Skip to content

Instantly share code, notes, and snippets.

@tonicanada
Last active April 15, 2022 23:58
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save tonicanada/6fd35c00b062c589fb2184dc15c99f6f to your computer and use it in GitHub Desktop.
Save tonicanada/6fd35c00b062c589fb2184dc15c99f6f to your computer and use it in GitHub Desktop.
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding Central Limit Theorem and t-test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When I first heard about inferential statistics I was amazed by that topic, How can we take conclusions about the population with just samples of it? In some way it sounds like magic.<br>\n",
"<br>\n",
"Then someone taught me about the Central Limit Theorem and how to compare means with the t-test, but at that time I didn't fully understood those concepts, and I realized that I just learnt how to solve problems and exercises about it.<br>\n",
"<br>\n",
"I think it's important to understand the Why's and the intuition behind the theory before learning the mechanics of solving exercises about them. Now tools like Python facilitates us a lot to do that, that's the purpose of this article. We are going to use Python to illustrate the first steps towards inferential statistics, with key concepts like Central Limit Theorem or the t-tests. As I usually say: When I code it, then I understand it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Central Limit Theorem (CLT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CLT states that if we have a population of some variable and take m samples of n-size, and we calculate some parameter in each sample (for example mean, median, standard deviation, etc), the distribution of that m parameters will be normal as n increases, and its variance will decrease also as n increases (distribution curve will narrow). This is true even if the original population doesn't follow a normal distribution.<br>\n",
"<br>\n",
"As said before, I think the best way to understand CLT is to practice with some data and obtaining the expected results. We are going to use the CLT with 3 distributions computing the mean, the median and the standard deviation. <br>\n",
"1. Uniform distribution\n",
"2. Normal distribution\n",
"3. Binomial distribution"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import norm, t\n",
"import json\n",
"import pprint"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Generating 3 arrays with uniform, random, normal distribution\n",
"# We'll set random seed to obtain the same results if we reexecute the code.\n",
"np.random.seed(42)\n",
"uniform_distribution = np.random.uniform(4,5.5, 1600)\n",
"normal_distribution = np.random.normal(size = 1600, loc = 5, scale = 0.05)\n",
"binomial_distribution = np.random.binomial(15,0.05,1600)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(uniform_distribution, columns=['uniform'])\n",
"df['normal'] = pd.DataFrame(normal_distribution, columns=['normal'])\n",
"df['binomial'] = pd.DataFrame(binomial_distribution, columns=['binomial'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uniform</th>\n",
" <th>normal</th>\n",
" <th>binomial</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.561810</td>\n",
" <td>4.970916</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.426071</td>\n",
" <td>4.949262</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.097991</td>\n",
" <td>4.967536</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.897988</td>\n",
" <td>4.938803</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.234028</td>\n",
" <td>5.001704</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uniform normal binomial\n",
"0 4.561810 4.970916 0\n",
"1 5.426071 4.949262 1\n",
"2 5.097991 4.967536 0\n",
"3 4.897988 4.938803 1\n",
"4 4.234028 5.001704 0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Plotting the histograms for the 3 variables\n",
"df.normal.hist();\n",
"plt.title('Random Normal Histogram \\n (\\u03BC = 5, \\u03C3 = 0.05)');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.uniform.hist();\n",
"plt.title('Random Uniform Distribution Histogram \\n (a = 4.0, b = 5.5)');"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.binomial.hist()\n",
"plt.title('Random Binomial Distribution Histogram \\n (n = 15, p = 0.05)');"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Storing the mean, median and desvest of the population\n",
"parameters = {\n",
" \"uniform_dist\": {\n",
" \"mean\": df.uniform.mean(),\n",
" \"median\": df.uniform.median(),\n",
" \"std\": df.uniform.std()\n",
" },\n",
" \"normal_dist\": {\n",
" \"mean\": df.normal.mean(),\n",
" \"median\": df.normal.median(),\n",
" \"std\": df.normal.std()\n",
" },\n",
" \"binomial\": {\n",
" \"mean\": df.binomial.mean(),\n",
" \"median\": df.binomial.median(),\n",
" \"std\": df.binomial.std()\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"uniform_dist\": {\n",
" \"mean\": 4.747806528172976,\n",
" \"median\": 4.759842615679644,\n",
" \"std\": 0.43976063753401085\n",
" },\n",
" \"normal_dist\": {\n",
" \"mean\": 5.001599754267358,\n",
" \"median\": 5.000861698922361,\n",
" \"std\": 0.04901432417153433\n",
" },\n",
" \"binomial\": {\n",
" \"mean\": 0.715625,\n",
" \"median\": 1.0,\n",
" \"std\": 0.822452811574653\n",
" }\n",
"}\n"
]
}
],
"source": [
"print(json.dumps(parameters, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building a function to generate m samples of the (mean, median, std...) of n-sized samples"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_sample_parameter_from_variable(m, n, df, parameter, variable, random_state=42):\n",
" \"\"\"\n",
" The following function takes m random samples of n-size of the specified dataframe column and applies\n",
" the selected parameter (mean, median, std) to each n-size sample. Returns an m-size array\n",
" with the sample parameters.\n",
" \"\"\"\n",
" result = []\n",
" for x in range(m):\n",
" df_sample = df[variable].sample(n=n, random_state = random_state)\n",
" if parameter == 'mean':\n",
" result.append(df_sample.mean())\n",
" elif parameter == 'median':\n",
" result.append(df_sample.median())\n",
" elif parameter == 'std':\n",
" result.append(df_sample.std())\n",
" random_state+=1\n",
" return list(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example os usage: Let's say we want to get a list of the mean of 20 random samples (each one with size 35), from the binomial distribution variable."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.8571428571428571,\n",
" 0.4857142857142857,\n",
" 0.6571428571428571,\n",
" 0.6857142857142857,\n",
" 0.7714285714285715,\n",
" 0.7714285714285715,\n",
" 0.7142857142857143,\n",
" 0.5714285714285714,\n",
" 0.7142857142857143,\n",
" 0.7428571428571429,\n",
" 0.5714285714285714,\n",
" 0.8571428571428571,\n",
" 0.9714285714285714,\n",
" 0.7142857142857143,\n",
" 0.8571428571428571,\n",
" 0.6857142857142857,\n",
" 0.8,\n",
" 0.8,\n",
" 0.7714285714285715,\n",
" 0.8571428571428571]\n"
]
}
],
"source": [
"sample_mean_from_binomial = get_sample_parameter_from_variable(20, 35, df, 'mean', 'binomial')\n",
"pprint.pprint(sample_mean_from_binomial)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \"Checking\" Central Limit Theorem by plotting histograms and distribution curves increasing n (sample-sizes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are going to \"check\" the CLT by plotting histograms of sample parameters and show how they change if we increase n (sample-size). We'll also plot the associated normal curve, and for that we'll need to know the \"standard error\".<br><br>\n",
"It's important to say that CLT is usually applied for the mean, but actually, as we'll see, we can apply it for any parameter (median, variances, standard deviation…). This is relevant because, for example, sometimes the median tells us much more than the mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Standard Error (SE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is the Standard Error? It's the standard deviation of the sampling distribution. It decreases as n (sample size) increases. Each parameter will have associated a different standard error formula. Next we show the SE expression for the SE mean, SE median, SE std."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>Standard error of mean:</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma_{\\overline{X}} = \\frac{\\sigma}{\\sqrt{n}}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>Standard error of median (works only for normally distributed data):</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma_{median} = 1.2533 \\ \\sigma_{\\overline{X}}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>Standard error of the standard deviation:</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma_{K_nS} \\approx \\sigma \\ \\frac{1}{\\sqrt{2 \\ (n-1)}}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can \"prove\" this is true using the previous function. We'll compute \"manually\" the standard deviation and compare it to the formula value, they should be similar."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"sample_size_n = 200\n",
"sample_mean_from_binomial = get_sample_parameter_from_variable(400, sample_size_n, df, 'mean', 'binomial')\n",
"sample_median_from_normal = get_sample_parameter_from_variable(400, sample_size_n, df, 'median', 'normal')\n",
"sample_std_from_uniform = get_sample_parameter_from_variable(400, sample_size_n, df, 'std', 'uniform')\n",
"sample_mean_from_binomial_std = np.std(sample_mean_from_binomial)\n",
"sample_median_from_normal_std = np.std(sample_median_from_normal)\n",
"sample_std_from_uniform_std = np.std(sample_std_from_uniform)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"sample_mean_from_binomial_se = df.binomial.std()/np.sqrt(sample_size_n)\n",
"sample_median_from_normal_se = 1.2533 * (df.normal.std()/np.sqrt(sample_size_n))\n",
"sample_std_from_uniform_se = df.uniform.std() / np.sqrt(2*(sample_size_n-1))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"binomialdist_example (mean, n=200)\": {\n",
" \"computed_se\": 0.0504901583850754,\n",
" \"formula_se\": 0.05815619602703789\n",
" },\n",
" \"normaldist_example (median, n=200)\": {\n",
" \"computed_se\": 0.004247561325654817,\n",
" \"formula_se\": 0.004343732383749954\n",
" },\n",
" \"uniformdist_example (std, n=200)\": {\n",
" \"computed_se\": 0.013055146058296647,\n",
" \"formula_se\": 0.02204320895687334\n",
" }\n",
"}\n"
]
}
],
"source": [
"#Comparing results of computing SE \"manually\" versus formula\n",
"se = {\n",
" f\"binomialdist_example (mean, n={sample_size_n})\": {\n",
" \"computed_se\": sample_mean_from_binomial_std,\n",
" \"formula_se\": sample_mean_from_binomial_se\n",
" },\n",
" f\"normaldist_example (median, n={sample_size_n})\": {\n",
" \"computed_se\": sample_median_from_normal_std,\n",
" \"formula_se\": sample_median_from_normal_se\n",
" },\n",
" f\"uniformdist_example (std, n={sample_size_n})\": {\n",
" \"computed_se\": sample_std_from_uniform_std,\n",
" \"formula_se\": sample_std_from_uniform_se\n",
" }\n",
"}\n",
"print(json.dumps(se, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Building a function to plot histograms and distribution curves as n (sample size) increase"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def setup_plots(ax, parameter, population_params_dict, variable, df_samples, i):\n",
" \"\"\"\n",
" Configures plots setting xlim, ylim, and adds distribution curve based on parameter ('mean', 'median', 'std'),\n",
" with its corresponding standard error\n",
" Parameters\n",
" ----------\n",
" ax: matplotlib ax\n",
" Subplot ax to setup\n",
" parameter: str\n",
" Can be one of following: 'mean', 'median', 'std'\n",
" population_params_dict: dict\n",
" Dictionary with population parameters \n",
" (keys: ['mean', 'median', 'std'])\n",
" variable: str\n",
" Pandas dataframe column (in our example can be one of following: 'normal', 'uniform', 'binomial')\n",
" df_samples: pandas dataframe\n",
" Pandas dataframe where each column represent sample parameter variables for different sample sizes\n",
" i: int\n",
" Column of df_samples to plot:\n",
" i = 0 is the case n = n_min\n",
" i = 1 is the case n = n_min + n_step\n",
" ...\n",
" \"\"\"\n",
" ax.set_xlim(population_params_dict[parameter]-population_params_dict['std'], \n",
" population_params_dict[parameter]+population_params_dict['std'])\n",
" ax.set_ylim(0,30)\n",
" x = np.linspace(population_params_dict[parameter]-population_params_dict['std'],\n",
" population_params_dict[parameter]+population_params_dict['std'],\n",
" 30)\n",
" std_error_dict = {}\n",
" std_error_dict['mean'] = population_params_dict['std']/np.sqrt(df_samples.columns[i])\n",
" std_error_dict['std'] = population_params_dict['std'] * np.sqrt(1/(2*(df_samples.columns[i]-1)))\n",
" if variable == 'normal':\n",
" std_error_dict['median'] = 1.2533 * std_error_dict['mean']\n",
" else:\n",
" #We use bootstrap strategy to calculate standard error of median\n",
" std_error_dict['median'] = df_samples[df_samples.columns[i]].std()\n",
" y_norm_curve = norm.pdf(x, population_params_dict[parameter], std_error_dict[parameter])\n",
" ax.plot(x, y_norm_curve, 'b--', linewidth=2)\n",
" ax.tick_params(axis='both', which='major', labelsize=12)\n",
" ax.set_title(f\"n = {df_samples.columns[i]}\", fontsize=18)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def plot_histograms_sample_parameter(m, n_min, n_max, n_step, parameter, df, variable):\n",
" \"\"\"\n",
" Plot \"(n_max-n_min)/n_step\" histograms for the selected variable. On top of histogram will also plot\n",
" the corresponding distribution curve.\n",
" Parameters\n",
" ----------\n",
" m: int\n",
" Number of samples to take.\n",
" m_min: int\n",
" Minimun sample size\n",
" n_max: int\n",
" Maximum sample size\n",
" n_step: int\n",
" Difference between sample sizes\n",
" parameter: str\n",
" Can be one of following: 'mean', 'median', 'std'\n",
" df: pandas dataframe\n",
" Dataframe where each column is a dataset\n",
" variable: str\n",
" Pandas dataframe column (in our example can be one of following: 'normal', 'uniform', 'binomial')\n",
" \"\"\"\n",
" samples_dict = {}\n",
" \n",
" params_dict = {\n",
" \"mean\": df[variable].mean(),\n",
" \"std\": df[variable].std(),\n",
" \"median\": df[variable].median(),\n",
" }\n",
" \n",
" for n in range(n_min, n_max, n_step):\n",
" sample_parameter = get_sample_parameter_from_variable(m, n, df, parameter, variable) \n",
" samples_dict[n] = sample_parameter\n",
" df_samples = pd.DataFrame.from_dict(samples_dict)\n",
" \n",
" fig, ax = plt.subplots(1, \n",
" int(np.ceil((n_max-n_min)/n_step)), \n",
" sharex='col', \n",
" sharey='row', \n",
" figsize=(20, 3.2))\n",
" fig.suptitle(f\"Histograms for {m} sample {parameter} (sample size between {n_min} and {n_max}) for variable with {variable} distribution\", fontsize=20, y = 1.1)\n",
" for i in range(len(df_samples.columns)):\n",
" df_samples.hist(column=df_samples.columns[i], ax=ax[i], alpha=0.5, color='red', bins = 30)\n",
" setup_plots(ax[i], parameter, params_dict, variable, df_samples, i)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'mean', df, 'normal')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'median', df, 'normal')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'std', df, 'normal')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'mean', df, 'uniform')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'median', df, 'uniform')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIAAAAEDCAYAAABNtH+LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABnmklEQVR4nO3dd5wU9f3H8deH3kWqIAoWioIRK3YPG3YSSxSNscSSWKOJxiQaL2qaaZYY/WmMJHbR2LsoKvauoNIEBEXpwtHL9/fHZ5Yblt273bttt/d+Ph772Lupn5mdz5TvfOc7FkJARERERERERETKV5NiByAiIiIiIiIiIvmlAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAqCImQUzG1PsOKR2Zna+mX1iZsui3+2nxY5Jiqsc8tfMKqLlqMxyvBZmNsnMnsxTaCXHzE6J1tUpJRDLGDMLxY4DyiMPpGEppVysDzPbNDqnuLqO4x9kZq+Z2cJofTyc4xBLnpn1iZZ9ZD2nk/Wx0Mwqo3Eq6jPvfDKzaWY2LctxSn65kqVbTjPrYGbXR/1XR8s1uPARFkaqc4O6nuflMKaUOWpmI6PufYoRVxRDUddNY1NWBUDRhlPjiXi048npRp6rg57UzsyOB64DlgPXAr8F3ihCHBeb2ZPR9lRlZovM7GMz+5uZ9aphvG3N7H4zm21my81sgpn91sxa1zDOHtG85kcnqB+Z2U/NrGl+lk7qqkgXQ+cDWwOXFXCe0kjV5SKmnJnZNtE+/BEz+yJxHmJmzWoZr5OZXRutzxVm9pWZ/bum40c5MLOO0fHzruhGTuJC8IBaxmtqZhdGx79l0fHwSTPbo4ZxWke/zYToeDs7Ov5ukzxsCOFL4GbgIjPbLMtl6gM8AmwB/Bs/L7k3m2lI41QuBagZugY4D/gY+AOeJ18XNaIGpiFvL7pWLi01nqA0MtsAS4sdhNTq8MR3COGrIsZxFlAFvAR8AzQHdgAuBH5kZhUhhPfjI5jZEOCFaNgHgBnAfsBvgP3NbP8QwoqkcYYDD+IFXvcB84EjgL8DewLH5msBpfSZWVvg18BzIYT3ih1PI/VDoE2xg5CiGYbvw9cAk/B9dauaRjCzzsBrQD/8mHAvMAA4FTjMzHYPIXyez6CLqA9+IQgwE5gLdK9pBDMzfB0dA0wA/gF0Ao4DXjazo0MIjySN0xJ4Dj9OvoPfONoMP2YeZmb7hRDeTJrVn/EL1MuBM7NYpgPw3/xnIYS7sxiv3HyJn0t/W+xAStT+xQ6gQNIt5+HAxBDCEYUMpsS8hefI3CLNv5RztNjrplFRAVAkhPBZsWOQjPQEKHLhD8CgEMLy5I5mdgZwC/A74NBY96bA7fiF4vAQwqNR9ybA/cDReOHRH2PjdABuxS8sKkII70TdL8cvGo4xs+NDCLrT2HidAHQERhY3jMYrhPBFsWOQonoKeB34KISwLKod1buWcX6PF/78LYTws0RHMzsfL6j4J3BwfsItuul4gcn7IYT50d3gk2sZ53i88Oc1YP/EsdfMbgbGArea2QshhMWxcS7CC38eAI4LIayNxrkPeBj4t5ltl+gOfl5hZs8BJ5jZxSGETC+SekbfxT4vKaoQwipA59JphBCmFDuGQqhhOXsCLxcyllITQlhKEXOklHO02Oum0QkhlM0HCL5INQ4zLRquT4pxxyR1a4/fCRoHLAIWA1Pwmhg7RcNUJuab4nNKbFpNgB8Db+M1R5ZEf/8EaJIm1hOB94BlwGzgDnwHOiZ5OYGKaJ6VwK7AE3htkXXLCgzFCyc+iZZnWbRsVwCtUsw/sWwVwAjgXbyW1FfA34CW0XD7RTEtAhZEcXZOMb3vAPdEv8EKYE60fNcCzWv53dKu56Th9geejpZ9BTARL1TZKMU0x0TTaIHfwZ0QjTOyHtvgRtE0JyV13y/q/lKKcbaM+k0DLNb9tKj7f1KMk3Z6NcR2JDAamBUt51d4Daazk4bbCb8I+TBaj8vxO9t/BTZOMd1TolhOAQ4EXsG38Tl4oVfHaLgdgMejbaQKeJSkPEz6XVoCVwNTo3inRNtqizS5PyZF92bA2fhjgoui7fd94FzS5F2adbclnjuT8byZj1djvjmxrcfiTvXpE5tWd+A2vObYMuAD/AKoIhq2Mou43ojWTZsU/WrdfyX9hg8Cn0cxLQJeBX6QZr6JZW2O586UaDuZAJwRG+7H0Xpaht/t/23yesdrBAS8EGsAfnE2H99HjgUOqmmbS9GvF15D4PNo3cyLtrVdsszlTPNlDBvuh9JtB4lPZdLwnfAq8Z9G6+rbaN4bLHstMYconp74fnh2NL13gRNqGG8Y8CR+5y2Ra38myt1omArSL8/IaJivgC9TTH96NNzlSd0PibpfmdS9DfBLPDeW4PuL14ER9VmG2LDTok/baJgvonEmA78gth/O9kP1OUazNP3b4fuhKqB9Ur8msfG3zHB+9dlfD422l8V4zj8BbJNmPlsDo/D99xK8MOYwasjFDOMfGY1/QA3DvBwNMzRFv/9G/U6NdbPYNrdFltM7Oep3Vgax15QTFbHh+kZxfgmsxPPkv0DfFNOsTIyPF/C/GW0r02qJ5bNo2l3S9P9FNN1zY93qc064QWzE9uVJ4/XDz8Pewc8LVkS/zy1ArxrWayWwO/A8vk9cDDwD7FxTbCn6DYi2sxnROvoGuBvon+E22j+a9l1J3beI/d57J/X7U9R9v6R9w7TY/2Nq2H76pFjnx+C1JJbiuX4vsGkWuVbTOkr3241MxIPXfP8Y38d8E/1+G6WYVqbLOSY2TFbXSInxgU2Af+G5tYZoPxTr3x1/LPMbqvdbe0fDJPb/0/FtcjxwbKbrMxbL8fgxNqtrtaTuOTnHpO45mvidt8QLzz+LfueZ+BMHHdL9BmnWybrtJimuVJ9Talo3oX770HrlTDl/VAMojajK8dPAHviJ57+A1fjFxVD8IvddPCE7AhfgJ2APxybzQezvO/BknBFNKwDfw+/07YUX9sTnfwl+AFkA/Ac/+B2IX5TVdFdqd/zEeSy+0+uCJwv4CcAAfAf4BF5leU88WSrM7IAQwpoU0zwPP1F/OFreg/DaKp3M7BE8oZ7Ad157AD+I5ntIbHm+g++IAn4xNhXogJ9Uno23X7KqhuUaE32fgt9d/W3yAGZ2FnATvpMfhe+IK6LlPsLM9gwhLEwx7QeBXfA7uQ9H49VVomrrR0nd94u+n04eIYTwuZlNxE+StsQvXGocBz+BXQrsYWYtQ9KjY8nM7Ezg//DnrR/DL5K64YVyp+LbYcIZ+Lb5En7i1QS/yLgIOMTMhoT177QmHIlX8X0cP2jtgf9efczsl/gF7St44cd2+Lra0sy+E2J3YWPux3+XB/BtYzi+re5sZkeGaE9fwzI3j5Z1GF4wcTd+QBsK3AAMAU6qaRrRdHrgJyId8AvMB/Hc2SIa/x94IcNIYGEU5yOsn/8Lo2l1wfNvSzxHxwI98PX1bG2xJMW1EbAz8HbwOyfxfpnuvxJuwk9+XsYLPDrjNdjuMLP+IYTL04RxL74en8R/o2OAW8xsFb5tnYxvD6Px7eM3+Hb7pxTT2iKK9WN8W+2BP+LxlJmdEEK4L4N1siO+HjvhFwr/w/dF3wXGmtn3Qgi1NpadZb6kssH+KXIS/tuv+73MrDe+f+uD/y5P4yemhwNPm9lZIYRba4s5ZmN8G1tIVAALfB+4y8w2DSH8OT6wmV2B59V8/LeajS/nz4FDo8eRFuEn9b8FfhqNem1sMh9E3y8AJ5rZgBDVqjWzrYHNo/77A1fFxks8KjA6Fk/HaDo74DcI/o3vg4YBd5vZwBDCeu1dZbEMcc3xbaQnvu9fjW8nf8TzO91vWF+7Aa2BZ5P3oyGEtWb2DP740VC8ELM