{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "25195a14", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.style.use(\"../styles/hda.mplstyle\")" ] }, { "cell_type": "markdown", "id": "7f1ad7ea", "metadata": {}, "source": [ "(chp-statistics-essentials)=\n", "# Statistics Essentials: Who Reads Novels?" ] }, { "cell_type": "code", "execution_count": 2, "id": "9f6438dd", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [], "source": [ "# HIDE THIS CELL\n", "import random, numpy; random.seed(1); numpy.random.seed(1);" ] }, { "cell_type": "markdown", "id": "a2f7a056", "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": "0a5b096c", "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": "7dbe1aaa", "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": "087ec723", "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": "4e8de994", "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": "e787fbdc", "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": "06df3998", "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": "72807e54", "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": "ad9e31cf", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_11_0.png" }, "needs_background": "light" }, "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": "2b6eb08d", "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": "19b52a4b", "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" }, "needs_background": "light" }, "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": "d25d3465", "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": "95ca154c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "749.1342599999999\n" ] } ], "source": [ "print(df['realrinc2015'].max() / df['realrinc2015'].min())" ] }, { "cell_type": "markdown", "id": "b751bf65", "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": "dd25cb7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "51296.749024906276\n" ] } ], "source": [ "print(df['realrinc2015'].mean())" ] }, { "cell_type": "markdown", "id": "79b917ae", "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": "23194de6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37160.92814781022\n" ] } ], "source": [ "print(df['realrinc2015'].median())" ] }, { "cell_type": "markdown", "id": "98180600", "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": "7b845476", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "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": "5ba5a2e3", "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": "f7be529d", "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": "4799a243", "metadata": {}, "source": [ "By contrast, the median is not changed:" ] }, { "cell_type": "code", "execution_count": 14, "id": "550c3b74", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "47387.5\n" ] } ], "source": [ "print(np.median(realrinc2015_corrupted))" ] }, { "cell_type": "markdown", "id": "c9ae6140", "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": "4024a631", "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": "12b893d2", "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": "4dbaa6b7", "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": "3b4df791", "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" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "df['realrinc2015'].plot(kind='hist', bins=30);" ] }, { "cell_type": "markdown", "id": "159a5b3b", "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": "431f67d1", "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_32_0.png" }, "needs_background": "light" }, "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": "b9648e3b", "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": "82c086cf", "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": "8582f315", "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": "13db008a", "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": "a484ca87", "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": "2da45354", "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": "48c89b42", "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": "dcc3ccdf", "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": "b21819c6", "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": "8cf1f405", "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" }, "needs_background": "light" }, "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": "7f1be50c", "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": "09284fde", "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": "3368392a", "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": "9e9caaa9", "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": "5fd038f3", "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": "d24f5f87", "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": "7d6968d1", "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": "f09f301c", "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": "615171ac", "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": "fcf84347", "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": "8a5c3e37", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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" ], "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": "ff2d4705", "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": "f07c9d65", "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": "f6bb1406", "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": "ab4bdcb6", "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": "b0aeac55", "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": "8a6e8267", "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": "41b1a6f2", "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": "c0eed396", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_61_0.png" }, "needs_background": "light" }, "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": "3105c318", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 35, "id": "ca2ea662", "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": "c97495d9", "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": "dcdf13ed", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_65_0.png" }, "needs_background": "light" }, "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": "7bc1cde1", "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": "48824be9", "metadata": { "tags": [ "remove-cell" ] }, "outputs": [ { "data": { "image/png": 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" ], "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": "1c04d542", "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": "550b26c1", "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
e. sou. central9045135
w. sou. central12353176
mountain663197
pacific15436190
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" ], "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": "d6405440", "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": "17193833", "metadata": {}, "outputs": [ { "data": { "image/png": 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//zs33ngjgwYN4oADDmh//emnp60Y39EyWh980PDvYQHYeOONOeus4u+I//3vf3fY97///W+n9XaHLbbYghtvvBGARx55hI022qhh39dee4277rqLXr16scMOO8ypEucrBiBS83QWK79cOV6+2hARSwI/Ab5Isbl3Zxas89oKlePxXRhjbjNX1L9A7z4stepHe+rykiRJkiRJc43hw4fzne98h6lTp3LjjTfOsLF5re9///tcc801HHnkkdO9vvDCC7cfv/POOw3Pf/bZZzusZ6eddqJPnz5MmTKFu+++m7feeqvhZu633dbZYi3d43Of+1x7AHLTTTfxxS9+sWHfCy64gCOPPJLNNttshgCkT59pfxddGwzdeeedjB07lq233poPf/jDTay+9SzQ0wVILaTjiBqqi/61L1gYEYsBtwMHUoQf5wAbAAtnZrQ9gAPo2KKV43lxgcF5vX5JkiRJkqSWstJKK7XP5HjxxRf5zW9+07Bv2+yPXr16ceihh07XtvHG0xZDaZtRUs8ll1zSYT0DBw7k85//PFDsr9E2G6TWe++917Ctuw0fPpw11lgDgN///veMHVt/0ZjJkye3v59f/vKXZ2gfMGBA+3Htcl4/+tGPOOCAA7o0K2d+ZwAiNU9n/z31rRy/WTn+FsWeHgAXZuYhmflAZjaOw+ur/kvYt2Gvude8Xr8kSZIkSVLLOe2001hnnXUAOOKII7jqqqtm6POXv/yFPfbYgylTpnDMMcew6qqrTtd++OGHt88cOfnkk/n9738/XfsHH3zAcccdx+jRozut5yc/+Un7XiDHHnvsDOe888477L///h3ONOlOffr04bLLLmPRRRdlypQp7L777jzzzDPT9Zk0aRIHHHBA+xJZI0aMmGGcDTbYoP09u++++9pff/nll7nzzjuJCDbYYINuvZdW4BJYUvMs1kn7MpXj6gbme1eOT5+N648DPlYeD5yNcXrKvF6/JEmSJEnSXOmss85i7Nix0y0xNXr0aN58s/gb3Y033pjhw4fXPXeRRRbh9ttvZ++99+bPf/4ze+65J+uttx6bbLIJmckjjzzCPffcQ0Rw5JFHcsIJJ8wwxrrrrstvfvMbDjroICZPnszee+/NhhtuyPrrr0+vXr247bbbGDt2LDvvvDPXXXfddPX179+fY445pn2sAQMGcMsttzBs2DCeffZZhg0bxlZbbcW6667LG2+8wc0338zUqVO54YYb2HTTTQEYO3YsRx99NAD77rsvG220EaNGjZouWKjtB3DooYeyyiqrcP/993PZZZe192nT1rf2/VtvvfUYM2YMe+65J//6179YY401+NSnPsUKK6zAc889xx133MErr7zC+uuvz7XXXkvv3jN+Tb/MMsvw+c9/ngsvvJDf/e53vPbaawwaNIibbrqJN998k/3335/BgwfX/cw0TWRmT9cgzbMi4njguPLpkZl5Wgd9jwW+Xz4dmZkHlK+/y7Q9PRbJzLcbnP8l4Lzy6QGZObKm/X+Bk8unF2TmlzqpfQegF3BXZr5W0zYS2L98unJmPtVgjDHANgDlMl0dXe94pr1X22bmmO6qf1ZExDhgUL+ll2OXC/85u8NJkiR1yekTlu3pEtRDlhvh/4tLkuacoUOHcvvttzds33///Rk5cmSn41xzzTVcfPHF/OUvf2H8+PH06tWLFVZYga222oqDDz64ww2/AR588EFOO+00brvtNl544QV69+7NoEGD2Hrrrfna177Ga6+9xrbbbjvdOSuttBJPPfXUDGO9/vrr/OxnP+P3v/89Tz75JL169WLFFVdkp5124n/+53/40Ic+RMSMX1ddcMEFjBgxghEjRnDhhRd2WO9tt93G0KFDGTly5HSbutdq9P61LXP1hz/8gYceeoiJEyey2GKL8dGPfpThw4fz5S9/mQUXrLfVb+G9997jpJNOYtSoUTz55JMAfPjDH2a//fbjf//3f6fbJ2Qu0uF3hHOaAYg0G2q+1L82M3ftoO+dwJbl010z89ry9VeBJcvXB2TmKw3OPw44vnxaLwAZAjxBEQo8B6yYmXX3JYmIjwH/AKYAS2fmGzXtI5nzAUjT6p8VBiCSJKknGIDMvwxAJElSi5qrAhD3AJGaZ4eIWK1eQ0RszLTw41Hghkpzda7dLg3O7wXs09HFy5CiLbYeBOzXQffDy5+XNyM8aIZ5vX5JkiRJkiRJcxcDEKl57gKujIgVqi9GxCrApeXTKcBXMvP9SpfTK8c/jYhtas5fEDgHWKsLNRwB/Ls8/kVEbF0zVq+IOAo4AHgd+E4XxpyTjmDerl+SJEmSJEnSXMJN0KXmORE4FHg0IkYDzwIrAMOAvsDbwBcz8+7qSZl5Q2V/kAHAmIi4C3iYYm+Q7Sk2UL8Z+HR52vCIWAcYm5lnVcZ6owwNrgCGlmPdCTxEsczWJsCqwCvAbpn5dNu5ZVBzaPm0umDjMRHxetu1ImIjYN+ybZXK+adUzhmdmaMjYimmhRQfr7QfGhE7AxMz86Rm1C9JkiRJkiRJVe4BIs2G2n0tgNuBLwAjgHWAJYAXKMKLn2Tm2A7G2pJiaactgYHAZOBp4M/Az8vXL6g57fbMHNpgvN0olpHarBxvCvAf4DrgzMwcX9N/KHBbB7d7e2YOjYgRdeqodUJmHl/u6/FkB/2ezswhzah/drkHiCRJ6gnuATL/cg8QSZLUouaqPUAMQKTZ0NnG3pp3GIBIkqSeYAAy/zIAkSRJLWquCkDcA0SSJEmSJEmSJLUcAxBJkiRJkiRJktRyDEAkSZIkSZIkSVLLMQCRJEmSJEmSJEktp3dPFyDNayJiKeA75dOPV5oOjYidgYmZedKcr0ySJEmSJEmS1MYARJp5SwBH1Xl9n/Ln04ABiCRJkiRJkiT1IAMQaSZl5lNA9HQdkiRJkiRJkqTG3ANEkiRJkiRJkiS1HAMQSZIkSZIkSZLUclwCS5Iq+vddgFE7DezpMiRJ0nwje7oASZIkqWU5A0SSJEmSJEmSJLUcAxBJkiRJkiRJktRyDEAkSZIkSZIkSVLLMQCRJEmSJEmSJEktxwBEkiRJkiRJkiS1HAMQSZIkSZIkSZLUcgxAJEmSJEmSJElSyzEAkSRJkiRJkiRJLad3TxcgSZIkSZIkSZp5w68f39Ml9IhROw3s6RI0j3AGiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSaWnnnqKiGj4eOqpp6brP2TIkC71mzhxIt///vfZZJNNWHrppVlooYVYfvnl2XHHHTn33HOZMmVKp7XdcsstDB8+nCFDhtC3b1/69evHhz/8Yfbaay/OPfdcXn755Sa+E/O+3j1dgCRJkiRJkiRJc4sllliCo446inHjxjFq1CgAVlllFXbbbbf29qqDDjqIiRMn8uc//5kHHniAffbZhxVWWGG6ftdeey37778/r776KgMGDGCHHXZgqaWW4j//+Q+33norN954I6eccgrXXXcdH/nIR2ao6YMPPuCggw7ivPPOA+DjH/84n/jEJ+jVqxf//ve/ufLKK7nyyiv52te+xjnnnMOIESO6582Zx0Rm9nQNktTjImIcMGjQoEGMGzeup8uRJEmSJEnq1PDrx/d0CT1i1E4D58h1Jk+ezHLLLcdrr73GkksuyQsvvEDfvn0b9l9jjTV48cUXeeGFF+jXr1/769dffz277bYbU6dOZdddd+Xiiy9mscUWa29/+OGH2WGHHXj22Wf50Ic+xIMPPsgyyywz3dhnnHEGRxxxBAsuuCDXX389n/zkJ6drv/vuu9l9992ZMGECxx13HMcff3xz3oSZFz114XpcAkuSJEmSJEmSpBp9+/Zl3333BeC1117j6quvbtj37rvv5rHHHmPfffedLvx45ZVXGDFiBFOnTmWFFVbgkksumS78AFh77bW56KKLAHjxxRc56qijZhj/17/+NQDbbLPNDOEHwBZbbMEPf/jDmb7HVmcAIkmSJEmSJElSHQcccED78QUXXNCw3/nnnz9Df4Bf/vKX7ftyHHbYYSyyyCJ1z99mm21Ya621ALjssst46aWXpmsfO3YsAL17N97VYrvttmP11VdnwIABDfvMbwxAJEmSJEmSJEmqY5NNNmkPJm699da6S6e/9dZbXH755ay99tpsuumm07Vdeuml7cc77LBDh9dqO3fKlCn86U9/mq7tQx/6EFBsgn7HHXfUPX+VVVbh0Ucf5Wtf+1ondzX/MACRJEmSJEmSJKmBtlkdH3zwARdeeOEM7VdccQVvvvnmDLM/Jk6cyKOPPgrAAgsswBprrNHhdZZddtn243/84x/Tte2zzz4ATJ06le22247Pfe5z3Hrrrbz//vszf0PzEQMQSZIkSZIkSZIa+MIXvtC+9NTIkSNnaL/gggvo3bs3n//856d7vTpb5IMPPqBv375ERMPHj3/84/b+tUtgHXfccWy77bYAvP/++1x66aVsv/32DBw4kBEjRnDdddcxderUZt1yy2i8YJgkSZIkSZIkSfO5ZZddlmHDhnHdddfxxBNPcOedd7LVVlsBxd4cd955J7vuuut0MzgA3njjjfbj3r17841vfKPL11x33XWne77IIovwpz/9iUsuuYRf/vKX3HvvvUAxy+TCCy/kwgsvZMUVV+Tkk09u37hdBiCSJEmSJEmSJHXogAMO4LrrrgOKGR9tAcj5559PZvKlL31phnMWW2yx9uPM5JRTTpmtGiKC/fbbj/32248nn3ySUaNGcckll/Cvf/0LgGeeeYbPfvazTJgwgcMPP3y2rtUqIjN7ugZJ6nERMQ4YNGjQoLqbWUmSJEmSJM1thl8/vqdL6BGjdho4x685ZcoUll9+eV5++WUWXXRRXnzxRfr168dKK63ElClTGDduXPsyWW1eeeUVBgwYMN3z/v37N722++67j+9+97uMHj0aKGaLPPXUU9Ndew6KnrhoI+4BIkmSJEmSJElSB/r06cN+++0HwJtvvskVV1zB6NGjGTdu3HR7hFQtvfTSrLnmmu3PH3rooU6vc/7553PiiSfOsAn6U089xcsvv1z3