFdX11yzU0ePHs01i3WJl5eXpk6dqrNnz2rfvn06cOCAIiMj5efnp+DgYKe2Z8+e1cyZM9W/f/9CqbfEBqs/JtGXX35ZycnJmjRpkmrWrKlJkyapRo0a2rZtW662BTV06FClpqY6loMHDxbaGAAAAABcHQ8PDzVo0EBLly512r506VLFxcVdsa+7u7sqVaokV1dXzZw5UwkJCXJxcY4+n376qTIyMtSrV69CqbfEBqukpCRFRUU51suWLauuXbvqtddeU1JSkipUqKBx48ZJkqpVq6Y9e/Zc9oa0K7Hb7fL393daAAAAABS/p556SpMnT9bUqVOVlJSkwYMH68CBA3r44YclXZwk6dOnj6P9rl279NFHH2n37t3auHGjevTooe3bt+uVV17Jte8pU6aoc+fOKlu2bKHUWiKD1YoVK7Rt2zbdc889eb7u4eGhqlWrKj09XZLUs2dPnTlzRu+8806e7Xl4BQAAAFD6dO/eXRMmTNDo0aNVr149ffPNN1q4cKEiIiIkSYcPH9aBAwcc7bOzs/Xaa68pOjpa8fHxOn/+vNatW6fIyEin/e7atUtr167Vgw8+WGi1FvtTATMyMpScnKzs7GwdOXJEixcvVmJiohISEtSnTx8tWLBAM2fOVI8ePVStWjUZYzR//nwtXLhQ06ZNkyQ1btxYzz77rIYMGaLff/9dd911lypUqKBffvlFkyZNUrNmzZyeFggAAACgdHjkkUcu+5C66dOnO63fcsst2rx581/u81KuKEzFHqwWL16ssLAwubm5KTAwUNHR0XrzzTfVt29fubi4qGbNmvL29taQIUN08OBB2e123XzzzZo8ebJ69+7t2M+YMWPUoEEDvf3225o0aZJycnJUtWpVdenSRX379i3GEQIAAAC43tlMYUe1Ui4tLU0BAQFKTU3lfisAQInx+HKuvABw43irzRvFXYKkgmWDEnmPFQAAAACUJgQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWuRV3AQAA4K+91eaN4i4BAHAFzFgBAAAAgEUEKwAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCK34i4AAAD8tY82tC/uEtQr9uviLgEASixmrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAFNg777yjqKgoeXp6qkGDBlqzZs1l2x4+fFg9e/ZU9erV5eLioieffDLPdrNnz1bNmjVlt9tVs2ZNffHFF0VUPQAUPoIVAAAokFmzZunJJ5/UsGHDtHnzZt12223q2LGjDhw4kGf7jIwMlStXTsOGDVN0dHSebdavX6/u3burd+/e2rp1q3r37q1u3brp22+/LcqhAEChKZHBql+/frLZbLLZbHJ3d1dISIji4+M1depU5eTkONpFRkZqwoQJjvXNmzcrISFB5cuXl6enpyIjI9W9e3cdP368GEYBAMD1afz48XrwwQfVv39/3XLLLZowYYLCw8M1ceLEPNtHRkbqjTfeUJ8+fRQQEJBnmwkTJig+Pl5Dhw5VjRo1NHToULVp08bp/+cBoCQrkcFKkjp06KDDhw9r3759WrRokVq1aqVBgwYpISFBWVlZudofPXpUbdu2VXBwsL7++mslJSVp6tSpCgsL09mzZ4thBAAAXH8uXLig77//Xu3atXPa3q5dO61bt+6q97t+/fpc+2zfvr2lfQLAteRW3AVcjt1uV2hoqCSpYsWKiomJUWxsrNq0aaPp06erf//+Tu3XrVuntLQ0TZ48WW5uF4cVFRWl1q1bX/PaAQC4Xh0/flzZ2dkKCQlx2h4SEqLk5OSr3m9ycnKh7xMArqUSO2OVl9atWys6Olpz5szJ9VpoaKiysrL0xRdfyBiT731mZGQoLS3NaQEAAFdms9mc1o0xubaVhH0CwLVSqoKVJNWoUUP79u3LtT02NlbPPfecevbsqeDgYHXs2FH/+te/dOTIkSvuLzExUQEBAY4lPDy8iCoHAKD0Cw4Olqura66ZpKNHj+aacSqI0NDQQt8nAFxLpS5YXenbq5dfflnJycmaNGmSatasqUmTJqlGjRratm3bZfc3dOhQpaamOpaDBw8WVekAAJR6Hh4eatCggZYuXeq0fenSpYqLi7vq/TZp0iTXPpcsWWJpnwBwLZXYe6wuJykpSVFRUZd9vWzZsuratau6du2qxMRE1a9fX+PGjdMHH3yQZ3u73S673V5U5QIAcN156qmn1Lt3bzVs2FBNmjTRe++9pwMHDujhhx+WdPFLy99//10zZsxw9NmyZYsk6cyZMzp27Ji2bNkiDw8P1axZU5I0aNAgNW/eXGPGjNGdd96