2dd1fH47vq57C9z/b4jm/i5ltG0JYVw3fzPriudk5Gv4D/Nj9cPR/3phZK3w/thTPj2RP4Xm1H769A2yFb3MTQwhT04yzdzTOi0n9Xo2+D8T3ATWZhm8nFcC++LnatFg/zGwX/Hdpj5/7fIKfh/0AGB6dd72dYto/i2J4LIpxo1pi+Q9es2wEfnxLdjJ+Lhh/RK2u54TZxnYUfnH/YjSvlcBA4HT83Gzn4G0wJRuCn88+D9yIb3NHAfuY2UEhhFTbw3rM7GD8OJA4H5iMHwePwh8FHBpqeXw6hDDBzL6k+pwsYf+kv19J+n95tLzpjKSWc4aYs/Fj6KN4rg/Bj4/bm9ng2s4Bc+AafB/8GH6MHYrve7Zmw/WSbCR+jLsCL2wZGXWfFhsmq2ukSCf8JlgV/huvxQt6Ejri+bwYv/ncCS+secbMdsfzuxN+zGiO5859ZjYjhJBR26JmdiF+Q3whXiCxEF9Pr5HhY1a5PMeMyTZHE/4O7IOffz8SLctPgb3NbK+Q4smHDI0hs2vlDdRjH1rsnCltxS6ByuWH2N3VGj4LyaAGEH6BGoCHUsynCbE7a6QpUY31HxH1fw9oF+veFr8jEojdncUvEFbhd0o2i3U3fCcWSF+qHEhz5yqa7gZ3NvET8oBXk453r4y6f0vsriBeM2M8Xto+D9g3ad08F403ONb9r1G34SnmvzEZ1sYgRYl61L03XoK/CBiQ1O+f0bxvSTUtvLAm5V2zDOI5PVpPf8EvJtbgB7W+ScONiuZ1dJrpPB71PyTW7e2o205pxhkX9U95xzZp2Hej9dMtRb8uSf/3BpqmGO5H0fx+kdT9lKj76hq2hfnAiUnj3ZZqm4j9LhNZP89a4RchATgpaZwN7kTEtt8b4ssDNE037zTr7rxo2AtS9GsLtE6xLk5JM61bov5/T+q+M57zKe9+pJnWwYnlS9Ev4/1X1G2rFMO1wC/MV5F0xyT2G73N+rVEtsRP7hfghbybxvp1xAtS5hCrHUH1/jMAf06zXhYQuwOVaj3jtb0m4yfd+yZNpyd+52gWUc3FHObLGFLsk1KMd2oU82vE7q5H468Fjk8aviN+YrQM6J7hNpFYj/cT26fiJ5Lzo99my1j3obGYOiZNK7GOk7fVaaSpjUB1rcVzYt3Oiro9S1JtNbw23lJitfqovnN4SdK0W+GFY2tZ/9hS12UI+Ml2PH+74ecIC6mlVmoNv0Fi2ulqAJ1DmryN+v886v+nDOdXn/31/kn9/pBm3T9Lin0gfiGS2OZOySTeFHEmfu+UNYDwgoIAfJym/85R/zdj3Q6Luj2WZpxjov73pem/AJidxTJUkqJmBX7O9mnUL/n4d1zU/TPWz9XEtJYAO2QRQy/8/OOdFP12iab5YFL3up4TpoyN9LULNiXFfhe/mbgGuCmpe0Vsuzo3qV9im5uUZr1VxLptHP2Wc4Ftk6YzCC84eC/D9ZuoaTYw1u0e/Hj2PvBK0nzXAKOTpjGNpH0ntZ8zJJZrEbBdUr+7o37fr892WstvNzLq/gWweax7M6pr0u1a23JG3QOpa2pndY0Um1aIfpcN9rWx/jcnbScnUX1O+hjrH4v3Js15U5r12Qc/ps5n/VreTfBCnEAGNYDI7Tlm4jfONkcTv/NcoHeaZUmuwZvy90yaXp/a5l3LuqnPPrTeOVPOn7J6C1jMFTV8NspyWsuSO4QQ1oYQFmQxjdOi70tDCFWx6SzB78CAFyQknIDvXG8IIcyIDR+AS/EDSzofhBBS3rUKIXweTSPZ36PvYWmmeX0I4dPYdFbgj5E0AZ4IIbwU67cWuDP6d/sU00q1PheE1DVAsvED/IL1H2HD9px+jd8BOClqGDLZ5SF2tzNLp+Pb1c/wk5l38RPZSUnDJba7dHcEEt071nOcmqwmRS2r5GUPIUwPqe/6/RvfoabbTu5JsS3cEf07LoRwV9Lw/42+B6eZ3lXxPAt+5+GX0b+npR7FRW0rnYfX4LgwvjzR3z8jOqDUNJ0kqbbdJSGEDbqnial5NL/F+AEqPp13gOT1U5tErYpZNQyT0f4rpHhmP4SwEr/r2oz0jTpeGmK16oI3XDsW3yavCrG7utFwj+E1cjZNMa1vgSuTYkisl4743cCaHIbf+b8hvh1G0/kKv4O5SQ3LkiyjfMmEme2P3238HC90TLRhsj1ec+DBkNSWV7S+rsALPo7OYnZr8Iv+eNsmU4Hr8buc8Vpv50ffZ4Sk2pEhhJF4AVQ2OZKoyZN8Z/ybaP4t8Lu5iYaQtwfGRttaotsP8IvYa2LTSOT/L/ATwhNytAznx/M3hDAbv+u5Ef7YRz7kdL9ej/31vSGE0Undbom+d010MH8r2YF4ge4/kub9CH5nNZ+Kcez8Guga1T6qjz3wO9WvJx//gtdoHItvZ3ulGPeWkPQiiZqEEGbi+beTmQ1M6n1y9P2fpHHqek6YbWxfhhR320MIz+I3E9PNZzJJtS1j29zW+AV7TX6I/8ZXhBA+SZrOOLyNxR3MbNsMFiPVvm0/vLbi88CQ6KUM4IXSTWLj5ML1IYSPk7olaobumjxwHlwZYu3dhRBWU13jrr7zz/YaKWEl8PMollSWAhcnXV/cjR/bN8YLXNbVaAleo2wa6c9Jk52IH1NvCCFMi01nLXAxfrMiG/U6x0ySVY7GXBdCmB6bf3xZajzvzpP67EOLnTMlrSwfAQshWLp+GTbQCF7F7ANgRFRF/xF8Q3sncbKahR3x5BmTot9L+An7DrFuib/HJg8cQphuZjPwktRU3koXRHRwugC/kOqHV6eLr6tUF2XgJfDJEo0dvpuiX+KiL/462/uieT9sZg/gB8xXU1141tGO0fcLyT1CCAvM7H28WuMAvPphXNp1VpsQwm6w7sJlR7zx53fN7PshhGfqOt08uAuvhfWJmd2Lb3evhhDmJA8YFVSchVeV3RY/mY4XFudzO4lLdWExlg3zJZV+eNXeScBl/kTUBpbhbxyozaN41fobzWwYXtPrVeCTNCfP6QzA2zZ5JaRuXHQMtTeGGtc5+k5VGJ3V/svMNsdPtPbHC5ZaJw2Sj998elK/90LqR1XG4OtlB5IuYJLsHn33NrPKFP37Rt/b4DU/apJxvtQmurh4EL/bfGjSNBIxb5Qm5q6xmDP1RUj92MsYvEApnju744Vcx5pZqjcKtsAvhDuHEObVNuPo+PQ5/vhIE1j3HP7z+DpcjW9jiUcIjPX32bvgNfRCmvXRPPqOr4+6LsO3IYTJKYZP3HTZON1ylpIc769TLfu685E0BU1j8ELMcjI/+u6Ct4FRV2nPS2Ld98LX8ctJ/epyXjISL6w7GbgEwMxa4DUsZpO036vHOWFWsUWPJJ+I117YHt++msYGSXdO/Uqam4Nj8G1uB2ougEzsX7dPsz/pF31vgx8za5L4DfcHrjezQXiNwdF43vwcP8d8iupHotL97nWRab7mSz7nn+01UsK0qNA+nYnJ5xQhhDVm9g3QNqR+0+KX+KNCmUjk9wbbYPCmHWaQ2fVmrs4x4+p6XVPTsvQxs47JN1ryrD770GLnTEkrywKgXIh2EvvhbVYcQ3WbFYvN7D/AL+Ml1bXYCJif6sIrhLDazBLtS8SHh/WfZY37hvQFQF+n6hidJL6Al3qOwwtk5lB9h/sK/NGuVFJdrK7OoF/iZJ0QwltmtjdeG+cYorvQZjYB+G0I4Z40885UYp2lqw2R6N4xRb+U6ywb0YXFc2b2Nl4d8Q4z6x0ruU+sp3Q10BLdF8a61WWcdPH9LdrOzsbvmP8Uv8h6Cb9DEt9R3oefEH6OFxx8jT+6QTRe3raTJBts/2nyJZVE4UhffNtOp10t00lc1O6K19o5GG87AGCGmf0lhHB9bdOI1JbX2W6HiW1rg7vU2ey/zGxL/GRhY7wNg2fx32sNvp85mTS/eZqCrLr+5rWtl9pqbyZ+81SFAHGZ/ObZ5EtaZrYJ0WNGwIEhhAlpYj4w+tQ55phs1mNn/DygphxJzL/WAqDIaLxtiB3x40tX/FGIxdH+MXEHfYP2f6heH7tEn5riiY9Tl2VYmGa4xDbaNE3/+srZfj1S1/31BtOP9q+w/rLner+VrWIcOxMF4HW5855qPoU6L3kIr/X1AzP7ZVRgdzh+M+TaeE2Jep4TZhvb3/BtcRZ+cfsl1ev2FNJfJOfqmHBGLcNlckyYYWaTgH3N3+oa3399ja+3/fECoP3x3yFVuyR1tTBFt3zvqwo1/2yvkRJq2w7T1QBcXUu/TK+NM9k31loAlMNzzOR510Vty7IRmR+bcqE++9CFKboVMmdKmgqAahA9JnEhcGHUkOW++J22c/GNrdYGZCPf4g0mNw/+Cr51zKwZfpcp3kBl4u/uePXYZN1rCjtN9+H4gX5kCOHUpBh6UPvJc72FEF4HDo8ew9oJ39GdhzfsOSeE8Hw9Jp/YmW9C6nXWI2m4eFx1LWHfQAhhoZm9jjcmOpDqEujEhV+/VONRXTthYqzbBLx9g34k1aaItpst8J1ZJg2FEkL4L/DfqJHVPfCLhtPwBvEGhBDmmNnOUffn8faI4ieMTYjuKhZId/y583XS5Esqid/5oRDCUTUOmYHgj0AeF81/e/w1xucB15nZkhDCbRlMJhFTuvzdJMuwEne+OqfqmcX+66JoGqdGj8ysY2YjyK5WUn3Utl5qa1Ax0X94COHR+gaTSb7UNL6ZtcEfeeuNv00t+e5UPOYL6niSl0o26/Fb/Nn5TjmaN/hF5Rl4jiRO6EfH+v3SzDrhF0nf4u0+xOMBb7Pnogznl49lyKe6HAtSKtD+Otf7rWxNwQujtzSzZike90h37IS6r+PO+LF1fpr+mYqfl6SS9ryE9OdyaYUQlpnZ/fjjMgfibWalfPyL+p0TZhybmXXDC9HHAXsk18iIjjHp5OqYsH0I4aPaYs3AC/gxdBd8/zU9UYvdzN4CDjCznnht38fT1JgrpkRtqlTXfh0LGEeybK+REnJ27l5H8X1jquuOjPeNOTrHXG+SWQ6f0J3q/WdcqpwLpC9H6FjH+Serzz5UalCubQDlXAhhcpSA++JV+YfHeid28ulKFN/H1/U+KfrtE433XtLwkOKZxuhxjs0yj3ydraPv/6XoV9Dq2yGEFSGE10IIv6G6/YbhNY2TgcQ6q0juEV3ADcYbh/00uX8eJKpNx09UE9UXD04eOKqB0Q9/JObzTMbBt5s2wGshy5bsQwgLQwhPhhDOwKuMd6J620xsJ4+mONHelQ0fDcqnVNvlXni+1PZs82d46f9u0Z3OnAghrA4hvBtC+BNerR68sC+hpn3BZ/gz6YPN3+CVrCLLcBIntANqG7CW/VfiN38wxaiF3DfsaGbtU3SviL5r+80Tb+2orV2IrNSSLylFF9934wW4v0l+dj0mHzFvbmZ9UnSviL7j6/ENYOMUbYbUZA013z17AT8x3B9/FOLzUN0+wmj8WPhD/CJ8TNJF0lv4RUo266Muy1BMb+C1H/ZM3t6j7eag6N8XM5hWIfbX685HopoPySpyMI+0grfR8Rp+vEu1XSTeNhp/RGAKfvOgn5ltkeE4AJhZO/wY/lEObg6lPS+JDI2+a3wLVZZGRt8nm1lXfFk/CiF8kDRcoc4Jt8RzfoO33pm3L7VlDePuFeVEsorou9DHhERB9jD8GDA6qd938IZpIfP2f2q7fsilxOPiqa4hdi7A/NPJ9hqpVCRi2iBfovP6rK/V6nmOmQs1Lcu0pMe/FpBiGaPjxOAU065L7MXYhzYKKgBKw8y2iDb6ZBvj1WLjVYMX4Ce8m6cYHrwxRoA/RHeFE/Nog79yFvytRAmJRsrOM7PNYsMb/paOuiT+tOi7It4xWsZUr2TOKTPbw8xSnYwm7vAsTdEvG3fiVXDPi2o7xF2Fv17xzmwLS1Ixs83NLOWdKfNX0e+CP2cab3zsJbzwaR8zOzI2fBOq1//NSSecD+At8h8f3elNjNMKuDr696YMYx4abT/JEtVqE+t/WvRdkTR+N7xB4EK63MzWPacbLfcfon9vTz2Kiy6GbsDvDlyfatszsx6WQeOPZrZTmgKbVNtu4hGTDfYF0Z2tu/B2FiqT5rEz2TW2C37HaQ7+WunkmLPZf02LviuSpjGM1A0v5stG+CNr8RgS6+Vb/PGGmjyCX/idY2aHphrAzHaP74PTySJf0vkbXsj2nxDCVekGih4lewU4ysxSNrBoZttF+ZeppsCf4hdO0UXw+fhx5c7YsInGXm+N7lwnz7utmSVvX/PwNnVSFi5EbTKMx18nnXyR9BpeEJ9ozP2FFOPeBexsZpenKnAws62SLurrsgxFEz16eQf+dpfKpN7n4o9dPpOmfYpk06LvinjHXO6vgzcu/Bxe4/TcpPkMpzCFxInj3NUWa5jZ/PXAx+H7wXUF2NFx9Obo32uScmE4XiDwCanbj0m0Q5VJAVxtXsXvpu9lZsfEe0T/743XQtqgvce6CiG8ird9Nxx/9XpzqguF4qZF3xVJceX6nDAxn/UKEKOCtlup+UmEvvhjuPH4EtvcZNZ/7Xoqt+M3gq4wf8RmPWbWxMwqaplG3Iv4uf7Z+PEqvm97AW8/6dLY/5lIe86QB4l2YU6NapoAEF1n/Cb1KAWR7TVSqbiL6uuOPomO0f7mz2R4jZ2rc8wcuSCqaJCILb4syefdb+E3nA5K6n4ZqR99q+1aOZWC70MbCz0Clt72wP/M2yz4FG/YtCt+UG1O7AAZQqgyszeBvc3sLnxjXIPflfsohHB3dND6PjDezB7Gk+C7+EnVffE7xCGEKWb2G7xRsA/N7D78AuhA/O7zh/idhmw8hh8wLzKz7fBS1c3x58OfIP8Hn0uA/czsFfxtIlX4I1KH4DuFW2oYt1YhhGlm9lP8pPc982rQc/AThd3x2he/SD+FrOwIjDJ/1Gsy/sxsZ/xCfDt82U4KSW+eMrNT8ZOCB8wbwv4Cv0u+M76T+3t8JiGERWZ2Bl4QNMa8Mdr5wJF4q/cP4M/tZ+IhoMrM3sBPyAzfce6CP16WePzu7SiWo8zsNXyn2h3/nSZQ3cBvIXyK58sD+EF2OP6WpyeofrtYTa7C8/jHwBFm9gLe9kA3/MRyT7xNqtoafzwJOMvMxuIFDAuiOI7A29q4Njbs6/jB+qfmDYMnnsO+IWov51f4b/7TqHBjLF5IdRzeVsyRZCiEEMzsIeBMMxsYQohXQc54/4W/ZeVUfJt+IBp2EF7z7H6q72jm28vA6WY2BN8GE+ulCXBWCKHGx/5CCKvM7Ci8jYknou33A/z32Azf1reMpltbAU6m+bKB6ELjAryg40tL3fjomBDCmOjvE/D9wm1mdj7wJn7R0gvfzw/C92E1NXYZ9xHeiOW7ZvYsXhX7+9H3JSHW8H4IYbSZXYoXrE4ysyfx/XM7/ARuX3wbjddCHI2vh6fN7GU8Bz4MITyWNMyg2N+J+a0ws1dJ3f5Pwrl4fl6Jv7lxLL6P7Yk31roLfmd0aj2WIWfMrAvwl1inLtH3bWaWKND/Y1j/7ZS/wi+8LzKzwfiJ9DZ4fs7GXxWfiULtr8/B923XRif7H+I1SL6Hn1sckc3EzOwvVK+nRE3ni83sB9HfD4cQHo6Nci/eLsYxwPtm9hh+zD0OL6w5I8X+4W/4+c0xwJtmNho/zzkWz//TQuoGhhMXM6lqRGYl2kefjBeg3Wdmj+DnIv3x87/FwA/TxFEf/8WPf5fjhb6paiAW5JwwhPB1dO5yPPBBtE/aCD+fXY7vowenGf1p4K9mdgjV29xR0Xjpfr/4vOdFF4kPAW9E28B4/Px7M3y/2pkU7eilmd5cM/uI6jfcxgt5Esf+bvi5Z/Lbh9Kp7ZwhZ0IIb0b77H2At6Jzou54/j5D3Z4uyEVcWV0jlYrouuNS/IUR78eu1Ybhx9uPyOxaLZfnmPX1Kp6n8WXZHj/vuSZp2L9E/R+Jhp+PPy6/Bd6gd0V84NqulVMFU8R9aPkLJfAu+lx98B1GqGWYadFwfVKMOyb2fy+8AOZVqhtVnIk38HZIiulujR9Q5+FV2ANwSqx/E/yuwTt48i7FE+ocvP2CVLGehB+Ul+MHlDvxk+BxwMKkYSuieVbWsOyb4ScCiQb4xuMFM82Slz8avjLqXpFiWqckL2NNseAnVbfjF9vfAkvwE9Trgd5Z/MZjavqNo/k8i+9AV+AnONcAHbOdVg3z2Bzf8b2JX5iswndCH0bdN6th3G2BUXjNnhX4DvC3QOsaxtkTLxxYEP1uH+NtuzTNIuYf4ydBn0fb3vxo27oEaJ80bCe8UGBatO1NwXOhTdRtWn22hVi/PlG/kal+F7ymytX4xdyKKPYrgJZpcn9Miu6G59HoaJlX4tv/WPwiLO1vFZvGEPwO9IfRNJZF29XtwKAUwx+MH6SrorjW29/gzzL/G8/pZfgJ8Ck1rasaYts+GudPSd2z3X/tgZ/MLoi25bH4wTVlTNSQO/jd5g32sVG/SpL2KfHtAL8IfiSKY2kU/7AU06lpm+uG3zUcF02jCr8j/gD+ivFmOc6X9dZFbJ3V9Elen+2j7fHdKN5l+Hb/BHAm/saSTLaHEMXTEz9ezMZz+D3ghBrG2wsv7PsKz5E50Xb5N2DnpGHb4vkwE7+4TJXDR0Td1wLdkvr9Mur3dQ3xtMALgl7Djxcr8ALz0Xhjsp3ruQzTSNqP1bSN1rLOE9tvTZ8NpoXvZ6/DH/1diTdm+W+gV6b5H5tOTvbX8W0oRfet8RxaiB+/XwcOq216aeYxrZb1VZlinGb4ce9jPD8W4MfFPWqYTxu8IHFStA3NwY+/26YZvglee/eDLH+DGrcZ/GLljug3XhV93wn0r+/2l2Z+m+MXVgF4rIbhcnZOmJQLyfuDNvgbUidH2+gM/GZdZ1IcS4gdd/BCmufxNmAW4+d3u2Sz3qK4/hFtB8ujaX0W/SbfzXLd/jWaz/gU/Z6J+t1Xw3Y/LUX3tOcMGSzXBuu7lvg74jWvZkc5MQ4/xqT77UbG40n3O2W4nCn3LbHcy/gaqaZpZTCvlPFF/TbYHjNYpyPwY2zytVqN23asW87OMWvaXmrJ0cTvvCXwMzw/luP7hmuBDmmmd2T0my3Hr3/vxW+8pNxuqOFaOd32FPXLyT403fI3xo9FK0QaCDPrgBc6fBBC2L224UUaGjMbA+wbQkj1CI4kMbNn8LtMW4bqt841GFHV6an441KnFDcaEWmszOwI/JXMJ4UQ7qxteBERkYZIbQCVKDPrakmN10bP7P4Vr676UFECE5FS83P88a6zaxtQREQ2ZGaG18Z9h9SPTImIiJQFtQFUuo4GrjSz5/Hqsok3z/TDq7XfULzQRKRUhBA+Nm9AONUbtEREpHab4LV/Hg6qGi8iImVMBUCl6028HY598OekwR+T+B3e3keDe9RDRPIjhPDfYscgItJQhRBmseFb2URERMqO2gASERERERERESlzagNIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQBIRERERERERKTMqQCokTOzdmZ2hZk9amYzzSyY2ZhaxjnUzF4zsyVmNt/MRpnZFgUKWaTRMbPdzOwBM5tsZoujz7godzdKM84QM3s+GnaRmT1tZoMLHLpIo2FmfaJjaKrPuDTjKE9FCsjMRtaQp8HMJqUYR3kqUmBmtpWZ3WVm35jZiugc+Ldm1irN8MrTDDUrdgBSdF2ASuAb4F2ge00Dm9lRwAPAh8DFwEbAT4FXzWznEMJX+QxWpJHqB7QB7gK+wgvvdwF+DRxjZruGEJYlBjaz3YAxwJfAb6LO5wKvmNkeIYSPCxi7SGPzEPC/pG4LkwdSnooUxf8Bz6fovh9wKvBYvKPyVKTwzGwA8DpeVnEjMBXYHbgcGGJmh4QQQmx45WkWLLbupBEys5ZA1xDCzOj/KuCdEEJFimGbA9OA1cDAEEJV1H0wXnh0WwjhzMJELiJmdjFwDXBcCOH+WPe3gAHANiGEL6NumwKfAm+EEA4qRrwi5czM+uAnqb8NIVRmMLzyVKREmNkzwEHAoBDC+Fh35alIgZnZw8CRwF4hhNdi3X8J/B44KYRwZ6y78jQLegSsQMzslKhq6X5m9nMzmxJVZ5toZicXK64QwopE4U8G9gV6Av9KFP5E0/gAL3U9LiokEmmQSjVPazA9+t440cHMtsZrB41KHAQBor9HAQeY2SYFjVIkhxpCnppZKzNrU0N/5amUtYaQpwlm1hs4AL9QjBf+KE+lrJVwng4FJsYLfyIjo+9TEx2Up9nTI2CF93ugNV4FdQXwE2CkmU0OIbxa28hm1iWLeX0bQlhVtzBT2iX6fj1Fvzfw6rP9gPEp+os0JCWZp9EFZeKzE/AnYCXrV2evLU9Pi8Z9IosYRUpRSeYp8DO8CrqZ2UzgduB3IYQVsWGUp9JYlGqexp2K3xT/V1J35ak0FqWWpy2BpSm6J7rtamYWPQamPM2SCoAKryWwSwhhJYCZPQB8jj+nWGuCAXOymNdQvGZOrvSMvr9M0S/RbVNUACQNX6nm6ZX4xWXCeOCIEMKUWLdM81SkoSu1PF0LvAA8jNfO6wp8H2+zYHczOziEsCYaVnkqjUWp5el6zKwJXgBUBdyX1Ft5Ko1FqeXpeGBbM9skhPB10rgA7fDa7/NRnmZNBUCF989EcoFXTzOziUDfDMc/MIt5fZhVZLVLVGdfkaLf8qRhRBqyUs3T/wOeBjrijeFV4A25xylPpbEoqTwNIXwB7J/U+TYzuwU4Azgeb8gdlKfSeJRUnqaZ/uZ4O5ZVSf2Up9JYlFqe/hU/Xj5iZpfgbdAOAa4DVgHN8dybj/I0ayoAKrzPU3SbB/TOZOQQQqo3FxRKotpdyxT9WiUNI9KQlWSehhAmAYlX1D5gZsOAp6NasPdE3ZWn0liUZJ6m8Du8AOgwqguAlKfSWJR6nv4o+k5+/AuUp9J4lFSehhDuNrPOwFVU1xZaiT+qdhj+2NeiqLvyNEsqACq8NWm6WyYjZ9mI1fx4aW4OJF7xnmhVPS5RtS5V9TuRhqZB5GkI4Rkz+wY4G0gUAMXzNJnyVMpJg8hTYAYea7y2nvJUGouSzdPoAnM4MC6E8EaKQZSn0liUXJ6GEG6IatBuhxfujA8hLDSzc4BZIYREAZDyNEsqAGp4ZmUxbK7bAHo7+t6d9RudBdgNL4mdmMP5iTRUhczTVkCn2P/xPE2+o7kbEIB36zE/kXJRqDzdEmgKfBPrpjwVyUw+8/SHQAvgtjT9lacimclLnkYvT3gn8b+Z7Yy3rxfPWeVpllQA1PAUsw2gl/AEP93M/p54VtrMtsfbIrk9x28dE2mocpqnKRrBS3Q/GdgIeDDRLYQw2czeAY41s8tDCF9Fw/YEjgVeSDUtkUYo13naOYQwL6lbE+Dq6N/HEt2VpyIZy+d574/wx0ruSNVTeSqSsbxfn5pZK+BavK2fvyS6K0+zpwKgBiYfz0Kb2bl4o7LgjWr1NrPLov8/DCE8Fs17lZldgL8l4RUzuxXoAFyIt/5+Ra5jE2mI8pCnT5rZPPwVl1/ghT574VXXZwKVScNfALyI5+kNUbfz8Nfc/gwRyUee3mpmHYDX8Me+ugBH46+ffQR4IGl45alILfLVBpCZDQEGAvcnF9wmUZ6K1CLXeWpmA4GRwOP4eW534GRgK+DUEMJnSaMoT7OgAiAB+DnrN/LVB290C+A/rH/XcpSZLQMuw0tfVwCjgV+EEPR8pUh+3IpfSJ6OX1SuAqYAfwL+knzyGkJ4zcwq8JoHV+PVX18Djg0h5LpmoIi4J4CTgDPxxzJX4K+yPQe4OYSwNj6w8lSkqGpq/Hkd5alIUczFC37OALoB3wKvACeFEN5KHlh5mh0LIRQ7BhERERERERERyaMmxQ5ARERERERERETyK6MCIDO708xmmdkiM5toZqfH+u1vZp+Z2VIze9HMetc0LRERERERERERKayMHgGLGmKaHEJYYWYD8Fe3HQZMx9uhOB1vJ+YqYO8Qwm55i1hERERERERERLKSUSPQIYTx8X+jz1b4my3GhxBGAZhZJTDXzAakaJ1bRERERERERESKIOM2gMzsn2a2FPgMmAU8ib8+cV3L2iGEJXiNoIE5jlNEREREREREROoo49fAhxDONrPzgN2BCvz1pu2AOUmDfgu0Tx7fzM7EX41Kq1atdtp8883rGHJhrF27liZNSruNbMWYG/WNceLEiXNDCF1zGFLRKE9zL22Mq1ZB8+aFDyiFBr0eM6Q8LZ7GsH0VQmOIUXlaPI1h+yqExhCj8rR4GsP2VQiNIcYa8zSEkPUHuBk4H7gO+GdSv4+Bo2sav1+/fqHUvfjii8UOoVaKMTfqGyPwTqhDHpX6R3maG2ljvOKKQoZRowa9HjOkPC2exrB9FUJjiFF5WjyNYfsqhMYQo/K0eBrD9lUIjSHGmvK0rsVKzfA2gMYD2yc6mlnbWHcRERERERERESkBtRYAmVk3MzvezNqZWVMzGwaMAEYDDwGDzOxoM2sF/Ab4KKgBaBERERERERGRkpFJDaAA/ASYCSwA/gL8NITwaAhhDnA08Luo3xDg+DzFKiIiIiIiIiIidVBrI9BRIc++NfR/HhiQy6BERERERERERCR3Srv5axERERERERFpmCorix2BxKgASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzNX6FjApYbNmpW5USw1tiTQslZXKWxERERERySvVABIRERERERERKXMqABIRERERERERKXMqABIRERERERERKXMqABIRERERERERKXMqABIRERERERERKXMqABIRERERkYZFb88UaXiUt0WnAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAiARERERERERkTKnAiARERERESkPesuQiEhaKgASERERERERESlztRYAmVlLM7vNzKab2WIz+8DMDon69TGzYGZVsc/l+Q9bREREREREREQy1SzDYWYA+wJfAIcC95vZdrFhOoYQVuchPhERERERERERqadaawCFEJaEECpDCNNCCGtDCI8DU4Gd8h+eiIiIiIiIiIjUV9ZtAJlZd6AfMD7WebqZzTSz282sS86iExERERERERGRerMQQuYDmzUHngKmhBDOMrN2wADgA6AzcCPQPoQwLMW4ZwJnAnTt2nWn+++/v/7R51FVVRXt2rUrdhg1qpo/n3YrVmzYo0ePwgeTRoNYj/WMcejQoe+GEHbOYUhFozzNvbQxzppVnavxv4ugQa/HDClPi6cxbF+F0BhiVJ4WT4PcvtIdO4t4TG2Q6zFLytPiabDbVwmd80IDXo9ZqDFPQwgZffDaQvcCTwLN0wyzCRDwQqC00+rXr18odS+++GKxQ6jVi3ffHcIVV2z4KSENYj3WM0bgnZBhHjWkj/I0N9LGGM/VIudtg16PGVKeFk9j2L4KoTHEqDwtnga5faU7dhbxmNog12OWlKfF02C3rxI65w2hAa/HLNSUp5k0Ao2ZGXAb0B04NISwKs2giepEer28iIiIiIiIiEiJyKgACLgJ2AY4IISwLNHRzIYAC4FJwMbA9cCYEMK3OY5TRERERERERETqqNaaOmbWGzgLGAx8bWZV0edEYEvgaWAxMA5YAYzIX7giIiIiIiIiIpKtWmsAhRCmA1bDIPfkLhwREREREREREck1tdUjIlIqKiv9IyKlRXkpUpqSc1O5KlKalJslQwVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiLFokafRURE6k/HUhGRjKgASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzDUrdgBSQOnekKA3J4iIiIhIQ6bzWRGRWqkGkIiIiIiIiIhImVMBkIiIiIiIiIhImVMBkIiIiIiIiIhImVMBkIiIiIiIiIhImVMBkIiIiIiIiIhImdNbwMqd3oggIiJSf4njqY6rIiIi0kCpBpCIiIiIiIiISJmrtQDIzFqa2W1mNt3MFpvZB2Z2SKz//mb2mZktNbMXzax3fkMWEREREREREZFsZFIDqBkwA9gX2Ai4DLjfzPqYWRfgf8DlQCfgHeC+PMUqIiIiIiIiIiJ1UGsbQCGEJUBlrNPjZjYV2AnoDIwPIYwCMLNKYK6ZDQghfJb7cEVEREREREREJFtZtwFkZt2BfsB4YCDwYaJfVFg0JeouIiIiIiIiIiIlwEIImQ9s1hx4CpgSQjjLzG4D5oQQLo0N8ypwawhhZNK4ZwJnAnTt2nWn+++/Pwfh509VVRXt2rUrdhgbmjVr3Z9VLVvSbsWK+k+zR4/6TyONkl2PMfWNcejQoe+GEHbOYUhFozzNvbQxxnIZ8DyMd8tjXiZr0OsxQ8rT4imL7Ss5N2fNKmiOQpmsx1ooT4unQW5fibxMPn4muhVBg1yPWVKeFk+D3b5KJD8TGux6zEJNeZrxa+DNrAlwB7ASODcRG9AhadAOwOLk8UMItwC3APTv3z9UVFRkOuuiGDNmDCUZY+z1s2P696diwoT6T3PEiPpPI42SXY8xDSHGQlGe5l7aGJNfJT1ixPrd8piXyRr0emyElKe5V2uMyblZWVnQHIUyWY+NiPI09zaIMZGXycfPRLciaJDrsRFTnuZeyhhLJD8TGux6zJGMHgEzMwNuA7oDR4cQVkW9xgPbx4ZrC2wVdRcRERERERERkRKQaRtANwHbAEeEEJbFuj8EDDKzo82sFfAb4CM1AC0iIiIiIiIiUjpqLQAys97AWcBg4Gszq4o+J4YQ5gBHA78DFgBDgOPzGK+IiIiIiIiIiGQpk9fATweshv7PAwNyGZSIiIiIiIiIiORO1q+BFxGRAqis3LDRPBERERERkTpSAZCIiIiIiIiISJlTAZCIiIiIiIiISJlTAZCIiIiIiIiISJlTAZCIiIiIiIiISJlTAZBUNzarBmdFRERqpmOlSOlTnoqULuVnUakASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzDUrdgAiIiIiJSPxdpLkbxEpDcpJEZE6Uw0gEREREREREZEypwIgEREREREREZEypwIgEREREREREZEypwIgEREREREREZEypwIgEREREREREZEypwIgEZFC0FtLREpbco4qZ0UaBuWqSOlKl5/K26JRAZCIiIiIiIiISJnLqADIzM41s3fMbIWZjYx172NmwcyqYp/L8xatiIiIiIiIiIhkrVmGw30FXA0MA1qn6N8xhLA6Z1GJiIiIiIiIiEjOZFQAFEL4H4CZ7Qz0ymtEIiIiIiIiIiKSU7lqA2i6mc00s9vNrEuOpikiIiIiIiIiIjlgIYTMBza7GugVQjgl+r8dMAD4AOgM3Ai0DyEMSzHumcCZAF27dt3p/vvvr2/seVVVVUW7du2KHcaGZs1a92dVy5a0W7Eif/Pq0aPekyjZ9RhT3xiHDh36bghh5xyGVDTK09xbF2Msd+nRY/3/03VLdM+zBrUe60h5WjwNZvtavDh9HqaSGLYAOQoNaD0qTwHlaT6kPJ7WpkD5mdCg1mMdKU+Lp0FuXzXla4HzM6FBrscs1ZSn9SoAStF/E2AW0CGEsDjddPr37x8mTJiQ8XyLYcyYMVRUVBQ7jA3FXpk3pn9/KvK5HnPwer6SXY8x9Y3RzMrmQBinPM2NdTHG86myMvUrp1PlXAFek9mg1mMdKU+Lp8FsX2PGpM/DVBLDFuhVtg1mPSpPN6A8zY2Ux9PaFPhV0w1qPdaR8rR4GuT2VVMOFulV8A1yPWappjzN9WvgE6VJer28iIiIiIiIiEiJyKgRaDNrFg3bFGhqZq2A1cBOwEJgErAxcD0wJoTwbV6iFRERERERERGRrGVaU+cyYBlwKfCD6O/LgC2Bp4HFwDhgBTAi92GKiIiIiIiIiEhdZfoa+EqgMk3ve3IVjIiIiIiIiIiI5F5GBUBSQInGsJIbjBUREZH80vFWpLwUsKF2EZGGQI01i4iIiIiIiIiUORUAiYiIiIiIiIiUORUAiYiIiIiIiIiUORUAiYiIiIiIiIiUOTUCXarUYJ1Iw5bI4f790/errZuIiIiISDlSI+1FoRpAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIlJYegtYwakASERERERERESkzKkASERERERERESkzKkASERERERERESkzKkASERERERERESkzDUrdgAiIiIiIiIiUr4+/qYbn83tsu7/jVqtYP8tPqdpEWNqjFQAJOmla5VdrbWLiIiISENQWalzV5FiSMq7e8Ztxx/G7r1et/989yF+qBwtKD0CJiIiIiIiIiI5EwJc/fI+jBq/LWuDMajbbI7ZdjzHbDuenXp8BcAbM3sVOcrGJ6MCIDM718zeMbMVZjYyqd/+ZvaZmS01sxfNrHdeIhURERERERGRkjdzUQcuf3E/zn7yMIzACdt9zKhjRzHq2FH8fv/RAIyb3a3IUTY+mdYA+gq4Gvh3vKOZdQH+B1wOdALeAe7LZYAiIiIiIiIi0nAkCncGdZuN2fr9BnWbDcCk+Z0KHVajl1EbQCGE/wGY2c5AvJ7WUcD4EMKoqH8lMNfMBoQQPstxrCIiIiIiIiJS4tYVAHWdvUG/Hu0W8+k5/2DrTvMLHVajV99GoAcCHyb+CSEsMbMpUXcVAImIiIiISM6FADMWbcTaYBv0a9diJV3aLC1CVCKSMG5OdQ2gZGYwoMvcQockgIUQMh/Y7GqgVwjhlOj/24A5IYRLY8O8CtwaQhiZNO6ZwJkAXbt23en++++vd/D5VFVVRbt27Qozs1mz6jRaVcuWtFuxIsfBZKBHj4wHLeh6rKP6xjh06NB3Qwg75zCkolGe5kgsp+udp1nkW12V7HqMUZ5WU57mXlVVFe0WL85upB49PNcLkKPQgNaj8hRQnubDuhijY+wfbx7CMy9vmXJYs8BffvUiOw78xjsoT9dRnlZTnuZePMYzT/0Ok6Z14vornmO7/rUU9hQoR6Hhrce6qClP61sDqArokNStA7DBWVQI4RbgFoD+/fuHioqKes46v8aMGUPBYqzja+/G9O9PxYQJuY0lEyNGZDxoQddjHTWEGAtFeZojsZyud55mkW91VbLrMaYhxFgoytPcGzNmDBXvvJPdSCNGeK4XIEehAa3HEo+xUJSnubcuxugY+59FAwBo32IFnVovWzdc86ZrGHXsKAY3+xoSh1/l6ToNIcZCUZ7mXiLGNWtgxsxVAJy0/F06Tli+4bDT+vCr0fuzc8+vuP7NIQWPsZTlM8b6FgCNB05O/GNmbYGtou4iIiIiIiI5d/vwR/j3kY9s0LisiBTf3LmwRceFLFvdjI6tNiz8AWhigddnbsbqtZm+l0pyIaMCIDNrFg3bFGhqZq2A1cBDwJ/N7GjgCeA3wEdqAFpERERERPJJhT8ipal7d/jknBtZszZ9kibaBho/pytr10ITlQMVRKar+TJgGXAp8IPo78tCCHOAo4HfAQuAIcDxeYhTRERERESE8bO7smhFy7T9/zR2T7a98Ryem5K6jSARKYymTdK3N9yp9TJ6tl/E0lUtmDatcDE1dhkVAIUQKkMIlvSpjPo9H0IYEEJoHUKoCCFMy2fAIiIiIiLSeH3vvuPp+MdLGT+7a8r+s5e05dO5XXl1xuYFjkxEAKqqMhsuUQto3Lg8BiPrUUUrERERaZzq+BIGESmC6O1fc5a0YdL8zrRuvor+XealHHSPzWYA8NqMzbyDcl2koIYMgW5/vpiJ8zrXONygrlEB0HWjCxGWUP9GoEVERETKypwlbXjli96EpJrrO/f8it4dvy1OUCICwOszvVBnyKZf0qzJ2pTD7L7ZTADemNmLNWutxsdQRCS3VqyAiRNhzeo29OqwqMZh19UAmt2tEKEJKgASERERWc8xo77Py9P7bNB95PCHOHnwh4UPSETWSdTqSdTySaVn+8X06biAaQs3Zvycbnyn+zeFCk+k0Zs4EVavhq07zadN81U1Drv7ZjM5f9c32LfPdGC7wgTYyKkASERERCQybWFHXp7eh1bNVnFo30nr9dt8I9X+ESm2VzMoAEr0n7ZwY16bsZkKgEQKKNGeT6J2T00GdJnLdYc8neeIJE4FQCIiIiKRb6raMniTWfTvPI97j3lgg/5fLW7PqPHbcsyXsGkR4hNpzFauhLe/9MzbrdfMGofdo9cM7v74O7w2YzN+vPM7hQhPRKguANougwIgKTwVAImIlLLKSjVeKVJAQ3p9yftn/R8rVjdN2f/8pw7hwU+3Ze1pT3Ph7gUOTqSRGzcOVqxpxjZd5tCp9bIahz1gy8+5dM9XOGirKQWKTkQguxpAAFPmb8zrMzdjuw9h++3zGJgAeguYiIiIyAZaNluTsvv3B44H4L7xgwoZjogAO+4IX130F+486n+1Dtu/yzz+cMBohm4xLf+Bicg62RYA3f7BDpz00FE8sGGlW8kD1QASERERAcZ+sTld2ixlQJe5aYc5rO9E2jRfyZtf9mLawo70KVx4IgL0aF9Fj/ZVxQ5DRNL497/ho4+g7+x5GQ2/7k1g4/IZlSSoBpCIiIgIcO6Th7LNjecy+vMt0g7TtsUqjug3EYD7xw8sVGgiUgczF3Xgxrd24Z6PVWNPpFD23RfOOw+aN12b0fAqACosFQCJiIhIozdhbmc+/GYTNmq5nL02/6LGYY8b6Gep96kASKRgvp7Thr594adPH5zxOB9/041znzqMG9/eNY+RiUh99Os8j2ZN1jBlCixdWuxoyp8KgERERKTRS7Tp890Bn6Vt/yfhkL6Tad9iBe/N6snkyYWITkTGT+rK5MkweX6njMdJvCnsna96smJFviITkYSnn96E3/wGPvkk83FaNF1D/87zCAE+/TR/sYlTAZCIiIg0brNmravNk6jdU5NWzVZz7LbjObTvRJb+9aZ8RyciwPiJXQDYY7MZGY+zcevlbNt1NivWNOP99/MVmYgkjB7djauuIuubI3oMrHDUCLSISAFVjqnglnd3IqTo17b5Ku4++kF23fTLgscl0phNndmBT+Z0o1PrpRyw5ecZjfOvIx/FLM+Bicg64+pQAASwR68ZfDKnG6+9Brvtlo/IRCRh6tS2AAwaBLyX+XiDus3m4ckwL7N2o6UeVANIRKRAQoDFK1owq6o9X6f4HNZ3Itt1+6bYYYo0Oi++3huAowZ8mnGjlSr8ESmcqiqY8kVHmtpadumZ3U2SRIHRa6/lIzIRSZg3D+bNa0nb5ivp0ye7cS/a/XWWLIGLLspLaBKjGkAiIgViBn8d9iyn7fA+nVov26B/x1bLad18dREiE2nc1qwx2rVYwXGDxmc33lrjpel9