nI033pibbrqpfY+Qt956a4bz51cGIJIkSZIkSZIkdeKAAw5oP77gggs4//zzAeouf9WmLTQB2pfQamTChAkcfPDBHHvssfTp02e6tpVXXpmDDz644bkRwW677db+/PXXX+/wWvMLAxBJkiRJkiRJkjqx3nrr8bGPfQyAO++8k6uvvprNNttsulketQ477DAGDiyW7Pr1r3/N+PGNly370Y9+xNSpU9lmm21m2AQd4O677+bdd99teP6zzz7bflzv/PmRAYgkSZIkSZIkSV3QNgskM5kyZUqHsz8AllpqKS6++GL69OnDpEmT2GmnnXjmmWem6zN16lR++tOfctppp9GvXz9++ctf1h3rpZde4sADD+Sdd96Zoe3ee+/l7LPPBmDYsGGsvvrqs3J7LcdN0CUJN0GXJEmSJEnzHjdBn/NeeeUVll9+ed577z0WXnhhXnjhBRZffPFOz7vlllvYb7/9mDBhAgsttBCf/OQnWXnllRk/fjx//etfefrppxkwYACXX355+14eVeussw4PP/wwAP3792e77bZj0KBBvP766zz22GPcc889AGyxxRZcc8013bLZehfNVZugG4BIEgYgkiRJkiRJ6pq99tqLK6+8ki984Qv89re/7fJ5r7/+OmeffTbXXHMNjz76KJMmTWKxxRZjzTXXZOedd+bggw/uMLi45557+OMf/8g999zD448/zquvvsoCCyzAwIED2XDDDdl3333Ze++9WWCBHl34yQBEkuY2BiCSJEmSJEnqiv/5n//h9NNP57bbbmPo0KE9Xc7cxgBEkuY2BiCSJEmSJEnqzPvvv8/gwYNZeOGFeeKJJ4iYq77vnxvMVW+Im6BLkiRJkiRJktQFN998My+++CIjRoww/JgHGIBIkiRJkiRJklTxzjvvcMghh3D33XdP9/o555xDr169GDFiRM8UppnSu6cLkCRJkiRJkiRpbvLuu+9yzjnn8P7777PFFlsAcN9993Hdddex++67s8IKK/RwheoKAxBJkiRJkiRJkuo477zzePnllxkwYABXXHEFCy+8MD/60Y96uix1kUtgSZIkSZIkSZJU0bdvX/bYYw8GDx7MjTfeyB/+8Ac233xzxowZw0c+8pGeLk9dFJnZ0zVIUo+LiHHAoEGDBjFu3LieLkeSJEmSJEmaF81VO8M7A0SSJEmSJEmSJLUcAxBJkiRJkiRJktRyDEAkSZIkSZIkSVLLMQCRJEmSJEmSJEktxwBEkiRJkiRJkiS1HAMQSZIkSZIkSZLUcgxAJEmSJEmSJElSy+nd0wVI0txk4uQPGH79+J4uQ1Idp09YtqdL0ExabkT2dAmSJEmSpPmYM0AkSZIkSZIkSVLLMQCRJEmSJEmSJEktxwBEkiRJkiRJkiS1HAMQSZIkSZIkSZLUcgxAJEmSJEmSJElSyzEAkSRJkiRJkiRJLccARJIkSZIkSZIktRwDEEmSJEmSJEmS1HJ693QBkiRJkiRJkqSZ98LI6OkSesRyI7KnS9A8whkgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIqlbRMQ6EfHfiPi/iFi+p+uRJEmSJEmSNH8xAJFaXEQ8FREZEU/N4Ut/B1gZ+Chw+By+tiRJkiRJkqT5nAGIpO4SDY4lSZIkSZKkudqQIUOIiOkexx9/PAB33HEHu+22G8svvzwLLbQQQ4YM4ZBDDuG5557r0ti33XYbI0aMYJVVVmHhhRdmiSWWYO211+bwww/noYce6sa7mv8YgEjqLicBTwEPAb/o2VIkSZIkSZKkrjvooIM46qij2GeffaZ7/ec//zk77rgjvXr1Yq+99mK77bbj2Wef5ZxzzmHjjTfm6aefbjjmO++8w/Dhw9luu+248MILWWqppdh///3Ze++9effdd/nFL37B+uuvz7e//W0ys7tvcb7Qu6cLkNSaMvNBiiWwJEmSJEmSpHnKMcccA8CYMWO4/PLLAbj77rt54okn+L//+z9WWWWV9r5XX301u+++Oy+88ALf+ta3uPTSS2cY7/3332fHHXdkzJgx9O7dm4svvpjhw4e3t2cmP/jBDzjuuOP48Y9/zKRJk/jVr37VzXfZ+pwBIkmSJEmSJElSJ2699VbOOuus6cIPgN12242NN94YgD/84Q+89dZbM5x70kknMWbMGAC++93vThd+AEQE3/ve99hzzz0BOOuss7j66qubfxPzGQMQ9bjKJt3Vx/Fl2zYRcUNETIiI1yLirxHxpYjoVTm/T0QcFhEPRMRbEfFKRFwbEZt28fqrRsSpEfFgeY13IuLJiPhdROzYwXljGtXdhX4juthvZNm2XkRcHBHPRMS7EfFc+XyNBrUNbRsDWKl8eaU642dEDKk5d+GI2DciLoyIhyPijYh4LyJejIibI+LgiOjbwfsyss41xnTn/UqSJEmSJEndbYUVVmDYsGF127bccksA3n333Rn28XjjjTc49dRTAVhwwQU54ogjGl7jW9/6VvvxCSecMJsVyyWwNDf4NdAfWAFoX1QvIr4OnADcAIwCtgA2KR/bRsQXKX6HrwQGAfdQ7Dexc/n4dETskpk3N7pwRHwL+D7QB3gG+APwDrA+8DngcxFxI/DZzJxUc/oo4P7auuto67c2UP9fyOn7LQ4cWKlxL+AC4A7gamAwsBOwH7BrRAzNzH/UjPUscGp5fGA55uvAuXWu235fEbEy8M+yP8A/gCuAD4DVgE8CnwK+Xr63/60z3o3Ay+XxUXPofiVJkiRJkqRutfnmmzdsW2GFFdqPazdDv+GGG5g0qfgK7uMf/zhLLLFEw3E23HBDlllmGSZMmMA///lPHnnkEdZaa63ZrHz+ZQCiHpeZJ0Exa4FpQcIWFKHG2pn5fNkeFF/gfxn4PEUwshbwl7Yxyn6DKb5YXxY4JyJWycz3a68bET8Avls+PR04utovInagCFd2AP4cEVtk5uRK3WfVqbve/bX1G0EHAUil3xCmBQKrAmcA22XmfZXaNgHuBBYDfglsXjPWWODosu9eFCHDq5l5dKPrlxYp+04F9snMP1QbI+JjwCUU7/u1EbFh9T0prz2KItwgIhoGIM28X0mSJEmSJKm7VUOOWosuumj78Ztvvjld2913391+vPbaa3d4jYhgzTXXZMKECe3nGoDMOpfA0tzqk8ChbeEHQGYmcFylz7eAjavhR9lvHHBW+XQlYOvawSNiG+A75dPbgCNrQ5LMvBH43/LpBsCPZ/luZt0WwA+qYQBAZv4N+GP5dLOIWLXJ1z2zNvwor/sAsFf5dC1gRJOv21P32+6DqVN49YkH6z7emfhSd11WkiRJkiRJc7lFFlmkYVufPn3ajz/44IPp2saNG9d+PHDgwE6vU+1TPVczzxkgmlv9NzNvr30xM5+LiGeAFYGPAj9rcP7dleONKUKOqu8BUR7/pAxX6vk1xRJZSwGHRMSPMnNOfgs+GbioQdtdwN7l8YbAE0243njghx1ck8x8OCIeA1YH9gDObsJ128zp+53Bu5Ne5pYjtq/bttZnj2ad/b7ZHZeVJEmSJEnSXK5YoGbmvfHGG+3Hffs23Fq3bp/XX399lq6pggGI5lZ/66DtBYoABOAvDfo8XzkeXG2IiGWB7cqn7wJ/bnShzHwvIv4M7AksVP78VQe1NduDmflWg7ZnK8eDmnGxzBzPtGXBOvISRQDy0WZct2KO3m89Cy0xgK1PuLRuW9/+y3bXZSVJkiRJktSiFltssfbjyZMnd9Bzxj6LL754Bz3VGQMQza06mttV/VeiUb93KseL1rRtUTl+IjPf66SWhymCj7Zz52QA8mwHbdXFBGvvcbZFxFrAphQB0mJMv2Teh8uf/Zt82R673zYL9O7DUqs2O9eRJEmSJEnS/Grw4Gl/n/3SS50vLjN+/Pj24472HVHnDEA0t3p7NvtVl7Sq3eumOiNkPJ2r9hncsFf3aDQbAmBK5bhp+/lExGbAzymWDutMn867zJQ5fr+SJEmSJElSd9pyyy0588wzAXj44Yc77JuZPPLII+3Pt9hiiw56qzN+iai5VaM9Oabv1Hjvjo4sVjnufM7Z9H3m9JyzWbm/WRYR2wO3U4QfLwMHAysAvTMz2h5ln+4wR+9XkiRJkiRJ6m477rgjSy65JAD33nsvkyZNatj3/vvv5+WXXwZg/fXXZ80115wTJbYsAxDNj96oHHe+69D0fWZn16FZ2yVpDomIXsB5wIIUQcQ+mfnrzByXme/3bHWSJEmSJEnSvGnRRRflyCOPBOC9997j9NNPb9j35JNPbj8+7rjjuru0lmcAovlRdd+QruxqPbByXG+Pig8qx706GGeRLlyrJ21KMdsDis3Ib+vJYiRJkiRJkqRWccwxxzB06FAATjzxREaNGjVde2bygx/8gCuvvBKAQw89lN12220OV9l63ANE86O7K8erRMRCmfluB/3XbnBum+o+JB2FHCt1pbgeVK3v8U76Gp5KkiRJkiSpZZ111lmMHTuWZ5+d9vfQo0eP5s0336R///4cc8wx0/Wr7u0xatQoHnrooen69erVixtuuIH999+fK664gn333Zef/vSnbLLJJkyZMoUxY8bwxBNP0KtXL44++mh+9KMfzdkbblEGIJrvZOZLEfEn4BPAQsB2wI31+kbEgmU/gHeBq+p0e65y3NEm6ZvPfLVN0bZ5+HShRUQsBuwJvJ6ZVzF9kNOvkzFX6KRdkiRJkiRJ3Wy5EW6n2l1GjRrF7bdPvw3uvffey7333stKK63UHmzU63fTTTdx0003TdcPoF+/flx++eWMGTOG888/n7vuuouRI0fSq1cvBg8ezFe/+lUOOeQQ1llnne6/wfmEAYjmV9+nCD4C+GZE3NRgQ/WDgCXL47My86XaDpn5QkQ8BwwCNo2IqB0rItam5wKQl4FVmX7zd4A1gQuAByiCnfsrbVtHxBKZOcOOTBGxOTCke0qVJEmSJEmSet6YMWOa2q9q6NCh7cthqXu5jI3mS5l5B/DD8um2wM8iYrpAMCKGAT8pn/4D+HYHQ/6u/LkiRWhSHWdg2X7LbJY9q+4rfy4ZEatWXt+9/Hk/QGY+B1xRvrY4cGlELFMdKCI+AlzcjbVKkiRJkiRJUlM4A0Q9LiIOBVZh+mWVPhURiwITM/OkiFgK+E7Ztkrl3FPKw8sy8/6I+BTwKYov8NtsVOn3w8x8FSAzj42It4ETgCOAPcqlsd4B1gc+Xp5zE7BvZk7u4DZOAnahmFVxdkQMBx4ClgZ2BM4rn3+67D88ItYp6zi6vJfhwMaNaq/Tr9771d6v4lfAgUBf4LaIuB5Yvqz3HeD0St+DKGZ3bAzsADweEbcDz1DsETIMGAv8B/hIWU/tZ9BWX9UqHfRr9v1KkiRJkiRJElF/1R9pzomIMcA2DZqfzswhETEEeLKDYQ7IzJERcTxwXAf9Vs7Mp2quvypwGLA9xQyOhYCXgHuAizPz+i7cBmVI8y2KmRUrAm8C/wTOzMw/RsQIiiWnppOZUZ4/Eti/0fgz26+mtk2BY4HNKJb0eo3i/k7IzL/X9F2QIgj5LMUG8IuU/R8CrqQIc25kxs+s7TPosL6u9pud+50VETEOGNRv6eXY5cJ/NmNISU12+oRle7oEzSTXI5YkSZKk+U5TvqtrFgMQScIARJoXGIDMewxAJEmSJGm+M1cFIO4BIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJaTu+eLkCS5ib9+y7AqJ0G9nQZkurKni5AkiRJkiTNQ5wBIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSW07unC5CkucnEyR8w/PrxPV2GJEkz7fQJy/Z0CdIcs9yI7OkSJEmSNA9wBogkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEia60VE1nmM6Om6JEmSJEmSJM29evd0AZLUBaeWP9cGhvVkIZIkSZIkSZLmDc4AkeZTETGyMptiSE/X05HMPDozjwZG9XQtkiRJkiRJkuYNBiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgTRIRx1c2lK59jKzT/4iy7b4G49UbZ0yTa+4dEV+OiNER8VJEvBcRr0fEvyLivIjYIyIW6sI425Ybao+NiLcjYlJEPBwRZ0bEOh2cV+8eh3ax35DZuvmZFBF9I+JrEfGniHixfK9ei4h/RsRvyveqbydjLBwRR0bEHRExvhzjxYi4rXx9kQbn1fvdeqpsGxgRJ0fE4xHxTkRMLD/PT3dQR0ZEAvtXXn6yzjVGdOH6S0bEsRHxf+XnnpXxq9dcKiK+FBGjIuI/EfFWREyOiHER8ceI+GxE9OrCRyFJkiRJkiRJXdK7pwtoIfcApwK7AKuVr50HvAbUCzk+U/7cMCKWz8zna9pPLX9+FlgK+BUwtlnFRsQA4EZgI+A94FbgaWAxYEPgS+XjlYjYNjP/VWeMfsBIYJ/ypb8Do4E+wFDga8ChEfFT4JjMzJoh2u7x48DmHZTb1m8YsHaXb7JJImIj4EpgRWAyxT0+AwwGNgW+XD4mRMSBmfnHOmNsWo4xCHgDuAl4sRxze4r36+iI2D0z/1pzetvvFsCBwOLlmGuWtTwP3AIsCuxYjrd9RBycmb+uc0v13s9zgddr+j3UyfU/Ut5HArdRfP47A8vU3PsWFL9fbQHRXcCfy+drAruWj8PK+3+5Ts2SJEmSJEmSNFMMQJokM0cDoyPieaZ9WXx7Zl5U2zci+gNbtT2l+PL37Jrxji4DhkOBWzLz6CaXfC5F+PE0sFVmPlupLyi+6D4LWLp81N5DL+AGii/upwKfz8xRNWMcC5wAfAtYAjisOkbbPUXE8XQQgFT6DWAOByAR8TFgDLAIRSCwc2Y+XWnvB/wAOIrii/+PAX+sGWMD4E/lGH8Fds3M8ZX2wcB1wHrAnyJio8x8tK297Xer7LsXRQCxIDAKODEzz6mMtXR5jVWAn0XElZn5SrWeBu/nSZn5VL33oIPrX1HW8N3M/KByL/+mCGPaLEURdrwBDMvMe2ren22BS4Aty/E+Ua8OSZIkSZIkSZoZLoHVfNdWjndp0GdnoLrcz2ca9PsksDBwTRPqahcRAyvX/HU1/ADIwq+ByzoY5hiK8AOKL+FHVRvLMb5PMesBipkgu81u7XNSRPSheA8WAaYAu1XDD4DMfKcMFP7cwRiXlmO8CexRDT/KMcYBe1EESYsA9WZt1FoOeKQafpRjvQL8rHy6CLB7F8aaFcsB4zPzmLbwo7z+OOCOBud8tzb8KM+5jWIGDcB2ETGs6dVKkiRJkiRJmu8YgDRZZv4HeKx8+umIWLBOt90olsZq67ddRCxWp99ngA8oZgc004cpZp5A8aV7I9dR1Ph29cWy1qPKp+8Bp3cwxo8rx8fNVJU973NMW87s95nZ0RJk5zZ4/bOVMX5XZ6kzADLzCcpZFsBW5cyTzjQKSu6qHG/YhXFm1RkNXv88sELl+X+AH1LM7mjkZqb9nu0x+6XNug+mTuHVJx6s+3hn4ks9WZokSZIkSZKkmeASWN3jGuCbFEsFbUOxPwNQbKYNfKrs8zTF8lALUuzHcEWl3wIUM0X+mpnN/tb1xcrxYRFxUWa+UNspMy+lmL1Qa0eKJa0A7snMSR1c6+/ABIrlodaPiLUy85FZrHtO+2zl+KZO+t4GnAPc38EYN3Yyxl8p3luAHYAHOuibwF8atFVn9Azq5Jqzo+5Mj8x8FXi18vwx4LsdDZSZ70fEyxR7ony0mUXOrHcnvcwtR2xft22tzx7NOvt9cw5XJEmSJEmSJGlWGIB0j2spAhAolsG6pdK2PcXSRH+k2Ej7W+Xrn6ESgFBsrr0sHc+umFVPA38DNgFWAh6NiF8DF2Xmg104f4vK8cMddczMjIh/M21j7C2AeSUA+XjluMOay5DqkNkZA6gGXRt00ndiZr7doO3NyvGiDfrMrlcy842ZPSkiNqTYe+ZDFLVFpXnJ8mf/2a5uNiy0xAC2PqFe7gd9+y87h6uRJEmSJEmSNKsMQLrHPcArFJuH7wJ8vdL2GYr9JG6k+KJ6PDAQ2DEiemdm25JUu5Y/m7r/B7SHEvtTLDu0IsVMlaOBoyNiLHA1cFlm1s5maDO4cjy+QZ+qap/BDXvNRSJiCaC6LFlX7rN2jMUp3ts2jxd7w3dJZ9+0v9WoITOnVK7TXcvcNbx+PRGxE3AqsHoXutdbNm6OWaB3H5ZatUcnoUiSJEmSJElqAgOQblAu53MD8AVgSESsm5n/Kpe12gUYk5mvA0TEdcCXgKWArZm2mfauwBPdtVxUZj4aEWsDh1NsQL1K2bQKxf4eR0XEX4CvZuY/ak6vBgOTu3C5ap/FG/aau9TOnOjKfdaq3dflV8A7XTz3uU7ac+bLaaouXz8iRgDnU8z2eBL4DsXv+YTqBuoR8RTFjCRJkiRJkiRJmm0GIN3nWooABIrQ41/A5hSzPaqzOq6hCECgCD3+HBGrAmsBp3VngZn5JvAj4EcRsSkwHNgXWK7sshlwd0Rsk5l/q5xaXfqobxcuVe3z+myU3OXpE01Qu7xTV+6zszF+nJnP1u3ZoiKiP3AmxWf3LrBDuSeIJEmSJEmSJHWr7loeR8Wm2e+Vx23LWe1W/qwGILcwbVbAZ2r6N335q0Yy86+ZeSSwArAn8HjZ1JfiC+yqcZXjrmyKMLByXC8A+KBy3KuDcRbpwrWaopyhUw1rBjbqOxNjLNOobwsbxrTZNDcZfkiSJEmSJEmaUwxAukm5QfTt5dNNImJZioDjn5n5TKXf28Ct5dMhEfFRigBkInBXd9QWEX0jYkhEzLBBdma+n5lXUWxW/nL58kYR0a/SrVrX2p1cKyhms7S5u0636mbeHYUcc3p5pGqtazXsBUTEkhGxc0Ts0MEY63R2wYjYIyK+GxHbzUSdc7PqZ/Z4w14F/z2SJEmSJEmS1DR+4di92mZwBPBN4CPAHzvoB8VyWFsCN1Q2RG+2zSj2YjigUYfMfBm4s3y6ANMHEzcAr5XHm5cbhjeyETCgPP5nZv67Tp/qfhd1N0kvw5p1O7hOd7ikcjysk74HUCx79r2a139XOd65owEiog9wDvAD5sxslymV4+n+LYiIvSNiREQsPJvXqIZb/Rp1iogFmYVZNpIkSZIkSZLUiAFI97q2cvyN8me9Za2uZdqm0l+lWAZqhn4RMSAibouI1yPiF02o7xOdtK9Q/nwBeKXtxXLvkJ+VTxcEjuhgjP9XOT6hQZ/7KsebN+jzBWChDq7TLiK+FxGvRcR9ETGkK+c0MAp4tDzeKyJWqdcpIvoCXymfnldnjLbQZ/dy4/lGDqUIi8ZShEzd7eXKce2G7RcCFwB9ZvMa1c92h4hotMTZnnTx85UkSZIkSZKkrjAA6UaZ+TTF5udQbDj/bGb+o06/l4C/Vfq9R7GHSK3/AYZSfFn91YjoLMDozK4R8bXaF6PwVYrZGwBnZGbWdDsJGFMefzcihtcZ41iKL7YBzsrMq+sVkZlPMO3+94qI9WrG2hj4LvWXz6qt/SMUQcsSZf0/6OycRjJzCsWm8G9SBAF/iIgVa663BEVQsBZwPzCyZoypFJvLv0Hx2V4TEevUjBER8UXgFIr9UA7JzPdnte6ZUA0nNq7U82mK2Rr/ycxJs3OBzLyHaZ/tKsBvapdei4hNmHGfGUmSJEmSJEmaLb17uoD5wDVMW7rp2k76bVoejyn3EOkuE4DxFEsOnRkRR1CEC69SzEDYGFit7Psr4Ke1A2Tm+xGxI8VMgb2ByyLimxRfdvehCGpWBd6n+GL/253UdBhwG0W485eIuJ5iaaxVgG0olpjamWJvEoBjIuJ1YGxmnlUZJ7r0DnRRZv5fRAwFrqT4HB+NiNEUm7kPArYGlgb+CexSb9myzPxXRGwNXAV8GHggIm6nmF2yJLAhsAbwFvCVzLy1en5EbEQRxAAs1fYzIk4pj8/KzLE1/dqsUtuv0nY9xb4cqwE/j4itKN6/Pcr2k7p4fTLz6Nr7rtgH+BPFZzkCGBYRd1HMLFqDYibSX8o6BjS4t7bn1Rk0w9vCpE6uL0mSJEmSJGk+FDP+Yb+aKSI2pfhyF2BYZt7coN/awEPl069l5i/r9BkIXAFsAFyUmYfNRl0LAjuWj42AlSnCh3eBZ4B7gPPKv+DvbKyhTNu75EMUocc4ii+9z87MhxqePP04q1PM9NgOWIYipLkDOLkMIkYC+9ecdntmDq0Z5wSKZbmeAPbOzP925fqd1Na2zNXuFJuZ96eY1fEgxTJX52Xme52MsRDwZYqAYd1yjLeB/wA3U3zZP67OeSMoZpk0sm1mjulqv5qxl6eYJfMpis/u3fKeTs/My7t4fTKzw+ApIhYDDqe499UplruaCDxAsdfKpRSfV+1G92331uE/VJ1dvysiYhwwqN/Sy7HLhf+c3eEkSZrjTp+wbE+XIM0xy43w/2MlSZLmUk39A/XZZQAiSRiASJLmfQYgmp8YgEiSJM215qoAxD1AJEmSJEmSJElSyzEAkSRJkiRJkiRJLccARJIkSZIkSZIktRwDEEmSJEmSJEmS1HIMQCRJkiRJkiRJUssxAJEkSZIkSZIkSS3HAESSJEmSJEmSJLUcAxBJkiRJkiRJktRyDEAkSZIkSZIkSVLL6d3TBUjS3KR/3wUYtdPAni5DkqRZkD1dgCRJkiTNVZwBIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJaTu+eLkCS5iYTJ3/A8OvH93QZkjRHnD5h2Z4uQfOp5UZkT5cgSZIkaT7gDBBJkiRJkiRJktRyDEAkSZIkSZIkSVLLMQCRJEmSJEmSJEktxwBEkiRJkiRJkiS1HAMQSZIkSZIkSZLUcgxAJEmSJEmSJElSyzEAkSRJkiRJkiRJLccARJIkSZIkSZIktRwDEEmSJEmSJEmS1HIMQCRJkiRJkiRJUssxAJE0V4uI4yMiax5P9XRdkiRJkiRJkuZuvXu6AEnqxD3AqeXxgcDiPViLJEmSJEmSpHmEM0Ck+VBEDKnMphjZ0/V0JDNHZ+bRmXk08GpP1yNJkiRJkiRp3mAAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjADIHRcRaEXFmRPw7It6IiLci4r8RcXFEfLoH6lkzIn4ZEQ9HxJsR8V5EvBARoyPi2xGxehfGWCIivhURd0fE+Ih4NyKej4ibIuKgiOjT4LzjK5twtz3GdLHfyNm/+5kTERtGxNkR8Vj5Xk2OiHHlfXb1vdosIs6LiCci4u3yd+DxiDg3Ijbr4Lza+8+IGFG27RoRt5Tv/eRyvJ9ExFINxjo+IhJ4svLy/nXGf6qL198uIq6MiGcjYmql/fg69/7jiLgzIiZExJSIeC0iHoiIUyJi5c7eP0mSJEmSJEmaGZGZPV3DfCEijgO+C/QGHgduB6YCGwEbl92uBT6bmW/NgXoOBH4J9Cnr+RvwOjAE2A7oW3Y9NzMPajDGp4GLgQHluTcDLwErA9sDCwL/AXbLzEdqzv0U8Kny6VHlz9szc2iDfosDB5YvX5iZI2bylmdJRPQCzgAOAwJ4FLirPF4d2KI8Brg1M7evM0Zv4BfAweVLDwB/ofhd2BJYs3z9XOCwzJxac/4p5eHawLDy+ABgHeArwA3AK8BHga3L9n8Dm2fmpJqx6r2fDwM31ZQ9MTNP6uT6qwDfBG4FngLWArYt20/IzOPL868BdilfH0/xuz8BWBHYjOL3ZzJwcGb+lg6UwcxKwNOZOaSjvjMrIsYBg/otvRy7XPjPZg4tSXOt0ycs29MlaD613Aj/H0SSJElqUdF5lzmnd08XMD+IiB8B3yqffg/4YWZ+UGnfC/gdxZfEV0fEp7Ibk6mI2AA4m2IG0LFlPVlpXxEYRfHl9GoNxhhGEdj0BsYAe2XmK5X2j1B8Mf8R4O6I2CwzH2trz8zRwOiy71E00NYvIoYw7Qv7Oeki4LPl8VGZ+bNqY0RsAlxO8aX8JxuM8dtyjA+AAzPz/JoxDqcIWQ4EegFfrrZn5tFlvxFMCyA+CwwEVsvM8ZWxjgBOowhVvgV8u2aseu/n/W3XqKfB9fehCETWq36uEXEacETNEMuXPy8CDsnMtyv9FwdOKWs5PyKeycwxjWqRJEmSJEmSpK5yCaxuFhFDgf9XPr0qM39QDT8AMvP3wEnl009S/HV9d/oyxWf/DvCj2rAlM58B9m90ckQsTfGlfm9gIrBHNfwox/gPsCfFl/5LApdGxDz1+xYR+zMt/Di/NvwAyMy/AV/o4hin14Yf5RhnAheWT78UEZ/oQnmfBD5fDT9KZwIvlsef78I4s2oH4HPV8KN0SYP+LwFfroYfAJn5OsXMmL9RhD8/bnahM+uDqVN49YkH6z7emfhST5cnSZIkSZIkqYucAdL9vse0aT8/7aDf2cBxZd9vADN8Ud5Eq5Q/PwDqzjTJzMcj4h7gmTrNhwHLlMfnZuarDcZ4MCJuBHYCPgbsClw9G3XPMWVYc2zlpZMb9c3MOyPiMYolsWrH+G759APg1A4u+StgRHn8deBPnZR4d2Y+XKeW9yPiXmB3YHBELJuZ3fGt/f2ZeXed1/8BrECxJFqb84DXMnNKvYEyMyPiD8AmwKYRsXxmPt/0irvo3Ukvc8sRM6xkBsBanz2adfb75hyuSJIkSZIkSdKsMADpRhGxLNP2RJgI/LVR38x8qdzfYGXgoxGxXGa+0E2ltc0QWIRidsqPGtS0RYPzP1c5vrGTa91EEYC0nXd110rscZswLSh6MjMf76T/T5m2l0ubjYFVy+MHO/lS/wHgPYp9Uz4ZEb1r9wKpcW8Hbc9WjgdRzL5otjvqvZiZ7wPjal47qwvjVWv8KNBjAchCSwxg6xMurdvWt79r5UuSJEmSJEnzCgOQ7lUNEB7twr4ebRuIA2wIXNctVRV7VrQtcXVSROxEMQPhmsx8s6MTy+Wv1qi8NMMshBrV9kaBytzo45XjRxr2KmXmeRQzHaqq99vhGJk5NSImAh8CFqZ4jx/q4JRnO2irfoaLdnTd2fDkzJ4QEf2BrSjubUmgT6V57cpx/9mqbDYt0LsPS6360Z4sQZIkSZIkSVITGIB0r8GV449HxMxsbN5tf2qemTdExE+BtrV8tigf70bErcBVwJWZOanO6YMqxx9k5sudXK66R8VyEbFA7R4oc6kVKse1+2x0VfXz/1xEfK5hzxktS8cByFsdtFWXmuqufVc6uv50ImJJ4CfAF4GFunDKgrNYkyRJkiRJkiS1MwDpXotVjp+kCBa6qnZz6abKzP+NiKuBoymWqFqQ4svpncrHGRHxM+DEmr0bqvf0bhcuNblyHOX59YKVuU115sTkhr06Vn2v/knn+3pUvdhJ+8yEad2hS9ePiMWA2ymWtQI4p3w8mpnvVPqNAC5oco2SJEmSJEmS5mMGIN3rjcrxk5l5dI9VUkdm3gPsUf6F/p7AvsB2FLMGFqXYwH2dsq1N9Z668tf8fauXrDl/ZkTnXZqqWmffhr26PsY/5rbPfw75FtPCjwsz85CeLEaSJEmSJEnS/KO7lsdRoboZ9DI9VkUnMvO1zDwvM7en2IPkF0DbMlV7RMQOle7PVY4XiIjO7mtg5fj5Bstftc0m6NXBOIt0cp1mq352Axv26voYc+3n3832rhyf3lNFSJIkSZIkSZr/GIB0r7srx6tFRId7G0TEghHxnYj4brnZeLeIiA9FxOB6bZn5TGYeDny/8vJWlfZXgH9X2qqbV9dTbb+7QZ+3y58dhRwrdXKdZrurcrxWZ50jYpOI2DkiVmkwxjpdGGNA+dl/JyJaZXZW9XN7vIN+/lskSZIkSZIkqan80rEbZeZLwK3l04WAT3Zyyk7AicDXgVe7sbTLgP/rpM8fKseL17RdUjnegY5V2y9p0KdtVkndUKa0eSfXabb7gf+UxytHxGqNOpZhxQ3AtcCqlab7mPal/8oR0VmQ8gXgB8AemTl1lqruuoYbpUfERyJiRER8vAnXebty3K+Dfit00CZJkiRJkiRJM80ApPt9n2nLSR0TEXXf84joA/y/8umv6i0VFRFfiYiXI+LRiNhgNuvqHxHrd9Be/UL6XzVtvwImlMdfiYil6g0QER8FhpVP/0ERENRzX/lzmZoZFG3jLAR8sYNaq31XjYi/R8RrEfHtrpxTT/n+/6Dy0v9r1Bf4HLA08DSVjc4zM4ETKv2ObTRAuQ/L4eXTM2ey3FkxkWlLjy1W07Y7xYbkezXhOvdVjnep1yEiegH7NOFakiRJkiRJktTOAKSbZeadTFtOagvgNxGxcLVPRPQHfgtsCjwK/KR2nPKcX1F80b46cEYTyrsgImb4y/uI+BBwcvn0RYoZI+0ycyJFIDEV6A9cVbtkV0SsCvye4nfsNeBzDfb/APhd5fiEakhUBkO/Bh7p4j2dAGwALAGcVNYxSzLzIuDi8umXIuKI2j4R8QmKPVMAjqiduZGZlwAjy6f7RsQPa5e3Kj+DKyn2XxlD8bvQrTLzHeCh8unHyhCC8r3/TPn6/U241OmV459GxDbVxnJZuHPowjJjkiRJkiRJkjQzWmWfgblaZp4QEe9QzCg4ANglIm4FXgaGAFsCS1IsS7VzZr5dZ5hoYkmPAFsD6wNPRMRtFMs99abYs+ETwILA88AumTmpzj3dFBE7UwQEQ4H/RsRNwEsUX+R/qhzjCWC3zHysUTGZeWNEXEIxk2I/YIOIGFPWsz3FzIqvAp8uT9koIk4pj8/KzLGz9jZ0yQiK5ci+BpwWEQdR7O2xAMW+HptSBEFfz8yrG4zxZYoQ6OvAMcD+EfFn4A1gFYrPoh/F7JE9a4OiiDiGImiq7qcyPCLWAcZm5lk1/apLVx1afk7t/Sp+RjHTYyXgrxHxN2BjYCPgMeCqLlwfYHRmjq5345l5Q0QcSxECDgDGRMRdwMMUvx/bU2wQfzPTPt/p7i0iPkXx+wTQNttoqcrvQMPrS5IkSZIkSZp/RbFKj+aEiPgwcBjFF70rUnzp/QrwAHA5cHFHez9ExMHASRTByX6ZOct/oV/OjNiTIrxYm+JL6N4UX/Y/RLFc1W8y841OxlkCOBTYFViNYr+QiRRhzlXABZn5Xhfq6QUcQhE4rEmxPNN/gAspZr4MAp6sc+q2mTmmMs5qFO/lysBPMvOHnV27K8olxw4Ctitr6QWMA24Dfp6ZtcuE1RtjfeDgyhh9gPEUy0RdDPwh6/wHGRFP0XgT+Nszc+jM9KsZez+KcGcdoC/FjJ8bgWPLPWw6GxfghMw8voN2ImJLiiW+tgQGApMpgq0/Az8vX7+gXs0RcTxw3OxcvysiYhwwqN/Sy7HLhf+c3eEkaZ5w+oRle7oEzaeWG+H/g0iSJEktqpl/yD/bDEAkCQMQSfMnAxD1FAMQSZIkqWXNVQGIe4BIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJbTu6cLkKS5Sf++CzBqp4E9XYYkzSHZ0wVIkiRJktRtnAEiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYgkiRJkiRJkiSp5RiASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJbTu6cLkKS5ycTJHzD8+vE9XYYkSZJm0ekTlu3pEiSppS03Inu6BEnqMmeASJIkSZIkSZKklmMAIkmSJEmSJEmSWo4BiCRJkiRJkiRJajkGIJIkSZIkSZIkqeUYgEiSJEmSJEmSpJZjACJJkiRJkiRJklqOAYgkSZIkSZIkSWo5BiCSJEmSJEmSJKnlGIBIkiRJkiRJkqSWYwAiSZIkSZIkSZJajgGIJEmSJEmSJElqOQYg6nERMSYisuYxsqfr6g4RMaTOvWZEDO3p2iRJkiRJkiSplRiAaG4wCjgVOLenC5kDJlHc66nAvT1cyxwVEd+MiNci4oyerkWSJEmSJElS6zMAUbeJiKfK2Q1PddQvM8/KzKOBk+ZMZd2jMptjTKM+mflqZh5d3u/oOVddc9XMZBnZhf6LUny+SwBfj4jVu7tGSZIkSZIkSfM3AxBJc0o0OJYkSZIkSZKkpjMAkdTtMvNN4DvA68CvMvPRHi5JkiRJkiRJUovr3dMFSJo/ZObJwMk9XYckSZIkSZKk+YMzQCRJkiRJkiRJUssxAJkLRETviPhyRIyOiJci4r2IeD0i/hUR50XEHhGxUBfG2TYiRkbE2Ih4OyImRcTDEXFmRKzTwXlZ5zG0i/2G1PQZ2tYGrFS+vFJXzm1Q23oRcXFEPBMR70bEc+XzNTo7t6siYt2I+F5E/CkiXqy8/w9HxFkRsW4H546o3G+bberd72zU9+GI+GZE3BgR48r34c2I+E9EXBgRW3Ryfr33fkTZtmtE3BIR4yNickQ8HhE/iYilGox1fHkvT1Ze3r/O+E915fqd1L1deX//LX+f346IpyLimoj4RkSs2IW3T5IkSZIkSdJ8yiWwelhEDABuBDYC3gNuBZ4GFgM2BL5UPl6JiG0z8191xugHjAT2KV/6OzAa6AMMBb4GHBoRPwWOyczaL+NPLX9+HNi8g3Lb+g0D1m7Q59lKvwOBxSn2fTi3Tt9JHVyLiNgLuAC4A7gaGAzsCOwH7BoRQzPzHx2N0ZmI+DlwePn0DYr3/3lgELAxcAhwUER8NzN/VGeIh5h2v0eVP8cBo2anrkp9RwE/pdg0/F3gFuAZYBlgA+CLwBcj4pfA1zPzgzrDtNW3NsVn1zb2KcBXgBvKej8KbA18E9g5IjbPzNrP6J5yvMUpPl+Ah4GbavpN7Oz6HdzzwsCFwF7lS38HbgYWBtYCdikfp0bEhZn55c7GlCRJkiRJkjT/MQDpeedShB9PA1tl5rNtDRERFF8ynwUsXT6mExG9KL7AHgpMBT6fmaNqxjgWOAH4FrAEcFh1jMw8uux7PB0EIJV+A2gQgGTmWKCt314UX5S/2nbuTFgVOAPYLjPvq9zPBsDdFAHRLzuqt4uWL3/eCuyTma9WrrUQcAzwPeCkiBiXmRdVT87M+4H7y/5tAcjYWbjfRpajCD/+BQzLzOcr9S0AHAycCXwVGA98v3aAyuc2gmkBxGeBgcBqmTm+MuYRwGnAmhS/L9+uGWs0MLqcvdMWgNzf0f02uH5dEdGbIhDcmjq/z2WfTwOXAUsCn+hoPEmSJEmSJEnzL5fA6kERMRD4TPn019XwAyALv6b4sreRYyjCD4ATa78sLsf4PnBl+dKhEbHb7NY+B2wB/KAafgCUMz7+UD7dLCJWbcK1pgCfrYYf5bXezczjgCvKl04qv6DvCZ+vhh8AmflBZp4F/Kx86f9FxAwhWQOfLMccX/P6mcCLbdec5Wpn3Xcowg+A79f+PgNk5s3A17urgA+mTuHVJx6s+3hn4kvddVlJkiRJkiRJTeYMkJ71YYq/7ofir90buY5iOay3qy9GxGJMW3bpPeD0Dsb4MbBneXwcxZJSc7PJwEUN2u6hmMEAxfvyxGxc5/fAnzLz5Q76XAXsTbEE1ybl9eeUW4GJmflgB32uoli2amHg08AlXRj37sx8uPbFzHw/Iu4FdgcGR8SymTlHvvUvf5//p3z6LsUMoEZGAb/ojjrenfQytxyxfd22tT57NOvs983uuKwkSZIkSZKkJjMA6VkvVo4Pi4iLMvOF2k6ZeSlwaZ3zd6RY0grgnjr7NVT9HZhAsXfE+hGxVmY+Mot1zwkPZuZbDdrGVY4Hzc5FMrOj2TVtqgHAR5mDAUhm3sSM+2vUqq2vKwHIvR20VWciDaoZvzvtxPS/z6836piZ70XEDyv9m2ahJQaw9Qn1/nODvv2XbfblJEmSJEmSJHUTA5Ce9TTwN4pZBSsBj0bEr4GLOvmL/zZbVI5n+Gv+qszMiPg3RQDSdu7cHIA800FbNRhZtBkXi4hFKJZeWhtYClio0rxC5bh/M643syJiQWBLioBjaaAv02YPLV7p2tX6nu2g7c3KcVPe3y76eOW409/NzPxJdxSxQO8+LLXqR7tjaEmSJEmSJElzkAFIDypDif2Bm4EVKb7IPho4OiLGUixTdVm50XY9gyvHtXs51FPtM7hhr7nD2x20Takcz9Y+NhHRl2JJsK/RtS/7F5yd682scqPzIyk2JO/K/h5dra/R7Bpo4vs7k6pBU1d+nyVJkiRJkiSpoTny5WZE9C7/wl41MvNRilkHxwBjK02rUOzvcV9E3BsRG9Q5fbHK8eQuXK7aZ/GGveYO2d0XiIhewLUU4cKiFBvFbwEsmpnR9gC27e5aOnAe8FOK8OM2is3Ll6ypb+VZGLfb399ZUA2guvL7LEmSJEmSJEkNdUsAEhG9ImK/iLguIl6m2ND49Yh4JyL+EhHHRETT1+6fV2Xmm5n5o8xcFdgMOA2o7gWyGXB3RGxSc+obleO+XbhUtU/D/RW6IDrvMk/4EkWgADAG2Dsz7+lg75E5KiK2B0aUTx8DdsjMP3Wy18u8bGZ/nyVJkiRJkiSpoZkKQCJi9Yh4pnw8HREfr9NnNYoNt38L7ECxJ0GUj4WAjYEfAP+NiB1n9wZaTWb+NTOPpFgOaE/g8bKpL3BmTffqZuBd2Z15YOW43h4QH1SOe3UwTqvM5tm7cnxGZs5tsyKq9Z2dme/2WCVzRvX3eWDDXpIkSZIkSZLUBTM7A+RTFHtHDAYGUbOMTkSsQLFMz7pMCz1qtb2+FPDHiBg2kzW0jIjoGxFDImKGvScy8/3MvIpiSaaXy5c3ioh+lW53VY7X7uRaAaxVeenuOt2q+250FHKs1NG15iHV+3i8Ya85uw9G1dxeX7NVf5/XatirFBFDI2LniFi+G2uSJEmSJEmSNI+a2S9OP1H+vAlYLjPvrWk/E1iOIuD4G8UeFttSfJn5EWADYDhwLsUmzL2AiyNiMeZPmwFPAgc06pCZLwN3lk8XYPpg4gbgtfJ4806WFdsIGFAe/zMz/12nz3OV47qbpJdhzbodXKeqbTPt6X7PImKxiBgREXt0cZzuUg18+jXsNf3m3B2ZWv6svd/lyvv91MwUR/Pra6aGG6VHxEfK+51hhlgnqr/PH4+IhvvURMQywK0Ue7jMr/9+SJIkSZIkSerAzAYgHwEmAcMzc0K1ISKGALsC7wEjMnOzzDwtM2/PzEczc2xm/jMzr8jMg4HVKEKSpYCDZ/tO5m2f6KS97QvuF4BX2l7MzDeBn5VPFwSO6GCM/1c5PqFBn/sqx5s36PMFiqXMuqJt5krtF9RrAhcA3+3iON2ler+7dNDvc10cr9H9bkVxv4d0cZw2za6vmSYybQZY7f3uTnG/e83MgDW/zwsB3+ig+6EUAerdmfnYzFxHkiRJkiRJ0vxhZgOQDwG3ZOYbddq2K3/+MDN/29lAmfkC8BngTWDnmayj1ewaEV+rfTEKX6WYvQH196k4iWIDb4DvRsTwOmMcS7GfCMBZmXl1vSIy8wmKUApgr4hYr2asjSlCi3rLZ9XT9gX+khGxauX13cuf93dxnO5yJvB+efz/ImK6L+wjYoGIOIFi6beuaLvf1WtmNc3q/Z7HtI3BvxgRXy2XMqvW+BXgwJkcd7Zl5jvAQ+XTj0VEr7KeBSj+u4ZZ+3yrv8/HRsTetR0i4nPA9yhm3BwxC9eQJEmSJEmSNB+Imdn3OSLeBs7NzBn+MjsivgccBwzJzHobbDcacxQwNDO7sol3S4mItYE/M23D57EU4cKrFMtVbUwxUwbgV8DhmflBnXH6ARcybdPsv1MEGX2AocCqFF/0nwJ8u6PNviNiQ4p9XBYDJgPXUyyNtQqwDcVyXTsD+5ennAu8DozNzLNqxloDeIBiA/dx5VjLU8xmeAfYKDMfKUObjYHFmfZl/sMUS62RmUeX47X1WwHYp+x3L3BPtd/MiIgRwK8p3iso3rt/Urxf25b3fQPTQrq2603MzJNqxtqOYlmmAB6jeB/XoPgMxlMsHTYF+E55yseZNtPmcoqN6acbNyI+DfweaNsn5t8UQctb5fnrAX9kWuhQ7307BuhPsU9M2547N5V92z+3Sr96ddX7fEdQzPRoe9/+RvH5bFTe//qZOTkiTin71Lv+DJ9bnd/n+8tHP+BjwEcplgf7YmZeSZNExDhgUL+ll2OXC//ZrGElSZI0h50+Yb77X0tJmqOWG9H17xIlzZfq7QveY2Y2APkvcFdmfrFO2/HAscBCmTm1tr2DMc8ADs7Mvl0upIVExILAjuVjI2BlivDhXeAZii/bz8vMe7ow1lDgS8CWFLN13qcIHv4EnJ2ZDzU8efpxVqeY6bEdsAzFl/d3ACdn5v9FxEimBSBtbs/MoXXG2pTi92IzYEmKPR7uAU7IzL+XfeqN1y4zY2b6zayI+CjwPxSBx3IUMwueo7jnX5R131Zz2tOZOaTOWMOA/6XY72ZRimWx/gx8LzOfKJeKe7KDcmYYNyJWBo4EPs205dBeongfzwGerjdm5X17isYb17d/bl3tV1PbfsDXgHUogq4XgRuBYzPzpbJPh//INPrcImJbisBtS4rPJYGngNHA6Zn5VEfjziwDEEmSpNZgACJJ3csARFIn5ukA5AZg9cxcpU7blyn+kn61zBw7E2NeA2ycmct1uRBJajIDEEmSpNZgACJJ3csARFIn5qoAZGb3APkDMKTeuvzAzRQ395WuDhYRq1Hsr/DoTNYhSZIkSZIkSZLU0MwGIJcCzwPnRsQnqg2ZOQ74HXBUuXF3hyJiI4p9APoATVvHX5IkSZIkSZIkqffMdM7MNyPiEIpNl0dHxFXARcBtmfkGcATF3gc/j4ivUcwY+T/gFYr1+5cAVge2B7amCGDGAr9pyt1IkiRJkiRJkiQxkwEIQGZeHxGfBy4E9igfRMSLwASgF8VSWKsD/6+DoYIiGBmemZNntg5JkiRJkiRJkqRGZnYJLAAy8zJgE+BuiiAjgOWAdYGPUMz2aNsRKRo87gS2yMx/zEb9kiRJkiRJkiRJM5ilAAQgM/8vM7cGtgTOAv5bNtUGHVUvUyyZNSwzt8nMx2f1+pIkSZIkSZIkSY3M9BJYtTLzHuAegIhYDFgT6A8sXo7/JjAJeDwzX5jd60mSJEmSJEmSJHVmtgOQqnIj9L81c0xJkiRJkiRJkqSZNctLYEmSJEmSJEmSJM2tmjoDRJLmdf37LsConQb2dBmSJEmaZdnTBUiSJGku4QwQSZIkSZIkSZLUcgxAJEmSJEmSJElSy+nxACQiVo6IrXu6DkmSJEmSJEmS1Dp6PAABDgNu6+kiJEmSJEmSJElS65gbAhBJkiRJkiRJkqSmMgCRJEmSJEmSJEktp3dXO0bEF7uphrW7aVxJkiRJkiRJkjSf6nIAAowEspvqkCRJkiRJkiRJapqZCUAmAwsB0Q11GKxIkiRJkiRJkqSmmZkA5BjgZ8DrwB+aWMNGwFpNHE+SJEmSJEmSJM3nuhyAZObpEbErsA1wXWZe2YwCIuKnGIBIkiRJkiRJkqQmWmAm+48A3gB+FRHLNL8cSZIkSZIkSZKk2TdTAUhmPgN8HVgGOK9bKpIkSZIkSZIkSZpNM7MHCACZ+duI2A3YLSK+kpm/mc0aRgNvzuYYkiRJkiRJkiRJ7WY6ACl9DlgWeHd2C8jMW4BbZnccSZIkSZIkSZKkNrMUgGTmZODpJtciSZIkSZIkSZLUFDO7CbokSZIkSZIkSdJczwBEkiRJkiRJkiS1nKYEIBGxdUSs3IyxJEmSJEmSJEmSZlezZoDcBhzapLEkSZIkSZIkSZJmS7MCkGjSOJIkSZIkSZIkSbOtdxPHGhYRA2bx3KnAa8B/gTGZ+WjTqpIkSZIkSZIkSfOdZgYga5eP2RYRtwNfzcx/N2M8SZIkSZIkSZI0f2nWElhQLIMVNccdPTrqNxS4LyK2bGJ9kiRJkiRJkiRpPtGsGSArAx8Gfgf0Ay6m2Bh9LPAG8D7QC1is7Lct8Hngn8DhwNvAIsAQYAtgf+BDwO8jYtXMfLNJdUqSJEmSJEmSpPlAZObsDxKxKPAg8DSwV2a+0oVz+gNXUIQw22Xm+zXjXQl8EvjfzDx1touUpA5ExDhg0KBBgxg3blxPlyNJkiRJkiTNi6LzLnNOs5bAOgJYEtijK+EHQGZOBPYG1gUOrml7E9gPeAvYpUk1SpIkSZIkSZKk+USzApDdgJsz89WZOakMQW6mCDtq214G/gys2YwCJUmSJEmSJEnS/KNZAcjKwPhZPHcCsFqDtqcpZpZIkiRJkiRJkiR1WbMCkH7AR2bx3I9QbIBezxLAe7M4riRJkiRJkiRJmk81KwB5BvhERHx0Zk6KiI9RbHT+bIMu6wEvzWZtkiRJkiRJkiRpPtOsAOR6oA8wOiI+05UTImIP4KayhmvqtO9OEYA82qQaJUmSJEmSJEnSfKJ3k8b5CTACWAa4KiKeAm4F/g28DEwG+pbta1LM+lgJCGAi8NO2gcpZIfsC/wMkMKZJNUqSJEmSJEmSpPlEUwKQzHypnLFxLbAYMAT4SienBfAmsFtmVjdQvwYYVB5/AFzZjBolSZIkSZIkSdL8o1kzQMjMOyJiY+BsYGgXTrkNOCQz/1Pz+mHAwuXxW5n5ZLNqlCRJkiRJkiRJ84emBSAAmfk4sF1ErAfsAWwELA8sSjHb43ngPuAPmfl/Dca4tpk1SZIkSZIkSZKk+U9TA5A2ZbhRN+CQJEmSJEmSJEnqbgv0dAGSJEmSJEmSJEnN1u0BSBQW7O7rSJIkSZIkSZIktWl6ABIRS0TENyLipogYD0wFTqy0j4iIn0bEh5t9bUmSJEmSJEmSJGhyABIR+wFPAj8DtgcG1Ok2CDgKeDQijmvm9SVJkiRJkiRJkqCJAUhEHAj8FlgSiPJRz43AtUAC34uInzSrBkmSJEmSJEmSJGhSABIRg4GfU4QejwP/C2wDrElNEJKZ/8jMzwAfB8YBR0bEBs2oQ5IkSZIkSZIkCZo3A+SrwELARcC6mXlKZt6ZmY81OiEz/w4ML2s4qEl1SJIkSZIkSZIk0btJ42wPvAgclJlTunpSZv4lIu4Btm5SHZI0WyZO/oDh14/v6TIkSXPY6ROW7ekSVFpuRPZ0CZIkSZJaRLNmgAwBbsnMd2fh3EeAFZpUhyRJkiRJkiRJUtMCkEWBV2fx3AR6NakOSZIkSZIkSZKkpgUgE4A1ZvHcTQDXm5EkSZIkSZIkSU3TrADk78B2EfGxmTkpInYH1gP+0qQ6JEmSJEmSJEmSmhaAXEKxofqNEbFjZ52jcCDwO4olsC5uUh2SJEmSJEmSJEn0bsYgmXl5RHwD2By4NiIeBW6i2OAcYLWI2A9YBlgH2AH4EBDAnzLzumbUIUmSJEmSJEmSBE0KQEq7AXcCq1HsB9K2J0gCu5SPNlH+/BewdxNrkCRJkiRJkiRJatoSWGTmBGBj4ELgA4qQo9FjKnAusHlmvtasGiRJkiRJkiRJkqC5M0DIzDeAAyLiBGAfYAtgMLAY8AYwDrgLGJWZzzTz2pIkSZIkSZIkSW2aEoBExPmVpwkcmZk/acbYkiRJkiRJkiRJM6tZM0BGUAQfUf48FpjUpLElzcci4njguJqXn87MIXO+GkmSJEmSJEnziqbtAUIRfBwBLJuZzzdxXEnzt3uAU8vH6z1ciyRJkiRJkqR5RLMCkPeA6zPz55n5cpPGlNRNImJIRGT5GNnT9XQkM0dn5tGZeTTwak/XI0mSJEmSJGne0KwA5Hng6SaNJUmSJEmSJEmSNFuaFYDcCawxKydGxD41m6hLkiRJkiRJkiTNlmYFIGcCW0XEJrNw7sbA/k2qQ5IkSZIkSZIkqTkBSGbeT7EB+rURMbwZY0qSJEmSJEmSJM2qpgQgEfE9YFngn8AlEfFCRFwRET+JiOMi4nuNHsDHm1HD3CYijq9sMl37GFmn/xFl230Nxqs3zpgm1fpUnbGPL9u2joirI+L5iHi37Ht2RAyaifG3jYiRETE2It6OiEkR8XBEnBkR63Rw3phG711ErBYRv4qIJyLinWa/J3Vq2bC878ci4s2ImBwR4yLipoj4dkSs3oUxNouI88qa346INyLi8Yg4NyI26+C8ep/9iLJt14i4JSLGlzU9Xv53t1SDsY6PiASerLy8f53xn+ri9beLiCsj4tmImFr7+1Nz7z+OiDsjYkJETImI1yLigYg4JSJW7uz9kyRJkiRJkqSZ0btJ4xwPZHkcFGHIHl08NyrntpJ7gFOBXYDVytfOA14D6oUcnyl/bhgRy2fm8zXtp5Y/PwssBfwKGNukWn8N9AdWAPZpezEivg6cBNwM/B74CPAp4