21장.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b4f8795-b172-46de-ae7d-ad7cb8e7d905",
   "metadata": {
    "id": "0b4f8795-b172-46de-ae7d-ad7cb8e7d905"
   },
   "source": [
    "# 21장 무작위 시험과 가설 검정"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17a2a83-49a0-4f41-9f5a-e8694d50b98e",
   "metadata": {
    "id": "a17a2a83-49a0-4f41-9f5a-e8694d50b98e"
   },
   "source": [
    "<table align=\"left\"><tr><td>\n",
    "<a href=\"https://colab.research.google.com/github/rickiepark/python4daml/blob/main/21장.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"코랩에서 실행하기\"/></a>\n",
    "</td></tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c918aed3-8625-4a7b-9f99-ac7f79b0d351",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c918aed3-8625-4a7b-9f99-ac7f79b0d351",
    "outputId": "3e6e8128-21bc-4568-d82e-c8680910b7ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (This frontend requires a controlling tty.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "dpkg-preconfigure: unable to re-open stdin: \n",
      "Selecting previously unselected package fonts-nanum.\n",
      "(Reading database ... 122352 files and directories currently installed.)\n",
      "Preparing to unpack .../fonts-nanum_20180306-3_all.deb ...\n",
      "Unpacking fonts-nanum (20180306-3) ...\n",
      "Setting up fonts-nanum (20180306-3) ...\n",
      "Processing triggers for fontconfig (2.13.1-2ubuntu3) ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://raw.githubusercontent.com/rickiepark/python4daml/main/bm_results2012.csv\n",
      "To: /content/bm_results2012.csv\n",
      "783kB [00:00, 10.2MB/s]                   \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#선 두께\n",
    "plt.rcParams['lines.linewidth'] = 4\n",
    "#제목 폰트 크기\n",
    "plt.rcParams['axes.titlesize'] = 16\n",
    "#축 레이블 폰트 크기\n",
    "plt.rcParams['axes.labelsize'] = 16\n",
    "#x 축 텍스트 크기\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "#y 축 텍스트 크기\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "#x 축 눈금 크기\n",
    "plt.rcParams['xtick.major.size'] = 7\n",
    "#y 축 눈금 크기\n",
    "plt.rcParams['ytick.major.size'] = 7\n",
    "#마커 크기\n",
    "plt.rcParams['lines.markersize'] = 10\n",
    "#범례를 표시할 때 마커 표시 개수\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "#범례 텍스트 크기\n",
    "plt.rcParams['legend.fontsize'] = 14\n",
    "\n",
    "# 한글 폰트 사용을 위한 코드입니다.\n",
    "import sys\n",
    "# 코랩의 경우 나눔 폰트를 설치합니다.\n",
    "if 'google.colab' in sys.modules:\n",
    "    !sudo apt-get -qq -y install fonts-nanum\n",
    "    import matplotlib.font_manager as fm\n",
    "    font_files = fm.findSystemFonts(fontpaths=['/usr/share/fonts/truetype/nanum'])\n",
    "    for fpath in font_files:\n",
    "        fm.fontManager.addfont(fpath)\n",
    "\n",
    "# 나눔 폰트를 사용합니다.\n",
    "import matplotlib \n",
    "\n",
    "matplotlib.rc('font', family='NanumBarunGothic')\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 코랩의 경우 bm_results2012.csv을 깃허브에서 다운로드합니다.\n",
    "if 'google.colab' in sys.modules:\n",
    "    import gdown\n",
    "    gdown.download('https://raw.githubusercontent.com/rickiepark/python4daml/main/bm_results2012.csv', quiet=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ba08c1c-51c9-485c-b066-c4636d25a731",
   "metadata": {
    "id": "2ba08c1c-51c9-485c-b066-c4636d25a731"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.seed(148)\n",
    "treatment_dist = (119.5, 5.0)\n",
    "control_dist = (120, 4.0)\n",
    "sample_size = 100\n",
    "treatment_times, control_times = [], []\n",
    "for s in range(sample_size):\n",
    "    treatment_times.append(random.gauss(treatment_dist[0],\n",
    "                                        treatment_dist[1]))\n",
    "    control_times.append(random.gauss(control_dist[0],\n",
    "                                      control_dist[1]))\n",
    "control_mean = sum(control_times)/len(control_times)\n",
    "treatment_mean = sum(treatment_times)/len(treatment_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "336d22cf",
   "metadata": {},
   "source": [
    "그림 21-1 사이클 선수의 완주 시간"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7449b89a-ecb8-437a-a760-644d29300040",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "7449b89a-ecb8-437a-a760-644d29300040",
    "outputId": "590029b8-ab9e-47e1-f4bd-038cd4e82823"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(treatment_times, 'co', markersize = 8,\n",
    "         label = '실험군 (평균 = ' +\n",
    "         str(round(treatment_mean, 2)) + ')')\n",
    "plt.plot(control_times, 'kv', markersize = 8,\n",
    "         label = '대조군 (평균 = ' +\n",
    "         str(round(control_mean,2)) + ')')\n",
    "plt.title('PED-X 테스트')\n",
    "plt.xlabel('사이클 선수')\n",
    "plt.ylabel('완주 시간 (분)')\n",
    "plt.ylim(100, 145)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b154aca8-251c-4529-a4f1-9236768373f0",
   "metadata": {
    "id": "b154aca8-251c-4529-a4f1-9236768373f0"
   },
   "source": [
    "## 21.1 유의성 검증하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10323ac-e69d-4b71-bd22-e3180fe612ce",
   "metadata": {
    "id": "c10323ac-e69d-4b71-bd22-e3180fe612ce"
   },
   "source": [
    "예제 21-1 t-점수 그리기\n",
    "\n",
    "그림 21-3 t 점수 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a7d5bcf-ccc9-4aa3-9a86-1d20e521d3a4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "6a7d5bcf-ccc9-4aa3-9a86-1d20e521d3a4",
    "outputId": "996c5ec6-f202-48b9-cb6e-90879f12d5eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "t_stat = -2.11 #PED-X 예제의 t-점수\n",
    "t_dist = []\n",
    "num_bins = 1000\n",
    "for i in range(10000000):\n",
    "    t_dist.append(np.random.standard_t(198))\n",
    "plt.hist(t_dist, bins = num_bins,\n",
    "         weights = np.array(len(t_dist)*[1.0])/len(t_dist))\n",
    "plt.axvline(t_stat, color = 'w')\n",
    "plt.axvline(-t_stat, color = 'w')\n",
    "plt.title('자유도가 198인 T 분포')\n",
    "plt.xlabel('T 점수')\n",
    "plt.ylabel('확률')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94cfca30-bf8e-4cdb-9774-8710ab13c89e",
   "metadata": {
    "id": "94cfca30-bf8e-4cdb-9774-8710ab13c89e"
   },
   "source": [
    "예제 21-2 t-점수와 p-값 계산하여 출력하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e96273c1-be62-471a-a754-55a41281cf54",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e96273c1-be62-471a-a754-55a41281cf54",
    "outputId": "b3a2b35f-89ea-46f5-ab89-69def1a6d2f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "실험군의 평균 - 대조군의 평균 = -1.38 분\n",
      "2표본 검정의 t-점수는 -2.11\n",
      "2표본 검정의 p-값은 0.04\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "control_mean = round(sum(control_times)/len(control_times), 2)\n",
    "treatment_mean = round(sum(treatment_times)/len(treatment_times), 2)\n",
    "print('실험군의 평균 - 대조군의 평균 =',\n",
    "      round(treatment_mean - control_mean, 2), '분')\n",
    "two_sample_test = scipy.stats.ttest_ind(treatment_times,\n",
    "                                        control_times,\n",
    "                                        equal_var = False)\n",
    "print('2표본 검정의 t-점수는', round(two_sample_test[0], 2))\n",
    "print('2표본 검정의 p-값은', round(two_sample_test[1], 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2397b61",
   "metadata": {},
   "source": [
    "예제 21-3 자전거 경기 샘플을 만드는 코드\n",
    "\n",
    "그림 21-4 p 값의 확률"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f180aa05-8c06-478d-9aa9-26c71f1bb7e5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "f180aa05-8c06-478d-9aa9-26c71f1bb7e5",
    "outputId": "dc50bb7e-b7e5-4c7d-e77d-2cc4ec220004"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "\n",
    "treatment_dist = (119.5, 5.0)\n",
    "control_dist = (120, 4.0)\n",
    "sample_size = 100\n",
    "p_vals = []\n",
    "for _ in range(10000):\n",
    "    treatment_times, control_times = [], []\n",
    "    for s in range(sample_size):\n",
    "        treatment_times.append(random.gauss(treatment_dist[0],\n",
    "                                            treatment_dist[1]))\n",
    "        control_times.append(random.gauss(control_dist[0],\n",
    "                                          control_dist[1]))\n",
    "    two_sample_test = scipy.stats.ttest_ind(treatment_times,\n",
    "                                            control_times,\n",
    "                                            equal_var = False)\n",
    "    p_vals.append(two_sample_test[1])\n",
    "mean = sum(p_vals)/len(p_vals)\n",
    "num_below = 0\n",
    "cut_off = 0.05 \n",
    "for e in p_vals:\n",
    "    if e <= cut_off:\n",
    "        num_below += 1\n",
    "frac_below = round(num_below/len(p_vals), 2)\n",
    "plt.hist(p_vals, weights = np.array(len(p_vals)*[1])/len(p_vals),\n",
    "         bins = 100)\n",
    "plt.annotate('평균 = ' + str(round(mean, 2))\n",
    "             + '\\n' + str(cut_off) + ' 아래 비율 = ' +\n",
    "             str(frac_below),\n",
    "             size = 'x-large',\n",
    "             xycoords = 'axes fraction', xy = (0.25, 0.7))\n",
    "plt.title('실제 분포의 P 값')\n",
    "plt.ylabel('확률')\n",
    "plt.xlabel('p 값')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d918998a-07bb-42b5-9325-0af5f5518b9c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "d918998a-07bb-42b5-9325-0af5f5518b9c",