WPo4HH54noITEd5+G9aubcJOPb6ibYtVWY2bKAB69VW/EaPCW5H8GB8dQgd2m02TJr2yGrdN81XQNA9ByQZUACQiUgAr1zSlRVNvWHZgtzk1Dvvt8pZ8tbg923SdW4jQRBq9M47/iH8NemhdjmZq9NQtGXbnSWzzNhx2mC4sRfIlUXtn917ZPf4F/oahrTaeT9/BnViyBNq1y3FwIgJUt98zqOtsILsCoIQQ/NNEzynljVatiEievTi1D/1uOI9np2xV67Cfze3CoJvO5sh7R7B0VfMCRCciAK2br6Zpk1Stc6U3tM9UOrdeyqefVt/5FJHcGzYMTvreOL63zWdZj2sGk867nqeeUuGPSD6tKwCKGnTO1o9+BBttBK+/nsOgZAMqACqkysrqj4iUnxT5vWx5M3706HCmf9uR12fUfjdki44L6NhqOZPnd+ayF/arcdoiUj8hwOMn3M2KlXWrd9686VqO2sbfWXvffbmMTETidt4ZTjv2Y/bbYmqdxlftPJH8694devdewne61609yzVrYPFivQks31QAJCKSR7feuz1TF27M4E1m8au9X6l1+JbN1jBy+MM0tbVc+8ZujP1i8wJEKdI4vTurJ0fccwJn/PJgQnaVf9ZJvDb+vvuo8zREJP+WL4f3sngrkYhk54orYOTIt9l/y7oV1A4a5N8qAMqvnBQAmdkYM1tuZlXRZ0Iupisi0pCNmdaHh57tR7Mma7h9+CMZv11op56zuHSvsQSM0x4ZztKleQ5UpJG6b9xAAHb+zqw61xDYt890unaFSZPgww9zGJyIAPDCC3DttfDFV+3rPI0Vq5vSpQvssou/UUxESk+iAOjjj4sbR7nLZQ2gc0MI7aJP/xxOV0SkwVmysjmnPTIcgF/v/QqDN/k6q/Ev3+clBnX7hknzO3PZZfmIUKRxCwHu/8QLgIbu9kWdp9OsyVqOOcb/1mNgIrl3111w4YXw5gc96zyNls3WsM02sHYtvPVWDoMTEcBfAf/11/WrCRuvAaQatfmjR8BERPLgl6MPYOrCjdmq94KMHv1K1rKZ1xpqamu59lqYNK9T7oMUacTeegu++LYjm7ZfxMC+9Xvj3nHHweabQ+fOOQpORNZJNAg7sF/98nSPPfw78UYxEcmdkSOhRw+46abaX3iSzqabeiPQ8+bBN3VrRkgykMsCoD+Y2Vwze9XMKnI4XRGRBmfIpjPZtutsLj3rjaxfLZ2wc8+vuObA53j8cejbeX6OIxRp3EaN8u9jtv2k3q+b3XtvmDYNfv7zeoclIjGzZsGnn0Lr1tC3z4J6TWuvvfz7hRdyEJiIrCfRbs+mmy6r8zTM1A5QIVjIQf0qMxsCfAKsBI4H/gEMDiFMiQ1zJnAmQNeuXXe6//776z3ffKqqqqJdrt8VOWtW9d89eqTunoWqli1pt2JFPYPKofgyRfKyHnOsvjEOHTr03RDCzjkMqWiUp/WQIo9DgCWtcpynKfKsvkpqPaahPK2mPK2/EGDEiN345ptWXH/Fc2zxncX1y9NEXib2A8rTOlGeFk+pbl9PPrkJf/7zAHbbbR6/vvileuXporab8b3v7UmTJoGHH36Vtm3rdnOmJqW6HuOUp9WUp7nz4x/vyIQJHfjjH19jyObT/TiY7TVujx6MGdOVJUuaseuu8+jadWVeYi3l9ZiQzzxtVuepxoQQ3oz9+x8zGwEcCtwQG+YW4BaA/v37h4qKilzMOm/GjBlDzmOMv8J5xIjU3bMwpn9/KiaUUHvb8WWK5GU95lhDiLFQlKf1kCaPc5mnny/YmC2uHZHz19mW1HpMoyHEWCjK0/r75hvo1g2aLV3EOeE1Xl7Rr355Gh3/vv75X3h68tac/FCF8rSRU57mxo03+vdJJ3Wm3YoV9cvTyhHsvju8+qqxcuXeHHZYbmKMK9X1GNcQYiwU5WlurFoFM2b439tss5aK9yb4cTHba9wRIyjE4pXqeozLZ4z5agMoADk+9RERKX1nPXY4P336YGYvaZvT6V7w1MFsff35PPNMTicr0ih17w4ffQQf/vgmmlhuWpoMAXa65UxOfeS7fPBBTiYp0qitWgXPPut/H3JIbqZ56KH+rYagRXLn9ddh6VLYdlvo0GF1scORWtS7AMjMOprZMDNrZWbNzOxEYB/g6fqHJyLScEyc15l/vb8jN769C0tXNc/ptHt1WETAqKzUmxFEcqVzm7q3VZDMDIb399oJifaFRKTuFiyAAw6AXXeFLbbIzTRPOw0mTYI//CE30xMReDq66j/44NxM7/nn4eyz4bPPcjM9WV8uagA1B64G5gBzgfOA74YQJuZg2iIiDcbVL+/D2tCEUwd/QJ+OC3M67bN3eZsubZbw5pvVd0RFJHszZ/oFYD4cu+14wAuAVFArUj/dusGDD8Ibb+RumptsAltvnbvpiQi8+KJ/DxuWm+ndeSfcdBM8/nhupifrq3cBUAhhTghhlxBC+xBCxxDCbiGE53IRnGRnxrcdeG9WdcOTy1c34+nJW/P05K15cWoflqzMbY0EEak2aV4n7vp4O5o1WVOn177Xpm2LVVyyx6sAqgUkUg/XXw/9+sHvf5/7ae/Tezrd2lYxeTJ8+GHupy/SGOW6Pa2E5cvzM12RxuaFF+CZZ2CffXIzvURB0tN6nigvctIItNRBHRt+jluzxnhl+uY8MakfT07qy8ezu3N4vwk8NuIeAOYva80hd/1g3fAtm66mos80Dus7kUP7TmKrTvV7naZIo1ZZuV4eX/2K1/45bfC7Oa/9k3D2Lm9zzQcH8cYb8NxzcNBBeZmNSNkKAUbdugDYmL33BkbnaMLRvqBpEzhqwKfc/O4ujBoFgwfnaPoijczs2fDee7DvvtD6T5U5OW9OHLfHjYMTToBOnWDMmPpPVqSxa906xTlpXXI2GufAcysxg1degSVLoG1um9Vs9PLVCLTk0RMT+3L8A8fw3bOOYp+Rp/GnV/fi49ndaddiBYO6zl43XMumqxm21WSGbTWZnXt+yco1TXlmytac//ShbH3DBYyZ1qd4CyFSRiZPhrs++k7eav8ktG2xiosv9r9VC0gke+++C9MWbkyPHrDnnvmZx7EDPwH0GJhIfTzyiDf8fNJJuZ92r17wyScwdix8+23upy/SmOTjONelC+yyC6xcqULafFANoAbo9Zmbcd/4QQD06zyXw/pO4rC+E9lr8y9o2WzNuuE6t1nG0z+4c93/s5e05enJW/PEpL68+sXm7NZr5rp+944bxM49v2LrTvMLtyAiZeKRR2BNaMKPBr/HFhsvzOu8zj4b/n7VYvr2bc+yZdCmTV5nJ1JWEo0zH300NMnTLbB9ek+nZ09vtHbRIthoo/zMR6ScPfmkfx90EPBVbqfdsaMXAL/8stemPeaY3E5fpLFYswb69oXtt/d2e3JZU+fgg/1tfU8/DYcdlrvpigqASl4I8OyUrVi+uhnDB/jbRX62+2t0ar2M7gev5MRv3814Wt3aLuGH23/ID7f/kLXB1r369qvF7Tn1keGsWtOUUwZ/wOX7vETvjrolIpKpn/0M9vz0X/Rsvzjv82rXDiaddwPtfv+rvM9LpJyEUF0AdOyx+ZtPsyZr+fxzaNkyf/MQKWcrV/pbgCB6/fttuZ/HoYd6AdCTT6oASKSu3n0Xpk71v3N9Q/Lgg+HKK9UOUD7oEbAS9vL03uwz8lQOvuskzn3qUJav9vK6jVsv56LdX2fTTarqPO1E4Q+AEThh0McA3Pb+jvS94XzOeeJQZi1uV78FEGlEdus1k803KkzBabsWKwsyH5Fy8t57fqK6SbvFeXv8K0GFPyJ1N3YsVFXBoEGw2Wb5mcchh/j3U0/B2rX5mYdIuYu//j3XjbXvsovX1Pve97xQWHJHBUAl6Juqtpz4v6PYd+SpjP2iN51aL+W8Xd/K2/x6tK/ituGP8sk5N3LCdh+xem0T/vnOrvT/x3nc8OaurA15ev2CSEMVNVL3ySf+5oNizDsEeOIJOP10r4IrIjX77DO/Q3n0Np/StCm5aVS2BmvWeA2D6dPzOhuRsvPUU/59SIdXqzvmMl8rK9luO9h0U/j6a72xT6Su4gVAOVVZSbNmXhh8zTXQokWOp9/IqQCohKwNxi3v7sSAG8/l7o+/Q+tmq6jc90WmXnAdl+z5Kq2arc7r/Pt1nsddR/2Pj3/yT47oN4HFK1vy0GfbYKgVS5Fka9Yap50G++8Pd91V+PkvWQJnngm33QY33FD4+Ys0NCeeCHPmwBX7jinI/C6+2N9g9K9/FWR2ImUj0f7PoX0n5W0eZv4YWHx+IpK5+fPhzTeheXMYOrTY0Ug2VABUQlauaco1r+7JwuWtOXjrSYw/+0auqHiJDi1XFDSOgd3m8OiIe3j4uHu46bDH11Xp+2pxexavUBGsCMB1b+7Gm2/6HcTDDy/8/Nu1g5tv9r9//Wv4/PPCxyDS0LRpA13bLi3IvBIXl3obmEjmFi/2R7Lat4c9N/sir/P6yU/8JQ4XXJDX2YiUpeef91zday/P13yZMgVuvBFWFPZyuKypAKjIlq5qvq5QpVWz1dx6xKPcd8wonjzhrry/Tag2wwdMoH+XeYCfvJ788HfZ9p/n8PBnA4oal0ixTZ7ficte2A+A//u/4r3l54gjYMQIWLoUzjhDF5ki6Xz2WeFPHisq/FW2EybAuHGFnbdIQ9W+PXz6qV/0NW+a38Z5dtgBjjzSb6iISHby9vhXkqOOgnPP9cfBJDdUAFRET0zsy8B/ns0vnj9wXbehW0zj+wPH57whrfpasLw1C5a1Zuaijfjefccz/N7jmb5Q77aVxmftWjjjsSNYtro5J5xQ/FdTXnedX2S+8II/DiYi6wvB87RbNy8IKpRmzbzxSqh++5iIZKZr12JHICI1ufRS+Nvfqo9z+ZIoYNLbwHJHBUBFMHNRB46+//scfs+JTFu4Ma/P7LXuDV+lqlPrZbx5+q1cf/CTtG+xgkcnDGDbf57Dn1/dg1VrtBlJ4/Gvf8GYaVvQtc0Srruu2NH4SXKiDaCf/Qy+/LK48YiUmscf90ckW7eGvn0LO+/E6+b//W/4tjAvCRRpsFauhC/y+9TXBsaP95q0egxMJDv9+sGFF+b/uKoCoNwr7VKHUhR/C0G6v9NYvbYJN7y5K78ZM5SqlS1p23wlVw59kfOHvEmzJqX/DsqmTQLnDXmLo7f9lAufGcb94wdxyfMHcf8nA3njR/+iaSbrJs9vXRHJpzVr4K9/9b9vOORJunSJru6KuV1XVnLcFZXcU/kZb325KdOnt2fTTYsXjkgpmTfPG0sHuOQSaHpVpf9ToJzdbz9/le3bb8NFF6mWnkhNXnoJDjrIC07vvz9PM0nKfTO4916/mfL3v0MT3dMUyU5l5fp5NWtWTie/557Qtq0/Sv3ll+gcNwe0myuQlWuaMuRfp3PRswdTtbIlR23zCZ+e8w8u2v31BlH4E9ez/WLuO+YBnj7xDrbceD6H951I0yZqfETKX9Om8PrrcN3BT/H9geOLHc46ZnDrEY/xydk3sscexY5GpHScd56/5nmvvYpzh79pU/jvf6FnTzjwwNqHF2nMEm/j2nrrws1zm22gd29/Q+B77xVuviIN2Tnn+AtIZs/O/7xatPCbKQDPPJP/+TUGKgAqkBZN1zCgy1x6b7SQx0bczYPfv5/NNlpU7LDqZdjWUxj3k39y6V7VrXL9+/0duPT5A1iwoIiBieRRp05w/pA3S66drm5tl7Bx6+Xr/leD0NLYjRoF99zjb/4aOdILY4phwAB/BO3444szf5GGIlEAdMghhZunWfX89Dp4kdpVVcGtt8If/1i446oeA8stFQDlyYS5nfn+qGN5/vMt13X720HP8Mk5N3J4v4lFjCy3WjdfTctmawCv5XT5i0P506t7scUW8Pvfw7JlRTrjFsmhGTPg6qsbxisoly+HX/0KTjzRH1kTaYyWLvW3hgD85S+w1VbFjadly+q/C93GiUhDMHo0TJzob9XcfffCzvvQQ/37zjvVVpdIbV58EVatgl13hc6dCzPPgw/2dvyaNy/M/MqdCoBybOqCjpz+6JEM/Oc5jPpkIFe+tO+6ft3bLaFN81VFjC6/WjRdw0PH3ccBW07h22+9auCJJw7hhjd3ZclKZaw0TK++6ge5yy+H888vdjS1+/prfzPYPff4a+JVG08aozZt4IEH4PTT4cc/LnY01a67zhvMzFv7JiIN0PvvV79J6Oyz/Q16hXTggdC/P0yaBMOH+40UEUmtUK9/j9tySz+fveuuws2znKkAKAdWroRR47floDtOYqvrL+C293cE4Mwd3+Huox8scnSFteumX/LcSXcwerRfNC9Y0ILznz6Unn/7GRPnFaiYWCQHQoCbb4ahQ71QpaICfve7YkdVuz594LHH/K7MU095A7TjxhU7KpHC23tvr6ZeSo9rtmzp5wxnn+37FZHGrqrKa+AsXgzHHee1bQutVStvW6RnT5gyJedt2IqUjRD83BIKWwAE69eklfpRAVBcohXzLN8Octtt8P0Hvs9zn29Fi6Zr+OH2H/DpOf/g/454nF4dGnY7P3W1337wxhtw1VXj2K3XDLq0WcrWneav6//UU36wFylFy5d7zYGf/MSruV54ITz3HHT5R+WGb7UrhTfbJcWw337wzjsweLCfzA4ZohoH0jjMmAHPP1/sKKjOyaR9xFlneW2DefP8b7XVJY1du3b+mOYhh8B//gNNrqwsfBCVlfTuDc8+C6+9BltsUfgQRErd2rVwzTUwdaq3h7nzzikGyvU5cdL0Vq2CE07wa0ypOxUAZWHVKhj7xeZUjqngj2P3Wtf9+ONhl55fct3BT/HVz/7Kf777MH07z69hSo2DGey111xe/9FtvHPGLTQxP9P98ks4/HDo0QOOOcZrWUyerBNhKQ0LF8K++8K//+13Be+8E/72t8JXSa+vPn388bUf/MDbQznuOLj77mJHJZI/IXjB7YEHes2fUmTmN4022ggefdQveEUauxNPhCeeKP4d/oEDYbPNqv9/553ixSJSaqqq4Kab/O9f/7o4L1a4+WZv4mDvvb0wam3DepF2ychJAZCZdTKzh8xsiZlNN7MTcjHdYlu+HD74AK691gssOnWCvW8/jd++VMGNb++yrsBi443hrTNu5fwhb9Kp9bIiRly64m8nmjfPX8m7ZAk8+KDXsujb1++4nH46fPVVEQOVRitxEOnQwQsne/f2O4EnnljcuOqjTRt/BfV118H228ORR1b3Uw08KRfffgv/+Adst53fwe/Uydu/KlWbbQbXX+9//+hH8N3veiGtSGOxYoXfPH333epupfSoJsCVV/oj1IkLXpHGKnG926ED3HuvNzNw0UXFieWss7xW/urV8Itf+OOjhXgVfbnJ1T3tG4GVQHdgMPCEmX0YQhifo+nnzdKl8M03/rzvhPcHs8umXzGom29JN94IP//5+sNv02UOB2z5OQds+TkhjCi5A1ZD8J3vwEsveRXC55/3z+jRMH26v6r373+vHvYnP4FFi2Cbbfyz1VbQvTt07drwamRIaVm1ymvIPP64fy66CM48E5o08UKTlSuhS5diR1l/Zt549U9+Uv32hM8/9wYv99nHC7e7dm1d3CBF6uDTT7123t13VxegdO/utWo22aS4sdXmpJP8BtMNN/hNjzZtqvstXAgdOxYpMJE8WrPGjz+/+pU30v7ee/DJJ6V5Ptezp3+fc47HN3w4dOtW3JhECmn1arjqKq/589e/erfddituTC1a+HF/v/3g5JO97a7Bg71x6KFDixtbQ1LvXa6ZtQWOBgaFEKqAsWb2KHAScGmqcVatasLbb/vfyY/9dOgAAwYkhoO33/ZhQvA79GvW+PfatV6Q0L27Dzt+PHz4oY+zapXfXViyxDfaFi3gssuq53HggX4A+uYbH6bad/n9fs+vKwAaNAj69fPGjA88EPbfHza99cbqwfUAXb1ssQWccYZ/1q7132/cOGjfvnqYhx9O31DmZZf5jgn8RPq666Bt2+pPu3Z+Ut2iBYwY4a8PBHjzTZg/36suNmkCH33UEfC/u3b1gibwbej99/3vREFf/LtfP6/GX65WrWqy3t25uPbtffnBDxAffph+Oltu6bXkwAta09XwatrUd+IJH33khTBxif1Fjx7Qq5f/vXChXwiuWeOxJH8OO6y64OOaa7xWz9y5vq3FX/c6erQXAIHvh8pN/NWZ777rOffCC/6BIVxxhT821qWLP/aWGP6FF3w/2qzZhp+ePX0c8BpFn31WPY/kwvGBA6tzcOpUz8FU2rXzwinwGBM5OGFCu/X2DeDzTryC9JtvYObM1NMs54L6Yufpxx9vmKcJm2wCm27qfy9a5K94Tme77aof//j0U8/RELwm7oIF1Z/evf35f/Dp/etf/vd++/nbvoYP931+qTPzk9hLLvFtN2H8eNhxRxg2zPdxG2/sn06d4Msvu7DHHtXL9/HHfg5j5sev+HbepUt1bi5d6us0nQED/JgJ/or6OXNSD9e6NWy7bfX/qba7RJ5utln1xfLcuX6DJ50ddyzvHIWGlaeTJqWf/6BB1Xk6ebIff6H6/Dzx6djRa52C//a/+pVv25995udW4MfZUaNKs/AHvEb67Nn+qMuZZ/qnSxc/lt1xR/WjYu+84+utaVNflk8+6UDbtr5Nd+5c3Z7QsmVe2JVO//5+/IOa87BVK48h4b330jehEM/DefNg2jT/O9XxdIcdfD8C/jutf31UrVOn9MvQ0JVjnk6ZUp2nievpxN/xa+4lS/yx5G+/9eEXLoSXX4bXX/ft4qyzqpe/FBx+uP8GJ5wAr7zi1+gvv+xPmIDfGGrRwvdF7dp5PiaOM717V08nnhepZJMXuc71qVPbUFFR/X9dcj2dXOx2+wGrQwjxU7sPgX3TDM+cOS3ZddfU/Q480Ktwg2+Ee+6ZfsYPPABHH+1/P/zw+oU8cZ07r99vyhS/AAHfOLp395XWb8XHbNu1+lcYNgwmTEg/f8mdJk08yXbYobpbCP6I2KefVn+mT/cD8ty561+kT57stYfSOeKI6ovP3/ymehtzg9f9ddRRPk/wnfbuu6ef5jPPwEEHZbiADdCcOS1TN/DGhnmabjhYP09vv91PplLp1Ml3WglHHpn+ouHSS+EPf/C/33yz5jcRzJhRXVj0+uvwyCPV/bbZxg8ihx8Oe+yRfhrl5thj/WD5zDNe++mxx1bx+efN+fxzP7mMn5Cff76fuKdywQX+iCx4Iew++6Sf5yefVBeuXn55+ld57rWXH8zBL/6rt60NN7I77vA2jsCfCb/wwtTTbF3GFZyKnadHHJE+T3/5S/j97/3vN97wY2o606ZVn5T9+tfw0EOphxs2rLoA6LDD/Nj+gx9UFxo2NJtssn5tpbFj/eLiscdSDT2Iiy+u/u/00+Gtt1JP97TTvK0h8IKymn77N97whuIB/vjH9I+8bL+953nCkCF+sb8+n9GNN/qbzsD3uaefnn7+q1aVbiFArpRLnk6dWl2w+ItfwP/+l3q4YcOqXxXdsuX67c/16uUFvldcUV1IVKp++Uu/kXjffX4cnDvXa7DHC0EuuMBvLFXbcd1fP/xhdTtfU6bU/NuOHVt9zfOXv3jtwFQGDlz/zZ577FFdqJbsuuv8GA5+rD/llESfDQNZtsyP/+CPpq6/TNV++MP0y9DQlWOeXnJJ+jyNL9PixdXH1rhNNvHztVIq/Eno1ctvUl55pd+QiJ/Hn366b9OpXHedVyIBb3vs5JPTzyPTvDjpJH96ALySSS5yvXfvgZx6avX/meZ6bcsEYKGeLe+a2d7AqBDCJrFuZwAnhhAqYt3OBKL76wwCSv3FxF2AucUOohaKMTfqG2PvEELXXAVTTMrTvFCMuaE8jShP80Ix5obyNKI8zQvFmBvK04jyNC8UY27kLU9zUQC0A/BqCKFNrNvPgIoQQspmGM3snRBCDWVjxacYc0MxNlwNYb0oxtxQjA1XQ1gvijE3FGPD1RDWi2LMDcXYcDWE9aIYc6Oxx5iLVmwmAs3MrG+s2/ZAyTcALSIiIiIiIiLSGNS7ACiEsAT4H3ClmbU1sz2B4cAd9Z22iIiIiIiIiIjUX67eY3U20BqYDdwD/KSWV8DfkqP55pNizA3F2HA1hPWiGHNDMTZcDWG9KMbcUIwNV0NYL4oxNxRjw9UQ1otizI1GHWO92wASEREREREREZHSlqsaQCIiIiIiIiIiUqJUACQiIiIiIiIiUubyVgBkZp3M7CEzW2Jm083shDTDmZn9yczmRZ8/mZnlK646xnixmY0zs8VmNtXMLi5EfNnEGBu+hZl9amYzSy0+M9vRzF42syoz+8bMLiilGM2spZndHMU238weM7NNCxFjsShPCxtjbHjlaR1jVJ4qT/MdY2x45WkdY1SeKk/zHWNseOVpHWNUnipP8x1jbHjlaR1jzEuehhDy8sEbg74PaAfsBXwLDEwx3FnABKAXsCnwCfDjfMVVxxgvAXYEmgH9genA8aUUY2z4XwMvAzNLKT6gC95I+IlAS6A9sE2JxXgJ8CHQHWgF/Bf4XyFiLNZHeVrYGGPDK0/r9zsrT5WneYsxNrzytH6/s/JUeZq3GGPDK0/r9zsrT5WneYsxNrzytH6/c07zNF8L1BZYCfSLdbsD+GOKYV8Dzoz9/yPgjQKs9IxjTDHu9cANpRYjsAXwKXBIIRIsy9/598Ad+Y6pnjHeBFwT+/8wYEKhYy7RdaM8zVGMytN6x6g8VZ7mPUblab1jVJ4qT/Meo/K03jEqT5WneY9ReVrvGHOep/l6BKwfsDqEMDHW7UNgYIphB0b9ahsu17KJcZ2o+t/eQE2vuc+VbGO8AfgVsCzfgUWyiW83YL6ZvWZms6Pqa5uXWIy3AXuaWU8za4OXBj9VgBiLRXmaG8rTwsaoPFWe1oXytLAxKk+Vp3WhPC1sjMpT5WldKE8LG2PO8zRfBUDtgEVJ3b7Fq1WlGvbbpOHaFeA5y2xijKvE19vteYgpWcYxmtn3gKYhhIcKEFdCNuuwF3AycAGwOTAVr/qWb9nEOAmYAXwZjbMNcGVeoysu5WluKE/rT3manvI0N5Sn9ac8TU95mhvK0/pTnqanPM0N5Wn9FTVP81UAVAV0SOrWAVicwbAdgKoQ1XHKo2xiBMDMzgV+CBwWQliRx9gSMorRzNoC1wDnFyCmuGzW4TLgoRDC2yGE5cBvgT3MbKMSivFG/PnPznjVvP9R3ndClKe5oTwtbIzKU+VpXShPCxuj8lR5WhfK08LGqDxVntaF8rSwMeY8T/NVADQRaGZmfWPdtid1tbTxUb/ahsu1bGLEzE4DLgX2DyEUpAVzMo+xL9AHeMXMvsY3jB5m9rWZ9SmB+AA+AuI7zXzvQBOyiXEwMDKEMD/agd4A7GpmXfIfZlEoT3NDeVp/ytP0lKe5oTytP+VpesrT3FCe1p/yND3laW4oT+uvuHmax8aN7sWrULUF9iR9y9Y/xhuG2hToGS14oVpZzzTGE4GvKVCr4NnGiLf+vknscxTwVfR302LHFw23H7Ag2oibA38HXimVdRgNdzvwILBRFOOvgC8L/ZuX2vYVDac8rWeMytOcxag8VZ7mLUblac5iVJ4qT/MWo/I0ZzEqT5WneYtReZqzGHOep/lcqE7Aw8AS4AvghKj73ngVusRwhlcPmx99rgGsQCs+0xinAqvw6lqJz82lFGPSOBUU7jV7GccH/AR/fnEB8BiwWSnFiFetuwt/HeBCYCywayFiLNZHeVrYGJPGUZ7W7XdWnipP8xpj0jjK07r9zspT5WleY0waR3lat99Zeao8zWuMSeMoT+v2O+c8Ty2asIiIiIiIiIiIlKl8tQEkIiIiIiIiIiIlQgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJlTgVAIiIiIiIiIiJl7v8BhyH9hbzjntoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'std', df, 'uniform')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'mean', df, 'binomial')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'median', df, 'binomial')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIAAAAEDCAYAAABNtH+LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABki0lEQVR4nO3dd5gV1f3H8fd36bD0JopSpSgKKIhiW0TFrrH3khiNPYmJ0SQmmJj8YopJNImJib1j7xVcC1awI0WqCkiRuvRyfn+cuezs3blt7727d+9+Xs9zn7s79czc8505c+bMGXPOISIiIiIiIiIixaukrhMgIiIiIiIiIiL5pQogEREREREREZEipwogEREREREREZEipwogEREREREREZEipwogEREREREREZEipwogEREREREREZEi12ArgMzMmVl5XadDUjOzy83sczNbF/xuP6zrNEndKob4NbOyYDvGZjhfUzP7wsyey1PSCo6ZnRvsq3MLIC3lZubqOh1QHHEg9UshxWI2zGyHoExxfQ3nP9TM3jKzFcH+eCLHSSx4ZtYz2PY7s1xOxudCMxsbzFOWzbrzyczmmtncDOfJ2XbVJFbN7M5gnp7Zrr8u5Wo/5nJ/JEpTXZ/Ho/JpoRzn63rfFLN6XQEUZIykBfEgY+f0YJark56kZmanAn8H1gN/A64D3qmDdPzUzJ4L8lOFma0ys0/N7EYz655kvl3MbJyZLTaz9WY23cyuM7MWSeYZGaxrWVBA/cTMfmhmjfKzdVJTdXSSvBzoC/yyFtcpDVRNLmKKmZkNDI7hT5rZl7FyiJk1TjFfBzP7W7A/N5jZAjO7Pdn5oxiYWbvg/HlfcCNnc7C/Dk4xXyMz+1Fw/lsXnA+fM7ORSeZpEfw204Pz7eLg/Dswflrn3Hzg38CPzWzHDLepJ/Ak0Au4HV8ueTCTZUjDVCgX1lIc6vv1qMoXdSdpgaXIDQTW1nUiJKWjYt/OuQV1mI4LgQrgNWAR0AQYCvwI+J6ZlTnnPgzPYGYjgAnBtI8AXwEHAb8CRpvZaOfchrh5jgUexVd4PQQsA44G/grsC5yUrw2UwmdmrYBfAC875z6o6/Q0UGcDLes6EVJnxuCP4VuAL/DH6ubJZjCzjsBbQD/8OeFBYABwHnCkme3jnJudz0TXoZ7AH4O/vwaWAl2TzWBmht9HJwLTgX8AHYBTgNfN7ATn3JNx8zQDXsafJyfhbxztiD9nHmlmBznn3o1b1Z+Ay4BrgQsy2KaD8b/5lc65+zOYr9jMx5elV9Z1QgrU6LpOQA1cA/wB/9vWZ//AH0O+rOuEpKGur0cLOZ/W9b4pWg22Asg5N62u0yBp2R6gjit/AAY559bHDzSz7wO3Ar8DjggNbwTcgb9QPNY591QwvAQYB5yArzz6Q2ieNsB/8RcWZc65ScHwa/EXDSea2anOOd1pbLhOB9oBd9ZtMhou51x9KFBK/jwPvA184pxbF9y97JFint/jK39udM5dGRtoZpfjKyr+BRyWn+TWuXn4CpMPnXPLgjvV56SY51R85c9bwOjYudfM/g28CfzXzCY451aH5vkxvvLnEeAU59zWYJ6HgCeA281st9hw8OUKM3sZON3MfuqcS7ciY/vgu67LJXXKObcJUFk6AefcrLpOQ6accwuBhXWdjmw555biK5sLXl1fjxZyPq3rfVPUnHP19gM4vwlJp5kbTNczYt7yuGGt8XeCPgNWAauBWfiWGHsG04yNrTfic25oWSXAD4D38S1H1gR/XwSUJEjrGcAHwDpgMXAPvqBRHr+dQFmwzrHAXsCz+NYi27YVGIWvnPg82J51wbb9Gmgesf7YtpUBpwGT8TWvC4AbgWbBdAcFaVoFLA/S2TFiebsDDwS/wQZgSbB9fwOapPjdEu7nuOlGAy8E274BmIGvVGkbsczyYBlN8Xdwpwfz3JlFHmwbLPOLuOEHBcNfi5indzBuLmCh4d8Nht8VMU/C5SVJ2zHAePzJfEPwO74GXBw33Z74i5CPg/24Hn9n+y9A+4jlnhuk5VzgEOANfB5fgq/0ahdMNxR4JsgjFcBTxMVh3O/SDLgemBOkd1aQV5smiP3yiOGNgYvxjwmuCvLvh8ClJIi7BPuuNz52ZuLjZhnwKf6RgY5x6Y769AwtqytwG77l2DrgI/wFUFkw7dgM0vVOsG9aRoxLefyK+w0fBWYHaVoFTATOTLDe2LY2wcfOrCCfTAe+H5ruB8F+Woe/239d/H7Htwhw+EqsAfiLs2X4Y+SbwKHJ8lzEuO74u32zg33zbZDXhmcYy+nGSznVj0OJ8kHsMzZu+g7A/wFTg321Mlh3tW1PkWYXpGd7/HF4cbC8ycDpSeYbAzyHLyDHYu1PBLEbTFNG4u25M5hmATA/YvnzgumujRt+eDD8N3HDW+LvPH8U5IMKfCXLadlsQ2jaucGnVTDNl8E8M4GfEToOZ/qhsozROMH4UvxxqAJoHTeuJDR/7zTXl83xelSQX1bjY/5ZYGCC9fQFHsYfv9fgK2OOJEksppn+O4P5D04yzevBNKMixt0djDsvNMxCea5Xhss7Jxh3YRppTxYTZaHpdg7SOR/YiI+Tu4GdI5Y5NjY/voL/3SCvzE2RlmnBsjslGP+zYLmXhoZlUyasljZCx/K4+frhy2GT8OWCDcHvcyvQPcl+HQvsA7yCPyauBl4EhiVLW8S4AUE++yrYR4uA+4H+aebR/sGy74sb3iv0e+8fN+6GYPhBcceGuaH/y5Pkn54R+/xE4D388WMZvkXLDhnE2rlUxv6R+Bheg4/pRxLkxzvD6Yn/nYO/H8Qfd9cHv/FRCdbfDLgaXyZYi89zbwAnR0wbXkefIH3fBnngJfxNWIDOQT5aGKz/faLjOjJ/AMcB9+KvFdYEn8n4R+urlRGj9kca+31P/HVJ7Dj7Cj5fJ0qTI0/Xo6R3rTiXuONNDfNOOQmuyYk7b5BG+SLRvgmGt8WXoaYH+WA5/lhR7bwStw+GBPtgBT5PvgaMTPe3LaZPg20BFC9ocvwCMBJf8PwfsBl/cTEKf9CajM/g7YAr8AWwJ0KL+Sj09z34E+ZXwbIc8B38nb798JU94fVfhT+BLAfuwp/8DsFflCW7K7UPvuD8Jv5Z9E74Ex74AsAAfOA+i2+yvC8+CMrM7GDn3JaIZV6GL6g/EWzvofjWKh3M7En8wf9Z/EF4JHBmsN7DQ9uzO76w4PAXY3OANvhC5cX4/ks2Jdmu8uD7XPzd1eviJzCzC4Fb8Aelh/EXP2XBdh9tZvs651ZELPtRYDj+Tu4TwXw1dXTw/Unc8IOC7xfiZ3DOzTazGfhCUm/8QT3pPPgC7FpgpJk1c3GPjsUzswuA/wDfAE/jT9Zd8JVy5+HzYcz38XnzNfyJqgR/AvsxcLiZjXBV77TGHIN/RO8ZfMXISPzv1dPMrsFf0L6Br/zYDb+vepvZ7i50FzZkHP53eQSfN47F59VhZnaMC47kSba5SbCtY/AnhfvxJ4ZRwM3ACOCsZMsIltMNX6hog7/AfBQfO72C+f+BL5jciT+JHIvvD+Kj0GJWBMvqhI+/3vgYfRPoht9fL6VKS1y62gLDgPedc2vjxqV7/Iq5BZiCz1cLgY74Fmz3mFl/59y1CZLxIH4/Pof/jU4EbjWzTfi8dQ4+P4zH549f4fPtDRHL6hWk9VN8Xu2Gf8TjeTM73Tn3UBr7ZA/8fuyAP/k/hj8WHQe8aWbfcc6l7Cw7w3iJUu34FDgL/9tv+73MrAf++NYT/7u8gK+YOAp4wcwudM79N1WaQ9rj89gKggpY4GTgPjPbwTn3p/DEZvZrfFwtw/9Wi/Hb+RPgiOBxpFX4QuF1wA+DWf8WWsxHwfcE4AwzG+CCO3Vm1hfYKRg/GvhtaL5YU/PxofS0C5YzFH+D4Hb8MWgMcL+Z7eqcq9LfVQbbENYEn0e2xx/7N+PzyR/w8Z3oN8zW3kAL4KX446hzbquZvYh//GgUvhIzlZoer4/CH6uexx9/dsHH/HAz28X5O+YAmNnO+NjsGEz/Ef7c/UTwf96YWXP8cWwtPj7iPY+Pq4Pw+R38BeNOwAzn3JwE8+wfzPNq3LiJwfch+GNAMnPx+aQMOBBfVpsbGoeZDcf/Lq3xZZ/P8eWwM4Fjg3LX+xHLvjJIw9NBGtumSMtd+JZlp+HPb/HOwZcFw4+o1bRMmGnajsffDHg1WNdGYFfgfHzZbJjzfTDFG4Evz74C/BOf544HDjCzQ51zUfmhCjM7DH8eiJUHZuLPg8fjHwUc5VI8Pu2cm25m86ksk8WMjvv7jbj/1wfbm8idpCgzhFyMP4c+hY/1Efjz42AzG5KqDBjneHz5/HH8uWcIvhX6KDMb6ZybnuZyeuArpGbjr3Fij2U+GeSdbbFlZk3xx9sD8ZWV/8RX9J8IPBRsw88j1tETf+0wlcoKp+8A5Wa2D/58uQpfEdIB31rweTPr59JrnfsHYGuwjvn4vHwQvlJ9OGmUEZMJ+ih7BX+j+TF8/huC3+8T0lxGrq9HIfm1YjK5yjvx5pK6fBEpKDNMxJ/D3g/m7YQv97xkZhc556KO5cOAq6jcpzvht2V8kB9rui31U13XQGXzIXR3NclnBWm0AMJfoDrg8Yj1lBC6s0aCux6h8acF4z8ASkPDW+Fryx2hu7P4C4RN+DslO4aGG74FjSNxCyBHgjtXwXKr3dnEF8gdvpl0ePjYYPhKQncF8bX4U/CPJn0LHBi3b14O5hsSGv6XYNixEetvT5qtMUhQo4w/EW3AnwgGxI37V7DuW6OWha+sibxrlkZ6zg/205/xJ7ct+APZznHTPRys64QEy3kmGH94aNj7wbA9E8zzWTA+8o5t3LSTg/3TJWJcp7j/ewCNIqb7XrC+n8UNPzcYvjlJXlgGnBE3321ReSL0u8ygapw1xx+oHXBW3DzV7gqE8u/N4e0BGiVad4J9d1kw7RUR41oBLSL2xbkJlnVrMP6vccOH4WPekWYLIPwjIg64OWJc2sevYFifiOma4i/MNxF3lzH0G71P1VYivfGFiOX4St4dQuPa4StSlhBqHUHl8dMBf0qwX5YDbZLtZ3xrr5n4QveBccvZHl+4W0jQcjGH8VJOgrtccdOdF6T5LUJ314P5twKnxk3fDl/wWQd0TTNPxPbjOELHVHzl2rLgt+kdGj4qlKZ2ccuK7eP4vDqXBK0RqGy1eElo2IXBsJeIa62Gb423llCrPirvsF4Vt+zm+ELwVqqeW2q6DQ5fcRmO3y74MsIKUrRKTfIbxJadqAXQJSSI22D8T4LxN6S5vmyO16Pjxv1fgn3/EhHHQPyFayzPnZtOeiPSGfu9I1sA4SsKHPBpgvHDgvHvhoYdGQx7OsE8JwbjH0owfjmwOINtGEv0nXzDX7g6qp//TgmGT6NqrMaWtQYYmkEauuPLH5Mixg0Plvlo3PCalgkj00biFkA7EHHcxd9M3ALcEje8LJSvLo0bF8tzXyTYb2WhYe2D33IpsEvccgbhWy99kOb+jbU02zU07AH8+exD4I249W4BxsctYy5JWlakyFurgN3ixt0fjKvWgibBsmLrcsS10sFXGriINN9J4hZADvh13PRjguHPxQ2/Jjacquf/LlQeM0cmWMcv4pZ1LZXlyn/H5YOziD7mV8sfwfCosk8JvkLVASNS7Y8k+9vw8e2oXs6N7e+oNDnydz1aFlpvomvFZPk0k7xTTpotgJKtO9m+CYb9Jxj+H6o+RbEz/vp1Q1z+De+D+PXHyiv/SiemiulTr98CFvLrJJ+2GS5rXfwA59xW59zyDJbx3eD7audcRWg5a/B3YMBXJMScjr+Yudk591VoeodvPhl1RybmIxdd04lzbnawjHh/Db7HJFjmTc65qaHlbMDXtpcAzzrnXguN24pvTgkwOGJZUftzuYtuAZKJM/EXrP9w1Z8R/QW+ueRZQceQ8a51obudGTofn6+uxBdmJuMLsl/ETRfLd4lab8WGt8tynmQ2E9HKKn7bnXPzXPRdv9vxhZBE+eSBiLxwT/DvZ865++Kmvzv4HpJgeb8Nx5nz/T5cE/z73ehZvKBvpcvwLTh+FN6e4O8rCQrlyZYTJyrvrnHOVRueIE1NgvWtxhdGwsuZBMTvn1RirSqSPZ+f1vHLRTzz7ZzbiL9L15jEnQJe7UKt6pzvuPZNfJ78rQvd1Q2mexp/Z2aHiGWtBH4Tl4bYfmmHv+uXzJH4O/83h/NhsJwF+I5nt0uyLfHSipd0mNlofOFkNr4gGOvDZDD+juijLq4vr2B//Rpf8XFCBqvbgr/oD/dtMge4CX8XPHxH8/Lg+/surnWkc+5OfAVUJjESa8kTf2d8UbD+pvgWr7GOkAcDbwZ5LTbsTPxF7B9Dy4jF/8/wherTc7QNl4fj1zm3GH8nvi3+sY98yOlxPYvj9YPOufFxw24NvveKDTD/VrJD8BW6/4hb95P41gj5VBfnzm+AzkHro2yMxLeweTv+/Od8i8Y38flsv4h5b3VxL5JIxjn3NT7+9jSzXeNGnxN83xU3T03LhJmmbb6LaKHinHsJfzMx0XpmEtfaMpTn+uJbcSVzNv43/rVz7vO45XyG72NxqJntksZmRB3bDsK34ngFGBG8lAF8pXRJaJ5cuMk592ncsFjL0L3iJ05hgnPumbhh/8C3Pj8oaJWajnn4x/S3cc69iH+kNj5NsZsDP3bObQ5Nv5jKVqHnU91cQn1jBmL5uBnw07jrh/vx5+4h6WxAgrLPVnwLIEicN9MxEh/fr7u4Tuqp3N+ZyMX1aEzCa8UUcpV3ciJoWXYmvjL3mvDxLLgOi5U7zo6YfWJQRgi7HZ9/Mo2peq8oHgFzzlmicWl20Ai+me5HwGlBhn4Sf7KeFCusZmAP/F3L8ohxr+EL7ENDw2J/vxk/sXNunpl9ha/ljfJeokQEJ6cr8BdS/fBNksP7KuqiDHwrpXixzg4nR4yLXfSFX2f7ULDuJ8zsEfwJc2LUwbeG9gi+qzWpdM4tN7MPgQPwhbGP4yZJuM9Scc7tDdsuXPbAd/482cxODk6EheI+fCusz83sQXy+m+icWxI/YVBRcSG+Ke0u+MJ0uHI4n/kkLOrC4k2qx0uUfvjmwF8Av/QtaKtZh3+jQCpP4ZvW/9PMxuBbek0EPk9QeE5kAL7J8xsuunPRclJ3hhrWMfiOOvlndPwys53wF9ej8RVLLeImycdvPi9u3Acu+lGVcvx+GUrcBUycfYLvHmY2NmL8zsH3QPxdyGTSjpdUgouLR/EFlCPilhFLc9sEae4cSnO6vnTRj72U4yuUwrGzD76S6yQzi3qjYFP8hXBH59y3qVYcnJ9m4x8fKSG4u4k/3r9G0OoE36JkFP78Ez5mD8e30HMJ9keT4Du8P2q6DSudczMjpo/ddGmfaDsLSY6P11Hbvq08kqCiqRxfiVlMlgXfnfB9l9VUwnJJaPh++H38ety4mpRL7sRX1p2Df7QhdoF0Gv6xyCrHvSzKhBmlLXiE5Qz8Xf/B+PzVKDRJojL1GwluDpbj89xQkldAxo6vgxMcT/oF3wPx58xkYr/haOAmMxuEb70yHh83P8GXMZ+n8lGxtB7xSVO68ZqOavvMObfFzN7E30QZSvXzc5SPEhwTvqJy32NmrfEVdvMjbtBC5X6KKtdFrSNWxpgRX2YItmMRicuVVQRl95/iH3/tjW/VHZYoBtIRi/9U+zuVXF6PxtT0uidXeSdX+uPL1ROdc8sixk/AdzESlbeqxZRzblOQf+rF+T+XiqICKBeCDH0Qvs+KE6nss2K1md2Fr2msSLiAqtoCy6IC1Tm32cxi/UuEpwd/1zTKIhJXAH0TNTAoJE7A12p+hq+QWULlHe5f42vTo0RdrG5OY1yssI5z7j0z2x/fGudEgrvQZjYduM4590CCdacrts8StYaIDW8XMS5yn2UiuLB42czexzf5vMfMeoTuLsf2U6IWaLHhK0LDajJPovTdGOSzi/F3zH+Iv8h6DX8HJXwgfAhfIJyNP9F8g29CSTBf3vJJnGr5P0G8RIlVjuyMz9uJlKZYTuyidi98q53D8M9AA3xlZn92zt2UahmBVHGdaT6M5a1qd6kzOX6ZWW98YaA9/lnyl/C/1xb8ceYcEvzmCSqyavqbp9ovqVpvxn7zqEqAsHR+80ziJSEz247gMSPgEFf9mfJYmg8JPjVOc0gm+7Ej/ryfLEZi609ZARQYj++XZg/8+aUzvmn46uD4GLuDXq3/Hyr3x/Dgkyw94Xlqsg0rEkwXy6ONEozPVs6O64GaHq+rLT84vkLVbc/1cStTdXHujFWAp9W6M4naLpc8jm/1daaZXRNcOB+Fvxnyt3DLiyzLhJmm7UZ8XlyIv4Eyn8p9ey6Jb8rm6pzw/RTTpXNO+MrMvgAONP9W1/Dx6xv8fhuNrwAajf8dovp2qqkVEcNqeqzKdr/GrEgwfDNVK6GziYNq5YjQcSpRC7/NJC5XbhP0HfM+/hHp9/Ct0pcF87fDV44mioF05OTYmePr0YzWHSFXeSdXsslbKxLMs5n8nf8LliqAQoJmdT8CfhR0ZHkg/k7bpfjMlG7nYCvxHSY3cf41mduYWWP8XaZwB5Wxv7vim8fG65os2QmGH4s/0d/pnDsvLg3dSF14zppz7m3gqOAxrD3xF9OX4Tv2XOKceyWLxcdOBNsRvc+6xU0XTlcmrTiScs6tMLO38Z2J7kplDXPswq9f1HxUtk6YERo2Hd+/QT/iWlME+aYX/kCVTkehOOfuBu4OTnoj8RcN3wVeNN9p6xIzGxYMfwXfH1G4wFhCcFexlnTFNyXeJkG8RIn9zo87545POmUanH8E8pRg/YPxrzG+DPi7ma1xzt2WxmJiaUoUv9tlmKxYZ+Udo0ZmcPz6cbCM8+Kbw5rZaWTWKikbqfZLosIeceOPdc49lW1i0omXZPObWUv8I2898G9Ti7/DH07zFRlUJKaSyX5cie8/oUOO1g3+ovL7+BiJ3fQYHxp3jZl1wF8krcT3jRdOD/j+G36c5vrysQ35VJNzQaRaOl7n+riVqVn4yujeZtY4vI2BROdOqPk+7og/t0bdUc5EuFwSJWG5hMRluYScc+vMbBz+UZpD8H1mRT7+RXZlwrTTZmZd8JXon+H7eFkdN/60JLPn6pww2Dn3Saq0pmEC/hw6HH/8mhdrxW5m7wEHm9n2+Na+zyRoHVMIst2vmcomDvLpfHw5+jrn3NjwCPMdTF+R5fJzduzM4fXotkVmOH1MJnlnK/hye8Rxu10N1x+vUPNWvVMsfQDlnHNuZnCRdyC+Kf+xodGxg3yiGsMP8fv2gIhxBwTzfRA3PUQ8Fx40/9sx/ZRv0zf4fixiXK0233bObXDOveWc+xWV/Tccm2yeNMT2WVn8iOACbgi+c9ip8ePzINZkNHzAizVxPSx+4qAFRj98s8nZ6cyDzzctgbdcZm9/wDm3wjn3nHPu+/gm4x2ozJuxfPJUxAF7L6o/GpRPUflyP3y8pOp/YBq+dn/v4E5nTjjnNjvnJjvnbsA3qwdf2ReT7FgwDd/h7RDzb/CKV5ZhcmIF2gGpJkxx/Ir95o9GzFqbx4Y9gqbi8cqC71S/+TvBd6p+ITKSIl4iBRff9+MrcH/lqvd/FZOPNO9kZj0jhpcF3+H9+A7QPqLPkGS2kPzu2AR84XI0/lGI2c65ucG48fhz4dn4i/DyuIuk9/CFxkz2R022oS69g2/9sG98fg/yzaHBv6+msazaOF5vK48ELR/ileVgHQk53/fTW/jzXVS+iL1tNPy4zSz8zYN+ZtYrzXkAMLNS/Dn8kxzcHEpYLgmMCr6TvoUqQ3cG3+eYWWf8tn7inPsobrraKhP2xsd8tbfeme9fqneSefcLYiJeWfBd2+eEWEX2GPw5YHzcuN3xnXtD+v3/pLp+yIdqv28Q27FrjrT7d0pH8LvPAnYw/0bBePmIg3Tku+wT255U+zsjWV6PZiuTvBPrniDqmnVYguWnKl/Em44vVw8OrvXi1VXeqndUARQws17BhXm89vgmgeGmwcvxBd6dIqYH36kUwP8Fd4Vj62hJZedm4RYEsU7MLjOzHUPTG/4tHTUJ7LnBd1l4YLCNUa9kzikzG2lmUYXRWG3y2ohxmbgX3wT3sqB2POy3+Fd435tpZUkUM9vJzCJrwc2/in44/hnocId9r+Ernw4ws2NC05dQuf//HVfgfAT/9opTgzu9sXmaU9nx3i1ppnlUkH/ixR6liu3/ucF3Wdz8XfAdAtema81s23O4wXb/X/DvHclmDC6GbsbX/t8UlffMrJul0fmjme2ZoMImKu/GHjGpdiwIWv/dh+9nYWzcOoaRWWe74Fu6LcG/Vjo+zZkcv+YG32VxyxhDdKeM+dIW38Q5nIbYflmJf7whmSfxhcxLzOyIqAnMbJ/wMTiRDOIlkRvxhbK7nHO/TTRR8CjZG8DxZhbZsbmZ7RbEX7oaATeEL5yCi+DL8eeVe0PTxjp7/W9w5zp+3a3MLD5/fYvvUyeycsH5Tj2n4F8nHX+R9Ba+Ij7WmfuEiHnvA4aZ2bVRFQ5m1ifuor4m21Bngqb69+D7mhgbN/pS/GOXLzrfoXoqc4PvsvDAXB6vne9c+GX8nfJL49ZzLLVTSRw7z11voY6Zzb9i/RT8cXDbRVxwHv138O8f42LhWHyFwOdE9x8T64cqnQq4VCbiL1D2M7MTwyOC//fHt0Kq1t9jTTnnJuL7vjsW/+r1JlRWCoXNDb7L4tKV6zJhbD1VKhCDirb/kvzJg53xj+GG0xfLczOp+tr1KHfgbwT92vxj3FWYWYmZlaVYRtir+LL+xfjzVfjYNgHff9LVof/TkbDMkEcHmdlRccMuxffh8qpzLh99uNyO3z9/issHnfBv9YpNU5vmBt9l4YFmNpTKc1Q23sLH/wFBvg2L7e+Ucnw9mq1M8k6sn6Eqj2CafylGopZ/ScsX8YKuVWLl6iplLTPrgy/3bKLyhTSSgB4BqzQYeMx8nwVT8Z2OdcafVJsQOkE65yrM7F1gfzO7D39C34K/K/eJc+7+IPhPBqaY2RP4AD0OX6h6KHyH2Dk3y8x+he949mMzewh/AXQI/u7zx/g7DZl4Gn/C/LGZ7Yavpd0J/3z4s+T/5HMV/sDxBv5tIhX4R6QOxx+wbk0yb0rOublm9kN8ofcD882gl+ALCvvgW1/8LPESMrIH8LD5R71m4p+J7Yi/EN8Nv21nubg3T5nZefhCwSPmO8L+En+XfBi+oPjX8Eqcc6vM7Pv4iqBy853RLgOOwXd89gj+uf10PA5UmNk7+JOe4Qufw/GPl8Uev3s/SMvxZvYWvmDaFf87Taey873aMBUfL4/gD+DH4k8yz5Lewfy3+Dj+AXC0mU3A9z3QBV+w3BffJ1Wqzh/PAi4038ndLHx+7QMcje9r42+had/GVw780HzngrHnom8O+sv5Of43/2FQufEmvpLqFHxfMceQJuecM7PHgQvMbFfnXPjRx7SPX/i3rJyHz9OPBNMOwrc8G0flHc18ex0438xG4PNgbL+U4F9XmvSxP+c77zse38fEs0H+/Qj/e+yIz+u9g+WmqsBJN16qCS40rsBXdMy36M5Hy51z5cHfp+OPC7eZ2eXAu/iLlu744/wg/DFscfXFRPoEGIHvjP4lfFPrk4Pvq1yo433n3HgzuxpfsfqFmT2HPz6X4h9dOxCfR8OtEMfj98MLZvY6PgY+ds49HTfNoNDfsfVtMLOJRPf/E3MpPj5/g39z45v4Y+z2+M5ah+MLj3Oy2IacCS5g/hwa1Cn4vs3MYhX6f3BVOz/9Of6i48dmNgRfUB6Ij8/F+FfFp6O2jteX4I9tfzOzQ/FlkL74x8+exh8L02Zmf6ZyP8XuHP/UzM4M/n7COfdEaJYH8X2vnQh8aGZP48+5p+Ara74fcXy4EV++ORF418zG48s5J+Hj/7suuoPhWAusqFYBGQmO0efgK9AeMrMn8WWR/vjy32rg7ATpyMbd+PPftfhK36gWiLVSJnTOfROUXU4FPgqOSW3x5dn1+GP0kASzvwD8xcwOpzLPHR/Ml+j3C6/726Ci7XHgnSAPTMGXv3fEH1c7EtGPXoLlLTWzT6h8w224kid27u+CL3vGv7ErkVRlhnx4Gng8KD/MxO//w/Hly4uTzJeNPwfrOBZ/XfMcvlXfSfh99kfnXM4qQtN0N74D6L+Z2Sh8xenO+Bh4jCzLPkH8fw8f/4+a2WNU7u/R+PydznkpZ9ej2WxPIJO8cwd+/15j/o2nn+OfdjgcH5NRbzdNp3wR72p8+ezS4KbAq/jzy8n4iqFLXfSLMSTMFcC76Gv6wR/UXYpp5gbT9YyYtzz0f3d8BcxEKjtV/BrfwdvhEcvtiw+Mb/FN2B1wbmh8CT44JuEP9mvxFxKX4PsviErrWfiT8nr8CeVefCH4M2BF3LRlwTrHJtn2HfEFgVgHfFPwFTON47c/mH5sMLwsYlnnxm9jsrTgC1V34A8AK4E1+ALqTUCPDH7j8mS/cbCel/AX6RvwB6g/Au0yXVaSdeyEP5m9i78w2YQvyH0cDN8xyby7AA/jW/ZswB+crwNaJJlnX3zlwPLgd/sU/yxwowzS/AP8AXd2kPeWBXnrKqB13LQd8JUCc4O8NwsfCy2DYXOzyQuhcT2DcXdG/S74OxvX4y/mNgRp/zXQLEHsl0cMN3wcjQ+2eSM+/7+JvwhL+FuFljECfwf642AZ64J8dQcwKGL6w/CFuoogXVWON/hnlW/Hx/Q6fAH43GT7KknaBgfz3BA3PNPj10h8YXZ5kJffxF+gRKaJJLGDv9tc7RgbjBtL3DElnA/wF8FPBulYG6R/TMRykuW5LviWlZ8Fy6jAF+wewb8utHGO46XKvgjts2Sf+P3ZOsiPk4P0rsPn+2eBC4BWaeYHF6Rne/z5YjE+hj8ATk8y3374yr4F+BhZEuTLG4FhcdO2wsfD1/iLy6gYPjoYvhXoEjfummDcN0nS0xRfEfQW/nyxAV9hPh7fmWzHLLdhLnHHsWR5NMU+j+XfZJ9qy8IfZ/+Of/R3I76zytuB7unGf2g5OTleh/NQxPC++BhagT9/vw0cmWp5CdYxN8X+GhsxT2P8ee9TfHwsx58XRyZZT0t8ReIXQR5agj//7pJg+hJ8692PMvwNkuYZfIXPPcFvvCn4vhfon23+S7C+nfAXfQ54Osl0OSsTxsVC/PGgJf4NqTODPPoV/mZdRyLOJYTOO/hKmlfw/f6txpfvhmey34J0/SPIB+uDZU0LfpPjMty3fwnWMyVi3IvBuIeS5Pu5EcMTlhnS2K5q+ztJ2s8Npj8XX8nxNj6WV+ArPPtFzHNnOD3prDfqNw2GN8ef5z4L8lusrHFaptsWlT+T7edE+xFfJn8Kf65cgz8Hn59o/VH7I439vie+smd18HklyNeJ0lRl28jh9SjpXStG7b+M804w36744/RqfP4ux9+U2ba8uOmTli8S/e74G1w3UHmsX4GveDs0Ytqk+yBq+xvCx4KNlwJlZm3wlQ4fOef2STW9SH1jZuXAgc65qEdwJI6ZvYhvKdLbVb51rt4w31/NHPzjUufWbWpEpKEys6PxF4NnOefuTTW9iIhIMVAfQAXCzDpbXOe15t9A9Bd8LfrjdZIwESk0P8E3B85X020RkaIW9Pl1Hb6VdqJO20VERIqO+gAqHCcAvzGzV/DNZWNvnumHb9Z+c90lTUQKhXPuU/MdCEe9QUtERFLbDt/65wmnpvAiItKAqAKocLyLfzb2APxz0uAfk/gdvr+Peveoh4jkh3Pu7rpOg4hIfeWcW0j1t7KJiIgUPfUBJCIiIiIiIiJS5NQHkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFkIiIiIiIiIhIkVMFUANnZqVm9msze8rMvjYzZ2blKeY5wszeMrM1ZrbMzB42s161lGSRBsfM9jazR8xsppmtDj6fBbHbNsE8I8zslWDaVWb2gpkNqeWkizQYZtYzOIdGfT5LMI/iVKQWmdmdSeLUmdkXEfMoTkVqmZn1MbP7zGyRmW0IysDXmVnzBNMrTtPUuK4TIHWuEzAWWARMBromm9jMjgceAT4Gfgq0BX4ITDSzYc65BflMrEgD1Q9oCdwHLMBX3g8HfgGcaGZ7OefWxSY2s72BcmA+8Ktg8KXAG2Y20jn3aS2mXaSheRx4LG7YiviJFKcideI/wCsRww8CzgOeDg9UnIrUPjMbALyNr6v4JzAH2Ae4FhhhZoc751xoesVpBiy076QBMrNmQGfn3NfB/xXAJOdcWcS0TYC5wGZgV+dcRTB8CL7y6Dbn3AW1k3IRMbOfAn8ETnHOjQsNfw8YAAx0zs0Phu0ATAXecc4dWhfpFSlmZtYTX0i9zjk3No3pFaciBcLMXgQOBQY556aEhitORWqZmT0BHAPs55x7KzT8GuD3wFnOuXtDwxWnGdAjYLXEzM4NmpYeZGY/MbNZQXO2GWZ2Tl2lyzm3IVb5k4YDge2B/8Uqf4JlfISvdT0lqCQSqZcKNU6TmBd8t48NMLO++NZBD8dOggDB3w8DB5vZdrWaSpEcqg9xambNzaxlkvGKUylq9SFOY8ysB3Aw/kIxXPmjOJWiVsBxOgqYEa78CdwZfJ8XG6A4zZweAat9vwda4JugbgAuAu40s5nOuYmpZjazThmsa6VzblPNkhlpePD9dsS4d/DNZ/sBUyLGi9QnBRmnwQVl7LMncAOwkarN2VPF6XeDeZ/NII0ihagg4xS4Et8E3czsa+AO4HfOuQ2haRSn0lAUapyGnYe/Kf6/uOGKU2koCi1OmwFrI4bHhu1lZhY8BqY4zZAqgGpfM2C4c24jgJk9AszGP6eYMsCAJRmsaxS+ZU6ubB98z48YFxu2A6oAkvqvUOP0N/iLy5gpwNHOuVmhYenGqUh9V2hxuhWYADyBb53XGTgZ32fBPmZ2mHNuSzCt4lQaikKL0yrMrARfAVQBPBQ3WnEqDUWhxekUYBcz2845903cvACl+Nbvy1CcZkwVQLXvX7HgAt88zcxmADunOf8hGazr44xSllqsOfuGiHHr46YRqc8KNU7/A7wAtMN3hleG78g9THEqDUVBxalz7ktgdNzg28zsVuD7wKn4jtxBcSoNR0HFaYLl74Tvx7IibpziVBqKQovTv+DPl0+a2VX4PmhHAH8HNgFN8LG3DMVpxlQBVPtmRwz7FuiRzszOuag3F9SWWLO7ZhHjmsdNI1KfFWScOue+AGKvqH3EzMYALwStYB8IhitOpaEoyDiN8Dt8BdCRVFYAKU6loSj0OP1e8B3/+BcoTqXhKKg4dc7db2Ydgd9S2VpoI/5RtSPxj32tCoYrTjOkCqDatyXBcEtn5gw7sVoWrs3Ngdgr3mO9qofFmtZFNb8TqW/qRZw65140s0XAxUCsAigcp/EUp1JM6kWcAl/h0xpurac4lYaiYOM0uMA8FvjMOfdOxCSKU2koCi5OnXM3By1od8NX7kxxzq0ws0uAhc65WAWQ4jRDqgCqfxZmMG2u+wB6P/jeh6qdzgLsja+JnZHD9YnUV7UZp82BDqH/w3Eaf0dzb8ABk7NYn0ixqK047Q00AhaFhilORdKTzzg9G2gK3JZgvOJUJD15idPg5QmTYv+b2TB8/3rhmFWcZkgVQPVPXfYB9Bo+wM83s7/GnpU2s8H4vkjuyPFbx0Tqq5zGaUQneLHh5wBtgUdjw5xzM81sEnCSmV3rnFsQTLs9cBIwIWpZIg1QruO0o3Pu27hhJcD1wb9Px4YrTkXSls9y7/fwj5XcEzVScSqStrxfn5pZc+Bv+L5+/hwbrjjNnCqA6pl8PAttZpfiO5UF36lWDzP7ZfD/x865p4N1bzKzK/BvSXjDzP4LtAF+hO/9/de5TptIfZSHOH3OzL7Fv+LyS3ylz374putfA2Pjpr8CeBUfpzcHwy7Dv+b2SkQkH3H6XzNrA7yFf+yrE3AC/vWzTwKPxE2vOBVJIV99AJnZCGBXYFx8xW0cxalICrmOUzPbFbgTeAZfzu0KnAP0Ac5zzk2Lm0VxmgFVAAnAT6jayVdPfKdbAHdR9a7lw2a2DvglvvZ1AzAe+JlzTs9XiuTHf/EXkufjLyo3AbOAG4A/xxdenXNvmVkZvuXB9fjmr28BJznnct0yUES8Z4GzgAvwj2VuwL/K9hLg3865reGJFacidSpZ58/bKE5F6sRSfMXP94EuwErgDeAs59x78RMrTjNjzrm6ToOIiIiIiIiIiORRSV0nQERERERERERE8iutCiAzu9fMFprZKjObYWbnh8aNNrNpZrbWzF41sx7JliUiIiIiIiIiIrUrrUfAgo6YZjrnNpjZAPyr244E5uH7oTgf30/Mb4H9nXN75y3FIiIiIiIiIiKSkbQ6gXbOTQn/G3z64N9sMcU59zCAmY0FlprZgIjeuUVEREREREREpA6k3QeQmf3LzNYC04CFwHP41ydu61nbObcG3yJo1xynU0REREREREREaijt18A75y42s8uAfYAy/OtNS4ElcZOuBFrHz29mF+BfjUrz5s333GmnnWqY5MK2detWSrZsqTqwSZPMF7RpU2EsI2Tr1q2UlBRfv+HZbteMGTOWOuc65zBJdaZBxWkR5mUo3m1TnFZSnNZ/xbptitNKitP6r1i3TXFaSXFayzZtyvp6NF7BbFuO5TNOa/QaeDP7N/A5/jGwJs65i0PjPgXGOuceTTR///793fTp0zNeb31QXl5OWXl51YFjx2a+oPh56moZIeXl5ZSVlWW1jEKU7XaZ2WTn3LDcpagwFH2cFmFehuLdNsVpNMVp/VSs26Y4jaY4rZ+KddsUp9EUp7Vg7Nisr0fjFcy25Vg+47Sm1UqN8ZU/U4DBoRW1Cg0XEREREREREZECkLICyMy6mNmpZlZqZo3MbAxwGjAeeBwYZGYnmFlz4FfAJ+oAWkRERERERESkcKTTAsgBFwFfA8uBPwM/dM495ZxbApwA/C4YNwI4NU9pFRERERERERGRGkjZCXRQyXNgkvGvAANymSgREREREREREcmd4usyW0REREREGoYcdyorIlLMVAEkIiIiIiIiIlLkVAEkIiIiIiIiIlLkVAEkIiIiIiIiIlLkVAEkIiIiIiIiIlLkVAEkIpKNsWPVAaWIiIiIiBQ8VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBS5xnWdABEREREREREpQnpbbkFRCyARERERERERkSKnCiARERERERERkSKnCiARERERERERkSKnCiARERERERERkSKnCiARERERERERkSKnCiARkUI0dqzemiBSVxR/IvWb4lekMCk265wqgEREREREREREilzKCiAza2Zmt5nZPDNbbWYfmdnhwbieZubMrCL0uTb/yRYRERERERERkXQ1TnOar4ADgS+BI4BxZrZbaJp2zrnNeUifiIiIiIiIiIhkKWULIOfcGufcWOfcXOfcVufcM8AcYM/8J09ERERERERERLKVcR9AZtYV6AdMCQ2eZ2Zfm9kdZtYpZ6kTEREREREREZGsmXMu/YnNmgDPA7OccxeaWSkwAPgI6Aj8E2jtnBsTMe8FwAUAnTt33nPcuHHZp74AVVRUULp6ddWB3bplvqCFCzNfRvw88WqSjpCKigpKS0uzWkYhyna7Ro0aNdk5NyyHSaozDSpOc5WXY3GXZXzlarmK02iK0/qnTvNyvuI6oDiNpjitfwoiLy9cWD1Wo4ZlqCC2LQ8Up5UUp7Ug6vo0h+dWxWm0ZHGadgWQmZUA9wNtgGOdc5siptkOWAi0cc6tjh8f079/fzd9+vS01lvflJeXU1ZeXnVgTV53Fz9POstINU2Wr90rLy+nrKwsq2UUomy3y8yK5kQYVvRxmqu8HIurXL/WsobLVZxGU5zWP3Wal/MV1wHFaTTFaf1TEHl57NjocrPKvZEUp9EUp3kSFYc5PLcqTqMli9N0OoHGzAy4DegKHBFV+ROI1Sbp9fIiIiIiIiIiIgUirQog4BZgIHCwc25dbKCZjQBWAF8A7YGbgHLn3Mocp1NERERERERERGooZUsdM+sBXAgMAb4xs4rgcwbQG3gBWA18BmwATstfckVEREREREREJFMpWwA55+YBlmSSB3KXHBERERERERERyTX11SMiUhty0CGliIiIiIhITakCSERERERERESkyKkCSERERERERESkyKkCSERERERERESkyKkCSERERERERESkyKV8C5iIiIiIUNmRuzp0FylMik0RkaTUAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREQkytixequQiIiIFA1VAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFLnGdZ0AKTDxbzvR209E8icWX4ozkcIRFY+KUZHCE45LxaiISFrUAkhEREREREREpMilrAAys2ZmdpuZzTOz1Wb2kZkdHho/2symmdlaM3vVzHrkN8kiIiIiIiIiIpKJdFoANQa+Ag4E2gK/BMaZWU8z6wQ8BlwLdAAmAQ/lKa0iIiIiIiIiIlIDKfsAcs6tAcaGBj1jZnOAPYGOwBTn3MMAZjYWWGpmA5xz03KfXBERERERERERyVTGfQCZWVegHzAF2BX4ODYuqCyaFQwXEREREREREZECYM659Cc2awI8D8xyzl1oZrcBS5xzV4emmQj81zl3Z9y8FwAXAHTu3HnPcePG5SD5haeiooLS1aurDuzWLfMFLVyY+TLi54lXk2WE5qmoqKC0tDT1MuqZbLdr1KhRk51zw3KYpDrToOI0V3k5FjOp4itZfEbNm+5y4yhOoylO6586y8upzqVhNTm/ozhNRHFa/9RpXs5FuTcJxWk0xWn9U7BxmmWMguI0kWRxmvZr4M2sBLgH2AhcGksb0CZu0jZAXA0IOOduBW4F6N+/vysrK0t31fVKeXk5ZZMmVR142mmZLyj+dZbpLCPVKzBrsozQPOXl5RTj71as21UTDSpOc7VtsZhJFV/J4jNq3nSXG6dY83OxbldNKE7zLJPXSdfk/E7x5udi3a6aUJzWglyUe5Mo1vxcrNtVE4rTWpBp+TdDxZqf87ldaT0CZmYG3AZ0BU5wzm0KRk0BBoemawX0CYaLiIiIiIiIiEgBSLcPoFuAgcDRzrl1oeGPA4PM7AQzaw78CvhEHUCLiIiIiIiIiBSOlBVAZtYDuBAYAnxjZhXB5wzn3BLgBOB3wHJgBHBqHtMrIiIiIiIiIiIZSuc18PMASzL+FWBALhMlIiIiIiIiIiK5k3Yn0CIiIiJFJ9ZBZSadP4uIiEhiOqcWrHT7ABIRERERERERkXpKFUAiIiIiIiIiIkVOFUAiIiIiIiIiIkVOFUAiIiIiIiIiIkVOnUCLiOSaOpUVaRjGjlWci9QmxZuISFbUAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMjpLWD1RfxbD/QWBBERERFpCFTuFRHJCbUAEhEREREREREpcqoAEhEREREREREpcqoAEhEREREREREpcqoAEhEREREREREpcqoAEhEREREREREpcnoLmGQn6q0MelODiJduLMSmU+yIiIiISEOhMnCtUwsgEREREREREZEil1YFkJldamaTzGyDmd0ZGt7TzJyZVYQ+1+YttSIiIiIiIiIikrF0HwFbAFwPjAFaRIxv55zbnLNUiYiIiIiIiIhIzqRVAeScewzAzIYB3fOaIhERERERERERyalc9QE0z8y+NrM7zKxTjpYpIiIiIiIiIiI5YM659Cc2ux7o7pw7N/i/FBgAfAR0BP4JtHbOjYmY9wLgAoDOnTvvOW7cuGzTXpAqKiooXb266sBu3TJf0MKFycdHLbMm86RaRmieiooKSktLU6+zJttbhyK3KwOjRo2a7JwblsMk1ZkGFac1+c1j+T2cx5MNy1S6y00SY9nm50KlOK2kOM2RROe7TOO3Buc8xWk0xWn9U2t5uSbn1XBMK063UZxWUpzmUTox261bWmXbZBSn0ZLFaVavgXfOVQCTgn8XmdmlwEIza+2cWx037a3ArQD9+/d3ZWVl2ay6YJWXl1M2aVLVgaedlvmCUr0KL2qZNZkn1TJC85SXl1Ptd4taZ022tw5FblcD1aDitCbbFsvv4TyebFim0l1ukhgr1vxcrNtVE4rTHEl0vss0fmtwzivW/Fys21UTitMcq8l5NRzTitNtinW7akJxmkfpxOxpp6VVtk2mWPNzPrcr16+BjzUn0uvlRUREREREREQKRFotgMyscTBtI6CRmTUHNgN7AiuAL4D2wE1AuXNuZV5SKyIiIiIiIiIiGUu3pc4vgXXA1cCZwd+/BHoDLwCrgc+ADUD9ev5HRERERERERKTIpfsa+LHA2ASjH8hVYkREREREREREJPey6gRacqimncZms47aWKeIZEdxKiIiDZXOgSL1j+K2oKmzZhERERERERGRIqcKIBERERERERGRIqcKIBERERERERGRIqcKIBERERERERGRIqdOoEVE6ptw53rqaE8kN7KNpdj8ikkREREpUGoBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5PQWsIZMbyoRKSyKSRERkfzRWzRFCkd8PComa4VaAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFDlVAImIiIiIiIiIFDm9BSwXwj2W9+9fZ8nIi2LeNpFiEItRvTlBpG4o9kQKk2JTpPbkojyqN4HVCrUAEhEREREREREpcmlVAJnZpWY2ycw2mNmdceNGm9k0M1trZq+aWY+8pFRERERERERERGok3RZAC4DrgdvDA82sE/AYcC3QAZgEPJTLBIqIiIiIiIiISHbS6gPIOfcYgJkNA7qHRh0PTHHOPRyMHwssNbMBzrlpOU6riIiIiIiIiIjUQLZ9AO0KfBz7xzm3BpgVDBcRERERERERkQJgzrn0Jza7HujunDs3+P82YIlz7urQNBOB/zrn7oyb9wLgAoDOnTvvOW7cuKwTXzAWLtz2Z0WzZpRu2FB1fLduGS0jLVHLzHQZGapo1ozSDh0yX2c621+HKioqKC0trfH8o0aNmuycG5bDJNWZoo7TkBr/5rH8Hs7TyYbVpmD92ebnQqU4raQ4zZF8xWka5zzFaTTFaf1Tb+I0Fpfh5aWIVcVpNMVp/VMreTlcHs4mbjO8blScRksWp9m+Br4CaBM3rA2wOn5C59ytwK0A/fv3d2VlZVmuuoCEXldX3r8/ZdOnVx1/2mkZLSMtUcvM82vzyvv3p9rvls4609n+OlReXl59uxqooo7TkBr/5rH8Hs7TyYbVpmD9xZqfi3W7akJxmiP5itM0znnFmp+LdbtqQnGaI7mK01hchpeXIlaLNT8X63bVhOI0h8Ll4WziNsPrxmLNz/ncrmwfAZsCDI79Y2atgD7BcBERERERERERKQDpvga+sZk1BxoBjcysuZk1Bh4HBpnZCcH4XwGfqANoEREREREREZHCkW4LoF8C64CrgTODv3/pnFsCnAD8DlgOjABOzUM6RURERERERESkhtJ9DfxYYGyCca8AA3KXJBERERERERERyaVsO4GWhqYuOrcVkdRisdm/f50mQ6QgxOIh/pw1dqzOYyKFKFHMikjDomNB3mXbCbSIiIiIiIiIiBQ4VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBQ5VQCJiIiIiIiIiBQ5dQItIpIL6qxOpHCF4zPfsaoOLEVyQ7EqUtiiYkjxVPDUAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMipAkhEREREREREpMjpLWAiIjFRbzGIepuB3nAgIunS21FE6p5iT0QEUAsgEREREREREZGipwogEREREREREZEipwogEREREREREZEipwogEREREREREZEipwogEREREREREZEip7eAiYiISHHSm39ERERyL803XP761TJufGcftjqrMvyrH/2VDi3Wpb8unc9zRhVAIiIiIiIiIpIz6zc35sZ39qFiY7O6ToqE5KQCyMzKgb2BzcGg+c65/rlYtoiIiIiIiIjUHy/P6k3FxmYM3W4hb5x3e5VxLZtsYtm6Fjwzox+nDfqUJo221lEqG55ctgC61Dn3vxwuT0REREREJKEPF27Hje/sU2VYaZONXDeqnC6t1tRRqkRkt66Lua7sVXZss5JWTTdVGz/qrnP4ZNF2dCtdzSF9ZtdBChsmPQImIiIiIiL10vzVbbj3k8HVhjdptJWbDn++DlIkIgA9263gVwe+lnD8Mf2m88mi7Xhs6kBVANWiXL4F7P/MbKmZTTSzshwuV0REREREBIBPFnVlwerWAAzZ7hvuPu6xbZ+/H+Yrfe78aAirNzSty2SKSBLHD5wKwOPTBrJlq6WYWnLFnHPZL8RsBPA5sBE4FfgHMMQ5Nys0zQXABQCdO3fec9y4cVmvt2AsXLjtz4pmzSjdsKHq+G7dMlpGWqKWmekyMhS5belIZ/vDorYj02VkoKKigtLS0hrPP2rUqMnOuWE5TFKdKeo4DUn4m4fzXizP5Tmucq2iWTNKO3So62TknOK0UoOP03QUatwGx5Vs83OhUpxWUpymKT5Wu3VLK34vHXsw02Z15A9Xvcaw3b6pNv6K34xmzbom/PKSt+jZfVV6aYkraypOoylO65+c5+U0YnTcswPYvMUYs/8cOrZfHzmNc3DGD49m4ZJSbvrVy+w2YGniBSa4FlScRksWpzl5BMw5927o37vM7DTgCODm0DS3ArcC9O/f35WVleVi1YUh9Fq68v79KZs+ver4007LaBlpiVpmnl+PF7lt6Uhn+8OitiPTZWSgvLycosqPWSjqOA1J+JuH814sz9Wz106W9+9flPlZcVqpwcdpOrKM28+XdObp6f1w+DuSx/Sfzi6dl2S1TGDbcaVY83OxbldNKE7TFB+rp52WMn4/WNiNKTM607bZei7iLVpNr963SPkx82jXfD22Bki36BpX1izW/Fys21UTitMaShGjW51x2pNH8U1Fay5q8w5Du1WvpI05vW8P/rJkJHNeKuUym5h4oQmuBYs1P+dzu/LVB5AD1I5LRERE6pVVG5px6D1nMX91m23DerZbwS6dl+Ccf6xk+red+MPBr9RhKkUarpvf2wuA7w79MLJjWYD2LaJbHIhI/r39VXe+qWhNz3bLGbJd4sofgBMGfs5f3h7JY1MH8pdDX8RUg5B3WVcAmVk7YATwGv418KcABwBXZLtsERERkdr08/Gjmb+6DYO6LOLInb8AYGAn3/pn1vIOXPjM0Wza2oij+s1gv52+rMukijQ4S9e25IFPd8NwXDz8/ZTTf/FtB16b15Pz9/igFlInIgCPTt0FgBMGTk1ZoTOi+3y6t1nJTm1XsmxdCzq2XFcLKWzYctECqAlwPTAA2AJMA45zzs3IwbJFREREasXbX3XnX+8Pp3HJFu4//lF267q4yvi+HZbxs33f5Po3DuSCp4/mwwv/TbPGW+ootSINz/8+2IMNWxpzxM4z6NthWdJpV65vxm63XMymrSUc3Hs2PdutqJ1EijRgzsFjUwcClZ08J1NijumX/oOWTaJb80nuZf0WMOfcEufccOdca+dcO+fc3s65l3OROBEREZHa8qe39sVhXDVyYrXKn5hfHPAG/TouZerSztwwcb9aTqFIw7V5awn/en84AJft9V7K6ds238BJu05hqyvhlveLos9ikYL3wcJuzFvZju1KV7N396/TmkeVP7UrX30AFY+oTq4y7VwyF8uoz+K3tSFtu4iI1Bv3n/Ao/3xveNJHS5o33sytRz1N2V3n8bs39ufkXacwoFOSN5eISE4sX9ecPbotpGWTTRzaZ1bqGfAVRfd+Mpj/fbgHY8vKadFkc+KJx45VGVUkS7HWP98ZMI0SS/9t487B5IXb0610NTu0WZ14QsVp1lQBJCJSBx6esgs/fPGwKsPaNNvA7w6akFaTWRHJveaNN3PlyLdTTndgz3l8b+gH3PbhHlzw9NGUn3tnRgVdEclc51ZreeLUB1m/uXHa8bbXDvMZvv183l+wAw98thvfHfphnlMp0rDt3+NLTlo2hVN2/Syj+X7y0qHc+M5IflM2gWsPfD1PqRPIwSNgIiKS2sLVpbzzdfdt/6/d1IQFq9tU+Uxb2pkTxp3CtRNGsdXpNQgitcE5+NPEkSxb1yKj+f50yEt0bVXBsnUtWLymVZ5SJyLxmjdO0oonwmV7vQv4t4c51dOK5NVhfWcy7qSHObDnvIzmO6jXHAAemzYwH8mSELUAEhHJs3e+7s7xD53Cxi2NmHTBrfRst4ITd/mcg3vPrjLdg58N4qpXDmH2ivYYKqWK1IY7PhrKVa8cyt2fDObjH/w77ZYF7VusZ/zZd7Fzx2U0baSOoEXy6b+T96BX+xWM7jU749dEn7zrFK58aQwffdONt77akX13+io/iRSRGju492xaN93AR990Y/by9vRuv7yuk1S0VAEkIpJHt30wlIufO5KNWxpzQI+52zq6a9V0E62aVu307sqRb7PXDvPZc/uF2wq4zpFxYVdE0rOoohU/eelQAK7Z782MH+PatcuSfCRLREJWrm/Gj148jDWbmjL1kn9k3OdWs8ZbuGyvd/liWUe9Ylokj66dMIq9dpjPYX1n0qTR1ozmbdZ4C0f1m8EDn+3GY1MH8pORb+UplaJHwERE8mDjlkZc8uwRnP/0sWzc0pjL9nqXV866my6t1iSdb/8eX26rJFqzsQn73/Fdnprev2aJiHWUp87ypNjVMJ//8MXDWL6+BWP6zOS0QZ/WePVL17bk7Me/w9PT+yWfUPEoklxEfNz50RDWbGrKQb1m17jD9WsPfJ27v/O4OmwXqakU567Zy9tz/RsHcsZjJ7DF1ayK4YSBnwPw6NQ0HgPT+bTGVAEkIpJjazc14bB7z+Rfk/aiaaPN3H7ME9x0+PMZ3w25/cOhTPxqJ4598DT+/s6IPKVWpGF6cWYfHvxsN1o03sQtRz6TVUu7Bz8bxD2fDObS549gzcYmuUukSAO3eWsJ/3h/LwAuHZ761e8iUjdib/86st+MjPvpijms70xaNN7EO1/vyPxVrXOZPAlRBZCISI69+eVOvDavB91KV/PGeXdw3tCParScS/d6jz+MfhmAH754OLd/ODSHqRRpuNZtaszFzx0JwNiycnq1X5HV8i4a9j57dFvAlyvbMba8LPsEiggAd300mJnLOtK7/TKO7j8j6+W9NKsPB9xxnjpuF8mxWAXQCVm8ybZV000c1ncmnVquYfq3nXKVNImjCiARkRw7tM8sHj/lISaccxd77TC/xssxg5/tN5GbDnsOgO8/fTSPfL5LrpIp0mA9M6Mfs5d3YLcui/jR3qlf+55KoxLHf456hhLbyl/f2YePv+mag1SKNGzrNjXm1+WjALh+1AQal2TWijbKTe+O4I0ve3D96wdkvSwR8d79egfe/npHWjbZyOF9v8hqWf856mkWXvmXbW8Fk9xTBVCRca76R0TyzzmYt6Lttv+P6T89Z30NXDbiPa4re5WtroTTHz2Bl2b1yclyRRqqk3b9nAln38ltxzyZ8aOZiQzbfgGXDH+fLa6EC585mi1b1Xu7SDZumTSc+avbMHS7hZwyaEpOlvl/o1/BcPx70jBmL2+fk2WKNHS/mDAagCtGvFvtBSeZ6txqbU4qeyUxvQWsnvvgA/jPf2D6dJg26ScsWlNaZfzin/yRzq3WAnD1Kwfz+ZLODOi0tMqnQwu9EUEkW9eMP5hbJg3j2dPvZ7+dvsz58q894DWWrWvB39/dm4en7MKhfWblfB0iDcmoXnNzvszrD5rAo1MH8u787tw6eU8uGj4p5+sQaSi+N/QDlq1rQVnPuRm/oS+R3bou5uzBH3PXx0O49tVR3Hf8YzlZrkhDNWFOL8bP6U3bZuv56ciJOVvuivXNueX9Yfx4n7dp1nhLzpYrqgCq3nt4gfYm/tXKNrw0qw8vze7DkTt/wdnB8EWL4NZbY1OVJpjbmzCnF+8v2IGnZ1R9o9DATkv4/h6T+dE+7+Q83ZEKdB+L1NQNb+7LDRP3o3HJFlasb56XdZjBjWNeZNj2Czh9t5q/rahK/EXFYmyY4lSK0BPTBtC55Rr23emrvCy/TbMN/P2wFzjp4ZOZtbxDXtYhUu+leX5p23wD1x80Ieerv67sVR74bBD3f7o7P9nnLYZ2+ybn6xApShGxu9cO87l+1HhaN9tI+xbrc7aq4x48ldfm9aRlk01csfe7KdMh6VMFUIHauKURE+b04oWZfXlpVh+mLu28bdyGzY23VQDtuSf8/e8wYAAMeP6vdG+zKuFdkruOe5wpS7owbWmnbZ+pSzsxdWnnKi2H5ixvx7gpu3JUvxns2mVJPjdTpN67dfKeXD3+EAzHXcc9wVH9su+kMpESc5y5+yfb/l+9oSnfrmtJz3Yr8rZOkWKxZE1LvvfUMSxb15K3v/c/9u7+dV7Wc8LAz/n0on8xqMvivCxfpNgtW9eCFo030aJJzd4klEqPdiu5dPh73PjOSK4efzAvnnlvXtYj0hCUNt3ILw54I+fLvXKft3htXk9+98YBfG+PD1M0c5BMqAKoQJ0w7mSeCbXUad10Awf1msOhfWYxps9MYAAAXbrA5ZcHE721MukyB3ZeysDOVfsk2bSlhHfnd2e70optw56Z0Y+rxx/C1eMPYWCnJZy0yxRO3nUK9I9fokjD5RzcMHE/fj7eP/f8zyOeza5lToa+XduCw+47k/mr2vDEqQ9m1dm0SEPw05cPZdm6lhzcexYjdshP5Q/41nqq/BGpuZ++dAgvzurL3d95PG8dwf58/zf434d7MHnB9ixY3ZrtW6/Oy3pEitWWrca6zU0obboxL8s/qt8M9un+FW9/vSN/e2dvfpmXtTRM6gS6jm3cCM8/D+c9eSyTFmy/bfjhfWcyqMsifrH/67x+7u18e9UNPHHqg1w8/H36dFies/U3abSV/Xb6kr4dlm0bNmS7bzhvyId0aLGWqUs785vXyxh0yyWc+9Mj+P0b++ds3SL12UXPHsU14w/GYfz5kBdrva+Ppo22UNp0IwsrWnPAHedxz8e71+r6ReqTV+f05K6Ph9Cs0WZuOfJZrJb6Z54wpxdH3HcG6zbpfptIOj5f0pk7Px7CojWt2Klt8hub2ejYch1PnfoAsy7/uyp/RGrg/k93o/ffr8hb+dMMfj96PAB/emsky5almEHSphJJHdiwuREvPwOPPAJPPgkrVgAMpUPzdQzbfgEAPxg2iYuHv18n6du/x5fs3+NLNm0pYcKcXjz8+a48Pm0A8+a35e1W3bdNt3lrCZ8v6cxuXRbVWmFapFAcufMMHvhsEHcf9zjHDphe6+tv3WwjL515D5c/fzj/njycs584nk8Xd2XMzvnp20Skvlq2rgUXPXsUAL/Y//UqNzzyaaszfvjCYXy6uCs/Hz+aG8e8qHOlSAo/Hz+ara6EH+z5Xt5j9cCe8/K6fJFitXFLI35dPoola1uxxeWvPUlZz7kc0nsWL8/uww03wA035G1VDYoqgGrZj18cw20fDmXVhsphgwbBSZ1e5dRBn20blqu3HWSjSaOtjOk7izF9Z3HLkc/w1037s9+S2dvGl8/tySH3nE2/jks5ceDnnLDLVIZut1AFXClay9a12PbWvKP7z2DOFX+v07foNWm0lVuOepbduy7i8hcO509v7cvraxYw7Cho2zb1/CLFbuqSThx5/xnMWdGeAZ2WcNW+uXtDSSol5vj3Uc+w3+3f5W/v7sPCitb875in8tZcXqS+m/jljjw5fQCtmmzk2gNfr7X1rt3UhNs/HMqFe06iSa2tVaT++t8HezBnRXsGdlrCWbt/nNd1/X70eF6e3YebboKf/AQ6p55FUlAFUJ44B9OWduKFmX05Y/dP6dJqDQDrNjVm1YbmDB4MJ50EJ5zgO3Bm7Gt1m+AUmjTayl67fMPI6ZWtCxZVtKJTyzXM+LYTv3/zAH7/5gFsV7qaMX1mcVjfmZy865T8VWSlentbOr3Dqwf5hmnsWOjfv/qwqOlC7vpoMJc+fwTPnn4/B/Twdw3rsvIn7KLhkxjYeSknjjuZdz/envtOfza9FoSKAanP0ojbTi3XsnlrCXt2W8BjpzxU66+SHbnjVzxy8jjOeeI4HpoyiM8Wd+HxUx6EhQt9WqPOXYpLKVZJ8vamLSX87JVDAN/5a7hvynw76v7TeXVuL7Y64/LwmzBjcRqm+JSGJi7Pr93UhN++fgAA1x80gUYl+W20MGz7Bfxi/9c5oMc8Ov1jFkQ1NNAbbDOiCqAccQ5mL2/P+Ld68MBbO/PCrL58ubId4J8zPnuwrx29at+J/Hift9n55suTLK1+OGP3Tzll0BRen9eDh6fswtMz+jN/dRvu+ngIb32147YWTc75fhD26LYwp68HFMm3jVsa8cjnu3Dze3vxztc7AvDSrD7bKoAKSVnPubz//Vu5dt4hXDS4svJn+tKO9O/0bR2mTKR2bdzSiBJzNC7ZSudWaxl/9l3s2HYVzRvn541CqRw/cCoDOy3h+HGnMGVJF4b99wJ+0eptyprX/qOjIoXq0ueOYOJXO9Gp5RquHPl2ra77kuHv8ercXlzxwuG8v2B7/njwy3Sr1RSI1B83v7sX31S0Ztj28/nOgKm1ss7rD5pQK+tpKHJSAWRmHYDbgEOBpcA1zrn7c7HsQuQcLFsGHTv6/7c6Y6e//oj5q9tUma5zyzWM6TuT3u0rO23u1X5FLaY0/xqXbOWgXnM4qNcc/uWeZcqSLrwwsy8tm2zaNs3Xq9pw8D3nALBzh2/Za4f5jNjha4bvsIBdOi+hTbMNiRYvUicWri7lP5OH8Z/Je/JNRWsA2jVfxw0Hv8IFe06u49Ql1qv9Cs7f+xMsuK78fElndv3XJezd/Ssu6wcnnghN6zaJInm1YHVrTnr4JPbd8Sv+eMjLAOzcse57jhzYeSnvnv8/znvyWJ77YmfatdkAehJMGjjn2NZtwOUj3qV8Xk/+e/TTtV4uPH7gVH5TNoHfvXEA934ymCemDeBX28GQ7fSuHJGwBatb88tXDwLg9weNr/VuP7Y645Jnj+CY/tM5tM+svLc+Kla5agH0T3xRpiswBHjWzD52zk3J0fLrxNKl8PHsXsxZ0Z6ZyzrwxbIOzFzWgZl/gpIS33lzo0b+Of+d2q5k09YS+vZfwZHtpjOmz0yGdvumIPryqS2xV9/Gv/52+foWjNzxSz5Y2I0vlnXki2Udue/Tyh7jP7zw3wzZ7hvAt65Yvq45O7ZdRZdWa+jSag2tm26IbO0nkivL1rVg/ebG294Ect+nu3Pda2UADOqyiMv2eo8zdvuEVk03JV5IAZq6pBNtm63nna935J0z4Mor4dw+o9mj20J26byEXbssqeskimRlqzO++LYD73zdnXfnd+fRqQNZvKaUL1e25ef7v0G75oXT6rRNsw08ctI4PlvchW97tYfpsGAB3HIL7L03jBgBneo6kSK1YP3mxvxp4kgmL9yex095EDPYtcsSpl7yzzopN5vBtQe+zhm7f8qPXxzDk9MHcNVVMKD3wXx+5lT1bSkNlnPw+eLO28qL25VW0LPdCgZ0WsrBvWenmDv3Xp3Tk39PHs6/Jw9np7Yr+O6QDxm4S7NaT0d9l3UFkJm1Ak4ABjnnKoA3zewp4Czg6qh5Nm0qYVLEG5OdgzZtKrvn2LQJPvjAD4/6DBwInYLS0uzZMGMGbNkCW7fC5s3+FesbN0LjxnDaaZXr+cMfYPFiqKiAirdPoGJjU1ZvbMqSNa24pCtcdJGf7tVX4eSg5Uq8Tp3gyy+hVy///wtn3kvrpht4bUB/yqarWXfY7l0XMfG7t7NpSwmfLe7Cu/O78978HZi0YHu+WNaBXu0qW0j99Z29eWHmzlXmb9ZoM13ugOOOg5tu8sO+XduCX706itKmGyltupGWf4GmTSs/h60upVtr//z450s6M3dFOxrZVkrM0ajE0ci2Yq9Dq1ZV0/reez5vAVVO+Gaw446w3XbB+r+FOXNyuZcKz6ZNJUxO0NildWvo18//vXkzfJyk/7fevaF9e//3woX+gidKo0YwZEjl/59+6uM3LPbbbLcddA9eSLdyJUyb5sdt3Vr1s2UL7L+/zxMATz0F0ybuy2efdOOBcj/ftGmwePHPOHfIh9xx7JMAfHfoh7y/YHsuGjaJA3vMrbeFvxN2mcphfWdy7ye7c/Pco5kyBf7wzf4A9O+4lGmX/mPbtL8YfxDNGvtXy7dqspFW9/r4aNUKBg+Grl39dF9/7Y99jRpVj5FGjWCPPSqHRf2GMV27Vv6Gq1bBF18k3o5Bgyr/njUr9ubE6jLJl8WiEOM0ZrvtYIcd/N+rVvlzdCK77QbNgjLcjBn+GDtlShsaNYJ162DtWv/dpQuMHu2nm7ygG4fcczbL17eosqwDe8xl3EkPF1TlT4wZ7NZ1MeX4nf3GG3D99ZXj+3a4jBEzfcyVlsIPflAZZ++/7/d1SYkfFhtu5vf1Tjv5/1evTr6vd9kFWgS7bM4cEr5at7S0sjy2dSt8+GHiZfbsWdkqetEif5xItP3hY8SUKbC+8H6mnKtPcZrqWByL01mzYPlymD69Na1a+TwSOw+3bQu77uqnW7kS7rjD54vFLxzDojWlfLyoK1+v8m8qeG/+DozoPh+o+xeg9G6/nCdOfZAXZ/bhiklnMWrYvG1x9vwXfXlmRj+6lvjjUNeu0KGDj8fmzWH48MrlfPSR/y2j7LADdAueL1u+3F+/JLL77tAk6JX6iy/87xOlXTvo08f/vXGj/70T6du38u/58+Gbb6Kna9LErz/m448Tb1OxKNY4XbECpk8vpbTUD4uVpcPX3OvXQ3m5/459FiyAe+6BGdMvYt4P/0b3NqsoMcezp99Pz3Yr6qRsvFvXxfz+oFf434d7MHt5B8a+NoqSNxx39enI6YM+5YzQtBMmVF4Xxqe1Rw8fx+AbfMxL0rPDHntUzj91qi+PROnY0Z8LwU8zNcnTcQMGVF6DfvklLIm4Hzt9eilduvhzdkyi/An+OjW8TcnkogVQP2Czcy5c3PgYODDRDEuWNKtyoAw75BB46SX/98qV/q5YIo884jtRBnjgAfjlL6On69ixagXQv/4FX23ry3i3KtOGD8Q9e8IBPebSs90K+rZfRt8Oy9i54zL63HDBtsCO0WNMqTVptJWh3b5haLdv+MEwXwO41VmVE/7oXnNo0XgzC1a3ZvGaVixe04o1m5ry1Vc+P8QsXtOKf03aq3JAedV1lZ/TcVsF0H8n78Hf3t2neoLu8gF4yy2Vg8rK/EVGlBtvhB/9yP/9/PNw1llpbng9tWRJM4YNix4XH6eJpoOqcXrHHfCLX0RP16GDv+iLOfroxAfka66B3//e//3OO3DYYYnX/+WX/qAYW/8TQSeTYS2bbMS5yrNDhxbreOjERxIvtB5p1XQTFw6bzAVPH82rr8IL105k2tJO7NimMqCWrm3J7988oOqMz1b+OW6c77Qe4N57/f6P0q6dL9DGHHts4orSn/3MV8YDvPsuHHpo4m2YNavy76uv9nkqysEHw8v+qR9Wr06eL4tFfYrTMWMSr3/OnMqC09VXw+OPA+xRbboxYyorgPp0WM7y9S3oVrqavbt/ve2z745f1ptm4QMGwFVX+f3z/vswc1lHZt4H993nC62xG1IA553nK0yiXHEF/O1v/u+PPoIDDoieDvwyYgXKX/3Kx3SU/fbzFVQAGzYkzz933115TnzggcpzZbzmzaueY086KXkhuVgUY5xedRU89hjAntWmO/RQePFF//e6deH8UBnTg7os4qbDnt9W+VNIxvSdxSe3w+sPzYDg/PPWVzv6cmfEDezu3cPXFf43TXQB9tvfVl6vlJfD8ccnTseiRZUXdJdfDi+8ED3dccfFjpm+QidZHnruucoK4Ftugd/9Lnq6HXf05aeYQw6JvkgtJsUdp9UTHN6mVavg8MOjl9etdA0zvu1I9za+BrJfx7rrV7JLqzVcs/+b/Gy/ibw6pyf//WBPHp2+C8990Y+vVratUgE0ZkziSst//hMuvtj//eSTcP75ide5ebOvrAM45xx/ro7y3e/Cbbf5v2fMSJ5H3n0X9gouY//wh6rXopWGMXiwP6fH7LWXr2SPEr9NyZhz2RWSzGx/4GHn3HahYd8HznDOlYWGXQBcEPw7CPiM4tQJ3w9SMSrWbct2u3o454rirYSK06JQrNumOA0oTotCsW6b4jSgOC0KxbptitOA4rQoFOu25S1Oc1EBNBSY6JxrGRp2JVDmnDs6wTyTnHNFeW9W21b/FOt2ZauY94u2rf4p1u3KVjHvF21b/VOs25WtYt4v2rb6p1i3K1vFvF+0bfVPPrcrF93bzwAam1m445bBQL3uAFpEREREREREpFhkXQHknFsDPAb8xsxamdm+wLHAPdkuW0REREREREREspeLFkAAFwMtgMXAA8BFKV4Bf2uO1luItG31T7FuV7aKeb9o2+qfYt2ubBXzftG21T/Ful3ZKub9om2rf4p1u7JVzPtF21b/5G27su4DSERERERERERECluuWgCJiIiIiIiIiEiBUgWQiIiIiIiIiEiRy1sFkJl1MLPHzWyNmc0zs9MTTGdmdoOZfRt8bjAzy1e6spXBdo01s01mVhH69K7t9GbCzC41s0lmtsHM7kwx7Y/M7BszW2Vmt5tZs1pKZsbS3S4zO9fMtsT9ZmW1ltA6oDhVnBYKxWliilPFaaFQnCamOFWcFgrFaWKKU8VpoajLOM1nC6B/AhuBrsAZwC1mtmvEdBcAx+FfHb87cDRwYR7Tla10twvgIedcaegzu9ZSWTMLgOuB25NNZGZjgKuB0UAPoDdwXd5TV3NpbVfg7bjfrDy/SatzilPFaaFQnCamOFWcFgrFaWKKU8VpoVCcJqY4VZwWijqL07xUAJlZK+AE4FrnXIVz7k3gKeCsiMnPAf7inPvaOTcf+Atwbj7Sla0Mt6vecc495px7Avg2xaTnALc556Y455YDv6VAfzPIaLsaFMVp/aQ4bVgUp/WT4rRhUZzWT4rThkVxWj8pTnMvXy2A+gGbnXMzQsM+BqJqIncNxqWarhBksl0AR5vZMjObYmYX5T95tSbqN+tqZh3rKD25NNTMlprZDDO71swa13WC8khx6ilO6x/FqeK0WChOi4Pi1FOc1j+KU8VpsVCcpilfQV4KrIobthJonWDalXHTlZqZucJ7R30m2zUOuBVYBIwAHjWzFc65B/KbxFoR9ZuB3w/1+W7D68AgYB7+IPIQsBn4v7pMVB4pThWn9ZHiVHGqOC18ilPFqeK08ClOFaeK08KX8zjNVwugCqBN3LA2wOo0pm0DVBRgcEEG2+Wc+9w5t8A5t8U59xbwd+DEWkhjbYj6zSD69603nHOznXNznHNbnXOfAr+heH6zKIpTxWm9ozgFFKeK0wKnOAUUp4rTAqc4BRSnitMCl484zVcF0AygsZntHBo2GJgSMe2UYFyq6QpBJtsVzwEF23t8hqJ+s0XOufpcuxqlmH6zKIrT6orpN1ecFgfFaXXF9JsrTouD4rS6YvrNFafFQXFaXTH95orTNOWlAsg5twZ4DPiNmbUys32BY4F7Iia/G/ixme1gZtsDVwJ35iNd2cpku8zsWDNrb95ewOXAk7Wb4syYWWMzaw40AhqZWfMEzxjeDXzPzHYxs3bALynQ3wzS3y4zO9zMugZ/DwCupcB/s2woThWnhURxGk1xqjgtJIrTaIpTxWkhUZxGU5wqTgtJncapcy4vH6AD8ASwBvgSOD0Yvj++CV1sOgP+CCwLPn8ELF/pqsXtegD/vGEFMA24vK7Tnsa2jcXXKoY/Y4Gdgu3YKTTtj/HPj64C7gCa1XX6s90u4M/BNq0BZuOb2DWp6/Tned8oThWnBfFRnCbdN4pTxWlBfBSnSfeN4lRxWhAfxWnSfaM4VZwWxKcu49SCBYuIiIiIiIiISJHKVx9AIiIiIiIiIiJSIFQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5FQBJCIiIiIiIiJS5P4f1C8uVb9P5boAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x230.4 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_parameter(300, 10, 100, 20, 'std', df, 'binomial')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, as the CLT theorem states, with any kind of distribution, if we take m samples of n-size, and compute some parameter on them (for example mean, median or standard deviation), the distribution of that m parameters will be normal, and its variance will decrease as n increases (distribution curve will narrow)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding t-test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have a notion about what CLT is about, now we can apply this knowledge to understand the t-test. T-test is used normally for the following cases:\n",
"1. **One-sample test**: <br>We want to check if the population mean is equal or different from the sample mean. Here we are using directly CLT theorem with this t statistic:<br><br>\n",
"$$\n",
"t = \\frac{\\overline{x}-\\mu}{{\\frac{s}{\\sqrt{n}}}}\n",
"$$\n",
"We assume $s \\approx \\sigma$ because we don't know the population variance.<br><br>\n",
"2. **Two-sample test**: <br>We want to check if given 2 samples, the population mean of them is equal or different. Assumptions:\n",
" - **Homogeneity of Variance**: <br>Population variances are assumed to be equal. I think we can have an intuition about the reason of this assumption because the t-test is in reality using the CLT theorem to compare 2 means. We have to apply the proper \"standard error\". The standard error depends on the sample size and population variance. Different sample sizes and variances will lead to different standard errors. <br>We can find an \"adjusted\" standard error if sample sizes are different, but for different variances it's better to apply a whole different test (Welch test) These are the cases and its corresponding t statistics with their proper standard errors:<br><br>\n",
" - Equal sample sizes and variance:<br>\n",
"$$\n",
"t = \\frac{\\overline{x}_1-\\overline{x}_2}{s_p \\ \\sqrt{\\frac{2}{n}}}\n",
"$$\n",
"where:\n",
"$$\n",
"s_p = \\sqrt{\\frac{s^2_{x_1}+s^2_{x_2}}{2}}\n",
"$$\n",
"Being ${s^2_{x_1}}$ and ${s^2_{x_2}}$ the standard deviation of each sample.<br><br>\n",
" - Equal or unequal sample sizes, similar variances:<br>\n",
" $$\n",
"t = \\frac{\\overline{x}_1-\\overline{x}_2}{s_p \\ \\sqrt{\\frac{1}{n_1}+\\frac{1}{n_2}}}\n",
"$$\n",
"where:\n",
"$$\n",
"s_p = \\sqrt{\\frac{(n_1-1) \\ s^2_{x_1}+ (n_2-1) \\ s^2_{x_2}}{n_1 + n_2 -2}}\n",
"$$\n",
"Being ${n_1}$ and ${n_2}$ the sample-sizes.<br><br>\n",
" - **Sample independence**:<br>\n",
" It means that there are 2 different groups, that it's not the same group that has been measured twice. If the samples are paired (dependent) t-statistic is very similar to the \"One sample test\" one, but our variable is the difference between samples.<br>\n",
"$$\n",
"t = \\frac{\\overline{d}-\\mu_d}{{\\frac{s_d}{\\sqrt{n}}}}\n",
"$$\n",
"Where:\n",
"${\\overline{d}}$ is the sample mean difference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: In all t-tests (1 or 2 sample test) we assume population follows a normal distribution, but as we have seen, CLT theorem states that as n increases, the sample mean (or other parameters) will follow a normal distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we are going to do some examples for each test mentioned above, and we will also check that the t-statistics are correct plotting the histogram and the distribution curve (as done before with CLT)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One sample t-test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Having one sample, we want to check it the sample mean can be considered equal or not to the population mean. In this case we're going only to do 2 example exercises, because we have already check the t-statistic in the CLT theorem part."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example 1**:<br>\n",
"**One-sample one-tailed t-test**<br>\n",
"We have the potato yield from 12 different farms. We know that the standard potato yield for the given variety is $\\mu=20$. <br>\n",
"x = [21.5, 24.5, 18.5, 17.2, 14.5, 23.2, 22.1, 20.5, 19.4, 18.1, 24.1, 18.5]<br>\n",
"Test if the potato yield from these farms is significantly better than the standard yield."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"mean\": 20,\n",
" \"std\": 3.021175268004159,\n",
" \"n\": 12\n",
"}\n"
]
}
],
"source": [
"#We compute n, mean and standard deviation\n",
"x_example1 = [21.5, 24.5, 18.5, 17.2, 14.5, 23.2, 22.1, 20.5, 19.4, 18.1, 24.1, 18.5]\n",
"mu_example1 = 20\n",
"mean_example1 = np.mean(x_example1)\n",
"std_example1 = np.std(x_example1, ddof=1)\n",
"n_example1 = len(x_example1)\n",
"params_example1 = {\n",
" \"mean\": mu_example1,\n",
" \"std\": std_example1,\n",
" \"n\": n_example1\n",
"}\n",
"print(json.dumps(params_example1, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mu = 20\n",
"$$\n",
"$$\n",
"{\\overline{x}} = 20.175\n",
"$$\n",
"$$\n",
"s = 2.892555\n",
"$$\n",
"$$\n",
"n = 12\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With that information we can compute the 't-statistic', which standarizes the distribution of the sample means (seen abobe in the CLT chapter)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"t = \\frac{\\overline{x}-\\mu}{SE}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where SE is the 'Standard Error of the mean', that we approximate subsituting $\\sigma$ by s, because we don't now the population deviation standard."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"SE = \\frac{\\sigma}{\\sqrt{n}} \\approx \\frac{s}{\\sqrt{n}}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we assume the following hypotheses:<br>\n",
"H0: Null hypotheses. We can consider that $\\mu \\approx {\\overline{x}}$ <br>\n",
"H1: We can reject the null hypotheses, concluding that $\\mu < {\\overline{x}}$"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.2006562773994862\n"
]
}
],
"source": [
"t_value_example1 = (mean_example1-mu_example1)/(std_example1/np.sqrt(n_example1))\n",
"print(t_value_example1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"t = \\frac{20.175-20}{\\frac{2.892555}{\\sqrt{12}}} = 0.2006562773994862\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we calculate the probability that H0 can be true, we go to the t-student table and check. Here we're going to use t-student distribution because n < 30 (this is more accurate that using normal distribution), but as we'll see, results are very similar."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4223145946526807"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using t-student distribtion\n",
"t.sf(t_value_example1, df=n_example1-1)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.42048367493849975"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using normal distribution\n",
"norm.sf(t_value_example1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It means that there is a 42% chance that $\\mu = {\\overline{x}}$, based on our sample and it's mean and standard deviation. So we can't reject HO. We can't conclude that $\\mu < {\\overline{x}}$"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"def draw_t_score(x, cond, mu, sigma, dof, title, cond2=None):\n",
" y = t.pdf(x, dof)\n",
" z = x[cond]\n",
" plt.plot(x, y)\n",
" plt.fill_between(z, 0, t.pdf(z, dof), color='blue')\n",
" if cond2 is not None:\n",
" z2 = x[cond2]\n",
" plt.fill_between(z2, 0, t.pdf(z2, dof), color='blue')\n",
" plt.title(title)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_lin_example1 = np.arange(-3*std_example1/np.sqrt(n_example1),3*std_example1/np.sqrt(n_example1),0.001)\n",
"draw_t_score(x_lin_example1, x_lin_example1>t_value_example1, mean_example1-mu_example1, \n",
" std_example1/np.sqrt(n_example1), \n",
" n_example1-1, f\"Example 1 (One-sample 1-tailed t-test): \\n p-value on t-student distribution where t > {round(t_value_example1, 4)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a one-tailed test because we are checking if $\\mu < {\\overline{x}}$, now we're going to do a two tailed example checking if $\\mu = {\\overline{x}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example 2**:<br>\n",
"**One-sample two-tailed t-test**<br>\n",
"1 sample is extracted from normal-distributed population. The sample mean is ${\\overline{x} = 50}$ and standard deviation $s=5$. There are 30 observations. Considering the following hypothesis:<br>\n",
"* H0: $\\mu=48$<br>\n",
"* H1: $\\mu \\neq 48$<br>\n",
"\n",
"With significance of 5%, can we reject H0?"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.1908902300206643\n"
]
}
],
"source": [
"#We compute t-statistic\n",
"t_value_example2 = (50-48)/(5/np.sqrt(30))\n",
"print(t_value_example2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"t = \\frac{\\overline{x}-\\mu}{SE} = \\frac{\\overline{x}-\\mu}{\\frac{5}{\\sqrt{30}}} = 2.1908902300206643\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5% of significance means 2,5% per tail, as we can see in the following picture."