GBg14jYPDOfbjRoRPQDRlbG/DswGugDDAW+BhwaET8FjsnM2t+DUcD9wOLAgZVxdwUuAR78/+3dd7gtVX0/4M8XLi2KCCJIbChoDKJJFHtDI1bAGo0lEY2aWGJMgj+NGgMxEpNYSEysSQQ7sUYsCBawxhaNih0FsQKCggpIWb8/Zo532Oxzzj7lnnPv3Pd9nv2ctfesWbNm9rrDZn/2zOr7ds0k9+/bXVVVtW2Sf07yxHRj9atJ3tiXfyPd8bhnkqOq6v2ttYOmtLEhyb+mO25J8rl0x2FDkjsmeWySx1bVq5I8sbV26UQTc+/9TZPca9DuC/p135PuWN08yZ2TPC3Jwf3789OJtubG5fCYnprkhIl6586w/ef223p/kv9Osl+Su07Z/3em+3eQJGcl+VCSs5NcL8ltk/xlkidV1R+31l4zuT4AAAAAwHKsVgCSdF8IL/R8q9JaOzHJiVX1/Wz8AvmU1tprJ+tW1W5J7jT3NMmhSV4+0d7hfaDwhCQntdYOX8W+HtX348BsDCvukGTfJL/VWvtV0FJV90/y9iR7JXl+ukDmSvrg4D3pgo5LkzyytXbcYHkl+eskRyZ5RpJd0oUMw369rK+7dzZ+Wb9Pkv9M8ojW2n8P2rtfkncsZb9n9Nps3Me/bK29aLiwunlv/ivJ9ZPcfZ42XtO3cXmSx7XW/nOijT9NF7I8Lsm2Sf5ouHzuve6vupgLIB6WZI8kN26tnTVo66lJXpzkN9Md17+aaGtuXO6djcf0MwuNp3m2/5B0gchvtda+Ntj+i9PdDm/o1/u/r03yJ621XwzqXy3JC/q+/GdVfae1dvJ8fQEAAAAAmNVqTYKedL9Av+syHv+1in3YHB0/KB8yT52D033xPed+89S7e5JfS/LOVejXYu6e5AnD8CNJWmvvyMYrWB5QVVeZZ/1npgs/kuTvhuFH305rrf1tkrf2Lz2hD1cWc8ckLxqGH317/53k5zOsP7OqelQ2hh//ORl+9Nv9VJI/mLGNoyfDj76NlyQ5tn/6mKr63Rm6d/d0odJZE6+/JMkP+/IjZ2hnue6d5OHD8KP3hnnq/yjJHw3DjyRprZ2f7sqYT6X7N/D81e4oAAAAALB1Ws0rQM5srZ2y1JWq6uBV7MNmp7X2jar6WrrbJd2zqrZvrf1yotr9090a60d9vbtV1c6ttQsm6t0v3VUE79q0vU7SvZ+Tt0Wa89Ekt0qyQ5L9k3xyuLCqdk53W6Mk+WWSoxfYzvOTPKgv/00Wv4rjkiQvm2fZvkkuW2T9mVTVNumuUJnzD/PVba19ZPAeT7bx7P7p5dl4JdA0L01yWF9+SpIPLNLFj7XWTp3Sl8uq6hNJHpDkOlW1Z2vtR4u0tRyfaa19bMrr/5vuVmrnD177jyQ/aa1dMq2h1lqrqrcnuXWS28xzC7g1c/mll+S8b35h6rIdd9szO+225xr3CAAAAABYjtUKQM7IFecMWIrTknx4lfqxuXpnurkSrpbkLklOmltQVTumm0finemO4zOSbJ/uVkNvHtTbJt2VIp/cRF9oT/rEAsvOHJSnTYZ+n3S3tEqSj0+Zh2Los+nmg7hmkt+uqv1aa19eoP4XW2vnTVvQWvvhtNeX6dbpbreVJN9urX19kfr/lC4UGrpVulAmSb6wyJf6n0sXFm2f5O5VtWHKXCBDS3l/NsV4mfpvtrV2WZLvTrw2X2A1NOzjzZOsWwBy8U/PyUlPvdJULkmS/R52ePZ/xNPWuEcAAAAAwHKsSgDSWrvBCtZ9eSbmuxih49MFIEl3G6yTBssOSnKVdJNIfyddAJJ0V3u8eVDvNukmlz96U3Z04MwFlv1sUL7qlOV3GJSvdJXCUP/r/6+kC0Dm1l0oAPn2Qu2totsPygv1J0nSWvuPdFc6DA2Pw4JttNYurapzk1wr3W3ObpLkSwusspL3ZzUs+X0YzHVzkyRXzxUnrb/poLzbinq2QjvssnvufOQbpy7b0dUfAAAAALDFWM1bYDG/jyf5cZJrpAtAnjJYdr90t3V6b7ovrs9KN7n1fSauAji0/7sW838kC8+nMbyV0bR5ZK4zKE/OUTHNsM515q3VWdV5PhZw3UF5ln2YZrgvD6+qhy9h3T2zcACykvdnNcz8PlTV1ZP8Y5I/THfbtMVsv8w+rYptNmyXXfe9+Xp2AQAAAABYBQKQNdDPy/CedJNl711VN2utfbG/rdUhSU7uJ4NOVb0ryWOS7Jrkzkk+2DdzaJJvLnJ7qFXt9grW3XlQvmiG+sM6V1uk7kr6tRTDKydm2Ydphsfh81l8Xo+hxW7ntVbHYUXb7+eDOSXdba2S5BX946uttQsH9Q5L8upV7iMAAAAAsBUTgKyd49MFIEkXenwxye3SXe0xvKrjnekCkKQLPT5YVfsm2S/Ji9emqys2nLx9xxnqD+ucP2+ttbXUfVisjf9trR2+gv5sqZ6RjeHHsa21P1nPzgAAAAAAW49NdXscruyEdJNcJxtvZ3X//u8wADkpydwv4+83UX+tbn+1UsNJsGeZNGGPQXmhuS3W0nAf9pi31uxtXHPeWuP2e4Py0evVCQAAAABg6yMAWSOttQvS3QooSW5dVXumCzg+31r7zqDeL5K8v3+6d1XdPF0Acm6Sj65hl1di2M+bzlsrSVVVuqtb5nxsk/Ro6Yb7sN+8tXpVdeuqOriq9pmnjf1naGP3qnp2VT2rqsZyddb1B+WvL1DPuQgAAAAAWFW+dFxbc1dwVJKnJblRkv9eoF7S3Q7rjkneM5gQfXP3niQ/6cu3q6pdFqh7QJLd+/LnW2tf2ZQdW4LPJPlGX75BVd14vop9WPGedLc523ew6NPZ+KX/DapqsSDlD5I8N8kD1+C9nnei9Kq6UVUdVlW3X4Xt/GJQ3mmBetddYBkAAAAAwJIJQNbW8YPyn/V/p93W6vhsnGT6SUm2nVavv2LgQ1V1flX966r2dAVaaz9L8qL+6fZJnrpA9acPykeudNtVtW9VfbaqflJVf7Xcdlprl6cLI+Y8fb66SR6e5BpJzshgovPWWssV9+mv52ugqq6e5E/7py9ZYneX49xsHGM7Tyx7QLoJyR+8Ctv59KB8yLQKVbVtkoeswrYAAAAAAH5FALKGWmtnpJv8POkmoD+ztfa/U+r9KMmnBvV+mW4OkUl/nuTAdF9gP6mqfne1+7wCRyU5uS8/u6oeOlxYnb9O8qD+pZe11t6xCts9MsktkuyS5Kh+Avllaa29Nsnr+qePqaqnTtbpj/lc+PTUySs3WmtvSHJM//T3q+p5k7e3qqrrJnlrkhukO2avWW6fZ9VauzDJl/qnv9OHEKmqbbJx7pnPrMKmjh6U/6mq7jJcWFXbJ3lFZrjNGAAAAADAUoxlnoEtyTuT3KwvH79Ivdv05ZP7OUQ2iap6QpJ9csXbEN2jqq6a5NzW2lET9Ybzejy0qvYf1kuS1tplVXWfJMemmwj7TVX1tHTBznbpgpt9k1yW5AVJrnS1Rh+a3CrJ1QYvH1BVL+jLp7XWXrbsHZ/NYUnOS/LkJC+uqsenm9tjm3TzetwmyaVJnrJAgPNH6W4J9pQkz0zyqKr6YJIL0h3PO6e7PdQHkjyov/rkV6rqmUl2y/Tj/qtjMKg3vHXVE6rq4Ew/Vi9Kd6XH9ZN8sqo+le54H5Dka0neNsP2k+TE1tqJ03a8tfaePuj623S3Oju5qj6a5NR0VwcdlG6C+Pcluee0fauqeyS5R79s17m/g3Ew7/YBAAAAgK1XdXfpYa1U1W2S/E//9F6ttffNU++m2fgL/Se31v5tSp09krw53RUPr22tPXGZfTo5yV3mWXxGa23vpdSb0v6B2TiXybXShR7fTfeF/8tba1+aZ71jkjxqga6f0lo7cGKdGyf5r3RXU/xja+15C6w/s6q6RZLHJ7lbkmunuy3Zd5N8KMm/tNa+uMDqc238dpI/HrSxXZKz0t0m6nVJ3t6m/IOsqtNzxcnEh351DGatN9H2I9KFO/sn2THJD5O8N8lf91ciLdZukhzZWjtigeWpqjumu8XXHZPskeSidLcM+2CSf+lff/W0PlfVEUn+ZiXbn0VVfTfJtXe6xl455NjPr7Q5ALYwR5+953p3gd5eh/n/EwAA2ILVendgSAACEAEIwNZOALL5EIAAAMAWbbMKQMwBAgAAAAAAjI4ABAAAAAAAGB0BCAAAAAAAMDoCEAAAAAAAYHQEIAAAAAAAwOgIQAAAAAAAgNERgAAAAAAAAKMjAAEAAAAAAEZHAAIAAAAAAIzOhvXuAMDmZLcdt8lx991jvbsBwJpr690BAAAAVpkrQAAAAAAAgNERgAAAAAAAAKMjAAEAAAAAAEZHAAIAAAAAAIyOAAQAAAAAABgdAQgAAAAAADA6AhAAAAAAAGB0BCAAAAAAAMDoCEAAAAAAAIDREYAAAAAAAACjIwABAAAAAABGRwACAAAAAACMjgAEAAAAAAAYHQEIAAAAAAAwOgIQAAAAAABgdAQgAAAAAADA6AhAAAAAAACA0RGAAAAAAAAAoyMAAQAAAAAARkcAAgAAAAAAjI4ABAAAAAAAGB0BCAAAAAAAMDoCEAAAAAAAYHQEIAAAAAAAwOgIQAAAAAAAgNERgAAAAAAAAKMjAAEAAAAAAEZHAAIAAAAAAIyOAAQAAAAAABgdAQgAAAAAADA6AhAAAAAAAGB0BCAAAAAAAMDoCEAAAAAAAIDREYAAAAAAAACjIwABAAAAAABGRwACAAAAAACMjgAEAAAAAAAYHQEIAAAAAAAwOgIQAAAAAABgdAQgAAAAAADA6AhAAAAAAACA0RGAAAAAAAAAoyMAAQAAAAAARkcAAgAAAAAAjI4ABAAAAAAAGJ0N690BgM3JuRddnoe++6z17gYAAMBW7+iz91zvLgCs2F6HtfXuwlbNFSAAAAAAAMDoCEAAAAAAAIDREYAAAAAAAACjIwABAAAAAABGRwACAAAAAACMjgAEAAAAAAAYHQEIAAAAAAAwOgIQAAAAAABgdAQgAAAAAADA6AhAAAAAAACA0RGAAJu9qmpTHoetd78AAAAAgM3XhvXuAMAMXtj/vWmSe61nRwAAAACALYMrQGArVVXHDK6m2Hu9+7OQ1trhrbXDkxy33n0BAAAAALYMAhAAAAAAAGB0BCAAAAAAAMDoCEAAAAAAAIDREYAAAAAAAACjIwBZZ1W1X1W9pKq+UlUXVNXPq+pbVfW6qrrnGvXhsMFk2L969Mt2rqpnV9WX+r79pKo+UlWPWEL7u1TVM6rqY1V1VlVdXFXfr6oTqurxVbXdPOsdOK1fVbV3VW1TVY+pqlP6Ni8fLD9wdY7MFfqyY1U9uao+UFU/rKpf9sfi81X171X1wKracZE2fq2q/qKqPtz3+Zd9Wx/qX7/KPOsdMeUYnN4v26Oq/qGqvl5VF1bVuVV14kJjZ/D+Pmrw8renbOOwGbZ/9ar666r6v6r66eT4GWxz1/79Oq6qvtGPpYuq6rtV9d9V9bCq2naGtwIAAAAAYCYb1rsDW7Oq+pskz073Pnw9yRuTXJrkgCSPSPKIqjo+ycNaaz/fhF35UpIX9uWHJrlO379rJTkpSSX5SJL/SXLPJHdMcseq2q+19qyFGu6/iH9dkt2TnJ/kfUl+lOQGSQ7q2zu8qu7fWvvyxOpnDvp1ryQ37cs7JXlvuuP0viSnJrlLkv2WuuOzqKoDkrw1yfWSXJTkxCTfSXecbpPkj/rH2VX1uNbaf09p4zZ9G9dOckGSE5L8sG/zoCQHpjsOD2itfXJi9Y9n43F4XJKr9W3+Zt+X76d7n66a5D59ewdV1R+31l45ZZemHdNXpXt/hr60yPZv1O9HS/KhJJ9NcnCSa07s+x2SvD/JXED00SQf7J//ZpJD+8cT+/0/Z0qfAQAAAACWRACyTqrq75M8o3/6nCTPa61dPlj+4CSvT3JIkndU1T1aa+3KLa1ca+0zST7Tb/eA9AFIkmOTvDPJs+e2XVU7JflAktsleUZVvX5KcDG3D/dKcny6cXZykge31n48WH6jJO9JcqMkH6uq27bWvjbo12lJDu/r7p6NX9bPfRm/T2vtJ/3y7dJ9sX7rZR+I6fvwO33fr5IuEDi4tXbGYPlOSZ6b5C/TffH/O0n+e6KNW6Q7ZldJ8skkh7bWzhosv06SdyX5rSQfqKoDWmtfnVveWjsxXdAxNy6ulmT7JMcl+bvW2isGbV2j38Y+SV5UVW8dHvO+vWnH9KjW2unTjsEC239z34dnz43dfl++ki6MmbNrurDjgiT3aq19fOL43DXJG9IFa8cl+d1p/QAAAAAAWAq3wFoH/S2ant4/fVtr7bnD8CNJWmtvSXJU//TuSR69Zh3c6GqttWcNg5fW2oVJ/q5/uk2Sh09bsf8i/jXpwo9zkzxwyhfx30jyoCSXJ7l6kjdW1Sxj8lZJHjIXfvRtXZLkLbPt1mz6UOVN6YKLS5Lcfxh+9Nu9sA8UPrhAG2/s2/hZuuNw1rBOa+27SR6c7uqfqySZdtXGpL2SfHkYfvRt/TjJi/qnV0nygBnaWo69kpzVWnvmcOz2+/LhedZ59mT40a/zoXRX0CTJ3frgbN1cfuklOe+bX5j6uPDcH61n1wAAAACAJXAFyPp4TrrbSiXJPy1Q7+VJ/qav+2dJ/nMT92vSq+Z5/aOD8i3nqfPEbLwV0qtaa+dNq9Ra+0JVvTfJfdNdPXFokncs0q9jWms/nfL6v6ULG85eZP1ZPTzJjfvyW/orUubzqiR3m/L6wwZtvL619v1pK7fWvllVJ6a7hdWdqup3WmufW6R/8wUlk+/Pvy/SznL98zyvPzJd+DLnG0mel+7qjvm8L8kvkvxakgemu7XWurj4p+fkpKceNHXZfg87PPs/4mlr3CMAAAAAYDkEIGusqvZMctf+6bnpblc0VWvtR/1k0zdIcvOq2qu19oNN38tf+cQ8/Tq/qs5Pdyuka8+z7vDKkPcusp0T0gUgc+u9Y5H6U68waK39It2X6KvlYYPyYl/IfyjJK9LfSmyeNhY7Dp9MF4Akyb2TLBSAtHRzskxz5qA83/uzGuZ7H85Lct7g+dfSzXUzr9baZVV1Tro5UW6+mp1cqh122T13PvKNU5ftuNuea9wbAAAAAGC5BCBr7w6D8ldnmNdjbsLwpPs1/7s2Sa+mO3OBZT9LF4BcdXJBf/urmwxeOnWR7QyX32HeWht9e4Y6q+H2g/LUeU7mtNZ+lORPVtJGuvd6zi0WqXtuH/hM87NB+Urvzyr5cWvtgqWuVFW3TDd5/bXS9a0Gi6/e/91txb1bgW02bJdd913XDAYAAAAAWAUCkLV3nUH59lW1lInN1/rn5z9fYNkl/d9pc3YMrzq4vLV2ziLbGc6JsVdVbTM5J8oS+rUqqmqXJDsPXjprvroLtHG1dCHRnK9X1XzVJy32Xs97DFprlwy2s6nm+VnSe1BV9003ef1vzFB9+2X1CAAAAABgQACy9oZfqn87yduWsO7XVrkvC5rh6pT5DPfx4hnqXzQoV7/+tDk+5iy3X0sxeeXERVNrLWzniecvTXLhjOt+b5Hla3EMVmX7VXVYuvlrKt2Yf1a6SePPHgZd/e3err+qvQQAAAAAtloCkLU3vG3Qt1trh69bTzad4T7uMEP9HQflNrH+epnsw45Tay2tjee31ha6rdjoVNVuSV6SLvy4OMm9+zlBAAAAAAA2qU11exzm991B+Zrr1otNa3j1wjZVtdh+7jEof3+R21+tidba+UnOH7y0x3x1l9DGWN/vhdwrG6+mOUH4AQAAAACsFQHI2vvYoHzjqlpwvoOq2r6qnlVVz+4nF9/stdZ+nOQrg5duusgqw+Ufm7fW2hv2Zb+FKlbV1avq4Kq69wJt7L/YBqvqgf17fbcl9HNzNryl1dcXqet8BAAAAACsGl84rrHW2o+SvL9/ukOSuy+yyn2T/F2SpyQ5bxN2bbW9YVCeDAUmDZe/Yd5aa2/Yl3stUvfRSY5P8pyJ118/KB+8UANVtV2SVyR5bpKrzNjHlbhkUL7CuaCqfq+qDquqX1vhNn4xKO80X6U+CFzyVTYAAAAAAPMRgKyPv00yd5unZ1bV1Peh/0L86f3Tl067NVRVPbaqzqmqr1bVLTZNd5flpUnO7suPrapdp1WqqptnY7jwv+lChBWpqudU1U+q6tNVtfcKmjouyVf78oOrap95trdjksf2T/9jShtzV8M8oKoWuhrmCUl2T3Jakvcsq8dLc86gPDlh+7FJXp1kuxVu49OD8r2ratt56j0os80XAwAAAAAwEwHIOmitfSRdCJIkd0jy75O/tO8nj35Nktuk+xL+Hyfb6dd5aZJrJPmNJP+8Cbu9JK21c5P8YZJLk+yW5G2Tt/C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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_73_0.png" }, "needs_background": "light" }, "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": "1b9d8d61", "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": "61b98e96", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "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": "e55595a5", "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": "7580ddf4", "metadata": {}, "outputs": [ { "data": { "image/png": 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5jmHgtN18napq62zA0ygAAAAAwBWLUGJ5vXfQ/qv+97Qpmd6bdQsHPyHdFEaX61dVO1fVJ6rq11X1r0tQ3x0X2H/t/vdPk/x87sV+LYqX9ptbJzl8xhh/O2gfOU+fzw/at5qnz0OSbDPjPL9XVc+uql9W1eerao/FHDOP45Kc3LfvW1V7TetUVdsmeVS/+bopY8wFMffuFxefz+PSBTinpAt+ltvZg/bkotzHJnl9kq028hzDz/ZuVTXf9Fx/nkV+vgAAAADAFZdQYhm11k5Lt8B10i0q/qPW2pem9DsjyecG/S5KtybFpL9OclC6G8hPqKqFQoWF3LOqnjj5YnWekO4phyR5WWutTXQ7KskJffuZVXXYlDGele5mc5K8srX27mlFtNa+l3XXf9+q+uOJsW6W5JmZPvXTZO3XSxd+7NDX/7yFjplPa+3idAt/n5fu5vy7quo6E+fbId3N+32SfCHJMRNjXJJuAfFz0322/11V+02MUVX10CQvTre+xmNba7/b0LrXwzAwuNmgnrume6rhu621X23MCVprn866z3avJP8+OW1YVd08l1+3BAAAAADYBK1Z6QI2A/+dddMOvXeBfrfo2yf0a1Isl7OSnJluupyXV9Xh6W74/yLdN/VvluT6fd9XJHnR5ACttd9V1d3TfaP+fkneWlVPTXcDeqt04cneSX6X7mb73y1Q0+OTfCJd4PK/VfX+dNM67ZXkdummRzok3VoXSfL0qvp1klNaa68cjFOLegcWqbX2f1V1UJJ3pPscT66q49Mt2L17kgOTXD3JV5IcOm3Krdba16rqwCTvTPKHSb5cVSemewrjaklumuSGSX6T5FGttY8Oj6+q/dOFI0my49zvqnpx335la+2UiX5z9prsN9j3/nTrPFw/yb9U1W3TvX/36fcftcjzp7V2xOR1D9w/ycfSfZZrkxxcVZ9K9wTODdM9sfO/fR07z3Ntc9vDJ00Omwt4Fjg/AAAAALBK1OW/AM9SqqpbpLvhmiQHt9Y+PE+/fZN8vd98Ymvt36b02SXJ25LcJMkbWmuP34i6tk5y9/5n/yR7pgsEfpvkh0k+neR1/TfdFxrroKxbC+MP0gURp6e7Ef2q1trX5z34suPcIN0TEXdIco10wcn/JPmnPhw4JsnDJg47sbV20MQ4R6abUup7Se7XWvv+Ys6/QG1zUzTdO92C1Tule/rhq+mmaHpda+2iBcbYJskj0930/6N+jPOTfDfJh9PdgD99ynFr0z2NMZ/bt9ZOWGy/ibGvme5pkruk++x+21/T0a21/1rk+dNamxkGVdX2SZ6U7tpvkG6qpnOSfDnd2h1vSfd5TS5mPndtM/9DtdD5F6OqTk+y+3ZX3y2HHvuVjR0OAABgxR191q4rXQLAZm+3tZvd/fcF79MJJQAilAAAADY9QgmAlSeUuDxrSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKNYs9IFAKwmO227RY67xy4rXQYAAMASaCtdAABcjiclAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUaxZ6QIAVpNzLrw0h73/zJUuAwAAuAI5+qxdV7oEAFap3da2lS5h1fGkBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBLCqVdVzq6pN/Jy60nUBAAAAAOtvzUoXALCATyd5Sd9+dJKrrmAtAAAAAMBG8KQEbIaqao/BUwfHrHQ9s7TWjm+tHdFaOyLJL1a6HgAAAABgwwklAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglRlRV+1TVy6vqW1V1blX9pqq+X1VvrKq7rkA9N6qqf6uqb1TVeVV1UVX9tKqOr6q/q6obLGKMHarqaVV1UlWdWVW/raqfVNWHquovq2qreY577mCh5bmfExbZ75iNv/r1U1U3rapXVdW3+/fqwqo6vb/Oxb5Xt6yq11XV96rq/P5v4DtV9dqquuWM4yavv1XV2n7fPavqI/17f2E/3gurasd5xnpuVbUkPxi8/LAp45+6yPPfoareUVU/qqpLBvufO+XaX1BVn6yqs6rq4qr6ZVV9uapeXFV7LvT+AQAAAABXfNVaW+kaNgtV9Zwkz0yyJsl3kpyY5JIk+ye5Wd/tvUke2Fr7zQj1PDrJvyXZqq/nc0l+nWSPJHdIsm3f9bWttb+cZ4y7Jnljkp37Yz+c5Iwkeya5c5Ktk3w3yb1aa9+cOPYuSe7Sbz6l/31ia+2gefpdNcmj+5ePba2tXc9L3iBVtWWSlyV5fJJKcnKST/XtGyQ5oG8nyUdba3eeMsaaJP+a5DH9S19O8r/p/hZuk+RG/euvTfL41tolE8e/uG/um+Tgvv3wJPsleVSSDyT5eZIbJzmw3/+tJLdqrf1qYqxp7+c3knxoouxzWmtHLXD+vZI8NclHk5yaZJ8kt+/3H9lae25//H8nObR//cx0f/tnJblOklum+/u5MMljWmv/mRn6sOS6SU5rre0xq+/6qqrTk+y+3dV3y6HHfmUphwYAADZxR5+160qXAMAqtdvaze7+ey3UYc0YVWzuquofkzyt33x2kue31i4d7L9vkjelu3H77qq6S1vGtKiqbpLkVemelHlWX08b7L9OkuPS3TC+/jxjHJwuRFmT5IQk922t/Xyw/3rpbpZfL8lJVXXL1tq35/a31o5Pcnzf9ymZx1y/qtoj626ij+kNSR7Yt5/SWnvpcGdV3TzJf6W7UX6necb4z36MS5M8urX2HxNjPCld8PHoJFsmeeRwf2vtiL7f2qwLBR6YZJck12+tnTkY6/Ak/5wu6Hhakr+bGGva+/mFuXNMM8/5758upPjj4edaVf+c5PCJIa7Z/35Dkse21s4f9L9qkhf3tfxHVf2wtXbCfLUAAAAAAFdspm9aZlV1UJK/7Tff2Vp73jCQSJLW2tuTHNVv3indt9CX0yPTffYXJPnHyQCktfbDJA+b7+Cqunq6G+1rkpyT5D7DQKIf47tJ/jzdjfirJXlLVV2h/t6q6mFZF0j8x2QgkSSttc8lecgixzh6MpDox3h5kmP7zUdU1R0XUd6dkvzFMJDovTzJz/r2XyxinA11tyQPGgYSvTfP0/+MJI8cBhJJ0lr7dbonSD6XLpB5wVIXur4uveTi/OJ7X536c8E5Z6x0eQAAAABwheZJieX37