{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='text-align:center; font-weight:bold; color: orange'>Evaluating Marketing Campaign Effectiveness for New Menu Items: An A/B Testing Approach</h1>\n",
    "<h5 style='text-align:center; font-style:italic'>LingAdeu</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from scipy.stats import skew, kurtosis, kstest, shapiro \n",
    "from scipy.stats import levene\n",
    "from scipy.stats import kruskal\n",
    "from scikit_posthocs import posthoc_dunn \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import ptitprince as pt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1 Introduction**\n",
    "In today's competitive fast-food industry, choosing effective marketing strategies play crucial roles in maximizing the impact of new product launch on sales. Recently, a fast-food chain launched three different campaigns to promote its new menu items in 137 locations, selected randomly, in four weeks for a trial phase. Based on this experiment, the Marketing Manager aims to identify which promotional campaign should be chosen for the larger scale campaign. The ideal result is a campaign which can generate the largest amount of sales during the testing phase.\n",
    "\n",
    "Due to the criticality of the decision for selecting the most optimal campaign to promote new menu items, the challenge here is to find which campaign generated the highest sale in the experiment phase. For this reason, a systematic approach to evaluate performance of marketing campaign is crucial for a successful product launch. My goal in this project is to evaluate the effectiveness of three marketing campaigns in promoting new menu items by comparing the sales performance of each campaign to determine which promotion received the highest sales.\n",
    "\n",
    "Additionally, for clarity purposes, below is the null hypothesis ($H_0$) and the alternative hypothesis ($H_1$) in this project.\n",
    "- Null hypothesis ($H_0$): There is no significant difference in sales performance across three marketing campaigns. Therefore, any observed differences in sales are due to random chance.\n",
    "- Alternative hypothesis ($H_1$): There is a significant difference in sales performance across the three promotional campaigns. At least one campaign generates different sales (maybe highest or lowest) compared to the other two.\n",
    "\n",
    "The dataset was obtained from [Kaggle](https://www.kaggle.com/datasets/chebotinaa/fast-food-marketing-campaign-ab-test). This dataset contains information about marketing campaign captured via IBM Watson Analytics. It consists of seven columns but two variables will be mainly used for this project, namely `Promotion` and `SalesInThousands`, to achieve the objective.\n",
    "- `MarketingID`: unique identifier for market\n",
    "- `MarketSize`: size of market area by sales\n",
    "- `LocationID`: unique identifier for store location\n",
    "- `AgeOfStore`: age of store in years\n",
    "- `Promotion`: one of three promotions that were tested\n",
    "- `Week`: one of four weeks when the promotions were run\n",
    "- `SalesInThousands`: sales amount for a specific location, promotion, and week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2 Data Preparation**\n",
    "This section deals with data preparation, including data cleaning. This broad term \"data cleaning\" covers whether there are missing values, how many the duplicates are, and how many unique values are. These will be required because the cleanliness of the dataset will affect the trustworthiness of the conclusions drawn from this A/B test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "df = pd.read_csv(\"../data/Watson_Marketing_Campaign.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe contains 548 rows and 7 cols.\n",
      "- 6 are numeric cols\n",
      "- 1 are object cols\n"
     ]
    },
    {
     "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>ColumnName</th>\n",
       "      <th>Nrow</th>\n",
       "      <th>DataType</th>\n",
       "      <th>NAPct</th>\n",
       "      <th>DuplicatePct</th>\n",
       "      <th>UniqueValue</th>\n",
       "      <th>Sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MarketID</td>\n",
       "      <td>548</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MarketSize</td>\n",
       "      <td>548</td>\n",
       "      <td>object</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>[Medium, Small, Large]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LocationID</td>\n",
       "      <td>548</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AgeOfStore</td>\n",
       "      <td>548</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>[4, 5, 12, 1, 10, 15, 6, 22, 8, 19, 11, 13, 3,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Promotion</td>\n",
       "      <td>548</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>[3, 2, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>week</td>\n",
       "      <td>548</td>\n",
       "      <td>int64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>[1, 2, 3, 4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SalesInThousands</td>\n",
       "      <td>548</td>\n",
       "      <td>float64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517</td>\n",
       "      <td>[33.73, 35.67, 29.03, 39.25, 27.81, 34.67, 27....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ColumnName  Nrow DataType  NAPct  DuplicatePct  UniqueValue  \\\n",
       "0          MarketID   548    int64    0.0           0.0           10   \n",
       "1        MarketSize   548   object    0.0           0.0            3   \n",
       "2        LocationID   548    int64    0.0           0.0          137   \n",
       "3        AgeOfStore   548    int64    0.0           0.0           25   \n",
       "4         Promotion   548    int64    0.0           0.0            3   \n",
       "5              week   548    int64    0.0           0.0            4   \n",
       "6  SalesInThousands   548  float64    0.0           0.0          517   \n",
       "\n",
       "                                              Sample  \n",
       "0                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  \n",
       "1                             [Medium, Small, Large]  \n",
       "2  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 10...  \n",
       "3  [4, 5, 12, 1, 10, 15, 6, 22, 8, 19, 11, 13, 3,...  \n",
       "4                                          [3, 2, 1]  \n",
       "5                                       [1, 2, 3, 4]  \n",
       "6  [33.73, 35.67, 29.03, 39.25, 27.81, 34.67, 27....  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create function to inspect df\n",
    "def inspect_dataframe(df):\n",
    "    print(f'The dataframe contains {df.shape[0]} rows and {df.shape[1]} cols.')\n",
    "    print(f\"- {len(df.select_dtypes(include='number').columns)} are numeric cols\")\n",
    "    print(f\"- {len(df.select_dtypes(include='O').columns)} are object cols\")\n",
    "    summary = {\n",
    "        'ColumnName': df.columns.values.tolist(),\n",
    "        'Nrow': df.shape[0],\n",
    "        'DataType': df.dtypes.values.tolist(),\n",
    "        'NAPct': (df.isna().mean() * 100).round(2).tolist(),\n",
    "        'DuplicatePct': (df.duplicated().sum()/len(df)*100).round(2),\n",
    "        'UniqueValue': df.nunique().tolist(),\n",
    "        'Sample': [df[col].unique() for col in df.columns]\n",
    "    }\n",
    "    return pd.DataFrame(summary)\n",
    "\n",
    "# inspect df\n",
    "inspect_dataframe(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- As described earlier, the dataset consists of 7 columns (6 numeric columns and 1 object columns) and 548 rows.\n",
    "- This dataset is quite clean as there are no duplicates and missing values so no further cleaning will be required. If there are duplicates found, there is only one treatment, i.e., removal. But for missing values, the treatment will not be straightforward, e.g., direct removal, depending on the mechanism of missingness.\n",
    "- Moreover, as this project is mainly interested in seeking which promotional campaign performs better at generating sales, column `Promotion` will be the primary interest. This will be the main column for grouping. And as can be seen, this column contains three unique values, namely `1`, `2`, and `3`, representing each campaign.\n",
    "- Because this dataset is clean enough, an exploratory data analysis, particularly to check parametric assumptions, can be done. Parametric assumptions is a collection of assumptions or conditions for statistical tests to satisfy. If one or more assumptions are violated, the conclusions drawn from the test results will be invalid. For example, using a one-way ANOVA (analysis of variance) test will be misleading if the assumptions, e.g., normal distribution, homoscedasticity, and independence of observation, are not met. For this reason, other options such as Kruskal-Wallis test, Welch's ANOVA, or Friedman test.\n",
    "    - Just an additional note, Kruskal-Wallis is a non-parameteric counterpart of one-way ANOVA when the independent groups being compared is more than 2. The term \"non-parametric\" means that this statistical test does not assume parametric assumptions.\n",
    "    - Welch's ANOVA is a variation of ANOVA with an adjustment in equal variances between groups (homoscedasticity). While the assumption of homoscedasticity is okay to be violated, this test remains assuming normality of data distribution.\n",
    "    - Friedman test is more suitable for repeated measures. This is a non-parametric alternative when the parametric assumptions are not met. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3 Parametric Assumption Tests**\n",
    "The main focus of this exploratory data analysis is to understand whether variable `SalesInThousand` grouped by `Promotion` meets the parametric assumptions such as normality of data distribution, independent observation, and equal variances between groups. Before moving forward to testing each parametric assumption, one question might arise: Why do we bother to check parametric assumptions, not directly choose a non-parametric test which does not assume normal distribution?\n",
    "\n",
    "Unlike their parametric counterparts, non-parametric tests have some limitations such as less power, rank-based calculation, and lack of informativeness. Nonparametric tests usually have less statistical power to detect actual difference between groups when the sample size is small. Dissimilar to parametric tests relying calculations on the values themselves, nonparametric tests normally depend on ranks. As a consequence, a loss of information can occur here. Last but not least, the calculation of non-parametric tests rely on median as the measure of central tendency. While median itself is useful to inform the middle value in the data distribution, median does not take into account the magnitude of every value in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.1 Are sales by promotions normally distributed?**\n",
