{ "nbformat": 4, "nbformat_minor": 5, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "colab": { "provenance": [], "include_colab_link": true } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "6yDxo9u0d9Na" }, "source": [ "# Business Statistics: News Source\n", "\n", "**Marks: 60**" ], "id": "6yDxo9u0d9Na" }, { "cell_type": "markdown", "metadata": { "id": "SrLd-ICEmPSD" }, "source": [ "## Define Problem Statement and Objectives" ], "id": "SrLd-ICEmPSD" }, { "cell_type": "markdown", "metadata": { "id": "9Vt1PLLqmXNn" }, "source": [ "News Source, an online news website, has redesigned its landing page with the aim of increasing subscriptions. It collected data during an A/B test: some users were served the old landing page and some were served the new one.\n", "\n", "The data consists of:\n", "* user ID\n", "* test group\n", "* landing page version\n", "* time spent on landing page (minutes)\n", "* subscriber conversion flag\n", "* preferred language\n", "\n", "We will determine whether the landing page redesign has affected user behavior. Throughout, the level of significance is fixed at 5%." ], "id": "9Vt1PLLqmXNn" }, { "cell_type": "markdown", "metadata": { "id": "a6a3d218" }, "source": [ "## Import all the necessary libraries" ], "id": "a6a3d218" }, { "cell_type": "code", "metadata": { "id": "658c5dec" }, "source": [ "# math libraries\n", "import numpy as np\n", "import scipy.stats as st\n", "\n", "# data handling and visualization libraries\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "\n", "# I will import specific tests from the statsmodels library later if necessary" ], "id": "658c5dec", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6970dd99" }, "source": [ "## Reading the Data into a DataFrame" ], "id": "6970dd99" }, { "cell_type": "code", "metadata": { "id": "d3b95640" }, "source": [ "usage=pd.read_csv('dataset.csv')" ], "id": "d3b95640", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9d75cfa2" }, "source": [ "## Explore the dataset and extract insights using Exploratory Data Analysis" ], "id": "9d75cfa2" }, { "cell_type": "markdown", "metadata": { "id": "27201534" }, "source": [ "Let's look at the data. We'll start with the head and tail, or first five rows and last five rows respectively." ], "id": "27201534" }, { "cell_type": "code", "metadata": { "id": "e1ad11d4", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "93482c00-9eb5-4471-8a68-98301169ce32" }, "source": [ "usage.head()" ], "id": "e1ad11d4", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " user_id group landing_page time_spent_on_the_page converted \\\n", "0 546592 control old 3.48 no \n", "1 546468 treatment new 7.13 yes \n", "2 546462 treatment new 4.40 no \n", "3 546567 control old 3.02 no \n", "4 546459 treatment new 4.75 yes \n", "\n", " language_preferred \n", "0 Spanish \n", "1 English \n", "2 Spanish \n", "3 French \n", "4 Spanish " ], "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
user_idgrouplanding_pagetime_spent_on_the_pageconvertedlanguage_preferred
0546592controlold3.48noSpanish
1546468treatmentnew7.13yesEnglish
2546462treatmentnew4.40noSpanish
3546567controlold3.02noFrench
4546459treatmentnew4.75yesSpanish
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 80 } ] }, { "cell_type": "code", "source": [ "usage.tail()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "bJlxKq4G1cOy", "outputId": "93954e4f-7104-43ae-896c-3064f237caca" }, "id": "bJlxKq4G1cOy", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " user_id group landing_page time_spent_on_the_page converted \\\n", "95 546446 treatment new 5.15 no \n", "96 546544 control old 6.52 yes \n", "97 546472 treatment new 7.07 yes \n", "98 546481 treatment new 6.20 yes \n", "99 546483 treatment new 5.86 yes \n", "\n", " language_preferred \n", "95 Spanish \n", "96 English \n", "97 Spanish \n", "98 Spanish \n", "99 English " ], "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
user_idgrouplanding_pagetime_spent_on_the_pageconvertedlanguage_preferred
95546446treatmentnew5.15noSpanish
96546544controlold6.52yesEnglish
97546472treatmentnew7.07yesSpanish
98546481treatmentnew6.20yesSpanish
99546483treatmentnew5.86yesEnglish
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "code", "source": [ "usage.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "le87jyEz1ejv", "outputId": "a447df89-bc69-403e-a037-72808e273f74" }, "id": "le87jyEz1ejv", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(100, 6)" ] }, "metadata": {}, "execution_count": 82 } ] }, { "cell_type": "markdown", "source": [ "We have 100 rows of user data examining News Source site usage. Since this data is from A/B testing, we are given whether each user was in the control group or the treatment group, whether they were shown the old layout or the new layout. We also get a unique ID for each user, which we can utilize to check for duplicates. Time spent on the page is a continuous numerical variable, and I expect much of our analysis will revolve around time. The other key variable is found in the converted column, since the main business goal is to convert readers to subscribers. Lastly, we can study language preference.\n", "\n", "It looks like group and landing_page give the same information in two different ways. We can confirm they are perfectly aligned below." ], "metadata": { "id": "mx2bNYYZwjQR" }, "id": "mx2bNYYZwjQR" }, { "cell_type": "code", "source": [ "usage.groupby('group')['landing_page'].value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sV7lCIRABwdZ", "outputId": "e5a3ecc2-a10a-4d4f-9d6a-8a5c01b792a8" }, "id": "sV7lCIRABwdZ", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "group landing_page\n", "control old 50\n", "treatment new 50\n", "Name: landing_page, dtype: int64" ] }, "metadata": {}, "execution_count": 83 } ] }, { "cell_type": "markdown", "source": [ "We next review the column info." ], "metadata": { "id": "KFIgKtKiCUQZ" }, "id": "KFIgKtKiCUQZ" }, { "cell_type": "code", "source": [ "usage.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQY7oRU76HZh", "outputId": "1c4692cf-f44b-4abf-bcad-b80b01d7f311" }, "id": "WQY7oRU76HZh", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 100 entries, 0 to 99\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 user_id 100 non-null int64 \n", " 1 group 100 non-null object \n", " 2 landing_page 100 non-null object \n", " 3 time_spent_on_the_page 100 non-null float64\n", " 4 converted 100 non-null object \n", " 5 language_preferred 100 non-null object \n", "dtypes: float64(1), int64(1), object(4)\n", "memory usage: 4.8+ KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "Thankfully, there are no null entries reported. We see that four of the six columns have 'object' data type, that is, strings. We will convert these columns to the 'categorical' data type later. Time, a continuous variable, is stored as a float, which is to be expected, since we will be looking at decimal numbers.\n", "\n", "We review brief statistical analyses." ], "metadata": { "id": "qqW2cMHNzhdf" }, "id": "qqW2cMHNzhdf" }, { "cell_type": "code", "source": [ "usage.describe().T" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "U05xo1I21ehq", "outputId": "980f6342-52d0-4c75-d303-97a6cba2f8ae" }, "id": "U05xo1I21ehq", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count mean std min 25% \\\n", "user_id 100.0 546517.0000 52.295779 546443.00 546467.75 \n", "time_spent_on_the_page 100.0 5.3778 2.378166 0.19 3.88 \n", "\n", " 50% 75% max \n", "user_id 546492.500 546567.2500 546592.00 \n", "time_spent_on_the_page 5.415 7.0225 10.71 " ], "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
user_id100.0546517.000052.295779546443.00546467.75546492.500546567.2500546592.00
time_spent_on_the_page100.05.37782.3781660.193.885.4157.022510.71
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 85 } ] }, { "cell_type": "code", "source": [ "usage.describe(include=[object]).T" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "zchRF58k2KNT", "outputId": "c2d5c3e8-50e0-465e-d691-40a1fda1ebd8" }, "id": "zchRF58k2KNT", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " count unique top freq\n", "group 100 2 control 50\n", "landing_page 100 2 old 50\n", "converted 100 2 yes 54\n", "language_preferred 100 3 Spanish 34" ], "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countuniquetopfreq
group1002control50
landing_page1002old50
converted1002yes54
language_preferred1003Spanish34
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 86 } ] }, { "cell_type": "markdown", "source": [ "User ID is not an insightful numerical variable, since it is just an identifier. For time_spent_on_the_page, which I will just refer to as *time*, we see that the mean and median are fairly close. This indicates that the time data is likely not very skewed. The minimum is 0.19 minutes and the maximum is 10.71 minutes.\n", "\n", "For the categorical variables, the unique count is a good way to check that the data is intact: We expect to only have two possibilites for group, landing_page, and converted. Group and landing_page data are split perfectly in half, slightly more than half of the users were converted (yay!), and the most popular language is Spanish.\n", "\n", "Lastly, let's just check that the data is intact." ], "metadata": { "id": "i_nZ3xhX0XAD" }, "id": "i_nZ3xhX0XAD" }, { "cell_type": "code", "source": [ "usage.isnull().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a34L8jdJ2f6T", "outputId": "9f28204e-0ca9-42ab-93ac-f1900097e65f" }, "id": "a34L8jdJ2f6T", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "user_id 0\n", "group 0\n", "landing_page 0\n", "time_spent_on_the_page 0\n", "converted 0\n", "language_preferred 0\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 87 } ] }, { "cell_type": "code", "source": [ "usage.duplicated().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mIVA2zi02uyg", "outputId": "d9f7583d-785f-41b7-8f1f-07f8519f80b0" }, "id": "mIVA2zi02uyg", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0" ] }, "metadata": {}, "execution_count": 88 } ] }, { "cell_type": "code", "source": [ "usage['user_id'].duplicated().sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vADoYDNxys1X", "outputId": "e950c1f9-198b-4bea-8246-82bc702a9a14" }, "id": "vADoYDNxys1X", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0" ] }, "metadata": {}, "execution_count": 89 } ] }, { "cell_type": "markdown", "source": [ "There are no null entries and no duplicated rows. Additionally, we checked specifically to ensure that there are no duplicated user IDs, since we only want one result in the sample from each user." ], "metadata": { "id": "o67K4qk7Fy3j" }, "id": "o67K4qk7Fy3j" }, { "cell_type": "markdown", "metadata": { "id": "68f3b2c9" }, "source": [ "### Univariate Analysis" ], "id": "68f3b2c9" }, { "cell_type": "markdown", "source": [ "We start by converting the categorical variables to the categorical data type." ], "metadata": { "id": "neLv4ElwGHkR" }, "id": "neLv4ElwGHkR" }, { "cell_type": "code", "source": [ "# convert object columns to categorical columns\n", "# and create a list of these column names\n", "col_types=usage.dtypes\n", "cat_col=[]\n", "for col in usage.columns:\n", " if col_types[col]==object:\n", " usage[col]=pd.Categorical(usage[col])\n", " cat_col.append(col)" ], "metadata": { "id": "CG28Q61n6PMk" }, "id": "CG28Q61n6PMk", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The ```info()``` method allows us to verify that these columns have been successfully converted to the 'category' data type." ], "metadata": { "id": "-_-uc0IhGSQz" }, "id": "-_-uc0IhGSQz" }, { "cell_type": "code", "source": [ "usage.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A7-I1K3W6p8I", "outputId": "7d3acb5a-23af-4503-b2a3-a082010d7baf" }, "id": "A7-I1K3W6p8I", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 100 entries, 0 to 99\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 user_id 100 non-null int64 \n", " 1 group 100 non-null category\n", " 2 landing_page 100 non-null category\n", " 3 time_spent_on_the_page 100 non-null float64 \n", " 4 converted 100 non-null category\n", " 5 language_preferred 100 non-null category\n", "dtypes: category(4), float64(1), int64(1)\n", "memory usage: 2.6 KB\n" ] } ] }, { "cell_type": "markdown", "source": [ "Let's look at what the values are for our categorical variables." ], "metadata": { "id": "eTPlxevvGpb9" }, "id": "eTPlxevvGpb9" }, { "cell_type": "code", "source": [ "# print a table with value counts for each categorical column\n", "print('Value Counts by Categorical Column')\n", "print('~'*50)\n", "for col in cat_col:\n", " print('Column:',col)\n", " print(usage[col].value_counts())\n", " print('~'*50)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5F4P81sq78L6", "outputId": "d121c063-e87b-4dbc-e12a-916464bef34b" }, "id": "5F4P81sq78L6", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Value Counts by Categorical Column\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "Column: group\n", "control 50\n", "treatment 50\n", "Name: group, dtype: int64\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "Column: landing_page\n", "new 50\n", "old 50\n", "Name: landing_page, dtype: int64\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "Column: converted\n", "yes 54\n", "no 46\n", "Name: converted, dtype: int64\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "Column: language_preferred\n", "French 34\n", "Spanish 34\n", "English 32\n", "Name: language_preferred, dtype: int64\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" ] } ] }, { "cell_type": "markdown", "source": [ "Recall that our sample size is 100 users. We note immediately that the groups are split perfectly down the middle. As was noted earlier, group and landing_page convey the same data in two different ways, and since we are A/B testing the layout, it is helpful that our two groups have the same size. From the converted column, we find that slightly more than half of the users were converted to subscribers. I'd be interested to see how that conversion is distributed across the test groups! Lastly, we have language preference. The three languages have about equal representation in the data, around 1/3 of the sample preferring English, 1/3 Spanish, and 1/3 French. We will look later at whether there is any difference between these language preferences and the conversion rate." ], "metadata": { "id": "zpi7w_obS-8o" }, "id": "zpi7w_obS-8o" }, { "cell_type": "code", "source": [ "# use seaborn default plotting theme\n", "sns.set_theme()" ], "metadata": { "id": "_dqFI2flEyIT" }, "id": "_dqFI2flEyIT", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# create a plot of categorical variables\n", "# using a loop and subplots\n", "\n", "# figure setup\n", "plt.figure(figsize=(16,5))\n", "plt.suptitle('Ratio of Values',fontsize=20)\n", "\n", "# loop to create subplots\n", "for i,col in enumerate(cat_col):\n", " plt.subplot(1,4,i+1)\n", " sns.countplot(data=usage,x=col)\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 358 }, "id": "jSCAmK3g5sdE", "outputId": "5bcfbb6f-a2df-4978-b3a2-4be431fa7e67" }, "id": "jSCAmK3g5sdE", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "We find in the countplots what we found in the table above. The only new insight is perhaps a better visual sense of how close these values are. For example, when we look across the entire sample, the number of converted users is quite close to the number of users who weren't converted." ], "metadata": { "id": "8mHCDPvOVmm3" }, "id": "8mHCDPvOVmm3" }, { "cell_type": "code", "source": [ "# plot the numerical variable: time\n", "time='time_spent_on_the_page'\n", "\n", "# figure setup\n", "fig=plt.figure(figsize=(12,5))\n", "fig.suptitle('Time Spent on the Page',fontsize=20)\n", "\n", "# histogram subplot\n", "plt.subplot(1,2,1)\n", "sns.histplot(data=usage,x=time,kde=True)\n", "plt.xlabel('Time (min)')\n", "\n", "# boxplot subplot\n", "plt.subplot(1,2,2)\n", "sns.boxplot(data=usage,x=time)\n", "plt.xlabel('Time (min)')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 375 }, "id": "rI_4aoa05sa7", "outputId": "c68f6e19-1b47-4a84-cee6-5ac995e9b9e8" }, "id": "rI_4aoa05sa7", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "We see from the KDE on the left that time is normally distributed. The peak is between 5 and 6 minutes, as is the median, which can be read from the boxplot. The boxplot further illustrates that the spead of the data is not irregular, that is, there are no outliers: the minimum is just about 0 minutes and the maximum is around 11 minutes, both within bounds for these data. As can be seen in both the histogram and (especially) the boxplot, the middle 50% of the data is concentrated between about 4 minutes and 7 minutes.