{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "38bdee8b", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.style.use(\"../styles/hda.mplstyle\")" ] }, { "cell_type": "markdown", "id": "7900d085", "metadata": {}, "source": [ "(chp-statistics-essentials)=\n", "# Statistics Essentials: Who Reads Novels?" ] }, { "cell_type": "code", "execution_count": 2, "id": "629323ec", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# HIDE THIS CELL\n", "import random, numpy; random.seed(1); numpy.random.seed(1);" ] }, { "cell_type": "markdown", "id": "57a8f2ad", "metadata": {}, "source": [ "(sec-statistics-essentials-introduction)=\n", "## Introduction\n", "\n", "```{margin}\n", "See chapter {ref}`chp-intro-probability` for a cursory introduction to probability distributions.\n", "```\n", "This chapter describes the calculation and use of common summary statistics. Summary statistics such as the mean,\n", "median, and standard deviation aspire to capture salient characteristics of a collection of values or, more precisely,\n", "characteristics of an underlying (perhaps hypothesized) probability distribution generating the observed values. Summary statistics are a bit like paraphrases or abstracts of texts. With poetry you almost always want the poem itself (akin to knowledge of the underlying distribution) rather than the paraphrase (the summary statistic(s)). If, however, a text, such as a novel, is extremely long or staggeringly predictable, you may be willing to settle for a paraphrase. Summary statistics serve a similar function: sometimes we don't have sufficient time or (computer) memory to analyze or store all the observations from a phenomenon of interest and we settle for a summary, *faute de mieux*. In other fortuitous cases, such as when we are working with data believed to be generated from a normal distribution, summary statistics may capture virtually all the information we care about. Summary statistics, like paraphrases, also have their use when communicating the results of an analysis to a broader audience who may not have time or energy to examine all the underlying data.\n", "\n", "This chapter reviews the use of summary statistics to capture the location---often the \"typical\" value---and dispersion of a collection of observations. The use of summary statistics to describe the association between components of multivariate observations is also described. These summary statistics are introduced in the context of an analysis of survey responses from the United States General Social Survey (GSS). We will focus, in particular, on responses to a question about the reading of literature. We will investigate the question of whether respondents with certain demographic characteristics (such as higher than average income or education) are more likely to report reading novels (within the previous twelve months). We will start by offering a definition of a summary statistic before reviewing specific examples which are commonly encountered. As we introduce each statistic, we will offer an example of how the statistic can be used to analyze the survey responses from the GSS. Finally, this chapter also introduces a number of common statistics to characterize the relationship between variables.\n", "\n", "A word of warning before we continue. The following review of summary statistics and their use is highly informal. The\n", "goal of the chapter is to introduce readers to summary statistics frequently encountered in humanities data analysis. Some of these statistics lie at the core of applications that will be discussed in subsequent chapters in the book, such as the one on stylometry (see chapter {ref}`chp-stylometry`). A thorough treatment of the topic and, in particular, a discussion of the connection between summary statistics and parameters of probability distributions are found in standard textbooks (see e.g., chapter 6 of {cite:t}`casella2001statistical`).\n", "\n", "(sec-statistics-essentials-statistics)=\n", "## Statistics\n", "\n", "A formal definition of a statistic is worth stating. It will, with luck, defamiliarize concepts we may use reflexively,\n", "such as the sample mean and sample standard deviation. A statistic is a function of a collection of observations, $x_1,\n", "x_2, \\ldots, x_n$. (Such a collection will be referenced concisely as $x_{1:n}$.) For example, the sum of\n", "a sequence of values is a statistic. In symbols we would write this statistic as follows:\n", "\n", "\\begin{equation}\\label{eq:sum}\n", "T(x_{1:n}) = \\sum_{i = 1}^n x_i\n", "\\end{equation}\n", "\n", "Such a statistic would be easy to calculate using Python given a list of numbers `x` with `sum(x)`.\n", "\n", "The maximum of a sequence is a statistic. So too is the sequence's minimum. If we were to flip a coin 100 times and\n", "record what happened (i.e., \"heads\" or \"tails\"), statistics of interest might include the proportion of times \"heads\"\n", "occurred and the total number of times \"heads\" occurred. If we encode \"heads\" as the integer 1 and \"tails\" as the\n", "integer 0, the statistic above, $T(x_{1:n})$, would record the total number of times the coin landed \"heads\".\n", "\n", "Depending on your beliefs about the processes generating the observations of interest, some statistics may be more\n", "informative than others. While the mean of a sequence is an important statistic if you believe your data comes from a\n", "normal distribution, the maximum of a sequence is more useful if you believe your data were drawn from a uniform\n", "distribution. A statistic is merely a function of a sequence of observed values. Its\n", "usefulness varies greatly across different applications.\n", "\n", "```{note}\n", "Those with prior exposure to statistics and probability should note that this definition is\n", "a bit informal. If the observed values are understood as realized values of random variables, as they often are, then\n", "the statistic itself is a random quantity. For example, when people talk about the sampling distribution of a statistic, they are using this definition of a statistic.\n", "```\n", "\n", "There is no formal distinction between a summary statistic and statistic in the sense defined above. If someone refers to a statistic as a summary statistic, however, odds are strongly in favor of that statistic being a familiar statistic widely used to capture location or dispersion, such as mean or variance.\n", "\n", "(sec-statistics-essentials-location-and-dispersion)=\n", "## Summarizing Location and Dispersion\n", "\n", "In this section we review the definitions of the statistics discussed below, intended to capture location and dispersion: mean, median,\n", "variance, and entropy. Before we describe these statistics, however, let us introduce the data we will be working with.\n", "\n", "(sec-statistics-essentials-data)=\n", "### Data: Novel reading in the United States\n", "\n", "To illustrate the uses of statistics, we will be referring to a dataset consisting of survey responses from the [General Social Survey](http://gss.norc.org/) (GSS) during the years 1998 and 2002. In particular, we will focus on responses to questions about the reading of literature.\n", "The GSS is a national survey conducted in the United States since 1972. (Since 1994 the GSS has been conducted on even numbered years.) In 1998 and 2002 the GSS included questions related to culture and the arts.[^gss-details]\n", "The GSS is particularly useful due to its accessibility---anyone may download and share the data---and due to the care with which it was assembled---the survey is conducted in person by highly trained interviewers. The GSS is also as close to a simple random sample of households in the United States (current population ~320 million) as it is feasible to obtain.[^simple-random-sample] Given the size and population of the United States, that the survey exists at all is noteworthy. Random samples of individuals such as those provided by the GSS are particularly useful because with them it is easy to make educated guesses both about characteristics of the larger distribution and the variability of the aforementioned guesses. For example, given a random sample from a normal distribution it is possible to estimate both the mean and the variability of this same estimate (the sampling error of the mean).\n", "\n", "[^simple-random-sample]: A simple random sample of items in a collection is assembled by repeatedly selecting an item\n", " from the collection, given that the chance of selecting any specific item is equal to the chance of selecting any\n", " other item.\n", "\n", "[^gss-details]: The official site for the General Social Survey is http://gss.norc.org/ and the data used in this\n", " chapter is the cumulative dataset \"GSS 1972-2014 Cross-Sectional Cumulative Data (Release 5, March 24, 2016)\" which\n", " was downloaded from http://gss.norc.org/get-the-data/stata on May 5, 2016. While this chapter only uses data from\n", " between 1998 and 2002, the cumulative dataset includes useful variables such as respondent income in constant dollar\n", " terms (``realrinc``), variables which are not included in the single-year datasets.\n", "\n", "```{margin}\n", "Respondents were also asked if they went to \"a classical music or opera performance, not\n", "including school performances\". Answers to this question are recorded in the \n", "[``gomusic``](https://gssdataexplorer.norc.org/variables/1412/vshow) variable.\n", "```\n", "In 1998 and 2002 respondents were asked if, during the last twelve months, they \"[r]ead novels, short stories, poems, or\n", "plays, other than those required by work or school\". Answers were recorded in the [``readfict``](https://gssdataexplorer.norc.org/variables/2129/vshow) variable.\n", "(Those familiar with GSS will want to know these, because this question was asked\n", "as part of a larger survey, we have considerable additional demographic information about each person responding to the survey. This information allows us to create a compelling picture of people who are likely to say they read prose\n", "fiction.\n", "\n", "In addition to responses to the questions concerning reading activity and concert attendance, we will look\n", "at the following named variables from the sample:\n", "\n", "- ``age``: Age of respondent\n", "- ``sex``: Sex of respondent (recorded by survey workers)\n", "- ``race``: Race of respondent (verbatim response to the question \"What race do you consider yourself?\")\n", "- ``reg16``: Region of residence when 16 years old ([New\n", " England](https://en.wikipedia.org/wiki/New_England), [Middle\n", " Atlantic](https://en.wikipedia.org/wiki/Mid-Atlantic_states),\n", " [Pacific](https://en.wikipedia.org/wiki/Pacific_States), etc.) A map showing the nine regions\n", " (in addition to \"Foreign\") is included below.\n", "- ``degree``: Highest educational degree earned (None, High School, Bachelor, Graduate degree)\n", "- ``realrinc``: Respondent's income in constant dollars (base = 1986)\n", "\n", "![Geographic regions used by the United States Census Bureau.](./us_regdiv-0.png)\n", "\n", "```{tip}\n", "The GSS contains numerous other variables which might form part of an interesting analysis. For\n", " example, the variable ``educ`` records the highest year of school completed and ``res16`` records the type of place\n", " the respondent lived in when 16 years old (e.g., rural, suburban, urban, etc.).\n", "```\n", "\n", "```{note}\n", "\"Race\", in the context of the GSS, is defined as the verbatim response of the interviewee to the question, \"What race do you consider yourself?\" In other settings the term may be defined and used differently. For example, in another important survey, the United States Census, in which many individuals are asked a similar question, the term is defined according to a 1977 regulation which references ancestry {cite:p}`council2004measuring`. The statistical authority in Canada, the other large settler society in North America, discourages the use of the term entirely. Government statistical work in Canada makes use of variables such as \"visible minority\" and \"ethnic origin\", each with their own complex and changing definitions {cite:p}`governmentofcanada1998previous,governmentofcanada2015visible`.\n", "```\n", "\n", "The GSS survey results are distributed in a variety of formats. In our case, the GSS dataset resides in a file named\n", "``GSS7214_R5.DTA``. The file uses an antiquated format from Stata, a proprietary non-free statistical software package.\n", "Fortunately, the Pandas library provides a function, ``pandas.read_stata()``, which reads files using this format. Once we load\n", "the dataset we will filter the data so that only the variables and survey responses of interest are included. In this\n", "chapter, we are focusing on the above-mentioned variables from the years 1998, 2000, and 2002." ] }, { "cell_type": "code", "execution_count": 3, "id": "7500fda3", "metadata": {}, "outputs": [], "source": [ "# Dataset GSS7214_R5.DTA is stored in compressed form as GSS7214_R5.DTA.gz\n", "import gzip\n", "import pandas as pd\n", "\n", "with gzip.open('data/GSS7214_R5.DTA.gz', 'rb') as infile:\n", " # we restrict this (very large) dataset to the variables of interest\n", " columns = ['id', 'year', 'age', 'sex', 'race', 'reg16', 'degree',\n", " 'realrinc', 'readfict']\n", " df = pd.read_stata(infile, columns=columns)\n", "\n", "# further limit dataset to the years we are interested in\n", "df = df.loc[df['year'].isin({1998, 2000, 2002})]" ] }, { "cell_type": "markdown", "id": "6c86dd5e", "metadata": {}, "source": [ "Most respondents provide answers to the questions asked of them. In some cases, however, respondents\n", "either do not know the answer to a question or refuse to provide an answer to the question. When an\n", "individual does not provide an answer to a question, the GSS data records that value as missing. In\n", "a ``DataFrame`` such a value is recorded as NaN (\"Not a Number\", a standard value elsewhere used\n", "to encode undefined floating-point values). Although handling missing data adds additional\n", "complexity to an analysis, the methods for dealing with such data in surveys are well established\n", "({cite:t}`hoff2009first`, 115--123). Because missing data models are beyond the scope of this book we will\n", "simply exclude records with missing values.\n", "\n", "As the initial discussion of summary statistics describing location focuses on the responses to the\n", "question about the respondent's annual income, ``realrinc``, a question which some people decline to\n", "answer, we will exclude records with missing values for this variable using the code below:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad68fa9e", "metadata": {}, "outputs": [], "source": [ "# limit dataset to exclude records from individuals who refused\n", "# to report their income\n", "df = df.loc[df['realrinc'].notnull()]" ] }, { "cell_type": "code", "execution_count": 5, "id": "a80c85c3", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# HIDE THIS CELL\n", "# REALITY CHECK, using GSS provided values\n", "df.loc[df['year'] == 2000, 'realrinc'].astype(float).min() == 333;\n", "round(df.loc[df['year'] == 2000, 'realrinc'].astype(float).mean(), 2) == 22110.13;" ] }, { "cell_type": "markdown", "id": "bc0b6a9d", "metadata": {}, "source": [ "```{margin}\n", "{cite:t}`hout2004getting` provides useful guidelines about interpreting variables related to income in the GSS.\n", "```\n", "As a final step, we need to adjust for inflation in the US dollar. Respondent's income ``realrinc``\n", "is reported in constant 1986 US dollars. The following lines of code adjust the 1986 US\n", "dollar quantities into 2015 terms. This is an important adjustment because the value of the US\n", "dollar has declined considerably since 1986 due to inflation. Inflation is calculated using the [US\n", "Consumer Price Index](https://en.wikipedia.org/wiki/United_States_Consumer_Price_Index) which\n", "estimates the value of a dollar in a given year by recording how many dollars are required to\n", "purchase a typical \"market basket\" of goods regularly and widely consumed. Using the US CPI we can\n", "say that 100 dollars worth of groceries for a typical family in 1986 is equivalent to 215 dollars in\n", "2015, for instance." ] }, { "cell_type": "code", "execution_count": 6, "id": "c5a785bb", "metadata": {}, "outputs": [], "source": [ "# inflation measured via US Consumer Price Index (CPI), source:\n", "# http://www.dlt.ri.gov/lmi/pdf/cpi.pdf\n", "cpi2015_vs_1986 = 236.7 / 109.6\n", "assert df['realrinc'].astype(float).median() < 24000 # reality check\n", "df['realrinc2015'] = cpi2015_vs_1986 * df['realrinc'].astype(float)" ] }, { "cell_type": "markdown", "id": "1ac709fb", "metadata": {}, "source": [ "After this preprocessing we can make a histogram showing annual family income, grouped by\n", "self-reported race (coded as \"white\", \"black\", or \"other\" by the GSS):" ] }, { "cell_type": "code", "execution_count": 7, "id": "d2b3a73b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "findfont: Generic family 'sans-serif' not found because none of the following families were found: \"Roboto Condensed Regular\"\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_11_2.png" } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "df.groupby('race')['realrinc2015'].plot(kind='hist', bins=30)\n", "plt.xlabel('Income')\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "ed08034d", "metadata": {}, "source": [ "\n", "\n", "It is common practice to take the logarithm of data which are skewed or which, like income, are\n", "commonly discussed in multiplicative terms (e.g., in North America and Europe it is far more common\n", "to speak of a salary raise in percentage terms than in absolute terms). Converting data to a logarithmic scale has the benefit that larger differences in numeric quantities get mapped to a tinier scope. We will follow that practice\n", "here. The following lines of code create a new variable ``realrinc2015_log10`` and generate a new\n", "plot using the variable. In the new plot below, it is easier to visually estimate the typical annual\n", "household income for each group. We can see, for example, that the typical income associated with\n", "respondents who describe themselves as \"white\" is higher than the typical income associated with\n", "respondents describing themselves as something other than \"white\"." ] }, { "cell_type": "code", "execution_count": 8, "id": "a6298f63", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_13_0.png" } }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "df['realrinc2015_log10'] = np.log10(df['realrinc2015'])\n", "df.groupby('race')['realrinc2015_log10'].plot(kind='hist', bins=30)\n", "plt.xlabel(r'$\\log10(\\mathrm{Income})$')\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "9da29e50", "metadata": {}, "source": [ "\n", "\n", "## Location\n", "(sec-statistics-essentials-location)=\n", "\n", "In the surveys conducted between 1998 and 2002, 5,447 individuals reported their annual income to survey workers. The\n", "histogram above shows their responses. In this section we will look at common strategies for summarizing the values\n", "observed in collections of samples such as the samples of reported household incomes. These strategies are particularly useful when we have too many observations to visualize or when we need to describe a dataset to others without transferring the dataset to them or without using visual aids such as histograms. Of course, it is difficult to improve on simply giving the entire dataset to an interested party: the best \"summary\" of a dataset is the dataset itself.\n", "\n", "There is a great deal of diversity in reported household income. If we wanted to summarize the\n", "characteristics we see, we have several options. We might report the ratio of the maximum value to\n", "the lowest value, since this is a familiar kind of summary from the news in the present decade of the\n", "twenty-first century: one often hears about the ratio of the income of the highest-paid employee at a\n", "company to the income of the lowest-paid employee. In this case, the ratio can be calculated using\n", "the ``max()`` and ``min()`` methods associated with the series (an instance of ``pandas.Series``). Using\n", "these methods accomplishes the same thing as using ``numpy.min()`` or ``numpy.max()`` on the underlying series." ] }, { "cell_type": "code", "execution_count": 9, "id": "392043da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "749.1342599999999\n" ] } ], "source": [ "print(df['realrinc2015'].max() / df['realrinc2015'].min())" ] }, { "cell_type": "markdown", "id": "a8516b16", "metadata": {}, "source": [ "This shows that the wealthiest individual in our sample earns ~750 times more than the poorest individual. This\n", "ratio has a disadvantage: it tells us little about the values between the maximum and the minimum.\n", "If we are interested in, say, the number of respondents who earn more or less than $30,000, this\n", "ratio is not an immediately useful summary.\n", "\n", "To address this issue, let us consider more familiar summary statistics. Initially we will focus on\n", "two summary statistics which aim to capture the \"typical\" value of a sample. Such statistics are\n", "described as measuring the *location* of a distribution. (A reminder about terminology: a *sample* is always a sample from some underlying distribution.)\n", "\n", "The first statistic is the average or *arithmetic mean*. The arithmetic mean is the sum of observed values divided by\n", "the number of values. While there are other \"means\" in circulation (e.g., geometric mean, harmonic mean), it is the\n", "arithmetic mean which is used most frequently in data analysis in the humanities and social sciences. The mean of $n$\n", "values ($x_1, x_2, \\ldots, x_n$) is often written as $\\bar x$ and defined to be:\n", "\n", "\\begin{equation}\\label{eq:arithmetic-mean}\n", "\\bar x = \\frac{1}{n} \\sum_{i=1}^n x_i\n", "\\end{equation}\n", "\n", "In Python, the mean can be calculated in a variety of ways: the ``pandas.Series`` method ``mean()``,\n", "the ``numpy.ndarray`` method ``mean()``, the function ``statistics.mean()``, and the function\n", "``numpy.mean()``. The following line of code demonstrates how to calculate the mean of our\n", "``realrinc2015`` observations:" ] }, { "cell_type": "code", "execution_count": 10, "id": "9edac18b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "51296.749024906276\n" ] } ], "source": [ "print(df['realrinc2015'].mean())" ] }, { "cell_type": "markdown", "id": "fd97c9a5", "metadata": {}, "source": [ "The second widely used summary statistic is the median. The median value of a sample or distribution is\n", "the middle value: a value which splits the sample in two equal parts. That is, if we order the\n", "values in the sample from least to greatest, the median value is the middle value. If there are an\n", "even number of values in the sample, the median is the arithmetic mean of the two middle values. The\n", "following shows how to calculate the median:" ] }, { "cell_type": "code", "execution_count": 11, "id": "7f66956e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37160.92814781022\n" ] } ], "source": [ "print(df['realrinc2015'].median())" ] }, { "cell_type": "markdown", "id": "647345a8", "metadata": {}, "source": [ "These two measures of location, mean and median, are often not the same. In this case they differ by\n", "a considerable amount, more than \\$14,000. This is not a trivial amount; \\$14,000 is more than a third of the typical annual income of an individual in the United States according to the GSS.\n", "\n", "```{margin}\n", "While there was a recession in the United States between March 2001 and November 2001, it was brief.\n", "Naturally it would be inappropriate to regard samples from the year 2006, 2008, and 2010 as samples from roughly the\n", "same time period due to the financial crisis of 2007-–2008.\n", "```\n", "Consider, for example, the mean and the median household incomes for respondents with bachelor's degrees in 1998, 2000,\n", "and 2002. Since our household income figures are in constant dollars and the time elapsed between surveys is short, we\n", "can think of these subsamples as, roughly speaking, simple random samples (of different sizes) from the same underlying\n", "distribution. That is, we should anticipate that, after adjusting for\n", "inflation, the income distribution associated with a respondent with a bachelor's degree\n", "is roughly the same; variation, in\n", "this case, is due to the process of sampling and not any meaningful changes in the income distribution." ] }, { "cell_type": "code", "execution_count": 12, "id": "59a05df2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| | size | mean | median |\n", "|--------------------+--------+---------+----------|\n", "| (1998, 'bachelor') | 363 | 63805.5 | 48359.4 |\n", "| (2000, 'bachelor') | 344 | 58819.4 | 46674.8 |\n", "| (2002, 'bachelor') | 307 | 85469.2 | 50674 |" ], "text/plain": [ " size mean median\n", "year degree \n", "1998 bachelor 363 63805.508302 48359.364964\n", "2000 bachelor 344 58819.407571 46674.821168\n", "2002 bachelor 307 85469.227956 50673.992929" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_bachelor = df[df['degree'] == 'bachelor']\n", "# observed=True instructs pandas to ignore categories\n", "# without any observations\n", "df_bachelor.groupby(['year', 'degree'], observed=True)['realrinc2015'].agg(['size', 'mean', 'median'])" ] }, { "cell_type": "markdown", "id": "58a2749f", "metadata": {}, "source": [ "We can observe that, in this sample, the mean is higher than the median and also more variable. This\n", "provides a taste of the difference between these statistics as summaries. To recall the analogy we\n", "began with: if summary statistics are like paraphrases of prose or poetry, the mean and median are\n", "analogous different strategies for paraphrasing.\n", "\n", "Given this, information we are justified in asking why the mean, as a strategy for summarizing data,\n", "is so familiar and, indeed, more familiar than other summary statistics such as the median. One\n", "advantage of the mean is that it is the unique \"right\" guess if you are trying to pick a single\n", "number which will be closest to a randomly selected value from the sample when distance from the\n", "randomly selected value is penalized in proportion to the *square* of the distance between the\n", "number and the randomly selected value. The median does not have this particular property.\n", "\n", "A dramatic contrast between the median and mean is visible if we consider what happens if our data\n", "has one or more corrupted values. Let's pretend that someone accidentally added an extra \"0\" to one\n", "of the respondent incomes when they were entering the data from the survey worker into a computer.