    "outputId": "96e838e5-6fd7-470b-fd2f-cbc1671bc2ca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "\n",
    "treatment_dist = (119.5, 5.0)\n",
    "control_dist = (120, 4.0)\n",
    "sample_size = 3000\n",
    "p_vals = []\n",
    "for _ in range(10000):\n",
    "    treatment_times, control_times = [], []\n",
    "    for s in range(sample_size):\n",
    "        treatment_times.append(random.gauss(treatment_dist[0],\n",
    "                                            treatment_dist[1]))\n",
    "        control_times.append(random.gauss(control_dist[0],\n",
    "                                          control_dist[1]))\n",
    "    two_sample_test = scipy.stats.ttest_ind(treatment_times,\n",
    "                                            control_times,\n",
    "                                            equal_var = False)\n",
    "    p_vals.append(two_sample_test[1])\n",
    "mean = sum(p_vals)/len(p_vals)\n",
    "num_below = 0\n",
    "cut_off = 0.05 \n",
    "for e in p_vals:\n",
    "    if e <= cut_off:\n",
    "        num_below += 1\n",
    "frac_below = round(num_below/len(p_vals), 2)\n",
    "plt.hist(p_vals, weights = np.array(len(p_vals)*[1])/len(p_vals),\n",
    "         bins = 100)\n",
    "plt.annotate('Mean = ' + str(round(mean, 2))\n",
    "             + '\\nFraction below ' + str(cut_off) + ' = ' +\n",
    "             str(frac_below),\n",
    "             size = 'x-large',\n",
    "             xycoords = 'axes fraction', xy = (0.25, 0.7))\n",
    "plt.title('실제 분포의 P 값')\n",
    "plt.ylabel('확률')\n",
    "plt.xlabel('p 값')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8216e70f-dc26-4d6e-89c0-b14d794d59ab",
   "metadata": {
    "id": "8216e70f-dc26-4d6e-89c0-b14d794d59ab"
   },
   "source": [
    "## 21.3 단측 1표본 검정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91ee1060-660c-49e6-8521-9ab0e9648066",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91ee1060-660c-49e6-8521-9ab0e9648066",
    "outputId": "5f8450b0-fafc-43e1-e5ef-8295444be132"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1표본 검정의 t-점수는 -3.5327978570330476\n",
      "1표본 검정의 p-값은 0.0006264237879103175\n"
     ]
    }
   ],
   "source": [
    "random.seed(148)\n",
    "treatment_dist = (119.5, 5.0)\n",
    "sample_size = 100\n",
    "treatment_times= []\n",
    "for s in range(sample_size):\n",
    "    treatment_times.append(random.gauss(treatment_dist[0],\n",
    "                                        treatment_dist[1]))\n",
    "\n",
    "one_sample_test = scipy.stats.ttest_1samp(treatment_times, 120)\n",
    "print('1표본 검정의 t-점수는', one_sample_test[0])\n",
    "print('1표본 검정의 p-값은', one_sample_test[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3fa88c-5ecb-4b66-bebb-ce33342226ed",
   "metadata": {
    "id": "fc3fa88c-5ecb-4b66-bebb-ce33342226ed"
   },
   "source": [
    "## 21.4 유의한가요? 유의하지 않은가요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8326f3c-015a-47dd-8877-da0c587cab43",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e8326f3c-015a-47dd-8877-da0c587cab43",
    "outputId": "21baa9f0-6f99-4e92-e9f4-595a74157260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1표본 검정의 p-값은 0.0982205871243577\n"
     ]
    }
   ],
   "source": [
    "num_games = 1273\n",
    "lyndsay_wins = 666\n",
    "outcomes = [1.0]*lyndsay_wins + [0.0]*(num_games - lyndsay_wins)\n",
    "print('1표본 검정의 p-값은',\n",
    "      scipy.stats.ttest_1samp(outcomes, 0.5)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "624e718c-8ad2-49f6-bbd7-bed574a85b96",
   "metadata": {
    "id": "624e718c-8ad2-49f6-bbd7-bed574a85b96"
   },
   "source": [
    "예제 21-4 린제이의 게임 시뮬레이션"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6edea6d-418d-42e2-80f6-2756776a0408",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f6edea6d-418d-42e2-80f6-2756776a0408",
    "outputId": "a33fcf23-a388-4a36-eb9f-5d9ec8b2ddb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "적어도 우연으로 이런 극단적인 결과를 얻을 확률 = 0.0497\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "num_games = 1273\n",
    "lyndsay_wins = 666\n",
    "num_trials = 10000\n",
    "at_least = 0\n",
    "for t in range(num_trials):\n",
    "    l_wins, j_wins = 0, 0\n",
    "    for g in range(num_games):\n",
    "        if random.random() < 0.5:\n",
    "            l_wins += 1\n",
    "        else:\n",
    "            j_wins += 1\n",
    "    if l_wins >= lyndsay_wins:\n",
    "        at_least += 1\n",
    "print('적어도 우연으로 이런 극단적인 결과를 얻을 확률 =', \n",
    "      at_least/num_trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e163b02f-387d-4d9a-82d7-342f91ac2168",
   "metadata": {
    "id": "e163b02f-387d-4d9a-82d7-342f91ac2168"
   },
   "source": [
    "예제 21-5 올바른 게임 시뮬레이션"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1448214b-e153-46fe-8a5c-7f252072e719",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1448214b-e153-46fe-8a5c-7f252072e719",
    "outputId": "a52368a2-6e60-4d21-96e9-0710aa028c31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "적어도 우연으로 이런 극단적인 결과를 얻을 확률 = 0.1047\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "num_games = 1273\n",
    "lyndsay_wins = 666\n",
    "num_trials = 10000\n",
    "at_least = 0\n",
    "for t in range(num_trials):\n",
    "    l_wins, j_wins = 0, 0\n",
    "    for g in range(num_games):\n",
    "        if random.random() < 0.5:\n",
    "            l_wins += 1\n",
    "        else:\n",
    "            j_wins += 1\n",
    "    if l_wins >= lyndsay_wins or j_wins >= lyndsay_wins:\n",
    "        at_least += 1\n",
    "print('적어도 우연으로 이런 극단적인 결과를 얻을 확률 =', \n",
    "      at_least/num_trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4cd3664-f4d6-4c0a-bd14-e4802da94962",
   "metadata": {
    "id": "e4cd3664-f4d6-4c0a-bd14-e4802da94962"
   },
   "source": [
    "**뇌풀기 문제**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "520b1945-df03-439c-bb24-757e17acd60b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "520b1945-df03-439c-bb24-757e17acd60b",
    "outputId": "cf0fcdb8-5fac-47bf-f171-515d25bf12c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2표본 검정의 p-값은 0.04832710677432093\n"
     ]
    }
   ],
   "source": [
    "print('2표본 검정의 p-값은',\n",
    "      scipy.stats.ttest_ind([1.0]*607+[0.0]*666, [1.0]*479+[0.0]*443)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e657e01-07d7-413e-8e19-798583e2f207",
   "metadata": {
    "id": "4e657e01-07d7-413e-8e19-798583e2f207"
   },
   "source": [
    "## 21.5 표본 크기는?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67648ef",
   "metadata": {},
   "source": [
    "그림 21-5 p 값에 대한 표본 크기의 영향"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74b04c35-842f-4252-8e6a-2fc4985055ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "74b04c35-842f-4252-8e6a-2fc4985055ed",
    "outputId": "ce991d53-2a43-437b-dc50-6c14f079b085"
   },
   "outputs": [
    {
     "data": {
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xY8cit52fn0fVX7+6pip55ZkzZzBy5Ej8+OOPqFOnDiZPnqx129999x1CQkIK1h+dP38eo0ePBvC/QCwmJgYtWrTA48ePERsbiw8++AC//fabUltPnz7Fjz/+WGh/MplMYW3Sm+vyTp8+jdOnTxd8vXDhQp0FX/n/ZoWti8qf/ctf6KtpHW0WBmdkZODDDz/EiRMn8Ouvv+Kvv/7CihUr0KFDB7Rt2xZAXjAaGBiIrl274sCBAxq3rY2S/H68WUedovxsr1mzBrVq1cLixYsxb968gusWFhaYNWsWFi5cqNP/Np8/f46+ffsiJycHa9asKTT31sGDB9GlS5dC23NycsLMmTMxc+bMIo1HLpfj77//xsqVK3HmzBnIZDIsXLgQ8+fPV/gDzNnZGadOncLAgQNx6NAh+Pv7Y/z48Rg/fnyJzjKrsmDBAsTGxhZ8/fTpU6XHqvk/YwkJCQW/h4r6u61SKo0tlkSqZGdnC99++61gamoqABAsLS2FR48eKZSBmlQT+eRyuWBoaCgAEKKjowst++mnn6p9RUZGFuetFaR/qFatWpHq9+nTR+W2cZlMJlhZWQkuLi6Ct7e38M477wgff/yxcOjQIUEQ/rcNXRPTpk1Tytnz9OlTAYDg7+9fpLG/SV2qie7duwsAhFu3bqlsIyAgQAAgJCQkCIIgCHXq1BEACKmpqSrrODg4FKTiUCc7O1vo1KmTYGJiUjDOzMxMoXXr1oKlpaVw5coVQRDy8mwBEFq0aKFRu2LUpZoICQkRAAh9+/ZV2cb+/fsV0o/89ttvAgBhxowZKuusW7euSCkSxKj72ZbL5UJYWJhw+PBh4fLly4WmJtAm1cSbnj9/LjRu3FgAIAQFBaksp0mqCV2JiIgoyLHXunVrtXnV5HK5sHz5csHGxkYAIHTq1Ell2fyfG129zp49W9C2v79/kdro3Lmzzr53FR1nvqhMkclkmDx5Mlq1aoXWrVvj9evX+O677/DVV19p3dbNmzeRnZ0NGxsbtTuRVq9erba9Xr16oWbNmlqPI1+DBg3g6+uLu3fv4tWrV1rPFInNYFVE+d+Xwh4Rvnz5ElKptGBm4806YguAc3NzkZiYqPHpAitXrsSRI0ewfPnygk0WRkZG2LNnD5o1a4YuXbrg1KlTqFatGoC8TQAlRdPvx5tli1KnONT9bBsYGKB+/frF7keVW7duoXfv3oiMjMTgwYM1+u/5bQMGDMDu3bt1Mp78zTne3t44dOgQ5HJ5oRsm8hkYGGD69OkYN24cfvrpp4I0FmIsLCwKvZ9vx44dMDAwUHm8WL43N9HcvHlTtMyQIUOwY8cObN++XeMTNEgcgy8qkwICAjB27FisWbMGYWFhRWpj//79APJ2Parb2i9okfumOOzs7ACoXsBaFK9evcKJEydw+/ZtxMfHIzMzs+AsxDp16qBdu3Y4fPiwyj6vXbuGNWvW4J133inIoVWa8tf5PXr0SHTjRU5ODmJjY1GtWrWCR2fVq1fH2bNn8ejRI9GNETExMcjJydEoeM7JycGSJUsAQGk9kr29Pf7++280b94cnTp1wqxZswDkBR8lxcPDAzKZrNAzLfN3Dua/vze/h5rWKa7CfrYTExPx888/w8XFBR9++KFO+svva82aNZg3bx4yMjLw6aef4ptvvinSmsr27durXbuVf45mYUcsAYobHQIDA7Uei6WlJYKCggot4+DgoHSGq5gdO3bA0NBQo7KkPwy+qMzK3wqvbU4kIC8gyV/Qqul5iCUtNzcX9+7dg7W1tdZ5ssSkp6djxowZ2LRpE7KyslSWs7KywsyZMzFnzhzR+9HR0di2bRtMTEzKRPAVEBAAIC/Zqthf9teuXUNWVpZCeoSAgABs27YNV65cQcuWLZXqXLp0CQA0Sqnw4sULJCYmwsbGRjRHlo+PD37//Xd06tQJU6ZMAQD06dNHszdXBDKZDPXr18e1a9eQkJBQEOS86e335+npCQcHB4SGhiI3N1c0GLl06RJkMplO0ruo+9l+9uwZJk6ciKZNm+os+Pr333/RvXt33L9/H/b29tixY0exNkOMHTtW7eL/rVu3AkCZTNxcv359GBsb48qVK6U9FNIAk6xSqfnhhx8KXeybf9izn5+fVu2mpaVhwIABiIuLw+DBg0U/jEuCuoXLu3btwosXL9CjR49i7XbM9+GHHyI4OBg1atTA5s2bERkZibS0NMjlciQnJ+Off/7BypUrYWFhgXnz5pWbhbDt27eHubk5fv/9d9FZlPy/4Hv27FlwrVu3bpBIJCoPuxaro4q9vT0MDQ2RlJSkcuaoXbt2BUGEhYWFVgk2i6JHjx4QBEEhHUK+Z8+e4fjx46hRo0bBfysSiQTdu3fHq1evcOjQIaU6165dw507d9C2bVuNHpnq82e7YcOG+Oyzz9Rmpvf19UXnzp0xYcIEREZG6n0Xallz9+5dRERElPYwSEOc+dLS119/jQ0bNiAuLg7+/v5YsWIF2rdvX9rDKpd+/vln7Nq1C99//71SSonLly/jxx9/hEwmEz1SRZWDBw9i5syZuHnzJlq1aoVNmzbpeNSqTZgwAXZ2dpgyZQocHR0V7v3222/45JNPYGZmVpDfCMjbRTRq1ChIpVJs3bpV4xmxW7du4cCBA6hatSquXLmi9LjE0tIS9erVQ7169dCvXz/4+Phgw4YN+OKLL9Tm5enUqRMsLCwUZoksLCywYMGCEk2ums/MzAzjx4/H8uXLMW3aNKxevbog79HJkyfx3XffwdPTUyE/lJeXFz788EP897//xYoVKxRSDPz888/466+/0Lx5c43yxxkZGaF379749ddf0bdvX+zbt09h2396ejo+//xz7Ny5E87OzoiLi8N7772HM2fOFLq7rjg++eQTLF++HJ9//jnat29fkFMqMzMTo0ePRlZWltKOvKlTp+Knn37C5MmT0bBhw4JHYUlJSQUzPG/XGTRoEP766y989dVXCjPGRfnZLqrGjRujcePGastJJBIEBwcXu7+SsmzZMkRFRemkrS+//FL0qC1dysnJwYQJEwotkz+rtmXLFpw7d05lOXt7e3z55Zc6HV+FU9or/suTNWvWCJaWlsLu3buFO3fuCHPmzBFMTEyEiIiI0h5auZS/e08qlQqtW7cWPvroI2HkyJFCq1atCq5///33SvXw/7sg83chjhkzRujZs6fg5OQkABCMjIyEGTNm6P2w1+nTpxf037p1a2HUqFHCxx9/LDRr1kwAIFhYWAh//fWXQp0VK1YU7BTS5oDxs2fPanzAtVwuF1xdXQUAwoMHD5Tu79mzRwAgjBs3TuP+i0PdbkdBEITU1FQhMDBQACDUrVtXGDt2rNC9e3dBKpUKZmZmoodax8XFCV5eXgIAoXnz5sK4ceOE9u3bCxKJRHB0dBTu3bun8RifPn0q1K5dWwAgmJiYCN26dRM+/vhjoXfv3oK1tbUAQKhfv77w+PHjgn/3d999V8jKytL6+6HJwdqCIAi7du0SDAwMBFNTU6Fv377C6NGjhWrVqgkAhD59+ogeAL1s2TIBgGBjYyMMGTJEGDlyZMHO16lTpyqUffHiRcHP4tu7Wovys/2mO3fuiB7+XRqKs9tRKpVqdCC1IOQd5g7oZiei2H+3bzM2Nhasra2VrqenpwsZGRlq66enp+tsvNocwF5ZMfjSUG5uruDu7i7Mnz9f4XqdOnWECRMmlNKoyresrCxh48aNwrvvviu4uLgIhoaGgrGxsVCjRg1h5MiRwvXr10Xrvf0fuqmpqVClShWha9euwtKlS4Vnz57p9428Yc+ePUKfPn0ET09PwcTERDAxMRG8vb2FSZMmKaXMEARBuHHjhuDm5ib6YViYtLS0gg/RBQsWCCkpKaLl4uPjhY8++kgAIPj5+Yl+QOcHXzY2NoKvr6/Gr169emn+jXmDJsFX/nucP3++4OPjIxgbGwuOjo7CgAEDhH///VdlnZcvXwqTJk0SPD09BSMjI8HNzU0YPXq0EBsbq/U4U1NThaVLlwpNmjQRrK2tBalUKtjb2wtt2rQRNmzYIGRnZwuCkJeWonXr1gIAYdiwYVr3o2nwJQh5Qfd7770n2NraCiYmJkK9evWENWvWCHK5XGWdvXv3Cm3bthWsrKwEc3NzoWnTpsLPP/8sWrZfv36Cubm5sGrVKqV72v5svyk/+DIxMdH456tevXpqvx9Foa/gS9+MjY0FAwMDrf4bHj16dGkPu9KSCIKetnmVc3fu3IGfnx/OnDmD1q1bF1z/9NNPcfToUdy+fbsUR0flWXZ2NhwdHbFp0yb07dtX43oXL15Er1698Pz5c5iamqJ+/fpwd3eHkZER0tPTER0djRs3biA7OxvVqlXD33//Lbp+7s8//0Tv3r21Hnf9+vWLvBOVKpd///0XtWvX1qqOsbExMjIydD6WESNGYNu2bRolWX2btolp9cnExEQhEbImOnfuLLomkEoegy8N/fXXX+jZsycePXoEDw+PguurVq3CjBkzdJo6gCqP3NxczJ49G0eOHEFoaKjWOzuTk5OxefNmHDp0SCHVhImJCVxcXFCnTh1069YNQ4cOLfSoGSIi0p8Ku9sxIiICzZo1g0QiUbtge8+ePejQoQNsbW1hYmICX19fzJkzB4mJiQVlXr9+DQBK51hZWFggJycH6enpun4LVAmsXbsWFy5cwKFDh4qUUsPKygqTJ0/GoUOHEB0dXbDbMTU1Fffv38fevXsxduxYBl5ERGVIhQu+BEHA2rVr0aBBg4JDgAuzePFifPDBB7hz5w5GjhyJ+fPnw8vLC0uWLEGrVq2QlJRUaP3inD5P9Mknn+DcuXNaH6JLRETlV4VLNTF48GDs3LkT/fv3R7NmzQqSIIoJDw/HggUL4ObmhuvXrytso589ezaWLl2KefPmYe3atQW5cFJSUhS2/CYnJ8PIyIgzC1QkRkZGpT0EIiLSswo38xUbG4tt27Zh165davMZbdy4Ebm5uZg6dapS/qLPPvsMpqam2LZtG9LS0gqyrT948ECh3P379+Hr66vT90BEREQVV4Wb+dq3b5/Gh9yeOHECQN6Oj7dZWlqiSZMmOH36NK5evYq2bduiWrVqOHToUMFBu4Ig4MiRI2p3imVmZqrdhZKbm4uEhATY29vzUSYREVE5IQgCXr9+DTc3N41PeKhwwZemgVd2djYiIyMhkUhUHizr7e2N06dP48aNG2jbti3mzp2LyZMnIzAwEI0aNcKaNWsQFxeHSZMmFdrX119/jUWLFmn9XoiIiKh8ePz4MapUqaJR2QoXfGkqOTkZcrkc5ubmMDExES2Tf4BtQkICAGDMmDFISkrClClT8Pz5czRo0ACHDx+Gu7t7oX3NmTMHU6dOLbRMUlISPDw88PjxY9HDfImIiKjsSU5ORtWqVbU6XqzSBl9paWkACl/wbGxsrFAWAKZPn65wbpwmjI2NC9pSx8rKisEXERFROaPNkqEKt+BeU2ZmZgCArKwslWXysyvnlyUiIiIqrkobfFlbW0MmkyE1NVXlERbx8fEAAAcHB30OjYiIiCqwSht8yWQy1KpVCwBw9+5d0TJ37twBADRo0EBfwyIiIqIKrtIGX8D/Ukzs379f6V5cXBxCQkJgb2+PgICAEhtDcHAw/Pz8EBgYWGJ9EBERUdlRqYOvcePGwcjICKtXr0ZMTIzCvdmzZyMnJwfjx48v0SzkQUFBuH37Nq5evVpifRAREVHZUaF2Oz5+/Bi7d+8u+Dr/bMdbt25hxYoVBde7du0Kf39/eHt7Y+XKlZg4cSIaNWqEIUOGwNbWFocPH8b58+fRsmVLzJ07V+/vg4iIiCouiSAIQmkPQldOnTqFd955R225LVu2YMSIEQVfHzx4ECtXrkRISAgyMjJQo0YNDBgwANOnT9fbmY3JycmwtrZGUlISU00QEZUQuVyO7Ozs0h4GlWFSqRSGhoYaly/K53eFCr7KMwZfREQlRxAEPHv2DElJSeDHHqljbGwMBwcHjT6Pi/L5XaEeOxIREYlJSkpCYmIiHB0dYW5uzjN0SZQgCMjOzkZSUhJiY2MBoEQmRBh8lbLg4GAEBwdDLpeX9lCIiCokQRDw/PlzWFlZMW8jqWVqagpLS0vExMQgPj6+RIKvSr3bsSzgbkciopIll8shl8u5pIM0JpFIYG1tjczMzBJZI8jgi4iIKrScnBwAecm1iTSVv+i+JJ5MMfgiIqJKgeu8SBsl+fPC4IuIiIhIjxh8EREREekRH4ATERFVYPHx8WrLGBkZFWxI+Omnn/D5559j165daNasmUK5Y8eOITExUaN+7e3tNUp8Xhzp6elISkoSvSeXy5Gbm4uqVasCyEvE/vHHH+Prr79G7969S3Rc6jD4KmVMNUFERCXJ0dFRbZkOHTrg2LFjAPKShj569AgZGRlK5aZPn45//vlHo37btGlT4sHX7t27MXLkSJX3q1WrhgcPHgAAUlJScPfuXZXBmj4x+CplQUFBCAoKKsiQS0REpEt//fWX2jJGRkYICwsDgILkomKOHz+uUeqFWrVq6eV4vvbt22PPnj0FX0skEhgaGiI+Ph7Dhw9H8+bNS3wMRcHgi4iIqALr3r07jhw5gs6dO+OXX37BwIEDAQBTpkzBqlWrkJycjNOnT6Nhw4Zq27K3t9eoz5SUFNjY2BRn2Brx8PCAh4eH0vX//Oc/AKBwjnO+3NzcUk8/wgX3REREFZxUKgXwv5xnAApmsGQyGdq3b4/IyEhERkbiiy++KFZfqampkMvlsLOzK1Y7RRUREYHly5ejY8eO6NSpk9L9jz76CIaGhjA0NCyY7dM3znwREVGlM/v3cNyNe13aw9CYr7MllvSpV+T6+TM8b64vzg++DA0N8fz5c4SEhAAAHj16VIyRAnFxcQA0W2u2detWjBw5Elu2bIGDgwMWLVqEmzdvwsLCAu+88w4WL14MHx8fjfuOiIhAly5dYGVlhZ9++km0zMCBA9GkSRMAgLu7u8Zt6xKDLyIiqnTuxr3G9ejE0h6G3qib+bp27VrB48jiiomJAQC4uLhoXGfbtm24cuUKZs2ahdmzZ+PKlSv49ttvcfz4cYSGhqJatWqF1s/KysKGDRswb948ODk54eTJk3B1dRUt26lTJ9HHkfrE4IuIiKiCUzXzlR+Ude/eHYIgAADWrVuHiRMnFpSLjIzE6dOnNe4rNDQUABAeHo4ffvgBAFC7dm20bNlSZZ2zZ8/i7NmzBQvk+/TpA09PTwQFBWHBggXYtm2baL3o6Gj88ssv2LBhA6KjozF06FCsXr0atra2Go+3NDD4IiIiqqDyA6eHDx8CyAty8gOuu3fvAgDmzp2Lx48fIywsDF999ZVSG+fPn8eYMWNU9pHfHpC3mD0/iPvuu+8Kro8bN67Q4Kt///5KOxNHjx6NhQsXYt++faJ1Xr16hWbNmiE+Ph7du3fH77//joCAAJV9lCUMvoiIiCqotwOnHTt2YMeOHZBIJDAxMYGtrS127doFd3d3tGjRArVr11Za8zVixAjRx3QLFy7EokWLEBISggYNGgAAOnbsiHPnzonmCCtMmzZtlK4ZGRmhdu3aOHPmDOLj4+Hg4KBw39bWFmfOnIGNjQ0sLCwKDsIGgPv37+PVq1cFwVhiYiJSUlIQEREBb29vrcZWEhh8lTImWSUi0j9fZ8vSHoJWijpeVYFTYWxsbFCjRg295OnK5+zsLHo9//FhZmam6P2aNWsiJCQEgYGB2LJlS8F7/c9//oM///yzIBt/ZGQkBg4ciPXr1zP4IiZZJSIqDcXZOVje7d+/Hz///DNCQ0Px9OlTpKenw9TUFO7u7ggICMCwYcNw79490bqrV69Gamoq5s6dq9Mx5T+qfNuTJ08gkUiKnbYiLS0NgOZ5ykoagy8iIqJKQBAEDB8+HNu3b0fNmjXRs2dPVK9eHaampgWP5Pbv349ffvkFo0ePxqZNm5Ta2L59O+Lj43UefIkFe8nJybhz547G2fJHjhypcNTQmxMa586dAwBcu3YN/fr108GIi4fBFxERUSVw/PhxbN++HT169MBvv/0GIyMjpTKrVq3C4MGD8cMPP2DQoEGFns1YrVo1tG3bFhYWFgXX6tevDwMD7fO3r1+/HuPGjSs43BsA1qxZg5SUFAwZMkSjNpYtW4YePXoAAObMmYOTJ08CyNvVuXPnTgDApk2bMH36dKX1Y/rGDPdERESVQH7y05YtW4oGXkBeSopmzZoByHvkV5gRI0bg1KlTqFmzZsG1lStX4siRI1qP7cWLF2jUqBFWrVqF3377DZ988gkWLFiAxo0bY8qUKRq14erqilq1aqFWrVoKs16LFy/GrVu3sHHjRsjlcgwfPlwh31lp4MwXERFRJdC1a1c4OztjwYIFyM3Nxfvvvw8vLy+YmJggPT0dDx48wL59+7B48WI4OzujS5cuou28fv0a69at06jPevXqie5kfNuSJUsQGRmJZcuWIT4+Hm5ubpg2bRrmz5+v8cL/U6dOFeyyjIyMhCAImDFjBlasWIGhQ4di7NixkMvlGD9+PLp164bt27fDyclJo7Z1jcFXBSUIAlIyc/DidWbeKyXvf+P//3/zr71KzYaduRGaetmhjY8jmnjZwcRQqr4DIiIqV+zs7HD27FlMnDgRn332mei6LQMDA3Tu3Blr1qxRuTg9ISFBIQlrYYKCgjQKviwsLLB69WqsXr1ao3bF/Pjjj/jxxx8LvjY2Nsavv/6KXr16FSR7/eSTT5CUlISffvqpVLMMMPiqoHqvv4Cwx4kalY1NTMeN2CT8cO4BjGUGaOJlh7Y+jmjj4whvJwtIJJKSHSwREemFt7c3Dh06hPj4eFy/fh1Pnz5FZmYmTExM4ObmhgYNGhS6IzD//MeyJCAgQOVuyVevXsHa2lphHdrs2bMxZcoUGBsb62uIShh8VVBWpobqC4nIzMnF2ch4nI2MB/6+AxcrE/So74oJ7b1hXcQ2iYiobHFwcMC7775b2sMocaqOGSrNwAvggvtSFxwcDD8/PwQGBuq0XUcL3fxgPUvOwKazDzBmWwjkueJ/WRAREZHmGHyVsqCgINy+fRtXr17VabuOlrqN6q88TMDPlx6pL0hERKShESNGQBAErbPwl3cMviqowoIvY5kBqtiaoqGHDVp7O2j8OHHF4bt4/lq787qIiIhIEdd8VVCNPGzwSbsacLQwhqOl4svSWKawiF6eKyA8JhFnIuJxJvIFwh4nij5ifJ2Zg6/+voNVAxrq860QERFVKAy+KqiGHrZo6CG+0PBtUgNJQflPO3ojKT0b+8Of4LM9N5XK/hn2BP0CqqJFzdLNDkxERFRe8bEjKbE2NcTgpp7oWd9N9P68vTeRlZOr51ERERFVDAy+SKV53WrD0lh5cjTqRSo2nY0qhRERERGVfwy+SCUnKxNM6+Qjem/N8Ug8TkjT84iIiIjKP675okINbV4N/w2Nwa0nyQrXM3NysXDfLfwwPECnGfAj4l7jp4sPcehmHOS5uajjbo3GnrYI8LRDAw8bWIjMxBEREZUn/CSjQkkNJFjcqw4+2HABb5/ecPzf5zh6Ow6d/F2K1UeOPBfH7sRh24VHuBj1UuFeQbZ9AAYSoLarFQI8bdG4mh2aV7fXeT4zIiKiksbgi9Rq6GGLgU088MvlaKV7i/66jVbeDjAz0v5H6WVKJnZdfYwdlx7hSZL6/GG5AnDrSTJuPUnGtouPIDWQYGL7mvi0gzfPnyQionKDwRdpZFbnWjh88xlepmYpXI9NTMea4/cwu2stjdoRBAFXH77CrivR2B/+FFnyou+alOcKWHUsEjIDCSa09y5yO0RERPrEBfelrKTOdtQ1azNDzHmvtui9TWejMOGXa9gf/gQpmTmiZZ4lZSD45D28s+IU+m28iD+uxxYr8HrTiiMR2HGZRx8REZVV8fHxGDt2LFxdXWFqaop69erh+++/16qN1NRUzJw5E56enjAxMYGPjw+WLFkCuVyuVFYikah81aql2WRBSeLMVykLCgpCUFAQkpOTYW1tXdrDKVSfRu749epjXHmYoHBdnitgf/hT7A9/CiOZAdp4O6BLHVe09XFE6KME/BoSg1N3n0Obc7lNDA3g52qF20+TkZGtPkib9+dN2JoZ4b26rtq+LSIiKkGvXr1CixYtEBcXhzlz5qB69er4888/MW7cONy/fx9Lly5V20ZmZiY6deqEkJAQzJgxA/Xr18fp06cxd+5chIWFYdeuXUp1Bg4ciEGDBildt7Cw0Mn7Kg4GX6QxiUSC//Sqg25rziJHRSSVlZOLY3ee49id50Xqw8PODMOae6Jv46qwNjNEtjwXt58kI+TRK4Q+SkDIw1d4/jpTqZ4gAJN3hcHG1JDZ94mIypCFCxciMjISR48eRceOHQEA/fr1g4GBAZYvX47BgwejXr16hbaxfv16XLhwAZs2bcLo0aMBAH379oWNjQ2+/PJLDB48GD169FCo4+Pjg+7du5fMmyomPnYkrfi6WOKjVl46b7etjyM2jwjAyentMLp1dVib5R32bSg1QP2qNviolRfWD26My3M74MvedUTbyJLnYsxPIbgRk6Tz8RERkfays7OxdetWBAYGFgRe+ebMmQNBELBp0ya17WzatAkuLi4YOXKkwvVp06bByMhI60eYpY0zX6S1qZ18cP9FKo7diStWOzZmhvigYRUMbe4JLwdzjepIJBIMbuqJV6lZWHEkQul+apYcI7ZcwX8/bo7qjqU/tUxE5UDz5rptb8AA4NNPCy+zejUg8qhMycWLxR7OwoULsWjRIpw8eRIPHz7E8uXLce/ePdjb26Nr165YvHgxXF1VL9nIr6/K8OHDsXXrVtF7oaGhSE5OVgq8AMDf3x8uLi44fvx4oeN/9uwZ7ty5g4EDB0IqlSrcs7W1RaNGjXDy5EkIglBudr4z+CKtGcuk+GF4AMIeJ+Lgzac4dPMZHr3ULNu9RAK08XZEv4Cq6OjnBGOZVH0lEUHv1MTL1CxsOf9Q6d7L1CwM/fEKfv+kBVysTYrUPhFVIpcu6bY9TYK5R490368aixYtwo0bNzBnzhx4enri6NGj2LRpE06ePInr16+rXHc8aNAgBAQEqGy3atWqKu/dvXsXAODr6yt639fXFxcuXIBcLlcKrLRp49KlS3j8+DE8PDwU7gmCgMzMTJiYlK3PAgZfVGQNqtqgQVUbzO5SC/8+e41DN5/h0M1nuBv3Wqmsh50Z+jaugj6Nq8DNxrTYfUskEszv5odXqVn4M+yJ0v3YxHQM23wZW0c20Ul/msjIluN6dCIsTWSo7WoFqUH5+AuMiCqH69evIyQkBDVr1gQAfPjhh7C2tsby5cvxzTffqJzd8vHxgY+P+FFz6rx48QIA4OTkJHrfyckJ2dnZSEpKgp2dXZHbAPJ2VL4ZfC1btgxffvklcnJyYGxsjEaNGmHMmDEYMWJEqc+QMfiiYpNIJKjtaoXarlaY8q4Pol6k4NCtZwh/nAQHSyN0q+uGpl52MNBxMGJgIMHyvvWRmJ6NU3dfKN2PiEvBOytOYUzr6