\n", "\n", "We can combine these two plots into the violin plot below, which encapsulates the analysis above." ], "metadata": { "id": "0D9KIWtLXg8G" }, "id": "0D9KIWtLXg8G" }, { "cell_type": "code", "source": [ "# violin plot of time\n", "\n", "#figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Violin Plot of Time', fontsize=16)\n", "\n", "# violin restricted to data range (cut parameter)\n", "sns.violinplot(data=usage,x=time,cut=0)\n", "plt.xlabel('Time (min)');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "id": "gCUFyeIt5sYT", "outputId": "7862a643-da05-4065-df91-f42ed6afe922" }, "id": "gCUFyeIt5sYT", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "fad2de2f" }, "source": [ "### Bivariate Analysis" ], "id": "fad2de2f" }, { "cell_type": "markdown", "source": [ "Let's start with the most pressing question: Does the landing page design have any effect the conversion rate?" ], "metadata": { "id": "bbWZ10aejJqS" }, "id": "bbWZ10aejJqS" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Conversion Rate by Landing Page Design',fontsize=14)\n", "\n", "# countplot\n", "sns.countplot(data=usage, x='landing_page',hue='converted')\n", "plt.xlabel('Design')\n", "plt.ylabel('Count');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "id": "jN2yYeP8Cwir", "outputId": "a7f7d265-3f41-4138-dc49-be2b91a67767" }, "id": "jN2yYeP8Cwir", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Indeed, it appears that the new design has a positive impact on conversion rate. In particular, more than half the users served the new landing page were converted to subscribers, whereas the old design had a conversion rate less than 50%. Now this looks like a big improvement, but we cannot be sure it is statistically significant until we run the test later.\n", "\n", "Next, let's look at whether language preference and conversion rate seemingly interact." ], "metadata": { "id": "QlSJ1BLHmgm6" }, "id": "QlSJ1BLHmgm6" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Conversion Rate by Language Preference',fontsize=14)\n", "\n", "# countplot\n", "sns.countplot(data=usage,x='language_preferred',hue='converted')\n", "plt.legend(loc='upper left')\n", "plt.xlabel('Language')\n", "plt.ylabel('Count');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "id": "cd94TO7rEmKO", "outputId": "5c7fee6d-0f06-4485-9a13-72713116b22c" }, "id": "cd94TO7rEmKO", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "We are seeing a difference in conversion rate between the three languages! The change in landing page design has had a big effect on English-speaking users, while the effect was less for Spanish-speaking users. There appears to be no improvement in conversion rate for the French-speaking users; it seems, instead, that *fewer* users subscribed when served the new landing page design.\n", "\n", "We should check, though, that all three languages were evenly represented in the two groups." ], "metadata": { "id": "FYbY7yvcnmQz" }, "id": "FYbY7yvcnmQz" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Group Language Breakdown',fontsize=14)\n", "\n", "# countplot\n", "sns.countplot(data=usage,x='group',hue='language_preferred')\n", "plt.legend(loc='lower left')\n", "plt.ylabel('Count');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "id": "r-Rye_dHFMXQ", "outputId": "738784f2-54f4-4dc4-950d-1292ccc66c40" }, "id": "r-Rye_dHFMXQ", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Thankfully, the groups are evenly composed of users speaking the three languages. Turning then to our numerical variable, time, was there any difference between the three languages with respect to how much time users spent on the News Source landing page?" ], "metadata": { "id": "fyvE6Pylp909" }, "id": "fyvE6Pylp909" }, { "cell_type": "code", "source": [ "# kde of time based on language\n", "sns.displot(data=usage,x=time,hue='language_preferred',kind='kde')\n", "plt.title('Time on Landing Page by Language Preference',fontsize=16)\n", "plt.xlabel('Time (min)');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "id": "sJyIGBcDCwnr", "outputId": "01f6a5ba-0f97-4e4d-cc3d-7c4b0803483e" }, "id": "sJyIGBcDCwnr", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# precise calculation of variance difference\n", "usage.groupby('language_preferred')[time].var()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kpdqDRZUiVat", "outputId": "d8a362a4-3627-44d1-f2ac-710c656af9f0" }, "id": "kpdqDRZUiVat", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "language_preferred\n", "English 6.870054\n", "French 7.157835\n", "Spanish 3.305470\n", "Name: time_spent_on_the_page, dtype: float64" ] }, "metadata": {}, "execution_count": 101 } ] }, { "cell_type": "markdown", "source": [ "No, users preferring all three languages spent essentially the same amount of time, on average, on the landing page. Several important features of the plot worth noting: The time data for all three languages are normally distributed, and Spanish users seem to fall closer to the mean. That is, the density is greater in the middle for Spanish users and the variance is thus less. Our subsequent calculation further supports this observation." ], "metadata": { "id": "ryDkePyGgnx5" }, "id": "ryDkePyGgnx5" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Time by Trial Group',fontsize=14)\n", "\n", "# histogram\n", "sns.histplot(data=usage,x=time,hue='group',kde=True)\n", "plt.xlabel('Time (min)');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "id": "Qf_CYAxBCwlH", "outputId": "82c6367e-fc26-480f-fe7c-c68f8a2702f1" }, "id": "Qf_CYAxBCwlH", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# precise mean calculation\n", "usage.groupby('group')[time].mean()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UgVy8uxGjdhC", "outputId": "98e3864a-41b7-499f-a430-0cdbffc2652b" }, "id": "UgVy8uxGjdhC", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "group\n", "control 4.5324\n", "treatment 6.2232\n", "Name: time_spent_on_the_page, dtype: float64" ] }, "metadata": {}, "execution_count": 103 } ] }, { "cell_type": "markdown", "source": [ "Looking at time broken down by trial group, we see (from the KDE) that the mean time for the trial group is higher than the control. This is good! Recall that the goal of the News Source layout redesign was to keep people on the site longer and convert them to subscribers. Time is approximately normally distributed for both groups, with a peak around 6 minutes for the treatment group and around 4.5 minutes for the control." ], "metadata": { "id": "rPGAWzvghoyK" }, "id": "rPGAWzvghoyK" }, { "cell_type": "code", "source": [ "# time and conversion rate\n", "\n", "# figure setup\n", "plt.figure(figsize=(12,5))\n", "plt.suptitle('Time and Conversion Rate',fontsize=20)\n", "\n", "# boxplot subplot\n", "plt.subplot(1,2,1)\n", "sns.boxplot(data=usage,x=time,y='converted')\n", "plt.xlabel('Time (min)')\n", "\n", "# swarmplot subplot\n", "plt.subplot(1,2,2)\n", "sns.swarmplot(data=usage,x=time,y='converted')\n", "plt.xlabel('Time (min)')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 375 }, "id": "8Nuj1OJDCwfP", "outputId": "f69febf4-54b9-455a-9739-0921b5995ab5" }, "id": "8Nuj1OJDCwfP", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtkAAAFmCAYAAAC4OYajAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd1hUV94H8O9Ueu8qoICACIgKlmBvxB5L3CSvGmN61mzWTWKKb5oxyZtiNokpu9kYS5ol6kax90Ks2At2EVCK0tvU+/4xMjLMIAPecRC/n+fZZ51z7z33d8uc/OZw7rkSQRAEEBERERGRaKT2DoCIiIiIqKVhkk1EREREJDIm2UREREREImOSTUREREQkMibZREREREQiY5JNRERERCQyJtlE97kVK1YgKioKK1assHco94Ts7GxERUXh9ddft3co95V9+/YhKioKc+fOtXcoRERWkds7ACIST1RUVKPW/+ijj2wUCVly7NgxLF68GOnp6cjPz4dGo4Gfnx/i4uLw4IMPIiUlBTKZzN5hkpX27duHyZMnm5TJ5XJ4eXkhISEBjz/+OJKSku54P6+//jpWrlyJLVu2oE2bNndcHxHdHUyyiVqQadOmmZUtXLgQZWVlmDx5Mtzd3U2WdejQAW3atEGnTp3g7+9/t8K872g0GsyePRuLFy+GTCZDUlIS+vXrB6VSidzcXOzduxcbNmxASkoKvvrqK3uH2yzFx8dj7dq18PLysncoZlq3bo0xY8YAAKqqqnDy5Els2rQJmzdvxj//+U8MHTrUzhESkT0wySZqQV588UWzspUrV6KsrAyPP/54vb1gbm5utg7tvjZr1iwsXboUkZGR+PLLLxEWFmayXKfTYfXq1di6daudImz+nJycEB4ebu8wLGrdurXZd+/777/HnDlz8OmnnzLJJrpPcUw20X2uvjHZAwYMwIABA1BRUYEPP/wQffv2RXx8PEaPHo3NmzcDALRaLb777jsMGTIEcXFxGDRoEH7++ed697Vr1y48/fTT6N69O2JjYzFo0CB8/PHHKC0ttTrevLw8fP3113jkkUeQnJyM2NhY9OrVCy+//DLOnz9vtn7tMdTZ2dmYPn06unfvjri4OIwdOxbbtm2zuJ/y8nJ89NFH6NOnj3E4x/z58yEIgtWxAkB6ejqWLl0KT09PzJs3zyzBBgCZTIaHHnoIn332mUm5Xq/Hb7/9hnHjxqFz585ISEjAuHHj8Ouvv0Kv15vVExUVhUmTJqGwsBBvvfUWevXqhdjYWAwfPhzLly83WXfNmjWIiorChx9+aDFutVqNpKQk9OrVC1qt1mRZamoqJk2ahMTERMTFxWHo0KH49ttvoVar642poKAAM2fORO/evdGhQwfj/Xb9+nV8/PHHSElJQUJCAhITE5GSkoLXX38dWVlZxnpuNyb78uXLmDFjBnr37m28H2bMmIHLly+brTt37lxERUVh3759WL9+PcaPH49OnTqhW7dumD59OvLy8iyej8YaP348ACAnJweFhYUmyzZv3oxXXnnFeMwJCQkYO3YsFi1aZHZdo6KisHLlSgDAwIEDERUVhaioKAwYMMBkveLiYsyZMwdDhw5FfHw8unbtiscffxy7d+8W5XiIqPHYk01E9dJoNJg6dSqKi4sxcOBAaDQapKam4sUXX8SPP/6IX3/9FUePHkWfPn2gVCqxfv16vP/++/D29sawYcNM6vr6668xd+5ceHp6ol+/fvD29sbZs2fx448/YufOnViyZAlcXV0bjOngwYP4z3/+g+7du2PIkCFwdnZGZmYmNmzYgK1bt+K3335DdHS02XY5OTl4+OGHERwcjNGjR6OkpARr167FCy+8gPnz56NHjx7GddVqNaZMmYLjx48jOjoaI0eORFlZGb799lvs37+/Uedw6dKlAIAJEyY0OCRHqVSafH711VeRmpqKoKAgjB8/HhKJBJs3b8Z7772H9PR0zJkzx6yO0tJSPProo1AqlUhJSYFarcb69evx5ptvQiqVGoc1DBo0CG5ubkhNTcWMGTMgl5v+52Dz5s0oLS3F1KlTTZa98cYbWLFiBQIDAzFkyBC4u7vjyJEj+PLLL7Fnzx7Mnz/frK7i4mL85S9/gbOzM4YMGQKJRAIfHx9UVVXh0UcfxZUrV5CcnIwBAwZAEARcvXoVW7ZsQUpKCoKDg297zo4dO4YnnngCFRUVGDBgACIiInDx4kWsWrUKW7Zswfz58xEfH2+23a+//oqtW7diwIABSEpKwrFjx7B27VpkZGTgjz/+MLsWd0KhUJh8/uyzzyCVShEfH4+AgACUlZVh7969+OCDD3D8+HF8+umnxnWnTZuGzZs3IyMjw2TIV+2/PuXk5GDSpEnIyclBYmIievfujaqqKmzbtg1PPfUUZs2ahQkTJoh2PERkJYGIWrT+/fsLkZGRQlZWlsXly5cvFyIjI4Xly5db3O7ZZ58VVCqVsfzAgQNCZGSkkJSUJIwdO1YoKSkxLrty5YrQsWNHYfTo0SZ17dmzR4iMjBT+8pe/mKxfe/8ffPCBVcdz/fp1oayszKz89OnTQkJCgvDkk0+alGdlZQmRkZFCZGSkMHfuXJNlO3fuFCIjI4WnnnrKpPy7774TIiMjhWnTpgk6nc7k+JKSkoTIyEjhtddesyregQMHCpGRkUJaWppV69dYvXq1EBkZKTz00ENCeXm5sbyiokIYM2aMEBkZKaxatcpkm5rjfPPNNwWtVmssP3funNChQwdh6NChJuu/9dZbQmRkpLB161az/T/99NNCZGSkkJGRYSyruVZ//etfhaqqKpP1v/rqKyEyMlJYsGCBxZheffVVQaPRmCzbsmVLvddepVKZXOe9e/cKkZGRwldffWUs0+v1woMPPihERkYKf/zxh8n2a9asESIjI4WUlBSTa1gTZ+fOnU2OTRAE4R//+IcQGRkprFmzxiweS2pimjhxotmyb775RoiMjBRGjBhhtiwzM9OsTKfTCTNmzBAiIyOFI0eOmCx77bXXbvsdnjhxohAVFSWkpqaalJeUlAijRo0S4uLihIKCAquOiYjEw+EiRHRbb775pkmvXmJiItq0aYOSkhK88sorJg9TBgcHo3Pnzjh37hx0Op2x/KeffgIAvP/++2YPX44dOxYdOnTA6tWrrYrHx8fHYo93dHQ0unfvjn379kGj0Zgtb926NZ5//nmTst69e6NVq1Y4duyYSfmKFSsglUrx6quvQiq91UwGBwdj0qRJVsVZo6CgAAAQEBDQqO1qhne8/PLLcHFxMZY7Ozvj1VdfBQAsW7bMbDsnJye88cYbJrOUREREoEuXLrhw4QIqKiqM5Q899BAAGIcj1I559+7diImJMZmxZtGiRZDL5fjwww/h6Ohoss0LL7wAT09Pi9dRoVDgtddeM+vhrlG3LsDQq9/QXzYOHTqEixcvonPnzhg1apTJsmHDhqFr1664dOkS0tPTzbadNGmS2Ww8Dz/8MADg+PHjt91vXTk5OZg7dy7mzp2LTz75BJMnT8aXX34JV1dXvPfee2brh4SEmJVJpVLjTCW7du2yet8ZGRnYv38/hgwZguHDh5ssc3d3x4svvgiVSoUNGzY06piI6M5xuAgR1cvd3d1iQuDv74/s7GzExsaaLQsICIBWq8X169eNieWRI0egUCiwfv16rF+/3mwbjUaDwsJCFBUVWTV7xPbt27F48WKcOHECRUVFZmOGi4qKzIZmREdHW5weLzAwEEeOHDF+Li8vR2ZmJoKCgiwee7du3RqMTwynTp2CVCq1uL+kpCTIZDKcPn3abFloaKjF5DQwMBCAYThJTdLepUsXtG3bFtu2bUNJSQk8PDwAAKtXr4ZOpzMOLQEMs2ZkZGTAy8sLCxcutBizUqnEhQsXzMpbt24NHx8fs/Ju3bohICAA33//PU6ePIm+ffuiS5cu6NChg1VTGZ46dQoA0L17d4vLe/TogfT0dJw6dcpsKr24uDiz9YOCggAAJSUlDe67tpycHHz99dcmZR4eHli4cCE6dOhgtn5RURHmzZuHHTt2IDs7G5WVlSbL8/Pzrd734cOHARjuW0vj1WvGg1+8eNHqOolIHEyyiahe9c06UtMjaWl5zbLavcnFxcXQarVmiUhdlZWVDSbZCxcuxIcffggPDw888MADCAoKgpOTk3G8ckZGhsUH8Or2oNeOt/bDZuXl5QBgMSkEAF9f39vGV5efnx+ysrKQn5/fqNkxysrK4OHhYXFscM1czDdu3DBbdrvjBGDyFwYAGDNmDP75z39izZo1eOyxxwAYerYVCgVGjBhhXK+0tBSCIKCwsLDB61iXn5+fxXJXV1csXboUX331FbZu3Wp8SM/LywuPPfYYnn/+ebPxzLWVlZUBQL1j3Wv2W7NebZbu3ZrE3tJDpbfTrVs3419riouLsXHjRsyaNQvPPfccfv/9d5PjLy0txfjx45GdnW18kNjDwwNyuRylpaVYtGiRxfu3PsXFxQCAtLQ0pKWl1bte3USeiGyPSTYR2ZyrqysEQWj0Q4N11STqfn5+WLFihVlyVbtHuqlqeoEtJbCAYTaMxujatSuysrKwZ88e9OzZ0+rt3NzcUFJSAo1GY5ZoarVaFBUVWfWgaENGjx6NL7/8Ev/973/x2GOP4dSpUzh79iwGDhwIb29v43o1+4qJiTEbXtIQiURS77LAwEB8+OGHEAQB58+fx969e/HLL7/gm2++gV6vx9///vd6t61JlGuG5NRVUy7GebKWp6cnJkyYAI1Gg1mzZuG9994z+VGybNkyZGdnY9q0aWbT/h0+fBiLFi1q1P5qzsHMmTPNXoxDRPbFMdlEZHMJCQkoKSnBuXPn7qieoqIilJaWonPnzmYJdkVFBU6ePHlH9QOGhCw0NBR5eXm4cuWK2fLG/lComdVhyZIlDSbotXswO3ToAL1ej4MHD5qtd+DAAeh0OsTExDQqFkuCgoLQo0cPHD16FBcvXjQm0LWHigCAi4sL2rdvj3Pnzhl7T8UkkUjQvn17TJo0CfPnzwcAbNmy5bbb1AzFqO+a7Nu3DwDQsWNHESO1ziOPPIL27dtj06ZNJmPCMzMzAQBDhgwx2+bAgQMW66p5LsBSD3unTp0AwOJ9QkT2xSSbiGxuypQpAIC33nrL4jzElZWVVvVC+/j4wMnJCSdPnjR5gE+j0eCDDz5AUVGRKPGOHTsWer0en332mUlik5WVZRwWYK2uXbtiwoQJKC4uxlNPPWVx7ma9Xo/U1FTjA40AMG7cOADAnDlzUFVVZSyvqqoyTt1XMxfznapJqH///XesWbMGXl5e6Nevn9l6U6ZMgUajwZtvvmlxbvOSkpJG/dA5d+6cxR8eNWWWHoisrWvXrmjXrh3S09PNxvqvX78eBw8eRNu2bdG1a1erYxKLTCYz9lR/8cUXxvKaF0LV/WFw6tQp/Pvf/7ZYl6enJwDg6tWrZsvi4uKQmJiITZs24ffff7e4/ZkzZ+r9ywwR2Q6HixCRzfXs2RMvv/wyPv/8c6SkpKBPnz5o06YNKisrcfXqVRw4cABdunTBvHnzbluPVCrFpEmT8P3332PkyJHGubv37duHkpIS4+wid2rq1KnYvHkzNmzYgDFjxqBXr14oKyvDunXrkJiY2Og3M7799tuQSqVYvHgxhg0bhm7duiE6OhpKpRJ5eXnYu3cvcnNzkZKSYtxm5MiR2LJlC9atW4fhw4dj0KBBxnHn2dnZGDZsmNmMGk01ePBguLq6YtGiRdBoNJg0aZLFsdDjx4/HyZMn8euvv2Lw4MHo1asXgoKCUFJSguzsbBw4cABjx47FrFmzrNpvWloaPv30UyQkJKBt27bw8fFBbm4utmzZAqlUiieffPK220skEnz88cd44oknMH36dKSmpiIsLAyXLl3C5s2b4eLigk8++cRkhpi7aciQIejQoQP279+PXbt2oXfv3hg9ejTmzZuHDz/8EPv27UNoaCgyMzOxfft2DB48GGvXrjWrp2fPnpg3bx7eeustDBkyBC4uLnB3d8fEiRMBGH6