    "This first question deals with the normality of data distribution which can be tested both visually and numerically. Visually, normality can be examined by exploiting a histogram, a boxplot, or a quantile-quantile plot. Here, I will use a raincloud plot, a modification of a histogram and a boxplot. This plot also displays every single point in the dataset. The advantage of this visual test is that we can get the advantages of three types of data visualization techniques in one plot such as clearer examination on data shape and distribution, including central tendency, skewness, kurtosis, and presence of outlier.\n",
    "\n",
    "As the best practice of normality test is both providing the visual and numerical tests, we also include the numerical tests such as kurtosis, skewness, Kolmogrov-Smirnov, and Shapiro-Wilk test. Kurtosis and skweness test inform us the shape of the distribution numerically. In the interpretations, kurtosis displays the peakness of a distribution. Positive kurtosis (leptokurtic) indicates a sharp peak, and negative kurtosis (platykurtic) shows the distribution has a flatter peak. Furthermore, both Shapiro-Wilk test and Kolmogorov-Smirnov test are used to tell how well data fits a specific distribution and provide *p*-values to interpret the results. The combination of the two tests provides a more comprehensive assessment. If both tests failt to reject the null hypothesis at $\\alpha$ at 0.05, the data does not fit the normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Promotion 3', 'Promotion 2', 'Promotion 1'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert num to string for better readings\n",
    "df['Promotion'] = df[\"Promotion\"].replace({1: 'Promotion 1', 2: 'Promotion 2', 3: 'Promotion 3'})\n",
    "df[\"Promotion\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- For better readability, the numerical values in `Promotion` were converted into strings by adding \"Promotion\" at the beginning.\n",
    "- Moreover, as there are three groups in the variable, some options for testing the group differences are one-way ANOVA test and Kruskal-Wallis test. In general, they shared a common purpose: Both statistical test test differences between more than 2 independent groups with a null hypothesis of all groups having the same mean (for one-way ANOVA) or distribution (Kruskal-Wallis). However, as mentioned earlier, one-way ANOVA is a parametric test while Kruskal-Wallis is non-parametric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Promotion 1</th>\n",
       "      <th>skewness</th>\n",
       "      <td>0.8585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>0.0284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ks_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sw_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Promotion 2</th>\n",
       "      <th>skewness</th>\n",
       "      <td>0.9205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>0.6614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ks_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sw_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Promotion 3</th>\n",
       "      <th>skewness</th>\n",
       "      <td>0.7642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.1787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ks_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sw_pvalue</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Value\n",
       "Promotion                    \n",
       "Promotion 1 skewness   0.8585\n",
       "            kurtosis   0.0284\n",
       "            ks_pvalue  0.0000\n",
       "            sw_pvalue  0.0000\n",
       "Promotion 2 skewness   0.9205\n",
       "            kurtosis   0.6614\n",
       "            ks_pvalue  0.0000\n",
       "            sw_pvalue  0.0000\n",
       "Promotion 3 skewness   0.7642\n",
       "            kurtosis  -0.1787\n",
       "            ks_pvalue  0.0000\n",
       "            sw_pvalue  0.0000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create function to apply normality tests\n",
    "def test_normality(group):\n",
    "    return pd.Series({\n",
    "        'skewness': skew(group),\n",
    "        'kurtosis': kurtosis(group),\n",
    "        'ks_pvalue': kstest(group, 'norm').pvalue,\n",
    "        'sw_pvalue': shapiro(group).pvalue\n",
    "    })\n",
    "\n",
    "# apply the normality tests\n",
    "(df.groupby('Promotion')['SalesInThousands']\n",
    " .apply(test_normality)\n",
    " .to_frame()\n",
    " .rename(columns={'SalesInThousands':'Value'})\n",
    " .round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- All sales data by promotions deviate from normal distribution with positive skewness, indicating the presence of a few high sales figures in each promotion which affect the overall distribution.\n",
    "- Moreover, the normality tests also indicate non-normal distributions. For example, with *p* = 0.0000 on Shapiro-Wilk test, the amount of sales generated by `Promotion 1` is significantly different from a normal distribution.\n",
    "- Given the non-normality, non-parametric methods might be more appropriate for statistical analysis for the sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the figure\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# use raincloud to display distribution\n",
    "pt.RainCloud(x='Promotion', y='SalesInThousands', data=df, \n",
    "             palette='Set2', bw=.2, width_viol=.6, ax=None, orient='h')\n",
    "plt.title('Distributions of data in all promotions are not normal')\n",
    "plt.xlabel('Sales in Thousands')\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- All promotions in terms of sales indicate non-normal distributions, in line with the previous findings in Section 3.1.1 (numerical test).\n",
    "- As can be seen all promotions appear to be positively skewed since the majority of data are located on the left and some are on the right of the distribution. This kind of skewness, judging solely from the histogram in the raincloud plot, indicates the presence of outliers which can be further examined from data points beyond the lower and upper whisker of the boxplots.\n",
    "- To extend our understanding on the outliers, we can use interquartile range (IQR) based outlier detection to quantify the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of outliers in 'Promotion 1' is 12 or 6.98% of the total data (172 rows).\n",
      "The number of outliers in 'Promotion 2' is 24 or 12.77% of the total data (188 rows).\n",
      "The number of outliers in 'Promotion 3' is 10 or 5.32% of the total data (188 rows).\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create function to detect outliers using IQR\n",
    "def detect_outliers(df, column, category):\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    outliers = df.loc[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "    print(f\"The number of outliers in '{category}' is {len(outliers)} or {(len(outliers)/len(df)*100):.2f}% of the total data ({len(df)} rows).\")\n",
    "\n",
    "# apply the function to each group\n",
    "df.groupby('Promotion').apply(lambda x: detect_outliers(x, 'SalesInThousands', x['Promotion'].iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- The code above computed the number and rate of outliers in each promotion. \n",
    "- `Promotion 2` appear to have the highest number of outliers, constituting 24 points (12.77%).\n",
    "- `Promotion 1` and `Promotion 3`, on the other hand, have similar rate of outliers (6.98 vs 5.32%).\n",
    "- While this finding does not significantly adds information about the distribution, in addition to providing the number of outliers, this finding also guides us to select a statistical test which is more robust to outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.2 Are the variances of sales by promotions equal?**\n",
    "Homogeneity of variance is tested because some tests, especially a one-way ANOVA test, require us to meet this criteria. To test this assumption, I rely on Levene's test (Levene, 1960) with a null hypothesis of equal variances in the groups (`Promotion 1`, `Promotion 2`, and `Promotion 3`). For this reason, if the *p*-value is less than 0.05, the decision is to reject the $H_0$ and the assumption of equal variances across promotional campaigns is violated. \n",
    "\n",
    "But before doing the more formal inspection using Levene's test, I will firstly check the spread of data in each group numerically using variances. This initial inspection will provide a numerical sense of the data spread in each promotional campaign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>Promotion</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Promotion 1</td>\n",
       "      <td>274.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion 2</td>\n",
       "      <td>228.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion 3</td>\n",
       "      <td>281.106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Promotion  Variance\n",
       "0  Promotion 1   274.028\n",
       "1  Promotion 2   228.281\n",
       "2  Promotion 3   281.106"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get variance of each group\n",
    "df.groupby('Promotion')['SalesInThousands'].var().reset_index(name='Variance').round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: \n",
    "- The variances ($s^2$) in `Promotion 1` and `Promotion 3` are similar, indicating similar variability in these groups. Variance for `Promotion 2`, on the other hand, is lower than the other two although the difference is not large. From these variances, it is possible that the assumption of homoscedasticity will be met. \n",
    "- One important question here maybe why we still need to use Levene's test to assess homogeneity of variances while variances of each promotional campaign can be computed and compared.\n",
    "- One reason is about how they work. Variance indeed can give a sense of how data in different campaigns is distributed but variance is sensitive to outliers since it uses sample mean ($\\bar{x}$). When there are outliers and/or the distribution is not normal, variance may not be a good choice to evaluate the homogeneity of variances. \n",
    "    - To note, the distributions of all campaigns are not normal and they contain outliers.\n",
    "- Levene's test, on the other hand, is more robust when the distibution is not perfectly normal or small sample size ([Nordstokke & Colp, 2014](https://www.uv.es/revispsi/articulos2.14/10NORDSTOKKE.pdf)). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating separate arrays for each promotion\n",
    "sales_promo1 = df[df['Promotion'] == 'Promotion 1']['SalesInThousands']\n",
    "sales_promo2 = df[df['Promotion'] == 'Promotion 2']['SalesInThousands']\n",
    "sales_promo3 = df[df['Promotion'] == 'Promotion 3']['SalesInThousands']\n",
    "\n",
    "# performing Levene's test\n",
    "levene_stat, p_value = levene(sales_promo1, sales_promo2, sales_promo3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>Test Statistic</th>\n",
       "      <th>p-value</th>\n",
       "      <th>Conclusion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Levene's Test</td>\n",
       "      <td>1.269679</td>\n",
       "      <td>0.281751</td>\n",
       "      <td>Variances are not significantly different</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Test  Test Statistic   p-value  \\\n",
       "0  Levene's Test        1.269679  0.281751   \n",
       "\n",
       "                                  Conclusion  \n",
       "0  Variances are not significantly different  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display output\n",