vi4XY0SPZro0ctUDP3xCqL//7zL1t4O2DCkMY9DIqIyIygoqCDwyjdz5kx8++232Lt3r8rgKysrC1lZWaL3AMDQ0BDGxuKnjaSnpwOAyvv5M1JpaWkqgy9t2si3aNEi1K5dG/b29khNTcW9e/ewbds2jBo1CseOHcOOHTtUvh994CcD6Vx1RwuMb1dTfUEdMJQaYP3gRhjyw2Vci05Uup+Zk4t1J+9h19XHmNbJB/0Cqup8RiorJxfjtocWBF5A3rFIn++9iW/6NdBpX0RERdWmTRulaw4ODnBzc8O9e/dU1vvqq6+KvOYrP2DKyRHPAZmdna1QTldtfP7550rlJk+ejNGjR2Pz5s0YNmwYOnfurLLPksbgi8o9MyMZNo8IRL+NFxERlyJaJj4lE3P+uIGt5x/is2610cbHUWf9rzsRiX+fKT9q/eNaLNr5OqFnfTed9UVEJaBZM9225+mpWRld96uGs7Oz6HVbW1s8eaK8fCPfqFGjRBfMq2sXAGxsbAAASUniu9ATExMhkUhgZWVVrDYAqM2VKZFI8Pnnn2Pz5s04fPgwgy+i4rIxM8JPo5qi78YLeJyQrrLc3bjXGLb5Ctr6OGLKuz6oX8W6WM/+b8YmIfjUfZX3P9tzA409beGup3VnRFQEOthRqLVPP1W/I1LHBEE8P+OTJ0/g4KA6P6KHh4fSQnZN5T/mjIqKEr0fFRWFqlWrFjrzpUkbUqkUXl7q0yDl7+rMyFB/nnBJYp4vqjBcrE2wN6gV+jauAnXx1OmIF+gVfB7d1pzDjsuPVB6LVJjMHDmm/foP5IWk7n+dkYMpu8MKLUNEpA9ijxajoqIQHx+Phg0blkifgYGBMDIywtmzZ5XuxcbGIiIiAq1atSq0DS8vL7i7u4u2kZGRgUuXLqFp06YwNDRUO56QkBAAeWkuShODL6pQ7MyNsLxvfeyf2AotatirLX/7aTI+23MTTb88hjl/3MDNWM0TtK45Him6s/NtVx4k4LvTqmfHiIj0YeXKlQXro/J99dVXEAQBQ4YMKZE+LS0t0adPHxw+fBjh4eEK95YtWwYAGDFiRMG1Q4cOITAwENu3by+4JpFIMGzYMNy6dQsHDx5UaGPDhg1ITk5WaGPfvn0Ki+/zpaSkYMKECbCyskL//v118O6KTiKomockvco/2zEpKanQZ9+kOUEQcOLf5/jqwB3cf5Gqcb16VawxpKknejV0h5FM/O+Tfx4n4oMNFzSe0ZIZSPD7Jy1Qv6qNxuMgIt3IyMjAgwcP4OXlVebyPelDfpJUc3Nz+Pr6YvTo0bCzs8OePXuwe/dudO/eHfv27Sux9AuPHj1CkyZNAACzZs2Ch4cHDh48iM2bN2PIkCEKgVa3bt1w4MAB+Pv74+bNmwXXExMT0axZM8TExGD27NmoXbs2Ll68iFWrVqFNmzY4cuQIZLK8lVTt27fHjRs30L9/fwQEBMDGxgZRUVH49ttv8fLlS/z6668aHTuk6c9NUT6/ueaLKiyJRIIOtZ3RxscRu65E49tjkUhIVb1dOl94TBJmxoTj+7NR+KKnv9JZkRnZckz/r/jjxloulkjNylFad5aTK2Dy7jDsn9gK5mUs/YQgCLj/IhUu1iZMjUFUgW3evBkHDhzA559/juTkZFSrVg1ffvklpk+fXqJ5rzw9PXH58mXMnTsXS5YsQVJSEmrUqIEVK1Zg8uTJCmV79eqF06dPo2/fvgrXbWxscO7cOcybNw8bNmxAfHw83N3dMWfOHMydO7cg8AKAHTt24JtvvsHhw4exdetWZGZmwsXFBR07dsSsWbNQq1atEnuvmuLMVxnBma+Sl5yRjQ2n7uPni4/wWos1Xt3ruWJeN7+CbPlLDv4r+hhRZiDBvgmtkJ6dg77fXYTYpNiAwKpY0qfwA2T1KfplGkZsvYKoF6mQSIDpnXwxvl2NUk9ASKRLnPn63/FC7dq1K+3hlBslOfPFNV+lLDg4GH5+fggMDCztoVR4ViaGmNWlFi5/1gHL+tTT+BHg/vCnaL/yFDaevo+rDxPw/Rnx9VuTOnjDz80KjT3tMLG9t2iZXVcf49DNp0V9CzolCAIm7LyGqP9/JCsIwPLDd/HfkJhSHhkRUcXGZwylLCgoCEFBQQWRM5U8MyMZ+gVWRb/Aqrj1JAm/XI7G3rAnhe54TMuS4+uD/0IiyQtS3lbH3QqftKtR8PXE9jVxJvIFroskfp39xw1IJBLUcrFEVVsznWf+19TF+y8RHqO8weA/f99GGx9HnotJRFRCGHxRpebvZo0ve9fF3PdqY98/T7D1/MNCdzCKBV6GUglW9K0PQ+n/JpJlUgOs7t8QXVefQWqWXKF8Ylo2xm0PBQCYGBqgppMFvJ0s4e1sgdquVmhV00GhrZLy08VHotdfZ+Rg7p4b+HF4AB8/EhGVAK75KiO45qtsyJHnYsflaKw4chevMzRbFzajsy+C3hE/Tun30BhM++8/Wo3BxcoES/rURTtf8UNkdSE2MR2tl54QXZeWb2Xf+ujTuEqJjYFIXyr7mi8qGq75ItITmdQAw1tUw4lp7fChBoFHvSrWGNemusr7HzRyR/d6rlqN4VlyBkZsuYqvDtxBVk6uVnU19cvlR4UGXgCw6K9beJ5cvCzQoY8SsPZ4JPaHP0FGtlx9BSKiSoDBF5EIR0tjrOhbH79/0hx+ruJ/yRhJDbCib33ICnlEKJFI8GWvukU6Xuj7M1Hou/Eiol8qJwssjswcOXZdeay2XHJGDubuuanySBJ1tl98iD4bLmLl0QhM+OU6xm0PLbFgkoioPGHwRVSIxp522DehJRb19Ielyf+WSEokwBfv+8PH2VJtG9Zmhtg1thna+TpCpuXi+n8eJ6LbmrPYH6760FttHbjxFC81yHcGAMfuxGHfP9r3fffZayz667bCtdMRL7Djsvg6MyKiyoQL7onUyH8U2b2eKw7cfIaniel4p5YTAqvZadxGVTszbB3ZBNnyXDyMT0VEXAoi4l7j3vO8/30Qn4ocFc8BX2fmYMIv13H+Xjw+7+4PUyNpsd7PtgviAVBtVyvceZqsdH3BvltoUcMBjpaqD759U26ugHl/3hB9P+tO3EPfgKpM5kpElRp/AxJpyN7CGEObeRarDUOpAbydLeHtbIlu+N9asKycXOy6Go3Ff6te57XzymNceZCA1t6OsDc3gr2FMewtjOBgYQQ7c2M4WBjB0qTwg2XDYxIR9jhR6bqPswV+/qgJOn17RmlWLDEtG/P/vIkNQxpptPvxt2sxuPrwlei9l6lZ+PHsA3zaUTwPGhFRZcDgi6gMMJIZYFjzamjsaYuJO68XJD592/0XqYWeU1nX3RpL+tSFv5t4zjhV6SWGNa8GewtjfPF+HQT9ck3p/qFbz/D3jafoXs+t0PfxKjULXx+4U2iZTWejMKSZB+wtNJtJIyKqaLjmi6gM8Xezxl8TWmm001LMjdgkDPj+Em7GKidPfZWaJbp+y9JYht4N3QEA3eq54r26LqJtf773Fl6mZBba/5KD/+JVWnahZVIyc7D+lPgpAURElQGDL6IyxtxYhhV96+Pb/vVhXoT1Xa8zcjBs8xVEvpUsdnfIY9FHmn0aV1E47PuL9+vA1kz58WVCahaCfrmGpHTx4OrqwwTsDlG/ixIAtl98hNjEdPUFiYgqIAZfRGVU74ZVsH9Sa9Rx1z7pbkJqFgb/cBkP4/MeUcpzBWxX8chxaHPFdWwOFsZY2NNftOylqAR8uOECHicopr/Iludi3p6bonXEdnhmyXOx6miE2vdBRFQRMfgiKsO8HMzx+yctML+7H+pXsRadkVLl+etMDP7hMmJepeHkv89FZ5paezughqOF0vWe9d3wrp+zaLuRz1PQe/15XI/+36L6zeceiB7LZGYkxZaRgRDLsPH7tRil2Tkiqpji4+MxduxYuLq6wtTUFPXq1cP333+vVRupqamYOXMmPD09YWJiAh8fHyxZsgRyueoEzkeOHIG5uXmZOyqNC+6JyjhjmRQftfLCR628AOQdgfQqLRsvUzPxMiUL8SmZ+C00Bmcj45XqxiamY/APl2FvbiTa9rDm1USv5yWHrYNrj16J5gSLT8nCgO8v4dv+DVCvijVWHYsUbWdyR2+09nZE38ZVlR5J5grAiiN3sXFoQGFvn4jKuVevXqFFixaIi4vDnDlzUL16dfz5558YN24c7t+/j6VLl6ptIzMzE506dUJISAhmzJiB+vXr4/Tp05g7dy7CwsKwa9cuhfIZGRlYvnw5Fi9ejNzcspfcmcEXUTkjkxrA0dJYIe9WZ38XjNhyBZeiEpTKP3qZhkciWfLdbUzRvpbq8yOdrEzw00dNMGrrVcQlKy+0z8zJxfgd1+DlYI50kaODarlYYmTLvIDx047e2BMWq7Tm7PCtOFyPfoWGHraq3zARlWsLFy5EZGQkjh49io4dOwIA+vXrBwMDAyxfvhyDBw9GvXr1Cm1j/fr1uHDhAjZt2oTRo0cDAPr27QsbGxt8+eWXGDx4MHr06FFQftmyZVixYgW+/vprhIWFYfv27SX3BouAjx2JKgATQyl+GB6Ihh42GtcZ0swTUjUZ9/3drPFnUEvUVnHEEgA8iBdPfbG4Vx0Y/v/RS242phjeXDxH2tJD/xb5CCMiKtuys7OxdetWBAYGFgRe+ebMmQNBELBp0ya17WzatAkuLi4YOXKkwvVp06bByMhI6RHmhx9+iMePH2Pq1KkwMCh7oQ5nvogqCAtjGbaObIJBmy7h1hPlTPVvMpIZoH9gVY3adbU2xX8/bo5JO6/jxL/PNarTP6AqAt46AWB8u5rYdeUxXmfmKFy/FJWAs5HxaOPjqFHbRLrWe/15pWs96rlh1P8/6lflWvQr/Gf/baXr87v7oZGa2dzN5x7gL5Fjw/aMb6lmtNpbuHAhFi1ahJMnT+Lhw4dYvnw57t27B3t7e3Tt2hWLFy+Gq6ur2vqqDB8+HFu3bhW9FxoaiuTkZKXACwD8/f3h4uKC48ePFzr+Z8+e4c6dOxg4cCCkUsUd4La2tmjUqBFOnjwJQRAK1nb5+fkV2mZpY/BFVIFYmxpi+0dN0X/jRUQ+T1FZrkc9N9ipWAcmxsJYhu+HNsbiv+9g64WHhZa1NTPE7K61lK+bG2Fsm+pYKbLLcdnhf9GqpgMMtDz7kkgXrkcnKl1TFzwBeWldxOq+zshRLvyW2MR00boladGiRbhx4wbmzJkDT09PHD16FJs2bcLJkydx/fp1WFuLJ2ceNGgQAgJUr82sWlX1H3J3794FAPj6+ore9/X1xYULFyCXy5UCK23auHTpEh4/fgwPDw+VYylLGHwRVTB25kbYMbop+m28iIcia70AYHgL7Y9JkkkNsLCnPzztzfCf/beh4ihKzHmvNmxVBHajWnlh28WHiE9RXMR/MzYZB26qz6BPREV3/fp1hISEoGbNmgDyHs1ZW1tj+fLl+Oabb1TObvn4+MDHx6dIfb548QIA4OQkvr7UyckJ2dnZSEpKgp2d+Hm5mrQB5O2oLC/BV9l7EEpExeZkZYIdY5rB3cZU6V5DDxvUq2JT5LZHtvTC90MDYCaSALZJNTt82Eh1dn5zYxkmthc/1/HLv+/glcjOSiLSjaCgoILAK9/MmTMhk8mwd+9elfWysrKQkpKi8pWZqfrki/T0vBQ3xsbix4mZmJgAANLSxP9Q1FUbZQ2Dr1IWHBwMPz8/BAYGlvZQqIJxtzHFjtFN4WlvVnDN3EiKpX0K31WkiY5+zvh1XHN42P2v7ZpOFlg1oIHaR4cDm3igiq1yUPg0KQMzfvuHi++JSkibNm2Urjk4OMDNzQ337t1TWe+rr76CpaWlyte4ceNU1s0PmHJyxB/FZmdnK5QrqTbKGj52LGVBQUEICgpCcnKyyuftREVVzcEchye3wcGbT5GelYsudVy0WutVmDru1jgxrS3ORL6AkVSK+lWtYWmiPgmskcwA0zr5YMruf5TuHbvzHD+ee4DRravrZIxEmhDbJSw2a/w2SxOZaF1LE/Ufre42plrtTtYFZ2fxxMm2trZ48kR58X++UaNGiS6YV9cuANjY2AAAkpKUz5sFgMTEREgkElhZqd5RrUkbAMrVZyiDL6IKzsRQit4Ni3ZQtzoyqQHa11L9i1eV9+u747fQGJy/91Lp3pKD/6KRp61GC56LQhAE7Lgcjb/Dn0JqIMG4ttXR2ps7LSuzou4wbORhW+S6o1p5qd1NqWuqZpWfPHkCBwcHlfU8PDyKvJYq/zFnVFSU6P2oqChUrVq10FkrTdqQSqXw8tLv97M4+NiRiPTOwECCb/s3gIOF8i/cnFwBE3+5jqQ08QO8gbwPkdBHr7Dx9H0cux2n1aPKNcfvYd6fN3Ex6iXO3YvHyC1Xcedp4ak5iCoCsUeLUVFRiI+PR8OGDUukz8DAQBgZGeHs2bNK92JjYxEREYFWrVoV2oaXlxfc3d1F28jIyMClS5fQtGlTGBpqfvxaaSty8CUIAi5fvozly5dj2LBh6NSpE9q2bYv3338f06ZNw86dO/H8uWY5gYio8nGyNMHqAQ0gduRabGI6pqtY/3XveQqG/ngFfTZcwNcH/8Xon0IwYed1yFVtv3xD2ONErD6umOoiJ1fAuhOq17sQVRQrV64sWB+V76uvvoIgCBgyZEiJ9GlpaYk+ffrg8OHDCA8PV7i3bNkyAMCIESMKrh06dAiBgYEKGeklEgmGDRuGW7du4eDBgwptbNiwAcnJyQptlAdaP3Z88eIFVqxYge3btyMuLg6A+FSmRCKBRCJBu3btMGnSJPTs2bP4oyWiCqVlTQdMbO+NNceVz4Y8ejsOm88/LDjTMjUzB2tP3MOP56KQLVf8nfN3+FPUcLTA1HdVb4fPyJZjxn//EU2RcSbiBbJycmEk48MAqrhu3ryJZs2aYfTo0bCzs8OePXuwe/dudO/eHQMHDiyxfr/++mscP34c7777LmbNmgUPDw8cPHgQmzdvxpAhQ/Duu+8WlF27di1CQkKwdOlSDB06tOD6zJkz8ccff6Bv376YPXs2ateujYsXL2LVqlV45513lDLfl3UaB1/JyclYunQp1qxZg9TUVBgYGKBx48Zo3749qlWrBmdnZ5ibm+P58+eIi4tDSEgITpw4gRMnTuDkyZMICAjAl19+WeiiPSKqfD7t4I0rD16Knku55OAdNPa0ReyrdCz++zaeJmWobGftiUgEeNqqzJS/+nikysSzrzNzcPVhAlrWVL3uhai827x5Mw4cOIDPP/8cycnJqFatGr788ktMnz69IDN8SfD09MTly5cxd+5cLFmyBElJSahRowZWrFiByZMnK5Tt1asXTp8+jb59+ypct7Gxwblz5zBv3jxs2LAB8fHxcHd3x5w5czB37lzIZOVrCbtE0GCxxMmTJzF8+HDExMTA398fkyZNKjjQUp3Q0FBs2LABv/zyCzIzMzF8+HAEBwfD1FT9TpLKJH+3Y1JSUqG7PogqoufJGXhvzVml5KtA3u7Itw/kVsXO3AgHJrWGi7WJwvWwx4n4YP15lYlhAWBky2pY0MNfq3FT+ZCRkYEHDx7Ay8urICdUZfLm8ULt2rUr7eGUG5r+3BTl81vtHPuePXvQqVMnODo64tixY7hx4wbGjBmjUeAFAI0bN8YPP/yAmJgYzJkzBzt37kTr1q01qktElYOTlQm+7S++/kvTwAsAElKzMHHnNeTI/1ensMeNbzp+5zlzjBGRXqgNvqKjo/Htt9/i6tWraN++fZE7srOzw+LFixEeHg57e/sit0NEFVNrb0cEtaupvqAaVx++wooj/1tUX9jjxjdFJ6ThngbliIiKS+1D0k8//VSnHXp7e+Pw4cM6bZOIKobJHb1x5WECrjxQXv/1pqZedvji/Tr49mgEDt16pnT/u9P3EVjNFvYWxth4+r7G/R+78xzezpYalRUEoUTXyRBRxaWXrT1bt27FqFGj9NEVEZVjMqkB1gxoqDILv5OlMVYPaIBdY5vB18USSz+sp3DE0Zum/voPpu4OE33cWMvFEjKRY5CO34lTO8Y912Pw7jen0XjxMcz78way5Zo/FiUqDQsXLoQgCFzvVYboJfg6f/48tm3bpo+uiKicc7E2wdqBDRUO7pYaSDCmtReOT2uL9xu4F8w4WZsaYv3gRjCSKv8qS0rPRlR8qtJ1Q6kEqwY0QGA1O6V716JfIaGQw70v3IvHtF//QeTzFCSkZuHnS9FYvP92Ud4mEVViTGpDRGVOy5oO2BvUEpM7emPquz44OqUNPuvmJ3p2ZB13a8zv4adx25Pae6OWixU61HZSupcrAKfuiieHFgQBy4/cVZpJ+/lyNGJepWncPxGRVsHXV199hUmTJqm8v2jRIrRsWbRzroiI3uTtbInJHX0wqYM3qjtaFFp2SFMP9KjvprbNOu5W+LhdDQBAx9riZ1IevyMefIU8eoXr0YlK1+W5Ajafe6i2byKifFoFX9u3b0dwcLDK+/fu3cOlS5eKPSgiIm1IJBJ8/UFdVHcwV1nGUCrBir71Yfj/jyirOZijhqNy+dP/n+3+bYUt3N91NbrQsyipbGAqEdJGSf688LEjEVUIFsYyBA9uBGMVRwTlP258k9jsV0pmjtJuy8i41zimYkYMANKy5Nhx5VERRk36kJ/9PCcnp5RHQuVJ/jmYUqlUTUntMfgiogqjtqsV/vN+HaXrbz5ufFMHFY8ej7216/H7M1Fq+956/iEyc+QajpT0SSqVQiqVIjk5ubSHQuWEIAhISkqCsbExDA2V15oWV/k6DImISI2+AVWQlpWDZYfvIi1LjibV7BA8uFHB48Y3NfKwgbWpIZLSFR8ZHv83Dgt6+EEikeBZUgb+DItV2+/z15nYG/YE/QKq6uy9kG5IJBI4OTnh6dOnMDY2hrm5OXO0kShBEJCdnY2kpCSkpKTA3d29RPopkeDr6NGjCs9KY2JiSqIbIiIlEokEI1p6YWjzanidkQ0bM/GcYUBeXrF3fB3xZ9gTheuPE9IR+TwFPs6W2HLhAbLlmq392HQmCh82qgIDkRxiVLqsra2Rnp6O+Ph4vHjxorSHQ2WcsbEx3N3dS+ysZZ0HX4IgoEuXLkrX+FcGEemT1EBSaOCVr0NtZ6XgC8jb9ehibYJfLkUr3TOQAO62pnickK5wPfJ5Ck5HvMA7tZTTWABAeEwiFuy7hYhnr9HAwwZf964HD3vxJLGkWxKJBK6urnBycipYy0MkRiqVlsijxjfpPPiSSCT44IMPFK6Fhobi0SMuRiWisqetryNkBhLkvJXA6/idOBhIgNeZyou036vriva1nDD113+U7m08c180+Loc9RIjtlxFenbeurDz915i0A+XcPDT1qL5y6hk5K//IipNJbLg/r///a/Cq0OHDiXRDRFRsVmZGKKJl3i2+01nxRfaj2tTAz3qu8HFykTp3qWoBITHJCpcC3mYgJFb/xd45Yt5lY5lh+4WffBEVC5xtyMRVXpiux5zBSA+RfmooRY17FG3ijUMpQYY1aqaaHtv7o4MffQKwzdfQVqW+E7I7Zce4erDwg8SJ6KKhcEXEVV6HUWOGlJlXNv/pawY2MQDlsbKqzcO3HiKxwlpCHuciBGbryBVReCVb9bv4cjIZpoKosqiSGu+6tWrJ3qduxqJqDzytM/Ldn//hfJB3G+q5WKJNt4OBV9bmhhiUFMPbHwrD1iuAHy+9yZCHr0SXTP2tqgXqVh34h6md/Yt2hsgonJFq+BLEAQIgoCbN2+qLMNdjURUHnWs7Yz7LwpPpvpx2xpKv+NGtKyGH889UFqwf/KudukMvjt9H93quaK2a8lsbSeiskOrx47nz5/H06dPC309eaK8ZbuiSUtLw6BBgyCRSHDq1KnSHg4R6YCqbPf53G1M0a2eq9J1V2tT9Gyg/lDvfF38XVCvirXS9ZxcAbN+D0eOXPlcSSKqWLSa+bK3ty+pcZQbUVFR6N27NzIyMkp7KESkQ408bGBjZohEFQdkf9TKSzRLPgCMaV0df1xTnwX/XT9nrBnYEPeep6DnunNKs2XhMUnYfP4BxrZRPgqJiCoOvSy4r0gnya9ZswYtWrTArl27SnsoRKRDednuxRfeW5saon+g6mODartaoY2PY6Htd6jlhOBBjWAkM4CfmxXGta0uWu6boxF49LLwtWdEVL7pJfiaOnUqDhw4oI+uStykSZOwYcOGEs9+S0T610HFrsehzTxhLrKr8U3j2ogHUwDQztcR64fkBV75Jrb3RnVHc6WyGdm5mPPHDa3/aI1+mYYZ//0H/TdexNJD/3L3JFEZppfgy8/PD507d9ZHVyWuenXVv2CJqHxrX8tJKXGqtakhhreoprZuixr2qC+ylqu1twO+G9IYxjLFrOomhlIs7SO+c/zC/Zf4NeSxxuO+cC8e3dacxX9DY3D5QQI2nLqPwT9cZgBGVEaVyzxfERERaNasWd4BuiNGFFp2z5496NChA2xtbWFiYgJfX1/MmTMHiYmJehkrEZUfZkYyrB3UEB52eectetiZYcPgRnC0NFZbVyKRYPWAhnC1/l/w1r2eKzYNC4CJofhxNoHV7DC0mafovcV/38G95ylq+90bFovhW64opbQIffQKs38Pr1DLPogqCrUL7pOTk9G9e3eddSiRSHD69Oki1RUEAevWrcOsWbOQlaWcefptixcvxvz58+Hq6oqRI0fC3t4eZ8+exZIlS/DXX3/h/PnzsLa2xqlTp/DOO++obOfgwYNKh4UTUcUUWM0Op6a3Q0pWDsyNZJAaaJ4+p5qDOc7MfAdhjxNhY2oIb2dLtXVmdvHFsTtxeJqkuInndUYOuq05i+mdfDGqlZfSOARBwPdnovD1wX9Vtv1n2BN4O1si6J2aGr8HIip5aoOv7OxsnDt3TmcdFicP2ODBg7Fz5070798fzZo1w5QpU1SWDQ8Px4IFC+Dm5obr16/Dyel/azlmz56NpUuXYt68eVi7di2aNm2KyMhIlW25uWm+jZyIyj8DAwmsinjYtaHUAIHVlM+KVMXSxBBf9q6DUVtDlO5l5uTiywN3cOjWMyz7sB5qOFoAAOS5Av6z/za2Xniotv3lh++iuoM5utZVTpNBRKVDbfBlZ2dXaGCiT7Gxsdi2bRuGDRuGrVu3Flp248aNyM3NxdSpUxUCLwD47LPPsGbNGmzbtg1Lly6FmZkZatbkX4ZEVDra13JGz/pu2PePeJ7E0Eev8N7qvFmwwc08MO3Xf3Dw5jON25/yaxiq2pmhjrvymjQAeJmSiW0XH+HqgwRUczBH0Ds1UMXWrEjvhYjUUxt8SSQS1KhRNnLO7Nu3D9bW4r883nbixAkAEF3ob2lpiSZNmuD06dO4evUq2rZtq9Nxvi0zMxOZmZmFlklOTi7RMRBR2faf9+vg3vMU3H4q/rsgfxZszfHIQo8skkiAt5d5ZWTn4qNtV7FvQis4v7GhICUzBz+cjcKmM1EF509ejHqJ03efY++EVhqtdSMi7ZXYgvsTJ07g5cuXOm1T08ArOzsbkZGRkEgkKme0vL29AQA3btzQagwvXrzAs2fPCt5bQkICnj17hoSEBJV1vv76a1hbWxf6qlpVdQ4hIqr4rM0M8dsnzTFCzc5KVYGXRAIs7OGHRT39Re/HJWdizE8hSM+SIysnF1vPP0DbZSex6lik0sHfT5Iy8NWBO0V6H0SkXpEO1lbn7t276NWrF9zd3XHjxg3IZCXSjUrJycmQy+UwNzeHiYmJaBk7u7w1GYUFTWICAwPx6NGjgq/79OkDAGjbtq3Ko4bmzJmDqVOnqh0zAzCiys3MSIaFPf3RtY4LZv4ejkcv0zSqZyQzwOr+DQrWdUXGpWD7pUdK5cJjkjBq61XEJqYjOqHwtvdcj0XfxlXQoqZDoeWISHs6j4qePHmC999/H6mpqRgxYoTeAy8g7+xFADAyMlJZxtjYWKGsph4+fKj1eIyNjQv6IyJSp2l1exz8tDWWHbqrdlG9takhfhgeoLDIf0EPPzyIT8W5e/FK5S9Gaf5EYt7emzj4aWulHGVEVDw6fewYGRmJli1bIiIiAp988glmzZqly+Y1ZmaWt1C0sHQU+Wcz5pclIipL8mfBdo9tBk978d9T7jam+P2T5kq7K2VSAwQPaiSaQV8bUS9SselMVLHaICJlOgu+1q9fj4CAADx69AizZ8/GunXrdNW01qytrSGTyZCamqryAOz4+Ly/CB0cOKVORGVX/izYyJbV8Gamnrru1vhjfAvUdBLPJWZtZogfhwfC2lSzlBnVHcQDtbUn7iFaw8efRKQZjYOvM2fO4OTJk7h06RKio6MB5CX5++2339C0aVNMmDABpqam2L9/P7766qsSG7AmZDIZatWqBSBv/ZmYO3fyFpM2aNBAX8MiIioSMyMZFvTwx/GpbbGwhx/WD26EPeNbKOxcFOPlYI4NQxpBVkiiWH83K/w0qgmOTm2LOu5WSvczc3Lx+b6bzJRPpEMaL8gaN24cIiIiCr42MTGBubk5Xr58CZlMhnnz5mHGjBmwtFSf0VkfOnfujJs3b2L//v2oX7++wr24uDiEhITA3t4eAQEBpTTCPMHBwQgODoZczjPYiKhw1R0tUP3/E61qqkUNByzuVQez/1Dc2e1pb4ZpnXzRva4rDP4/OPuyV130Wn9eKVXFqbsvcOjmMyZqJdIRjWe+2rRpg+7du6NHjx4ICAiApaUl4uPjIQgCcnJycOzYMZw9e7Ykx6qVcePGwcjICKtXr0ZMTIzCvdmzZyMnJwfjx48vdFG+PgQFBeH27du4evVqqY6DiCquAU088MOwALTzdURrbwd82bsOjk5pi5713QoCLwCoX9UGQ5qKnzW56K/bSCkkvxgRaU4iFGMuOSoqCkePHsWPP/6IkJAQSCQS9OjRAxs3boSzs7Mux4nHjx9j9+7dBV+HhIRg9+7dCAgIQP/+/Quud+3aFf7+eXlu1q1bh4kTJ8LR0RFDhgyBra0tDh8+jPPnz6Nly5Y4duyYylQU+pacnAxra2skJSXBykp56p+ISB+S0rPRYeVpxKcoJ4b+qJUX5nf3K4VREZVdRfn8Llbw9aYzZ85g8uTJCAsLg4uLCw4ePKj0uK841B1+nW/Lli0YMWJEwdcHDx7EypUrERISgoyMDNSoUQMDBgzA9OnTYWpqqrPxFReDLyIqK/aGxeLTXWFK16UGEuyb0BL+btYQBAHPkjMQGZeCiLjXeJyQBgsTGQY28eDRRFSplGrwBQC5ubmYNWsWVq5cCRsbGxw6dAhNmjTRVfMVGoMvIiorBEHAkB8v4/w95ZxgHnZmsDM3wr3nKaKPIc2MpPh+aABaeXMnOVUORfn81mmeLwMDAyxfvhxr165FYmIievXqpbTeioiIyjaJRIIv3q8DI6nyR0R0QhrCHieqXP+VliXH6J+u4pIWyVyJKpsSOdsxKCgIX3zxBeLi4hAaGloSXVQYwcHB8PPzQ2BgYGkPhYioQA1HC3zctnqR6mZk52LU1qsIeajd8W1ElYVOHzu+7eDBg+jatWtJNV+h8LEjEZU1GdlydPr2jNpzIFWxMJbh59FN0aCqjW4HRlSGlPpjx7cx8CIiKr9MDKX44n3/ItdPyczBsB8v42Zskg5HRVT+lWjwRURE5Vs7Xycs6OEHU8O8w7UNJHmZ89/1c8b4djXwbf/62D+xFd5v4CZaPzkjB0N+vIx/nyXrc9hEZVqJPnYkzfGxIxGVZSmZOXiVmgVHS2OY/H8g9qYceS4m7bqOAzeeida3NzfCrrHN4O1cNk5BIdKVMvfYkYiIKgYLYxmq2pmJBl4AIJMaYPWAhuhYWzzB9svULAz64TKiXqSU5DCJygUGX0REpBOGUgMED26Idr6OovdfvM7Exz+HIkeeq+eREZUtDL6IiEhnjGVSfDekMVrVFE+yGhGXgkO3xB9NElUWeg2+jh07hj/++EOfXZZ5zPNFRBWNiaEUm4YFoKmXnej9E3ee63lERGWLxgvuq1cvWrK9iRMnYsqUKQCA2rVrIzIyEjk54pmRKzMuuCeiiiY1Mwedvj2D2MR0het25ka4+llHSA0kpTQyIt0pyue3TNPGHz58WKRBvXr1SuFrbq4kIqoczI1leNfPGVsvPFS4npCahfCYRDT0sC2dgRGVMo2Dr/R0xb9cBEGAmZkZPvnkE3zzzTeqO5Bp3AUREVUw79RyUgq+AODkv88ZfFGlpfGaL2NjY4WXiYkJAEAqlSrde/MllYpvSyYiooqvqZddQYLWN524y3VfVHlxtyMREZUYE0MpWta0V7p+MzYZz5MzSmFERKWPwRcREZWod2o5iV4/dfeFnkdCVDZovCBr+/bt+Ouvv5SuHzp0CP369Sv4esGCBfD3L/pBrEREVLG08xUPvk78+xz9AquqrZ+WlQNDqQEMpZwvoIpB4+ArPDwcv/32W8HXEkneFuF79+7h3r17Bdc+/vhjBl9aCA4ORnBwMORyeWkPhYioRLjbmKKWiyX+ffZa4fq5e/HIysmFkUw8qErLysHU3f/g8O1nMJQaYExrL0zp6AMZgzAq5zT+Cf7kk09w8uRJnDx5EqtXr4YgCFi2bFnBteXLl5fkOCusoKAg3L59G1evXi3toRARlRix2a+UzByEPExQWWfB3ls4dOsZBAHIyslF8Mn7GL/jGjKy+ccqlW8az3xVr169INFqfvqIevXqoW3btgDAxKlERKRS+1pO+O70faXrJ/59jhYiRxHde56C367FKF0/cjsOo7ZexffDAmBhzFRGVD6V2Nxteno6oqOjFV7Z2dkl1R0REZVhjTxsYGWiHCydVJFyYv3Je1CVk/vC/ZcYvOkSElKzdDlEIr3RafD1Zvb6ffv2wcvLS+EVFRWly+6IiKickEkN0MbHUen6/RepiH6ZpnDtYXwq/gyLLbS9f2KS0G/jRTxNSi+0HFFZpNM52/xF+ABga2uLunXrKlwjIqLKq30tJ+wPf6p0/cS/cRjR0qvg6+CT95CrwUl0956n4MMNF/Hz6KbwcjDX5VCJSpTGwVdWVhYyMvIS4qWkpAAA0tLSkJycDABITU1VKN+pUyd06tRJV+MkIqJyrq2PIyQSKD1OPHn3RUHw9TghDX9cL3zW602xieno+90FbBvVBP5u1rocLlGJ0Tj4+uyzzxTOcJRIJOjTp0+JDKpS69AB0NV5mAMGAJ9+WniZ1auBXbt001++ixcLv//8OfD++7rtc9IkYODAwst89hlw4oTu+nR0BPbtK7zMjRvA2LG66xMAFi/O+zkpzJgxwM2buuuzTh1g06bCyxw/Dsybp7s+AeD774G6dQsv07Mn8EKHyTrbtwe+/LLwMjt3AmvW6K5PANi7F3ASz4dVoHlz3fap598R9gAOPnuNLv2XKly/GPUSaVk5MDOSYf2pe7B5/Qqb/viPQhkbUyOkZcuRlSO+0zHnOwlSnCxUL8Ln7whF/B1RdJr8jlBD4095Ozs7eHh4qC1namparAFVeiEhumtLk1/Ujx4Bly7prk9NZGXpvs++fdWXiYjQbb/u7urLpKTo/r0mqN6aX+DmTf3/uyYk6L7P/59lL9S1a0Cs5jMlalWpor7M06e6f69ZGiwe13WfpfA7opbItaycXFy49xK1XC3xW2gM7OU5aPTkrvaNF/ZjwN8Rivg7oug0+R2hhsbB15w5czBnzpxid0hERPS2k3ef41TEc2TLNVjsRVTOMUlKKWOGeyKqbBwtjfHidabCtcO3niE5nfkiqXIocvAll8tx48YN3Lt3D3FxcUhNTUVOTg7Mzc1ha2uLmjVrom7durC0tNTleCucoKAgBAUFITk5GdbWXCxKRBXfO76O+DVEMYFqfApzdlHloXXwFRYWhmXLluHAgQN4/fp/53S9meMrP72ETCZDu3btMHPmTHRQt/iP8gQE6G7BvaenZmWaNdNNf5oyMtJ9n66u6sv4+Oi2X0flnEVKLCx0/17t7NSXqVNHt31q0p6dne7fq4WF+jKNGgFV1R/OrDEfH/VlXF11/16NjNSX0XWfpfQ74h1fJ6Xg603ZUhmuufkCAPzcrGGi4uzHZ8kZeJKonOerqq0ZHC2NFS9q+DsiuUFj3Hsuvo7I1EiGWi6W0DiBEn9HKKpMvyPUkAiCqhzCylauXInZs2dDLpfDxcUFLVq0gLe3N1xcXGBmZgaZTIb09HS8fPkSDx8+xKVLl3Dnzh1IJBJMmzYNy5YtK/aAK6r8ma+kpCRYWVmV9nCIiErM64xsNPrPUbXru3o3dMe3/RuovP84IQ2tl51Uut6kmh1+/Vj7naGPE9LQY905JKapPo1lUU9/DG9RTeu2qeIqyue3xlMse/fuxYwZM+Dt7Y01a9agU6dOGiVQvXbtGsaOHYuVK1eiUaNGGDBggKZdEhFRBWRpYojAana4cP+lyjISCRD0Ts1C26lqZ4aGHja4Hp2ocP3KwwQ8SUyHm43mu+/Ts+QYtz200MALAFYcuYv36roqz6wRaUHj44VWrlwJOzs7nD9/Hp07d9Y4c32jRo1w/PhxODk5Ye3atUUeKBERVRzv+Bae06x7PTfUdFL/SKlHPTfR6/vDn2g8FkEQMHfPDdx+mqy27OuMHHx94I7GbROJ0Tj4+ueff9CzZ084OCifPq+OtbU13nvvPdy4cUPrukREVPG8U6vw4Gti+8JnvfJ1r+cKA5G5gH3/aB58bbvwEHu0yKr/x/VYXI5SPWtHpI7GwZdUKkVmZqb6gipkZ2dDKpUWuT4REVUcNRzN4WFnJnrvvbou8HHWbKe8k5UJmlW3V7p+MzYZUS/UJ+C88iABi/8Wn8nydbZEZ39n0Xvz995EtjxXozESvU3j4KtJkybYt28foqKitO7k2bNn2L9/Pxo2bKh1XSIiqngkEgne8RXfDTjhHW+t2upZX/zRo7rZr2dJGRi/IxQ5Iqd4W5rIsHFoYyzqWQfmRsoTBxFxKdh6/qFW4yTKp3HwNXfuXGRkZKBly5bYtGlTweHa6hw6dAjt2rVDUlISpk6dWuSBEhFRxTKsRTXI3npm+EEjd/i5abfju2sdVxhKlZ89/vXPE6ja0J+ZI8fHP4eK5heTSIDVAxqgmoM5XKxNMOVd8dQCq45F4FlShlZjJQK0TDWxc+dOjBkzBunp6TA0NES9evXg7e0NV1dXmJqaQiqVIiMjAwkJCXj06BFCQ0Px6tUrSCQSLFmyBNOnTy/J91KuMdUEEVVGp+4+x/LDdxGfkolOfi74rFttmBhqv0Rl9LarOHbnudL1vye1gr+bcgLrz/bcwI7L0aJtTenog087/m/2LVuei+5rzuFu3Gulst3quSJ4UCOtx0sVR1E+v7UKvgDg6dOnWLVqFfbu3YuIiIhCyzo4OKB79+6YOnUq6ug6oVsF8ebxQhEREQy+iIiKYG9YLD7dFaZ0fVzb6pjTtbbCtd9CYzD9v/+IttOxthO+HxoAg7dm5K48SEC/jRdF6/z8UVO08tZ+MxpVDHoJvt6UkJCAyMjIguOFcnNzYWpqCltbW1SvXh2emmRPJgCc+SIiKo7UzBw0XnwUGdmKi+DdbUxxduY7BcHUrSdJ+GD9BWTmKC+W93Iwx94JLWFlYijax9Rfw/DHNeVdkdUdzHFwcmsYy7iprDIq0SSrYuzs7NC0adPiNEFERFRs5sYydKztjP3hTxWuxyam41r0KwRUs0NSWjY++fmaaOBlZiTFxqGNVQZeADCna20cvR2H1xmKB4BHxadiycF/8Xl3P41zYFLlpvGCe02kp6dj+/btmDJlCiZMmIBVq1bh/v37uuyCiIhIVGG7HnNzBUz9NQzRCWmiZZb2qac2vYWjpTFmdPYVvbfl/ENsPKN9NgCqnDSe+erUqRP69OmDcePGid4PDQ1F7969ERsbq7C7ZMaMGRg5ciTWrl0LY2Mex0BERCWjra8jrExkSH5rZurAjaewNzfG8X+VF+QDwMiW1dBDReD2tsFNPfFryGPcjFXOhr/k4L+wNzdC3wAdHuJMFZLGM1/Hjh3D3bt3Re+9fPkS3bp1Q0xMDHr27ImdO3fi8OHDWL16NXx9ffHjjz/iww8/1NmgiYiI3mYsk6JLHRel6/EpWfj2mPgGsQBPW8x9r7boPTFSAwm+7l0PRlLxj8/Zf9zAsdtxGrdHlZNOHjuuXbsWz58/x8yZM7Fnzx70798f7777LiZOnIiwsDB0794dBw4cwK+//qqL7oiIiET1rO+ucVkHC2MED24EQxWBlCp1q1jj2/4NILa8S54rIOiXawh5mKBVm1S56CT42r9/P8zNzTFnzhylezKZDJs3b4aNjQ02b96si+6IiIhENa9hDwcL9UtcpAYSrBvUEM5WJkXqp1s9V3zR01/0XmZOLkZtvYoIkbxgRICOgq/IyEi0atUK1tbKiewAwN7eHp07d8a1a9d00R0REZEoqYEE3eoqP3p826wuvqJnQmpjaPNqmKTiAPDkjBwM+/EKYhPTi9UHVUw6Cb7S09NRpUqVQsvY2dkhMTFRF90RERGp1LNB4Yvnu9