IPf7445g5cyZ++ukndOrUCW5ubsjNzcXZs2dx9uxZLFmypN7nDIjINphkE9Fd8cwzz6BLly746aefkJ6ejq1bt8LV1RUBAQGYMGGCyUN2t/PSSy/B29sby5Ytw5IlS+Dm5oYHHngAf//73y3OrtAUSqUSCxYswNy5c7F27VosWrTIOAXg4MGDG51kKxQKvPfeexg7diyWLFmC9PR0HD16FBqNBj4+PoiNjcVrr72GBx980GS7zz//HElJSVi+fDmWLFkCAAgPD8fUqVPx6KOPinKsgGHavwcffNDYE1oztZ8l77zzDvr06YPFixfjzz//ND6gGRQUhCeffLJRiX/v3r1x7do1HDhwAFu2bEF5eTn8/f2RnJyMKVOmoEuXLg3W0alTJ/z+++/47rvvsGfPHmzbtg1eXl4YPnw4XnjhBYtv2LxbJBIJ/va3v+H555/HF198gd69eyMgIAC//PILPvvsM6Snp2P37t0ICwvDO++8g549e1pMsnv37o3XX38dS5cuxcKFC6HRaNC6dWtjkh0YGIjly5fj559/xsaNG42zw/j6+iIiIgITJ05EZGTk3T58ovueRBAa+Y5gIiIiIiK6LY7JJiIiIiISGZNsIiIiIiKRMckmIiIiIhIZk2wiIiIiIpExySYiIiIiEhmTbCIiIiIikTHJJiIiIiISGZNsIiIiIiKRMckmIiIiIhIZk2wiIiIiIpExySYiIiIiEhmTbCIiIiIikTHJJiIiIiISGZNsIiIiIiKRMckmIiIiIhIZk2wiIiIiIpExySYiIiIiEhmTbCIiIiIikTHJJiIiIiISGZNsIiIiIiKRMckmIiIiIhKZ3N4B2EpRUQX0eqHe5T4+rrhxo/wuRmRbLe14gJZ3TC3teAAek9ikUgm8vFzssm97u9/abKDlHVNLOx6g5R1TSzseoHm32S02ydbrhds22DXrtCQt7XiAlndMLe14AB4TieN+bLOBlndMLe14gJZ3TC3teIDme0wcLkJEREREJDIm2UREREREImOSTUREREQkMibZREREREQiY5JNRERERCQyJtlERERERCJjkk1EREREJDIm2UREREREImOSTUREREQkshb7xkcy9euvi5CVlWnvMKxSUlIMAPD19YFGo7PZfoKDQ/HYY5NtVj8RERHdv5hk3yeysjJx5tx5yBw97R1Kg3TVhiS7oFRr830QERER2QKT7PuIzNETzqED7R1GgyoztwCATWOt2QcRERGRLXBMNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJ9k1paTuRlrbT3mEQ3Zf4/SMiopZGbu8Amovdu3cAAJKT+9g5EqL7D79/RETU0rAnm4iIiIhIZEyyiYiIiIhExiSbiIiIiEhkTLKJiIiIiETGJJuIiIiISGRMsomIiIiIRMYkm4iIiIhIZEyyiYiIiIhExiSbiIiIiEhkTLKJiIiIiETGJJuIiIiISGRMsomIiIiIRMYkm4iIiIhIZEyyiYiIiIhExiSbiIiIiEhkcnsHQERERPcPvV7A0QvXUVKhRucIX3i4Otg7JCKbYJJNREREd81Xy4/h2IUbAIBlDufxxsSuaOPnaueoiMTH4SJERER0V1y6VmpMsAGgSqXDpgNZdoyIyHbYk01ERPccnV6PK3nl8HF3hLuL8o7qKi5XIa+wEu2C3KFUyESKsOXT6wVk5pXBy80BnrcZ8pFfXIVdR68CAEL8zXustTq9zWJsDgpLq3H1RgXCW3nAyYFp1/2EV5uIiO4puYWVmLP4MG6UqiCTSvDIwPYY2LVNk+raeigbv20+B51egJuzAv+YkIDQQDeRI7YfrU6P8irNbZPghpzNKsbhcwUI8HJGclwgFHIZrpdUYc7iI8grqoJMKsFDvdthyqg4s22LylR4f8EBVFRrAQDODjIE+7siK78cACCTStC/c9OuXUN0ej2Wb7+Ivady4eXmiAn9wxEV4mWTfdVn2+Ec/LLxLPSCACcHOaZP6ISI1h53NQayHw4XISKie8rKnRdxo1QFANDpBSzddh6V1ZpG16NS67Bs2wXo9AIAoKxSg+U7L4gaqz2ln8nHP75Owz++TsP7Cw+gqEzV6DoOZuTj418OYcP+LCzacAbfrjwBAEj98zLyiqoAGK7Bf3ddQmFptdn2+07lGRNsAKhU6dAt2h+PDWqP4T1D8faUJES0sU3SuelANtbvv4LicjUuXSvFV8uPo1qtbXhDkWi0Oizbdh56wXB/Vam0WLGj5dxf1DD2ZBMR0T3leolpMqfR6lFSoYazo6JR9VSqtFBpdCZlTUlE61NWqcbiLedwLrsEEW088MjA9nB3vrOhLdZSaXT4cW0GqlSGpPLStTL8vv0Cnh4Z06h6tqRnQ6j1+eiFG7heXGV2DXR6AdeLq+DlZJpWODqYD79xc1GiT6dWjYqjKU5nFpl8rlJpcflaGaJD705vdrVah2q16f1VXK6+K/um5oE92UR0TykuLsL//d8slJQU2zsUspOkaH+Tz8H+rgjycWl0PV5uDogM9jQp6xETcEex1TZ/bQb2nMzD9ZJq7D2Zhx/XnDZZnp1fhi3p2TibJf69XFSmMibYNXKul992myqVFst3XMDnS45g3b5M6PR6KBSmaYJEAsjlUrNr4O/lhPA2t85lXlElVqddglqjR5CPs7G8tZ8LItq435Vx2KGBpuO/5TIpWvs1/j5pKjdnJWLDvE3KesYG3rX9k/2xJ5uI7imrV6/EuXNnsGrVCkyaNNXe4ZAdpHQLhlQKHDl3HYHezhiZ3M64TKPVQSG3/uHFaWPjkPrnZVy7UYGECF/069za6m21Oj1kUgkkEomxTBAEaHUCFHIpTly6YbL+yUuFxn+nnynAd3+cgP7mUJURD4RibJ9wq/dds3+pVAJprf0DgF4Q4O3uAH8vJ+TfHNIBAHFhPmZ1aLR6yGWGY/gh9RQOn7sOADhxqRBlFRoM7xGKM1eKodEakuJ+Ca3h6eqAvgmtoRcEHMwogK+HI0Y+0BYyqSGOrPxyfPDTQag1hm2C/V3wwkOxKK1QY+vhHPzvf/bD1UmBqcM7ICHC1yQend6wjUx6K7nPK6wEJECAlzMs0Wj1UMjN+wyHJAUjp6ACR85dh5uzAo8Mag+3m39JaOx9cmtfjdvu+dGxWLcvE9n5FYgP90HfBNv34N+ORquHXhDM7hnTdaw7xsaci6ae73sdk2wiumcUFxdh9+4dEAQBu3fvxKhRY+Hh4dnwhtRi6PUCftl8FruOXoWDQoYukX7wcnPApWul+CH1FK7dqEREGw88O7IjfDwcb1uXVqfHki3nsPdUHpwd5UiM9jdJmOuj0ujw45rTSD9TADcXBR4d2B7dOgTg2IXr+GnDGRSWqhAf7oMgHxfjA34ATHpRU/+8bEywAWDD/iwM79EWDsqGExGdXo+fNpxB2vFcODnIMbZvGPolGH4c7D52Dcu2n0dltRbx4T4I9HZGXmElEtr7YlStHyMV1Rr8Z/UpHL9wA97ujnh0UASO3Eywa+w7nYfRvdrhLwMicO1GJTpH+CKmnTcEQcCybRew9VA2ZDIJYtp6wdfTybjdtsM5xgQbALLyK+DkIMeek7m4er0CAFBepcGCtafx2V+TIZcZEuQVOy9g44EsSCQSpCQFY8QDbfHdf08YE/+uUX54bnRHYwKekVmEBesykF9chZi2XnhmZEe4uyihFwQs2XIe24/kQC6TYkyfMAzrEQqpVIJrNyrw/apTyMwrQ0iAK54Z2RGtfBvu3c7MLcMPqaeQc70C4a3c8cyojvCrdcz12XMyF9sO5UCl0cHTVQlBMPw14G7T6vRYuD4De0/mwclBjof7haN3nSE7uYWV+Peqk8jMLUOIvyueHhmD1hbmL792owLfrz51a71RHdG6nnNYUFyF71edxIWrpWjt64KnRsS0qAeLG2LXJDsqKgrTp0/Hpk2bUFxcjBkzZiAlJQUAsHPnTnz++efQ6XTw9vbGrFmzEBoaarNYSkqKUVJSgo8/ft9m+7AlhUIGTZ2xhbVduZIJve7++xVZH722GleuZN7V693QNboXiXVMV65kwsOj4YefVq9eaUxM9Ho9e7PvsubQZqeduIZth3IAAFqdFr9uPoeoEE98v+qk8UG889kl+GXTWfxtfPxt69qano20E7kADA89Llx3Bh1CveDrcfvkad3eTBzIyAcAlJSr8UPqaYS1cse/V50yDtE4euEGukX7Q6XWIb+4Cn4ejogL88FPG88gtp232XAJnU6ASqNDbmElAryd4Kis/z/PO45cxc6j1wAYktWf1hvilkulWLAuw/ig3eFz1zGhfwT+/nAnszpW7LxonK/6Rmk1flxzGq7OCpRV3nqA1M1Zgf/9YR9u3HygMSu/HNGhXjh0tgDr918xrKQFlu+4iKhgL/j5GZInmYUsUiqVGBPsGqWVGpRVauDl5oBjF24g9c9M47JVaZeh1QnGBBsw9P4fPnsdidH+0Or0+PeqkyipMIxxPnW5CEu3ncdTI2Kw/3QeNh00zL2t0eqxYudFdGznjXZB7vhx7Wlk5pUBAK7klWP+2tOYOTmx3nNdoybBBoALV0vx04Yz+MdfEm67zdXrFfh541nj5+1HriI00A19E6z/a4lYth7KQdpxw71eXqXBgvUZhnu91g+FBWtPIzP35rnJL8e8Nafx9pQks7oWrMswWe/HNafw1uPm6wHATxvP4MLVUgBAzvUK/Cf1FGY/1V3UY2vO7D4m29XVFcuXL8cnn3yC2bNnAwBu3LiBGTNm4LPPPsPq1asxYsQIvPLKK3aOlIjsbc+eNOh0hiRGp9Niz540O0d0/7F3m335WnoAAUUAACAASURBVJlZ2dmsEmOCXeNSbmmDdV3KNa1LLwi4knf7ccsAcLnOdlqdHicvFZqNgb5RWo2Pnu2Bf05LRkQbD6zZk4lth3Iwd/lxs97T+HBvvDVvH95bcAAvf5OGI+dNe5VN9l/nHAg3y67klRkT7FuxWj4PdeuoVOnwYLcQY6+ys4McIQFuxgQbMEzld+LSDYvn9lxOMRZvOoOvVxyHs5PcZD7oiDYeCA10RXy46dCQEH9XuDjKodcLFuPMtFBWUGK4zkVlKmOCfetYyywem6Gs1OKySxbWrUut0RkT7FvbNXx/Zeaa123N/myh7vkVBBh/bNxax/RzZm4ZhDr3E2B+DJbOd33Lrl6vMHvYuCWz+3CRYcOGAQASEhKQn58PlUqFo0ePIjo6GhEREQCAcePG4b333kN5eTlcXW3z6lUPD094eHjitdfeskn9tubn54aCgvpv9I8/fh/ns+pvtO83UrkjQoJ97+r1buga3YvEOiZr/6LQs2cydu7cDp1OC5lMjp49k+9439Q49m6zo0I8se1wjvGzVCJBXJg3QvxdcaXW0IxoK+ZDjgrxxL5TecbPcpkU4VbMYRwV4mny1kJHpQyd2/th5a5LKK2V+EWFeEEikUAhl2Fvrf0AQHZBOWY/+wB2H8lGsJ8rNqdnGXuRq1Q6/LzxDDqF+1gcvhIV4ondx68ZP8ukErRv4wG5TAq5TGrSS+7t5oDPlx5BtUqHvgmtkBwXBACIDvE0SRQ9XJUYnBSM3p1aIaegHG0D3fHf3RfN9l2l0iE6xAvr9l4xlkkAnLhYaJzN49DZAgxKbIMAL2eo1DqknbiGaf/chWB/FyTHBeLi1VIEejtDq9Pj+c93wM1JgT4Wxio/EBeEjCvFxikW5TIJOrf3AwD4eDjC18PRZJaTqBDPm8fmhY213iIpARB5836IDvHEycu3Zh2JDm14uJlSIUO7IHeT82XNDCXt23hAKpGY/PCxZn+2EB3ihb0na9/rErP5uqNCvHD84o1anz0t3n/RIZ44Uev5gprzbklUiCfSzxQYP7cLcoPDffTCJ7v3ZDs4GCbIl8kMJ12rvXtzWBLRvWXkyDGQ3ny4SiqVYtSosXaO6P5j7za7W4cAjOkTBk9XJQK9nfHMqBj4eznjuYdi0SHUCy43x1b/z+DIBuvq06kVhvcMhYeLEq39XPDCmFh4WPH2yMGJwRicGAw3ZwVCAlzx4tg4uLso8eK4OIS1coerkwJ9E1phVHJbAIaEpu6DeY5KGTpF+mFC/wj0jA1EQbHplHhFpSpodea9iADwQGwgRiW3hYerEq18XfD8Q7Hwvvnmy7+OiUUbPxe4uygxsEsbbD2cgxMXC3E+pwTz1pw2JlGjerVDn06t4OqkQHgrd7w4Nh5ymRSuTgpEhXjBQSlDclyQSdxebg7oFOGDuDAfPDIgwvhw5aSUKGTUmS7v8NkCDOzaBvtO5+HajUoAhrHZ14ur8cHTPRDs74rjFwshCIZhI+v3XcG4vuHwvZk8T06JQs+OgZg+oRM6hfsgPswHo5LboeLmfOhSiQTTxsahfRsPuDjK8UBsIMb3NTw4mtDeFw/3C4eXmwMCvJzw1IgY45jhqcNj0CncBy6OcsSH+2DqsA4NXm8AeHZUDDq2NdxfXSP9MGlIVIPb+Ho64bnRHRHk4wxPVyUe6tUOPWLsM7tIr/ggDO8ZCi83h5v3ehw86rygaMrQaCRE+MLFUY64MB88OdzydI9PDOtgXC8+3AdPjah/WshJKVHoGuUHF0c5Ytp64dlRHUU9rubO7j3ZliQkJODNN9/EhQsXEB4ejpUrVyImJsZmvdhEdG/w9PRCr159sX37FvTq1YcPPTYTd7vNHvlAW4x8oK1JmYujHIHezlBpdAj0drKqt0wqkWBc33CM69u4WT3kMikeHdQejw5qb1Ie3soD/2thfK9SIcOInm2xYqehZ1gmlZg8hAgAiVF+2H7kqvFzpwhfizNmAIBEIsFDvcPwUO8ws2WdInzR6eaMHXtO5mLLoWyT5YfPXUdcmA8cFDJMGRqNKUOj6z3ONn6ueOvxROw+dg2OShn6JrQ2jhUf0i0EQ7qFALj5ZsUdF0xeOuPu4gC9XjB58BMALt8colB3KIVWJ6BtoCuQ0Ap7T+Yh/Uw+QgPdENPWG4Hezvjw53Qcu3gD2Al06+CP50bHIiTADW9M7Gox9qE9QjG0h/kzAV5uDnjJwhj1hvh7OePlRzo3ervEaH8k1pnu0B5q7vXnxifU+9dHLzeHBp9jaMx6AODurMRfx5i/CfR+0SyTbG9vb3zyySd45ZVXoNVq4e3tjU8//dTeYRFRMzBy5Bjk5GSzF7sZaQ5t9r/+OGkcrnDxaimqVTo8ZkVv9t0y4oG2iAvzufnwoKfZw5WPDmoPV2clMq4UoW2gGx7q1a6emqwX6G0+5V2gV8MzYtTWxs8Vjwxsf9t1ZFIpJgyIwKL1Z6DTC3BQyjC+XzikUgkigz1N5gHvcHPYRodQLxytM+Qmp6ACy3cYfojkXK/A5dwj+Oyvydh0MAuFpbdeErT/dD5SupWiXZB7o46F6G6za5J95syZej/36dMHffr0udshEVEz5+nphddff9veYdyXmmubXaXSmr3dL/1sQbNKsgEgNNCt3unLFHIZxvYx75m+E+2C3DG0ewg2HsiCTi+gYzsvm70MpXd8K/RNDMGxjDyEtXI3vn3zmZEx+GnDGVy8ZkiKnRzl+OjndMSFeSOlWzD2ncqDp6sDHu4fgY37r5jUWVGtxcWcEpTXmvGkRnmVeRlRc9Mse7KJiIis5aCQwcNViZJar6z2t2IO4/vBw/0j8GD3EOw9lYc1f17G3+fuRtdIPzw5PMaqObkbw8fDCbF1Xnjj7e5oHJ4xe9FB4wOj57JLMCq5LT6f1su47olLN0x6t2VSCQJ9XJAcF4Q/T+ai5vlBXw9Hqx5sJbI3uz/4SEREdCekUgkmD4mCk4MhafR0VTY4xOF+IgBYtu08Sis1EATg4JkCrNuX2eB2YiosrcbFq6bTyB2sNesEAAzvEYoON2ftcFDK8Oig9vByc0B0qBdmPNoZveKDMLRHCN6Y2LXe8epEzQl7somI6J7XOdIPn7fthfziKgT5OBvneyYgJ7/cbKaSunMi25qrkwJODnKTucT96ryR09lRgVcf7YzichWcHOQmD69GhXghir3XdI9hK0RERC2Cg1KGYH9XJth1tA1yh2OdoSEdrJjnWUxKhQz/M7g9lDd7oL3dHTC+n+VZXTxdHe6ruZSp5WJPNhERUQvm5CDH38bFY8m28yguV6FnTCAGJba563E8EBuEhAhfFBRXo42/C2RS/hiilo1JNhERUQsXHeqFd6Yk2TsMODsqEBqosHcYRHcFf0YSEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyOT2DqC56NWrr71DILpv8ftHREQtDZPsm5KT+9g7BKL7Fr9/RETU0nC4CBERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkciYZBMRERERiYxJNhERERGRyJhkExERERGJjEk2EREREZHImGQTEREREYmMSTYRERERkcjkt1v45ZdfWlXJSy+9JEowRETUdGyziYiaj9sm2bm5ucZ/q1QqbNy4EbGxsWjdujWuXr2K48ePY8iQITYPkoiIGsY2m4io+bhtkv3RRx8Z/z19+nTMmTMHKSkpxrKNGzdi/fr1touOiIisxjabiKj5sHpM9s6dOzFo0CCTsgEDBmDHjh2iB0VERHeGbTYRkX1ZnWSHhobil19+MSn77bffEBISInpQRER0Z9hmExHZ122Hi9Q2e/ZsTJs2DT/88AMCAgKQl5cHuVyOuXPn2jI+IiJqArbZRET2ZXWSHRMTgw0bNuDo0aPIz8+Hn58fEhISoFAobBkfERE1AdtsIiL7avI82UlJSdBoNKisrBQzHiIisgG22UREd5fVPdlnzpzB888/D6VSiby8PAwbNgwHDhzAypUr8cUXX9gyRiIiaiS22URE9mV1T/a7776Lv/3tb1i/fj3kckNunpSUhPT0dJsFR0RETcM2m4jIvqxOss+fP4/Ro0cDACQSCQDA2dkZKpXKNpEREVGTsc0mIrIvq5Ps1q1b48SJEyZlx44d43RQRETNENtsIiL7snpM9ksvvYRnn30WjzzyCDQaDf79739j8eLFeP/9920ZHxERNQHbbCIi+7I6ye7fvz9++OEHLF26FElJScjJycHcuXMRGxtry/hIRLrqYlRmbrF3GA3SVRcDgE1jNezD12b1E9kb22wiIvuyOslet24dhg4dinfffdekfP369XjwwQfFjotEFhwcau8QrFZSYrgtfX19oNHobLQX33vqnBA1FttsIiL7sjrJnjlzJoYOHWpW/vbbb7PBvgc89thke4fQaH5+bigoKLN3GET3JLbZRET21WCSnZWVBQAQBMH479rLlEqlbSIjIqJGY5tNRNQ8NJhkDx48GBKJBIIgYPDgwSbLfH198eKLL9osOCIiahy22UREzUODSXZGRgYAYOLEifj5559tHhARETUd22wioubBqnmydTodcnNzoVarbR0PERHdIbbZRET2Z1WSLZPJIJPJUF1dbet4iIjoDrHNJiKyP6tnF5k8eTKmT5+OZ599FoGBgcbX9AJAcHCwTYIjIqKmYZtNRGRfVifZNW8JS0tLMymXSCQ4ffq0uFEREdEdYZtNRGRfVifZNQ/TEBFR88c2m4jIvqwak13btWvXcOTIEVvEQkREImObTURkH1Yn2VevXsUjjzyCoUOH4oknngBgeD3vzJkzbRYcERE1DdtsIiL7sjrJfvvtt9GvXz8cOnQIcrlhlElycjL+/PNPmwVHRERNwzabiMi+rE6yjx8/jmeeeQZSqdT4lLqbmxvKyspsFhwRETUN22wiIvuyOsn28fFBZmamSdn58+cRFBQkelBERHRn2GYTEdmX1Un21KlT8dxzz2H58uXQarVITU3F9OnT8fTTT9syPiIiagK22URE9mX1FH7jx4+Hp6cnlixZgqCgIPz3v//FSy+9hEGDBtkyPiIiagK22URE9mV1kq3T6TBo0CA20ERE9wC22URE9mX1cJHk5GS8++67SE9Pt2U8REQkArbZRET2ZXWS/eOPP8LZ2Rkvv/wyBgwYgDlz5uDMmTO2jI2IiJqIbTYRkX1ZPVwkJiYGMTExmDFjBvbv34/U1FQ8/vjj8PPzw+rVq20ZIxERNRLbbCIi+2r0a9UBICwsDOHh4WjVqhVycnLEjomIiETENpuI6O6zuie7tLQUGzZsQGpqKo4ePYrk5GQ89dRTGDhwoC3jIyKiJmCbTURkX1Yn2b1790bnzp0xYsQIzJ07F+7u7raMi4iI7gDbbCIi+7I6yd60aRP8/f1tGQsREYmEbTYRkX1ZnWT7+/vj4sWLyMjIQGVlpcmy8ePHix4YERE1HdtsIiL7sjrJ/te//oVvvvkG0dHRcHR0NJZLJBI22HRf+fXXRcjKymz0dgqFDBqNzur1S0qKAQAeHp6N3pc1goND8dhjk21SN9kf22wiIvuyOsleuHAhli1bhujoaFvGQ9TsZWVl4vL5DAS6Wv31AQBUN3I/ReVaAIBD1fVGbtmw3Jt1U8vFNpuIyL6szhIcHR0RFhZmy1iI7hmBrnI8Ee9t033MP1YIADbZT03d1HKxzSYisi+r58l+6aWXMHv2bOTn50Ov15v8j4iImhe22URE9mV1T/brr78OAFi2bJmxTBAESCQSnD59WvzIiIioydhmExHZl9VJ9pYtW2wZBxERiYhtNhGRfVmdZLdu3RoAoNfrcf36dfj6+kIqbdJb2YmIyMbYZhMR2ZfVLW55eTlmzJiB+Ph49OnTB/Hx8XjttddQVlZmy/iIiKgJ2GYTEdmX1Un27NmzUVVVhdWrV+PYsWNYvXo1qqqqMHv2bFvGR0RETcA2m4jIvqweLrJr1y5s3rwZTk5OAIB27drho48+wuDBg20WHBERNQ3bbCIi+7K6J9vBwQGFhaZz6xYVFUGpVIoeFBER3Rm22URE9mV1T/b48eMxdepUTJkyBa1atcLVq1exYMECPPzww7aMj4iImoBtNhGRfVmdZD///PMICAjA6tWrkZ+fD39/fzz11FNssImImiG22URE9mX1cJEPPvgA7dq1w4IFC7B27VosWLAA4eHh+OCDD2wZHxERNQHbbCIi+7I6yU5NTUVsbKxJWWxsLFJTU0UPioiI7gzbbCIi+7I6yZZIJNDr9SZlOp3OrIyIiOyPbTYRkX1ZnWQnJibiyy+/NDbQer0ec+fORWJios2CIyKipmGbTURkX1Y/+Dhz5kw8++yz6NWrF1q1aoVr167Bz88P//rXv2wZHxERNQHbbCIi+7I6yQ4MDMTKlStx7NgxXLt2DUFBQYiPj4dUanVnOBER3SVss4mI7MvqJBsApFIpEhISkJCQYKt4iIhIJGyziYjsh10aREREREQiY5JNRERERCQyJtlERERERCJjkk1EREREJDIm2UREREREImOSTUREREQkMibZREREREQiY5JNdywtbSfS0nbaOwwiI96TRERkb416GQ2RJbt37wAAJCf3sXMkRAa8J4mIyN7Yk01EREREJDIm2UREREREImOSTUREREQkMibZREREREQiY5JNRERERCQyJtlERERERCJjkk1EREREJDIm2UREREREImOSTURERNRMCIIAQa+1dxgkAr7xkYiIiKgZ0FzYD9WeXyFUlUIe1g2OfadCIlfaOyxqIvZkExEREdmZvqoU1du/h1BZDAh6aC/shfroOnuHRXeASTYRERGRnekLswGd6TARXcFFO0VDYuBwESIiImoxBHUVqncvgjbzCKSeQXDsNQkyv3aNrkd9YhNyMg9Ap3SHMnEsZF6tbBDtLTLfUEDhCGiqjWXyVtE23SfZFnuyiYiIqMVQHVgO7fk9gKYK+oKLqNo4F4Je16g61Ke3Q/XnL1DlnIX20kFUrfkEgs62DyNKHFzgNHgapN7BkDi6QRE7GIrYITbdJ9kWk2wiIiJqMXTXzph8FioKIZQVNKoO7eVDpnVUFls9dEN79TSq//wF6hObIGhVVu9TX1kMzdk0QK+FPKwbHJLGQSKVNSpual44XISIiIhaDJl/GPSFWcbPEid3SFx9G1WH1CMAuqxaBRIppG5+DW6nuXgA1Zu/MX7WZh6B8/BXrdpn9ZbvjD8Q9MXXAJ0ajn2fbFTc1LywJ5uIiIjuedrMI6ja8i8IUhlkrWMASCDxCIDjwOchkTWuT1GZMAJS31DDB5kcDt3/AqmLV4PbaU5tNfmsyzkJfUleg9sJmmqzHnjtlaPWB0zNEnuyiYiI6J6muZyO6o1zjZ+lHoFwmfo9pHJFk+qTOntAmTAC8vxT0HiFQRnV27oN5Q6mnyUSwJp5ruUOkLj6QCi/cSsGr9aNiJiaI/ZkExERUbMhqCuhOfcntNknIAh6q7bRnk0z+awvyYVw/XKTY1AdWI7qzd+g/Ng2qHbMg+rgCpPluqKr0FeVmsWt7DjAJKlWdOjfYA+4oKqA9sJeKDoOgsTJAwAg8QiAwwP/0+T4qXlgTzYRERE1C/rSfFT+MRvCzQRWFhwP56H/aHA7iaO7eZmTW6P3Db0eUs9AqE9uNlmmPrEZDoljIVSXo3LdHOgLLgESGZSdR8AhcQxU+5ZCfXwDIOghC+0CeUg8pJ5BkAdG3n6fxbmG41WVAwBkbbvAsedjkLj6QCKRNCp+an6YZBMREVGzoD6xyZhgA4Au6xh0uecgC2x/2+2UCcOgzToKoaIIAKDoOBBSj0Cr9ino9aje+i9oL+4HAMhCOkEiU0JAlXGdmlebq4+tNyTYACDooD70B6QeQVAfXXsr5svpUIQmNJhgA4D6+Hpjgm3Y9hCELqMhdWvcg5rUPDHJJiIiouZBYz7lnVXT4CmdoIh7EEJpHuQRPSFvICmvTXvlsDHBBgDdlaOQt0+G9tytISjKLqMB3OztrkOXf868rCgH1owGF2q9eOZ2ZQCgvXYGumtnIPMPh7xNRytqbzpdYTaqd86H/sYVyFrHwLHvk5A6mf+1gG6PY7IJxcVF+L//m4WSkmJ7h0LUrFn6rlhbRkQNU0T3AWrNDS31DIKsgbce6qtKUbn8baj3/gbNqa2o3vovCKoKq/cplF43K5O6+8F53Cz4Pvg0nMfNgjKmPwBA3q6ryXoSRzcoovubxAwA8uB4q/atiO4HSG6lYlLvNpAFmP9AUJ/YhKrVH0F9cAWq1n4K1aE/rKq/qaq3fAt9/gVAp4HuylGo0n626f5aKibZhNWrV+LcuTNYtWpFwysT3ccsfVesLSMSi778Rr29ndYQtGrob/NyFkGvg740H4L+9g8d6qtKIVSX33adxpIFRMD5obcgj+4LZZfRcB41ExKp+R/dBb3+Zow6aM/vMQ4TAQCh/AY0F/ZD0GuhLy247cOT+spiyIIigdr7kMggb9sVEmdPuHR4ADKfEEO9gh4SJ3coEoZDGhgFeVgSnEbMgNQrCA69HofUPwJSn1A49HkC+rICVO+YB82ZXcb9Wzqv8lbRcBr5JuSRvaBMGgvnkW9AIjWkZkJ1OfSVJQAA9dF1JnGrj66DIAj1HpfhGpv/eAAAQWf5vAjqKujLCw37LbpqskyXZ9pbL2iqoa81E8rtWHO/Gs5NQaPfzNnwvgshqKtMymqfV2OZIBj2L/JbPTlc5D5XXFyE3bt3QBAE7N69E6NGjYWHh6e9wyJqdix9Vwz/briM3ykSg766DFUbvoQ+7zwgd4BDj79AGTOgUXVoLuxD9a4FgLoKUp9gOKX8HVJXH+Nybe45VG/+BkJlMSSuPnAa9FfI/MNM6hD0OlRv/w+05/cBEikUHQfAUaSZMAR1FVT7f4cu+wQgUwByBRwSRpiso8u/iKpNX0OoKITExQvytolm9eiLc1Hxy8sQqkogcfOD0+BpkNXMew3DEJSqzd9Cd+UoIJVDHt4dUFdCEPRQdBwI9bH10J77ExUSCRQd+kHZbQKq1nxsHI8tC4qCY/9nob9+GRW/vQKhoggSF284DZ4Gzfk90JzYZDjfZ3ZBUXQVirZdULXlW5P1ZP5h0F2/jOqt30EovwGJsydkgVGQB0Wh+s9foDm5FRD0kId3A1B/Ql2X5vweVO9edPMah8Ap5SXjNa68eAQVK7+AUF0GiXsAnIb8DTLv1lAf2wDVgeWATg1pYBQk7gEQSm/N7y3zDzf+W31qG1R7FwNaFaQBEXAa8jeLQ0mE6nJUbfwKutyzgFwJh+4ToOw4yGw9Xf4FVG365ub19IbToBcgC4iw+ngtEdSVqNo4F7qrpwGZEg5JY6CMH4rqPb9Bc2Kz4byGJcFxwDPQl+SjauOXEEryIHF0g2P/p63+S0RD7kqS/cMPPyAnJwfvvPMOAOD69esYNWoUNm7ciG+++QYHDhyAWq1GVFQU3n33Xbi4uGDJkiVYsGABlEol9Ho9vvjiC4SHhzewJ2qs1atXQq83fHn1ej1WrVqBSZOmNqqOkpJilJSU4OOP3xc1NoVCBo1G3F+1YrhyJRPOsG5aqeaqXK1H/pXMO75mzfkaeXh4iFqnpe+K4d8NlzX2O2VvbLObJ/Wh1YYEGwC0Kqj+/AXytl0hdbbuXhc0KlTvXABoDD17+htZUO3/HU4DnjWuo9o1H0KlYZiTUH4D1Wk/wWXMOyb1aM/vhfb83puV6qA5sQny0M6AX/c7O0AYhkXosk8YPug0UO9fDkXbREg9bz3EWL1rIYSKQsPuK4qgzT4BiYuXsTdb4uoDbWY6hCpDb6VQVgBV2s9wHj3TWIfm1DZDgg0Aei2059LgPG4WZD4h0Jzfe2s8tiDcfMGM9NYDjzC8ul17OR3qw6uN+xUqClG9ayH0JddMjkmTsR26rGOm6+1eCJex76F690/GubGFymJU75wPx96PG5N0ANBe2Ad5RE9oz+8xlik7DbU4+4igrkL1roXAzZ5j/Y0rUB1YAaf+T0MQ9ChI/RZCdZlh3dI8qPb8Csc+T0C1bzFws2dcn3sG8qg+0CscoS+8AlmrGDgkTzQsqyyB6s+fgZs9zvq881AfXm3xR5bqSKohwQYArRqqP38z3K91pjSs3rWg1vU0nEOX8Xf23yb1sfWGBBsAdGqo9i2FxNkLmuMbjOtoL+6HNjgOmgv7INx8YZBQXYbqnfPh8ugc418U7sRdGS4yfvx4bNy4ERUVhjFSS5YswYgRI7Bo0SK4ubnh999/x6pVq+Dv74/vv/8eAPDJJ59g4cKF+OOPP7B8+XK0atXqboR639mzJw26m38e0em02LMnrYEtiO5Plr4r1pbda9hmN0/64qt1CnQWH8Srj1BRZEywLdUpCILhdd61lxflNBxHPes1hXndAnR1yuquI5TmGcZNd30IirgUOI2aCaHOUImG6gBgHCJR9xwAgL7U/K2NQlUp9EV1zldxDiRKZ5MyidLZbH8129UdliGU5EFXmG22L6mLF5xGvgFF55FQdBwESOUWh2voK4qMCfatmG7uQ1MNXdkNs2X6klxjgm2Mo7oc8vAkSP3DIXHxNCbVQmm+8d+3jsX8XFosF3QW335pfg4t19cY5vsWoMu1/ICq2TWw8D1pqrvSk+3p6YkBAwbgjz/+wIQJE7Bs2TIsWLAAr7zyCsrLy7Fhg+GXhVqtRnS04QGHHj164PXXX0f//v3Rr18/BAcH341Q7zs9eyZj587t0Om0kMnk6NkzudF1eHh4wsPDE6+99paosfn5uaGgoEzUOsXw/Yv+LgAAFgJJREFU8cfvozr3vL3DuCOuSil8A0Pv+Jo152sktvq+K9aW3UvYZjdP8pCEW728ACRO7pD5tbV6e4lHAKQegYakqladxuUSCWTB8bd6eOssryEL6QQcWVOrYhnkwXFWx3E78pCEW73kAKBwhDwoqs46naC9dNDks+bEZqgPrwIEAdrMw5AGRkJf04t6cx3TY0iAJmNnrUqVkAa2h6BVQx4SD/WhVTAO0ZBIoYgdZHjtuU59My4nyNslQpdzCtrMwybxy0MTUL1jniFxlUjg0G08tBf2m64X2sn4/7WPVxYcC0VwPNTSxSbJrCykE2T+YVDtXmhMCtVHUuEy5l1IPQKM60k9A82GetRcQ4nSGY7BHVCdddrkvMgCIgAHF6DWw6ISiQTq/b8DMPRW6wsy4fLwbEj92kLi5GH8K4HhGMzvkZpyXdaxW3U6upkNPaqJQXs53SzeO1H3HoGDCxQd+kKTsd3kvMpDEgCNCprT24xl0oAISBxc7jgG4C6OyZ44cSJeeeUV+Pj4IDw8HG3btoUgCHjnnXfQs2dPs/W//vprHD9+HHv37sXkyZPx7rvvom/fvncr3PvGyJFjsHv3Duh0gFQqxahRY+0dElGzZOm7UjP+uqGyexHb7OZH0XEgBG01tOf3QeLqDYek8ZDIrH9tuEQigdOD06Havwz64lzI23aGsstIk3Wc+v1/e3cfHFV973H8c/Y5CQ8JCSEB5EGREGqBlAgKQSSIICZBqV4QoaWizL2tPGiZEelVbNVWbMfSqba1rQ8zndFp9XpzK/SOmREEQUH0apUmIkQkSCAhECFP7GZ3z/0juBKSQB5OstnD+/VX9mT3nO/uZr/zydnf+f3ukX/3XxWqLJUzbbS8k25vsR9X2mj5cv9dgb1FMhwueSbktXtO6otxj7pG5pkaNe7bISOur7wTb2kReHzX/UB+bx+FKj6TM3WU3N+6QfX/vT5yNtY8XSln6ig5Rg9U+PhBOdPHtHge7hHfkTltqRpL3pLhjZcRn6j6vz4oKSz32JnyzfwPBT55Qx6vR8bYG+UaNl6OeesU+NebkiTPt+fIkZAk3/Rl8r/3N4UqDsgxYJjMoF/+DzfJNWqKnIPHyJU+Ro5+A+Ua+m35d/9NocoDcg4aLe/kpnp8Od+X3xOv0NFPZSQPl3fS7XL0GaC42asU+GizzGCjPFfdIFd6hhq/+KD5WddAgxo/3Sbv5H+LbDIMh+Jvuk/+3a8ofKpCrpHfkSfrmzHtqbfer/J/PB+Zms876TYZLq/i566R//3XZNafkvvKKWo89x8dSeHqLxU+dUyO/mmKm7tG/j2vyqw9KdcVk+X+1szW38vMGTIDZxQsfVdGwgB5s+dH5htv9n5Ov0t+Xx+FKvbLOWiUvJMXXOSv5OLcGdNk+uvVuH+njPj+8k68Vc7kYYqbvVqBjzZ987oOHiNn6kjJ6VboyL/kSB7W7PXsqh4L2RkZGUpMTNTPf/5zPfzww5Kk3Nxcvfjii8rKypLP51Ntba0qKio0fPhwlZeXa9y4cRo3bpzKyspUUlJCw+4GiYlJysmZrrfeelM5OddxgRbQhrY+K+3dFmvo2b2PYRjyTshrcSFgRzj6D1LcrHvbPoavj3zTl110P+5R18g96ppO13EhnqtmyXPVrDZ/b3gT5LtuaeR2qOJAy+EO/tqLrhTpybxenszrFTxSrIbNT0a2N37yhpw3rlDCLQ81+7YuXF2u0KGPmmZUOVMr34zlTa/XdU3XXNS9tl7hqkOSpOCpYzI8PjkypjXV7Osj3/SW12YYnjh5r71DZ7Y9p2DpLtUf+kjeq+fLc9UsheuqFdjzXzrz9osKVR1qmgWlxQ5ajst29E9T3I0rWn3Orr4Dmo3B/5pz4EjF3/TjyO1Q5ecKn7ssvcsbWfLdmXyZ4ufc1+r+m5dmyDthrrwT5l74ft4E+a77wUX311GecbPlGTe72TbXZd9u8a2L4fLKN2WR5ceXengKv9tvv10Oh0MzZjTNN7l8+XKNGTNGt912m/Lz87Vo0SKVlpYqHA5r7dq1ys/PV0FBgY4fP64FC7r+nw1al59/q668MiNmz7gBPaW1z0p7t8UiejZigWPgSBn9Upttc1/R/oswvw7GzbeVNb/dcFpntj9/9qLBpiEpgXOGzITrv2qxn2DZx2qPxuItTUNGTFNqbJD/nZcULPtE/u0vNq1+GQyo8ZM3ZDbUyHF2OkFJMrx9mubZ7gbeq78ro9/ZYShOj3xT7pThieuWY9lZj07ht3v3bt15551ynL1i0+1267777tN997X8j+ill17qydIuaYmJSVq79uFolwH0eq19Vtq7LRbRsxELDIdT8XkPKPDhJoVrT8jRN0X+//sfnXn3JXnGTJdn0m0yjLbPKTqHZEoydO40ec4hY5vdJ1xdLp03h3LoxDdB3PD1bTFW2TlgaLvqD50X6CVTwbIPm9UjSeGqLxQ/7ycKfr5HZqBBrssntXtmmY5y9B+k+NsfV+NHm5rmVHe5ZZpmqzOaoG09cia7oqJCs2fP1qFDh3TnndbMpQkA6B70bMQaR59k+aZ9v2n6u5JtTbNg+OsU+Oc/FPzswjP8OFNGyHf93XIkDpbRP02ub92g4IFdCnxSpPDZZd6dA0dI580a4joniBsOp3zTl8k4O1+0I2movNcsbFftLZZId7rkGnVti6EgzrTRMlxeuUfnyHPVrG4L2F8LvPuyAh8UKvjZTp3Z8qwCHxR26/HsqEfOZA8aNChyNToAoHejZyNWhY7tl8zmU8wFj+6T++zY6La4R0+Ve/RUNX66XWe2Px/ZXln1mZwz7pXh9iluzmr5d/9NZu3JpjHpV93YbB+uYePkzfmegp+/L+fAkTLOmw+6zWNfOUXh2pNq/HSb5ImXe9g4hasOyXvNHQrsLZIZaJAnc0ZT8O4hZjisxn3bmm1rLNkqb/atPVaDHbDiIwAAsAVn6uVNZ4DPuRCytWnj2hI4Zyo3Sarf/74SJlXLkZAkV9poueb95wUe+5b8b78oSQqW7lKwvETxc1a367jerDx5s/JU/79PKfDh600bXV7FF6xrtlJljzEMGS6fzFDtN9sYk91hPXrhIwAAQHdx9EuV77q7moZtON1yj53ZoYsDW1zc53C1Ou1caxpL3mp2O1T2kcJnV89sj1DVoWbzSivoV+CTonY/3kqGYchz9XfVNFZdkuGQNzu2L+SOBs5kAwAA23BnTJNjwFD5339NocoDaix+84JTAp7Lk1WghmMHIovOJF5ToFA7FyZpNaB3YB7z81dSbHNbD/GMnSHn4AyFKw/KmXalHOfN4IKLI2QDAADbMAMNqv/HryIrGPqrDsnwxMs9+uKrr7oGj1HCHU8qeHivwscPqv7ABwp9WSrv1d+V85zp81rjmXiLGipKIwHdM+HmDq0c6Ey9XM600Qp9vVKlw9XmQi89xZk4WM7EwVGtIZYRsgEAgG2Eju1vtkS4JAXLPmpXyJYkR3yiFDyjxuI3z245pIbjB5Vwx68uOHTElZ6hhDt+qVB5iRyJ6Z0aSx0398dq3P+uzLpquS6fJOeAIR3eB3oPQjYAALANR2Kazp/32pGY3qF9BMv+2ey22XBaoaov5EprZdXFc48d31+OLqyEabi88mRe3+nHo3fhwkcAAGAbjn6p8k6+XTo7Hto5OFOecXM6to/zh0g4nIxJRodxJhsAANiKZ/xcuTOvlxlokKNPcscfn5WncOXnClXsl1xeea9Z0DSMBOgAQjYAALAdwxMv47xVGtvL4eur+Hk/UZLXr5O1pgy3z+LqcClguAgAAEArXP1SCNjoNEI2AAAAYDFCNgAAAGAxQjYAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgMUI2AAAAYDFCNgAAAGAxQjYAAABgMUI2AAAAYDFXtAtA7MvJmR7tEoBm+JsEAEQbIRtdNnXqddEuAWiGv0kAQLQxXAQAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALCYK9oFALHoWG1QL3x8stuPIalbjnOsNqgRlu8VAAB8jZANdNBllw3v1OPcbqcaG0Ptvn/Sqa8kSb7+iZ063oWMUOefBwAAuDhCNtBBixZ9r1OPGziwr44fr7G4GgAA0BsxJhsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwGCEbAAAAsBghGwAAALAYIRsAAACwmG2XVXc4DEvuE0vs9nwk+z0nuz0fiedkh+P2Bpdiz5bs95zs9nwk+z0nuz0fqff2bMM0TbOHagEAAAAuCQwXAQAAACxGyAYAAAAsRsgGAAAALEbIBgAAACxGyAYAAAAsRsgGAAAALEbIBgAAACxGyAYAAAAsRsgGAAAALHbJheyDBw9qwYIFmj17thYsWKAvvvgi2iV1SXV1te655x7Nnj1b+fn5uvfee3Xy5Mlol2WJp59+WhkZGfrss8+iXUqX+f1+rV+/XjfeeKPy8/P10EMPRbukLtm6datuueUWzZs3TwUFBSoqKop2SR22YcMG5ebmtvgbs1uPiHV2ez/o2bHBbj1biv2+HZM927zELFmyxCwsLDRN0zQLCwvNJUuWRLmirqmurjZ37doVuf3EE0+YDz74YBQrssbevXvNZcuWmTNmzDD37dsX7XK67NFHHzUff/xxMxwOm6ZpmsePH49yRZ0XDofN7OzsyPtSUlJiTpgwwQyFQlGurGP27NljlpeXt/gbs1uPiHV2ez/o2bHBTj3bNO3Rt2OxZ19SZ7JPnDih4uJi5eXlSZLy8vJUXFwc02cREhMTNXny5MjtCRMmqLy8PIoVdV0gENDPfvYzPfLII9EuxRJ1dXUqLCzUqlWrZBiGJCklJSXKVXWNw+FQTU2NJKmmpkapqalyOGKrnWRnZys9Pb3ZNjv2iFhmx/eDnt372bFnS7Hft2OxZ7uiXUBPOnr0qAYNGiSn0ylJcjqdSk1N1dGjRzVgwIAoV9d14XBYL7/8snJzc6NdSpf85je/UUFBgYYOHRrtUixx+PBhJSYm6umnn9bu3buVkJCgVatWKTs7O9qldYphGNq4caN++MMfKj4+XnV1dfrjH/8Y7bIsYfceEWvs/n7Qs3snu/Vsyb59u7f3iNj5FwYX9eijjyo+Pl6LFy+Odimd9uGHH2rv3r1atGhRtEuxTCgU0uHDhzV27Fi99tprWrNmjVasWKHa2tpol9YpwWBQzz77rH73u99p69at+v3vf6/Vq1errq4u2qUBMYWe3TvZrWdL9O1ouaRCdnp6uioqKhQKhSQ1fZAqKytbfP0QizZs2KBDhw5p48aNMfX1z/n27Nmj0tJSzZw5U7m5uTp27JiWLVumHTt2RLu0TktPT5fL5Yp8nTV+/HglJSXp4MGDUa6sc0pKSlRZWamJEydKkiZOnKi4uDiVlpZGubKus3OPiEV2fj/o2b2X3Xq2ZN++3dt7ROx+sjshOTlZmZmZ2rRpkyRp06ZNyszM7BVfKXTFU089pb179+qZZ56Rx+OJdjldsnz5cu3YsUNbtmzRli1blJaWpueee045OTnRLq3TBgwYoMmTJ2vnzp2Smq6EPnHihIYPHx7lyjonLS1Nx44d0+effy5JKi0t1YkTJzRs2LAoV9Z1du0Rscqu7wc9u3ezW8+W7Nu3e3uPMEzTNKNdRE8qLS3V2rVrdfr0afXr108bNmzQ5ZdfHu2yOm3//v3Ky8vTiBEj5PP5JElDhw7VM888E+XKrJGbm6s//OEPGj16dLRL6ZLDhw9r3bp1+uqrr+RyubR69WpNnz492mV12t///nf96U9/ilwUtHLlSt1www1RrqpjHnvsMRUVFamqqkpJSUlKTEzU5s2bbdcjYp3d3g96dmywW8+WYr9vx2LPvuRCNgAAANDdLqnhIgAAAEBPIGQDAAAAFiNkAwAAABYjZAMAAAAWI2QDAAAAFiNkw1Zuvvlm7d69u0eOdeDAAc2fP1+dmaCnvLxcWVlZkQn0L6Sqqko33XSTAoFAZ8oEgF6Nvg27Ygo/xJSsrKzIzw0NDfJ4PHI6nZKkn/70pyooKOixWlasWKE5c+bo5ptv7vZjPfLII7riiiu0ZMmSbj8WAFiJvk3fvlQRshGzcnNz9dhjj2nKlCk9fuzKykrl5eXp7bffltfr7fbjffDBB1q/fn1kVSsAiEX0bVxKGC4CW8nNzdU777wjSfrtb3+rlStXas2aNcrKylJ+fr4OHjyoZ599Vtdee62mT5+uHTt2RB5bU1OjdevWKScnR9OmTdOvf/3rNr8WfOeddzR27NhmjTo3N1d//vOflZ+frwkTJmjdunWqqqrS3XffraysLC1dulSnTp2SJH355ZfKyMhQMBiUJC1ZskQbN27UwoULlZWVpbvuuksnT56M7Hv8+PE6fPiwjhw5YvlrBgDRRN+GXRGyYWtbt27VvHnztGfPHmVmZmrZsmUKh8Pavn27fvSjH+nhhx+O3Hft2rVyuVwqKipSYWGhdu7cqVdeeaXV/e7bt08jR45ssb2oqEgvvPCC3njjDW3dulX33HOP7r//fu3atUvhcFh/+ctf2qx106ZN+sUvfqF3331XjY2Nev755yO/c7lcGjZsmD799NMuvBoA0PvRt2EXhGzYWnZ2tqZNmyaXy6U5c+aourpay5cvl9vt1ty5c3XkyBGdPn1aVVVV2rZtm9atW6f4+HglJydr6dKl2rx5c6v7rampUUJCQovtixcvVkpKigYNGqTs7GyNGzcucuZk1qxZKi4ubrPW+fPna+TIkfL5fJozZ45KSkqa/T4hIUE1NTVde0EAoJejb8MuXNEuAOhOycnJkZ99Pp+SkpIiF9z4fD5JUn19vSorKxUMBpWTkxO5fzgcVnp6eqv77devn+rq6lpsT0lJifzs9Xqb3fb5fKqvr2+z1oEDB0Z+jouLa3Hfuro69e3bt83HA4Ad0LdhF4RsQFJaWpo8Ho927doll+viH4uMjAwVFhb2QGVNgsGgysrKNGbMmB47JgD0ZvRt9HYMFwEkpaamaurUqXriiSdUW1urcDissrIyvffee63ef+rUqSouLpbf7++R+j7++GMNGTJEQ4YM6ZHjAUBvR99Gb0fIBs568skn1djYqLlz5+rqq6/WypUrdfz48Vbvm5KSosmTJ+vNN9/skdpef/11LVy4sEeOBQCxgr6N3ox5soFOOnDggB544AG9+uqrMgyj245z4sQJLV68WIWFhT0ytysA2BV9Gz2JkA0AAABYjOEiAAAAgMUI2QAAAIDFCNkAAACAxQjZAAAAgMUI2QAAAIDFCNkAAACAxQjZAAAAgMUI2QAAAIDF/h/TVOPV1/Fl/gAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Now looking at conversion rate, we see that spending more time on the landing page is generally a good indicator of conversion status. Since the boxplots contain outliers, we'd like a better idea of the spread and concentration of the data. We accomplish this with a swarm plot, which shows every data point. The swarm plot illustrates that non-converted users have a wider spread of times. This is supported by the wider IQR that can be observed in the boxplot. Times for converted users are generally greater with less variability." ], "metadata": { "id": "WA21KcmYoGg8" }, "id": "WA21KcmYoGg8" }, { "cell_type": "code", "source": [ "# language differences \n", "sns.catplot(data=usage,\n", " x='language_preferred',\n", " y=time,\n", " col='converted',\n", " kind='box');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 365 }, "id": "3vGg46cYx30T", "outputId": "c1c24686-6349-499d-e1fc-d805b6ee02fb" }, "id": "3vGg46cYx30T", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "What is apparent from this plot is that time has a greater connection with conversion status than language. Generally, the time is greater for converted users than non-converted users, independent of language preference." ], "metadata": { "id": "Njqb4fa_qPUk" }, "id": "Njqb4fa_qPUk" }, { "cell_type": "code", "source": [ "# conversion and group vs time\n", "sns.catplot(data=usage,\n", " x='converted',\n", " y=time,\n", " col='group',\n", " kind='box');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 365 }, "id": "aqOC_XjJyz3s", "outputId": "3d79b283-5ebc-4be2-9e77-9eef1066f0bd" }, "id": "aqOC_XjJyz3s", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Here again, time is more closely connected with conversion status than trial group. We see that converted users reliably spend more time on the News Source landing page than non-converted users, independent of the page layout. What is of note is that the medians are higher for all users in the treatment group. Equivalently, the redesign appears to have had a positive effect on user retention." ], "metadata": { "id": "qRs55IuTqyF9" }, "id": "qRs55IuTqyF9" }, { "cell_type": "code", "source": [ "# split violin plot of time\n", "sns.catplot(data=usage,\n", " x=time,\n", " y='language_preferred',\n", " hue='group',\n", " hue_order=['treatment','control'],\n", " kind='violin',\n", " split=True)\n", "plt.title('Time by Language and Group',fontsize=16)\n", "plt.xlabel('Time (min)')\n", "plt.ylabel('Language');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "id": "iYuqRj0Eyz06", "outputId": "fef83dcd-3aa9-4d60-8b28-c5c5b9ee35d1" }, "id": "iYuqRj0Eyz06", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa0AAAFuCAYAAAAyKkctAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3gU1cLH8e/W7G567w1CAgkkofcqUixXFFGwIKLotXFFr8q9NrwqoihIERB97aCogIqiFEUUkF6F0AKBQEJ632TL7Lx/RFZigoSQZHeT83mePA87zMw5s5nsb8+ZM2cUsizLCIIgCIILUDq6AoIgCIJQXyK0BEEQBJchQksQBEFwGSK0BEEQBJchQksQBEFwGSK0BEEQBJchQqsRJSQkXPJnyJAhnDlzhoSEBFasWOHoKgPV9Z49e7bLlyE0vqlTpzJkyJB6rVtRUcHixYsZPXo0Xbp0oWPHjgwaNIjJkyezfv16xN01QmNQO7oCLcmyZctqvH744YdJSEjgkUcesS/TarUEBQWxbNkyoqKimruKgtAkcnJyuPvuu8nLy2PcuHFMnjwZvV5PZmYmP/zwAw899BCff/45KSkpjq6q4OJEaDWi1NTUGq+1Wi2+vr61lte1riC4sn//+98UFBSwfPnyGl/GevTowejRo9myZQseHh5/uw+z2YxWq23qqgouTnQPOkBd3YNTp05lwIABHDhwgLFjx5KcnMzw4cP5+eefAXj//fcZMmQIXbp04YEHHqCwsLDGPq1WK2+//TYjRoygY8eO9OvXjxkzZmAymepdr4ULFzJgwACSk5O5/fbbSUtLs//fiy++SJ8+fbBYLDW2KS8vp3Pnzrz++usNeCf+ZDKZmD59Otdddx2dO3emb9++/POf/yQ9Pb3GeitWrCAhIYG9e/fy+OOP06VLF/r168dLL71U61gzMzOZNGkSKSkp9O7dmxkzZrBs2TISEhI4c+aMfb2EhATmzZtXY9u6fkf79+9n8uTJ9vdo+PDhzJo1i6qqqhrbSpLE7Nmz6devHykpKYwfP5709PQ6yzl8+DD//Oc/6d69O8nJyYwdO5adO3de8v06deoUTzzxBEOGDCE5OZmrrrqK559/npKSkhrrnT+vDh06xG233UZKSgrDhg3j008/rbXP3377jRtvvJFOnToxdOhQPvvss0vWA2Dv3r1s376dBx544KK9B3369KFt27a16rVnzx77+f7aa68BcOLECR566CG6detGcnIyt9xyC7/88kut46qr2/LOO+/kzjvvtL/etm0bCQkJrFmzhqlTp9K9e3e6dOnC448/TlFRUb2OT3AuoqXlRMrLy3nqqaeYOHEiQUFBLFq0iEceeYTbb7+djIwMnnvuOfLz85k+fTovvPACc+bMsW/7xBNPsGHDBu699166dOlCeno6c+bM4ezZs7U+KOvy1VdfERoaynPPPYfZbGbOnDlMmDCBNWvW4OPjw7hx4/jkk09Yt24d11xzjX27b7/9lsrKSm699dYrOnaz2UxFRQUPPPAAgYGBlJSUsHTpUsaOHcvq1asJDAyssf6TTz7Jtddey/z589mzZw/z58/Hy8uLyZMn2/d39913YzabmTZtGn5+fnzxxResWbOmwXXMzs6mffv23Hjjjbi7u3Ps2DEWLFhAZmZmjet1c+fO5e233+aee+6hT58+HDx4kAcffLDW/g4ePMjtt99Ohw4dePHFF9Hr9Xz66adMmDCBzz77jI4dO160Lrm5uYSGhvLf//4Xb29vMjMzefvtt7nvvvtqdVOXl5fz+OOPc9ddd/HQQw+xYsUKpk2bRmxsLL169QIgPT2dSZMm0bFjR2bPno3ZbGbevHkYjUZUKtXfvi+//fYbAIMGDarvWwlAWVkZjz32GBMnTmTKlCnodDpycnK47bbbcHd359lnn8XT05MlS5Zw//33s2jRIgYOHHhZZZw3ffp0+vTpwxtvvMGpU6eYNWsWubm5fPzxxw3an+BAstBkBg8eLD/++OO1lmdmZsrx8fHy8uXL7cueeuopOT4+Xt6+fbt9WVpamhwfHy8PGzZMtlqt9uXTp0+XExMT7ct27Nghx8fHyytXrqxRztdffy3Hx8fLhw4d+tt6xsfHyz169JArKipq1DExMVGePXu2fdkdd9whjx8/vsa2o0aNkidOnPi3+z9fxqxZsy653nlWq1U2Go1yamqq/P7779uXL1++XI6Pj5fnzJlTY/377rtPHjZsmP31Z599JsfHx8v79u2zL7PZbPL1118vx8fHy5mZmTXqNnfu3Br7q+t3dCGbzSZbLBb5q6++khMSEuTCwkJZlmW5uLhYTk1NlZ9//vka67/33nu1yhk/frw8YsQI2WQy1TjuESNGyA888MAl3qGaLBaL/Tw4ePCgffn58+q3336zLzOZTHKPHj3kZ555xr7sscceq3UOZGVlyUlJSfLgwYP/tuznnntOjo+Pr3EcsizLkiTJFovF/iNJUq16rVu3rsY2M2bMkDt06CBnZGTYl1mtVnnYsGHyqFGjamxfV73uuOMO+Y477rC/3rp1qxwfH1/rHD3/t7Fly5a/PTbB+YjuQSdiMBjo3r27/XWbNm2A6q6VC7/ttmnTBqvVSl5eHgC//vorGo2G4cOHY7Va7T/9+vUDYMeOHZcse+DAgRgMBvvriIgIUlJS2Lt3r33ZuHHj2LZtGxkZGUB1d9mhQ4cYO3Zsww/6AqtXr2bMmDF069aNxMREUlNTMRqNnDhxota6f/1WHx8fT1ZWlv313r17CQsLIzk52b5MoVAwbNiwBtevvLycmTNnMnToUDp16kRSUhJPPvkksixz6tQpAI4ePYrRaGTEiBE1th0+fHiN11VVVezYsYMRI0agVCrtvzNZlunTp88luwjNZjOLFi1ixIgRJCcnk5SUxO233w7AyZMna6yr1+vtLSqovtYaExNT6/366zkQGhpK586dL+MdqmnatGkkJSXZfy7sGQDQaDQMHjy4xrIdO3aQkpJCdHS0fZlKpeK6664jLS2N8vLyBtVl5MiRNV6ff9/37NnToP0JjiO6B52Ip6dnjdfnL0p7eXnVWK7RaADs13AKCgqwWCwXHdxRXFx8ybL9/f1rLQsICODYsWP211dffTUBAQEsW7aMp556is8++4ygoKBaHzwN8dNPPzFlyhRuvPFGHn74YXx9fVEoFNx3332YzeZa63t7e9d4rdVqa6yXl5eHn59fncfUUP/5z3/YsmULkydPpkOHDuj1evbv38///vc/++/i/BeJv76ffy23pKQESZJYsGABCxYsqLM8m82GUln398pZs2bxySef8OCDD9K5c2fc3d3Jycnh4YcfrnVt76/nD9T9fl3sHDh79myddTgvJCQEgKysLGJiYuzL//nPfzJmzBgAbr755lrb+fr61up6LCkpoUOHDnXWQ5ZlSkpKLjmgoy5/ff+1Wi1eXl7k5ORc9r4ExxKh1QL4+Pjg5ubGkiVL6vz/oKCgS+6joKCg1rL8/HyCg4PtrzUaDWPGjGHp0qXce++9rF69mrvvvhu1+spPo++++47o6GhmzJhhX2axWGoNLKivwMDAWoM4oPqY/kqr1dYaYPLXoDeZTPz44488/PDD3HXXXfblR48erVUuVL+f7dq1u2i5np6eKJVKbr/9dm644YY6j+FigQXV79cNN9xQ41rZ1q1bL7r+pQQGBl70HLiU8624n3/+mQkTJtiXh4WFERYWdtHtFApFrWXe3t51lpmfn49CobB/Wflr6J5XXFyMj4/PJY/DbDZTWlpa4/wWXIPoHmwB+vfvj8lkory8nE6dOtX6qc8f5saNGzEajfbXZ86cYd++fbVab2PHjqWsrIx//etfmM1mbrnllkY5hqqqqlrfur/++mskSWrQ/lJTU8nKymL//v32ZbIss3bt2lrrhoWF1Qqf86M2zzObzUiSVCugV65cWeN1fHw8BoOBH374ocbyv742GAx069aNw4cPk5SUVOfv7e9UVVXVqsuV3Kyemppa6xzIzs6uV/dZ586d6datGwsXLuT06dMNrgNA9+7d2bdvX43RnZIksXr1ahITE+2trPDwcAoKCmqMoj19+nStrtHzvv/++xqvf/jhB2w22xV1fwqOIVpaLUDPnj257rrrmDx5MhMmTCA5ORmlUsnZs2fZuHEj//73v4mNjf3bfeh0OiZOnMi9996L2Wxm7ty5eHh41PjmDBAcHMyQIUNYt24dgwcPJjQ0tN71PHnyZK0Pb4DevXvTv39/1q9fz/Tp0xk8eDAHDhzgk08+qbNrqz5uvPFG3nnnHR5++GGmTJliHz1YWloK1GzFXHvttSxcuJCFCxeSmprKzp07+fbbb2vsz9PTk9TUVN5//32CgoLw9fVl+fLltbqXvL29GT9+PG+//Tbu7u720YPLly8HarYupk6dyh133ME999zDzTffTGBgIEVFRRw6dAhJkvj3v/990ePr378/X331FfHx8URHR7N27doruj7z4IMPsmbNmhrnwPz58+vsMqzLG2+8wV133cXo0aO57bbb6Nq1KwaDgcLCQjZt2gSAu7v7JfczYcIEVq5cycSJE3nkkUfw8PBg6dKlZGRk8Pbbb9vXGzFiBHPmzOGJJ55gwoQJFBUVsXjx4jpbWQDHjx/nP//5D9dccw0ZGRnMnj2bHj160Lt373odn+A8RGi1EDNnzuTjjz9m+fLlLFq0CK1WS3h4OP369avXdZxRo0ah1+v53//+R1FREZ06dWL27Nl1fgiMGDGCdevWXfYAjDVr1tQ55PzLL7/klltuITs7m+XLl7Ns2TI6derEokWLePjhhy+rjPO0Wi3vvfceL774Is8//zwGg4HrrruO5ORk3njjjRrXD++//35KS0tZsmQJixcvZuDAgcycOdN+Pea8N954g2nTpvHCCy+g0+kYOXIkTz/9NPfff3+N9c4Pu//yyy/5+OOPSUlJ4ZVXXmHcuHE1yk1KSuLLL79k/vz5vPTSS5SVleHn50diYiLjxo372+N75plnkGWZN998E4ABAwbwxhtv1KpzfbVt25bFixfz2muv8eijjxIcHMykSZPs92BdSkhICMuXL+eTTz5h7dq1fPTRR1gsFgICAkhOTmbhwoX1mg4qODiYpUuX8vrrrzNt2jTMZjMdOnTg7bffZsCAAfb1oqOjmTt3Lm+++SYPPfQQMTExTJ06tUawXejpp5+2XzeVJIkhQ4bw9NNP1/8NEpyGQpbFhGDC5Xn88cfZs2cP69ev/9vrLs7o/vvvJz09nfXr1zdruT/88AP/+te/WLJkCd26dWvWsluzbdu2MX78eN5//3369Onj6OoIjUC0tIR627t3L2lpaXz//fdMnTrV6QPr/fffx2AwEB0dTUVFBT/88AM///wz06ZNa9Jy9+3bx88//0xKSgpubm78/vvvvPPOO6SmptK1a9cmLVsQWjoRWkK93XrrrRgMBkaNGsVtt93m6Opcklar5YMPPiA7OxtJkoiNjeWll15qcBdafRkMBnbu3MnSpUspLy/Hz8+PESNG8Pjjj9c5Yk4QhPoT3YOCIAiCy3Du/h1BEARBuIAILUEQBMFliGtaDVRQUI7N1nJ6Vn19DRQVGS+9Yisi3pPaWtt7EhjoeemVhGYlWloCAGr13z9+ojUS70lt4j0RHE2EliAIguAyRGgJgiAILkOEliAIguAyRGgJgiAILkOEliAIguAyRGgJgiAILkOEliAIguAyxM3FgtBEjMYKzp49Q15eLkVFhZSVlWE2m7DZbGg0GvR6A97ePgQHhxAeHomvr6+jqywITk+EliA0kpycc6SlHeTIkTSOHT9KYUF+jf9XKNUoVWpAiSxL2CQzXDBftY+PH0lJHenUKZWUlFTc3HTNfASC4PxEaAlCA8myzIkT6ezatY1du3aSl5cDgEpjQKHzRxuYjMrNG6XWE4XGgEKp/sv2NmSrCZu5DJupmHJjHr9t28bmzb+g0Wjp2rU7gwcPJS4uXjzSRBD+IEJLEC5Tfn4emzf/wqbNv1CQn4dCoURpCMItuAtq9xAUWs96hYxCoUSh0aPU6ME9CPzikWUbUmU+1pLTbN+xna1bNxMT04Zrr72BLl26ifASWj3xPK0GamkT5gYGepKXV+boajiVC98TWZb5/ff9/PjjGvbv3wfIqNyD0XhFo/aMQKHSNnr5ss2CpSQDa9FRJFMZEZFRjLl5HJ06pTR6WfXV2s4TMWGu8xGh1UAitFq+wEBPsrOL2Lp1M6u/X8W57CyUGj1q7zZofNqg1Lg3Sz1k2Ya19BSW/INI5nISEzsxbtydhIdHNEv5F2pt54kILecjQquBRGi1bFarlf37t7Nk6acUFRag0vmg8UtA7RWFQuGYmc5lWcJSdBxL/kGQrVx99UhuuGE0Ol3zDdhobeeJCC3nI0KrgURotUyyLLNr13a++OIz8vJyUOn90fonofIIdZrrSTZrFea8/ViKT+Dj68dd4+8hJaVzs5Td2s4TEVrOR4RWA4nQanlOncpgyZIPOH78KCqdN9qAZFQeYU4TVn9lNeZhPrcTyVRCj569uf22u/D09GrSMlvbeSJCy/mI0GogEVotR2WlkRUrPuenn9ahULmhCeiExicWhcL5J4yRZQlzfhqWgkMY3N0Zf+fddO/eq8nKa23niQgt5yNCq4FEaLUMe/fu5sMP/4+SkiI0vu1wC+zUJCMBm5pUVYzp3HakykK6du3OnXdOxMvLu9HLaW3niQgt5yPu0xJaJaPRyNKlH7Jly6+odD4YYq5Gpfd3dLUaTKXzQR89FHPBYXbv3sXhw2nceedEevRoulaXIDiCaGk1kGhpua6jRw/z9uK3KCoqROvXAW1gksNGBDYFyVSCKXs7UmUBXbt25447JuLt3TitrtZ0noBoaTkj0dISWg2bzcbq1d+wcuUXKDXuGKKvQqUPcHS1Gp3KzRt99FX2Vlda2iHuuGMCPXv2cdpBJYJQX6Kl1UCipeVaysvLWbx4Pr//vh+1VxS6kO4oVBpHV6vJXdjqSknpzJ13TsTPr+HdoC39PPkr0dJyPiK0GkiElus4fTqDuXNnVXcHBndB49O2VbU4ZNmGpfAY5vwDaDVqxowZx6BBV6FUXv7oyJZ8ntRFhJbzEaHVQCK0XMP27Vt59/8WYkODLryvSw+2uFI2czmmczuwVuQQGxvH3XffS0RE1GXto6WeJxcjQsv5iNBqIBFazs1ms/HNNyv45psVqAyB6ML7olSL51PJsoy1NANz7j5kyczw4dfwj3/cVO+poFraeXIpIrScjwitBhKh5bzMZjPv/t9Cdu7Yhto7Fl1otxY1OrAxyFYTptx9WEpO4OPjy2233UXXrt0v2W3aks6T+hCh5XxEaDWQCC3nVFpawpw5r3PyZDpuQSlo/Nq3qutXl0sy5mPK2YlUVUxiYkduu+0uwsLCL7p+SzlP6kuElvMRodVAIrScT3Z2FrNmzaCwqBi30F5ovJr/0R2uSJZtf8we/zvIVoYOHcE//nEjBkPtR6+0hPPkcojQcj4itBpIhJZzOXr0MHPmvI7JYkMX0b9VD7hoqAtnj3f38GT0TbcwYMDgGqMMXf08uVwitJyPCK0GEqHlPLZv38o77y4AlQFdxACUWg9HV8mlSZWFmHL3IBnziIiIYty4O+nQIQlw7fOkIURoOR8RWg0kQsvxZFlmzZrv+PzzpagMgejD+6FQuzm6Wi2CLMtYyzKx5O1HMpeTmtqFMWNuIzk5weXOkyshQsv5iNBqIBFajiVJEkuXfsiGDetRe0aiC+uFQilGCDY22SZhLjyCtTANZIlrrrmGq6++rsmf2+UsRGg5HxFaDSRCy3EqKytZ9PY8Duzfi8avPW5BKWKEYBOrvt71O5aSdNy0blx//SiGDh2BVut6j3G5HCK0nI8IrQYSoeUYhYUFvPnmTM6czcQtuAta33aOrlKrIplKMOfuw1qehY+PHzfffCu9evVt0JRQrkCElvMRodVAIrSaX3r6MebMfQOjsRK3sD6oPUIdXaVWy1qRgzlvH1JlIRERUdxyyziSkpJbXItXhJbzEaHVQCK0mtcvv2zg44/fA5Uet4j+qNwa/6m8wuWpnhLqNJb8A0jmchISOjBmzDjatIlzdNUajQgt5yNCq4FEaDUPi8XMkiUf8ssvG1C7h6AL741CJUYIOhNZlrAUpWMpOITNWkXnzt246aZbCA93/Zu7RWg5HxFaDSRCq+mdO5fNwoVzycw8hda/A9rATigULfPaSUsgS5bqkYZFR7BJVnr27M0NN4wmJMR1u3FFaDkfEVoNJEKr6ciyzK+//szSpR9htYFbSA/UnhefD09wLrLVhLkwDUvRcZAlevXqy3XXjSI0NMzRVbtsIrScjwitBhKh1TQKCwv48MN3OXBgH2r3INxCe6HUGBxdLaEBbNYqzAVpWIvTkW0S3br34Npr/kF0dKyjq1ZvIrScjwitBhKh1bisVis//riGlSu/xGKV0AYmo/Ft1+JGo7VGNmsVlsIjWIuPY5MsdOiQxPDh19KxY7LTD5UXoeV8RGg1kAitxiHLMrt37+SLLz8lN+ccao9Q3IK7ivkDWyBZMmMuSsdafAybxUhQUAhXXXU1ffr0x93dOX/fIrScjwitBhKhdWVsNht79uzk22+/5tSpk6jcvNAGpqL2dL3rHsLlkWUJa2kmlqJjSJUFqNUaunbtRp8+A0hM7IhK5TzTcYnQcj4itBpIhFbDFBcX8dtvm/jpp/UUFOShcvNE49cBtXeMGBnYCklVRViKTyCVncJmNePu4Un3bj3o0qUbCQmJaDQah9ZPhJbzcUhoDRkyBK1Wi5vbn/fbvPXWW0RENOy+joSEBHbv3o27uzuTJk3i2WefJSoqql7rN5QIrfrLz8/jwIF97Ny5ncOHDyHLNlSGQDS+7VB7RoiwEpBtEtaKbKwlp5AqspFtVrRaNzp0SCIxMYmEhA5EREQ1+zUwEVrOR+2ogufOnUt8fHyj7/edd95p9H0Klyc/P4/ff9/PiRPpHDmSRl5eDsAfrar2aLxjULo17izhsixjKTqOtTwLZGuj7vuSZUsWZJsFhVKDQtUILQOFGrVnGBqfuFYzEEWhVKHxjEDjGYFssyJV5GItz+L3w8fYt283AFqtGzExscTExBIREUVoaDihoaF1PmFZaLkcFlp1SUhIYMqUKaxbt47i4mKefPJJhg8fDsCaNWuYPXs2Op2OESNGMHv27DpbS0OGDGHRokXEx8czf/58vv32W9zc3FAoFHz00Ud4eVV/WH788cd1liPA5s2/sGnTxgZvf+JkOhazGVCAUoNC44FCpUVWqJAq85Aq8xqvsn+wWSqRLeWNvt/60Ol0DBs5jLVr11JlLG6UfUoV2ZgLjqDU6Btlf81B490Gjc+VD2dXKKtD+/z1TZulAsmYh1RZQHpmHseOH0O2Sfb19XoD/gEBdOncjVGjbr7i8gXn5rDQmjx5sr17UKVSsWLFCgA8PDxYvnw5u3bt4tFHH2X48OHk5+fz3HPPsWzZMmJiYvjggw8uuf/i4mI++OADNm3ahE6no7y8HJ1OZ///usppKps3/8KST9536uG9CgWc7yi2Wq1YJenvN/gbNpvtj3/JYDMj28zIliuvo7MaNmwYkyZNQpZlVq1a1Wj7lS3lSA4K4oaQjHmYsrc1S1kXtj+rKo2czTxN1pnTrF/3fXVdJAmT2VzntpMmPUjv3v2aoZZCU3C67sFrrrkGgNTUVHJzczGZTOzbt4/ExERiYmIAGD16NK+88srf7t/T05OoqCiefPJJ+vXrx6BBg/Dw+HNYbV3lXHiNrTGdPn2KKpOp1nKlAnx1Kvz0KnzcVCidpidIjZM1wi8pp8JKRoljknHt2rXIssy6desadb8x3hqC3V3r99DULDYoMUmUmiRKTDbM0p/XlWUZjJWVl9xHSUlJU1ZRaGJO9xdxYesLqr/1N4RKpeLzzz9n9+7dbN26lZtuuol3332X9u3bX7ScpgqtIUOGolarKS4uoqiokMKCfAqLCrFarRRUShRUSqiUCvz1aoIMSoIMGkI91IR6aPDQOm/rzJnIsszO7EqOFJqwNPMAGZPVwq9rv8NHrcDN+8qvaWmUChL83OgWqm8117TOk2UZo0WmoNJKQZVEYaVEUVX1T4lZptxU+/PA3WDA29sHL28fvLy88fT0wtPTE3d3jz9+3NHr9ej1BvR6Pb6+fg44MqGxOF1o1SUlJYX//ve/nD59mqioKFauXHnJbcrLyzEajfTo0YMePXqwd+9ejh07Zg+t5hQcHMqYMeNqLJNlmeLiYvLycsjJOce5c9lkZZ3hTOYpfj9VaF/PW6cmwkNFpJeW6D++eStb2QdZfSgUCrqHGegeJqZ8cgU2Waa4SiLPKJFntJJfaSXfKJFfZaPK8mfXtFKpxM/Pj6DIENoGBOLvH0hAQAB+fv74+vrh6+uLRtOyn54s1OQU17QAXnrppYuuGxAQwLRp05g0aRJ6vZ5Bgwah0WjQ6y9+kbq8vJxHHnmEqqoqZFkmMTGRYcOGNeoxXAmFQoGvry++vr7Ex9cMUqPRyOnTGWRknOTEieOkpx/l4IkiAHQaFTFeatr6aGnrq8VP7xLfO4RWSpZlSkw2co1Wcius5Bqt9qCyXtAi9vbyIjQ8nHah4QQHhxISEkJwcAj+/oGo1eIcF/7kMjcXl5eX269JLV++nC+//JJPP/3UYfVp7vu0CgryOXr0MGlphzh0cD+FRdWtsQCDhna+GhL83Yjy0ohWmOAwRouN3AorOX8EVM4fAWWy2uzr+Hj7EBEZSVhYJGFh4fYfZx22Lu7Tcj4uE1oLFy7khx9+QJIkvL29+d///kfbtm0dVh9H3lwsyzI5Oec4cGAf+/fv4cjhQ1glCYNWRXs/LYkBbsR6a1E5z8gOoQWx2mTyjVZyKv74MVrJNdoou+B6k0GvJyIyivDwKCIiIgkPjyAiItJpw+liRGg5H5cJLWfjTDNiVFZW8vvv+9i9ewd79+7GZDJh0KhIDNCSHKgj0kvT6i7oC42j0mLjXIWV7HIL5yqs5Bgl8iqs2P742FCrVISGhRMZGU1EROQfARWFj49PizjnRHm2OMwAACAASURBVGg5HxFaDeRMoXUhi8XMgQP72LZtC3v37sZiseCnV5Mc6EZqsB4fnfNMRio4F5NkI7vMytlyC1llFrIqJIoq/2w9+Xh70zYujpCQcCIiooiIiCIkJNSpJrhtbCK0nI8IrQZy1tC6UGVlJbt372Dz5l84cvgQALE+WrqE6Gnv74ZadB+2aqUmiVMlFk6Xmskss5JTYbHfYO7v509MbFtiYtoQHR1DVFQ0Xl7eDn/uWnMToeV8RGg1kCuE1oXy8/Oqp2f6dQMFhYW4a1WkBrnRLdSAr2h9tQpGi42TxWZOFJs5WWKh8I9WlJublrZt2tE2Lp62beOIiWlrn+7sr0RoCY4mQquBXC20zrPZbBw8eICNG39k795dyDaZOD83eobpaeOjFaMPWxBZlskxWjlaYOJokYWzZWZkGXRubiS0T6R9+0QSEjoQGRld7y4+EVqCo4nQaiBXDa0LFRYWsHHjT2z8eT2lZWX4GzT0DNWRGqxHqxLh5YpsssyZMgtp+SbSCswUV1W3pmKiY0hO6ULHjsnExrZt8HUoEVqCo4nQaqCWEFrnWa1WjhzZx/LlK8jIOIlOo6RrsI6eYQa83ETXobOTZZnsCisHcqs4mG+m1GRFrVKRmNSJLl26k5ycio+Pb6OUJUJLcDQRWg3UkkILqv84c3NLSU8/xpo1q9m9ewdKoFOQG33C3QkSE7c6nRKTxP7cKvbnmsgzWlCpVHRMSqZHz96kpHTBYGj8Ka1EaAmOJj6JBDuFQkFcXDxxcfHk5uawbt33/PLLBvbmFJDg50a/SAORXmKeN0ey2mQOF5jYk1PJiSIzMtAuLp6RffrTrVvPGk8yEISWSLS0GqgltrTq+gZdVlbKjz+u5cf1P1BhNBLro6V/pDux3uKG5eaUW2Fl97lK9ueZMFokfH196ddvEH369Cc4OKTZ6iFaWoKjidBqoNYSWudVVVWxceOP/PD9t5SUlhDhpWFApDvtfLUivJqIWZI5mFfFrpwqzpSaUalUdO7cjQEDBpOY2NEhDxUVoSU4mgitBmptoXWexWJm06ZfWP3dVxQUFhLmoWFAlIEEPzcRXo0kq9zC7uxKDuSbMFlthASHMGDgVfTp0/+i9081FxFagqOJ0Gqg1hpa51mtVn77bRPfrlpBXn4+oR4aBkQaaO8vwqshjBYbB/Kq2JNTxblyCxq1mm7dezFw4BDatUtwmvdUhJbgaCK0Gqi1h9Z5kiSxdetmvvlmOXl5eYR4aBgYaSDB303cqHwJkk0mvdjM3pxKjhSakWwyUZFRDBh4FT179sbd3fkGVYjQEhxNhFYDidCq6Xx4rVq1gtzcXILdq1teHQJEeF3IJsucKbXwe14VBwvMVJgl3N3d6d27P337DiA6OsbRVfxbIrQERxOh1UAitOomSRLbt//Gqm9WcC7nHIEGDf0i9XQM1KFqpeF1PqgOFVTPUlFSZUWtVpOS0oU+ffrTqVOKyzydV4SW4GgitBpIhNbfs9ls7NixlW9XreRs1ll89Wr6hOtJDdajaQWzy5usNk4UmzlaWD3vX4VZQq1SkZSUTPcevejcuSt6fePf/NvURGgJjiZCq4FEaNWPzWZj795dfPft15zMOIGHVkX3UB3dQw0YNM0/ZLupSLLMuXIr6UXVs6hnllqQZBmdTkdycmdSU7uSkpLqkkF1IRFagqOJ0GogEVqXR5ZlDh8+xPerv+H3gwfQqJR0CnSjR6ieEA9Nk5XbVKw2maxyC6dLLJwqNXO61IrJagMgMiKSjp1S6dQphbi4eJfp+qsPEVqCo4nQaiARWg139uwZ1q5dzdatm7FYLER4aekc7EZSgA6d2vlaX7IsU2KycbbMwpk/frLLrVj/+P2HBAfTvkNHEhI60KFDEl5e3g6ucdMRoSU4mgitBhKhdeUqKsrZvPkXNv78I9nnstGolLTz1dAxUEecr5tDHo8iyzKlZhvZ5dXBdP6x8xVmCQC1SkVMTCxt4xKIi2tHu3YJLTqk/kqEluBoIrQaSIRW45FlmRMn0tmy5Rd27thKWXk5aqWCaG8Ncb5aor21hLirG33ofJXVRr5RItdoJafCSq7RyrkKiUpLdUApFApCQ0KJbRNHTEwb2rSJIzIyqkV1910uEVqCo4nQaiARWk1DkiSOHj3Mvn272bd3Nzm5OQBoVUqC3dWEuKvw16vx1anwclNi0CjRq5WoldhDTZZlzJKMSZIxWm1UmG2UmW2UmCSKqyQKqyQKq2TKTFZ7uVqNhrDwCKKiYoiMjCY6OobOnZMoK7M45H1wVs5ynjQXEVrOR4RWA4nQah6FhQUcPXqE9PRjnD51kszMU1SZTHWuq1QokGWZv/uteHl6EhQUQnBIKCEhoYSFhRMWFk5gYHCtCWid9T1xpNb2nojQcj6tt59DcAl+fv706tWHXr36ANWtqLKyUnJzcygpKaasrAyjsQKLxYLVakGhUKJUKtHpdOh0etzd3fHy8sbb2wc/P3+0WvE8MEFwZSK0BJeiUCjw8vJuVYMfBEH4k/ONLxYEQRCEixChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMEVqCIAiCyxChJQiCILgMtaMrIAiuorCwgMzM0xQW5mOzyRgMBkJCQomMjEatFn9KgtAcxF+aIPyNiopyNm78iS1bNpGVdabOddzcdHTsmMzAgUNITOyIUik6MAShqShkWZYdXQlXVFBQjs3Wct66wEBP8vLKHF0Np2GxmPn11/V8/vkXmM0mVIZA1B7hqPQBKDTuoFAgSyZsplKkinNI5WexWasIC4tg9OhbSE3tikKhcPRhNLrWdp4EBno6ugrCX4jQaiARWi3XyZPpLFo0n7y8HNSeEWgDklDpfP92G9kmYS3LxFJwCMlUSocOHRk//m6Cg0ObqdbNo7WdJyK0nI8IrQYSodXyyLLMunXf8/nnS1Go9WiDu6H2uLzQkWUblqJ0LPn7USpgzJixXHXV8BbTZdjazhMRWs5HhFYDidBqWaxWKx9++H9s3rwRtUc4urCeKFTaBu/PZqnEdG4H1vIsEhM7cd99D+Ll5d2INXaM1naeiNByPi3j658gXIGqqirefPM1Nm/eiDYgCV1EvysKLAClRo8uoj9uId1IO3yIZ5+dypEjaY1UY0FovURoCa2a0VjBzJkvc+jQQXShPXAL7NRoAygUCgVa3zj00VdjNNt47bWXWbPmO0TnhiA0nAgtodWqqChn5szpZGScRBfeF41PmyYpR6XzQR89DJVHGMuWLeHtt+djMlU1SVmC0NKJ0BJapcpKI6+/PoPTp09VB5ZXRJOWp1Bp0IX3RRuYzPbtv/HSS8+Tm5vTpGUKQkskQktodUwmE7PfnMmp0xnowvui9gxvlnIVCgVuAYnoIweSnZPLCy88zf79e5ulbEFoKURoCa2K1Wrlrbdmc/zYUXRhvZotsC6k9ghFH301ZtmNN9+cyVdffYnNZmv2egiCKxKhJbQaNpuNd95ZwO+/78ctpBsaryiH1UWp9UAffRVq72i++WYFb7wxg9LSEofVRxBchQgtoVWQZZklSz5gx46tuAWloPVt6+gqoVCq0YX2xC2kO4ePpPHMs09x8OABR1dLEJyaCC2hVfj66+Vs2LAejV97tP4dHF0du+ph8W3RR19NpUXBG2+8wtKlH2E2mx1dNUFwSvUOLVmW+fzzzxk/fjzXX389ADt27GD16tVNVjlBaAzr16/hm29WoPaOxS0oxdHVqVP1sPir0fi2Y/36H3ju+akcP37U0dUSBKdT79CaM2cOX375JbfeeivZ2dkAhISE8O677zZZ5QThSm3dupmlSz+qnpoptLtTz7yuUKrRhXRFHzmIgqIypk9/gU8+eR+j0ejoqgmC06h3aK1cuZJFixZx7bXX2v/wIyIiyMzMbLLKCcKV2L9/D+++uxCVIRBdeG8UCtfoDVd7hKCPGYHGN46fflrP1P88xqZNG8UIQ0HgMkJLkiTc3d0B7KFVUVGBwWBompoJwhU4fPgQ8+e/icLNB31EfxRK13reqUKlQRfSFUPM1VRaNbz33tu8+OKzYv5CodWrd2gNHDiQV155xX6BWJZl5syZw+DBg5uscoLQEOnpx3nzzZnIagO6iIEoVBpHV6nBVHo/9NFD0YX15ExWDq+++iKzZr3KqVMZjq6aIDhEvR9NUl5ezlNPPcUvv/yC1WrFzc2Nvn378uqrr+Lh4dHU9XQ64tEkzunkyRPMnPkyFpsKXdRVKDV6R1ep0cg2K+aiY1gL07BZzXTu3I1//OMmoqNjmq0OLeU8qS/xaBLnc9nP08rPzycrK4vQ0FACAwObql5OT4SW88nIOMHMmdMxS0p0UYNRatwdXaUmIUtmzIVHsRYdxSaZ6dQpheuuG0W7dglNXnZLOE8uhwgt51Pv0LrYReCW8kTWyyVCy7mcPJnOzNdfwSIp0UUOQqlt+a1/WTJXt7yKjmKzmoiLi2fkyOtJSencZH+Xrn6eXC4RWs6n3qHVvn37OocLq1QqgoKCGDZsGI888oh9sEZLJ0LLeRw5ksabb87EKqtbdAvrYmSbFUvxCaxFR5DMFQQFhzBi+LX06dMfrfbKHmb5V658njSECC3nU+/QWrJkCevXr+e+++4jJCSE7Oxs3n33XQYOHEhsbCxvvfUWcXFxvPzyy01dZ6cgQss57N27iwUL5iKrDOgiB6LUtN7RrLJsw1p6GkvRUaTKQtzdPRg8eCiDB1+Nr69vo5ThqudJQ4nQcj71Dq2hQ4eycuVKPD3//CWWlpZy0003sX79enJycrjpppvYvHlzk1XWmYjQcrxfftnAhx++i1Lniy5iIEq1m6Or5BRkWUYy5mEpPIK1/CxKpZLu3XsxdOhw2rSJu6IbrF3xPLkSIrScT71vXikvL6eysrJGaFVWVlJWVn0CBwQEUFUlnsYqND2bzcby5cv4/vtVqN1D0UX0QaF03WHtjU2hUKB2D0LtHoTNXIa56Bg7du5g27YtREXFcNVVw+jRozdubiLkBddT79AaNWoUEydOZPz48YSEhJCTk8NHH33EjTfeCMCmTZuIjY1tsooKAlR/UXr33YXs2bMTjU9b3EK6usxMF46g1HqiC+6CHNAJS0kGZ3OP8/77i/n0s0/o17c/AwYMISIi0tHVFIR6u6zRg8uWLeOHH34gNzeXwMBARo4cyS233IJKpcJkMiHLMjqdrqnr7BRE92Dzy87OYv782WSfy8ItKBWNb7xTzyXojOxdh8XHkcrOIMs2YmLa0L//IHr06IW7+9+PunSF86Qxie5B53PZ92kJ1URoNa+tW7fwwQfvYLUpcAvrhdo9xNFVcnk2qwlrSQbW0pNIVcWoVGqSU1Lp1bMPycmd6+w+dPbzpLGJ0HI+lxVa+fn57N+/n6KiIi7c7Oabb26SyjkzEVrNo7LSyCeffMBvv22qnvg2rHerHiHYFGRZxlZVhKUkA6k8E5ulEo1GS3JyCl26dKdTp1T7rDfOep40FVcJLavVilrtWvNrNlS9Q2v9+vU88cQTREdHc/z4ceLi4jh27BhdunTh448/bup6Oh0RWk3v99/38d5771BcUoTWPwltQKK4ftXEZNmGZMzDWpqJVHEWm6UShUJBmzbtSE5OoW/fnnh7B6NSqRxd1Wbh6NA6ePAgTz/9NKdOnaJ///4olUqio6Pp06cPTzzxBHfccQcffvghffr04eWXX2bmzJl8//33AIwcOZInnngCrVbLihUr+OKLL/j000/t+05ISGDt2rVER0czdepUtFotmZmZ7N27l6SkJF599VXCw8MddegXVe9ofvPNN5k+fTojR46ke/fufPXVVyxfvpzjx483Zf2EVqikpJhPP/uY7dt+Q6XzxhA9FJXe39HVahUUCiVq92DU7sHIcldsVYVYy7PIyMomPf0LVq78Ajc3HQkJ7UlI6EB8fHuio2Nbzbf85mQ2m3n44YeZMGECt912Gxs2bOCxxx7jnnvuAap7vkpKStiwYQM2m42FCxeyb98+vv76axQKBQ8++CALFizg0UcfrVd5q1atYvHixaSkpPDaa6/x73//u0bIOYt6n2lZWVmMHDmyxrIbb7yRvn378tRTTzV6xYTWx2w2s27d96xa9RUWiwVtQBJa/0QUytbxrd7ZKBQKVHr/6i8MgZ2wWauQjLlIFTkcPHKC/fv3AqBWa4ht05Z2cfG0bduOtm3j8PLydnDtXd++ffuwWq2MHz8ehULBsGHD6NSpk/3/lUolkydPts96smrVKp599ln8/au/4D300EM8//zz9Q6tQYMG0b17dwCmTJlCt27dyM7OJjQ0tJGP7MrUO7T8/f3Jz88nICCA8PBw9uzZg6+vr3gwnXDFLBYLmzZt5OtvVlBaUozaIxxDZCpKrWtcT2gtlGodSq8oNF5RANislUjGfKTKPE5k5nLs2FGQqz8P/AMCa4RYRESUaI1dptzcXIKDg2uMkL0wQHx9fWsMlsnNzSUsLMz+OiwsjNzc3HqXFxLy5+Amd3d3vL29yc3Ndd3QGjNmDLt27WL48OFMmDCB8ePHo1Qqufvuu5uyfkILVl5ezq+/bmDN2u8pLSlGZQhEHzUEtXuQo6sm1INSrUfpFYnGq/o+L9lmRaoqwlaZT0llATt27Wbr1uoZcjQaLbGxbWjXLp62beOJi2uHh4f4UvJ3AgMDycnJQZZle3BlZ2cTGVn9fv/1do+goCCysrJo166dfd2goOq/Jb1eX2Pyh7y8vFrlnTt3zv7viooKSkpK7Ns7k3qH1n333Wf/96hRo+jRoweVlZW0bdu2SSomtEw2m41jx47w668/s33HVqwWCyr3YPRRg1AZgsV9Vy5MoVSjNgSCofqRRbIsI1uNf7TGCkjPzOPoBa2xoOCQC1pj7QgPj2i1T42oS2pqKiqVik8++YRx48bx888/c+DAAXr06FHn+tdeey0LFy60dyG+9dZbXH/99UD1hOfHjh0jLS2NNm3aMG/evFrbb9y4kZ07d5KcnMycOXNISUlxulYWXEZo/dWFzdArMWTIELRarb2Z27NnT/773/82yr4vJiEhgd27d7eaGekdzWIxc/ToEfbu3c3OndspKSlCqdKg8ozC4NsOlc7H0VUUmoBCoUChcUfp7Y7GOxo43xorRDIWUFCRR/627Wze/AsAWq0bsbFtiI1tS0xMG2JiYgkMDGq1X2S0Wi3z5s3jmWeeYdasWfTv359BgwZddOb+Bx98kIqKCv7xj38AMGLECB588EEAYmNjeeihh5gwYQI6nY7HHnuMZcuW1dj+uuuu46233mLv3r0kJiYyc+bMpj3ABqr3kPeBAwde9OT5+eefG1yBIUOGsGjRIuLj4+v8f0mSGn14bWOElhjyXjdZlikqKuT06QxOnEjn2PGjpB8/htVqQaFUoTKEoPaKRO0ZgUIprnG0drIsI1vKkSrzkSoLsVUVYKsqRv6jNebmpiMyMoqIiEjCwyMICQkjJCQUX1+/ZmmVOXrI+1+NGTOGsWPHMnr06Ebd79SpUwkODmbKlCmNut+mUO9Pjb+mbl5eHh999BHXXHNNo1ZoxYoVfPPNN7i7u3Pq1ClmzpyJ2Wzm9ddfp6KiAoDJkyczaNAgzpw5w+jRoxk7diwbN26ksrKSl19+mW7dugGwYcMG5s2bh9VqRalUMmPGDNq3bw/Axx9/zLp16yguLubJJ59k+PDhjXocrcmZM6dZvfobzp07x7lz2VRVVVb/h0KBys0HpVcsekMIKvegRg0qWZaxFB3HWp4FsrXR9nvR8iQLss2CQqlBoWqmCXoVatSeYWh8rmx2dmelUChQaD1Raj3ReFfPXSrbJGymkurrY6YiTmYVcSIjA5vVbN9OpVLj5+dPQEAAfn7++Pj44uXljZeXF4mJHfH09HLUITWq7du3Exsbi6+vL6tWreLIkSP079/f0dVyqHp/gtTVj9qjRw/uvfde7rrrriuqxOTJk+3dg+PGjbPfaxAVFUVpaSnjx49n8eLFBAUFkZuby80338y3334LQHFxMampqUyZMoVvvvmG119/nc8++4yTJ0/yzDPPsGTJEmJiYjCbzZjNf570Hh4eLF++nF27dvHoo482WWgdPnyIb7/9CmefLUujUWGxSDWWFRcXU1JSdMltzWYzVmsdoSHLSFXFSFXFWAqPNVZVLyygCfZ5cTqdjmEjh7F27VqqjMXNVq5UkY3p3C7AhUJLoaz+adR9qkCuPkclyUpeXg55eTm1Vhs06CrGj7+ncct2kJMnT/Loo49SWVlJREQEc+fOdcrBEc3pir72arVazpw5c8WVmDt3rr17cMWKFXTp0oWoqOphtXv27OHMmTNMmjTJvr5CoeDUqVP4+vpiMBgYPHgwUH3h8tVXXwVgy5YtDBgwgJiYGHtdL+wLPt9CTE1NJTc3F5PJ1CSPati3bw+HDv1e5//56FR4apVO+1FkMtuQLJe+pUEFqFTNfxQWGzRnD+2wYcOYNGkSsiyzatWq5isYUCpA40JjFNw14H4FD022yVBqkigzX/r8O39zc0VFOVarlaSkTpfcxlXceuut3HrrrU1ezowZM5q8jMZS79CaM2dOjddVVVVs3LiRAQMGNHqlLrzWJMsyCQkJLFmypNZ6Z86cqRFESqWy7m/8dTgfUOevl1mt1iYJra5du5OVdZbi4kKKiwopKy+3/19xlYTRIuOvVxFoUBHsribYXU2YhwaDK31COciOLCPfpTff1FNr165FlmXWrVvXbGWeN7KtJ91DW96ci1abTJ7RSm6FlVyjlTyjlYIqmaJKK7a/9E54uLvj7eODt3d1V6CHhweBgcEMHTq8RXadCnWrd2hdOIYfqsf933333dxwww2NXqkLde7cmVOnTrF161Z69eoFwP79+2vcGV6Xvn37smDBAjIyMmp0D56f+LO5xMXFM2XKk/bXFouZwsJC8vJyyc3N4dy5bM6dO0vmmUz25/7Z5eSrVxPpqSbSS0OstxZ/vUr8Yf5Ft1A9AEcKTViaocllslr4de13+KgVuHk3zzUtjVJBgp8b3UL0zVJeU5JlmYJKicwyC2dKLWSVV4eV9Ec4qVUqgoKCiYmNoFdICIGBwQQEBOLvH4Cfnx8azRU03YQWo96h9corrzRlPS7K29ubBQsWMHPmTKZPn47FYiEyMpJFixb97XYxMTG8+OKLTJkyxT4CccaMGSQkJDRTzeum0WgJDg4hOLj2ozXKy8vJzDxFRsYJ0tOPc/zYYfbnVrckPN3UtPVRE+frRltfLXq1aIkpFAq6hxnoHtbyWiAtgSzL5BklTpaYySg2c7rMSoW5+pqUXqcjJjae1Jg2REVFExERRXBwiJg1Q7ikeg95/+233+pcrtVqCQkJccrZgJtScwx5l2WZ3NxzpKUdIi3tdw7+vh9jZSVKhYIYbw0dAtzo4K/DQysCTHAOlVYb6UVmjheZSC+2Umaq7q739/enffsk2rVLoG3bdoSGhrnEjcTONuRduIzQGjJkiH0eKx8fH4qLq7uyzs9JmJCQwKxZs+wDH1o6R9ynJUkSJ04cZ+/e3ezatY3c3FwUCoj11pISrKODvw6tAwZECK1bQaWVIwUmjhSaySw1Y5PBoDeQ1LETSUnJdOiQRGCga454E6HlfOodWgsWLKCsrIx//etf6HQ6qqqqmDdvHh4eHtx1113MmDGDzMxM3n///aaus1Nw9M3Fsixz5kwm27f/xtatmygoKECrUtIx0I0uwXrCPdXiGpjQJGRZ5lyFlbR8E4cLzeRWWAAID48gNbUrKSmdadMmziVaUpdSV2hNffo58gsa/5aHAH8fZrz8v0uuN2/ePO6///6LzozREOvXrycoKIjk5ORG22dTlVnv0OrVqxebNm2q0edssVjo378/W7duxWg0MnDgQHbs2HF5NXdRjg6tC9lsNo4fP8qvv25kx/YtmC0WQjw0dAvR0SlIh5vK9T88BMeyyTJnyiyk5ZtIKzBTXGVFoVAQH9+eLl2607lzVwICAh1dzUZXV2jd+8/JGL16NnpZhtJtvLto7iXXu9iMPlfy9OKpU6fSsWNH7rjjjgZt35xl1vsIDQYDBw4coHPnzvZlBw8eRK+vHtXUEr5VuSqlUkl8fHvi49tz223j2bZtCxt+Wsu3xzNZn1FB52AdPcIM+OrEc6mE+pNkmVMlFtLyqzhcaKHMZEWlUpGY2JEbu/UkNbVLi5l5wlW88MILAIwdOxalUkl4eDi+vr6cPHmSiooKvv76a1auXMnSpUuRJAkPDw+mTZtGmzZtOHLkCC+88AKVlZWYTCZuueUWJkyYwK+//spPP/3Eli1b+OKLL7j77rsJDQ3l5ZdfJjk5mX379qFWq3nttdeYP38+x44dIzQ0lHnz5mEwGDCbzcyePZsdO3ZgNptJSEhg2rRpuLu725+InJGRwblz5+z30m7atKlWmaNGjarXe1Dv0Jo8eTITJ05kyJAhhIaGcu7cOTZs2MCzzz4LVA/UEFMhOZ5er2fQoKsYOHAI6enHWL9+Ddt2bmNrlpH2fm70iTAQ6SWGDgt1M0nVAymOFJg4WmSh0iKh0Wjo1KkzXbt256qrBmA0imfoOcrzzz/P0qVL+eyzz+yhkJaWxieffILBYGDnzp18//33LFmyBK1Wy8aNG/nvf//LZ599Rnh4OB988AFarZaKigrGjBlD//796d+/P0OGDKnR6tm2bRvp6em8+uqrvPTSS7zwwgvcc889fP7554SEhDBp0iS+++47xowZw7vvzAW/BQAAIABJREFUvounpydffvklUD3l3+LFi+3zGB47dowPPvgAhULBjTfeyJYtW+oss77qHVqjRo2iY8eOrFmzhtzcXGJiYli2bBlxcXEADB482D4zheB4CoWCuLh44uLiKSy8jZ9+WsfPG9aRtq+ISC8tfcL1JPi7oRTXvVo1WZbJr5RILzJzrMhERokFySZj0BtI7dabLl2607Fjsv3Ge3d3d4zG5ruhW7i0ESNGYDBU3/bx008/cfjwYcaMGQNU/35LS0uB6gkhpk2bxpEjR1AoFOTm5nL48OGLPl4qNjaWDh06AJCYmEhWVpb9QZFJSUmcOnXKXmZ5eTlr1qwBqqd1Oz/HK8DQoUPt509iYiKnT5+mb9++DT7ey+oAjYuLs4eU4Dr8/Py5+eaxXH/9KH79dSNr1nzLsrQCAgxqeofrSQnSo1aK8GoNZFmmqEridKmFk8VmMkqtlFRVD0sPCQ7hqqFdSE3tQrt2CY3+dAWhaZwPLKj+/Y4ePZp//etftdabNWsWgYGBzJgxA7VazcSJEzGZTBfd74UDPVQqVY0Zg1QqlX1bWZZ5/vnn6d27d537+et2kiTVuV591Tu0iouLee+990hLS8NoNNb4v7qmWBKcj5ubjqFDhzN48FB27drO6u++ZtWx02w4baRXqJ6uoXpx03ILYpNliqskco1WzpVbySq3kFUuUf7HDb7u7u60T6oelp6U1Mllh6W3Nu7u7pSXl9f5aKUhQ4bw1FNPceuttxISEoIkSaSlpdGxY0fKyspISEhArVZz9P/bu/PwqMr77+Pvc2af7CtZICEJssoeZBGKxoKIAhVptVb8+Wtr+1Stj9tVW/SnlZ8rj14qqK3WKlVRXNmrRgERDSAgIAiKgUDCkmWyJzOZ7Zznj2AqIhKyzZzk+7queIWZM+d853hPPnPOuc9979/Ptm3buOyyy4DmAcTr69t2BJ2Xl8fixYsZOXIkdrudhoYGysrKzjhBcFu32erQuv322/H5fFxyySUtnS+EMZlMJs47bzxjxoxj7949vPvuKj7cu4eNR9yM6mVnbJqTWOm00Sq+oI7br+EOaDQFdLwBDb+mE9Cax9XTdNC/Mxq9ioKqgElVMClgVpUTPyf/blKUlmUUpfl1+ok1BU+s26/peIM6TQENt1+n0a9R5w1S6w1S7W0evy9wooerokCv5BSGju5Hv379Oeec/qSlyUzBbZGYEIurckunrLc1fv3rX3Pttddit9tPGdRhzJgx3HLLLfzhD38gGAzi9/uZNm0a5557Ln/4wx/405/+xFtvvUVWVhZjxoxped3MmTP5y1/+wnvvvdfSEaO1fve73/HUU08xZ86c5qlmFIWbbrrpjKH1/W22tiNGq7u8jxo1is2bN3fovQFGFk5d3jtCXV05r732Blu3bgJdZ3CinfHpTtKjumjeqDDlD+pUegK4PEGqPEGqmwLUejXqfDr1viDeQHh1SnA6HMTFx5OcnEJycgopKaknJlDsg91ub/f6O2qyUKOQm4vDT6uPtAYMGEBpaWnLlCGie8nJyeH3v7+JK664kg8/fJ+PN6xlT0UVvaOtjE21MyjR3q2ve+m6To1Xo7TBT2ljgLLGAOUejWq3/6RZu6KjokhISCIzIZG4uDhiYuKIiooiMjKSiIhI7HYHNpsNq9WKyWTGZDKhqgrNc2HpaJpGMBhs+RYcCATw+5sHc/b7/fj9vhOP+QkGgwQCAXRdQ9M0FEVFVZt/rFYrFosVh8OBw+EkKiqKqKjoTpmpQIhw0uojrSeffJI1a9Ywe/ZsEhMTT3puzpw5nVJcOOtuR1rf/wbt8bj55JOPWfvhe5RXlBNhNTEy2cbIFAcJDuMPalrvC3K03s/R+gDH6v0cbwziPjEJpqIo9EruRU6/HBISepGWlk5KShrJyb065GjFyORIS4Raq0Nr7ty5P7wCReGll17q0KKMoLuH1rc0TePLL3ezbl0+X3yxE13XyYyxMjzZzqBEmyE6bgQ0ndKGAEfq/Ryp93GkPkjNiR5zqqqSnpZOVnY/MjOzyMzsS+/eGVit1h73B7o1eto+kdAKP60OLXGynhJa31VdXcWnn37MJ598RHl5OSZVISfWwqBEO/3jbESEwWjzuq5T1dR8FHXkxJFUaWOA4In/V3FxcWRnn0NOzjlkZ+eQmZl12lNqPe0PdGv0tH0ioRV+2hRauq7z3Zf1xB5IPTG0vqXrOocOHWTz5gK2b9tCVXUVCpAWZSE71krfGAu9oy2dPuZhUGu+Mbassfk61PGGAMcbgzSdOM1ntVjom5V9IqT6kZ3dj7i4+Favv6f9gW6NnrZPJLTCT6tDq6ysjPnz57Nt27aWO6y/tW/fvk4pLpz15ND6ruYAK2L37p188cUODh06iKbpKAokOS2kRphIjjCT6DATZzcRY1fPKsy8QY0Gn0ZNU5Bar0aVJ0ClJ4irSaPS/Z8p2c0mE7179yGzbzZZWTn07ZtNenrvdt0g29P+QLdGT9snElrhp9VX1O+9917sdjuLFy/mmmuuYcmSJSxatIjJkyd3Zn0izCmKQlZWNllZ2cycORuPx0Nh4X4OHPiGoqIDFB0uYlf5yV9yrCYVp9WE3cSJe5JO9K3TIaCDX4OmoI7Hr+EPntylXFVVkhKTSOuXzpj03qSn96ZPnwxSUtJk1lvRJebf/Rdqq1wdvt6Y+ETuub/rZoivq6vj9ddf5/rrr2/T60MxMjycRWjt2LGD9evX43Q6URSFgQMH8sADD3DVVVfxi1/8ojNrFAbicDgYOnQ4Q4cOb3msoaGB0tJjuFwuKitd1NfXUl9fT1NT82jTzd26dVRVxWKxYrVacTojcDqdxMTEEBMTS3x8AgkJicTHJ8jwQiKkaqtcXN2348+yvHqo44Pwx9TV1fH888+fNrTaM9VJZ2p1RaqqtryB6OhoqqqqiIyMpKysrNOKE91DZGRky+C9Qoj22bFjBwsWLKCxsRGAP/3pT0RHR/PAAw/gdrtxOp3cddddDBs2jCNHjnDFFVdw1VVXsWHDBjweDw888AC5ubnMnz+f+vp6Zs2ahcPhYOnSpcydO5eBAweya9cuYmJi+Pvf/86jjz7Kxo0bAZg0aRJ33HFHSL84tjq0hg8fzoYNG5gyZQoTJ07klltuwW63M3To0M6sTwghxAk1NTXcdNNNLFq0iFGjRhEMBqmurmbOnDk89NBDjB8/noKCAm6++Wby8/NbXjNixAhuvfVWVq5cyaOPPsrSpUu55557uOKKK1ixYsVJ2ygpKeHVV1/FbDbz6quvsm/fPt555x0Arr/+el5//XWuvvrqLn/v32r1FfEFCxa0jFU1b948xo0bR3Z2Nn369Om04oQQQvzHzp07ycnJYdSoUUDzOKKVlZVYLJaWUdYnTJiAxWKhqKgIaB4F/ttpo0aMGEFJScmPbmPGjBktZ9U2bdrE5ZdfjtXafNp+9uzZbNq0qbPeXqu0+kgrOvo/M5Ta7XZuuOEGvF4vI0aM4IEHHuiU4oQQQrTPd8eLVVWVQCDwo8t/d6qTcNSuG2kURUHuTRZCiK4xYsQIDhw4wI4dOwAIBoMkJCTg9/vZvHkz0Hx0FAgEyMrK+tF1RUZG0tTU9KMhNn78eJYvX35iXEw/y5cvZ8KECR33htqg3V1DFJn5VgghukRsbCyLFi3i4Ycfxu12o6oqd955JwsXLjypI8aTTz55xhk5YmNjmTFjBjNmzCAmJoalS5eessyVV15JcXExl19+OQATJ04MeW/xM95c/GPnL/1+P7///e/l5uJuoKfdNNoask9O1dP2yQ/dXNxd7tMyqjMead11110/+vzZTBYmhBBGJ8ESWmcMrXXr1nVFHUIIIcQZ9byRboUQQhiWhJYQQgjDkNASQghhGBJaQgghDENCSwghhGFIaAkhhDAMCS0hhBCGIaElhBDCMCS0hBBCGIaElhBCCMOQ0BJCCGEYElpCCCEMQ0JLCCGEYUhoCSGEMAwJLSGEEIYhoSWEEMIwJLSEEEIYhoSWEEIIw5DQEkIIYRgSWkIIIQxDQksIIYRhSGgJIYQwDAktIYQQhiGhJYQQwjAktIQQQhiGhJYQQgjDMIe6ACG6M03TqKgop76+DovFSlpaGhaLNdRlCWFYElpCdIJjx47y4YfvsWXLJjwed8vjqqoyZMhQfvrTaZx77jAURQlhlUIYj4SWEB3I43Hz5puvsWHDOlBMmCLTscUmo5qd6FqAoKeSvV99w+7duzj33GFcd931xMcnhLpsIQxD0XVdD3URRlRZ2YCmdZ9dl5QURUVFfajLCCtnu0+Kig7w9NNPUlVViSXuHKyJQ1DNtlOW03UNf/U3+Cp243Q6uPGG/8vAgYM7svRO09PaSVJSVKhLEN8jHTGE6ACbNxfw4EP3UdvgwZl5EfaUUT8YWACKomKNH4Cj71Sa/CqPPfYwO3Zs6+KKhTAmCS0h2ik//98899xTYI3HnjkVkzOxVa8z2aJxZOaBNYannnqCXbt2dHKlQhifhJYQ7bBq1TKWLn0Fc1RvHH0mn/bo6nQUkw1HnwtQ7bE8/fQTFBbu76RKhegeJLSEaKNVq5azbNmbmKMzsadPQFFNbVqPYrJg7/0TdJOdhQsfw+Wq6OBKheg+JLSEaIP33lvDsmVvNAdW2lgUpX0fJdVsx5Y+CbfHy8JFj+Hz+TqoUiG6FwktIc7SRx+t5Y03lmCO6tMhgfUtky0aW9o4jpQU88orL3bIOoXobiS0hDgLmzcX8NJLL2COTMOePq7DAutb5sg0rAmD+eSTDRQUbOzQdQvRHUhoCdFKO3du5x//eAaTM6n5GpbStmtYZ2JNOheTM4mXXnqBsrLSTtmGEEYloSVEK+zb9yVPP/Mkqj0WR+9JKGrnDSajKCr2tHEENHj22acIBAKdti0hjEZCS4gzKCzczxNPPgrmSBy9J6OYLJ2+TdUSgbXXaA4dOsjq1cs7fXtCGIWElhA/4uDBQh577GE0xYa9z2SUs7wPqz0s0RmYY/qyatVyDhwo7LLtChHOJLSEOI39+/fz6KMPEdDN2PtcgGp2dHkN9l6jUC0Onnvuabzepi7fvhDhRkJLiB/wzTdfc9ddd+HXTNgzLkS1OENSh2KyYk05j4qKMt5887WQ1CBEOJHQEuJ7vvxyN48++hB+3YI9Iw/VEhHSeswRvbDED2Ddug/Ys+eLkNYiRKhJaAnxHVu3buaJJxagmZwnAis0R1jfZ0saiskWw/P//DsNDQ2hLkeIkJHQEuKEDz54j7/9bRHY4nFk5IXkGtbpKKoZW+pY6urqeOmlfyLT4ImeSkJL9HiaprFkyb947bWXMEem4egzGcVkDXVZpzA54rEmnsu2bVtktAzRY3XeHZJCGIDb7ebvf1/Enj27sMT3x5Y8osOHZupI1oSBBBtLefnlF+nXrz+9eqWEuiQhulT4fjqF6GTHjx/jf//3f9jz5W5sKbnYe40K68CCb0fLGEtA03nmbwvx+/2hLkmILhXen1AhOsnOnduZP/9uKiqrcfSZjDWuX6hLajXVEoEtZSwlxYd4/fUloS5HiC4lpwdFj6JpGsuXv8Xq1csxOeJx9Dk/5F3a28IclX6iG3w+/fr1Z9y4CaEuSYguIaEleoy6ulqeffZp9u3bgzkmC3vK6E4d+Laz2ZKHozVV8eKLz5Ge3ps+fTJCXZIQnU5OD4oe4euv93HPvX/hq6/3YUsdgyNtrKEDC769vjUBDTNPPPn/qKurC3VJQnQ6CS3RrWmaxsqV77Bgwf00NgVxZP4Ua2xOqMvqMKrFgS19IjU1NSxc+Cg+ny/UJQnRqSS0RLdVVVXJggX3s3z5W5iiMnBkTsVkjwt1WR3O5IjHljqOgwcLefbZpwgGg6EuSYhOI6EluqUtWwr4n/+5k8IDB7CnjsWRPr5L5sEKFUt0H2y9RrFjxzZefPE5NE0LdUlCdApjn9QX4nvq6up4ZcmLbNu6BZMjAUffC1GtUaEuq0tY4/ujB30UFGzEZDLxX//1W1RVvpeK7kVCS3QLuq6zadMnvPrqS3g8HqxJQ7EmDAr7m4U7mjVxCOgaGzd+hM/n4ze/+T+YzfIxF92HtGZheMXFh1iy5F98883XmJ2JOPpOwmSPDXVZIaEoCrbkYaCa2bKlgNraGm644RYiIyNDXZoQHULRZbjoNqmsbEDTus+uS0qKoqKiPtRlnJXjx4+yatVyNm/+FNVsx5J4LpbYHBRFCXVpYcFfW4T3+FYSEhK56aZbycjIbPc6jdhO2iMpqWecWjYSCa02ktAKDU3T2LfvS9at+4AdO7ajqCYscec0nwoMw5HZQy3odtF07FMU3c8vfv5LLrro4nZd5zJKO+koElrhR0KrjSS0uk5DQz2FhfvZs+cLtm/fRm1tNarZhjkmB0t8f1SzPdQlhjUt0IT3+GcEGo6RnXMO1879bzIy+rZpXeHcTjqDhFb4kdBqIwmtU2maRlVVJS5XBZWVLurqamloaKCpyYPf72+5f0hVVcxm84kfC2azGZPJBEAwGMTr9dLY2EB1dRXHS49TW1MNNE+EaHL2whyTiTkyHUU1te9N9yC6rhOoO4SvfCdawMeECROZOXM2ycm9zmo9Eloi1LostN59912effZZdF3H6/UyZMgQHnvssQ7fzu7du1m8ePGPrvudd97ho48+YuHChW3eTk8PrcbGBoqLD3P4cBElJcWUlBRTWnqcQODkqTIURUUxWU4MmaSAAug66Bq6rqFrQXQ92PxY8wtQVTOKyQYmO4olAtUei8mRgMmeIEHVTnrQh9f1JYGaQtA1Ro3K5YILfsqgQUNaddpQQkuEWpf0HiwvL+e+++5j2bJlpKamous6+/bt65RtDR06tFPCsKfSdZ2amhqOHCmmuPgQhw8foqjoIJWVFS3LmCxOsMagRmdjs0ajWiNQzU4UswNUc6s6Rui6Lh0ouoBismLvNRItfiD+6v3s2LWL7du3EhMTR27uGIYOHc455wzE4XCEulQhflCXhJbL5cJsNhMb29wNWVEUBg8eDMCAAQO48cYbWbt2LU1NTdx2221cfPHFANx+++0UFRXh9/vJyMjgwQcfJCYmhi1btvDggw8yfPhwduzYgaIoPP744+Tk5LBlyxYeeeQR3nnnHSorK7n99tuprKwEYPz48cybNw+AhoYGbrnlFr755huioqJYtGgRSUlJXbE7wk4gEODYsWMcPHjkxOk9F+XlpZSWHufYsaN4PO6WZU22KBRrLNakYZjscaj2eFSzrd01dHVg6bqOv7qQQMMx0ANtW0fQj675UVRL60fbUMyYo9KwxPYLaUirFge25OFYE88lUH+Exrpi1q1fy9q1+SiKSnrv3vTNzKZ3794kJ6eQmZlFXFz3GwJLGE+XhNbAgQMZNmwYF1xwAWPHjmXUqFHMmjWr5UOgqiorVqzg4MGD/PKXvyQ3N5eEhATuuusu4uPjAXj88cf5xz/+wR133AFAYWEhDz30EPPnz+dvf/sbzzzzzClHWKtWrSIjI4PFixcDUFtb2/Lc7t27WblyJampqdx999288sor3HrrrV2wN87M5/OxbNmb1NXV4nQ6MZs7d/ihgk0bqT/TCOEmO6rJhq6o6P4GNH8DgfqSM65bD3rRA2E4iKuuAe0b6shutzP1kqnk5+fT5K5p9euCjcfxlu6ALr7xWTFbm0+7nu55SxR6wIMe9HKkpJgjJcUtz6mqypQpl+BwWPB4Wj9bst/vw+1207dvFlOnTm9X/UJAF4WWqqo888wz7N+/n61bt/Lhhx/yz3/+k1WrVgHw85//HIDs7GwGDx7Mzp07ueiii1ixYgWrVq3C7/efaPh9W9aZlZXVcrQ2YsQI1q9ff8p2hw8fzuLFi3nkkUc477zzmDhxYstzo0aNIjU1tWW5goKCznr7Z62k5DDvv7/mrF9nVhXUNnx713Ud85n+fupNEGg663Vr+n8uV3U3U6dO5frrr0fX9Za23HraieDsOkoggBp0/+gyast/vvdadNZ/+G7Lv33Bs6t98+ZPycubKqNziHbr0hbUv39/+vfvz69+9SumT5/OZ599dtplt23bxmuvvcbSpUuJj49n1apVvPHGGy3PW63/uSdHVVUCgVNP8YwcOZJly5ZRUFDAihUreO6553jttdcAsNn+843TZDKF1cjYWVk53HjjrdTUVOPxuHG7G2loaKChoZ66ulrqamuoravD7z/5G6+m60RYTcTaFGJtJuLs3/lxmIi0qHLd6IStx9ysOdC+DgX5+fnous4HH3xw1q+9tF8UY1Kd7dp+ZwvqOtWeIOXuAC53AJcnSJUnSGWTBt/7uJhMJqKjoomNjSM6JoaoqGgiIyNxOiNwOiNITU2TwBIdoktaUVlZGceOHWPkyJEAlJaWUlVVRe/evQF4++23ueGGGzh06BB79+5lxIgR7Nq1i8jISGJjY/H5fLz99ttnvd2SkhJSUlK49NJLyc3NZcqUKYYY/VpVVUaPHvOjy+i6jsfjobq6iurqSqqqqlq6mrtc5RwuL+OLitqTjnLMqkKs3dwcanYTsTYTMTYTMXaVGJuJSKuKqYeEWm5qc0eDr6u8+NvYC9Qb8LMxfw2xZgVbTOtO4VpUhQHxNnJTwqujgy+oU9rg51hDgNJGP2WNQSrcAQLf2TdxsXFkZPehX1wSycm9SExMJiEhkfj4BKKiomRwXtEluiS0AoEAixYt4ujRo9jtdjRN45Zbbmk5vRcMBvnZz36Gx+Nh/vz5JCQkMGnSJFauXMnFF19MXFwcubm57N69+6y2+9lnn7F48WJUVUXTNO67775u88FSFAWn04nT6SQ9vfcPLuP3+3C5XLhcFVRUlONylVNRUU5FeRl7XRW4PQ0nrxOItJmJsipEW1WirCpRVhPRtubfo0+EnNVk/GBTFIUxaU7GpIX30U5n0HUdlydISZ2fkjofxxqClLv9LV9woiIjycg8h+F9MkhP70NaWm9SU9Ow2+09rsu7CD8hv7l4wIABfP7550RERISyjLPWHe7T8njcVFVVUVlZgd/v5vDho9TUVFNVVUlNdRU1NdW4PZ5TXue0moi1qcTbVeLtZhKdJpKdZhKdZsyq8QOtu9F1nUpPkIM1PopqfBTXB2j0NZ/fi3A6ycruR3Z2P/r2zSIzM4vY2LjTnkbuaaEl92mFHznJ3IM5HE7S05uP1E73x8jr9VJTU90SZlVVlVRWuigvL6O0vJQvj1Ty7fcek6KQHGEmPdJMRoyFzBgrMTa5GTgUAprOoVofX1d6+abaT01T8zXf+Lh4hucOoX//gZxzTn969UrtNmcfRM8Q8iMto+oOR1rf1dZv0H6/n7KyUo4cKaGk5DCHDh2kqOgATU3NPQ0TnBZyYs0MiLfRN8aKSY7EOk1Q1ymq8bG7vImvq/00+YNYLBYGDz6XYcNGMGTIMJKSktvVGUeOtESoSWi1kYTW6WmaxpEjxXz11V6+3LObr776En8ggMNiYlCClaFJdjJjLG3qni9OVeUJ8HmZh13lPuq9Aex2O6NHn8fo0WMYPHjoST1t20tCS4SahFYbSWi1ntfrZe/e3WzduoUdO7bh9XqJtZsZ1cvGqBQHkVY5hXi2dF3nYI2Pzcc8FFZ5QVEYNnQE50+czPDhI7BYOmeaFgktEWoSWm0kodU2Pp+Pzz/fyscfr+err/ZiUhSGJNmYkO4kJbJzR/7oDjRdZ6/LyydH3JQ2+ImOiuKCC6cweXIecXHxnb59CS0RahJabSSh1X7Hjx9j3boP+GTjerw+H+fE2/hJHyd9omUyx+/7Nqw+KnbjcvtJSUnhkktmMm7chE47qvohEloi1CS02khCq+M0Njawbt2H5OevobGxkX5xVvL6RpImR17ous6BGh8fHmqktMFPWmoaM2fNITf3vJD0+pPQEqEmodVGElodz+ttYt26D/j3mhU0ut0MTbKT1zeSOHvPvOZV1hjg/YP1HKzxkZiQyOWzf8HYsRNC2kU9HNpJV5LQCj8SWm0kodV5PB437767mvffX4MeDDA+3cnEPk5spp5xP5Hbr7H+cAPbSj047A5mzprDhRf+FIsl9Eee4dROuoKEVviR0GojCa3OV1VVyVtvLWXz5k+JtpmZmhXBkERbtx30V9N1Pi/1sPawm6aAxoUX/pSf/WwOkZHh84czHNtJZ5LQCj8SWm0kodV1Cgv38/LLL1BSUkxOnJVLc6KId3SvwVyO1vtZc6CeY/V++vcfwDXX/De9e2eEuqxThHM76QwSWuFHQquNJLS6lqZprFv3Ae+8vZSA38fkjAgmpDsNP8KGJ6Cx7lAD2457iIqO5sorr2HcuPPD9mgy3NtJR5PQCj8SWm0koRUa1dVVLFnyLz7/fCspkRZm9osiLSr013rOlq7r7K5oIr+okUa/Rl7eVC6//Oc4neE96rxR2klHkdAKPxJabSShFVrbt2/llZdfoK6+lgnpTi7IiMRikClTKtwB1hTWc6jWR1bfbK79r9+QmZkV6rJaxWjtpL0ktMJP97owIHqM0aPHMGjQYF5/fQkbN37EV1U+ZvSLom9M+N6Y7A1obChpZPNRD3a7g2uvnctPfnKhjLIuxFmQI602kiOt8LF37x4Wv/gcrkoXo1McTMmKxG4OnyDQdZ0vKpr48FAj9d4gEydOZs6cXxIdHR3q0s6akdtJW8iRVviR0GojCa3w4vU2sWzZW3zwwbtEWlWmZUUyOAy6x5fU+Xj/YCNH6n307ZvFr3713+Tk9AtpTe1h9HZytiS0wo+EVhtJaIWnoqKD/GvxcxSXFJMTZ+OS7EgSnV1/FtzlDrD2UAP7Kr3EREdzxZxfMmHCJMOfCuwu7aS1JLTCj4RWG0loha9gMMi6dfksW/Ymfq+X89Ic/KRPBA5L5wdGlSfAxyWN7Cpvwmqxcsn0mUydOh273d7p2+4K3amdtIaEVviR0GojCa3wV1tbwzvvvME6kcv7AAAIC0lEQVQnGz/CZlGZkOZgbJoTWydc7zre4KfgiJs9riZMJjMXXjiF6dNnEhMT0+HbCqXu2E5+jIRW+JHQaiMJLeMoKSlm2bI32Lnzc+xmldEpdnJTne0eiNcX1NnnamJ7WRPFtT5sVisXXDiFqVOnExcX10HVh5fu3E5+iIRW+JHQaiMJLeMpKjrAu++uYvv2raDr9I21MjjRxoB4G9G21gVYg0+jqMbH11VN7K/y4wtqJCUmcWHeVH7ykwtwOiM6+V2EVk9oJ98loRV+JLTaSELLuFyuCgoKNlLw6ceUV5QDEOcwkxphIt5hIspiwmpW0HXwB3XqfEGqm4KUNmpUefwAREZEMGr0eYwbdz79+w80fAeL1upJ7QQktMKRhFYbSWgZn67rHDt2lD17dlFYuJ+S4sO4Kl1omnbScqqqEh8fT0ZGX7Kychg0aAiZmVmYTD1vnq+e1k4ktMKPhFYbSWh1T8FgEI/HjcfjITExivp6P5GRkT3mSOpMelo7kdAKPzKMkxDfYTKZiIyMIjIyiqSkKBSl5/yBFsII5OujEEIIw5DQEkIIYRgSWkIIIQxDQksIIYRhSGgJIYQwDAktIYQQhiGhJYQQwjAktIQQQhiGhJYQQgjDkNASQghhGBJaQgghDENCSwghhGFIaAkhhDAMCS0hhBCGIaElhBDCMCS0hBBCGIaElhBCCMOQmYvbSFWVUJfQ4brje2ov2Senkn0iQknRdV0PdRFCCCFEa8jpQSGEEIYhoSWEEMIwJLSEEEIYhoSWEEIIw5DQEkIIYRgSWkIIIQxDQksIIYRhSGgJIYQwDAktIYQQhiGhJSgqKuLKK6/k4osv5sorr+TQoUOhLink8vLymDZtGrNmzWLWrFls3Lgx1CV1uUceeYS8vDwGDBjA/v37Wx6X9iJCSUJLcO+993L11Vfz/vvvc/XVV3PPPfeEuqSwsHDhQlasWMGKFSuYNGlSqMvpchdddBFLliwhPT39pMelvYhQktDq4SorK9m7dy+XXXYZAJdddhl79+6lqqoqxJWJUMvNzSU1NfWkx6S9iFCTUd57uOPHj9OrVy9MJhMAJpOJ5ORkjh8/Tnx8fIirC6077rgDXdcZPXo0t912G9HR0aEuKeSkvYhQkyMtIX7AkiVLWLlyJW+//Ta6rjN//vxQlySEQEKrx0tNTaWsrIxgMAhAMBikvLz8lNNCPc23799qtXL11Vfz+eefh7ii8CDtRYSahFYPl5CQwKBBg1i9ejUAq1evZtCgQT36VI/b7aa+vh4AXdf597//zaBBg0JcVXiQ9iJCTSaBFBw4cIA///nP1NXVER0dzSOPPEJ2dnaoywqZkpIS/vjHPxIMBtE0jZycHO6++26Sk5NDXVqXuv/++8nPz8flchEXF0dsbCxr1qyR9iJCSkJLCCGEYcjpQSGEEIYhoSWEEMIwJLSEEEIYhoSWEEIIw5DQEkIIYRgSWqJbuPTSS9myZUuXbKuwsJDZs2fTlo63x44dY+TIkS035/4Yl8vFJZdcgs/na0uZQnRL0uVdGMLIkSNbfvd4PFit1pbx7+677z5mzpzZZbX88Y9/ZNq0aVx66aWdvq2//vWv5OTkMHfu3E7flhBGIKElDCcvL4/777+fCRMmdPm2y8vLueyyy9i4cSM2m63Tt7d9+3buvffelhEohOjp5PSg6Bby8vIoKCgAYNGiRdx8883ccccdjBw5khkzZlBUVMSzzz7L+PHjmTx5Mp988knLa+vr65k3bx4TJ05k0qRJPP7446c9fVdQUMDgwYNPCqy8vDyef/55ZsyYwYgRI5g3bx4ul4vf/va3jBw5kuuuu47a2loAjhw5woABAwgEAgDMnTuXJ554gquuuoqRI0fy61//+qRpPoYPH05JSQlHjx7t8H0mhBFJaIluaf369cyaNYutW7cyaNAgfvOb36BpGh9//DE33njjSRMX/vnPf8ZsNpOfn8/y5cv59NNPefPNN39wvV9//TVZWVmnPJ6fn8+LL77I+++/z/r167n++uu57bbb2Lx5M5qm8fLLL5+21tWrV/PQQw+xadMm/H4/L7zwQstzZrOZjIwMvvrqq3bsDSG6Dwkt0S3l5uYyadIkzGYz06ZNo7q6mt/97ndYLBamT5/O0aNHqaurw+VysWHDBubNm4fT6SQhIYHrrruONWvW/OB66+vriYiIOOXxa665hsTERHr16kVubi7Dhg1rOSKbMmUKe/fuPW2ts2fPJisrC7vdzrRp09i3b99Jz0dERLQM4CtETyeTQIpuKSEhoeV3u91OXFxcS8cNu90ONI/mXl5eTiAQYOLEiS3La5p22qk2oqOjaWxsPOXxxMTElt9tNttJ/7bb7bjd7tPWmpSU1PK7w+E4ZdnGxkaioqJO+3ohehIJLdGjpaSkYLVa2bx5M2bzmT8OAwYMYPny5V1QWbNAIEBxcTEDBw7ssm0KEc7k9KDo0ZKTkzn//PN5+OGHaWhoQNM0iouL+eyzz35w+fPPP5+9e/fi9Xq7pL4vvviC9PR00tPTu2R7QoQ7CS3R4y1YsAC/38/06dMZM2YMN998MxUVFT+4bGJiImPHjmXt2rVdUtuqVau46qqrumRbQhiB3KclxFkqLCzkzjvv5K233kJRlE7bTmVlJddccw3Lly/vknvChDACCS0hhBCGIacHhRBCGIaElhBCCMOQ0BJCCGEYElpCCCEMQ0JLCCGEYUhoCSGEMAwJLSGEEIYhoSWEEMIw/j9qVUiwVdko5wAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Lastly, we can visualize time with respect to language preference and trial group. For all three languages, we see that users in the treatment group stay on News Source for longer than those in the control group; that is, the peak of the curve is greater (further to the right) for users in the treatment group. What's more, the split violin plot illuminates the differences between the languages and time, as no two languages have similar looking violins." ], "metadata": { "id": "Q4FSspslrlAs" }, "id": "Q4FSspslrlAs" }, { "cell_type": "markdown", "metadata": { "id": "27906576" }, "source": [ "## 1. Do the users spend more time on the new landing page than the existing landing page?" ], "id": "27906576" }, { "cell_type": "code", "metadata": { "id": "58e9a1d0", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "bf02469e-00de-4847-d213-d4081254a3f8" }, "source": [ "# initial numerical comparison\n", "usage.groupby('landing_page')[time].mean()" ], "id": "58e9a1d0", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "landing_page\n", "new 6.2232\n", "old 4.5324\n", "Name: time_spent_on_the_page, dtype: float64" ] }, "metadata": {}, "execution_count": 108 } ] }, { "cell_type": "markdown", "source": [ "It appears that the mean time for the new landing page is greater than the mean time for the old landing page. Now what we must determine is whether the difference is significant." ], "metadata": { "id": "wg3q43qFImsL" }, "id": "wg3q43qFImsL" }, { "cell_type": "markdown", "metadata": { "id": "e5fdf0c8" }, "source": [ "### Perform Visual Analysis" ], "id": "e5fdf0c8" }, { "cell_type": "code", "metadata": { "id": "6eZJa41eg21n", "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "outputId": "db3f4cbd-3e20-40f9-d5cc-2f27fb801cc4" }, "source": [ "# kde of time for old and new layouts\n", "sns.displot(data=usage,x=time,hue='landing_page',kind='kde')\n", "plt.title('KDE of Time Spent on Landing Page', fontsize=16)\n", "plt.xlabel('Time (min)');" ], "id": "6eZJa41eg21n", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "We have two time variables now: time for the new layout and time for the old layout. From the Kernel Density Estimate, we find that both are approximately normally distributed. Moreover, the peak of the new layout data is higher and further to the right, indicating a high concentration of data above the mean of the old layout data." ], "metadata": { "id": "7BxbwflILZXZ" }, "id": "7BxbwflILZXZ" }, { "cell_type": "markdown", "metadata": { "id": "56b3dafd" }, "source": [ "### Step 1: Define the null and alternate hypotheses" ], "id": "56b3dafd" }, { "cell_type": "markdown", "metadata": { "id": "d2ac77ef" }, "source": [ "To test if the new layout does indeed make a statistically significant difference, we must assume at the outset that there is no population-level difference. That is, our null hypothesis is that the mean time for the old layout ($\\mu_{\\text{old}}$) is the same as the mean time for the new layout ($\\mu_{\\text{new}}$):\n", "$$ H_0: \\mu_{\\text{old}}=\\mu_{\\text{new}}.$$\n", "The alternative hypothesis then adresses Question 1 above, namely, do users really spend more time on News Source now that the layout has been redesigned? We formulate this as:\n", "$$ H_a: \\mu_{\\text{old}}<\\mu_{\\text{new}}.$$" ], "id": "d2ac77ef" }, { "cell_type": "markdown", "metadata": { "id": "c7ee4907" }, "source": [ "### Step 2: Select Appropriate test" ], "id": "c7ee4907" }, { "cell_type": "markdown", "metadata": { "id": "2c183cd7" }, "source": [ "Since the populations are independent, simply randomly sampled, and time is normally distributed (as was shown in Step 1), the two sample independent t-test is the best test to study the effect (if any) of the layout change." ], "id": "2c183cd7" }, { "cell_type": "markdown", "metadata": { "id": "3f58c9a7" }, "source": [ "### Step 3: Decide the significance level" ], "id": "3f58c9a7" }, { "cell_type": "markdown", "metadata": { "id": "92d7d7ee" }, "source": [ "Per the problem statement, we will be assuming a significance level of 5%." ], "id": "92d7d7ee" }, { "cell_type": "markdown", "metadata": { "id": "5fed2eac" }, "source": [ "### Step 4: Collect and prepare data" ], "id": "5fed2eac" }, { "cell_type": "code", "source": [ "# separate out the time data\n", "old_time=usage[usage['landing_page']=='old'][time]\n", "new_time=usage[usage['landing_page']=='new'][time]" ], "metadata": { "id": "qsRpIvukHTlZ" }, "id": "qsRpIvukHTlZ", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We create two pandas Series with the time data for the old layout and the new layout." ], "metadata": { "id": "c7qdKdYPNOAM" }, "id": "c7qdKdYPNOAM" }, { "cell_type": "markdown", "metadata": { "id": "f5c5258d" }, "source": [ "### Step 5: Calculate the p-value" ], "id": "f5c5258d" }, { "cell_type": "code", "metadata": { "id": "fac8594a", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "801c93e0-dc51-4f20-dfdb-d51934bc4995" }, "source": [ "# apply the t-test\n", "test_stat, p_val=st.ttest_ind(old_time,\n", " new_time,\n", " equal_var=False,\n", " alternative='less')\n", "print('The p-value is',p_val)" ], "id": "fac8594a", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.0001392381225166549\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "359b12f8" }, "source": [ "### Step 6: Compare the p-value with $\\alpha$" ], "id": "359b12f8" }, { "cell_type": "code", "metadata": { "id": "7be47289", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9476b452-067a-464c-bac3-0d0ec0379b1f" }, "source": [ "p_val<0.05" ], "id": "7be47289", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 112 } ] }, { "cell_type": "code", "source": [ "0.05/p_val" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JY6i7SHpOItf", "outputId": "d0d9c06b-a020-4e08-e946-012eb7e12457" }, "id": "JY6i7SHpOItf", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "359.0970568711832" ] }, "metadata": {}, "execution_count": 113 } ] }, { "cell_type": "markdown", "source": [ "The p-value is indeed less than the level of significance $\\alpha$. In fact, the p-value is about 359 times smaller than $\\alpha$!!" ], "metadata": { "id": "Ur-KJ8cDOAah" }, "id": "Ur-KJ8cDOAah" }, { "cell_type": "markdown", "metadata": { "id": "d9affc84" }, "source": [ "### Step 7: Draw inference" ], "id": "d9affc84" }, { "cell_type": "markdown", "metadata": { "id": "6b16dd4b" }, "source": [ "As the p-value is less than the level of significance, we must reject the null hypothesis in favor of the alternate. Thus, the new layout does keep users on News Source for longer than the old layout." ], "id": "6b16dd4b" }, { "cell_type": "markdown", "metadata": { "id": "1ad0823a" }, "source": [ "**A similar approach can be followed to answer the other questions.**" ], "id": "1ad0823a" }, { "cell_type": "markdown", "metadata": { "id": "353e9d24" }, "source": [ "## 2. Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?" ], "id": "353e9d24" }, { "cell_type": "code", "metadata": { "id": "b7f52898", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "714c4ee2-06ae-4a52-c5b7-beae9adf5ba8" }, "source": [ "# numerical comparison\n", "usage.groupby('landing_page')['converted'].value_counts(sort=False)" ], "id": "b7f52898", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "landing_page \n", "new no 17\n", " yes 33\n", "old no 29\n", " yes 21\n", "Name: converted, dtype: int64" ] }, "metadata": {}, "execution_count": 114 } ] }, { "cell_type": "markdown", "source": [ "A first look at our data shows a greater conversion rate for the new layout: 66% versus 42%." ], "metadata": { "id": "mItT91b55zVY" }, "id": "mItT91b55zVY" }, { "cell_type": "markdown", "source": [ "### Perform Visual Analysis" ], "metadata": { "id": "qQpeLZbgtcW3" }, "id": "qQpeLZbgtcW3" }, { "cell_type": "code", "source": [ "# visual analysis\n", "\n", "# prepare figure\n", "plt.figure(figsize=(8,5))\n", "plt.title('Ratios of Landing Page Conversions',fontsize=16)\n", "\n", "# countplot\n", "sns.countplot(data=usage,x='landing_page',hue='converted')\n", "plt.xlabel('Landing Page Design')\n", "plt.ylabel('Count');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "id": "mzZcPMXfIKd1", "outputId": "6ce163f7-08ac-4092-eaa9-356a1b5c18ce" }, "id": "mzZcPMXfIKd1", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Indeed, the contrast noted numerically above is illustrated by the count plot. The orange bar shows converted users. The new landing page has about double the converted users as those that weren't converted, while the old landing page converted less than half the users." ], "metadata": { "id": "DlbAc1R666ko" }, "id": "DlbAc1R666ko" }, { "cell_type": "markdown", "source": [ "### Step 1: Define the null and alternate hypotheses" ], "metadata": { "id": "NehrWjAotnOp" }, "id": "NehrWjAotnOp" }, { "cell_type": "markdown", "source": [ "Since we are interested in whether the new layout had any effect on the conversion rate, we must assume at the outset that there is no difference. That is, we assume the conversion rate for users shown the new page ($r_{\\text{new}}$) is equal to the conversion rate for the old page ($r_{\\text{old}}$):\n", "$$ H_0: r_{\\text{new}}=r_{\\text{old}}.$$\n", "The alternate hypothesis addresses our inquiry, namely that the new rate is greater than the old rate:\n", "$$ H_a: r_{\\text{new}}>r_{\\text{old}}.$$" ], "metadata": { "id": "F_1eHHHr8HRA" }, "id": "F_1eHHHr8HRA" }, { "cell_type": "markdown", "source": [ "### Step 2: Select appropriate test" ], "metadata": { "id": "MmkpCqP_tuZw" }, "id": "MmkpCqP_tuZw" }, { "cell_type": "markdown", "source": [ "We will use the two proportion z-test. Our two populations are binomially distributed, independent, and simply randomly sampled. The last condition to satisfy is the sample size inequalities: $nr\\geq10$ and $n(1-r)\\geq10$.\n", "* Old: $nr_{\\text{old}}=21$, $n(1-r_{\\text{old}})=29$.\n", "* New: $nr_{\\text{new}}=33$, $n(1-r_{\\text{new}})=17$.\n", "\n", "Since all four values are greater than 10, we can confidently use this test." ], "metadata": { "id": "89wjscoe9Gg2" }, "id": "89wjscoe9Gg2" }, { "cell_type": "markdown", "source": [ "### Step 3: Decide the significance level" ], "metadata": { "id": "365QHjTBt2Uv" }, "id": "365QHjTBt2Uv" }, { "cell_type": "markdown", "source": [ "Again, the problem statement fixes the level of significance at $\\alpha=0.05$." ], "metadata": { "id": "Jl6UoT6XAfPs" }, "id": "Jl6UoT6XAfPs" }, { "cell_type": "markdown", "source": [ "### Step 4: Collect and prepare data" ], "metadata": { "id": "JHr09OMot6bM" }, "id": "JHr09OMot6bM" }, { "cell_type": "code", "source": [ "# prepare the conversion data\n", "yes=np.array([21,33])\n", "size=np.array([50,50])" ], "metadata": { "id": "mOMwaXUoAp5c" }, "id": "mOMwaXUoAp5c", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Step 5: Calculate the p-value" ], "metadata": { "id": "CN923ciyt6BD" }, "id": "CN923ciyt6BD" }, { "cell_type": "code", "source": [ "# two proportion independent z-test\n", "\n", "# import the required function from statsmodels\n", "from statsmodels.stats.proportion import proportions_ztest\n", "\n", "# test\n", "test_stat,p_val = proportions_ztest(yes,size)\n", "print('The p-value is',p_val)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UJx57iPHPFQo", "outputId": "f2d768b9-af82-448b-afac-620b7e595480" }, "id": "UJx57iPHPFQo", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.016052616408112556\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Step 6: Compare the p-value with $\\alpha$" ], "metadata": { "id": "Ey0VYt5luD02" }, "id": "Ey0VYt5luD02" }, { "cell_type": "code", "source": [ "p_val<0.05" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WxzRCDEEAusZ", "outputId": "dfaf8019-a975-4944-d5b2-57aef9978947" }, "id": "WxzRCDEEAusZ", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 118 } ] }, { "cell_type": "markdown", "source": [ "The p-value is less than the level of significance." ], "metadata": { "id": "TzlQjgoaAtZl" }, "id": "TzlQjgoaAtZl" }, { "cell_type": "markdown", "source": [ "### Step 7: Draw inference" ], "metadata": { "id": "3lUWX5iiuKz_" }, "id": "3lUWX5iiuKz_" }, { "cell_type": "markdown", "source": [ "Since the p-value is less than $\\alpha$, we reject the null hypothesis. Therefore the population conversion rates are genuinely different. In particular, the conversion rate for users served the new landing page is greater than the rate for the old page. Put another way, the News Source redesign has had a positive impact on subscriptions." ], "metadata": { "id": "Nv-R1PwBA9C1" }, "id": "Nv-R1PwBA9C1" }, { "cell_type": "markdown", "metadata": { "id": "d49bfa2d" }, "source": [ "## 3. Is the conversion and preferred language are independent or related?" ], "id": "d49bfa2d" }, { "cell_type": "code", "metadata": { "id": "b0e63f17", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "facf3611-00a8-483c-ec39-aa0f9210f3bc" }, "source": [ "# contingency table WITH margins (totals)\n", "pd.crosstab(usage['language_preferred'],\n", " usage['converted'],\n", " margins=True,\n", " margins_name='Totals')" ], "id": "b0e63f17", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "converted no yes Totals\n", "language_preferred \n", "English 11 21 32\n", "French 19 15 34\n", "Spanish 16 18 34\n", "Totals 46 54 100" ], "text/html": [ "\n", "
\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
convertednoyesTotals
language_preferred
English112132
French191534
Spanish161834
Totals4654100