Kx7ZOVFM/q9Kslz+r5/leRyN6+X0F7970uTTH0io7X2nar6dJIfTtn9+CTX6Nuvba39Yp4xvlpVH0xyjyR/muSeSd69EXWPpg9QnjV46Z/m69ta+2RVfTvddE6TYzyz37w0yUtmnPIVSdb27Scn+dgCJZ7UWvvGlFp+V1WfSXLvJNeqql1ba8txJ/0LrbWTprz+pSTXTjed15zXJflla+3iaQO11lpVvSvJzZPcoqqu2Vr7yZJXvEi//dXZ+cjhl5uFK0myzwOPyH4PfurIFQEAAADApkMosYyqatesm2P/nCSfna9va+2Mfr78PZPcuKp2a639dJlKm/sm/ZXTPcXxj/PUdMA8xz9o0P7gAuf6ULpQYu64dy+uxBV386wLb37QWvvOAv1flHVrg8y5WZK9+/ZXF7jR/uUkF6Vbh+NOVbVmcm2JCZ+Zse9Hg/bu6Z5SWGr/M+3F1trvkpw+8dorFzHesMYbJ1mxUGKbHXbOgUe+Zeq+bXcyTywAAAAAbAyhxPIa3tQ/eRHrRMwtEp0kN03yvmWpqlsDYW56pqOq6h7pvqn/362182Yd2E/ddMPBS5f7tv6E4f75Qo7V6NaD9jfn7dVrrb0u3RMBQ8PrnTlGa+2SqjonyR8kuVK69/jrMw750Yx9w8/wKrPOuxF+sL4HVNVOSW6b7tqulm6R9Tn7Dto7bVRlG2mLNVtlx71vvJIlAAAAAMAmSyixvK41aN+6qtZn8epl+0p2a+0DVfWiJHPz0BzQ//y2qj6a5J1J3tFa+9WUw3cftC9trZ29wOmGax7sVlVbTK6psUpde9CeXLdhsYaf/4Oq6kHz9ry8XTM7lPjNjH3DaZKWax2PWee/jKq6WpIXJnlokm0WccjWG1gTAAAAALDKCSWW1/aD9g/S3exfrMkFhJdUa+3/VdW7kxyRbnqlrdPdML5H//Oyqnppkn+YWAtgeE2/XcSpLhy0qz9+Wtix2gyfMLhw3l6zDd+rr2ThdSKGfrbA/vUJuJbDos5fVdsnOTHdlExJ8ur+5+TW2gWDfmuTvH6JawQAAAAAVhmhxPI6d9D+QWvtiBWrZIrW2qeT3Kf/JvufJ3lAkjuk+3b9VdIt0r1fv2/O8JoW8633bYennDh+fdTCXZbUsM5t5+21+DG+tNo+/5E8LesCiWNba49dyWIAAAAAgJW1XFO70Bku+HuNFatiAa21X7bWXtdau3O6NS3+NcncFEv3qaq7Dbr/eNDeoqoWuq5dBu2fzDN109y37recMc6VFzjPUht+drvM22vxY6zaz3+Z3W/QPnqligAAAAAAVgehxPI6adC+flXNnCu/qrauqmdU1TP7BaWXRVX9QVVda9q+1toPW2tPSvL3g5dvO9j/8yTfGuwbLlA8zXD/SfP0Ob//PSt4uO4C51lqnxq091moc1XdvKoOqaq95hljv0WMsXP/2T+jqjaVp5iGn9t3ZvTz3yIAAAAA2Ay4EbiMWmtnJPlov7lNkjstcMg9kvxDkicn+cUylvbWJP+3QJ93DdpXndj35kH7bpltuP/N8/SZe/pialDSu9UC51lqX0jy3b69Z1Vdf76OfYDwgSTvTbL3YNfns+5G/J5VtVC48ZAkz0tyn9baJRtU9eLNuxh2VV2vqtZW1a2X4DznD9rbzeh37Rn7AAAAAIBNhFBi+f191k2F9PSqmvqeV9VWSf6233zFtGmOqupRVXV2VZ1cVTfZyLp2qqo/mbF/eJP4axP7XpHkrL79qKracdoAVXXjJAf3m19Kd9N+ms/3v68x8aTB3DjbJHnojFqHffeuqi9W1S+r6u8Wc8w0/fv/vMFLfztf3yQPSnL1JKdlsJh1a60lOXLQ71nzDdCv6/GkfvPl61nuhjgn66bN2n5i373TLTp93yU4z+cH7UOndaiqLZPcfwnOBQAAAACsckKJZdZa+2TWTYV0QJJ/r6orDftU1U5J/jPJLZKcnOSFk+P0x7wi3c3vGyR52RKU9/qqutw31KvqD5L8U7/5s3RPVvxea+2cdCHBJUl2SvLOyemmqmrvJG9P9zf2yyQPmmc9iSR506B95DC46cOa1yT55iKv6cgkN0myQ5Kj+jo2SGvtDUne2G8+oqoOn+xTVXdMtwZHkhw++YRDa+3NSY7pNx9QVc+fnJqp/wzekW49jxPS/S0sq9baBUm+3m/+aR8MpH/v/6x//QtLcKqjB+0XVdXthjv7Kc1enUVMkQUAAAAAXPFtKvPWr2qttSOr6oJ037x/eJJDq+qjSc5OskeS2yS5WroplQ5prZ0/ZZhawpK+meTAJH+S5HtV9Yl0UxWtSbcGwB2TbJ3kJ0kOba39aso1faiqDkl30/6gJN+vqg8lOSPdzfW79GN8L8m9Wmvfnq+Y1toHq+rN6Z44eHCSm1TVCX09d073BMITkty1P2T/qnpx335la+2UDXsbFmVtuqm0npjkn6vqL9OtFbFFunUibpEunHlya+3d84zxyHTBzJOTPD3Jw6rq40nOTbJXus9iu3RPWfz5ZHhTVU9PF/4M1+c4rKr2S3JKa+2VE/2G0y49rv+cft9v4KXpnoi4bpLPVtXnktwsyf5Jvp3knYs4f5Ic31o7ftqFt9Y+UFXPShfM7ZzkhKr6VJJvpPv7uHO6RcA/nHWf72Wuraruku7vKUnmnsrZcfA3MO/5AQAAAIDVpboZZhhDVf1hksenu/l6nXQ3on+e5MtJ/ivJG2etJVBVj0lyVLow48GttQ3+Jnv/BMGfpwsU9k13Y3hNuhvwX0831dK/t9bOXWCcHZI8Lsk9k1w/3foT56QLWN6Z5PWttYsWUc+WSR6bLgS4Ubqphb6b5Nh0T4jsnuQHUw69fWvthME410/3Xu6Z5IWttecvdO7F6KfL+sskd+hr2TLJ6Uk+keRfWmuTU1xNG+NPkjxmMMZWSc5MN8XRG5O8q035B1lVp2b+hb5PbK0dtD79JsZ+cLrAZb8k26Z7MuaDSZ7Vr4my0LhJcmRr7bkz9qeqbpNueqrbJNklyYXpwqaPJ/mX/vXXT6u5qp6b5Dkbc/7FqKrTk+y+3dV3y6HHfmVjhwMAADYjR5+160qXAMAqtdvaze7++4JfrhdKAEQoAQAAbDihBADzEUpcnjUlAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUaxZ6QIAVpOdtt0ix91jl5UuAwAAuEJpK10AAFxheFICAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYxZqVLgBgNTnnwktz2PvPXOkyAACAJXD0WbuudAkAbOZ2W9tWuoRVx5MSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSrLiqOqGq2sTPMStd13Koqj2mXGurqoNWujYAAAAAgOUmlGA1OC7JS5K8dqULGcGv0l3rS5J8ZoVrGVVVPbWqfllVL1vpWgAAAACAlSGUYNlU1an9UwCnzurXWntla+2IJEeNU9nyGDz1cMJ8fVprv2itHdFf7/HjVbe0Jp74OGYR/a+S7vPdIcmTq+oGy10jAAAAALD6CCWAsdQ8bQAAAABgMyGUAJZda+28JM9I8uskr2itnbzCJQEAAAAAK2DNShcAbB5aa/+U5J9Wug4AAAAAYOV4UgIAAAAAABiFUGIVqKo1VfXIqjq+qs6oqouq6tdV9bWqel1V3aeqtlnEOLevqmOq6pSqOr+qflVV36iql1fVfjOOa1N+Dlpkvz0m+hw0ty/JdfuXr7uYY+ep7Y+r6o1V9cOq+m1V/bjfvuFCxy5WVf1RVT27qj5WVT8bvP/fqKpXVtUfzTh27eB659xu2vVuRH1/WFVPraoPVtXp/ftwXlV9t6qOraoDFjh+2nu/tt93z6r6SFWdWVUXVtV3quqFVbXjPGM9t7+WHwxeftiU8U9dzPkXqPsO/fV9v/97Pr9fPP2/q+qvquo6i3j7AAAAAIBVxPRNK6yqdk7ywST7J7koyUeTnJZk+yQ3TfKI/ufnVXX71trXpoyxXZJjkty/f+mLSY5PslWSg5I8McnjqupFSZ7eWpu8Qf6S/vetk9xqRrlz/Q5Osu88fX406PfoJFdNt47Aa6f0/dWMc6Wq7pvk9Un+J8m7k1wryd2TPDjJPavqoNbal2aNsZCq+pckT+o3z033/v8kye5JbpbksUn+sqqe2Vr7xylDfD3rrvcp/e/Tkxy3MXUN6ntKkhelWxj6t0k+kuSHSa6R5CZJHprkoVX1b0me3Fq7dMowc/Xtm+6zmxv7xUkeleQDfb03TnJgkqcmOaSqbtVam/yMPt2Pd9V0n2+SfCPJhyb6nbPQ+Wdc85WSHJvkvv1LX0zy4SRXSrJPkkP7n5dU1bGttUcuNCYAAAAAsDoIJVbea9MFEqcluW1r7UdzO6qq0t34fWWSq/c/l1FVW6a7qXxQkkuS/EVr7biJMZ6V5MgkT0uyQ5LHD8dorR3R931uZoQSg347Z55QorV2SpK5fvdNd/P6F3PHroe9k7wsyR1aa58fXM9NkpyULrT5t1n1LtI1+98fTXL/1tovBufaJsnTkzw7yVFVdXpr7Q3Dg1trX0jyhb7/XChxygZc73x2SxdIfC3Jwa21nwzq2yLJY5K8PMkTkpyZ5O8nBxh8bmuzLhR4YJJdkly/tXbmYMzDk/xzkhul+3v5u4mxjk9yfP+Uy1wo8YVZ1zvP+aeqqjXpQroDM+Xvue9z1yRvTXK1JHecNR4AAAAAsLqYvmkFVdUuSf6s33zNMJBIktZ5TbobsPN5erpAIkn+YfIGbj/G3yd5R//S46rqXhtb+wgOSPK8YSCRJP2TEe/qN29ZVXsvwbkuTvLAYSDRn+u3rbXnJHlb/9JR/U3zlfAXw0AiSVprl7bWXpnkpf1Lf1tVlwuu5nGnfswzJ15/eZKfzZ1zg6vdcM9IF0gkyd9P/j0nSWvtw0mePGpVAAAAAMCSEEqsrD9M9y34pPtW+Hzel+TbSc4fvlhV22fdlEEXJTl6xhgvGLSfs15VrowLk7xhnn2fHrRvupHneXuSv2qtnT2jzzv739dKcvONPN/6+miSZ7TWvjqjz1x9V0py10WOe1Jr7RuTL7bWfpfkM/3mtapq10VXupH6v+e/7jd/m+5Jmfkcl25asCV36SUX5xff++rUnwvOOWM5TgkAAAAAmw3TN62snw3aj6+qN7TWfjrZqbX2liRvmXL83dNNx5Qkn54y///QF5OclW4tgj+pqn1aa9/cwLrH8NXW2m/m2Xf6oL37xpyktTbrKZQ5wzvRN85lQ5Fl1Vr7UC6/XsOkyfrevIihPzNj3/CJnd0nxl9O98hl/57nDR1aaxdV1fMH/ZfMb391dj5y+J2n7tvngUdkvwc/dalPCQAAAACbDaHEyjotyefSffv+uklOrqrXJHnDAt+Mn3PAoH25b70PtdZaVX0rXSgxd+xqDiV+OGPfMKy4ylKcrKqunG7aoH2T7Jhkm8Huaw/aOy3F+dZXVW2d5DbpQoerJ9k2656yueqg62Lr+9GMfecN2kvy/i7SrQftBf82W2svXI4ittlh5xx45LQMMNl2p9EeHAEAAACATZJQYgX1QcHDknw4yXXS3Vw+IskRVXVKkncneWu/mPI01xq0J9cGmGbY51rz9lodzp+x7+JBe6OmIKuqbdNNZ/XELO4G/NYbc7711S9m/TfpFp1ezHoRi61vvqdQkiV8f9fTMPxZzN/zsthizVbZce8br9TpAQAAAGCTZk2JFdZaOzndt/OfnuSUwa690q0X8fmq+kxV3WTK4dsP2hcu4nTDPledt9fq0Jb7BFW1ZZL3prvhf5V0i4EfkOQqrbWa+0ly++WuZYbXJXlRukDiE+kWqL7aRH17bsC4y/7+boBhKLSYv2cAAAAA4ApmlFCiqtb00+MwRWvtvNbaP7bW9k5yyyT/nGS4tsQtk5xUVZOLLJ87aG+7iFMN+2zMIsG1cJcrhEeku8mfJCckuV9r7dMz1rIYVVXdOcnafvPbSe7WWvvYAmuHXJGt798zAAAAAHAFsyyhRFVtWVUPrqr3VdXZSX6b5NdVdUFV/W9VPb2qlnyB2k1Ba+2zrbW/STeVzZ8n+U6/a9skL5/oPlzweTGT3e8yaE9bU+DSQXvLGeNsKgHT/Qbtl7XWVtvTA8P6XtVa++2KVTKO4d/zLvP2AgAAAACusNYrlKiqG1TVD/uf06rq1lP6XD/JF5P8Z5K7pVt4t/qfbZLcLMnzkny/qu6+sRdwRVZV21bVHlV1ubUMWmu/a629M910Qmf3L+9fVdsNun1q0N53gXNVkn0GL500pdtwHYdZwcN1Z53rCmR4Hd+Zt9fKTXO22utbasO/533m7dWrqoOq6pCquuYy1gQAAAAALKH1vZl5l3QLJF8rye6ZmJe+qq6dbt77P8q6IGLS3Os7JnlPVR28njVsSm6Z5AdJHj5fh9ba2Uk+2W9ukcuGBR9I8su+fasFnj7ZP8nOffsrrbVvTenz40F76kLYfYDyRzPOMzS3YPJl/s6qavuqWltV91nkOMtlGMJsN2+vyy7APMsl/e/J692tv967rE9xWfr6ltK8i2FX1fX6671caLmA4d/zratq3nVPquoaST6abk2Q7efrBwAAAACsLusbStyx//2hJLu11j4zsf/lSXZLFzp8Lt1CzbdP963n6yW5SZLDkrw2yW/STRH0xqra3G8q3nGB/XM3nX+a5OdzL7bWzkvy0n5z6ySHzxjjbwftI+fp8/lB+1bz9HlIuideFmPuCY/Jz/dGSV6f5JmLHGe5DK/30Bn9HrTI8ea73tumu97HLnKcOUtd31I6J+tCycnrvXe6673v+gw48fe8TZK/mtH9cen++3FSa+3b63MeAAAAAGDlrG8ocb0kv0pyWGvtrOGOqtojyT2TXJRkbWvtlq21f26tndhaO7m1dkpr7Suttbe11h6T5Prpgosdkzxmo6/kiu2eVfXEyRer84R0Tzkk09c9OCrdIs1J8syqOmzKGM9Ktz5FkryytfbuaUW01r6X7jNJkvtW1R9PjHWzdEHCtKmfppm7qX61qtp78Pq9+99fWOQ4y+XlSX7Xt/+2qi5zE72qtqiqI9M9IbQYc9d7g4mgbUOv93VZt/jzQ6vqCf00XMMaH5Xk0es57kZrrV2Q5Ov95p9W1ZZ9PVsk+bP+9Q35fId/z8+qqvtNdqiqByV5dronUw7fgHMAAAAAACuk1mdt36r6eZKPtdbuP2XfI5L8e5LntNaet8jxdk03V/6XW2sHLbqQTURV7Zvk41m3qO8p6W74/yLdVEs3SxfeJMkrkjyptXbplHG2S3Js1i2M/MV04cJWSQ5Ksne6m+8vTvJ3sxZ0rqqbppuCa/skFyZ5f7ppnfZKcrt0U00dkuRh/SGvTfLrJKe01l45MdYNk3w53SLdp/djXTPdt/4vSLJ/a+2bfZBysyRXzbob7N9I90ROWmtH9OPN9bt2krm/wc8k+fSw3/qoqrVJXpPuvUq69+4r6d6v2/fX/YH+mofnO6e1dtTEWHdIN6VQJfl2uvfxhuk+gzPTTXt1cZJn9IfcOuueSPmvdIuPX2bcqrprkrcnmVt35Fvpwo/f9Mf/cZL3ZF0QMO19e3q6tV32TTI3XdqH+r6//9wG/abVNe3zXZvuiYi59+1z6T6f/fvr/5PW2oVV9eK+z7TzX+5zm/L3/IX+Z7skf5rkxummtnpoa+0dWSJVdXqS3be7+m459NivLNWwAADACjr6rF1XugQANnO7rV38/fdNxLQlHS7bYT1DifOTvLa1drlpVarq2Umek2SP1tqP1mPM45Ic1FrbLP+XQlVtneTu/c/+SfZMFwj8NskP090Af11r7dOLGOugJI9Icpskf5DuxvrpST6W5FWtta/Pe/Blx7lBuici7pDkGuluqP9Pkn9qrf1fVR2TdaHEnBOnBUtVdYskz0q3fsbV0q0Z8OkkR7bWvtj3mTbe77XWan36ra+qunGSv04XQuyW7hv4P053zf/a1/2JicNOa63tMWWsg5P8v3RTlV0l3ZROH0/y7Nba9/onin4wo5zLjVtVeyb5myR3zbqpvM5I9z6+Oslp08YcvG+nZv7FyX//uS2230RtD07yxCT7pQuffpbkg0me1Vo7o+8z8z8y831uVXX7dCHYbdJ9Li3JqUmOT3J0a+3UWeOuL6EEAABseoQSAKw0ocSUDusZSnw/yadaaw+dsu+56W4+b9Nau2Ry/4wxX5bkMa21bRddCMASE0oAAMCmRygBwEoTSlze+q4pcXKSA+bZN/d0xHzftJ7PnummKwIAAAAAADZh6xtKvCvJHtMWn03y4XQpyKMWO1hVXT/dIsInr2cdAAAAAADAFcz6hhJvSfKTJK+tqjsOd7TWTk/ypiRPqaonLDRQVe2fbrHbrZIs2WK1AAAAAADA6rRmfTq31s6rqscmeU+S46vqnUnekOQTrbVzkxyeboHff6mqJ6Z7suL/kvw83SK1OyS5QZI7JzkwXShySpJ/X5KrAQAAAAAAVq31CiWSpLX2/qr6iyTHJrlP/5Oq+lmSs5JsmW4apxsk+dsZQ1W6sOKw1tqF61sHAAAAAABwxbK+0zclSVprb01y8yQnpQsXKsluSf4oyfXSPRUxt6x4zfPzySQHtNa+tBH1AwAAAAAAVxAbFEokSWvt/1prBya5TZJXJvl+v2syfBg6O910Twe31m7XWvvOhp4fAAAAAAC4Ylnv6ZsmtdY+neTTSVJV2ye5UZKdkly1H/+8JL9K8p3W2k839nwAAAAAAMAV00aHEkP9YtefW8oxAQAAAACATcMGT98EAAAAAACwPpb0SQmAK7qdtt0ix91jl5UuAwAAWBJtpQsAACZ4UgIAAAAAABiFUAIAAAAAABjFiocSVbVnVR240nUAAAAAAADLa8VDiSSPT/KJlS4CAAAAAABYXqshlAAAAAAAADYDQgkAAAAAAGAUaxbbsaoeukw17LtM4wIAAAAAAKvIokOJJMckactUBwAAAAAAsIlbn1DiwiTbJKllqEPYAQAAAAAAm7j1CSWenuSlSX6d5F1LWMP+SfZZwvEAAAAAAIBVaNGhRGvt6Kq6Z5LbJXlfa+0dS1FAVb0oQgkAAAAAANjkbbGe/dcmOTfJK6rqGktfDgAAAAAAsKlar1CitfbDJE9Oco0kr1uWigAAAAAAgE3S+qwpkSRprf1nVd0ryb2q6lGttX/fyBqOT3LeRo4BAAAAAACscusdSvQelGTXJL/d2AJaax9J8pGNHQcAAAAAAFjdNiiUaK1dmOS0Ja4FAAAAAADYhK3vQtcAAAAAAAAbRCgBAAAAAACMYklCiao6sKr2XIqxAAAAAACATdNSPSnxiSSPW6KxAAAAAACATdBShRK1ROMAAAAAAACbqDVLONbBVbXzBh57SZJfJvl+khNaaycvWVUAAAAAAMCqsJShxL79z0arqhOTPKG19q2lGA8AAAAAAFh5SzV9U9JN4VQT7Vk/s/odlOTzVXWbJawPAAAAAABYQUv1pMSeSf4wyZuSbJfkjekWvz4lyblJfpdkyyTb9/1un+QvknwlyZOSnJ/kykn2SHJAkocl+YMkb6+qvVtr5y1RnQAAAAAAwAqp1trGD1J1lSRfTXJakvu21n6+iGN2SvK2dMHIHVprv5sY7x1J7pTk/7X/3959h9tS1fcD/nzh0lREUEGCRhQ1RtFExd6wYAVsUWOLmFhiiZKIP40ahRiJsaIm1hjBGmIXC4IK2GKNvYuiYKEICiid9ftj5niHwz793Nn3nvu+z3Oes/aeNWutPXsP9zCfPWu19rIVDxJgHlV1SpLddtttt5xyyinTHg4AAAAAbIpqoQqrNX3TgUmukuSBiwkkkqS1dmaSBye5SZInzNp2bpJHJPldkv1WaYwAAAAAAMAUrVYocf8kH2utnbWUnfpg4mPpAojZ285I8skkf7oaAwQAAAAAAKZrtUKJ6yQ5bZn7np7kBnNs+2m6OzAAAAAAAIBN3GqFEtsluf4y971+ukWuJ9khyYXLbBcAAAAAANiIrFYo8bMkd6uqmy5lp6q6WbrFrE+eo8qfJTl1hWMDAAAAAAA2AqsVSnw4yVZJjqmq+y1mh6p6YJKj+zF8cML2B6QLJb63SmMEAAAAAACmaN0qtfPiJAckuXqS91bVSUk+nuS7Sc5Icn6Sbfvtf5ru7ohrJ6kkZyZ5yUxD/d0Tf5nk75O0JMev0hgBAAAAAIApWpVQorV2an9nw1FJtk+ye5LHLrBbJTk3yf1ba8NFsj+YZLe+fGmS96zGGAEAAAAAgOlarTsl0lr7VFXdMsnrkuy9iF2OS/K3rbUfznr+SUmu0Jd/11r7yWqNEQAAAAAAmJ5VCyWSpLX2gyR3rao/S/LAJHsl+aMkV0p3V8Qvknwpyftaa1+fo42jVnNMAAAAAADAxmFVQ4kZfeAwMXQAAAAAAAA2T1tMewAAAAAAAMDmYYOHEtXZekP3AwAAAAAAbNxWPZSoqh2q6mlVdXRVnZbk4iT/Mth+QFW9pKquu9p9AwAAAAAAG69VDSWq6hFJfpLk5Un2SXK1CdV2S/L0JN+rquevZv8AAAAAAMDGa9VCiap6XJK3JLlKkup/JvlokqOStCTPq6oXr9YYAAAAAACAjdeqhBJVdc0kr0oXRPwgyf9Lcuckf5pZ4URr7f9aa/dLcrskpyT5h6q6+WqMAwAAAAAA2Hit1p0ST06yTZK3JrlJa+2lrbVPt9a+P9cOrbWvJHloP4bHr9I4AAAAAACAjdS6VWpnnyS/SvL41tpFi92ptfb5qvpckjut0jgAVuTM8y/NQz982rSHAQAAAEmSw07fZdpDAFZg1wPatIew0VmtOyV2T3Jsa+2CZez7nSTXWqVxAAAAAAAAG6nVCiWulOSsZe7bkmy5SuMAAAAAAAA2UqsVSpye5IbL3PdWScyVAgAAAAAAa9xqhRJfSXLXqrrZUnaqqgck+bMkn1+lcQAAAAAAABup1Qol3pFu0eyPVtV9FqpcnccleXu66ZvetkrjAAAAAAAANlLrVqOR1tr/VNXTktw2yVFV9b0kR6dbxDpJblBVj0hy9SR7Jrl3kmskqSSfaK19aDXGAQAAAAAAbLxWJZTo3T/Jp5PcIN36EjNrTLQk+/U/M6r//c0kD17FMQAAAAAAABup1Zq+Ka2105PcMskRSS5NFzzM9XNxkjcmuW1r7TerNQYAAAAAAGDjtZp3SqS1dk6Sx1TVIUkekuT2Sa6ZZPsk5yQ5JclnkhzZWvvZavYNAAAAAABs3FYllKiq/xo8bEn+obX24tVoGwAAAAAAWBtW606JA9KFEdX//qckv12ltoHNWFUdnOT5s57+aWtt9/FHAwAAAACsxKqtKZEujDgwyS6ttV+sYrvA5u1zSV7W/5w95bEAAAAAACuwWqHEhUk+3Fp7VWvtjFVqE9hAqmr3qmr9z+HTHs98WmvHtNYOaq0dlOSsaY8HAAAAAFi+1QolfpHkp6vUFgAAAAAAsAatVijx6SQ3XM6OVfWQWQtlAwAAAAAAa9BqhRKvTnLHqrrVMva9ZZJHr9I4AAAAAACAjdSqhBKttS+nW+T6qKp66Gq0CQAAAAAArC2rEkpU1fOS7JLka0neUVW/rKp3VdWLq+r5VfW8uX6S3G41xrCxqaqDBwsJz/45fEL9A/ttX5qjvUntHL9KYz1pQtsH99vuVFXvr6pfVNUFfd3XVdVuS2j/LlV1eFWdWFW/r6rfVtW3q+rVVbXnPPsdP9exq6obVNVrqupHVXXeah+TCWO5Rf+6v19V51bV+VV1SlUdXVX/WFV/sog2blNVb+rH/PuqOqeqflBVb6yq28yz36T3/oB+2/5VdWxVndaP6Qf9ebfjHG0dXFUtyU8GTz96QvsnLbL/u1bVe6rq5Kq6ePbnZ9Zrf1FVfbqqTq+qi6rqN1X11ap6aVVdZ6HjBwAAAABs+tatUjsHJ2l9udIFFA9c5L412Hct+VySlyXZL8kN+ufelOQ3SSYFD/frf9+iqv6otfaLWdtf1v9+WJIdk7wmyYmrNNY3JNkpybWSPGTmyap6apJDk3wsybuTXD/JPZI8Icn+VXXb1tqcC5xX1XZJDh+0+ZUkxyTZKsneSZ6S5IlV9ZIkz26tzf4cHJnky0munORxg3b