    "pd.DataFrame({\n",
    "    'Test': ['Levene\\'s Test'],\n",
    "    'Test Statistic': [levene_stat],\n",
    "    'p-value': [p_value],\n",
    "    'Conclusion': ['Variances are significantly different' if p_value < 0.05 else 'Variances are not significantly different']\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: From the Levene's test output, the variances across three different marketing campaigns were similar (*F* = 1.269, *p* = 0.281).Based on *p* > 0.05, the assumption of homogeneity of variances is tenable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.3 Are there any potential measurement errors?**\n",
    "This section is concerned with checking whether there are weird values in the dataset. For example, the number of weeks within a month is not possible for either negative integer or greater than 4. However, since our main interest is column `Promotion` and `SalesInThousand`, other columns will not be investigated further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketID</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>AgeOfStore</th>\n",
       "      <th>week</th>\n",
       "      <th>SalesInThousands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MarketID  LocationID  AgeOfStore   week  SalesInThousands\n",
       "count     548.0       548.0       548.0  548.0             548.0\n",
       "mean        6.0       480.0         9.0    2.0              53.0\n",
       "std         3.0       288.0         7.0    1.0              17.0\n",
       "min         1.0         1.0         1.0    1.0              17.0\n",
       "25%         3.0       216.0         4.0    2.0              43.0\n",
       "50%         6.0       504.0         7.0    2.0              50.0\n",
       "75%         8.0       708.0        12.0    3.0              60.0\n",
       "max        10.0       920.0        28.0    4.0             100.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hZGQkIiMj1S6XU8HBwQLIuAMgs9wm0t+9eyfMzMxUkuVCCLFp0yYBQAwZMiTHccnbmOvXrytNl38+srqrRNPnNStr1qwRAMSyZcuUpsuPq8aNG6ssI5PJFHffZD6XZ9VmeXl5CQBqf8BLT08XtWrVEvr6+uLVq1dCCCHOnz8vAIiuXbvmeD+IiIio4BiAiIiIiszkyZPx+++/47fffsPEiRNRtmxZteXCw8ORmJiIevXqwdPTU2X+F198gTFjxuD06dNIT0/P8cMO69atizJlyqhM9/DwwI0bN/DixQvFmMH//PMPACiNXZtZ06ZNAQAXLlxQTDt9+jQAoEePHmq3UatWLVy+fFnt+p49e4Z9+/bhzp07SEhIUIypK5VKcf/+/RztX3aaNWsGJycnHD16FNHR0bCzswOQMU7url27YGZmhm7duinKy+uga9euautYPmZ65jrQpHv37hgxYgR27tyJ5cuXw9DQEEDGvoeGhsLV1RXe3t4qyxVF3aiTk2Ng2bJluHDhgqLe6tati1OnTmHIkCEYN24cqlWrluftv3nzBkFBQbhx4wbi4uKQlpYGIGMM6Ddv3iAmJgalS5dWWsbBwQH16tXL074AyPF7qUlYWBgAoG/fvirzjIyM0KNHDyxduhRhYWFq3+/cys3nWh5br169VMqXKVMGbdu2xZ49e/Idk1zz5s0hlUqVphkYGKBChQq4fPky3rx5AwcHhxyvr23btirTPDw8AGSMrV1Qmjdvrvh8yrm5uQHIeHDlx2Nty+dfuXIl3+dRuTp16qBcuXI5jvnEiRNIS0tDx44dFWP458TVq1fxzz//4NGjR/jw4QOEEHj79i0A5Pv8YmZmhi5dumDLli04cOCAUj389ddfAIB+/fqpLJecnIzDhw/jwoULePXqFWQyGQDg33//VcRVo0YNleU6d+6cpzhPnTqFEydO4NmzZ0hKSoIQQnE8ZVUH6j5DhoaG8Pf3x5IlSxAWFoYmTZpkuc3o6Ghcu3YNlSpVQvXq1VXmSyQSeHt74+rVqwgPD4evry88PT1hZmaGAwcOYOHChejbty8cHR3ztM9ERESUe0ykExERFSFbW1uMGjUKCxYswPz58/Hrr7+qLSd/GOHHD8KTs7KygqWlJeLj4xEbG6s2iaaOk5OT2umlSpUCkJG8kIuMjASAbBN9r1+/VryWJx7Kly+vtqyzs7PaRPrixYvx3XffISUlReO28ktPTw+9e/fGwoULsW3bNnz99dcAgP379yMhIQF9+vSBmZmZory8DqZMmYIpU6Zkud6kpKQcbd/CwgKdOnXCjh07cPjwYXTq1AkAsHXrVqSnp6NPnz4qD5QsqrpRR77/2SXzMh8DK1asQNeuXbF+/XqsX78eZcuWhY+PD7p37w5/f3/o6+vnaNtbt27FsGHD8O7duyzLvH37ViWR7uzsrHFf+vbtqzbBrW5f8iq7z698+rNnz/K9LSB3n2t5bFklWrOKuShiy+v68rouTdQd8+bm5gAAe3t7tcexfH5+z6NyWR3LWXny5AkAwN3dPUflZTIZBg4ciK1bt2ZZRp5Qz4++fftiy5Yt+OuvvxSJ9JcvX+Lo0aNwcnJCs2bNlMr/+++/6Ny5s6LuchNXbussPj4e3bt3x7Fjx3K9rew+Q+oeKpyZfP/u37+f5YOe5eTHh4WFBdasWYNhw4Zh0qRJmDRpEjw8PNCiRQt88cUXBfLDHBEREWWNiXQiIqIiNnHiRKxcuRK///47Jk2alOf1ZPfFW52cXrkOQHHVs7+/v1Jy+WPqrpjPjXPnzmH8+PGwtLTE0qVL0bx5c9jb28PIyAgA4OjoWKBXmvbr1w8LFy7Eli1bFIl0+ZWRHydY5XXQpEmTHCencrL9HTt2YMuWLYpEelbbL8q6ke+rumkDBgzQuGzDhg0Vr2vWrIlbt27h8OHDOHjwIE6cOIHt27dj+/bt+Oyzz3DixAmVK5Q/9ujRIwwcOBAAsGTJEnTo0AHlypWDiYkJAKBx48Y4e/YshBAqyxobG2vcv3bt2mV5JwgA2NjYaIytIOTls6vu/ZHLzee6qBV0bAW1Pk31md12iuo8mtWxXFAWL16MrVu3okaNGliwYAHq1KkDa2trGBoa4t69e6hcubLaz1hutW3bFra2tjhw4ADi4+NhaWmJv//+G2lpaejdu7dSfQoh0LNnT0RGRmL48OEYPnw43NzcYG5uDolEgh9++AHz5s3LMq7c1tnkyZNx7Ngx+Pj4YObMmahevTqsrKygr6+Pf/75B76+vgVSB+rIjw17e3v4+vpqLJs5ad+7d2+0bt0ae/fuxT///IPQ0FCsWrUKq1atwrhx47Bo0aJCiZeIiIiYSCciIipyNjY2+PrrrzFv3jzMmzdP7W3Z8mmPHj1Su474+HjExcXBxMQE1tbWhRKnk5MT7t69i++++w5169bN0TIODg6IjIzEkydPULlyZZX58ismMwsMDAQAzJkzRyVhm5iYiKioqDxEn7WaNWuievXqOHfuHB4+fAhra2scPHgQtra2KsNGyK9+7dq1K8aPH18g2//8889RunRpBAUF4d27d3j8+DGuXr2K2rVro2rVqkplC7Ju5MlrdVd4p6WlqV2Xk5MT/vvvPyxatCjHdz0AGcmsrl27omvXrgCAmzdvok+fPjh79izWrl2LkSNHalz+4MGDkMlkmDBhAr755huV+Q8fPsxxLHLy93Lo0KHw8/PL9fK54ejoiLt37+LRo0dqh7ZRd6V/Xt6fvHBwcFDE9vHxBmR9zimONNWpunNRYcjLeTSv5HcC/ffffzkqLz+/bN26VeU4zctnLCsGBgYICAjA//73P+zatQuDBw/OcliXO3fu4M6dO6hXrx5+++03lXUVZFxARh3o6+sjKCgIFhYWudpWVp8V+fTshlyRn5NsbGywcePGHEacwdbWFkOHDsXQoUMhhMCRI0cQEBCAxYsXY/DgwfkaUouIiIiypruXrxAREX3Cxo8fj1KlSmH16tVqh3eoW7cuTExMEB4ernZ81s2bNwPIGC6gsK5GbdOmDYD/S7bkhPy28l27dqnMe/DgAa5cuaIyPTY2FoD6IRt27Nih9mpAeYIsNTU1x7FlJr/ye8uWLdi5cydkMhkCAgJgYKB8jUFe6iA7hoaG6NGjBz58+IA9e/ZkeTU6kLe6yYp8HOp79+6pzDt+/LjaoWMKav+rVauGUaNGAQBu3LiRbXlN+33y5Em8fPky1zEUxnuZFfm41+qGzJDJZNixY4dSOSBv709+Ytu+fbvKvJiYGMWY3p8CTXUaHBxcJDEU5XHXvHlz6Ovr48iRIzn6oUDT50zd8ZEfmc+59+/fx8WLF1G9enXUrFkzxzHFxsYW+PsWGxsLCwsLlSQ6kH0dqJufmpqqaP80jY8OZOyjp6cnbt26pfYYzSmJRIJ27dqhQ4cOADJ+uCQiIqLCwUQ6ERGRFpQpUwZjxoxBcnIy1q1bpzLfzMwMgwcPRnp6OkaNGoX3798r5t27dw+zZ88GAIwZM6bQYvzqq69gZ2eHBQsWYPXq1SpDIaSmpuLIkSNKidGvvvoKQMaQAbdu3VJMT0xMxJgxY9QOpyB/UOC6deuUkoW3bt3C5MmT1cYmv9Lv7t27edo3+Vjk8nF7AfWJ7IYNG6JNmzY4ffo0Ro0ahYSEBJUy165dw+HDh3O1fflVmH/99Re2bt2qGLv9Y3mpm6zIxyHevHmz0tjDERERWR5H48ePh4mJCSZMmIDdu3erzE9OTsbOnTvx9OlTAMCHDx+wbNkyxMXFKZVLT09X1FFW4+dnJt/vzZs3Kx37z549w/Dhw7NdXh0/Pz9UrVoVf/31F2bNmqUynrYQAqdPn1Y8MDc/hgwZAhMTE/z99984cOCAYnp6ejp++OEHPHv2DHXr1lUazzgv709eDBo0CEZGRvjrr78QEhKimJ6SkoJvv/1Wqb6LOx8fHwDAb7/9hjdv3iimX716FdOmTSuSGPJyHs0rR0dH9O/fH0lJSRgwYIDSPgMZD7c8f/684m/55+z3339XKrdz50788ccf+Y4ns0aNGsHd3R3Hjx/HL7/8AkD9ObdixYrQ09PDsWPHlH5ETkpKwvDhwxETE1OgcXl4eCA2Nhbbtm1Tmv7rr7/i+PHjGpc9deoU1q9frzRt+vTpePz4MWrWrKn0Q1lWpk6divT0dPj5+eHq1asq89+8eYM1a9Yo/r5y5Qp2796tePiqXExMjOK9zck5loiIiPKGiXQiIiItGT9+PCwsLJCYmKh2/rx581C3bl0EBwfDzc0NPXv2RIcOHeDl5YWoqCiMGTNGMcZ2YbCyssLevXthaWmJr776Cq6urmjfvj369u2LVq1awdbWFu3atcODBw8UyzRt2hRjx47FmzdvUKdOHXz++ecICAiAu7s7bt26pYg38xjZgwYNgr29Pfbt24fKlSsjICAAbdq0Qa1atdC0aVO1D3RzdXVFzZo1cenSJTRo0ACDBg3C0KFDERQUlKN9c3Z2RtOmTXH79m2EhobC3d0djRo1Ult28+bNqF27NlauXAkXFxe0aNECffv2RceOHeHs7IxatWrlOpHu7e0NFxcXHD58GBEREWjRooXaYQDyUjdZcXd3R//+/REbG4tatWqhc+fOaN26NWrUqIHq1aurXVfFihWxdetWpKSkwM/PD5UqVULnzp3Ru3dvNGvWDGXKlEGPHj0UD8KTyWT45ptvYGdnh88++wy9e/eGn58fXF1dERgYCFdXVwwbNizbWDt37oxq1arh0qVLqFixIvz9/dGxY0d4eHjA2toajRs3zvF+yxkYGGDPnj2oUKECpk2bBmdnZ7Rp0wZ9+/aFr68v7O3t0aRJE1y8eDHX6/6Ys7MzVq1ahfT0dHTq1AlNmzZFnz59ULVqVSxatAhly5ZV3FUil5f3Jy8qVKiARYsWISUlBb6+vmjRogV69+4NDw8P7N27V+ODWIubFi1awMfHBw8ePEDVqlXRvXt3NGvWDA0bNiyy/czLeTQ/li5disaNG+P48eNwcXFB+/bt0atXL3z22WcoX7680l0SkyZNgr6+Pr777jvUq1cPffr0Qf369dGjRw98++23BRJPZn379kV6ejpWr14NiUSCPn36qJSxs7PDkCFDkJCQAC8vL3Ts2BE9evSAq6srjh07pnh2QkH5/vvvAQC9evVCs2bN0KdPH1SrVg0TJkzItg5GjBiBoUOHomHDhujTpw+qV6+OuXPnwsLCIsdDtfTp0wc//PADbty4gbp166Ju3bro2bMnevTogTp16qBs2bKYOHGiovyjR4/g5+cHOzs7tG7dGv369UPHjh3h6uqKhw8folOnTvjss8/yXB9ERESkGRPpREREWmJtbY2xY8dmOb9UqVIIDQ3FzJkzYWNjg6CgIISFhaFevXrYsmULli5dWugxNmrUCP/++y8mTZoECwsLhIaGYs+ePXj06BF8fHywceNGtG7dWmmZxYsX4/fff4eHhweOHz+OEydOoG3btjh37pziR4PM422XKVMGFy9eRJ8+fSCTyRAUFIRnz55h1qxZaofGkNu1axe6du2Khw8f4o8//sC6detw+fLlHO9b5kSapqSanZ0dzpw5g2XLlqFq1aq4cuUKdu7cievXr8PNzQ0LFy7EhAkTcrxdACpJpKy2n9e6ycqaNWvw3XffwcLCAkeOHEFkZCS+//57jevq0qULrl+/jpEjR0IikSA4OBgHDhxAdHQ0OnXqhO3btyvG2jY3N8eKFSvQqVMnvHr1CkFBQTh27Bisra0xc+ZMhIeH52isdalUirCwMIwYMQLGxsbYv38/bt++ja+//hrBwcEwNDTM9b4DQKVKlXDlyhXMnj0bTk5OOHfuHHbv3o179+6hdu3aWLFihcqYzXn1xRdfICwsDB07dsTt27exc+dOJCYmYsSIEQgPD1f7cMm8vD95MWrUKAQGBqJ+/fo4f/48jhw5Ai8vL5w7dw4VK1Ys0G1pk0Qiwd69ezF8+HBIJBIcPHgQMTExWLp0KRYuXFhkceTlPJpXpUqVwvHjx7F06VJUq1YNYWFhCAoKwqtXr9C3b1/0799fUbZZs2Y4deoUWrZsiYcPH2L//v2QSqXYtWuXYiimgpT5PNe0aVM4OzurLffbb79h0aJFqFChAo4ePYqwsDC0bt0aly5dKrAflDLHdODAATRq1AhXr17FoUOH4OjoiGPHjqFz584al+3ZsyeCgoKgr6+PvXv34unTp+jSpQvOnj2L2rVr5ziGOXPmIDQ0FH5+foiKisKePXtw/PhxpKWlYcSIEUo/EDdq1AizZ89G3bp1cffuXezYsQOXLl1CzZo1sX79erXDqhEREVHBkYjCegw5ERERUSbv3r1DhQoVkJSUhLi4OOjr62s7JCIiIiIiIqIc4RXpREREVKBu376NDx8+KE1LSEjAsGHD8Pr1a/Tq1YtJdCIiIiIiIipWeEU6ERERFajhw4dj8+bNqFu3LhwcHPD69WtcuXIFMTExcHNzw7lz52Bra6vtMImIiIiIiIhyzEDbARAREdGnpXv37oiKikJ4eDguXLgAIOMBh0OHDsWkSZNyNEY2ERERERERkS7hFelERERERERERERERBpwjHQiIiIiIiIiIiIiIg2YSCciIiIiIiIiIiIi0oCJdCIiIiIiIiIiIiIiDZhIJyIiIiIiIiIiIiLSgIl0IiIiIiIiIiIiIiINmEgnIiIiIiIiIiIiItKAiXQiIiIiIiIiIiIiIg2YSCciIiIiIiIiIiIi0oCJdCIiIiIiIiIiIiIiDZhIJyIiIiIiIiIiIiLSgIl0IiIiIiIiIiIiIiINmEgnIiIiIiIiIiIiItKAiXQiIiIiIiIiIiIiIg2YSCciIiIiIiIiIiIi0oCJdCIiIiIiIiIiIiIiDZhIJ/pESSQSNG/eXNthEBERUQFxdXWFq6urtsMgIiKiLNy/fx/dunWDvb09JBIJrKystB0SERUgJtKJ8kkikUAikUBPTw///fdfluVatGihKLtx48aiC7AQNG/eHBKJJNv5J06cUDtd/s/AwADW1tbw9PREz549sWHDBrx7966Qoyciok+dvJ0pbrJrX7XlxIkTan+gl0/P/M/U1BQODg5o1qwZJk6ciCtXrmgnaCIiKjbmzJmjaEfu3r2r7XAghMDOnTvRpUsXODo6QiqVokyZMmjSpAkWL16MDx8+qF0uLS0NXbt2xcGDB9GxY0dMnz4d3333nWL+8+fP8e2336Jq1aowNTWFiYkJnJ2d4ePjgylTpqjkE3S1X0BUkhloOwCiT4GBgQFSU1Oxbt06zJ07V2X+/fv3ceLECUW5km7AgAFwdXWFEAJv377Fw4cPERISgh07duCHH37AunXr0L59e22HSUREpFOOHj2q7RDUcnFxwcCBAwEAMpkMr169wuXLl/HLL7/gl19+QZ8+fbBq1SqYm5trN1AiItI5QgisXbsWEokEQgisWbMGv/zyi9biiYuLQ8+ePREcHAxLS0u0b98erq6uiImJwZEjRzB+/HgsX74c+/fvR7Vq1ZSWjYiIwK1bt/Dll19i9erVSvNu3LgBHx8fxMTEoEaNGhgwYABKly6N6OhoXLhwAXPnzkWFChXg7u5elLtLRLnERDpRAShbtiwcHBywYcMG/PTTTzAwUP5orV27FgDQqVMnBAYGaiNEnTJw4ECVq9qSkpKwaNEiTJs2Dd26dUNwcDCaNWumnQCJiIh0kK5+uXZ1dcWMGTNUpl+9ehX9+/fHli1bEBMTg0OHDhV9cEREpNP++ecfREZGYuDAgTh8+DA2bdqEuXPnQiqVFnks6enp6NGjB0JCQuDr64u//voLZcqUUcxPTU3FtGnTMG/ePLRt2xaXL19G2bJlFfOfP38OAHB0dFRZ99ixYxETE4MZM2Zg+vTpKvMfPnwImUxWCHtFRAWJQ7sQFZAvv/wSUVFR2L9/v9L0lJQUbNy4EY0bN0bVqlXVLhseHo5vvvkGXl5eKF26NIyNjVGpUiWMHz8esbGxKuU3btyoGCLm8OHDaN68OSwtLXN029fChQuhp6cHb29vxMTEKKafP38e/v7+sLe3h1QqRfny5fHVV18pOgMAEBkZCYlEgtDQUABQupU7v+OxGxsbY8qUKfjxxx8hk8nwzTff5Gt9REREOZGcnIz58+ejRo0aMDU1hYWFBZo2bYrt27dnucyFCxcQEBCAcuXKwcjICA4ODmjbtq3KMhs3boSfnx/c3NxgYmICCwsLeHt7Y/PmzUrlctq+ZjVGem72Qb6tgQMHIjIyEr169YKNjQ2MjY1Rr149lX5MftSqVQshISGwtbXF4cOHsWfPngJbNxERfRrWrFkDIOP7dN++ffH69essLz578eIFBg0aBDs7O5iYmKBWrVrYtGmTYqgxdT/qxsTE4Pvvv0eVKlVgYmICS0tLtGrVCv/8849K2S1btiAkJATu7u7YvXu3UhIdyLgTfe7cuQgICMDz58/x448/KuZJJBL4+PgAAGbOnKlox+UxnTlzBgCy/J7r5uYGT09PALn73h0eHg4/Pz/Y2dnByMgILi4uGDlyJF68eKGyjYEDB0IikeDhw4dYvnw5atasCRMTE6V15qa+iEoiXpFOVEB69+6NcePGYe3atejatatielBQEKKjo/Hzzz/jwYMHapdds2YNAgMD4ePjg9atWyM9PR3h4eFYvHgxDh06hPPnz6NUqVIqy+3cuROHDx/G559/juHDh+PRo0dZxpeeno6xY8di+fLl6N69O/766y8YGxsDANavX49hw4bByMgInTt3Rvny5XH//n2sXbsW+/btw7lz5+Ds7AwrKytMnz4dGzduxKNHj5R+SS+oh59NmDABCxcuxNWrV3Hz5k2V2+WIiIgKikwmg6+vL0JDQ+Hp6YlRo0bhw4cP2LlzJwICAnD16lWVIdvWrFmDESNGQF9fH507d0alSpUQHR2NS5cuYeXKlejZs6ei7IgRI1CtWjU0a9YMDg4OePPmDQ4ePIgvvvgCd+/exaxZswAgX+1rXvYBAB49eoQGDRrAzc0NX3zxBWJiYrBt2zZ06dIFISEhaNGiRT5q9v/Y2dnhq6++wuzZs/HXX38p9ZGIiKhke/nyJYKCguDh4YHGjRvDwsICixYtwurVqxEQEKBUNjo6Gp999hkePXqEZs2aoXHjxoiKisLIkSPRtm1btet/9OgRmjdvjsjISDRt2hTt2rXD+/fvsX//frRr1w6rVq3Cl19+qSgvT+qPHz8epqamWcY9bdo0bNu2DX/++SeWL18OY2NjTJ8+HZGRkdi0aRN8fHwUyWn5/2XKlMHTp09x7949NGjQQGO95LRfsH//fvj5+UEIAX9/f7i4uCA8PBy//fYb9u7di1OnTqFChQoq6//mm28QFhaGDh06oH379tDX189TfRGVSIKI8gWAKFeunBBCiCFDhgh9fX3x5MkTxXxfX19hYWEh3r9/L6ZMmSIAiA0bNiitIzIyUqSmpqqse+3atQKAmD9/vtL0DRs2CABCIpGIQ4cOZRmXj4+PEEKIxMRE0b17dwFAjB49WqSlpSnK3b17VxgaGgp3d3fx9OlTpXWEhIQIPT090bVrV6XpPj4+QtPpQz7/+PHjOZr+sSZNmggAYv369RrLERERqQNAYzslN3fuXAFAfP755yIlJUUx/eXLl8LFxUUAEKdPn1ZMv3nzpjAwMBDW1tbixo0bKuvL3P4LIcSDBw9UyiQnJ4uWLVsKAwMDlXY3u/bVxcVFuLi45GsfIiIiFPUzY8YMpXUdPnxYsa7Mjh8/rtSvyG76x0JCQgQA4ezsrLEcERGVLPPmzRMAxNy5cxXT6tatKyQSibh//75S2cGDBwsAYtKkSUrTr169KqRSqQAgpk+frjTPx8dHSCQSsXXrVqXpsbGxwsvLSxgbG4uoqCghhBApKSmK9dy7dy/b2B0dHQUAERYWppgmbxc/jkMIIcaPHy8AiLJly4oZM2aI0NBQER8fr3EbmvoFb9++FaVLlxZ6enri5MmTSvPmz58vAIg2bdooTR8wYIAAIBwdHcXDhw/Vbi+n9UVUUnFoF6IC9OWXXyItLQ3r168HkPGLbnBwMPr27avxF20XFxfFr8CZDR48GBYWFjhy5Ija5bp06YJ27dppjCkmJgatW7dGYGAgfv75Zyxfvhx6ev/30f/tt9+QkpKCpUuXoly5ckrLtmrVCp07d8a+ffvw9u1bjdspSPI4Xr16VWTbJCKikmf9+vWQSCRYvHix0vNN7OzsMHXqVAD/95wTIKPNTE1NxdSpU9XeMeXk5KT0t7oxzaVSKUaNGoXU1NQCeXhobvdBzsXFRemWdADw9fWFs7MzLly4kO+4MmO7TkREHxP//yGjenp66N+/v2L6wIEDFQ8dlZPJZNi6dSssLS1V2i4vLy+l5eWuXbuG0NBQ+Pn5oVevXkrzrKysMHPmTCQlJWHXrl0AMr43y8coL1++fLbxy8tkHgpVkzlz5uDLL7/EmzdvMGPGDPj4+MDKygqenp4YO3YsHj58mKP1yO3duxcxMTEICAhA06ZNleaNHz8erq6uCA4OxuPHj1WWnTRpksqV6rmtL6KSikO7EBWghg0bokaNGli/fj1+/PFHrF27Funp6dne/pSSkoJVq1bh77//xq1btxAfH4/09HTF/GfPnqldLrtbwl6+fAlvb288fPgQmzdvRp8+fVTKnD17FgAQGhqKixcvqsyPjo5GWloa7t27h7p162rcXkERQgBAjsZ8JyIiyou3b9/iwYMHKFeunGJM0sxatmwJALhy5Ypi2rlz5wAAn3/+eY628fjxY/z88884evQoHj9+jMTERKX5WbXvOZWXfZCrVauW2h/xy5cvr+gbFBS260RE9LFjx47hv//+g6+vr9IFXX369MH48eOxceNGzJ49G4aGhrh79y4SExNRr149tUOeNmnSROVHY3lbFh8fr3bsdPmPu7dv3y7AvcqakZERVq9ejVmzZuHw4cM4f/48Ll++jEuXLmHp0qVYvXo1tm/fjo4dO+ZofZcvXwbwf219ZgYGBmjWrBkiIyNx5coVODs7K81Xl0fQtfoi0lVMpBMVsC+//BJjxozBoUOHsGHDBtStWxe1a9fWuExAQAACAwPh5uaGLl26wN7eHkZGRgCAJUuWIDk5We1y9vb2GtcbFRWFhIQEODk5oUmTJmrLvHnzBkDGQ0g1effuncb5BUn+q76trW2RbZOIiEqW+Ph4AICDg4Pa+fLpcXFximny1x/fwaXOw4cP0aBBA8TGxqJp06Zo27YtLC0toa+vrxhDNav2vTD3Qc7KykrtMgYGBko/5hcEtutERPSx1atXA8i4Aj2z0qVLo1OnTti1axf27t0Lf39/RXtXtmxZtetSN13+PTc4OBjBwcFZxiH/nlu6dGlIpVLIZDI8efIElSpV0hj/kydPAACOjo4ay6mLdcCAARgwYACAjCvhJ0+ejLVr12Lw4MF4+vQppFJptuvJTx9AXR4ht/VFVFJxaBeiAvbFF1/AxMQEw4cPx7NnzzBs2DCN5S9duoTAwEC0bt0ad+/exYYNGzBv3jzMmDED06ZNU9xepk52V3Z5eXlh06ZNePbsGZo1a6b2djFLS0sAGQ2xECLLf/InkBe2t2/fIjw8HEDGFf5ERESFQd7+RUVFqZ3/4sULpXLA/yWfc3Il+eLFi/HmzRusW7cOJ06cwLJlyzBr1izMmDEDvr6++YweSrHlZh+04fjx4wDYrhMRUYZXr15hz549AIDevXtDIpEo/ZMPHyJPtltYWADIuONaHXXT5W3f0qVLNX7P3bBhA4CMH5Ll7VRISIjG+G/fvo3nz5/DyMgI9erVy+XeKytdujRWrVoFZ2dnvHr1Cjdu3MjRcvnpA6jLI+S2vohKKibSiQqYlZUV/P398fTpU5iZmaF3794ayz948AAA0LlzZ6WxTQHgwoULKreB51a/fv3w999/4/nz52jWrBnu3bunNL9Ro0YAgLCwsByvU34reFpaWr5iU2fhwoVITExEnTp1UKVKlQJfPxEREQCUKlUK7u7uePbsGe7fv68yX578rVOnjmKavM08dOhQtuuXt+9+fn4q80JDQ9Uuk9v2NS/7UNSio6OxatUqAEDfvn21FgcREemOTZs2QSaToW7duhgyZIjaf7a2tggJCUFERAQ8PT1hYmKC69evq31216lTp1Sm5eV77tChQwFk/Biu6Xv47NmzAWRcRGdsbJzj9WdFT08PZmZmAP5vODRAc79Aftf7iRMnVOalpqYq9junfYC81BdRScREOlEhmD17NgIDA3HkyBG1Y7hl5urqCkC1AYyOjsaoUaMKJB5/f3/s3LkTr1+/ho+PD27evKmYN3r0aBgaGuLbb79VSbIDGQ92+bgxLVOmDACofXBJXiUlJWHu3LmYM2cOpFIpli5dWmDrJiIiUmfw4MEQQmDixIlKX1Jfv36NWbNmKcrIjRgxAgYGBpg1axZu3bqlsr6nT58qXmfVvh85ckTtwz+BvLWvud2HonTt2jW0adMGr1+/Rvv27dG5c2etxEFERLpF/iDRlStXYu3atWr/ffXVV4oHkkqlUgQEBCA+Pl6RxJa7du0a/vjjD5Vt1KtXD02bNsXu3buxfv16tXH8+++/iI6OVvzdt29ftGjRAg8ePIC/vz9iY2OVyqelpWHatGnYsmULHBwcFO1sTsycORORkZFq5+3cuRN37tyBtbU1qlevrpiuqV/QtWtXlC5dGlu3blU8w0VuyZIliIiIQOvWrVXGR89KXuqLqCTiGOlEhcDZ2TnHDVb9+vXh7e2N3bt3o3HjxmjSpAlevnyJQ4cOoXLlyrkecy0rnTt3xt69e9GtWzc0b94cISEh8PLygqenJ9avX4/BgwejWrVqaNeuHTw8PJCSkoLHjx8jLCwMtra2uHPnjmJdrVq1wo4dO9C9e3e0b98eJiYmcHFxwRdffJGjWDZu3KhILLx9+xYPHz7EyZMnERMTAwcHB6xfvz7LMd2JiIhy6uNxVzNbuXIlJkyYgEOHDmHv3r3w8vJC+/bt8eHDB+zYsQPR0dGYNGmSUntUtWpVrFy5EsOHD0ft2rXRpUsXVKpUCW/evMHFixdhYWGhuAp85MiR2LBhA3r06AF/f384Ojrixo0bOHz4MHr27Ilt27apxJSX9jW3+1AYIiMjFQ8mS0lJwevXrxEeHq4Yqq1fv374/fffCzUGIiIqHk6cOIF79+6hRo0aah96KTdkyBDMmTMHGzZswMyZMzF//nwcO3YMCxYswPnz59G4cWO8ePEC27dvR/v27bFnzx7o6SlfK7plyxa0bNkSQ4YMwbJly9CwYUNYWVnh6dOnuH79Om7cuIGzZ8/Czs4OQMYV4Lt27YK/vz8OHjwINzc3dOjQAS4uLoiJicGRI0cQEREBV1dX7Nu3L9tnlmX266+/YsaMGahduzbq1asHW1tbxMfH4/Llyzh79iwMDAzw+++/K56VBmjuF5ibm2P9+vXo0aMHfHx80KNHDzg7OyM8PBz//PMP7O3tFXeE5VRu64uoRBJElC8ARLly5XJUdsqUKQKA2LBhg9L0N2/eiBEjRggXFxdhZGQk3NzcxPfffy/ev38vXFxchIuLi1L5DRs2qF3Px3H5+PioTD9+/LgwNzcX1tbW4sKFC4rp169fFwMGDBDOzs5CKpUKa2trUa1aNTFs2DBx9OhRpXWkpqaK77//XlSoUEEYGBiobMvHx0cAEMePH1daTj5d/k9fX19YWlqKypUri549e4oNGzaId+/eaapCIiKibGVua7L6FxsbK4QQIjExUcyZM0dUq1ZNGBsbC3Nzc+Ht7S22bNmS5frPnDkjunfvLmxtbYWhoaFwcHAQvr6+YseOHUrlTp8+LVq0aCGsrKwU6w0MDBTHjx8XAMT06dOVymfXvqrrE+R2HyIiIgQAMWDAALX7Jm+rM5PH+3G/Qj498z9jY2Nhb28vmjZtKiZMmCCuXLmSVTUSEVEJ1KdPHwFALF26NNuybdq0EQDE7t27hRBCPH36VPTv31/Y2NgIY2Nj4eXlJTZu3Ch27NghAIhff/1VZR0JCQlizpw5ok6dOsLMzEwYGxsLV1dX0b59e7Fq1Sq13z/T09PFtm3bRMeOHYW9vb0wNDQU1tbWonHjxuKXX34R79+/VxtvVu27EEKEhYWJH374QXh7e4vy5csLqVQqTE1NhYeHhxg6dKi4fv26yjLZ9QuEEOLChQuia9euwsbGRhgaGory5cuL4cOHi2fPnqmsb8CAAQKAiIiIUBt/XuuLqCSRCJFpACYiIiIiIiIiIqJiYsqUKZg7dy4OHz5cYA/0JiJSh4l0IiIiIiIiIiLSac+fP1cZ+vTff/9F48aNIZVK8ezZswJ5+CcRUVY4RjoREREREREREem0evXqoWLFiqhevTrMzMxw//59HDhwAOnp6Vi1ahWT6ERU6HhFOhERERERERER6bSZM2diz549iIyMxNu3b2FlZYVGjRphwoQJaN68ubbDI6ISgIl0IiIiIiIiIiIiIiIN9LQdABERERERERERERGRLmMinYiIiIiIiIiIiIhIAybSiYiIiIiIiIiIiIg0MMjvCmJjY5GamloQsXwSbG1t8erVK22HUWKx/rWL9a9drH/tKoz6NzAwgLW1dYGuEyj4tpvHnnaw3rWD9a4drHftYL3nXlG13Xxv8of1lz+sv/xh/eUP6y9/WH+qctN25zuRnpqaipSUlPyu5pMgkUgAZNQJn+Fa9Fj/2sX61y7Wv3YVt/ovyLa7uO37p4L1rh2sd+1gvWsH6123ZG67+d7kD+svf1h/+cP6yx/WX/6w/vKPQ7sQEREREREREREREWmQ7yvS6dMghIBMJtN2GPkikUiQlJSE5ORk/rJWxIQQSElJgYmJCZKSkrQdTokkP/557BPRp9Cm66ri3teQt9cAYGhoqLgqSdcV93pXRyqVFpv6J9IlBdHGafOcUlzPw5npyjmZ51Ei0gYm0gkAIJPJMHHiRG2HQUT59Msvv0AqlWo7DCLSIrbpRLpv4cKFMDIy0nYYRMUO2ziS43mUiLSBQ7sQEREREREREREREWnAK9JJxRjpBxhqOwgqVlIEsCzFFAAwxvADDHmHXZFKAbBMZqrtMIhIB7FNp8zYXmsX22uiglUc2zieh/OH51Ei0jYm0kmFIQApG3TKI0MJj58i92kMF0tEhYBtOmWF7bUWsL0mKlDFvY3jeTgPeB4lIi3j0C5ERERERERERERERBowkU5EREREREREREREpAET6UREREREREREREREGjCRTkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKSBgbYDkBNCQCaTAQCkUikkEomWIyIiIsqevP1KSkqCEELb4RSpkrzvRERUfJXk754led+JiIoznr91g85ckS6TyTBx4kRMnDhRcWAQERHpOplMhgkTJqBfv34lrv0qyftORETFV0n+7lmS952IqDjj+Vs36EwinYiIiIiIiIiIiIhIFzGRTkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKQBE+lERERERERERERERBoYaDsAIiIiIiIiIiIiIio8v/76Kx4+fAg3NzeMHTtWK9uPiIhAhQoV8O233xb58gWBV6QTERERERERERERfaKioqLw8OFDAMDDhw8RFRVV5NuPiIgAAEREROR6+/ldvqAwkU5ERERERERERET0iVq0aJHGv3V9+9qOX05nhnYRQihey2QyLUaSdxKJBElJSUhOTlban+Igc50LAUCivViIKHcyn26K4/mnuFM+f5asus+8vzz2ipamPgfbdCLdlPmjWhy+7xTn7zbZYdudoTgchx9jG1eyFbfzqDqf8rm1KJTk+stP2xUSEoLk5GSlacnJyQgJCUHr1q0LJL7C3L62488sx4n0lJQUpKSkKP6WSCQwMTEpsEAyr3vKlCkFtl7KvVQARtoOgohyLDXTa54/tUsmk8HISHfOoGy7iW06ke5ge62bUlJSYGxsrO0wFNh25xzbuJKH51GiDLlpu1JTUxEUFKR2XlBQEJo3bw4Dg8K7zjq/29d2/B/L8ZYCAwOxc+dOxd8VKlTAzz//XChBERERUf6x7SYiIipe2HYTEVFBOnLkSLbzO3TooLPb13b8H8txIr1bt27o2LGj4m+JpGDvoTI0NFS8njNnDqRSaYGuvyhIJBLY29sjKiqq2N1iIpPJFL/o6sx4P0SUI5k/s8X1/FmcZT5/6lrds+3+dGnqc7BNJ9JNxa29Ls7fbbKT+TyZuS3TBWy7NWMbV7IVt/OoOp/yubUolOT6y2vb5evrqzEZ7evrm+/YCnP72o7/YzluewwNDQu1k5G5gyCVSnXq1vickkgkMDY2hpGRUbH+QBdwX42IClnmz6yRkVGx7FB+Kgr6y25+FWXbzWOvaOW0z6FjhyRRiZb581gcvu98Kt9tslOS2+7icBxqomNvHRWB4nYeVaeknFsLC+svQ27aLgMDA3Tu3Fnt8ChdunQp9GFR8rt9bcf/Mb0i3RoRERERERERERERFYnWrVur/PBkZGSEVq1aFYvtazv+zJhIJyIiIiIiIiIiIvpEjR8/XuPfur59bccvx0Q6ERERERERERER0SfK3t4ebm5uAAA3NzfY29sX+fYrVKgAIONB2rndfn6XLyh8PgcRERERERERERHRJ+zbb7+Fg4MDXrx4oZUx5r/99lutLl8QeEU6EREREREREREREZEGTKQTEREREREREREREWnARDoRERERERERERERkQZMpBMRERERERERERERacBEOhERERERERERERGRBgbaDkBOKpVi4cKFitdERETFgVQqxS+//AJ7e3vExMRoO5wiVZL3nYiIiq+S/N2zJO87EVFxxvO3btCZRLpEIoGRkZG2wyAiIsoVeftlbGwMiUQCIYS2QyoyJXnfiYio+CrJ3z1L8r4TERVnPH/rBg7tQkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKQBE+lERERERERERERERBowkU5EREREREREREREpAET6UREREREREREREREGhhoOwDSPSkAILQdBRUnKUL9ayoaKdoOgIh0Ftt0yozttXaxvSYqWMWxjeN5OH94HiUibWMinVQsk5lqOwQqxpal8PghItIVbNMpK2yviai4K+5tHM/DRETFD4d2ISIiIiIiIiIiIiLSgFekEwBAKpVi4cKF2g4jXyQSCezt7REVFQUheJ9cURJCICUlBWXLlkVsbKy2wymR5Md/TEyMtkMhIi37FNp0XVXc+xry9hoADA0NIZFItBxRzhT3eldHKpVqOwSiYqkg2jhtnlOK63k4M105J/M8SkTawEQ6AchoDI2MjLQdRr5IJBIYGxvDyMjok/mSVZyYmJjAysoKiYmJrH8tkB//EomE9U9Uwn0Kbbqu+hT6GsbGxtoOIdc+hXonooJREG2cts8pxfE8nJm264+ISJs4tAsRERERERERERERkQb5viLdwIAXtX+MdaJdrH/tYv1rF+tfuwq6/gvr/SyM9fLY0w7Wu3aw3rWD9a4drPfcKcq2m+9N/rD+8of1lz+sv/xh/eUP609ZbupDIngvDhERERERERERERFRlji0SwFKTEzE5MmTkZiYqO1QSiTWv3ax/rWL9a9dJbn+S/K+axPrXTtY79rBetcO1rvu4nuTP6y//GH95Q/rL39Yf/nD+ss/JtILkBACERERfOCGlrD+tYv1r12sf+0qyfVfkvddm1jv2sF61w7Wu3aw3nUX35v8Yf3lD+svf1h/+cP6yx/WX/4xkU5EREREREREREREpAET6UREREREREREREREGjCRXoAMDQ3h7+8PQ0NDbYdSIrH+tYv1r12sf+0qyfVfkvddm1jv2sF61w7Wu3aw3nUX35v8Yf3lD+svf1h/+cP6yx/WX/5JBAfGISIiIiIiIiIiIiLKEq9IJyIiIiIiIiIiIiLSgIl0IiIiIiIiIiIiIiINmEgnIiIiIiIiIiIiItKAiXQiIiIiIiIiIiIiIg2YSCciIiIiIiIiIiIi0sBA2wF8CgIDA3HhwgU8e/YMUqkUHh4e6NevHxwdHbUdWom0Z88ebNmyBe3bt8fAgQO1HU6JEBMTg82bN+Pq1atITk6Gvb09Ro4cCXd3d22H9slLT0/H9u3bERYWhri4OJQuXRo+Pj7w8/ODRCLRdnifnFu3biEoKAgRERGIjY3FhAkT0KBBA8V8IQS2b9+Oo0eP4v379/D09MTQoUPh4OCgxagLz+HDh7Fv3z7ExcXBxcUFgwcPRsWKFbUdVrGVk/6ETCbDH3/8gTNnziAlJQVeXl4YOnQorKysFGVev36NNWvW4ObNmzA2NoaPjw/69OkDfX19LexV8aOuH8F6LxzZ9R9yck599+4d1q9fj/DwcEgkEjRs2BCDBg2CsbGxtnZLp+Wk38B6131sf/Nm+/bt2Llzp9I0R0dHLFmyRDsB6Tj2e/Mnu/pbsWIFQkNDlZbx8vLClClTijpUnVRQ/eKSKif1N2PGDNy6dUtpudatW2PYsGFFHW6xwkR6Abh16xZ8fX3h7u6OtLQ0bN26FbNnz8bixYvZmSxiDx48QHBwMFxcXLQdSonx7t07TJ06FdWqVcMPP/wACwsLvHjxAmZmZtoOrUTYs2cPgoODMWrUKDg5OeHhw4dYuXIlTE1N0b59e22H98lJTk6Gq6srWrZsiV9++UVl/t69e3Ho0CGMGjUKdnZ22LZtG+bMmYPFixdDKpVqIeLCc+bMGfzxxx/48ssvUalSJRw4cABz5szBkiVLYGlpqe3wiqWc9Cc2bdqEy5cvY9y4cTA1NcW6deuwaNEizJo1C0BGkmzevHmwsrLC7NmzERsbi//973/Q19dHnz59tLl7xUJW/QjWe8HLSf8hJ+fUZcuWITY2Fj/++CPS0tKwcuVKrFq1Ct988422dk2n5aTfwHrXbWx/86d8+fKYOnWq4m89Pd6knxX2e/Mnu/oDgFq1amHkyJGKvw0MmKKTK4h+cUmW0zxlq1atEBAQoPibn93ssdUoAFOmTEHz5s1Rvnx5uLq6YtSoUXj9+jUePnyo7dBKlKSkJCxfvhxfffUVk7hFaO/evShTpgxGjhyJihUrws7ODl5eXrC3t9d2aCXCvXv3UK9ePdSpUwd2dnZo1KgRatasiQcPHmg7tE9S7dq10atXL6WrSeSEEDh48CC6d++O+vXrw8XFBaNHj0ZsbCwuXryohWgL1/79+9GqVSu0aNECTk5O+PLLLyGVSnH8+HFth1ZsZdef+PDhA44dO4YBAwagevXqcHNzw8iRI3H37l3cu3cPAHDt2jU8ffoUX3/9NVxdXVG7dm0EBATgyJEjSE1N1ebu6bys+hGs98KRXf8hJ+fUp0+f4urVqxg+fDgqVaoET09PDB48GGfOnEFMTIw2d09nZddvYL3rPra/+aOnpwcrKyvFPwsLC22HpLPY780fTfUnZ2BgoHQ8mpubF2GEuq0g+sUlWU7zlEZGRkrHoKmpqZYiLj6YSC8EHz58AACeBIvY2rVrUbt2bdSsWVPboZQoly5dgpubGxYvXoyhQ4di0qRJCAkJ0XZYJYaHhwdu3LiB58+fAwAiIyNx9+5d1K5dW8uRlTzR0dGIi4tTOgeZmpqiYsWKn1xnLjU1FQ8fPkSNGjUU0/T09FCjRo1Pbl+16eP+xMOHD5GWlqZU7+XKlYONjY2i3u/duwdnZ2elW1pr1aqFxMREPHnypOiCL4ay6kew3gtHdv2HnJxT7927BzMzM6Wh5GrUqAGJRMIflLOQXb+B9a7b2P7mX1RUFL766iuMHj0ay5Ytw+vXr7UdUrFUkvq9henWrVsYOnQovvnmG6xZswZv377Vdkg6Ky/9Yvo/WeUpw8LCMGTIEIwfPx5btmxBcnKyNsIrVnjfSAFLT0/Hxo0bUblyZTg7O2s7nBLj9OnTiIiIwLx587QdSokTHR2N4OBgdOjQAd26dcN///2HDRs2wMDAAM2bN9d2eJ+8rl27IjExEd9++y309PSQnp6OXr16oWnTptoOrcSJi4sDAJXbqi0tLRXzPhUJCQlIT09XGX/QyspKkZyh/FHXn4iLi4OBgYHKXVeZj7G4uDiV90V+TH5qx2FB0tSPYL0Xjuz6Dzk5p8bFxalcTaqvrw9zc3PWexay6zew3nUb29/8qVSpEkaOHAlHR0fExsZi586dmDZtGhYtWgQTExNth1eslKR+b2GpVasWGjZsCDs7O0RFRWHr1q2YO3cu5syZwyGHPpLXfjFlyCpP2aRJE9jY2KB06dJ49OgR/vrrLzx//hwTJkzQYrS6j4n0ArZu3To8efIEP/30k7ZDKTFev36NjRs34scff+R4TlqQnp4Od3d3xRiwFSpUwOPHjxEcHMxEehE4e/YsTp06hTFjxqB8+fKIjIzExo0bYW1tzfonKsbYnyg67EdoB/sP2sF+A5Vkme/YdHFxUSTWz549i5YtW2oxMiqJvL29Fa+dnZ3h4uKCr7/+Gjdv3lS6yprYL86vrOqvdevWitfOzs6wtrbGTz/9hKioKA7VqwET6QVo3bp1uHz5MmbOnIkyZcpoO5wS4+HDh4iPj8fkyZMV09LT03H79m0cPnwYW7Zs4S+6hcja2hpOTk5K05ycnHD+/HktRVSybN68GV26dFF0xJydnfHq1Svs2bOHX4iLmPzqsPj4eFhbWyumx8fHw9XVVTtBFRILCwvo6empXO2h7qpcyr2s+hNWVlZITU3F+/fvla6+iY+PV9S7lZWVytAK8fHxinmkKrt+xJQpU1jvhSC7/kNOzqlWVlZISEhQWkdaWhrevXvHes9Cdv0G1rtuY/tbsMzMzODo6IioqChth1LslKR+b1EpW7YsSpUqhaioKCbSM8lPv5hyl6esWLEiADCRng1mFwuAEALr1q3DhQsXMG3aNNjZ2Wk7pBKlRo0a+OWXX7BgwQLFP3d3dzRp0gQLFixgEr2QVa5cWeVW0ufPn8PW1lZLEZUsycnJKse4np4ehBBaiqjksrOzg5WVFf7991/FtA8fPuDBgwfw8PDQYmQFz8DAAG5ubrhx44ZiWnp6Om7cuPHJ7WtRyq4/4ebmBn19faVj7Pnz53j9+rWi3j08PPD48WNFEhcArl+/DhMTE5WkJWXIrh/h7u7Oei8E2fUfcnJO9fDwwPv375UenHXjxg0IIRRfBklZdv0G1rtuY/tbsJKSkhAVFcWkWx6UpH5vUXnz5g3evXun9MNESVYQ/eKSLC95ysjISADgMZgNXpFeANatW4dTp05h0qRJMDExUVwhYGpqyluEi4CJiYnKePRGRkYoVaoUx6kvAh06dMDUqVOxe/duNG7cGA8ePMDRo0cxbNgwbYdWItStWxe7d++GjY0NnJycEBkZif3796NFixbaDu2TJP/CJRcdHY3IyEiYm5vDxsYG7du3x+7du+Hg4AA7Ozv8/fffsLa2Rv369bUYdeHo2LEjVqxYATc3N1SsWBEHDx5EcnIy74TIh+z6E6ampmjZsiX++OMPmJubw9TUFOvXr4eHh4fiC4OXlxecnJzwv//9D3379kVcXBz+/vtv+Pr6wtDQUIt7p7ty0o9gvRe87PoPEokk23Oqk5MTatWqhVWrVuHLL79Eamoq1q9fj8aNG6N06dLa3D2dlV2/gfWu+9j+5t0ff/yBevXqwcbGBrGxsdi+fTv09PTQpEkTbYemk9jvzR9N9Wdubo4dO3agYcOGsLKywsuXL7F582bY29vDy8tLi1HrjoLoF5dk2dVfVFQUTp06hTp16sDc3ByPHz/Gpk2bUKVKFbi4uGg3eB0nEbxsMd969uypdvrIkSPZodGSGTNmwNXVFQMHDtR2KCVCeHg4tmzZgqioKNjZ2aFDhw5K421R4UlMTMS2bdtw4cIFxMfHo3Tp0vD29oa/vz8MDPhbaUG7efMmZs6cqTLdx8cHo0aNghAC27dvR0hICD58+ABPT08MGTIEjo6OWoi28B0+fBhBQUGIi4uDq6srBg0ahEqVKmk7rGIrJ/0JmUyGP/74A6dPn0Zqaiq8vLwwdOhQpavpXr16hbVr1+LmzZswMjKCj48P+vbtC319/SLYi0/Dx/0I1nvhyK7/kJNz6rt377Bu3TqEh4dDIpGgYcOGGDx4MIyNjbWxSzovJ/0G1rvuY/ubN0uWLMHt27fx9u1bWFhYwNPTE7169eIQBllgvzd/NNXfl19+iYULFyIiIgLv379H6dKlUbNmTQQEBPAOif+voPrFJVV29ff69WssX74cT548QXJyMsqUKYMGDRqge/fuMDU1LeJoixcm0omIiIiIiIiIiIiINODg0UREREREREREREREGjCRTkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKQBE+lERERERERERERERBowkU5EREREREREREREpAET6UREREREREREREREGjCRTvSJio6ORs+ePREUFKTtUIiIiCiftm/fjp49e2o7DCIiItLgxYsXmD17NgYMGICePXviwoUL2g6JiAqQgbYDIPrUnThxAitXrgQA/PTTT/D09FSaL4TAyJEj8ebNG9SpUwffffedNsLM1qlTpxAfH48OHTooTY+Ojsbo0aPRr18/dO7cGQBw8+ZNzJw5U1HGwMAAZmZmKFeuHGrWrInWrVvDwsKiSOMnIiLKTN4+z5s3D+7u7toOBwCQnJyMvXv3olq1aqhWrVqRb1/efo8bNw6NGjUCoNyPAQBDQ0OYm5vD2dkZtWvXRosWLWBiYlLksRIRUcly5MgRrFu3DhUrVsTcuXOLbLuvX7/G7t27ce3aNcTGxsLU1BQeHh7o3Lmzynd7AFixYgWio6PRu3dvmJqaKvoYjx8/xo4dO/Dff/8hPj4e5ubmcHJyQr169fD5558rlt+9ezecnJzQoEGDIttHIso5JtKJioihoSFOnTql0tjeunULb968gaGhoZYiy5lTp07hyZMnKol0TT7//HO4u7sjPT0dCQkJuHfvHnbs2IEDBw7g22+/RfXq1QsxYiIiouIlOTkZO3fuBACVRLqfnx+6du2qhagy9OzZE3Z2dkhLS0NcXBxu3bqFTZs24cCBA5g0aRJcXFy0FhsREX36Tp06BVtbWzx48ABRUVGwt7cv9G3euXMH8+bNAwC0atUKTk5OiIuLw4kTJzB9+nQMHDhQKQkuk8lw7949dO/eHe3atVNMv3v3LmbOnAkbGxu0atUKVlZWePPmDe7fv4+DBw8qrSMwMBCNGjViIp1IRzGRTlREateujbNnz2LQoEHQ19dXTD916hTc3Nzw9u3bAtlOeno6UlNTC2Rd+VWlShXFFW1ykZGRmDNnDhYtWoTFixfD2tpaS9EREREVH/r6+kr9h6JWu3ZtpSv3u3Xrhhs3bmD+/PlYsGABfv31V0ilUq3FR0REn67o6GjcvXsXEyZMwOrVqxEWFoYePXoU6jbfvXuHxYsXQyqVYtasWUqJ+44dO2LOnDnYuHEj3NzcULlyZQBAQkICAMDU1FRpXbt374apqSnmzZsHMzMzpXnx8fGFuh8AkJSUBGNj40LfDlFJwEQ6URFp0qQJLl68iOvXr6N27doAgNTUVJw7dw5+fn44dOiQUvmgoCBcuHABz58/R3JyMpycnNCtWzeVxHTPnj3h6+sLDw8PBAYG4sWLF/j222/h6uqqEoMQAqtXr8aJEycwduxYNGzYEABw8uRJHDhwAE+fPoVUKoWXlxf69esHGxsbAMCMGTNw69YtxfYAwNbWFitWrMh1Pbi6umLAgAFYtmwZDh8+jN69e+d6HUREREUhIiICW7duxd27d5Geno5KlSqhV69e8PDwUCr3/v177NixAxcvXkRsbCwsLCxQvXp19O/fHxYWFkhNTcWuXbtw+fJlREVFIT09HRUqVEDPnj0Vd2fJh0oDgJ07dyquTPf390fPnj2xfft27Ny5E9u3b1dsNy0tDYGBgQgNDcWbN29gbW0Nb29v9OjRQ+lOt1GjRqF8+fLo2rUrNm3ahMePH8Pa2ho9evSAj49PnuunevXq8PPzw9atW3Hy5Em0bt06z+siIiLKSlhYGMzMzFCnTh00atQIp06dUkmkv337Fhs3bsSlS5cgkUhQr149dOzYEZMmTcLIkSPRvHlzRdlnz57h77//xo0bNyCTyVC+fHn4+/ujXr16ijIhISGIi4vD6NGjVa5+l0qlGDVqFMaMGYOdO3diypQpinYaADZv3ozNmzcrvjO/fPkS5cuXV0miA4ClpaXitfy7dmhoKEJDQwEAPj4+GDVqFICc9UvkQ7LNmDEDZ86cwblz55CWloYNGzYAAK5cuYLAwEBERERAIpGgSpUq6NevH8qXL5/r94WoJOLDRomKiK2tLTw8PHD69GnFtCtXruDDhw9o3LixSvlDhw7B1dUVPXv2RO/evaGvr4/Fixfj8uXLKmVv3LiBTZs2oXHjxhg4cCDs7OxUyqSnp2PFihU4efIkJk6cqEii7969GytWrICDgwMGDBiADh064N9//8X06dPx/v17AED37t3h6uqKUqVKYfTo0Rg9ejQGDBiQ57po1KgRpFIprl+/nud1EBERFaYnT55g2rRpePToETp37gw/Pz9ER0dj5syZuH//vqJcUlISpk2bhsOHD6NmzZoYOHAg2rRpg2fPniEmJgYA8OHDBxw7dgzVqlVD37590aNHDyQkJGDOnDmIjIwEAFhYWGDo0KEAgAYNGijaW3l7rc7vv/+O7du3o0KFChgwYACqVKmCPXv2YMmSJSplo6KisGjRItSsWRNffPEFzMzMsHLlSjx58iRf9dSsWTMAYJtORESF5tSpU2jYsCEMDAzg7e2NFy9e4MGDB4r56enp+Pnnn3H69Gn4+PigV69eiIuLU3vh15MnTzBlyhQ8e/YMXbt2xRdffAEjIyMsXLhQ6cGg4eHhMDQ0xGeffaY2Jjs7O3h6eiqS8Q0bNlR8R/b29lb6zmxra4uHDx/i8ePHGvdz9OjRMDQ0RJUqVRT9gDZt2ijizkm/RG7t2rV4+vQp/P390aVLFwAZF9DNnz8fxsbG6Nu3L/z8/PD06VNMmzYN0dHRGmMjogy8Ip2oCHl7e2Pr1q2QyWSQSqUICwtD1apVUbp0aZWyS5cuVbpFul27dpg8eTL279+POnXqKJV9/vw5Fi1aBCcnJ8W0zA1hWloali9fjkuXLmHSpEnw8vICALx69Qrbt29HQEAAunfvrijfoEEDTJ48GUeOHEH37t1Rs2ZNlC5dGu/fv1d8Yc4PAwMDODg4ICoqKt/rIiIiKgx///030tLS8NNPP6Fs2bIAMq4KGzt2LDZv3qx4qHZQUBCePHmCCRMmKI1n6ufnByEEAMDc3BwrVqyAgcH/db1btWqFsWPH4tChQxgxYgSMjY3RqFEjrF27Fs7Oztm2t5GRkQgNDUXLli0xfPhwAICvry8sLS2xb98+3LhxQ+lZJM+fP8fMmTNRpUoVAEDjxo0xYsQIHD9+HP37989zPZUpUwampqZ4+fJlntdBRESUlYcPH+LZs2cYNGgQAMDT0xNlypTBqVOnULFiRQDAxYsXce/ePQwcOBDt27cHALRt2xazZ89WWd/GjRthY2ODefPmKe7e8vX1xbRp0/DXX38p2vKnT5/C0dFR47PMXFxccOvWLURFRcHFxQUmJibYtGkTKlSooNSOd+rUCXPnzsWkSZNQsWJFeHp6okaNGqhWrZpS36BZs2ZYs2YN7OzsVPoBOe2XyJmbm2PatGnQ08u4fjYpKQkbNmxAy5Yt8dVXXynKydcRGBioNJ2I1OMV6URFqHHjxpDJZAgPD0diYiIuX76MJk2aqC2bOYn+7t07fPjwAVWqVEFERIRK2apVqyol0TNLTU3F4sWLER4eju+//16RRAeA8+fPQwiBxo0bIyEhQfHPysoK9vb2uHnzZj73OGvGxsZISkoqtPUTERHlVXp6Oq5fv4769esrvqwCUAydcufOHXz48AFARlvq4uKi9qFgEokEAKCnp6f4opyeno53794hLS0N7u7uatv1nLhy5QqAjHFaM+vUqRMAqNzB5uTkpEiiAxlXwDs6OhbIFWjGxsZITEzM93qIiIg+FhYWBktLS8WPwxKJBJ999hlOnz6N9PR0AMDVq1ehr6+PVq1aKZbT09ODr6+v0rrevXuHGzdu4LPPPkNiYqLi++/bt2/h5eWFFy9eKO4mS0xMhImJicbY5PPlfYKs1KxZE7Nnz0a9evXw6NEjBAUFYc6cORg+fDguXbqUbR3kpl8i16pVK0USHci4c+z9+/fw9vZW+u6vp6eHSpUqFep3f6JPCa9IJypCFhYWqFGjBk6dOoXk5GSkp6erjHkuFx4ejt27dyMyMhIpKSmK6fIv5ZmpG8pFbs+ePUhKSsIPP/yAatWqKc2LioqCEAJjxoxRu2zmX8cLGh94QkREuiohIQHJyclwdHRUmefk5AQhBN68eQNTU1NERUVpHH5F7sSJE9i/fz+ePXuGtLQ0xXRNbbgmr169gkQiURm31crKCmZmZnj9+rXSdPlzTzIzMzNTDOOWH0lJSUpjvBIRERWE9PR0nDlzBtWqVVP64bdSpUrYv38//v33X3h5eeH169ewtraGkZGR0vIft5Hy77/btm3Dtm3b1G4zPj4epUuXhomJSbY/EsvnZ5dwB4CKFStiwoQJSE1NRWRkJC5cuIADBw5g0aJFWLhwYZYXxgG565fIfdy/ePHiBQDgp59+UruNnOwDETGRTlTkmjRpglWrViEuLg61atVS+8CR27dvY8GCBahSpQqGDBkCa2tr6Ovr48SJEzh16pRK+cxXr3/My8sLV69exd69e1G1alWlsunp6ZBIJPj++++Vfq2WK6xEd2pqKl68eMEHmhARUYlw8uRJrFy5EvXr10fnzp1hYWEBPT097NmzJ99Doqj7gV0dde08AMXwM3n15s0bfPjwQekKOSIiooJw48YNxMbG4syZMzhz5ozK/LCwMKU7rrMjv4K9U6dOWS4nT76XK1cOERERSElJyXJ4l8ePH0NfXx8ODg45jsHAwAAVK1ZExYoV4ejoiJUrV+Ls2bMqD0/Nr49zBPL2fvTo0bCyslIpr6+vX6DbJ/pUMZFOVMQaNGiA1atX4/79+xg7dqzaMufPn4ehoSGmTJmi1GifOHEi19urVKkS2rRpg59//hmLFy/GxIkTFY2kvb09hBCws7NT++t2YTl37hxkMlmuOj1ERERFxcLCAkZGRnj+/LnKvGfPnkEikaBMmTIAMtrS7B7Yee7cOZQtWxYTJkxQSnzv2LFDqVxOk+JAxoPLhBB48eKF0lVscXFxeP/+vdor0AvDyZMnAQC1atUqku0REVHJIR/WZciQISrzzp8/j4sXL0Imk8HGxgY3btxAcnKy0lXpHz+TS/6jr76+PmrWrKlx23Xq1MG9e/dw9uxZtc8tiY6Oxu3bt1GjRg2NF7Zp4ubmBgCIjY1VTFPXF8hNvyQr8n23tLTMdt+JKGscI52oiBkbG2Po0KHo0aMH6tWrp7aMnp4eJBKJ4hdzIKOhvnjxYp62WbNmTXzzzTe4du0ali9frlhvgwYNoKenh507d6pckSaEwNu3b5Xizm7st5yIjIzEpk2bYGZmhnbt2uV7fURERAVNT08PNWvWxKVLl5RuJY+Li8OpU6fg6empuH26YcOGePToES5cuKCyHnnbKr8aPHNbe//+fdy7d0+pvPzLf07a29q1awMADh48qDR9//79AKDyYPLCcOPGDezatQt2dnZZPvOFiIgoL2QyGS5cuIA6deqgUaNGKv/atWuHxMREXLp0CV5eXkhLS8PRo0cVy6enp+PIkSNK67S0tES1atUQEhKilLyWS0hIULxu06YNLC0tsXnzZpW7x2QyGX777TcIIeDv75/tvty4cUPtHWDy551kvqjNyMhIZdi13PRLsuLl5QUTExMEBgYiNTVVZX7mfSeirPGKdCItaN68ucb5derUwf79+zF37lzFw0COHDkCe3t7PHr0KE/bbNCgAUaMGIEVK1bA1NQUw4YNg729PXr16oUtW7bg1atXqF+/PoyNjRVJ+1atWqFz584AMn4tP3PmDDZt2gR3d3cYGxtn+UOA3O3btyGTyRQPVrtz5w7Cw8NhamqKiRMnqr2ljIiIqCgdP34cV69eVZneo0cPXL9+HdOmTUPbtm2hr6+PkJAQpKamol+/fopynTt3xrlz57B48WK0aNECbm5uePfuHS5duoQvv/wSrq6uqFu3Li5cuIBffvkFderUQXR0NIKDg+Hk5KT04G2pVAonJyecOXMGDg4OMDc3R/ny5eHs7KwSn6urK3x8fBASEoL379+jatWqePDgAUJDQ1G/fn3FQ9kKypUrV/Ds2TOkp6cjLi4ON2/exPXr12FjY4NJkybl+Wo8IiIidS5duoTExMQsv3NWqlQJFhYWCAsLw8SJE1GxYkX88ccfiIqKgqOjI8LDw/Hu3TuV5YYMGYKpU6diwoQJaNWqFezs7BAfH4979+4hJiYGCxcuBACUKlUK48aNw/z58zF58mS0bNkSTk5OiIuLQ2hoKKKiojBw4EBUrlw5233ZsGEDkpOT0aBBAzg6OiI1NRX37t3DmTNnYGtrixYtWijKurm54d9//8X+/fthbW0NOzs7VKpUCb169cpRvyQrpqam+PLLL7F8+XJMnjwZ3t7esLCwwOvXr3H58mVUrlxZ7ZX/RKSMiXQiHVS9enUMHz4ce/fuxaZNm2BnZ4e+ffsiOjo6z4l0AGjWrBmSkpKwdu1amJiY4IsvvkDXrl3h4OCAAwcOKG4xt7GxQc2aNZU6LW3btkVkZCROnDiBAwcOwNbWNttE+qFDhwBk3DpnZmaGcuXKoUePHmjdujUsLCzyvB9EREQF5Z9//lE7vXnz5vjpp5+wZcsW7NmzB0IIVKxYEV9//TUqVaqkKGdsbIyffvoJ27dvx4ULFxAaGgpLS0tUr15dcZt18+bNERcXh5CQEFy7dg1OTk74+uuvcfbsWdy6dUtpu8OHD8f69euxadMmpKamwt/fX20iXV62bNmyOHHiBC5cuAArKyt07dq1wMdZBYDt27cDyBjb1dzcHM7OzhgwYABatGjBB5QREVGBCwsLg6GhYZbDkOjp6aFOnToICwvD+/fv8d1332Hjxo0IDQ2FRCJBgwYN4O/vj6lTpyr92Ovk5IT58+djx44dOHHiBN6+fQtLS0u4urrCz89PaRtVqlTBwoULERgYiHPnziE2NhampqaoXLkyRowYAU9PzxztyxdffIGzZ8/iypUriuS3jY0N2rZtCz8/P6Xnpg0YMACrVq3C33//DZlMBh8fH1SqVAnly5fPUb9EkyZNmsDa2hp79uxBUFAQUlJSULp0aVSpUkUpmU9EWZOI/D5hiIiIiIiIiIiISIfI7wb76aefcpz0JiLShGOkExERERERERFRsSWTyZT+Tk9Px+HDh2FiYqJ4qCcRUX5xaBciIiIiIiIiIiq21q9fD5lMBg8PD6SkpODChQu4e/cuevfuzed4EFGB4dAuRERERERERERUbJ06dQr79u1DVFQUUlJSYG9vj7Zt26Jdu3baDo2IPiFMpBMRERERERERERERacAx0omIiIiIiIiIiIiINGAinYiIiIiIiIiIiIhIAybSiYiIiIiIiIiIiIg0YCKdiIiIiIiIiIiIiEgDJtKJiIiIiIiIiIiIiDRgIp2IiIiIiIiIiIiISAMm0omIiIiIiIiIiIiINGAinYiIiIiIiIiIiIhIAybSiYiIiIiIiIiIiIg0YCKdiIiIiIiIiIiIiEgDJtKJiIiIiIiIiIiIiDRgIp2IiIiIiIiIiIiISAMm0omIiIiIiIiIiIiINGAinYiIiIiIiIiIiIhIAybSiShXJBIJmjdvru0wiIiohChJ7c7AgQMhkUgQGRmp7VCKpcjISEgkEgwcOFDboRARERHRJ4iJdCIiIiLKs7S0NKxZswY+Pj4oXbo0DA0NYWdnh5o1a2Lo0KEICgrSdoi5Ik9mb9y4Mc/rcHV1hUQiyfG/GTNmFFj8RERERERUOAy0HQARERERFU9paWno2LEjDh8+DCsrK3To0AFOTk6QyWS4efMmtmzZgjt37qBz587aDrVIjR07FnFxcUrTNm7ciEePHmHAgAFwdXVVmldSrrgnIiIiIirOmEgnIiIiojzZunUrDh8+DC8vL4SGhsLS0lJp/ocPH3D+/HktRac9Y8eOVZl24sQJPHr0CAMHDmTinIiIiIioGOLQLkQ64t27d5BKpfD29laanpiYCGNjY0gkEvz5559K83777TdIJBKsX79eMS0mJgbff/89qlSpAhMTE1haWqJVq1b4559/stz21q1b0aJFC1hZWcHY2BhVqlTB7NmzkZycnOP4Fy5cCD09PXh7eyMmJibHyxERUfF15swZABnDoXycRAcAU1NTtGjRQvF3fHw8Fi5ciJYtW8LJyQlSqRS2trbo3Lkzzp49m6ttp6amYuXKlWjUqBEsLCxgamqK2rVr43//+x/S09NVygcFBaFVq1ZwcHCAkZERHB0d4ePjg5UrV+Zoe82bN4dEIkFqairmzp2LSpUqwcjICOXLl8fkyZMhk8lyFX92Vq1ahRo1asDY2Bhly5bFsGHDEB8fr7ZseHg4/Pz8YGdnByMjI7i4uGDkyJF48eJFlvuhzsaNG9UOa3P9+nX07t0brq6uMDIygq2tLerUqYOxY8ciJSVFUe758+f46aef4O3tDXt7e0ilUjg6OqJPnz64deuWyvYyj2keGRmJXr16wcbGBsbGxqhXrx7279+vNs63b99i3LhxcHJygrGxMTw9PbF48WK17zsAvHz5EhMmTEDlypVhZmYGKysrVK5cGQMHDsTDhw/VLkNERERE9DFekU6kI8zNzdGgQQOcP38eb9++RalSpQAAp0+fViS0jx49ii+++EKxzNGjRwEArVq1AgA8evQIzZs3R2RkJJo2bYp27drh/fv32L9/P9q1a4dVq1bhyy+/VNru4MGDsWHDBjg5OcHPzw9WVlY4d+4cpk6diqNHjyI4OBgGBlmfKtLT0zF27FgsX74c3bt3x19//QVjY+MCrRsiItJNZcqUAQDcu3cvR+Vv376NKVOmoFmzZujQoQOsra3x+PFjBAUF4dChQ9i3bx/atWuX7XpSUlLQqVMnHDlyBJUrV0afPn1gbGyM48eP4+uvv8b58+eVfnxevXo1vvrqK9jb26NTp06wsbFBdHQ0rl+/jg0bNmDkyJE53uc+ffogLCwMn3/+OSwsLHDw4EEsWLAA0dHR2LBhQ47Xo8mkSZNw5MgRdOrUCW3btsXx48exZs0aPHjwAMeOHVMqu3//fvj5+UEIAX9/f7i4uCA8PBy//fYb9u7di1OnTqFChQp5juX69eto2LAhJBIJOnfujAoVKiAhIQEPHjzAypUrMXv2bBgaGgIATp48ifnz56NFixbw8/ODubk57t+/j507dyIoKAinT5+Gl5eXyjYePXqEBg0awM3NDV988QViYmKwbds2dOnSBSEhIUo/xiQnJ6NVq1a4ePEivLy80LdvX8TFxWHWrFkIDQ1VWfeHDx/g7e2N//77D23atEGnTp0ghMCjR4+wd+9e+Pv7w83NLc/1Q0REREQliCAinTF16lQBQOzfv18x7bvvvhP6+vqiZcuWwsnJSTE9LS1NlC5dWri5uSmm+fj4CIlEIrZu3aq03tjYWOHl5SWMjY1FVFSUYvqGDRsEANGtWzfx4cMHpWWmT58uAIglS5YoTQcgfHx8hBBCJCYmiu7duwsAYvTo0SItLS3fdUBERMXH5cuXhaGhoZBIJKJfv35i165dIjIyMsvycXFx4tWrVyrTnzx5IhwcHISnp6fKvMztjpy8jRo9erRITU1VTE9NTRWDBw8WAMSePXsU0+vUqSOkUql4+fKlyvo/jmfAgAECgNiwYYPSdB8fHwFA1KlTR7x580Yx/d27d8Ld3V3o6emJFy9eZLnv8uWPHz+eZRn5tsuXLy8ePXqkmJ6SkiKaNm0qAIjz588rpr99+1aULl1a6OnpiZMnTyqta/78+QKAaNOmjdo41JH3CzLv+7hx41TqUy4mJkap7X/58qVISEhQKXf16lVhZmYm2rVrpzQ9IiJCABAAxIwZM5TmHT58WAAQn3/+udL0OXPmCACie/fuStt++PChsLa2FgDEgAEDFNODgoIEADF27FiVuJKTk9XGS0RERESkDod2IdIh8ivL5Veay1/XrVsX3bt3x9OnTxVX/V29ehUxMTGKZa5du4bQ0FD4+fmhV69eSuu1srLCzJkzkZSUhF27dimmL126FAYGBli/fj1MTEyUlpk6dSrKlCmDv/76S22sMTExaN26NQIDA/Hzzz9j+fLl0NPjKYWIqCSpXbs2Nm/ejLJly2Lz5s3w8/ODq6srypQpg27dumHfvn1K5S0tLWFjY6OyHicnJ/j7++POnTt4/Pixxm2mp6dj+fLlsLe3x6+//gp9fX3FPH19fSxatAgSiUSl/TIwMFBcOZ2Zung0+fnnn1G6dGnF32ZmZujbty/S09Nx6dKlXK0rK9OmTYOzs7PibwMDAwwaNAgAcOHCBcX0vXv3IiYmBgEBAWjatKnSOsaPHw9XV1cEBwdnW6c58XE/AQCsra2V2n47OzvFHXWZeXl5oWXLljh+/LjSUDByLi4u+PHHH5Wm+fr6wtnZWWl/AWDDhg3Q09PDggULlLZdoUIFjBkzJlfxS6VStfESEREREanDoV2IdMhnn30GExMTRSI9Pj4ely9fxqRJk9CyZUsAGYl1Dw8Pxa3d8unysWXj4+MxY8YMlXW/evUKQMZt9UDGrc7Xrl2DjY0NlixZojYeIyMjRfnMXr58CW9vbzx8+BCbN29Gnz598r7TRERUrPXs2RPdunXD8ePHcerUKVy5cgWnTp3Cnj17sGfPHvTv318x9jaQMWTZ0qVLcfbsWURHR6uMLf7s2TOlJPLH7t27h5iYGFSqVAmzZ89WW8bExESp/erbty/Gjx+PqlWrolevXvDx8YG3tzdsbW1zvb/16tVTmVa+fHkAQGxsbK7Xl59tXL58GcD/9QUyMzAwQLNmzRAZGYkrV65orFNNAgICsHTpUnTt2hX+/v5o3bo1vL294e7urrb8gQMH8Pvvv+PSpUt4/fo1UlNTlea/fv0aDg4OStNq1aql9IOIXPny5ZXGzn/79i0ePHiA8uXLq91+8+bNMXPmTKVpPj4+KFeuHObPn4/Lly+jffv28Pb2znKbRERERERZYSKdSIdIpVI0adIEISEhePXqFc6cOYO0tDS0atUKVapUgYODA44ePYoRI0bg6NGjkEgkii/Pb968AQAEBwcjODg4y228e/cOQMYXcSEEXr16pfKlMztRUVFISEiAk5MTmjRpkse9JSKiT4WhoSHatm2Ltm3bAgDS0tKwa9cuDB48GH/88Qe6deuGrl27IjAwEP7+/jA2NkabNm3g7u4OMzMz6Onp4cSJEwgNDc32Qdfy9u7+/fsa2y95ewcA48aNg42NDVauXIlly5ZhyZIlkEgk8PHxwcKFC9UmrrNiZWWlMk3+LJG0tLQcr6cgtiF/+OjHiWk5+fS4uLg8x9KgQQOEhYVhzpw52Llzp2Ls+cqVK2P69Ono3bu3ouzSpUsxduxYWFtbo02bNnB2doapqSkkEgn27NmDa9euqX1/1e0vkLHPmR8gKt/fsmXLqi1vb2+vMs3CwgLnzp3D9OnTERQUhCNHjgDIuBNh5MiR+PHHH9XeqUBERERE9DEm0ol0TMuWLREcHIyjR4/izJkzMDY2hre3t2LeoUOHkJycjLCwMFSrVg12dnYAMm6XBzK+xGq6tVlOXr527dqKK9pyysvLC0OHDsXAgQPRrFkzHDt2jA/qIiIiBX19ffTs2RP//vsvZs+ejWPHjqFr166YOnUqpFIpLl26hCpVqigt89VXX6l9WOTH5O1Xt27dsHv37hzH1L9/f/Tv3x9xcXE4c+YMAgMDsX79evj6+uLOnTt5ujpd2+R1ERUVpXb+ixcvlMoBUAyHkpqaqvIw8awS7p999hn279+P5ORkhIeH4/Dhw1i+fDn69OkDW1tbtG7dGqmpqZgxYwbs7e1x+fJlleR+5ivL80q+Hy9fvlQ7P6t6cHJywrp16yCEwK1bt3Ds2DGsWLECP/30E9LT0zFr1qx8x0ZEREREnz4OaEykYzKPk37s2DE0btwYxsbGinkxMTH47bff8P79e0VZAGjUqBEAICwsLEfbMTc3R7Vq1XDz5k3ExMTkOs5+/frh77//xvPnz9GsWTPF2O1ERERy8vGnhRAAgAcPHqBq1aoqSfT09HScOnUqR+v09PSElZUVzp07p3a87exYWVmhffv2WLNmDQYOHIiYmBicPHky1+vRBbVr1wYAnDhxQmVeamqqok9Qp04dxXRra2sAwJMnT1SWyW6MdyMjIzRu3Bg//fQTli1bBiBjnHYgY8iWuLg4NG7cWCWJ/u7du1z/aK9OqVKlULFiRTx79gz//fefynx19ZCZRCJBtWrV8PXXXyvu3tuzZ0++4yIiIiKikoGJdCIdU6dOHVhaWmLv3r24efOmUrJcPozLvHnzlP4GMsZTbdq0KXbv3o3169erXfe///6L6Ohoxd/jxo2DTCbD4MGD1V6FFhsbq/GLr7+/P3bu3InXr1/Dx8cHN2/ezNW+EhFR8bZ161YEBwcrDb8hFxUVhTVr1gAAmjVrBgBwdXXF/fv38fz5c0U5IQRmzJiBW7du5WibBgYG+Prrr/HixQuMGTMGiYmJKmVevHihtL7jx48rkvmZydtEU1PTHG1b13Tt2hWlS5fG1q1bce7cOaV5S5YsQUREBFq3bq00PnqDBg0AQPHeyB09ehRbt25V2caZM2fU1rH8qnB53dnZ2cHU1BTh4eFKw+qkpKTgm2++wevXr/O4l8oGDRqE9PR0TJ48Wem4i4iIUCT3M7t586baK9g/jp+IiIiIKDsc2oVIx+jr66N58+aKK7wyJ9JdXFzg7u6O//77D/r6+vDx8VFadsuWLWjZsiWGDBmCZcuWoWHDhrCyssLTp09x/fp13LhxA2fPnlUMBzN48GCEh4dj5cqVcHd3h6+vL5ydnRETE4OIiAicPHkSgwYNwu+//55lvJ07d8bevXvRrVs3NG/eHCEhIfDy8iqEmiEiIl1z/vx5LF26FPb29mjSpAkqVKgAICOpeeDAASQmJqJLly7w9/cHAHz77bcYPnw4ateuDT8/PxgaGuL06dO4desWOnXqhH379uVou1OnTsW1a9fw+++/Y9++fWjZsiXKlSuH6Oho3L9/H6dPn8acOXNQtWpVABnDwJibm6NRo0ZwdXWFEAJhYWG4ePEi6tati9atWxdOBRUyc3NzrF+/Hj169ICPjw969OgBZ2dnhIeH459//oG9vT1WrVqltMygQYOwcOFCzJs3D9euXUPVqlVx7949HDp0CN26dcOuXbuUyi9YsADHjh1D06ZNUaFCBZibm+PmzZs4dOgQrK2tMWzYMAAZQ8aMGTMG8+fPR40aNdClSxfIZDIcP34cMTExaNGiBY4fP57vfR4/fjz27NmDXbt2oU6dOvD19UVcXBy2b9+OZs2aISgoSKl8cHAwJk6ciM8++wweHh6ws7PD06dPsXfvXujp6WHixIn5jomIiIiISgYm0ol0UKtWrbB3715YWFioPACtVatW+O+//1C3bl2lMU+BjDFAw8PDsXz5cuzatQt//fUX0tLSYG9vj6pVq+Lrr79GjRo1lJZZsWIFPv/8c/z+++8ICQlBXFwcSpcuDWdnZ0ycOBH9+vXLNl5fX18cPHgQnTp1QosWLXDkyBHUr18//xVBREQ6bfz48ahUqRJCQkJw/fp1HDlyBElJSShTpgyaN2+OPn36oE+fPpBIJAAyxkE3MjLCkiVLsGnTJpiYmKBp06bYsGEDdu3aleNEuqGhIfbs2YPNmzdj48aN2L9/P969ewdbW1tUqFABs2bNQt++fRXl58+fjyNHjuDy5cs4ePAgjI2N4eLigp9//hkjRowo1g+b7NKlC06fPo25c+fiyJEjiI+Ph729PYYPH46pU6fC0dFRqbydnR1CQ0MxceJEnDx5EqGhoahXrx6Cg4MRERGhkkgfOXIkrK2tcf78eZw6dQqpqalwcnLCyJEjMX78eLi4uCjKzpo1C7a2tli7di1WrVoFS0tLtGnTBrNnz8b06dMLZH+NjIwQEhKCGTNmYNu2bVi6dClcXV3x448/olu3biqJdF9fXzx+/BgnT57E3r17kZCQAAcHB7Rp0wbjxo1D48aNCyQuIiIiIvr0SYS6+1yJiIiIiIiIiIiIiAgAx0gnIiIiIiIiIiIiItKIiXQiIiIiIiIiIiIiIg2YSCciIiIiIiIiIiIi0oCJdCIiIiIiIiIiIiIiDZhIJyIiIiIiIiIiIiLSgIl0IiIiIiIiIiIiIiINmEgnIiIiIiIiIiIiItKAiXQiIiIiIiIiIiIiIg0M8ruC2NhYpKamFkQsAABbW1u8evWqwNb3qWH9aMb60Yz1oxnrRzPWj2aFUT8GBgawtrYu0HUCBd925xWPKd3H90j38T3SfXyPdF9BvkeF1XYTERGR9uU7kZ6amoqUlJSCiAUSiUSxTiFEgazzU8L60Yz1oxnrRzPWj2asH82KW/0UZNudV8Wtzkoivke6j++R7uN7pPv4HhEREVFO5TuRTiWTEAIymUzbYSiRSCRISkpCcnIyO8EfEUIgJSUFJiYmSEpK0nY4OklXjx+pVKr4gkdEpOt0sX+Qmbw9BABDQ8Nif35V13ax3SAiIiIiKhxMpFOeyGQyTJw4UdthEH3yFi5cCCMjI22HQUSUI+wfaB/bDSIiIiKiwsGHjRIRERERERERERERacAr0infxkg/wFDbQZBGKQJYlmIKABhj+AGGvONbp6UAWCYz1XYYRET5oov9g0+1PWS7QURERERU+JhIp3wzBCD9RL6IlgSGEr5fOk93hmgnIsozXe8ffFLtIdsNIiIiIqJCx6FdiIiIiIiIiIiIiIg0YCKdiIiIiIiIiIiIiEgDJtKJiIiIiIiIiIiIiDRgIp2IiIiIiIiIiIiISAMm0omIiIiIiIiIiIiINGAinYiIiIiIiIiIiIhIAwNtByAnhIBMJkNSUhKEENoOh4iIKEdKcvsl33cAkEqlkEgkWo6IiIg04XmbiIiIKO905op0mUyGCRMmoF+/forOHRERka4rye2XTCbDxIkTMXHixBK370RExRHP20RERER5pzOJdCIiIiIiIiIiIiIiXcREOhERERERERERERGRBkykExERERERERERERFpwEQ6EREREREREREREZEGTKQTEREREREREREREWnARDoRERERERERERERkQYG2g6AiIiIiIiIitatW7ewZ88e1K9fH6dPn4ZMJkNqairatGmD6OhoXL16FYaGhqhWrRquXbsGAwMD6OnpwdDQEI0bN8bFixfh7++PGjVqKNb577//4s8//0RycjK8vLzw6NEjtWW2bNkCiUSCihUr4tq1azAyMoKnp6fidbNmzbJc/86dO+Hv7w8AitdZlck8/eP59evXx8WLF9GjRw84ODgURhUTERHRJ4aJdCIiIiIiohJm165dSEhIwD///KM0PfPfKSkpuHr1quI1ACQnJyM4OBhCCGzfvh2VK1eGVCqFTCbDtm3bkJSUBACK5dSVef/+vVKZpKQkpddZrX/79u2Ij4/H9u3bIYRAQkKCxjLy6XKZ58u3sW3bNvj4+BR09RIREdEniEO7EBERERERlTBv377N87JCCABAQkICgoODAQDBwcFISEhQKZuTMrldf3x8vOJ1VmUyT5fLPD/zNgIDA3O660RERFSC6cwV6fKODJBxlUPmvymDRCJBUlKSTtSPTCZTvBYCgER7sRB9ajJ/vDN/1rRJl84/ukb5fFiy6ibz/ub2WOUxpfvy8h6xf6AduthuUAZdO9cVdJslhEBISAgqVqyokrRWV+bjq99zu351MWdVRj69QYMGsLW1xatXr9SuQwiBwMBAVK1aFTY2NrmKj4iIiEoWichhDyolJUVxOx+Q0Sk0MTHBq1evlKbn1du3bzFlypR8r4eK3hjDDzDjvQ06TSaARTJTAMB46QdImdjQae/TgWUpptoOg/Jgzpw5KFWqVIGtz9DQELa2tnlenm03aZMu9g8+1faQ7QZpm6mpKT58+KCxjImJCRITE3O9bj09PRgbGyMxMTHL5L+8fUtKSkJ6errSsh4eHhg+fDh+//133Lt3T2l+5uUrV66MESNGQCLJ34khv203ERER6a4cX5EeGBiInTt3Kv6uUKECfv7550IJioiIiPKPbTcRERWF7JLoAPKURAeA9PT0bNcvhFBbJj09HXfu3MGtW7dw584djcvfuXMHL1++hL29fZ7iJCIiok9fjhPp3bp1Q8eOHRV/5/eX+o8ZGhoqXs+ZM0fpoTCUQSKRwN7eHlFRUVq/NVQmkymuQtSZ8YGIPhGZP1O6cj7UpfOPrsl8PtSF9yozXW67eUzpvry8R+wfaIcuthuUQdfOdcnJyfjxxx8LfL05uSI9J2XU0dPTg4mJCT58+KDxinRTU1MkJiaqXJFeuXJlVK1aFZ6enhqvSPf09ETZsmVzHR8RERGVHDn+jmNoaKj0hbmgZf5yb2RkxC8AakgkEhgbG8PIyEgnOuJyBZyXISrxMn+mpFIpjIyMtBfM/6er5x9dU9CJ6vwqyrY7t8cqjyndl9/3SMc+Dp80XWw3KENJONfp6+tj0KBB+O2339QmqTOXWblyZa7rQSKRYODAgfjtt9+yXFZPT09R5uNl/f39oaenhx49emDOnDlZLt+jRw+da8eJiIhIt+jYyJVERERERERU2AoiaSyRSNC6dWtUrlwZbdq0ybZM27Zt87V+dTFnVUY+XT5eua2trdp1SCQSdOvWjeOaExERUbaYSCciIiIiIiph8vNwbHky2tLSUpFAb9OmDSwsLFTK5qRMbtdvaWmp9DqrMh8n9zPPz7yNbt265XTXiYiIqARjIp2IiIiIiKiE8fPzg7W1Ndq2bQszMzMYGhpCIpGgbdu2qFWrFoCMIcJq1aoFiUQCQ0NDGBkZwdzcHG3atIG1tTV69OihGJJTKpUiICAAxsbGkEgkqFWrVpZlzMzMYG5urli3sbGx0uus1t+zZ09YW1ujZ8+eCAgI0Fgm83S5zPPl2+jZsyeHQyIiIqIc4XOgiIiIiIiISpiqVauidu3aAKD0YOqcUrdMjRo1sGDBAo3L1ahRA/Pmzcvz+mvUqKH0d3ZlNM3v2LEjx0UnIiKiHOMV6UREREREREREREREGjCRTkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKQBE+lERERERERERERERBoYaDsAOalUil9++QX29vaIiYnRdjhEREQ5UpLbL6lUioULFypeExGRbuN5m4iIiCjvdCaRLpFIYGRkBGNjY0gkEgghtB0SERFRtkpy+yXfdyIiKh543iYiIiLKOw7tQkRERERERERERESkARPpREREREREREREREQaMJFORERERERERERERKQBE+lERERERERERERERBowkU5EREREREREREREpIGBtgOg4i8FAIS2oyBNUoT616SbUrQdABFRAdDF/sGn2h6y3SAiIiIiKnxMpFO+LZOZajsEyoVlKXy/iIio8Ol6/4DtIRERERER5QaHdiEiIiIiIiIiIiIi0oBXpFOeSKVSLFy4UNthKJFIJLC3t0dUVBSE+ITu1y4AQgikpKSgbNmyiI2N1XY4OklXjx+pVKrtEIiIckwX+weZydtDADA0NIREItFyRPmjru1iu0FEREREVDiYSKc8kUgkMDIy0nYYSiQSCYyNjWFkZKRTiVBdYWJiAisrKyQmJrJ+1ODxQ0SUf7rYP/iYsbGxtkMoMGy7iIiIiIiKDod2ISIiIiIiIiIiIiLSgIl0IiIiIiIiIiIiIiIN8j20i4FBwY8OUxjr/JSwfjRj/WjG+tGM9aMZ60ezgq6fwqpvXXofdSkWUo/vke7je6T7+B7pvoJ6j/heExERfbokggMqEhERERERERERERFlSaeGdklMTMTkyZORmJio7VB0EutHM9aPZqwfzVg/mrF+NGP95B7r7P+1d+9hUVf5H8Dfw00YkDsDjoMgIpcUAUXz8is0DEkjK1nX9dqWlqm5a6vQ5mXFS6Wmu21huYqaKYm2iBZoqOWqiSF5gVEuiiKskoDAIPfLzO8PH745AgNYzMw679fz+DzynfOd+ZzPOc93mM8czlf/cYz0H8dI/3GM9B/HiIiIiDpLrwrpKpUKN27cABfJt4350Yz50Yz50Yz50Yz50Yz56TrmTP9xjPQfx0j/cYz0H8eIiIiIOkuvCulERERERERERERERPqGhXQiIiIiIiIiIiIiIg30qpBuamqKiIgImJqa6joUvcT8aMb8aMb8aMb8aMb8aMb8dB1zpv84RvqPY6T/OEb6j2NEREREnSVScTM4IiIiIiIiIiIiIqJ26dWKdCIiIiIiIiIiIiIifcNCOhERERERERERERGRBiykExERERERERERERFpwEI6EREREREREREREZEGJtp8sStXruDQoUO4ceMGysvLsXjxYgwbNkzjOZcvX8auXbtQWFgIBwcHTJo0CaNHj9ZOwFrW1fxcvnwZ0dHRrY7/61//gq2tbTdGqhsHDhxAWloabt26BTMzM3h5eWH69OmQSqUaz0tNTUV8fDxKSkrg4uKCadOmYfDgwVqKWjseJTcnTpzA5s2b1Y6Zmppiz5493R2uTqSkpCAlJQUlJSUAAJlMhoiICAQGBrZ7jiHMnRZdzY+hzZ8HJSYmIi4uDuPHj8crr7zSbjtDmj+adOb61NDQgF27duHMmTNobGyEv78/Zs+e/Vi+l+m7tuY3x0c/lJWVYffu3bh48SLq6+vh4uKCefPmoV+/fgAAlUqFffv24fjx46iuroaPjw9mz56NXr166Tjyx59SqcS+fftw6tQpVFRUwN7eHsHBwZg0aRJEIhEAjo8udPTZqjNjUlVVhe3bt+Onn36CSCTCk08+iT/+8Y8wNzfXRZeIiIhIx7RaSK+vr4e7uzueeeYZfPjhhx22Ly4uxgcffIBnn30Wb731FuRyOT777DPY2toiICCg+wPWsq7mp8U//vEPiMVi4Wdra+vuCE/nrly5gnHjxqFfv35obm7Gl19+iTVr1mDTpk3t/jKbk5ODjz76CFOnTsXgwYNx+vRpbNiwAevWrUOfPn203IPu8yi5AQALCwt89NFHWoxUd+zt7TF16lT06tULKpUK//nPf7B+/XqsX78erq6urdobytxp0dX8AIY1f1pcu3YNR48ehZubm8Z2hjZ/NOnM9enzzz/H+fPn8fbbb0MsFiM2NhYbN27E6tWrdRy9YWlvfnN8dK+qqgrLly/HgAED8O6778La2hpFRUWwtLQU2hw8eBCHDx/G/PnzIZFIEB8fj7Vr12LTpk0wMzPTYfSPv8TERBw9ehTz58+HTCbD9evXsXnzZojFYowfPx4Ax0cXOvps1Zkx+ec//4ny8nIsW7YMzc3N2Lx5M7Zs2YI//elP2u4OERER6QGtbu0SGBiIKVOmdLgKvUVKSgokEglmzpwJmUyGsLAwDB8+HElJSd0cqW50NT8tbGxsYGtrK/wzMno8d+xZunQpRo8eDVdXV7i7u2P+/PkoLS3F9evX2z0nOTkZAQEBeOGFFyCTyTBlyhR4eHjgyJEjWoy8+z1KbgBAJBKpzZ3HeXVhUFAQBg8ejF69ekEqleIPf/gDzM3NcfXq1TbbG8rcadHV/ACGNX8AoK6uDh9//DHeeOMNteJVWwxt/mjS0fWppqYG3333HWbNmoWBAwfCw8MD8+bNQ05ODnJzc3UcveFob35zfPTDwYMH4eDggHnz5sHT0xMSiQT+/v5wcXEBcH9lbXJyMl5++WUMHToUbm5uWLBgAcrLy3Hu3DkdR//4y83NFd5HJRIJhg8fjkGDBuHatWsAOD66oumzVWfG5L///S8uXryIuXPnon///vDx8cGrr76KM2fOoKysTNvdISIiIj2g1xXXq1evws/PT+2Yv78/P7g9JDIyEq+//jpWr16N7OxsXYejNTU1NQAAKyurdtvk5ua2OYc0FQcfB53JDXC/cDJv3jy8+eabWL9+PQoLC7URns4plUr88MMPqK+vh5eXV5ttDHXuAJ3LD2B482fbtm0IDAzEoEGDOmxryPOnIw9fn65fv47m5ma1fPXu3RuOjo58v9ei9uY3x0c/pKenw8PDA5s2bcLs2bMRGRmJY8eOCY8XFxejoqJCbfzEYjE8PT05Tlrg5eUFuVyO27dvAwDy8/ORk5MjbI/G8dE/nRmT3NxcWFpaCtsnAYCfnx9EIpHwJQkREREZFq1u7dJVFRUVsLGxUTtmY2OD2tpaNDQ0GPyfQdrZ2WHOnDno168fGhsbcfz4cURHR2Pt2rXw8PDQdXjdSqlUYufOnfD29ta4TUJ7c6iioqKbI9SdzuZGKpXizTffhJubG2pqanDo0CEsW7YMmzZtgoODgxYj1p6CggIsXboUjY2NMDc3x+LFiyGTydpsa4hzpyv5MbT588MPP+DGjRt4//33O9XeEOdPZ7R1faqoqICJiUmrVf7Ml/Zomt8cH/1QXFyMo0ePYsKECXjppZeQl5eHHTt2wMTEBKNHjxbGgtcd3XjxxRdRW1uLRYsWwcjICEqlElOmTMFTTz0FABwfPdSZMamoqGi1ZaaxsTGsrKw4bkRERAZKrwvppJlUKlW7WZu3tzfu3LmDpKQkvPXWWzqMrPvFxsaisLAQq1at0nUoeqezufHy8lJbbezl5YVFixbh6NGjmDJlSneHqRNSqRQbNmxATU0Nzp49i5iYGERHR7dbLDY0XcmPIc2f0tJS7Ny5E8uWLTP4L3B/LV679Q/n9/8GpVKJfv36YerUqQCAvn37oqCgAEePHsXo0aN1GxwhNTUVp0+fxsKFC+Hq6or8/Hzs3LkTdnZ2HB8iIiKix4heF9JtbW2hUCjUjikUClhYWPDDXjs8PT0f++1dYmNjcf78eURHR3e48rW9OfS47uXcldw8zMTEBH379sXPP//cTdHpnomJibCfrIeHB/Ly8pCcnIzXX3+9VVtDmztA1/LT1rmP6/y5fv06FAoFoqKihGNKpRJZWVk4cuQI4uLiWt2bwhDnT0fauz7Z2tqiqakJ1dXVaqueDT1f2tLR/F66dCnHRw/Y2dm1+lJTJpPhxx9/BABhLBQKBezs7IQ2CoUC7u7u2grTYO3evRsTJ07EqFGjAAB9+vRBSUkJEhMTMXr0aI6PHurMmNja2qKyslLtvObmZlRVVfH6R0REZKD0upDev39/XLhwQe1YRkaGxj17DV1+fr7aL4OPE5VKhe3btyMtLQ0rV66ERCLp8BwvLy9kZmZiwoQJwrGMjAz079+/O0PVukfJzcOUSiUKCgqE/TwNgVKpRGNjY5uPGcrc0URTftpq+7jOHz8/P3z44Ydqxz799FNIpVJMnDixzRs8c/78oqPrk4eHB4yNjZGZmYnhw4cDAG7fvo3S0lK+32tBR/Pb0dGR46MHvL29hf23W9y+fRtOTk4AAIlEAltbW2RmZgpFwJqaGly7dg2hoaHaDtfg1NfXt3ovMDIygkqlAsDx0UedGRMvLy9UV1fj+vXrwraZcrkcKpUKnp6eugqdiIiIdEirhfS6ujq11YrFxcXIz8+HlZUVHB0dERcXh7KyMixYsAAAEBoaim+//Ra7d+/GmDFjIJfLkZqainfeeUebYWtNV/OTlJQEiUQCV1dXNDQ04LvvvoNcLseyZct01YVuFRsbi9OnTyMyMhIWFhbC3oRisVj4C4VPPvkE9vb2wp8+jx8/HitXrsTXX3+NwYMH44cffkBeXl6nVtj+L3mU3Hz11Vfo378/XFxcUF1djUOHDqGkpAQhISG66ka3iouLQ0BAABwdHVFXV4fTp0/jypUrWLp0KQDDnTstupofQ5o/FhYWre430KNHD/Ts2VM4bujzR5OOrk9isRjPPPMMdu3aBSsrK4jFYmzfvr3V9kHUPTozvzk+ujdhwgQsX74cCQkJGDlyJK5du4bjx48L1xSRSITx48cjISEBvXr1gkQiwd69e2FnZ4ehQ4fqOPrH35AhQ5CQkABHR0fIZDLk5+fjm2++wZgxYwBwfHSlo89WHY2JTCZDQEAAtmzZgjlz5qCpqQnbt2/HyJEjYW9vr6tuERERkQ5ptZCel5eH6Oho4eddu3YBAIKDgzF//nyUl5ejtLRUeFwikeCdd97B559/juTkZDg4OGDu3LkICAjQZtha09X8NDU1YdeuXSgrK0OPHj3g5uaG5cuXY+DAgVqPXRtSUlIAACtXrlQ7Pm/ePGH/ydLSUohEIuExb29vLFy4EHv37sWXX36JXr16YcmSJRpvwvm/6FFyU1VVhS1btqCiogKWlpbw8PDAmjVrHtv9whUKBWJiYlBeXg6xWAw3NzcsXboUgwYNAmC4c6dFV/NjaPOnI4Y+fzTpzPVp1qxZEIlE2LhxI5qamuDv74/Zs2drOVJqD8dH9zw9PbF48WLExcXh3//+NyQSCWbNmiXczBIAJk6ciPr6emzZsgU1NTXw8fHBu+++y+0QteDVV19FfHw8tm3bBoVCAXt7ezz77LOIiIgQ2nB8tK+jz1adGZOFCxciNjYWq1atgkgkwpNPPolXX31V630hIiIi/SBStfzNIRERERERERERERERtdJ6Y1ciIiIiIiIiIiIiIhKwkE5EREREREREREREpAEL6UREREREREREREREGrCQTkRERERERERERESkAQvpREREREREREREREQasJBORERERERERERERKQBC+lERERERERERERERBqwkE5EREREREREREREpAEL6USk0YkTJzB58mTk5eXpOhQiIiKNVq5ciZUrV+o6jF8lJiYGM2bM0HUYOldcXIzJkyfjxIkTug6FiIiIiAgAYKLrAIiIiIjIcBUUFGD//v3Iy8uDQqGAlZUVZDIZgoKC8Nxzz+k6PEFxcTEWLFiA6dOn44UXXuj0eSdOnMDmzZs7bOfk5ISYmJhfEyIREREREXUjFtKJiIiISCdycnIQHR0NR0dHhISEwNbWFnfv3sXVq1eRnJysV4X0R+Xr64sFCxaoHduyZQs8PT0REhIiHDM3N9d2aERERERE1AUspBMRERGRTiQkJEAsFuP999+HpaWl2mMKhUJHUf22nJ2d4ezsrHZs69atkEgkePrpp3UUFRERERERdRUL6UR66ubNm1iyZAkiIyMRFBQEALh+/Treeecd9O3bF+vWrRPavvfee6iqqsJ7770HALhw4QIOHDiAGzduQCQSwdfXF9OnT4erq6vaa9y6dQt79+6FXC5HQ0MDXF1dERERIbxee6qqqrB27VqUl5djxYoVkEqlv3HviYjIENy5cweurq6tiugAYGNjI/z/+++/x8mTJ1FYWIiamho4OzvjueeeQ2hoaIev0djYiAMHDuDUqVO4e/cubGxsMGrUKPz+97+Hqamp0C4jIwP79+9HYWEhmpubYW9vjyeffBJTp05t97lbtm1ZtWoVfvzxR5w8eRINDQ0YNGgQ3njjDVhbW3cxI78oKyvDtm3bkJmZCTMzMwQHB2P69OkwMvrlFkd1dXXYt28fUlNToVAo4OTkhJCQEISHh0MkEgH4ZUuaefPmYfTo0WqvMXnyZERERGDy5MkAgNraWsTHx+PcuXMoLy+HWCyGm5sbpk2bBg8PDwBAVlYWDh8+jKtXr0KhUMDGxkbIk5mZmfDcMTExOHv2LD766KMO+1FdXY2dO3ciLS0NIpEIQUFBeP7551vlpKKiAnFxccjIyEBlZSWsrKzg6emJV155BRKJ5JFzTURERETUGSykE+mplsLClStXhMJ2VlYWRCIR8vPzUVNTA7FYDKVSiZycHIwdOxYAcPLkScTExMDf3x/Tpk1DfX09UlJSsGLFCqxbt074oFlYWIjly5fD3t4eL774Inr06IHU1FRs2LABf/nLXzBs2LA246qsrMSaNWtQVVWFlStXwsXFRTsJISKix46TkxNyc3NRUFCAPn36tNsuJSUFrq6uCAoKgrGxMX766Sds27YNSqUSYWFh7Z6nVCqxfv16ZGdnIyQkBDKZDAUFBUhKSsLt27cRGRkJ4P574gcffAA3NzdMnjwZpqam+Pnnn5GTk9OpfuzYsQOWlpb43e9+h+LiYiQnJyM2NhaLFi3qWkIeiHvt2rXw9PTEjBkzkJmZiW+++QYuLi7ClwcqlQrr16/H5cuXMWbMGLi7u+PSpUvYvXs3ysrK8Morr3T5dbdu3YqzZ88iLCwMMpkM9+7dQ3Z2Nm7duiUU0lNTU1FfX4/Q0FD07NkT165dw5EjR1BWVoa33377kfuRnZ2NZ599FjKZDGlpaW3uF79x40YUFhYiLCwMEokECoUCGRkZKC0tZSGdiIiIiLodC+lEesrIyAje3t7Izs4WjmVlZWHo0KFIT09Hbm4uAgICcPPmTdTW1sLX1xd1dXXYsWMHnnnmGbzxxhvCecHBwfjzn/+MAwcOCMd37twJR0dHvP/++8KKvHHjxmHFihXYs2dPm4X0iooKrF69Gg0NDYiOjoaTk1M3Z4GIiB5n4eHheO+99xAZGQlPT0/4+PjAz88PAwYMgInJL7+mRkdHq612DgsLw9q1a5GUlKSxkH769GlkZGQgOjoaPj4+wnFXV1ds3boVOTk58Pb2RkZGBpqamvDXv/71kVaRW1lZYdmyZcIqcJVKhcOHDwtfendVY2MjRowYgYiICABAaGgooqKi8N133wkF6PT0dMjlckyZMgUvv/wygPt52bRpEw4fPoywsLAuf9l9/vx5hISEYObMmcKxiRMnqrWZPn262liMHTsWLi4u+PLLL1FaWgpHR8cu9yMrK0vtJq6hoaGIjo5We93q6mrk5OS0utnrSy+91KU+EhERERE9KqOOmxCRrvj4+ODGjRuoq6sDcP+mbIMHD4a7uzuysrIA/LJK3cfHBxkZGaiursaoUaNQWVkp/DMyMkL//v1x+fJlAPe3ZpHL5RgxYgRqa2uFdvfu3YO/vz+KiopQVlamFsvdu3fxt7/9DU1NTSyiExHRb2LQoEFYs2YNgoKCcPPmTRw6dAhr167F3LlzkZ6eLrR7sHBbU1ODyspKPPHEE7hz5w5qamraff6zZ89CJpNBKpWqvS8OHDgQAIT3xZatZdLT06FUKrvcj7FjxwpFdOD+DUaVSiVKSkq6/FwtHt62xsfHB3fu3BF+vnDhAoyMjFrdkPX555+HSqXCxYsXu/yalpaWuHbtWqvfAR704FjU1dWhsrISXl5eUKlUuHHjxiP1w9jYWK2dkZFRqy9IzMzMYGJigitXrqCqqqrLfSMiIiIi+rW4Ip1Ij/n6+qK5uRm5ublwdHSEQqGAj48PCgsLhZXq2dnZkMlksLKyQlFREQBg1apVbT6fhYUFAODnn3+GSqVCfHw84uPj22yrUChgb28v/PzJJ5/A2NgYf//732Fra/sb9pKIiAyZp6cnFi9ejKamJuTn5yMtLQ1JSUnYuHEjNmzYAJlMhuzsbOzfvx+5ubmor69XO1/Tqu+ioiLcunULs2fPbvPxlhuajhw5EsePH8dnn32GPXv2wM/PD8OGDcPw4cPV9vJuz4OrsIFfCvPV1dUdntsWU1PTVivjLS0t1Z6vpKQEdnZ2wnt7C5lMJjzeVdOmTUNMTAzefPNNeHh4IDAwEMHBwWo3Sy0tLUV8fDzS09Nb9e/hLzU62w9bW1uYm5urtXv4/iumpqaYNm0adu3ahTlz5sDLywuDBw9GcHAwfy8hIiIiIq1gIZ1Ij/Xr1w+mpqbIysqCo6MjbGxsIJVK4evri5SUFDQ2NiIrK0vYhkWlUgEAFixY0OaHSmNjYwAQVtuFh4fD39+/zdd++M/Bhw0bhpMnTyI5OVnjjdeIiIgehYmJCTw9PeHp6QmpVIrNmzcjNTUVTz31FFavXg2pVIqZM2fCwcEBJiYmuHDhApKSkjSuIFepVOjTp4/aViUPaimAm5mZITo6GpcvX8b58+dx8eJFnDlzBgMHDsSyZcs6LKa393jL+3JXdaZ431kPrpR/UFt5GzlyJHx9fZGWloZLly7h66+/xsGDB7F48WIEBgZCqVRi9erVqKqqwsSJE9G7d2/06NEDZWVl2Lx5c6v+/pb9AIAJEyZgyJAhOHfuHC5duoT4+HgkJiZixYoV6Nu372/6WkRERERED2MhnUiPtRQVsrOz4eDgIOzv6uPjg8bGRpw6dQoKhQK+vr4AIKwYs7GxwaBBg9p93pZ2xsbGGts96LnnnoOLiwv27dsHsViMF1988Vf0jIiIqH0tN7YsLy/HTz/9hMbGRkRFRamt/G7ZlkUTZ2dn3Lx5E35+fu0WlFsYGRnBz88Pfn5+mDVrFhISErB3717I5fJOv1dqk5OTEzIzM1FbW6u2Kv3WrVvC40D7q+PbW7FuZ2eHcePGYdy4cVAoFIiKikJCQgICAwNRUFCAoqIizJ8/H8HBwcI5GRkZv6ofcrkcdXV1aqvSb9++3WZ7FxcXhIeHIzw8HEVFRYiMjMTXX3+NhQsXPnIMRERERESdwT3SifScr68vrl69isuXLwsFc2tra/Tu3RsHDx4U2gCAv78/LCwscODAATQ1NbV6rsrKSgD3C+0DBgzAsWPHUF5e3m67h0VERCA8PBxxcXFISUn5TfpHRESGSy6Xt7lq+8KFCwDub+/Rsqr5wXY1NTU4ceJEh88/YsQIlJWV4fjx460ea2hoEO5B0tae2+7u7gDQ5vupPmhZIX7kyBG140lJSRCJRAgICAAAiMVi9OzZU7i3Sotvv/1W7WelUtlqaxYbGxvY2dkJOWhrLFQqFZKTk39VP5qbm9V+r2irX/X19WhoaFA75uzsDHNzc70dIyIiIiJ6vHBFOpGe8/HxQUJCAu7evSusSAfuF8+PHTsGJycnODg4ALj/YXnOnDn4+OOPERUVhVGjRsHa2hqlpaU4f/48vL298dprrwEAXnvtNSxfvhyLFy9GSEgIJBIJFAoFcnNzUVZWhg0bNrQZz4wZM1BTU4PY2FiYm5vj6aef7v4kEBHRY2nHjh2or6/HsGHDIJVK0dTUhNzcXJw5cwZOTk4YM2YMFAoFTExMsG7dOowdOxZ1dXU4fvw4rK2t2/wy+EFPP/00UlNTsXXrVsjlcvj4+ECpVOLWrVtITU3F0qVL0a9fP3z11VfIyspCYGAgnJycoFAokJKSovbXYPpmyJAhGDBgAPbu3YuSkhK4ubnh0qVLSE9Px/jx49W2aAsJCUFiYiI+++wzeHh4ICsrS7ivSova2lrMnTsXw4cPh5ubG8zNzZGZmYm8vDxhaxypVApnZ2d88cUXKCsrg1gsxo8//virbv45ZMgQeHt7Y8+ePSguLoZMJkNaWlqron5RURFWrVqFESNGQCaTwdjYGGlpaVAoFBg5cuQjvz4RERERUWexkE6k57y9vWFkZIQePXoIq+OAXwrpLavRW/zf//0f7OzskJiYiEOHDqGxsRH29vbw9fXFmDFjhHYymQwffPAB9u/fjxMnTuDevXuwsbGBu7s7Jk2apDGmOXPmoK6uDp9++iksLCwwdOjQ37TPRERkGGbMmIHU1FRcuHABx44dQ1NTExwdHREaGopJkybB0tISlpaWePvttxEfH48vvvgCtra2CA0NhbW1NT799FONz29kZIQlS5YgKSkJJ0+exLlz52BmZgZnZ2eMHz8evXr1AgAEBQWhuLgY33//Pe7du4eePXviiSeewOTJk9u9kamuGRkZISoqCvHx8Thz5gy+//57SCQSTJ8+HeHh4WptIyIiUFlZibNnzyI1NRUBAQF499131W7C2qNHD4wbNw6XLl1CWloalEolXFxcMHv2bISGhgK4v+VcVFQUduzYgcTERJiammLYsGEICwvDkiVLflU/du7ciVOnTkEkEiEoKAgzZ85EZGSk0M7BwQGjRo2CXC7HyZMnYWxsjN69e2PRokUYPnz4I702EREREVFXiFSPehckIiIiIiIiIiIiIiIDwD3SiYiIiIiIiIiIiIg0YCGdiIiIiIiIiIiIiEgDFtKJiIiIiIiIiIiIiDRgIZ2IiIiIiIiIiIiISAMW0omIiIiIiIiIiIiINGAhnYiIiIiIiIiIiIhIAxbSiYiIiIiIiIiIiIg0YCGdiIiIiIiIiIiIiEgDFtKJiIiIiIiIiIiIiDRgIZ2IiIiIiIiIiIiISAMW0omIiIiIiIiIiIiINGAhnYiIiIiIiIiIiIhIg/8HRLTVQwKLiH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x700 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# specify columns to plot\n",
    "numerical_columns = df.select_dtypes(include='number')\n",
    "\n",
    "# setup up figure size\n",
    "plt.figure(figsize=(15, 7))\n",
    "\n",
    "# create boxplots\n",
    "for i, col in enumerate(numerical_columns):\n",
    "    plt.subplot(5, 3, i+1)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f'{col}')\n",
    "plt.suptitle('No negative values are found in numerical variables', size=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- While the data type of `MarketID` and `LocationID` is numeric, examining potential measurement errors in these columns are not done because these columns only serve to indicate unique identifier of each observation, not the measurement itself.\n",
    "- Column `AgeOfStore`, `week`, and `SalesInThousand` do not seem to contain any measurement errors since the values are possible in real-worldd."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3.4 Summary**\n",
    "Before moving forward to the statistical analysis, the central part of this A/B test, I will recapitulate the key findings on the parametric assumption tests in Section 3 to better inform which statistical test is best-suited for the testing. In short, the distributions of all sales by promotions are not normal. Outliers are also present in sales based on promotions. Additionally, it is not possible to move forward with one-way independent ANOVA since the homoscedasticity assumption is also violated based on Lavene's test output. For these reasons, Kruskal-Wallis test will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4 Statistical Analysis**\n",
    "In this section, Kruskal-Wallis H test will be carried out to check the overall differences between sales by promotions. This test will be proceeded by Dunn's post-hoc test to examine specific group difference if the Kruskal-Wallis test output is significant. Since these tests can only provide a half story of the group difference (promotional campaign difference in terms of sales), effect size ($\\eta^2$) for Kruskal-Wallis *H* will be computed. Last, the analysis will be supplied by computation of confidence intervals for the effect size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.1 Perform Kustkal-Wallis *H* Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis H Test statistic:\n"
     ]
    },
    {
     "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>H</th>\n",
       "      <th>p-value</th>\n",
       "      <th>dof</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.2948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         H  p-value  dof  significant\n",
       "0  53.2948      0.0    2         True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate data by groups\n",
    "sales_promo1 = df[df['Promotion'] == 'Promotion 1']['SalesInThousands']\n",
    "sales_promo2 = df[df['Promotion'] == 'Promotion 2']['SalesInThousands']\n",
    "sales_promo3 = df[df['Promotion'] == 'Promotion 3']['SalesInThousands']\n",
    "\n",
    "# number of groups\n",
    "num_groups = 3\n",
    "\n",
    "# Perform Kruskal-Wallis H test\n",
    "h_stat, p_value = kruskal(sales_promo1, sales_promo2, sales_promo3)\n",
    "\n",
    "# Degrees of freedom\n",
    "dof = num_groups - 1\n",
    "\n",
    "# Print Kruskal-Wallis H Test results\n",
    "print(f\"Kruskal-Wallis H Test statistic:\")\n",
    "results = pd.DataFrame({\n",
    "    'H': [h_stat],\n",
    "    'p-value': [p_value],\n",
    "    'dof': [dof],\n",
    "    'significant': [p_value < 0.05]\n",
    "})\n",
    "\n",
    "results.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# create boxplot \n",
    "sns.boxplot(x='Promotion', y='SalesInThousands', data=df, palette='Set2', width=0.5)\n",
    "sns.stripplot(x='Promotion', y='SalesInThousands', data=df, color='k', alpha=0.5, jitter=True)\n",
    "plt.title(f'Kruskal-Wallis test shows a significant difference ($H({dof})={h_stat:.2f}, p={p_value:.4f})$')\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Sales in Thousands')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- The Kruskal-Wallis test indicates a significant difference in sales based on promotional campaigns (*H*(2) = 53.29, *p* = 0.00).\n",
    "- As mentioned earlier, Kruskal-Wallis test can only check the overall difference between groups. To identify whether the differences between each group (promotional campaign), a post-hoc test will be required. Dunn's test for a pairwise comparison will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.2 Perform Dunn's Post-Hoc Test**\n",
    "Dunn's test ([Dunn, 1964](https://doi.org/10.1080/00401706.1964.10490181)) here is used as a post-hoc pairwise comparison test between promotional campaigns in terms of sales. Probability (*p*) value needs to be adjusted to prevent obtaining a difference between a promotional campaign pair by random chance (hereby, reducing the risk of a false positive). Holm's method ([Holm, 1979](https://www.jstor.org/stable/4615733)) is adopted to control the [family-wise error rate](https://www.statology.org/family-wise-error-rate/) (FWER)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        1    2       3\n",
       "1  1.0000  0.0  0.0486\n",
       "2  0.0000  1.0  0.0000\n",
       "3  0.0486  0.0  1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separate data by groups\n",
    "sales_promo1 = df[df['Promotion'] == 'Promotion 1']['SalesInThousands']\n",
    "sales_promo2 = df[df['Promotion'] == 'Promotion 2']['SalesInThousands']\n",
    "sales_promo3 = df[df['Promotion'] == 'Promotion 3']['SalesInThousands']\n",
    "\n",
    "# perform Dunn's test\n",
    "dunn_results = posthoc_dunn([sales_promo1, sales_promo2, sales_promo3], p_adjust='holm').round(4)\n",
    "\n",
    "display(dunn_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the figure\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# create the boxplot with data points\n",
    "sns.boxplot(x='Promotion', y='SalesInThousands', data=df, palette='Set2', width=0.5)\n",
    "sns.stripplot(x='Promotion', y='SalesInThousands', data=df, color='k', alpha=0.5, jitter=True)\n",
    "\n",
    "# calculate the vertical offset for annotations\n",
    "max_y = max(df['SalesInThousands'])\n",
    "offset = max_y * 0.1  # 10% of the y-axis range as an offset\n",
    "\n",
    "# extract unique promotions for easy indexing\n",
    "promotions = df['Promotion'].unique()\n",
    "promotions_dict = {promo: idx for idx, promo in enumerate(promotions)}\n",
    "\n",
    "# plot Dunn's test results\n",
    "for i, promo1 in enumerate(promotions):\n",
    "    for j, promo2 in enumerate(promotions):\n",
    "        if i < j:\n",
    "            p_value = dunn_results.iloc[i, j]\n",
    "            if p_value < 0.05:\n",
    "                # determine positions for the bracket\n",
    "                x1, x2 = promotions_dict[promo1], promotions_dict[promo2]\n",
    "                y = max_y + offset * (i + j + 1)    # vertical position of the bracket\n",
    "                h = offset * 0.5                    # height of the bracket\n",
    "                col = 'k'                           # color of the line and text\n",
    "\n",
    "                # add a bracket-style line indicating significant difference\n",
    "                plt.plot([x1, x1, x2, x2], [y, y + h, y + h, y], lw=1.5, c=col)\n",
    "                # add a label for the p-value\n",
    "                plt.text((x1 + x2) * 0.5, y + h, f'$p$ = {p_value:.4f}', ha='center', va='bottom', color=col)\n",
    "\n",
    "plt.title('Dunn\\'s test reveals significant difference across promotions ')\n",
    "plt.ylim(0, 160)\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Sales in Thousands')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- The results show significant differences between all pairs of groups, given that all p-values are less than 0.05.\n",
    "    - `Promotion 1` vs `Promotion 2`: *p* = 0.00\n",
    "    - `Promotion 2` vs `Promotion 3`: *p* = 0.00\n",
    "    - `Promotion 1` vs `Promotion 3`: *p* = 0.04\n",
    "- While other pairs indicate strongly signficant differences, the probability value of the `Promotion 3` and `Promotion 1` pair is only slightly below the alpha ($\\alpha$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.3 Compare Median Sales**\n",
    "While we already obtained Kruskal-Wallis H and Dunn's test outputs to examine whether the differences between promotional campaigns are statistically significant, we have not explicitly test which campaign outperforms others in terms of sales. For this reason, I will use median to indicate the best performing campaign. In its interpretation, the promotion with the highest median is the best in terms of its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promotion</th>\n",
       "      <th>SalesInThousands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Promotion 1</td>\n",
       "      <td>55.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion 3</td>\n",
       "      <td>51.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion 2</td>\n",
       "      <td>45.385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Promotion  SalesInThousands\n",
       "0  Promotion 1            55.385\n",
       "2  Promotion 3            51.165\n",
       "1  Promotion 2            45.385"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_promotion =( df.groupby('Promotion', as_index=False)\n",
    "                   .agg(func={'SalesInThousands':'median'})\n",
    "                   .sort_values(by='SalesInThousands', ascending=False))\n",
    "median_promotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5), dpi=100)\n",
    "# create barplot\n",
    "colors = {'Promotion 1': '#8e9fca', 'Promotion 2': '#fc8d62', 'Promotion 3':'#66c2a5'}\n",
    "ax = sns.barplot(x='Promotion', y='SalesInThousands', data=median_promotion, palette=colors)\n",
    "\n",
    "# add data labels\n",
    "ax.bar_label(ax.containers[0], fmt='%.2f', label_type='edge')\n",
    "plt.title('While Promotion 1 has the highest median sales, differences in medians are small')\n",
    "plt.ylim(0, 65)\n",
    "plt.ylabel('Sales in Thousands')\n",
    "plt.xlabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "- In general, the median sales of Promotion 1 is the highest (USD 55.39) among two other campaigns.\n",
    "- But since the median of each campaign is close from one to another, the differences in median sales across different promotional campaigns do not seem to be large (USD4.23K for `Promotion 1` vs `Promotion 2`; USD 10K for `Promotion 1` vs `Promotion 3`; and USD 5.76K for `Promotion 2` vs `Promotion 3`.\n",
    "- For this reason, the significant differences found previously should be interpreted with caution, and to examine the practical significance of the differences, I will use effect size ($\\eta^2$) for Kruskal-Wallis test statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4.4 Confidence Intervals and Effect Size**\n",
    "Confidence intervals (CIs) allows us to evaluate a range within which we can be confident that the true mean difference lies. And effect size quantifies the magnitude of difference between groups, not just whether the difference is statistically significant.\n",
    "\n",
    "The confidence intervals here are used to understand the precision of the estimates and effect sizes to understand the practical significance. So it gives context to the statistical significance in the real-world impact of the difference. Effect size used here is eta-squared ($\\eta^2$), measuring the proportion of variance explained by promotions in the data (see [rstatix 0.7.2](https://rpkgs.datanovia.com/rstatix/reference/kruskal_effsize.html#:~:text=Compute%20the%20effect%20size%20for,the%20total%20number%20of%20observations.) for the R documentation).\n",
    "\n",
    "$$\\eta^2 = \\frac{H - k + 1}{n - k}$$\n",
    "- $H$: Kruskal-Walllis test statistic\n",
    "- $k$: number of campaigns\n",
    "- $n$: sample size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eta-squared: 0.0941\n"
     ]
    }
   ],
   "source": [
    "# define grouped data\n",
    "grouped_data = [sales_promo1, sales_promo2, sales_promo3]\n",
    "\n",
    "# specify k and n\n",
    "k = len(grouped_data)\n",
    "n = sum([len(group) for group in grouped_data])\n",
    "\n",
    "# calculate eta-squared\n",
    "eta_squared = (h_stat - k + 1) / (n - k)\n",
    "print(f\"Eta-squared: {eta_squared:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: As eta-squared ($\\eta^2$) represents the proportion of variance in sales (`SalesInThousands`) that is explained by promotional campaigns (`Promotion`), the effect size ($\\eta^2$) means only 9.4% of the variance in sales is explained by the differences between promotional campaigns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% Confidence Interval for eta-squared: [0.0518, 0.1474]\n"
     ]
    }
   ],
   "source": [
    "# bootstrapping for confidence intervals\n",
    "n_bootstraps = 1000\n",
    "bootstrapped_eta_squared = []\n",
    "\n",
    "for _ in range(n_bootstraps):\n",
    "    # resample data with replacement\n",
    "    resampled_data = [np.random.choice(group, size=len(group), replace=True) for group in grouped_data]\n",
    "    resampled_H_stat, _ = kruskal(*resampled_data)\n",
    "    \n",
    "    # calculate eta-squared for resampled data\n",
    "    resampled_eta_squared = (resampled_H_stat - k + 1) / (n - k)\n",
    "    bootstrapped_eta_squared.append(resampled_eta_squared)\n",
    "\n",
    "# calculate the confidence intervals (e.g., 95% CI)\n",
    "ci_lower = np.percentile(bootstrapped_eta_squared, 2.5)\n",
    "ci_upper = np.percentile(bootstrapped_eta_squared, 97.5)\n",
    "\n",
    "print(f\"95% Confidence Interval for eta-squared: [{ci_lower:.4f}, {ci_upper:.4f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The 95% confidence interval for $\\eta^2$, obtained via bootstrapping, was [0.0567, 0.1463]. This interval suggest that the true effect size lies between 5% and 14% of the variance in sales being attributable to the promotional campaigns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5 Conclusions**\n",
    "### **5.1 Summary**\n",
    "In brief, this analysis seeks to identify which promotional campaign generated largest sales. Since the data did not meet the assumptions for parametric tests, Kruskal-Wallis H test was adopted along with Dunn's post-hoc test. The results revealed that Promotion 1 had the highest median sales among three campaigns. While these three campaigns were found to be statistically different, the practical significance of their differences is small.\n",
    " \n",
    "Taken altogether, despite finding the significant difference between promotional campaigns, with the Promotion 1 as highest median sales, the practical difference is not big enough. This small impact implies that while the campaigns affect sales, the impact is relatively small to warrant major changes based on these results alone.\n",
    "\n",
    "### **5.2 Recommendations**\n",
    "- Although there are differences between promotional campaigns, the impact on sales is not large. As a result, consider reviewing the campaigns to see if there are ways to improve their effectiveness.\n",
    "- Since the practical differences are small, take into account other factors such as target audience or cost when deciding which campaign to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **6 References**\n",
    "- Dunn, O. J. (1964). Multiple comparisons using rank sums. *Technometrics*, 6(3), 241-252. https://doi.org/10.1080/00401706.1964.10490181\n",
    "- Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. *Scandinavian Journal of Statistics*, 6(2), 65–70. http://www.jstor.org/stable/4615733\n",
    "- Levene, H. (1960). In *Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling*, I. Olkin et al. eds., Stanford University Press, pp. 278-292.\n",
    "- Nordstokke, D. W., & Colp, S. M. (2014). Investigating the robustness of the nonparametric Levene test with more than two groups. *Psicológica*, 35(2), 361-383."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:orange; font-weight:bold; text-align:center'>---END---</h1>"
   ]
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