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAEXCAYAAAAN0FvQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABEU0lEQVR4nO3dd3xddf348dc7uyNNmzYdSboHpYuO0FJkCkIBbYGWLUNFQMXxcyIqIIqKKEPlq4AgmwqUUaCATKFQ2qZ70zQdSbrSpEnaJM18//74nMjtJfsmOfcm7+fjkUfuPfN9xj3vMz7n8xFVxRhjjAkHUX4HYIwxxtSxpGSMMSZsWFIyxhgTNiwpGWOMCRuWlIwxxoQNS0rGGGPChiUl8z8ico2ILO6gecWLyEYRGdQR8wtXIvKoiPzW7zjqIyKvi8jV3udW7xsduV+1hogsEJFz/I7DOJaUOoiI7BCRchE5HPD3N7/jaisi8icR2Soih0Rks4hc1cQo1wEfqOqegGmcKCLvetMoFpFXRGRc+0YemUSkv4g8IyK7vXX1kYjMaGIcFZFRzZ2Hqp6jqo+FHm2jMQ3z4oppYrgWxd7IdG4TkSeDOt8JhOWJQVdkSaljfUVVewb83eh3QG2oFPgKkARcDdwnIic2MvwNwBN1X0RkJvAf4GUgFRgOrAE+EpER7RV0BOsJLAemAcnAY8BrItLT16gikKouA3qJSIbfsRhLSmFBRP4uIgsCvt8pIu+I00dEXhWRfBE56H1ODxj2fRH5rYh87F19vSIifUXkKREpEZHlIjIsYHgVke+JSLaIHBCRu0Sk3v1ARMaKyFsiUigiW0Tk4oaWQVVvVdXNqlqrqkuBD4GZDUx3CDACWBrQ+Y/A46p6n6oeUtVCVf0l8AlwmzfeaSKSKyI/EpH9IrJHRL4WMN1474ptl4jsE5F/iEi3Rtb7z0Qkz7sy2yIiZ3jdp4vIEhEp8ubxNxGJC1qH3w64MvyNiIz0tkGJiDxbN3xAzDd763uHiFzRSExfFpHV3rw/FpFJDazvbFW9W1X3qGqNqj4IxAHHNDDdD7yPa7z95JJm7lvXNjC9BvcNb/9b6K2LZcDIhpYXqIuryIvrc/tMfbE3ta7q27YiMgu4GbjEm86agNm8D5zXSJymo6iq/XXAH7ADOLOBft2BT4FrgJOBA0C6168vMNcbJhF4DngpYNz3gSzcDz8J2OhN60wgBngc+FfA8Aq8hzu7HuINe63X7xpgsfe5B5ADfM2bzhQvrnHNWNZuwB5gVgP9zwM2BC1/DXB6PcN+DdjjfT4NqAZuB2KBc4EyoI/X/x5gobdsicArwO8biOEYb/lSve/DgJHe52nACd5yDwM2AT8IWocvA72A8UAF8A4u0dZtg6uDYr4biAdOxV1VHuP1fxT4rfd5CrAfmAFE4644dwDxzVjnk4EjQFIjwygwKuB7c/atFu8bwHzgWW+4CUBe3bj1xDTMiyumieULjr3BddXEtr0NeLKe6f8QeCHg+1rgcr+PG13xz/cAusqf94M5DBQF/H0zoP8MoBDYCVzWyHQmAwcDvr8P/CLg+5+B1wO+fwVYHfBdCUgWwLeBd7zPgQeeS4APg+b9AHBrM5b1MeANQBrofwXwScD3dC+usfUMOwuo8j6fBpQHHsC8A9MJgOAO9iMD+s0EtjcQwyhv3DOB2CaW5wfAi0Hr8AsB31cAPwvaBvcGxFwN9Ajo/yzwK+/zo3yWlP4O/CZo3luAU5uIrxewDvh5E8MddWBv5r5VX1JqcN/AJYiqwG0J/I62T0oNrqvGti0NJ6VvAu82tW/bX/v/2e27jnW+qvYO+Huoroe6W17ZuIPrs3XdRaS7iDwgIjtFpAR3u6O3iEQHTHdfwOfyer4HP2fICfi8E/cMJ9hQYIZ3a6RIRIpwyWRgYwsoInfhzo4vVu/XXo+DuDPzwO+1QH0l8QbhzsLrFKhqdcD3MtzypeDO+FcExPuG172uJFldAZMrVDULl2xuA/aLyHwRSfWGHePdytrrrfPfAf2C4mrJOj+oqqUB3xtb5z8KWueDGxgWL9ZuuCvCT1T19wHdNwQs78kNjNucfas+je0bKbirp+B9rNmaEzuNrKvGtm0jEnEnisZnlpTChIh8B3frYTfw04BeP8Ldjpihqr2AU+pGCWF2gwM+D/HmGSwH+G9QEu2pqt9qZBl+DZwDnKWqJY3Mfy0wXLwSV94BewlwUT3DXoy7NdaUA7hkMD4g3iRV7enN4xz9rIDJU163p1X1JNwBTnGlsMCdhW8GRnvr/GZCW999RKRHwPfG1vkdQeu8u6o+U99ERSQeeAnIBa4P7Keq4wOW98MG4mrtvtXYvpGPuzIM3sca8rkTl2bG3ui6amTbNnSidCyuYI3xmSWlMCAiY3BFUr8KXAn8VEQme70TcQfbIhFJxt0iCdVPvIfcg4HvA/+uZ5hXgTEicqWIxHp/x4vIsQ0sw8+By3HPzQoam7mq5uKeg00P6HwTcLW4QhiJXny/xd2C+3VTC6SqtcBDwD0i0t+LKU1Ezm4g3mNE5Ivegf0Ibh3Xer0TgRLgsIiMBRpMxC3waxGJ8878v4x7fhPsIeAGEZkhTg8ROU9EEoMHFJFY4Hkv7qu95W/KPtxzrzqt3bca3DdUtQZ4AbjNuxIbh3ve05B83HpvqoRlcOwNrqsmtu0+YJh8vnDPqcDrzVl4074sKXWsV+To95Re9K4WngTuVNU1qroVd2b+hPejuhdXcOAAriTaG20Qx8u45yCrgdeAh4MHUNVDwFnApbiz+r24s834Bqb5O9wZcVbA8t3cSAwP4BJw3fwWA2cDF+IKSezEPcw+yVsnzfEzXLL7xLsd9TYNlEbzluMPuPW6F+gP/Nzr92Ncgj2EO/jVl7RbYi/uFuVu4CngBlXdHDyQqmbinm38zRs+C/cspz4n4pLbWXxWcq2x213gbmc95t3uuphW7lvN2DduxN2+3It7ZvavRqZVBtyBK/pfJCInNCf2JtZVY9u27mSgQERWAojI8cBhdUXD8bptkEZKSZr2Iw3f9jedkYgo7rZUls9xxAOrgDM04AXazkZETsM9WE9vYlDjE3GvYzysqov8jsW4B5LGdDhVrQCstgbjO1Wd63cM5jN2+84YY0zYsNt3xhhjwoZdKRljjAkbYZuUpJF6t4wjPjZ7IEHNEXglv9qk4lRx9cT90/vcrFqkWzDtIV6sTb0g2i7aenlM+PDz99iZhG1SigShJE7xKups65haoy1i8V50zG6L+ajq71S1TU5IxFWAembAtHd5sda0xfQjSXsmRBH5iYisF1cB6nYR+Ukjw8aJyPPetlGvhGJg/94i8pi4Snf3i8ht9SzHeyJSJq6ZlDMD+sWLyD3imvQ4KCL/573T5avgk7h2ntfVIrJCXIW4uSLyx8a2uYg8KK7S2loRuSaoX6PrU0SSvVdbSsXVDHJ5QD8RkV+IqyC5RFzNGr2ait+SkgkrdgXRfO2UXJJbeRAX4CqgD66+whtF5NJGhl+Me1l8bz397sFVGTUM94L1lRJQGzzwDO51gr7AL4DnRSTF63cTkIGr6moMMBX4ZSuWJ6yIyIAWDN4dV81SP1ydmmfg3r1ryBpcHZgr6+nX1Pq8H6gEBuCqmvq7iIz3+l2FexfxC7iqsroBf20y+pZWloerpuN7uHraDgB3AVH1DBePq0tqQkC3FNzb1f1xO++ruDe6D3qf0wOGfZ/PKoO8jYBKFAmqxBFXM/PDuJcu83C1I0Q3EH/dC6m7vb978WphxlWemYurfmW/N72vNTCdO3A1Wx/BVbT6twaGOxdXa/QhL7Yf42pPrnvL/LD3l0pA5ZyB8QR8n4LbcQ7hXuicHzT8l3EvxBYBHwOTAvrt8Oa9Fij2xk9oKJZ6lqMvrgbuEmAZ8BsCKtkkoMLMFi7zbbiaCZ70pn1t4PYO2NbXedtrD/DjgPk2uM5w7TXVevM9jKu+qW56dftOqrdchbgXMAMryb0NVw/h496ybAAyGtjOvwb+6n2OxVUOe5f3vRtuP0kOmP/VwC7cbyiwQt0o3IFgG1DgzT85aF18wxv3A6/713E1mR8E3gSGNhDjLm/8uvU/s55hLsHVevBnAn67rThO/KVufTQxXC5wWlC3A8DxAd9vxqsAFndgrAASA/p/iHshGSATuCig3+VATiPzvw9XZVEJ7oXyk5u7/Wni9xgw3LHe9q/x1ntRM9dhd9xB/V1gYwjb4ofAK80YbjFwTVC3Btcn7jddCYwJ6P8E8Afv8/PATwL6neith+6NxdHaK6ULcNlzKjAH96M4irr3UF4ALgvofDGuzqz9uB/fv3B1Uw3BHTha2xLro7j6tkbhdpSzcAe3+vwCV6v0ZOA43JlYYOYfiEtyabgf//0i0id4Iqr6C9yP4UZtvMG+h4HrVTURd7bxrrq63s4BdutndXzVVxfa/4hrn+cl3EZPxr2ZPjeg/xTgEVwdaH1xNSYs9F5SrXMx7ix2ODAJtwM2N5b7cTvUINz2/tw2D2GZ5+B24N64Gg/qczowGrdtfxZ4y6Yhqnol7kBc17jiH+sZbD7uwJgKzAN+JyJfDOg/2xumNy55NbSP/heXEAGOx10B1NUlNxPYoqqFAcOfhKtt4gzgFvms+qbvAufjqr1JxSWa+4PmdSruQHe2iMzBHbQvxJ30fYi7kqhPXTy9vfWxJHgAVf23F1Mt8B9x7XF9u77fQENERHBNsGxo7jj1TSbo8wTv83ggW12tEnXWeN0bGjddRJIamM9y3LEgGXgaeE5EEgL617v9m/o9BlLVTbhGLZd46713A7HgTXumiDyEO6G7Cvd7mtbYOE04hbbdFnXrcwxQraqfBvRvalvE437HDWptUrpTXSNsu3BXGpc1MNzTuKpI6lzudUNVC1R1gaqWeTvYHbgfW4t4l7Xn4tq7KfUS3j1B8w10BXC7qu5X1XzcGe6VAf2rvP5V6t7wPkzDVdU0RxUwTkR6qepBVa3vErk5TsCdgd/rxfY87gdV5zrgAVVdqq7Rt8dwZ5SB1bb8RVV3ewfHV3A/xiZ5hQLmArd463g9rnmKhrR0mZeo6kvqGggsb2CYX3vzXoc7mWlon2s2cXX/fQHX7MQRVV0N/BN3IKizWFUXqXsG9QTuRKbeZQBGi0hf3EHgYSBNXEuwp+KSVvDylKvqGtwPuW66N+CunHK9E7vbgHlBt+pu89ZFuTf871V1k7ra038HTBaRoS1fI46qrlfVn+AqVb0Nl2y3N/eZgDdO3Ulna7wB3CSuHrtRuBOg7l6/nrgr/UDFfFbr/BvA90UkRUQG4u7qEDD+UVT1Se9YVK2qf+az9pjqNLT9m/o9tpiIXCwim3En2duBiar6JVV9qpHfRVPT/DruAuJPrQyrsfXZE3eFGSh4W1zrPQNMwlUDVjdug1qblOpt+kA+X+X8e0B3cZUmDsMdBF/0hm1ttfnBhuJ2jj3yWRX2D+BuEdYnlaOr0g9uRqChphGaJK7UWN3y/8PrPBeXNHeKyH+lnpY1mykVyFPvOjgg9jrNafYg8P59s5eLljdH0NJlzmmif/AwDTX90FKpQGHQWfdO3FVyneB1llDfsxzvoJGJS0Cn4JLQx7ikV19SamhbDAVeDNiGm3C3fQKfKQSui6G4pufrhi/EnZEGLkOreAfidbikWYi7Wmn0eZOI3IhL6ud5SbU1voe7c7IVV0/jM7irWXAnicGJsRfuFhq4k9tVuNvYH+OuZqo4ummRwHh/LCKbRKTYW39JHN1MSUPbv6nfY2uk47bbatw6r+9521FE5IqAY87rQf3OB34PnKOqB+qdQNMaW59NbYtHcNvufdyV2nte90YLO7U2KdXb9IEGVTnv7dTP4s5qLwNeDTgAtKTa/FKOzq6Bbfrk4K4I+ulnVdj3UtXx1G837of8ufhb4ag3j9WVGqtb/hu8bstVdQ4uSb7EZ20l1ffWcmPLuQd35h24fgKbBGhRsweNLUc9WtQcQQuXuTnzp555122zxtZZU9PeDSTL0bVwD8HdNmmN/wJfxN1CXu59Pxt3i/iDRsYLlIM7iARuxwRVDYxJg4a/Pmj4bqr6cT3Tbtab8iLSU1xpsXdxz0zSgEtUdYI2UgO8d1Z+E64+w1aX5vTuwlyhqgO933EU7jkmuIPbiKBtdpzXHe/q80ZVTVPVEbjnciu0nlrUvRPnn+Jua/fxbqsV07xmSpr6PX5usZqaoKrejVvX7+AeM+R6Jd+mNDLOUwHHnHPquotr+v0h3K3rdU3Nu5HpN7Y+PwViRCTwdlzgtqhV1VtVdZi6uh834H5bjf6+WpuUmtP0QZ2ncQ9Pr/A+12lJtfmrgVPEvWOSxGc1/qKuMs//AH8WkV4iEiUiI0WkoVuBzwC/9C5H+wG34B6yt0ZwdfpHEVf09QoRSVLVKtylbmAV+n2D7nWvBs71SkANxJWgqbMElxi+J66pgAs5uumHZjd70MByBMfyP9qC5ghasczN9Stv3uNxzXDX7XOraXid1c2z3m2kqjm4s7/fi0iCiEzCPUds7f7wX9xVwkZVrcQrrINr/Ta/mdP4B3BH3e03bz+d08TwP/fWCyKSJCL1tUsFzWgmwjuY7cb9Zh8A0lT126ra6K0pcTVq/w74kjbxaoA3fLx89uwmzlv/4vUbKSJ9RSRaRM7B3Zr+LYC65xergVu9cS7APR9d4I2bJiKp3m/gBOBXNHxsScT9pvJxB9db+PyZf0Oa+j0G24d7FhPX2ERVtURVH1TVE3FX2EdwrQs0p00xAMQ9E30KmKsBNZ83Mnycty0EiPXWa5TXr8H1qe458QvA7d7x5gu458NPeOMme9tSvGPG3bhHI403s6ItL8mhfFb6rgBXSqfekm4B42ThLv/jArql4n60h3EZ93qOLhX1Pl7pO+/7/bhSZVm4KusDh03CNcyWizvTWQVc2kAsCbiSQXu8v78ACV6/0wgo7eZ124FrI6i+ac30Yj+Ie14T3D8Od1/1IO7gvBzXFENd/0e8dVjkrY8E3MG2BFdK7v9xdOm7DG/Z6kr7/JujS57N8uZR5C3bc3illIKXg8+XaDwqlnqWJQVXQrLR0netWOaj4giOjc+XvtsL/DRoeza2zubgCjsU4UoB1k2vbt9J95arEFfi7YZG1tFR49azjnribm3c6n0XXCnOvzc2DY4uaRqFKy21xdvO24DfNTZ/3DPRdd46yAEeaeS3eDvuIFwEnFBP/+H1bf9mHBe2e8t+OODvHwH9NwBXBP2uNOhvmNfvYm9bl+ES0NlB8xrmrbNybz0F7teneNMu8/pd0UjM0bj9sQT3e/kpAb+TprY/Tfwe6zkWvObtZwdauG6jqKekZCPDv4dLmIHb4vWA/q8DNwftf8Hb4rTmrE9cIY+XcHcsdgGXB/Qb441Thru1+cPmxN/iuu8kTJo+MMYY0/nYy7PGGGPChiUlY4wxYcOarjDGGBM27ErJGGNM2Iioyi/79eunw4YN8zsMY4yJKCtWrDigqilND+m/iEpKw4YNIzMz0+8wjDEmoohIqLVNdBi7fWeMMSZsWFIyxhgTNiwpGWOMCRshJyURmSWuKd0sEbmpkeHmimv6OCOg28+98baIyNmhxmKMMSayhVTQQVwzE/cDX8LVO7dcRBaq6sag4RJxFbcuDeg2Dtfm0XhcHWhvi8gYdZV/GmOM6YJCvVKaDmSpara6WpHn4yrADPYb4E5cjbd15gDzVbVCVbfjKlptrJZdY4wxnVyoSSmNoxscyyWocTERmQoMVtXXWjquN/51IpIpIpn5+c2t/d8YY0wkatf3lLw2Oe4GrmntNFT1QeBBgIyMDKsTyYSNyupa1uUVs3FPCcVllcRER5HauxsZQ/uQ2rub3+EZE5FCTUp5HN0iaDpHtyqYiGtC+X2v/a6BwEIRmd2McY0JS7uLynnwg2xeXp3HwbKqeoeZmJbEVTOHcsGUNGKirZCrMc0ValJaDowWkeG4hHIpcHldT1UtJqC9exF5H/ixqmaKSDnwtIjcjSvoMJrPmjw2JuxUVtfyt3e38o8PskHhrPEDOG/iII4b3Jt+PeOprKllZ0EpH2cV8NyKHH7y/Foe+jCb38yZwIwRff0O35iIEFJSUtVqEbkReBOvFUdV3SAitwOZqrqwkXE3iMizwEZcK4nfsZJ3JlztKijj20+vYH1eCedPTuXHZx9Dep/uRw0TFxPF+NQkxqcmce3Jw3lzwz7uWLSRyx76hO+dMZrvfnE00VHi0xIYExkiqumKjIwMtbrvTEdbtesg1z6WSXWtcufcScyaMLDZ45ZWVPOrl9bzwqo8zps4iLsvOY74mOh2jNaYzxORFaqa0fSQ/ouoClmN6WjLthdy9SPL6JcYx6Nfm87IlJ4tGr9HfAx/vvg4jh3UizsWbaK4vIp/Xp1BQqwlJmPqY09gjWnA2twivv7ocgb1TuCFb32hxQmpjojwzVNG8KeLjuOjbQe48elVVNfUtnG0xnQOlpSMqUdOYRnX/Gs5vbvH8tS1M0hJjA95mvOmpXP77PG8vWkfN7+4jki6dW5MR7Hbd8YEKaus5ronVlBVU8vjX5/JoKS2e+foypnD2H+ogr++m8XEtCSunDmszaZtTGdgV0rGBFBVblqwjs17S/jLZVMY0cpbdo35f2eO4Yyx/fn1KxvJ3FHY5tM3JpJZUjImwAsr81i4Zjc/PHMMpx/Tv13mERUl3H3JZNL6dON7z6yiuLz+F3CN6YosKRnjySks49aFG5g+LJlvnz6qXeeV1C2W+y6dwr5DFfx64YZ2nZcxkcSSkjFAba3yo2fXIMCfLz6uQ15ynTy4N985fRQvrMpj0bo97T4/YyKBJSVjgGeW72LZjkJu+co4Bid3b3qENvLdL45iYloSt7y8nuIG6tEzpiuxpGS6vAOHK7jz9c2cMCKZedPSO3TesdFR/P7CiRSWVnLXfzZ36LyNCUeWlEyX97vXNlFeVcNvz5+IV5t9h5qQlsRVM4fx1NJdrM4p6vD5GxNOLCmZLm1pdgEvrMrj+lNGMqp/2xf/bq4fnTWGlJ7x/PKlddTW2ku1puuypGS6rNpa5Y5FmxiUlMB32rm0XVMSE2K5+dxjWZ9XwkurrVkx03VZUjJd1itrd7M2t5gfnXUM3eL8ryB19nGpTExL4k9vbuFIlbXiYromS0qmS6qoruGuN7cwblAvLpiS5nc4gHup9uZzj2V38RH+9dEOv8MxxheWlEyX9PjHO8k9WM7N5x4bVg3vzRzZlzPG9uf/3suisLTS73CM6XAhJyURmSUiW0QkS0Ruqqf/DSKyTkRWi8hiERnndR8mIuVe99Ui8o9QYzGmOQ5XVPN/72dxypgUThrdz+9wPuemc8ZyuLKaBz/I9jsUYzpcSElJRKKB+4FzgHHAZXVJJ8DTqjpRVScDfwTuDui3TVUne383hBKLMc31+JIdHCyr4odfGuN3KPUaPSCR2cel8viSHRQcrvA7HGM6VKhXStOBLFXNVtVKYD4wJ3AAVS0J+NoDsPKuxjelFdU89EE2px2TwuTBvf0Op0Hf/eJoyqtqeOjD7X6HYkyHCjUppQE5Ad9zvW5HEZHviMg23JXS9wJ6DReRVSLyXxE5ub4ZiMh1IpIpIpn5+fkhhmu6uic+2cnBsiq+f8Zov0Np1Kj+Pe1qyXRJHVLQQVXvV9WRwM+AX3qd9wBDVHUK8EPgaRHpVc+4D6pqhqpmpKSkdES4ppMqq3RXSaeMSWHKkD5+h9OkuqulBz+0Z0um6wg1KeUBgwO+p3vdGjIfOB9AVStUtcD7vALYBoTnTX7TKTz1yS4KSivD/iqpzqj+PfnKpFSeXLLTKms1XUaoSWk5MFpEhotIHHApsDBwABEJPAKcB2z1uqd4BSUQkRHAaMBOCU27qKyu5eHF2zlxZF+mDQ3/q6Q6N5w6ktLKGp5cutPvUIzpECElJVWtBm4E3gQ2Ac+q6gYRuV1EZnuD3SgiG0RkNe423dVe91OAtV7354EbVNXahjbt4tW1u9lbcoTrThnhdygtMi61FyeP7sejH++wWh5MlyCqkVMYLiMjQzMzM/0Ow0QYVeWc+z5EFd74wcm+1AQeio+yDnDFP5fyhwsncun0IX6HYyKQiKxQ1Qy/42gOq9HBdHofbj3A5r2HuPbk4RGXkABOHNmX8am9ePDDbKtB3HR6lpRMp/fQh9kM6BXPnMnhUcddS4kI1586kuz8Ut7etM/vcIxpV5aUTKe2cXcJH249wDUnDicuJnJ393MnDCS9Tzf+aS/Tmk4ucn+lxjTDPxdn0yMumstnRPazmJjoKK6aOZRlOwrZuLuk6RGMiVCWlEynVXC4glfX7GHetHSSusX6HU7ILs4YTEJsFI99vMPvUIxpN5aUTKf178wcKmtquXLmUL9DaRO9u8dxwZQ0Xlqdx0Fr1sJ0UpaUTKdUU6s89ckuThzZl1H9E/0Op81cfeIwKqpr+XdmTtMDGxOBLCmZTumdTfvIKyrnqpnD/A6lTY0d2IsZw5N5YslOaqx4uOmELCmZTunxJTtJTUrgzGP7+x1Km/vaF4aRV1RuxcNNp2RJyXQ62/IPszjrAJfPGEJMdOfbxc88dgCpSQlW4MF0Sp3vF2u6vCeW7CQuOqrTVskTEx3FFScM5eNtBWTnH/Y7HGPalCUl06mUVlSzYEUu504cSL+e8X6H024uykgnJkr493Ir8GA6F0tKplN5de1uDlVU89UTOkcx8Ib0T0zgzGMH8NyKXCqqrfZw03lYUjKdyjPLchjdv2dEtZnUWpfNGEJhaSVvbbQCD6bzsKRkOo3Ne0tYnVPEJccPjsjawFvq5FH9SOvdjWeW7fI7FGPajCUl02nMX5ZDXHQUF05N9zuUDhEVJVw2fTAfZRWw40Cp3+EY0yZCTkoiMktEtohIlojcVE//G0RknYisFpHFIjIuoN/PvfG2iMjZocZiuq4jVTW8uCqPs8YPILlHnN/hdJiLMgYTHSU8s9yulkznEFJSEpFo4H7gHGAccFlg0vE8raoTVXUy8Efgbm/cccClwHhgFvB/3vSMabE3N+yluLyKS4/vnMXAGzKgVwJnjO3P85m5VFbX+h2OMSEL9UppOpClqtmqWgnMB+YEDqCqgfXs9wDq6kaZA8xX1QpV3Q5kedMzpsXmL8thcHI3ThzZ1+9QOtxlM4ZQYAUeTCcRalJKAwJflMj1uh1FRL4jIttwV0rfa+G414lIpohk5ufnhxiu6Yx2HChlSXYBl2QMJiqq8xdwCHbK6BTSendjvt3CM51AhxR0UNX7VXUk8DPgly0c90FVzVDVjJSUlPYJ0ES0f2fmECXu+UpXFB0lzJ2WzuKsA+wuKvc7HGNCEmpSygMCjwTpXreGzAfOb+W4xnxOVU0tz6/I5Ytj+zOgV4Lf4fhm3tR0VOGFlbl+h2JMSEJNSsuB0SIyXETicAUXFgYOICKjA76eB2z1Pi8ELhWReBEZDowGloUYj+li3t28n/xDFV2ugEOwIX27M2N4Ms+vyEXVmrQwkSukpKSq1cCNwJvAJuBZVd0gIreLyGxvsBtFZIOIrAZ+CFztjbsBeBbYCLwBfEdVrb4U0yLPZeaSkhjPacfYrd2LMgazo6CM5TsO+h2KMa0WE+oEVHURsCio2y0Bn7/fyLh3AHeEGoPpmgoOV/D+lv18/aThnbKJipY6d+JAbn15Pc9l5jB9eLLf4RjTKvZLNhHr5dW7qa5V5naRGhya0j0uhvMmDeK1dXsoraj2OxxjWsWSkolYC1bmMiGtF8cMTPQ7lLBxUcZgyiprWLRuj9+hGNMqlpRMRNq8t4QNu0vsKilIxtA+DO/Xg+dWWCk8E5ksKZmItGBFLjFRwuzjUv0OJayICPOmpbNseyE7C6ySVhN5LCmZiFNdU8uLq3Zz+tj+9O3Ercu21gVT0hCB5+1qyUQgS0om4ny49QAHDlfYrbsGpPbuxkmj+rFgRS61tfbOkokslpRMxHl+ZS59usfyxbH9/Q4lbF2UMZjdxUf4eFuB36EY0yKWlExEKS6r4q2N+5h9XCpxMbb7NuSscQPolRDD8ytymh7YmDBiv2oTUV5dt5vK6lrmTrNbd41JiI3my8el8saGvRw6UuV3OMY0myUlE1EWrMhldP+eTExL8juUsDdvWjpHqmrtnSUTUSwpmYiRnX+YlbuKmDstHZGu125SS00Z3JsRKT2sFJ6JKJaUTMR4YWUeUeKKPJumiQhzp6azfMdBdhywd5ZMZLCkZCJCba3y4qo8Thqd0qXbTWqpC6e6d5asnSUTKSwpmYjwSXYBeUXlzJ1qV0ktMSjJe2dpZZ69s2QigiUlExGeX5lLYnwMZ48f6HcoEWfetHTyisr5ZLu9s2TCnyUlE/ZKK6p5Y/1ezps0iITYaL/DiThnjRtIYnyMFXgwESHkpCQis0Rki4hkichN9fT/oYhsFJG1IvKOiAwN6FcjIqu9v4XB4xoD8Pr6vZRV1ti7Sa3ULS6a8yYN4o31e62dJRP2QkpKIhIN3A+cA4wDLhORcUGDrQIyVHUS8Dzwx4B+5ao62fubjTH1WLAil6F9u5MxtI/foUSsedPSrZ0lExFCvVKaDmSparaqVgLzgTmBA6jqe6pa5n39BLDTXdNsuQfLWJJdwIVT7N2kUEwb2odhfbvbLTwT9kJNSmlAYOVauV63hnwDeD3ge4KIZIrIJyJyfn0jiMh13jCZ+fn5IYZrIs1Lq/IAV7TZtF7dO0tLtxeSU1jW9AjG+KTDCjqIyFeBDOCugM5DVTUDuBy4V0RGBo+nqg+qaoaqZqSkpHRQtCYcqCovrMxjxvBkBid39zuciHfhtHREXDPyxoSrUJNSHjA44Hu61+0oInIm8AtgtqpW1HVX1TzvfzbwPjAlxHhMJ7I6p4jsA6XWblIbSevdjRNH9mXBSmtnyYSvUJPScmC0iAwXkTjgUuCoUnQiMgV4AJeQ9gd07yMi8d7nfsAXgI0hxmM6kQUrc4mPieKcifZuUluZOzWdnMJylu8o9DsUY+oVUlJS1WrgRuBNYBPwrKpuEJHbRaSuNN1dQE/guaCi38cCmSKyBngP+IOqWlIyAFRU1/DKmj2cPX4giQmxfofTacyaMJAecdFW4MGErZhQJ6Cqi4BFQd1uCfh8ZgPjfQxMDHX+pnN6b/N+isurrIBDG+seF8O5EwexaN0efj1nPN3jQj4EGNOmrEYHE5YWrMyjf2I8J43q53conc68aemUVtbwxvq9fodizOdYUjJhp+BwBe9t3s/5U9KIibZdtK0dPyyZIcn2zpIJT/aLN2HnlTW7qa5Vu3XXTqKihAunprEku4Dcg/bOkgkvlpRM2HlhVR7jBvVi7MBefofSac2dmo4qvLjyc29wGOMrS0omrGzdd4i1ucV2ldTOBid354QRySxYmYuqvbNkwoclJRNWXliVR3SUMGeyJaX2NndqOjsKylix86DfoRjzP5aUTNioqVVeXJnHqWNSSEmM9zucTu/ciYPobu8smTBjScmEjSXbCthbcsRu3XWQHvExzJowkNfW7qG8ssbvcIwBLCmZMPLCylwSE2I489gBfofSZcybls6himr+s9HeWTLhwZKSCQulFdW8vn4vX7YmzzvUCcP7kta7m93CM2HDkpIJC2+s30t5VY3VCN7BoqKEuVPTWJx1gD3F5X6HY4wlJRMeFqzMZUhyd6ZZk+cdbu40987SC/bOkgkDlpSM7/KKyl2T51PTrMlzHwzt24Ppw+ydJRMeLCkZ3720Kg9VuHCK3brzy9xpaWTnl7Iqp8jvUEwXZ0nJ+EpVWbAyl+OH9WFIX2vy3C/nThxEQmyUFXgwvrOkZHy1ctdBsvNLuShjsN+hdGmJCbGcM2EQr6zZzZEqe2fJ+CfkpCQis0Rki4hkichN9fT/oYhsFJG1IvKOiAwN6He1iGz1/q4ONRYTeZ5dnkv3uGjOmzjI71C6vLlT0zl0pJq3Nu7zOxTThYWUlEQkGrgfOAcYB1wmIuOCBlsFZKjqJOB54I/euMnArcAMYDpwq4hY0asupKyymlfX7ua8iYPoEW8toPpt5si+pCYl2C0846tQr5SmA1mqmq2qlcB8YE7gAKr6nqrWNdryCVD3NPts4C1VLVTVg8BbwKwQ4zERZNG6vZRW1nDx8XbrLhxERwkXTE3jw6357Cs54nc4posKNSmlATkB33O9bg35BvB6S8YVketEJFNEMvPz80MM14STZzNzGN6vBxn2blLYmDs1nVqFF1fZO0vGHx1W0EFEvgpkAHe1ZDxVfVBVM1Q1IyUlpX2CMx1u+4FSlm0vZN60dHs3KYyMSOnJtKF9WLDC3lky/gg1KeUBgfde0r1uRxGRM4FfALNVtaIl45rO6fkVOUQJVq1QGJo7NZ2t+w+zNrfY71BMFxRqUloOjBaR4SISB1wKLAwcQESmAA/gEtL+gF5vAmeJSB+vgMNZXjfTydXUKgtWuHaTBiYl+B2OCXLepEHEx9g7S8YfISUlVa0GbsQlk03As6q6QURuF5HZ3mB3AT2B50RktYgs9MYtBH6DS2zLgdu9bqaT+3BrPntLjti7SWEqqVssZ48fyMI1u6motneWTMcKuRyuqi4CFgV1uyXg85mNjPsI8EioMZjI8lxmLn26x3LGsf39DsU0YO60dBau2c07m/Zzrr1DZjqQ1ehgOtTB0kre2riP86ekER9j7SaFq5NG9WNAr3i7hWc6nCUl06FeXp1HZU0tF02zW3fhLDpKuGBKOv/9NJ/9h+ydJdNxLCmZDqOqzF+ew4S0XoxL7eV3OKYJ86alUVOrvLxqt9+hmC7EkpLpMKtyiti89xCXTR/idyimGUb1T2Ty4N48b+8smQ5kScl0mKeX7qJHXDRzJjdW6YcJJ3OnpbNl3yE27C7xOxTTRVhSMh2iuLyKV9fuZvbkNHpa5asRY/akVOKio3guM6fpgY1pA5aUTId4aVUeR6pquWKG3bqLJEndYzl7wkBeXJVHeaW9s2TanyUl0+5UlaeX7mJiWhIT0pL8Dse00BUzhlBypJpX1lqBB9P+LCmZdrdy10G27DvE5XaVFJFmDE9mdP+ePPXJTr9DMV2AJSXT7p5emkOPuGhmH5fqdyimFUSEK2YMYU1uMeusklbTziwpmXZVXOYKOMyZkmaty0awC6am0y02miftasm0M0tKpl29sCqXiupaLrd3kyJaUrdYZh+XysI1uykur/I7HNOJWVIy7aaugMNx6VbAoTP46glDKa+q4cWVVh+eaT+WlEy7WbKtgK37D/PVE4b6HYppAxPTkzguPYknl+6yGh5Mu7GkZNrNox/voE/3WL5iBRw6jStOGErW/sMs3W5Nn5n2YUnJtIvcg2W8vWkfl04fQkKsNVHRWXxlUiq9EmKswINpNyEnJRGZJSJbRCRLRG6qp/8pIrJSRKpFZF5QvxqvNdr/tUhrOocnP9kFYLfuOplucdHMmzaYNzfsZX+JNWlh2l5ISUlEooH7gXOAccBlIjIuaLBdwDXA0/VMolxVJ3t/s+vpbyLQkaoa5i/fxVnjBpLWu5vf4Zg2dtXMoVTXql0tmXYR6pXSdCBLVbNVtRKYD8wJHEBVd6jqWqA2xHmZCLFw9W6Kyqq46kS7SuqMhvXrwRlj+/Pk0l0cqbL68EzbCjUppQGB1Qfnet2aK0FEMkXkExE5v74BROQ6b5jM/Pz8EEI1HUFVefTjHRwzIJGZI/r6HY5pJ1//wnAKSytZuNrqwzNty++CDkNVNQO4HLhXREYGD6CqD6pqhqpmpKSkdHyEpkVW7DzIxj0lXHXiUETE73BMO5k5si9jBybyyEfbrXi4aVOhJqU8YHDA93SvW7Ooap73Pxt4H5gSYjzGZ//6aAeJCTGcbw35dWoiwte+MIzNew+xJLvA73BMJxJqUloOjBaR4SISB1wKNKsUnYj0EZF473M/4AvAxhDjMT7KKSzj9fV7uHzGEKvnrguYMzmN5B5xPLJ4h9+hmE4kpKSkqtXAjcCbwCbgWVXdICK3i8hsABE5XkRygYuAB0Rkgzf6sUCmiKwB3gP+oKqWlCLYw4u3EyXC104c7ncopgMkxEZzxYwhvLN5HzsLSv0Ox3QSIZ/OquoiYFFQt1sCPi/H3dYLHu9jYGKo8zfhoaiskmczc5g9OZWBSQl+h2M6yFdPGMo//ruNf320g9tmj/c7HNMJ+F3QwXQSTy3dRVllDd88eYTfoZgONKBXAl+elMpzmTkUlVX6HY7pBCwpmZBVVNfw6Mc7OHl0P44d1MvvcEwHu/7UEZRW1vDEEnuZ1oTOkpIJ2cLVu8k/VGFXSV3U2IG9OP2YFB79eAfllfYyrQmNJSUTElXloQ+zGTswkZNH9/M7HOOTb502ioLSSp5bkdP0wMY0wpKSCcm7m/fz6b7DfPPkEfaybBd2/LA+TB3Smwc/yKa6xmoUM61nScm0mqry13ezSO/TjdmTrc2krkxE+NZpo8g9WM5r6/b4HY6JYJaUTKt9lFXA6pwivnXaSGKjbVfq6s4Y25/R/Xvy9/e3WdVDptXsSGJa7a/vbmVAr3jmTfvca2imC4qKEq4/dSSb9x7i/S1WebJpHUtKplWWbS9k6fZCrj9lJPEx1rKsceZMTiW9TzfufWerXS2ZVrGkZFrlb+9l0bdHHJdNH+J3KCaMxEZHcePpo1iTU2RXS6ZVLCmZFluTU8QHn+Zz7ckj6BZnV0nmaHOnpZPepxv3vP2pXS2ZFrOkZFrsnrc/JalbLF89wa6SzOfFRkfx3S+OYm1uMe9t2e93OCbCWFIyLbJ8RyHvb8nnhlNHkpgQ63c4JkxdODWdIcndufdte7ZkWsaSkmk2VeWuN7eQkhjP1ScO9TscE8Zio6O40btaemeTXS2Z5rOkZJrtw60HWLa9kBtPH0X3OGvEzzTugilpDO3bnbvf+pTaWrtaMs1jSck0i6ryp/9sIa13Ny6dPtjvcEwEiI2O4odfGsPGPSUsXLPb73BMhAg5KYnILBHZIiJZInJTPf1PEZGVIlItIvOC+l0tIlu9v6tDjcW0nzc37GNtbjHfP3O0vZdkmu0rk1KZkNaLP/1nCxXVVoO4aVpISUlEooH7gXOAccBlIjIuaLBdwDXA00HjJgO3AjOA6cCtItInlHhM+6iqqeWuNzczIqUHF05J8zscE0GiooSbZh1L7sFya2/JNEuoV0rTgSxVzVbVSmA+MCdwAFXdoaprgeCqg88G3lLVQlU9CLwFzAoxHtMO5i/bxbb8Um6aNZYYq+POtNBJo/tx8uh+/O29LIrLq/wOx4S5UI8waUBgAyq5Xrc2G1dErhORTBHJzM+3N8Q7WnF5FXe/9SkzR/TlS+MG+B2OiVA3nTOW4vIq/vHfbX6HYsJc2J/2quqDqpqhqhkpKSl+h9Pl3P9eFkXlVfzivGOtvSTTauNTkzh/chqPLN5O7sEyv8MxYSzUpJQHBBbFSve6tfe4pgPsLCjl0Y92MG9qOhPSkvwOx0S4n5x9DFEi3PHaJr9DMWEs1KS0HBgtIsNFJA64FFjYzHHfBM4SkT5eAYezvG4mTPzh9c1ERwk/PvsYv0MxnUBq72585/SRvL5+Lx9lHfA7HBOmQkpKqloN3IhLJpuAZ1V1g4jcLiKzAUTkeBHJBS4CHhCRDd64hcBvcIltOXC7182EgQ8+zef19Xv59mkjGdArwe9wTCdx7ckjGJLcnVsXbqDKmk039ZBIqpcqIyNDMzMz/Q6j0ztSVcOsez9ARHjjByfbe0mmTb29cR/XPp7JL887lmtPHuF3OF2CiKxQ1Qy/42iOsC/oYDreA//NZkdBGbfPGW8JybS5M47tz6ljUrjv7a3sP3TE73BMmLGkZI6y40Ap97+fxZcnDeLk0Vba0bQ9EeHWr4yjoqaWXy/c6Hc4JsxYUjL/o6rcsnADcdFR/OrLwRVzGNN2RqT05PtnjOa1dXv4z4a9fodjwoglJfM/z6/I5YNP8/nxWWOscINpd9edMoKxAxP51cvrKTliNT0Yx5KSAWBv8RFuf3Uj04clc9XMYX6HY7qA2Ogo/jhvEvmHKrjz9c1+h2PChCUlg6py84vrqKqp5Y/zJhEVZTU3mI4xKb033zhpOE8t3cWSbQV+h2PCgCUlw4KVeby7eT8/PXssw/r18Dsc08X8vy+NYXi/Hvzo2dVWYauxpNTV5RSW8etXNpAxtA/XnDjM73BMF9Q9LoZ7L5nMvkMV3PLyer/DMT6zpNSFVdfU8v35q0Dhnksm220745vjBvfmB2eM5uXVu3l5tVWB2ZVZUurC7ntnKyt3FfHbCyYwOLm73+GYLu5bp41k6pDe/PKl9VaTeBdmSamLWrKtgL+9l8VF09KZM9lakzX+i4mO4p5LJqMK33l6lTWf3kVZUuqC9h86wg/+vYrhfXtw2+zxfodjzP8M7duDP100iTU5RdbERRdlSamLqayu5dtPrqSkvJr7r5hKj/gYv0My5iizJgzimycP5/ElO+35UhdkSamL+c2rG8nceZA/zpvEsYN6+R2OMfX66ayxHD+sDzctWMfmvSV+h2M6kCWlLuTZ5Tk88clOrj9lBF85LtXvcIxpUGx0FH+7fCqJCTF849FM8g9V+B2S6SCWlLqIj7MO8IuX1nHSqH78xFqSNRFgQK8EHr76eApLK/nm45kcqbKCD11ByElJRGaJyBYRyRKRm+rpHy8i//b6LxWRYV73YSJSLiKrvb9/hBqLqd/mvSVc/8QKhvfrwf1XTCUm2s5FTGSYmJ7EPZdMZk1uET96bg21tZHTKKlpnZCOTiISDdwPnAOMAy4TkeA2D74BHFTVUcA9wJ0B/bap6mTv74ZQYjH121Ncztf+tZzu8dE8+rXpJHWL9TskY1pk1oSB/GzWWF5bu4c7Fm0iklrLNi0X6inzdCBLVbNVtRKYD8wJGmYO8Jj3+XngDBGxqgM6QMHhCq5+ZBmHjlTzr2umk9q7m98hGdMq158ygmtOHMbDi7fzl3ey/A7HtKNQk1IakBPwPdfrVu8wqloNFAN9vX7DRWSViPxXRE6ubwYicp2IZIpIZn5+fojhdh1FZZV89eFl7Cwo48GrpjEu1UramcglItzy5XHMnZrOPW9/yiOLt/sdkmknfr6ksgcYoqoFIjINeElExqvqUeU/VfVB4EGAjIwMu25vhpIjVVz1yDK27T/MQ1dncOLIfn6HZEzIoqKEO+dOpLSimttf3UhstHCltf3V6YR6pZQHDA74nu51q3cYEYkBkoACVa1Q1QIAVV0BbAPGhBhPl1dYWsmV/1zKpj0l/P2rUzl1TIrfIRnTZmKio7jvssmcMbY/v3p5Aw9+sM3vkEwbCzUpLQdGi8hwEYkDLgUWBg2zELja+zwPeFdVVURSvIISiMgIYDSQHWI8Xdqe4nIufmAJm/Ye4u9XTOOMYwf4HZIxbS4+Jpp/XDmN8yYN4neLNnPPW59a4YdOJKTbd6paLSI3Am8C0cAjqrpBRG4HMlV1IfAw8ISIZAGFuMQFcApwu4hUAbXADapaGEo8XVl2/mGufHgZJeVVPP716Zwwom/TIxkToWKjo/jLpVPoFhvNfe9spaC0gtu+Mt5ed+gEJJLOMDIyMjQzM9PvMMLOR1kHXH12xcJladP53Y+T/A7JmA5RW6vc+cZmHvggm2N6pfD8D6eQmGCvPQQTkRWqmuF3HM1hpxUR7vElO7jq4WUcyo8n919foHq/JSTTdURFCT8/91gK3pjI5qIDnPqbJewqsLaYIpklpQhVXlnDz55fyy0vb6BiZwp5j51IdXF3EhL8jsyYjle9ZQj7n53OgbJyTv/Dhyxas8/vkEwrWVKKQFv2HmL23xbz78wcSpePJG9+BpWldsvCdG1HdvZjz79O5khBd779TCY/e3oTVTW1fodlWsga04kgtbXKU0t38tvXNlFzJIbi16ZTtNmKfBtTp7q4O7sfP5G+Z2zk32SzeGsBj95wHKMHJPodmmkmS0oRYvuBUm5asJal2wupzu1H4aLjKD9o9+qM+ZyaaAr+M5HyHf2onbWes/68mO+ePobvnTXcSudFAEtKYa6iuoaHF2/nvre3Ul0ZRcm7Ezm4YjBg1Qca05iyTwdxJDeZlFnr+UvUZhYs3829V47n+OHJfodmGmFJKUypKm+s38vvX9/MrsIyKrcPoOjtCZQX2tWRMc1VWxbPvhem0v2YvdScsZGLHljCmaPSuOPisQzoZb+lcGRJKQwtzS7gz299yrLthUSV9KT4PXt2ZEzrCWVbBlGenULvmdt4qyab9+7YwxUzhvH9s0bQt2e83wGaAJaUwoSqsiS7gPve3srS7YVEV8VxaPEECpcPBrX74MaESqtiOPjBMRxaM5jkUz/lMbJ5ZtlOrjlxGNedPpyUREtO4cCSks+OVNXw2to9PLZkB2tzi4mujOfQknEUrxxCTWW03+EZ0+lUF3dn/8LJxHw0kr6nbuUhtvHwx9v58oRUvn3mcMYOtGZe/GRJySdZ+w+xYGUe85flcLCsEjnUk8MrJlC4Ih2ttmRkTHurLkhk3wtTiUk+TJ8Z23m5KpeF63MZ3z+Za05J59yJg+gRb4fIjmZrvAPtKznCK2t289KqPNbvLgGFiuwBHFk3jKItfbESdcZ0vOrCnuS/PpGo946h15Qc1kzM4Sf713Lzgg2cPX4gczNSmTmyLwmxdrLYESwptaPaWmXD7hLe2byPdzftZ21eMQBakETphnEc2jCIyhIrAWRMOKg9EkfRkpEULRlBfNpBek/JZeGRPby6Po+4qGhOP6Y/5x43gJNHp5DcI87vcDstS0ptqLZW2bLvEMu2F7JsRyGfbCukoLQCFGrze1O+bQylWwZRvq+n36EaYxokVOQlsy8vGaLHkzCkgKTx+1h0aB9vbtoDwIg+vThtXF9OHNmX44clk9TdqvlqK5aUWqm2VtleUMqG3SVs2F3Mxt0lrN5VxKGKagCkPIGyHX2p3Z1C0aYUqkutZI8xEacmmiPb+3Nke39gAnGpRSSOLGDz4ANsy9/JIx9tB2Bgz+5kDO/N5CFJTErvzTEDEi1RtZIlpUaoKsXlVewoKGPHgVKyD5Sy40Ap2w+UkrXvMOXVNQBIrVBTlEh53iDYn0zJtmQqD3bDnhEZ05kIlbv7ULC7DzAKomuITztIz6FF7BpYRN6eQl5dt/t/Q/dOiGfMgJ6MTe3JqP49GZzcnfTe3Ujr043ucXbobUjIa0ZEZgH34Vqe/aeq/iGofzzwODANKAAuUdUdXr+fA98AaoDvqeqbocbTFFXlcEU1RWVVFJVVcbCskoNllRSWVrK35Aj7io+wp/gIe72/isBahhUo60bFgR7UHBwMB3txOLcX5XsTodbeJTKmS6mJpmJXPyp29ftfp6geR4gfUEyP1MNUJB9mf9JhlvbLQ+Kqjxq1V1wcqX26MbRfAimJ8fTtEU+/xHj69YijX2I8fXvE0bdHPD0TYoiO6lontyElJRGJBu4HvgTkAstFZKGqbgwY7BvAQVUdJSKXAncCl4jIOFzT6OOBVOBtERmjqjWhxFSfrP2H+daTKyg4VEVJRSXVtQ20tlsbBWXxVJUkUFWchBwZAOUJVB3szuE9Pagq6g41VgLHGFO/2tIEyrMTKM8eENBVie5ZQUxSGfHJ5SQkl1ORWE5+j3LW9SwlpmchklBV7/QE6BYbQ2J8LKeN6c+dF0/okOXwU6hXStOBLFXNBhCR+cAcIDApzQFu8z4/D/xNRMTrPl9VK4DtIpLlTW9JiDF9TmJCDDFlPdm5JBapioOKWPRIHDVHYqk6HEfV4VhqyuOoLYsj0m+5RVDr9sa0mar6j+lhQqg5nEDN4QQq8qCk3kFqie5eSVT3SqJ7VBDfq4KYnpVIfDWlcVXkx1ax+ZVu3HlxR8fe8UJNSmlATsD3XGBGQ8OoarWIFAN9ve6fBI2bFjwDEbkOuA5gyJAhrQpyQK8Erp8wjTd+BT2DCr5FAfF1HyK8UNzhwzB0qN9RGNPx0tJg/36IidhHNVFAApQnQDnUHHDPNOpUV0O/fg2N27mE/SZU1QeBBwEyMjJafR1w/vl2FWFMZ7Vzp98RmLYS6tP5PGBwwPd0r1u9w4hIDJCEK/DQnHGNMcZ0IaEmpeXAaBEZLiJxuIILC4OGWQhc7X2eB7yrqup1v1RE4kVkODAaWBZiPMYYYyJYSLfvvGdENwJv4oqEP6KqG0TkdiBTVRcCDwNPeAUZCnGJC2+4Z3GFIqqB77RHyTtjjDGRQzSCHrRkZGRoZmam32EYY0xEEZEVqprhdxzNYW98GmOMCRuWlIwxxoQNS0rGGGPChiUlY4wxYSOiCjqISD4Qbq/J9QMO+B1EC0RSvJEUK0RWvJEUK0RWvOEY61BVTfE7iOaIqKQUjkQkM1JKtUBkxRtJsUJkxRtJsUJkxRtJsYYju31njDEmbFhSMsYYEzYsKYXuQb8DaKFIijeSYoXIijeSYoXIijeSYg079kzJGGNM2LArJWOMMWHDkpIxxpiwYUmpDYjIb0RkrYisFpH/iEiq3zE1RkTuEpHNXswvikhvv2NqiIhcJCIbRKRWRMKymK2IzBKRLSKSJSI3+R1PY0TkERHZLyLr/Y6lKSIyWETeE5GN3j7wfb9jaoyIJIjIMhFZ48X7a79jikT2TKkNiEgvVS3xPn8PGKeqN/gcVoNE5Cxcu1bVInIngKr+zOew6iUixwK1wAPAj1U1rKqJF5Fo4FPgS0Auro2xy1R1o6+BNUBETgEOA4+r6gS/42mMiAwCBqnqShFJBFYA54fxuhWgh6oeFpFYYDHwfVX9xOfQIopdKbWBuoTk6QGEdaZX1f+oarX39RNcq79hSVU3qeoWv+NoxHQgS1WzVbUSmA/M8TmmBqnqB7h2zcKequ5R1ZXe50PAJiDN36gaps5h72us9xfWx4JwZEmpjYjIHSKSA1wB3OJ3PC3wdeB1v4OIYGlATsD3XML4wBmpRGQYMAVY6nMojRKRaBFZDewH3lLVsI43HFlSaiYReVtE1tfzNwdAVX+hqoOBp4Ab/Y226Xi9YX6Ba/X3Kf8ibV6spusSkZ7AAuAHQXclwo6q1qjqZNzdh+kiEta3SMNRSM2hdyWqemYzB30KWATc2o7hNKmpeEXkGuDLwBnq84PFFqzbcJQHDA74nu51M23AezazAHhKVV/wO57mUtUiEXkPmAWEfaGScGJXSm1AREYHfJ0DbPYrluYQkVnAT4HZqlrmdzwRbjkwWkSGi0gccCmw0OeYOgWv4MDDwCZVvdvveJoiIil1JVlFpBuu8EtYHwvCkZW+awMisgA4BldKbCdwg6qG7dmyiGQB8UCB1+mTcC0tKCIXAH8FUoAiYLWqnu1rUEFE5FzgXiAaeERV7/A3ooaJyDPAabjmFfYBt6rqw74G1QAROQn4EFiH+20B3Kyqi/yLqmEiMgl4DLcfRAHPqurt/kYVeSwpGWOMCRt2+84YY0zYsKRkjDEmbFhSMsYYEzYsKRljjAkblpSMMcaEDUtKxhhjwoYlJWOMMWHj/wNpIbWQiM4CSQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_lin_example2 = np.arange(-3.5,3.5,0.001)\n",