3T/KOJN/ox3b1JPfv211VVbVlklcmeVK6z+r3kryzL/9JuuNxzySHVtXHW2v7TGhjXZJ/T3fckuSr6Y7DuiR3SPLYJI+tqjcmeVJr7eJZTcy89zdOcq9Buy/t9/1IumN10yR3SvKMJPv2789vZ7U187kcHtNvJzl6Vr0zF9H/C/q+Pp7kA0lulOQuE17/B9OdB0lyWpLjkpye5I+T3CbJ05M8uaqe0Fp7y+z9AQAAAIC1Y7VCiaS7SDvf481Ka+2YJMdU1S+y/qLuCa21t86uW1U7JbnjzMMk+yd53az2Duov8j8xybGttYNWcayH9uPYO+sDhNsnuV6SP2ut/SH8qKr7J3lfkl2TvChdSHI5/cX8j6QLHy5O8sjW2pGD7ZXkn5IckuRZSXZId+F/OK7X9nV3z/oL6Hsk+a8kj2itfWDQ3v2SvH8pr3uR3pr1r/HprbWXDzdWt47K/yS5dpK7z9HGW/o2Lk3yuNbaZRZ2r6q/Sxd8PC7Jlkn+Zrh95r3u706YCQUelmTnJDdorZ02aOvAJK9I8qfpjus/zmpr5nO5e9Yf0y/P93mao/+HpAsp/qy19v1B/69IN5Xb0B/1v9+a5G9ba78f1L9ykpf2Y/mvqvpZa+34ucYCAAAAAGzaVmuh66T7pvZdlvHzP6s4ho3RUYPyfnPU2TfdxegZ95uj3t2TXCHJB1dhXAu5e5InDgOJJGmtvT/r7/R4QFVdcY79n50ukEiSfxkGEn07rbX2z0ne0z/1xD7wWMgdkrx8GEj07X0gye8Wsf+iVdWjsz6Q+K/ZgUTf7xeTPGqRbRw2O5Do23h1kiP6h39dVXdbxPDuni7oOW3W869O8qu+/MhFtLNc907y8GEg0XvHHPVPTfI3w0AiSVprZ6e7g+SL6c6BF632QAEAAACAjcdq3ilxcmvthKXuVFX7ruIYNjqttR9W1ffTTfVzz6raurV24axq9083rdOpfb27VtX2rbVzZtW7X7pv239ow446Sfd+zp7SZ8ZnktwyyTZJ9kzyheHGqto+3ZQ8SXJhksPm6edFSR7Ul5+fhe92uCjJa+fYdr0klyyw/6JU1Rbp7uSY8W9z1W2tfXrwHs9u47n9w0uz/o6ZSV6T5IC+/NQkn1hgiJ9trX17wlguqar/TfKAJNesql1aa6cu0NZyfLm19tkJz/9fumnAzh4896Ykv2mtXTSpodZaq6r3JblVklvPMX3ZaC69+KKc9aNvTNy27U67ZLuddhl5RAAAAACwdqxWKPHTXHYO+qU4McmnVmkcG6sPppt7/8pJ7pzk2JkNVbVtunUJPpjuOD4rydbppsl516DeFunuqPjCBrrIPNv/zrPt5EF50oLX90k3HVOSfG7CugZDX0m3vsDVk/x5Vd2otfadeep/s7V21qQNrbVfTXp+mW6VbqqoJPlJa+0HC9R/SbqgZuiW6YKSJPnGAhfav5ouwNk6yd2rat2EtSWGlvL+bIjPy8RztrV2SZJTZj03V4g0NBzjTZNMLZS44Ldn5NgDL7c0SJLkRg87KHs+4hkjjwgAAAAA1o5VCSVaa9dZwb6vy6z1E9ago9KFEkk3hdOxg237JLliuoWCf5YulEi6uyLeNah363QLiB+2IQc6cPI8284dlK80YfvtB+XLfZt/qP+W/HfThRIz+84XSvxkvvZW0e0G5fnGkyRprb0p3R0BQ8PjMG8brbWLq+rMJNdIN0XXDZN8a55dVvL+rIYlvw+DtVNumOQquezC5DcelHda0chWaJsdrpY7HfLOidu2dZcEAAAAAKzIak7fxNw+l+TXSa6aLpR46mDb/dJNSfTRdBeTT0u3gPF9Zn1bfv/+9xjrSSTzr88wnIZn0rok1xyUZ695MMmwzjXnrNVZ1XUj5nGtQXkxr2GS4Wt5eFU9fAn77pL5Q4mVvD+rYdHvQ1VdJcmLk/xVuim/FrL1Mse0KrZYt1V2vN5NpzkEAAAAAFizhBIj6Of5/0i6BZF3r6qbtNa+2U/JtF+S4/sFf1NVH0ry10l2THKnJJ/sm9k/yY8WmNpoVYe9gn23H5TPX0T9YZ0rL1B3JeNaiuEdBot5DZMMj8PXsvA6EUMLTUU11nFYUf/9+iInpJuSKUle3/98r7V23qDeAUnevMpjBAAAAAA2MkKJ8RyVLpRIuiDim0lum+6uiOHdDx9MF0okXRDxyaq6XpIbJXnFOENdseEC3dsuov6wztlz1hrXUl/DQm38X2vtoBWMZ1P1rKwPJI5orf3tNAcDAAAAAEzXhprahcs7Ot1Cxsn6qZju3/8ehhLHJpn5Bvn9ZtUfa+qmlRoudLyYSfh3HpTnWythTMPXsPOctRbfxtXnrLW2PXhQPmxagwAAAAAANg5CiZG01s5JN41NktyqqnZJFzp8rbX2s0G93yf5eP9w96q6abpQ4swknxlxyCsxHOeN56yVpKoq3V0gMz67QUa0dMPXcKM5a/Wq6lZVtW9V7TFHG3suoo2rVdVzq+o5VbVW7mK69qD8g3nq+W8RAAAAAGwGXAgc18ydDpXkGUmun+QD89RLuqmc7pDkI4NFrzd2H0nym75826raYZ66eyW5Wl/+WmvtuxtyYEvw5SQ/7MvXqaobzFWxDxA+km6KrusNNn0p6y/EX6eqFgo3HpXkBUkeOMJ7Pedi2FV1/ao6oKputwr9/H5Q3m6eeteaZxsAAAAAsEYIJcZ11KD8tP73pCmZjsr6hYSfnGTLSfX6b9YfV1VnV9W/r+pIV6C1dm6Sl/cPt05y4DzVnzkoH7LSvqvqelX1lar6TVX943Lbaa1dmi4gmPHMueomeXiSqyb5aQaLWbfWWi77mv5prgaq6ipJ/q5/+OolDnc5zsz6z9j2s7Y9IN2i03+xCv18aVDeb1KFqtoyyUNWoS8AAAAAYCMnlBhRa+2n6Ra4TrpFxk9urf3fhHqnJvnioN6F6dakmO3vk+yd7qLyk6vqbqs95hU4NMnxffm5VfXQ4cbq/FOSB/VPvba19v5V6PeQJDdPskOSQ/tFwpeltfbWJG/rH/51VR04u05/zGcCoQNn3+HQWntHksP7h39ZVS+cPTVTVV0ryXuSXCfdMXvLcse8WK2185J8q394sz4YSFVtkfVrmXx5Fbo6bFB+SVXdebixqrZO8vosYoosAAAAAGDTt1bmrd+UfDDJTfryUQvUu3VfPr5fk2KDqKonJtkjl51C5x5VdaUkZ7bWDp1Vb7hOxEOras9hvSRprV1SVfdJckS6xY7/u6qekS5s2SpdmHK9JJckeWmSy93V0AcZt0xy5cHTe1XVS/vyia211y77hS/OAUnOSvKUJK+oqsenWytii3TrRNw6ycVJnjpPqPI36aazemqSZyd5dFV9Msk56Y7nndJNbfSJJA/q79L4g6p6dpKdMvm4/+EYDOoNp116YlXtm8nH6uXp7oi4dpIvVNUX0x3vvZJ8P8l7F9F/khzTWjtm0gtvrX2kD5/+Od00XcdX1WeSfDvdXTT7pFsE/GNJ7jnptVXVPZLco9+248zvwedgzv4BAAAAgI1LdTPMMJaqunWSz/cP79Va+9gc9W6c9d9kf0pr7T8m1Nk5ybvS3Rnw1tbak5Y5puOT3HmOzT9tre2+lHoT2t8769fGuEa6IOKUdBfhX9da+9Yc+x2e5NHzDP2E1tres/a5QZL/SXfXwYtbay+cZ/9Fq6qbJ3l8krsm2S3dlFqnJDkuyataa9+cZ/eZNv48yRMGbWyV5LR0Uxy9Lcn72oQTsqpOymUXjB76wzFYbL1ZbT8iXeCyZ5Jtk/wqyUeT/FN/x85C7SbJIa21g+fZnqq6Q7rpqe6QZOck56eb7uqTSV7VP//mSWOuqoOTPH8l/S9GVZ2SZLftrrpr9jviayttDgAAAFbFYafvMu0hACuw6wGb3fX3WrCCUAJAKAEAAMDGSSgBmzahxOVZUwIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABjFumkPAGBjstO2W+TI++487WEAAABAr017AACryp0SAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKIQSAAAAAADAKNZNewAAG5Mzz780D/3wadMeBgAAwGbvsNN3mfYQAFZs1wPatIew0XGnBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBAAAAAAAMAqhBLDRq6o24eeAaY8LAAAAAFiaddMeAMAivKz/feMk95rmQAAAAACA5XOnBGymqurwwV0Hu097PPNprR3UWjsoyZHTHgsAAAAAsHxCCQAAAAAAYBRCCQAAAAAAYBRCCQAAAAAAYBRCCQAAAAAAYBRCiSmrqhtV1aur6rtVdU5V/a6qflxVb6uqe440hgMGCx7/4afftn1VPbeqvtWP7TdV9emqesQS2t+hqp5VVZ+tqtOq6oKq+kVVHV1Vj6+qrebYb+9J46qq3atqi6r666o6oW/z0sH2vVfnyFxmLNtW1VOq6hNV9auqurA/Fl+rqv+sqgdW1bYLtHGFqvqHqvpUP+YL+7aO65+/4hz7HTzhGJzUb9u5qv6tqn5QVedV1ZlVdcx8n53B+/vowdM/mdDHAYvo/ypV9U9V9fWq+u3sz8+gzx379+vIqvph/1k6v6pOqaoPVNXDqmrLRbwVAAAAAMAmbN20B7A5q6rnJ3luuvfhB0nemeTiJHsleUSSR1TVUUke1lr73QYcyreSvKwvPzTJNfvxXSPJsUkqyaeTfD7JPZPcIckdqupGrbXnzNdwf3H8bUmuluTsJB9LcmqS6yTZp2/voKq6f2vtO7N2P3kwrnsluXFf3i7JR9Mdp48l+XaSOye50VJf+GJU1V5J3pPkj5Ocn+SYJD9Ld5xuneRv+p/Tq+pxrbUPTGjj1n0buyU5J8nRSX7Vt7lPkr3THYcHtNa+MGv3z2X9cXhckiv3bf5pP5ZfpHufrpTkPn17+1TVE1prb5jwkiYd0zeme3+GvrVA/9fvX0dLclySryTZN8nVZ7322yf5eJKZ0OYzST7ZP/7TJPv3P0/qX/8ZE8YMAAAAAKwBQokpqap/TfKs/uHzkrywtXbpYPtfJHl7kv2SvL+q7tFaa5dvaeVaa19O8uW+373ShxJJjkjywSTPnem7qrZL8okkt03yrKp6+4QwYeY13CvJUek+Z8cn+YvW2q8H26+f5CNJrp/ks1V1m9ba9wfjOjHJQX3dq2X9BfSZC+R7tNZ+02/fKt3F7lst+0BMfg0368d+xXQX6fdtrf10sH27JC9I8vR0F+NvluQDs9q4ebpjdsUkX0iyf2vttMH2ayb5UJI/S/KJqtqrtfa9me2ttWPShQ8zn4srJ9k6yZFJ/qW19vpBW1ft+9gjycur6j3DY963N+mYHtpaO2nSMZin/3f1Y3juzGe3fy3fTReQzNgxXQBxTpJ7tdY+N+v43CXJO9KFXUcmudukcQAAAAAAmz7TN01BP73QM/uH722tvWAYSCRJa+3dSQ7tH949yWNGG+B6V26tPWcYhrTWzkvyL/3DLZI8fNKO/cXxt6QLJM5M8sAJF8d/mORBSS5NcpUk76yqxXwmb5nkITOBRN/WRUnevbiXtTh90PHf6cKEi5LcfxhI9P2e11/k/+Q8bbyzb+PcdMfhtGGd1topSf4i3V0yV0wy6e6G2XZN8p1hING39eskL+8fXjHJAxbR1nLsmuS01tqzh5/d/rV8ao59njs7kOj3OS7dnSZJctc+zJqaSy++KGf96BsTf84789RpDg0AAAAANnnulJiO56WbEilJXjJPvdcleX5f92lJ/msDj2u2N87x/GcG5VvMUedJWT+Nzxtba2dNqtRa+0ZVfTTJfdPdZbB/kvcvMK7DW2u/nfD8f6QLAE5fYP/FeniSG/Tld/d3bszljUnuOuH5hw3aeHtr7ReTdm6t/aiqjkk3/dIdq+pmrbWvLjC+ucKL2e/Pfy7QznK9co7nH5kuEJnxwyQvTHcXxFw+luT3Sa6Q5IHppoWaigt+e0aOPXCfidtu9LCDsucjnjHyiAAAAABg7RBKjKyqdklyl/7hmemm2pmotXZqv6DwdZLctKp2ba39csOP8g/+d45xnV1VZ6ebxme3OfYd3kHx0QX6OTpdKDGz3/sXqD/xm/ittd+nu7C9Wh42KC90kfy4JK9PPw3WHG0sdBy+kC6USJJ7J5kvlGjp1viY5ORBea73ZzXM9T6cleSswePvp1s7ZU6ttUuq6ox0a2zcdDUHuVTb7HC13OmQd07ctu1Ou4w8GgAAAABYW4QS47v9oPy9RawTMbModNJ96/1DG2RUk508z7Zz04USV5q9oZ+66YaDp769QD/D7befs9Z6P1lEndVwu0F54roZM1prpyb525W0ke69nnHzBeqe2Ycwk5w7KF/u/Vklv26tnbPUnarqFukWKL9GurHVYPNV+t87rXh0K7DFuq2y4/WmmosAAAAAwJollBjfNQfl21XVUhavHvtr2r+bZ9tF/e9Ja0AMv51/aWvtjAX6Ga6xsGtVbTF7jY0ljGtVVNUOSbYfPHXaXHXnaePK6YKbGT+oqrmqz7bQez3nMWitXTToZ0OtG7Ok96Cq7ptugfI/WUT1rZc1IgAAAABgoyeUGN/wQvdPkrx3Cft+f5XHMq9F3MUxl+FrvGAR9c8flKvff9KaETOWO66lmH2HwfkTa81v+1mPX5PkvEXu+/MFto9xDFal/6o6IN16KJXuM/+cdAuDnz4Mn/qpyq69qqMEAAAAADYqQonxDae8+Ulr7aCpjWTDGb7GbRZRf9tBuc3af1pmj2HbibWW1saLWmvzTYm15lTVTklenS6QuCDJvfs1JgAAAACAzdCGmtqFuZ0yKF99aqPYsIbf8t+iqhZ6nTsPyr9YYOqmUbTWzk5y9uCpneequ4Q21ur7PZ97Zf1dJ0cLJAAAAABg8yaUGN9nB+UbVNW88+dX1dZV9Zyqem6/gPRGr7X26yTfHTx14wV2GW7/7Jy1xjccy43mq1hVV6mqfavq3vO0sedCHVbVA/v3+q5LGOfGbDgd0w8WqOu/RwAAAACwxrkIOLLW2qlJPt4/3CbJ3RfY5b5J/iXJU5OctQGHttreMSjPvlA/23D7O+asNb7hWO61QN3HJDkqyfNmPf/2QXnf+Rqoqq2SvD7JC5JccZFjXImLBuXL/Legqh5cVQdU1RVW2MfvB+Xt5qrUh3NLvhsFAAAAANi0CCWm45+TzExR9Oyqmvg+9Bepn9k/fM2kaY2q6rFVdUZVfa+qbr5hhrssr0lyel9+bFXtOKlSVd006y/4/1+6C/srUlXPq6rfVNWXqmr3FTR1ZJLv9eW/qKo95uhv2ySP7R++aUIbM3eNPKCq5rtr5IlJrpbkxCQfWdaIl+aMQXn2otxHJHlzkq1W2MeXBuV7V9WWc9R7UBa3/ggAAAAAsAkTSkxBa+3T6YKJJLl9kv+c/Y30foHgtyS5dboL4y+e3U6/z2uSXDXJnyR55QYc9pK01s5M8ldJLk6yU5L3zp5+qqqul+Td6T6Hv0ny8JWuJ1FV109ySJIdkuyV7q6DZWmtXZTkL5Ocm+7i/Puq6o9n9bdDuov3N0ry5SSHz2rj4iQPTbfo9bokH6yqPWe1UVX1V0lemi6s+tvW2iXLHfcSDAODWw7Gc890dzX8sLX225V00Fr7XJIv9g/3SPdZv9KwTlXdKt1i2AAAAADAGrdu2gPYXLXWDqmq89JdNH9Mkv2q6uPpvr2+e5I7JLlKkq8n2be19vsJzdRqjKW/A+CJ/cM9Bs+/tC/+d2vty7Pqzdz5sOPsejP7t9aOrqp9k7wtyd5JflxVRyc5Ncl1ktwjydZJfpTk/rMXQe7vrnhO/3CvwaZnV9XMAtKvba2dONxt8a98Ya21r1fV3knek+QmSb5XVcckOTnJbknulC4U+lqS/foQYnYb36yqOyV5b5LrJvlqVZ2QLmy6SpJbJLlhkt8leWxr7ePD/atqr3ThSDL5uL+2tXbirHoz9phdb7Dtw+nWebhBkldV1R3THb8H9tsPXWT/aa0dNPt1DzwkySfSfbYOSHKvqvpMkl/2r/tuST7fj+Nqc7y2mcfDO00eOhPwLNA/AAAAALCRqNbatMewWauq6yZ5UpJ7JvnjdN9Q/3WSryb5nyRvm3She7D/E9JdPD4jySOGocASxrB3kuPmqfKY1trhi603of0d0oUZ+6e78HzlJGemC1zem+TNrbULJ+y3e5KfLDD8u7TWjp+13yFJDkwXdjy4tfbjBdpY0GCKpgekW7B6p3R3P3wj3RRNb5r0Gma1sU2Sv0l30f8mfRu/T/LDJB9LdwH+lAn7HZDuboy53KW1dvxi681q+4/SBWP3SHKNJBf0r+mw1tr/LLL/tNbmDYOqavskf5futf9Juqmazkz3OX9Hkneme7+uPWvXmdc273+oFup/MarqlCS7bXfVXbPfEV9baXMAAACs0GGn7zLtIQCs2K4HbHbX3xe8TieUAIhQAgAAYGMjlADWAqHE5VlTAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGMW6aQ8AYGOy07Zb5Mj77jztYQAAAJA27QEAsAG4UwIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABiFUAIAAAAAABjFumkPAGBjcub5l+ahHz5t2sMAAADYLBx2+i7THgLABrXrAW3aQ9jouFMCAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVACAAAAAAAYhVBiM1JVz6qqNuvnpBW0d/6E9g6YVWfvCXVaVe2+wpezSaqqAyYdj2mPCwAAAABgDEKJTURVPbyqzqyqd1fVct+3LyR5Wf9z9ioM6xV9W0fPU+fkQZ/fXoU+N3XfyvrjccqUxzKaqtqiqt7Vf4YfPu3xAAAAAADTIZTYdLwsyY5JHpRkn+U00Fo7rrV2UGvtoCRnrXRArbV/7Ns6cp46Jw76/PJK+9xYzboj5OC56rXWvjw4HieON8LVNeuOjwMWscs+Sf4i3Wf4ZRt0cAAAAADARksosemoOcqwKfD5BQAAAACEEpuQg5L8Jsn7kxw71ZHA0h2b5H3pPsPPmO5QAAAAAIBpWTftAbA4rbW3JXnbtMcBy9FauyTJA6c9DgAAAABgutwpAQAAAAAAjEIosQRVddJgcd/LLGpcVXeuqo9U1elV9Zuq+kJV/XVVbTnYf6uqelJVfbWqfldVv66qo6rq1svpc559tqmqp1fVl6rq7P7nW1X1gqq62jJe9y2q6m1VdUpVXdD//mBV3WepbS2hz6qqh1TV+wf9/rqqvlJVL6qqa61SP1tU1d2r6rD+PTuzqi7q+/p8VR1SVTvPs//hVdWSHDd4+vkT3rPjVzDG2/Sv+dP95+ui/jP21ap6aVVdZ559954wllZVu/efx6f1x/Tsqjqn/8w8Zfi5ndXe8f3rffPg6TdPaP/whfpf4DVvWVWPrqoPVdXPq+rCfozf6T+Lj6yqKy/9aAIAAAAA02T6pqV5Q5KdklwryUNmnqyqpyY5JMlHkhyZ5PZJbtX/3KWq/irdsX5Pkt2SfC7Jt5Ls2//cs6r2a619bLF9zqWqdk1yTJI9+6e+3P9sk+RRSQ6oqnsv9gVX1YFJXppkyyRnp1sX4PQk10/y/qr6jyRfX2x7i+xz576f2yW5JMknk3wvyY5J9knyzCQHVtVTW2tvWGF3X0tyk7780yRHp1v34Lrp3sdbJ3lqVf3lHO/PR5Ockcu+P/+b7j0eOnE5g6uqDybZr394Wrrw4/Qkf5zkNkmenuTJVfWE1tpbJjRxcpKX9eV7JblxX75iunUerp3k40m+mOSuSfbqf26b5BET2jsy3efpxn17SXfMvj2r3pcW6H9OVbVHurVT9kxycbr3/4dJdk5yi35cj0hyTlX9v9ba6xZqEwAAAADYOAgllqC1dmjSffs76y9A3z5d0HDj1tov+u2V5I1J/ibJI9OFFTdK8vmZNvp610x3gXeXJK+vqj36ufcX6nOiqtomycfSXcy9NMljhheqq2pduoDhXUm2Xej1VtXDk7yif/j5JPu31k4fbL9J39+fL9TWYvXffv9Ukj9Jd/H9Pq21Lw+2b5cuqHlkumN2XmvtrSvo8o/63/+W5LmttYsHfV0jyeuS3C/Je6rqlq217w53bq0dmeTIWe/PMa21g1cwpknje2uSv22t/X4wviunez8fl+S/qupnrbXjZ43vxHSLpKe/S2YmFHh5uhBmn9baRf32LdOFDg9K8vCqesvsIKa19tq+7gFZH0oc2Vo7fNLg5+l/ov4OmM+lCyB+nu79/8Zg+7okT0vykiTbpwtmhBIAAAAAsIkwfdPK3T3JE2cCiSRprbUkzx/UeVaSWw4Dib7eKUle2z+8dpI7rXAsz8z6b/3/x+xvzrfWLm6tHZjum/27zNdQVe2Y5FX9w/OT/MUwkOjb+2aSRyfZe4XjHnplukAiSf5mGEj0fZ6XLuz5Qf/Uq/uxrsRXW2vPGgYSfV+/SvLQJKeku7Pg+ZN2HsGp6Y7F74dPttbOTvKEdHc5bJnkRUto84ZJHj8TSPTtXZLkeYM6j1r2iJfvLekCiSR52DCQSP7wGX5ZLjt9FAAAAACwiRBKrNyPW2snzH6ytfbzJD/rH940yTvn2P+zg/ItlzuIqto6yVMGT718nuqvXESTByS5al8+sn89l9NaOzaXn7pnWfq1EWYuhH+vtXbUHH1emOQ/+4c79GNdrpclefZcG1trFyT5cP9w36raagV9Lcebkvz9MDwY6gOw9/UPb11VfzSp3gRH9K9tdnvfSXJW//AWSx3sSlTVnbM+4Prf1tqn56n+xg01jksvvihn/egbE3/OO/PUDdUtAAAAAGwWTN+0cl+cZ9sv0839n3TTH03yi0H5misYxx2SXL0v/6i1dtI8dY+bZ9uMBwzKxy5Q97gsYq2ARXhIum/8J906BfP5wqB876yfZmpJWmv/uohqM1eir5hkj3TrW4xiZrqkBQyvlN80l/1MzeV/59l2crr1O3ZbRDur6WGD8kLv/1fSTds013m1bBf89owce+A+E7fd6GEHZc9HPGO1uwQAAACAzYZQYuVOmWfb+Yuod96gfKUVjGOvQfm7c9ZK0lr7dVX9Nt1dBpfTr4kx/Jb8vO1lmYs4T3D7Qfk7C9QdXoi/+Uo77hcIv0O6BbyvnMueG7cblHdaaV/LUVU7JbljummXrpJkeMfGMBBa7PhOnmfbuf3vlXwel2N4nOd9//s7R564IQaxzQ5Xy50OmXxj07Y7zTvrGQAAAACwAKHEyv1+4Srz1muD8kqm0xpO23PGIuqfkzlCif75KyyhvXMW0d9iDO8UeUNVvWGR+121qracvUj4YlTVbkkOS3dnyJbz106SbL3UPlaiqq6S5MVJ/irJNovYZbHj+90822amiqpFtrVarjUonzZy33+wxbqtsuP1bjqt7gEAAABgTRNKrFxbuMof5v7fkIbfaj9/zlrrzXcBf/Y35Bdqb8lhwBy2H5SPyvrFrBdjq6WOo6qumeRz6S6GX5zkX9MttPyT4XoLVXVwprDIdVVtn+SEdFMyJcnr+5/v9Qt+z9Q7IEtf+HlDfx6XY6mfYQAAAABgEyOUWDvOHZS3XUT9+e4KOHfW44XaW8wdBosxvOPiXa21t65Su3P5t6z/dv4LW2sHb+D+lupZWR9IHNFa+9tpDmYE56RbyyJZ3GcYAAAAANjErGS6IDYuw8WNrz5nrfW2n2fbb3PZ6X0Wam++tpZiuO7GYl7DslXVulx2Me/DNmR/y/TgQfmwaQ1iRMP3f+epjQIAAAAA2GCEEmvHlwblP52vYr9o8lzrScxMNfWVxbaX5LoLjm5xPjMo77lQ5aq6RVU9t6oes4y+rp5ku758emvtN/PUndZ5cu1Beb6prNbKeTx8/280X8Wq2qaq9u1/FrPWBgAAAACwEVgrFzNJPpvk9L68R1XNFxTcZRHtvW9QvscCdRfT3mIcmW5thyS5d1UtNC3U85K8IIsIMCYYLjy+bVXNt6jztebZNuOiQfky51VV3ayqDqiqpa6ePBzjdnPWWtz4Vtt8r/eO/etdalj1jkH5XgvUvU+6dUfekOTCJfYDAAAAAEyJUGKNaK1dmOTfB0/9wzzVn7qIJo9IcmZffnC/KPTlVNVds7xQ4HJaaz/N+gWbr5HksXPVrapbJLl3usWtX7uMvn6b5If9w+2T3HmOfrZPsu8imjxjUJ49ndXj0r2uvZc2ysvc/bLfpAp9cPOQJba7GuZ7vf+Y7vUu6XPRWvtMkk/0D29bVXecVK8PkJ7cP/yvERaRBwAAAABWiVBibfm3JN/sy0+qqgOGG6tqXVW9LMnNc9lFpS+ntXZWkr/rH26b5N1VdZl1HqpqzyRvyWXXs1ipv0/yrb58WFU9cHaFqrpNkvcm2SrJoa21Hy2zr8MG5f+cfSdDH0j8d5KrLaKtHyU5qy/vNWhjm3ThSZJ8eQXje0lVXSY4qaqtk7w+C0x1tIH8X7pAKEluORjT1ZLcMUnr6yzVo5P8qi+/o6puMtxYVdsmeVmSuyX5WZIXL6MPAAAAAGBK1k17AJuSqnpikj1y2ely7lFVV0pyZmvt0KraMclz+m17DPZ9aV/879bal6vqHummRbryoK29BvVe2Fo7q6qenWSnOfr8Q70kaa1dUFX3THJMum+pv7mqnpzuG/dbp7uQu3OSv0zy6nTfcN9x0OcxrbVjZjpprb2jqnZO8tIkt07yo6r6aJLTklwvyd2TnJDk3Ule1+/20D6sSGvtoKraI8kTZ17f4DU8u6rOTnJia+0Pdzq01n7XX3x/V5K7JnlPVX09yReTVLoL8LdLd9H75Umen+V7bZI/T3cnwx5JvlJVx6dbv2GHdFMIretf40wg8MSq2jfJl1prRw7GfUlVvaofzx36dr7b77d7kuNba5+rqr3SHf9k8ufjD+221j5SVf+U5J/TBSPHV9Vnknw73fu5T7q1MT6W5J79/jPH/8TW2mtnfR4nHf9jWmvHLOJzO/uzcXpVvS1diPCIqrpKkp/3x+xKSY5orZ2yjPf/51V1uyQfSHKTJP9XVZ9IF/rsnO693y3JSUnu3Vo7OwAAAADAJqPMfLJ4/YXmidP8JPlpa233qto9yU/maeYxrbXDq+rgzH9B/TqttZOq6qRcdsHjifVmjXPrJE9J8vAkf5LuYv4vkhyb5BWttR/N0e4hrbWDZ3dQVTdP8vR00w9dPd0dAd9K8vYkhyf5q6yfdukPWmtVVXsnOW6e8Z/QWtt70oaq2j/JI5PcJt0F6UuSnJzk00le31pb6p0HE1XVfkken+RW6QKg36e7CP6xdOHNE3L59+qI1toBs9qpdHeXPCbdcV/Xj/e96Y7tuf3dK5c7Vgu0e4e+3TukOw7nJ/lpkk8meVX//Ow2T2it7b2Iz+MhrbWDF1tv1ri2TvLsJA9Ncp3+6R+n+1y8uLV20XLf/35aqkemm5rqZulCmfPSBT3vS/Ka1tq8d/ssVVWdkmS37a66a/Y74mur2TQAAABzOOz0XaY9BIANatcDNrvr7/Ot3dtVEEoACCUAAACmQSgBrHVCicuzpgQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADAKoQQAAAAAADCKddMeAMDGZKdtt8iR99152sMAAADYTLRpDwCAkblTAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGIVQAgAAAAAAGMW6aQ8AYGNy5vmX5qEfPm3awwAAANgsHXb6LtMeAsCq2vWANu0hbHTcKQEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAEAAAAAAIxCKAGbsKrau6rahJ/dpz221VRV35vwGg+e9rgAAAAAgKVZN+0BACtycpKX9eV7JbnxFMeyIf1Xkp2TXCvJQ6Y8FgAAAABgmYQSsAlrrZ2Y5KAkqaqrZY2GEq21FyfdnSERSgAAAADAJsv0TQAAAAAAwCiEEgAAAAAAwCiEEgAAAAAAwCiEEgAAAAAAwCiEEpuoqjqpqtqsn4P7bXeqqvdX1S+q6oK+7uuqarcltH/PqnpHVf20qs6vqt9U1beq6lVVdaM59jl+wphmfg6YUP/9/baXTNi29xztHLzog3T5NquqHtL3e0p/bH5dVV+pqhdV1bXm2G+usexeVVtV1dP6Ns6uqnOq6ktV9ZSq2nKR47pFVb1tMKZfVdUnquqv+zEfMKn/FRyHm1TV8/o+flVVF/Zj/3ZVvbaqbjLPvrvPcSz27sf66Kr6TFWdWVW/q6pvVtVzq2q7RY7t+lX1+qr6Sf+5mzkWj1ju6wUAAAAANh7rpj0Alu0NSXZKcq0kD5l5sqqemuTQJB9L8u4k109yjyRPSLJ/Vd22tfbTuRqtqismeXuS+/VPfTrJUUmukOQuSf4uyZOq6oWttefP2v3IJF9O8qQk2yU5N8nr+23fmtXPdkn26R/un+QZs9o6OcnLkmyZ5MAkP+jH8bm5xj6fqto5yfuS3C7JJUk+meR7SXbsx/HMJAdW1VNba2+YYyxJcq8kN+7LV0xybJJrJ/l4ki8muWuSvfqf2yaZ92J6VR2Y5KX96zy7H+Pp6d631yV5WLr3ccbRSb696Bd++f5ele49TJJz+nH/IsluSW6Z5G+TPL6qntta+9cJTfw264/F7dK9xqQLOP87yZ37MX6t33bzJHsm2aeq7tZau3iesT0kyeHpPjvn9+2cnO4z/saqun+6YwIAAAAAbKKEEpuo1tqhSfct/qwPJW6f5HpJ/qy1duJM3f5i7vuS7JrkRekudF9OVa1L8tEkd0xyXpIHtdY+Omv7C5P8vyTPq6qLW2svGIzptX29GyTZL8mVkryqtfazCd3dI13QkSQ3qKobtta+N2jrxCQHVdWd04USr2utvWKh4zLH67pykk8l+ZN0F/zv01r78mD7dulCnkcmeX1Vnddae+vssfR1r5b1ocTLk/w0yT6ttYv67VumC2celOThVfWW1trH5hjXw5PMvKbPJ9m/tXb6YPtN0oVLew52O7K1dvhyjkPvj/rfH0/ykNbaWYP+tkny7CTPS3JoVZ0yPA5J0tefORYHZ30o8cz+9/Vaa+cO2nxFuvfvTkn+JutDqsuoqjulC8PWJflhknu11n482H7tJB9J8twlv2IAAAAAYKNh+qa15e5JnjgMJJKktfb+JF/qHz6gvxtikuekCySS5JnDQKJv5+LW2jOTHN8/9byq+pMJ7Rw1KO87R1/3W+DxjP373x+cY/tivDJdIJEkfzMMJJKktXZeugvmP+ifenVV7biIdm+Y5PEzgUTf1iXpLurPeNSkHfv2X9U/PD/JXwwDib6tbyZ5dJJrLGIsS3FRkocNA4m+vwv6u1/e1T91aB9ELcbtkjxyGEj0Du77S+Y+FuuS/GfWh6QPGwYS/dh+mi7ouWM2sEsvvihn/egbE3/OO/PUDd09AAAAAKxpQom15eTW2tFzbPtM/3ubXPab90mSqto+yd/3D3+T5I3z9POa/ve6dFM1zfahJDNrHuw/e2N/N8G+6YKSmXrzhRLfmR20LFZVXSfrL4Z/r7V21KR6rbUL010YT5IdkhywiOaPaK1dMKGt7ySZueB/izn2PSDJVfvyka21n88xrmOzgumaJnh3kqe11s6Yp857+9/XTHKrRbb7vtmhSpK01n6b9VN33ayqJv03Z79001UlyQmtta9M6qC/k2auz/equeC3Z+TYA/eZ+HPiR9+yobsHAAAAgDXN9E1ry//Os+3kQXnSgtf3SXcxPkmOb62dP09bXxiU753kacONrbVfVtWX061RsHdVXWnWN+hvl+Tq6abiuSTJbZLcuqp2aa394avo1S2ofb0kk9Y2WKyHpFuvIVn4gvbs17XQdFELHe8dM/lYJ8kDBuVjF+jnuKyfMmpFWmv/vYhqw9sBbprFreOx0LG4Wbrpuq6S5MxZ25d6LO67iPEs2zY7XC13OuSdE7dtu9MuG7JrAAAAAFjzhBJry8nzbBuGAleasP32g/J3FuhneNH6elW1fWvtnFl1jkoXSmyTbv2I9w623T/dHRJHpbtb4Dbp7trZL+vvVkjW3z2xkqmblvu6br6IthdzvC93rKuqctk7KL67QD/LuktkPv0UXndKF3bsmO59mnGtQXmnRTa5lM/e7FBir0F59GMx2xbrtsqO17vphu4GAAAAADZLQom15XfzbLtoUJ40hc41B+VnV9WzF9lnJdk5yexQ4oNJ/rkv75/LhhL3S/Kl/o6KDyY5dPD8MJTYP11QMLyDYamGr+sNVfWGRe531arasl8jYi6LOd41YdsOWb/Id5LMN5VScvlju2xVtW2S5yd5SiaHU7NtvcimV/LZ+6NBebRjAQAAAACMTyixtrSFq8xp+0H5U1m/MPZi/P5yA2nt61X1syR/nOQ+VbVFa+3SqtozyR5J/quv9+2q+nGS6ya5e1VdobX2+6raJd16Bm9ura3W6zoq6xezXoyt0k0vNZfljmt2GDDfVFlZYAyL1q/lcVS6BdGT5D1JXp7k66213w3q7Z1umqSlWMl7NDweoxwLAAAAAGA6hBLMGH4D/eOttResQpsfSrcQ9tXTTdH0uUyekumDSQ5Msm26qZ7en24qpy2ysqmbksu+rne11t66wvZWw7mzHm+7QP0tF9i+WH+d9YHE8UkevMLAZ7Wcm/XrmYx1LAAAAACAKZg0lQqbp1MG5auvUpvDQGH//vf9k/y4tfatOerdb1D/vCy88PFCNsTrWqnf5rLTHS00ru0X2L5YDx6UX7mRBBJJ8otBeaxjAQAAAABMgVCCGZ8ZlPdcqHJVXa+qnltVB85T7fisvytgv6raLd0Cz7Pvfvh0krP68r5VtX26b/Qf21o7bxFjn89SX9ct+tf1mBX2O6c+DPjK4Kk/XWCX665S19celOebxmrs/y4Mpwob61gAAAAAAFMglGDGR7I+GLh9VV1lgfp/l+QFSe44V4XW2gVJPtY/vFGSv0+38PMHZtW7uO8/Sa6WbiHm7bLyqZuS5MgkF/fle/frKszneele14IBxgq9b1C+xwJ177JKfQ7X/thunnrXWqX+FmsaxwIAAAAAmAKhBEmS1tq5SV7aP9w6yTPnqltV107yV/3DVy/Q9FGD8tPSBR+fmVDvg7PqXZpuTYoVaa39NMmb+4fXSPLYuepW1S2S3DvdYsqvXWnfCzgiyZl9+cFVdc05xnTXrF5AMrwjYb956j18lfpbrA8l+WFfvmNV3XJSpaq6QZJ7jTYqAAAAAGDVCSUY+rckn+jL/6+qnjS7QlX9abo7Ha6S5K2tteMXaPPD6QKGpFtY/cP9nRGzHZ3kwkG9L7bWTl3S6Of290lm1rA4rKoeOLtCVd0myXuTbJXk0Nbaj1ap74laa2elu9sk6RZ3fndVXWY9haraM8lbkpywSt2+Ol3gkiTPrKq/mNXfFlV1SBa+W2FV9Z+Hx2b9HS3vqKrLTNNUVddK8p4kp405NgAAAABgda2b9gBYnqp6YpI9ctmpdu5RVVdKcmZr7dBZ9W48qPfQ/oL3H+olSWvtkqraN92dBX+Z5D/6NSM+nS4wuEGSOyfZMsk7M89dB4M2z6iq/01y+/6piVMytdbOrqoTkuwzX73laK39rqrunORdSe6a5D1V9fUkX0w3ndSNktwuSUvy8nTTR/1BVe2Y5Dn9w70Gm55dVWcnOaa1dsysensM9p+5A+WY1toxg3G9o6p2TneHyq2T/KiqPpruwvv10q2r8dokX0133C+nqvZI8sR5xnZia+21fX/frKrHJnlDuiDkXVX1lSRfSxdW3KUf94eS7Nu3M+kzNfN6bjfo74n9Z+dLrbUjZ9WbNK4/1OvH9qmqekSSw/vX/u2qOjrJyUmumeSe6e6m+Lt04cRwbEnywj7oAQAAAAA2YtWtucumpqqOzxwXqpP8tLW2+1LqTWj/Tkn+Osmd0k17VEl+meRzSd7cWvvEpP3maOuZSV6ULti4WmvtnDnqPTnJv/cP92ytfXuxfSxhLPsneWSS2yTZOd3F+JPTBS+vb619ecI+uyf5yTzNHtJaO3ix9Sa0f/MkT0+yd7o1NX6dLoh4fWvtg1X110ne1Fd/VGvtbYN9905y3Dx9ntBa23tWfzdNd/fIXZLsmu4OhZ8n+VS643+VCW0OP1Pz/UfjiNbaAUupN2ts10vyjHQhxK5Jzkm3KPe7kvxHuiBk0uu9TmvtpHn6W1BVnZJkt+2uumv2O+JrK2kKAACAZTrs9F2mPQSAVbXrAZvd9fdasIJQAjZuVfV3SV7VP9yvtbbitTa4PKEEAADA9AklgLVGKHF51pSAjd/ug/KPpzUIAAAAAICVEkrAlFTVv1bV96tqqwWqzqzHcVqS727gYQEAAAAAbDBCCZieHdMtHn6PuSpU1a3SLYKdJK9r5lsDAAAAADZhQgmYvtdX1Y1mP9kvSP0//cPvJXnxqKMCAAAAAFhl66Y9ANiMndv/3i3JN6vqhCQ/SnJxkhsluUOSLZN8Ncn9Wmu/m8ooAQAAAABWiVACpqS1dlBVvTfJ/klum+SGSW6XboX6M5J8OMm7kryztXbJ1AYKAAAAALBKhBIwRa21zyX53LTHAQAAAAAwBmtKAAAAAAAAoxBKAAAAAAAAozB9E8DATttukSPvu/O0hwEAALCZatMeAAAbmDslAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUQglAAAAAACAUayb9gAANiZnnn9pHvrh06Y9DAAAYBN22Om7THsIAGwkdj2gTXsIGx13SgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSgAAAAAAAKMQSsACqupqVfWiqvpmVZ1bVb+vqp9U1Yeq6klVte20xwgAAAAAsClYN+0BwMasqnZP8pkkuyX5bZIPJzkzyW2T3Lf/+UiSk6YzwtVVVXsm+WCSc5Lcu7X2iykPCQAAAABYQ4QSML/XpAskfpPkz1trP02SqqokH0pyn+kNbYN4TpLr9OW/S/KPUxwLAAAAALDGCCVgDlV1lST37B9+dCaQSJLWWquqV6e7o+B3UxjehlJzlAEAAAAAVkwoAXO7ftavu3LS7I2ttaOTHD3mgEZwaJJbJzk3yb9PeSwAAAAAwBojlIC5XWFQvnBqoxhRa+0bWT99EwAAAADAqtpi4Sqw2TJ9EQAAAADAKhJKwCxVdVJVtSTHDZ5+flW1wc9JE/bboaqeVVWfrarTquqCqvpFVR1dVY+vqq3m6G/vWW3P/OxeVVtU1V9X1Ql9m5cOtu89oa2dqup5VfXFqvr1YAwfqarHzTOGwyf0f/wijtWDquqYqjq9qs6vqp9V1ZFVdZeF2l3gdW9VVU+rqq9U1dlVdU5VfamqnlJVWy40LgAAAABg42T6Jri8NyTZKcm1kjykf+5/k3xuUOfM4Q5Vdc8kb0tytSRnJ/lYklPTTYW0T7oFsw+qqvu31r4zq7+Tk7ysL98ryY378nZJPppkr769bye5c5IbTRp0Ve2X5IgkOyY5o9/3rHRrY9w9yb37MezbWvvhrN0/2u+TJE+f1P6svtb1r/eh/VM/S/LBJBf04/1kVb1w1m5vTHdsTlzgdV8xybFJrp3k40m+mOSufbt7JbltkkcsNEYAAAAAYOMjlIBZWmuHJt03+bM+lDimtXbwpPpVda8kR6U7n45P8hettV8Ptl8/yUfShQOfrarbtNa+P+jvxCQH9XWvlvUX52cu2O/RWvtNv32rJJ9JcqtZY7hvkvf2Y/hgkke21s4ZbL9xuuDhBkk+VVU3ba2dPhjDkUmO7OsuGEok+Y+sDyRel+TvWmsXD/r7yyRvzfqgI0kOba2dtIjX/fIkP02yT2vton77lv34HpTk4VX1ltbaxxYxTgAAAABgI2L6JliBqrpqkrekCwPOTPLAYSCRJP1dCQ9KcmmSqyR5Z1Ut5ty7ZZKHzAQSfVsXJXn3hDEc3o/h5CQPHwYS/X7fTvKo/uE1sj7wWLKqunOSx/cPv5bkKcNAou/vv5P8W9/XUt0wyeNnAom+vUuSPG9Q51GX2wsAAAAA2OgJJWBlnpTk6n35ja21syZVaq19I92dCklysyT7L6Ltw1trv53w/H+km1rqf/vHT043bVSSvKa19rs5xnBCkpmpo/6yqnZZxBgm+YdB+ZV9YDDJq9MFMUt1RGvtgtlP9tNezRzfWyyj3UW59OKLctaPvjHx57wzT91Q3QIAAADAZsH0TbAyDx+UPzpnrc7RSe472O/9C9T/1KQnW2u/T/L7wVMPW8IYvpBuTYqtktwtyTsWqH8ZVbVtuvUfZhw7V93W2qlV9Z0key6lj6wPWyY5Od2aGbstsc1Fu+C3Z+TYA/eZuO1GDzsoez7iGRuqawAAAABY84QSsEz9tEk3HDz17QV2GW6//SK6+MkixrDTYAyXJvneArsMv+p/8ywxlEhy0yRb9+VzWms/X6D+iVl6KHHyPNvO7X9faYltLto2O1wtdzrknRO3bbvTcm8uAQAAAAASoQSsxPDb+pe21s6Ys2bntEF516raorU23/RGE6dhmuWag/IWSc6vqkXsliRZzhX2PxqUF3q9SXLOwlUuZ77XPbPOxKJf5FJtsW6r7Hi9m26o5gEAAABgsyaUgOXbflC+3BoIE5w/KFe//6Q1I2a0JY7h4iSvXMQ+M765hLozhnconD9nrfXmWm9iPot53QAAAADAJkgoAcs3vAtgm0XU33ZQblneXQTzjaFaawetQpvzOXdQ3nbOWuttuaEGAgAAAABseraY9gBgEzZcT2GLqrr6AvV3HpR/scDUTcsZw5b9GhMb0i8G5YVeb3LZOzkAAAAAgM2cUAKWqbX26yTfHTx14wV2GW7/7AYaw4KLSlfVX1fVc6vq5svo8utJLuzLV6qqa85XOcl1l9EHAAAAALBGCSVgZd4xKN97gbrD7e+Ys9bSvX1Q3ne+iv3dHK9P8oKsXzR60VprFyT56OCpe8zT185ZOKgBAAAAADYjQglYmdckOb0vP7aqdpxUqapumuRe/cP/S3LUKo/htL78+D4MmMs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" ] }, "metadata": { "filenames": { "image/png": "/Users/folgert/projects/hda/_build/jupyter_execute/statistics-essentials/notebook_77_0.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(\n", " df_subset['reg16'], df_subset['readfict'], normalize='index').plot.barh(stacked=True);\n", "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, 1.15), ncol=2, title=\"Read fiction?\");" ] }, { "cell_type": "markdown", "id": "759fdb71", "metadata": {}, "source": [ "\n", "\n", "From this plot it is possible to see that the observed density of ``readfict`` \"yes\"\n", "responders is lowest in states assigned the ``south atlantic`` category (e.g., South\n", "Carolina) and highest in the states assigned the ``pacific`` category.\n", "\n", "The differences between regions are noticeable, at least visually. We have respectable sample sizes\n", "for many of these regions so we are justified in suspecting that there may be considerable\n", "geographical variation in the self-reporting of fiction reading. With smaller sample sizes, however, we would\n", "worry that a difference visible in a stacked bar chart or a contingency table may well be due to\n", "chance: for example, if \"yes\" is a common response to the ``readfict`` question and many people grew\n", "up in a ``pacific`` state, we certainly expect to see people living in the ``pacific`` states and\n", "reporting reading fiction in the last twelve months even if we are confident that fiction reading is\n", "conditionally independent from the region a respondent grew up in.\n", "\n", "### Mutual information\n", "\n", "This brief section on mutual information assumes the reader is familiar with discrete probability distributions and random variables. Readers who have not encountered probability before may wish to skip this section.\n", "\n", "Mutual information is a statistic which measures the dependence between two categorical variables ({cite:t}`cover2006elements`, Chp 2). If two categorical outcomes co-occur no more than random chance would predict, mutual information will tend to be near zero. Mutual information is defined as follows:\n", "\n", "\\begin{equation}\\label{eq:mutual-information}\n", "I(X, Y) = \\sum_{x \\in \\mathcal{X}}\\sum_{y \\in \\mathcal{Y}} \\Pr(X = x, Y = y) \\log \\frac{\\Pr(X = x, Y = y)}{\\Pr(X = x)\\Pr(Y = y)}\n", "\\end{equation}\n", "\n", "where $X$ is a random variable taking on values in the set $\\mathcal{X}$ and Y is a random variable taking on values in $\\mathcal{Y}$. As we did with entropy, we use the empirical distribution of responses to estimate the joint and marginal distributions needed to calculate mutual information. For example, if we were to associate the response to ``readfict`` with $X$ and the response to ``reg10`` as $Y$, we would estimate $\\Pr(X = \\text{yes}, Y = \\text{pacific})$ using the relative frequence of that pair of responses among all the responses recorded.