\n", "(This particular error is common enough that it has a moniker: it is an error due to a \"fat\n", "finger\".) That is, instead of \\$143,618, suppose the number \\$1,436,180 was entered. This small mistake has a severe impact on the mean:" ] }, { "cell_type": "code", "execution_count": 13, "id": "a0de719c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "220802.375\n" ] } ], "source": [ "realrinc2015_corrupted = [11159, 13392, 31620, 40919, 53856, 60809, 118484, 1436180]\n", "print(np.mean(realrinc2015_corrupted))" ] }, { "cell_type": "markdown", "id": "b110947e", "metadata": {}, "source": [ "By contrast, the median is not changed:" ] }, { "cell_type": "code", "execution_count": 14, "id": "748e06c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "47387.5\n" ] } ], "source": [ "print(np.median(realrinc2015_corrupted))" ] }, { "cell_type": "markdown", "id": "08bcddbc", "metadata": {}, "source": [ "Because the median is less sensitive to extreme values it is often labeled a \"robust\" statistic.\n", "\n", "An additional advantage of the median is that it is typically a value which actually occurs in the\n", "dataset. For example, when reporting the median income reported by the respondents there is\n", "typically at least one household with an income equal to the median income. With respect to income,\n", "*this particular household* is the typical household. In this sense there is an identified household which receives\n", "a typical income or has a typical size. By contrast, there are frequently no households associated\n", "with a mean value. The mean number of children in a household might well be 1.5 or 2.3, which does\n", "not correspond to any observed family size.\n", "\n", "If transformed into a suitable numerical representation, categorical data can also be described\n", "using the mean. Consider the non-numeric responses to the ``readfict`` question. Recall that the\n", "``readfict`` question asked respondents if they had read any novels, short stories, poems, or plays\n", "not required by work or school in the last twelve months. Responses to this question were either \"yes\"\n", "or \"no\". If we recode the responses as numbers, replacing \"no\" with 0 and \"yes\" with 1, nothing\n", "prevents us from calculating the mean or median of these values." ] }, { "cell_type": "code", "execution_count": 15, "id": "cb01d0e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "37731 0\n", "42612 1\n", "37158 1\n", "35957 1\n", "41602 1\n", "42544 1\n", "35858 0\n", "36985 1\n", "Name: readfict, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "readfict_sample = df.loc[df['readfict'].notnull()].sample(8)['readfict']\n", "readfict_sample = readfict_sample.replace(['no', 'yes'], [0, 1])\n", "readfict_sample" ] }, { "cell_type": "code", "execution_count": 16, "id": "f7f4258c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean: 0.75\n", "Median: 1.0\n" ] } ], "source": [ "print(\"Mean:\", readfict_sample.mean())\n", "print(\"Median:\", readfict_sample.median())" ] }, { "cell_type": "markdown", "id": "0f8574da", "metadata": {}, "source": [ "## Dispersion\n", "(sec-statistics-essentials-dispersion)=\n", "\n", "Just as the mean or median can characterize the \"typical value\" in a series of numbers, there also exist many ways to describe the diversity of values found in a series of numbers. This section reviews descriptions frequently used in quantitative work in the humanities and social sciences." ] }, { "cell_type": "code", "execution_count": 17, "id": "aa43c518", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_30_0.png" } }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "df['realrinc2015'].plot(kind='hist', bins=30);" ] }, { "cell_type": "markdown", "id": "59077c81", "metadata": {}, "source": [ "\n", "\n", "The plot shows all respondents' reported household income. The mean income is roughly\n", "\\$51,000. Reported incomes vary considerably. Contrast the figure above with the histogram\n", "of a fictitious set of simulated incomes below:" ] }, { "cell_type": "code", "execution_count": 18, "id": "13392af3", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_32_0.png" } }, "output_type": "display_data" } ], "source": [ "# simulate incomes from a gamma distribution with identical mean\n", "alpha = 5\n", "sim = np.random.gamma(alpha, df['realrinc2015'].mean() / alpha, size=df.shape[0])\n", "sim = pd.Series(sim, name='realrinc2015_simulated')\n", "sim.plot(kind='hist', bins=30);" ] }, { "cell_type": "markdown", "id": "38885b97", "metadata": {}, "source": [ "\n", "\n", "The two series visualized above may look different, but they are also similar. For example, they each have the same number of observations ($n$ =\n", "5447) and a common mean. But the observations clearly come from different distributions. The figures\n", "make clear that one series is more concentrated than the other. One way to quantify this\n", "impression is to report the *range* of each series. The range is the maximum value in a series minus\n", "the minimum value." ] }, { "cell_type": "code", "execution_count": 19, "id": "4c776b9f", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# HIDE THIS CELL\n", "# REALITY CHECK\n", "import math\n", "math.isclose(df['realrinc2015'].mean(), np.mean(sim), rel_tol=0.01);" ] }, { "cell_type": "code", "execution_count": 20, "id": "c1b6f950", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observed range: 505,479\n", "Simulated range: 203,543\n" ] } ], "source": [ "# Name this function `range_` to avoid colliding with the built-in\n", "# function `range`.\n", "def range_(series):\n", " \"\"\"Difference between the maximum value and minimum value.\"\"\"\n", " return series.max() - series.min()\n", "\n", "\n", "print(f\"Observed range: {range_(df['realrinc2015']):,.0f}\\n\"\n", " f\"Simulated range: {range_(sim):,.0f}\")" ] }, { "cell_type": "markdown", "id": "13e1cd91", "metadata": {}, "source": [ "The range of the fictitious incomes is much less than the range of the observed respondent incomes.\n", "Another familiar measure of the dispersion of a collection of numeric values is the sample\n", "*variance* and its square root, the sample *standard deviation*. Both of these are available for Pandas `Series`:" ] }, { "cell_type": "code", "execution_count": 21, "id": "a37e6a71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observed variance: 4619178856.92\n", "Simulated variance: 536836056.00\n" ] } ], "source": [ "print(f\"Observed variance: {df['realrinc2015'].var():.2f}\\n\"\n", " f\"Simulated variance: {sim.var():.2f}\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "b69268f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Observed std: 67964.54\n", "Simulated std: 23169.72\n" ] } ], "source": [ "print(f\"Observed std: {df['realrinc2015'].std():.2f}\\n\"\n", " f\"Simulated std: {sim.std():.2f}\")" ] }, { "cell_type": "markdown", "id": "711039f3", "metadata": {}, "source": [ "The sample variance is defined to be, approximately, the mean of the squared deviations from the\n", "mean. In symbols this reads:\n", "\n", "\\begin{equation}\\label{eq:sample-variance}\n", "s^2 = \\frac{1}{n-1} \\sum_{i=1}^n (x_i - \\bar x)^2\n", "\\end{equation}\n", "\n", "```{margin}\n", "The estimate is more reliable in the sense that it will be closer to the variance of the\n", "underlying distribution as the number of samples increase, when the underlying \n", "distribution has a defined variance.\n", "```\n", "The $n-1$ in the denominator (rather than $n$) yields a more reliable estimate of the variance of\n", "the underlying distribution. When dealing with a large number of observations the\n", "difference between $\\frac{1}{n-1}$ and $\\frac{1}{n}$ is negligible. The ``std()`` methods of a\n", "``DataFrame`` and ``Series`` use this definition as does Python's ``statistics.stdev()``.\n", "Unfortunately, given the identical function name, ``numpy.std()`` uses a different definition and must be\n", "instructed, with the additional parameter ``ddof=1`` to use the corrected estimate. The following\n", "block of code shows the various ``std`` functions available and their results." ] }, { "cell_type": "code", "execution_count": 23, "id": "5878807c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "statistics.stdev: 23169.7\n", " sim.std: 23169.7\n", " np.std: 23167.6\n", " np.std(ddof=1): 23169.7\n" ] } ], "source": [ "# The many standard deviation functions in Python:\n", "import statistics\n", "\n", "print(f\"statistics.stdev: {statistics.stdev(sim):.1f}\\n\"\n", " f\" sim.std: {sim.std():.1f}\\n\"\n", " f\" np.std: {np.std(sim):.1f}\\n\"\n", " f\" np.std(ddof=1): {np.std(sim, ddof=1):.1f}\")" ] }, { "cell_type": "markdown", "id": "8d74273a", "metadata": {}, "source": [ "Other common measures of dispersion include the mean absolute deviation (around the mean) and the interquartile range\n", "(IQR). The mean absolute deviation is defined, in symbols, as $\\frac{1}{n} \\sum_{i=1}^n \\lvert x_i - \\bar x \\rvert$. In\n", "Python we can calculate the mean absolute deviation using the ``mad()`` method associated with the ``Series`` and\n", "``DataFrame`` classes. In this case we could write: ``df['realinc'].mad()``. The IQR is the difference between the upper\n", "and lower quartiles (the interquartile range or IQR). The IQR may be familiar from the ``boxplot`` visualization. Box\n", "plots use the IQR to bound the rectangle (the \"box\"). In our series, the 25th percentile is \\$20,000 and the 75th\n", "percentile is \\$61,000. The boxes in the box plots shown in figure {ref}`fig-statistics-essentials-realrinc-boxplots` have width equal to the\n", "IQR." ] }, { "cell_type": "code", "execution_count": 24, "id": "9443ec8c", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_42_1.png" } }, "output_type": "display_data" } ], "source": [ "# HIDE THIS CELL, JUST FOR PLOT\n", "from myst_nb import glue\n", "\n", "fig, axes = plt.subplots(2, sharex=True)\n", "df['realrinc2015'].plot(kind='box', vert=False, ax=axes[0])\n", "axes[0].set_yticklabels([\"Respondent's income\"])\n", "sim.plot(kind='box', vert=False, ax=axes[1])\n", "axes[1].set_yticklabels([\"Respondent's income (simulated)\"]);\n", "\n", "glue(\"fig_income\", fig, display=False)" ] }, { "cell_type": "markdown", "id": "db555a3a", "metadata": {}, "source": [ "```{glue:figure} fig_income\n", "---\n", "name: fig-statistics-essentials-realrinc-boxplots\n", "---\n", "\n", "Box plots of observed and simulated values for household income in constant 2015 US dollars.\n", "```\n", "\n", "Depending on the context, one measure of dispersion may be more appropriate than another. While the\n", "range is appealing for its simplicity, if the values you are interested in might be modeled as\n", "coming from a distribution with heavy or long \"tails\" then the range can be sensitive to sample size.\n", "\n", "Equipped with several measures of dispersion, we can interrogate the GSS and ask if we see patterns\n", "in income that we anticipate seeing. Is income more variable among respondents who graduate\n", "from university than it is among respondents whose highest degree is a high school diploma? One piece of\n", "evidence which would be consistent with an affirmative answer to the question would be seeing\n", "greater mean absolute deviation of income among respondents with a bachelor's degree than among\n", "respondents with only a high school diploma:" ] }, { "cell_type": "code", "execution_count": 25, "id": "29b9c8a3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "degree\n", "lt high school 19551.0\n", "high school 23568.0\n", "junior college 33776.0\n", "bachelor 45055.0\n", "graduate 77014.0\n", "Name: realrinc2015, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('degree')['realrinc2015'].mad().round()" ] }, { "cell_type": "markdown", "id": "e13357b9", "metadata": {}, "source": [ "Given the question we began this chapter with, we might also investigate whether or not there is an\n", "association with reading fiction and variability in respondents' incomes. To keep things simple, we\n", "will limit ourselves to respondents with bachelor's or graduate degrees:" ] }, { "cell_type": "code", "execution_count": 26, "id": "ee375af8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "degree readfict\n", "bachelor yes 48908.0\n", " no 119523.0\n", "graduate yes 82613.0\n", " no 133028.0\n", "Name: realrinc2015, dtype: float64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_bachelor_or_more = df[df['degree'].isin(['bachelor', 'graduate'])]\n", "df_bachelor_or_more.groupby(['degree', 'readfict'], observed=True)['realrinc2015'].mad().round()" ] }, { "cell_type": "markdown", "id": "20656323", "metadata": {}, "source": [ "The greater variability is being driven largely by the fact that respondents who do not report\n", "reading fiction tend to earn more. Looking at the means of these subgroups offers additional context:" ] }, { "cell_type": "code", "execution_count": 27, "id": "544887d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "degree readfict\n", "bachelor yes 71251.0\n", " no 139918.0\n", "graduate yes 113125.0\n", " no 153961.0\n", "Name: realrinc2015, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_bachelor_or_more.groupby(['degree', 'readfict'], observed=True)['realrinc2015'].mean().round()" ] }, { "cell_type": "markdown", "id": "55d4b74e", "metadata": {}, "source": [ "One can imagine a variety of narratives or generative models which might offer an account of this difference. Checking any one of these narratives would likely require more detailed information about individuals than is available from the GSS.\n", "\n", "The question of who reads (or writes) prose fiction has been addressed by countless researchers. Well-known studies include {cite:t}`hoggart1957uses`, {cite:t}`williams1961long`, {cite:t}`radway1991reading`, and {cite:t}`radway1999feeling`. {cite:t}`felski2008uses` and {cite:t}`collins2010bring` are examples of more recent work. Useful general background on the publishing industry during the period when the surveys were fielded can be found in {cite:t}`thompson2012merchants`.\n", "\n", "### Variation in categorical values\n", "(sec-statistics-essentials-variation)=\n", "\n", "Often we want to measure the diversity of categorical values found in a dataset. Consider the\n", "following three imaginary groups of people who report their educational background in the same form\n", "that is used on the GSS. There are three groups of people, and there are eight respondents in each group." ] }, { "cell_type": "code", "execution_count": 28, "id": "c90972da", "metadata": {}, "outputs": [], "source": [ "group1 = ['high school', 'high school', 'high school', 'high school', 'high school',\n", " 'high school', 'bachelor', 'bachelor']\n", "group2 = ['lt high school', 'lt high school', 'lt high school', 'lt high school',\n", " 'high school', 'junior college', 'bachelor', 'graduate']\n", "group3 = ['lt high school', 'lt high school', 'high school', 'high school',\n", " 'junior college', 'junior college', 'bachelor', 'graduate']" ] }, { "cell_type": "code", "execution_count": 29, "id": "32e86407", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 5, 5]\n", "[0.25, 0.625, 0.625]\n" ] } ], "source": [ "# calculate the number of unique values in each group\n", "print([len(set(group)) for group in [group1, group2, group3]])\n", "# calculate the ratio of observed categories to total observations\n", "print([len(set(group)) / len(group) for group in [group1, group2, group3]])" ] }, { "cell_type": "markdown", "id": "f532fa5f", "metadata": {}, "source": [ "The least diverse group of responses is group 1. There are only two distinct values (\"types\") in group\n", "1 while there are five distinct values in group 2 and group 3.\n", "\n", "Counting the number (or proportion of) distinct values is a simple way to measure\n", "diversity in small samples of categorical data. But counting the number of distinct values\n", "only works with small samples (relative to the number of categories) of the same size. For example, counting the number of distinct `degree`s reported for each region in the United States will not work because all possible values occur at least once (i.e., five distinct degrees occur in each region). Yet we know that some regions have greater variability of `degree` types, as table {numref}`tbl-statistics-essentials-proportion-degree-type` shows. To simplify things, Table {numref}`tbl-statistics-essentials-proportion-degree-type` shows only three regions: East South Central, New England, and Pacific." ] }, { "cell_type": "code", "execution_count": 30, "id": "7c9045c9", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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degree
reg16
new englandhigh school0.5
bachelor0.3
graduate0.1
junior college0.1
lt high school0.1
e. sou. centralhigh school0.6
lt high school0.1
bachelor0.1
junior college0.1
graduate0.1
pacifichigh school0.5
bachelor0.2
junior college0.1
graduate0.1
lt high school0.1
\n", "
" ], "text/org": [ "| | degree |\n", "|---------------------------------------+----------|\n", "| ('new england', 'high school') | 0.5 |\n", "| ('new england', 'bachelor') | 0.3 |\n", "| ('new england', 'graduate') | 0.1 |\n", "| ('new england', 'junior college') | 0.1 |\n", "| ('new england', 'lt high school') | 0.1 |\n", "| ('e. sou. central', 'high school') | 0.6 |\n", "| ('e. sou. central', 'lt high school') | 0.1 |\n", "| ('e. sou. central', 'bachelor') | 0.1 |\n", "| ('e. sou. central', 'junior college') | 0.1 |\n", "| ('e. sou. central', 'graduate') | 0.1 |\n", "| ('pacific', 'high school') | 0.5 |\n", "| ('pacific', 'bachelor') | 0.2 |\n", "| ('pacific', 'junior college') | 0.1 |\n", "| ('pacific', 'graduate') | 0.1 |\n", "| ('pacific', 'lt high school') | 0.1 |" ], "text/plain": [ " degree\n", "reg16 \n", "new england high school 0.5\n", " bachelor 0.3\n", " graduate 0.1\n", " junior college 0.1\n", " lt high school 0.1\n", "e. sou. central high school 0.6\n", " lt high school 0.1\n", " bachelor 0.1\n", " junior college 0.1\n", " graduate 0.1\n", "pacific high school 0.5\n", " bachelor 0.2\n", " junior college 0.1\n", " graduate 0.1\n", " lt high school 0.1" ] }, "metadata": { "scrapbook": { "mime_prefix": "application/papermill.record/", "name": "df" } }, "output_type": "display_data" } ], "source": [ "from myst_nb import glue\n", "\n", "# HIDE THIS CELL, JUST FOR TABLE\n", "# East South Central States are Alabama, Kentucky, Mississippi, Tennessee\n", "regions_oi = sorted(['pacific', 'e. sou. central', 'new england'])\n", "df_regions = df.loc[df['reg16'].isin(regions_oi)].copy()\n", "df_regions['reg16'] = df_regions['reg16'].cat.remove_unused_categories()\n", "glue(\"df\", df_regions.groupby('reg16')['degree'].value_counts(normalize=True).round(1).to_frame(), display=False)" ] }, { "cell_type": "markdown", "id": "94296cdb", "metadata": {}, "source": [ "```{glue:figure} df\n", ":name: tbl-statistics-essentials-proportion-degree-type\n", "\n", "Proportion of respondents in indicated region of the United States with named degree type. Data for three regions shown: East South Central, New England, and Pacific. \n", "```\n", "\n", "We would still like to be able to summarize the variability in observed categories, even in situations when the number of distinct categories observed is the same. Returning to our three groups of people, we can see that group 2 and group 3 have the same number of distinct categories. Yet group 3 is more diverse than group 2; group 2 has one member of classes \"high school\", \"junior college\", \"bachelor\", and \"graduate\". This is easy to see if we look at a table of degree counts by group, {numref}`tbl-statistics-essentials-degree-counts-by-group`.\n", "\n", "```{table} Degree counts by group\n", "---\n", "name: tbl-statistics-essentials-degree-counts-by-group\n", "---\n", "\n", "| | | Count |\n", "|---------|----------------|-------| \n", "| Group 1 | high school | 6 |\n", "| | bachelor | 2 |\n", "| | lt high school | 4 |\n", "| | high school | 1 |\n", "|---------|----------------|-------|\n", "| Group 2 | junior college | 1 |\n", "| | bachelor | 1 |\n", "| | graduate | 1 |\n", "| | lt high school | 2 |\n", "| | high school | 2 |\n", "|---------|----------------|-------|\n", "| Group 3 | junior college | 2 |\n", "| | bachelor | 1 |\n", "| | graduate | 1 |\n", "```\n", "\n", "\n", "```{margin}\n", "The Simpson Index and the Herfindahl–Hirschman Index are other frequently encountered\n", "measures of diversity. These measures capture essentially the same information as Shannon\n", "entropy. \n", "```\n", "Fortunately, there is a measure from information theory which distills judgments of diversity among categorical values\n", "into a single number. The measure is called *entropy* (more precisely, *Shannon entropy*). One way to appreciate how this measure works is to consider the task of identifying what category a survey respondent belongs to using only questions which have a \"yes\" or a \"no\" response. For example, suppose the category whose diversity you are interested in quantifying is highest educational degree and a survey\n", "respondent from ``group2`` has been selected at random. Consider now the following question: what is the *minimum*\n", "number of questions we will have to ask this respondent *on average* in order to determine their educational background?\n", "Group 2 has respondents with self-reported highest degrees shown above. Half the respondents have an educational\n", "background of ``lt high school`` so half of the time we will only need to ask a single question, \"Did you graduate from\n", "high school?\", since the response will be \"no\". The other half of the time we will need to ask additional questions. No\n", "matter how we order our questions, we will sometimes be forced to ask three questions to determine a respondent's\n", "category. The number of questions we need to ask is a measure of the heterogeneity of the group. The more heterogeneous,\n", "the more \"yes or no\" questions we need to ask. The less heterogeneous, the fewer questions we need to ask. In the\n", "extreme, when all respondents are in the same category, we need to ask zero questions since we know which category a\n", "randomly selected respondent belongs to.\n", "\n", "If the frequency of category membership is equal, the average number of \"yes or no\" questions we\n", "need to ask is equal to the (Shannon) entropy. Although the analogy breaks down when the frequency\n", "of category membership is not equal, the description above is still a useful summary of the concept.\n", "And the analogy breaks down for very good reasons: although it is obvious that with two categories one\n", "must always ask at least one question to find out what category a respondent belongs to, we still\n", "have the sense that it is important to distinguish---as entropy does in fact do---between situations\n", "where 90% of respondents are in one of two categories and situations where 50% of respondents are in\n", "one of two categories.\n", "\n", "```{tip}\n", "A useful treatment of entropy for those encountering it for the first time is found in {cite:t}`frigg2011entropy`.\n", "```\n", "\n", "While entropy is typically used to describe probability distributions, the measure is also used\n", "to describe samples. In the case of samples, we take the observed frequency distribution as an\n", "estimate of the distribution over categories of interest. If we have $K$ categories of interest and\n", "$p_k$ is the empirical probability of drawing an instance of type $k$, then the entropy (typically\n", "denoted $H$) of the distribution is:\n", "\n", "\\begin{equation}\\label{eq:entropy}\n", "H = - \\sum_{k=1}^K p_k \\log(p_k).\n", "\\end{equation}\n", "\n", "```{margin}\n", "In the calculation of entropy, $0 \\cdot \\log(0)$ is equal to zero. This can be the source of some confusion since in other\n", "settings $\\log(0)$ is not defined. In Python ``0 * math.log(0)`` raises a ``ValueError``, for\n", "instance. The argument in favor of $0 \\cdot \\log(0) = 0$ rests on an investigation of the limit of\n", "the expression, $\\lim_{x \\searrow 0} x \\cdot \\log(x)$.\n", "```\n", "The unit of measurement for entropy depends on the base of the logarithm used. The base used in the calculation of entropy is either 2\n", "or $e$, leading to measurements in terms of \"bits\" or \"nats\" respectively. Entropy can be calculated in Python using the\n", "function ``scipy.stats.entropy()`` which will accept a finite probability distribution or an\n", "unnormalized vector of category counts. That ``scipy.stats.entropy()`` accepts a sequence of category\n", "counts is particularly useful in this case since it is just such a sequence which we have been using\n", "to describe our ``degree`` diversity.\n", "\n", "The following block illustrates that entropy aligns with our expectations about the diversity of the\n", "simulated groups of respondents (``group1``, ``group2``, ``group3``) mentioned earlier. (Note that\n", "``scipy.stats.entropy()`` measures entropy in nats by default.)" ] }, { "cell_type": "code", "execution_count": 31, "id": "7c43fc70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Group 1 entropy: 0.6\n", "Group 2 entropy: 1.4\n", "Group 3 entropy: 1.6\n" ] } ], "source": [ "import collections\n", "import scipy.stats\n", "\n", "# Calculate the entropy of the empirical distribution over degree\n", "# types for each group\n", "for n, group in enumerate([group1, group2, group3], 1):\n", " degree_counts = list(collections.Counter(group).values())\n", " H = scipy.stats.entropy(degree_counts)\n", " print(f'Group {n} entropy: {H:.1f}')" ] }, { "cell_type": "markdown", "id": "697fccfe", "metadata": {}, "source": [ "As we can see, ``group1`` is the least diverse and ``group3`` is the most diverse. The diversity of\n", "``group2`` lies between the diversity of ``group1`` and ``group3``. This is what we anticipated.\n", "\n", "Now that we have a strategy for measuring the variability of observed types, all that remains is to apply it to the data of interest. The following block illustrates the use of entropy to compare the variability of responses to the `degree` question for respondents in different regions of the United States:" ] }, { "cell_type": "code", "execution_count": 32, "id": "be71dbdb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "reg16\n", "foreign 1.505782\n", "new england 1.345351\n", "middle atlantic 1.321904\n", "e. nor. central 1.246287\n", "w. nor. central 1.211067\n", "south atlantic 1.261397\n", "e. sou. central 1.196932\n", "w. sou. central 1.290568\n", "mountain 1.214591\n", "pacific 1.283073\n", "Name: degree, dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('reg16')['degree'].apply(lambda x: scipy.stats.entropy(x.value_counts()))" ] }, { "cell_type": "markdown", "id": "2331f5d2", "metadata": {}, "source": [ "Looking at the entropy values we can see that respondents from the New England states\n", "report having a greater diversity of educational backgrounds than respondents in other states.\n", "Entropy here gives us similar information as the proportion of distinct values but the measure is\n", "both better aligned with our intuitions about diversity and usable in a greater variety of\n", "situations.