pL7/8UsuWLdPatWuv+fgA4GqUqmC1YsUKbdu2TYMHD85Xew8PD1WtWlXp6elFXBkAADeO7t2768SJExo9erQOHz6s2rVra+HChYqIiJB08QeB//ybVvXr13f8+fvvv9d//vMfRUREOC7vj4uL08yZM/X888/rhRdeUNWqVTVr1iw1btz4mo0LAKwoscEqIyNDycnJys7O1pEjR7R48WIlJiYqISFBffr0ydV+wYIFmjlzpnr06KFq1arJGKP58+dr4cKFmjZtWjGMAACA69cjjzyiRx55JM/Xpk+fnmtbfh4s1aVLF3Xp0sVqaQBQLEpssFq8eLHCwsLk5uamwMBARUdH680331Tfvn3l4pL71rCaNWvK29tbQ4YM0cGDB2W323XzzTdr8uTJ6t27dzGMAAAAAMCNwmYK8mzyG0BaWpoCAgKUmpoqf3//4i4HAABJ0kcb2hd3CeoV+3VxlwAA11RBskGpeyogAAAAAJQ0BCsAAAAAsIhgBQAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCgAAAAAsIlgBAAAAgEUEKwAAAACwiGAFAAAAABa5FXcBAADgr/WK/bq4SwAAXAEzVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFvFUwD8xxkiS0tLSirkSAAAAAMXpUia4lBGuhGD1J6dPn5YkhYeHF3MlAAAAAEqC06dPKyAg4IptbCY/8esGkpOTo0OHDsnPz082m624y7khpaWlKTw8XAcPHpS/v39xl4MbGOciSgrORZQUnIsoSa7F+WiM0enTp1WhQgW5uFz5LipmrP7ExcVFlSpVKu4yIMnf358PbZQInIsoKTgXUVJwLqIkKerz8a9mqi7h4RUAAAAAYBHBCgAAAAAsIlihxLHb7RoxYoTsdntxl4IbHOciSgrORZQUnIsoSUra+cjDKwAAAADAImasAAAAAMAighUAAAAAWESwAgAAAACLCFYAAAAAYBHBCiXCyy+/rLi4OHl7e6tMmTL56mOM0ciRI1WhQgV5eXmpZcuW2rFjR9EWiuveqVOn1Lt3bwUEBCggIEC9e/dWSkrKFfv069dPNpvNaYmNjb02BeO68c477ygqKkqenp5q0KCB1qxZc8X2q1evVoMGDeTp6akqVapo0qRJ16hSXO8Kci6uWrUq1+efzWbTTz/9dA0rxvXom2++0e23364KFSrIZrNp7ty5f9mnuD8XCVYoES5cuKCuXbvq73//e777jB07VuPHj9e///1vbdq0SaGhoYqPj9fp06eLsFJc73r27KktW7Zo8eLFWrx4sbZs2aLevXv/Zb8OHTro8OHDjmXhwoXXoFpcL2bNmqUnn3xSw4YN0+bNm3XbbbepY8eOOnDgQJ7t9+7dq7/97W+67bbbtHnzZj333HN64oknNHv27GtcOa43BT0XL/n555+dPgNvvvnma1Qxrlfp6emKjo7Wv//973y1LxGfiwYoQaZNm2YCAgL+sl1OTo4JDQ01r776qmPb+fPnTUBAgJk0aVIRVojr2c6dO40ks2HDBse29evXG0nmp59+umy/vn37mjvvvPMaVIjrVaNGjczDDz/stK1GjRrmn//8Z57tn332WVOjRg2nbQMHDjSxsbFFViNuDAU9F1euXGkkmVOnTl2D6nCjkmS++OKLK7YpCZ+LzFihVNq7d6+Sk5PVrl07xza73a4WLVpo3bp1xVgZSrP169crICBAjRs3dmyLjY1VQEDAX55Xq1atUvny5VWtWjUNGDBAR48eLepycZ24cOGCvv/+e6fPM0lq167dZc+79evX52rfvn17fffdd8rMzCyyWnF9u5pz8ZL69esrLCxMbdq00cqVK4uyTCBPJeFzkWCFUik5OVmSFBIS4rQ9JCTE8RpQUMnJySpfvnyu7eXLl7/iedWxY0d9/PHHWrFihV577TVt2rRJrVu3VkZGRlGWi+vE8ePHlZ2dXaDPs+Tk5DzbZ2Vl6fjx40VWK65vV3MuhoWF6b333tPs2bM1Z84cVa9eXW3atNE333xzLUoGHErC56LbNTkKbkgjR47UqFGjrthm06ZNatiw4VUfw2azOa0bY3JtA/J7Lkq5zynpr8+r7t27O/5cu3ZtNWzYUBEREfrqq6909913X2XVuNEU9PMsr/Z5bQcKqiDnYvXq1VW9enXHepMmTXTw4EGNGzdOzZs3L9I6gT8r7s9FghWKzGOPPaYePXpcsU1kZORV7Ts0NFTSxW8nwsLCHNuPHj2a69sKIL/n4o8//qgjR47keu3YsWMFOq/CwsIUERGh3bt3F7hW3HiCg4Pl6uqaa0bgSp9noaGhebZ3c3NT2bJli6