\n", "\n", "Looking closely at the mutual information equation, it is possible to appreciate why the mutual information between two variables will be zero if the two are statistically independent: each $\\frac{\\Pr(X=x,Y=y)}{\\Pr(X=x)\\Pr(Y=y)}$ term in the summation will be 1 and the mutual information (the sum of the logarithm of these terms) will be zero as $\\log 1 = 0$. When two outcomes co-occur more often than chance would predict the term $\\frac{\\Pr(X=x,Y=y)}{\\Pr(X=x)Pr(Y=y)}$ will be greater than 1.\n", "\n", "We will now calculate the mutual information for responses to the ``reg16`` question and answers to the\n", "``readfict`` question." ] }, { "cell_type": "code", "execution_count": 43, "id": "e4cc6350", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.006902379486167156\n" ] } ], "source": [ "# Strategy:\n", "# 1. Calculate the table of Pr(X=x, Y=y) from empirical frequencies\n", "# 2. Calculate the marginal distributions Pr(X=x)Pr(Y=y)\n", "# 3. Combine above quantities to calculate the mutual information.\n", "\n", "joint = pd.crosstab(df_subset['reg16'], df_subset['readfict'], normalize='all')\n", "\n", "# construct a table of the same shape as joint with the relevant\n", "# values of Pr(X = x)Pr(Y = y)\n", "proba_readfict, proba_reg16 = joint.sum(axis=0), joint.sum(axis=1)\n", "denominator = np.outer(proba_reg16, proba_readfict)\n", "\n", "mutual_information = (joint * np.log(joint / denominator)).sum().sum()\n", "print(mutual_information)" ] }, { "cell_type": "markdown", "id": "1c7ab969", "metadata": {}, "source": [ "In the cell above we've used the function ``numpy.outer()`` to quickly construct a table of the\n", "pairwise products of the two probability distributions. Given an array $v$ of length $n$ and an\n", "array $u$ of length $m$, ``numpy.outer()`` will multiply elements from the two arrays to construct an\n", "$n \\times m$ array where the entry with index $i, j$ is the product of the $i$th entry of $v$ and\n", "the $j$th entry of $v$.\n", "\n", "Mutual information is always non-negative. Higher values indicate greater dependence. Performing the same calculation using the `degree` variable and the `readfict` variable we find a higher mutual information.\n", "\n", "There are many applications of mutual information. This section has documented the quantity's usefulness for assessing whether or not two variables are statistically independent and for ordering pairs of categorical variables based on their degree of dependence.\n", "\n", "\n", "(sec-statistics-essentials-conclusion)=\n", "## Conclusion\n", "\n", "This chapter reviewed the use of common summary statistics for\n", "location, dispersion, and association. When distributions of variables are regular,\n", "summary statistics are often all we need to communicate information about the\n", "distributions to other people. In other cases, we are forced to use summary statistics\n", "because we lack the time or memory to store all the observations from a phenomenon of\n", "interest. In the preceding sections, we therefore saw how summary statistics could be used\n", "to describe salient patterns in responses to the GSS, which is a very useful study in\n", "which to apply these summary statistics.\n", "\n", "It should be clear that this chapter has only scratched the surface: scholars nowadays\n", "have much more complex statistical approaches at their disposal in the (Python) data\n", "analysis ecosystem. Nevertheless, calculating simple summary statistics for variables\n", "remains an important first step in any data analysis, especially when still in exploratory\n", "stages. Summary statistics help one think about the distribution of variables, which is\n", "key to carrying out (and reporting) sound quantitative analyses for larger\n", "datasets---which a scholar might not have created herself or himself and thus might be\n", "unfamiliar with. Additionally, later in this book, the reader will notice how seemingly simple means and variances are often the basic components of more complex approaches. Burrows's Delta, to name but one example, is a distance measure from stylometry which a researcher will not be able to fully appreciate without a solid understanding of concepts such as mean and standard deviation.\n", "\n", "The final section of this chapter looked into a number of basic ways of measuring the\n", "association between variables. Summary measures of association often usefully sharpen our\n", "views of the correlations that typically are found in many datasets and can challenge us\n", "to come up with more parsimonious descriptions of our data. Here too, the value of simple\n", "and established statistics for communicating results should be stressed: one should think\n", "twice about using a more complex analysis, if a simple and widely understood statistic can do the job.\n", "\n", "(sec-statistics-essentials-further-reading)=\n", "## Further Reading\n", "\n", "While it is not essential to appreciate the use of summary statistics, an understanding of\n", "probability theory allows for a deeper understanding of the origins of many familiar summary statistics. For an excellent introduction to probability, see {cite:t}`grinstead2012introduction`. Mutual\n", "information is covered in chapter 2 of {cite:t}`cover2006elements`.\n", "\n", "## Exercises\n", "\n", "The Tate galleries consist of four art museums in the United Kingdom. The museums --\n", "Tate Britain, Tate Modern in London, Tate Liverpool, and Tate St. Ives in Cornwall --\n", "house the United Kingdom's national collection of British art, as well as an international\n", "collection of modern and contemporary art. Tate has made available metadata for\n", "approximately 70,000 of its artworks. In the following set of exercises, we will explore\n", "and describe this dataset using some of this chapter's summary statistics.\n", "\n", "A CSV file of these metadata is stored in the `data` folder, `tate.csv`, in compressed\n", "form `tate.csv.gz`. We decompress and load it with the following lines of code:" ] }, { "cell_type": "code", "execution_count": 44, "id": "4f5c7005", "metadata": {}, "outputs": [], "source": [ "tate = pd.read_csv(\"data/tate.csv.gz\")\n", "# remove objects for which no suitable year information is given:\n", "tate = tate[tate['year'].notnull()]\n", "tate = tate[tate['year'].str.isdigit()]\n", "tate['year'] = tate['year'].astype('int')" ] }, { "cell_type": "markdown", "id": "996480c9", "metadata": {}, "source": [ "### Easy\n", "1. The dataset provides information about the dimensions of most artworks in the\n", " collection (expressed in millimeters). Compute the mean and median width (column `width`), height (column\n", " `height`), and total size (i.e., the length times the height) of the artworks. Is the\n", " median a better guess than the mean for this sample of artworks?\n", "2. Draw histograms for the width, height, and size of the artworks. Why would it make sense\n", " to take the logarithm of the data before plotting?\n", "3. Compute the *range* of the width and height in the collection. Do you think the range\n", " is an appropriate measure of dispersion for these data? Explain why you think it is or\n", " isn't.\n", "\n", "### Moderate\n", "1. With the advent of postmodernism, the sizes of the artworks became more varied and\n", " extreme. Make a scatter plot of the artworks' size (Y axis) over time (X axis). Add a\n", " line to the scatter plot representing the mean size per year. What do you observe?\n", " (Hint: use the column `year`, convert the data to a logarithmic scale for better\n", " visibility, and reduce the opacity (e.g., `alpha=0.1`) of the dots in the scatter\n", " plot.)\n", "2. To obtain a better understanding of the changes in size over time, create two box plots\n", " which summarize the distributions of the artwork sizes from before and\n", " after 1950. Explain the different components of the box plots. How do the two box plots\n", " relate to the scatter plot in the previous exercise?\n", "3. In this exercise, we will create an alternative visualization of the changes in shapes\n", " of the artworks. The following code block implements the function `create_rectangle()`,\n", " with which we can draw rectangles given a specified width and height [^credits].\n", "\n", " ```python\n", " import matplotlib\n", " \n", " def create_rectangle(width, height):\n", " return matplotlib.patches.Rectangle(\n", " (-(width / 2), -(height / 2)), width, height, \n", " fill=False, alpha=0.1)\n", " \n", " fig, ax = plt.subplots(figsize=(6, 6))\n", " row = tate.sample(n=1).iloc[0] # sample an artwork for plotting\n", " ax.add_patch(create_rectangle(row['width'], row['height']))\n", " ax.set(xlim=(-4000, 4000), ylim=(-4000, 4000))\n", " ```\n", "\n", " Sample 2,000 artworks from before 1950, and 2,000 artworks created after 1950. Use the\n", " code from above to plot the shapes of the artworks in each period in two separate\n", " subplots. Explain the results.\n", "\n", "### Challenging\n", "\n", "1. The `artist` column provides the name of the artist of each artwork in the\n", " collection. Certain artists occur more frequently than others, and in this exercise, we\n", " will investigate the diversity of the Tate collection in terms of its artists. First,\n", " compute the entropy of the artist frequencies in the entire collection. Then, compute\n", " and compare the entropy for artworks from before and after 1950. Describe and interpret\n", " your results.\n", "2. For most of the artworks in the collection, the metadata provides information about\n", " what subjects are depicted. This information is stored in the column `subject`. Works\n", " of art can be assigned to one or more categories, such as \"nature\", \"literature and\n", " fiction\", and \"work and occupations\". In this exercise we investigate the associations\n", " and dependence between some of the categories. First calculate the mutual information\n", " between the categories \"emotions\" and \"concepts and ideas\". What does the relatively\n", " high mutual information score mean for these concepts? Next, compute the mutual\n", " information between \"nature\" and \"abstraction\". How should we interpret the information\n", " score between these categories? (Hint: to compute the mutual information between\n", " categories, it might be useful to first convert the data into a document-term matrix.)\n", "3. In the blog post, [The Dimensions of\n", " Art](https://web.archive.org/web/20190708205952/https://ifweassume.blogspot.com/2013/11/the-dimensions-of-art.html),\n", " that gave us the inspiration for these exercises, James Davenport makes three\n", " interesting claims about the dimensions of the artworks in the Tate Collections. We\n", " quote the author in full:\n", " > 1. *On the whole, people prefer to make 4x3 artwork*: This may largely be driven by\n", " > stock canvas sizes available from art suppliers.\n", " > 2. *There are more tall pieces than wide pieces*: I find this fascinating, and\n", " > speculate it may be due to portraits and paintings.\n", " > 3. *People are using the Golden Ratio*: Despite any obvious basis for its use, there\n", " > are clumps for both wide and tall pieces at the so-called \"Golden Ratio\",\n", " > approximately 1:1.681 [...].\n", " \n", " Can you add quantitative support for these claims? Do you agree with James Davenport on\n", " all statements?\n", "\n", "[^credits]: The idea for this exercise was taken from a blog post, [\n", "The Dimensions of Art](https://web.archive.org/web/20190708205952/https://ifweassume.blogspot.com/2013/11/the-dimensions-of-art.html), by James Davenport." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "formats": "md:myst", "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.10.3" } }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.9" }, "source_map": [ 16, 21, 26, 31, 147, 160, 175, 181, 188, 203, 209, 214, 219, 232, 239, 258, 260, 285, 287, 295, 297, 315, 320, 340, 343, 347, 349, 368, 374, 377, 384, 388, 396, 402, 412, 421, 431, 437, 442, 445, 467, 475, 486, 499, 520, 522, 528, 531, 536, 538, 551, 560, 565, 574, 585, 677, 687, 694, 696, 729, 740, 746, 753, 757, 765, 779, 787, 795, 807, 829, 831, 873, 876, 885, 889, 911, 914, 919, 923, 957, 972, 1032, 1038 ] }, "nbformat": 4, "nbformat_minor": 5 }