\n", "\n", "\n", "(sec-statistics-essentials-measuring-association)=\n", "## Measuring Association\n", "\n", "(sec-statistics-essentials-measuring-association-numbers)=\n", "### Measuring association between numbers\n", "\n", "When analyzing data, we often want to characterize the association between two variables. To return to\n", "the question we began this chapter with---whether respondents who report having certain\n", "characteristics are more likely to read novels---we might suspect that\n", "knowing that a region has an above average percentage of people with an advanced degree\n", "would \"tell us something\" about the answer to the question of whether or not an above average\n", "percentage has read a work of fiction recently. Informally, we would say that we suspect higher\n", "levels of education are associated with higher rates of fiction reading. In this section we will\n", "look at two formalizations of the idea of association: the correlation coefficient and the rank correlation coefficient.\n", "\n", "In this section we have tried to avoid language which implies that a causal relationship exists between any two variables. We do not intend to discuss the topic of causal relationships in this chapter. Two variables may be associated for any number of reasons. Variables may also be associated by chance.\n", "\n", "One association that is visible in the data is that older individuals tend to have higher incomes. To examine the relationship more closely we will first restrict our sample of the GSS to a\n", "relatively homogeneous population: respondents between the ages of 23 and 30 with a bachelor's\n", "degree. To further restrict our sample to individuals likely to be employed full-time, we will also\n", "exclude any respondents with an annual income of less than \\$10,000. The first block of code below\n", "assembles the subsample. The second block of code creates a scatter-plot allowing us to see the\n", "relationship between age and income in the subsample." ] }, { "cell_type": "code", "execution_count": 33, "id": "1354d194", "metadata": {}, "outputs": [], "source": [ "df_subset_columns = ['age', 'realrinc2015_log10', 'reg16', 'degree']\n", "min_income = 10_000\n", "df_subset_index_mask = ((df['age'] >= 23) & (df['age'] <= 30) &\n", " (df['degree'] == 'bachelor') &\n", " (df['realrinc2015'] > min_income))\n", "df_subset = df.loc[df_subset_index_mask, df_subset_columns]\n", "# discard rows with NaN values\n", "df_subset = df_subset[df_subset.notnull().all(axis=1)]\n", "# age is an integer, not a float\n", "df_subset['age'] = df_subset['age'].to_numpy().astype(int)" ] }, { "cell_type": "markdown", "id": "4b2fb25b", "metadata": {}, "source": [ "In the block of code above we have also removed respondents with NA values (non-response, \"I don't\n", "know\" responses, etc.) for ``degree`` or ``age``. Without any NaN's to worry about we can convert\n", "``age`` into a ``Series`` of integers (rather than floating-point values)." ] }, { "cell_type": "code", "execution_count": 34, "id": "7b4dba7a", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_61_0.png" } }, "output_type": "display_data" } ], "source": [ "# Small amount of noise (\"jitter\") to respondents' ages makes\n", "# discrete points easier to see\n", "_jitter = np.random.normal(scale=0.1, size=len(df_subset))\n", "df_subset['age_jitter'] = df_subset['age'].astype(float) + _jitter\n", "ax = df_subset.plot(x='age_jitter', y='realrinc2015_log10', kind='scatter', alpha=0.4)\n", "ax.set(ylabel=\"Respondent's income (log10)\", xlabel=\"Age\");" ] }, { "cell_type": "markdown", "id": "4303c793", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 35, "id": "a5136116", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# HIDE THIS CELL\n", "# calculate values used in paragraph below\n", "x, y = np.arange(23, 31), df_subset.groupby('age')['realrinc2015_log10'].median()\n", "slope, intercept = np.polyfit(x, y, deg=1)\n", "[slope.round(3), intercept.round(2), (10**slope - 1).round(2), y[[23, 30]].round(1), (10**y[[23, 30]]).round(-3)];" ] }, { "cell_type": "markdown", "id": "dad93dd0", "metadata": {}, "source": [ "The income of respondents with bachelor's degrees tends to increase with age. The median income of a\n", "23-year-old with a bachelor's degree is roughly \\$25,000 ($10^{4.4}$) and the median income of a 30-year-old with a bachelor's degree is roughly \\$48,000 ($10^{4.7}$). Looking at the incomes for\n", "respondents with ages between 23 and 30, it seems like median income increases about 8% each\n", "year. Using the $\\log_{10}$ scale, we express this by saying that log income rises by 0.035 each year\n", "($10^{0.035} - 1 \\approx 8\\%$). This account of the relationship between income\n", "and age (between 23 and 30) is sufficiently simple that it can be captured in an equation which\n", "relates log income to age: $\\text{log income} \\approx 0.035 \\times \\text{age} + 3.67$. This equation conveniently provides a procedure for estimating log income of a respondent given their age: multiply their age\n", "by the number 0.035 and add 3.67.\n", "\n", "This equation also describes a line. The following code block overlays this line on the\n", "first scatter plot:" ] }, { "cell_type": "code", "execution_count": 36, "id": "5ffa7e38", "metadata": {}, "outputs": [ { "data": { "image/png": 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ft2PHDm3dulVSbP3cw4Png30dtGLFimOOfXibfv361aluAAAAoLnI8pkamO3V6pKw0t3GEWtO+kxDnduYyvQY8pqsOQmgeTn8/OX3urTfchSK2mrjNuR1GUpzG/KZhkyXlJ/pVronFrR2SXdpc1lUwYijomA0oWC1KBjrJ+I4yk8/OvQ9aF/YVpppKOrY8pgu7Qs76tDm6HaGYSS1PgA1S2iph3Xr1skwDN12222Evt+zePHi+HZ9Z8yed9558e3qLoR20Lx58+Lb559//lGPn3zyyerWrZuk2PP27bffVttXMBjUkiVLJMVCfZ5bAAAAtGRZPlOnd/WpTwevumaY6prpVoHfra6ZbnXNMNWng1end/UR+gJodrJ8pobmpKm9zyWvy5A/zVDndFPtfC5lpbmUk2HqhHaeeOgrxWbWppmGAmFbgYgjq57LKVi2o8CBN8vSzKNn8B5kO7ELzVm25DEl25ZCliO7mnGTVR+AY0so+A2Hw5JUrwuXpbINGzZo1qxZ8dvjxo2rVz9nnXWWcnJyJEnvv/++Vq5cWWU7y7L06KOPxm9PnDixynZXXHFFfPuRRx6pdtypU6eqvLxckjR+/Hilp6fXuXYAAACgOXG7DOX73RqS69PZ+T6dmRf7PiTXp3y/m+UdADRbhmGovc9U53RT3f1unZjlUe8sj45r51F7nylXFecvj8uQdSBPteqZqx6+v6eGc+TB3NaW5DEMHVxJ3a5uhyTVB+DYEgp+D84gDYVCSSmmuXv00Uf1ySef1Nhm1apVGjNmTPzfZPTo0RoyZMhR7WbOnCnDiF1VeMSIEVX2ZZqmHnjggfjt6667Tjt37jyq3X/8x3/oX//6lyTp9NNP15gxY6rs7xe/+IX8fr8k6fHHH9cbb7xxVJvly5frN7/5jSTJ7XZrypQp1f+wAAAAQAtkumJLO5iEvQBaANM49D3qxN7IqirsPVzEdo7YL9FxIzXMyj1YiktSxHHiQVNNgVMy6gNwbAktpDJ+/Hj9+9//1ocffqjrrrsuWTU1W4sWLdI999yjnj17auTIkerXr586duwo0zS1bds2LVy4UPPmzZNtx97X6t69u2bMmJHQmJMnT9acOXP07rvv6t///rf69++vyZMn6+STT9aePXv04osv6qOPPpIUW0v4qaeeqrav7OxsPfbYY7rhhhtk27YuvvhiTZw4UaNGjZJpmvr444/17LPPxkPrhx56SCeddFJC9QMAAAAAgPozXYb8HkN+r0v7glGFona1yy5IUihqq9Jy1LmNKb+n/m9y1XZclxFbazjokiKW5HLF1lCvLpxOVn0Aji2h4Pfuu+/WtGnT9Nxzz+nOO+/UD3/4wySV1bxt2rRJmzZtqrHNmDFjNH36dHXt2jWhsdxut/7xj3/oqquu0ltvvaXt27frd7/73VHt8vPzNXv2bPXt27fG/q6//npVVFTo3nvvVSgU0gsvvKAXXnjhiDamaer+++/Xr371q4RqBwAAAAAAicvLdKssbMtjGNpRYaub36jyQmuO42hHRaxdpsdQXmZiF06r7bjtvC4VBaNyG4YilqN23qrD3GTXB6BmCS310KVLF82ZM0dt27bVqFGjNHv2bDlO6i7O8vDDD2vatGmaPHmyTjvtNPXo0UOZmZnyeDzq1KmTBg8erLvuukvLli3TggULEg59D/L7/XrzzTf12muv6ZJLLlFBQYHS0tLUqVMnDRkyRH/605/05Zdf6kc/+lGt+rvtttu0Zs0a3XvvvTr55JPl9/uVkZGhE088UbfeeqtWrFihhx56KCm1AwAAAACAxORkmPKahnIzTe2P2toSsBSKHrmKbujA/fujtnIzY+1zMhK7aGVtx90XtmU5UpcMl6K2VBZ2GqU+ADUznASS2kmTJkmSvvvuOy1evFiGYSg7O1uDBw9Wx44d5XLVnCsbhqFnnnmmvsOjBcrPz1dRUZHy8vJUWFjY1OUAAAAAQLNk2Y4sJ7b+KR+FhySVhiyt3BlWIGKrOGgp4jhKMw15XIYitqNKy5HHiIW0fo9LA7O9yvIlHqzWdtwsn6E9+211bGNqb8hutPoAVC+h4NflclU5xb8uLMtKaH+0LAS/AAAAAFC1qO1oe7mlomBUgcihl+r+Ax+Jz8kw5SYEbtVKQ5ZWl4QVthwFI44CB2bamobk97qU6YldvLJ/p+SGqrUdt4ffrW8D0UavD0DVEl5MJZGlHRINjQEAAAAASAXHCtbKwrY27SMwa+2yfKZO7+o79AaB99AnrRvyDYK6jJvndzd6fQCqllDwu3nz5mTVAQAAAABAq1TTR+lDlqN9wWj8I/Ird4b5iHwr53YZyve7le93N+qSILUdt6nqA3C0hILf7t27J6sOAAAAAABanajtaHVJLPTdGoiqjdul/HRTPvehmZKhqK0dFbHHC/xurS4J6/SuPmZNQqbLUFO8BVDbcZuqPgAxNV99DQAAAAAANJjt5ZbClqPioKU2bpe6+Y8MfSXJd+D+Nm6XioOx9tvLuV4OAKBmBL8AAAAAADSRomBUwYijiOOoS3r1F1A3DENd0l2KOLE1gIuC0UauFADQ0iR8cbfv++6777Rs2TIVFxcrEAjI7/era9euGjJkCEtDAAAAAABwgGU7Chy4kFuaaRw10/f7fG6X0kxDgbCtgNcly3ZYNxUAUK2kBb+vvvqq/vjHP2rVqlXVthkwYIB+9atf6ZJLLknWsAAAAAAAtEiWc+i7p5YBrsdlHLEf66cCAKqT8FIPtm3r+uuv1xVXXKFVq1bJcZxqv1atWqUJEybohhtukOM4yagfAAAAAIAWyTQOfY/YtXuNHLGdI/YDAKA6Cc/4vfvuuzVr1qz47Z49e2r06NHq1auXMjMzFQwGtWHDBr377rv6+uuvJUmzZs2S3+/XY489lujwAAAAAAC0SKbLkN9jyO91aV8wqlDUrnG5h1DUVqXlqHMbU36PwTIPAIAaGU4CU29XrlypU089VZKUlZWlJ598Updffnm17V955RXddttt2rNnj1wulz799FMNHDiwvsOjBcrPz1dRUZHy8vJUWFjY1OUAAAAAQJMqDES1bk9Ym0qj8piGuvnNKi/w5jiOtgQsRSxHPbPc6tPBq3x/0i/bAwBIIQkt9fD000/LcRx5PB699957NYa+kjRhwgS9++678nq9chxHTz/9dCLDAwAAAADQouVkmPKahnIzTe2P2toSsBSK2ke0CR24f3/UVm5mrH1OBqv7AgBqllDw+8EHH8gwDF1zzTUaMGBArfYZMGCArr32WjmOo/fffz+R4QEAAAAAaNHcLkP9O3nl97hU4HcrYjnaXBbVN/si2hqIfd9cFlXEclTgd8vvcal/J6/cLPMAADiGhILfoqIiSdKZZ55Zp/2GDx8uSdq2bVsiwwMAAAAA0OJl+UwNzPYqK82lnllu5We65Ttw5TafaSg/062eWW5lpbli7XzM9gUAHFtCCwJFo1FJktfrrdN+B9sf3B8AAAAAgNYsy2fq9K4+bS+3VBSMKuA9NE/L7zGUl+lWTobJTF8AQK0lFPxmZ2dry5YtWr16tSZOnFjr/dasWSNJ6ty5cyLDAwAAAACQMtwuQ/l+t/L9blm2I8uRTEMyCXsBAPWQ0FIPQ4YMkeM4mjFjhvbu3Vurffbs2aNnnnlGhmFo6NChiQwPAAAAAEBKMl2GvKZB6AsAqLeEgt8rrrhCkrRr1y6dd955KiwsrLH91q1bNXbsWO3atUuS6jRLGAAAAAAAAABQO4bjOE4iHZx55pn66KOPZBiG0tPTdcUVV2j06NHq1auXMjIyVF5ero0bN+qdd97RSy+9pIqKCknSGWecoQ8++CApPwRajvz8fBUVFSkvL++YbxQAAAAAAAAAqJ+Eg9+SkhINHz5c69evj3VoVP8xlINDnXTSSVqyZIk6duyYyNBogQh+AQAAAAAAgIaX0FIPktSpUyd99tlnuu222+Tz+eQ4TrVfPp9Pd9xxh1asWEHoCwAAAAAAAAANJOEZv4crKSnRvHnztHz5chUXFysQCMjv9ys3N1dDhgzRBRdcQODbyjHjFwAAAAAAAGh4SQ1+gWMh+AUAAEBrY9mOLEcyDcl0Vb80HlJTYz3/HGcAgO9zN3UBAAAAAJBqoraj7eWWioJRBSKH5tr4PYbyMt3KyTDlJpxLWY31/HOcAQBqQvALAAAAAElUGrK0uiSssOUoGHEUCNvxmZh+r0tlYVub9hnq38mrLJ/Z1OUiyRrr+ec4AwAcS0IXd9u7d68uvfRSXXLJJVq0aFGt9lm0aJEuueQSTZgwQcFgMJHhAQAAAKBZKQ1ZWrkzrNJKW5tKoyoMRhWyYjMxQ5ajwmBUm0qjKq20Y+1CVhNXjGRqrOef4wwAUBsJrfH7t7/9TbfffrsyMjJUXFyszMzMY+4TDAbVtWtXlZeXa+rUqbrpppvqOzxaINb4BQAAQKqK2o4+3hZSaaWtrYGo2rhd6pLuks99aL5NKGprR4Wt/VFbBX63stJcOr2rj4/jp4DGev45zgAAtZXQjN933nlHkjRmzJhahb6SlJmZqbFjx8pxHC1YsCCR4QEAAACg2dhebilsOSoOWmrjdqmb3zwijJMk34H727hdKg7G2m8vZzZmKmis55/jDABQWwkFv6tXr5ZhGPrRj35Up/2GDh0a3x8AAAAAUkFRMKpgxFHEcdQl3SXDqHp2pWEY6pLuUsSJrc1aFIw2cqVoCI31/HOcAQBqK6Hgt7i4WFLs4/t10bVrV0nStm3bEhkeAAAAAJoFy3YUOHCBrTTTOGoG5vf53C6lmYYCYVuBiCPLrvcKfGgGGuv55zgDANRFQsHvweWBbduu034H20ejvOMIAM2NZTsKW7wwAACgLg5cV0uWI3lquY6qx2UcsR9arsZ6/jnOADRHvIZsvtyJ7NypUydt27ZNmzZtqtN+33zzjSSpQ4cOiQwPAEiSqB1b960oGFUgcuiXtd9jKC/TrZwMk4uBAABQA9M49D1Uy3QtYjvyHdjR5Ndsi9ZYzz/HGYDmgteQLUNCM35POeUUOY6jOXPm1Gm/OXPmyDAM9enTJ5HhAQBJUBqy9PG2kNbtCWtbuaVtwai2BqLaFoxqW7mldXvCsStHh7ggCAAA1TFdhvweQ36vS5WWo1C05k9FhqK2Ki1Hfq9Lfo8hkxfHLVpjPf8cZwCaA15DthwJBb9jxoyRJK1atUrTp0+v1T7Tpk3TypUrJUljx45NZHgAQIJKQ5ZW7gyrtNLWptKoCoPR+OyRkOWoMBjVptKoSivtWDt+cQMAUK28TLcyPYY8hqEdFXZ8abzvcxxHOypseQxDmQdmRqHla6znn+MMQFPiNWTLklDwe9NNN8WXa7jtttv0yCOPyLKqfkIty9LDDz+sO+64Q5LUrl07TZ48OZHhAQAJiNqOVpeEFYjY2hqIymMaOq6tW8e386jAH/t+XFu3PKahrYGoAhFbq0vCirJuEwAAVcrJMOU1DeVmmtoftbUlYB01IzN04P79UVu5mbH2ORlmE1WMZGqs55/jDEBT4TVky2M41b09WEsvvfSSrrrqKhlG7CMjOTk5Gjt2rE4++WRlZmYqGAxq7dq1mj9/vrZv3y7HcWQYhmbNmqWrrroqKT8EWo78/HwVFRUpLy9PhYWFTV0O0KoVBqJatyesTaWxX9jd/Gb8XH44x3G0JWApYjnqmeVWnw5e5fuZMQIAQFUOzoQKRGwVBy1FHEdppiGPy1DEdlRpOfIYsdDO73FpYLZXWT4CuVTRWM8/xxmApsBryJYn4X/1iRMnqqSkRPfee6+i0ai2b9+uGTNmVNnWcRy53W79z//8D6EvADSxomBUwYijiOMoP73qX9iSZBiGuqS7tLks1r4oGOWXNgAA1cjymRqY7dXqkrDS3YaCEUeBsC3LkXymoc5tTGV6DHlNQ/07EcalmsZ6/jnOADQFXkO2PEn5V7/zzjt12mmnacqUKXrnnXeqXGPIMAydf/75evDBBzV48OBkDAsAqCfLdhQ48AIhzTTkc9e88o/P7VKaaSgQthXwumTZDhcHAQCgGlk+U6d39R262rn30O9Zrnae+hrr+ec4A9CYeA3ZMiUtbj/ttNM0f/58lZSU6KOPPlJhYaHKysrUtm1b5efna/jw4erYsWOyhgMAJODA2vuyHMlTy1++HpdxxH7MGwEAoHpul6F8v1v5frcs24n97jTEi95WorGef44zAI2F15AtU9LnWXfq1Ek//vGPk90tACCJTOPQ94NXYD2WiO3Id2BHk9cSAADUmukyeLHbijXW889xBqAh8RqyZap5XjYAICWZLkN+jyG/16VKyznqStDfF4raqrQc+b0u+T0Gs0gAAAAAoBXhNWTLRPALAK1UXqZbmR5DHsPQjgq7yvXZpdiFOXdU2PIYhjIPrBcHAAAAAGhdeA3Z8iT1X962bW3atEl79+5VKBSq1T5nnnlmMksAANRSToapTfsM5Waa2hqIaktA6pLuOmKR/lDU1o4KW/ujtgr8bnlNQzkZfIgQAAAAAFobXkO2PEkJfj/55BP9+c9/1rvvvlvrwFeSDMNQNBpNRgkAgDpyuwz17+TVyp1hFfjdKg5a2lwWVZppyOMyFLEdVVqOPIahAr9bfo9L/Tt5uTI0gJTDBZFQWxwrAIDWjNeQLU/Cwe/DDz+s++67T47jVDvFGwDQPGX5TA3M9mp1SVjpbkPBiKNA2JblSD7TUOc2pjI9hrxm7Bd8lo93agGkhqjtaHu5paJgVIHIob9h/Qc+jpiTYfIiBZI4VgAAOByvIVuWhILfJUuW6Je//KUMw5DjOMrLy9PZZ5+t/Px8paWlJatGAEADyvKZOr2r79CLWu+hj+nwohZAKioNWVpdElbYco54sWIakt/rUlnY1qZ9vFgBxwoAAFXhNWTLkVDw+3//93/x7d/97nf6z//8T7lcXC8OAFoat8tQvt+tfL+bj7ECSGmlIUsrd4YViNgqDlqKOE7844khy9G+YFQeI7Z23cqdYQ3MJtBrrThWAACoHq8hW4aEgt+lS5fKMAz9+Mc/1v3335+smgAATch0GeJlK4BUFLUdrS6JBXlbA1G1cbuUn25WeUGSrYGoCvxurS4J6/SuPmastDIcKwAA1B6vIZuvhKbn7t69W5J04YUXJqUYAAAAoKFsL7cUthwVBy21cbvUzX9kkCdJvgP3t3G7VByMtd9ebjVRxWgqHCsAACAVJBT8durUSZKUkZGRlGIAAACAhlIUjCoYcRRxHHVJd8kwqp6ZaRiGuqS7FHFi67oWBaONXCmaGscKAABIBQkFvz/84Q8lSZs2bUpGLQAAAECDsGxHgQMX50ozjaNmb36fz+1SmmkoELYViDiybKeRKkVT41gBAACpIqHg96abbpLjOHrppZeSVQ8AAACQdJZz6LunlmuwelzGEfuhdeBYAQAAqSKh4Pfiiy/WpZdeqjVr1uj//b//l6yaAAAAgKQyjUPfI7WckRmxnSP2Q+vAsQIAAFJFQsGvJD333HO66qqr9PDDD+vcc8/Vm2++qZKSkmTUBgAAACSF6TLk9xjye12qtByFonaN7UNRW5WWI7/XJb/HkFnLmZ9o+ThWAABAqnAnsrNpmvFtx3H0/vvv6/3336/1/oZhKBrlAggAAABoeHmZbpWFbXkMQzsqbHXzG1VetMtxHO2oiLXL9BjKy0zoT2a0QBwrAAAgFSQ049dxnPjX92/X9gsAAABoDDkZprymodxMU/ujtrYErKNmc4YO3L8/ais3M9Y+J8OspkekKo4VAACQChJ6S/rMM8+s8p1vAAAAoLlxuwz17+TVyp1hFfjdKg5a2lwWVZppyOMyFLEdVVqOPIahAr9bfo9L/Tt55eaj+60OxwoAAEgFhsO0WzSi/Px8FRUVKS8vT4WFhU1dDgAAaIVKQ5ZWl4QVthwFI44CYVuWE7sol9/rUqbHkNeMBX9ZPmZwtmYcKwAAoCVjESoAAAC0Klk+U6d39Wl7uaWiYFQB76HVz/wH1mnNyTCZvQmOFQAA0KIR/AIAAKDVcbsM5fvdyve7ZdlOfBanSYCH7+FYAQAALRXBLwAAAFo102WID+mjNjhWAABAS+I6dhMAAAAAAAAAQEtSqxm/kyZNkiQZhqFnnnnmqPvr6/v9AQAAAAAAAAASZziO4xyrkcvlkmHE1rCyLKvK++vr8P6Q+vLz81VUVKS8vDwVFhY2dTkAAAAAAABASqr1Gr+O41QZ8tYiN65WoqExAAAAAAAAAOBotQp+N2/eXKf7AQBAauKK9mhKzf34a+71AWjeajqH1Pb8UpfzEOes1qWuzzfHB5Lt8GNKEsdXI6lV8Nu9e/c63Q8AAFJH1Ha0vdxSUTCqQOTQJ338HkN5mW7lZJhy8wcbGkhzP/6ae30AmreaziE5GabkSNsrrBrPL3U5D3HOal3q+nxzfCDZDj+m9lXa2he2VVppS5Ky0lxql+ZSO6+L46sB1WqNXyBZWOMXAFqW0pCl1SVhhS1HwYijQNiOvzvv97qU6THkNQ317+RVls9s6nKRYpr78dfc6wPQvNV0DnEbOhSO+FyK2qry/NLD79a3gWitzkOSOGe1InX9HcXvNCTb4cfUrgpL3waiilqOoo4kRzJdksdlqEc7tzq3MTm+GgjBLxoVwS8AtBylIUsrd4YViNgqDlqKOI7STEMel6GI7ajScuQxDOVmmvJ7XBqYzR9qSJ7mfvw19/oANG81nUPKw7aKKyzZB16puwwpJ8NUpsd1xPmlvc+l3fstdWjjUmnIqfE8ZEqSEQuPOWelvrr+jurZzq1N+6L8TkPSHH4Mfrcvqh37LUlSKGLHgl/FlnhoYxqSIWW3MdWjnZvjqwEQ/KJREfwCQMsQtR19vC2k0kpbWwNRtXG71CXdJZ/bFW8TitraUWFrf9RWgd+trDSXTu/q4yNaSFhzP/6ae30AmreaziG27ejrfRGVhW1tL7ckx1BOhktt00ydkOWWyzAUisYe+64sqnSPoYqoo25+t3IzzCrPQ+URS5YjGZJMw1C6h3NWKqvr76i8TFM7K2xlp7tUFLT4nYaEHX4MbimLqrTSksc0FAg78roM+b2x4yYQthW2Y0uJRGypXZqh7m09HF9J5jp2EwAA0NpsL7cUthwVH3gB0M1/5ItJSfIduL+N26XiYKz99nKriSpGKmnux19zrw9A81bTOWRf2JZlSxVhR1lel9r7YsGuZTvad2DpB5/bpbZeQ26XtKPclmlI7byuas9Djgzt2W9r935btiPOWSmurr+jvimNqjJq65t9UX6nISkOPwZtJ3Z+qjgQ+nbwueQxDXnM2KcWvC6pIuqondeQI4PjqwEQ/AIAgKMUBaMKRmIfG+2S7pJhVP2Ou2EY6pLuUsSJrQdXFIw2cqVIRc39+Gvu9QFo3mo6h5RW2gpZjixJbdNc8ntdshwpZDnxNX8laV/Ykcc0FD3wkfx9YbuKkWLnIbchVVqxj+u7XeKcleLq+juqLGKrLOKoLGzzOw1Jcfgx6HZJlbZkSbGZvocdXoZhxM9xlXZsbXOOr+Qj+AUAAEewbEeBAxf1SDONo2Z9fJ/P7VKaaSgQthWIxGYlAfXV3I+/5l4fgOatpnOIbTsKWY4qo7Gw5OCsOLdLqozGHrMdJ97OtiWPKVm24o99n+04siU5khxHsg+MUx3OWS1bXX9Hec1YChc88MaB9xgfref4wLEcfgx6DUO2dMQ57fsOP8fZkjwucXwlGcHv/8/encfHdZWH//+cc++dTTOWbMurZCdhCxAgxGFpEpZAEvgCaYDSlD2BlJS2lG+/hfZXCjQJLd+Wpc2XfV+SsBQKJYUWkpSSQFjTgMEkbEmME9uKZVu2Jc1oNHOXc35/3Bl5JI3WGWkWPe/XKy/Z0dy5V76PnnPvmXOfRwghxJJExuJHMhB3s8ie/OotsraWp9W07YRYrvniz1hLaGZPbqxk/M3MefL7IdqRjM2dY94cV/PVqVl16VQmTwCMnf46d8b3Zqr9f9W3rL82+CTJWZ1rqWOUsXF8hcaigHARLaAajQ/JV51nsecsMpbJML5Oiyy4lf5sM3PaTLU5zlWSf5rNbfUBCCGEaH+hiessDRVC8sHJETjnKQayLlt7HCm+30WqH8Y7Kl5BtBiBsaQqG9b5MF+IRZsZf8bGdS2rjz9XpRxFX1LTm9RNj7/5ct62Hgdjrfx+iJaTsbkzzTfG6pqvQc0EXGQtXmXSRCvi5buV14XWntyuzumu/X+2Zrv5SM7qXEu5hjPWMl42nChHlENLYAz3nQhJeyfHV11nsm458SH5qvMs9pzNfJ0xlt+MBhQDi6fAcWbntJlqc1xoLQkl+aeZZOJXCCHEvEZLEXtGfPworreU9w2RjQfiXEIz7hv2jinO7E/Ql3JafbiiCRytyHlxza2xQkgpNPM+KlgKDeXIsintkPMUjly4iwbUxt/RsYBfHfPjLvaVx58N8Q1EyVWUQsuDhZDQwqZerynxt5icN5QPSbqKsm/l90O0hIzNnWu+MVZrRcpRlFzFZNkSVCbuQgPZZPw9reIamSlHkdcQROBoTn5vBq0UmkpZTRXnTz1PHpKc1dkWew1XDAwHCxGl0OBX4qwUWY6XI3qMphRajk4aBrMOGe/k9suJD8lXnWex5+zUnMv9+XDW6yYDS2AsY4HBreS1MIIgsrPKPQSRncpxGghMvA/JP80jE79CCCHmNFqK2H3EJx8YDhUigkoDEU/HkzBjhRBPKbZlHXYf8dm1WS7YusVA1mXcN3hKcbho2JlTdZt9WGs5XIxfl62sABCiUQNZl+GJkLFy3IFeUXmkWcePAwbWMlmOV7lZ4hVtGttw/C0252U8xfHJ+Njk90OsNhmbO998Y2xfMp50c4DxskGpeLKl+pRDVW9C8WDB4ipFObL0Jup/AGWtJbSQrEy2hCb+f5KzutdC13DFwLA/H8Yfnk5EWAOuo3CBvG8JIoOjoTep2Z+37My5ZDy9rPiQfNV5FnvO+lKKX4z4bEw7nCiZaa/zNIyVLcUAEk5cIsLaOL42pE42eLM2nix2FCQ1ca7Skn+aTf4lhRBC1BUay56ReNA/kA9Ju5rBjDNt1UApNBwuxt/fkXPZM+Jz3vaUPKrVBbb2OOwdiy/ED+RD9udhS0bXPf+ToWFHziXhKLb2yMW6aFx/WjNcNKRdxYOFiKSj2JTR01YdFQPD0WK88mh71mG4aOhPL799xVJy3ljZEFmLBSaCSH4/xKqRsbk7zDfG9iY0RycjMgnF8EQEVrG1R+NoRW9l4rcUGsb9eJXclh5NMbSM+YaMp+rGgsKyIa1RxB+U7c9HkrO62HzxZazlYCGi4BuGJwwoy+A6l1JoySU0o2UTf6hq4XjJsCGlOViIGMxajk7aJcWH5KvOs9hzdmgi4hfHAjKu4tBExCnrXAZ73KnXDfQ4/Oq4z9HJuFSXVpakoyEyHC9BLhGf37xv8E1cPmLMt/QmYVtW8k+zycSvEEKIuoYnIvzIcqgQkXY1O3POrNUhKVezM6fYn4dDhYiMG9+kDOZkeOl0ro4fudt9xGdHzuVQIWLfeDj1SX5gLOUorse1I+eS8zRn9ifkQl00xcikYVtGM1QI6U1qlLWM+5ZiGOEoRWTjCY+0E+ehydCyLaMZmTQM5pY3+bvUnDfhG7SCdUlNMbDy+yFWhYzN3WGhMdaYeGVcujKJkg8sPQnLUCGall8e05/g2GTEab2a0dLceeiUdR4OgIqbJcmY3t3mi69yaDk6GXGibPAUbM/Gk3oP7XU4OhmhlWasZAhtHBNDBUvWM+R9RW/CWVJ8SL7qPIs9Z72JuNfB4QnD+pRiXWL6h05aK07r9dAqAGCsHH+ohKsphxHHS/HrHK1IO4p8YNmcdjhlneSflbDiv02Tk5N85CMf4bvf/S5hGPL4xz+eP/mTP2Hbtm0rvWshhBANGCqEFAJLYC2DmdmDfpVSii0Zzb7x+PVDhVAu1rpEX8ph1+YEe0Z8Mq6aVrsr5Sg2pR2yniLhSF020VxDhZCIeHWbq5hV49dTaqrepak8xhyhGso/S855oWFDUlOKLA/tc+X3Q6wKGZu7x3xj7LqkZkMqXn0Zv1YTGurml9/ZmuT+fMjGlF0wDwEypq8Rc8XXsUkDFtKOYmvGIeUpBrNxKYe0qzhYCElkHEqRZcI3jPo2fr2reWifu6T4kHzVeRZ7zsZ8Q9JRhNbgOZox37IhPf01GU9zyjoPYwOsBU9DMbL0JjWhBWxcn9zTilN7XTalHck/K6Sh36af/vSnXH755Sil+MhHPsI555wz7fvj4+M89alP5e677576f1//+tf58Ic/zH/9139x1llnNbJ7IYQQKyQylnzlAjHpqHkbF0H8yW/SUeR9Qz6hiYyVYvxdoi/lcN721MluvTU1BKUTs1gJtfknl9CcmnMZ8+NHBUvuyY7Q1XqXvQnN/fmwofyz3JwXWhjMujxivcfwRCS/H2JFydjcfRYaY7f2OGBhuBiRD+y079Xml4Gcu+hxWsb0tWNmfI15ijHfEBlFwlXsWOfSm9BTzf4ynuZhvd7UmJtyFVZFeEqRdhWPWO8xkHUXFR+SrzrPYs+ZsZZSZIkMeA4YEzcGNMbOahyZ8TSP2pDAWJ/AwIaUYkuPZtyP81lfUtNbuZaT/LNyGpr4/fKXv8zdd9/Nli1b+J3f+Z1Z33/LW97CXXfdNev/Hzt2jBe96EX86le/IplMNnIIQgghVkCluS+RjT+FXQxPq2nbyee03cPVisGcy2DOJTJ2qquvXJCLlTAz/2itWJ9yWJ9yMCZe8TuzK32j+aeRnKe1YluPyynrPPn9ECtKxubutJgx9pTe+fPLUsZpGdPXltrzPRnETUkfLEQoBevrrKqcOeamHYVWip3rXLb1LG7SFyRfdaLFnjNTeV31CSzDyb/XmyrWWk2t4B3ocTh3e4q0q6b2Jfln5S2/AwZwxx13oJTioosumrUEPJ/P88lPfhKlFDt37uTGG2/kZz/7GX/0R38EwAMPPMBnP/vZRnYvhBBihVQaP+MoCIyd/8UVgbHTthPdydHxY6BygSZWynz5R2uFW5kMrtVo/mlWzpPfD7GSZGzufvPlkMXml6XkIclZa0vCicdQVy8uh2gdT+pVF34uJYdIvuo8iz1n1XShgcDaqUnF+SYXq+dW63j1uKOV5J9V1NDE79DQEEDdkg033XQTpVJcsfmTn/wkz3/+83nc4x7HRz7yER73uMcB8O///u+N7F4IIcQKcbQi5ylyCU05spRCM+/rS6GhHMXdgHOeDOBCiOVrRf6RnCc6gcSpEKIRq5lDJF91nsWeM63iHguOhiACrePyWzM/lK+Sc9t6DU38joyMANRt1Pad73xn6nsXXHDBtO9deumlWGv5+c9/3sjuhRBCrKCBrEvWU3hKcbhosLb+J7/WWg4XTdxsqVIfTgghGtGK/CM5T3QCiVMhRCNWM4dIvuo8iz1nvZXJYVcpgsjSm6g/mSvntj00NPE7NjYWv4me/TY//OEPUUrNmvQF2LlzJwBHjx5tZPdCCCFW0NaeuLPqtqzDZGjYn49mffJbqvz/ydCwLRu/fmuPVOQSQjSmFflHcp7oBBKnQohGrGYOkXzVeRZ7zsZ8Q2RhS48mNDDuz14hLOe2fTQ03Z7JZMjn87MmcMfGxqZW85577rmztkulUgBEUdTI7oUQQqwgVyvO7E+w+4jPjpzLoULEvvGQpKPwtKIcGUqRJaEUO3IuOU9zZn9COrEKIRpWm38Gsg4PFiJ+OxaRcjWeVgTGUo4sXhPzz0I5byX2KdpDtcEV1oJSbd1oRuJUCNGImTlkKB9y31hEylEkHd3UHCL5qvMs5ZydsdHj+KTh1HWa4yXDb8eCBa/TFOBHtq3H2W7U0MTvqaeeyl133cX3vvc9Xve61039///8z//EGINSivPOO2/WdseOHQOgt7e3kd0LIYRYYX0ph12bE+wZ8cm4inHfMDwRcWzSEBlL0lU4DhybjHhEn0s20dCDJEIIAUBoLIXAApZDExHGQjmCydCQcRW5hGJTOn4cMeHENyl9dbqTL9XMnFcILPnKqpaUo9iUdpq+T9EaobEMT0TszwcMFSJGy/GHmSlH0ZfUDGRdduZctvY4bTcJIXEqhGhENqHZ1qO5/1BAZOM6rYWywdGWdQnF9h6HdQndlBwi+arzLOacZVwohpadOZf9hXDe6zRHw7aM5jcnAvLBydIRuUrph3YcZ7tNQxO/T33qU/n5z3/O1772Nfbs2cOZZ57J+Pg473rXuwDYvn07j3nMY2Ztd/fddwNw2mmnNbJ7IYQQq6Av5XDe9hT3nAg4eKiEsXF338jE/zluXOfpYCHiyGRJLtqEEA0ZLUXsGfHxo3jyN+Nq8r7B2pOdpIsh7MwpHrE+0fQbhmrOG56IGCqE5Gs+0JKblO5QjbHRUsTesZCJwFCO4slgVyuGHTiQDxkpuvSmnLYc1yROhRDLUTvG9iY0pTAiqjyh72owFsbLhkdtSPCI9V5Tcojkq84z3znTWMb9+PqsGM5/nba1x+VwMeJgIZo2gewoyCU0475h75hM+q+0hiZ+r7zySj784Q9TKpV40pOexFlnncXevXs5fvw4SimuvPLKutvdeuutKKV43OMe18juhRBCrJJCZaXvhrRDKQpJWMW6tCbhxo/yPFiMODoZ12/afcRn12YZvIUQSzdaith9xCcfGA4VIgJrSTqKbEKTdC2lIH48cHuPBuJGIStxo+hqxWDOZTDnTpUBkMcSu0M1xo5MhvzyWEApsviV6nNKWUqRZcKHQmAZLRvO2JggMu05rkmcCiGWYq4xdkuPxg/j/Odq2JB2GJ6I2N7jNC3vSb7qPPXOWd437Dnqo5Rl31g473VaMbD8dixeVV4bb55WlCLLWCHEU0ruH1eBsnO16Vukv//7v+fqq6+O30ypqa5/j3vc4/jhD39IOp2e9vq77rqLM888E6UUn/rUp7j88ssb2b3oMIODgwwNDTEwMMDBgwdbfThCiEUIjeX7D5YYLRsO5EPSrmZLRpNyT37yWwoNh4uGydCwI+fSl9Sctz0ln9wLIRZNco1YadUYO16Z/LA2/n9pV5FLaDwn7k6e9y2TocHVCgXs2pJgQ2X1k8SaEKITyRgrGrWUGJoIIiILCnCUIuNJvLVSw8UY//Zv/5Z///d/53nPex6PeMQj2LVrF29605u4/fbbZ036Arz//e8HwFrLs5/97EZ3L4QQYoUNT0T4keVQISLtanbmnGmDNkCq8v/TruZQIX798IQ08BRCLJ7kGrHSqjH229EQR8U3pGlXsT4VT/oCeI5iQ0qTdjXKxqvffjsWSqwJITqajLGiUUuJIYvi+KTh2KTBWCTeWqyhUg9Vl1xyCZdccsmiXvuxj32Mj33sY83YrRBCiFUwVAgpBJbAWgYzDkrV/xRWKcWWjGbfePz6oULIYK4pw4wQYg2QXCNWWjXGxgNDylEUjCWX0LNjTUEuoThWUnhO3NhUYk0I0clkjBWNWkoMuQrKUVwNwNVIvLWYtF8XQggxp8hY8pVC/ElHzfqkdqaUq0k6irxvyAeWyDRUTUgIsUZIrhErrRpjY+Wo8vf4ZrS60ncmz1Fxo6NK06PxssSaEKIzyRgrGrWUGDLWYgALWAsGMPPEkMTbypOJXyGEEHOqfFBLZMFbZL0lT6tp2wkhxEIk14iVVo2R0MT1Bg3x1/nUvi40099HCCE6hYyxolFLiaHaedvqMGsWeH+Jt5UlE79CCCHmVF0I5SgIFvnpa2DstO2EEGIhkmvESqvGiKshshZN/HU+ta+rLm6SWBNCdBoZY0WjlhJDtfPC1WF2oYlHibeVtejiGX/3d3837/evuuqqhg9GCCFEe3G0IufF3c7HCiGl0Mz7aE8pNJQjy6a0Q85TONKVVQixCJJrxEqrxlhv0gFCHA2TAQSRrVvuIYgsoYGkB0SwLqkl1oQQHUnGWNGopcSQVgpN3EAVFU/66nliSOJt5S164veaa66ZsyAzyMSvEEJ0q4Gsy7hv8JTicNGwM6fqjgfWWg4X49dlPcVAVgrzCyEWT3KNWGnVGFvnafKBQQN537A+NaPBm4W8b9HWEkSwLqEl1oQQHU3GWNGopcRQaCFZ+VA1NPH/k3hrnSWVerDWzvmfEEKI7rS1xyHhKLZlHSZDw/58RCmcXqmpVPn/k6FhWzZ+/dYep0VHLIToRJJrxEqrxthD+lwiGzeemQwtJ0qGoFJUMIgsx0uGydBgK7V9H9LrSqwJITqajLGiUUuJIYVlQ1qzMa3RCom3FlNWZm3FKhocHGRoaIiBgQEOHjzY6sMRQizSaCli9xGffGA4VIgIrCXpKDytCIylHFk8FV8I5DzNrs0J+lIycAshlkZyjVhp1Rg7Mhnyy2MBpcjiR/H3lLJYC8pCwlUkHcUZGxNsSjsSa0KIjidjrGjUUmLIAVBxszaJt9aS5m5N9KpXvQql1NR/11xzTUPvVy2vsdT/zj///Lrvd9111y3pfRo9fiFE9+hLVW56k5qH9rkMZl1Slcd3Uo5iMOvy0D6XvqQM2kKI5ZNcI1ZaNca29bicvTnB1ozDxlT8iGlSK7KeZkNasyXj8ITNCbb2yKSvEKI7yBgrGrWUGHrKQIqnbE9JvLUBKaDRJDfddBPXX399qw8DgIc85CGtPgQhRBfqSzmctz3F8ETEUCEknzj52WGuUpNpa4+DKwX5hRANkFwjVlptjG3KBAwVIkbLhlJkSTmKvqRmIOuyMyexJoToLjLGikYtNYYk3lqvoYnfG264AYBnPvOZDA4OLnq7Bx98kP/+7/8G4LLLLmvkENrC+Pg4r33tawHo6elhYmKiKe/7kpe8hMc//vELvi4IAl7xilfg+z4AV1xxxYLbvP71r+eZz3zmvK955CMfuajjFEKsHa5WDOZcBnMukbFEFhyFdF8VQjSV5Bqx0urFWFznQUmsCSG6moyxolFLiSGJt9ZraOK3WtrgxhtvXNLE71133cWrXvUqtNZdMfH7V3/1Vxw4cIAdO3Zw6aWXcu211zblfR/5yEcuavL1xhtvnJr0Pf3003nKU56y4Da7du3iBS94QaOHKIRYwxytkAdyhBArTXKNWGknY0xuQoUQa4uMsaJRS4khibfWaGmN327oK3frrbfy8Y9/HIAPfehD5HK5VT+GT33qU1N/XsxqXyGEEEIIIYQQQgghRHdrycRvdcJXqc7+VL1YLHLllVdireXFL34xF1988aofw6FDh7jpppsAcF23K1ZQCyGEECspMhY/skSm8z+AFtMt59xKPAghWmUl8o/kNCHESpIc03la0tzt6NGjAGSz2Vbsvmn+5m/+ht/+9rds2LCB9773vS05huuvv54oigB43vOex9atW1tyHEIIIUQ7C4092VgiOHmhKo0lOt9yzq3EgxCiVVYi/0hOE0KsJMkxnW3VJ37L5fJUU7jTTjtttXffND/4wQ/4wAc+AMA//dM/sWXLlpYcx6c//empP//hH/7horf70Ic+xDvf+U4OHDiAMYb+/n4e//jH85znPIfLL7+cTCazEocrhBBCrLrRUsSeER8/shQCS943U40lcgnNuG/YO6Y4sz9BX0oqj3WS5ZxbiQchRKusRP6RnCaEWEmSYzrfoid+r7/+eq6//vq633vrW9/Ke97znnm3t9YyMTHBr3/9ayYmJlBKceGFFy7pYNtFqVTiiiuuwBjDBRdcwKtf/eqWHMd3v/td7rnnHgC2bdvGc5/73EVve+edd077+4EDBzhw4AD/8R//wdVXX82nPvWplpSuEEIIIZpptBSx+4hPPjAcKkQE1pJ0FJ5WlCLLWCHEU4ptWYfdR3x2bZaL1k6xnHMLSDwIIVpiJcYjGeOEECtJckx3WPTE7/3338+3v/3tWXV5rbX84he/WPQOq/V9t27dyhvf+MZFb9dOrrrqKn7zm9+QTqf56Ec/2rLjqG3qdvnll+M4C/+COY7DOeecw1Of+lQe8YhHkM1mGR0d5Sc/+Qn/+q//yvHjxzl69CiXXHIJn/vc53jpS1+6kj+CEEIIsWJCY9kzEl+sHsiHpF3NYMYh5Z5scVAKDYeL8fd35Fz2jPictz0lj6u1ueWc291HyyiUxIMQYtWtxHgkY5wQYiVJjukeSy71UJ24Xej/zaSUIpvNctppp3HhhRfyxje+sSPr0d55551ce+21ALztbW/joQ99aEuOI5/P86UvfWnq71dcccWC2zzlKU/h/vvvZ3BwcNb3XvOa1/Cud72LK6+8ki9+8YtYa7niiis477zz2LlzZ1OPHSAIAnbv3l33e9u2bWPbtm1N36cQQoi1ZXgiwo8shwoRaVezM+fM+gA75Wp25hT783CoEJFxFcMTEYO5lrRBEIu0nHNbTmkUcKxkJB6EEKtqJcYjGeOEECtJckz30Au/JHb11VdjjJn2H8QTuv/+7/8+63sz/4uiiLGxMX72s5/xT//0Tx05sef7PldccQVRFLFr1y7e8IY3tOxYvvjFLzIxMQHAU5/6VB7+8IcvuM3DHvawupO+Vblcjs997nOcf/75QFzS4p3vfGdTjnemI0eOcPbZZ9f9r5WrqIUQQnSPoUJIIbAE1rIlo2ddrFYppdiS0QQ2rl02VAhX+UjFUi3n3A4XIw5NRBIPQohVtxLjkYxxQoiVJDmmezQ8Db+Y1b7d4u1vfzt33303juPw8Y9/fFGlFVZKbZmHpTR1W4jjOLz97W/nKU95CgD/+Z//yQc/+MGmvX/V5s2buemmm+p+rxM/FOgkkbFTxdgdeQRDCNGgds0pkbHkKw0oko6a9lhaPSlXk3QUed+QT2giY9vq5xEnLefcehpGivGihU3p6Y8pGmsxFrQCXbmpkXgQzdCu+VGsrpUYj2SME60kua271Dufi80x1WuohKNalmMkHhfW0MRvddXvWrBnzx7e8Y53APCGN7yBXbt2texYfv3rX/PDH/4QgHXr1nHppZc29f3POeccUqkUpVKJ/fv3UywWyWQyTd2H53kt/Tdca0JjGZ6IGCqE5IOTH9bkPMVA1mVrjyN1eIQQi9YJOSWyJ796izwWT6tp20lriva0nHPrKkX1qtV14huVsbJhtGwoRSdjOOUo+pKa3qSWeBDL0gn5UayulRiPZIwTq01yW3dZ6HxuSMUTvfVyzFzXUAXfkvUUW3vsiucYicelkcIbi3TdddcRBAFaazzP4+1vf3vd191+++3T/lx93emnn960CdpPfvKTU39+yUte0vRJWa01GzZs4MEHHwRgdHS06fsQq2e0FLFnxMeP4kcv8r6Z+kQsl9CM+4a9Y4oz+6UDpxBiYZ2SUxx18mvtRel8AmNJVTZ05FqxbS3n3IbWTtU3m/At942GRMZSiizl0GKI65+VXEUptBydNETW0pvQ0/YpxHw6JT+K1bUS45GMcWI1SW7rLos5n65WFAMzK8cUA8PBQlT3GmoiNIRGcd+o4uzNho3plYkFicelk4nfRaqWtDDG8A//8A+L2ua2227jtttuA+D5z39+UyZ+wzDkM5/5zNTfm1nmocoYw4kTJ6b+3tfX1/R9iNUxWorYfSTuxHmoENc1TDoKTytKkWWsEOIpxbasw+4jPrs2S3IUQsytk3KKoxU5T5FLaMYKIaXQzPsobCk0lCPLprRDzlPyqFgbW865DQysS2r80HJ4MiLnKYphvCLF1eAoRWAtk2WLoyDjKvKBZWNKSzyIRemk/ChW10qMRzLGidUiua27LOV8Hi1GZDxF2beUQoOxsD8fUo7iFb+111CTkSXvWyyK9cay5+jKxILE4/I0beI3DEPuvPNO7r77bk6cOEGpVFrUdldddVWzDmFN+PrXv87hw4cBeMxjHsOTnvSkpu/jRz/6EZOTkwAMDg7Kat8OFRrLnpE4KR7Ih6RdzWBmel3DUmg4XIy/vyPnsmfE57ztKXksQggxSyfmlIGsy7hv8JTicNGwM6fqNqaw1nK4GL8uW3lETLS35ZzbvpTiYD7CWBieMKxPxWUdvJqlb0EUrxwZnjCkPcVoybB1m9wwiPl1Yn4Uq2slxiMZ48RKk9zWXZZ6PtclNKMlgwNxc9zIUo4sx0uGhObkNZSF4yXDOk+RdKAYWvKBaXosSDwuX8NZ3xjDu971Lv7f//t/jIyMLHn7Tpn4fc973sN73vOeBV93zTXX8La3vQ2Aq6++mmuuuaapx1Fb5mGlVvvWnpOLL7646fsQq2N4IsKPLIcKEWlXszPnzLoYTLmanTnF/jwcKkRkXMXwRMRgTi4IhRDTdWJO2drjsHcs/tT/QD5kfx62ZHTdC8TJ0LAj55JwFFt7ZKKv3S3n3IbGkvIUlAFlsbb+TYC18feh8v21fa8gFqET86NYXSsxHskYJ1aa5LbustTzWQji7gjZhGZ4IqQYWoIIEg6sT2mUUpUPzC2+sWzMOKQcSDp6RWJB4nH55m//uQBrLZdeeilvectbGBkZwVq7pP/Wsuuuuw6l4k9lzz///EVtMzw8zE033QRAIpHgFa94xaL398Mf/pCPfexj867EnpiY4LLLLuNb3/oWAMlkkr/+679e9D5EexkqhBQCS2AtWzK67goAAKUUWzKawMY1coYK4SofqRCiE3RiTnF1XN8r52l25FyCyLJvPOS3YwEH8vHXfeMhQWTZkXPJeZoz+xNrflVAJ1jOuV2f1GxIarSKJ0yMhWMlw8hkxInK12Ol+FHGrT0OWsWrWYYnolb/uKLNdWJ+FKtrJcYjGePESpPc1l2Wej4jG18H+ZEl7WpKoWXMN/jGMlq2U9dNkbFsSGmSjmJHzmNrj7MisSDxuHwNTXvfcMMN3HjjjQA4jsPv//7vc9FFFzE4OEgymWzKAYqTbrjhBsIwDtrnP//59Pf3L3rbw4cP89rXvpY3vvGNXHTRRZx99tns2LGDnp4exsbG2L17N1/4whc4duwYEP+yfOITn+DUU09diR9FrLDIWPKVQudJR81b8wviT8aSjiLvG/IJTWSs1P4SQkzp5JzSl3LYtTnBnhGfjKumNYFIOYpNaYesp0g40gSi0yzl3D5mg8dPRwJCC9syDlpDj8u0xiSeUmSTipSjcDRkXQgt5AMr46KYUyfnR7G6VmI8kjFOrBTJbd1luecztLAprTlYCOnx4oWLaUfVvW4azLpkvPh9mx0Liz3+0FgmAkshMByfNByZKPMzrdh9xOdPzlzX0DF0soYmfq+//noAUqkUN998M0972tOaclCivk996lNTf15umYdCocCNN944NWFfz9atW/nEJz7B8573vGXtQ7RetfFmZMFbZJL1tJq2nVwWCiGqOj2n9KUcztueYngiYqgQkk+cvFjMVeodbu1xZBVUB1rsuTUWICCy0JPQDPQ4jPmG0bKh5J58Ci3lxHV/exOaoYmobWJYtK9Oz49ida3EeCRjnFgJktu6SyPnM+lqTlvnYWyIpw09NTmm9rpJ17xvs2MhsmBsPKFbCi33jQZMBIZCYJnw4/8/EVhKka27/d7RgD9+XG7OVcLdrqGJ35///OcopXjNa14jk74r7Pvf/z6/+c1vANixYwcXXXTRkra/8MIL+epXv8odd9zB//zP/3DgwAGOHTvG6OgomUyGzZs3s2vXLp73vOfxB3/wB6RSqZX4McQqqfapcRRzJr+ZAmNJVTZ01mY+FELMoRtyiqsVgzmXwZxLZGx8EaqQ1ShdYDHnNopnfqdiWGvF+pTD+pSDMfGKXw3TblraLYZFe+qG/ChW10qMRzLGiWaT3NZdGjmfGtCOojepSbqKU3Nu3eumetvW7ns+xlrGyoZjJcOxyYiRScOx0smvxybjD+uXWzA2MDDuW3qTazMwG5r4nZiYAODcc89tysF0g2uuuWZRDd1e9apX8apXvWrR73veeec1VBc5m81yySWXcMkllyz7PUTncLQi5ylyCc1YIaQUmnkf5yiFhnJk2ZR2yHlKLhKFENN0W05xtJJVKF1qrnM7XwxrrWY1vWj3GBbto9vyo1hdKzEeyRgnmkFyW3dp5Hz2JuPXTQSWsUKIb+ySYkEryPuVCd3KxO6xmt4KxyYjjpcMi5yPXraRyWjqZ1lrGpr43b59O/fffz/GmGYdjxCiSQayLuO+wVOKw0XDzpyq+2iDtZbDxfh12crjYEIIMZPkFNHpJIbFSpHYEkJ0I8lt3aXR8znXtn50stRCwTc8OBFRDCy/HQ0JjOUDPxvHb4Mpw2Mlw0NbfRAt0tBv5NOe9jTuv/9+fv7zn/Oyl72sWcckhGiCrT0Oe8cU27IOB/Ih+/OwJaOnfTpXCg2Hi4bJ0LAj55JwFFt7ZI2AEGI2ySmi00kMi5UisSWE6EaS27rLcs5nvHDbcqxk+O1YyLFSxJFixO7DEFpLMbBtMak7H61gfVJPlf1ai5RtoH7A7t27efKTn8zGjRu59957yeVyzTw20YUGBwcZGhpiYGCAgwcPtvpwut5oKWL3EZ98YDhUiAisJekoPK0IjKUcWTwVJ/+cp9m1Wbr9CiHmtpo5RWoUipWwnBjOJbTEoliQXHOJ5ZLxTqyW5cSa5LbuUns+h/IhE6HF2rgmbzG0TASGIIqbqU2GczdLaycJDSlXkXQUrlZk3PjDh/6UwznbEuxc56353NrQxC/AO9/5Tv7mb/6GZzzjGXz5y19m/fr1zTo20YVk4nf1jZYi9oz4lUcwLHnfTA34uYQm6ykSjuLMfhmkhRALW8mcEhp7sit5cPLyRLqSi2ZaTAw7GrZlHMZ9K7EoFk2uucRiyXgnVkszYk1yW+eZ1SytpmnakWK8arcY2mU3S1stPZ5iY8qhP63ZmHLYmNakHcXxckTSUVgLxdBKPC6goYnf22+/HYCPfexjfP7zn2fjxo1cdtllnHPOOfT396P1woWTn/a0py1396IDycRva8jFpRCimVYip8hNhVhN88VwLqE5XIwIjcSiWDq55hILkfFOrJZmxprktvZhbfyh9LGa5mitaJbWqKSj2JjS0yZ1+9PO1J83pvScTeQkHpemoYlfrfW0YtDW2rrFoefcuVKEYbjc3YsOJBO/rSePkwkhmqkZOUUeIxStVBvDed9ILIqmkWsuMZOMd2K1rGSsSW5bWcXATJvQrV2xW/17u9fVdTXxBG5KszHt0F/5Wv37xrSmx63fXG6pJB4X1nC7xZnzxg1WjhBCrDBHK+TyUQjRLI3mlNBY9ozENyYH8iFpVzOYceo2mjiQD9mRc9kz4nPe9pR8ki+aohrDEoui2eSaS9SSHCNWy0rHmuS25fMjy7FSxLFJw7FSXHph5ordybC959SqzdKqE7n9tRO6la/rEgrdhEndxZB4XFhDE79XX311s45DCCGEEGvQ8ESEH1kOFSLSrmZnzpn16X/K1ezMKfbn4VAhIuMqhiciBnMNf34txBSJRSHESpIcI1aLxFprhMZyomQYqZnYnTbBW4rI++09qQvQm4zLLEzV1p2xYrcvqWVlbYeRiV8hhBBCtMxQIaQQWAJrGczMvjGpUkqxJaPZNx6/fqgQys2JaCqJRSHESpIcI1aLxFrzGWsZLZs5J3SPTRpGy6btm6VlPXVyZW61rm61pm7aYUNS4zkyqdtt5LdaCCGEEC0Rmbg5Rd43JB01ZwOHqpSrSTqKvG/IJzSRsbLiQDSFxKIQYiVJjhGrRWJt6bqpWVpto7SpFbs1f0+5a+vciphM/AohhBCiJaoX0JEFb5E3GZ5W07aTml6iGSQWhRArSXKMWC0Sa7N1Q7M0T8OGOs3Said2M01qlia6z4pM/Pq+z/Hjx/F9n507d67ELoQQQgjR4apPkjkKSotcRhEYS6qyoTyJJppFYlEIsZIkx4jVstZirW6ztBl/76RmaVMTudWmaZW/r0vIpK5YvqZN/N5zzz28973v5ZZbbmHfvn1AXDMmDMNpr/vCF77Ab3/7W7Zu3coVV1zRrN0LIYQQosM4WpHzFLmEZqwQUgoNKVdjjJ1aeeIo0JUVK6XQUI4sm9IOOU+tuUcRxWxRJVaqN6rVPy81NuaKxblILIqVUBvPElPdpRk5RuJDLEZtrI2OhxR8Q8ZVU9dSM7XzeFZtlladyJ3ZNG2kg5ql1TZHm9ksbX1Ko1dpUrfZeaRVeUny4dI0ZeL3ne98J3/7t39LFEVYO/8v3sTEBG9961txXZeLL76YzZs3N+MQhBBCCNGBBrIu477BUXDfaIg1htHAElYeuXM1rE84bO3RFAKLpxRZTzGQlWpVa1VoLMMTEUOFkLGyYcyPG6oA9CU1vUlNb0IzkHXZ2uPgLvKGoBqLnlIcLhp25uqvrrHWcrhoJBZFU9TGcz44eR+Vq8TWUmJYtLfl5JiMq3C14o5DJYkPsSihsbhacbQYMlKKOFGOWJ/SpF0dj5EJPTUJ3MrxzFjLWNnULb0wMtk5zdJ6PDWrlm67NUtr9jjTqnFLxsvlU3ahmdoFvOMd7+Atb3kL1locx+FJT3oSjuPwve99D6UUURRNe32xWKS/v59yucxHP/pRXvOa1zT0A4jOMjg4yNDQEAMDAxw8eLDVhyOEEKLFQmP55gOT3DVS5kA+ii/wbfwpvgJcR+EpiIC0q3n8pgRbexzO256Si7s1aLQUsWfEx48sR4sR9+dDwsgSWsCCo+Nahaf2umxKOyQcxZn9CfpSC1csDI3l+w+WGC0bDuRD0q5mS0ZPW5VXCg2Hi4bJ0LAj59KX1BKLYtlq47lQacZUXcGUS2iynlpSDIv2ttQcsyntMFo2nLrOoRgi8SEWVM0ppdDwi2NB5cNRS0JDX0qT8zSOhsGsi1as2HhmbZzTZjZJ68RmaXHJherq3M5rltbscaZV45aMl41paOL33nvv5dGPfjTGGM444wy+9KUvcfrpp/PVr36VF77whXUnfgEuvvhibrrpJl760pfy2c9+tqEfQHQWmfgVQghRa7QU8c0HivzkiM9Y2VCs1GHTCpQCY+P/ElrRl9QM5Bx+76E9nNLrtfjIxWobLUXsPuKTDwwPjIUcnoyvMUuBoVq+z9GKtKNAwea0w6m9LjlPs2vz4m9oqvs4VIgIrCXpKDytCIylHMWrzrdlnSW9rxAzSaytTYs97+tTmmOTERvSmtGSlfgQC5oZWxOhiSfHDOQDS2gsnoJsUqMVrEtoely9rBjqhmZpriYuu5ByZpVe2FiprdvT4c3Smj3OtGrckvGycQ2t5f/ABz5AFEX09fVxyy23sG3btkVt94QnPIFvfOMb3HXXXY3sXgghhBAdLDSW3UfLHJyICCq1utYlFAlHYwBr41UjQRTf9E6GhiDS/HY8ZCDnyirLNSQ0lj0j8UX//vGQMT8il1Dkfcu6pEMuEcdC3jf4Jn7sb9w3PDAecMo6jz0j/qJWMvWlHHZtTrBnxCfjqmmrSlKOYlPakVUlomG18Vxd+TmYcequ/DyQD9mRcxcdw6K9LSbHZFzF/eMhG9KakUkj8SEWVC+nnJ7zMBYOFiI2G0shMJwoGQq+jRuFAaf1uqTc6ePZfM3Sqit2O6lZ2lSTtBkTu+sSatXq6rZCs8eZVo1bMl42R0MTv7feeitKKS677LJFT/oCnHbaaQAcOHCgkd0LIYQQooMNT0SMFA3HJw0WxfYeRcrVTEYna/yCwlXxjUgpNOR9y0gxYngiYjAntVXXiuGJCD+yHCpEGAu9iXhCJKEVG1I6rgsCrE9pTpTileP9KY1FcagQkXHVomOmLxWXEpmqI5c4eXMhdeREM9TGc9rV7Mw5s1aVpVzNzpxif54lx7BobwvlmDi3WPaNSXyIxZkvpzysTzFWNoyW45rRR4qGicDiKMve0ZCEA/8zXJ6a4O2UZmnV1brTautWJnb7knrNN/xq9jjTqnFLxsvmaOhfojpx+4QnPGFJ2+VyOQAKhUIjuxdCCCFEBxsqhAwXI8pRXH9uXdLBcxQ9xCt9DaABpRRBZDlShHJkOVS5WZYLurVjqBBSCOKV366GYhjXfe5LqKlJX4hjJZfQHCsZygYyLgSVWoNLiRlXKwZzLoM5VzpHi6arjefBzOyb2CqlFFsymn3j4ZJjWLS3+XLMHYdKFEMkPsSiDRVC8r6hEBo2unFMTASWgh9P8k4EhkJgp8ppxSIgaNUhzynrqZMrc9u0WVonaPY406pxS8bL5mjoX6JcLgOQSqWWtF11wrenp6eR3QshhBCiQ0XGMuYbxisdm5OumnYhr5Si9iF6z1EkXUXRt4z7hrGyITJWJuLWgMhY8pXHoRNK4VtLOYwngOvd/HmOwtVQDi0pV+HpuAREPqGXFTOOnh6LQjSiNp6Tjpr2uGo9KVeTdFRDMSzaW22OkfgQ9Vgbx0VtDd2pZmmThoOFeKK33dfqphw11RSt+rXTmqV1gmbnkVblJcmHzdPQxO+mTZsYGhpiaGhoSdv98pe/BGDLli2N7F4IIUQLRcbiV9rxJhw158Aqq+VEPVGlaVu1ooOziDprjlJYDKG1hLYSV4vdn8RhR6h3nqpdvyMLrgN+GMfNfDHjKDUVW65S095jrpiZue/q37EWlJLY6WLNzg/zvV9tLHqL3JenFxfDoj0tJb4WEx/GWoyN65hqpSQ+ukA3NEvzNGxInayhW22WVjuxm9RgkPF0pTV7nFmNcWuh6z8ZLxvT0MTvmWeeycGDB/nv//5v/vzP/3xR21hrufHGG1FK8eQnP7mR3QshhFhlobEM5UN+ccxnaCKiVBlZU45ioMfljH6PgWw8tEzVrgtOrj+Q+piiylGVm9bK3yM79zoVYy2l0HKiHFEKLMZa7jsRsj5ZZkdu7ngKjZU47AALnadN6ThKHBVP+kIcN8E8MRPZuMMzQGgticqfZy4QnrlvU1mJXgwMoYXQgG8sKUfRl9QMZF12zhNzonM0Oz8s9v2qMegopsbQhQSVGKxuJ9rfcuNrrvgw1lbqtJpp/z/lKAqBYX1ST9tetI9ua5Y2NZFbbZpW+fu6hKr7GH71d+HuEV+uxVZJs8eZlRq3lnL9J+NlYxqa+P3d3/1dvv71r3PzzTdz55138sQnPnHBbd7//vdz7733opTi+c9/fiO7F0IIsYpGSxE/PFRi31j8OFnej28+FPFj+qMlw/3jAVszDulKc5LabtWOglxCM+4b9o5N7yAs1h5HK3oTmnXJOCbKoSWI7KxH9/3IcqJsCEJLMbBEBpI2vgAcLkYUgvrxNFqK2DPi40dW4rCNLeo8OQqNJZfQjPkhmjjnTJbrx0xQaQ6YTap4gtjE75Xzpj+ZMHPfI5MRhycifGOYCCAyBoMiqeMyI8MOHMiHjBRdelOOxE4Ha3Z+WOr75by4FvVYIaQUmnkfXy2FhnJk2ZR2ZsWwaE+NxJej1az4MBYOFiIiYylFcambag38QqWUjbWwMyePNa+20FhOlGomcutM8HZCs7SUo0g6kHIVaVfRl3TYkIq/PmlLglN6PfQinsyaSa7FWqNeHmlknGn2+8Eyrv9kvGxIQxO/l19+OX/3d3/HoUOHuOSSS/i3f/s3zj333LqvDYKAa6+9lre+9a0opTj99NP5vd/7vUZ2L4QQYpWMliK+N1TintGAo5OGQmDQMNV5ulS2jJYMKVexPx/Sl9KkHY3WkHTixxBLkWWsEOIpxbasw+4jPrs2y4XeWjaQddmaCTlajJgI44u+9Sk9tWLEjyzHS4YgsoyWDX5lkq/Hi+u2HpwjnkZLEbuP+OQDw6FCRGCtxGEbWsp5CiNwdbyKtxxZkjp+fC/vWzakTjZ4szaOI0dBUkNoIakV2crqkbn2PREaxv04xo5NGgIT10p0FZSVJWniGpyFII7FMzYmiIzETidqdn5YzvsNZF3GfYOnFIeLhp25+ivlrLUcLsavmxnDoj01I75q42N/PsIYS9nEK34jC66Oy9kExjIyGU8qmhScKFlGS5HkpCaprrKeWXqhdsXuaKVPQTtLOtDjabJefP0UGFA2nuTtT2uMjdu8VeM0MJZyZLFWkXIV+8Yj1qecJceVXIu1VrPHmWa+33Kv/2S8XL6G/jWSySSf+9zneNaznsWRI0d46lOfyjnnnMP69eunXvNXf/VXHDhwgNtuu42RkRGstaRSKT772c82fPBCCCFWXmgsu4+WuXcs4HAxohxCX0KzPnWyq24QWY6XIo4UDVpBPjBsTructdkj4528iCuFhsNFw4F8yI6cy54Rn/O2p+QRrzVqa49Df0azIa3xJyJOlCyhiSd/HQ0nyvFK4NGyIbKWlAvrU5rtWYeH93n4xs6KpydvTbJnJL6YPJAPSbuawYwzbYWAxGHrhcYu6TwNZB2Gi4YtPZqD+YjRsiGTUOT9+MOBXCI+d3nf4Jv4McEx39KbhG1Zl4Sj2Nrj1N13ylG4Ku4kvr8UNxBxFFjix1tzCY1WcS3zchjX1rx7xGfXloTETodZatwtlB+W+35P3pok4cQ3tQfyIfvzsCWj6243GRp25KbHsGhPzYqvrT0Oe8cUW3o0u4/4WBu/d7wSM772CiJL3oeUa3G1Yqxs0NpKTloka08+6TGrWVrl78dLZqpWaLuqNkvbkNKEJv5QoBRa1iU0A1mHDWlnqjZqNfYmgojIxp+XBgYynp4z/yz3OqnZuVYsXTWPNGucadb7NXL9N1SIZLxcJmXtPAXSFummm27ila98JcePH687+w5xcgXo6+vjX//1X7nwwgsb3a3oQIODgwwNDTEwMMDBgwdbfThCiEU4mA/5wYMl7hrxmQgtvQk1bVVm1YRvOFQIGQ8snlY8pNfloX0e62d8em+tZX8+IogsD+1zedSGBIM5+VR2rZprNbkFyqGhHMWvy3iKdQnN5ozDw/o8Ml58wTcznjakHI6XIvaOhniOYmfOmXNlgMRh6xzMh/zquL+k87S9RxMYcB3FA2Mhhyfj4ChV6vFC/Dhi2olXAG9OO5za65Lz9LSVRDP3nfPgcNFwMB9SiuKJXUdD2oHJSiO5nKfJJTTlyBJGBtdRZBOax/UnJHY6yHLibr780Mj7ZT0154qn6oq76oqnmTEs2lMz42u0FPGt/ZPsGw85mI9AWdYlFK7WRDYuZ+MAvSlNQse5T6NkPKtYK83SMu7J1Y/zraKszSkZTzFejkuI9CT0ilwnNTvXiuVZbEwsdpxpxvs1ev0n4+XyNOW36jnPeQ53330373rXu/jMZz7DsWPHZr2mt7eXl7/85bz5zW9m+/btzditEEKIVTBUCBkuRpQjS0LHq9/qDdCTkcV1NCqIUArG/fgRuJkTv0optmQ0+8ZDCoFlqBDKRd4a1pdyeMpACkdDaixkItDk/fiGDRSuA72eIu3FTUQe0utOTfrC7HganvDJJjSBtQxm6l9M1ttO4nB1DRXif/elnKcIxfpUXLMts9Gjv6i5Px+S1Dqe+K1M2HpacWqvy6a0Q8KZXTtw5r4PVRpVlqJ45Vw5ihuDaK1IufGqsMBYJiNDb0JzrKTwHMW4byR2Osxy4m6+c9zI+z15W4pdm+NV4xl3ek38lKPYlHbIeqpuDIv21Mz46ks5rE9pHshDj6foSWiMiT+I8pQim1SkHIWjYTDrohVrZjzrtmZpU03SFtksbS59KWdROeVAPmRDSnOsbNiSqX9ND41dJzU714rlWWxMLHacacb7NXz9J+PlsjTtt2rr1q1ce+21XHvttfzyl7/k/vvvZ3R0lGw2y+DgII9//OPReu5CzEIIIdpPVOlwP16pYZZ01axGSgDWQGjA2vjTWI1iMoLJ0GKsndUQIuVqko4i7xvyCU1kpCHJWtaXcrjolAxD+ZBfHPM5UAiJgGJlNm9H1mNb1mF9UqPrxEk1nsbKEfkgXrGZdNS8DSBqt5M4XF2RseQrF+1LPU+5hMt5W5McnTSsS4T0px3GKh8yAfQlNb1JTW9C1+0WPnPfiUo9uVIQb2+IG4tU48DRCkfFq+vix2gVrgZTWSk2XjbkJHY6QiNxVy8/NOP9+lIO521Pnexqnjj5HtLxvrOsRHwZFBtSDgrIJvS0rvYpJy770Js4OS52w3gWmrh8T+3Ebic2S+tN6nilbqqyQrdmxe7GlKYvqVfk/CyUU7b2OFgLw8Voxa6Tmv27IBrT7HGmkfdr1vWfjJdLtyIfpzz60Y/m0Y9+9Eq8tRBCiFUUWTA2ngyBuJlIPdVLcEP86KElngS2le3rjcGeVlO10yIbbyfWLlcrTun1OKXXYzIwfPtgiaFCiFawc5234PaeVoQmDqjQMFXTbjHbSRyurtp/7+WcJ6UUgzmXwZxLZOxUF+jqe9ZO3C6072puiyrvay3MTHNKnXydtXEejCeIFaGp2e+ifhLRKo3G3cxz3Kz3c3X9eJaJj86yUvFliSdaduRcjLEYQEPdD0HbfTwzNq7ZP9eEbqc0S+vx1MkJ3UrJhf7K141phw1JXXeRxGqZL6f4keXe0bBpcVpPs38XROOaPc4s9/1W6vpPxsuFyTp6IYQQc3JUPGlb/Uw1mqMsfHW41cQTKA7x5IlS9Sd9AQITP05d3Y8QVQknXlmecNS0FU7zCUxcioQobm5SNovfTuJwdVX/nR3Fks5vvfPkaDXtBnGhm8WZ+67mNoe4npzWamo1b5W1dupDL6XiPOgpRWAt1cUqEjvtr5lxtxLvB7PjWXSO1YgvrRXzrY9r5XhmbbyS71i9ZmmVid1OapZWndCdOcG7MeWQcjsn4c8aI1cgb83a5yrsQyxfs8eZpbzfSl7/ifnJxK8QQog5OVrRm9CsS2rGfUM5tASRnbWSQel4sk0pCCOLciDtKNKumlXmAeLuq+XIsintkPOUfFIrpnG0IucpcgnNWCGkFJp5Hwc7GU8ujjJkE5qDS9pO4nA1Lf/8Nn6eZu7br9xQpDwNpbixYGCZetS0uqIk6cQ5LjRx2YekB0SwLqkldjpEs+OulXEs2k+3x1dts7SZTdI6vVnaxpqmabXN0rrRasRVu8WuaB8SG62zqInf22+/fcUO4GlPe9qKvbcQQojGDWRdtmZCjhYjJsK4LtP61OxmEGlHMRpFU2Ue1iXiGmYzWWs5XDRxc5JKTSYhZhrIuoz7cZwcLhp25urfjM2Mp525BMdL0ZK3kzhcXcs9v804TzP33ZtQlMK4UVIpsijilShpLKUwfmrB04q0o8n7Fm0tQRTnOImdztLsuGtlHIv206nxVbdZWu2K3Q5qllbbHK3RZmndajXiSnKjmIvERmss6l/v/PPPX5EkqZQiDMOmv68QQojm2drj0J/RbEhr/ImIEyVLaOLJ3+rK3yCyFALDZBQ3F3E0lKN4lVytUmg4XDRMhoYdOZeEEzeaEGKmrT0Oe8cU27IOB/Ih+/OwJaOnrQyoF09nbPS4Y9gseTuJw9W13PPbjPM0c9/WKoy19Gc0+/MRAOXw5KRvLqExWCaj+KkHV8e1fR/SK7HTaZodd62MY9F+2jG+QmM5UdMsbaRDm6X1VZulpeuv2F2f0nWfMBOzrUbektwo5iKx0RrK2jkKNtbQev5ue8veuVJEUbQi7y3a0+DgIENDQwwMDHDw4MFWH44QYpFGSxHfGypxz2jA0UlDIYgfiY47p8aPPlsLKVehFfSlNGlHo3U8EexpRTkylCJLQikGci45T7Nrc4K+lAzkor7RUsSPD5cZ8w2HJyIiTsZTYCzlKK61ui3rTIun0VLE7iM++cBwqBARWLuo7cTqqp6nMT/iwUJEZC0pV6/KeZoZIxOhYdw38cq3SUNgLBZwlcJRkHAVLvHXpKM4Y2OCTWlHYqdJVrNJS7Pzg+QbUWs146sURuQDizHQ42kiGzceKwR2qrZuJzRLy3rq5ITujGZp/WmHDSldud4UzRCZuBbzz0d8JkK7YJz2uIrH9SfYmHaWlJ8lN4q5SGysvkWt+L366qvn/f6Pf/xjvv71rwPQ19fHU57yFB72sIfR09PDxMQE9913H9/73vcYHR1FKcXznvc8zj777MaPXgghxKroSzk8ZSCFoyE1FjIRaPK+qTwWrehJKnKepsdTbM04pD2FqxXjvmF4Ir75iIwl6SocB45NRjyizyWbWJkPFkVnC41leCJiqBBSDC2HixGRjbubB5HFUXEDlk1ph6wXN4E7s//kRWFfKp6Q2zPik3Hjm+C8HzeVmW87sXpCYykEFrAcmogwNn5KYDI0ZFxFLqHYlHZX7DzVi5GRyYjDExFeD0wEEBmDQZHUiqSrSDrx5MrDel16U47EToNqf8/zwcmpqVzlkc6tPc6KTPY0Oz9IvhG1mhUP1sY58kTZ4Cg4kA8ZK8cTuaNlQym0BIa2n9Sd2Sytf8aK3Q0d1iytU9XLt8UwngDekNI4GiYCOxWnG1OayMTX6yrt8PNjARAsKT9LbhRzkdhYfYta8Tuff/mXf+HVr341nufxzne+k9e85jUkEolZr/N9n09+8pO86U1vwvd9rrvuOl784hc3smvRgWTFrxCdLTSWoXzIL475DE1EUx1ZU45ioMfljH5vqgbTPScC7jhUohBYSpGdekR6XUKxJeOwLqllQBezjJYi9oz4+JGduhAMTfzYV2jiBoKOVgxmHbZknHlvQFo1sSTmN/Mcj5UNeT+uIakqk/qOVjxyvcsj1idW9DzNjBFjLGO+oRgYQguhYaoBXF9SM5B12ZmT2GlUvd/z6orfXKV28kqPD83OD5JvRK2F4mFdQjHm22nN0WY2Teu4ZmmVCd7+monebm+W1gnmy7c9rsJYKASGjWmHjKfjRn6TEVlPoxVMhLah/Cy5UcxFYmP1NDTx++tf/5pdu3YRhiHf/va3Offccxfc5gc/+AHnn38+iUSCH//4xzzykY9c7u5FB5KJXyG6R2QsfmXiN+FM77Ra+wjPg4UQP4oncxKuPMIj5raYR79cRfzBQULzhC3JRcfNaj5KLua20DkuBRZXw/asw7rE6pZSmBkj1b9jLVRKPkjsNK4dH/Fsdn6QfLO2zWyWdrQY19M9XjYcr9TW7ZRmadUSDP1paZbWiZaSb3tcxanrPO4fDxZVAmI5+Vlyo5iLxMbKaqg13vvf/35KpRKXX375oiZ9Ac4991xe9rKXccMNN/CBD3yAD3zgA40cghBCiBZxtCI9xyrLPSPxReaBfEja1ezI1S/afyAfsiPnsmfE57ztKflUdw2rFzeDGadu3Dw4EeFotaS4cbRCPlporaWc44OFiB25pZ3jRs2MkZN/l7zULEuJgdUcH5qdHyTfdK9uaZbWW2mWNrP0gjRL6x5LzbcDWYdvHZhkc0YzVIhWJD9LbhRzkdhYWQ1N/H7zm99EKcX555+/pO2e8YxncMMNN/DNb36zkd0LIYRoQ8MTEX4UrxRIu5qdOWfWipCUq9mZU+zPw6FCRMZVDE9EDOYaGpZEB5O46X5yjoXEgGhnxsalZ2aWXqhO6HZKszRXwbqkZkvGYTDrTmuWtjHtsCGp8RyZ1O12S823vx0N8TT8dszQl3QkPwvRRRr6DR0aGgIgmUwuabvq66vbCyGE6B5DhZBCYAmsZTAz+6KxSinFloxm33j8+qFCKBeOa5jETfeTcywkBkSrWGvJB9Nr6h4rGUZq/n68FNc+bWeuhmylme7srwpHw8FCxGDWZXuPw5O3pVp9yKJFlppv9+eDqZIOp6/3JD8L0UUa+g1NpVKUSiV+9rOf8ZKXvGTR2/3sZz8Dlj5hLIQQor1FJr6xyvuGpKOmPR5WT8rVJB1F3jfkE5rIWKnrtAZJ3HQ/OcdCYkCspGJgZjVHm/m145qlVUouDBUiiqFBKXhE39wTclVJx8jvzRq31HybqKwAL/iGpKtILBAzkp+F6CwNTfyeccYZfO973+OTn/wkb3jDG9i8efOC2xw5coRPfvKTKKU444wzGtm9EEKINlNdKRNZ8BZ5AehpNW07qe+09kjcdD85x0JiQCyXH9lpK3Prrdjt1mZpfmS5fajEgXwIsKhmavJ7I5aab40FRykia3GUwgDzTxVLnAnRSRqa+H35y1/O9773PY4fP84zn/lMvvjFL847mfvLX/6SF7/4xRw7dgylFK94xSsa2b0QQog2Uy0Z5ygoLfJ5ycBYUpUNpeTc2iRx0/3kHAuJAVFPaCzHa5qlHZvZLG0yIh+096QurFyzNPm9Ecux1LjRisqkb/x1oUlfkDgTopM0NPF75ZVX8slPfpIf//jH/OpXv+Kss87iwgsv5IILLuBhD3sYmUyGYrHIfffdx6233so3v/lNoigC4IlPfCJXXnllU34IIYQQ7cHRipynyCU0Y4WQUmjmfbysFBrKkWVT2iHnKXlMbI2SuOl+co6FxMDaY6xltGzqT+h2ULO0Hk+dnNCtNElbrWZp8nsjlmOpceNXJoezCU1gLL6xpOaJHYkzITpLQxO/Wmtuvvlmnv3sZ/OTn/yEMAy55ZZbuOWWW+q+3to4oZx99tl8/etfX9SjKkIIITrLQNZl3Dd4SnG4aNiZm/3oIsRjwuFi/LqspxjISmOItUzipvvJORYSA91j3mZplYndTmiWlnLUVKmF6tf+GX9Pua29Z5XfG7EcS42bdZ7G0xBYiTMhuk3Dv6UbNmzghz/8Ie9+97t53/vex+HDh+d87ZYtW/jzP/9z/vIv/xLXlQQhhBDdaGuPw94xxbasw4F8yP48bMnoaSsNSqHhcNEwGRp25FwSjmJrj1QHW8skbrqfnGMhMdA5ioGZVVN3ZvO0TmyWtjE9fWI349af3Gon8nsjlmOpcfOQPpcjRcOOTNxQUOJMiO6hbHUZbhOEYcj3v/99/ud//ocHH3yQQqFANptlYGCAJz3pSZx77rky4bvGDQ4OMjQ0xMDAAAcPHmz14QghVshoKWL3EZ98YDhUiAisJekoPK0IjKUcWTwVX4zmPM2uzQn6UnLhuNZJ3HQ/OcdCYqD1/MjOWXqh+vdubZbWqeT3RizHUuPmob0ue8dCiTMhukxTJ36FWIhM/AqxdoyWIvaM+PiRpRBY8n78yKejIJfQZD1FwlGc2S8XjeIkiZvuJ+dYSAysnNBYTtQ0SxupM8Gb99v/9q/aLG2q9MKMZml9Sb3m6orK741YjqXGjcSZEN1HJn7FqpKJ3+aIjJ0agNfaRa/oLOXQ8OBExPBExETN6qFcpSbY1h4HV2JYzBAay/BExFAhnNbNXeKmeyzlHMuY153a/fe8lXE31767qVnazFq6q9UsbTlaEQtz7bPdf29Ee1pq3Mj1u1iO2rwFyLVbG5GJX7GqZOJ3+eRCT3SKuWK1x4WtGZftWYfkPJ2Fhaglk37dr945ljFvbWmX3/NWxl0QGfaOhdx7IuDIpKEQGCYCSzk0+BHkAyPN0lZRK2Jhqftsl98b0VmW+qGCXL+L+dTGzVjZMObHH0AC9CU1vUlNb0LLtVuLNX3i1/d9RkdHKZVKi3r9zp07m7l70eZk4nd55JEb0SkkVoUQjZI8IlphpeNuvmZpR4oRx0uGNi+rO2eztI01TdM6oVnaQlqRgyTviVaS+BPLURs3R4sR9+dDwsjGY5kFR4OnFaf2umxKOxJDLdSUid99+/bx3ve+l5tuuom9e/ey2LdUShGGYaO7Fx1EJn6XTpo5iE4hsSqEaJTkEdEKjcadH1lGaiZ0a79W/780S+sMrchBkvdEK0n8ieWojZsHxkIOT0YAlIKTH2I6WpF2FCjYnHY4tdeVGGqRhid+v/jFL/KHf/iHTE5OAix60hfiid8oihrZvegwMvG7NKGxfP/BEqNlw4F8SNrVbMloUjWP2ZRCw+GiYTI07Mi59CU1521PyWMUYlVJrAohGiV5RLTCQnFnrOX4ZMTBQsS4b0i7itBAwlEcL3VOs7S+arO0dP0Vu+tTGt3lk7oLaUUOkrwnWkniTyxHbdzsHw8ZLUd4jiLvWxJakUvEsZH3Db6Jy9UEBnqTilPWeRJDLeA2svFdd93FK1/5SqIowlpLKpXiCU94AoODgySTyWYdoxBr1vBEhB9ZDhUi0q5mZ86ZtdIi5Wp25hT783CoEJFxFcMTEYO5hn69hVgSiVUhRKMkj4jVZqzlN8cDDuZD9o6GhBY8bbj3hJ2qsVts85W6AFlPnZzQ7YBmae2qFTlI8p5oJYk/sRy1cWMs9CY0I5OGhFZsSGmohND6lOZEyVAMLf0pjUVJDLVIQ//S7373uwnDEKUUr3nNa3j3u99Nb29vs45NiDVvqBBSCCyBtQxmZg/EVUoptmQ0+8bj1w8VQkmkYlVJrAohGiV5RDSTtZZ8YONaupPxytxjM752QrM0T0OPp0m7CmMtm9IO23scnrgtRX9Ks6FDmqV1glbkIMl7opUk/sRy1MaNq6EYQgT0JdTUpC/EcZNLaI6VDGUDGRcCayWGWqChf+nvfOc7KKU4//zz+djHPtasYxJCEHdczVeK6ycdNe2Rm3pSribpKPK+IZ/QRMZKh1+xKiRWhRCNkjwilqoYmKnmaHPV1g1Mq49yfo6CpKNIOor+tKbHi5so9XiabCL+mtBMTcb8diwg5Si2Z13O2OBJzDdRK3KQ5D3RShJ/Yjlq4yahFL61lMN4ArjekyWeo3A1lENLylV4GomhFmho4vfIkSMAvPjFL27KwQghTqquQIls3A3TWIuxcfONuWqweVpN205KpovVMDNWF0NiVQhRq9V5JDJ2qoO53IS0Xrc0S9tQU3phY2p6s7Scp/nZ0TIHC3G/kx2LWPkkY+fKaUUOmm+fc133L7RPyWViserF30L3m5KDRO35dx3wQzCAM0+NeEcpqp/Dump2DEneWnkNTfz29vZy9OhRNmzY0KzjEUJUOCoefIuB4UTZUKhZtpJyFH1JTW9yeiOOwFhSlU/apJSbWC3VWHMUlBb5zKzEqhCiVivySGgswxMRQ4WQfHBynzlPMZB12drjSOORFRAaO9UUbWbphZHJeIK39ny0q7Sr2NbjxBO5M5ql9ac1fcn5m6VFxqKUkrGzTbQiB83cp7GWsbJhtGymHUPtdX+9fUouE8tRjR+F5UTZEBo7Z9xVc5nkIFGbt/ww/rMmLuEwl8havEoMhdaSUPGituGJkEMTkeStVdDQxO+jH/1ovvOd73DgwIFmHY8QoiLvG4byIcXAMl4ZjLVSaKDkKkqh5eikYTDrkPE0pdBQjuLabzlPyadlYtU4WpHz4hpOY4WQUmjmfVxMYlUIMdNq55HRUsSeER8/imvN5X0ztdokl9CM+4a9Y4oz+xP0pWRN02IZaxktm/oTupW/j5YN7T6tm3SYKruQ9TQ9la+eA8dLhtN6XXZkXZ68LbXsfcjY2V5acT5q93l0LOBXx3y0UpSi+NFpA9Ou+x8sxA0IN/V6U/uUXCaWy9EKTfxB3MhkRClhiQyz4q56v6kVkoPE9Fzph2gg6Somy5YgsrPKPQSRJTSQTcbzGIEBV8NQPkSB5K1V0tDE72WXXca3v/1tvvKVr/AXf/EXzTomIda80VLE7iM+jlaM+4ZSGD/+0JtQBFgmyxZHQW9Ssz9v2ZF1GCnFn6RlK5+SCbGaBrIu477BU4rDRcPOnKrbIMJay+GikVgVQsyyWnmkOsbmA8OhQkRgLUlH4el4wmWsEOIpxbasw+4jPrs2y40HTG+WVq/0wrHJzmiWlqrU091YWanracVkaBgrG7IJzcP6XBLO7Ak/ay378xE5T9Ob0E0Zv2TsbC+tOB8DWZfhiZCxssHYuC+SIZ4YcZQisPF1vwYslUfwsQxkXclloiGjpYgTpbi0QzGI66b3JuMJ3WrcTd1vjlu0VpKDBDA9V5YjS1LHJRvyvmVD6mSDN2vjSV1HQVJDaAELxycN65KavaOh5K1V0tBv7OWXX851113Hd7/7Xd7//vfz+te/vlnHJcSaFRrLnpH4Im6sbEg4sD3nkPctFuhNxDcjed9wvGTIeYqfj8QD9SnrPBKOYmuPJEexurb2OOwdiwfpA/mQ/XnYktHTVsuUQsPhomEyNOzIuRKrQohpViOP1I6xB/IhaVczmHHq7uNAPmRHzmXPiM9521Nd/8hhMTBzTuhWv/pt3izN07AhFZdaqK2tW/v3jDt9Mi80lu8/WGK0HJ/zQxOGLRlWZfySsbO9tOJ89Kc1w0VD2lU8WIhIOopNGU3GO7nPYmA4WoxXGG/POgwXDX1JxU+OSC4Ty1MdC7W2lQ+91NTTpVlP4TmaIIon7Y4WI1ytUAp2bU5IDhLTcuX+8ZDRsiGTUOT9eAV5LhHnmLxv8E1cvmHMt+QSFovCWhgrGzKe5K3VoqydpxjHIhw/fpwXvehF3H777Vx++eX8xV/8BY997GObdXyiywwODjI0NMTAwAAHDx5s9eG0pYP5kF8d99k7GuI5iv6U4kAhohxZxkqGiHgVgAbGK8m0L6F5+HqPwawrn4qJlplv5UlgLOXITn2Cm/O0xKoQYpaVziMzx9idOWfOFX378xFBZHlon8ujNiQYXETzrXbVLc3S1idPNkeb2SxtY8phXaL+Cs2FtHL8krGzvaz2+TiYD9l9uMSPj/jxil9rMUpNrfiNbPyYtLYWqxRawRM2JxjIeRwvRWsul4nmqB0LDZbIWHzDtHtNRylCYxj3LVjFYM7htHUuF+xMSw4S03LlA2MhhyfjRqWlwFC9nHC0Iu3EK4A3px3WpzTjlacbehJa8tYqauhf7yEPeQgAYRhireX666/n+uuvp6enhw0bNqD13HWRAJRS7N27t5FDEKLrDBVCCoElsHbqE7CdOcXBQkgi40yr+5XzNMXQsi6hCY2VmwHRUn0ph12bE+wZ8cm4alrNppSj2JR2yHqKhCM1m4QQ9a10Hpk5xs41SaiUYktGs288fv1QIWzbm44Fm6WVIvJ+e0/qAvQl9VSTtJnN0jamNOtT8zdLa2jfLRy/ZOxsL6t9PoYKIRGK3qTGVcyq8espRTapSDlxM6TQQoTil8d8sgndVblMrJ7asfC0dS7GUvdeM6E1mzMw4dv4w7eUlhwkgBm5cqNHf1Fzfz4kqfVUSQdHg6cVp/a6bErHT1JsSGmOlQ1bMlry1ipq6F/v/vvvnzpZSimqi4cLhQKFQmHB7ZfzibwQ3Swycf28vG9IOmrqsYeMp3lYr8eYX+n06568gXPLhh5PMZh1ySXm/7BFiJXWl3I4b3vqZHfpmpiULq1CiMVYqTwy1xg7l5SrSTqKvG/IJzSRsavezGZms7SRydkTvJ3QLC3rqZMTupWSC/3VUgxphw1JPashzGpr5fglY2d7Wa3zUZuTcgnNqTm37rV+ylH0JePa0vfnQ8bLhvHAEFVWI3dCLhPtY66xcK57zZSjSDuG9SkHg5L4EVNqc+W6REh/2pmKIYg/0O2t5K6tPQ7WwnAxkrzVAg1N/O7cuVMmb4VoompDlMjGn47V0lqxPuXEg6452el3aCKa+n5k48LqQrSSqxWDOZfBnEtk7FSXVhmshRCLtRJ5ZL4xdi5eZWytbtfMMbbaLG1kcvZK3erXTmmWtrFeTd3Kit0NKYeU2xn5v5Xjl4yd7WU1zsfMnDTXtb6u2aenFWGl1nZo2iOXic4y11g4X/wdyIdTHzBK/Ihac+VKYFre9CPLvaNh21yDrTUNr/gVQjRPNUk6Ckrz3Olprah+RhYYS6qyYYsXywgxi6OVDNJCiIY0K48sdoyt1cgYWwwMI3Vq6tbW1g06pFnazFq68zVL6xatHL9k7GwvK3U+5stJtdf6tQJjSWoFUVyH1Tcrn8tEd1nMWDgz/iR+xGLMzJXT/rzK12BiOimUIUQbcbQi5ylyCc1YIaQUmnkfgyiFcYffTWmHnKdkVYgQQggxh2aOsd3SLG1DavpK3WY1SxNCLKyRnKQVZBOag3K/IJZI7jdFK0jctZZM/ArRZgayLuO+wVOKw0XDzlz9my5rLYeL8euylXpjQgghhJjbYsZYYy0F3/DbsZCJwFIOAw7kQ75zsDQ1wZsP2ntSF1rbLE0IsTjLve7fmUtwvBTJ/YJYFrnfFK0gcdc68i8oRJvZ2uOwd0yxLRt3vtyfhy0ZPe0TsVJoOFw0TIaGHTmXhKPY2iMPBQohhBDz2ZzR/HzE4jmwPx/wwHiIVpZSCIXAMBFYim2+Uhfmb5bWn3bYkNLSCEyIDrDc6/4zNnrcMWzkfkEsi9xvilaQuGsdZa1t2tXtyMgIX//61/nRj37EoUOHyOfz5HI5tm/fzpOf/GSe97zn0d/f36zdiQ40ODjI0NAQAwMDHDx4sNWH07ZGSxG7j/jkA8OhQkRQ6drraUVgLOXI4qk4aeY8za7NCfpSK5MQpcGIqCXxIFpB4k4sJDKW0MSTtqNlU7f0wrFJaZYmGjNf4xohlmu51/3tdL8gOs9y4ieX0JL3REMkb7VGUyZ+i8Ui/9//9//xqU99inK5POfrkskkr3nNa3jnO99JOp1udLeiA8nE7+KNliL2jPj4kaUQWPK+mRpocwlN1lMkHMWZ/c1PhqGxDE9EDBXCaY+z5iqPWmztcWQl0Roi8SBaQeJOzFQMpk/oHi1GHCiEHC5GjJXj1brtPqnraabX1F1DzdI6VW0uGvMNY2XDaDnuyteX1PQmNL1JLXlJNGS51/2tvF8QnW8x8eNo2JZxGPetXI+JppC8tfoanvgdGRnh6U9/Or/+9a9ZzFsppXjUox7Fd77zHTZu3NjIrkUHkonfpWnFxIckYlFL4kG0gsTd2uNHlmOliJFJaZYm2kdtLjo6GXH/WEhgLJEBFLgKXEdxas5lU8aRvCQastzrfvmgVDRivvjJJTSHixGhkesx0VySt1ZXwxO/T3/60/nud78LQDqd5qUvfSnPfvazecQjHkE2m6VQKHDPPfdwyy238IUvfIFisQjA0572NL797W83/AOIziITv8u3Go86y6MXopbEg2gFibvuExrL8ZLhWCni2OT0ryOVr3m/vSd1FdBb0yytv86K3b6kNEvrJrW56P6xkCOTEViYjCyRiePVVZDy4tqEW9IOp/S6kpdEUyz3ul9KI4lG1MZP3jdyPSZWheStldfQxO+NN97Ii170IpRSPP7xj+crX/kKp5xyypyvf+CBB/j93/99fvKTn6CU4itf+QrPf/7zl7t70YFk4rd9hcby/QdLjJYNB/IhaVcvWGy9L6k5b3tKPo3rQhIPohUk7jqPsZWaunNM6B6bjB+Lb+9p3ZPN0vorE7sba75Ks7S1pzYXPTAeMFa2eBrygSWh45VuAHnf4htLLqEIIktf0mHnOslLQojOJtdjQnQXt5GNv/CFLwCwadMmvvnNb7Jhw4Z5X3/KKadw8803c8YZZ3D06FE+//nPy8SvEG1ieCLCjyyHChFpV7Mz58x6HDXlanbmFPvzcKgQkXEVwxMRg7mGUoloQxIPohUk7tqLtXE9v2klF2ZM8HZCszRPxyUYtmTcqSZpUyt3pVmaqKM2F1kUvQkYKRkSGtan9FRe2pBSHC8Zir6lP60xVvKSEKLzyfWYEN2lod/KO+64A6UUV1xxxYKTvlUbN27kD//wD/nHf/xH7rjjjkZ2L4RooqFCSCGwBNYymJk9uFcppdiS0ewbj18/VAhlgO9CEg+iFSTuVlcxMIyUZtfUrf3qm1Yf5fwcBT2eoseLaw3WfvU0jJQiTsm5DGRdnrwt1erDFR2iNhe5CooRRDZu5jYtLynIJRTHSpaygYwLgbWSl4QQHU2ux4ToLg39Vh45cgSAxz3ucUva7rGPfey07YUQrRWZeFVX3jckHTXtMZ56Uq4m6SjyviGf0ETGSj2eLiLxIFpB4q65/MhONUXrlmZpG5KaYyWDH1m0htPXe6Sc+ZulTVYbBAZWYkQsSm0u8jQEBsqhxdXgObPjx3MUro5fk3IVCSV5SQjRueR6TIju09DEbyKRoFwu4/v+krarvt7zvEZ2L4RokupjupEFb5EDtafVtO2klH/3kHgQrSBxt3j1mqWNTE6f4K3tkNyu+mqapdWWXqg2T5vZLM2PLLcPlTiQDwFIL3AzCms3RsTy1caLqxQBFgM483zA4ChFdXG8o5GYE0J0LLkeE6L7NDTxu337dn7zm9/w3e9+l8suu2zR291+++0ADAwMNLJ7IUSTVBewOApKiyzWGBhLqrJhnQUwooNJPIhWkLiLVZuljcyopXusQ5ulbays2K2trbvcZmkSI2I11MZZudIDWxOXcJhLZOPu9gCRgYQz/b2EEKJTyFgrRPdpaOL3/PPP59e//jWf+cxn+LM/+zPOPPPMBbf52c9+xmc/+1mUUpx//vmN7F4I0SSOVuQ8RS6hGSuElEIz72M9pdBQjiyb0g45T8njPF1G4kG0wlqIO2sted8yMseEbqc0S0s5alaTtNVolrYWYkS03rQ48w0aSLqKybIliOyscg9BZAkNZJMKDfjWsjkhMSeE6Ewy1grRfRqa+H3Na17DRz/6UYIg4MILL+SjH/0ov/d7vzfn67/yla/w2te+Ft/30Vpz5ZVXNrJ7IUQTDWRdxn2DpxSHi4adufp1E621HC7Gr8t6ioGsFPDvRhIPohWaFXeRsfGjhopVvQGZr1ladQVv0ObN0jzNVE3d2q/9NX/PuPPX1V1JkpvEaqiNs7KxJHWcT/K+YX2qpsGbhbxvcYCkhtBA0lm9mGtVrhPNJedRtBsZa9emdsxF7XhMnaih38xdu3bxx3/8x3z4wx/m+PHjXHrppTzkIQ/hoosu4hGPeAQ9PT1MTExw77338s1vfpO9e/dirUUpxR//8R9z1llnNevnEEI0aGuPw94xxbasw4F8yP48bMnoaZ/wlkLD4aJhMjTsyLkkHMXWHqni1I0kHkQrNBJ3obEMT0QMFcJp9W1zlRuRrT3OkksL1OrGZmkzJ3T7Uw65ROsmdRdDcpNYDbVx9sB4wJhvybiKfGA5UTLkEnG85X2Lbyy5hGLMN/QlHbZlnRWNuZXOdWJ1yHkU7UzG2rWjHXNROx5Tp1PWzlOwahGMMVx++eV87nOfi99wnpuF6q5e8YpXcN1116H1wk05RHcZHBxkaGiIgYEBDh482OrDETOMliJ2H/HJB4ZDhYjAWpKOwtOKwFjKUVzDblvWIedpdm1O0JeSAb5bSTyIVlhO3AHsGfHxI0uh0om6ujogl9BkPUXCUZzZXz9G6zVLm9k0rd2bpSmgN6mnTeTOnOCd2SytU0luEquhNs7uHws5MhmBhcnIEpk4H7gKUl58P7Ml7XBKr7uiMTdaihrKdaI9yHkUnUDG2u7XjrmoHY+pGzQ88Vv1pS99iXe+853s3r17ztecffbZvOlNb+JFL3pRM3YpOpBM/LY/SbailsSDaIWlxB0w741JKTRYq8gmFNZCX1JTDO205mmd0iytv05N3UaapXUyyU1iNdTG2dHJiPvHQgJjiQyg4olf11GcmnPZlHFWNOZkEqY7yHkUnUTG2u7VjrmoHY+pWzRt4rdq//793HHHHRw6dIh8Pk8ul2Pbtm08+clPZufOnc3clehAMvHbGeTxClFL4kG0wmLizlrLtw5McmgiYv94iLEKR0MpjG9QJgLDRGDbflI35ai41MIqN0vrdJKbxGqojbMx3zBWjj8sgviDpN6EpjepVzTmQmP5/oMlRsuGA/mQtKsXfOy6L6k5b3tKfgfaiJxH0YlkrO0+7ZiL2vGYuknTJ36FmI9M/HYeKaguakk8iNVUbZZ2tBhydNJwomQ4UT5ZW/foZETYAc3SNqROrsxtt2Zp3UJyk1gNtXEGrFrMHcyH/Oq4z97REM9R7Mw5czZa2p+PCCLLQ/tcHrUhwWBOmi21CzmPotPJWNsd2jEXteMxdRP5FxJCzMvRCnmAQlRJPIhm6dZmaTMneNe1ebO0biG5SayGmXG2WjE3VAgpBJbAWgYz9W+GIe61siWj2Tcev36oEMoNcRuR8yg6nYy13aEdc1E7HlM3WZF/oSNHjvDggw9OlXrYvn07mzdvXoldCSGEEKLNdEOzNIgf465XeqH69/Wp7miWtlZUVyphLSg1a8WSrGQSrbBQ3EXGkq/U1kw6atpjr/WkXE3SUeR9Qz6hiYyVeG4Dch6FEO2gHXNROx5Tt2naxO8DDzzA+9//fr785S9z4MCBWd/fuXMnl156Ka973es45ZRTmrVbIYQQQqwiYy2jZVN/QrfDmqVNTehWSy+s4WZp3apam3B/PmSoEDJaNpQiS8pR9CU1W3s0GdehHFmKodQuFKtjKTUzo8q3IwveImPR02radrJCr/XkPAoh2kE75qJ2PKZu05SJ3w9+8IP89V//NZOTk0Bcd2Om/fv388///M986EMf4l3vehd/+qd/2oxdCyGEEKJJrI0/cT82OX0it/br8ZKZutBqV56GHi/uNl37NR9ErPM0p/V5PGtnWlYHdLlqN/KxUsR9YyETgaEcxZNurlYcUJbdR2BjUqO1IpeIJ/ur3crHfcPeMelWLpqrGpd+FDehzPtmasVvvbir1hN2FJQWmXwDE3+4Ud1OtJ6cRyFEO2jHXNSOx9RtGp74/cd//Efe+ta3AvENo9aaRz/60Tz84Q+np6eHiYkJ7rvvPn75y19ijKFYLPL617+e8fFx3vSmNzX8AwghhBBicarN0mbW1K2trRu0ebM0R8HGlGZTxqH6OXPZWE7JOaxPOfR4moRmVm2wUmjYN27ZlnXZkNQy6dvlRksRu4/4HJ2M+MUxn3Jk8UOLVaAUTASWcmRRCo5NGnIJTTZh2JZx0FoxVgjxlGJb1mH3EZ9dm2XyVzSuGpf5wHCoEBFYS9JReFpRiuyccZfz4g8mxgohpdDM+xhsKTSUI8umdLyd5Lr24Ggl51EI0XLtmIva8Zi6TUMTv7t37+aqq67CWovjOPzv//2/eeMb38j27dtnvfbQoUNce+21vOc97yGKIv72b/+WZz/72Zx11lmNHIIQQgghmLtZWu1Eb7s3SwNIOYpcQrEuoemprNRNOFAOwWJ5SK/L+pTDedtTDE9EUx2AXa1Yn9RzdgA+XDR4SpGtPEotuldobLzS14+4e8THAgriCbSEwtEwPBGBbykGcV24UmTY6nkY4CE5F9/EMXMgH7Ij57JnxOe87Skp+yCWrRqX+SCOq7SrGcw4025uS6GpG3cDWZdxP85hh4uGnbn6TSMl17U3OY9CiHbQjrmoHY+pmzT0r/T+97+fKIpQSvHZz36WF7/4xXO+dtu2bbz73e/miU98Ii95yUswxvC+972PT3/6040cghBCCNH1qs3S6k3sVr92arM0hWWsbBiZNPR4ilPWuXNe6O3PRwxPGHo8zfBExNYeh71j8eq4A/mQ/XnYktF1J1ImQ8OOnEvCUWztkZWb3Wx4IsKPLL8dC3E1hJEl7Wo2pDSoeOV7NcI8R+EqcJRipBiRyrmM+Yb1KYedOcX+PBwqRGRcxfBEJJ2jxbJV4/JQISLtanbmZnctT7m6btxJrusOch6FEO2gHXNROx5TN2no6vW2225DKcXFF18876RvrT/4gz/g85//PF/72te47bbbGtm9EEII0fGqzdLmqqnb7c3S7jhUIjCgy4atPbMnQqqUUmzJaPaNhxQCy1AhZDDncmZ/gt1HfHbkXA4VIvaNh1OPTgcmfpzfU4odOZecpzmzPyGrNrvcUCGOkfFKd2jfQC6hqM72FkNLaMCiyHkwGYFbedS+FMW/j+tTzpwxJ8RyVOMysJbBjOS6tcjVSs6jEKLl2jEXteMxdZOGrl4PHz4MwMUXX7yk7Z73vOfxta99bWp7IYQQohtZa8n7lpE5JnRHShEnOqBZWtpVs1bqVr/2pzQbUg4pd+kXXpGJm8nlKxN089Xzgng1XNJR5H1DPqGJjKUv5bBrc4I9Iz4ZV01rlpRyFJvSDllPkXCkSddaUI2p8XJcrNoYcHW8shfi38nQQFBpppVwNb4xU/WiJ0NDylUYY9Fa1Y05qSUnlkpynaiS8yiEaAftmIva8Zi6RUMTv319fRw5coS+vr4lb1f7VQghhOhE3dAszdOwMeWwMa2nvvbPmNjNePNPUixXdcI7suAtcjLN02radg7xhWK15u9QISSfOHm8uUr9r609jqwKWAOqsRGauHyDIf5aVf11tDZu8gbxCktT/WpPvq4aRfViToilkFwnasl5FEK0g3bMRe14TN2goYnfRz/60Rw5coR77713Sdvdd999U9sLIYQQ7WiuZmnHamrttnuzNK1gQ23phZkTvJVmV3M9crzSKoswcRSUFrnsOTCWVGVDp+awXa0YzLkM5lwiY+OJEoWszlxjqjHhaohs/EhgYE/GVvX2Qal4NTDEq4AdpYisRVd+F2o/6pgr5oRYLMl1YiY5j0KIdtCOuagdj6nTNTTx+4pXvILbbruNG264gb/8y78kkUgsuI3v+1x33XUopXjlK1/ZyO6FEEKIZemGZmkK6K00S+tPT5/QrU709iX11ERWO3K0IucpcgnNWCGkFJp5H4EuhYZyZNmUdsh5as4LQEcrWZW5RlVjal0yjiOtIQwgiCyeE3/I4WrwFBQt+GH8CKGrAQtpV5NyFLoSW4uNOSHmI7lOzEfOoxCiHbRjLmrHY+pEDU38vupVr+Izn/kM3/72t3n5y1/ODTfcQDqdnvP1pVKJyy67jHvvvZdnPvOZvOpVr2pk90IIIcQs3dgsrX9Gbd35mqV1moGsy7hv8JTicNGwM1d/BbK1lsPF+HXZyqNeQtRTjal1CU3BN+hKre0NqbjBW8ZVBJFFYckH4Kq4NETKUaQcRV9l0lhiTjST5DohhBBCtEJDVxJKKb761a/y6le/mn/7t3/jzjvv5M/+7M+44IILePjDH05PTw8TExPcd999fPOb3+RDH/oQ+/fv5/d///f51Kc+1ayfQQghxBrRLc3SUo6KSy80uVlaJ9ra47B3TLEt63AgH7I/D1syetpquFJoOFw0TIaGHTmXhKPY2iOf/4v6qjH1kF6X3Yd9rFJMhobjJcglFClXMVqOXxtEFqMVnrL0Z1wcDb0JLTEnmk5ynRBCCCFaQVlrl3177DgnL0SqbzNfncDFvKb6/TAMl3tYoo0NDg4yNDTEwMAABw8ebPXhCLHmtHudpG5tljazxm7GbV1d3WZodhyNliJ2H/HJB4ZDhYjAWpKOwtOKwFjKUVyrdVvWIedpdm2WTr5iftWYOjoZ8YtjPuXI4ocWq+L6vmEE5ciiVNzoLZfQZBOKbRkHrZXEnFgRkuuEEO2q3e8RhBDL19CK33pzxouZR25grrmtvepVr+L666+f+vvVV1/NNddc09B7nn/++XznO99Z9Ov37dvHqaeeuuDr7rvvPj760Y9y0003ceDAAaIoYmBggAsvvJArr7ySxz/+8cs/aCFEWwmNPdkZtaZu7Wp3Ru22ZmlTE7s1NXZb3SxtJa1kHPWlHHZtTrBnxCfjKgqBJe/HK7dTjmJT2iHrKRKO4sx+mQgRC6uNqbST4L6xkInAUI7iWO5xFVpZAgMbkxqt4/qrrlY4Cok5sSIk1wkh2km73CMIIVZWQxO/T3va07ry5nY5brrppmmTvu3sYx/7GP/n//wfJicnp/3/e+65h3vuuYePfvSjXHXVVVx11VUtOkIhRLOMliL2jPj4kZ12g+moeIXbuG/YO9b4DWa3NEvrS+pZK3U7qVnaSlmNOOpLOZy3PXXyBiRx8vFnuQERy1EbU/2ZkKFCyGjZUIrsVC3frT2ajOtQjizFUG56xcqTXCeEaAerdY8ghGi9hiZ+v/3tbzfpMDrb+Pg4r33tawGm6hqvhBtvvHHB12zevHne73/2s5+dOlatNS95yUu44IILcF2X73//+1x//fWUy2Wuvvpqkskkf/3Xf92UYxdCrL75HiktRZaxQjj1SOnuI/6cj5R2S7O0XE2ztJlfu6lZWrM1K44Ww9WKwZzLYM6VRw5FU9SLKawFpWbFlsScWC2S64QQrbSa13ZCiNaTNrFN8Fd/9VccOHCAHTt2cOmll3LttdeuyH5e8IIXNLT90aNHed3rXgfEk7433ngjl1xyydT3L7vsMl796ldzwQUXUCwWeetb38oLXvACTj/99Ib2K4RYfaGx7BmJL+gO5EPSrmYw48xqIjM8EbF3NGB9SrM/H7A543KiFE/oVid6O6FZWtpVsyZyZ07wJh25oV6qpcTRA2MhO9c57BnxOW97quFJdEcr5BZDNNPJmJoem7UTbwnJE22tXSZJGzmOmdtKrhNCrKbFXtsdLsbf35Fzm3ZtJ4RoDZn4bdCtt97Kxz/+cQA+9KEP8eMf/7jFRzS3f/qnf2J8fByA173uddMmfat+53d+h7//+7/njW98I2EY8ra3vY3Pf/7zq32oQogGDU9E+JHlwHhIaEFh2TceMuFbCoGhEFgmAsNEYNt+Urdes7Taid3+lCbj6YXfSCxZNY4OFSLSrmZnzpkq8WSMZcyPV3tPhoYTZcPIkYjBrEPW05y5KSE3CKJtSV3DztEu56qR42iXn0EIIea7tqtKuZqdOcX+PBwqRGRcxfBExGBOpo+E6ETym9uAYrHIlVdeibWWF7/4xVx88cVtPfH7xS9+cerPf/EXfzHn66688kquuuoqJiYm+NrXvsbk5CTpdHo1DlEIsQTlyE6roTtSU1P3YD5kzDeEptVHOb+13CytEwwVQgqBJbCWwczJG4NiYDhYCIkMlCJLOYw/QCj4liOThp8cLlMIjNSFE21J6hp2jnY5V40cR7v8DEIIAXNf282klGJLRrNvPH79UCGUiV8hOlTDv7nHjx8HIJvNkkgkZn3/F7/4BW9+85v57ne/SxiGnHXWWbz5zW/m2c9+dqO7brm/+Zu/4be//S0bNmzgve99b6sPZ16//OUveeCBBwB41KMexWmnnTbna3O5HE996lO5+eabmZiY4Dvf+Q7/63/9r9U6VCEE0ixNtF5kLPnKJEXSUVOPABYDw/58SDmyjJUMEeDquGalVYZjJYMfWjZltNSFE21H6hp2jnY5V40cR7v8DEIIAXNf280l5WqSjiLvG/IJTWSs1CIXogM1NPF70003cfHFF6OU4oc//CFPfOITp33/nnvu4dxzz6VQKGBtPEHxve99j+c+97l8+tOf5rLLLmtk9y31gx/8gA984ANAXEJhy5YtK77Piy++mJ/+9KccPXqUnp4etm/fzrnnnsvLXvYynvGMZ8y77V133TX155nnqZ4nPvGJ3HzzzVPbysSvEM0jzdJEJ6iWAIksePpkeYeDhXjS93jJkNCKvoTCq6mLGhmLVor9+ZBT1ympCyfahtQ17Bztcq4aOQ6gLX4GIYSoqndttxBPq2nbyUdTQnSehiZ+//M//xNrLY94xCPqTia+4Q1vIJ/PA3EzsXXr1jE6OgrAn/3Zn/GsZz2LrVu3NnIILVEqlbjiiiswxnDBBRfw6le/elX2+/Wvf33qz6Ojo4yOjvLLX/6ST3ziEzzzmc/ks5/9LNu2bau77W9+85upP8+32rfea2q3FULMz1pL3reMzJjIrf17pzVL668zsSvN0rpf9fQ6Ki7nADDmGyIDY5VJ3w0pPa1PVmQtnlb0JjUpV0ldONFWpK5h52iXc9XIcQBt8TMIIURVvWu7hQTGkqpsKJf+QnSmhq4qfvrTn6KU4oILLpj1vYMHD3LTTTehlOLpT386X/7yl9mwYQP/8i//witf+UomJib4xCc+wVvf+tZGDqElrrrqKn7zm9+QTqf56Ec/uuL7W79+PRdddBFPeMITGBgYwHEchoaG+Na3vsVNN92EtZZbb72Vc845hx/96Ed1J9OrE+4A/f39C+5z48aNdbcVYq0rBmZaLd2RydkrdoM2r6vrKOjxFFlP0+MpUo5iMrJs73E4dZ3LM3akpVmawNGKnKfIJTRjhZBSGK9EL0WWCOhLqGmTvkFkCQ1kk4qUq9jW40hdONFWpK5h52iXc9XIcQBt8TMIIURVvWu7+co9lEJDObJsSjvkPCVlHoToUA1dVRw5cgSAxzzmMbO+941vfANrLUopPvzhD7NhwwYAXvrSl3LDDTdwyy238N///d8dN/F75513cu211wLwtre9jYc+9KErur9//Md/5Oyzz65bP/kNb3gDP/7xj3nRi17E/v37eeCBB7jiiiv4xje+Meu1hUJh6s+pVGrB/dY2c6uu2m6mIAjYvXt33e9t27ZtzpXLYrbI2KkmITIYN6a2WdpctXUnw/ZeqlttlpbzNFrFk3EZT7Mj55BNnJzord6AWmvZn48IIstD+1wetSEhk75iykDWZdw3eCpehTYZGsqhxdVMK+8Qr3Q3OApSjqIvqaUunGgrUtewc7TLuWrkOMbc+IOxVv8MQsxF7h/Wrtpru8NFw85c/SbK1loOF+PXZT3FQFY+kBJzk5zS3hr67R0ZGQHqryC9/fbbATjjjDM4/fTTp33v2c9+Nrfccgu//vWvG9n9qvN9nyuuuIIoiti1axdveMMbVnyf55xzzrzff8ITnsDNN9/MWWedRblc5qabbuLOO+9cVB3fVjpy5Ahnn3123e9dffXVXHPNNat7QB0mNJbhiYihQjitwVeuMihv7XGkPtwMa61ZWmgs33+wxGg5rh2oKhdt9WoLToaGHTmXhKPY2iOVu8RJW3sc9o7FjYceGAs5UY7LlNTmlyCKJ0Z8E3/o4FRKPYDUhRPtQ+oado52OVeNHIcBsK3/GYSoJfcPAqZf2x3Ih+zPw5aMlnsEsWSSUzpHQxO/xWIRgHK5POt7P/zhD1FKcf7558/6XnU1Z6eVEHj729/O3XffjeM4fPzjH8dx2iP5PepRj+KVr3wln/jEJ4C49vLMid9sNjv151KptOB7Tk5OTv05l8s16UhP2rx5MzfddFPd78lq3/mNliL2jPj4kaVQWYlS/XQtl9CM+4a9Y4oz+9dOZ2hpljabq+MY2H3EZ0fO5VAhYt94ONVNPDCWcmTxlGJHziXnac7sT8jgLKapjaOd6xxGjkQUfItVcT2TyMblHZzKSvOkoxjMOujKyhGpCyfahdQ17Bztcq4aOQ4NoFr/MwhRJfcPokruEUQzSE7pLA1N/Pb29nL8+HGGh4en/f/9+/ezb98+lFJ1V6waE98wzlXrqh3t2bOHd7zjHUBcYmHXrl0tPqLpnvGMZ0xN/P7qV7+a9f2+vr6pP1dXas/n2LFjdbdtFs/z2u7fsBOMliJ2H4k7RB8qRATWTg3SpcgyVgjxVPwJ7u4jPrs2d36inatZ2rFSNDXR22nN0mZ+7V+hZml9KYddmxPsGfHJuGraoJxyFJvSDllPkXBkUBZzq42jwazDkUnDsZIhMnEjt2wyLh/i6HjSt1oqROrCiXYidQ07R7ucq0aOo/rUw0RgJd5Ey63F+wcxP7lHEI2QnNJ5Gpr4Pf300/nBD37A17/+dd74xjdO/f8vfelLU39+ylOeMmu76kTxYpqMtYvrrruOIAjQWuN5Hm9/+9vrvq5a4qL65+rrTj/9dC699NIVO75NmzZN/bneSurachv79u1b8P1qXzOzVIdojdBY9ozECfZAPiTtagYzTt3Hcg7kQ3bkXPaM+Jy3PdXWn9DObJZWr2lauzdL8zRzll6oTuy2qm5uX8rhvO2pk4/hJE4ehzyGIxarGkdZT/OTw2X80KJVXNIh5cY1fXsrZUZA6sKJ9iR1DTtHu5yrRo+jHX4GsbZ16/2DaJzcI4jlkJzSmRq6snjWs57FD37wA77zne/woQ99iCuuuIKf/OQnvPOd70QpxeMe9zh27Ngxa7uf/exnADzsYQ9rZPerytp4OaExhn/4h39Y1Da33XYbt912GwDPf/7zV3Tit3YVb70Vuo997GOn/nznnXcu+H61r6nXvE+svuGJCD+yHCpEpF3NztzsDtEpV7Mzp9ifh0OFiIwbN2RqVXdoP7Jz1tQd6bBmaVMTuTMmePtTDrlE/Zu5duFqxWDOZTDnSuF9sWyuVpy5KUEhMGzKaPbnQ1KuYltP/Ys9qQsn2o3UNewc7XKuGj2OdvgZxNrWifcPYvXIPYJYKskpnamhf/nXvva1/PM//zOFQoHXv/71vP71rwfiSVKlVN3mZ9ZabrnlFpRS8qh/E1UnmKH+Ct1HP/rR7Ny5k/379/OrX/2K+++/n1NPPbXuexUKBb773e8CkMlkePrTn74ixyyWZqgQUggsgbUMZmYn2CqlFFsymn3j8euHCuGKJNlqs7Spkgtd3iytWzhaScMYsWy1deFOXaekLpzoKFLXsHO0y7lq9Dja4WcQa1u73T+I9iX3CGIxJKd0pob+5bds2cIXvvAFXvziF1MoFKZ977LLLuOVr3zlrG2++c1vcuTIEZRSHTWh+J73vIf3vOc9C77ummuu4W1vexsAV199Nddcc83KHhhwzz338JnPfGbq7xdffHHd1734xS/m3e9+NwDXXnst73vf++q+7mMf+xgTExMAXHLJJWQymSYfsViqyFjyldpLSUfNWycO4k/Zko4i7xvyCU1k7JI+uZVmaUKIuUhdONHJJH47R7ucq0aOo11+BrE2rfb9gxCiu0lO6VwNT7k/5znP4Te/+Q2f//znue++++jp6eFZz3oWz3rWs+q+/kc/+hFPf/rTUUpx4YUXNrr7jnXdddfx6le/GoCnP/3pfPvb3571mve973084QlP4Nxzz53zfX7605/ye7/3e5RKJSAuv/HkJz+57mv/8i//ko985CPk83k++MEPcuGFF3LJJZdMe80dd9zB3/7t3wLgui5XX331cn480WTVxmWRBW+RydLTatp21VuJbmyW1j9H07RmN0sTQsSkLpzoZBK/naNdzlUjx9EuP4NYe5p5/yCEEJJTOldT1lpv27ZtWnO3+Vx11VVcddVVzdht17v11lv58z//cx760Idy4YUX8pjHPIaNGzfiOA4PPvgg3/rWt/jGN76BMXHnq1NOOYVPf/rTc77f5s2bef/738+rXvUqjDG88IUv5CUveQkXXXQRjuPw/e9/n+uvv35qEvltb3sbj3zkI1flZxXzq85fOgpKC8zI+pGlEBgOTYREJq7D8+vjflyWoYOapfXPsVK31c3ShBAxqQsnOpnEb+dol3PVyHG0y88g1pal3D9UBcaSqmwo6yeEELUkp3QuKbLRAfbu3cvevXvnfc2zn/1sPvWpT7F9+/Z5X3f55ZdTLBZ5wxveQKlU4vOf/zyf//znp73GcRze8pa38OY3v7nhYxfN4WhFzlOkXcWhiZB9gB/BRGAoBHba13af1HVqmqXVq63bCc3ShBDTSV040ckkfjtHu5yrRo6jXX4G0f2q9w+5hGasEFIKzbyPZpdCQzmybEo75DwlH0wIIaaRnNK5ZOK3jf3zP/8zv/u7v8sdd9zBnj17OHLkCCMjI5TLZXp7ezn11FM555xzePnLXz5neYd6/uRP/oQLL7yQj3zkI9x8880cOHAAYwzbt2/nggsu4I/+6I8466yzVvAnE/VUm6WN1GmSNlIyHC1GFMPqJ2tBS491LvM1S+uvfO22ZmlCCCGEEEK0o4Gsy7hv8JTicNGwM1d/cYW1lsPF+HXZSgkSIYSYSXJKZ1LW2qZU8hwdHeXjH/84t9xyC7/85S85fvw4YRgShuG01916660MDw/T398/Zx1g0b0GBwcZGhpiYGCAgwcPtvpwVk29ZmkzJ3jHOrBZWv+MCV5pliaEEEIIIUR7CI3l+w+WGC0bDuRD0q5mS0ZPW6VXCg2Hi4bJ0LAj59KX1Jy3PSXX9EKIWSSndKamTLt/8Ytf5LWvfS35fB6IZ/eBujP/e/bs4Y1vfCPpdJpDhw6xbt26ZhyCEC01XjZTzdKmJnRrmqZJszQhhGgvUmdTCCFEu1ipMcnVijP7E+w+4rMj53KoELFvPCTpKDytCIylHFk8pdiRc8l5mjP7EzJBI4SoS3JKZ2p4xe8NN9zAq1/96qnJ3m3btpHL5bjnnntQShFF0bTXnzhxgi1bthBFETfccAMvf/nLG9m96DDduuL3j/57hLFy+xbXlWZpQggRr1IYnogYKoTkg5OXP7nKI2hbexy5MBVCCLEqVnNMGi1F7BnxK02gLXnfTE005xKarKdIOPGETl9KqlALIeYnOaWzNLTi98EHH+SP//iPsdayfft2rrvuOi688EK++tWv8sIXvrDuNuvXr+dpT3sat912G7feeqtM/Iqu0J/SLZv4VYCrwdOKpKPoS8aPW6xPaXZtSnJqryfN0oQQa95CF6jjvmHvmFygCiGEWHmrPSb1pRzO2546OdGcOLngQz78FEIsleSUztLQxO8HPvABSqUS6XSab33rW5x++umL2u5JT3oSt956K3v27Glk90K0jY1ph71j4cIvXKJ6zdJ6XMVo2aAUFHwL1pJ0NQkHQsvUoxXrUw4jJcPOdRalZDWvEGLtGi1F7D7ikw8MhwoRgbVTj6SVIstYIcRTim1Zh91HfHZtlslfIYQQK6NVY5KrFYM5l8GcK+WOhBANk5zSORqa+P2v//ovlFK87GUvW/SkL8DDHvYwAO6///5Gdi9E29iYWt7EarVZ2swmaXM1S6sWU/ccxYF8SDYxdzH1A/mQHTmXPSO+FFMXQqxZobHsGYlvsKtNKAYzjuRNIYQQq65dxiRHK+TjTSFEs0hOaW8NTfzu27cPgKc85SlL2q63txdgqhmcEJ1uY3p2mqs2S5uvtu5Sm6UNT0T4keVQISLtanbmnFklHFKuZmdOsT8PhwoRGVcxPBExmGtKL0chhOgokjeFEEK0CxmThBBCrLaGRo+JiQkAstnskrabnJwEIJVKNbJ7IdrGmZsSpN3ctInelWiWNlQIKQSWwFoGM7MvFKuUUmzJaPaNx68fKoRysSiEWJMkbwohhGgXMiYJIYRYbQ2NHhs3bmR4eJjDhw8vabt7770XgE2bNjWyeyHaxs6cy84VvhiLjCVfaf6QdNS0R8LqSbmapKPI+4Z8QhMZK/V2hBBdZaF6YpI3u4/UkBOtJjEoFjJXjMiYJIQQohUamql69KMfzfDwMLfffjt/+qd/uujt/uM//gOlFGeffXYjuxdiTYnsya/eIi/6PK2mbSd1d4QQnS409mQH4cBO/f96HYQlb3aHpZxzIVaCxKBYyGJixMiYJIQQogUamvh9znOew7e+9S2++tWvcu+99/Lwhz98wW2+8IUv8LOf/QylFM997nMb2b0Qa0q1HLCjoBTZ+V9cERhLqrLhEssJCyFE2xktRewZ8fEjS6Gyaqq6qiqX0Iz7hr1jijP74w7okjc731LPuRDNJjEoFrLYGHnMBg+QMUkIIcTqaqgI6Wte8xo2bNiA7/tccsklU83e5vLFL36RK6+8EqUU27dv52Uve1kjuxdiTXG0IucpcglNObKUQjPv60uhoRxZcglNzlPyaJgQoqONliJ2H/EZLRv2joYcLIRTN86lyHKwELJ3NGS0bOLXlSLJmx1uOedciGaSGBQLWUqM7BkJ0FgZk4QQQqyqhlb8rlu3jg9/+MO85CUv4Z577uExj3kML3zhC0kmk1Ov+eAHP8iBAwe4+eabueuuu7DW4jgOn/rUp/A8r+EfQIi1ZCDrMu4bPKU4XDTszKm6TSGstRwuxq/LVh4xE0KIThUay54Rn3xgOJAPSbuawYwzrT5iKTQcLsbf35Fz2TPic972lOTNDtXIOZdH7kUzSAyKhSwnRoy1ZFwtY5IQQohV0/AIcumllzI6OsrrX/96Jicn+Zd/+ReAqQHsf//v/z31WmstiUSCj370o1x00UWN7lqINWdrj8PeMcW2rMOBfMj+PGzJ6LoXmJOhYUfOJeEotvbIo4dCiM41PBHhR5ZDhYi0q9mZm90JPeVqduYU+/NwqBCRcRXDE5HkzQ7VyDmXzveiGSQGxUKWEyOn9TpMhlbGJCGEEKumoVIPVVdeeSV33nknL3jBC1BKYa2d9R/Ac5/7XO644w4uv/zyZuxWiDXH1XENuZyn2ZFzCSLLvvGQ344FHMjHX/eNhwSRZUfOJedpzuxPyMoTIURHGyqEFAJLYC1bMrru6iiIP3TektEENq6zOFQIJW92qEbOuRDNIDEoFrKcGCmGsC6hZEwSQgixapr2cfRjH/tYvvKVrzA2Nsb3v/997r//fkZHR8lmswwODvLUpz6VTZs2NWt3QqxZfSmHXZsT7BnxybhqWhOJlKPYlHbIeoqEI41G1qLI2KmGIlILTnSDyFjylTyXdNS0VVHGWowFrUBXbrhTribpKPK+IZ/QRMZK3uwwfmg4UTaMlaNZ57yeeudc8p9oxHx5p57VjEEZ59tDIzGSS7ictSnB3ceWPibJ+RdCCLFUTX8Oqbe3l+c+97mLeu3Q0BADAwPNPgQhul5fyuG87SmGJyKGCiH5xMmLzVylDtjWHkdWB6wRobEnYyE42SVaYkF0g2rj88iCpxXGWsbKhtGymdYVPeUo+pKa3qTG02radg6SN9tdbR47UTbcOxpwomTwtGJ9MqI3qacm9+upd86FWK6ZeWcxVjIGZZxvP43GSC6hFz0myfkXQgjRiJYUoBoeHub//t//yyc/+UmKxWIrDkGIjudqxWDOZTDnyqf/a9hoKWLPiI8f2WkrRhwV31SM+4a9Y7KKUXQuR538OuYb7hu1RMZSiizl0GKI61aVXEUptBydNETW0lu5iXZqUqLkzfY0M4+Nlw0nSoZiYFAKhicURycNg1mHjFd/VV1gLKnKyXbkdIoG1ead2g+Y5rNSMSjjfHtqRow4ixiT5PwLIYRo1KpO/B49epR3vOMdfOQjH6FUKq3mroXoao5WsrppDRotRew+EneTPlSICKwl6Sg8rShFlrFCiKfipla7j/js2iw3BaLzOFqR8xSujhvp5DxFMYxvkF0NjlIE1jJZtjjq/2/v3uPjquv8j7+/55yZTJKZNpQ2TZs0FAqWi2y1XV2xAuWqiKKACOqKUGRRwV1FdhUvXBTR9QKLIlflIjeRRRR0QRDKIvArW6yUm3LpBdI0TQklyUySycw55/v7Y9Jp0+baziSZyev5eNRMZr7ne77HfDjfM5/zPd+vVOUZJbNWu8ccJSJm0KQu582JYbDzWNSRekzuseiNCjSt0tHrSavGhLdD8jfth+oNrGZUukP+zYGR2nLeSUQddaR8pf1wyEf5ixWD9PMTV6FjZKA+ib8/AKAQdjrx+/LLL+vll19WT0+PGhsb9fa3v13V1dUDln3rrbf0/e9/X1dddVV+hK+1Vp7HircAsDP80GpVW+7LQFPSV6XnqKHKHXBV6KakrzkJT6vaMlo8O8bjgCg5dVWu/tLaq9BKG7tC7RbLTesQ2WZYXTbIjYTa2BWqMmLUng5VN4svwBPZUOex3SocbewK1GKtegOrzT2hplU6Wp8KtHeNyU/7YK1Va3eoiDGK9z32DBRCfdxTZyYXW63doRoTZsDFu4oVg/TzE18xY4S/PwCgUIaehX4Af/jDH7T//vtrv/3200c+8hGdcsopeu9736s5c+bo4osvlrX9H3W54oortPfee+tHP/qRurq68gnf008/XS+++GLBDgQAJpONXYEygVVLKlCl56gx4e4w0iTW936l56gllSu/sSsYpxYDu2Db77DGyg7yVK21uc8H3A4TzlDnsalRR64j7V7lKuoqn/wNwtwcz1Iu6fF6MlCPH2pW3FXUNaqrJtmPwqirzsXUrLirnr5YS/thvzLFjEH6+YmvmDHC3x8AUCijuiX9y1/+UkuXLpW1docEb3t7u7797W9r7dq1uummm9TZ2amPfexjevjhhyXl7nRWVFRo6dKl+upXv6rGxsbCHQUATDLNKV+prFXW6z9lxwAAa95JREFUWjVUuQOOMJEkY4xmVjla25kr35zy1ZBgRBxKy8auQDUVjpq7AtVVu+rOWL2ZDvNTPQTWyg9ziynVVbtKZqxq+kaM7jElMt7NxyCGOo85jlFD3NPrSV+7V7p6sztQRyZUqNw8l7tX5h6bjhijOQlPiYijBdOjjHRDwXhObt7UlZsympPw1JIKtLbTzz9qnw1tUWOQfn7iK2aM8PcHABTKiHuFTZs26Ytf/KLCMHcXMxKJ6IADDlAsFtNLL72kt956S9Za3XLLLfr0pz+tSy65RI899pistaqsrNTnP/95nXfeeaqrqyvawQDAZBCEVsm+BT4qXDPknHJSbkRIhZubKzMZdRSEljkwUTK2xLtvpVlVrhxHqvbUb3G3iDGKVxjFXCPXkeKe5FspmbXE+wQ1kvNYVcRRY8LT+pSvWXFPtiuQtVbZ0CrqSDMqPcUjRlGXhY1QHDUxVwtro1rVllGVZ/otrhVzjWZUukWJQfr50lGMGOHvDwAopBEnfm+++WYlk0kZY3TCCSfommuu0e677y5JCoJAP/3pT3XeeefJWqtTTz1VLS0tkqRPfvKT+tGPfkTCFwAKZMvi0YGVIiO8sI84pt92pEdQKraN2+qoo/pqVx2ZUO29odLe1qePYm5u3t+p0dzIYOJ9Yhvpeawq4mjvqRF1ZEKlsqGyodFuMUd11Z52q3BUH/dUV+0y0hdFUxNztXh2TBu7AjWnfCWjW5Nwib75Wgsdg/TzpaXQMcLfHwBQSCNO/G6ZsmGvvfbS7bffrkhk66OTruvqS1/6kl599VVdddVVamlpkTFGl19+uf71X/+18K0GgElsy3pWrsmNehyJbGgV69vQJT+CErJ9vDuO0W4xV7vFXIVhbsSvo9zUAFsQ7xPfaM5jW/7mu1eGqnCMZla7Orwhpugwo+CAQvEco4aEp4aEpyC0ucSaUdFGVdLPl55Cxgh/fwBAIY34ivnFF1+UMUaf/OQn+yV9t3XGGWdIys019E//9E8kfQGgCFzHKBExSkQd9QZ2h4VEtpf2c3NhJqKOEhHD438oKUPFu+MYeY7pl/Ql3kvDzp7HplQ42q3CIemLceM6ucf2i3luoZ8vbbsaI/z9AQCFNOKr5s2bN0uS9t1330HLbPvZiSeeuAvNAgAMpT6em9syYoxau8MdFtzcwlqr1u4wNwdq3+OGQKkh3ssTf1dgcPz3Mbnx9wcAFMqIE7/d3d2SpOrq6kHLVFZW5l83NjbuQrMAAEOpq3YVdY1mxV31+KFeTwY7jAhJ973f44eaFc+Vr6tm1jeUHuK9PPF3BQbHfx+TG39/AEChFO2WYEVFRbGqBoBJz3Nyq0Ov3JTRnISnllSgtZ2+KlyjiGOUDa16A6uIMZqT8JSIOFowPTqqxWeGm6NuLOY5BKSxiXeMPf6uwOD476N0FON6iL8/AKBQeBYEAEpUTczVwtqoVrVlVOUZpbJWyUyowEox12hGpat4JDfP3ILpUdXEhh8F4od266rU2a2PFW5ZlXp6paO2nnDQzwu9sjmwRTHiHeOPvyswOP77mLiGu14qxPUQf38AQCGMOvFrzMg6sJGWAwDsvJqYq8WzY1u/fES3zuAz2i8f7elAq9oyygS235cL10iJqKPWLl8t3aHqqhyFMjt83pkJtbqDLx8onkLGOyYO/q7A4PjvY+IZ7nqpkNdD/P0BALvK2MFmit+O4zgjSuZuqW4kZY0x8n1/JLtHmWhoaFBzc7Pq6+u1fv368W4OUHZ29nHD9nSglZsySmZDtaQCZa3t9zhhMhOqozdUpWfU41tNrXCUiDo7PG44K+4qEXG0sJbkL4qP6UbKE39XYHD89zG+hrteKvb1EH9/AMBo7dRUD0PlirdN+I4wpwwAKBDXMRrt1ws/tFrVlvsS05T0Vek5aqhyFfNyo0rC0OpvmzMKrbQhFWhqhSPPSHOneHL6zvlpP1Rrd277OQlPq9oyWjw7xggUFNXOxDsmPv6uwOD472P8DHe9JBX/eoi/PwBgtJzhi2xlrR02mbulDElfACgNG7sCZQKrllSgSs9RY6L/l5iOTCjHGJm+US1GkmOMOnq3ri4d69uu0nPUksrVt7ErGIejAQAAKLzhrpckrocAABPPiBO/YRgW/F8Q0AkCwHhrTvlKZa2y1mpm1Y7T+rT3hkoHVqExmlHlKJSUDqzat0n8SrknPmZWOcra3Jx3zSmm8gEAAOVhuOulLbgeAgBMJKMa8QsAKC9BaJXsW5ikwjU7jFwJQ6t0YNXrW3mOVBVx5DlSr597P9zu6Y6Y56jCzS38lsxaBSFPfwAAgNI23PXS9rgeAgBMFCR+AWASC+zWn5EB5p8Lt/np9o1scY3Z+v4A32MijulXLwAAQCkb7nppIFwPAQAmAhK/ADCJuWbrz+wAWVxnm59B3+jewNqt7w/w3Scb2n71AgAAlLLhrpcGwvUQAGAiIPELAJOY6xglIkaJqKPewCrt95+313GMYq5RhWfkh1J3NpQfShVe7n1nu/nt0n6o3sAqEXWUiBi5BVrFGgCAchaEVpmAKQEmquGul7bH9RAAYKLwxrsBAIDxVR/31JkJFTFGrd2hGhOm34IlNRWO0r6VY63e6Laq7Ev61lT0v3dorVVrd66eeMSoPk4XAwDAYPzQamNXoOaUr2R2a8I30deH1lW78kgYThjDXS9twfUQAGAiYcQvAExyddWuoq7RrLirHj/U68mg30iWqVFHobWyxqg3sLKSQms1dZvEb7pvux4/1Kx4rr66anccjgYAgImvPR3oiQ1p/W1zRhu6Am1I+WpK+tqQ8rWhK9DfNmf0xIa02tPBeDcVfYa7XpK4HgIATDzcfgSASc5zjBZMj2rlpozmJDy1pAKt7fRV4RpFHKNsaOXb3Hy+s+Ouevzc7+s6/fznvYFVxBjNSXhKRBwtmB5llBIAAANoTwdauSmjZDZUSypQ1tp8n5sOrDpSviIml2BcuSmjhbVR1cRIHo63kVwvcT0EAJhoSPwCAFQTc7WwNqpVbRlVeUaprFUyEyqwUsw1mjE1IldWLd2h6qochTL9P690FY8YRd3clyK+oAIAsCM/tFrVlkv6NiV9VXqOGqpcxbz+T9G0duc+n5PwtKoto8WzYyQQJ4Bhr5e4HgIATDAkfgEAknJfZhbPjm2dbzC69UvolvkGp1c6ausJB/2c+QgBABjcxq5AmcCqJRWo0nPUmHB3mCc25jlqTBi9npRaUoGqPKONXYEaEnx1mwhGcr3E9RAAYKLg6gEAkOc5Rg0JTw0JT0FoFVjJNeq3GnVDwhnycwAAMLDmlK9U1iprrRqqdkz6bmGM0cwqR2s7c+WbUz6J3wlkJNdLAABMBFw9AAAG5DpGQz2gONznAABgqyC0SvZNDVDhmn7TOwwk5jmqcHNTKyWjjoLQklicgLgeAgBMZCR+AQBA0TASCgByArv1Z2SE58OIY/ptR4IRI0X/i0LYNo4kEVNACSLxCwAACsoP7da5D7M2/z5zHwKYzLYkTlwjpQM7dOE+2dAq1rehy2kTw6D/RSFsG0cdvaE6MqHae0NJUk2Fo6kVjqZGHWIKKBEkfgEAQMG0pwOtassoE9h+q527RkpEHXVmQq3uYLVzAJOP6xglIkaJqKOOlK+0Hw453UPaD9UbWM2odJWIGEbYYUj0vyiEbePoje5A65K+/MDKt5KstMHJPYkwd6pHTAElgsQvAAAoiPZ0oJWbMkpmQ7WkAmWtVYVrFHGM0oFVR8pXxBjNirtauSmjhbV8UQAwudTHc8mSiDFq7Q7VmDADLvBmrVVrd65cvG+0JjAY+l8UwrZx9FqHr9aeQJKUzoa5xK9yN7AqXemFN7OqrQw1d6pHTAET3NArCgAAAIyAH1qtast9WWhK+oq4RntO8bTX1IjmJHI/95ziKeIaNSV9JbOhVrVl5Icje9wZAMpBXbWrqJtLwPX4oV5PBkr7Yb8y6b73e/xQs+K58nXVJFQwMPpfFMK2cfR6p6+OTKBENHdTakqFqz2meNpjiqepUSOZ3PQhnZlQr3VmiSlggiPxCwAAdtnGrkCZwKolFajSc9SYcHd4hDnW936l56gllSu/sSsYpxYDwNjznNxj0YmIozkJT9nAam2nrzUdWTUlcz/XdvrKBlZzEp4SEUcLpkeZQxODov9FIWwbR6GVpkYddWesoo7RtJijiGsUcY12izmKOlK3bzU1amRliClggiPxCwAAdllzylcqa5W1VjOrnAEfXZYkY4xmVjnK2twchM0pf4xbCgDjqybm5h6LrnA0r8ZTQ9zLL+AWc40a4p7m1XiqqXB4fBrDov9FIWwbR54j9YZSIOVG/W4TUsbk5ikPbK6MZ0RMARMck0UBAIBdEoRWyb6FZCpcM+RiRVJu5FGFa5TMhEpGHQWhZdEiAJNKTczV4tkxbewK1JzylYxuPW8m+ub0rat2GemLIdH/ohC2jaOoMcpYq14/lwCOuDvGR8Q1ueSwbxXzjCKOiClgAiPxCwAAdklgt/6MjPBiP+KYftsxng3AZOM5Rg0JTw0JT0Foc+dCI5ImGDH6XxTCtvHguVLGl0JJ7iCjx9X32ZbZyT1DTAETGVM9AACAXbJlMIhrpOwIF/bIhrbfdgAwmbmOUdQ1JH0xKvS/KIRt48Hvm6bXkRTYwWMqsDafTPItMQVMZCR+AQDALnEdo0QkN+dbb2B3WKF+e2k/VG9glYg6SkRIdAAAsDPof1EI28ZRpi+hW+EZ+aGUDXZM/mYDKz/MlXEkZUMRU8AERuIXAADssvq4p3jEKGKMWrtD2UFGiVhr1dodKmKM4n3zWAIAgJ1D/4tC2DaO/FCqcHJTNiQzVtompKzNzQXsmlwZ34qYAiY4Er8AAGCX1VW7irpGs+KuevxQryeDHUYepfve7/FDzYrnytdVMxMcAAA7i/4XhbBtHDlG6siEqooaZUKrzelQ2cAqG1i9lQ6VCaUqz6gjY2VkiSlgguOWDDBBsKgHgFLmOUYLpke1clNGcxKeWlKB1nb6qnCNIo5RNrTqDawixmhOwlMi4mjB9Cgr1gMAsAvof1EI28ZR4xRPtkNq7clN+NvZG2hzOlfOdYwqXaNk1qq20tUeU4gpYKIj8QuMIz+02tgVqDnlK5nd+gxNou9Rmbpqlw4UQMmoiblaWBvVqraMqjyjVDb3OGBgpZhrNKPSVTySW8BowfSoamKMDAEAYFfR/6IQ+sXR7hFN73a0LumrwnHkW0lWch0p4hjNneppRqVLTAElwNjBJgECiqChoUHNzc2qr6/X+vXrx7s546o9HWhVW0aZwPa7OHNNbnJ8Ls4AlCpuagEAMPbof1EI28ZRR2+ojkyo9t7c9CE1FY6mVjiaGnWIKaBEkPjFmCLxm9OeDrRyU0bJbKiWVKCstQM+jjUr7ioRcbSwluQvgNLENDYAAIw9+l8UwrZxJImYAkoQUz0AY8wPrVa15ZK+TUlflZ6jhipXMW/rWotpP1Rrd+7zOQlPq9oyWjw7xt1UACXHdYy4bQUAwNii/0UhbB9HxBRQepzhiwAopI1dgTKBVUsqUKXnqDHRP+krSbG+9ys9Ry2pXPmNXcE4tRgAAAAAAAClhsQvMMaaU75SWaustZpZ5ciYgUfxGmM0s8pR1ubmAG5O+WPcUgAAAAAAAJQqEr/AGApCq2TfQm4VrtlhpO/2Yp6jCtcomQmVzFoFIVNyAwAAAAAAYHgkfoExFNitPyMjnK834ph+2wEAAAAAAADDIfELjKEtq6G6RsqOcPRuNrT9tgMAAAAAAACGQ+IXGEOuY5SIGCWijnoDq7QfDlk+7YfqDawSUUeJiJE7wlHCAAAAAAAAmNxI/AJjrD7uKR4xihij1u5Q1g488tdaq9buUBFjFI8Y1ce9MW4pAAAAAAAAShWJX2CM1VW7irpGs+KuevxQryeDHUb+pvve7/FDzYrnytdVu+PUYgAAAAAAAJQahhACY8xzjBZMj2rlpozmJDy1pAKt7fRV4RpFHKNsaNUbWEWM0ZyEp0TE0YLpUXlM8wAAAAAAAIARIvELjIOamKuFtVGtasuoyjNKZa2SmVCBlWKu0YxKV/GIUdTNJYlrYoz2BQAAAAAAwMiR+AXGSU3M1eLZMW3sCtSc8pWMbp15JdE3p29dtctIX0x4QWgVWMk1YgFCACWDcxcKgTjCYIgNAKWAc1X5I/ELjCPPMWpIeGpIeJxwUVL80G69aZHdukAhNy0ATGScu1AIxBEGQ2wAKAWcqyYXEr/ABOE6RkzogFLQng60qi2jTGD7TVPiGikRddSZCbW6g2lKAEwsnLtQCMQRBkNsACgFnKsmH2OttcMXAwqjoaFBzc3Nqq+v1/r168e7OQBGqT0daOWmjJLZUC2pQFlrB1yYcFbcVSLiaGEtFwwAxh/nLhQCcYTBEBsASgHnqsmJEb8AgBHxQ6tVbbkLhaakr0rPUUOVq5i3dX7qtB+qtTv3+ZyEp1VtGS2eHeNRIQDjhnMXCoE4wmCIDQClgHPV5OUMXwQAAGljV6BMYNWSClTpOWpM9L9QkKRY3/uVnqOWVK78xq5gnFoMAJy7UBjEEQZDbAAoBZyrJi8SvwCAEWlO+UplrbLWamaVI2MGvvNrjNHMKkdZm5s3qjnlj3FLAWArzl0oBOIIgyE2AJQCzlWTF4lfAMCwgtAq2Tf5f4Vrdrg7vL2Y56jCNUpmQiWzVkHIdPIAxh7nLhQCcYTBEBsASgHnqsmNxC8AYFiB3fozMsI5niKO6bcdAIw1zl0oBOIIgyE2MJEFoVUmIGmH8T1XDRaHxOfYYXE3AMCwXLP1Z3qEPX82tIr1beiyHgCAccC5C4VAHGEwxAYmGj/MzcnanPKVzG6NyUTEqD7uqa7aZaGuSWisz1WDxWGVJ1W4jnoDq26f+BwrJH4BAMNyHaNExCgRddSR8pX2wyEfEUr7oXoDqxmVrhIRI5cOHMA44NyFQiCOMBhiAxNJezrQqraMMkFubtZkJlRgc0m7RNRRZybU6g6jBdOjqom5491cjKGxPFcNFofZwCqVDeUZybe5mPQcQ3yOAaZ6AACMSH3cUzxiFDFGrd2hrB34brG1Vq3doSLGKN539xYAxgvnLhQCcYTBEBuYCNrTgVZuyqi9N9Tqdl/rU35+ZGc6sFqf8rW63Vd7b5grlw7GucUYa2NxrhosDnt9qw1dvjZ2B1rTEai1O9D6lK9ePyQ+xwCJXwDAiNRVu4q6RrPirnr8UK8nA6X9sF+ZdN/7PX6oWfFc+bpq7tgCGD+cu1AIxBEGQ2xgvPmh1aq2jJLZUE1JXxHXaM8pnvaaGtGcRO7nnlM8RVyjpqSvZDbUqraMfOZWnVSKfa4aLA7nTvEUKjeq15HkOZKRNCXqKJQ0N+ERn0Vm7GBpfqAIGhoa1NzcrPr6eq1fv368mwNglLbcxU1mQ7WkAmWtVYVrFHGMsqFVb2AVMbkLikTE0cJaHtUBMP44d6EQiCMMhtjAeFqf9PW3zRmtbs8l2xoTrozZ8dF8a61eTwbKBlbzajztNy2qhgQjzyeTYp6rBovDt9KBNnYFeqMnkGuk3SocvdWbW9RtRpWrumpXu8Vc4rOI+H8RADBiNTFXC2ujWtWWUZVn+s3bFHONZlS6ikeMoi7zMwGYODh3oRCIIwyG2MB4ak75SmWtstaqoWrgpK8kGWM0s8rR2s5c+eaUT2JtkinmuWqwOGzvzU3nEFippsKRcYwSUenNtFU6sGrvDbVbzCU+i4j/FwEAo1ITc7V4dmzrSq3RrbMGsSIrgImKcxcKgTjCYIgNjIcgtEr2Je8qXDPkgl2SFPMcVbhGyUyoZNRREFoWGZxkinGuGiwOQ2vzc/x6jhRxc3VGXCPPkXp9q7RnFYZWjmOIzyIh8QsAGDXPMWpIeGpIeApCm18xmI4ZwETGuQuFQBxhMMQGxlrf+m0KrBQZYZxFHNNvO8afTz6FPlcNFodbpukNJbnbjUR3jdGWGYZDbV2AjPgsPBK/AIBd4jqGDhlAyeHchUIgjjAYYgNjoW8ApVwjpYORLd+UDa1ifRu63JOY9ApxrhosDrfkgB1J2e2WFwtsbj7hLZ9vQXwW3tDPAQAAAAAAAGDCcR2jRMQoEXXUG1il/XDI8mk/VG9glYg6SkQMo9FREIPFoWOMYq5RhWfkh1K2LymcDaz8UKrwcp87fXFIfBYHiV8AAAAAAIASVB/3FI8YRYxRa3coawce+WutVWt3qIgxivfN5QoUymBxWFPhKOYauUZKZkLZ0CqZsXKVW1CupiKXliQ+i4fELwAAAAAAQAmqq3YVdY1mxV31+KFeTwY7jPxN973f44eaFc+Vr6tmMhIUzmBxOLXCkesYTa1w1OPbfBxOjTlyHWlq1CE+i4wUOgAAmDBYDAcAAGDkPMdowfSoVm7KaE7CU0sq0NpOXxWuUcQxyoZWvUFuPtU5CU+JiKMF06PyuM5CAQ0Vh45yo31DSX4ouY7UmQlV7blal/SJzyIj8QsAAMaVH1pt7ArUnPKVzG59PDHR95hXXbXLxR8AAMAgamKuFtZGtaotoyrPKJW1SmZCBTb3OP2MSlfxiFHUzSXnamKMpkThDRqHjjS72lMqG8ozkm+lRNSR5+SmgCA+i4vELwAAGDft6UCr2jLKBLbflxTX5C4IOzOhVndwEQgAADCUmpirxbNjW2+mR7fO7MnNdIyVoeKwypMq3NwCcN0+gz3GColfAAAwLtrTgVZuyiiZDdWSCpS1Nv9YYjqw6kj5ipjcXGErN2W0sJbkLwAAwGA8x6gh4akh4TF9FsbNSOKQ+Bw7JH4BAMCY80OrVW25pG9T0lel56ihylXM2zoqIO2Hau3OfT4n4WlVW0aLZ8cYCQAAADAM1zHidjnG22BxSHyOHWf4IgAAAIW1sStQJrBqSQWq9Bw1JvonfSUp1vd+peeoJZUrv7ErGKcWAwAAAEBpIfELAADGXHPKVyprlbVWM6scGTPwKF5jjGZWOcra3BzAzSl/jFsKAAAAAKWJxG8BnXbaaTLG5P9ddNFFu1xnMpnU3XffrXPOOUfvfe97NWPGDEUiEU2ZMkX77ruvTj31VD3wwAOy1g5b10033dSvfcP9K0T7AQDYXhBaJfsWcqtwzQ4jfbcX8xxVuEbJTKhk1ioIh+/zAAAAAGCyY47fArn//vt18803F7TOyy67TN/4xjeUTqd3+CyZTOqll17SSy+9pFtuuUUHH3ywbr31VjU2Nha0DQAAFFpgt/6MjHC+3ohj+m3HnGAAAAAAMDQSvwXQ2dmps846S5JUXV2trq6ugtT78ssv55O+9fX1OvLII7Vo0SLV1tYqnU5r+fLluvXWW5VKpfTnP/9ZS5Ys0fLly1VbWzts3V/84hd1+OGHD1lm3333LchxAACwLdds/ZkORjZ6Nxtaxfo2dFnbDQAAAACGReK3AP793/9dTU1NmjNnjk466SRddtllBanXGKOjjz5a5513no444gg5Tv9HYT/zmc/oa1/7mt7//vfrpZde0tq1a/W1r31NN9xww7B1L1y4UB/96EcL0k4AAEbDdYwSEaNE1FFHylfaD4ec7iHth+oNrGZUukpEjNwRjhIGAAAAgMmMOX530SOPPKLrr79eknTVVVcpkUgUrO7vfve7+uMf/6ijjjpqh6TvFnvssYfuvPPO/O933nmnuru7C9YGAACKoT7uKR4xihij1u5w0LnqrbVq7Q4VMUbxiFF9nHvWAAAAADASJH53QXd3t84880xZa3XyySfrQx/6UEHrnzZt2ojKLViwQPPnz8+36dVXXy1oOwAAKLS6aldR12hW3FWPH+r1ZKC0H/Yrk+57v8cPNSueK19Xzey+AAAAADASDJvZBeeff77WrFmjadOm6YorrhjXtkyZMiX/uqenZxxbAgDA8DzHaMH0qFZuymhOwlNLKtDaTl8VrlHEMcqGVr2BVcQYzUl4SkQcLZgelcc0DwAAAAAwIoz43UlPPvmkrrzySknSj370I82cOXPc2pLJZPTyyy/nf99jjz2G3eaqq67Sfvvtp3g8rqqqKjU2Nuq4447T1VdfzVQRAIAxURNztbA2qpoKR/NqPDXEvfwCbjHXqCHuaV6Np5oKJ1cuxmhfAAAAABgpRvzuhHQ6raVLlyoMQx1xxBE6/fTTx7U9t99+uzo6OiTlFm2rq6sbdpsVK1b0+72pqUlNTU267777dOGFF+qGG24o+NQVAABsrybmavHsmDZ2BWpO+UpGt96TTvTN6VtX7TLSFwAAAABGicTvTrjgggv00ksvqbKyUtdee+24tuWNN97QV7/61fzv3/zmN4cs77quDjroIB188MF629vepng8rvb2dv3lL3/Rr3/9a23evFlvvPGGjjvuON122236xCc+UZR2Z7NZrVy5csDPZs2apVmzZhVlvwCAicdzjBoSnhoSnoLQKrCSaySXZC8AAAAA7DQSv6O0YsUKXXbZZZKkiy++WPPmzRu3tmQyGZ144onatGmTJOmjH/2ojj/++EHLv+9979O6devU0NCww2ef/exn9YMf/EBnnnmm7rzzTllrtXTpUi1evFiNjY0Fb/umTZu0aNGiAT+78MILddFFFxV8nwCAic91jJjQAQAAAAB2HYnfUchkMlq6dKmCINDChQt17rnnjltbwjDU0qVL9ec//1mSNG/ePN1www1DbrP33nsP+XkikdBtt92m1tZWPfroo0qn0/rP//xP/exnPytYu7eora3V/fffP+BnjPYFAAAAAAAAdg2J31G45JJL9Pzzz8t1XV1//fVy3fEZk2St1ec+9znddtttkqTGxkb96U9/0m677bbLdbuuq0suuUTve9/7JEm///3vi5L4jUQiWrhwYcHrBQAAAAAAACA5wxeBJK1atUrf//73JUnnnnvuuCUtrbX6whe+oOuvv16S1NDQoEceeURz584t2D4OOuggxWIxSdLrr7+u7u7ugtUNAAAAAAAAoPgY8TtCN910k7LZrBzHUSQS0SWXXDJguccee6zf6y3l5s+fr5NOOmmX2mCt1dlnn61rrrlGklRfX69ly5YVfJ5hx3E0bdo0bdiwQZLU3t6uqqqqgu4DAAAAAAAAQPGQ+B0ha62k3Ny6l1566Yi2WbZsmZYtWyZJ+shHPrJLid8tSd+rr75akjR79mwtW7Zs2Hl7d0YYhnrrrbfyv9fU1BR8HwCKIwitAiu5JrdIVqHKjnXbAACYaOjHIBEHAIDSQuK3BGyf9J01a5aWLVumffbZpyj7W758uXp6eiTlppJgtC8wsfmh1cauQM0pX8mszb+fiBjVxz3VVbvy+r6YjKbsWLcNAICJhn4MEnEAAChdxm4ZyoqCuOiii3TxxRdLki688EJddNFFu1zn2WefrauuukqSVFdXp0cffVTz58/f5XoHEoahjj76aD388MOSpM997nP5hHMhNDQ0qLm5WfX19Vq/fn3B6gUmq/Z0oFVtGWUCq1TWKpkJ86NQElFH8YhR1DVaMD0qSSMuWxPb9cUrR9O2QuwPAIBCoh+DRBwAAEobi7uNk5tuuknGGBljtGTJkkHLffGLXyxI0vf//b//p+uuu07pdHrQMl1dXTr11FPzSd+Kigp99atfHfW+AIyN9nSglZsyau8Ntbrd1/qUr3SQu5eXDqzWp3ytbvfV3hvq8ea0Ht+QHlHZlZsyak8HY9a2QuwPAIBCoh+DRBwAAEofUz1MYN/85jd15ZVXSpKMMfq3f/s3/e1vf9Pf/va3IbdbuHChGhsb+73X2tqqs846S1/5yld01FFHadGiRZozZ46qq6vV0dGhlStX6le/+pXefPPN/P5+/vOfa+7cuUU5NgC7xg+tVrVllMyGakr6qvQcNVS5inlb7+el/VCt3aFe68wqsJKR5BqjqsjgZZuSvuYkPK1qy2jx7NhOPbY4mrYVYn8AABQS/Rgk4gAAUB5I/E5gjz/+eP61tVbnn3/+iLa78cYbddpppw34WSqV0j333KN77rln0O3r6ur085//XMcee+yo2gtg7GzsCpQJrFpSgSo9R40JV8b0/5IR8xw1Joxe3Bxqc09uBMr0SnfIsq8npZZUoCrPaGNXoIbE6LuJ0bStEPsDAKCQ6McgEQcAgPJAjzRJHHnkkfrd736np556Sv/3f/+npqYmvfnmm2pvb1dVVZVqa2u1cOFCHXvssfr4xz+uWCw23k0GMITmlK9U1iprrRqqdvwisoUxRp6RevseS/QcDVl2ZpWjtZ25uptT/k59cRlN2wqxPwAACol+DBJxAAAoDyzuhjHF4m7ArgtCq2Xr09rQN8/cXlMjg5YNrdVLb2X1eqcvWalxqqf5NRE5QzyCuKYjq5hrNDvu6bCGmNxRPK44mrYVYn8AABQS/Rgk4gAAUD5Y3A2YAILQKhNYBSH3YTC8vsG7CqwUGeZLxbYhtWWgSjhM/RHH9NtHsdpWiP0BmHyK1WfSF0MafT8WWitHUrYvbujHygPXM5ho6KMA7CyeQQHGiR9abewK1Jzylcxu7cATEaP6uKe6apeFITAg12z9mR7mm8W2IbTl+Y7h7vhlQ6tY307cUYbgaNpWiP0BmByK1WfSF2N7I+nHQmvV0RuqvTdUOrBq6wkUMUahld5W42l23CNuShzXM5gI6KMAFAKJX2ActKcDrWrLKBNYpbJWyUyowOYuEhNRR52ZUKs7jBZMj6om5o53czHBuI5RImKUiDrqSPlK+2G/Faa35RgjR5JR7n8cachpHtJ+qN7Aakalq0TEjPoxxdG0rRD7A1D+itVn0hdjIMP1Y93ZUOtTgYLQKh1YdWVDdfRaTYlKPdnc9EprO33ipsRxPYPxRh8FoFCY6gEYY+3pQCs3ZdTeG2p1u6/1fXOHSbkRBetTvla3+2rvDXPl0sE4txgTUX3cUzxiFDFGrd2hBpuu3Vor30oVrlGFa+SHGrJsa3eoiDGK940kKHbbCrE/AOWrWH0mfTGGMlg/1p0N9XrSV48f6o2eQO3pUMmMlSsrI6knsMRNGeF6BuOFPgpAIZH4BcaQH1qtassomQ3VlPQVcY32nOJpr6kRzUnkfu45xVPENWpK+kpmQ61qy8hnLidsp67aVdQ1mhV31eOHej0ZKO33n7033fe+kdW0Ske7VzpyjIYs2+OHmhXP1V1XvXOjB0bTtkLsD0B5KlafSV+M4QzUj3VnA61PBeoNrDanQ1mbm04p5hnNTniaWe1qv90ixE0Z4XoG44E+CkChcTsSGEMbuwJlAquWVKBKz1FjwpUx/R8Fi3mOGhNGryelllSgKs9oY1eghgT/uWIrz8k92rVyU0ZzEp5aUoHWdvqqcI0ijlE2tOoNrCLGaI8pEbmSZHKLjQxVdk7CUyLiaMH06E7PGTaathVifwDKU7H6TPpiDGegfuxvm311ZXOPXBtZVbhGnjGaFnNU4Ro1xD1VRRw1RoibcsH1DMYDfRSAQmPELzCGmlO+UlmrrLWaWeXs0IlvYYzRzCpHWZv7gtGc8se4pSgFNTFXC2ujqqlwNK/GU0Pcyy8qEuv7EjqvxlNNhaP31cf0vtmxEZVdWLvrc4WNpm2F2B+A8lOsPpO+GCOxfT9W6TmSlUIrJSKOdou5mlHlqtIzakzkkr4ScVNuuJ7BWKOPAlBo3BICxkgQWiX7JuavcM2QC0RIuTu5Fa5RMhMqGXUUhJaFIrCDmpirxbNjW1f8jW6Nq4FW/B1N2bFuGwBsUaw+k74Yo7GlH2tO+VrX6SvqGk11pN0rXcVco5oKR1Ojzg6LphI35YXrGYwV+igAxUDiFxgjffPxK7BSZIQdcsQx/bZjDAEG4jlGDQlPDQlPQWjzK/4OdOE3mrJj3TYA2KJYfSZ9MUbLc4xmVXvac2pErjGyspoT93ZI9m6PuCkvXM9gLNBHASgGpnoAxkjfU2FyjZQd4eT72dD22w4YjusYRV0zoi8ioylbCGO9PwClq1h9Jn0xdsaWv7vn5BIrwyV9JeKmnHE9g2KhjwJQDCR+gTHiOkaJiFEi6qg3sDusCry9tB+qN7BKRB0lIlxcAgAmj2L1mfTF2BnEDYCxwLkGQDGQ+AXGUH3cUzxiFDFGrd2hrB34Tq61Vq3doSLGKN43dxgAAJNJsfpM+mLsDOIGwFjgXAOg0Ej8AmOortpV1DWaFXfV44d6PRnscCc33fd+jx9qVjxXvq6a2ZoAAJNLsfpM+mLsDOIGwFjgXAOg0Iwd7BYSUAQNDQ1qbm5WfX291q9fP97NGRft6UArN2WUzIZqSQXKWqsK1yjiGGVDq97AKmJynX0i4mhhbVQ1MTpyADuHRWhQyorVZ9IXY2cQNwDGAucaAIVE4hdjisRvTns60Kq2jDKBVSprlcyE+cRMIuooHsktGrFgOp04gNHzQ6uNXYGaU76S2a3dfKLvUcC6alceSWCUiGL1mfTF2BnEDYCxwLkGQKGQ+MWYIvG7FYkZAMXAFwWUo2L1mfTF2BnEDYCxwLkGQCGQ+MWYIvE7MB7FBlAIPBqIyaBYfSZ9MXYGcQNgLHCuAbCzWPoRmABcx4jUC4Bd4YdWq9pySd+mpK9Kz1FDlauYt3Ud17QfqrU79/mchKdVbRktnh1jtAhKSrH6TPpi7AziBsBY4FwDYGc5wxcBAAAT3cauQJnAqiUVqNJz1Jjon/SVpFjf+5Weo5ZUrvzGrmCcWgwAAAAAKCYSvwAAlIHmlK9U1iprrWZWOTJm4FG8xhjNrHKUtbk5gJtT/hi3FAAAAAAwFkj8AgBQ4oLQKtm3kFuFa3YY6bu9mOeowjVKZkIls1ZByHT/AAAAAFBuSPwCAFDiArv1Z2SE8/VGHNNvOwAAAABAeSHxCwBAiXPN1p/ZEY7ezYa233YAAAAAgPJC4hcAgBLnOkaJiFEi6qg3sEr74ZDl036o3sAqEXWUiBi5IxwlDAAAAAAoHSR+AQAoA/VxT/GIUcQYtXaHsnbgkb/WWrV2h4oYo3jEqD7ujXFLAQAAAABjgcQvAABloK7aVdQ1mhV31eOHej0Z7DDyN933fo8falY8V76u2h2nFgMAAAAAiolhPgAAlAHPMVowPaqVmzKak/DUkgq0ttNXhWsUcYyyoVVvYBUxRnMSnhIRRwumR+UxzQMAAAAAlCUSvwAAlImamKuFtVGtasuoyjNKZa2SmVCBlWKu0YxKV/GIUdTNJYlrYoz2BQAAAIByReIXAIAyUhNztXh2TBu7AjWnfCWjW2d1SvTN6VtX7TLSFwAAAADKHIlfAADKjOcYNSQ8NSQ8BaFVYCXXSC7JXgAAAACYNEj8AgBQxlzHiAkdAAAAAGDycYYvAgAAAAAAAAAoJSR+AQAAAAAAAKDMkPgFAAAAAAAAgDJD4hcAAAAAAAAAygyJXwAAAAAAAAAoMyR+AQAAAAAAAKDMkPgFAAAAAAAAgDJD4hcAAAAAAAAAygyJXwAAAAAAAAAoM954NwDAxBGEVoGVXCO5jhnv5gAAAIw7ro8AlDvOc0D5IvELTHJ+aLWxK1Bzylcya/PvJyJG9XFPddWuPDp/AAAwiXB9BKDccZ4DJgcSv8Ak1p4OtKoto0xglcpaJTNh/k5vIuqoMxNqdYfRgulR1cTc8W4uAABA0XF9BKDccZ4DJg8Sv8Ak1Z4OtHJTRslsqJZUoKy1qnCNIo5ROrDqSPmKGKNZcVcrN2W0sJZOHwAAlDeujwCUO85zwORC4heYhPzQalVbrrNvSvqq9Bw1VLmKeVvXe0z7oVq7c5/PSXha1ZbR4tkxHvcBAABliesjAOWO8xww+TjDFwFQbjZ2BcoEVi2pQJWeo8ZE/85ekmJ971d6jlpSufIbu4JxajEAAEBxcX0EoNxxngMmHxK/wCTUnPKVylplrdXMKkfGDHz31hijmVWOsjY391Nzyh/jlgIAAIwNro8AlDvOc8DkQ+IXmGSC0CrZN4F/hWt2uMO7vZjnqMI1SmZCJbNWQWiHLA8AAFBquD4CUO44zwGTE4lfYJIJ7NafkRHO0xRxTL/tAGAiC0KrTMAXFIwuFoibyY3rIwDlbqDzXGit/NAqtAOfxDjPAaWPxd2AScY1W3+mR9h7Z0OrWN+GLnP6A5iA/DA3/1xzylcyu/XclogY1cc91VW7LEoySYwmFogbbMH1EYByt+U8ZWT1Vm8oP7T9zncx16imwtHUCkdO3xQQnOeA0kfiF5hkXMcoETFKRB11pHyl/XDIx3zSfqjewGpGpatExMjlCzCACaY9HWhVW0aZIDcPXTITKrC5LyiJqKPOTKjVHUYLpkdVE3PHu7kootHEgiTiBnlcHwEod65j5MhqczpUW0+gdNQqCKVQuUfB055R2rd6oydUQ9yVY8R5DigDJH6BSag+7qkzEypijFq7QzUmzIAT+1tr1dqdKxfvG/0EABNJezrQyk0ZJbOhWlKBstaqwjWKOEbpwKoj5StijGbFXa3clNHCWpJ45Wo0sfD4hrRkpUAibpDH9RGActaeDvRW2iq0UndW6s6GmlqRS+hmrVVPr5VrpKkVjl7vtHIcw3kOKAP81wtMQnXVrlZ35L7QNiV9vZ6UZlY5/Ua2pP1Qrd2hevxQcxKeoq5RXTVfegFMHH5otaotl+hrSvqq9Bw1VLkDnsuakr7mJDytasto8ewYj++XmdHEwuudvgJrZZUb3VsdcYkbSOL6CED52tJPOo5VR2+oeNTID62cvsRuxHWUDXJPv7zRHchzjIyRFtZGOc8BJY7ELzAJeU7u0dWVmzKak/DUkgq0ttPPj3bKhla9gVXEGM1JeEpEHC2YHuULL4AJZWNXoExg1ZIKVOk5aky4O4zOi3mOGhNGrydzIzurPKONXYEaElwClZPRxMKLb4Z6syeUJE2vcokb5HF9BKBcbeknW7tCzYq7uQVNQ6kjHerNtJXnSK4xCmzfvL++1JBwFYbiPAeUOK5egUmqJuZqYW1Uq9oyqvJMv/kNY67RjEpX8YhR1GV+QwATU3PKVyprlbVWDVU7Ju+2MMZoZpWjtZ258s0pnwRemRlNLHhObs5CSfKMiBv0w/URgHK0bT+55xRPoZXWp3xFq1ylA6te3yqUFHUc1VZJXRkrx0i7xRzOc0CJ4+oVmMRqYq4Wz45tXdE8uvVRRlY0BzCRBaFVsi8hU+GaIRdhknIjOCtco2QmVDLqKAgti5SUidHEQhjmvthaK8nkFrQJrc2vXr494mZy4voIQDkZrJ/ce2pEHZlQ7b2h0p7Nl4+5RpVuqN1irkIZ+j6gxJH4BSY5zzFqSHhqSHgKQptf0ZzOHcBE1jdgU4GVIiM8X0Uc0287xq+Uh9HEQrjlxTbFQisNtRlxMzlxfQSgXAzWTzqO0W4xN5fg7bsx6vS935T0ZbfZnr4PKF1DD48BMKm4Tu7RRb7UAJjoXLP1Zza0Qxfukw1tv+1QHkYTC/kL322KDdflETfg+ghAKRtJP+k4Rp5j5PSd5+j7gPJB4hcAAJQc1zFKRIw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"text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_65_0.png" } }, "output_type": "display_data" } ], "source": [ "ax = df_subset.plot(x='age_jitter', y='realrinc2015_log10', kind='scatter', alpha=0.4)\n", "slope, intercept = 0.035, 3.67\n", "xs = np.linspace(23 - 0.2, 30 + 0.2)\n", "label = f'y = {slope:.3f}x + {intercept:.2f}'\n", "ax.plot(xs, slope * xs + intercept, label=label)\n", "ax.set(ylabel=\"Respondent's income (log10)\", xlabel=\"Age\")\n", "ax.legend();" ] }, { "cell_type": "markdown", "id": "14c22c72", "metadata": {}, "source": [ "\n", "\n", "There are other, less concise, ways of describing the relationship between log income and age. For\n", "example, the *curve* in {numref}`fig-statistics-essentials-quadratic-household-income-age` does seem to summarize the association between log income and age better. In particular, the curve seems to capture a feature of the association visible in the data: that the association between age and income decreases over time. A curve\n", "captures this idea. A straight line cannot." ] }, { "cell_type": "code", "execution_count": 37, "id": "086805f2", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [ { "data": { "image/png": 