xWXN+u5lzMS2xsrD766KPCLg+4opLwuUiwQpEJDg5WcHBwkew7KipKoaGhWrp0qerXry/p4rXhq1ev1pgxY4rkmCi98nsuNmnSRKmpqdq4caMaNWokSfr222+VmpqquLi4fB/vxIkTOnjwoFPoBy7Hw8NDDRo00NKlS3XXXXc5ti9dulR33nlnnn2aNGmi+fPnO21bsmSJGjZsKHd39yKtF9evqzkX87J582Y+/3DNlYjPxWv2mAzgCvbv3282b95sRo0aZXx9fc3mzZvN5s2bzenTpx1tqlevbubMmeNYf/XVV01AQICZM2eO2bZtm7n33ntNWFiYSUtLK44h4DrRoUMHU7duXbN+/Xqzfv16U6dOHZOQkODU5o/n4unTp82QIUPMunXrzN69e83KlStNkyZNTMWKFTkXkW8zZ8407u7uZsqUKWbnzp3mySefND4+Pmbfvn3GGGP++c9/mt69ezva//rrr8bb29sMHjzY7Ny500yZMsW4u7ubzz//vLiGgOtEQc/F119/3XzxxRdm165dZvv27eaf//ynkWRmz55dXEPAdeL06dOOfw9KMuPHjzebN282+/fvN8aUzM9FghVKhL59+xpJuZaVK1c62kgy06ZNc6zn5OSYESNGmNDQUGO3203z5s3Ntm3brn3xuK6cOHHC3HfffcbPz8/4+fmZ++67L9djhP94Lp49e9a0a9fOlCtXzri7u5vKlSubvn37mgMHDlz74lGqvf322yYiIsJ4eHiYmJgYs3r1asdrffv2NS1atHBqv2rVKlO/fn3j4eFhIiMjzcSJE69xxbheFeRcHDNmjKlatarx9PQ0gYGBplmzZuarr74qhqpxvbn0KP8/L3379jXGlMzPRZsx/39XFwAAAADgqvC4dQAAAACwiGAFAAAAABYRrAAAAADAIoIVAAAAAFhEsAIAAAAAiwhWAAAAAGARwQoAAAAALCJYAQAAAIBFBCsAQKlls9k0d+7c4i4DAACCFQCg5Dp69KgGDhyoypUry263KzQ0VO3bt9f69euLuzQAAJy4FXcBAABczj333KPMzEx98MEHqlKlio4cOaLly5fr5MmTxV0aAABOmLECAJRIKSkpWrt2rcaMGaNWrVopIiJCjRo10tChQ9WpU6c8+4wePVohISHasmWLJGndunVq3ry5vLy8FB4erieeeELp6emSpLfeekt16tRx9J07d65sNpvefvttx7b27dtr6NChkqSRI0eqXr16+vDDDxUZGamAgAD16NFDp0+fdrQ3xmjs2LGqUqWKvLy8FB0drc8//9zx+qlTp3TfffepXLly8vLy0s0336xp06ZJki5cuKDHHntMYWFh8vT0VGRkpBITEwvnzQQAFDmCFQCgRPL19ZWvr6/mzp2rjIyMK7Y1xmjQoEGaMmWK1q5dq3r16mnbtm1q37697r77bv3444+aNWuW1q5dq8cee0yS1LJlS+3YsUPHjx+XJK1evVrBwcFavXq1JCkrK0vr1q1TixYtHMfZs2eP5s6dqwULFmjBggVavXq1Xn31Vcfrzz//vKZNm6aJEydqx44dGjx4sHr16uXY5wsvvKCdO3dq0aJFSkpK0sSJExUcHCxJevPNNzVv3jx9+umn+vnnn/XRRx8pMjKy0N5PAEDRshljTHEXAQBAXmbPnq0BAwbo3LlziomJUYsWLdSjRw/VrVtX0sWHV3z22Wf68ssv9d1332np0qWqVKmSJKlPnz7y8vLSu+++69jf2rVr1aJFC6Wnp8tut6t8+fKaNGmS7rnnHtWvX1/du3fX66+/riNHjmj9+vVq3ry5Tp06JV9fX40cOVL/+te/lJycLD8/P0nSs88+q2+++UYbNmxQenq6goODtWLFCjVp0sRxzP79++vs2bP6z3/+ozvuuEPBwcGaOnVqrrE+8cQT2rFjh5YtWyabzVaUbysAoAgwYwUAKLHuueceHTp0SPPmzVP79u21atUqxcTEaPr06Y42gwcP1vr167VmzRpHqJKk77//XtOnT3fMfPn6+qp9+/bKycnR3r17ZbPZ1Lx5c61atUopKSnasWOHHn74YWVnZyspKclxLF9fX8c+IyMjHaFKksLCwnT06FFJ0s6dO3X+/HnFx8c7HXPGjBnas2ePJOnvf/+7Zs6cqXr16unZZ5/VunXrHPvq16+ftmzZourVq+uJJ57QkiVLiuptBQAUAYIVAKBE8/T0VHx8vIYPH65169apX79+GjFihOP1+Ph4/f777/r666+d+uXk5GjgwIHasmWLY9m6dat2796tqlWrSrp4OeCqVau0Zs0aRUdHq0yZMmrevLlWr16tVatWqWXLlk77dHd3d1q32WzKyclxHE+SvvrqK6dj7ty503GfVceOHbV//349+eSTOnTokNq0aaOnn35akhQTE6O9e/fqxRdf1Llz59StWzd16dKl8N5IAECR4qmAAIBSpWbNmk6/XXXHHXfo9ttvV8+ePeXq6qoePXpIuhhUduzYoZtuuumy+2rZsqUGDRqkzz//3BGiWrRooWXLlmndunUaNGhQgeqy2+06cOCA031Zf1auXDn169dP/fr102233aZnnnlG48aNkyT5+/ure/fu6t69u7p06aIOHTro5MmTCgoKyncdAIDiQbACAJRIJ06cUNeuXfXAAw+obt268vPz03fffaexY8fqzjvvdGp711136cMPP1Tv3r3l5uamLl266B//+IdiY2P16KOPasCAAfLx8VFSUpKWLl2qt956S5JUu3ZtlS1bVh9//LG+/PJLSRfD1pAhQyRJzZo1y3e9fn5+evrppzV48GDl5OSoWbNmSktL07p16+Tr66u+fftq+PDhatCggWrVqqWMjAwtWLBAt9xyiyTp9ddfV1hYmOrVqycXFxd99tlnCg0NVZkyZQrh3QQAFDWCFQCgRPL19VXjxo31+uuva8+ePcrMzFR4eLgGDBig5557Llf7Ll26KCcnR71795aLi4vuvvturV69WsOGDdNtt90mY4yqVq2q7t27O/rYbDa1aNFCc+fO1W233SZJqlu3rgICAlSlShX5+/sXqOYXX3xR5cuXV2Jion799VeVKVNGMTExjno9PDw0dOhQ7du3T15eXrrttts0c+ZMx3jHjBmj3bt3y9XVVbfeeqsWLlwoFxeu2geA0oCnAgIAAACARXwNBgAAAAAWEawAAAAAwCKCFQAAAABYRLACAAAAAIsIVgAAAABgEcEKAAAAACwiWAEAAACARQQrAAAAALCIYAUAAAAAFhGsAAAAAMAighUAAAAAWPR/iM428UPeF3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Skewness data for Model 2\n",
    "skewness_model2 = [-0.05, -0.38, -0.62, -1.17, -1.19, 0.97, 0.10]\n",
    "\n",
    "# Variable names for Model 2\n",
    "variables_model2 = ['ACC', 'SDC', 'DUS', 'DSS', 'DPS', 'DSC', 'DIS']\n",
    "\n",
    "# Plotting skewness for Model 2\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=skewness_model2, y=variables_model2, palette=\"viridis\")\n",
    "plt.title('Skewness for Model 2 Variables')\n",
    "plt.xlabel('Skewness')\n",
    "plt.ylabel('Variables')\n",
    "\n",
    "# Adding data points to the bars\n",
    "for i in range(len(skewness_model2)):\n",
    "    ax.text(skewness_model2[i], i, f'{skewness_model2[i]:.2f}', ha='left', va='center', color='black')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96470b9b",
   "metadata": {},
   "source": [
    "## 5.5. Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4189dbe",
   "metadata": {},
   "source": [
    "### Hypothesis Testing for Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81cfe0fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1 F-statistic: 41.184773278043544\n",
      "Model 1 p-value (F-test): 1.7719819035065024e-13\n",
      "Individual Coefficient p-values (t-test) for Model 1:\n",
      "const: 0.3459100134441736\n",
      "internet_access: 9.831638939501509e-05\n",
      "mobile_utility_payment: 0.09440462482524704\n",
      "digital_payment: 3.032239454019748e-06\n",