ZxwZjW1XXS15R3fTCwiYfovWfJGRj642UkpCofYUSVm06CL2traxgYFN7U8+fPYWmp2Sn1RERERdXIwxbuNqai96o7mmPZh/V0lo9LIpFgca866OIvPtsW9SIVo7ZeRWaOXCf9UcWgVZLV27dv46efflJuRCbDixcvCq17+fJleHtrd1I9ERGRtiQSCXo3dMe6k/cUrpsaSvHdkMawLCSRalFIDSRYNaABRmy5gktRygvtwx4nYtWxSMzqUqvYfSVnZGPenps4fy8eNZwsMKtLLTT2tC12u6RfGh8vpG5my8fHB//++6/ovWPHjqFTp06YP38+Fi1apP0oKwEeL0REpDupmTnotuYsHr7MS6pqYmiA1QMaorOKGSpdSM7IRv+Nl3DnqXIOMAMJ8PsnLdDQo+iBUm6ugIGbLuHyg/8FeFYmMuyf2Boe9mZFbpeKp0TPdjx9+nSh96VSKVq1aqV0XRAEBAQEICIiAhEREXB1ddVoYJUNgy8iIt1Ky8rBnuuxyMjORVsfR9R0sijxPp+/zkCfDRfwOEF5oX0NR3P8Pak1TAyLdgbk3+FPEfSL8sa13g3d8W3/BkVqk4pP7wdrayo6OhomJiZwcnIq6a7KLQZfREQVw52nyei57hyy5cofr2PbVNcqqWu+HHkuOq06g6gXqUr3JBLgyOQ28FZzPBKVjKJ8fuv0bEdVPDw8GHgREVGlUNvVCpPai69x3nQ2CqGPtE/A+sf1WNHACwAEAfjmqHgGfyqb1AZfmzdvxh9//KGzDuPj4zFixAidtVfeBQcHw8/PD4GBgaU9FCIi0pFP2tVAXXfl3JeCAEz/bzjSszTf/ZiZI8fqY5GFljl48xluxiZpPU4qHWqDL0NDQ3z44Yfo3r27ygX1msjOzsaGDRvg6+uLc+fOFbmdiiYoKAi3b9/G1atXS3soRESkIzKpAVb2qy96BuSD+FQsPyx+VrKYXy5Ha5SsdeURzduk0qU2+Bo6dCh27NiB8+fPw9/fH++99x4OHz4MTZeKxcXF4YsvvoCnpycmTJiAwMBAXLp0qdgDJyIiKst8nC0x5V0f0XtbLjzA5aiXattIzcxB8FspM1Q5efdFkR5pkv5pvOA+NjYWc+fOxY4dOyAIAmxsbNC2bVu0a9cOXl5ecHZ2hqmpKV68eIG4uDhcu3YNx48fR3h4OHJzc+Hh4YFFixZh+PDhJf2eyiUuuCciqnhy5Ln48LuLCHucqHTPw84Mhya3hpmR6pSb605EYsUR5fVcMgMJcnKVP76bV7fHzrHNijVm0o5edjveunULixcvxp9//onMzEyVWYLzm61ZsyYmTZqEcePGwdBQt4ntKhIGX0REFdP9Fyl4b/VZZObkKt0b1twTX7xfR7ReYloWWi87idcZOUr31g1qiLl/3ECyyL0do5uiZU2H4g+cNFKUz2+tMtwDgL+/P3bu3ImkpCQcOXIEZ8+exa1bt/Ds2TNkZ2fDysoK1atXR+PGjfHuu++iUaNGWr8RIiKiiqKGowVmdPbF4r/vKN376eIjNK9uj651lXNgfnc6SjTwauplh251XfHoZZro2rHlh++iRQ17nR2hRLqnlzxfpB5nvoiIKi55roD+Gy8i5NEr0fvd6rlifjc/uFibAACeJ2egzfKTyMhWni37/ZPmaOxph9TMHLRZdhIvRQ7u/nF4ADrUdtbtmyBRZTbPFxERUWUmNZBgRd/6MDEU/9j9O/wpOqw8hR/ORiFHnot1J++JBl4dajmhsacdAMDcWIZP2tUQbW/FkQjkiqwJo7KBwRcREZEeVHMwx+xCDtdOzZJj8d930G3NOey8Ei1aZlonX4WvhzTzhLOVsVK5O0+TceDm0+INmEoMgy8iIiI9Gda8Gno1cCu0zN2416JHE/Ws7wY/N8XHWiaGUkxUkU3/m6MRyJErz55R6WPwRUREpCcGBhJ8068BFveqAysTzfe8SQ0kKnOG9Quoiiq2pkrXo16k4s+wJ0UeK5UcBl9ERER6ZGAgwZBmnjgxvR36NKqiUZ1+AVXh5WAues9IZoDJHcUDs2+O3EVqpvKOSSpdDL6IiIhKgYOFMVb2q4/dY5vBx9lCZTkjmQEmdahZaFu9GrihuqNycPYkKQPf8tDtMofBFxERUSlqWt0ef09qjTlda8HUUKp0f3QrL7haKz9WfJNMaoCpKh5Lbj7/gIdulzFFDr5++eUXLFmyRJdjISIiqpQMpQYY17YGjk9ri5713WBqKIWJoQEGNvFQ2uGoSre6rmjiZad0PVcA5u65ATlTT5QZRU6y2rVrV5w/fx7Jycka1zl16hS8vLzg6elZlC4rNCZZJSKifJk5cghC3m5Gbdx7noKuq8+I7pZc0MMPI1t66WqI9P/KdJLVR48eoX///mjfvj3S0tL01S0REVG5YyyTah14AUBNJwt80k58fdiKw3fxNCm9uEMjHdBon2taWhrOnTuncC0+Ph5yuRxHjx7Fm5Nn7du3h0ym2Oz169fx/vvvIz4+Hl27doWZmZkOhk5ERERvG9+uBvb/8wRR8akK11Oz5Fiw9xa+HxZQSiOjfBo9drx79y5q166tcEhnfrW3D+4MDw9HZmYm7O3tER8fjx9++AE//vgjcnJy0LVrV/z5558wNDTU8dso//jYkYiIdOXC/XgM2nRZ9N7GoY3R2d9FzyOquIry+a3RzJe1tTWGDRum0Qnphw8fxvTp05XKTps2DUuXLoWBATdYEhERlaQWNRzwYeMq+C00Runewn230LKmAyyMNU/ySrpV5AX3qpw9exbff/89srKyEBYWhsjISACAra0tduzYgS5duuiyuwqDM19ERKRLCalZ6LDyFF6lZSvdG9myGhb08C+FUVU8ZWLBfevWrbF9+3bs3r0bd+/exb179zBixAi8evUK77//Po4cOaLrLomIiOgtduZG+Kybn+i9bRceIjwmUb8DogIaB18vXrzAv//+i9xc7Q7prF69OjZv3oxNmzYhJycHAwcORFxcnNYDraiCg4Ph5+eHwMDA0h4KERFVMH0auaN5dXul67kCMOePGzx4u5RoHHxduHAB/v7+cHZ2xqeffoonT8QP60xLS0NUVJTS65133sGwYcPg6ekJR0dHnb2B8i4oKAi3b9/G1atXS3soRERUwUgkEnzZuw6MZMof97eeJGPn1celMCrSOPiSyWQQBAEvX77E2rVr4evriw0bNiiV27NnD7y9vUVfP/30E/755x8uuiciItKT6o4WmPCOeO6vrecfQMdLv0kDGkdBUqkUEokEv/zyC3744Qc4ODhgwoQJmDp1qkK56tWrY9CgQRg0aBAGDhwIQRBQp04dDBo0CDVrFn4wKBEREeneuLbVUUPk4O37L1Jx5UFCKYyoctM4+JJIJBAEASYmJhg1ahRu3bqFXr16YfXq1Vi6dGlBuebNm2P79u3Yvn07fv75ZwBAnz59sH37drRv317374CIiIgKZSyTYlIHb9F7Oy5H63k0pPXzv+zsvC2rZmZm+PXXX/Huu+9i/vz5CAkJ0fngiIiISDe61HGBnbmR0vVDN58hITWrFEZUeWkdfL35bFgqlWL79u2wsbHBmDFjVNbRJDkrERERlRxjmRR9G1dRup4lz8VvoVx4r0/FXvnu6OiIhQsXIjw8HHv27MGjR4/g5uZW8JJIJPj2229RvXp1/PLLL7oYMxERERXBwCYeotd3XnnMhfd6pFXwpWoGa+zYsXBxccGqVavyGjUwgFQqhVQqhZubG8zNzZGdnQ0rKyu4ubkVe9BERESkvWoO5mhZUznv14P4VFy8/7IURlQ5aXWwk6qoWCaTYcyYMVi8eDEMDAwQE6N8lhQRERGVvkFNPHH+nnKgteNKNFrUdChSm1k5uVh/6h72/fMEpoZSjG9XE93quRZ3qBWWxjNfzZo1w9mzZ9GhQwfR+4MHD0b16tW5vouIiKgMe9fPGQ4Wygvvj9x6hviUTK3by8rJxfgdoVh1LBJRL1Jx60kyJu68hrORL3Qx3ApJ4+DL2toaLVu2hL298nQlAHh7eyMkJARVqigv5iMiIqKywUhmgH4BVZWuZ8sF/DdEuydXWTm5CPrlGo7dea5wPVcA/rP/NuS5XEcmRqep5jU9zZuIiIhKz8AmHhB7ULXzSjRyNQyYsuW5mLjzGo7eFj+vOSIuBX9c4zIkMTznh4iIqJKpameG1t7K5yxHJ6Th/P14tfWz5bmYtPM6Dt8SD7zyfXs0AhnZ8iKPs6Ji8EVERFQJDVKRdmLHpcIz3ufIczF5VxgO3nymto8nSRn4+dKjIo2vImPwRUREVAl1qO0EJ0tjpetH78TheXKGaJ0ceS4m7w7D3zeeatzPupP3kJyRXeRxVkQMvoiIiCohQ6kB+gcqL7yX5wr4NUQ5432OPBdTf/0H+8PFAy9zI6no4d2Jadn4/nRUscaalpWD8/ficTM2qVjtlBVa5flS59mzZ3jy5AmysrJgY2ODmjVrQibTaRdERESkIwOaeCD45D28vcZ+55XH+KRdTWTLc3Ep6iVOR7zAqbsv8CA+VbQdcyMpto1qgix5LgZtuqx0/8dzDzCsuSecrEy0HuOFe/GYvDsMz1/npcF4r64L1g1sBAOD8pvaqtiRUXZ2NlauXInvv/8ejx4pPtc1NDRE+/btMW3aNJX5wYiIiKh0uNuYop2vE078q5gqIjYxHX02XMCdp8nIzMkttA0zIym2jmqCgGp2AIA2Po44E6GY4ys9W441JyKxuFddrcZ34MZTTN4Vhix57hvXnuHn6o8wrHk1rdoqS4r12DEjIwPt27fHZ599hocPH0Imk8HT0xONGjVC9erVIZfLcejQIXTq1Alz587V1ZiJiIhIR1QtvA97nKg28DI1lGLLiEAE/n/gBQAzO/uKlt115TEeqpg5E/PzpUcI+uWaQuCV7/szUcgRuV5eFCv4WrFiBc6fP4+qVatiy5YtSElJQVRUFK5evYrIyEgkJCRg48aNsLOzw9KlS/Hnn3/qaNhERESkC+18HeFqrf3jQFNDKbaMDETT6orJ1+u4W6NnfeVznHNyBaw4cldtu4IgYM3xSMz78yZUnfUd8yodBzTYbVlWFSv42r17N6ytrXH+/HkMHz4choaGCvctLS0xZswYnDx5EkZGRlizZk2xBktERES6JVOx8L4wrtYm2DIyEM2qi596M62TD2Qia7L2hz/FjRjVi+ZzcwUs3HcL3xyNUDuGjafvqzxzuqwrVvB1//59dO/eHe7u7oWWq1OnDt577z2EhYUVpzsiIiIqAQMCPWAoLXwBu62ZIXrWd8PKvvVxfFpblYEXAHjam2NQU/HHmcsO/yt6PSsnL43Ftoua5QW79SQZF+4rHxBeHhRrwb2ZmRlMTDSbqnRxcUFmpvYHdhIREVHJcrE2wZyutfHF/tsF1wwkQP2qNmjn44S2vo6o624NqRY7DCe298ZvoTFIy1LMcH82Mh7Tfv0H9hZGMDWUwtRIClNDKY7dicPZSPXZ9d/03en7aFnTQas6ZUGxgq/27dvj0KFDyMzMhLGxcqK2N127dg21atUqTndERERUQka18kLT6na49ugV7C2M0by6PWzNjYrcnqOlMUa3ro41xyOV7v2uxZmPBhJgUU9/rD5+D/EpipM4ZyPjcftJMvzcytfZ0sV67Pj1118jNTUVw4cPR2qq6h0MGzZswJUrVzBhwoTidEdEREQlyN/NGkObV8N7dV2LFXjlG9PaC3bFaMdIZoANQxpjaPNqGNmymmiZTWeLl8C1NEiEYqxW++mnnxAWFoZVq1bBzs4Ow4cPh7+/Pxwc8qYAk5KSsGfPHuzduxe1atXCrFmzRNsZNmxYUYdQYSQnJ8Pa2hpJSUmwsipfETwREZEqm889UHicqSkLYxk2DQtA8xp5a8uS0rLRfMlxpceYUgMJzsx8B+42pjoZr7aK8vldrODLwMAAEomkYLeBRKL8LPjN5sXuA4BcXn5OPE9PT8cXX3yBX3/9FXFxcahVqxbmz5+P999/v1jtMvgiIqKKKDNHjt7BF3D7abLGdRwsjLB1ZBPUcbdWuP7FX7ex+fwDpfIftfLC/O5+xR5rURTl87tYa76GDRumMqCqqCZMmIADBw7g+++/R61atfDjjz/igw8+wMWLF9GkSZPSHh4REVGZYizLO3po2aF/ceH+SyRnZCMjW45sufjcT/0q1lg9oCGqOSifE/lRay9su/gQ8rfOQ9p5JRqT2nvD2sxQqU5ZVKyZr8omKSkJ9vb2+O677zB69GgAeTN7Xl5e+OCDD/DNN98UuW3OfBERUWWSLc9FerYcGVlypGXJkZ4th7mRDFVsTQs9t3Hyruv4M+yJ0vUZnX0R9E7NkhyyKL3PfFU2VlZWePLkicI3VyKRwNnZGfHx2m2PJSIiqswMpQYwlBrAykS72aqxbWqIBl9bzj/ER628YGIo1dUQS0yxdjtWNhKJBE5OTgq5zZ48eYJ//vmHjxyJiIj0wM/NCq29lXN7xadk4s/rsaUwIu2Vy+ArIiICzZo1g0QiwYgRIwotu2fPHnTo0AG2trYwMTGBr68v5syZg8TExGKPQy6XY+TIkXB2dsbIkSOL3R4RERGp93HbGqLXvz8bhdzcsr+aqlwFX4IgYO3atWjQoAFCQkLUll+8eDE++OAD3LlzByNHjsT8+fPh5eWFJUuWoFWrVkhKyjtf6tSpU5BIJCpfhw4dUmo7JycHAwcOxMWLF/HHH3/A3Fx5YSARERHpXosa9vAXSawa9SIVx+7ElcKItFOu1nwNHjwYO3fuRP/+/dGsWTNMmTJFZdnw8HAsWLAAbm5uuH79OpycnAruzZ49G0uXLsW8efOwdu1aNG3aFJGRyhl487m5KZ7OnpWVhX79+uH06dM4cuQIGjduXPw39/8GbboEQ9P/BXI96rlhVCuvQutci36F/4jkUJnf3Q+NPGwLrbv53AP8Fa787HzP+JZqxzpp53U8fpWmcK1BVRss6OFfaL34lEyM+Uk5eB7Rohreb1D4OaFHb8dh/al7StdX928ID3uzQusuP/yv0jlg9uZG+GF4YKH1gLx/l/RsxZQoHWs7q13c+e+zZMz544bS9emdfNUeibHrSjR2hzxWur5jdFOYGRX+n+7s38NxN+61wjVfZ0ss6VOv0HppWTkY/MNlpev9A6piQBPxc9rynb8XjxVH7ipd//qDuqjlUvgi1OCT95R+YZoaSvHLmGaF1gOA0duu4mVqlsK1FjXsMaNz4SdqRL9Mw6e7rytdH9+uJt71cy607t6wWGy98FDp+qZhAXCwKPy0j0V/3ULY40SFa1VtzbBmYMNC6wFA7/Xnla7xd4Qi/o6oPL8jsuW5omU3nolCJ3+Xgq9L43eEOjoLvg4cOICwsDDMnTtXV00qiY2NxbZt2zBs2DBs3bq10LIbN25Ebm4upk6dqhB4AcBnn32GNWvWYNu2bVi6dCnMzMxQs6bmOyQ++ugjnD9/HqdOnUL9+vWL8lZUCo9JgoFxdsHX6n4xAsDrjBxcj04Uva5ObGK6aF1N3HmajMjnKQrXLIzV/0hly3NF++xWV/3Zny9TMkXrZuSozxX3ID5Vqa6LlWZnk4bHJCElU/H76eNkqbZeaqZcdLyJadnKhd/yNClDtO7bW6zF3I17XaR/V3muIFqvjbej2rqJadmidVMz1f/bRL9MU6qryc8SANyMTcaz5AyFa67W6v9dM3LE/21epqj/OXzxWvznUNWHwZvuPU9RqpuiwX+rAET75O8IRfwdUXl+R5gbS1HF1hQxr9IVroc+eoWL918WJGctjd8R6ujsseOePXswf/58XTUnat++fRpnwz9x4gQAoHPnzkr3LC0t0aRJE7x+/RpXr17Vagxr167FH3/8gUOHDmkceGVmZiI5OVnti4iIiDQjgQSjVcz6rj4eoefRaEeva74iIiIQHh5e5PrW1tbqCwHIzs5GZGQkJBKJyhktb29vAMCNG8rTvaqkpKTg888/x/jx4+Hu7o5nz54VvF68eKGy3tdffw1ra+tCX1WrVtV4HERERAT0C6wqenbkpagEXI56KVKjbNA4+Lp8+TIuXLiAsLAwPHjwoEi7Bd9//300atRI63raSk5Ohlwuh5mZmUJaiDfZ2dkBABISEjRuNzQ0FImJiVixYgVcXV0VXoGBqtcEzJkzB0lJSYW+Hj9Wfm5PREREqpkZyTCmdXXRe6uPq17LXdo0XvM1YsQIREQoTuNJpVI4ODjA1dUVcXGa7S7QR0L9tLS8BZ5GRqpPUjc2NlYoq4m2bdsWafzGxsYF/alTr4q1woJ7TQ4KtTSRoaGHjeh1ddxtTEXraqK2qxUs3uqjppOF2nqGUgPRPh0t1X+P7C2MReuayNQn1fNyMFeqay/yF5OYelWslRbTqlu8C+StSRAbr40GR2C4WpuI1pUWkvk5n6+z8loTsWtibYv1qcn6CBszQ9G65sbq/2087M2U6ppqmCixjrsVXG0Ux+clcizJ20xk4v829moWzAN5P6tidQ2l6v+erelkobQ2qKqt+p8lAKJ98neEIv6OqHy/I4Y198T3Z+7j1Vvr5C7cf4mrDxNK5XeEOhofL1S7dm1ERETg/fffL1ij9OrVK8TGxiIjI28hm0QiQUhIiFKA4ubmBhcXl4I2dHGQ9tatWzFy5EgMHz5cafH9y5cv4eDgAHNzc6SkpIjWnzlzJpYvX45Fixbh888/L/Z4iovHCxERERVN8Ml7WH5YeSdlq5oO+Hl00xLtWy/HC/3xxx9K1+Lj4zF+/Hj89ttvCAgIULq/YMECvQY41tbWkMlkSE1NRUZGhuijx/zjgBwcCt/KS0RERGXb8BbVsOlslNIu0XP34hHyMAEB1exKaWTidJJqwsHBAdbW1pBKpejYsaPSfW3SOOiCTCZDrVq1cPPmTdy9e1d0V+KdO3cAAA0aNNDr2N4WHByM4OBgncwGEhERVUYWxjKMbuWFFUeUdzmuPh6J7R+V7OyXtnSaZNXQ0BAHDx7UZZNF1rlzZ9y8eRP79+9XCr7i4uIQEhICe3t70Zk6fQoKCkJQUFDBtCURERFpL2/26wGS0hVnv85GxiP00Ss09lSfE09fytXxQtoYN24cjIyMsHr1asTExCjcmz17NnJycjB+/PhCF+UTERFR+WBpYoiPVOb9Kls7H8vN8UKPHz/G7t27C77OP9vx1q1bWLFiRcH1rl27wt/fH97e3li5ciUmTpyIRo0aYciQIbC1tcXhw4dx/vx5tGzZskSz8RMREZF+jWhZDT+cjULyW6c3nIl4gevRr9BQgxMh9EGnwVdWVhbatGmjdH3UqFEYMWJEsdq+f/8+ZsyYoXQ9JCRE4ZBtBwcH+PvnnRs2YcIE1KhRAytXrsTmzZuRkZGBGjVq4IsvvsD06dNV5gAjIiKi8sfKxBAftaqOb4+Jr/3aOrJJKYxKmU6Dr9zcXJw7d07putgifG21a9euSDm2unbtiq5duxa7fyIiIir7RrSshh/ORSmdXXrq7guEPU5Eg6o2pTOwN+g0+JJIJKLHB719sDURERFRSbA2NcSoll6i67zWHI/E5hGqT6TRF52v+cp/5EeaYaoJIiIi3RrV0gubzz3A67dOkzjx73OExySiXhWb0hnY/yux3Y5jx46FmZmZwuvt44koL9XE7du3cfXq1dIeChERUYVgbWaIkS2rid5bUwZ2PmocfJmZmcHcXP1ZSPlkMlnBmYb5LysrKx6dQ0RERCVuVCsvWBgrP+A7duc5bsQklcKI/kfj4Cs0NBTJyckaN7x+/Xq8evVK9EVERERUkmzMjDCiRTXRezsuP9LvYN5SYZOsEhERUeX2USsvmBtJC76uameKpX3q4j+96pTiqHS44N7e3h4eHh66ao6IiIioWGzNjTC8RTX8feMpgt6pid4N3WEoLf15J4lQlORZpHP5ZzsmJSVxXRwREZGOpGfJYSiVQFZCQVdRPr9LP/yr5IKDg+Hn54fAwNLPO0JERFTRmBpJSyzwKiqNZr4ePnyIM2fOaNVwjx49YGtri6SkJOzYsQPx8fHo0qULmjQpG6n9yxrOfBEREZU/Rfn81mjN1+XLlzFixAhIJBK1ZQVBgEQiwdWrV5GWloZWrVohOjoagiDgiy++wJo1azB+/HiNBkdERERU0WgUfPn5+WHWrFkAgCdPnmD79u34+OOPYWdnh6+++goBAQHo0KGDQh0XFxeMGzcOjx49Qo8ePeDn54d169ZhypQp6NKlC6pXr677d0NERERUxmm94P7y5cto0aIF7ty5Ax8fHxgYGGDy5MlYuXIl0tPTYWJiAgMDAzx+/Bienp549913cfjwYQDAzp07MXjwYHz22Wf4z3/+UyJvqLziY0ciIqLyp1QX3N+4cQOWlpb49ttvAQCnTp2CRCLBxx9/XFBmwIABsLOzw6lTp3TVLREREVG5onGer7i4ONy/fx9SqRT16tWDiYkJAKBevXpwd3eHv78/jI2NERoaCgCIjMw7O+nNXXwSiQT169fH7du3dfkeiIiIiMoNjYOv3377DZMmTcL169dx/fr1guthYWEF/9/T0xNRUVEAgMTERACAs7OzQjuOjo4F94iIiIgqmyJluN+7dy+MjIxgaGio8LKwsMDz588BANnZ2XkdyBS7kEqlkMvlxRx2xREcHIzg4GB+T4iIiCqJIgVfvXv3Fk07IQgCHB0dAQDm5uYAgNevXyssQHv9+nXBPQKCgoIQFBRUsGCPiIiIKrYiBV/Tp09HTk4OcnJykJ2dXfC/586dQ1xcHACgatWqAICIiAgEBAQU1I2MjCy4R0RERFTZFCn4WrZsmej1MWPGYMuWLQCAxo0bQxAE7Nu3ryD4ioyMxN27dzF8+PAiDpeIiIiofCtS8FWY/LRhLVq0QJUqVfDNN9+gSZMm8Pf3x8iRIyGRSNC/f39dd0tERERULug0+HozX6uBgQFWrlyJ/v374/333y+436VLF3Tp0kWX3RIRERGVG0UKvmxtbSGXyxVeubm5SuX69u0LIO8xZWJiIrp164avv/66eCMmIiIiKseKFHwZGhrC1NQUMpms4CWVShEXF4ekpCSFsn379i0IwoiIiIgquyIFX/m5vN42ZswYbN68uVgDIiIiIqrIND7b0crKCh4eHjAyMirJ8RARERFVaBLhzVXyxXTjxg1ER0ejW7duumqywnszw31ERIRWp6ITERFR6cpPkq7N57dOgy8quqL84xEREVHpKsrnt8aPHYmIiIio+Bh8EREREekRgy8iIiIiPWLwRURERKRHDL6IiIiI9IjBFxEREZEeMfgiIiIi0iMGX0RERER6xOCLiIiISI8YfBERERHpEYOvUhYcHAw/Pz8EBgaW9lCIiIhID3i2YxnBsx2JiIjKH57tSERERFTGMfgiIiIi0iMGX0RERER6xOCLiIiISI8YfBERERHpEYMvIiIiIj1i8EVERESkRwy+iIiIiPSIwRcRERGRHjH4IiIiItIjBl9EREREesTgi4iIiEiPGHwRERER6RGDr1IWHBwMPz8/BAYGlvZQiIiISA8kgiAIpT0IApKTk2FtbY2kpCRYWVmV9nCIiIhIA0X5/ObMFxEREZEeMfgiIiIi0iMGX0RERER6xOCLiIiISI8YfBERERHpEYMvIiIiIj1i8EVERESkRwy+iIiIiPSIwRcRERGRHjH4IiIiItIjBl9EREREesTgi4iIiEiPGHwRERER6RGDLyIiIiI9YvBFREREpEcMvoiIiIj0iMEXERERkR4x+CIiIiLSIwZfRERERHrE4IuIiIhIjxh8lbLg4GD4+fkhMDCwtIdCREREeiARBEEo7UEQkJycDGtrayQlJcHKyqq0h0NEREQaKMrnN2e+iIiIiPSIwRcRERGRHjH4IiIiItIjBl9EREREesTgi4iIiEiPGHwRERER6RGDLyIiIiI9YvBFREREpEcMvoiIiIj0iMEXERERkR4x+CIiIiLSIwZfRERERHrE4IuIiIhIjxh8EREREekRgy8iIiIiPWLwRURERKRHDL6IiIiI9IjBFxEREZEeMfgiIiIi0iMGX0RERER6xOCLiIiISI8YfBERERHpEYMvIiIiIj1i8EVERESkRwy+iIiIiPSIwZeWXr16hfHjx8Pd3R3Gxsbw8/PDpk2bSntYREREVE7ISnsA5c2HH36I58+fY+fOnXBzc8O2bdswduxYVKlSBV27di3t4REREVEZx5kvLcTGxuLatWtYu3Yt2rRpg5o1a+I///kPvL298ccff5T28IiIiKgc4MyXFtzd3fHq1SvRezIZv5VERESkHiOGYsjIyMDatWvx9OlTfPzxx6U9HCIiIioHyuVjx4iICDRr1gwSiQQjRowotOyePXvQoUMH2NrawsTEBL6+vpgzZw4SExOLNQZ/f3+YmZnhu+++w9GjR1G/fv1itUdERESVQ7kKvgRBwNq1a9GgQQOEhISoLb948WJ88MEHuHPnDkaOHIn58+fDy8sLS5YsQatWrZCUlAQAOHXqFCQSicrXoUOHlNo+cOAALl++jG7duqFLly64cOGCzt8vERERVTwSQRCE0h6EpgYNGoSdO3eif//+aNasGaZMmYLhw4dj69atSmXDw8PRsGFDuLi44Pr163Byciq4N3v2bCxduhQTJkzA2rVrkZ6ejtjYWJX9urm5wczMTOX9999/H4mJiTh9+nSR31tycjKsra2RlJQEKyurIrdDRERE+lOUz+9yNfMVGxuLbdu2YdeuXbCxsSm07MaNG5Gbm4upU6cqBF4A8Nlnn8HU1BTbtm1DWloaTE1NUbNmTZWv/MArOjoaO3bsQG5urkJ7tWvXxt27d3X6XomIiKhiKlcL7vft2wdra2uNyp44cQIA0LlzZ6V7lpaWaNKkCU6fPo2rV6+ibdu2GrUZFRWFIUOGwMvLCy1atCi4fuvWLVSpUkVlvczMTGRmZhbadv4j0OTkZI3GQkRERKUv/3NbmweJ5Sr40jTwys7ORmRkJCQSCWrWrClaxtvbG6dPn8aNGzc0Dr7atGmDwMBAjBo1CsHBwfD09MSePXuwf/9+7Ny5U2W9r7/+GosWLdKoj6pVq2pUjoiIiMqO169faxynlKvgS1PJycmQy+UwNzeHiYmJaBk7OzsAQEJCgsbtGhgY4ODBg5g1axb69++PtLQ0+Pj4YNu2bRgwYIDKenPmzMHUqVMLbTs3NxcJCQmwt7eHRCLReExA3vutWrUqHj9+zPViGvi/9u49OKryDAP4s5vdzdVgCBtyERIISbFBTCEaSaDByQwGFKSSWHSgUAkJghqHQaotVazSUoFpKQMBQdQWrJeOE6StBEoFLHILFUMBDYSSaSMElpCIIQmb7Ns/7G677CV7luxJzub5zeyg32X3+97JefNm9+w5jJcyjJdyjJkyjJdyjJkygYyXiODq1atITEz0eU5QFl/Xrl0DAJhMJo9jQkNDncb6KjY2Fps2bcKmTZt8nhMaGup4PW+6Oo+tK9HR0TwIFWC8lGG8lGPMlGG8lGPMlAlUvHx9x8tOUyfc+8p+gvz169c9jmlra3MaS0RERKSGoCy++vXrB4PBgJaWFkeRdSOLxQIAGDBggJpLIyIioj4uKIsvg8GA4cOHA4DHS0CcOnUKAJCZmanWsoiIiIiCs/gC/neJiT/+8Y8ufQ0NDaiqqkJsbCyysrLUXhoRERH1YUFbfJWWlsJkMmH16tX497//7dT37LPPoqOjA/Pnz/d6Uj4RERFRd9PMtx3/9a9/4Z133nH8v/3ejidOnMDKlSsd7RMnTkRGRgbS0tKwatUqPPnkkxg1ahRmzJiBmJgYVFZWYv/+/cjNzcWPf/xj1fdBREREfZtmiq/a2lo888wzLu1VVVVON9keMGAAMjIyAABPPPEEUlNTsWrVKmzevBltbW1ITU3Fz372MyxatMjjNcCIiIiIAkUzxdf48eMVXbrfbuLEiZg4cWIAVtR7hIaG4oUXXvDpWmLEeCnFeCnHmCnDeCnHmCnT2+KlE38qGiIiIiLyS9CecE9ERETUG7H4IiIiIlIRiy8iIiIiFbH4IiIiIlIRiy+Nunz5Mp588kkMHjwYYWFhGDZsGJYsWYLW1taeXlrApaSkQKfTeX3ceGFdf+JVV1eH2bNnIyEhAeHh4RgxYgRWrlyJzs7OQG/xpogISktLodPpkJKS4nWsP3usrq7GtGnTYDabERkZiaysLGzevNnr6+zbtw8FBQWIiYlBdHQ0xo0bh4qKCj92Fxi+xqyrnzuDwf0XyNWKsxo++ugjfO9730N8fDxMJhPi4uIwefJk7Nmzx+14tY69YIiXP7kNCK78duXKFSxfvhw5OTno378/TCYTBg0ahJkzZ6K6utrtHE3mMSHNaWhokJSUFAEgY8eOlZKSEsnMzBQAkpWVJdeuXevpJQZUcnKyJCUlSVlZmcdHU1OTY7w/8Tp16pT0799fdDqd3HfffTJ37lxJS0sTAPLAAw9IZ2enmlv22ZUrV+TRRx8VAAJAkpOTPY71Z4979uyR8PBwMRqNMnXqVHnsscckKSlJAMj8+fPdvs5bb70lISEhEhERIdOnT5dZs2ZJTEyMAJBXXnmlu7buNyUxAyC33367x5+7hQsXusxRK86B1tzc7IhTeHi4TJ48WUpKSuTee+8VAKLT6eT11193mqPWsRcs8VKa20SCK799/vnnMmDAAMdx+Oijj0pxcbGMHj1aAIjRaJQPPvjgpvfSG/IYiy8NevDBBwWA/OpXv3JqLysrEwDy9NNP98zCVJKcnCx5eXk+j1caL5vNJt/5zndEp9PJ+++/72i3Wq3y0EMPCQD59a9/fTNbCIjPPvtM4uLiBIA88cQTXgsJf/Z47do1SUhIkLCwMDlw4ICj/euvv5acnBwBIBUVFU5z6uvrJSIiQsxms9TU1DjaGxoaJC0tTfR6vRw9erQbdu8fJTET+ab4mjVrls/Pr1ac1XDkyBEJCQmRRYsWuRQAb7/9tgCQmJgYaWlpcbSrcewFU7yU5jaR4Mpvu3btkiFDhsj27dtd+tasWSMAxGw2OwpKLecxFl8ac+LECQEg99xzj0tfe3u7DBo0SMLDw6WxsbEHVqcOJQnKn3j9+c9/FgDy/e9/32WOxWKRiIgISUxM7HXvflVUVEhubq7s3r1bRMRrIeHPHtetWycA5Ec/+pHLnJMnTwoAyc7OdmpfvHixAJDy8nKXOR9++KHHNahFSczs/UqKL7XirIazZ8/KX//6V4/9I0aMEADy0UcfiYh6x16wxEtEefEVbPntwoULXn932WP24Ycfioi28xiLL435+c9/7vGHQOR/PyS///3vVV6ZepQkKH/iVVJS4nSA3+jhhx8WAE5/NfUGHR0dTv/vrZDwZ48TJkwQAHLq1Cm3c+6++27R6XRy/vx5R1t6eroYjUZpbm52Gd/Z2Snx8fFyyy23yPXr17vaXkAoiZm9X0nxpVace4MpU6YIANm6dauIqHfsBUu8RJQXX30pv4mIFBUVCQDHx7VazmM84V5jPvnkEwDA2LFj3fbb2+3j+jp/4vXJJ59Ap9MhJyfH5zm9QUhIiM9j/dnjgQMHEBsbi+HDh3ucIyKOOZcvX0ZNTQ1GjhyJ6Ohol/F6vR45OTm4evUqjh8/7vPau5OSmPlDjTj3FufPnwcA3HrrrQDUO/aCJV7+6Ev5DQCampoAAMnJyQC0ncdYfGnM6dOnAQBpaWlu++3tZ86cUW1NvZnSeIkIamtrMXDgQLcHmrs5WuPPHi9cuICrV696jKO7OV3F3t2cYKJWnHuDkydPoqqqCrfccovjF54ax14wxcsffSm/nT17Fvv27UNCQgLGjBmj+TzG4ktjGhoaEBMT4/HmoPHx8Y5xwezIkSO47bbbEB4ejrCwMKSkpGDmzJk4ePCg0zil8frqq6/Q2tqKhIQEj6+t9Rj7s0f7v4GeowUVFRVITExEWFgYwsPDkZ6ejnnz5uHkyZNO49SKc08TEcyfP99xuQ77L0I1jr1gipedr7kNCP78ZrVaUVdXh/LycuTl5UFEsH79eoSFhWk+j7m/KA31Wi0tLTCbzR77IyIiHOOCldlsRmpqKpKSkhAVFYXz58/js88+w5YtW7B161Y8//zzWLp0KQDl8bL/Gx4e7vMcrfFnj2rN6e0SExNxxx13ICEhAaGhoaivr8fRo0exYcMGbN68GevWrUNxcTGAvhOz1atXY+/evRgyZAheeOEFR7sax14wxQtQltuA4M9vZrMZzc3NAICsrCxs27YNo0aNAqD9PMbiS2NsNpvX81TsF3ns6OhQa0mqO3LkiEubiGD79u2YM2cOXnzxRWRmZmLq1KmK42Wz2QB4PxdI6zH2Z49qzent6uvrXdqsViu2bt2KBQsWoLS0FKNGjcKoUaP6RMyOHTuGZ599FkajEVu2bEFUVJSjT41jL5jiBSjLbYA6Me5J8+bNw4ULF1BVVYWqqirMmTMHr7/+OjIzMzWfx/ixo8aEh4ejra3NY7/9isaRkZFqLalX0Ol0mDJlCjZu3Ajgm78uAeXxsv91E8wx9mePas3RIqPRiNmzZ2PZsmWw2WxYs2YNgOCP2ZUrV/Dwww+jvb0dK1ascDnpWY1jL5ji5Ymn3AYEf35bvnw53njjDfzjH//AH/7wB5w4cQJjx45FfX295vMYiy+NiYmJQVNTE0TEbf/ly5cd4/qiyZMnIyIiAn//+98BKI9XdHQ09Ho9GhsbPb6G1mPszx7t/wZ6jpZNnz4dABw/e2rFuSd0dHSgqKgIp0+fxg9+8AOUlZW5jFHj2AumeHXlxtwG9K38Nm3aNMybNw8tLS1YvXq15vMYiy+NGTp0KKxWK7788ku3/efOnQMADBs2TMVV9R4hISHo168frFYrAOXxMhqNGDRoEOrr6z3eF0zrMfZnj4MHD4bBYEBdXZ3H571xztChQwFA0Rwt69+/PwA4fvbUinNPeOqpp7B7927k5ubi1VdfdTtGjWMvmOLVlRtzG9D38lt+fj6Ab74tqvU8xuJLY7KysgAAhw8fdttv/0aM/aTEvqapqQkXL150HAT+xCsrKwttbW0eb+IaDDFWukeDwYA777wTtbW1Hv8CvHFOcnIyBgwYgKNHjzrOm3A3x2Aw4I477rip/fQGX3zxBQDnBKxGnNW2bt06lJeXIzk5Ge+//77Hb9qpcewFU7y6cmNuA/pefjOZTAD+9xGgpvOYz5djpV5hz549AkCKiopc+mw2m4wcOVL0er2cO3euB1YXeFar1Wv/smXLBIAsWbJERPyL1xtvvCEA5JlnnnGZ09raKmazWaKionr9Dczh5Wrt/uxx6dKlAkDWrl3rMuf8+fNiMpkkNTXVqX327NkCQP70pz+5zDl69KgAkPz8fIU7CxxvMevqZ2/u3LkCQDZt2uRoUyvOavnLX/4iBoNBoqKipLq62utYtY69YImX0twmEnz5bceOHV5va7RgwQIBIIsWLRIRbecxFl8adNddd4lOp5N3333Xqf3ll18WAPLII4/00MoCb8qUKbJixQr56quvnNptNpusXbtWjEajxMXFyaVLlxx9SuPV1tYmt912m4SGhsrf/vY3p9ew/4J97rnnArC77uWtkPBnjw0NDRIZGSmxsbFOt+Zoa2uT+++/XwDIhg0bnOZUV1eLXq+XtLQ0+fLLLx3tTU1NMnr0aAEglZWV3bDb7uEtZtnZ2bJx40Zpa2tzardarfL8888LALn99tulvb3d0adWnNVQU1MjMTExotfr5YMPPvBpjhrHXrDEy5/cJhJc+S0vL0/uvfde+fTTT1363nrrLdHr9WIymeT06dMiou08xuJLgz7//HPp37+/6HQ6yc/Pl9LSUsnOzhYAkpaWJhaLpaeXGDCFhYUCQCIjIyU/P1/mzp0rJSUljhuuxsXFyaFDh5zm+BOvffv2SVhYmBgMBpk8ebKUlJRIRkaGAJCcnBxpbW1Va8t+81ZIiPi3x7ffflv0er2Eh4dLUVGRFBcXS0pKigCQadOmic1mc5nzyiuvCAC59dZbZcaMGfLDH/5QBg4cKABk4cKF3bnlm+YtZllZWY59TJo0SUpLS+Wxxx6ToUOHCgAZNmyY45fC/1MrzoE2adIkASBDhgyRsrIyj4//v1ehWsdeMMTLn9wmElz5bc2aNWIwGASAjBgxQmbOnCnFxcVy1113CQAxGo2yZcuWm95Lb8hjLL40qq6uTmbPni2JiYliMpkkJSVFFi5cKE1NTT29tICyWq3y5ptvyqRJkyQpKUlMJpNERETIiBEj5Cc/+YnHwtOfeB0/flwKCwvFbDZLaGiopKeny4svvujyzkdv1VXxJeLfHj/++GOZNGmSxMTESFhYmIwcOVJ+85vfeP24YNu2bZKXlyfR0dESGRkp2dnZLkm0N/AWs2vXrsmaNWskPz9fBg4cKEajUaKioiQrK0uWL18uX3/9tcfnVSvOgZSXlycAunzceONxtY49rcfL39wmElz5rba2VhYvXiyjR4+Wfv36idFolKSkJJk5c6bHj261mMd0Ih6+o0pERERE3Y7fdiQiIiJSEYsvIiIiIhWx+CIiIiJSEYsvIiIiIhWx+CIiIiJSEYsvIiIiIhWx+CIiIiJSEYsvIiIiIhWx+CIi6qMaGxthsVh6ehlEfQ6vcE9E1EfFx8ejoaEB/DVApC6+80VE5Mby5ctRUFCAXbt2dTl28eLFKCgowIkTJ1RYGRFpnaGnF0BE1B0ef/xxWK1WRXP69euHVatWue07duwYKisrMX369C6f5/Dhw9i7dy+WLFmi6PXnzJmDzs5ORXOWLVuGpKQkp7YVK1bgiy++cDveZrOhs7MTnZ2dePPNNxESEqLo9Yio+/FjRyIKCmFhYWhvb1c0Z+DAgbhw4YLbvunTp+Odd97BmDFjMHToUK/Ps2vXLly8eBEff/wxxo4d6/PrGwwGxcVXbW2ty3rGjx+PvXv3uozV6/UwmUxoa2tzu1d+7EjUM/jOFxEFhba2NgDfvNNjs9lgMLimNxFBZ2cn9Ho99Hrfzro4cOAADhw40K1rtevo6PB57H333YedO3ciMjLSpW/37t0uBZR9j6dPn0Z6ejq+9a1v3fR6iah78JwvIgoqDz30EIxGIw4fPuzSt3PnThiNRsyZM8fn53vvvfcgIl4feXl53bkFt1paWgDAbfEVEhICg8Hg9LAXl/bC8bvf/W7A10hEvmHxRURBJTQ0FADcnv91/fp1pzFa0tLSgtDQUERFRSma99577wEACgoKArEsIvIDiy8iCirBWnxZLBbExcUpmnP27Fns2LEDw4cPR25uboBWRkRK8ZwvIgoqYWFhALwXX/YxvigqKuqehfmhvb0dFosFly5dgsViQUZGhqL5S5cuRUdHB55++unALJCI/MLii4iCSne98zVu3DiXk/YtFgsqKythNpsxYcIElzlK3pnas2cPHnjgAbd9VqsVVqvV5ST6+Ph4n59/x44d+N3vfoeMjAwUFxf7PI+IAo/FFxFp3rFjx7Bp0yYAwP79+wEA69evx86dO53G2S+CunPnTjQ2NgIAysrKkJaW5vKcCxYswIIFC5zaDh48iMrKSqSnp2PLli03tWaz2YypU6e6tIeEhMBkMiE0NBTR0dFISEhAY2Mjli5disGDB/v03DU1NZgxYwaMRiM2btzIa3sR9TIsvohI886cOYO1a9c6tW3fvt3j+EOHDuHQoUMAgMLCQrfFV6BlZGT4XMD99re/BQAkJyd3ObampgYTJkzA5cuXsXr1aowZM+am1klE3Y8n3BOR5hUWFjou+3Dp0iUAQHZ2tsslIe6//34AwPHjxx1t48ePdzxPSkoKdDqdx4e9kNm/f7/XcTqdDi+//HK37a+mpgZA18XXrl27kJubi7q6Orz00kt46qmnum0NRNR9+M4XEdF/FRYWwmKxdMtz3Xnnnd3yPAActw5KTU1129/Y2Iif/vSnKC8vh8lkwvr161FaWtptr09E3YvFFxHRf61cudLx38899xyOHDmCDRs2eCx61FJdXQ0Abq9Sv3fvXjz44INobm52fJSZmZmp8gqJSAkWX0REbnz66afYvXs3rl692qPraG1txZkzZxAfH4/o6GiX/nHjxuGRRx7Bt7/9bTz++ONub6tERL0Lj1Ii0qyKigrs2LHDqc1+j8d//vOfmDdvnlPf8ePHAQAvvfQSYmJinPpmzZqlysnpv/jFL1BbW+vz+ObmZthsNnR0dLhcMuKXv/wlYmNjUV5e3t3LJKIAYvFFRJp18OBBbNiwwW3fxYsXPfa9++67Lm333HOPKsXXtm3bHN+0VMJiseC1115zaluyZAliY2O7a2lEpBKd3HgVPyIiQkFBASorK5GSkuLz7Yjuvvtux2UhtCA+Ph4NDQ0uF3MlosDiO19ERF6cO3fO57FKrkBPRH0X3/kiIiIiUhEvskpERESkIhZfRERERCpi8UVERESkIhZfRERERCpi8UVERESkIhZfRERERCpi8UVERESkIhZfRERERCpi8UVERESkIhZfRERERCpi8UVERESkIhZfRERERCpi8UVERESkov8A54g8cqFDFe0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "num_trials = 50\n",
    "gaussian_1, gaussian_2 = [], []\n",
    "for _ in range(3100):\n",
    "    gaussian_1.append(random.gauss(100, 5))\n",
    "    gaussian_2.append(random.gauss(100.5, 5))\n",
    "p_vals_means = []\n",
    "for sample_size in range(50, 3040, 50):\n",
    "    p_vals = []\n",
    "    for t in range(num_trials):\n",
    "        sample_1 = random.sample(gaussian_1, sample_size)\n",
    "        sample_2 = random.sample(gaussian_2, sample_size)\n",
    "        p_vals.append(scipy.stats.ttest_ind(sample_1, sample_2)[1])\n",
    "    p_vals_means.append(sum(p_vals)/len(p_vals))\n",
    "plt.plot(range(50, 3040, 50), p_vals_means, label = '평균 p 값')\n",
    "plt.ylabel('평균 p 값 (50번 시도)')\n",
    "plt.xlabel('표본 크기')\n",
    "plt.title('SD = 5, 평균 100 & 100.5인 가우스 분포')\n",
    "plt.axhline(0.05, color = 'r', linestyle = 'dashed', label = 'p = 0.05')\n",
    "plt.axhline(0.01, linestyle = ':', label = 'p = 0.01')\n",
    "plt.yticks(np.arange(0, 1, 0.1))\n",
    "plt.semilogy()\n",
    "# 지수 부분의 음수 표시를 위해\n",
    "plt.yticks(fontname=\"Liberation Sans\")\n",
    "plt.ylim(10e-4, 1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31aca7f2-c97e-4aaf-be59-af6ebe925314",
   "metadata": {
    "id": "31aca7f2-c97e-4aaf-be59-af6ebe925314"
   },
   "source": [
    "## 21.6 다중 가설"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "560f55b9-30bd-4300-b4df-ad8eff34136b",
   "metadata": {
    "id": "560f55b9-30bd-4300-b4df-ad8eff34136b"
   },
   "outputs": [],
   "source": [
    "def get_BM_data(filename):\n",
    "    \"\"\"주어진 파일의 내용을 읽습니다.\n",
    "       파일은 CSV 포맷이고 다음과 같은 6개의 항목으로 이루어졌다고 가정합니다.\n",
    "       0. 이름 (문자열), 1. 성별 (문자열), 2 나이 (정수)\n",
    "       3. 부문 (정수), 4. 국적 (문자열), 5. 시간 (실수)\n",
    "       반환값: 6개 항목에 대한 리스트를 담은 딕셔너리\"\"\"\n",
    "\n",
    "    data = {}\n",
    "    with open(filename, 'r') as f:\n",
    "        f.readline() #첫 번째 줄은 버립니다\n",
    "        line = f.readline()\n",
    "        for k in ('name', 'gender', 'age', 'division',\n",
    "                  'country', 'time'):\n",
    "            data[k] = []\n",
    "        while line != '':\n",
    "            split = line.split(',')\n",
    "            data['name'].append(split[0])\n",
    "            data['gender'].append(split[1])\n",
    "            data['age'].append(int(split[2]))\n",
    "            data['division'].append(int(split[3]))\n",
    "            data['country'].append(split[4])\n",
    "            data['time'].append(float(split[5][:-1])) #remove \\n\n",
    "            line = f.readline()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12d9492-4cd2-49f9-bb10-9627f13a74d5",
   "metadata": {
    "id": "a12d9492-4cd2-49f9-bb10-9627f13a74d5"
   },
   "source": [
    "예제 21-6 몇몇 나라의 평균 완주 시간 비교하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afc91877-c2ce-4247-baf2-d45dd10a3cee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "afc91877-c2ce-4247-baf2-d45dd10a3cee",
    "outputId": "94ab4de9-6683-494e-e55f-9b5074646a66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITA 와 JPN 는 평균이 크게 다릅니다, p-값 = 0.025\n"
     ]
    }
   ],
   "source": [
    "data = get_BM_data('bm_results2012.csv')\n",
    "countries_to_compare = ['BEL', 'BRA', 'FRA', 'JPN', 'ITA']\n",
    "\n",
    "#여성 선수의 완주 시간의 리스트를 딕셔너리에 매핑합니다\n",
    "country_times = {}\n",
    "for i in range(len(data['name'])): #각 선수에 대해\n",
    "    if (data['country'][i] in countries_to_compare and\n",
    "        data['gender'][i] == 'F'):\n",
    "        try:\n",
    "            country_times[data['country'][i]].append(data['time'][i])\n",
    "        except KeyError:\n",
    "            country_times[data['country'][i]] = [data['time'][i]]\n",
    "\n",
    "#완주 시간 비교\n",
    "for c1 in countries_to_compare:\n",
    "    for c2 in countries_to_compare:\n",
    "        if c1 < c2: # 매 국가 쌍마다 검정하기 때문에 != 가 아니라 <을 사용합니다.\n",
    "            pVal = scipy.stats.ttest_ind(country_times[c1],\n",
    "                                         country_times[c2],\n",
    "                                         equal_var = False)[1]\n",
    "            if pVal < 0.05:\n",
    "                print(c1, '와', c2, '는 평균이 크게 다릅니다,',\n",
    "                      'p-값 =', round(pVal, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d845d513-1283-4500-ac5c-ddd8593129ec",
   "metadata": {
    "id": "d845d513-1283-4500-ac5c-ddd8593129ec"
   },
   "source": [
    "예제 21-7 다중 검정 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f89173ea-bf4e-4dbd-a24d-eccbb1aee990",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f89173ea-bf4e-4dbd-a24d-eccbb1aee990",
    "outputId": "fbe1e4fe-11af-4643-b377-3c9ce71e9ead"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "통계적으로 유의하게 다른 (p < 0.05) 쌍의 개수 = 3\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "num_hyps = 50\n",
    "sample_size = 200\n",
    "population = []\n",
    "for i in range(5000): #큰 모집단을 만듭니다\n",
    "    population.append(random.gauss(0, 1))\n",
    "sample1s, sample2s = [], []\n",
    "#작은 크기의 표본 쌍을 많이 생성합니다\n",
    "for i in range(num_hyps):\n",
    "    sample1s.append(random.sample(population, sample_size))\n",
    "    sample2s.append(random.sample(population, sample_size))\n",
    "#통계적으로 유의한 차이가 있는 쌍을 확인합니다\n",
    "numSig = 0\n",
    "for i in range(num_hyps):\n",
    "    if scipy.stats.ttest_ind(sample1s[i], sample2s[i])[1] < 0.05:\n",
    "        numSig += 1\n",
    "print('통계적으로 유의하게 다른 (p < 0.05) 쌍의 개수 =', numSig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f15cc7-b117-466a-b696-0f823660b994",
   "metadata": {
    "id": "c4f15cc7-b117-466a-b696-0f823660b994"
   },
   "source": [
    "## 21.7 조건부 확률과 베이즈 통계"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71132799-7a37-427e-9127-07f8f0597cae",
   "metadata": {
    "id": "71132799-7a37-427e-9127-07f8f0597cae"
   },
   "source": [
    "### 21.7.1 조건부 확률"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "624f0ec4-606a-4997-9a03-8575e2712422",
   "metadata": {
    "id": "624f0ec4-606a-4997-9a03-8575e2712422"
   },
   "source": [
    "**뇌풀기 문제**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11c812fe-c253-485f-8370-8ccb3d5ca02a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "11c812fe-c253-485f-8370-8ccb3d5ca02a",
    "outputId": "50142979-8f58-47de-fa61-39e13b269caf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43785918794570444\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "print(0.5 * (1 - norm.cdf(80, 95, 13)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18998305-fb0c-49aa-b800-ca4ea6d837ff",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "18998305-fb0c-49aa-b800-ca4ea6d837ff",
    "outputId": "cea19e68-39d4-4a51-a7b7-63a09b530a3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8757183758914089"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - norm.cdf(80, 95, 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63fc3dc6-546e-4736-96ba-61f1c0be0afd",
   "metadata": {
    "id": "63fc3dc6-546e-4736-96ba-61f1c0be0afd"
   },
   "source": [
    "### 21.7.2 베이즈 정리"
   ]
  }
 ],
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