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AAAAAaMna+myd2NWnLaWOioJRBbwH5mn5PZbyst3KzbKZ6QsAqLWEgt9OnTppw4YNWr58uS699NJa77dixQpJUseOHRMZHgAAAACAtOF2Wcr3u5Xvd8uJGTlGsi3JJuwFANRDQks9DB06VMYYTZ48Wd9//32t9tm5c6eef/55WZalYcOGJTI8AAAAAABpyXZZ8toWoS8AoN4SCn4vueQSSdL27dt1xhlnqLCwsNr2Gzdu1Jlnnqnt27dLUp1mCQMAAAAAAAAAascyxphEOhg5cqQ++ugjWZalzMxMXXLJJTr99NPVq1cvZWVlqbS0VF9//bXee+89vfbaa9qzZ48k6aSTTtL8+fOT8iDQfOTn56uoqEh5eXk1vlEAAAAAAAAAoH4SDn6Li4s1YsQIrV69urxDq+qPoewf6phjjtGHH36oDh06JDI0miGCXwAAAAAAAKDhJbTUgyTl5ORoyZIlmjBhgnw+n4wxVX75fD7dcsstWrx4MaEvAAAAAAAAADSQhGf8Hqy4uFgzZ87UokWLtHnzZgUCAfn9fnXp0kVDhw7VuHHjCHxbOGb8AgAAAAAAAA0vqcEvUBOCXwAAALQ0TszIMZJtSbar6qXxkJ4a6/nnOAMAHMqd6gIAAAAAIN1EY0ZbSh0VBaMKRA7MtfF7LOVlu5WbZctNOJe2Guv55zgDAFSH4BcAAAAAkqgk5Gh5cVhhxygYMQqEY/GZmH6vS7vDMa3dZWlAjldtfXaqy0WSNdbzz3EGAKhJQhd3+/7773XhhRfqggsu0Jw5c2q1z5w5c3TBBRfooosuUjAYTGR4AAAAAGhSSkKOlm4Lq6QsprUlURUGowo55TMxQ45RYTCqtSVRlZTFytuFnBRXjGRqrOef4wwAUBsJrfH79NNP6+abb1ZWVpY2b96s7OzsGvcJBoPq2rWrSktL9eyzz+qnP/1pfYdHM8QavwAAAEhX0ZjRx5tCKimLaWMgqlZulzpnuuRzH5hvE4rGtHVPTHujMRX43Wqb4dKJXX18HD8NNNbzz3EGAKithGb8vvfee5KksWPH1ir0laTs7GydeeaZMsbo3XffTWR4AAAAAGgytpQ6CjtGm4OOWrld6ua3K4RxkuTb9/NWbpc2B8vbbyllNmY6aKznn+MMAFBbCQW/y5cvl2VZOuGEE+q037Bhw+L7AwAAAEA6KApGFYwYRYxR50yXLKvy2ZWWZalzpksRU742a1Ew2siVoiE01vPPcQYAqK2Egt/NmzdLKv/4fl107dpVkrRp06ZEhgcAAACAJsGJGQX2XWArw7YOm4F5KJ/bpQzbUiAcUyBi5MTqvQIfmoDGev45zgAAdZFQ8Lt/eeBYLFan/fa3j0Z5xxEAmhonZhR2eGEAAEBd7LuulhwjeWq5jqrHZVXYD81XYz3/HGcAmiJeQzZd7kR2zsnJ0aZNm7R27do67fftt99Kktq3b5/I8ACAJInGytd9KwpGFYgc+M/a77GUl+1WbpbNxUAAAKiGbR34HqpluhaJGfn27Wjz32yz1ljPP8cZgKaC15DNQ0Izfo899lgZYzR9+vQ67Td9+nRZlqU+ffokMjwAIAlKQo4+3hTSqp1hbSp1tCkY1cZAVJuCUW0qdbRqZ7j8ytEhLggCAEBVbJclv8eS3+tSmWMUilb/qchQNKYyx8jvdcnvsWTz4rhZa6znn+MMQFPAa8jmI6Hgd+zYsZKkZcuW6YUXXqjVPpMmTdLSpUslSWeeeWYiwwMAElQScrR0W1glZTGtLYmqMBiNzx4JOUaFwajWlkRVUhYrb8d/3AAAVCkv261sjyWPZWnrnlh8abxDGWO0dU9MHstS9r6ZUWj+Guv55zgDkEq8hmxeEgp+f/rTn8aXa5gwYYIee+wxOU7lT6jjOHr00Ud1yy23SJLatGmj8ePHJzI8ACAB0ZjR8uKwApGYNgai8tiWjmjt1pFtPCrwl38/orVbHtvSxkBUgUhMy4vDirJuEwAAlcrNsuW1LXXJtrU3GtOGgHPYjMzQvp/vjcbUJbu8fW6WnaKKkUyN9fxznAFIFV5DNj+WqertwVp67bXXdPnll8uyyj8ykpubqzPPPFN9+/ZVdna2gsGgVq5cqVmzZmnLli0yxsiyLE2dOlWXX355Uh4Emo/8/HwVFRUpLy9PhYWFqS4HaNEKA1Gt2hnW2pLy/7C7+e34ufxgxhhtCDiKOEY927rVp71X+X5mjAAAUJn9M6ECkZg2Bx1FjFGGbcnjshSJGZU5Rh6rPLTze1wa1Mmrtj4CuXTRWM8/xxmAVOA1ZPOT8L/6pZdequLiYt1xxx2KRqPasmWLJk+eXGlbY4zcbrf+93//l9AXAFKsKBhVMGIUMUb5mZX/hy1JlmWpc6ZL63aXty8KRvlPGwCAKrT12RrUyavlxWFlui0FI0aBcEyOkXy2pY6tbGV7LHltSwNyCOPSTWM9/xxnAFKB15DNT1L+1W+99VYdf/zxmjhxot57771K1xiyLEtnnXWW7rvvPg0ZMiQZwwIA6smJGQX2vUDIsC353NWv/ONzu5RhWwqEYwp4XXJihouDAABQhbY+Wyd29R242rn3wP+zXO08/TXW889xBqAx8RqyeUpa3H788cdr1qxZKi4u1kcffaTCwkLt3r1brVu3Vn5+vkaMGKEOHTokazgAQAL2rb0vx0ieWv7n63FZFfZj3ggAAFVzuyzl+93K97vlxEz5/52WeNHbQjTW889xBqCx8BqyeUr6POucnBydd955ye4WAJBEtnXg+/4rsNYkEjPy7dvR5rUEAAC1ZrssXuy2YI31/HOcAWhIvIZsnqqflw0ASEu2y5LfY8nvdanMMYddCfpQoWhMZY6R3+uS32MxiwQAAAAAWhBeQzZPBL8A0ELlZbuV7bHksSxt3ROrdH12qfzCnFv3xOSxLGXvWy8OAAAAANCy8Bqy+Unqv3wsFtPatWv1/fffKxQK1WqfkSNHJrMEAEAt5WbZWrvLUpdsWxsDUW0ISJ0zXRUW6Q9FY9q6J6a90ZgK/G55bUu5WXyIEAAAAABaGl5DNj9JCX4/+eQT/c///I/ef//9Wge+kmRZlqLRaDJKAADUkdtlaUCOV0u3hVXgd2tz0NG63VFl2JY8LkuRmFGZY+SxLBX43fJ7XBqQ4+XK0ADSDhdEQm1xrAAAWjJeQzY/CQe/jz76qO666y4ZY6qc4g0AaJra+mwN6uTV8uKwMt2WghGjQDgmx0g+21LHVrayPZa8dvl/8G19vFMLID1EY0ZbSh0VBaMKRA78Devf93HE3CybFymQxLECAMDBeA3ZvCQU/H744Yf61a9+JcuyZIxRXl6eTjnlFOXn5ysjIyNZNQIAGlBbn60Tu/oOvKj1HviYDi9qAaSjkpCj5cVhhR1T4cWKbUl+r0u7wzGt3cWLFXCsAABQGV5DNh8JBb//7//9v/j273//e/3Xf/2XXC6uFwcAzY3bZSnf71a+383HWAGktZKQo6XbwgpEYtocdBQxJv7xxJBjtCsYlccqX7tu6bawBnUi0GupOFYAAKgaryGbh4SC3wULFsiyLJ133nm65557klUTACCFbJclXrYCSEfRmNHy4vIgb2MgqlZul/Iz7UovSLIxEFWB363lxWGd2NXHjJUWhmMFAIDa4zVk05XQ9NwdO3ZIkn70ox8lpRgAAACgoWwpdRR2jDYHHbVyu9TNXzHIkyTfvp+3cru0OVjefkupk6KKkSocKwAAIB0kFPzm5ORIkrKyspJSDAAAANBQioJRBSNGEWPUOdMly6p8ZqZlWeqc6VLElK/rWhSMNnKlSDWOFQAAkA4SCn5/+MMfSpLWrl2bjFoAAACABuHEjAL7Ls6VYVuHzd48lM/tUoZtKRCOKRAxcmKmkSpFqnGsAACAdJFQ8PvTn/5Uxhi99tpryaoHAAAASDrHHPjuqeUarB6XVWE/tAwcKwAAIF0kFPyef/75uvDCC7VixQr9+te/TlZNAAAAQFLZ1oHvkVrOyIzETIX90DJwrAAAgHSRUPArSS+99JIuv/xyPfroozrttNP0z3/+U8XFxcmoDQAAAEgK22XJ77Hk97pU5hiForFq24eiMZU5Rn6vS36PJbuWMz/R/HGsAACAdOFOZGfbtuPbxhjNmzdP8+bNq/X+lmUpGuUCCAAAAGh4edlu7Q7H5LEsbd0TUze/VelFu4wx2rqnvF22x1JedkJ/MqMZ4lgBAADpIKEZv8aY+Neht2v7BQAAADSG3CxbXttSl2xbe6MxbQg4h83mDO37+d5oTF2yy9vnZtlV9Ih0xbECAADSQUJvSY8cObLSd74BAACApsbtsjQgx6ul28Iq8Lu1Oeho3e6oMmxLHpelSMyozDHyWJYK/G75PS4NyPHKzUf3WxyOFQAAkA4sw7RbNKL8/HwVFRUpLy9PhYWFqS4HAAC0QCUhR8uLwwo7RsGIUSAck2PKL8rl97qU7bHktcuDv7Y+ZnC2ZBwrAACgOWMRKgAAALQobX22Tuzq05ZSR0XBqALeA6uf+fet05qbZTN7ExwrAACgWSP4BQAAQIvjdlnK97uV73fLiZn4LE6bAA+H4FgBAADNFcEvAAAAWjTbZYkP6aM2OFYAAEBz4qq5CQAAAAAAAACgOanVjN/rrrtOkmRZlp5//vnDfl5fh/YHAAAAAAAAAEicZYwxNTVyuVyyrPI1rBzHqfTn9XVwf0h/+fn5KioqUl5engoLC1NdDgAAAAAAAJCWar3GrzGm0pC3FrlxlRINjQEAAAAAAAAAh6tV8Ltu3bo6/RwAAKQnrmiPVGrqx19Trw9A01bdOaS255e6nIc4Z7UsdX2+OT6QbAcfU5I4vhpJrYLf7t271+nnAAAgfURjRltKHRUFowpEDnzSx++xlJftVm6WLTd/sKGBNPXjr6nXB6Bpq+4ckptlS0bassep9vxSl/MQ56yWpa7PN8cHku3gY2pXWUy7wjGVlMUkSW0zXGqT4VIbr4vjqwHVao1fIFlY4xcAmpeSkKPlxWGFHaNgxCgQjsXfnfd7Xcr2WPLalgbkeNXWZ6e6XKSZpn78NfX6ADRt1Z1D3JYOhCM+l6IxVXp+6eF367tAtFbnIUmcs1qQuv4fxf9pSLaDj6ntexx9F4gq6hhFjSQj2S7J47LUo41bHVvZHF8NhOAXjYrgFwCaj5KQo6XbwgpEYtocdBQxRhm2JY/LUiRm/j97dx4fyVkf+P/zPFXVl7olza0ZacYnNtgE4wFCsDkMNrBZvCYbQiAJ2ODgsLsJmw0kv1zENllemwCJlwRCuGMDYWGTjZNsiPGSYG5wgIHBYONjPPbMaA6NZkZSH+qu43l+f1S31LqP7lYf+r5fr3mppa7qKk199TxV33rq+1CJLJ5S7M465DzN/p1yoiaap9Pjr9P3TwjR2ZZrQ4q+4UQpwlSv1LWCoT6HrKfntC9bUpoz0xFb05qJsl22HXIAVJw8ljar9621j7powOXQZCh9mmia+hh8cjLk1HQEQDkwceKXuMRD2lGgYGfa4fwBV+KrBSTxKzaUJH6FEKI7hMby9eNlJiqGo/mQtKvZldGkXD2zTDk0nCoZpkPD3pzLYFJz9Z6UPKIlGtbp8dfp+yeE6GzLtSHGWB6bDJjyDSeLEVjFUJ+mP+lw8aCLVopyGL/35FRIxlOUQsu+nMvuPmfRdqgYREQWFOAoRcaTNquXrbWPGs46jJUMOzOa0UIkfZpoWH0MHpkKmahEeI4i71sSWpFLxHGT9w2+iUuJBAYGkorz+j2JrybTKy8ihBBCiM3mZDHCjywnqhcA+3JzLyYBUtWfp13NiUK8/Mli1KY9Fr2k0+Ov0/dPCNHZlmtDJn1DZKDkWwYTmi2pOLEbGctktfRDytX0JxSuhlNFg6NgIKGXbIcsirPThjPTBmORNqvHrbWPenwipBIaHp8MpU8TTVEfg8bG7VOpmvTdmtJ4jsJz4qcWEhpKoWUgobAoia8WkMSvEEIIIRYYLYQUgvix0V0ZjVKL33FXSrErowlsXA9utBBu8J6KXtTp8dfp+yeE6GzLtSETFUM5skRAf1KTS2giC+XIztT8BZj0LZ6jCKuP5E/6ZpEtxe2Qq6ASxY/ruxpps3rcWvuoqcAwFVimfCN9mmiK+hh0NVQMRBCP9K0LL6XUTBtXMXFtc4mv5pPErxBCCCHmiIwlX53UI+moBaM+5ku5mqSjyPuGfBCPShJivTo9/jp9/4QQnW25NsQYSzmyVMI4WVIbFedqqITxe8bameWMAc+ByDDz3nzGWgxgAWvBVLezFGmzutta+6iEE2fhCtUbB4kVHq2X+BArqY/BhFIYmNOmzVffxhnA00h8NZkkfoUQQqxJZCx+JB1xL4vs7FdvlbW1PK3mrCfEei0Xf8ZaQrMwudHK+Jvf5snfh+hE0jd3j2XbuLqvTt2oS6eaPAEwdu5y7rz35qv/We0jFx8bPEvarO611j7K2Di+QmNRQLiKKaAajQ9pr7rPao9ZZCzTYXyeFllwq/OzzW/T5qtv41wl7U+zue3eASGEEJ0vNHGdpdFCSD6Y7YFznmI46zLU50jx/R5SuxnvqHgE0WoExpKqrrjIzXwhVm1+/Bkb17WsPf5ck3IUg0nNQFI3Pf6Wa/N29zkYa+XvQ7Sd9M3dabk+Vtd9DeoScJG1eNWkiVbEw3ery4XWzq63yOGu/5mtW2850mZ1r7WcwxlrmaoYzlUiKqElMIbHzoWkvdn+VS+SrFtPfEh71X1We8zmL2eM5eGJgFJg8RQ4zsI2bb76Ni60loSS9qeZJPErhBBiWRPliIPjPn4U11vK+4bIxh1xLqGZ8g2HJhVXbE8wmHLavbuiCRytyHlxza3JQkg5NMs+KlgODZXIsiPtkPMUjpy4iwbUx9/pyYCHzvjxLPbVx58N8QVE2VWUQ8vxQkhoYceA15T4W02bN5oPSbqKim/l70O0hfTN3Wu5PlZrRcpRlF3FdMUSVBN3oYFsMn5Pq7hGZspR5DUEETia2ffm0UqhqZbVVHH7qZdph6TN6m6rPYcrBYZjhYhyaPCrcVaOLGcrEX1GUw4tp6cNI1mHjDe7/nriQ9qr7rPaY3Z+zuWJfLhguenAEhjLZGBwq+1aGEEQ2QXlHoLIzrRxGghMvA1pf5pHEr9CCCGWNFGOODDmkw8MJwoRQXUCEU/HSZjJQoinFLuzDgfGfPbvlBO2XjGcdZnyDZ5SnCoZ9uXUopN9WGs5VYqXy1ZHAAjRqOGsy8liyGQlnoFeUX2kWcePAwbWMl2JR7lZ4hFtGttw/K22zct4irPT8b7J34fYaNI3d7/l+tjBZJx0c4CpikGpONlSe8qhZiChOF6wuEpRiSwDicVvQFlrCS0kq8mW0MQ/kzard610DlcKDEfyYXzztBhhDbiOwgXyviWIDI6GgaTmSN6yL+eS8fS64kPaq+6z2mM2mFL8aNxnW9rhXNnMWc7TMFmxlAJIOHGJCGvj+Nqamp3gzdo4WewoSGritkpL+9Ns8j8phBBiUaGxHByPO/2j+ZC0qxnJOHNGDZRDw6lS/P7enMvBcZ+r96TkUa0eMNTncGgyPhE/mg85koddGb3o8Z8ODXtzLglHMdQnJ+uicdvTmpMlQ9pVHC9EJB3FjoyeM+qoFBhOl+KRR3uyDidLhu3p9U9fsZY2b7JiiKzFAsUgkr8PsWGkb+4Ny/WxAwnN6emITEJxshiBVQz1aRytGKgmfsuhYcqPR8nt6tOUQsukb8h4atFYUFi2pjWK+EbZkXwkbVYPWy6+jLUcK0QUfMPJogFlGel3KYeWXEIzUTHxTVULZ8uGrSnNsULESNZyetquKT6kveo+qz1mJ4oRPzoTkHEVJ4oR5/W7jPS5M8sN9zk8dNbn9HRcqksrS9LREBnOliGXiI9v3jf4Ji4fMelbBpKwOyvtT7NJ4lcIIcSiThYj/MhyohCRdjX7cs6C0SEpV7MvpziShxOFiIwbX6SM5KR76Xaujh+5OzDmszfncqIQcXgqnLmTHxhLJYrrce3NueQ8zRXbE3KiLppifNqwO6MZLYQMJDXKWqZ8SymMcJQisnHCI+3E7dB0aNmd0YxPG0Zy60v+rrXNK/oGraA/qSkFVv4+xIaQvrk3rNTHGhOPjEtXkyj5wNKXsIwWojnty9O3JzgzHXHBgGaivHQ7dF6/hwOg4smSpE/vbcvFVyW0nJ6OOFcxeAr2ZOOk3kUDDqenI7TSTJYNoY1jYrRgyXqGvK8YSDhrig9pr7rPao/ZQCKe6+BU0bAlpehPzL3ppLXiggEPrQIAJivxTSVcTSWMOFuOl3O0Iu0o8oFlZ9rhvH5pf1qh5X9N09PTfPCDH+SrX/0qYRjyzGc+k//8n/8zu3fvbvWmhRBCNGC0EFIILIG1jGQWdvo1Sil2ZTSHp+LlRwuhnKz1iMGUw/6dCQ6O+2RcNad2V8pR7Eg7ZD1FwpG6bKK5RgshEfHoNlexoMavp9RMvUtTfYw5QjXU/qy5zQsNW5OacmS5aNCVvw+xIaRv7h3L9bH9Sc3WVDz6Ml5WExoWbV9+aijJE/mQbSm7YjsESJ++SSwVX2emDVhIO4qhjEPKU4xk41IOaVdxrBCSyDiUI0vRN0z4Nl7e1Vw06K4pPqS96j6rPWaTviHpKEJr8BzNpG/Zmp67TMbTnNfvYWyAteBpKEWWgaQmtICN65N7WnH+gMuOtCPtT4s09Nf0ve99j5tuugmlFB/84Ad53vOeN+f9qakpXvCCF/DDH/5w5mef+9zn+Mu//Ev+3//7f1x55ZWNbF4IIUSLRMaSr54gJh217MRFEN/5TTqKvG/IJzSRsVKMv0cMphyu3pOana23roagzMQsWqG+/cklNOfnXCb9+FHBsjs7I3St3uVAQvNEPmyo/VlvmxdaGMm6XLLF42Qxkr8P0VLSN/eelfrYoT4HLJwsReQDO+e9+vZlOOeuup+WPn3zmB9fk55i0jdERpFwFXv7XQYSemayv4ynuXjAm+lzU67CqghPKdKu4pItHsNZd1XxIe1V91ntMTPWUo4skQHPAWPiiQGNsQsmjsx4mqdtTWCsT2Bga0qxq08z5cft2WBSM1A9l5P2p3UaSvz+7d/+LT/84Q/ZtWsXP/VTP7Xg/d///d/ngQceWPDzM2fO8KpXvYqHHnqIZDLZyC4IIYRogerkvkQ2vgu7Gp5Wc9aT+7S9w9WKkZzLSM4lMnZmVl85IRetML/90VqxJeWwJeVgTDzid/6s9I22P420eVordve5nNfvyd+HaCnpm3vTavrY8waWb1/W0k9Ln7651B/v6SCelPR4IUIp2LLIqMr5fW7aUWil2NfvsrtvdUlfkPaqG632mJnqcrUnsAyz3y+WKtZazYzgHe5zuGpPirSrZrYl7U/rrX8GDOD+++9HKcVLX/rSBUPA8/k8H/vYx1BKsW/fPu6++26+//3v8yu/8isAPPnkk3zqU59qZPNCCCFapDrxM46CwNjlF64KjJ2znuhNjo4fA5UTNNEqy7U/WivcajK4XqPtT7PaPPn7EK0kfXPvW64NWW37spZ2SNqszSXhxH2oq1fXhmgdJ/VqAz/X0oZIe9V9VnvMas2FBgJrZ5KKyyUXa8dW63j0uKOVtD8bqKHE7+joKMCiJRvuueceyuW4YvPHPvYxXvnKV/KMZzyDD37wgzzjGc8A4O///u8b2bwQQogWcbQi5ylyCU0lspRDs+zy5dBQieLZgHOedOBCiPVrR/sjbZ7oBhKnQohGbGQbIu1V91ntMdMqnmPB0RBEoHVcfmv+TfkaObbt11Did3x8HGDRidq+/OUvz7x37bXXznnv1a9+NdZafvCDHzSyeSGEEC00nHXJegpPKU6VDNYufufXWsupkoknW6rWhxNCiEa0o/2RNk90A4lTIUQjNrINkfaq+6z2mA1Uk8OuUgSRZSCxeDJXjm1naCjxOzk5GX+IXvgx3/zmN1FKLUj6Auzbtw+A06dPN7J5IYQQLTTUF8+sujvrMB0ajuSjBXd+y9WfT4eG3dl4+aE+qcglhGhMO9ofafNEN5A4FUI0YiPbEGmvus9qj9mkb4gs7OrThAam/IUjhOXYdo6G0u2ZTIZ8Pr8ggTs5OTkzmveqq65asF4qlQIgiqJGNi+EEKKFXK24YnuCA2M+e3MuJwoRh6dCko7C04pKZChHloRS7M255DzNFdsTMhOrEKJh9e3PcNbheCHi8cmIlKvxtCIwlkpk8ZrY/qzU5rVim6Iz1Ca4wlpQqqMnmpE4FUI0Yn4bMpoPeWwyIuUoko5uahsi7VX3Wcsxu3ybx9lpw/n9mrNlw+OTwYrnaQrwI9vR/Wwvaijxe/755/PAAw/wta99jV/91V+d+fk//dM/YYxBKcXVV1+9YL0zZ84AMDAw0MjmhRBCtNhgymH/zgQHx30yrmLKN5wsRpyZNkTGknQVjgNnpiMuGXTJJhp6kEQIIQAIjaUQWMByohhhLFQimA4NGVeRSyh2pOPHERNOfJEyuMjs5Gs1v80rBJZ8dVRLylHsSDtN36Zoj9BYThYjjuQDRgsRE5X4ZmbKUQwmNcNZl305l6E+p+OSEBKnQohGZBOa3X2aJ04ERDau01qoGBxt6U8o9vQ59Cd0U9oQaa+6z2qOWcaFUmjZl3M5UgiXPU9zNOzOaB4+F5APZktH5KqlHzqxn+01DSV+X/CCF/CDH/yAf/zHf+TgwYNcccUVTE1N8e53vxuAPXv28PSnP33Bej/84Q8BuOCCCxrZvBBCiA0wmHK4ek+KR84FHDtRxth4dt/IxP8cN67zdKwQMTZdlpM2IURDJsoRB8d9/ChO/mZcTd43WDs7k3QphH05xSVbEk2/YKi1eSeLEaOFkHzdDS25SOkNtRibKEccmgwpBoZKFCeDXa046cDRfMh4yWUg5XRkvyZxKoRYj/o+diChKYcRUfUJfVeDsTBVMTxta4JLtnhNaUOkveo+yx0zjWXKj8/PSuHy52lDfS6nShHHCtGcBLKjIJfQTPmGQ5OS9G+1hhK/t9xyC3/5l39JuVzmJ3/yJ7nyyis5dOgQZ8+eRSnFLbfcsuh6X/ziF1FK8YxnPKORzQshhNgghepI361ph3IUkrCK/rQm4caP8hwvRZyejus3HRjz2b9TOm8hxNpNlCMOjPnkA8OJQkRgLUlHkU1okq6lHMSPB+7p00A8UUgrLhRdrRjJuYzk3JkyAPJYYm+oxdjYdMiDZwLKkcWvVp9TylKOLEUfCoFlomK4fFuCyHRmvyZxKoRYi6X62F19Gj+M2z9Xw9a0w8lixJ4+p2ntnrRX3WexY5b3DQdP+yhlOTwZLnueVgosj0/Go8rr483TinJkmSyEeErJ9eMGUHapafpW6b//9//ObbfdFn+YUjOz/j3jGc/gm9/8Jul0es7yDzzwAFdccQVKKT7+8Y9z0003NbJ50WVGRkYYHR1leHiYY8eOtXt3hBCrEBrL14+XmagYjuZD0q5mV0aTcmfv/JZDw6mSYTo07M25DCY1V+9JyZ17IcSqSVsjWq0WY2eryQ9r45+lXUUuofGceHbyvG+ZDg2uVihg/64EW6ujnyTWhBDdSPpY0ai1xFAxiIgsKMBRiown8dZODRdj/IM/+AP+/u//nle84hVccskl7N+/n9/5nd/hK1/5yoKkL8D73vc+AKy1vPzlL29080IIIVrsZDHCjywnChFpV7Mv58zptAFS1Z+nXc2JQrz8yaJM4CmEWD1pa0Sr1WLs8YkQR8UXpGlXsSUVJ30BPEexNaVJuxpl49Fvj0+GEmtCiK4mfaxo1FpiyKI4O204M20wFom3Nmuo1EPNDTfcwA033LCqZT/84Q/z4Q9/uBmbFUIIsQFGCyGFwBJYy0jGQanF78IqpdiV0RyeipcfLYSM5JrSzQghNgFpa0Sr1WJsKjCkHEXBWHIJvTDWFOQSijNlhefEE5tKrAkhupn0saJRa4khV0EliqsBuBqJtzaT6deFEEIsKTKWfLUQf9JRC+7UzpdyNUlHkfcN+cASmYaqCQkhNglpa0Sr1WJsshJVv48vRmsjfefzHBVPdFSd9GiqIrEmhOhO0seKRq0lhoy1GMAC1oIBzDIxJPHWepL4FUIIsaTqjVoiC94q6y15Ws1ZTwghViJtjWi1WoyEJq43aIi/Lqd+udDM/RwhhOgW0seKRq0lhurztrVu1qzw+RJvrSWJXyGEEEuqDYRyFASrvPsaGDtnPSGEWIm0NaLVajHiaoisRRN/XU79crXBTRJrQohuI32saNRaYqg+L1zrZldKPEq8tdaqi2f84R/+4bLv33rrrQ3vjBBCiM7iaEXOi2c7nyyElEOz7KM95dBQiSw70g45T+HIrKxCiFWQtka0Wi3GBpIOEOJomA4giOyi5R6CyBIaSHpABP1JLbEmhOhK0seKRq0lhrRSaOIJVFFx0lcvE0MSb6236sTv7bffvmRBZpDErxBC9KrhrMuUb/CU4lTJsC+nFu0PrLWcKsXLZT3FcFYK8wshVk/aGtFqtRjr9zT5wKCBvG/Ykpo3wZuFvG/R1hJE0J/QEmtCiK4mfaxo1FpiKLSQrN5UDU38M4m39llTqQdr7ZL/hBBC9KahPoeEo9iddZgODUfyEeVwbqWmcvXn06FhdzZefqjPadMeCyG6kbQ1otVqMXbhoEtk44lnpkPLubIhqBYVDCLL2bJhOjTYam3fCwdciTUhRFeTPlY0ai0xpLBsTWu2pTVaIfHWZspK1lZsoJGREUZHRxkeHubYsWPt3h0hxCpNlCMOjPnkA8OJQkRgLUlH4WlFYCyVyOKp+EQg52n270wwmJKOWwixNtLWiFarxdjYdMiDZwLKkcWP4veUslgLykLCVSQdxeXbEuxIOxJrQoiuJ32saNRaYsgBUPFkbRJv7SWTuzXRG97wBpRSM/9uv/32hj6vVl5jrf+uueaaRT/vzjvvXNPnNLr/QojeMZiqXvQmNRcNuoxkXVLVx3dSjmIk63LRoMtgUjptIcT6SVsjWq0WY7v7XJ61M8FQxmFbKn7ENKkVWU+zNa3ZlXF49s4EQ32S9BVC9AbpY0Wj1hJDzx9O8fw9KYm3DiAFNJrknnvu4a677mr3bgBw4YUXtnsXhBA9aDDlcPWeFCeLEaOFkHxi9t5hrlqTaajPwZWC/EKIBkhbI1qtPsZ2ZAJGCxETFUM5sqQcxWBSM5x12ZeTWBNC9BbpY0Wj1hpDEm/t11Di9xOf+AQAL3nJSxgZGVn1esePH+df/uVfALjxxhsb2YWOMDU1xZvf/GYA+vr6KBaLTfnc1772tTzzmc9ccbkgCHjd616H7/sA3HzzzSuu85a3vIWXvOQlyy7z1Kc+dVX7KYTYPFytGMm5jORcImOJLDgKmX1VCNFU0taIVlssxuI6D0piTQjR06SPFY1aSwxJvLVfQ4nfWmmDu+++e02J3wceeIA3vOENaK17IvH7W7/1Wxw9epS9e/fy6le/mjvuuKMpn/vUpz51VcnXu+++eybpe+mll/L85z9/xXX279/Pz/zMzzS6i0KITczRCnkgRwjRatLWiFabjTG5CBVCbC7Sx4pGrSWGJN7ao601fnthXrkvfvGLfOQjHwHgAx/4ALlcbsP34eMf//jM69WM9hVCCCGEEEIIIYQQQvS2tiR+awlfpbr7rnqpVOKWW27BWstrXvMarr/++g3fhxMnTnDPPfcA4LpuT4ygFkIIIVopMhY/skSm+29Ai7nWc2wlHoQQ7dKK9kfaNCFEK0kb033aMrnb6dOnAchms+3YfNP87u/+Lo8//jhbt27lz/7sz9qyD3fddRdRFAHwile8gqGhobbshxBCCNHJQmNnJ5YIZk9UZWKJ7reeYyvxIIRol1a0P9KmCSFaSdqY7rbhid9KpTIzKdwFF1yw0Ztvmm984xu8//3vB+BP/uRP2LVrV1v246/+6q9mXv/yL//yqtf7wAc+wLve9S6OHj2KMYbt27fzzGc+k5/+6Z/mpptuIpPJtGJ3hRBCiA03UY44OO7jR5ZCYMn7ZmZiiVxCM+UbDk0qrtieYDAllce6yXqOrcSDEKJdWtH+SJsmhGglaWO636oTv3fddRd33XXXou+9/e1v573vfe+y61trKRaL/PjHP6ZYLKKU4rrrrlvTznaKcrnMzTffjDGGa6+9lje+8Y1t2Y+vfvWrPPLIIwDs3r2bf//v//2q1/32t7895/ujR49y9OhR/u///b/cdtttfPzjH29L6QohhBCimSbKEQfGfPKB4UQhIrCWpKPwtKIcWSYLIZ5S7M46HBjz2b9TTlq7xXqOLSDxIIRoi1b0R9LHCSFaSdqY3rDqxO8TTzzBl770pQV1ea21/OhHP1r1Bmv1fYeGhnjb29626vU6ya233srDDz9MOp3mQx/6UNv2o35St5tuugnHWfkPzHEcnve85/GCF7yASy65hGw2y8TEBN/97nf53//7f3P27FlOnz7NDTfcwF//9V/zC7/wC638FYQQQoiWCY3l4Hh8sno0H5J2NSMZh5Q7O8VBOTScKsXv7825HBz3uXpPSh5X63DrObYHTldQKIkHIcSGa0V/JH2cEKKVpI3pHWsu9VBL3K70s/mUUmSzWS644AKuu+463va2t3VlPdpvf/vb3HHHHQC84x3v4KKLLmrLfuTzef7mb/5m5vubb755xXWe//zn88QTTzAyMrLgvTe96U28+93v5pZbbuGzn/0s1lpuvvlmrr76avbt29fUfQcIgoADBw4s+t7u3bvZvXt307cphBBiczlZjPAjy4lCRNrV7Ms5C25gp1zNvpziSB5OFCIyruJkMWIk15ZpEMQqrefYVlIaBZwpG4kHIcSGakV/JH2cEKKVpI3pHXrlRWK33XYbxpg5/yBO6P793//9gvfm/4uiiMnJSb7//e/zJ3/yJ12Z2PN9n5tvvpkoiti/fz9vfetb27Yvn/3sZykWiwC84AUv4ClPecqK61x88cWLJn1rcrkcf/3Xf80111wDxCUt3vWudzVlf+cbGxvjWc961qL/2jmKWgghRO8YLYQUAktgLbsyesHJao1Sil0ZTWDj2mWjhXCD91Ss1XqO7clSxIliJPEghNhwreiPpI8TQrSStDG9o+E0/GpG+/aKd77znfzwhz/EcRw+8pGPrKq0QqvUl3lYy6RuK3Ech3e+8508//nPB+Cf/umf+Iu/+IumfX7Nzp07ueeeexZ9rxtvCnSTyNiZYuyOPIIhhGhQp7YpkbHkqxNQJB0157G0xaRcTdJR5H1DPqGJjO2o30fMWs+x9TSMl+JBCzvScx9TNNZiLGgFunpRI/EgmqFT20exsVrRH0kfJ9pJ2rbestjxXG0bUzuHSjiqbW2MxOPKGkr81kb9bgYHDx7kj//4jwF461vfyv79+9u2Lz/+8Y/55je/CUB/fz+vfvWrm/r5z3ve80ilUpTLZY4cOUKpVCKTyTR1G57ntfX/cLMJjeVkMWK0EJIPZm/W5DzFcNZlqM+ROjxCiFXrhjYlsrNfvVXui6fVnPVkaorOtJ5j6ypF7azVdeILlcmKYaJiKEezMZxyFINJzUBSSzyIdemG9lFsrFb0R9LHiY0mbVtvWel4bk3Fid7F2pilzqEKviXrKYb6bMvbGInHtZHCG6t05513EgQBWms8z+Od73znost95StfmfO6ttyll17atATtxz72sZnXr33ta5uelNVas3XrVo4fPw7AxMRE07chNs5EOeLguI8fxY9e5H0zc0csl9BM+YZDk4ortssMnEKIlXVLm+Ko2a/1J6XLCYwlVV3RkXPFjrWeYxtaO1PfrOhbHpsIiYylHFkqocUQ1z8ru4pyaDk9bYisZSCh52xTiOV0S/soNlYr+iPp48RGkratt6zmeLpaUQrMgjamFBiOFaJFz6GKoSE0iscmFM/aadiWbk0sSDyunSR+V6lW0sIYw//4H/9jVevcd9993HfffQC88pWvbEriNwxDPvnJT85838wyDzXGGM6dOzfz/eDgYNO3ITbGRDniwFg8E+eJQlzXMOkoPK0oR5bJQoinFLuzDgfGfPbvlMZRCLG0bmpTHK3IeYpcQjNZCCmHZtlHYcuhoRJZdqQdcp6SR8U62HqObWCgP6nxQ8up6YicpyiF8YgUV4OjFIG1TFcsjoKMq8gHlm0pLfEgVqWb2kexsVrRH0kfJzaKtG29ZS3H83QpIuMpKr6lHBqMhSP5kEoUj/itP4eajix532JRbDGWg6dbEwsSj+vTtMRvGIZ8+9vf5oc//CHnzp2jXC6var1bb721WbuwKXzuc5/j1KlTADz96U/nJ3/yJ5u+jW9961tMT08DMDIyIqN9u1RoLAfH40bxaD4k7WpGMnPrGpZDw6lS/P7enMvBcZ+r96TksQghxALd2KYMZ12mfIOnFKdKhn05tejEFNZaTpXi5bLVR8REZ1vPsR1MKY7lI4yFk0XDllRc1sGrG/oWRPHIkZNFQ9pTTJQNQ7vlgkEsrxvbR7GxWtEfSR8nWk3att6y1uPZn9BMlA0OxJPjRpZKZDlbNiQ0s+dQFs6WDf2eIulAKbTkA9P0WJB4XL+GW31jDO9+97v5n//zfzI+Pr7m9bsl8fve976X9773vSsud/vtt/OOd7wDgNtuu43bb7+9qftRX+ahVaN964/J9ddf3/RtiI1xshjhR5YThYi0q9mXcxacDKZczb6c4kgeThQiMq7iZDFiJCcnhEKIubqxTRnqczg0Gd/1P5oPOZKHXRm96AnidGjYm3NJOIqhPkn0dbr1HNvQWFKeggqgLNYufhFgbfw+VN/f3NcKYhW6sX0UG6sV/ZH0caLVpG3rLWs9noUgnh0hm9CcLIaUQksQQcKBLSmNUqp6w9ziG8u2jEPKgaSjWxILEo/rt/z0nyuw1vLqV7+a3//932d8fBxr7Zr+bWZ33nknSsV3Za+55ppVrXPy5EnuueceABKJBK973etWvb1vfvObfPjDH152JHaxWOTGG2/kX//1XwFIJpP89m//9qq3ITrLaCGkEFgCa9mV0YuOAABQSrErowlsXCNntBBu8J4KIbpBN7Ypro7re+U8zd6cSxBZDk+FPD4ZcDQffz08FRJElr05l5ynuWJ7YtOPCugG6zm2W5KarUmNVnHCxFg4UzaMT0ecq349U44fZRzqc9AqHs1yshi1+9cVHa4b20exsVrRH0kfJ1pN2rbestbjGdn4PMiPLGlXUw4tk77BN5aJip05b4qMZWtKk3QUe3MeQ31OS2JB4nH9Gkp7f+ITn+Duu+8GwHEcfu7nfo6XvvSljIyMkEwmm7KDYtYnPvEJwjAO2le+8pVs37591eueOnWKN7/5zbztbW/jpS99Kc961rPYu3cvfX19TE5OcuDAAT7zmc9w5swZIP5j+ehHP8r555/fil9FtFhkLPlqofOko5at+QXxnbGko8j7hnxCExkrtb+EEDO6uU0ZTDns35ng4LhPxlVzJoFIOYodaYesp0g4MglEt1nLsX36Vo/vjQeEFnZnHLSGPpc5E5N4SpFNKlKOwtGQdSG0kA+s9ItiSd3cPoqN1Yr+SPo40SrStvWW9R7P0MKOtOZYIaTPiwcuph216HnTSNYl48Wf2+xYkHhsTEOJ37vuuguAVCrF5z//eV74whc2ZafE4j7+8Y/PvF5vmYdCocDdd989k7BfzNDQEB/96Ed5xStesa5tiParTbwZWfBW2cB5Ws1ZT04LhRA13d6mDKYcrt6T4mQxYrQQkk/MnizmqvUOh/ocGQXVhVZ7bI0FCIgs9CU0w30Ok75homIou7NPoaWcuO7vQEIzWow6JoZF5+r29lFsrFb0R9LHiVaQtq23NHI8k67mgn4PY0M8beira2Pqz5t03ec2OxYkHhvTUOL3Bz/4AUop3vSmN0nSt8W+/vWv8/DDDwOwd+9eXvrSl65p/euuu45/+Id/4P777+ff/u3fOHr0KGfOnGFiYoJMJsPOnTvZv38/r3jFK/j5n/95UqlUK34NsUFq89Q4Kh7NtBqBsaSqKzpyXiiEqNMLbYqrFSM5l5GcS2RsfAKo2NR3/3vFao5tFGd+Z2JYa8WWlMOWlIMx8YhfDXMuWjothkVn6oX2UWysVvRH0seJZpO2rbc0cjw1oB3FQFKTdBXn59xFz5sWW7d+2+3a/2btQzdrKPFbLBYBuOqqq5qyM73g9ttvX9WEbm94wxt4wxvesOrPvfrqqxuqi5zNZrnhhhu44YYb1v0Zons4WpHzFLmEZrIQUg7Nso9DlENDJbLsSDvkPCUniUKIOXqtTXG02tR3/XvZUsd2uRjWWi2Y9KLTY1h0jl5rH8XGakV/JH2caAZp23pLI8dzIBkvVwwsk4UQ39gNjwWJx8Y0NLnbnj17ADDGNGVnhBDNM5x1yXoKTylOlcySNw6stZwqmbhGT/VxMCGEmE/aFNHtJIZFq0hsCSF6kbRtvaWR49kJsdAJ+9CtGkr81so7/OAHP2jKzgghmmeozyHhKHZnHaZDw5F8RDmce5OmXP35dGjYnY2XH+qTMQJCiIWkTRHdTmJYtIrElhCiF0nb1lsaOZ6dEAudsA/dStkG6gccOHCA5z73uWzbto1HH32UXC7XzH0TPWhkZITR0VGGh4c5duxYu3en502UIw6M+eQDw4lCRGAtSUfhaUVgLJXI4qm48cx5mv07ZbZfIcTSNrJNkRqFohXWE8O5hJZYFCuScy6xXtLfiY2ynliTtq23NHI8OyEWOmEfulFDiV+Ad73rXfzu7/4uL37xi/nbv/1btmzZ0qx9Ez1IEr8bb6IccXDcx48shcCS981Mh59LaLKeIuEortgujaIQYmWtbFNCY2dnJQ9mT09kVnLRTKuJYUfD7ozDlG8lFsWqyTmXWC3p78RGaUasSdvWWxo5np0QC52wD92mocTvV77yFQA+/OEP8+lPf5pt27Zx44038rznPY/t27ej9cqVJGrlIsTmIInf9pCTSyFEM7WiTZGTOLGRlovhXEJzqhQRGolFsXZyziVWIv2d2CjNjDVp23pLI8ezE2KhE/ahmzSU+NVao9Tsf6a1ds73K25cKcIwXO/mRReSxG/7yeNkQohmakabIo9tiXaqj+G8byQWRdPIOZeYT/o7sVFaGWvStvWWRo5nJ8RCJ+xDp2t4erv5eeMGK0cIIVrM0Qo5fRRCNEujbUpoLAfH4wuTo/mQtKsZyTik3Nmnhsqh4VQpfn9vzuXguM/Ve1JyJ180RS2GJRZFs8k5l6gnbYzYKK2ONWnbeksjx7MTYqET9qHTNZT4ve2225q1H0IIIYTYhE4WI/zIcqIQkXY1+3LOgqeHUq5mX05xJA8nChEZV3GyGDGSa/j+tRAzJBaFEK0kbYzYKBJrQoh6kvgVQgghRNuMFkIKgSWwlpHMwguTGqUUuzKaw1Px8qOFUC5ORFNJLAohWknaGLFRJNaEEPVWnn1NCCGEEKIFImPJVycbSTpqziOIi0m5mqSjyPuGfGCJjJSXEs0hsSiEaCVpY8RGkVgTQswniV8hhBBCtEVkZ796q6xf6Gk1Zz0hmkFiUQjRStLGiI0isSaEmK8liV/f9zl58iRHjhxpxccLIYQQogc4avZrsMoRJoGxc9YTohkkFoUQrSRtjNgoEmtCiPmaVsDlkUce4c/+7M+49957OXz4MBDXjAnDcM5yn/nMZ3j88ccZGhri5ptvbtbmhRBCCNFlHK3IeYpcQjNZCCmHhpSrMcbOjDhxFOjqiJVyaKhElh1ph5yncGSW800vqsZK7UK19nqtsbFULC5FYlG0Qn08S0z1lma0MRIfYjXqY21iKqTgGzKumjmXmk/6s82l2e1Iu9olaQ/XpimJ33e96138wR/8AVEUYe3yd5WKxSJvf/vbcV2X66+/np07dzZjF4QQQgjRhYazLlO+wVHw2ESINYaJwBKa+H1Xw5aEw1CfphBYPKXIeorhrEw+slmFxnKyGDFaCJmsGCZ9w0QlDpjBpGYgqRlIaIazLkN9Du4qLwhqsegpxamSYV9OLTohjrWWUyUjsSiaoj6e88HsdVSuGltriWHR2dbTxmRchasV958oS3yIVQmNxdWK06WQ8XLEuUrElpQm7eq4j0zomSSw9GebQ7P7mXb1W9Jfrp+yK2VqV/DHf/zH/P7v/z7WWhzH4Sd/8idxHIevfe1rKKWIomjO8qVSie3bt1OpVPjQhz7Em970poZ+AdFdRkZGGB0dZXh4mGPHjrV7d4QQQrRZaCxfeHKaB8YrHM1HWAAb38VXgOsoPAURkHY1z9yRYKjP4eo9KTm524QmyhEHx338yHK6FPFEPiSMLKEFLDg6rlV4/oDLjrRDwlFcsT3BYMpZ8bNDY/n68TITFcPRfEja1ezK6Dmj8sqh4VTJMB0a9uZcBpNaYlGsW308F6qTMdVGMOUSmqyn1hTDorOttY3ZkXaYqBjO73cohUh8iBXV2pRyaPjRmaB6c9SS0DCY0uQ8jaNhJOuiFdKfbQLN7mfa1W9Jf9mYhhK/jz76KJdddhnGGC6//HL+5m/+hksvvZR/+Id/4D/+x/+4aOIX4Prrr+eee+7hF37hF/jUpz7V0C8guoskfoUQQtSbKEd84ckS3x3zmawYSmF8WqIVKAXGxv8SWjGY1AznHH72oj7OG/DavOdio02UIw6M+eQDw5OTIaem43PMcmAIa6VBtCLtKFCwM+1w/oBLztPs37n6C5raNk4UIgJrSToKTysCY6lE8ajz3VlnTZ8rxHwSa5vTao/7lpTmzHTE1rRmomwlPsSK5sdWMTRxcsxAPrCExuIpyCY1WkF/QtPnaomhHtbsfqZd/Zb0l41raCz/+9//fqIoYnBwkHvvvZfdu3evar1nP/vZ/PM//zMPPPBAI5sXQgghRBcLjeXA6QrHihFBtVZXf0KRcDQGsDZ+DDGI4ove6dAQRJrHp0KGc66MStlEQmM5OB6f9B+ZCpn0I3IJRd639Ccdcok4FvK+wTfxY39TvuHJqYDz+j0OjvurGsk0mHLYvzPBwXGfjKvmjCpJOYodaUdGlYiG1cdzbeTnSMZZdOTn0XzI3py76hgWnW01bUzGVTwxFbI1rRmfNhIfYkWLtSmX5jyMhWOFiJ3GUggM58qGgm/pTygUcMGAS8qV/qwXNbufaVe/Jf1lczSU+P3iF7+IUoobb7xx1UlfgAsuuACAo0ePNrJ5IYQQQnSxk8WI8ZLh7LTBotjTp0i5mulotsYvKFwFfmQph4a8bxkvRZwsRozkpBbdZnGyGOFHlhOFCGNhIBEnRBJasTWl47ogwJaU5lw5Hjm+PaWxKE4UIjKuWnXMDKbiUiIzdeQSsxcXUkdONEN9PKddzb6cs6DWa8rV7MspjuRZcwyLzrZSGxO3LZbDkxIfYnWWa1MuHlRMVgwTFUXW05wtGyJjyXqa7WmHK3YkpD/rQc3uZ9rVb0l/2RwN/U/UErfPfvaz17ReLpcDoFAoNLJ5IYQQQnSx0ULIyVJEJYrrz/UnHTxH0Uc80tcAGlBKEUSWsRJUIsuJ6sWynNBtHqOFkEIQj/x2NZTCuO7zYELNJH0hjpVcQnOmbKgYyLgQ2Lge3FpixtWKkZzLSM6VmaNF09XH80hm4UVsjVKKXRnN4alwzTEsOttybcz9J8qUQiQ+xKot16ZopdiSctiScjDWUgoMR/IhOzLOzERwovc0u59pV78l/WVz6JUXWVqlUgEglUqtab1awrevr6+RzQshhBCiS0XGMukbpioGCyRdhefMnswppXDU7IznnqNIugprYco3TFbiESui90XGkq8+Dp1QCgNUwjgBXB8zNZ6jcHW8jAE8HZeAyAd2XTHj6Li0gyR9RTPUx3PSUXMeV11MytUkHdVQDIvOVt/GSHyItVpLzGilyCbix+QlZnpXs9uRdrVL0h42T0Mp8B07djA6Osro6Oia1nvwwQcB2LVrVyObF0II0UaRsfhR3KEulxSR0XJiMVF10rZaRQdniTv49RylsBhCawltNa5Wuz2Jw66w2HGqNjNEFlwH/DCOm+VixqkmiAFcpeZ8xlIxM3/bte+xFpSS2OlhzW4flvu8+lj0VrktT68uhkVnWkt8rSY+jLUYG0+CqpXqifiIjCW0EJi41FPt/yyylshAWP2dQzP7s9mv1fery9V+tti6tf+7+F98c7D+e1t3bjJ32er785af8z5AdZlausnaJV5Xvwdb95oFr6n7HuJJb2H2QZfa98Xq5G0A3xvzZ5ev/1q3bu38Peko7js6Hd9or36eU42rOL5m40wx93ut4lGE9d+r2vvM/qwW99V5V9Eqjm3Pid93lcLRc5dz6n9Wfe1WP8/VdT/TCq/6nqtZcjToZtPsfmYj+q2Vzv+kv2xMQ4nfK664gmPHjvEv//Iv/Pqv//qq1rHWcvfdd6OU4rnPfW4jmxdCCLHBQmMZzYf86IzPaDGiXO1ZU45iuM/l8u0ew9m4a5mpXRfMnrFKfUxR49RdHEB8gbYUYy3l0HKuElEOLMZaHjsXsiVZYW9u6XgKjZU47AIrHacd6ThKHBUnfSGOm2CZmIlsPMMzxBf9ierr+QOE52/bVEeilwJDWE0U+MaSchSDSc1w1mXfMjEnukez24fVfl4tBh3FTB+6kqAag7X1ROdbb3wtFR/G2mqdVjPn5ylHUQgMW5J6zvqrYa0lqLZxQWTxDdWv8Y39+u8Xvh8nagITf0Zo44RtWPve2JnXtWTuYu/Vvsq4vCYK1vK/aRmbNisv1iWcalLZ1bNf3frvVTVZ7My+rl82/jr7OuEoEnWvPU31a917jiJR/cxE9X1HtTcJ3ex+plX91lrO/6S/bExDid//8B/+A5/73Of4/Oc/z7e//W2e85znrLjO+973Ph599FGUUrzyla9sZPNCCCE20EQ54psnyhyeDClWH7spRxZF/Jj+RNnwxFTAUMYhXZ2cpH62akdBLqGZ8g2HJmUG4c3O0YqBhKY/GcdEJYwvLOc/uu9HlnMVQxBaSkE8eidp4xPAk6WIQrB4PE2UIw6O+/iRlTjsYKs6To5CY8klNJN+iCZuc6Yri8dMUJ0cMJtUcYLYxJ+V8+Y+mTB/2+PTEaeKEb4xFAOIjMGgSOq4zMhJB47mQ8ZLLgMpR2KnizW7fVjr5+W8uBb1ZCGkHJplH18th4ZKZNmRdhbEsOhMjcSXoxVZF1Ku4vR0xHgpxDdwvBgRRJZyZPHDeGScraZLp0NLylUcyUc8ei6gEsWJ28oKydugd/J9QgDVkd6RjScBaOPtBAUknPqEsZrzfbKaLE65zHxf+5dwFClH1f2cJX4eJ6IXSzA7WjW1n2n258E6zv+kv2xIQ4nfm266iT/8wz/kxIkT3HDDDfyf//N/uOqqqxZdNggC7rjjDt7+9rejlOLSSy/lZ3/2ZxvZvBBCiA0yUY742miZRyYCTk8bCoFBw8zM0+WKZaJsqhceIYMpTdrRaE31xERRjiyThRBPKXZnHQ6M+ezfKYmTzWw46zKUCTldiiiG8UnflpSeOYn1I8vZsiGILBMVg19N8vV58cnusSXiaaIccWDMJx8YThQiAmslDjvQWo5TGIGr41G8lciS1PHje3nfsjU1O8GbtXEcOQqSGkILSa3IVkePLLXtYmiY8uMYOzNtCEycUnEVVJQlaRQOUAjiWLx8W4LISOx0o2a3D+v5vOGsy5Rv8JTiVMmwL6cWvXi31nKqFC83P4ZF+1kbJ1LLYZyQLYeW06WQH50JmPIN49MG38Q3yC3xSDQ/issJuFpx92MlPB0/WVBL1pajdSZkK5YTRQMEzf0lhRBrZoFKFP9dr20E9trECWZFyplNIKfcOEFsidum6dByNB+xLaXjEhvVUcozI6IVjE8brI3nRBjKLN7fNbPfWu/5n/SX69fQ/0YymeSv//qvednLXsbY2BgveMELeN7znseWLVtmlvmt3/otjh49yn333cf4+DjWWlKpFJ/61Kca3nkhhBCtFxrLgdMVHp0MOFWKqIQwmNBsSemZkXZBZDlbjhgrGbSCfGDYmXa5cqdHxps9gSiHhlMlw9F8yN6cy8Fxn6v3pOSR6U1qqM9he0azNa3xixHnypbQxMlfR8O5SjwSeKJiiKwl5cKWlGZP1uEpgx6+sQvi6blDSQ6OxyeTR/MhaVczknHmjBCQOGy/0Ng1HafhrMPJkmFXn+ZYPmKiYsgkFHk/vjmQS8THLu8bfBM/JjjpWwaSsDvrknAUQ33OottOVR/5zHqKI+V4AhFHxRduujryRKv4oqoSxjUdfzjus39XQmKny6w17lZqH9b7ec8dSpJw4ovao/mQI3nYldGLrjcdGvbm5sawWD9rLZUIpkPDdGgpVRMj06GlFMz/mZl5r5bcrX9dDq2UKBBCtE2cYI7btJVGOD85tfLnffNkBSji6riMTNqdTSYnHUUpjAtVVyLLYxOKwaQi4+qZchcWS96PS7ns63dxNAv6rUbO/0YLkfSX66SsXaZA2irdc889vP71r+fs2bNL1jKpbWZwcJD//b//N9ddd12jmxVdaGRkhNHRUYaHhzl27Fi7d0cIsQrH8iHfOF7mgXGfYmgZSKg5ozJrir7hRCFkKrB4WnHhgMtFgx5b5o2UstZyJB8/snjRoMvTtiYYycld2c1qqdHkFqiEpnoyCxlP0Z/Q7Mw4XDzokfHiE7758bQ15XC2HHFoIsRzFPtyzpIjAyQO2+dYPuShs/6ajtOePk1gwHUUT06GnJqOg6NcrccL8eOI6eoMMjvTDucPuOQ8PWfU5vxt5zw4VTIcy4eUozix62hIOzBdnUgu52lyCU0lsoSRwXUU2YTmGdsTEjtdZD1xt1z70MjnZT215IinwMSP6ddGPM2P4c2qVu+9GFiKoaEUxK9LoaEY1CVwa0ndYG4Ct5bQlYneRb25k5ZVJyVjkYnMZiYnWzjxWe29+snXFPEPZl/X/by6bP1rZt5Tc9+rvj9/cjiYO0FcEMGUH98or0RxuxN/TpyQM3Z2/6PqUy21m5q1SeXmfo1HtMcj1JmpW1ub1M4uMQme/HmJep6GjKtIu5q0G99YD0x88yzhKLYmdVwvua7URcJReLWRyFhG+jShVbiOkv5ynZpylvrTP/3T/PCHP+Td7343n/zkJzlz5syCZQYGBvilX/olfu/3fo89e/Y0Y7NCCCE2wGgh5GQpohJZEjoe/bbYhe10ZHEdjQoilIpPPicqZkHiVynFrozm8FRIIbCMFkJJmmxigymH5w+ncDSkJkOKgSbvG86UDaBwHRjwFGlPsz3tcOGAO5P0hYXxdLLok01oAmsZySyehFlsPYnDjTVaiP/f13KcIhRbUnHNtsw2j+0lzRP5kKTWceK3mrD1tOL8AZcdaYeEs7CO5vxtn6hOVFmOLK6OS0mkHIXWipQb154LjGU6MgwkNGfK8SQuU76R2Oky64m75Y5xI5/33N0p9u+MR41n3Lk18VOOYkfaIeupRWO4W62UuK1/XQqqX0NLMYiXLckI246kVfy4uKfjxMzM6+rEWt68r3Nfz0665em4fY8fP1c41WSjq1V1G7M/c6oTJTrVdXV1uTnv1f+sbt35idx2TsLVbCvVTc16auZJlzMVwwX97oo1Uw9PhYxkXfb0OTx3d2rFfbDW8q0TZUaLEccKIedlXTxHY2z89zubJI4TxuXQMFqM2J5y2JLSXLolQVSdLNBYS2jjiQQjW63hW30d1iYNnDexYP1kgnMnFKy+Z+MnFSM7d4LB2lfRXIGBSd8y6UeLvn80v/jP62nFTL1jTyscFd8ccao3LrKeIutpTpcitm3RjE3H52cZT9HnaRJL1ELeTJp2ljo0NMQdd9zBHXfcwYMPPsgTTzzBxMQE2WyWkZERnvnMZ6L10o2KEEKIzhNVZ7ifqhgs8YRK8ydSArAmrlFnbTwaT6OYjqiOrLHoeZ1tytUkHUXeN+QTmshYKcC/iQ2mHF56XobRfMiPzvgcLYREED9ShmVv1mN31mFLUqMXiZNaPE1WIvJBfCERP5q2/HmHxGF7RMaSr16QrvU45RIuVw8lOT1t6E+EbE87TFZvMgEMJjUDSc1AQsc1pPucOY/oz992olpPrly92jPMJhWglkCoXUTOJipM9eJwqmLISex0hUbibrH2oRmfN5hyuHpPanZW88TsZ9RmNZ8fw50gNHFSqRgYCr6lEJjqP0uxekOk9n2h7vtSIInbVqtPhMyMnps3sdT8iaZq3ydmvqfu57Pf195P6PhcL1FtD+ef44n2WalNGepzsBZOlqKWnScZC8UQSoEl42pyydpNq6XWc6iY+IbXlpTD5du8tvWn1s4mledOjAh+tUZ3/fdBtSb37M/j9fzIztT0riWj/br3apMv1v5Jwnl5xlJ9egNWGlP+hSPlBT9zFPR5irc8c4Bn7Ei0Zic7XEuGJ1x22WVcdtllrfhoIYQQGyiq3ZWvfu8sVc6n+tUQT7g086hYdf3Fzt88rYjs7Ha6fyyTaISrFecNeJw34DEdGL50rMxoIUQr2Nfvrbi+pxVh9fnd0MTfr4bE4car//9ez3FSSjGScxnJuTMjf2r3o2qvl7ponL/tWtsWVT/X2tlHbmuUml3O2rgdjBPEitDUbXdVv4lol0bjbv4xbtbnuXrxeN6IxIexloJvq6PXDXk/TmTPJG2rid3ivO/L0fIX3mJp9aNfIW5H+qoJuVo9zbjOuOJEMSQfWBwFOzMOmviphoSOb8LXbkS5Kv680UKIUoq9OZcXDqdILHKjXvS+5doUP7I8OhE2rR1cTLPb2o2kVDyC3NWKlcc2N4+xtWQwcxLCtUkf5/6MRX5WnRyyrv54pfp1OrIz5yqbVWRhyrescJ+jp8lzaUIIIZbk1OqXVb+PligLXzut08QJFIdq7TK1eNIX4rvfqepFiVybiHoJJ76oTThq1QmGwMSlSIjiWnSVVRZxlDjceLX/Z0expuO72HFytJpzgbjSxeL8bdfaNod4pI/WamY0b421duaml1JxO+gpRWBnLyIkdjpfM+OuFZ8HC+N5LYyNSyXkfcOUP5vEzdcldGf+VZcrygjcZTkqri+fro6MTLvxJEcpZ+7XtBuPgn1iKqIQxDVW9+ZcPK2q/2ZLH9Q/bvz4ZEDKUezJurx4JLVgRPl9x8ocL8S1xy8cWPkGaGghJW2SqLOgj2xBu7VgmxuwjV6jVbVNaVF2LjSzCeH65PD8r9N1CeNyZBZMajld968b+46MtwmDq0oSv0IIIZbkaMVAQtOf1Ez5hkoYP8Y0v9yD0lQvaCCMLMqBdHU22MUeASyHhkpk2ZF2yHlKHpEWczhakfMUuYRmshBSDs2KNejieHJxlCGb0Bxb03oShxtp/ce38eM0f9t+9WIz5WkoxxMLBtUago5WM6Olkk7cxtXqBCY9IIL+pJbY6RLNjrtWx3Fo4pG4kxXDpG/IV6qll2YSu3MTugVJ4s6YP5lQ2lVxArf6s4yrZn/uatJe/ffVny1R2mo5958oc7xa13RLUnd0fInNaSPiSmK387hakdWK7Mr3j1alNkK5NGcSzXgizdK8BHFtss3p6nLj5ShOMIfx+dVG9lt9m3jI76oSv1/5yldatgMvfOELW/bZQgghGjecdRnKhJwuRRTD+CJzS2rhBG9pRzERRTNlHvoTmsHkwg7WWsupksFTcQ264azcgxQLDWddpvw4Tk6VDPtyatGJGebH075cgrPlaM3rSRxurPUe32Ycp/nbHkgoymE8eq8cWRTxKKU0lnIYP7XgaUXa0eR9i7Zxfb/+6kQ5Ejvdo9lxt5bPO1GMsAb8yFAMNF8/XmaqmtSdqtbSn6wmcicrplrjfHOqJWD7PEXG0/S51a+emvM6XkaTqb7OeJq0s/aEbbO0M76kPxOrtRFxJbHb2+pHKG9dY02MY/mQh876HJoIcTUMZ52ZOsmVeTWQx6qTi2fc+KkLCzOTfRarE3+upYxFn4z4Xd4111zTklnwlFKEYdj0zxVCCNE8Q30O2zOarWmNX4w4V7aEJk7+1i6ugiiu/TcdxZNqORoqUTxKrl45NJwqGaZDw96cS8KJ69oJMd9Qn8OhScXurMPRfMiRPOzKzB1BtVg8Xb7N4/6TZs3rSRxurPUe32Ycp/nbtlZhrGV7RnOkOrt0JZxN+uYSGoNlOoqfenB1XNv3wgGJnW7T7LjbmtJUIovnwLF8wLF8iKfjWcynq7UVp4M4ibuZJu9REM+ynogTtFkvvknSV/26WOK2r5rgTbvdO+Kv2fHVznZS9K6NiCuJXbGU+bFxvGjYldEMpmaPfS02hvpgb85lMKm5ek9q0YlO/chSCkw1ETz7ulSdgLRUTRAXA0vK7c6+pRmUtUsUbKyjdWuGRCuliKKoJZ8tOtPIyAijo6MMDw9z7Nixdu+OEGKVJsoRXxst88hEwOnpeOIXDdUOOH702VpIuQqtYDClSTsareNEsKcVlchQjiwJpRjOueQ8zf6diTkdvRD1JsoR3zlVYdI3nCpGRMzGU2Di0QCeik8e6+NpohxxYMwnHxhOFCICa1e1nthYteM06UccL0RE1pJy9YYcp/kxUgzjUZd+ZDkzbQhM/Ni8qxSOgoSrcIm/Jh3F5dsS7Eg7EjtNspGTmq3UPkyHBmsUAykFVrEzo/ENTFYME9V/tde9PirX1cwkbbOeJpuYTd7Wvo/fr3+tqucCm/MCu9n9z3KfJ+dVYj0iYzkzHfGDcZ9iaFeM0z5X8YztCbalnTW1z3IuJpYisbHxVjXi97bbblv2/e985zt87nOfA2BwcJDnP//5XHzxxfT19VEsFnnsscf42te+xsTEBEopXvGKV/CsZz2r8b0XQgixIQZTDs8fTuFoSE2GFANN3jfVx6IVfUlFrjqCZyjjkPbi2aanfMPJYsSZaUNkLElX4ThwZjrikkGXbGLz1loSSwuN5WQxYrQQUgotp0rRTB2wIIpnOE85ih1ph6wXTwJ3xfbZk8LBVJyQOzjuk3EVhepERpFdfj2xcUJjKQQWiB+BNzZ+SmA6NGRcRS6h2JF2W3acFouR8emIU8UIrw+KAUTGYFAktSLpKpIO9HmaiwdcBlKOxE6D6v/O88FsAjVXfdx3qM9ZdHTPelgb1yI8Vzacq8QJ/sOTAXk/rqNbDGZnRu/VkblJR9FfTc7mEppcQtGfiF9nvbrXibg/zyY0iXmTkYmVNbv/mf95cl4l1mOx9rYUxgngrSmNo6EY2Jk43ZbSRCaOK5V2+MGZAAjW1D7LuZhYisTGxlvViN/l/K//9b944xvfiOd5vOtd7+JNb3oTiURiwXK+7/Oxj32M3/md38H3fe68805e85rXNLJp0YVkxK8Q3S00ltF8yI/O+IwWo5nZelOOYrjP5fLt3kx9rkfOBdx/okwhiGeErT0i3Z9Q7Mo49Ce1dOhigYlyxMFxHz+yMyeCoYkf+wpNPIGgoxUjWYddGWfZC5CNTCyJ1Zt/jCcr8eRU06FFVZP6jlY8dYvLJVsSLT1O82PEmHgCrVJgCC2EhpkJ4AaTmuGsy76cxE6jFvs7r434rSUiV9M/WBs/2nmuYqpJ3Yhz5Xg07tlybYRuxNmy6amErqeZSdT2J1T1/2zu97mEnplgKZeI+1uxcZrd/4TGynmVWJfl2ts+V2EsFALDtrRDxtOUAsOZ6Yisp9EKiqFdV/tcI+diYikSGxunocTvj3/8Y/bv308YhnzpS1/iqquuWnGdb3zjG1xzzTUkEgm+853v8NSnPnW9mxddSBK/QvSOyMTF9wESztyafPWP8BwvhPhRnMxJuPIIj1jaah79chXxBW5C8+xdyVXHzUY+Si6WttIxLgcWV8OerEN/YmNLKcyPkdr3WAvVkg8SO41b1d85sD3joIA9fQ6+YSa5O1Ex1dcRE5XeSOg6CgaSmv5E/G8gqepe6wWvN3Odwm7UjP5HzqvEeqzlkfo+V3F+v8cTU8GqSkCsJ87kXEwsRWKjtRqaNvF973sf5XKZm266aVVJX4CrrrqKX/zFX+QTn/gE73//+3n/+9/fyC4IIYRoE0cr0kuMsjw4Hp9kHs2HpF3N3tziEzoczYfszbkcHPeXLNovNofF4mYk4ywaN8eLEY5Wa4obRyvkEri91nKMjxUi9ubWdowbNT9GZr+XdqlZQmP53ukKp0oRT0yFWBQJDXl/dvKVQvWr6eLyuQrIJdSiSdvZ12rmdcZdfMZ70Rsa7X/kvEqsx1r63KP5kOGsw78enWZnRjNaiFZcfj1xJudiYikSG63VUOL3C1/4AkoprrnmmjWt9+IXv5hPfOITfOELX2hk80IIITrQyWKEH8UjBdKuZl/OWXBBm3I1+3KKI3k4UYjIuIqTxYiRXEPdkuhiEje9T45x76tElrPluLTC2WnD2WqZhfhfxOlSxJQfT5zXjXJenMwdTOrFvybi17mElhFLommk7RTrsda4eXwixNPw+KRhMOlInAnRQxr6Cx0dHQUgmUyuab3a8rX1hRBC9I7RQkghsATWMpJZeNJYo5RiV0ZzeCpefrQQyonjJiZx0/vkGHc3P7KcKceTSp0pR4xPx3Ugz5Tj78+W45G63abPXTqZW/+6P6Fl9KRoC2k7xXqsNW6O5IOZkg6XbvEkzoToIQ39haZSKcrlMt///vd57Wtfu+r1vv/97wNrTxgLIYTobJGx5KsTRyQdNefxsMWkXE3SUeR9Qz6hiYyVUVKbkMRN75Nj3NkiE0+QNl5L5E5XE7t1id4pv7uSurmEYktSM5h02JLSbEnquq/OTGJXJj0TnUzaTrEea42bWjtY8A1JV5FYIWYkzoToLg0lfi+//HK+9rWv8bGPfYy3vvWt7Ny5c8V1xsbG+NjHPoZSissvv7yRzQshhOgw1bneiCx4qzwB9LSas57Ud9p8JG56nxzj9rHWMuXbOUndM+Vqkrf6+mzZdE35hf6EYjDpsDUVJ27nJ3NrP5fRuaIXSNsp1mOtcWMsOEoRWYujFAZYPlUscSZEN2ko8ftLv/RLfO1rX+Ps2bO85CUv4bOf/eyyydwHH3yQ17zmNZw5cwalFK973esa2bwQQogOUxs45SgoR6tLIwTGkqquKAOvNieJm94nx7h1jLWcrSZyT0/XvkacLsWvx6cjfNPuvVyZq+OyC32eJuPFX6dDQ87TjORcrtubYmvakYSu2FSk7RTrsda40Ypq0jf+ulLSFyTOhOgmDSV+b7nlFj72sY/xne98h4ceeogrr7yS6667jmuvvZaLL76YTCZDqVTiscce44tf/CJf+MIXiKIIgOc85znccsstTfklhBBCdAZHK3KeIpfQTBZCyqFZ9vGycmioRJYdaYecp+QxsU1K4qb3yTFev6BaW/f0tOF0KVqQ4D1bNqwyH9Q2/QnF1pSDBpSKEwbDWYfBpEPGU2Q9jaeZU1OyHBoOT4WMZF329Dns7JMakmLzkbZTrMda48avdiLZhCYwFt9YUsvEjsSZEN2loTMorTWf//znefnLX853v/tdwjDk3nvv5d577110eWvjBuVZz3oWn/vc55YsGC6EEKJ7DWddpnyDpxSnSoZ9ObVoe2+t5VQpXi7rKYazclG/mUnc9D45xovzI8tYKU7ijpXi2rqnqyN1x6cN5yqdO1xXK9iS1GxLO2xLxWUWtqac6tf49ZakxqsOBzuWD3norM+hiRDPUQxnF59waLPFgBDLkbZTrMda46a/egMusBJnQvSahv9Kt27dyje/+U3e85738Od//uecOnVqyWV37drFr//6r/Obv/mbuK40EEII0YuG+hwOTSp2Zx2O5kOO5GFXRs8ZaVAODadKhunQsDfnknAUQ31SHWwzk7jpfZv1GEfGcqZsGCvFid2xaoL3dClibNow0cGJ3YGkZltKsy3lsD0df92W1myvJnoHk3pNI702awwI0Qj5uxHrsda4uXDQZaxk2JvRjBYiiTMheoiytWG4TRCGIV//+tf5t3/7N44fP06hUCCbzTI8PMxP/uRPctVVV0nCd5MbGRlhdHSU4eFhjh071u7dEUK0yEQ54sCYTz4wnChEBNaSdBSeVgTGUoksnopPRnOeZv/OBIMpOXHc7CRuel8vHmNjLRMVU03mGsama0ndOMF7pmwwHViKoc9VbFskmVsbvbst5cyM1G2mXowBIVpN/m7Eeqw1bi4acDk0GUqcCdFjmpr4FWIlkvgVYvOYKEccHPfxI0shsOT9uA6loyCX0GQ9RcJRXLFdThrFLImb3teNx3g6NJwqRpwqxf/GSmamNMPp6YigwwbtagVbU5odaYftaYcdtcRuWrO9muhNL1PvsdW6MQaEaDf5uxHrsda4kTgTovdI4ldsKEn8Nkdk7EwHLMX0RSerhIbjxYiTxYhiONvd5Ko1wYb6ZIZ2sVBoLCeLEaOFkHwgcdOL1nKMN6LPs9Zyrjpq91Qp4lQx4mRpNtGb9zvrdNnTzEno1r/ekY5r7Hb6+UGn/52381xLzvM6SzuOx1Lb7PS/G9GZ1ho3cv4u1qO+3QKkH+sgkvgVG0oSv+snJ3qiWywVq30uDGVc9mQdkm0caSa6iyRAet9ix7gVfV5o7Exid6wuqXuyGH/vd9Co3bSr5iRyt6fnjt7tT2p0D02S3Cl/5+0815LzvM7SjuOx1m12yt+N6C5rvakg5+9iOfVxM1kxTPqzcxcMJjUDSc1AQks/1mZNT/z6vs/ExATlcnlVy+/bt6+ZmxcdThK/6yOP3IhuIbEqhGhUI+3IdGg4WR2tO1YduVtL8I5PGzpltENCw86Mw85MnNitvd6Z1uzIOPR5cnG90drZf0nf2VnacTwkBkQ7SfyJ9aiPm9OliCfyIWFkCS1gwdHgacX5Ay470o7EUBs1JfF7+PBh/uzP/ox77rmHQ4cOsdqPVEoRhmGjmxddRBK/ayeTOYhuIbEqhGjUatoRZSGbUAQGsp5mohIne0+UIiYrnTFs11FxKYadGT2b2E077KgmeAcSCtVDI3a7XTv7L+k7O0s7jofEgGgniT+xHvVx8+RkyKnpCIByYKhVB3G0Iu0oULAz7XD+gCsx1CYNJ34/+9nP8su//MtMT08DrDrpC3HiN4qiRjYvuowkftcmNJavHy8zUTEczYekXc2ujCZV95hNOTScKhmmQ8PenMtgUnP1npQ8RiE2lMSqEKJR9e3IE5MBFoWnYTq0TPqWqeojhNNh+8ftKmBLStclc+PXtZG7W1O9VYqhl7Wz/5K+s7O043hIDIh2kvgT61EfN0emQiYqEZ6jyPuWhFbkEnFs5H2Db+JyNYGBgaTivH5PYqgN3EZWfuCBB3j9619PFEVYa0mlUjz72c9mZGSEZDLZrH0UYtM6WYzwI8uJQkTa1ezLOQtGCKVczb6c4kgeThQiMq7iZDFiJNfQn7cQayKxKoRYq0oU14U7WQw5WYp4bCLkyFTA2bLpiHq7noZdGSf+1+fMvq6WZ/AcuWDpBe3sv6Tv7CztOB4SA6KdJP7EetTHjbEwkNCMTxsSWrE1peO748Q3yM+VDaXQsj2lsSiJoTZp6H/6Pe95D2EYopTiTW96E+95z3sYGBho1r4JsemNFkIKgSWwlpHMwo64RinFrozm8FS8/GghlIZUbCiJVSHEYoy1nJ42HC+EHC9GnChEnCiGnChGnCm3P7ubS6g5Cd36JO9gj02gJhbXzv5L+s7O0o7jITEg2kniT6xHfdy4GkohRMBgQs0kfSGOm1xCc6ZsqBjIuBBYKzHUBg39T3/5y19GKcU111zDhz/84WbtkxCCeMbVfLW4ftJRcx65WUzK1SQdRd435BOayFiZ4VdsCIlVIcSUHyd3TxQjThSjmdcnSxFhG/O7Ctie1kuO3M3IJGqbWjv7L+k7O0s7jofEgGgniT+xHvVxk1AK31oqYZwAXuxJKM9RuBoqoSXlxiW8JIY2XkOJ37GxMQBe85rXNGVnhBCzIjv71dMKYy3GglYsOQLJ02rOelIyXWyE+bG6GhKrQnQfv1qa4USxOnq3LsFbCNpXd1cBOzMOu/vif7syDkN98b8daWdVNeQiY2dmMJeLkM2jnf2X9J2dpR3HY7ltLnXev9I2pS0Tq7VY/K10vSltkKg//q4DfggGcJZ5QspRitoYAFctjCFpt1qvocTvwMAAp0+fZuvWrc3aHyFElaPizrcUGM5VDIVgdshUylEMJjUD8x5DDYwlVb3TJqUHxUapxZqjoBytLvkjsSpEZ7LWcq5iOF6IGK2WZzheLc8wPm1oV3pXEZdl6E/EfV9/QlMKDVuSmosGPa7dm17zxUJo4kT2aCEkX5e4znmK4azLUN/qksaie7Wz/5K+s7O043jM36axlsmKYaJi5uxD/Xn/YtuUtkysRy1+FHG/Hxq7ZNzVrjelDRL17ZYfxq81cQmHpUTW4lVjKLSWhIoHtZ2slv6Sdqv1Gkr8XnbZZXz5y1/m6NGjzdofIURV3jeM5kNKQTyTeWgsWik0UHYV5TCumziSjR9VLYeGSmTZkXbIeUrulokN42hFzotrOE0WQsqhWfZxMYlVIdovMpZTpYjRugTvaCFktBAxHbYnvauAjKdmJlXbmnJmEr25hJpzo7McGg5PhQz1uQwk9JrbkYlyxMFxHz+Ka83lfTMz2iSX0Ez5hkOTiiu2JxhMyZimXtXO/kv6zs7SjuNRv83TkwEPnfHRSlGO4kenDcw57z9eCAkt7BjwZrYpbZlYL0crNJazZcP4dEQ5YYkMC+Kudr2pFdIGibltpR+igaSrmK5YgsguKPcQRJbQQDYZ5zECA66G0XyIAmm3NkhDid8bb7yRL33pS/zd3/0dv/Ebv9GsfRJi05soRxwY83G0Yso3lMP48YeBhCLAMl2xOAoGkpojecverMN4Ob6Tlq3eJRNiIw1nXaZ8g6cUp0qGfTm16AQR1lpOlYzEqhAbpBwaRgvRzAje0WqC92QxYpWD2ppuW0qzJ+uwuy8e0bG7WprBjyyPTgQcmgjxHLXo7OLQeDtS62PzgeFEISKwlqSj8HSccJkshHhKsTvrcGDMZ/9OufDoZe3sv6Tv7CztOB7DWZeTxZDJisHY+AaYIU6MOEoR2Pi8XwOW6iP4WIazrrRloiET5Yhz5bi0QymAUmAYSMYJ3VrczVxvTlm0VtIGCWBuW1mJLEkdl2zI+5atqdkJ3qyNk7qOgqSG0AIWzk4b+pOaQxOhtFsbpKG/2Jtuuok777yTr371q7zvfe/jLW95S7P2S4hNKzSWg+PxSdxkxZBwYE/OIe9bLDCQiEcf5H3D2bIh5yl+MB531Of1eyQcxVCfNI5iYw31ORyajDvpo/mQI3nYldFzRsuUQ8OpkmE6NOzNuRKrQjSJrT4efKwQcbwYVhO98dcz5fbMrJZ2FXv6nJkE754+h91Zh6GMS8pdfJRQaCxP5sOWtiP1fezRfEja1YxknEW3cTQfsjfncnDc5+o9KXnksEe1s/+SvrOztON4bE9rTpYMaVdxvBCRdBQ7MnrOxJOlwHC6FI8w3pN1OFkyDCYV3x2TtkysT60v1Do+f8gm1MzTpVlP4TmaIIqTdqdLEa5WKAX7dyakDRJz2sojUyETFUMmocj78QjyXCJuY/K+wTdx+YZJ35JLWCwKa2GyYsh40m5tFGXtMsU4VuHs2bO86lWv4itf+Qo33XQTv/Ebv8FP/MRPNGv/RI8ZGRlhdHSU4eFhjh071u7d6UjH8iEPnfVnRjxtTymOFiIqkWWybIiIRwFo4lnUfQODCc1TtniMZF25KybaZrmRJ4GxVCI7cwc352mJVSHWyFrLmbLhWCHkWD7iWCFkNB8neIttKM/gqLgkw+4+hz1Zd2ZytT1Zl4HE4iPlVtLqdmR+H7vcqOIj+Yggslw06PK0rQlGcjLCqVe1s/+SvrOzbPTxOJYPOXCqzHfG/HjEr7UYpWZG/EY2fkxaW4tVCq3g2TsTDOc8zpYjacvEutT3hQZLZCy+Yc61pqMUoTFM+RasYiTncEG/y7X70tIGiTlt5ZOTIaemIwDKgaF2SupoRdqJRwDvTDtsSWmmqk839CW0tFsbqKH/vQsvvBCAMAyx1nLXXXdx11130dfXx9atW9F66bpIAEopDh061MguCNFzRgshhcASWDtzB2xfTnGsEJLIOHPqfuU8TSm09Cc0obFyMSDaajDlsH9ngoPjPhlXzanZlHIUO9IOWU+RcKRmkxDLsdYyPj03wXusjfV3B5N6ZsTuzOjdPoedmeZPvNHqdmR+H7tUclopxa6M5vBUvPxoIZSLjh7Wzv5L+s7OstHHY7QQEqEYSGpcxYIav55SZJOKlBNPhhRaiFA8eMYnm9DSlol1qe8LL+h3MZZFrzUTWrMzA0XfohVsSWlpgwQwr63c5rG9pHkiH5LUeqakg6PB04rzB1x2pOMnKbamNGcqhl0ZLe3WBmrof++JJ56YOVhKKWqDhwuFAoVCYcX11zMSRIheFhlLvnqCmXTUzGMPGU9z8YDHpF+d6dedvfB3K4Y+TzGSdckllr/ZIkSrDaYcrt6Tmp1dui4mZZZWIeYytQRvPqwmdyOOVUfwrnZW+WbRCoYycWmG4azLcPXrnj5nziPHG6FV7chSfexSUq4m6SjyviGf0ETGymQ2Payd/Zf0nZ1lo45HfZuUS2jOz7mLnuunHMVgUjOQiBMrUxXDVGCIqqORpS0Ta7FUX7jUtWbKUaQdw5aUg0FJ/IgZ9W1lfyJke9qZiSGIBw4MVNuuoT4Ha+FkKZJ2qw0aSvzu27dPkrdCNFHtOj+y8d2xelortqScuNM1szP9jhajmfcjGxdWF6KdXK0YybmM5FwiY2dmaZXOWmxWxlrGShGjhXj07tF8daK1Qkgl2th9STmqmtydTex2YlKpFe3Icn3sUrxq31pbT/rY3tbO/kv6zs6yEcdjfpu01Lm+rtumpxVhtXR7aKQtE2u3VF+4XPwdzYfYuvUlfkTNUm0lMKfdjCfwDeUcrE0aHvErhGieWiPpKJYd7aW1onaPLDCWVHVFR64NRIdxtJJOWmwa1lrOVQxH8nFy92g+5Gg+5Fg+xN/gOda2JPXM6N1acnc467A1tfSjdZ2qWe3IavvYetLHbl7t7L+k7+wsrToey7VJ9ef69QJjSWoFUVyH1TfSlom1WU1fOD/+JH7EasxvK+e8lnOwtpJCGUJ0EEcrcp4il9BMFkLKoVn2MYhyGM/wuyPtkPOUjAoRQogNUqjOpH40H3JkKuJoIX5dDDauRIMCdmYcRurKM+ypfu3b4PIM3UD6WCFEJ2mkTdIKsgnNMWnLxBpJXyjaQeKuvSTxK0SHGc66TPkGTylOlQz7covPjG6t5VQpXi5brTcmhBCiuSpRPKlEnOANOVqIR/KeLW/cEF4FDPXFCd6RrMtwzmVvNcmbkCEQayJ9rBCik6y3TdqXS3C2HElbJtZF+kLRDhJ37SP/g0J0mKE+h0OTit3ZeObLI3nYldFz7oiVQ8OpkmE6NOzNxRf+Q33yUKAQQqxXZCwnirPlGY5USzWcKkVs1Bje2gRrw1mXvblaktdhT58keJtF+lghRCdZb5t0+TaP+08aacvEukhfKNpB4q59lLW2adcz4+PjfO5zn+Nb3/oWJ06cIJ/Pk8vl2LNnD8997nN5xStewfbt25u1OdGFRkZGGB0dZXh4mGPHjrV7dzrWRDniwJhPPjCcKEQE1Vl7Pa0IjKUSWTwVN5o5T7N/Z4LBVGsaRJlgRNSTeBDt0Oy4m6wYnqyO4D2SD3lyKuRYIZyZMKfVHFUbwevGo3hzLiNZl919Dp4keNdlLTHSSX2s6C7LTVwjxHqtt02Stkw0Yj3xk0toafdEQ6Tdao+mJH5LpRL/3//3//Hxj3+cSqWy5HLJZJI3velNvOtd7yKdTje6WdGFJPG7ehPliIPjPn5kKQSWvG9mOtpcQpP1FAlHccX25jeGobGcLMazzufr6lXmqo9adNrs76K1JB5EOzQj7oLIMlqME7xP1pK8+YjJysZkeGslGvblXEZyDnuz8azHu+VvpikaiZF29rGiu9TH2aRvmKwYJqptyGBSM5DQDCS19IeiIettk6QtE41YTfw4GnZnHKZ8K9cBoimk3dp4DSd+x8fHedGLXsSPf/xjVvNRSime9rSn8eUvf5lt27Y1smnRhSTxuzbtSLhJQyzqSTyIdlhr3FlrOVcxs8nd6tfjhYhVThzcsG0pzd6cy96cy76cw96cy7DU4G2ZZrRNclNLrKQ+zk5PRzwxGRIYS2QABa4C11Gcn3PZkXGkPxQNWW+bJG2ZaMRy8ZNLaE6VIkIj1wGiuaTd2lgNJ35f9KIX8dWvfhWAdDrNL/zCL/Dyl7+cSy65hGw2S6FQ4JFHHuHee+/lM5/5DKVSCYAXvvCFfOlLX2r4FxDdRRK/67cRj9jLoxeinsSDaIeV4q4UGvwQXA3FwBIYy/FCNOeksZVynppJ8Mb/4hG9GW/pmYlFc7WibZIyNmK++jh7YjJkbDoCC9ORJTJxe+MqSFX/9nelHc4bcKU/FE2x3jZJ2jLRiPr4yftGrgPEhpB2q/UaSvzefffdvOpVr0IpxTOf+Uz+7u/+jvPOO2/J5Z988kl+7ud+ju9+97sopfi7v/s7XvnKV65386ILSeK3c4XG8vXjZSYqhqP5kLSrVyy2PpjUXL0nJXfjepDEg2iH+XGnlcJVlqnAcnbacKYcP2a9ESnepKPYWy3PsLe/OpI36zCQ1IvOQCw2hrRNYiPUx9mTUwGTFYunIR9YEjoe6QaQ9y2+seQSiiCyDCYd9vVLzAkhupv0tUL0FreRlT/zmc8AsGPHDr7whS+wdevWZZc/77zz+PznP8/ll1/O6dOn+fSnPy2JXyE6xMlihB9ZThQi0q5mX85ZkNxIuZp9OcWRPJwoRGRcxclixEiuoaZEdCCJB7GRjI0f9/ruqQoPjPsczUcUQoMftX7bCtiTjUft7su57OuPv25Pa7QkeDuOtE1iI9THmUUxkIDxsiGhYUtq9ubP1pTibNlQ8i3b0xpjJeaEEN1P+lohektDf5X3338/SiluvvnmFZO+Ndu2beOXf/mX+aM/+iPuv//+RjYvhGii0UJIIbAE1jKSWdi51yil2JXRHJ6Klx8thNLB9yCJB9EqlchyZCrkieqEa09MBRzJh1Q2IMmb8xTn9c8md8/rdxmROrxdRdomsRHq48xVUIogsvFkbnNiTkEuoThTtlQMZFwIrJWYE0J0NelrhegtDf1Vjo2NAfCMZzxjTev9xE/8xJz1hRDtFZl4lta8b0g6as5jPItJuZqko8j7hnxCExkr9Xh6iMSDaJaJiuGJqaCa4A15YjLkRDFqeakGR8FwbRRvv8t51a9bpExDV5O2SWyE+jjzNAQGKqHF1eAtcpPIcxSujpdJuYqEkpgTQnQv6WuF6D0NJX4TiQSVSgXf99e0Xm15z/Ma2bwQoklqs95HFrxVdtSeVnPWk1L+vUPiQayVtZaxacPhyYDDk9Uk71TIRMW0fNtJB7anHa7YnuCCAZfz+j2GszITcC+StklshPp4cZUiwGIAZ5mbRo5S1Fo7RyMxJ4ToWtLXCtF7Gkr87tmzh4cffpivfvWr3Hjjjate7ytf+QoAw8PDjWxeCNEktQEsjoJytLqxeIGxpKorylPSvUXiQSzHWMvxQsThqXBOorcUtnYcryKurbktpdmactiW1uR9Q39Csyfr8uKRlIww6XHSNomNUB9nleoc2Jq4hMNSIhvPbg8QGUg4cz9LCCG6hfS1QvSehhK/11xzDT/+8Y/55Cc/ya/92q9xxRVXrLjO97//fT71qU+hlOKaa65pZPNCiCZxtCLnKXIJzWQhpByaZR/rKYeGSmTZkXbIeUqSLT1G4kHUhMZyrBByeLL6byrkyamg5fV4HQXb05odaYdtaYdtKc1gUs+JrXJomPINuYSWuNskpG0SG2FOnPkGDSRdxXTFEkR2QbmHILKEBrJJhQZ8a9mZkJgTQnQn6WuF6D0NJX7f9KY38aEPfYggCLjuuuv40Ic+xM/+7M8uufzf/d3f8eY3vxnf99Fac8sttzSyeSFEEw1nXaZ8g6cUp0qGfTm1aC1May2nSvFyWU8xnJUC/r1I4mHz8SPLkXwtyRtweCrkSD4kbHG1hh1pzXn9Luf3u2Q9zXRoOFWMSCwxizSsHHeRsfGjhgq5AOkx0jaJjVAfZxVjSeq4Pcn7hi2pulrhFvK+xQGSGkIDSWfjYk7aut4gx1F0GulrN6dObIs6cZ+6UUN/mfv37+c//af/xF/+5V9y9uxZXv3qV3PhhRfy0pe+lEsuuYS+vj6KxSKPPvooX/jCFzh06BDWWpRS/Kf/9J+48sorm/V7CCEaNNTncGhSsTvrcDQfciQPuzJ6zh3ecmg4VTJMh4a9OZeEoxjqkypOvUjiobdNh4Ynp2ZH8R6eDDhWiDAtrNYQT7gWJ3jP73c5fyCedC2bmI2p0Fi+fryMo9Wa4y40lpPFiNFCSD6Y/UVy1QuRoT6p+9sLpG0SG6E+zp6cCpj0LRlXkQ8s58rxkwYQJ319Y8klFJO+YTDpsDvrtDTmpK3rDXIcRSeTvnbz6MS2qBP3qdspa5cpWLUKxhhuuukm/vqv/zr+wGUmPqht6nWvex133nknWi8/Q6ToPSMjI4yOjjI8PMyxY8favTtinolyxIExn3xgOFGICKwl6Sg8rQiMpRLFNex2Zx1ynmb/zgSDKenge5XEQ28oh5YnpgIenwx5fDLg0ETIiWJEKyvypl01k+CtjeYdyboLHpFezHriDuDguI8fWQrVmahrowNyCU3WUyQcxRXbJUZ7gbRNYiPUx9kTkyFj0xFYmI4sUfUumasg5cXXM7vSDucNuC2NuYlyJG1dD5DjKLqB9LW9rxPbok7cp17QcOK35m/+5m9417vexYEDB5Zc5lnPeha/8zu/w6te9apmbFJ0IUn8dj5pbEU9iYfu4keWJ6biBO/jkyGHJgJGC61N8uYSigv6PS4YcON//S47Mw56mRvBK1lL3AFyYbIJSdskNkJ9nJ2ejnhiMiQwlsgAKk78uo7i/JzLjozT0piTJExvkOMouon0tb2rE9uiTtynXtG0xG/NkSNHuP/++zlx4gT5fJ5cLsfu3bt57nOfy759+5q5KdGFJPHbHeTxClFP4qEz1WryPj4Z8PhEyKHJkGOFsKXlGramNBf0VxO8Ax7n97tsq6932USriTuArx8vM1ExHM2HpF294qOIg0nN1XtSErM9QNomsRHq42zSN0xWDBOVuPj5YFIzkNAMJHVLY65WBkfauu4mx1F0I+lre08ntkWduE+9pOmJXyGWI4nf7iMF1UU9iYf2CE0tyRvy+ETAocmQo/mQqIU9+M605oIBb2YU7/kDHoPJ9pRoWirujuVDHjrrc2gixHPUspPBHclHBJHlokGXp21NMJKTCUh6ibRNYiPUxxmwYTEnbV1vkOMoup30tb2hE9uiTtynXiL/Q0KIZTlaIQ9QiBqJh9aLjOVYIeLQRLVcw2TAkXxIaFqzPQXs7nNmRvFeUJ14Let1Th3+peJutBBSCCyBtYxkFj9BhHj+gV0ZzeGpePnRQigniT1G2iaxEebH2UbFnLR1vUGOo+h20tf2hk5sizpxn3pJS/6HxsbGOH78+Eyphz179rBz585WbEoIIYToWtZaTk8bHpsIeKw6kvfwZEAlas32FDCcdbhwwOPCak3e8/vdOY9RdYqVRpVExpKv1ptLOmrF3yHlapKOIu8b8glNZKyMVtlkajGFtaDUgtiSkUyiHaSt2xzkOAohOkEntkWduE+9pmmJ3yeffJL3ve99/O3f/i1Hjx5d8P6+fft49atfza/+6q9y3nnnNWuzQgghRNeYqhgOTQbVRG88mjfvt6ZeQ20k74UDLhcOelzUwUnemrXUkauVuYgseKs82fO0mrOejFrpfbWYOpIPGS2ETFQM5ciSchSDSc1QnybjOlQiSymU2oViY0hbt/nIcRRCdIJObIs6cZ96TVMSv3/xF3/Bb//2bzM9PQ3EI5jmO3LkCH/6p3/KBz7wAd797nfzX/7Lf2nGpoUQQoiOVA5NXKqhmuB9bCLg9HSL6jUAQxmHCwddLhyYTfJmOqhcw0pWmjl6yjccmpydObpWY9NRUF5lsePAxAm/2nqit9ViarIc8dhkSDEwVKI46eZqxVFlOTAG25IarRW5hMbVasmYE6IZpK3bnOQ4CiE6QSe2RZ24T72m4cTvH/3RH/H2t78diBO+Wmsuu+wynvKUp9DX10exWOSxxx7jwQcfxBhDqVTiLW95C1NTU/zO7/xOw7+AEEII0W61ydcOTYQcmgh4bDLgWD6iVXOv7crEE69dOOByUXUCtr4uSvLON1GOODDmkw8MJwoRgbUkHYWnFeXIMlkI8ZRid9bhwJjP/p1xQiTnxcm6yUJIOTTLjmYuh4ZKZNmRjteTR8J6Wy2mTk9H/OiMTyWy+KHFKlAKioGlElmUgjPThlxCk00YdmcctFZLxpwQjZC2bvNytJLjKIRou05sizpxn3pNQ4nfAwcOcOutt2KtxXEc/ut//a+87W1vY8+ePQuWPXHiBHfccQfvfe97iaKIP/iDP+DlL385V155ZSO7IIQQQmyoWl3eR88FPDoRcGgi4PBUSNCiwbw70guTvLlE9yZ55wuN5eB4nAg5mg9Ju5qRjDPnhK8cGk6V4vf35lwOjvtcvSfFcNZlyjd4SnGqZNiXU0vOAHyqFC+XrT5KLXpXLaYm/YgfjvtY4tIngymHXELhaDhZjMC3lIK4Llw5Mgx5Hga4MOfiG7tozEnZB7Fe0tYJOY5CiE7QiW1RJ+5TL2nof+l973sfURShlOJTn/oUr3nNa5Zcdvfu3bznPe/hOc95Dq997WsxxvDnf/7n/NVf/VUjuyCEEEK0VCkwHJoMefRcXK7h0YmAqRbV5c0lFBcNeFw86HLxoMeFAx4Dyd5J8i7mZDHCjywnChFpV7Mvt3Am35Sr2ZdTHMnDiUJExlWcLEYM9TkcmoxHxx3NhxzJx6OhF0ukTIeGvTmXhKMY6pORm72sFlOPT4a4GsLIknY1W1MaVPw3XYswz1G4ChylGC9FpHIuk75hS8pZNOZk5mixXtLWCTmOQohO0IltUSfuUy9p6Oz1vvvuQynF9ddfv2zSt97P//zP8+lPf5p//Md/5L777mtk80IIIURTGWs5mo/iBG91RO9ooTUlG5IOXDDgcfGAy0WDHhcPeuxI60Xvbvey0UJIIbAE1jKSWZgIqVFKsSujOTwVLz9aCBnJuVyxPcGBMZ+9OZcThYjDU+HMo9OBiR/n95Rib84l52mu2J6QUZs9rhZTU9XZoX0T31SpZXtLoSU0YFHkPJiOwK0+al+OLBOVOPG7VMwJsR7S1glXKzmOQoi268S2qBP3qZc0dPZ66tQpAK6//vo1rfeKV7yCf/zHf5xZXwghhGiHiXLEoxMhj07Eo3kPTYSrnlRgLbSCfTmXiwZdLh6Ik7zDWWfT16SKjCVfndwo6ahl63lBPBou6SjyviGf0ETGMphy2L8zwcFxn4yr5kyWlHIUO9IOWU+RcGSSrs2gFlNTlbj2ijHg6nhkL8SPCIYGgupkWglX4xtDbV7i6dCQchXGWLRWi8bcZv+7FWsnbZ2okeMohOgEndgWdeI+9YqGEr+Dg4OMjY0xODi45vXqvwohhBCt5keWw5PhTLmGxyYCTk+3pjDvUMaJk7yDHhcNeJw/4JKUKWcXqOXYIwveKpNpnlZz1nOITxSv3pPiZDFitBCSr6uBnKvW/xrqc2RUwCZQi43QxOUbDPHXmtpfvLXxJG8Qj7A0ta92drlaFC0Wc0KshbR1op4cRyFEJ+jEtqgT96kXNJT4veyyyxgbG+PRRx9d03qPPfbYzPpCCCFEs9VPwPZwtTbvE1MhLRjMy0BS15VriCdgy/bQ5GutVMuFO4pVj7QOjCVVXbE+l+5qxUjOZSTnEhkbJ0oUMjpzk6nFhKshsvEjgYGdja3aX6ZS8WhgiNsLRykia9HVbHD9X/BSMSfEaklbJ+aT4yiE6ASd2BZ14j51u4YSv6973eu47777+MQnPsFv/uZvkkgkVlzH933uvPNOlFK8/vWvb2TzQgghBDA7mveRcwGPTAQ8ci5gotL80byehgv6XZ6yJS7X8JRBj+2bsC5vszhakfMUuYRmshBSDs2yj0CXQ0MlsuxIO+Q8teQJoKOVjMrcpGox1V+dFFFrCAMIIovnxDNEuxo8BSULfhg/QuhqwELa1aQcha7G1mpjTojlSFsnliPHUQjRCTqxLerEfepGDSV+3/CGN/DJT36SL33pS/zSL/0Sn/jEJ0in00suXy6XufHGG3n00Ud5yUtewhve8IZGNi+EEGKTOluOePhcMDOi9/Bka0bz7so4PKVasuEpWzzO73fl0aImG866TPkGTylOlQz7cmrRRLq1llOleLls9VEvIRZTi6n+hKbgG7S15H3L1lQ8wVvGVQSRRWHJB+CquDREylGkHMVgNWksMSeaSdo6IYQQQrRDQ2cSSin+4R/+gTe+8Y38n//zf/j2t7/Nr/3ar3HttdfylKc8hb6+PorFIo899hhf+MIX+MAHPsCRI0f4uZ/7OT7+8Y8363cQQgjRw0JjeWKqOpq3+u9MufmjedOu4imDLk8ZjEfzXjzozYwaFK0z1OdwaFKxO+twNB9yJA+7MnrOaLhyaDhVMkyHhr05l4SjGOqT+/9icbWYunDA5cApH6sU06HhbBlyCUXKVUxU4mWDyGK0wlOW7RkXR8NAQkvMiaaTtk4IIYQQ7aCsteseI+U4sycitY9Z7nHX1SxTez8Mw/XuluhgIyMjjI6OMjw8zLFjx9q9O0JsOt1QJ2miEtfmrSV5D00GBE3O8ypgX787O5p30GNP1pmp7ymW1+w4mihHHBjzyQeGE4WIwFqSjsLTisBYKlFcq3V31iHnafbvlJl8xfJqMXV6OuJHZ3wqkcUPLVbF9X3DCCqRRal4ordcQpNNKHZnHLRWEnOiJaStE0J0qm64RhBCrE9DI34XyxmvJo/cQK65o73hDW/grrvumvn+tttu4/bbb2/oM6+55hq+/OUvr3r5w4cPc/7556+43GOPPcaHPvQh7rnnHo4ePUoURQwPD3Pddddxyy238MxnPnP9Oy2E6CihsbMzowaz7W8nzIxqrOVIPuThs3GS99GJgFOl5o/mHUxqnjLo8ZQt8YjeCwfcZesrioVaGUeDKYf9OxMcHPfJuIpCYMn7ce3VlKPYkXbIeoqEo7hiuyRCxMrqYyrtJHhsMqQYGCpRHMt9rkIrS2BgW1KjdVx/1dUKRyExJ1pC2johRCfp5GsEIUTzNJT4feELXygT2lTdc889c5K+nezDH/4w/+2//Temp6fn/PyRRx7hkUce4UMf+hC33nort956a5v2UAjRLBPliIPjPn5k51xgOioe4TblGw5NbtwFZjm0PDYR1+V9uDqidzps7s1AreD8fpdLtnhcUq3Nu0MmYGvIRsTRYMrh6j2p2QuQxGxiXi5AxHrUx9T2TMhoIWSiYihHdqaW71CfJuM6VCJLKZSLXtF60tYJITpBp10jCCFap6HE75e+9KUm7UZ3m5qa4s1vfjPATF3jVrj77rtXXGbnzp3Lvv+pT31qZl+11rz2ta/l2muvxXVdvv71r3PXXXdRqVS47bbbSCaT/PZv/3ZT9l0IsfGWe6S0HFkmC+HMI6UHxvyWPFJ6rjoJ24/PxoneJ6ZCTJMf+sglFJduics1XLLF48IBj5QrF8zNspFx5GrFSM5lJOfKI4eiKRaLKawFpRbElsSc2CjS1gkh2qkTrhGEEBtHpoltgt/6rd/i6NGj7N27l1e/+tXccccdLdnOz/zMzzS0/unTp/nVX/1VIE763n333dxwww0z799444288Y1v5Nprr6VUKvH2t7+dn/mZn+HSSy9taLtCiI0XGsvB8fiE7mg+JO1qRjLOopPIHM2H7M25HBz3uXpPat2jjIy1jBaimSTvw2d9xqabW7ZBAXtzbpzo3RJ/3ZVxZDRvi6w2jk4WI56cDNnX7zQcRzWOVsglhmim2ZiaG5v1ibeEI21JJ+uUJGkj+zF/XWnrhBAbqR3XCEKI9pLEb4O++MUv8pGPfASAD3zgA3znO99p8x4t7U/+5E+YmpoC4Fd/9VfnJH1rfuqnfor//t//O29729sIw5B3vOMdfPrTn97oXRVCNOhkMcKPLCcKEWlXsy+3MDmacjX7coojeThRiMi4ipPFiJHc6roGP6or21Ct0VtsctmGPlfxlC3xSN6nbPG4eMAl40lt3o2yXBwZY5n0DROVeAb6cxXD+FjESNYh62mu2JGQCwTRsaSuYffolGPVyH50yu8ghBAbcY0ghOgs8pfbgFKpxC233IK1lte85jVcf/31HZ34/exnPzvz+jd+4zeWXO6WW27h1ltvpVgs8o//+I9MT0+TTqc3YheFEE0yWggpBJbAWkaWGRGrlGJXRnN4Kl5+tBAueVI3WTEzI3l/fC7g8GQYPzbdRCNZJ070Vss27Mk6aBnN2zZLxVEpMBwrhEQGypGlEsYj2Aq+ZWza8N1TFQqBkbpwoiNJXcPu0SnHqpH96JTfQQghoDXXCEKIztbwX+7Zs2cByGazJBKJBe//6Ec/4vd+7/f46le/ShiGXHnllfze7/0eL3/5yxvddNv97u/+Lo8//jhbt27lz/7sz9q9O8t68MEHefLJJwF42tOexgUXXLDksrlcjhe84AV8/vOfp1gs8uUvf5l/9+/+3UbtqhCiQZGx5KsXl0lHzXl0azEpV5N0FHnfkE9oImPRCk5PGx466/PjswEPnQ04UYyaup+ehosHPS7dEv+7ZItHNiGjeTvFUnFUCgxH8iGVyDJZNkSAq+OalVYZzpQNfmjZkdFSF050HKlr2D065Vg1sh+d8jsIIQQ05xpBapEL0X0aSvzec889XH/99Sil+OY3v8lznvOcOe8/8sgjXHXVVRQKBayNh4V97Wtf49//+3/PX/3VX3HjjTc2svm2+sY3vsH73/9+IC6hsGvXrpZv8/rrr+d73/sep0+fpq+vjz179nDVVVfxi7/4i7z4xS9edt0HHnhg5vX847SY5zznOXz+85+fWVcSv0J0j9oo3MiCt8qTM1fBRMUwUfF54LTPIxMBZ8vNrc/bX52E7dKtCS7d4nHhgCuPtnawxeLIGMuxQpz0PVs2JLRiMKHw6uqixjcOFEfyIef3K6kLJzqG1DXsHp1yrBrZD6AjfgchhKhZzzWCp9Wc9eTWlBDdp6HE7z/90z9hreWSSy5ZNJn41re+lXw+D8STifX39zMxMQHAr/3ar/Gyl72MoaGhRnahLcrlMjfffDPGGK699lre+MY3bsh2P/e5z828npiYYGJiggcffJCPfvSjvOQlL+FTn/oUu3fvXnTdhx9+eOb1cqN9F1umfl0hROer5eAcFT+GvxhjLePThhPFiJPFkOPFiLC5eV729DlcutXjqVs8Lt3qMSSTsHWVxeJo0jdEBiarSd+tKT1nnqzIWjytGEhqUq6SunCio0hdw+7RKceqkf0AOuJ3EEKImtVcI8wXGEuquqLMfypEd2rorOJ73/seSimuvfbaBe8dO3aMe+65B6UUL3rRi/jbv/1btm7dyv/6X/+L17/+9RSLRT760Y/y9re/vZFdaItbb72Vhx9+mHQ6zYc+9KGWb2/Lli289KUv5dnPfjbDw8M4jsPo6Cj/+q//yj333IO1li9+8Ys873nP41vf+taiyfRawh1g+/btK25z27Zti64rhOh8jlbkPEUuoZkshJRDg6MVY6WIk8X431gpopnzsLkaLhqISzY8dWs8EVu/lG3oaovF0UTFUI4sETCYUHOSvkFkCQ1kk4qUq9jd50hdONFRpK5h9+iUY9XIfgAd8TsIIUTNYud2y5V7KIeGSmTZkXbIeUrKPAjRpRo6qxgbGwPg6U9/+oL3/vmf/xlrLUop/vIv/5KtW7cC8Au/8At84hOf4N577+Vf/uVfui7x++1vf5s77rgDgHe84x1cdNFFLd3eH/3RH/GsZz1r0frJb33rW/nOd77Dq171Ko4cOcKTTz7JzTffzD//8z8vWLZQKMy8TqVSK263fjK32qjtZgqCgAMHDiz63u7du5ccuSwWioydmSREOmNRsyWpeWDc58nJkAdO+0z5lmbOw5bzFJdujevyxmUbPBIyDKDnDGddpnyDp+JRaNOhoRJaXM2c8g7WxvXiHAUpRzGY1FIXTnQUqWvYPTrlWDWyH5NufGOs3b+DEEuR64fNq/7c7lTJsC+nFr0xZa3lVCleLusphrNyQ0osTdqUztbQX+/4+Diw+AjSr3zlKwBcfvnlXHrppXPee/nLX869997Lj3/840Y2v+F83+fmm28miiL279/PW9/61pZv83nPe96y7z/72c/m85//PFdeeSWVSoV77rmHb3/726uq49tOY2NjPOtZz1r0vdtuu43bb799Y3eoy4TGcrIYMVoIyQez6bxctVMe6nOkPtwmM1GOePBswINnAn58zudovrkTse1Ma566NcFTt8Yjevf0SdmGzWCoz+HQZDzx0JOTIecq8Wz09e1LEMWJEd/A1pTGqZZ6AKkLJzqH1DXsHp1yrBrZDwNg2/87CFFPrh8EzD23O5oPOZKHXRm9aP3x6dCwN+eScBRDfdIqibmkTekeDSV+S6USAJVKZcF73/zmN1FKcc011yx4rzaas9tKCLzzne/khz/8IY7j8JGPfATH6YzG72lPexqvf/3r+ehHPwrEtZfnJ36z2ezM63K5vOJnTk9Pz7zO5XJN2tNZO3fu5J577ln0PRntu7yJcsTBcR8/shSqI1Fqd9dyCc2Ubzg0qbhiu8wM3cvOTMeJ3ofO+Dx4NuBEsbmJ3r05h6duSfC0aqJ3W1piaTNyddyWHBjz2dfvMD4WUfAtVsUFoSMbl3dwVJz0TTqKkayDrt4UkLpwolNIXcPu0SnHqpH90ACq/b+DEDVy/SBq6s/t9uZcThQiDk+FJB2FpxWBsVQii6cUe3MuOU9zxfaEJPDEHNKmdJeGEr8DAwOcPXuWkydPzvn5kSNHOHz4MEqpRUesGhNfMHbTaLGDBw/yx3/8x0BcYmH//v1t3qO5XvziF88kfh966KEF7w8ODs68ro3UXs6ZM2cWXbdZPM/ruP/DbjBRjjgwFs8QfaIQEVg700mXI8tkIcRT8R3cA2M++3dKQ9srxkoRD531efBMwENnfU6VmjcTmwbO63d5+vY40XvpFo+s1OcVVYMph/07Exwc9xnJOoxNG86UDZGJJ3LLJhUpJ677NpJ1yHhx7EhdONFJpK5h9+iUY9XIftSeeigGVuJNtJ1cP4j56s/tMq6ak7hLOYodaYesp0g4krgTC0mb0n0aSvxeeumlfOMb3+Bzn/scb3vb22Z+/jd/8zczr5///OcvWK+WKF7NJGOd4s477yQIArTWeJ7HO9/5zkWXq5W4qL2uLXfppZfy6le/umX7t2PHjpnXi42kri+3cfjw4RU/r36Z+aU6RHuExnJwPG5gj+ZD0q5mJOMs+ljO0XzI3pzLwXGfq/ek5A5tl4lrakU8eCaIR/We9Rmfbl6i11WwM+OwL+fwzB1JnjOUlESvWNZgyuHqPSmynua7pyr4oUWruKRDyo1r+g4k9cxIX6kLJzqR1DXsHp1yrBrdj074HcTmJtcPYim1c7uZR/XrrgXkUX2xFGlTulNDZxYve9nL+MY3vsGXv/xlPvCBD3DzzTfz3e9+l3e9610opXjGM57B3r17F6z3/e9/H4CLL764kc1vKGvjx7SMMfyP//E/VrXOfffdx3333QfAK1/5ypYmfutH8S42QvcnfuInZl5/+9vfXvHz6pdZbPI+sfFOFiP8yHKiEJF2NftyC2usplzNvpziSB5OFCIybjwhk8wO3dmstZwo1mr0+jx0NuBsuXmJ3j5P8dQtHk/bmuCSLS57cy5JR0YUibVxteKKHQkKgWFHRnMkH5JyFbv7Fj/Zk7pwotNIXcPu0SnHqtH96ITfQWxucv0gluNqxUjOZSTnyuRcYlWkTelODf3Pv/nNb+ZP//RPKRQKvOUtb+Etb3kLECcxlFKLTn5mreXee+9FKSWP+jdRLcEMi4/Qveyyy9i3bx9HjhzhoYce4oknnuD8889f9LMKhQJf/epXAchkMrzoRS9qyT6LtRkthBQCS2AtI5mlJ9ZSSrErozk8FS8/Wgilke0w1lqOFaKZJO9DZwMmKs1L9G5Jap62zavW503MqbkqRCPq68Kd36+kLpzoKlLXsHt0yrFqdD864XcQm5tcP4jVcrSSSSXFiqRN6U4N/c/v2rWLz3zmM7zmNa+hUCjMee/GG2/k9a9//YJ1vvCFLzA2NoZSqqsSiu9973t573vfu+Jyt99+O+94xzsAuO2227j99ttbu2PAI488wic/+cmZ76+//vpFl3vNa17De97zHgDuuOMO/vzP/3zR5T784Q9TLBYBuOGGG8hkMk3eY7FWkbHkq7WXko5atk4cxHfZko4i7xvyCU1krNy5bSNrLaOFiB+d8flRtUbvlL+6yV5WY3ta87StCS7b6nHZNo9dy3TCQjRK6sKJbibx2z065Vg1sh+d8juIzUmuH4QQzSRtSvdqOOX+0z/90zz88MN8+tOf5rHHHqOvr4+XvexlvOxlL1t0+W9961u86EUvQinFdddd1+jmu9add97JG9/4RgBe9KIX8aUvfWnBMn/+53/Os5/9bK666qolP+d73/seP/uzP0u5XAbi8hvPfe5zF132N3/zN/ngBz9IPp/nL/7iL7juuuu44YYb5ixz//338wd/8AcAuK7Lbbfdtp5fTzRZbULoyIK3ysbS02rOenIpsXGstZwsRfzoTMCPzsQTsjVzRO+uTDXRuy0u37AzI0dXbCypCye6mcRv9+iUY9XIfnTK7yA2H7l+EEI0k7Qp3aspY6137949Z3K35dx6663ceuutzdhsz/viF7/Ir//6r3PRRRdx3XXX8fSnP51t27bhOA7Hjx/nX//1X/nnf/5njIkTSueddx5/9Vd/teTn7dy5k/e973284Q1vwBjDf/yP/5HXvva1vPSlL8VxHL7+9a9z1113zSSR3/GOd/DUpz51Q35XsTxHzX4tR6sbKRoYS6q6oiPXEi03VqqN6I1H9TazRu/uPofLtno8bVs8qndbWrpM0X5SF050M4nf7tEpx6qR/eiU30FsLnL9IIRoJmlTupcU2egChw4d4tChQ8su8/KXv5yPf/zj7NmzZ9nlbrrpJkqlEm9961spl8t8+tOf5tOf/vScZRzH4fd///f5vd/7vYb3XTSHoxU5T5FLaCYLIeXQLPtoRTk0VCLLjrRDzpNJvFphfHq2dMODZ3xOTzcv0TucdbhsZkSvxxZ59FN0OKkLJ7qZxG/36JRj1ch+dMrvIHqfXD8IIZpJ2pTuJYnfDvanf/qn/If/8B+4//77OXjwIGNjY4yPj1OpVBgYGOD888/nec97Hr/0S7+0ZHmHxfzn//yfue666/jgBz/I5z//eY4ePYoxhj179nDttdfyK7/yK1x55ZUt/M3EegxnXaZ8g6cUp0qGfTm1aB1Xay2nSvFy2eojhKJx58qzpRt+dCbgVClq2mfvyzlzSjcMJJevlySEEEIIIcRK5PpBCNFM0qZ0J2WtbcoMQxMTE3zkIx/h3nvv5cEHH+Ts2bOEYUgYhnOW++IXv8jJkyfZvn37knWARe8aGRlhdHSU4eFhjh071u7d6SqhsXz9eJmJiuFoPiTtanZl9Jy7bOXQcKpkmA4Ne3Mug0nN1XtSUjduHSYrhgerSd4fnfE5Xmxmotfl8m0el29L8NStHrmEJHqFEEIIIURzyfWDEKKZpE3pTk1Ju3/2s5/lzW9+M/l8Hoiz+8Cimf+DBw/ytre9jfT/396dx8dV1/sff3/PObMkmWnSLU2bNJS1rBbK1atWBARURFFUxBWhyMV9QbyKC4siel0viqyyySZ63UCvCEK5KP7KhVspm7J0oWlIm6Ztkpkkk5lzzvf3x6TTps3azGSZvJ6PR80k8z3nfMf58P2e8znf8/1WVKilpUUzZswoRhWAsuc5+RWfV7VmtTDpqSUdaF2nr5hr5BopG1jlQquo42hh0lMy4mjJnCgN7Ah150L9Y1tOT7Vl9fTWrJpSxUv0NiRcHTY7qsNmR3To7CiJXmASYJ5NAMBkUao+aajrh4hjlAutegOriDFcPwAYFm3K1DTmEb8/+9nPdPbZZxeSvfPnz1cymdTzzz8vY4yCoH/yZPv27Zo3b56CINDPfvYzfeADHxjL4THFMOJ37NozgVa3ZZXxQ7V0hdrcHag3sHIkxTyj6qijuZWOXr8grtmVPFIxmGxg9fz2nJ7emtXTbVmt6fAVFuX5B2lBVf9EL1M3AJODH1pt6grUnPaVyu38Dz7Z9whaXZXLiSkAYFyMZ5+04/ohG1ilc1apbFhINCejjhIRo6ibT+jUsLYEgGHQpkwtY0r8vvzyyzrggAOUyWS0YMEC3XzzzTrxxBP1u9/9TqeddtqAiV9JOvHEE7VixQqdddZZuuGGG8b0ATC1kPgtjraeQH9tzqi121dH1qrXtwolxV2j2kpX86tcbEVYzQAAejxJREFUxT0a2l2F1mpth58f0duW1XPbc8oVaT22eZVuYeqGQ2dHNIv/z4FJhxNUAMBkMRF9Ejc/ARQTbcrUMabhgFdeeaUymYwqKir0wAMPaPHixSPa7lWvepUefPBBrV69eiyHB6al9kygJ7dkZYwUWqOYIyUrHEVdybdSKhcq02E1P+FqVWtWS2unZxLDWquN6UBP903d8OzWnLr94gzpnVvh6NC+Eb2HzY5qTsX0+/8XmEraM4FWtWaVyoVqSQfKWVt4JC0TWHWkfUWMmfbtJgCg9CaqT/Ico4akp4akx3RHAMaMNmXqGFPi97777pMxRu9///tHnPSVpAMOOECStH79+rEcHph2/NBqdVv+RHHHZOoNSXfAydSbUr4WJj2tbstOm8nUt3QHenprVk+15Rdla+8tzpDeWXGnkOQ9bHZUtZUkhICpYsB2s5J2EwAw/iZLn+Q6RpzNAigW2pTJbUyJ33Xr1kmSXve6141qu+rqakkqLAYHYGQ2dQXKBlYt6UAVnqPGpLvHIopxz1Fj0mhDSmpJB6r0jDZ1BWpIlt98v529YWGO3qe3ZrW5uziJ3mTU6PDZUR0+Jz+qt65yz/+fAUwNtJsAgMmCPgkAMN7G1Ht0dXVJkhKJxKi26+npkSTF4/GxHB6YdprTvtI5q5y1ahgiGWmM0bxKR+s68+Wb035ZnCxmA6t/bMvqqbacnmrLan2nX5T9xl2jQ/tG9B4+J6LGpCeHRC9QFqZ7uwkAmDzokwAA421Mvcfs2bO1adMmbd68eVTbvfDCC5KkuXPnjuXwwLQShFapvsUfYq7p90jYQOKeo5hrlMqGSkUdBaGdcvPthNZqfaevJ7fkp28o1oJsniMdVBPR4XOiOnx2VPvXeDzSDUxBw80nNh3bzXLHHHKYaMQghjNYjNAnAQAmwpgSv4ceeqg2bdqkhx9+WB//+MdHvN0999wjY4yOPvrosRwemFYCu/NnZIQnfRHH9NtuKsy709od6Km2rJ5sy+qZtmy/FUL3lpG0b7XXN31DRAfPiirmcuIMTEWjWUF4urSb5Y5VozHRiEEMZyQxEtInAQAmwJgSvyeffLIeeOAB/e53v9MLL7ygAw88cNhtfv7zn+uJJ56QMUZvectbxnJ4YFrZkad0jZQJRpYMzYVW8b4NJ2ueM50L9UxbfvqGJ9uy2twdFGW/C6pcHTEnP0/vobMjSkSGHlUBYPJrzwRa3ZZVNrBK942a2jGqKhl11JkNtabDaMmc/Aro5dpuTiej/c6BYiMGMZyRxsjhsyKS6JMAAONrTInfj3zkI7r88su1fft2nXrqqfrv//5v7bvvvoOWv+uuu3TuuefKGKMFCxbo/e9//1gOD0wrrmOUjBglo4460r4yfjjkI2IZP1RvYDW3wlUyYibNo2F+aPX89nyS96m2rNa0+xr7mF5pdtzpm7ohP4XDLC6+gLLSngm0qjW/EnpLOlDOWsVco4hjlAmsOtK+IsZofsLVqtasltbmkzDl0G5OV3v7nQPFQgxiOKOJkdVtOTmy9EkAgHE1psTvjBkzdPXVV+u9732vnn/+eR1++OE67bTTFIvFCmV+8pOfqKmpSffee6+eeuopWWvluq5uvPFGRSKRMX8AYDqpT3jqzIaKGKPN3aEak2bARSGstdrcnS+X6HvEbKJYa9WUDvTUlvz0Df/YllVvEQb1VnlGh82J6og5ER0xJ6q6IRbIADC1+aHV6rb8hXVTyleF56ih0u13wZzxQ23uzr+/MOlpdVtWyxbEp2S7ibF95zxyj2IgBjGcvYmR0FpVeg59EgBg3Iy5Bzn99NPV3t6uT33qU+rp6dGdd94pSYUO7NOf/nShrLVW0WhU1157rU466aSxHhqYduqqXK3pyI8aaEr52pCS5lU6A55g9vihFiY9RV2juqrxHX3S0RvqqbasVvcle9t7x74im2ukxTPzo3lfMSeq/ao9Rj4A08SmrkDZwKolHajCc9SY3PNGT9xz1Jg02pCSWtKBKj2jTV3BlGk30d9YvnNWvkcxEIMYzt7EyL7Vrnp8S58EABg3RTkrOffcc/XqV79aF198se6++26F4Z5Jnh1z+n7zm9/UkiVLinFYYNrxnPwccqtas1qY9NSSDrSu0y88UpYLrXoDq4gxWpj0lIw4WjInWvKRJ35o9dz2XD7RuyWrdZ1+Ufa7MJmfp/cVc6I6ZFZk2NWPAZSn5rSvdM4qZ60ahhjdb4zRvEpH6zrz5ZvTvhqS3qRsNzG0sX7nwFgRgxjO3sRIty9VR40kQ58EABgXRTsrOeKII/TrX/9aHR0deuSRR7R+/Xq1t7crkUiooaFBxxxzjObOnVuswwHTVk3c1dLaqFa3ZVXpmX6LSMRdo7kVrhIRo6hbuoVGrLXa1B1o9Zb8qN5ntubUO8JFKoYyM+boiF2mb5jJPHmjFoS2sKAII6JRDoLQKtXXzsVc0+8GUGitQis5RnL6LrjjnqOYa5TKhkpFHQWhnRTtJkYu64fa3huqozfY4zsfyEDfOe0fxmKodmcg4xmD9POTw1hiJBn1dNTcqJ7eOvo+ie8fADBaRb8dXV1drbe85S0jKtvc3Kz6+vpiVwEoezVxV8sWxLWpK1Bz2lcquvNkM9k3D1hdlVvU0QHdufz0DU/2TeGwpWfs0zfEXKPDZu+cvqEhwTy9e8MP7c5YyO1MwJcqFoDxtOOeUmCliGMUWquO3lDtvWG/VdHjrlFNzFF1zFHEMf22czUx7SZGbtd2bHtvqBfac9qeCRVxjGbGAlXHnEJyfyADfefA3tq93RmJUsYg/fzkM9YYSUadEfdJfP8AgLGYkOeQNm3apG9+85u64YYb1N3dPRFVAKY8zzFqSHpqSHolufsfWqs17b5Wt+Wnb3ihPadwjIN6jaQDary+Ub1RHTQzwonqGLVnAq1uyyob2H4jRlyTv6jozIZa08EoRkxdrtn5syMb6sV2qyC0ygRWvb5VKMmRlPGMMr7Vlp5QgbWq7ruIdndpYkrdbmLv7N6OdfaG2p4J1Z0LZYy0qctoS0+ohoSrysjAo+pyoVW878t2+ToxRru2O5kRPtFUqhikn5+cihEj7gj6JL5/AMBYjWvid8uWLfr2t7+ta665RplMZjwPDZQ11zFFGVnS1hPoyS1ZrW7L6qm2rLpyY8z0Kr9gxSvmxPSKuVEdNjuiqkEu2jF67ZlAq1rzq0m3pAPlrC3MEZcJrDrSviImv6jVqtasltZyUYCpx3WMkhEjz8kvpJOMGHX7+Qtkz5FcY5SzVj29Vq6RKj2jVM5qdtxRMmIGTeoWq93E2AzWjkUdqcfkH4vepECzKhxtSFk1Jr09kr8ZP1RvYDW3wh3yOwdGake7k4w66kj7yvjhkI/ylyoG6ecnr2LHyEB9Et8/AKAY9jrx+/zzz+v5559XT0+PGhsbdfjhh6uqqmrAstu3b9e3v/1tXXXVVYURvtZaeR4LHwATKRtYPbstW5irtzkdjHmfFV5++oZXzIlqydyo6qr477wU/NBqdVv+YqAp5avCc9RQ6Q64KnRTytfCpKfVbVktWxBnlDWmnLpKV/+3uVehlTZ1hZoZz0/rENllWF0uyI+E2tQVqiJi1J4JVTefC+DJbKh2bGbM0aauQC02v9DRtp5QsyocbUwHOqDGFKZ9sNZqc3eoiDFK9D32DBRDfcJTZzYfW5u7QzUmzYDTUZUqBunnJ79SxgjfPwCgWEZ9ZvKHP/xBX/jCF/Tcc8/1+3t1dbU+85nP6KKLLurX4V1xxRX6+te/rvb2dlmbHz0YiUT0wQ9+UBdeeOEYqw9gNHYsyvZEa1ZPbMnq2a1ZZcc4Va+RtG+1pyVz8/P0Mn3D+NjUFSgbWLWkA1V4jhqTe86PHPccNSaNNqSklnSgSs9oU1fAauOYenYNbWNl7cBtjLX59wsb0BRNakO1Y9VRR1t6As2udLW1Jygkf+uqjDp6Q82Mu4WkR48famHSU9Q1qqsi2Y/iqKtytaYjP5qyKeVrQyr/FNNAibdSxCD9/ORXyhjh+wcAFMuoeoWf/exnWr58uay1hSTuDu3t7fr617+udevW6eabb1ZnZ6fe/e5364EHHpCUTzjFYjEtX75cX/ziF9XY2Fi8TwFgUBnf6pmt+RG9T2zp1ebusS/KNjPm6BVz8yN6j5gT1Ywo0zeMt+a0r3TOKmetGioHXxTPGKN5lY7WdebLN6d9Lggw5WzqClQTc9TcFaiuylV31mprJixM9RBYKz/ML6ZUV+UqlbWq6Rsxus+MyERXH4MYqh1zHKOGhKcNKV+zK1xt7Q7UkQ0VKj/P5eyK/GPTEWO0MOkpGXG0ZE6UG48oGs/Jz5u6qjWrhUlPLelA6zr9wqP2udCWNAbp5ye/UsYI3z8AoFhG3Cu0trbqU5/6lMIwnzSKRCI67LDDFI/H9dxzz2n79u2y1urWW2/Vhz70IV122WV6+OGHZa1VRUWFPvaxj+mCCy5QXV1dyT4MgPxNluZ0oCf6Er3/2JaTP8Zcb8SRDpmVT/S+Ym5UCxODn4Ci9ILQKtW3wEfMNUPOKSflR4TE3PxcmamooyC0zIGJKWNHvPtWml/pynGkKk/9FneLGKNEzCjuGrmOlPAk30qpnCXeJ6mRtGOVEUeNSU8b077mJzzZrkDWWuVCq6gjza3wlIgYRV0WNkJp1MRdLa2NanVbVpWe6be4Vtw1mlvhliQG6eenjlLECN8/AKCYRpz4veWWW5RKpWSM0Tvf+U5dc801mj17tiQpCAL9+Mc/1gUXXCBrrc4880y1tLRIkt7//vfre9/7HglfoIS6c6Ge2ZrT37f0avWWrNp6xj6qd2HSLczTe8isqKIskz5p7Fg8OrBSZIQn9hHH9NuO9Aimil3jtirqqL7KVUc2VHtvqIy38+mjuJuf97c6mh8ZTLxPbiNtxyojjg6ojqgjGyqdC5ULjWbGHdVVeZoZc1Sf8FRX5TLSFyVTE3e1bEFcm7oCNad9pXZ5yinZN19rsWOQfn5qKXaM8P0DAIppxInfHVM27LfffrrjjjsUiex8dNJ1XX32s5/Viy++qKuuukotLS0yxuiHP/yhPv3pTxe/1sA0Z63VSyk/P31Da1bPbc8VTvb2ViJi9Iq+eXqXzI1qFiOnJq0dOXjX5Ec9jkQutIr3bUgOH1PJ7vHuOEYz465mxl2FYX7Er6P81AA7EO+T32jasR3f+eyKUDHHaF6Vqzc0xBUdZhQcUCyeY9SQ9NSQ9BSENp9YMyrZqEr6+amnmDHC9w8AKKYRJ36fffZZGWP0/ve/v1/Sd1fnnHOOrrrqKhlj9K//+q8kfYEiSudCPdWWT/Su3pLV9t6xjeo1kvav8XTk3JiOnBvV/jVeYZV0TG6uY5SMGCWjjjrSvjJ+OORjgBk/Pxfm3ApXyYjh8T9MKUPFu+MY7R75xPvUMJZ2bGbMIemLCeM6puSjKennp7axxgjfPwCgmEac+N22bZsk6eCDDx60zK7vvetd7xpDtQBYa7UhFejvrb36e2tWz7fnFI5xVG911GhJX6L3FXOjSrIo25RVn/DUmQ0VMUabu0M1Js2A8y5ba7W5O18u0fe4ITDVEO/lie8VGBz/fUxvfP8AgGIZcc/Q3d0tY4yqqqoGLVNRUVF43djYOLaaAdNQxg/1VFt+rt4nWrPamhnbqF7HSAfVRLRkblRH1ka1aAajestFXZWrNR1G8xOumlK+NqSkeZVOvxEhGT/U5u5QPX6ohUlPUdeoroopPDD1EO/lie8VGBz/fUxvfP8AgGIp2S3BWCxWql0DZaWly9ffW7P6e2uvnt2Wkz/Gddlmxhwd2ZfoPXxOVIkIo3rLkefkV4de1ZrVwqSnlnSgdZ2+Yq5RxDHKhVa9gVXEGC1MekpGHC2ZEx3V4jPDzVE3HvMcAtL4xDvGH98rMDj++5g6SnE+xPcPACgWngUBxlkusHp2W7Yv2ZvVpu5gTPtzjbR4ZkRH1kZ15NyYGpPugI+CofzUxF0trY1qdVtWlZ5ROmeVyoYKrBR3jeZWuEpEjKJu/uKhZgQL9vmh3bkqdW7n3CI7VqWeU+GorScc9P1ir2wO7FCKeMfE43sFBsd/H5PXcOdLxTgf4vsHABTDqBO/I00okXgCdmrrCfT31qye2NKrp9qy6h1brldzKvpG9c6N6fA5EVWwyM20VRN3tWxBfOfFxy7zNo/24qM9E2h1W1bZwPa7uHCNlIw62tzlq6U7VF2lo1Bmj/c7s6HWdHDxgdIpZrxj8uB7BQbHfx+Tz3DnS8U8H+L7BwCMlbHWjmi5KMdxRpTM3bG7kZQ1xsj3/ZEcHmWioaFBzc3Nqq+v18aNGye6OiUThFbPt+cKUzhsSI19VO8hsyI6sjamo+ZGVZ9gVC8GtrePG7ZnAq1qzSqVC9WSDpSztt/jhKlsqI7eUBWeUY9vVR1zlIw6ezxuOD/hKhlxtLSW5C9Kj+lGyhPfKzA4/vuYWMOdL5X6fIjvHwAwWns11cNQueJdk1EjzCkDZaGjN9QTW3r199asntySVZc/tvifGXN0ZG1US2tjOnx2RJXM1YsRcB2j0V5e+KHV6rb8RUxTyleF56ih0i0sIBKGVv/YllVopZfTgapjjjyjfosF7lhgpCnla2HS0+q2rJYtiDMCBSW1N/GOyY/vFRgc/31MnOHOl6TSnw/x/QMARmtUid+RJHJJ9mK6eTnt6yerO7Wm3ddYot9IOnBmREfNjWppbVT7zPAY1YtxsakrUDawakkHqvCcPeaJ7siGcoyR6RvVYiQ5xqijN9TMvlEscc9RY9JoQ0pqSQeq9Iw2dQVqSDKVPAAAmPqGO1+SOB8CAEw+I+6BwjAsZT2AKasm5mhdx94lfZMRoyVzozqqNqYlc6NKRhnVi/HXnPaVzlnlrFVD5Z4XMe29oTKBVWiM5lYadWatMoFV+y6JXyn/xMe8SkfrOvP7a077XOgAAICyMNz50g6cDwEAJhN6IGCMKiOODpkV0dNbcyMqv+8MT0fV5pO9B9TsfFQemAhBaJXqW5gk5pp+jytK+WkeMoFVr2/lOfl47/YD9fpWGc8qtLZfDMc9RzE3v/BbKuooCC1z0AEAgCltuPOl3XE+BACYLEj8AkVwVG1s0MRvhWd0xJz89A1L5kY1iwWvMIkEdufPyAAXJOEuP92+BK9rzM6/W2n3zSKO6bdfIh4AAExlw50vDYTzIQDAZEDiFyiCo2qjuvUfO3+vT7g6qm8Kh4NnRVjgCpOWa3b+zAR7Tlji7PIz1zeHe2DzK1ZLeyZ9JSkXWsXdHUniYtcYAABgfA13vjQQzocAAJMBiV+gCBZUuTquIa79qj0dVRtTbSX39DE1uI5RMmKUjDrqSPvK+GG/xxcdxyjuGmU8o55eq+5cKD+UErH833efqiTjh+oNrOZWuEpGDI81AgAwAkFo86NCjeg7J6Hhzpd2x/kQAGCyIPELFIExRh9bMmOiqwHslfqEp85sqIgx2twdqjFp+i1YUhNzlPGtHGu1pduqwssnfWti/S94rLXa3J3fTyJiVJ+giwEAYDB+aLWpK1Bz2lcqt3MUabKvD62rcnlqbBIZ7nxpB86HAACTydCz0gMAyl5dlauoazQ/4arHD7UhFSjjh4X3q6OOQmtljVFvYGUlhdaqepfEb6Zvux4/1PxEfn91VYx8BwBgIO2ZQI+8nNE/tmX1clegl9O+mlK+Xk77erkr0D+2ZfXIyxm1Z4KJrir6DHe+JHE+BACYfLj9CADTnOcYLZkT1arWrBYmPbWkA63r9BVzjSKOUS608vsWcVuQcNXj539f3+kX3u8N8vP+Lkx6SkYcLZkTZZQSAAADaM8EWtWaVSoXqiUdKGdtoc/NBFYdaV8Rk08wrmrNamltVDUsDjzhRnK+xPkQAGCyIfELAFBN3NXS2qhWt2VV6Rmlc1apbKjASnHXaG51RK6sWrpD1VU6CmX6v1/hKhExirr5iyIuUAEA2JMfWq1uyyd9m1K+KjxHDZVuv/liM36ozd359xcmPa1uy2rZgjgJxElg2PMlzocAAJMMiV8AgKT8xcyyBfGd8w1Gd16E7phvcE6Fo7aecND3mY8QAIDBbeoKlA2sWtKBKjxHjUl3j3li456jxqTRhpTUkg5U6Rlt6grUkOTSbTIYyfkS50MAgMmCswcAQIHnGDUkPTUkvUFXGG9IOkO+DwAABtac9pXOWeWsVUPlnknfHYwxmlfpaF1nvnxz2ifxO4mM5HwJAIDJgLMHAMCAXMdoqAcUh3sfAADsFIRWqb6pAWKu6Te9w0DinqOYm59aKRV1FISWxOIkxPkQAGAyI/ELAABKhpFQAJAX2J0/IyNsDyOO6bcdCUaMFP0vimHXOJJETAFTEIlfAABQVH5od859mLOFvzP3IYDpbEfixDVSJrBDF+6TC63ifRu6NJsYBv0vimHXOOroDdWRDdXeG0qSamKOqmOOqqMOMQVMESR+AQBA0bRnAq1uyyob2H6rnbtGSkYddWZDrelgtXMA04/rGCUjRsmoo460r4wfDjndQ8YP1RtYza1wlYwYRthhSPS/KIZd42hLd6D1KV9+YOVbSVZ62ck/ibCo2iOmgCmCxC8AACiK9kygVa1ZpXKhWtKBctYq5hpFHKNMYNWR9hUxRvMTrla1ZrW0lgsFANNLfSKfLIkYo83doRqTZsAF3qy12tydL5foG60JDIb+F8Wwaxy91OFrc08gScrkwnziV/kbWBWu9MzWnGorQi2q9ogpYJIbekUBAACAEfBDq9Vt+YuFppSviGu07wxP+1VHtDCZ/7nvDE8R16gp5SuVC7W6LSs/HNnjzgBQDuqqXEXdfAKuxw+1IRUo44f9ymT6/t7jh5qfyJevqyKhgoHR/6IYdo2jDZ2+OrKBktH8TakZMVf7zPC0zwxP1VEjmfz0IZ3ZUC915ogpYJIj8QsAAMZsU1egbGDVkg5U4TlqTLp7PMIc7/t7heeoJZ0vv6krmKAaA8D485z8Y9HJiKOFSU+5wGpdp6+1HTk1pfI/13X6ygVWC5OekhFHS+ZEmUMTg6L/RTHsGkehlaqjjrqzVlHHaFbcUcQ1irhGM+OOoo7U7VtVR42sDDEFTHIkfgEAwJg1p32lc1Y5azWv0hnw0WVJMsZoXqWjnM3PQdic9se5pgAwsWribv6x6Jij/Ws8NSS8wgJucdeoIeFp/xpPNTGHx6cxLPpfFMOuceQ5Um8oBVJ+1O8uIWVMfp7ywObLeEbEFDDJMVkUAAAYkyC0SvUtJBNzzZCLFUn5kUcx1yiVDZWKOgpCy6JFAKaVmrirZQvi2tQVqDntKxXd2W4m++b0ratyGemLIdH/ohh2jaOoMcpaq14/nwCOuHvGR8Q1+eSwbxX3jCKOiClgEiPxCwAAxiSwO39GRniyH3FMv+0YzwZguvEco4akp4akpyC0+bbQiKQJRoz+F8Wwazx4rpT1pVCSO8jocfW9t2N2cs8QU8BkxlQPAABgTHYMBnGNlBvhwh650PbbDgCmM9cxirqGpC9Ghf4XxbBrPPh90/Q6kgI7eEwF1haSSb4lpoDJjMQvAAAYE9cxSkbyc771BnaPFep3l/FD9QZWyaijZIREBwAAe4P+F8Wwaxxl+xK6Mc/ID6VcsGfyNxdY+WG+jCMpF4qYAiYxEr8AAGDM6hOeEhGjiDHa3B3KDjJKxFqrzd2hIsYo0TePJQAA2Dv0vyiGXePID6WYk5+yIZW10i4hZW1+LmDX5Mv4VsQUMMmR+AUAAGNWV+Uq6hrNT7jq8UNtSAV7jDzK9P29xw81P5EvX1fFTHAAAOwt+l8Uw65x5BipIxuqMmqUDa22ZULlAqtcYLU9EyobSpWeUUfWysgSU8Akxy0ZYJJgUQ8AU5nnGC2ZE9Wq1qwWJj21pAOt6/QVc40ijlEutOoNrCLGaGHSUzLiaMmcKCvWAwAwBvS/KIZd46hxhifbIW3uyU/429kbaFsmX851jCpco1TOqrbC1T4ziClgsiPxC0wgP7Ta1BWoOe0rldv5DE2y71GZuiqXDhTAlFETd7W0NqrVbVlVekbpXP5xwMBKcddoboWrRCS/gNGSOVHVxBkZAgDAWNH/ohj6xdHsiOZ0O1qf8hVzHPlWkpVcR4o4RouqPc2tcIkpYAowdrBJgIASaGhoUHNzs+rr67Vx48aJrs6Eas8EWt2WVTaw/U7OXJOfHJ+TMwBTFTe1AAAYf/S/KIZd46ijN1RHNlR7b376kJqYo+qYo+qoQ0wBUwSJX4wrEr957ZlAq1qzSuVCtaQD5awd8HGs+QlXyYijpbUkfwFMTUxjAwDA+KP/RTHsGkeSiClgCmKqB2Cc+aHV6rZ80rcp5avCc9RQ6Sru7VxrMeOH2tydf39h0tPqtqyWLYhzNxXAlOM6Rty2AgBgfNH/ohh2jyNiCph6nOGLACimTV2BsoFVSzpQheeoMdk/6StJ8b6/V3iOWtL58pu6ggmqMQAAAAAAAKYaEr/AOGtO+0rnrHLWal6lI2MGHsVrjNG8Skc5m58DuDntj3NNAQAAAAAAMFWR+AXGURBapfoWcou5Zo+RvruLe45irlEqGyqVswpCpuQGAAAAAADA8Ej8AuMosDt/RkY4X2/EMf22AwAAAAAAAIZD4hcYRztWQ3WNlBvh6N1caPttBwAAAAAAAAyHxC8wjlzHKBkxSkYd9QZWGT8csnzGD9UbWCWjjpIRI3eEo4QBAAAAAAAwvZH4BcZZfcJTImIUMUabu0NZO/DIX2utNneHihijRMSoPuGNc00BAAAAAAAwVZH4BcZZXZWrqGs0P+Gqxw+1IRXsMfI30/f3Hj/U/ES+fF2VO0E1BgAAAAAAwFTDEEJgnHmO0ZI5Ua1qzWph0lNLOtC6Tl8x1yjiGOVCq97AKmKMFiY9JSOOlsyJymOaBwAAAAAAAIwQiV9gAtTEXS2tjWp1W1aVnlE6Z5XKhgqsFHeN5la4SkSMom4+SVwTZ7QvAAAAAAAARo7ELzBBauKuli2Ia1NXoOa0r1R058wryb45feuqXEb6YtILQqvASq4RCxACmDJou1AMxBEGQ2wAmApoq8ofiV9gAnmOUUPSU0PSo8HFlOKHdudNi9zOBQq5aQFgMqPtQjEQRxgMsQFgKqCtml5I/AKThOsYMaEDpoL2TKDVbVllA9tvmhLXSMmoo85sqDUdTFMCYHKh7UIxEEcYDLEBYCqgrZp+jLXWDl8MKI6GhgY1Nzervr5eGzdunOjqABil9kygVa1ZpXKhWtKBctYOuDDh/ISrZMTR0lpOGABMPNouFANxhMEQGwCmAtqq6YkRvwCAEfFDq9Vt+ROFppSvCs9RQ6WruLdzfuqMH2pzd/79hUlPq9uyWrYgzqNCACYMbReKgTjCYIgNAFMBbdX05QxfBAAAaVNXoGxg1ZIOVOE5akz2P1GQpHjf3ys8Ry3pfPlNXcEE1RgAaLtQHMQRBkNsAJgKaKumLxK/AIARaU77SuesctZqXqUjYwa+82uM0bxKRzmbnzeqOe2Pc00BYCfaLhQDcYTBEBsApgLaqumLxC8AYFhBaJXqm/w/5po97g7vLu45irlGqWyoVM4qCJlOHsD4o+1CMRBHGAyxAWAqoK2a3kj8AgCGFdidPyMjnOMp4ph+2wHAeKPtQjEQRxgMsYHJLAitsgFJO0xsWzVYHBKf44fF3QAAw3LNzp+ZEfb8udAq3rehy3oAACYAbReKgTjCYIgNTDZ+mJ+TtTntK5XbGZPJiFF9wlNdlctCXdPQeLdVg8VhpSfFXEe9gVW3T3yOFxK/AIBhuY5RMmKUjDrqSPvK+OGQjwhl/FC9gdXcClfJiJFLBw5gAtB2oRiIIwyG2MBk0p4JtLotq2yQn5s1lQ0V2HzSLhl11JkNtabDaMmcqGri7kRXF+NoPNuqweIwF1ilc6E8I/k2H5OeY4jPccBUDwCAEalPeEpEjCLGaHN3KGsHvltsrdXm7lARY5Tou3sLABOFtgvFQBxhMMQGJoP2TKBVrVm194Za0+5rY9ovjOzMBFYb077WtPtq7w3z5TLBBNcY42082qrB4rDXt3q5y9em7kBrOwJt7g60Me2r1w+Jz3FA4hcAMCJ1Va6irtH8hKseP9SGVKCMH/Yrk+n7e48fan4iX76uiju2ACYObReKgTjCYIgNTDQ/tFrdllUqF6op5SviGu07w9N+1REtTOZ/7jvDU8Q1akr5SuVCrW7Lymdu1Wml1G3VYHG4aIanUPlRvY4kz5GMpBlRR6GkRUmP+CwxYwdL8wMl0NDQoObmZtXX12vjxo0TXR0Ao7TjLm4qF6olHShnrWKuUcQxyoVWvYFVxORPKJIRR0treVQHwMSj7UIxEEcYDLGBibQx5esf27Ja055PtjUmXRmz56P51lptSAXKBVb713g6ZFZUDUlGnk8npWyrBovD7ZlAm7oCbekJ5BppZszR9t78om5zK13VVbmaGXeJzxLi/0UAwIjVxF0trY1qdVtWlZ7pN29T3DWaW+EqETGKuszPBGDyoO1CMRBHGAyxgYnUnPaVzlnlrFVD5cBJX0kyxmhepaN1nfnyzWmfxNo0U8q2arA4bO/NT+cQWKkm5sg4RsmotDVjlQms2ntDzYy7xGcJ8f8iAGBUauKuli2I71ypNbpz1iBWZAUwWdF2oRiIIwyG2MBECEKrVF/yLuaaIRfskqS45yjmGqWyoVJRR0FoWWRwmilFWzVYHIbWFub49Rwp4ub3GXGNPEfq9a0ynlUYWjmOIT5LhMQvAGDUPMeoIempIekpCG1hxWA6ZgCTGW0XioE4wmCIDYy3vvXbFFgpMsI4izim33aMP59+it1WDRaHO6bpDSW5u41Ed43RjhmGQ+1cgIz4LD4SvwCAMXEdQ4cMYMqh7UIxEEcYDLGB8dA3gFKukTLByJZvyoVW8b4NXe5JTHvFaKsGi8MdOWBHUm635cUCm59PeMf7OxCfxTf0cwAAAAAAAACYdFzHKBkxSkYd9QZWGT8csnzGD9UbWCWjjpIRw2h0FMVgcegYo7hrFPOM/FDK9SWFc4GVH0oxL/++0xeHxGdpkPgFAAAAAACYguoTnhIRo4gx2twdytqBR/5aa7W5O1TEGCX65nIFimWwOKyJOYq7Rq6RUtlQNrRKZa1c5ReUq4nl05LEZ+mQ+AUAAAAAAJiC6qpcRV2j+QlXPX6oDalgj5G/mb6/9/ih5ify5euqmIwExTNYHFbHHLmOUXXMUY9vC3FYHXfkOlJ11CE+S4wUOgAAmDRYDAcAAGDkPMdoyZyoVrVmtTDpqSUdaF2nr5hrFHGMcqFVb5CfT3Vh0lMy4mjJnKg8zrNQREPFoaP8aN9Qkh9KriN1ZkNVea7Wp3zis8RI/AIAgAnlh1abugI1p32lcjsfT0z2PeZVV+Vy8gcAADCImrirpbVRrW7LqtIzSuesUtlQgc0/Tj+3wlUiYhR188m5mjijKVF8g8ahIy2o8pTOhfKM5FspGXXkOfkpIIjP0iLxCwAAJkx7JtDqtqyyge13keKa/AlhZzbUmg5OAgEAAIZSE3e1bEF858306M6ZPbmZjvEyVBxWelLMzS8A1+0z2GO8kPgFAAAToj0TaFVrVqlcqJZ0oJy1hccSM4FVR9pXxOTnClvVmtXSWpK/AAAAg/Eco4akp4akx/RZmDAjiUPic/yQ+AUAAOPOD61Wt+WTvk0pXxWeo4ZKV3Fv56iAjB9qc3f+/YVJT6vbslq2IM5IAAAAgGG4jhG3yzHRBotD4nP8OMMXAQAAKK5NXYGygVVLOlCF56gx2T/pK0nxvr9XeI5a0vnym7qCCaoxAAAAAEwtJH4BAMC4a077SuesctZqXqUjYwYexWuM0bxKRzmbnwO4Oe2Pc00BAAAAYGoi8VtEZ511lowxhX+XXHLJmPeZSqX0q1/9Sp/85Cf12te+VnPnzlUkEtGMGTN08MEH68wzz9S9994ra+2w+7r55pv71W+4f8WoPwAAuwtCq1TfQm4x1+wx0nd3cc9RzDVKZUOlclZBOHyfBwAAAADTHXP8Fskf//hH3XLLLUXd5w9+8AN95StfUSaT2eO9VCql5557Ts8995xuvfVWHXPMMbrtttvU2NhY1DoAAFBsgd35MzLC+Xojjum3HXOCAQAAAMDQSPwWQWdnp8477zxJUlVVlbq6uoqy3+eff76Q9K2vr9eJJ56oo48+WrW1tcpkMlq5cqVuu+02pdNp/eUvf9Fxxx2nlStXqra2dth9f+pTn9Ib3vCGIcscfPDBRfkcAADsyjU7f2aCkY3ezYVW8b4NXdZ2AwAAAIBhkfgtgi984QtqamrSwoULdfrpp+sHP/hBUfZrjNEb3/hGXXDBBTrhhBPkOP0fhf3whz+sL33pS3rTm96k5557TuvWrdOXvvQl3XjjjcPue+nSpXrHO95RlHoCADAarmOUjBglo4460r4yfjjkdA8ZP1RvYDW3wlUyYuSOcJQwAAAAAExnzPE7Rg8++KCuv/56SdJVV12lZDJZtH1/85vf1J/+9CeddNJJeyR9d9hnn3101113FX6/66671N3dXbQ6AABQCvUJT4mIUcQYbe4OB52r3lqrzd2hIsYoETGqT3DPGgAAAABGgsTvGHR3d+vcc8+VtVZnnHGG3vrWtxZ1/7NmzRpRuSVLlmjx4sWFOr344otFrQcAAMVWV+Uq6hrNT7jq8UNtSAXK+GG/Mpm+v/f4oeYn8uXrqpjdFwAAAABGgmEzY3DhhRdq7dq1mjVrlq644ooJrcuMGTMKr3t6eiawJgAADM9zjJbMiWpVa1YLk55a0oHWdfqKuUYRxygXWvUGVhFjtDDpKRlxtGROVB7TPAAAAADAiDDidy/97W9/05VXXilJ+t73vqd58+ZNWF2y2ayef/75wu/77LPPsNtcddVVOuSQQ5RIJFRZWanGxkadeuqpuvrqq5kqAgAwLmrirpbWRlUTc7R/jaeGhFdYwC3uGjUkPO1f46km5uTLxRntCwAAAAAjxYjfvZDJZLR8+XKFYagTTjhBZ5999oTW54477lBHR4ek/KJtdXV1w27z2GOP9fu9qalJTU1Nuueee3TxxRfrxhtvLPrUFQAA7K4m7mrZgrg2dQVqTvtKRXfek072zelbV+Uy0hcAAAAARonE71646KKL9Nxzz6miokLXXnvthNZly5Yt+uIXv1j4/atf/eqQ5V3X1Wte8xodc8wxOuigg5RIJNTe3q7/+7//0y9+8Qtt27ZNW7Zs0amnnqrbb79d73vf+0pS71wup1WrVg343vz58zV//vySHBcAMPl4jlFD0lND0lMQWgVWco3kkuwFAAAAgL1G4neUHnvsMf3gBz+QJF166aXaf//9J6wu2WxW73rXu9Ta2ipJesc73qHTTjtt0PKve93rtH79ejU0NOzx3kc+8hF95zvf0bnnnqu77rpL1lotX75cy5YtU2NjY9Hr3traqqOPPnrA9y6++GJdcsklRT8mAGDycx0jJnQAAAAAgLEj8TsK2WxWy5cvVxAEWrp0qc4///wJq0sYhlq+fLn+8pe/SJL2339/3XjjjUNuc8ABBwz5fjKZ1O23367NmzfroYceUiaT0X/8x3/oJz/5SdHqvUNtba3++Mc/Dvgeo30BAAAAAACAsSHxOwqXXXaZnn76abmuq+uvv16uOzFjkqy1+uhHP6rbb79dktTY2Kg///nPmjlz5pj37bquLrvsMr3uda+TJP3+978vSeI3Eolo6dKlRd8vAAAAAAAAAMkZvggkafXq1fr2t78tSTr//PMnLGlprdXHP/5xXX/99ZKkhoYGPfjgg1q0aFHRjvGa17xG8XhckrRhwwZ1d3cXbd8AAAAAAAAASo8RvyN08803K5fLyXEcRSIRXXbZZQOWe/jhh/u93lFu8eLFOv3008dUB2utPvGJT+iaa66RJNXX12vFihVFn2fYcRzNmjVLL7/8siSpvb1dlZWVRT0GAAAAAAAAgNIh8TtC1lpJ+bl1L7/88hFts2LFCq1YsUKS9Pa3v31Mid8dSd+rr75akrRgwQKtWLFi2Hl790YYhtq+fXvh95qamqIfA0BpBKFVYCXX5BfJKlbZ8a4bAACTDf0YJOIAADC1kPidAnZP+s6fP18rVqzQgQceWJLjrVy5Uj09PZLyU0kw2heY3PzQalNXoOa0r1TOFv6ejBjVJzzVVbny+i5MRlN2vOsGAMBkQz8GiTgAAExdxu4YyoqiuOSSS3TppZdKki6++GJdcsklY97nJz7xCV111VWSpLq6Oj300ENavHjxmPc7kDAM9cY3vlEPPPCAJOmjH/1oIeFcDA0NDWpublZ9fb02btxYtP0C01V7JtDqtqyygVU6Z5XKhoVRKMmoo0TEKOoaLZkTlaQRl62Jj33xytHUrRjHAwCgmOjHIBEHAICpjcXdJsjNN98sY4yMMTruuOMGLfepT32qKEnf//f//p+uu+46ZTKZQct0dXXpzDPPLCR9Y7GYvvjFL476WADGR3sm0KrWrNp7Q61p97Ux7SsT5O/lZQKrjWlfa9p9tfeG+mtzRn99OTOisqtas2rPBONWt2IcDwCAYqIfg0QcAACmPqZ6mMS++tWv6sorr5QkGWP0mc98Rv/4xz/0j3/8Y8jtli5dqsbGxn5/27x5s8477zx9/vOf10knnaSjjz5aCxcuVFVVlTo6OrRq1Sr9/Oc/19atWwvH++lPf6pFixaV5LMBGBs/tFrdllUqF6op5avCc9RQ6Sru7byfl/FDbe4O9VJnToGVjCTXGFVGBi/blPK1MOlpdVtWyxbE9+qxxdHUrRjHAwCgmOjHIBEHAIDyQOJ3EvvrX/9aeG2t1YUXXjii7W666SadddZZA76XTqf1m9/8Rr/5zW8G3b6urk4//elPdcopp4yqvgDGz6auQNnAqiUdqMJz1Jh0ZUz/i4y456gxafTstlDbevIjUOZUuEOW3ZCSWtKBKj2jTV2BGpKj7yZGU7diHA8AgGKiH4NEHAAAygM90jRx4okn6ne/+50effRR/e///q+ampq0detWtbe3q7KyUrW1tVq6dKlOOeUUvec971E8Hp/oKgMYQnPaVzpnlbNWDZV7XojsYIyRZ6TevscSPUdDlp1X6WhdZ37fzWl/ry5cRlO3YhwPAIBioh+DRBwAAMoDi7thXLG4GzB2QWi1YmNGL/fNM7dfdWTQsqG1em57Ths6fclKjdWeFtdE5AzxCOLajpzirtGChKfjG+JyR/G44mjqVozjAQBQTPRjkIgDAED5YHE3YBIIQqtsYBWE3IfB8PoG7yqwUmSYi4pdQ2rHQJVwmP1HHNPvGKWqWzGOB2D6KVWfSV8MafT9WGitHEm5vrihHysPnM9gsqGPArC3eAYFmCB+aLWpK1Bz2lcqt7MDT0aM6hOe6qpcFobAgFyz82dmmCuLXUNox/Mdw93xy4VW8b6DuKMMwdHUrRjHAzA9lKr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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_67_1.png" } }, "output_type": "display_data" } ], "source": [ "# HIDE THIS CELL; CREATES PLOT\n", "fig, ax = plt.subplots()\n", "ax = df_subset.plot(x='age_jitter', y='realrinc2015_log10', kind='scatter', alpha=0.4, ax=ax)\n", "coef2, coef1, intercept = np.polyfit(x, y, deg=2)\n", "label = f'$y = {coef2:.2f}x^2 + {coef1:.2f}x + {intercept:.2f}$'\n", "ax.plot(xs, coef1 * xs + coef2 * xs**2 + intercept, label=label)\n", "ax.set(ylabel=\"Respondent's income (log10)\", xlabel=\"Age\")\n", "ax.legend();\n", "glue(\"fig_age\", fig, display=False)" ] }, { "cell_type": "markdown", "id": "d642e773", "metadata": {}, "source": [ "```{glue:figure} fig_age\n", "---\n", "name: fig-statistics-essentials-quadratic-household-income-age\n", "---\n", "\n", "Relationship between household income and age (jitter added) of respondent. The curve proposes a quadratic relationship between the two variables.\n", "```\n", "\n", "#### Spearman's rank correlation coefficient and Kendall's rank correlation coefficient\n", "\n", "There are two frequently used summary statistics which express simply how reliably one variable will\n", "increase (or decrease) as another variable increases (or decreases): Spearman's rank correlation\n", "coefficient, often denoted with $\\rho$, and Kendall's rank correlation coefficient, denoted $\\tau$.\n", "As their full names suggest, these statistics measure similar things. Both measures distill the\n", "association between two variables to a single number between -1 and 1, where positive values\n", "indicate a positive monotonic association and negative values indicate a negative monotonic association. The\n", "``DataFrame`` class provides a method ``DataFrame.corr()`` which can calculate a variety of correlation\n", "coefficients, including $\\rho$ and $\\tau$. As the code below demonstrates, the value of $\\tau$ which\n", "describes the correlation between age and log income is positive, as we expect." ] }, { "cell_type": "code", "execution_count": 38, "id": "4fe5d2ab", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| | age | realrinc2015_log10 |\n", "|--------------------+----------+----------------------|\n", "| age | 1 | 0.159715 |\n", "| realrinc2015_log10 | 0.159715 | 1 |" ], "text/plain": [ " age realrinc2015_log10\n", "age 1.000000 0.159715\n", "realrinc2015_log10 0.159715 1.000000" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_subset[['age', 'realrinc2015_log10']].corr('kendall')" ] }, { "cell_type": "markdown", "id": "b6f8f960", "metadata": {}, "source": [ "There are innumerable other kinds of relationships between two variables that are well approximated\n", "by mathematical functions. Linear relationships and quadratic relationships such as those shown in\n", "the previous two figures are two among many. For example, the productivity of many in-person\n", "collaborative efforts involving humans----such as, say, preparing food in a restaurant's kitchen---\n", "rapidly increases as participants beyond the first arrive (due, perhaps, to division of labor and specialization) but witnesses diminishing returns as more participants arrive. And at some point, adding more people to\n", "the effort tends to harm the quality of the collaboration. (The idiom \"too many cooks spoil the\n", "broth\" is often used to describe this kind of setting.) Such a relationship between the number of\n", "participants and the quality of a collaboration is poorly approximated by a linear function or a\n", "quadratic function. A better approximation is a curvilinear function of the number of participants. In such settings, adding additional workers improves the productivity of\n", "the collaboration but eventually adding more people starts to harm productivity---but not quite at\n", "the rate at which adding the first few workers helped. If you believe such a relationship exists\n", "between two variables, summary statistics such as Spearman's $\\rho$ and Kendall's $\\tau$ are\n", "unlikely to capture the relationship you observe. In such a setting you will likely want to model\n", "the (non-linear) relationship explicitly.\n", "\n", "(sec-statistics-essentials-measuring-association-categories)=\n", "### Measuring association between categories\n", "\n", "In historical research, categorical data are ubiquitous. Because categorical data are often not\n", "associated with any kind of ordering we cannot use quantitative measures of monotonic association.\n", "(The ``pacific`` states, such as Oregon, are not greater or less than the ``new england`` states, such as\n", "New York.) To describe the relationship between category-valued variables then, we need to look for\n", "new statistics.\n", "\n", "In our dataset we have several features which are neither numeric nor ordered, such as information\n", "about where in the country a respondent grew up (``reg16``) and the highest educational degree they\n", "have received (``degree``). The variable ``readfict`` is also\n", "a categorical variable. It is easy to imagine that we might want to talk about the association\n", "between the region an individual grew up in and their answers to other questions, yet we cannot use\n", "the statistics described in the previous section because these categories lack any widely agreed\n", "upon ordering. There is, for example, no sense of ordering of gender or the region the respondent\n", "lived in at age 16, so we cannot calculate a correlation coefficient such as Kendall's $\\tau$.\n", "\n", "Traditionally, the starting point for an investigation into possible associations between two\n", "categorical-valued variables begins with a table (a *contingency table* or *cross tabulation*)\n", "recording the frequency distribution of the responses. The ``crosstab()`` function in the Pandas\n", "library will generate these tables. The following contingency table shows all responses to the\n", "question concerning fiction reading (``readfict``) and the question concerning the region of\n", "residence at age 16 (``reg16``):" ] }, { "cell_type": "code", "execution_count": 39, "id": "6e7b5d63", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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readfictyesnoAll
reg16
foreign6733100
new england732699
middle atlantic19872270
e. nor. central24787334
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south atlantic17898276
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mountain663197
pacific15436190
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" ], "text/org": [ "| reg16 | yes | no | All |\n", "|-----------------+-------+------+-------|\n", "| foreign | 67 | 33 | 100 |\n", "| new england | 73 | 26 | 99 |\n", "| middle atlantic | 198 | 72 | 270 |\n", "| e. nor. central | 247 | 87 | 334 |\n", "| w. nor. central | 109 | 28 | 137 |\n", "| south atlantic | 178 | 98 | 276 |\n", "| e. sou. central | 90 | 45 | 135 |\n", "| w. sou. central | 123 | 53 | 176 |\n", "| mountain | 66 | 31 | 97 |\n", "| pacific | 154 | 36 | 190 |\n", "| All | 1305 | 509 | 1814 |" ], "text/plain": [ "readfict yes no All\n", "reg16 \n", "foreign 67 33 100\n", "new england 73 26 99\n", "middle atlantic 198 72 270\n", "e. nor. central 247 87 334\n", "w. nor. central 109 28 137\n", "south atlantic 178 98 276\n", "e. sou. central 90 45 135\n", "w. sou. central 123 53 176\n", "mountain 66 31 97\n", "pacific 154 36 190\n", "All 1305 509 1814" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_subset = df.loc[df['readfict'].notnull(), ['reg16', 'readfict']]\n", "pd.crosstab(df_subset['reg16'], df_subset['readfict'], margins=True)" ] }, { "cell_type": "markdown", "id": "ab4d5a2a", "metadata": {}, "source": [ "Contingency tables involving categorical variables taking on a small number of possible\n", "values (such as the one shown above) may be visualized conveniently by a stacked or\n", "segmented bar plot. The relative density of (self-reported) fiction readers across the\n", "regions of the United States is easier to appreciate in the visualization below, which is\n", "created by using the ``plot.bar(stacked=True)`` method on the ``DataFrame`` created by the\n", "``pandas.crosstab()`` function:" ] }, { "cell_type": "code", "execution_count": 40, "id": "1b266423", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_73_0.png" } }, "output_type": "display_data" } ], "source": [ "pd.crosstab(df_subset['reg16'], df_subset['readfict']).plot.barh(stacked=True);\n", "# The pandas.crosstab call above accomplishes the same thing as the call:\n", "# df_subset.groupby('reg16')['readfict'].value_counts().unstack()" ] }, { "cell_type": "markdown", "id": "34997c81", "metadata": {}, "source": [ "\n", "\n", "While the data shown in the table has a clear interpretation, it is still difficult to extract\n", "useful information out of it. And it would be harder still if there were many more categories. One\n", "question to which we justifiably expect an answer from this data asks about the geographical\n", "distribution of (self-reported) readers of fiction. Do certain regions of the United States tend to\n", "feature a higher density of fiction readers? If they did, this would give some support to the idea\n", "that reading literature varies spatially and warrant attention to how literature is consumed in\n", "particular communities of readers. (This suggestion is discussed in {cite:t}`radway1991reading, 4.) Is it reasonable to believe that there is a greater density of fiction\n", "readers in a Pacific state like Oregon than in a New England state such as New Jersey? The stacked\n", "bar plot does let us see some of this, but we would still be hard-pressed to order all the regions\n", "by the *density* of reported fiction reading.\n", "\n", "We can answer this question by dismissing, for the moment, a concern about the global distribution\n", "of responses and focusing on the *proportion* of responses which are \"yes\" or \"no\" in each region\n", "separately. Calculating the proportion of responses within a region, given the cross tabulation,\n", "only requires dividing by the sum of counts across the relevant axis (here the rows). To further\n", "assist our work, we will also sort the table by the proportion of \"yes\" responses. The relevant\n", "parameter for ``pandas.crosstab()`` is ``normalize``, which we need to set to ``index`` to normalize the rows." ] }, { "cell_type": "code", "execution_count": 41, "id": "533d4c16", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/org": [ "| reg16 | yes | no |\n", "|-----------------+----------+----------|\n", "| pacific | 0.810526 | 0.189474 |\n", "| w. nor. central | 0.79562 | 0.20438 |\n", "| e. nor. central | 0.739521 | 0.260479 |\n", "| new england | 0.737374 | 0.262626 |\n", "| middle atlantic | 0.733333 | 0.266667 |\n", "| w. sou. central | 0.698864 | 0.301136 |\n", "| mountain | 0.680412 | 0.319588 |\n", "| foreign | 0.67 | 0.33 |\n", "| e. sou. central | 0.666667 | 0.333333 |\n", "| south atlantic | 0.644928 | 0.355072 |" ], "text/plain": [ "readfict yes no\n", "reg16 \n", "pacific 0.810526 0.189474\n", "w. nor. central 0.795620 0.204380\n", "e. nor. central 0.739521 0.260479\n", "new england 0.737374 0.262626\n", "middle atlantic 0.733333 0.266667\n", "w. sou. central 0.698864 0.301136\n", "mountain 0.680412 0.319588\n", "foreign 0.670000 0.330000\n", "e. sou. central 0.666667 0.333333\n", "south atlantic 0.644928 0.355072" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(df_subset['reg16'], df_subset['readfict'], normalize='index').sort_values(\n", " by='yes', ascending=False)" ] }, { "cell_type": "markdown", "id": "df2517b0", "metadata": {}, "source": [ "A stacked bar plot expressing the information on this table can be made using the same method\n", "``plot.bar(stacked=True)`` that we used before:" ] }, { "cell_type": "code", "execution_count": 42, "id": "30b2909e", "metadata": {}, "outputs": [ { "data": { "image/png": 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