      "mobile_phone_ownership: 0.07693681973223013\n",
      "mobile_bill_payment: 0.8790651297196372\n"
     ]
    }
   ],
   "source": [
    "# Line of Best fit for Model 1\n",
    "ols_model1\n",
    "\n",
    "# Overall Model Significance Test (F-test) for Model 1\n",
    "f_statistic, f_p_value = ols_model1.fvalue, ols_model1.f_pvalue\n",
    "print(\"Model 1 F-statistic:\", f_statistic)\n",
    "print(\"Model 1 p-value (F-test):\", f_p_value)\n",
    "\n",
    "# Individual Coefficient Significance Test (t-test) for Model 1\n",
    "t_test_results = ols_model1.t_test(np.eye(len(ols_model1.params)))\n",
    "\n",
    "print(\"Individual Coefficient p-values (t-test) for Model 1:\")\n",
    "for idx, variable in enumerate(indep_var_model1.columns):\n",
    "    print(f\"{variable}: {t_test_results.pvalue[idx]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa1d9f2",
   "metadata": {},
   "source": [
    "### Hypothesis Testing for Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a79e0fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 2 F-statistic: 7.352573148811251\n",
      "Model 2 p-value (F-test): 8.644605466870827e-05\n",
      "Individual Coefficient p-values (t-test) for Model 2:\n",
      "const: 0.7898080501069904\n",
      "source_data_capacity: 0.011083716383894056\n",
      "spi_data_use: 0.38538856115979137\n",
      "spi_data_services: 0.0023663090448723797\n",
      "spi_data_products: 0.15678092693555912\n",
      "spi_data_sources: 0.0001999381497366182\n",
      "spi_data_infrastructure: 0.0075410358992607385\n"
     ]
    }
   ],
   "source": [
    "# Line of best fit for Model 2\n",
    "ols_model2\n",
    "\n",
    "# Overall Model Significance Test (F-test) for Model 2\n",
    "f_statistic, f_p_value = ols_model2.fvalue, ols_model2.f_pvalue\n",
    "print(\"Model 2 F-statistic:\", f_statistic)\n",
    "print(\"Model 2 p-value (F-test):\", f_p_value)\n",
    "\n",
    "# Individual Coefficient Significance Test (t-test) for Model 2\n",
    "t_test_results = ols_model2.t_test(np.eye(len(ols_model2.params)))\n",
    "print(\"Individual Coefficient p-values (t-test) for Model 2:\")\n",
    "for idx, variable in enumerate(indep_var_model2.columns):\n",
    "    print(f\"{variable}: {t_test_results.pvalue[idx]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f353b4",
   "metadata": {},