"draw_t_score(x_lin_example2, x_lin_example2>t_value_example2, 0, \n",
" 50/np.sqrt(30), \n",
" 30-1, f\"Example 2 (One-sample 2-tailed t-test): \\n p-value on t-student distribution where t > {round(t_value_example2, 4)} and t < {round(-t_value_example2, 4)}\", x_lin_example2<-t_value_example2)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.018322341974252625"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using t-student distribtion\n",
"t.sf(t_value_example2, df=30-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\alpha}{2} = \\frac{0.05}{2} = 0.025 \\implies \\text{p_value} < 0.025 \\implies 0.018 < 0.025 \\implies \\text{We can reject H0}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can reject H0, so we can conclude with 5% of significance that $\\mu \\neq 48$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Two sample t-test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As said before, we want to check if given 2 samples, the population mean of them is equal or different. Here we're going to check that the following t-statistic is correct by plotting histograms, for the general case: \"Equal or unequal sample sizes, similar variances\". After that we'll do an example exercise for each case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to do this check we'll tweak \"plot_histograms_sample_parameter\" function, generating m samples of the difference between their sample means (with different n size). Better than describing it with words, it's easy to understand reading code:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"def plot_histograms_sample_mean_diff(m, n_min, n_max, \n",
" n_step, df, variable):\n",
" \"\"\"\n",
" Plot \"(n_max-n_min)/n_step\" histograms for the \n",
" difference between the means of 2 random samples of\n",
" different sizes. On top of histogram will also \n",
" plot the corresponding distribution curve.\n",
" \n",
" Parameters\n",
" ----------\n",
" m: int\n",
" Number of samples to take.\n",
" m_min: int\n",
" Minimun sample 1 size\n",
" n_max: int\n",
" Maximum sample 1 size\n",
" n_step: int\n",
" Difference between sample sizes\n",
" df: pandas dataframe\n",
" Dataframe where each column is a dataset\n",
" variable: str\n",
" Pandas dataframe column (in our example can be one \n",
" of following: 'normal', 'uniform', 'binomial')\n",
" \"\"\"\n",
" \n",
" diff_mean_dict = {}\n",
" std_sample1_dict = {}\n",
" std_sample2_dict = {}\n",
" \n",
" params_dict = {\n",
" \"mean\": df[variable].mean(),\n",
" \"std\": df[variable].std(),\n",
" \"median\": df[variable].median(),\n",
" }\n",
"\n",
" for n in range(n_min, n_max, n_step):\n",
" for x in range(m):\n",
" #We are going to generate the difference between the \n",
" #mean of 2 samples. First sample with size = n, \n",
" #second sample with size round(n/4)\n",
" \n",
" n1 = n\n",
" n2 = round(n/4)\n",
" df_sample_1 = df.sample(n=n1)[variable]\n",
" df_sample_2 = df.sample(n=n2)[variable]\n",
" if (n in diff_mean_dict.keys()):\n",
" diff_mean_dict[n].append(df_sample_1.mean()-df_sample_2.mean())\n",
" std_sample1_dict[n].append(df_sample_1.std())\n",
" std_sample2_dict[n].append(df_sample_2.std())\n",
" else:\n",
" diff_mean_dict[n] = [df_sample_1.mean() - df_sample_2.mean()]\n",
" std_sample1_dict[n] = [df_sample_1.std()]\n",
" std_sample2_dict[n] = [df_sample_2.std()]\n",
"\n",
" df_diff_means = pd.DataFrame.from_dict(diff_mean_dict)\n",
" df_sample1_std = pd.DataFrame.from_dict(std_sample1_dict)\n",
" df_sample2_std = pd.DataFrame.from_dict(std_sample2_dict)\n",
"\n",
" fig, ax = plt.subplots(1, \n",
" int(np.ceil((n_max-n_min)/n_step)), \n",
" sharex='col', \n",
" sharey='row', \n",
" figsize=(20, 3))\n",
" \n",
" fig.suptitle(f\"\"\"Histograms for {m} (difference between 2 samples) mean:\n",
" sample_1 size between {n_min} and {n_max}\n",
" sample_2 size between {round(n_min/4)} and {round(n_max/4)}\n",
" for variable with {variable} distribution\"\"\", \n",
" fontsize=16, \n",
" y = 1.45)\n",
"\n",
" for i in range(len(df_diff_means.columns)):\n",
" df_diff_means.hist(column=df_diff_means.columns[i], \n",
" ax=ax[i], \n",
" alpha=0.5, \n",
" color='red', \n",
" bins = 30)\n",
" ax[i].set_xlim((-1.5*params_dict['std'],\n",
" 1.5*params_dict['std']))\n",
" ax[i].tick_params(axis='both', which='major', labelsize=12)\n",
" ax[i].set_title(f\"n1 = {df_diff_means.columns[i]} \\n n2 = {round(df_diff_means.columns[i]/4)}\",\n",
" fontsize=16)\n",
" \n",
" #Here we compute the standard error for this kind of test\n",
" n1 = df_sample1_std.columns[i]\n",
" n2 = round(n1/4)\n",
" s1 = df_sample1_std[df_sample1_std.columns[i]].mean() \n",
" s2 = df_sample2_std[df_sample2_std.columns[i]].mean()\n",
" sp = np.sqrt(((n1-1)*s1**2 + (n2-1)*s2**2)/(n1+n2-2))\n",
" std_error = sp*np.sqrt((1/n1) + (1/n2))\n",
" \n",
" #Here we plot the distribution curve\n",
" x = np.linspace(-6*params_dict['std'], \n",
" 6*params_dict['std'], \n",
" 1000)\n",
" y_normcurve_2 = norm.pdf(x, 0, std_error)\n",
" ax[i].plot(x, y_normcurve_2, 'b--', linewidth=2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x216 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_histograms_sample_mean_diff(300, 10, 100, 20, df, 'normal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, the blue normal curves fit well in the histograms, it means that our t-statistic is correct."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example 3**:<br>\n",
"**Two-sample independent t-test with different sample sizes**<br>\n",
"We can measure person's fitness by measuring body fat percentage. The normal range for men is 15-20%, and the normal range for women is 20-25% body fat. We have 2 sample data from a group of men and women. The following dictionary shows the data."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"example3_data = {\n",
" \"men\": [13.3, 6.0, 20.0, 8.0, 14.0, 19.0, \n",
" 18.0, 25.0, 16.0, 24.0, 15.0, 1.0, 15.0],\n",
" \"women\": [22.0, 16.0, 21.7, 21.0, 30.0,\n",
" 26.0, 12.0, 23.2, 28.0, 23.0]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using t-test we want to know if there is difference significance between the population mean of men and women group. These will be our hypothesis.\n",
"* H0: $\\mu_{\\text{men}}=\\mu_{\\text{women}}$<br>\n",
"* H1: $\\mu_{\\text{men}} \\neq \\mu_{\\text{women}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we have to compute ${\\overline{x}_{\\text{men}}}$, ${s_{\\text{men}}}$, ${n_{\\text{men}}}$ and ${\\overline{x}_{\\text{women}}}$, ${s_{\\text{women}}}$, ${n_{\\text{women}}}$"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"x_men = np.mean(example3_data[\"men\"])\n",
"s_men = np.std(example3_data[\"men\"], ddof=1)\n",
"n_men = len(example3_data[\"men\"])\n",
"x_women = np.mean(example3_data[\"women\"])\n",
"s_women = np.std(example3_data[\"women\"], ddof=1)\n",
"n_women = len(example3_data[\"women\"])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"x_men\": 14.946153846153846,\n",
" \"s_men\": 6.842589103623397,\n",
" \"n_men\": 13,\n",
" \"x_women\": 22.29,\n",
" \"s_women\": 5.319659554687478,\n",
" \"n_women\": 10\n",
"}\n"
]
}
],
"source": [
"params_example3 = {\n",
" \"x_men\": x_men,\n",
" \"s_men\": s_men,\n",
" \"n_men\": n_men,\n",
" \"x_women\": x_women,\n",
" \"s_women\": s_women,\n",
" \"n_women\": n_women\n",
"}\n",
"print(json.dumps(params_example3, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we compute the standard error with formula seen before:\n",
" $$\n",
"t = \\frac{\\overline{x}_1-\\overline{x}_2}{s_p \\ \\sqrt{\\frac{1}{n_1}+\\frac{1}{n_2}}}\n",
"$$\n",
"where:\n",
"$$\n",
"s_p = \\sqrt{\\frac{(n_1-1) \\ s^2_{x_1}+ (n_2-1) \\ s^2_{x_2}}{n_1 + n_2 -2}}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.235617003466411\n"
]
}
],
"source": [
"sp_value_example3 = np.sqrt(((n_men-1)*s_men**2+(n_women-1)*s_women**2)/(n_men+n_women-2))\n",
"print(sp_value_example3)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.622839252120241\n"
]
}
],
"source": [
"se_example3 = (sp_value_example3*np.sqrt(n_men**(-1) + n_women**(-1)))\n",
"print(se_example3)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.7999604428329192\n"
]
}
],
"source": [
"t_value_example3 = abs((x_men-x_women)/se_example3)\n",
"print(t_value_example3)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21\n"
]
}
],
"source": [
"dof_example3 = n_men + n_women - 2\n",
"print(dof_example3)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEXCAYAAABGeIg9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABBx0lEQVR4nO3deXwU9f348dc7dwgkEAhHEkJAQLmvgCfWAyseFRU8qFb71Xr8KrXXt61trW212la/tfaw9a71QETxQKtSL0RFhHCJXALhSrhCAiEJ5Nz374+Z6JJskk3YZHaT9/PxyCO7M5+Zec/s7rxn5jPz+YiqYowxxviL8joAY4wx4ceSgzHGmAYsORhjjGnAkoMxxpgGLDkYY4xpwJKDMcaYBiw5mDYhIt8WkY/aaVnxIrJORPq1x/LCjYicISL5XsfRGiIyWkQWex2HaciSQwQSkW0ickREyvz+/u51XKEiIveKyE4ROSQi20XkF81MciOwSFV3i8ibftukWkSq/N4/1B7xRxIReVJEftdMmd+IyDMhWp6KyOC696r6GXBQRL4Rivmb0InxOgDTat9Q1Xe8DqKNPA78VlXLRSQD+K+IbFDVlxopfzNOgkBVz6sbKCJPAvmqentbB2yOybPATcBrXgdivmJnDh2MiPxTROb5vf+jiLwrjh4i8rqIFIrIAfd1pl/ZhSLyOxFZ7B5pvyYiPUXkWfcofpmIZPuVVxG5VUTyRGS/iNwnIgG/UyJygoi8LSLFIrJRRC5vbB1UdaOqlvsN8gGDA5UVkSxgEPBpM9vlAxGZ7r4+1Y39Avf92SKyyn0dJSK3u2cs+0TkKRFJaWK+k0Qk190+e0Xkfr9xL4jIHhEpEZFFIjLCb9yTIvIPvzOdj0Wkr4g84H42G0RknF/5bSLyc/fy2QER+ZeIJDQSU7qIzHM/560icmsj5W4ErgJ+Wvd5BygzFfgFcIVbZrU7PEVEHheR3SJS4H5vot1xg93tXeJ+L553hy9yZ7vandcV7vuFwNkiEt/Ydjbtz5JDx/NjYJQ41/wnA9cD16rTTkoU8C9gAJAFHAHqX466EvgWkAEcB3ziTpMKrAd+Xa/8JUAOMB6YBlxXPyARSQLeBmYDvd1l/ENEhje2EiJym4iUAflAkjttIKOAPFWtaWxerg+AM9zXXwPygNP93n/gvv62+3cmTtLpSsNt5O8vwF9UNRlne831G/cmMARnnVfgHCH7uxy4HegFVOJs6xXu+xeB++uVvwo4113OUHfao7jJ+TVgNc5neDbwAxE5t35ZVX3EjeleVe2qqg0u7ajqW8A9wPNumTHuqCeBGpykPQ74OvAdd9xdwH+BHkAm8Dd3XnXbe4w7r+fd4QVANXC8uw7fFJHP6sdi2pclh8j1iogc9Pu7AUBVD+Ps3O8HngG+p6r57rgiVZ2nqodVtRS4G2fH6O9fqrpFVUtwdm5bVPUdd+f7As6OwN8fVbVYVXcADwAzA8R6IbBNVf+lqjWquhKYB1zW2Mqp6h+AbjhJ52mgpJGi3YHSxubj5wO+WtfTgd/7vfdPDlcB96tqnqqWAT8HrhSRxi7BVgODRaSXqpap6hK/dXhCVUtVtRL4DTCm3lnIy6q6XFUrgJeBClV9SlVrgedpuK3/rqo7VbUY57MLtK0nAmmqeqeqVqlqHvAoTkIOCRHpA5wP/EBVy1V1H/Bnv2VU4xyApKtqhaoGc2NCKc5niarOVtXRoYrXtI4lh8h1sap29/t7tG6Eqn6Kc2Qs+B3JikgXEXnYvWRyCFgEdK+7HODa6/f6SID3XevFsdPv9XYgPUCsA4AT/ZMZzk64b1MrqI6V7nJ/20ixAzhJpDmfAEPdHdtY4Cmgv4j0AibhbAvc+LfXW6cYoI+IXCVfVW6/6Y6/HucofoN72e1CABGJFpE/iMgWd1tvc8v38pt3W23r9Hrb+hdAnwBlG2hkHQMtIxbY7beMh3HOkAB+ivPdWyoia0WkwdlkAN2Ag8HEaNqHVUh3QCJyCxAP7ML5of7eHfVjnFP3E1V1j4iMBVbi/JBbqz+w1n2d5S6zvp3AB6p6TiuXEYNzKSWQz4CBIhLT1KUlVT0sIsuB7wOfq2qVOLdQ/gjn7Gi/W3QXzs6vThbO5ZO9qvos9S4NqeomYKZ7OedS4EUR6em+ngZMwUkMKTiJ7Fi3tX9cjW3rrao6JMh5HtUsc6B1rF/GXUYl0CvQNlfVPcANACJyGvCOiCxS1c2BAhDnpoM4YGOQMZt2YGcOHYyIDAV+B1yNc3npp24SAOfo7AjOrYOpNKw/aI2fiFPR3R9nx/t8gDKv4xy1f0tEYt2/iSIyLED8USJykztPEZFJwC3Au4EW7l4y24xz9N+cD4BZfHUJaWG99wDPAT8UkYEi0pWvrrcHTDwicrWIpKmqj6+OfH0427oSKAK6uPM5VreISKb72f2SwNt6KVAqIj8TkUT3DGakiExsZJ57cepWmrIXyHYTIKq6G6dO4U8ikux+ZseJyNcAROQy+epGhwM4ycXXxPK+BrznXn4zYcKSQ+R6TY5+zuFl97r4Mzj1AKvdo9pfAE+7d4I8ACQC+4ElwFshiONVYDmwCvgPzm2oR3HrN76Oc016F7AH+CPO2U0glwBbcK5DP4NTofm3JmJ4GCcRNucDnJ32okbeAzyBU8exCNgKVADfa2KeU4G1buX5X4ArVfUIzmWr7UABsA5nex+r2Tg75Tyc7dPg+QS3vuJCnEtnW3E+68dwzlwCeRwY7l4eeqWRMi+4/4tEZIX7+hqco/11OAngRaDuIcSJwKfuNpkPfN+t+wCn7uXf7vLq7li7CvjyGRT30lbd2ajxiFhnP6a1RESBIY1dLmjHOOJxLo+d7R7Vdjgisg34Tkd7tkVERgMPq+rJXsdijmZ1DibiuZcjGr0t1oQv9wlpSwxhyC4rGWOMacAuKxljjGnAzhyMMcY00GGTgzjtBH2n+ZKdlwTRImcbLvuoJr3dO66au6Uy2Hn/QkQec19ni9OOUkjq10Qky401uvnSoRfq9TEtIxHcPHpLddjkEAmOJYGF05c0FLG4be3kNVUm2OWo6j2qGpIDA3EavJviN+8dbqy1oZh/JGnLxCQiPxGRz0WkVJzGAn/STPnLRWS9W36diFwc6phaQ+o1Sd6GyxkqIq+K07hisYgsEJHjmyifKiLPi0iROI0hPisiyU0tw5KD6VDsiDp4bbSTTxWR2NZMivPsRA+cZ0dmiUjA9qDcJ6qfwXm6PRn4CTBbRHoHKh8p3IcWg2kKBpx2qObjtHjQB+fhx1ebKP87nG07EKe1gT44z5w0TlXb9Q/naclbcR7k2Q/cB0QFKBeP88TpSL9haThP+PZ2V/R1oBDnIZzXgUy/sgtx7gvH3QjP+I3LduOIcd+n4DwMtBvnoaXfAdGNxF/3MNku9+8BIN4ddwZOK6I/Bva58/ufRuZzN1CL85BVGU6jaoHKnY/zoFGpG9v/4rRSegTnqdMy9y8dp6XM3/lNewZOfwZ178fhtPpZivN07Zx65S/EeZjtILAYGO03bpu77M9wGsF7HkhoLJYA69ET58t8COeLfBfwUb3vxeBWrPNvcB7Aesad93f8P2+/z/pG9/PaDfyv33Ib3WY4D8P53OWW4TRFUje/uu9OurtexThPat/gN6/f4LRt9ZS7LmuBnEY+598Cf3NfxwLlwH3u+0Sc70mq3/KvBXbg/IZ+6TefKOA2nIfkitzlp9bbFte70y5yh1+H0+LuAWABMKCRGHe409dt/5MDlLkC5ynoP+H3223FfuKvddsjwLgTgX31hhUGiscddwHOczCHcJr++E2AfUFj2zPR/Y4cwPlO/gS/31S95Sxy51Xubp8rglzXk3Ae5DzQ2DoEMY9Ud9k9Gxn/JvBdv/e3AAuanGdrP7xj+NAVeN9dmSzgC9ydeICyTwB311uht9zXPYHpOE0TdMN5ivMVv7ILCT45vOx+OEk4iWcpcFMjMd2J87Rrb5xktRi4yx13Bk47PHfi/MDPBw4DPRqZ15cxNrG9dgOT3dc9gPF+y8qvV/ZJGt/RxeE8sftDN7YZOK1n/s4dPw4noZ0IRLs/lm18lfi2udslna+a7765sVgCrMccnB1VEjASZ6ffWHJoyTr/xl2Pi3F2jIkETg7PucsehbMjmdLcNvNb7ylNfHcWAf/ASZRj3Xmf5Rdbhfs9iMZp42pJI9vnLGCN+/oUnJ37p37jVtdb/qPuuo7BaaZjmDv++zjfz0ycA5mHgefqTfuUuy0Scdp/2gwMw3nu6XZgcSMxHrXuTXzWI3EO+nYBy4Dv0shvoJHpBWdnfnMj46Nxnm6/yH19MW7T7o2UP8P93KOA0TjJ6+Igt+cfgA9xvvP9gc9p4ruO3/e4mXXsh3Owsd79rH8NDAx2GwWY38XA7ibGXwi8gfN76gG8h9OqbuPzbG0wx7ASCkz1e/9d4N1Gyk7BaRSt7v3HwDWNlB0LHPB7v5AgkgPO6VUlkOg3fibwfiPL2QKc7/f+XJzmqOu+hEf8fzw4O9yTGpnXlzE2sb124PSSlRzgC9+S5HA6zo9V/MYv5qvk8E/cJOc3fiPwNff1NuBqv3H3Ag81Fku9+UTj7MBP8Bt2D40nh5as829wj4DrDaufHE6oF/vjzW0zv/UOmBxwdha1QDe/8b8HnvSL4x2/ccOBI41so7qzg544R/6/wNnhdcU5q/hrveX7nyUvxWm2A5ydzdl+4/q52z7Gb9pBfuPfBK73ex+Fc0AzIECMX657c79zv8/9ApyDgoM4BwjJQUz3W5z+KOKbKHM9ztF5jRvvBcHE5E77APDnILdnHkfvr26s/x2sN+8mkwPOAfHrOGcJT+D8LiXY2BuZZybOwdbMJsqkA+/gnAn7cPpXiWtqvl7VOQRselic5n3r2gqajHOG0UVEThSnB7KxOEf5wTY/HYwBNN38cH2BmnT2bzq5SI9upO0wDZteDsi9y6Z+f8fTcY48t4vTu1ZrnyZNBwrU/ab4xV5nAPBjObqp5/4cvW57/F4HvV44Z1gxNPzcG9PSdd7ZzPj6ZRpr7rql0oFiddqO8p93ht/7+tssIdC1fnXaY8rFaYTudJwj48XAqRzd30Rj8637LAYAL/t9hutxEph/k93+22IA8Be/8sU4R+7+69Aq6lTar8HZ0RfjnFE0WR8hIrNw6h4u0EYa4nNvELgXJ5HH4Wyfx+SrBibrlz9RRN53K29LcLqV7VWvWGPbM53gv7fBSAJG4CT+1cD6er/JgOTodtSy/Ian4bS39Q9Vfa6JWczFuUrTDaeeZgvOpdhGeZUcAjY9rKoj1LkTpKuqfuh+uebiHMnPBF73+yH6Nz+dzFe9egVqErkc5/JTHf9+BPybH+7u/iWr6ggCC9Skc6Cmk4Nx1JdCnbts6tb/ZnfYMlWdhpOsXuGr/hkCfaGaWs/dQIaI+G+fLL/XO3Eu4XX3++vSzBcu4HoEUIhzhFf/cw88s5atczDLJ8Cy6z6zprZZc/PeBaTWq0TMwjmKa40PcC4hjcO5HPMBzpmpf38TzdkJnFfvc0xQp7e1Olqv/E31yieq6uIA8w5mOyMiXd1bld/DqePKwLn+PlJVi5qY7jqcs6az1e2gqhFjcc4Wc1XVp6rLcLqJndJI+dk49UL9VTUFp5G/YJtO302Q39tgqOp6nFZpbwUmAJtEZL6IzJAmukn12y90VadjLUSkB05imK+qdzez6LE4bViVq9OJ1UM4B2CN8io5BNPMc53ZOJVcV3F0V5EtaX56FXC6e496Ck7vXkDzzQ8H8Bxwu4ikidNRzB00k4Gb0GRzySISJ04LlSmqWo1Toebf9HFPObpnsVXA+e4dI32BH/iN+wRnB32rOE1mX8rRzVw/CtzsHmWJiCSJyAVB3j0RKJYvuUn+JeA37hnfcJw6jVCsc7B+5S57BPA/fPWdW0Xj26xumQE/I1XdiXN0/3sRSRCnEbnraf334QOco+Z1qlqFe9kRp3+GwiDn8RBwt4gMAOfIUkSmNVP+5+52qesburEe+gpxPoumvrNTcZLmFThn4Bmq+l13B94oEbkK51LjOdrMLc04iXNy3ZmCOH1tT8a5WSKQbjhneBXiNAH/zWbm728uzvbpIU4z5E210AtBNIGujvdV9RqcxPMqzvdut/sdapZ7G+oC4GNVvS2ISZYB33HviErEuTzWdFesx3Ktq5XXx5Sv7lYqwrmrIeCdQX7TbMY5LY3zG5aO8+MpwzlduomjKwoX4nc9H3gQ57rnZpyOSPzLpuBcc8/HuRNnJe41xwCxJODcSbHb/fsrkOCOO4OG18S34XfNut64k93YD+BeU643Pg6nWe0DODvJZcBpfuOfcLfhQXd7JODs9A65H/wPOfr6eY67bnV3Kz3P0dfbp7rLOOiu2wu419PrrwcN63GOiiXAuqThXGtt8m6lVqzzUXHUj42GdyvtAX5a7/NsaptNw6kDOYhz11Td/Oq+O5nuehXjnKrf3MQ2OmraANuoK079wK/d94JTZ/XPpubB0fVrUTi3eG50P+ctwD1NLR+nufM1fHU3zxNN/BbvxEkSBwlQl4Zzq2SDzz+I/cJWd93L/P4e8hu/FrjK7/0snN9yKc6+5MdNzHsGzuWgUvez+nuA70dj27MLTgX+QZq5W8ktfzPOb+cgcHkLt8FgoG+QZa/l6Duj6v6y3PFXAWvrfS6v4fx2inF+Y0OaWka7t60kYdLMszHGmMbZQ3DGGGMasORgjDGmAWuy2xhjTAN25mCMMaaBsGukrFevXpqdne11GMYYE1GWL1++X1XTQjW/sEsO2dnZ5Obmeh2GMcZEFBE51qe3j2KXlYwxxjRgycEYY0wDQSUHEZkqIhtFZLOINPqotohMF6cnpBy/YT93p9soIueGImhjjDFtq9k6B7eV0weBc3Cal1gmIvNVdV29ct1w2kn61G/YcOBKnFYI04F3RGSodsIuFo0xJpIEc+YwCdisqnnqNAY2B6e9mfruAv6I0yZ9nWnAHFWtVNWtOG2hTAowrTHGmDASTHLI4Oj2zPOp19a7iIzHaQ73Py2d1p3+RhHJFZHcwsJgG580xhjTVo65QlpEooD7cfpXaBVVfURVc1Q1Jy0tZLfpGmOMaaVgnnMo4OjOLuq6pKvTDaeHp4VuPzJ9gfkiclEQ0xoT1mp9ytpdJazddYji8ipEIKN7IuP69yCrZ5fmZ2BMhAomOSwDhojIQJwd+5X4dZahqiX4dbknIguB/1XVXBE5AswWkftxKqSH4LTlb0xYO1BexWMf5fFCbj77SgP2Vsnxfbpx7SnZTJ+QQXxMS3unNSa8NZscVLXG7dd1AU6H4U+o6loRuRPIVdX5TUy7VkTm4nSSUQPcYncqmXDm8ylPL9nOfQs2Ul5Vw9kn9OYbY9IZ178HfVLi8flgR/FhFm/Zz7wV+fzi5TU8+mEed00byWlD6ndLbEzkCrtWWXNyctSazzBeOFBexfeeW8lHm/dz+tA0br9gGEP7NN5Lqqqy8ItC7nxtHVv3l/PdM47jR+cMJSbani017U9ElqtqTvMlgxN2bSsZ44Vt+8v5nyeXUXDwCPdcMoqZk/rj1qE1SkQ48/jenDyoJ799bS3/WLiFDXtK+cdV40mItctMJrLZIY7p9LbuL+eyhz/h4OEqZn/nRL55YlazicFfQmw0v790NHddPJL3N+7j2ieWUl5Z04YRG9P2LDmYTi3/wGGuenQJtT5l7k0nk5Od2up5feukATxwxViWbSvmu8+uoLrWF8JIjWlflhxMp1VWWcN1Ty6jrLKGp6+fxJAm6heCNW1sBndfMooPvijktnlrCLc6PWOCZXUOplPy+ZQfPr+KLYXlPHXdJEakp4Rs3jMnZbH3UAUPvLOJEenJXHfawJDN25j2YmcOplP6x8LNvL1uL7dfMIxTB4f+FtTvnz2Ec4b34Z431pO7rTjk8zemrVlyMJ3Oyh0H+PM7m7hoTDrfPiW7TZYhIvzfZWPI6JHILbNXUHK4uk2WY0xbseRgOpXyyhp++Pwq+iYncNfFI1t0V1JLpSTG8veZ49lfVsVvXlvbZssxpi1YcjCdyj1vrGd78WHuv3wMKYmxbb68UZkpzDpzMC+vLOCtz3e3+fKMCRVLDqbTWL79AM9+uoPrTh3IiYN6tttyZ501mBHpyfzy5c/t8pKJGJYcTKdQXevjly+voV9KAj86Z2i7Ljs2Oop7Z4zmwOEq/vT2xnZdtjGtZcnBdApPfLSVDXtK+c1FI0iKb/87uEekp3DNydk8s2Q7nxeUtPvyjWkpSw6mw9tXWsFf3t3ElGG9OXdEX8/i+OE5Q0lNiuNXr36Oz2cPx5nwZsnBdHh/fnsTVTU+br9guKdxpCTG8vPzhrFyx0Hmr97laSzGNMeSg+nQvthbyvPLdvCtkweQ3SvJ63C4ZFwGI9KT+b//bqSyxro2MeHLkoPp0P7w5gaS4mO49awhXocCQFSUcNt5J5B/4AjPLNnhdTjGNCqo5CAiU0Vko4hsFpHbAoy/WUTWiMgqEflIRIa7w7NF5Ig7fJWIPBTqFTCmMYu37Oe9DfuYdeZgeiTFeR3OlyYPSWPykF78/b1NHKqwW1tNeGo2OYhINPAgcB4wHJhZt/P3M1tVR6nqWOBe4H6/cVtUdaz7d3OI4jamSarKA29vom9yAte2URMZx+JnU0/gwOFqHvkgz+tQjAkomDOHScBmVc1T1SpgDjDNv4CqHvJ7mwTYrRjGU59sKWLptmK+e+ZxYdkr28iMFC4Y1Y8nF2/j4OEqr8MxpoFgkkMGsNPvfb477CgicouIbME5c7jVb9RAEVkpIh+IyORACxCRG0UkV0RyCwsLWxC+MQ2pKg+8s4k+yfFcntPf63AaNeuswZRV1vDEx9u8DsWYBkJWIa2qD6rqccDPgNvdwbuBLFUdB/wImC0iyQGmfURVc1Q1Jy0tLVQhmU7qy7OGMwaH5VlDnWH9kjl3RB/+9fFWSo5Y3YMJL8EkhwLA//Ar0x3WmDnAxQCqWqmqRe7r5cAWoH3bLjCdiv9ZwxUTw/esoc73zhpCaUUN/168zetQjDlKMMlhGTBERAaKSBxwJTDfv4CI+N8neAGwyR2e5lZoIyKDgCGA1cCZNrNs2wGWbivm/30tPOsa6huZkcKUYb15/KOtlNqdSyaMNJscVLUGmAUsANYDc1V1rYjcKSIXucVmichaEVmFc/noWnf46cBn7vAXgZtV1brFMm3mkUVb6NEllismZnkdStBmnTWEkiPVPL9sZ/OFjWknQbVApqpvAG/UG3aH3+vvNzLdPGDesQRoTLA27yvjnfX7uPXsISTGhf9ZQ52x/bszaWAqT3y0lWtPySY22p5NNd6zb6HpMB77MI/4mCiuPXmA16G02I2TB7GrpII31liHQCY8WHIwHcK+0gpeWlHAjAmZ9Owa73U4LXbWCb0ZlJbEox/moWqPCRnvWXIwHcJTi7dT7fPxncmDvA6lVaKihBsmD+LzgkN