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "execution_count": 119 } ] }, { "cell_type": "markdown", "source": [ "Here is a contingency table of the data with totals in the final row and column." ], "metadata": { "id": "IZGHgPbx69Dq" }, "id": "IZGHgPbx69Dq" }, { "cell_type": "markdown", "source": [ "### Perform visual analysis" ], "metadata": { "id": "eQcdJdc0uSPi" }, "id": "eQcdJdc0uSPi" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Heatmap of Contingency Table',fontsize=16)\n", "\n", "# heatmap\n", "sns.heatmap(pd.crosstab(usage['language_preferred'],\n", " usage['converted']),\n", " annot=True,\n", " vmin=0,\n", " vmax=25)\n", "plt.xlabel('Converted')\n", "plt.ylabel('Language');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "id": "DDpfWLAc7Fuh", "outputId": "c722e505-29a0-4690-da1f-10df5d718c2d" }, "id": "DDpfWLAc7Fuh", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "Above is a heatmap of the contingency table data. A darker color represents fewer observations in the data. The highest value (lightest color) is 21 and the lowest value (darkest color) is 11, both associated with English data." ], "metadata": { "id": "mwwa6K_L8Bi4" }, "id": "mwwa6K_L8Bi4" }, { "cell_type": "markdown", "source": [ "### Step 1: Define the null and alternate hypotheses" ], "metadata": { "id": "tHr7BKoNuSkS" }, "id": "tHr7BKoNuSkS" }, { "cell_type": "markdown", "source": [ "We are interested in whether conversion status and language preference are related variables. We will assume at the outset that they are not:\n", "$$ H_0: \\text{conversion status and language are independent}.$$\n", "\n", "Otherwise, they must be related:\n", "$$ H_a: \\text{conversion status depends on language}.$$" ], "metadata": { "id": "4at_vxV08TRC" }, "id": "4at_vxV08TRC" }, { "cell_type": "markdown", "source": [ "### Step 2: Select appropriate test" ], "metadata": { "id": "uLppmqvIuSkT" }, "id": "uLppmqvIuSkT" }, { "cell_type": "markdown", "source": [ "We are studying the connection of two randomly sampled categorical variables. Since the number of observations in each category is greater than 5, the best test is the chi-squared test of independence." ], "metadata": { "id": "W-ruGTLG83bP" }, "id": "W-ruGTLG83bP" }, { "cell_type": "markdown", "source": [ "### Step 3: Decide the significance level" ], "metadata": { "id": "HKty2LKsuSkT" }, "id": "HKty2LKsuSkT" }, { "cell_type": "markdown", "source": [ "We assume a significance level of $\\alpha=0.05$." ], "metadata": { "id": "BXzqY0D29p1i" }, "id": "BXzqY0D29p1i" }, { "cell_type": "markdown", "source": [ "### Step 4: Collect and prepare data" ], "metadata": { "id": "99mCDziquSkT" }, "id": "99mCDziquSkT" }, { "cell_type": "code", "source": [ "# generate the contingency table\n", "contingency=pd.crosstab(usage['language_preferred'],usage['converted'])" ], "metadata": { "id": "XFcJGjX2ujbX" }, "id": "XFcJGjX2ujbX", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Step 5: Calculate the p-value" ], "metadata": { "id": "E2t5nY5_uSkT" }, "id": "E2t5nY5_uSkT" }, { "cell_type": "code", "source": [ "# chi squared test for independence\n", "a, p_val, b, c =st.chi2_contingency(contingency)\n", "print('The p-value is',p_val)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UhQxxEGYUWtC", "outputId": "33c61777-a025-49e0-91ce-c2cdbc0509b3" }, "id": "UhQxxEGYUWtC", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.2129888748754345\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Step 6: Compare the p-value with $\\alpha$" ], "metadata": { "id": "D9xxNYqPuSkT" }, "id": "D9xxNYqPuSkT" }, { "cell_type": "code", "source": [ "p_val<0.05" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dKSEO-vZ9wSh", "outputId": "ebb69408-90d6-4d6f-b543-a854a0bd3b3e" }, "id": "dKSEO-vZ9wSh", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "False" ] }, "metadata": {}, "execution_count": 123 } ] }, { "cell_type": "markdown", "source": [ "The p-value is greater than $\\alpha$." ], "metadata": { "id": "aF9JTJJx9zix" }, "id": "aF9JTJJx9zix" }, { "cell_type": "markdown", "source": [ "### Step 7: Draw inference" ], "metadata": { "id": "AGp2ij9suSkT" }, "id": "AGp2ij9suSkT" }, { "cell_type": "markdown", "source": [ "Since the p-value is greater than $\\alpha$, we fail to reject the null hypothesis. Therefore we cannot conclude that conversion status and language preference are related." ], "metadata": { "id": "OE34bWZx9vt5" }, "id": "OE34bWZx9vt5" }, { "cell_type": "markdown", "metadata": { "id": "8d585a90" }, "source": [ "## 4. Is the time spent on the new page same for the different language users?" ], "id": "8d585a90" }, { "cell_type": "code", "source": [ "usage.groupby(['landing_page','language_preferred'])[time].mean()['new']" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5FfbvGduuD0J", "outputId": "80fd59b2-21b7-4ea0-c0c3-ffaabde1d1ab" }, "id": "5FfbvGduuD0J", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "language_preferred\n", "English 6.663750\n", "French 6.196471\n", "Spanish 5.835294\n", "Name: time_spent_on_the_page, dtype: float64" ] }, "metadata": {}, "execution_count": 124 } ] }, { "cell_type": "markdown", "source": [ "There's certainly a difference, albeit small, between the mean times for the three languages on the new News Source landing page." ], "metadata": { "id": "F4FjO77uvM4p" }, "id": "F4FjO77uvM4p" }, { "cell_type": "markdown", "source": [ "### Perform visual analysis" ], "metadata": { "id": "uif-zv7KuT8W" }, "id": "uif-zv7KuT8W" }, { "cell_type": "code", "source": [ "# figure setup\n", "plt.figure(figsize=(8,5))\n", "plt.title('Time on New Landing Page by Language',fontsize=16)\n", "\n", "# boxplot\n", "sns.boxplot(data=usage.loc[usage['landing_page']=='new'],\n", " x=time,\n", " y='language_preferred')\n", "plt.xlabel('Time (min)')\n", "plt.ylabel('Language');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "id": "1Lu80tJZw0Pz", "outputId": "02207bbf-33d1-417e-9daa-7f747f8d6831" }, "id": "1Lu80tJZw0Pz", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# kde of time for different languages\n", "sns.displot(data=usage.loc[usage['landing_page']=='new'],\n", " x=time,\n", " hue='language_preferred',\n", " kind='kde')\n", "plt.title('Time on New Landing Page by Language',fontsize=16)\n", "plt.xlabel('Time (min)');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "id": "lkN5c3THyE5E", "outputId": "8ff7f931-55e0-4c45-fba2-90abb33eeba1" }, "id": "lkN5c3THyE5E", "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "There is clear visual separation of the time data for the three languages. What's striking though is the contrast between the boxplot and the KDE plot: While the medians (in the boxplot) are quite variable, the means (in the KDE plot) are more concentrated. In the boxplot, the data looks far more variable, with differing ranges and outliers." ], "metadata": { "id": "lDqg7y8ezV8W" }, "id": "lDqg7y8ezV8W" }, { "cell_type": "markdown", "source": [ "### Step 1: Define the null and alternate hypotheses" ], "metadata": { "id": "cjpRzddtuUUB" }, "id": "cjpRzddtuUUB" }, { "cell_type": "markdown", "source": [ "We suppose at the outset that there is no population-level difference in the time spent on the new landing page for all three languages:\n", "$$ H_0: \\mu_E=\\mu_F=\\mu_S,$$\n", "where the quantities are the mean time for English, French, and Spanish respectively. The alternate hypothesis is that there is some difference:\n", "$$ H_a: \\text{at least one mean differs}.$$" ], "metadata": { "id": "-X7oDjCXz2uv" }, "id": "-X7oDjCXz2uv" }, { "cell_type": "markdown", "source": [ "### Step 2: Collect and prepare data" ], "metadata": { "id": "SlYWI2PruUUB" }, "id": "SlYWI2PruUUB" }, { "cell_type": "code", "source": [ "# define a new DataFrame for just new landing page data\n", "usage_new=usage.loc[usage['landing_page']=='new']\n", "\n", "# define pandas Series of time for each language\n", "en_time=usage_new[usage_new['language_preferred']=='English'][time]\n", "fr_time=usage_new[usage_new['language_preferred']=='French'][time]\n", "sp_time=usage_new[usage_new['language_preferred']=='Spanish'][time]" ], "metadata": { "id": "5bHFF5jEn7bb" }, "id": "5bHFF5jEn7bb", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "In this case, as we will need these collected and prepared data to run the Shapiro-Wilk test and the Levene test, we complete this step first." ], "metadata": { "id": "ZWEfU-1-pnvV" }, "id": "ZWEfU-1-pnvV" }, { "cell_type": "markdown", "source": [ "### Step 3: Select appropriate test" ], "metadata": { "id": "wUOwaTKfuUUB" }, "id": "wUOwaTKfuUUB" }, { "cell_type": "markdown", "source": [ "In the case of comparing more than two means, we require the ANOVA test. To invoke this test, however, a number of assumptions must be satisfied:\n", "* independent simple random samples,\n", "* normally distributed populations, and\n", "* common population variances.\n", "\n", "The first assumption is satisfied since the entire data set was simply randomly sampled and users are independent.\n", "\n", "For the second assumption, we use the Shapiro-Wilk test. We assume a level of significance of 5% for this test. Our hypotheses are:\n", "$$ H_0: \\text{time data for the new landing page is normally distributed};$$\n", "$$ H_a: \\text{time data for the new landing page is NOT normally distributed}.$$" ], "metadata": { "id": "RzbE2KFf0rW-" }, "id": "RzbE2KFf0rW-" }, { "cell_type": "code", "source": [ "# shapiro-wilk test\n", "a, p_val=st.shapiro(usage_new[time])\n", "print('The p-value is',p_val)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_modaDfb3b9o", "outputId": "19ecd411-0149-4cad-f081-21a2313147cc" }, "id": "_modaDfb3b9o", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.8040016293525696\n" ] } ] }, { "cell_type": "markdown", "source": [ "With a p-value of 0.8, we fail to reject the null hypothesis. Thus, time data for the new News Source landing page is normally distributed.\n", "\n", "Now we must test that the populations (different languages) have equal variances. We accomplish this with the Levene test. Our hypotheses:\n", "$$ H_0: \\text{all three languages have equal variance};$$\n", "$$ H_a: \\text{at least one population has a different variance}.$$" ], "metadata": { "id": "rwmqXIMBjhla" }, "id": "rwmqXIMBjhla" }, { "cell_type": "code", "source": [ "# levene test\n", "a, p_val=st.levene(en_time,fr_time,sp_time)\n", "print('The p-value is',p_val)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8GF4Wu-mp5SP", "outputId": "98958f1e-bcaf-4956-d124-f24d3e18d962" }, "id": "8GF4Wu-mp5SP", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.46711357711340173\n" ] } ] }, { "cell_type": "markdown", "source": [ "With a p-value of 0.47, we fail to reject the null hypothesis, and can therefore assume that the time data for all three languages have equal variance." ], "metadata": { "id": "pNiPokEfqKPv" }, "id": "pNiPokEfqKPv" }, { "cell_type": "markdown", "source": [ "### Step 4: Decide the significance level" ], "metadata": { "id": "zkM761AmuUUB" }, "id": "zkM761AmuUUB" }, { "cell_type": "markdown", "source": [ "From the problem statement, we assume a significance level of $\\alpha=0.05$." ], "metadata": { "id": "XoAkMk21qcuj" }, "id": "XoAkMk21qcuj" }, { "cell_type": "markdown", "source": [ "### Step 5: Calculate the p-value" ], "metadata": { "id": "Crm8f00IuUUB" }, "id": "Crm8f00IuUUB" }, { "cell_type": "code", "metadata": { "id": "b422f395", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e79a786f-f749-416e-91f8-1720ddee81d1" }, "source": [ "# one-way anova test\n", "a,p_val=st.f_oneway(en_time,fr_time,sp_time)\n", "print('The p-value is',p_val)" ], "id": "b422f395", "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The p-value is 0.43204138694325955\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Step 6: Compare the p-value with $\\alpha$" ], "metadata": { "id": "H_-2gyYfuUUB" }, "id": "H_-2gyYfuUUB" }, { "cell_type": "code", "source": [ "p_val<0.05" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "C_5Wg5ki6PkY", "outputId": "d3760469-cbeb-4348-92f4-bfb8b4d1fd72" }, "id": "C_5Wg5ki6PkY", "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "False" ] }, "metadata": {}, "execution_count": 131 } ] }, { "cell_type": "markdown", "source": [ "Our p-value of 0.43 is greater than $\\alpha$." ], "metadata": { "id": "rk3BL6q46JJS" }, "id": "rk3BL6q46JJS" }, { "cell_type": "markdown", "source": [ "### Step 7: Draw inference" ], "metadata": { "id": "zhBCmsDpuUUB" }, "id": "zhBCmsDpuUUB" }, { "cell_type": "markdown", "source": [ "We fail to reject the null hypothesis. We do not have strong evidence to conclude there is a difference between the time spent on the new landing page for the different language users." ], "metadata": { "id": "EM-q_jWI6TKb" }, "id": "EM-q_jWI6TKb" }, { "cell_type": "markdown", "metadata": { "id": "3acbc947" }, "source": [ "## Conclusion and Business Recommendations" ], "id": "3acbc947" }, { "cell_type": "markdown", "metadata": { "id": "21fb1063" }, "source": [ "* News Source users shown the new landing page stay on the site for longer.\n", "* The redesign had the intended effect: The new landing page has a higher conversion rate for subscribers than the old page. \n", "* Language preference is not a factor influencing conversion rate.\n", "* The time spent on the new page is likely the same for all three languages. More precisely, our evidence is not strong enough to conclude that different languages lead to (statistically significant) different times.\n", "* I recommend News Source conduct additional testing to quantify the relationship between time on landing page and conversion rate. What percent increase in conversion rate is observed for each additional minute a user spends on the landing page?\n", "* News Source could do an analysis of the type of stories that gets the greatest conversion rate. Some genres could include breaking news, gossip, opinion pieces, or politics. If the data science team determined that, for example, political stories garnered more subscribers, News Source could focus more on these stories. This facet could even differ by language or region of users.\n", "* News Source could next look at subscription pricing. There is likely an optimal price: high enough to make a profit but low enough to encourage the greatest number of users to subscribe. An analysis could be performed, based on survey data, to determine the ideal subscription price.\n", "* The data science team could offer a sign-up discount for the first month of subscriptions and measure whether this leads to an increase in the conversion rate. This could be accomplished through A/B testing, where some users are shown a pop-up offering the discount if they sign up now, while others do not get this pop-up. Further, more detailed analyses, could examine what percent discount is optimal." ], "id": "21fb1063" }, { "cell_type": "markdown", "source": [ "___" ], "metadata": { "id": "U0dAd4i0iCHo" }, "id": "U0dAd4i0iCHo" } ] }