   "source": [
    "# 6. Phase 5: Share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f5d3fe41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression results for Model 1.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Exporting regression result as an Excel File\n",
    "# Convert the regression results summary to a dataframe\n",
    "regression_results_model1 = ols_model1.summary()\n",
    "regression_results_df_model1 = pd.read_html(regression_results_model1.tables[1].as_html(), header=0, index_col=0)[0]\n",
    "\n",
    "# Export the dataframe to an Excel file\n",
    "regression_results_df_model1.to_excel(\"regression_results_model1.xlsx\")\n",
    "\n",
    "print(\"Regression results for Model 1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5da9441b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptive statistics for Model 1.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Exporting descriptive stats result as an excel file for Model 1\n",
    "desc_model1.to_excel('desc_model1.xlsx')\n",
    "\n",
    "print('Descriptive statistics for Model 1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "61d9f52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptive statistics for Model 2.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Exporting descriptive stats result as an excel file for Model 2\n",
    "desc_model2.to_excel('desc_model2.xlsx')\n",
    "\n",
    "print('Descriptive statistics for Model 2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "10795e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36.25275297, 67.71857471, 52.46712807, 47.98902335, 18.8266856 ,\n",
       "       28.58058205, 44.9136686 , 53.01701255, 67.08734359, 50.64486208,\n",
       "       56.00199781, 44.26275099, 62.90811648, 39.46190352, 59.06135446,\n",
       "       61.11429563, 45.06825223, 38.74550916, 53.30689915, 36.97488527,\n",
       "       33.13351753, 70.41003994, 44.86373269, 43.75912241, 46.66711801,\n",
       "       40.7967662 , 62.99003982, 34.50329914, 99.3950081 , 19.99720639,\n",
       "       41.06566196, 50.30056932, 51.74385254, 41.88368771, 59.68677996])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values_model2"
   ]
  },
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    "# 7. Phase 6: Conclusion (Act)"
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