8klfkdTjGWHIwke9wVQ1PL9nO14f3YWAYtLzaWpeMy6BX1zge+3Cr16EYY8nBRL6XVxZQcqSaGyL0rKFOQmw03zopm/c27GPzvjKvwzGdnCUHE9FUlacWb2dEejITBvTwOpxjdtVJWcRFR/HMku1eh2I6OUsOJqIt3VrMxr2lXHPyAETE63COWa+u8Zw/qi/zludTVlnjdTimE7PkYCLaU59sJyUxlovGNGgLMmJdc0o2pZU1vLyyqVZqjGlblhxMxNpTUsFba/dwxcT+EfXQW3PG9e/OyIxknv5km93WajxjycFErNlLd+BT5eoTI++ht6aICNeclM0Xe8tYkmetzRhvWHIwEamqxsfsT3dw5vG9yerZxetwQu6isel07xLL00u2eR2K6aQsOZiI9Obnu9lfVsk1EdhURjASYqO5PKc/C9buZXfJEa/DMZ2QJQcTkZ5dsoPsnl04fUjH7Rzq6hMH4FNlzlJrrdW0P0sOJuJsKSxj6bZirpyURVRU5N++2pisnl04bXAvXsjdSa3PKqZN+7LkYCLO88t2EhMlTB+f6XUobW7mpCx2lVSw6AvrW920L0sOJqJU1fiYtzyfKcP6kNYt8lpfbakpw/rQMymO55bu8DoU08kElRxEZKqIbBSRzSJyW4DxN4vIGhFZJSIfichwv3E/d6fbKCLnhjJ40/m8vW4vReVVXDkp/PuHDoW4mChmTMjk3Q372HeowutwTCfSbHJw+4B+EDgPGA7M9N/5u2ar6ihVHQvcC9zvTjscp8/pEcBU4B91fUob0xpzlu0go3sikztwRXR9V0zsT61PeXFFvtehmE4kmDOHScBmVc1T1SpgDjDNv4CqHvJ7mwTU1Z5NA+aoaqWqbgU2u/MzpsV2Fh/mo837uSwnk+gOXBFd36C0rpw4MJXnl+3EZxXTpp0EkxwyAP976fLdYUcRkVtEZAvOmcOtLZz2RhHJFZHcwkKreDOBzc11vkqX5XSOS0r+Zk7KYnvRYZZYR0CmnYSsQlpVH1TV44CfAbe3cNpHVDVHVXPS0jrP5QITvJpaHy/k5vO1oWlkdE/0Opx2N3VkX1ISY3lumT3zYNpHMMmhAPA/VMt0hzVmDnBxK6c1JqAPvihkz6EKrpyY5XUonkiIjeaScRks+HwPxeVVXodjOoFgksMyYIiIDBSROJwK5vn+BURkiN/bC4BN7uv5wJUiEi8iA4EhwNJjD9t0Ns8t3UmvrvGcPay316F4ZuakLKpqfbxiTXmbdtBsclDVGmAWsABYD8xV1bUicqeIXOQWmyUia0VkFfAj4Fp32rXAXGAd8BZwi6rWhn41TEdWWFrJ+xv3MX1CBrHRnffRnOP7dmN0ZgovLre7lkzbiwmmkKq+AbxRb9gdfq+/38S0dwN3tzZAY15dVUCtT7lsQsd/Iro5MyZkcsera1m7q4QR6Sleh2M6sM57GGYigqry4vJ8xvTvzuDe3bwOx3PfGJ1OXHQU85bbpSXTtiw5mLC2dtchNuwpZcb4jtMN6LHokRTHlOG9eWVVAVU1Pq/DMR2YJQcT1uatyCcuOopvjEn3OpSwMWNCJsXlVSzcuM/rUEwHZsnBhK3qWh/zV+3i7GG96d4lzutwwsbpQ9Lo1TXeKqZNm7LkYMLWwo2FFJVXdYqmuVsiJjqKS8dn8N6GfRSVVXodjumgLDmYsDVveT69usbxtePtqfn6po/PpManvLpql9ehmA7KkoMJSwfKq3h3w16mje3czzY0xp55MG3NfnUmLM1fvYvqWrVLSk2YMSGTdbsPsXZXidehmA7IkoMJS/NW5DOsXzLD05O9DiVsXTTGnnkwbceSgwk7m/aW8ll+CdPt2YYmde8SxznD+9gzD6ZNWHIwYefFFflERwnTxlpyaE7dMw/v2zMPJsQsOZiwUutTXllZwBlD00jrFu91OGFv8pBepHWLZ55VTJsQs+RgwspHm/ez91AlM6yRvaDEREdxyTh75sGEniUHE1bmLc8nJTGWszpxvw0tVffMw/zV9syDCR1LDiZsHKqoZsHaPVw0Jp34mGivw4kYx/ftxsiMZOatsEtLJnQsOZiw8Z/PdlNZ42O6XVJqsenjM/m84BAb9hzyOhTTQQSVHERkqohsFJHNInJbgPE/EpF1IvKZiLwrIgP8xtWKyCr3b379aY2pM295PselJTEm0zqxaSnnSXKximkTMs0mBxGJBh4EzgOGAzNFZHi9YiuBHFUdDbwI3Os37oiqjnX/LsKYALbtLyd3+wGmT8hERLwOJ+KkJsVx5vG9eXnlLmpq7ZkHc+yCOXOYBGxW1TxVrQLmANP8C6jq+6p62H27BLDrAqZFXlqRT5TApePsq9Na0ydksr+skg837fc6FNMBBJMcMoCdfu/z3WGNuR540+99gojkisgSEbm45SGajs7nU+atKODUwb3om5LgdTgR68zje9OjSywvWsW0CYGQVkiLyNVADnCf3+ABqpoDfBN4QESOCzDdjW4CyS0sLAxlSCYCfLq1mIKDR+zZhmMUFxPFtLEZvL12LyWHq70Ox0S4YJJDAdDf732mO+woIjIF+CVwkap++TSOqha4//OAhcC4+tOq6iOqmqOqOWlp1nZ/Z/PSiny6xsfw9eF9vQ4l4s2YkElVrY/XPrNnHsyxCSY5LAOGiMhAEYkDrgSOuutIRMYBD+Mkhn1+w3uISLz7uhdwKrAuVMGbyHe4qoY31uzm/FF9SYyzZxuO1Yj0ZI7v082eeTDHrNnkoKo1wCxgAbAemKuqa0XkThGpu/voPqAr8EK9W1aHAbkishp4H/iDqlpyMF/679q9lFfVWr8NISIiTJ+QwcodB9lSWOZ1OCaCxQRTSFXfAN6oN+wOv9dTGpluMTDqWAI0Hdu8Fflk9khkYnaq16F0GBePzeCPb21k3vJ8fjr1BK/DMRHKnpA2ntlTUsFHm/dz6fhMoqLs2YZQ6Z2cwOlDevHyygJqfep1OCZCWXIwnnl5ZQGqcOk467ch1KZPyGR3SQWfbCnyOhQToSw5GE+oKi+tyCdnQA+yeyV5HU6HM2VYH5ITYnhx+c7mCxsTgCUH44k1BSVs2lfGpVYR3SYSYqP5xph03lq7h9IKe+bBtJwlB+OJl1YUEBcTxQWj+3kdSoc1fUImFdU+3lyzx+tQTASy5GDaXVWNj1dXFXDO8D6kJMZ6HU6HNa5/dwb1SrLmNEyrWHIw7W7hxn0cOFzNDLuk1KacZx4yWbq1mB1Fh5ufwBg/lhxMu5u3Ip9eXeOZPKSX16F0eJeMy0AEe2LatJglB9OuDpRX8d6GfVw8Np2YaPv6tbX07omcelwvXlqZj8+eeTAtYL9O065e+2wX1bVqdym1o+kTMthZfIRl24q9DsVEEEsOpl3NW1HAsH7JDE9P9jqUTuPcEX1JiovmRetC1LSAJQfTbjbvK2P1zoNMH29PRLenLnExXDC6H2+s2c3hqhqvwzERwpKDaTcvrcgnOkq4aGy616F0OtPHZ1JeVcuCtfbMgwmOJQfTLmp9yssrCzh9SC96d7OuQNvbxOxU+qcmMm95g366jAnIkoNpF59sKWJ3SYVVRHskKkq4dFwmH2/Zz66DR7wOx0QASw6mXbywfCcpibGcM7yP16F0WtPHZ6LqtIZrTHOCSg4iMlVENorIZhG5LcD4H4nIOhH5TETeFZEBfuOuFZFN7t+1oQzeRIaSw9W8+fkeLh6bTkKsdQXqlayeXZg0MJV5y/NRtWceTNOaTQ4iEg08CJwHDAdmisjwesVWAjmqOhp4EbjXnTYV+DVwIjAJ+LWI9Ahd+CYSzP9sF1U1Pi7L6e91KJ3ejPGZ5O0vZ+XOg16HYsJcMGcOk4DNqpqnqlXAHGCafwFVfV9V6xpvWQLUXVg+F3hbVYtV9QDwNjA1NKGbSDF32U6G90tmZEaK16F0eueN6ktCbJQ982CaFUxyyAD8ewzJd4c15nrgzVZOazqYdbsOsaaghMtzrCI6HHRLiOW8kf14ffUuKqprvQ7HhLGQVkiLyNVADnBfC6e7UURyRSS3sLAwlCEZj72wfCdx0VFMG2vHBOFi+vhMDlXU8M76vV6HYsJYMMmhAPC/WJzpDjuKiEwBfglcpKqVLZlWVR9R1RxVzUlLSws2dhPmKmtqeWVlAeeM6EOPpDivwzGuk4/rSb+UBLu0ZJoUTHJYBgwRkYEiEgdcCcz3LyAi44CHcRLDPr9RC4Cvi0gPtyL66+4w0wm8u97pt+Fyq4gOK9FRwiXjMlj0RSH7DlV4HY4JU80mB1WtAWbh7NTXA3NVda2I3CkiF7nF7gO6Ai+IyCoRme9OWwzchZNglgF3usNMJzA3dyf9UhI4bbD12xBupk/IxKfwyip75sEEFhNMIVV9A3ij3rA7/F5PaWLaJ4AnWhugiUy7S46w6ItCbjlzMNFR4nU4pp7j0royLqs785YXcMPkQYjYZ2SOZk9Imzbx0ooCfAozJthdSuFqxoRMNu4tZXV+idehmDBkycGEnKoyN3cnJw1KZUDPJK/DMY34xph0EmOjmbN0h9ehmDBkycGE3Cd5RWwvOmwV0WEuOSGWi8akM3/1Lkorqr0Ox4QZSw4m5GZ/uoOUxFjOH9XP61BMM2aemMXhqlpeXbXL61BMmLHkYEJqf1klC9bu4dLxGdbIXgQYk5nCsH7JzP50hzXGZ45iycGE1IvL86muVa46McvrUEwQRIRvnpjFut2H+Mwqpo0fSw4mZHw+5bmlO5iUncrg3t28DscEadpYp2L6OauYNn4sOZiQWbzFqYj+pp01RJTkhFi+MaafVUybo1hyMCEze+l2enSJZerIvl6HYlpo5iSrmDZHs+RgQmJfaQX/XbuXGRMyrSI6Ao3t390qps1RLDmYkHghN58anzJzkl1SikQiwjcn9beKafMlSw7mmPl8ypxlOzh5UE8GpXX1OhzTStPGZZAYG83sT61i2lhyMCGwaFMhO4uPWEV0hEtOiOXicem8urqAg4ervA7HeMySgzlmT32ynbRu8Zw7wiqiI901J2dTUe3j+WU7my9sOjRLDuaYbNtfzvsb9/HNSVnExdjXKdIN65fMpIGpPL1kO7U+q5juzOzXbI7JU59sJ1rEnojuQL59Sjb5B47w3oZ9zRc2HZYlB9Nq5ZU1vJC7k/NH9aN3coLX4ZgQOWd4H/omJ/DUJ9u8DsV4KKjkICJTRWSjiGwWkdsCjD9dRFaISI2IzKg3rtbtOvTL7kNNx/DSygJKK2v49qnZXodiQig2OoqrT8riw0372byvzOtwjEeaTQ4iEg08CJwHDAdmisjwesV2AN8GZgeYxRFVHev+XRRgvIlAqsq/F29jdGYK4/p39zocE2JXTsoiLjrKzh46sWDOHCYBm1U1T1WrgDnANP8CqrpNVT8DfG0QowlDH28uYvO+Mq49Odv6H+6AenWN58LR/Zi3PN/aW+qkgkkOGYD/fW357rBgJYhIrogsEZGLAxUQkRvdMrmFhYUtmLXxypOLt9EzKY4Lx1iHPh3VtadkU15Vy7zl+V6HYjzQHhXSA1Q1B/gm8ICIHFe/gKo+oqo5qpqTlpbWDiGZY7Gj6DDvbtjLzElZxMdYO0od1Zj+3RmX1Z1/Ld5mt7V2QsEkhwLAvzPgTHdYUFS1wP2fBywExrUgPhOGHvsoj5go4ZqTB3gdimljN0wexPaiw7y9bo/XoZh2FkxyWAYMEZGBIhIHXAkEddeRiPQQkXj3dS/gVGBda4M13jtQXsXc3J1cPDbDbl/tBM4d0Zes1C48sijP61BMO2s2OahqDTALWACsB+aq6loRuVNELgIQkYkikg9cBjwsImvdyYcBuSKyGngf+IOqWnKIYE8v2U5FtY8bTh/kdSimHURHCd+ZPJAVOw6Su63Y63BMO4oJppCqvgG8UW/YHX6vl+Fcbqo/3WJg1DHGaMJERXUt/168jTOPT2NoH+sGtLOYMSGT+9/+gkcW5ZGTnep1OKad2BPSJmgvryygqLzKzho6mS5xMXzrpAG8vX4veYX2UFxnYcnBBMXnUx79MI9RGSmcPKin1+GYdnbNydnERkfx+EdbvQ7FtBNLDiYo727YR15hOTecPsgeeuuE0rrFM318Bi8uz6eorNLrcEw7sORgmqWq/HPhZjK6J3L+SOuzobO6/rRBVNX6+NfH27wOxbQDSw6mWYu3FLFix0FuPuM4YqLtK9NZDe7dlfNG9uXfi7dRcsSa1Ojo7JdumvXXdzfRJzmeyyY0uCHNdDKzzhxCaWUN/168zetQTBuz5GCatHRrMZ9uLeam048jIdaayujshqcnM2VYb574eCtllTVeh2PakCUH06S/vbeJXl3jmDnJenozju+dNYSDh6t5+pPtXodi2pAlB9OoVTsP8uGm/Xxn8iAS4+yswTjG9O/O6UPTeOzDPI5U1XodjmkjlhxMo/7+3ia6d4nl6pOsgT1ztO+dNZii8ipmL93hdSimjVhyMAGt3nmQd9bv47pTB9I1PqhWVkwnMjE7lZMGpfLQB1vs7KGDsuRgAvq//24kNSmO604b6HUoJkz9+OvHU1hayZN251KHZMnBNLB4y34+3LSf755xnJ01mEZNzE7lzOPTeOiDLfbcQwdkycEcRVX5vwUb6ZeSYHUNpln/e+7xlByp5lHr76HDseRgjvLehn2s2HGQW88eYs81mGaNSE/hwtH9eOLjrRSWWptLHYklB/Mln0+5b8FGsnt2YYY9DW2C9KNzhlJZ4+PB9zd7HYoJoaCSg4hMFZGNIrJZRG4LMP50EVkhIjUiMqPeuGtFZJP7d22oAjeh98qqAjbsKeWH5wwl1tpQMkEalNaVyyZkMvvTHewsPux1OCZEmt0DiEg08CBwHjAcmCkiw+sV2wF8G5hdb9pU4NfAicAk4Nci0uPYwzahdriqhnvf2sjozBS+MTrd63BMhPnBlKFERwl/eHOD16GYEAnm8HASsFlV81S1CpgDTPMvoKrbVPUzwFdv2nOBt1W1WFUPAG8DU0MQtwmxRxdtZc+hCn514XCioqy/BtMyfVMSuOlrg/jPmt0ss76mO4RgkkMGsNPvfb47LBhBTSsiN4pIrojkFhYWBjlrEyp7Sip46IMtnD+qLxOtj2DTSjedfhz9UhK487V1+HzqdTjmGIXFhWVVfURVc1Q1Jy0tzetwOp37Fmyk1qfcNnWY16GYCJYYF81Ppx7PmoISXl5Z4HU45hgFkxwKgP5+7zPdYcE4lmlNO/gs/yDzVuTzP6dlk9Wzi9fhmAg3bUwGY/p3594FGyi3Jr0jWjDJYRkwREQGikgccCUwP8j5LwC+LiI93Iror7vDTBio9Sm/euVzenWN55YzB3sdjukAoqKEOy4czt5DlfztPbu1NZI1mxxUtQaYhbNTXw/MVdW1InKniFwEICITRSQfuAx4WETWutMWA3fhJJhlwJ3uMBMGZi/dwer8En514TCSE2K9Dsd0EBMG9OCyCZk89mEeX+wt9Toc00qiGl4VRzk5OZqbm+t1GB1eYWklZ/1pIaMzU3jm+hMRsTuUTOgUl1dx1p8WMqR3V56/8WS7A64diMhyVc0J1fzCokLatL973lhPRXUtd04baYnBhFxqUhy/OG8Yy7Yd4MXl+V6HY1rBkkMntHjzfl5eWcBNpx/HcWldvQ7HdFAzJmQyMbsH97y5nuLyKq/DMS1kyaGTKa+s4WcvfUZ2zy7MOssqoU3biYoS7r5kFGUVNfz2tbVeh2NayJJDJ/PHtzaQf+AI9102xlpdNW1uaJ9uzDprMK+u2sVbn+/xOhzTApYcOpHFW/bz1Cfb+Z9TBtqT0Kbd3HLmYEakJ/PLl9dQVGbNekcKSw6dRHllDT+b51xO+sm5x3sdjulEYqOj+NPlYzhUUc0dr9rlpUhhyaGTuOv1dV9eTkqMs8tJpn2d0DeZH0wZyn/W7Gb+6l1eh2OCYMmhE3j9s13MWbaT755xnF1OMp656fRBjMvqzi9fWmP9PkQASw4d3M7iw/z8pTWMy+rOD6YM9Toc04nFREfx1yvHgcCs51ZSVVO/hX8TTiw5dGDVtT5unbMSFP565Tjr3c14rn9qF+6dPprVOw/yf//d6HU4pgm2t+jA7luwkZU7DnLPpaPon2otrprwcN6oflx9UhaPLMrj/Q37vA7HNMKSQwc1f/UuHlmUx7dOGsA3xli3nya83H7BcIb3S+b7c1aydX+51+GYACw5dEDrdh3ipy+uZmJ2D351Yf3uvo3xXkJsNA9/awLRUcINT+VSWlHtdUimHksOHcyB8ipueiaX7olx/OOqCcTF2EdswlP/1C48eNV4tu4v54fPr7KuRcOM7Tk6kIrqWm54Kpe9JZX88+rxpHWL9zokY5p0ynG9uOPC4byzfh/3WQV1WInxOgATGj6f8uO5q8ndfoC/f3Mc47J6eB2SMUG55uQBbNhTyj8XbiG9eyLfOmmA1yEZgjxzEJGpIrJRRDaLyG0BxseLyPPu+E9FJNsdni0iR0Rklfv3UIjjN6573ljPf9bs5vYLhnHhaKuANpFDRLhr2gjOPqE3d7z6uTXQFyaaTQ4iEg08CJwHDAdmikj9Ws7rgQOqOhj4M/BHv3FbVHWs+3dziOI2fv65cAuPfbSVb5+SzfWnDfQ6HGNaLCY6ir9/czxj+3fn1jkrWbrVehP2WjBnDpOAzaqap6pVwBxgWr0y04B/u69fBM4W616sXTz+0Vb++NYGLhqTzq8uHG69upmIlRgXzePXTiSzRyLXPbmM5dsPeB1SpxZMcsgAdvq9z3eHBSyjqjVACdDTHTdQRFaKyAciMjnQAkTkRhHJFZHcwsLCFq1AZ/b0ku3c9fo6zhvZl/svH0O09dNrIlxqUhyzv3MSvbrGce0TS1mxwxKEV9r6bqXdQJaqjgN+BMwWkeT6hVT1EVXNUdWctLS0Ng6pY3jqk2386pXPOfuE3vzlynHEWNMYpoPom5LAczeeRM+ucVz7uCUIrwSzRykA+vu9z3SHBSwjIjFAClCkqpWqWgSgqsuBLYC1/nYMVJW/vLOJO15dy5RhfXjwqvH2LIPpcPqlJDLnxpNI7RrH1Y99yqIv7IpCewtmr7IMGCIiA0UkDrgSmF+vzHzgWvf1DOA9VVURSXMrtBGRQcAQIC80oXc+Pp9y5+vr+PM7XzB9fCYPXT3euvo0HVa/lEReuOlkBvRM4ronl/HqqvrHpKYtNZsc3DqEWcACYD0wV1XXisidInKRW+xxoKeIbMa5fFR3u+vpwGcisgqnovpmVbXbEFrhcFUN3312Bf/6eBvXnTqQ+2aMtktJpsPrnZzA8zedRE52D74/ZxWPLNqCqj1J3R4k3DZ0Tk6O5ubmeh1GWCk4eIQb/p3Lhj2H+MX5w7j+tIF2V5LpVCqqa/nx3NX8Z81uLh2XwT2XjrKz5npEZLmq5oRqfvaEdJj7NK+IW2avpLK6lse/PZEzj+/tdUjGtLuE2Gj+NnMcx/ftxv1vf8HmwjIe/tYE+qUkeh1ah2XXJcJUrc+peJ756BK6JcTw8i2nWGIwnVpUlHDr2UN49Joc8grLueCvH/Hu+r1eh9VhWXIIQ3tKKrj6sU/58ztfMG1sBq997zQG9+7mdVjGhIVzhvfhlVtOpW9yAtf/O5c7Xv2ciupar8PqcOyyUhhRVZ5ftpO7/7OeGp9y34zRzJiQafULxtQzuHdXXr7lFO57ayOPfbSVT7YU8Yfpo5kwwBqcDBU7cwgTO4oOc/Xjn3LbS2sYkZHMm9+fzGU5/S0xGNOI+Jhobr9wOE9dN4nyyhpmPLSYO1793DoOChG7W8ljZZU1/OP9zTz24VbiYqL4+fknMHNiFlHWFIYxQSurrOFP/93Ik4u30btbPD+begIXj83oVL+jUN+tZMnBIzW1Pl5aWcB9CzZSWFrJpeMy+OnUE+ibkuB1aMZErFU7D3LHq5/zWX4JI9KT+eX5wzhlcC+vw2oXlhwiXE2tj1dW7eLv721iW9FhxmV1544Lh1vnPMaEiM+nvPbZLu59ayMFB48weUgvZp05mBMH9Wx+4ghmySFCHa6q4aUVBTz6YR7biw4zIj2ZH0wZypRhva1ewZg2UFFdy1OfbOORRXnsL6tiYnYPvnvmYL42JK1DXm6y5BBhdhYf5qlPtjFn2U5KK2oYnZnC984aYknBmHZSUV3LnKU7eHhRHrtLKsju2YWrTxrAjAmZdO8S53V4IWPJIQKUVlTz5po9vLQynyV5xcRECeeN6se3TxnA+KwelhSM8UBVjY831uzmmSXbyd1+gPiYKM4b2Zdp4zI4bXAvYiO8rTJLDmGq5HA1C7/Yx3/X7uWd9XuprPExsFcSl4zL4PKc/lbRbEwYWb/7EM8s2c5rq3dxqKKG1KQ4zh/Vl/NG9mNidmpENoNvySFM+HzKhj2lLN6yn3fX72Pp1mJqVenVNY7zR/XjknEZjO3f3c4SjAljlTW1LPpiP6+uKuCd9XupqPbRNT6Gkwf14pzhvTl9aFrEHNhZcvBIRXUtG/eUsnz7AZbkFbEkr5hDdQ/bHOxGybrevPT3PnxtZPcOWdllTEdXXlnDx5v38/ib+/h4ayEx3SoA6JPUhVOHpnLioB5MzE4lu2dSWP7GrVXWdnCgvIothWWs31PK5/klrCkoYePeUmp9TiKV8i6Ube0De3tSsqkn1SWJJCbCkFSIiryzUWMMkBQfw9dH9GXP8r68/mulOqmUpEFFVGUV8WLhPl5amQ9AQnQMw/slM2ZAMiPTUxienszAXkkdrgnxTpkcfD6lsKySgoNH2H2wgvwDh9m6v5wthWVs3lvOgSNVX5aV6lgqd6dQvW8Qvv0pHNranZpDDZsJtqtHxnQcUVFCdWEyBwuTOfjpQECJ6VlGl6wDJKaXsGTnIZZv24HE+AAQIC0pkSF9kxjcO4lBaV3J7JFIv5RE0rsnkJIYG3GXmINKDiIyFfgLEA08pqp/qDc+HngKmAAUAVeo6jZ33M+B64Fa4FZVXRCy6P34fErx4SqKy6soKnP+F5dXUlTuDiuvorC0koLiI+wtraDGd/TlNKmMo6qoK1X7+yClXakuTqKsoBs1JYk4H70xpvMSaoq6caioG4dW1g1SYlPLiOtTSlLfMnamlJOfXM5HqQVIXM1RU8dHR9O7WwL9UxPpmxJPalIcqV3jSO0SR2pSHD27xtGjSxw9u8aTkhjb/qsXQLPJwe0D+kHgHCAfWCYi81V1nV+x64EDqjpYRK4E/ghcISLDcfqcHgGkA++IyFBVDXn7uoVllZx4z7uBR1bF4jsSR01pPDWHesCRRChPpLokgcOFiVSXJKJV4fGBGGMihArVRd2oLupG+bqjRhCdVEl0yhHiUipISD1CRbcKDnU5wuakCmK6lhPVpQqJabgb7J+Uwoe/Oq3dVqEpwZw5TAI2q2oegIjMAaYB/ptjGvAb9/WLwN/FOYeaBsxR1Upgq9vH9CTgk9CE/5XUpDguzhrBo3+LQ6riqDoUR+2ROHxH4sDX9hUBNTXNlzHGRIbaYzp8FWrLE6gtT6BqF5Q1ViqmlqjEKqK7VBGdVElctyr2HYmm4ieQEAY3SAWTHDKAnX7v84ETGyujqjUiUgL0dIcvqTdtRv0FiMiNwI0AWVlZwcZ+lNjoKKaPyuYvG6BrV4gH52pQl1bNrsUqK53lGmMiW79+cORIe/yeo0GdqxiUQ+0+EB/EhElNcFiEoaqPAI+Acytra+czeTKE2Z25xpgIc845th+B4Dr7KQD6+73PdIcFLCMiMUAKTsV0MNMaY4wJM8Ekh2XAEBEZKCJxOBXM8+uVmQ9c676eAbynztN184ErRSReRAYCQ4CloQndGGNMW2n2spJbhzALWIBzK+sTqrpWRO4EclV1PvA48LRb4VyMk0Bwy83FqbyuAW5pizuVjDHGhJY1n2GMMR1AqJvPsMYejDHGNGDJwRhjTAOWHIwxxjRgycEYY0wDYVchLSKFwHav46inF7Df6yBaIJLijaRYIbLijaRYIbLiDcdYB6hqWqhmFnbJIRyJSG4o7wJoa5EUbyTFCpEVbyTFCpEVbyTF2lp2WckYY0wDlhyMMcY0YMkhOI94HUALRVK8kRQrRFa8kRQrRFa8kRRrq1idgzHGmAbszMEYY0wDlhyMMcY0YMkhSCJyl4h8JiKrROS/IpLudUxNEZH7RGSDG/PLItLd65gaIyKXichaEfGJSFjeHigiU0Vko4hsFpHbvI6nKSLyhIjsE5HPvY6lOSLSX0TeF5F17nfg+17H1BQRSRCRpSKy2o33t17H1FasziFIIpKsqofc17cCw1X1Zo/DapSIfB2nX40aEfkjgKr+zOOwAhKRYYAPeBj4X1UNq2Z5RSQa+AI4B6er22XATFVd1+SEHhGR03G6Ln5KVUd6HU9TRKQf0E9VV4hIN2A5cHEYb1sBklS1TERigY+A76vqkmYmjTh25hCkusTgSgLCOquq6n9VtcZ9uwSnF76wpKrrVXWj13E0YRKwWVXzVLUKmANM8zimRqnqIpx+VcKequ5W1RXu61JgPQH6mQ8X6ihz38a6f2G9L2gtSw4tICJ3i8hO4CrgDq/jaYHrgDe9DiKCZQA7/d7nE8Y7sEglItnAOOBTj0NpkohEi8gqYB/wtqqGdbytZcnBj4i8IyKfB/ibBqCqv1TV/sCzwCxvo20+XrfML3F64XvWu0iDi9V0XiLSFZgH/KDeWXrYUdVaVR2LczY+SUTC+tJdazXbTWhnoqpTgiz6LPAG8Os2DKdZzcUrIt8GLgTOVo8rl1qwbcNRAdDf732mO8yEgHvtfh7wrKq+5HU8wVLVgyLyPjAVCPvK/5ayM4cgicgQv7fTgA1exRIMEZkK/BS4SFUPex1PhFsGDBGRgSISh9NH+nyPY+oQ3Arex4H1qnq/1/E0R0TS6u78E5FEnJsUwnpf0Fp2t1KQRGQecDzOXTXbgZtVNWyPHkVkMxAPFLmDloTr3VUicgnwNyANOAisUtVzPQ2qHhE5H3gAiAaeUNW7vY2ocSLyHHAGTrPSe4Ffq+rjngbVCBE5DfgQWIPz2wL4haq+4V1UjROR0cC/cb4HUcBcVb3T26jahiUHY4wxDdhlJWOMMQ1YcjDGGNOAJQdjjDENWHIwxhjTgCUHY4wxDVhyMMYY04AlB2OMMQ38fxACq+YMDMnsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_lin_example3 = np.arange(-3.5,3.5,0.001)\n",
"draw_t_score(x_lin_example3, x_lin_example3>t_value_example3, 0, \n",
" se_example3, \n",
" dof_example3, f\"Example 3 (Two-sample t-test): \\n p-value on t-student distribution where t > {round(t_value_example3, 4)} and t < {round(-t_value_example3, 4)}\", x_lin_example3<-t_value_example3)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.005365303952098963"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using t-student distribtion\n",
"t.sf(t_value_example3, df=dof_example3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\alpha}{2} = \\frac{0.05}{2} = 0.025 \\implies \\text{p_value} < 0.025 \\implies 0.0054 < 0.025 \\implies \\text{We can reject H0}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can reject H0, so we can conclude with 5% of significance that $\\mu_{\\text{men}} \\neq \\mu_{\\text{women}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Example 4**:<br>\n",
"**Two-sample paired t-test**<br>\n",
"A study was conducted to investigate the effectiveness of hypnotism in reducing pain. Results are shown in the following dataframe. The \"before\" value is matched to an \"after\" value. Are the sensory measurements, on average, lower after hypnotism? Test at 5% significance level."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"example4_data = {\n",
" \"subject\": ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n",
" \"before\": [6.6, 6.5, 9.0, 10.3, 11.3, 8.1, 6.3, 11.6],\n",
" \"after\": [6.8, 2.4, 7.4, 8.5, 8.1, 6.1, 3.4, 2.0]\n",
"}\n",
"df_example3 = pd.DataFrame.from_dict(example4_data)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>subject</th>\n",
" <th>before</th>\n",
" <th>after</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>6.6</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>B</td>\n",
" <td>6.5</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>C</td>\n",
" <td>9.0</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>D</td>\n",
" <td>10.3</td>\n",
" <td>8.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>E</td>\n",
" <td>11.3</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>F</td>\n",
" <td>8.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>G</td>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>H</td>\n",
" <td>11.6</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject before after\n",
"0 A 6.6 6.8\n",
"1 B 6.5 2.4\n",
"2 C 9.0 7.4\n",
"3 D 10.3 8.5\n",
"4 E 11.3 8.1\n",
"5 F 8.1 6.1\n",
"6 G 6.3 3.4\n",
"7 H 11.6 2.0"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_example3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are our hypothesis:<br>\n",
"$$\n",
"H0: {\\overline{d}= 0}\n",
"$$\n",
"$$\n",
"H1: {\\overline{d} \\neq 0}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where d is the mean of the difference between before and after."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>subject</th>\n",
" <th>before</th>\n",
" <th>after</th>\n",
" <th>diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>6.6</td>\n",
" <td>6.8</td>\n",
" <td>-0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>B</td>\n",
" <td>6.5</td>\n",
" <td>2.4</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>C</td>\n",
" <td>9.0</td>\n",
" <td>7.4</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>D</td>\n",
" <td>10.3</td>\n",
" <td>8.5</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>E</td>\n",
" <td>11.3</td>\n",
" <td>8.1</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>F</td>\n",
" <td>8.1</td>\n",
" <td>6.1</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>G</td>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>H</td>\n",
" <td>11.6</td>\n",
" <td>2.0</td>\n",
" <td>9.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject before after diff\n",
"0 A 6.6 6.8 -0.2\n",
"1 B 6.5 2.4 4.1\n",
"2 C 9.0 7.4 1.6\n",
"3 D 10.3 8.5 1.8\n",
"4 E 11.3 8.1 3.2\n",
"5 F 8.1 6.1 2.0\n",
"6 G 6.3 3.4 2.9\n",
"7 H 11.6 2.0 9.6"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Here we compute the difference between (before and after)\n",
"df_example3['diff'] = df_example3.before - df_example3.after\n",
"df_example3"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"mean\": 3.125,\n",
" \"std\": 2.9114306743298166,\n",
" \"n\": 8\n",
"}\n"
]
}
],
"source": [
"#Now we compute mean and std of the diff column\n",
"mean_example4 = df_example3['diff'].mean()\n",
"std_example4 = df_example3['diff'].std()\n",
"n_example4 = len(df_example3)\n",
"params_example4 = {\n",
" \"mean\": mean_example4,\n",
" \"std\": std_example4,\n",
" \"n\": n_example4\n",
"}\n",
"print(json.dumps(params_example4, indent=4))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.029346186386568\n"
]
}
],
"source": [
"#Here we compute the standard error\n",
"se_example4 = std_example4/np.sqrt(n_example4)\n",
"print(se_example4)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.0359076871601824\n"
]
}
],
"source": [
"#And finally our t-value\n",
"t_value_example4 = mean_example4/se_example4\n",
"print(t_value_example4)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"dof_example4 = n_example4-1\n",
"print(dof_example4)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x_lin_example4 = np.arange(-3.5,3.5,0.001)\n",
"draw_t_score(x_lin_example4, x_lin_example4>t_value_example4, 0, \n",
" se_example4, \n",
" dof_example4, \n",
" f\"Example 4 (Two-sample pairted t-test): \\n p-value on t-student distribution where t > {round(t_value_example4, 4)} and t < {round(-t_value_example4, 4)}\", x_lin_example4<-t_value_example4)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.009477987786306376"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Using t-student distribtion\n",
"t.sf(t_value_example4, df=dof_example4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\alpha}{2} = \\frac{0.05}{2} = 0.025 \\implies \\text{p_value} < 0.025 \\implies 0.009478 < 0.025 \\implies \\text{We can reject H0}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we see, we can reject HO with a p-value of 0.009478. So, based on our data, it's very likely that hypnotism is reducing pain"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment