4 - Veri Bilimi Icin Istatistik.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Veri Bilimi için İstatistik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temel İstatistik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Örnek Teorisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Popülasyon içerisinden bu popülasyonu temsil ettiği düşünülen bir alt kümeye **örneklem** denir.\n",
    "- Belirli kurallara göre seçilir ve yansız olması gerekir.\n",
    "- Olasılıklı ve olasılıksız olarak iki türe ayrılır.\n",
    "- **Örneklem dağılımı**, birden fazla seçilen örneklemin birbirlerine göre dağılımlarına denir.\n",
    "- **Merkezi limit teoremi** büyük bir sayıda olan bağımsız ve aynı dağılım gösteren rassal değişkenlerin aritmetik ortalamasının, yaklaşık olarak normal dağılım göstereceğini ifade eden bir teoremdir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burada bir populasyondan örneklem seçme işlemini gerçekleştireceğiz. Bu işlemi numpy üzerinden yapacağız.<br>\n",
    "Öncelikle random modulunun her seferinde aynı sayıları üretmesi için **seed** fonksiyonuna rastgele bir sayı veriyoruz. seed fonksiyonununa aynı sayı verildiğinde random fonksiyonundan her zaman aynı sayılar üretilecektir.<br>\n",
    "Populasyon olarak 0 ile 80 yaşları arasında 10k kişiyi oluşturuyoruz. Bu populasyonun yaş ortalamasını örneklem üzerinden bulmaya çalışacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44, 47, 64, 67, 67,  9, 21, 36, 70, 12])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "pop = np.random.randint(0,80,10000)\n",
    "pop[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Örneklem seçme işini yine numpy içerisindeki random modulu altındaki **choice** fonksiyonu ile gerçekleştireceğiz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38, 12, 69, 55, 41, 14, 59, 65, 64, 66])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1 = np.random.choice(a=pop,size=100)\n",
    "sample1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.3897"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.03"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9400934762133248"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1.mean() / pop.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Betimsel İstatistik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Betimsel istatistik aşağıdaki yöntemlerden oluşmaktadır;\n",
    "* Merkezi Eğilim;\n",
    "    * Ortalama\n",
    "    * Medyan\n",
    "    * Mod\n",
    "    * Kartiller\n",
    "* Dağılım;\n",
    "    * Değişim Aralığı\n",
    "    * Standart Sapma\n",
    "    * Kovaryasyon\n",
    "    * Korelasyon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kovaryans** iki değişkenin birlikte ne kadar değiştiklerinin ölçüsüdür. Kovaryans, iki rastgele değişkenin beraber değişimlerini inceleyen bir istatistiktir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Korelasyon**, iki değişken arasındaki ilişkiyi, ilişkinin anlamlı olup olmadığını, ilişkinin şiddetini ve yönünü ifade eden bir istatistiksel tekniktir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting researchpy\n",
      "  Downloading researchpy-0.2.3-py3-none-any.whl (10 kB)\n",
      "Requirement already satisfied: scipy in c:\\users\\ea\\anaconda3\\lib\\site-packages (from researchpy) (1.5.2)\n",
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      "Requirement already satisfied: numpy in c:\\users\\ea\\anaconda3\\lib\\site-packages (from researchpy) (1.19.2)\n",
      "Requirement already satisfied: patsy>=0.5 in c:\\users\\ea\\anaconda3\\lib\\site-packages (from statsmodels->researchpy) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\ea\\anaconda3\\lib\\site-packages (from pandas->researchpy) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\ea\\anaconda3\\lib\\site-packages (from pandas->researchpy) (2020.1)\n",
      "Requirement already satisfied: six in c:\\users\\ea\\anaconda3\\lib\\site-packages (from patsy>=0.5->statsmodels->researchpy) (1.15.0)\n",
      "Installing collected packages: researchpy\n",
      "Successfully installed researchpy-0.2.3\n"
     ]
    }
   ],
   "source": [
    "!pip install researchpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total_bill</td>\n",
       "      <td>244.0</td>\n",
       "      <td>19.7859</td>\n",
       "      <td>8.9024</td>\n",
       "      <td>0.5699</td>\n",
       "      <td>18.6633</td>\n",
       "      <td>20.9086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tip</td>\n",
       "      <td>244.0</td>\n",
       "      <td>2.9983</td>\n",
       "      <td>1.3836</td>\n",
       "      <td>0.0886</td>\n",
       "      <td>2.8238</td>\n",
       "      <td>3.1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>size</td>\n",
       "      <td>244.0</td>\n",
       "      <td>2.5697</td>\n",
       "      <td>0.9511</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>2.4497</td>\n",
       "      <td>2.6896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Variable      N     Mean      SD      SE  95% Conf.  Interval\n",
       "0  total_bill  244.0  19.7859  8.9024  0.5699    18.6633   20.9086\n",
       "1         tip  244.0   2.9983  1.3836  0.0886     2.8238    3.1728\n",
       "2        size  244.0   2.5697  0.9511  0.0609     2.4497    2.6896"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import researchpy as rp\n",
    "rp.summary_cont(df.select_dtypes([\"float64\",\"int64\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sex</td>\n",
       "      <td>Male</td>\n",
       "      <td>157</td>\n",
       "      <td>64.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>Female</td>\n",
       "      <td>87</td>\n",
       "      <td>35.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>smoker</td>\n",
       "      <td>No</td>\n",
       "      <td>151</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td>93</td>\n",
       "      <td>38.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>Sat</td>\n",
       "      <td>87</td>\n",
       "      <td>35.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>Sun</td>\n",
       "      <td>76</td>\n",
       "      <td>31.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>Thur</td>\n",
       "      <td>62</td>\n",
       "      <td>25.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>Fri</td>\n",
       "      <td>19</td>\n",
       "      <td>7.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>time</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>176</td>\n",
       "      <td>72.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>Lunch</td>\n",
       "      <td>68</td>\n",
       "      <td>27.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Variable Outcome  Count  Percent\n",
       "0      sex    Male    157    64.34\n",
       "1           Female     87    35.66\n",
       "2   smoker      No    151    61.89\n",
       "3              Yes     93    38.11\n",
       "4      day     Sat     87    35.66\n",
       "5              Sun     76    31.15\n",
       "6             Thur     62    25.41\n",
       "7              Fri     19     7.79\n",
       "8     time  Dinner    176    72.13\n",
       "9            Lunch     68    27.87"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp.summary_cat(df.select_dtypes([\"category\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Kovaryans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>1.914455</td>\n",
       "      <td>8.323502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>8.323502</td>\n",
       "      <td>79.252939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tip  total_bill\n",
       "tip         1.914455    8.323502\n",
       "total_bill  8.323502   79.252939"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"tip\",\"total_bill\"]].cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Korelasyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>0.675734</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tip  total_bill\n",
       "tip         1.000000    0.675734\n",
       "total_bill  0.675734    1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"tip\",\"total_bill\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"tip\",y=\"total_bill\",data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Güven Aralıkları"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Güven aralığı, istatistik biliminde bir anakütle parametresi için bir çeşit aralık kestirimi olup bir çıkarımsal istatistik çözüm aracıdır. Bir anakütle parametre değerinin tek bir sayı ile kestirimi yapılacağına, bu parametre değerini kapsayabilecek iki (alt ve üst sınır) sayıdan oluşan bir aralık bulunur. Böylece güven aralıkları bir kestirimin ne kadar güvenilir olduğunu gösterir.\n",
    "* Anakütle parametresinin tahmini değerini kapsayabilecek iki sayıdan oluşan bir aralık bulunmasıdır.\n",
    "* Genelde 95% güven aralığı seçilmektedir.\n",
    "* Hesaplanırken n(birim sayısı), ortalama ve standart sapma kullanılmaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Problem : \n",
    "    * CEO fiyat belirleme konusunda bilimsel bir dayanak ve esneklik istiyor.\n",
    "\n",
    "* Detaylar:\n",
    "    * Satıcı,alıcı ve bir ürün bulunmakta\n",
    "    * Alıcılara ürüne ne kadar ücret öderdiniz diye soruluyor ve alıcılar birbirlerinin cevaplarından habersiz tutuluyor\n",
    "    * Optimum fiyat bilimsel ve esnek olarak bulunmak isteniyor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fiyatları müşterilerden toplamış gibi varsayarak 5 ila 200 arasında 1000 gözlem oluşturuyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = np.random.randint(5,200,1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bilimsel bir dayanağa göre fiyat belirlenmek sitenildiğinde en basit ve efektif yol ortalamayı almaktır. Fakat bu esneklik sağlamamaktadır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102.61"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burada esnekliği sağlayacak olan yöntem güven aralığıdır.<br>\n",
    "**Güven aralığı hesaplamak için alttaki yolu izleriz.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99.14589303185839, 106.0741069681416)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.stats.api as sms\n",
    "sms.DescrStatsW(prices).tconfint_mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sonuç olarak 95% güven aralığıyla müşteriler 99 ila 106 lira arasında fiyat ödeyeceklerdir.\n",
    "Kısacası 100 müşteriden 95inin ödeyeceği fiyat 99 ila 106 arasında olacaktır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Olasılığa Giriş"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Olasılık, olayların olabilirliğinin sayısal ifadesidir.\n",
    "* Rassal değişken, değerlerini bir deneyin sonucundan alan değişkenlere denir.\n",
    "* Dağılım, evrendeki gerçekleşen olayların veya durumların sayısal karşılıklarının ortaya çıkardığı yapıya denir.\n",
    "* Olasılık dağılımı, bir rassal olaya ait değerler ve bu değerlerinin gerçekleşme olasılıklarının bir arade ifade edilmesine denir.\n",
    "* Olasılık fonksiyonu, bir değişkenin herhangi bir değeri alması olasılığını hesaplamaya yarayan fonksiyondur.\n",
    "* Olasılık Dağılımları :\n",
    "    * Kesikli Olasılık Dağılımları\n",
    "        * Bernoulli\n",
    "        * Binom\n",
    "        * Poisson\n",
    "    * Sürekli Olasılık Dağılımları\n",
    "        * Normal Dağılım\n",
    "        * Uniform Dağılım\n",
    "        * Üstel Dağılım"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Bernoulli Dağılımı"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Başarılı-başarısız, olumlu-olumsuz şeklindeki binary olaylar ile ilgilenildiğinde kullanılan kesikli olasılık dağılımıdır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(x;p) = p^x(1-p)^{1-x}$<br>\n",
    "$x \\in \\{0,1\\}$<br>\n",
    "$E(X) = p$<br>\n",
    "$Var(X) = pq = p(1-p)$<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bernoulli dağılımını hesaplarken aradığımız değerin olasılığını bernoulli fonksiyonu içerisine verilir.<br>\n",
    "Daha sonra Probability Mass Function üzerinden hangi değeri arıyorsak k parametresine vererek sonucu buluruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.634"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import bernoulli\n",
    "\n",
    "p = 0.634\n",
    "rv = bernoulli(p)\n",
    "rv.pmf(k = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36600000000000005"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rv.pmf(k = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Büyük Sayılar Yasası"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir rassal değişkenin uzun vadeli kararlılığını belirleyen olasılık teoremidir.<br>\n",
    "Büyük sayılar kanunu ya da büyük sayılar yasası, bir rassal değişkenin uzun vadeli kararlılığını tanımlayan bir olasılık teoremidir. Sonlu bir beklenen değere sahip birbirinden bağımsız ve eşit dağılıma sahip bir rassal değişkenler örneklemi verildiğinde, bu gözlemlerin ortalaması sonuçta bu beklenen değere yakınsayacak ve bu değere yakın bir seyir izleyecektir.<br>\n",
    "\n",
    "Bir örnek olarak madeni para atılması olabilir. Bir madeni paranın peş peşe atılması durumunda, yazıların (ya da turaların) sıklığı, gözlem sayısı arttıkça, %50'e gittikçe yaklaşacaktır. Fakat yazı ve tura sayıları arasındaki mutlak fark atış sayısı arttıkça açılacaktır. Örneğin, 1000 atıştan sonra 520 ve 10000 atıştan sonra 5096 yazı görebiliriz. Ortalama ,52'den ,5096'ya gittiği, gerçek %50'ye yaklaştığı halde, ortalamadan toplam fark 40'tan 192'ye yükselmiştir.<br>\n",
    "\n",
    "Büyük sayılar yasası önemlidir, çünkü rastgele olaylardan kararlı uzun-vadeli sonuçlar alınacağını \"garanti eder\". Örneğin, bir gazino tek bir Amerikan Rulet dönüşünden para kaybedebilir, ama 1000lerce dönüşe oynanan paranın tamamının %5,3'üne yakınını neredeyse kesin olarak kazanacaktır. Bir oyuncunun herhangi bir kazancı, sonuçta oyunun başlıca parametreleri tarafından soğurulacaktır. Büyük sayılar yasasının büyük sayıda gözlem yapıldığı zaman etkili olacağı unutulmamalıdır. Küçük miktardaki gözlem için sonucun beklenen değere yaklaşacağını veya bir sapmanın hemen bir başkasıyla \"dengeleneceğini\" beklemek için bir neden yoktur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deney Sayisi :  2  Tura Orani : 50.00\n",
      "Deney Sayisi :  4  Tura Orani : 75.00\n",
      "Deney Sayisi :  8  Tura Orani : 75.00\n",
      "Deney Sayisi :  16  Tura Orani : 43.75\n",
      "Deney Sayisi :  32  Tura Orani : 62.50\n",
      "Deney Sayisi :  64  Tura Orani : 48.44\n",
      "Deney Sayisi :  128  Tura Orani : 46.09\n",
      "Deney Sayisi :  256  Tura Orani : 54.69\n",
      "Deney Sayisi :  512  Tura Orani : 48.24\n",
      "Deney Sayisi :  1024  Tura Orani : 51.46\n",
      "Deney Sayisi :  2048  Tura Orani : 51.17\n",
      "Deney Sayisi :  4096  Tura Orani : 50.24\n",
      "Deney Sayisi :  8192  Tura Orani : 50.59\n",
      "Deney Sayisi :  16384  Tura Orani : 49.55\n",
      "Deney Sayisi :  32768  Tura Orani : 49.84\n",
      "Deney Sayisi :  65536  Tura Orani : 50.06\n",
      "Deney Sayisi :  131072  Tura Orani : 50.20\n",
      "Deney Sayisi :  262144  Tura Orani : 49.87\n",
      "Deney Sayisi :  524288  Tura Orani : 49.87\n",
      "Deney Sayisi :  1048576  Tura Orani : 49.96\n",
      "Deney Sayisi :  2097152  Tura Orani : 50.03\n",
      "Deney Sayisi :  4194304  Tura Orani : 49.97\n",
      "Deney Sayisi :  8388608  Tura Orani : 50.03\n",
      "Deney Sayisi :  16777216  Tura Orani : 50.02\n",
      "Deney Sayisi :  33554432  Tura Orani : 50.00\n",
      "Deney Sayisi :  67108864  Tura Orani : 50.02\n",
      "Deney Sayisi :  134217728  Tura Orani : 50.00\n",
      "Deney Sayisi :  268435456  Tura Orani : 50.00\n",
      "Deney Sayisi :  536870912  Tura Orani : 50.00\n",
      "Deney Sayisi :  1073741824  Tura Orani : 50.00\n"
     ]
    }
   ],
   "source": [
    "# Örnekleme olarak yazı turayı gerçekleştirelim.\n",
    "import numpy as np\n",
    "generator = np.random.RandomState(0)\n",
    "for i in range(1,31):\n",
    "    deney_sayisi = 2**i\n",
    "    ornekler = generator.randint(0,2,deney_sayisi)\n",
    "    tura_orani = ornekler.mean()\n",
    "    print(\"Deney Sayisi : \",deney_sayisi,\" Tura Orani : %.2f\" % (tura_orani * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Binom Dağılımı"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bağımsız n deneme sonucu, k başarılı olma olasılığı ile ilgilenildiğinde kullanılan dağılımdır.<br>\n",
    "Olasılık kuramı ve istatistik bilim kollarında, binom dağılımı n sayıda iki kategori (yani başarı/başarısızlık, evet/hayır, 1/0 vb) sonucu veren denemelere uygulanır. Araştırıcının ilgi gösterdiği kategori başarı olarak adlandırılır. Bu türlü her bir deneyde, bağımsız olarak, başarı (=evet=1) olasılığının p olduğu (ve yalnızca iki kategori sonuç mümkün olduğu için başarısızlık olasılığının 1 - p olduğu) bilinir. Bu türlü bağımsız n sayıda denemeler serisi içinde elde edilen başarı sayısının ayrık olasılık dağılımı binom dağılım olarak tanımlanır. Bir binom dağılım sadece iki parametre ile, yani n ve p, ile tam olarak tanımlanır.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(k;n,p) = \\binom{n}{k}p^k(1-p)^{n-k}$<br>\n",
    "$ k \\in \\{0,...,n\\}$<br>\n",
    "$n \\geq 0$<br>\n",
    "$E(X) = np$<br>\n",
    "$Var(X) = np(1-p)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basit bir örnek olarak ; bir madeni para 4 kere atılıyor 2 kere yazı gelme olasılığı nedir?<br>\n",
    "$f(2;4,0.5) = \\binom{4}{2}(0.5)^2(1-0.5)^{4-2} = 0.375$<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36972963764971983\n",
      "0.0028977871237616114\n"
     ]
    }
   ],
   "source": [
    "# Bir reklama tıklanma oranı 0.01, 100 kişiden 1 ve 5 kişinin reklamı tıklama olasılığı nedir?\n",
    "from scipy.stats import binom\n",
    "p = 0.01\n",
    "n = 100\n",
    "rv = binom(n,p)\n",
    "print(rv.pmf(1))\n",
    "print(rv.pmf(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Poisson Dağılımı"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Belirli bir zaman aralığında, belirli alanda nadiren rastlanan olayların olasılıklarını hesaplamak için kullanılır.<br>\n",
    "$f(x,\\lambda) = \\lambda^xe^{-\\lambda} / x!$<br>\n",
    "$x = 0,1,2,...,n$<br>\n",
    "$E(X) = \\lambda$<br>\n",
    "$Var(X) = \\lambda$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Örnek verilebilecek olaylar;\n",
    "* 10 bin kelimeden oluşan bir kitaptaki hatalı kelime sayısı\n",
    "* 4k öğrencili okulda not girişinde hata yapılması\n",
    "* Bir iş gününde çağrı merkezine gelen taktir sayısı"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Poisson dağılımını;\n",
    "* n büyük\n",
    "* p küçük\n",
    "olduğu zamanlarda kullanılır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir olayın nadir olay kabul edilebilmesi için;\n",
    "* n > 50\n",
    "* n*p < 5 \n",
    "ise bu duruma nadir olay yorumu yapılır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rassal denemeler iki sonuçlu olmalıdır.\n",
    "* Aynı koşullar altında gerçekleştirilmelidir.\n",
    "* Rassal denemeler birbirinden bağımsız olmalıdır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9048374180359595\n",
      "0.00015080623633932676\n"
     ]
    }
   ],
   "source": [
    "# İlan sitesinde 1 yıl boyunca hata değerleri hesaplanmış ve lambda 0.1 bulunmuştur. Hiç hata olmama ve 3 kez hata olma durumlarını bulunuz.\n",
    "from scipy.stats import poisson\n",
    "lambda_ = 0.1\n",
    "rv = poisson(mu = lambda_)\n",
    "print(rv.pmf(0))\n",
    "print(rv.pmf(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Normal Dağılım"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normal dağılım, aynı zamanda Gauss dağılımı veya Gauss tipi dağılım olarak isimlendirilen, birçok alanda pratik uygulaması olan, çok önemli bir sürekli olasılık dağılım ailesidir.<br>\n",
    "\n",
    "Bu dağılım ailesinin her bir üyesi sadece iki parametreyle tam olarak tanımlanabilir: Bunlar konum gösteren ortalama (μ, aritmetik ortalama) ve ölçek gösteren varyans (σ2, \"yayılım\")dır.<br>\n",
    "\n",
    "Normal dağıldığı bilinen sürekli rassal değişkenler için olasılık hesaplaması için kullanacağız."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(x|\\mu,\\sigma^2) = \\dfrac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\dfrac{{(x-\\mu)}^2}{2\\sigma^2}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Örnek olarak yıllık satış ortalamasının 80K olduğu firmada gelecek yıl için tahminler yapılıyor.<br>\n",
    "80K ortalama, 5 Standart Sapma değeri ve noraml bir dağılım gösterildiği biliniyor.\n",
    "* 90K'dan fazla satış yapma\n",
    "* 70K'dan fazla satış yapma\n",
    "* 73K'dan az satış yapma\n",
    "* 90 ila 85K arası satış yapma olasılıkları nelerdir?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burada cdf fonksiyonu Cumulative Density Function'dır. Normal dağılımda eğrinin altında kalan alan bize olasılığı verdiği için integral alarak değerlerimizi bulmaktayız. Bu sonuç sürekli bir alanı temsil ettiği için cdf fonksiyonu kullanılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02275013194817921"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "1 - norm.cdf(90,80,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normal dağılım fonksiyonunu kullanırken cdf'ye parametre olarak sırasıyla **beklenen değer,ortalama ve standart sapma** verilir."
   ]
  },
  {
   "attachments": {
    "d3e8eda2-e86b-4b47-8ce3-9aeb700c32ee.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:d3e8eda2-e86b-4b47-8ce3-9aeb700c32ee.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9772498680518208"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1- norm.cdf(70,80,5)"
   ]
  },
  {
   "attachments": {
    "2b63e905-8cfb-45c1-b5ff-43128fca3f4d.png": {
     "image/png": 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v3ttTbw+9LWOJ+eI+mVpN00ZW0zK22U8+m+fl94qJtqZ95Ed+5H6rnpUhxcJwR/3NV7jy/Axv9DmerdjsoWRbzDxvsnZcivQLZgYt4/GLJTJcldC87P2dL5/7G2Cv8AqvsF8mkdFkggb/nM/5nHsACIahOZytKNjSo1IusUDgTdw9CL69/DJqe7K0ry8EzVKMLTNJEFxPgEgZcDzguwSHgb73e7/3nvTnPn0h5pb4EPxJ+i3/EZyK/cu8zMvsVwj04x7j1L+9he5VkZDAEL5D36qAr//6r7+/LsolaSBA++wPZZP7BsY/S4UaX/3v21tJ+g3imDH+72APvLmKNg40vfqx/iS3ZGHrWWTrLCvn8D3oTba2nqnASeQ8pKnyQzey3LbawR3yYUn/xQR1u5F+s4gN5Xyxma3fdR1trvd0Z34afJzPc+Q+99xppN+YzzvfY7E5i/QvK/3B8Tzjca2HoxXtrMAjZ4g9/iK+KxCKBRICnyNuPlf0wQvEj7O26c5xnWdsx+C0bPus9s/6/jr6PKbNi16T+bt/i/RLBGz9sQuEjVnFkehlG7lCnsTAr2T5RZ2lTGc/a2Ndfm/Ll+e9Xv3VX32fLIbXOtstQIdwW1V8xWQJpsQST5Z8KDriqLiIM7188Rd/8f02dEnL2nEp0q9Bk7AUgvBYpvby8ENe87N858E122QYhGp/shdbe17jNV5jD3QGq30ZsnsReXuwkiSYJHJs355Kq6r4t3zLt+wfjHO9fXQq9pIB2y8YYjIkWToQkTKVd/3kOJb0uweBl5AghBIZQorgCEL25QFeW35C+NOPs/v95nn2jklg3O9sfB4oXmaT8/7zvr9O0n/esdwp1wsqjFPSabk4OkS+lv4kj36twUEnEHxO3sqPrVlJXu+U+R4aZ0n/IWS2P78dSf/2iPvtdSLAR+R1nn7mPTdN+vV9ux7HkP5De/rPMy8FThVUSQTiZSsnHoH44xZeCD9OotqPfInrCkHn6ed2xfm845r6et57r/N6pH9rT79nLFX6FXf9KpStWYrAfgnKDy8g/Hlodm2cW7Jefocj+CEcu0gkjCHveCOy70F9fVoJwG9d64dB6KAiu2QE8Q/npIO2H9lybx5sY3lciPQvBy6oITqq1se+/ISlXzZB9JEoZ4TZw6v2xuVAmACvaiq7yeTcgxjLgBiW7UCSA4Zp64xxIMv22Kev5T52mZKfqkTyJzFDjLcq/dneY9760D6FkJCkUk943ktIPOhsHK6NIcwz7CiRn9K0MiER8kCWbU4Et8wmg81FziX9F0Ftt18eVuVB+smW/jnn5yr9B1U5yJZu0ClGF3vJOdfdaeeS/otJrKT/Yrj1rrsjkJjh05sk/bPfu4/o9vjrGNKvsHfZZ6r0o4iTVX3xWTxQ+MElbBu2zx+Bs6VH0VExCF/ocfsggPTjVYrOaw/y+k6ln96L4a63I8T2cjzO8yE4GX247KFtRXA7YJ78yZ98zxmReDrkVwKtAtA3RUT81osOSmI9l+oanFGyiZPgx7awK577CVvccjnOC5H+y0wUiAYsU7E33yCRJxP1t5/V9H0O4MvKLKnIYEzMy9LHczzHc+wzIKR9VsPjpJyRf8sw9lXb7mNrUEhb+kTa50M2iLGftbRcN7f3zD395qHCbyuHjIuxI/nathKhH+ND+P2aj+cTBP+1wzgplV8Gsj9cxgcLJJPTIDTXbB2uoYjmqy94eHE4xppDQuVBXsuRwdJ4rSpYhpQ0HXOYS/pLX/rVn9WLeRi7633u5f2cT94vr1vO2/fmoo/M0dmcr8IItZ+xzPEbL1l7nkOSGf2BG9JvHz9nkPktxz3bWmvf9/pgoPA3Jy/vBSrfzUMbPkt/3i/bNQbfw+usV9pZu853aVubhx7kfau3equ989KG6+drjt1732mXHM0x+uP9mpNyfebrbBw3cehryiTjjL4Z1zGHds7zIG/wgaV7l/2Yv++mrdMXugLXrUNb7oV95hNdM1f9XebI2I1l6rKxRrZr84kOHivb6I+xm4fzmu85NBf9aAMWzped96F+5ufmrd/o1MQfPsZy7PzPQ/rnGNbem3/kFSwjryUu5uAz93gZ71Keyz58n1gx5ZU+Ds15trvWb/pxv1V0v6x26EFez1SZo2O2BXPzSV85p+3l2bXmwN/b4y9G2/ftAUovsUBF1k87K1ayAePzCmazv9m+vnPNEtuzxjXbWb53b2QWfV+25/spI3M09mN9yrLP+XfmNeN29Mxn59H7tLscv88zT/ZFt8zBK3oW3Lcq/Xgm0q/Sn8N9cEC4tWW8+lqOYcrY+xyuI09zjU9MOz7zoy54r4q+3SJ++MP2YLxXguFa7aXPtIez4q5+4h1nxKVxEgmAFQL/Aav563seN0r6DRZwHpSQ2fjVgZBkBNvgfUdoORiO/2XPb+7PJ5qRVtt9/JqPNnNMYLx3aE87/uc1SyZZLUDSrRYg5pbgchwi/X6r1aoCIdjSlAd7ZGhZsUCkLe/JvmRhfkKJoDOW9LE8I/d+HQDpVzl4vdd7vX2yEOVbu58imJtrLBOZg4dLbK3yu8C2OPnNWU+hS6QogOVJPwl1EdKvP0o6M1+KqR/9yTwlOBwihTRvjkRmCn8Zq7FIwgQthhR5eQ9T97rGlifGYMxxRgxAG/rQp//8xNkYOH3VHH0ulXyJ9drfGUdwdtaOpBO2snFbxcg1pJ8e+d1c/+kSXTBuL3N1X5zMsr/04fvokv2i9E4/2RpHd/wsmO9gA6PgaWUh/Xmvvzgj1wlysIKLVaTly+f5Tjvz+/kdOeibnpH/FukX6DIGcw6mee9+8iRHcvZ7xOYbXTVfGNiLSMfoGozois+MM3pBpy4i56Usln9HJior9FRBIWOkb8ZK31R96Kdf9TDOyHTZnr+1eQzphw/9JTv4kAm/AK/YkmApAeX3BKfYAZ1h6/Bhe/RKvxkXrGDPD8Bx2pA5mSPsFR/4E3pmPGtH2sx3/ta+sUXvIlvji43CTPuRLV2ZsqWv5safr/WtDzhkDmKFtj1T5uxvczMGY1nTD2NlHzCCMV3XLxt3z9TfzO+iZ315kYNxsyPzj53nP28ydn6b/+Y76FTs7VDflyX95mm+dIvO8DN0wFjoEt1SIDJe44EZnSI7/gZm8DNe8lpinXnTOfKAM/lom7yc/e1/OSVz9mxMU+7a8Df7Ii/2SL5e/kMi98DWNWxli/SbJ0zpdXy6ObhPu+z9LMz1BQPYw4COK/r52U7kH8myHVeMNSfXwi39wVmf7o2MoyOujU6aH2zpJGwvo5OwIWd46d98tR0fAQ99kCP9I3f+wIs/Nxc+KPFnyie6aQ7LY8qfvZo3n4MjRO/pgM/4stj9sXOdfRoT/MgwcZSP1j59Flf4cd+5Bhbmaf++3SUpfioqT9Kv3egeOzAHbSz1lV7A0OfBWLyCsc/N3+f03fyNy/i0ZzxshKxhjvzbok4n9T2P6IrPvIeVefhRGrtY8Fmk/973vvf+uROJw1qcvBHSHwEBEQCMxAORk3z7nX9P2ZtsDvfZtmPLj+0uIVuEhIB7MHYqSfrJ/fOs7wc+8IF7QU+Cbgwe/vXfmefglP2HB4jxrPTnQV4OHNjIM0XJ0kqUR2Lx1m/91ntHyuC3xpU+GdwDHvCA/XKhXwGAEcdAoQ4dnBRnxlkTsP+UhSLbS2isHjiy7cjPj0qcOF6GLBk5L+k3Bw4syulXivRni5Z+yNOzEx5qtcfdT41yLsZHhhyi1RN9kzPDZyTBhiFSdgkYXMncPTEMjlKb2vDQi/48K+EBaXNWZbHfzfWM6LIHfeEsBWMO3VzN069LkXOSVc+KGIufXjRu85O8cWYckXbMMfOc4zJODlXgsPJlq5nVKPtFvbyX2PoO2YYXJ0E/7fEjV/P2XtCmL+wBlvYh0mGrOhmXsXn5e74Ofa59spTQCNzmcoj0L3+yc87Te/dyYoIOOWo3D9vDjxxVOSyZ++k9zjpEDEnwDJC5GpM9jYIR+Wj3MseUjfdkQtf8Bjo9VXXx4wH8jaV8Y/Ub3FY2+B+O2jjZ6ZqMjc13x5B+AZjN2BagekguzoiEoCHwsiWVRHI1DrgZG91kewob/Jw58FM5+CHBVdvZn0rH/AwxX2GOdFhRg48IScr95hZdzmc50znt82EqT2yUT9UmvLz4CT4RMRP0BEXkyfK1z8yVbPk9+k1XItvgKoAiROycLrB7bec/dYwf8HyVBMOYcmjDiywka+RLB+EomWezCKggedEjfWS82tEfP0B+bIetsnM/+AB7OkWGthrwMfw4YiBO8u+HjsuQfuND3snLFgA+l54bEz2y6hsdpw90nO+hU3TDL+iRMfw850QnE+eCAdnRPzpHJ8hXLNK2La/6UeSDP31mz/zXMnkQcxBS8tJvfMBnf/Zn70m3PmDMrxz6yc756z3mzTfDmX80LnHXf3SmaLTsf+JPjjBwrXvYC5zEW/Py63yKmar9ftjBtXyx5EB/7I6uGaeYzcYcsEImPTRKFvG77pF4nVcnIwNt8ymwgTHfAG8+hT8KCWZvYq2fMn/5l3/5vT+gm2R011137ecjRpkLeawds8/MCV7s1XOT9IV/ghWdjw6I5f7DMzsxEsO2ZLDsG3bsBG/E4chFDCEX/o1vE0fptx+bMQ/y4IP4C/E7vA2X8xlCTp/NlQz50/vf//57cg1DOzJgpjhirGKGv2FsnjDGj+iAz8VifEbb9ASu/KHP6JLkgJz4UXNxNi+YHjp8p29jtX9f0RyvTaXfA79s85aR/gycI+dYKRdiHBLvoQUBH+kT2BwmZfICvl+2cQ3hZFIIg+uBM48toAib08jDsmmPIOZ/uiUrPLSnn+LIHJEmvwCE8BuTtoDu6W7LNMbNMZvD1pHxErxMkOJQBAZJEfL9bMOckTDEmYPhPG078b/sUlzZq1UU+9Uog/9kzNYk4/K7s7LZuZfN+GFwaHsP5ReEGBVHT1a2Li37y2/VW2K1vMSYBQ3OVftWa8jdWMkBoTZHTlsw8ZmVDteoqDMORiwgc5ba1Las1vzohHmYs3v0YY5xUBO7+X5ieeg9gxIEEG7B0Fw9GT9Xm+ivv31utYfsrdIYNyIjWJnf8mAHiAdnQI8YqKTXvlBtmZOXRNc+Ud9xxhwOoglPz7eQq3n7lSeBRBDngAQUBMv3xuTluvnK5/O7+Vk+9/9akIskl96tkX5LiwIgskLfl1gLzAIPx47YcPzaNTdzjK56DwPztSeRA2UPSBJia/zwRZDggIicZV9L7Nf+Ni/tIGYCFBsRwG2x80AemZgjfTNWiR95swG/qyxQs0UyXSNq9HuN9FsRhBtS5RBE6C7CrG3z9aAW/0cPkQDJlWBGPsZBT4yNzbN1NkZf2alkLSskiDK/SzfZkKQ/95qTOfr5Qbapf88XCVgSK/L0WvO1vkf6+A5jY4P0zkOO2tS2FxtlQ+aD/CWp58eR9sgWnuwdIYrt6Bu2SFHmQA+0qW3zTx/aEej9ghufzibYGxk4yEdCaXWOr4GjttggIok8LOe5v/HIf4KVy5EeRQiJlmoc8jHtPDpFDuZCp9gyf+4ZNjGED1mzqYuQfmPjHxBx2PjxCwkHnYmeGwtdio7TB+Ohf8Yk2fUZHSJLiR3Zsx0Y6yP+DelBqmELY/qp7fhtOhh5+YUUD0giuci/dhxIuthgrOQaPUGg+AD6LT4dQ/r5IYUvvtm4zcG4EFA+jt+ET/qOyN2n0ov84QV8sbmzFziJe17ew1Hs0j4yhiw6s1k46w/pRbzxDLpGT9g9n+1e17jWfOkk4ukwruXYMsa1s2vxKYmdmE/36DvfIjmRgPIpbAU/sEU5/jg+hR27T8wXi0OIlzaSsTnDEM4KjeKxYiB/P+UfHRDLrZZL3vlCRJXNJomc81rOnZ6xV3omUUL04SeGxDfyC+ZELuRtHnAmR3qMC07SD6fMkR6Kd/yaeGucfAYfRVfZJn+CG/mbnZgnjOmUvt74jd94zwA9n5cAACAASURBVFuMi43TfzZm3vRATJfw0oEcy3nm83lma2RrZYAtmKcCtSK2h8uNhdyn78v9N1LpT2eqlCo7gr7JB2zKxgkzrEzexA3YwAUqE3G9JQygIT4SiCVA/l5+lv61r2pLKULeEDfB3a/rJCsS7LQ/K/2uI3hCREA5Z/v2MwftaRdBEeA5qwSa9L92zngpD0VH5p2RzrV5uE5AFACRagZM4HP1ImPKGamHm6DCgCm/hz2SdG2Rfg6VYXF4+ekqZCWrNOljnrVLXuTK2UmOGFdWTeDGuFRvyFhwR4B9lgQqW6/cT1+MWZv5fvaX9x6A5ogZNScaecJ9Dcs1eeQzY+JsyZNc9RG80l8+m5/DRQBATgQi+M2DfguQKprIFQdPd7bm5TvPnwgEbAGeyeqDtWAsOJozWUmQ5jgv855cVJs5+vOQfvpPvuzJ8xCINCw9j3PWeNg4R8lxcqJ0LvfADImySsIeLnOYE+JKJgKOVTZ6S0e3dNxYyIXtsUGBQcJgTHRnBkU4HCL9bArpp58SfwQKoWCf+rBUazwIAEIteNHzYLE8G7Ox81V8qvaskggC2oH91rz0J2AiLWQmeMPYfKYN8U8q7wopEk6E1tjOki19ZR98rj7Ya+ZqLuxcpYyvZjsIL3/I38GYDzurD3MQYOkOgmcOZOxgf0g4XYRDbBe5EYAlMMf47UM6ByP368dqg3iDJCAMdDj9LeWWv/lIuu+3wRFd8Q+xZUcT//OSfvdKKtkieUmcYQmrLd/Dz4jV4h2ShHSn0EUOwY0e0AlzVxHmf1VcxZutecNDe3SHPiCiKvHkRecUXqwMigH0Bk7u4QPoSQodZ5F+3INvVGiCKz8Ea3NnL2QkBsFo2m4ILH8t+eaDj41/ZA4zfgzJFdOMX3/aUiiAl3FZveTbc03in+QAz3GQ4dSBQzqYz83DKiQbSJzRv3hjbGIrn4KAhg9FD+eZnYjfqv+KjCrybHM5lui9xE1CFh2bXGO2O9/rnw5IEI2XDrB7beZIf+bFJ5E5uViNQMaN8Sz7co3iA1IuCZm+lM+YpB/PIhc45TpYIPaSDAk9u4QxvkjX0z+dJnNt0pdwn8zZ34pe4rYiMj3LkXnm7+XZ3MUK8V7RkE6yRboc/833K8Kt8cgbIf2ZhMAqkAg+QAkAAprqzgyWBCuoWHYSIKKUUVgVZJlW2l4Cs/Y5Ii74cmCEr39CogQqEtqjzJRWAKAUERZAOQqC5SxnoNKOBEHGJakhkLXDmNbGZa7Hfq5tJIHTCBkNjpmPOS1fucbnCXbe+9zcGD+CoKoxD1ksZyiLVrlzbdo61N/8nvPySp++4/S0J0PXH8wFYktead/ZfYyNIqfN5bwyh/k9+UiIVLSW85lz23pPFyU7KiKMM/2mn3nOd87mKWgLLMiGuc2Dg+CoZPmqKbMd72dbeT+vEYRngucaTkWAVcHkKBEF1ZXcf8x59rF8z3nTAXp6HtLPISIAql2c7NSB9LE2tnznen7CfGOHvpMEwNfS8RLfifUx75FKq2r8Cecev5AxOG+N0ff0VFJmqTr/r4Z2cwhcx5D+VIw48QQZfdN/OOjH32eNyTWu5R9U0fksyUnmtDaf+Rnb49+QZqszy+BrXoIUf26Vgy4bY2x3q598F/s2TnLO58gJ25Vw0x8xALETHNl1rnM2Zu3klTnkGoHQ2Kxa8O0OZNycEEw65R7X8/WKOUjxJBqR4THn+HB96A+BDWGYc8zYM96cM25nc5J8SdYkk4pBU6fOS/rNydxsdRFP6evs76wxGSO8ph2KxeKeKrnqqPGRmYo50kH/1nz3IXlpD0lWgLPNjQ/mP2wPUgnmVzNOPoCeiNV08RjSj9AbG1+JYJqLsfCffJwYNIt15CmJR/zER/1HX4JdZDfP+c5Z+2TvlXv1p7hgpwJSxr4UHMwx+LpekcuKHf13GE907Bh95K/FMDJH8sO5jMl7eJPPHPvyfebic7aKqykiqDIb+zzInt5LKq0g6UNfsw3t+MwrfeV7Z75ewUKyY97aXM4b6UXI2bViwxqpTtvznH58Bl9YO+dz9obDSQwlYvwxuZjzxC66F35hzpIAiUfaWp7nOLw3T3bIfx4bx+BApjggPWZ37CWEX7uw4O/FM7q7dtwY6ecQ7KNSrRGACB3ggpvqebbqhABzUkCnQECO82AIKmuIk+z9PAcFsk1BNjmrBhyX6jDHwWFzjsvtPQAFLsJqzDFOnwuQgoiATwEopeM8Bro2j+X9nJYlX8txMDGeKBc8/S14IQ1IloAniCLrlMw1rjfmvPzt8yXpZ9CqLaosiC+DiOK7h+xggRCrTOpPBVoliHOMvPSjfeeM1X0Ifkg/vDj2Sfpzn37c7yVQSdAkbQxRvypN+vJ95kYeHKhEUrC8yGH+HIskFZlj0OY6kz1jhLlsXvbPiFXokCUVPlWqBGpnAYXeCjCqAGQSTOiT+al8cnpkB08JsuSAw3VtsAiezmzCigTST3890KkKxk5UmTgBWBlfzvO97RhTZhmTvmCrGq1qsEX62bXKg3nSW/Yr4AgQZGG+c8zmww8IRuYZcooomA88XL+cr7HRfcvGxzrLyH9pTz5PhV+QIsfgrB9zN0Y6TSbGSL7GSFZT59xHP2xdYTO2HiUows2qFrlHf8yPg56V/kn6c92UOVvQj8qrMRmPFxmzcfYJs+BGN+HO3uha/C3CQw/YUKq35mm+ud+1+kC2VayR7xzImIDHN2uD7gQLZ+MwRnKigxkjWzWvyDZ9OZunM120vSqVfqTPFgf9BBPXmRPdUQEWqPkOlUhz459dkzmohhkvnWQf/DR/7fv0fRWkHz5kjrDaNsGnwiH96Cvyg03snC3SqWATLODI3yH+fCUsosMh/UsybCsDXyCGpXooMRbbkoTQiei5MU15GROdoB/0mZ7P8QcvYwzplyypzJo7vVeok+zk3ug6ffI5f2qLDL2IT9UHHTVf8ko1OaSffdKzYAM/pN8WGXHqLNKPKCHXqqpsTr/6pK/skG2KQbZ0sFc67h66p/rLPpb+WoGA3PgE8Y+uG/+8Fl55Zex0F+m3/Y5OIpi2h0zSDzPXIYUh/bG/Y8/mIXmWRMGevDIGc8/LvPgPtmcbJblYccOR4hNyHzuhj3w9HcwRvRf32GQKhOZuLny6pJ1uKUgh6/rCT+AYfSQPf7Md/kVBBm/LoR+FM4k7mRmP9oOx94mj5CKO0mVy0a655lp9efnb/Pj/kH66coj0w9JKsxUYNmY8W6SfXRuncYh1XvRFDBMX8Dp6sHawd7oIa/FOUZk8xRkyYsvkaPz8ns8k3OQzffZs+0ZIvwkxJgbEeENWKaFAIBMVfB0m6UW4ljXtI2RYMTgCFQBUPxjLeQ7j4AwpCwFHkSk3Im1plxA5Eo5MMkCJXLd8RVGcKbSgQ4CIXRxzzucZ49a1gq/lNUpHwBk/w+TEYCtIy5I9SIJwWXay/EXRBKAYVxRfGxR/SfopDKUWgCmSa1xLwfTNgG0hobj2e+rPg1b64xQoOFmlv4kfZ0YGluxV4tdI/7xef3RAoDAey3+ciwqyAKevkMrMy/hUdFTbQ7y3sF1+x2Eam2VrDoY+WJ2grxmbvgRZSSSSDQcveu4+uqQdB11VDTNeDsC9E08OV/vaYdQydU5PNg8rSQUcYuC535lTCek3V4mOirwgz37gxMaMLWef2aNpXh6wglfsMvOj1wilsXC+5rJW6RfklqRfJYg92F8uAKRN43e9RITjssJGbyRXxmRbVJ6hiI7n3pxD+i2vnqfSP+2RL6Dj/IgAjFDEx9B1Y0TG6DIZwNIYYYZskJWEjC5HJnRdEFNAQC44c4d+z0P6YR6CmznrQ19sEWkSZCyhe6kGmoO+EZjoRu51Jlt6h3QZHz1QxWK3dM52F7odYuAeNmXLAcIpgc0hGFpNErjJ1tj0yVfypYKt8QQ3Y6SH+mUrEts12WqDnrNxvh9BR+a0Q+bxQXCWhPN1qrC2s5GjfughMmFc/J1EgH6pepH5dZN+RNFWOIm/QknGDBs6RqdgQ37GxbbYpUBN7yUyU4bkxjbtC5d4Gz87FGfY4hrpF8tC+s1Zccz+altYIi/ype8KCsgqPecL6DmdMCaraXw8wkQfo+fRq5B+BTKFBr4HQfW8Bl2UTLiWrMkV2VaIoU8ItvhEJ+wnl6zRHfLi/9j2rPRfJem3d/wQ6Q8Bg69qqjHDP3MmR1iYn3jEF/AJ5mLcEhZJAv/GXpfxj4773NbOkH5+aI30i59XQfrJ1Hhj28bgZWzkz9/QSc9QkQ8/J457rmoZF+ijeGu8ijo5kFKrme6xXSg+QR/xO3y7mKbw5RkjiTE7EN/ovXjDXozNPXD0HCF/E9+NR9rpoBDJ/06fjdTjRmyPPfFx5EKX+ZTEUbgGg5zJN6Q/e/q3SL/2ziL9dEVfEh0PEhuH+XvhMLbAiau4buYXPJ3ZLg6Bw/ApfKgdHpIyGCchgzWdVOzFA8QayXK4x2zT+xsh/YyXERMwYVIEYBOSjIfTzp5Lk/cCBEfNgSDfMR73yBRDGJcT2vpbuwSFbGgzhizAe6AsVQPnZaU/1zob+/wb+DJKTlyFynyv8ggelh8pLkWPccCFsiICKiwSF6SP8/KShVphoXCCISM3/txvHt5P0q8/SsM4JVgzQHsvaMPL94KyoKIvhu8+qzbwE+DIa4kXp2cekeEW6afQgg4niWjIdvWFkKvEI1dIATI0g5IAi7AIlIcy3mNkxPAQdnqDlCI0kb15cTIcpMoUHAR9108dgKcHETlf9wcPZ46Ow+MQ2QH9gaNKAwdHfnDixBJ8079z2gjpNyf9IQcCF4yMCQnXrnPG6Qw/zt48EhS0S684VYRQAkNG5yH9lt3ZLuIVx5yxshV9WrUSOMyTjIyHPpGzRNV8EzzmnBFAeJ2X9E95qx65X9IB/+iOMxw4ViTIvltjhJ0xOruPE4Y58snhRqZ0W8C39KziSxZwuwzpD250HMmgDyp4fKYXgszm2CTM2PPEy9/8rkSFj+Df6AEdoWeq4GyZX0W8Mhd+DVnj18w5ukUf2BwC6Rp9wU2A4y/YQ3Cjg3Cjh3yTB88kmcYpKKavjDeVfnpvbh5yMy8kzbVefBD/oerFFzhip34MQUBFVlUc2Q1CmuvM+bq29xiH8dKbqffwhw3/CxsrcuzcmGBDFvTeChGdIuf4eLhEHyU1WUE6i/Qnlpkv0qA4A1uYa9OYxAKEhC3CEubapRNskc/zwG/0aup5xoV88PXxEfwJzMkrdq8fRIxd0wM24cXuJSf0C+lE7HEEpA4u7EY8kSDcBOk3PnPnu5Ex44JP/CLMJGT8jx8kMC52ASv38Q18BVvnV5CwEMzoN/291aQ/YzE2CR9b5j/EKCQT9uYhOWNnZDztFHdyDx8fWUr2FBslb8GLT8CtJEcIvqIljOCL3+mHj5QsILP8MH8CZwkjnMVc97FvfSkOI/Lildg5fQK/qxDAjug8ubAx8Y+c9CPJdF36CRbO5yX92d7DbuitWDDbMz6fxeaNw3i86D4OA4dDhxhFJoozkik2xffBRz/mzp7ppKTO3MUlNjz5x7L9ayf9BEW4DEHmBmyDpRAUxEA5DEJ1RIlUDRA8DoUwQvpVn1SLtMehnefQ9hrp16bfxeewBRqOiCMUKOMkpzCX7wlBsFRFU1ERhK/yUM3k7GHBkcQAYaLSLOu151o1jtHGqCiUeykZxVTBEQgZJfwzD+P3efb0I3hIrIqITD/XOlMu1TSOmUOmuGSlL2f3Umj3I7m24lD+KKnzsaTfuFQfs/0LBmSuL/pinoKIICrp4WAyJ31waHC56BYfMqQz+mN8KiCCQPB3FqA5Js7L/IO9gOVeB5nYJ8iB0uWMUVDkgOgaYsIhklfwjPx8bvkcmVgmN8bA6LOnX3/6hk/G47x86UdgMyeJtwAQZ2J85CywsU1ORJteW5V+AV+/xi0gWv2a5IW+ItMqMGxbuxyb672MkT4hQaog+hdwEkSCG4d/EdIfecCIjtINZISuZO6SVIkZ4kiH2Y4xGlswpYN0ylZBFViVMnMjC2d6yEYkNdGH8+zpn5X+tElPLCmTB503JuPxIksBjv2q2KvGZT5slmwtWyNwbJaPMx/yhLu/BXnPMc2klO/jo/ULC9fq1/YYCQRfrn19sXEVW0RJwI1sgxsc2IHABwu+hX/VRuTqHNLPhxqXfvk349APPMzPKkP2uhtX7FSskRDBHpGFl89c46BffhHqqvf0p3/YkIF5Zbz0V2xQ4UMUYENmxhSdit4bt0R7VljJgZ+nU3SODLRxVqUffnymYgL/PhM6ei4JERMkCOxh+p7Yorjh1+QkI+yOfkde5rUk/arjEgx+O6RffIU3/dMerIKXMUqA+Ec2j0jSEbi45qZIP/vI7gFYKJThBNE7c0a4VLnZg+0TfABfACuydCab+GsrcMv4R39vmvRbgUCg40eNgd0am8IMnTRmsTs+xTzojhjOrsnSfXCQ7KtcK6i5zrzpmGIq29S2a/kEXM2qQOzZtfwO2eoreLlfm9pOzFC4kwzSC3riXqsjfAedypjoJP9olUWVnlwmL+F7yInNkCsSrpix5CXnIf38Pj+nnzXSDwM2wG6MWd+JBXQltg+HQ4fiIS4q1on7EqFgy7fAWvFEUiWm0km2AyfYwnntuHbSb6IM1xIYJYvDMGD7B2UxnHQOIHgZuOyMgDiWOBvO6jpIv8qiAEO5OJ5DpJ8DpiwM1xxiCBTQcq6qiAwZWQD8VRyqCMbGABhFMFRBsRzKmQrah4RsDL4TACUOFGXpACbpN3YBRaBiePozT4aGyKmIWPqiuIcOY6awDFR/7g9WsDur0u9aSm4MlhyjzMv+OA1BgyOey7CqUgzFVpvLJmH00XyNQ5U683CWyTO4qcPLMSJpCGaqwpEfWVrBEMhhvnX4XkDlWFUR04YxLEn/liPRh+DOoSOJgh09SnJLj1VyLGtztuQYOdOhNdLvfoQE6We3xirQCJDBSrtIh0q1YJGVg0NzlsBKHFSfzTftmPdFSX/6Mh86IVnjg8xd+3yM4Ghbg+CBLB46tGGeiAJywC+5XzsIn3HTPfjBBJawppfmoE9yg7MEOjomQE7Sz8mzO9sv2J0+1/yKPgQ0yR/7cp9+jEWFiN3za2v36pvfM16+NXiYC58m6UbK6A07tMwsofa9F1/C9pBVOsomD+mgzyXQVhb4HLqjjegz0s+W2bSAKrAK5pLQqaNsm84h2RJmz1LBWryBBeImeRQ451i0GdIfeelbAiKp5rPWMDqkB/lcnwK7aif5ZU58GPxVAkPYt/w0fFUqsz0osiBHiY6iEh0wt0OkH9Ega0mWFQSJGLyQVuOiG3Cm5+KK9g4d8CQvMQa5mPIid76CbpGBa/UrZrNRc4etOKk/uknH9Mn+yMg9XmxN4mrMsMxx06TfOJA4eiW2ppAENzrC1hUc+fT4xYw1Z/qGRCNhtlolLmnD61aTfjpFjki27TMI8ZpOsmNchu/htxBN8sQJFNkkg+wLZookCjrm5xr+Hl7IMRzIVXvLF5/ipRgrZiDv+JV29CnhsIVPPNAP/bGKp33jYcNkhL+xG7p8SC7kw77YjevZZXyKMR9L+vkivo5twW6N9NN98V6M4cPW8I2+HDqbi5VLOiT5NEYvbSsCWP0ST+mqVVQ6l37o4PR7s49rJ/0h0anWZOACOQIpuHKWOTJYglOJtKeNcAjZvYJr9ply4Oc5AGKpiMJKJDIWSmx8HD7lPET6KZn+OXVkYS5HaYsCEg5F59iML0I4zziX13IwqmOEPMk6Mo00mBPlO+ugRAyD0SASxsy4KD6Skko/R8vJU+5kwwKF7R6ClwSDoW4d5u0ae+/JK8FLn8eQfv25jsKrfh06BAjKjohwGJGpAIfA2WZBjy5zmIvqgSVKVfE4NmfB1NYpmDjWDC0VdaQ3ZMw4bX9yLz0J0Vjen799TzclGO7LPI0hpB/p2zq0xa7IFwlVreU80hb9RfKQU5UXQTn9axcOh0i/5C6kRgLEGal0BCuylATYyynhOHTM/jjL5fYbY70s6Yc3G2c7SUi1S9fpqmSDvI85BCsFDYGdzmmHjK3I0UlYIzVws9IZe2IPh0i/e5Mc8HueZ2F3xqyttYN++J7vEdATzBAu/oodCniH/JFgbGnYvm9BJQGen1ZskNDRP4UYKzAhMuYr6NpbLgE6JFtyjWzpoAKEsZpbxqotPofu0CFjQjgFfcE/mNAp80Iq+DJbDZBhsSTbZtwbm5p4kf0h0o+8XpT0a9fWA4mIcZlLsLHliT0do1MwUnlVgaUH5pm2PJfF/hEgMWGL9CMBYq8VQkmUMbFvcpVAqLzb6oW0bZEk2Jmb9hAdPjU+bEn64U1efK45s3n9edEpspZU0mVJnwSRnPlv8cs4oiOR2a0g/fBX4Rbf6Rx9M2f489fmqGjhWI43f7NTvMZWFFi4XztecLnJPf3LSj9Z8HueD6Sz5GbcGXuwN0c+PZX1yJ1/kZRKfsiOXrNleEVX4aaarg9b/fgNJJlPh6+X9z7znV0WVp1xHNwkOiNZhLmVYf0g0eI8HPWlHzopsdVmjrX5+M5cbRvXF3lO+zoP6VeIMKZDpN+47ApYFrXn+PJ+nqcM8DXJtDg1/S38FR/pqO8VOw759dl23l856Q/YGTyjFQxkzZaZoxQUQkDnwGJABpX7Cce9AioF5ajcK1AiXsgDUrJ1ZAy5hiECKQ+gaY/yEDZnTWk4OI5ftUIVYwYkQV2Fy5gI095FyUPGpi1BmrAFfA7tLHKcsS3Pc+wJfJQ043G2cmIsMs047uC3PGsfpjJH+9qQu8iCgc1KPyNkREhbkgz9CbzmLfhr69CRvl1jr6CKArITp8HpnVXp158Ey9IWwnHoMG9KDweyyZwYM2eAqNt6c5ljSfrTB3kfQ/qjT/Q/zkobMGAb2p/yXhur711nxcB9cwyHSH/kkLN2BSIJJGI/V9C0RydU85A/ldOljPV/FumnG5JQezTJL+NkY0gvYsbG5jHnnnk6GwNywHeEUGuPXG0h4HQvYl/0BeFQdJh2YIzGTV7IUI7gN8eZ7wQiSYKtEqkKsifPvSBbkgLzPU+lH9nLfAVoSY7ihwA5q6BzPGRlXy0CrIiB4MFK8OEnVMhUhw/NBUkWxK0kuccc3B/SDy+6Q36qnbCKbBVlkBgEUzXurMMY+EZFBjabuWovlX7fG5P26JOqNLmnT+MzR7pPjxEBdqFa77kBWAiaEyPjIgs6JVngY2KPSPHWashZc9IXjJYrcYpAVn+RmqXep83IJH8nZkpApw2JmcgV0iK5UqVd7nW3eqjSj6SLjwom+pfcm68XP+QavllMXGJkHHNMdE4V3jMFc6vHkvTzD0iQyq4HHckrsk1sJC9jlIiyP/qqUikJpJ9Tv40DFp5lW85T25LDPLvANmyfmisa9AM5kihJeNm9WILE0lnxiB6JR3wTX8zuFexsdeMbxHbXsQlE1/Mgy/g38ct7WJiLSrmkGQ7agcNNk37kUDKdWO7MzlTncQdjXTvCl9ge30Z32B9S7qFzzxZJPsVX+oQv+d4ck5RL8u6666495lZ6Dr2sJtoWx9fF/5CfNiWJ7Ec/OOMs1PBPdgOIi7ikY+ru2ryM2SqFWBefbdx8mrjIx9GVtQd5yRB2Z5F+fAdPPUT618a1/IwuikWKLDOhir+VkLHxZZxetrP8+8pJfzoAvMGoPtnbJlMhxBi/SoNAxICidDEYbfhMFom0WSaJwlImoKtcCbjznvQ9z1MBCJKw80BrFFQFgpNKoAG0AEDhCNl1Xq6j3L6ngJSekxcwYhCu49QoOwdx1rabOdZD71WvODSOMuORXFB2FXkObYlD5r38XDC0dclcEtidkX7yEJgEDMvUAkUSGrKTLHHA5Lpsd23sruHMYctRBKPzkH6OnXIfOpB+xqGaYfUlsqInDEWgutWknxORIAp49D9j5OhUQc5zeH7B6kfa0N4h0p92yQFOZEv/JX10G6HUDp0iH05Q9U8gQLiWB5s8i/SrLpK5JeEEfH3QNw6fP1hL8vVlnPOVaiaCNsnPZUk/8ooMeXg929eMkY8S4JCqSdDM28vYlgfds2dzFhLYE5+Q55VggvQjpQKavtgCua1t71mSfgHb/wQuyZmkaI6HTUowlqQ/wQfR5U8nvnMu5G2MHrJ1T3zDJP2IH31FkHxuHl6CsLkaH6JwzCGg0iVVUDikrZB+q2PGZL4ItURVAQA2ZJaVX/fRX7qGpPkeIZFwKQzBBBFNYCTXVPrJIPZ4WdKf7TZWz5a6yoctZTcxWsqEnBBnJHPqJ38GZxVShaBDpJ+dSZSMSSJnZZu84AQ3K46q9uyUXueY+pTPnF0jubaaQs/5DbhN0i/hg7EXou55CraE+Og7sZ+83Bt5ie3sm38Uy5KspP+Q/uWvFF0n6VcYQy5DdGOryLuCCP81cctYl2d4shdJBp0IBrea9IuNkiOrEAorh+bClumIAkuwID+xAn/DfxBtnAKxx3tixzDj6xBpvp+P4F8PvRRJJO8S0uDkjIPQVdsLxQ5jkUjHbrXPr0pg2Y0j9rSUR/5GlBV/FSrEwYz5MqSf36XLaSt+9zKkX8EDtvyeZEjb5g3L8+zwyLxzvlbSz8Fy7JaMKYpBUwbLE7IXZC4VagOaTsd7CQHibK9YCAQSSjiWjQTnCHjem8mlzXynOqK95UMcDADJFdQEmUOkP5V1QYShIEcCCPIwM0bKaowqYoIVJcsY5tiOfS9xsMRFqRKM4SCxUGVMMF+2t9an1QwEguIkgXC2vYfTJTPVE5UOwSGkX7BQmbE9aB5rfeR733HiDAI+cKED9yTSDwMVQPq+JP1Wl3x3nkMCoa04F07gGNIv4SULZIi+536yJw/VdJUfujaJ5RwbnT+L9HNUCACbF1zSD33jsJGRSQzhEx3K+/wdkikZ4TPSlhRLKgAAIABJREFU1mVJP3uR/CASy3YlqJJsNp5jOa587sxeELS5asBGBTCOGRY3SfpVxWalP8EH6ZfsHJrLMaRfcUFwNdfp75BRskXUQ64nRmvvEVLPMqhmzxVgpB9J5tunDCSACJRKPr9HB8iOjoUk0A/24DOrlAi4YgniT//przbFAKTlKkk/XUWCkGL2FF01HwRyC5ulTLKsj8zM5AphQugRAWTtLNJvTPkFFjoR0o9IKWqxxdgaGc33U2Y+F6c9UwLTJeknE0TZNa7lP8hXMmybEkzoiLmI4xN38tIeYug6K8yxGfK6adLPfyGYVlRSqDJediSek/F5SL8tl4osdDV6ekqkH6dCxiVE+EL0PrbIFx77ogternfGPVS16SqdkIDA0me+1wfSrsDEl+BZ0eNDuux7vgDHswNhtnW7kX5xQ7FATOJHzBc2ikD8LT8wfeS02a33V0r6J9AMVjXCrwMYcCr1zrIWe6pUe1yXY97vveoCI1PxiCM1aU5d9iNbW96TttbOKiSW+ihnCCjlUfm3coCMaA/p5cgoFIOPIlvaVDk2LuN2pvT2mdnSE2WNwlIiAQxRO0SkluOc88l3s9KvD+NJpV8w59zX7sv988zR27okKGZeS9J/qNKvgqbSP2U22/Z+jsN7wYCsBPZgSZYCGtkiTZyo5AWZyPxcqzpySpV+88n8YM/pnLfSn4c1Izu6dhbpp3scBOfBYUT3tSEIS4L9Xjj7SOBeytXf5H4M6WeXfmIsibp+UumnW4dI/7JP/kH1GclbVk85PdvQ2OB5D6QfeV1W+iUmdBVBo5drB53Oy/eHKv18hwDFlyhe3FSlH+mf/g1ZscXhKkg/PyO4Hqr0k8eU7Rp++UyFkP3zvan00+WQfqtzM6BJJuAopqheWz0WF1yPUPEX7qdrznyaSqNfI1LAoP/8zHWR/lT6+bAUuIwlFWl7p9nW2jH1yff0UzFgWen3TJVCgSTmEOlH6Ncq/SH9bN971VNbJvTtcD7k133OHrJCcoj08zOZi3vYptVzVX/b0xQCrMTAJys1ZOVFfvSAvCQK7Mb9N0H69Wtbhy0iKVooVGWFgs/GW+i9+AeLs5Lb4Ckx87ygPqKjp0T67bYQN1T6J+mPDSLux77ohJfrnWFm9V5sQn7FDv5ZLNE++8Kx2BwdY4PR5ej1/oPFP5IDSYRVTfdrJ21ddHvPdVT66ZkiHJ/OboyRDnmvkACTi8S/ayH9AOf8ET7BORkz4+GgVe44bgPeEg7DQpi1wVGZdByENiw3HjK+tXYRLNtzkJGQL2cEVAU790zSL3hEKZB+yYqqGKfm5b3KACdKGTlE12uX8gq4VhG2iMRCJx/kT4HO/lcGkPE42w/r2YG5p/9Bbh4fCHqqkraHzKU4Y53bewRvFTLLaKnWUjaJjfnLlNdwh5/PgyN8kB3LaJxmML+nkX6VEMSD7gcDOiKJosNwCmZDXHd763vX2a9M96OT7IFzFOjXHuR1n/6tFCH3rnWv++gqnbLdyzUC3tah/7NIP2ImsRToZjUYGTHu5f+bYHyH5i7Z9VCgFbboobGn0n9e0p9+6K+9vYK4inywNEa6KmlRbV07Mt60hbySiW0MIXtsUwIx933eTqR/bV7HVPqzLC7Az2ApwZHk8dWSoGMOlXxbNz2UPmUb0r+s9GuT/vFhiAYCgFBpQ2FAlZheKC7Exui4uCGxyMPZSH/IKwLsGvK/7PYe+mLlGpGY1XnFFTolFrKNtSM6le/4e+QyD8NGP630CvawObSnP6QfPvRckmYbIX9PL70kguwTUZpEfTmOjEcsh7mYQM/FNbjx6WS/rPTnPmd6ZZVGoqI/v4BmtdH+bf5obnsxNn/zEx7elvTzAbC4zu09fOIk/bCzk0D8M0dzFf/Ef1tizCfJbfzAnLP3EjyVWnHbqlJ0TVunRPqt/knsxB+6EF0VW/gIOwg8J2VbmpXPQy9boPLKNQg+ws8fS4T1g1xbNYrdijHs21Y+duM4pMeREb3CP8lFTM6YjVdM4MfYjutsz2GH4XV0lI+avh3/ug7ST8ckMmzBaoaknw/gY2zFlhAfW0zO3J2vhfRTeKB5QIdxA4qQZG/2bslcLK+cdRCeCgmiaWmT4RCQtmxR4AAF3WW2sxS68QhYAoRtDAxYO4KDgGM/IUHnOLS9h3JRQoFDAHLoi8NXtbK/H4kwzyiloINQw2Jr60T6XjtzMrYJ2cOfAKl9WHLestZJ2DJ/5xzeU2JL2x6Kmkvq2pr/ORdlEhhllCGJsNKfarEtQrMKpw/twwTpd6aMcIEXhw3zYLIk/ZR7rdKPRJ1CpV/CZ4WIbkSH6R98VcBUUBNEIq/l2feuc32y/tjCIdLPLlQErbZNGZADHVX1F4Atm4aQTJ1ZjoFct0g/Ii3IC9T0ZFZkyNwzKZzV8idU1/o09uwnRaBjs+ZspVBAuCjpp7sSE5V4ASQkERliG2z1UCJtrHnRdckSgsA/ZWXDWP1SkUQLOUNUD5F+gQ4x0yb79JOd5Ju2BJvz7Ok/ptK/lKu/Q/ot08MhmCCwSKvKMx/DH9urTSZk4SVw8o1wI/s1ec4+9UVXFHPoyLQJc2XzbIactAVn71W+2AE99NIOfPk//oouG4cxZ/zaNj6FF9X+WbGePumypF+7sMn2g4mNmCMxZItr2Pgsn5tfigRiXMiG+Sgg2f6CgIsJbHH5gGtIvzaQToQJQbGNIXHJWUwSD/kU+umY45h/a4ctipOIB0z5kDXSTy7iLdlMeWnP34iZMYlDfARClQdmYaZd806cYeNiw5L0s1vjUaAzvss8yMt/Inup9EuqkD02nDhpXPqks/QNvzHXyG0P4P/84zO2Ykw4Cr8XfdDOIdJPlrEFuqlv/p7eXuQwPkR5+SAvDgHjy+7pt3UMKaWLSC/bzTz1QVfIUdKGX/Ehh178gaTZyzX+pic4k0ICu5Bg2KlhNSic0moeufDBdIHurckk+NF1KxMwUTQNnzLuLdJP111ziPTzu2t7+vlzMrjIwe+xIzhIHmFsFcw2bA+bw4WMz3tcKelP5wYqaKp+U64ogmCiUsC4OOvlsSYsGZ6H7jjzGKD2tGU/qIxNsMyhDUBMMDgFymRPnkoCw9OGigUnaWsPh5yDw1z7nf5U+gUgApmH+VgtQMxn9qgfQchSLeJ+qII42zKHiQUHYylWhjeX0DgPBiATZBw5zJ3yTwPQnhUJgdGcOf7IBR5Ifx7kRQCtisB3rrAwEFstBHfBYo4xY9a398YseZKUzK1E+jRulRV4wJJB2y+63N4DxzuN9EcG8wwrD4BxCnmQc+Jg9WXqRbCc+EpaVcnp0WyD7NZIv3vZBd0QMEMe9CugwBZx5mDTj3Pk531emYvvziL9dE7AUxW0ihBb4yzJXcXHikT6TNvLM2JjFcTq0tzaY/whhucl/enDPCS2AohVhARaxAp5R8TZShKh3DfHrA26y5epcrMNczQ+tsVObKlBdFy7Rfo9nK3tkH4kITK+KtKPFGdPf+Yzz5Issj1E+umoa+ihAMvvmGvmK6GNPqV6nPYnbt7D3gPjntWiE2nHWRWNj+LPxBF+lhwkEz5jS/xFDu15CeYKQApE9D0EV5v03eomn6at7OmPvFxzWdJP7wVhK6/kF73XNx+K/IhB5jPxMI/MwXsy8vOeHm5N8qItsUoFnJ8Uz8RFOrUkw3y71WzVanO1BUDSLw4nkTRfeu46hIJMDx3GC1ckWIygl5nbkvRH7uxfPJ3yypwzVzJkv2KvmJk4Y2zihQIaP2Ee5rycZ0i/GDNJPx9rnsbIriVKkm96Qz5kwAep4pM/m6eDIf2uYYd4Bbznr3vxs5IUiRcid6jKShfoq3gqucoKoLkZFx6Di3jmIfrNT8MhcdnY+b3bmfTnZ535QUUt2EQ3zEPlH1GlY5H7Rc50E07kDFO2wR7IjgzJxdYxySHdc22O6F3+pgd4B/5Bthkv2SD9h7b3HCL9fJREdkn6jYnu8XN04TzHHLP34oeXeXl5P3E8T9uuvRbSzyEJEkhcSCpws9SJwDIux6HBZ+ICjQqKgGL5lnC8OFPVC0GK8iHds63cT8jIkvs5hAR4bRgbBVJpmwGeI1n7yc4t0k/4HIGfjlTZoZTm7EVhBEUEliPj8Aju0JF55HsBjSOyj3X+Rru5CAwUldNIdYxipA0OiDxSPaKIiASDCZbGOLf3uEd1wXhhnuDorDJnqcnPm3HqxhasI0+YIzKpcsQJp797GunnFFXK6OpMgMhP1do+XQ+WIrqRW7D0t6BrK4MgrXpC98kMns5rpF/SrbpIrogA2bnWvaq0nieg93RjHlOW83Pv6ewW6WfzAqEXm0SG45z1LRD4aUWBQLWMnkwCp4+QN8u1AiP91kZ0x/mypN8cER2rc/mRAePzopv8hGCr2iQZYw/zMGayIjOyM57gG5tUEWTr7tUfEsEfhsyTA7lJMO4U0k+u5i64IknxI3wJG5cIKJbYyoK8TlIEA3aADKpUIXX8b0gO7Kds+RbXqyTSU9vj+HAVOnLhD+PnoptihYBuZcJ44uOc6RIbmqR/2tFlSb/5ma+qJn/M50cnvFfs4r/5gTWdovcqpqqndEpscx9c6JTxmT8CZZ5bpF+CEzIMJ8mBrYS2+ARnuqfSSV7iI1s0hxzeJ/ZKvv0CEN8/MTM+xM6DlsaVWCO58mMRKvGqydpXpTXHHNo3X9fSBfOLjYuV9AspNo/rJv2wILP8ZKdEXbzkp6y6RUfN3dgkPxIp1Wv40ilYuQ+OsECCYW5e7g/uznwMHQ3pR2bvVNKvuMcObd0SUxRj4cTmvM//wJ5f0TrEe2CA5/GpiqeSMz42PoS+0C++0kqjolVwZR94im1B4pOiAnnMI/bF5/PNYiAfEZ1zvijp59Oui/SbA58LZ8kFnsu3ms/EctrunPfa+ysn/QaCfMrgBYFUGDlATkYwNfgEe4NdG3A+c+bgOB6ZNwONM1dd4xwpmwzQ8o7qgf4t0VnC5zBUjgRyxhYhc1jGowrg+gngRUg/cCkag7fCoRIxg6L5c/yWmT2kNpOMNcHMz4yNkFUVOcgYlrkkIHCSHJEAqTrCgGAhwULABE2rJbOiEizgOUm/vhFNpHH5ECXMOX9B1PKwSrHtPvpTVYKdwAdzCYqqBkeXlz7vaaSfU+QYbHUiA7oX7MmSw1J9Uo21bGdZGJ7O/lYxRg45qlTaZhBhE/ZUcoj64iCsLNh7bc8jJ6w/9+hbBZ4d0hXJ3aEXR20pVHCm2/TwLNIfgkwHBX5JDp1J/8aqes9GkEcEkd6wCXokcRAAJUhsaJKMYHZZ0k+/Jensg12ozCaA6A/GqlaCPhJGBuTBpxirVQCrXYKMJIYMMzZVPfNT4eFX+C+4IaO3O+lHDuFu/vGxki6fkQvdcvCzcMueZ3N3PbtWGBEA+Ry+MLKlS/RNXEAg6fKUbfQ5lX4FFOQY+VI8sCLjHiSCb4E/30YmbMuLDllO9wsz9NyYtKuftUo/35l+L0v698D8Dzb5ieqQCuNgh3QKNoiHsUen2DkCaU7GSR9n7ICrVVfVdkE/5PpQpV8b2faCiLJhW3z4Y+2ac8ZEV33HFvluY2EX5IWs81lWmG3tzHyCGYwn6ecjJDXmL9axX1tWkCxJPLshI/FfUcIYxXVbgGdFnc/Q501V+pekH77inyRAXJ0VYQUINkEeVm9suyAHyQvCahWJ31BMlNTO4l98BHkuST9/cSdW+sUaBzu3AuI5Df6QjuA89B6H8GMn4hP9YqsIPYy9ZwcSIL8e5/+UgDmd5DMlVeGK+mHvnvOy9Sq7NvRFN9mNOKiogJfQe207x774bPfOldnI5SKkP4UMMscx0xadumyln7/VPswUjSTQOBffitvCPvE2fDl+aOt85aQfOWXcqhUcAhAIhYPgjBmSrM4gjx2oNjkL+5koVTLvAEz4/uMqGaAle1k2Auxnt2TljI8C5nrjkbFTMOQkqw4B6qKk33xkpozfwyv6FXDSL0XgBCUolH0mGvo+Cw/OWKVLdTjkb7aNDEkKKCAcvDhcDohCJwHLPTnDBunnwAQJB2UTeBkfzBNA3eM9w2Y85kkuVjiQPDIQ3KYTdw/M87onkf7IVJKHwCDv7AIWwd97lQuBEvmHo/93wNnfPkcmg1/OwZUsrAJwqgIv8kwOS713PR2U7NILsqUrSNXy5XMP6JEth8xJn4f0sym/kEP3rDTQmcxXIDQ2QY6fsCWCXltdk6hwnnHouWeeL0P6Iw9nwURgkcDCFwlMP8aooCBgIZn0m9O1tQXWkjcym/ewozxvgNTFljhvRO92Jv2SIOQF2Voj/QhgAkxWSZA1cpy6zM8j0HCjg/BCBlSDJQ9kqxAw9QHmaQPpR6aQQhU7RRvBnM/IdUiX1VQ6TCbIMvJoS5mVG0WJ+HtnAR7ZRjL5tWzvuQ7SLxCrevpxCD5wzpNO8cMqwKrgdEqcUghCVhBwc5sxA555Dk5cFV/o7laln37yNSksGZOCkRXaPNAbLI0PyUZi4WksCmFWAOiCRAH2cx6xkUn6Je+SNAk7m/ede2Cs0ERebJ2MJAAKHIigMUnmZmFAEcSzEeaATN5UpV8/sOLn2INtGfQXecM5JgZkZF62TXlYlbwVJcVENhG/QK/d5xUdD+nnH9kUPnSnk366JqnnS9l/7I+uiE9kKtGBJ1tFYhVU2C7fT9dgmWSXTrAJSSSZxG/rB4HPqvfkgt5LMsRL3E7sEkedXe9z+h/7Io/IxDgvS/rnVvbLkP7wUDqIJyL6uDN/Et9qXpKBxJjcc8z5ykm/5S2VXhUfgRuYQDZgwVOGgsSf52CEUSrOEmHnQON8GBhDAojqAlJAyQTtuQzkesooKxQcOB8Gl2CWMQkOghRyFgVx79b2ntxLOQlKciPwcQAZp7aM3S9NqMSqoJ7ngIFlLziaI8c62zYvSm3ejMYLJgwBXtNp5T5Kb1wcl2QkSpQsUwBDPI07SUPuodjkKijZdmKfqP7IfconfeVMVpZTES5BGPnicJGijNGYyO4U9vSTMR2z31DwTTV1OkY6jAzROXiSnbO/fZ4gEgxzJotJ+vUh6RSEOMHp1NwDV3IjJ3qiWuu89vIdAoWEqZxskX4B3TWxJefsa2UHdDBjJmNjQ7TpFb2hP/qiT+ZzaL7auAzpn/ZmPhJbgccY9R39c2ZfdBApMj6EnlyMmY4b48SXvPIfrtnOkMq48+1e6T8P6Uc8VdBU7edzP2QDN36CvOkY2fLJZCv5k1DQwejC8hzST3dUtREDCdbUH7oDf3IRaK1cWQlQnNAHuUUufI0tGkim9vg37xHTaX/GyucjmpHb1JVj3vP9sDF2q6T8/PT/sOEXgw2dosvRKWOdNgsn13guCkFU9Qz5OUT6+X+V/kn6+Vd6jjyYN38SGcCJvNgiDOJ7Ii++wrXBc8oLzvwT3JB+5ExMVaklH/i6z5zi28iXrMjM3I03VVf4eE+nPJuh0OCh/5sk/RIXfoEOsAlbySRAdAgWmX/mRZ/5CMmaJMA1U4YwgN/0Fa4RA0L6+ew7nfTDS6JOVggqAh2dgQFuwjbJnPwVBvEpxVr+Ib6BDrhP7FFQjF7F/vRjhYA/lSjwAenHvXCma+ElbIsuG4/Pp2wiy5wvSvrNW5Hsqkm/rY1WkBTCzIHvYKuwwZVsg1IYccQvBKet85WTfpm5bA6AIX6cA+H6uS77kS7iVE2KsO3LlMUBGDmIwCM4f+eVz3LmUADmp5oYmaWitUOlnyMT3GdgEHwsd3IGHMOhQ7VV2zJYyj3HSWiwQaRUzyRAW23NPmAgUfBgs0qIqoK25xgz1+UZJvqm+IIO2TAS1znP/5wrfeqPUlEuBshhc1hLzJd9pU0Gpi8BZRqb4EZpVT8F4UOkX+A4lvSrmqlgZyxkLWBa9bFKdJmDfAQg+qtakD7ggGiojNmDfNYBz5BMVVKGzDbS3taZjM1JYIHnlIHAY9nScrjAS15I7FZ75/nOdgzkCw6HtvdM0m+eDkkqO7BsGwJE7+bYD41DoDQvwWCZBCDfkn/Ek61d5pB08llkK1HiH6ZtZHzGvDZucqHPgguypeDBRum0AxYSoLVKv3mt7elf/nqP+Vr1sdKHVOYIzv7mU23JUNFGQJI06YPOzt/pz/3zfB7S7z5ExYoSu+NPkbdDsl3iRrb8CEK8TAJC+tkJYssX2y5ihZQuTAKqXfPk19jR/I7c9MFH8rUqhnyNFyz4uzmukH79XSQ+wSTy0Afij7jyc+KI8cXfTp2iP3McvjMPcoOFJFIByZaYWSwL6RdfkhBphxzck+09xsVuydc2NbaIVJsv3DKWrbMxihv8jnFlvIllYqVndPRhhYs/1Afi5Zpc76wtWPg8Oqpvn2vbqo4VSAUEOIo/th4tH+Tl2z3jQF4Kd6qeVhH4Y34yfdF9K96KkVYfVYkVsRQ5yMNLv2xfPyH9sQ3JuxVUc0RSM6elLCd+vjNHdqgf8qfn5ui6SfrpWir9y1/v4Veu60FeOFllWttxkLnzYbboWbGjY/TS+OmB5NE2E/EmB3mxW75UURUhJ4uJFZnDBuYwmfrhOjwBXuIjXbVFkl7Nw9/0QpFS8gRjbc1+pjzmezIwJvOBr/7z/RrpV9jA+/g21+lD4mJ7Kp0TP5B+K+WT9Jsf3cMz+YKLHDgH2+erjDXjhH/aho8jvueYfq6c9KtEI6RAImBnzh1pRFbPM7i1CVha1g5FtJ82gqOQBLJ8+ZwiUQrVf8uJlpcI6tBYrEYwNsqUeZgLR2Y/Gee7du/8jDLYS0YZOG+KbhzaceasEVJOJtXRtfkuP+PAtW0pjeJxkoig9s2VUk8M/K0/mbYqjgpLnBflj4wYGVy0vTxUmFT8Ecvs02csW/2RC/z0JbOnA8aoPwaHSFri0x/ngqh68DPBwJnThJ+AcuiAHQLCQViG1b6X/uFuH2wM41AbZ31Orpy/pWtym3KEh2BzHsNmzCoiHJbgRTfp2VKHyc7Ldwwd+RMYbAMgu4zDdwIg2xPYEHTyzPfB5CJnbZBLSL+tPpLuaRfImK0BAqogBq+8YJu91gIFUs0handtvj7TNtlzopIFclRNy/iRYsRAohVyfZYMt75nzwoKgojqkWQMyYlM1mzKWOgo20MskXcFCbYyDzjQUVtn4EhW7mUL7IDcEJzomC12ktesJMDKfBEXCdT0Fe7JAXf+xl571dfYtv7oLNvma+Y9873kiY2TLawjX215ZkrFc/atX31KmFSQEX+yRWjIdg2zyJYv4LvpA58cTNznc1UzFV5jkjjqw3K2bTECc/Rn2Qe/lz7YFN8Yvcwv1PA3sFjahxUJPt92z+U8g/F5zrBFgCQYKt/sfOrU9NHem4uxx++RubGraCqULcdkPlb0EAJElE7BD9GSgM9Kf+SMLEmUbYlSjaXn4sKWLUbHFe0QjZBq95ADH2/7hIRCP/rQt+IY+Yo50QnzW8478qJz7N3/Nq6wkOIgu0wRg+wzTz7B9l2xmo5IiPUZTuA6Okz3+WdVU3FCLFEwkKT73kvfbFM/SKwY6whu7pNQS3D9epo5uWf64IwLXmyXPtkyK5kwBn4CZohbSD9fqi9k2T50hRry0Fb8gwKq5Poih7bxHPJmC/rVttgIJ/wDec58l33wreRqZwEdS/xmv7YyKWRkazSsvLTFdq2e2mrMvuEBZ7Jes1mf+T6cAWZ8CpluFXUkwomj9IEfOdSPvuHvmjxrAoPoFFz4L2OmI+aleMMf24YauWjDFkVbX42PvhuHranigO+1Fb/L/sngIodYYkeIGChWwMkL/pJgOnNMsXHZ95WRfgIXBFRUBB7CBiKiQvkRplTWY0zLwRzzN+dHIJQRoROkPTBMeJSGsycgZ04WIZGZMVZ7CFVDZf2pmkRZZ9+UVmBA8jlrjo0Rc6YCe4iNe3L/ck6uEWAkKJwTBeNotMUxCzqySM5tVu/mOA69Z1gwUOWQSQrKiDUMOCM4cMiMnLLol5JYpqTEZGF7DSdrPM75ZaGJS/o3PkHGUpvqFbJKwRECyq0fL/3qn+wpqmAARyseAjbDFGiQGkbCuLRtPvarqqbQF3qjDc7S/LYq9e41ZttmJJbu9eLkOA/yOu82qsx7ylQwIEtVNE6DDJ0lL5zfefrgTMndw5EJJIKpKh2dDZacZcgveXHeqlHwRPzJVQASkDlw5IjOsUFkEobwzsu1x75g6FryoDfahIcgq+pMZ1yjbclWnPTSHvzNMbIpJEVbdINdcV7mG101XzrMVuAsiLuHbkoeYa4/1VO2L3kKGVr2Gxkec6ZDZGL7B/+gUqkPcyST6DifYqx03FjoMZIMe5VVfiW2PMfjvYqZuUu6s+XFPmcyRdYddIy+0mP2Za58Fx2zbUIQ4FfWDnNI9QvZIjfyM0aVTUUC5GKOa7Zj/sia5yvI073GyW/QUcTDvY7ZBtkit4iaQMjO2QW9jS7TZxjyF8gs2fIj7EbxRiXNXOmzCiHZBkvz1YfgpkjAb0gwzA+RojP8jj70R15sifxUMvmYuYWTr5BkITL8UXw7wgXjrWLQxOuY9wgLQkCn7JXnj6JT0XvEMTqlwsmWbecRG9icYgMdJ9/grm+ypHPmQY/g52Ue+UnJJWHShvsUKCQT9FHSQMeWvoe8ED36Qx/5YQUvsUysISv36Q+xCflIH3DUBy4g9tIJsdi8Z3zWL0wkRvRM4YCesyPzlahKOOmJh7jjvyQtfI5KNZnSfQRJUY/v55v4P6tveY4QHnwk3YsO0XGY5z8Y4z+j55FxYrk58kfGye75JH3QofQHG2Mgb0k03MiTPUzSD1NbN/TF9sQWyQhCaUywZXuePzkvaYyeOIszKvLIbHy2M95EPnjEvD5zdqZ3/Db/pthE5uYJL8U4fGCpY9qKPBLf+C99kn986dSBxDg2a1XPtic6yu7p06FDP5JC+kEm5kj27Cg8aPoFsjIW8UuCR/99lIx+AAAgAElEQVTxMHh74an6R+bpAd0jF/5TcY/P1wZdxaPoHD2VUOYXyXzvOpzUcwTuZ8MO2JznkGz6kQ28iE/m6+AnNiyLGedp+0pJPwEJnITNIFVfnS15WwqhYFd1AEQFlxFykIDx4IeHRQgfMWXwKoLGoVIJQEYwAfJ+/m18xsk5CAIUXvBAQKKM8/q1++ccEROOjPFrQ9UBaUOGJS6Ua0uxZ1vL94IZRySgI/IcrFUWTsY+MMkAwoTEmIulV9fDDR5wg40gqw0BKqQi88pcnZESBgFzAYDRMAgOUH+COcdlfki4pITDgru/3YPkwIKhwsbctc14BWP6Am9n1QL3Mr5Dh3uNWQVLNdW9+vArHpwd5wHjyx4cjACDlNEJgVXwEGiQHsH0vIdECplCfuiGCidHISDA1MqK1Rzz4aBhT0aINyfjHi8VaoQtCaSgydkES/fn2mPP7vHSBllp08HBcfZ0Jlgbiwqp+ZBHXhMP+LkXUaELdMTc2Czd8VItF3zpifGbL/JHLwQ/tmNM7k+Fb9rOWr9zDGe958Btm1DVIxN9CW7k4AE1fsU42Rhbo2Oqdcap2pgERD8ZS8503UqMYEEGdMic4KFPB4xCoLWtf0vlbDUPbc35pp+c+QP2IjHUvvv5BVjS3YwvY9p3+j//aBfWZGvPuzHSbzbIbxhj7ss59wv8ApvrECyyRUajy/SZn0DYzSeyhYktjvowVmOOn14GfNjQQT4zffBtfA6fz997z9/TS/YCMz5rHjAQo2zZoGd0ylztwSdHceUqD4SOXbDP6JQx0vulTimS8I3w4aPFoUMy87lCg4Qi8oIh2ZPDvHfOh+yMCfbBEgbGRF78jvjBFm0VEyOMR9zgY8UyJJ+syFJs58MmzvpgS+RFv/lIKzXsXR8IJ5l5L4kgL9iwO1iF8GuH3MnLGKbP4wNgSuf5f+fEtfgmvgvxVjTSpuv4SPfByfdkzyZxFpjAVb9rB9zYiPki7HRGG/RdPIAJbPQp9hk33baKgOSmyq0QB199OtieOCkGw9uY6CaOoK/z8ibjz4ttKrbCl7zEe237G17mlGuXczYu9mAuxsIf0DF48cnwgukaXvAmSzKVrJuT+KZwyodKjKID9IIO0BOygTG5H3PoBz/Qj/hPBmIIf0OP+R7+m36TEb8gVpGNpBr5NzYv8lR8pCP0AHb8Jh/PLsNn8Qr9wQZGfBU58V36dx2/Sw/IdRZbjplTriEbY2Hj8DEPemP1Bw/BmY7FKW06Xxnp11iUB2AGAwzAxYhnx5d5P5XMe+Ajd6o6jBHYCDoHxVEBjnIec2QOzsZtHnkdUvCz2qUYBAgPBJQSaPPYMWl/Oedln0gHQshAORMOBFlTReR89W8cOSIj8vHd2twmFrnPOc4A5gKHwC2IIjUCLXKnzRza0Z/PzHv2N/swhmDtmmP1xni8cn/u1Wdwyzljuug5uJFjdPuibeU+Y+fYBVaki5F70WcBi/PUbw7Xw0n/znOerjFXWMCBzgVzfx/7ihzW2te/PvUf/fFZZJn3Ge/yrE0kEskyX7rqxQnDYEmgtWfcE/ND8jz0+XIMy7+X95kf3AVMY4xfMU5ERgVsOc7MP23n75zNY+JKv5dYuXZiG/xd5/B9jrSbz5zJXbtrslnel/Zyv7/1M/1ebHD2lfdpb56Nl/3zAwIov0CXYcZfIGfmlyPjdV/w8N0cU67N566VZPBtyAdfrxDB/8TfB6/lvRm7MWjnGJ2abVzmPR1GUIwxscq4YcSP8t90CibLI+Oen/ss+MWuzWtt7vO+vHe/69kiP0NeZMWfW5lii8i863J4T07pLzLLNc55n3tci8ixd32QF3vyXnFN/2SxPNKWs3lqx3Vea/N0nfH4Pr7Rfcvx6Cfy5x+1u8Q897iOjvAFfLT3PsuY9EOnyU1b+vfyGcKHoFmVzXMNtp+oBCPA/F2OtJf5GVOwzTXHnrWVl3ti08ZqjM4TP9euHfk892dsxpXv3Dffr7XjenpEn8yZD8XRJHJ8K93D0cz5Mkf64bMlvngQ26LL6cMcMl7n6IHPl/PKWJa65zqYuH+25fPgay7Rk1yXa9PusWc6J/ngM8zF/HDepc4e296Vkv50mkkaVMCZ3+X9ec9pd3lflBLgAEKqvfwdg14CvtZWPsu18++8X/Y9/859+Wz+nfuNdYnJ2vX5bO0828332qRkDBoGXuZPmaN8udY548l5fjff5/tln8F8rT9yn/etvZ99zPdr1y77Xl6/1V+u3Woj1xxzXhvflOdF+wme0eHI7yz9zXiM3Xvt5H2+u6rzxCdt6m/5mtetvXcvmZnbnC9dmg513pv+cp7fXdX7tJ3zcox8CrmsjdM980gb8+z7+ffyfe5ffu7veczvD30+ZeKa3DOvn+9nH7k253l/PpvXL9tZ4nYIs9y3bNPfsen0vbzW93wb/Vnz97lPW7CYfRx6P+9Jf1d1Tp9LbKZO8dPGunbk/q3vcs08L6/Pd/nc38sxLeXl2tx31nm2m2vNafaROZPdIf82+4z8nPM+bee81q/vcuS6fDb/zme5dp5TrVaFlqio8CKo/JTDvctxIWRWIq0C2P5kG2MewrQVzdYP30n+lsfWuLbGuWxn+fey3fm3a/P38r78ne9zzufzXt+tHT6HEczCF47RgUPtrfWRceiHTqWf6HL0LPqTNjKfnNNOvs853+ec6/ydI9/Nc767zDnYsRfzMpflPM7T/rWQ/gxgApLPLnu+qjYjmMuO53a+/6qwup3naGyXMYCrmNvthPOtGkvs6Vb1fxVyPPU27iTZXJVNVy9vrVYH/4uO4rL3X7Rf+odgWUnynItnAOzltrXPNhFbzFSq7SZQgfWysm4Vw9Y8W888q2J/ueo+0q/ab0+8rWgSCCtiPYrATSNwraT/pifT/u6OwJ0U5O8+8v5VBIpAESgCReDWIKAqraKvIu8HPTyE66FrDzd7kNKDxJ4h8YMJfhLWy8PG9pB76NUDx37FJdt6nFX5fWfPt2d3rFT1KAI3jUBJ/00jfoP9lfTfINjtqggUgSJQBE4CAdtCEHMPGvulsVTq8/OqCL0Kvp/o9FOWXt77zHeuy5YeZw/x+lUyD7Pb+mN7S48icCsQKOm/FajfUJ8l/TcEdLspAkWgCBSBk0HAvmkPH/sVFj8j6z8z8ws8SzI/if3yveq+JMBP1fqtdz8ZbruQh1o9v9GjCNwKBEr6bwXqN9RnSf8NAd1uikARKAJF4GQQsKffg5N+Dc/PNfp5UcTd/+1gi4/fTJcEpMKvyp+qv9+G99vz/t8Ev6/upyP9XKa28vPTjc0noyp33ERK+u84kR0/4DqW47HqlUWgCBSBIlAElgj4NR4/Ler/BvB/0/hP1vynZbb9WAHwHwpKBDy06+c4/edw97vf/fa/Se//UPFztYd+inXZV/8uAteNQEn/dSPc9otAESgCRaAIFIE7EgFbcfzuvv/vxu+9+z8V/PqO/6TJf4zmPwPzn2rZCuQ/K/O/U/s/I/yHTf7vhfxHaVYPehSBW41ASf+tlkD7LwJFoAgUgSJQBO4IBKyg+zlPe/P9r8iIvf88yf/y6hd/PKTr13+WK+3+Xn52R0y4gzwpBEr6T0qcnUwRKAJFoAgUgSJwOyEQwl/SfztJ5Z45lpL+e6bcO+siUASKQBEoAkXgnAhM4j7frzUTsr/2XT8rArcCgZL+W4F6+ywCRaAIFIEiUATuaAS2SH8J/x0t2pMdfEn/yYq2EysCRaAIFIEiUASuE4FDxN+Du4e+u87xtO0isIVASf8WOv2uCBSBIlAEikARKAJFoAicAAIl/ScgxE6hCBSBIlAEikARKAJFoAhsIVDSv4VOvysCRaAIFIEiUASKQBEoAieAQEn/CQixUygCRaAIFIEiUASKQBEoAlsIlPRvodPvikARKAJFoAgUgSJQBIrACSBQ0n8CQuwUikARKAJFoAgUgSJQBIrAFgIl/Vvo9LsiUASKQBEoAkWgCBSBInACCJT0n4AQO4UiUASKQBEoAkWgCBSBIrCFQEn/Fjr9rggUgSJQBIpAESgCRaAInAACJf0nIMROoQgUgSJQBIpAESgCRaAIbCFQ0r+FTr8rAkWgCBSBIlAEikARKAIngEBJ/wkIsVMoAkWgCBSBIlAEikARKAJbCJT0b6HT74pAESgCRaAIFIEiUASKwAkgUNJ/AkLsFIpAESgCRaAIFIEiUASKwBYCJf1b6PS7IlAEikARKAJFoAgUgSJwAgiU9J+AEDuFIlAEikARKAJFoAgUgSKwhUBJ/xY6/a4IFIEiUASKQBEoAkWgCJwAAiX9JyDETqEIFIEiUASKQBEoAkWgCGwhUNK/hU6/KwJFoAgUgSJQBIpAESgCJ4BASf8JCLFTKAJFoAgUgSJQBIpAESgCWwiU9G+h0++KQBEoAkWgCBSBIlAEisAJIFDSfwJC7BSKQBEoAkWgCBSBIlAEisAWAiX9W+j0uyJQBIpAESgCRaAIFIEicAIIlPSfgBA7hSJQBIpAESgCRaAIFIEisIVASf8WOv2uCBSBIlAEikARKAJFoAicAAIl/ScgxE6hCBSBIlAEikARKAJFoAhsIVDSv4VOvysCRaAIFIEiUASKQBEoAieAQEn/CQixUygCRaAIFIEiUASKQBEoAlsIlPRvodPvikARKAJFoAgUgSJQBIrACSBQ0n8CQuwUikARKAJFoAgUgSJQBIrAFgIl/Vvo9LsiUASKQBEoAkWgCBSBInACCJT0n4AQO4UiUASKQBEoAkWgCBSBIrCFQEn/Fjr9rggUgSJQBIpAESgCRaAInAACJf0nIMROoQgUgSJQBIpAESgCRaAIbCFQ0r+FTr8rAkWgCBSBIlAEikARKAIngEBJ/wkIsVMoAkWgCBSBIlAEikARKAJbCJT0b6HT74pAESgCRaAIFIEiUASKwAkgUNJ/AkLsFIpAESgCRaAIFIEiUASKwBYCJf1b6PS7IlAEikARKAJFoAgUgSJwAgiU9J+AEDuFIlAEikARKAJFoAgUgSKwhUBJ/xY6/a4IFIEiUASKQBEoAkWgCJwAAiX9JyDETqEIFIEiUASKQBEoAkWgCGwhUNK/hU6/KwJFoAgUgSJQBIpAESgCJ4BASf8JCLFTKAJFoAgUgSJQBIpAESgCWwiU9G+h0++KQBEoAkWgCBSBIlAEisAJIFDSfwJC7BSKQBEoAkWgCBSBIlAEisAWAiX9W+j0uyJQBIpAESgCRaAIFIEicAIIlPSfgBA7hSJQBIpAESgCRaAIFIEisIVASf8WOv2uCBSBIlAEikARKAJFoAicAAIl/ScgxE6hCBSBIlAEikARKAJFoAhsIVDSv4VOvysCRaAIFIEiUASKQBEoAieAQEn/CQixUygCRaAIFIEiUASKQBEoAlsIlPRvodPvikARKAJFoAgUgSJQBIrACSBQ0n8CQuwUikARKAJFoAgUgSJQBIrAFgIl/Vvo9LsiUASKQBEoAkWgCBSBInACCJT0n4AQO4UiUASKQBEoAkWgCBSBIrCFQEn/Fjr9rggUgSJQBIpAESgCRaAInAACJf0nIMROoQgUgSJQBIpAESgCRaAIbCFQ0r+FTr8rAkWgCBSBIlAEikARKAIngEBJ/wkIsVMoAkWgCBSBIlAEikARKAJbCJT0b6HT74pAESgCRaAIFIEiUASKwAkgUNJ/AkLsFIpAESgCRaAIFIEiUASKwBYCJf1b6PS7IlAEikARKAJFoAgUgSJwAgiU9J+AEDuFIlAEikARKAJFoAgUgSKwhUBJ/xY6/a4IFIEiUASKQBEoAkWgCJwAAiX9JyDETqEIFIEiUASKQBEoAkWgCGwhUNK/hU6/KwJFoAgUgSJQBIpAESgCJ4BASf8JCLFTKAJFoAgUgSJQBIpAESgCWwiU9G+h0++KQBEoAkWgCBSBIlAEisAJIFDSfwJC7BSKQBEoAkWgCBSBIlAEisAWAiX9W+j0uyJQBIpAESgCRaAIFIEicAIIlPSfgBA7hSJQBIpAESgCRaAIFIEisIVASf8WOv2uCBSBIlAEikARKAJFoAicAAIl/ScgxE6hCBSBIlAEikARKAJFoAhsIVDSv4VOvysCRaAIFIEiUASKQBEoAieAQEn/CQixUygCRaAIFIEiUASKQBEoAlsIlPRvodPvikARKAJFoAgUgSJQBIrACSBQ0n8CQuwUikARKAJFoAgUgSJQBIrAFgIl/Vvo9LsiUASKQBEoAkWgCBSBInACCJT0n4AQO4UiUASKwP9j70yfpqiy/P8HzduJ+L2bmBczMRMzE7Pv+952O8q+L7KDIPuiKIKCguyKILgiu4AgCMi+yi6gjjbd/TD0/cXn2qf6VtaWS1U9lZnfJyIjszJv1lP5zXPP+dyT996UAlJACkgBKSAFmikg6G+mjo5JASkgBaSAFJACUkAKSIECKCDoL8BN1CVIASkgBaSAFJACUkAKSIFmCgj6m6mjY1JACkgBKSAFpIAUkAJSoAAKCPoLcBN1CVJACkgBKSAFpIAUkAJSoJkCgv5m6uiYFJACUkAKSAEpIAWkgBQogAKC/gLcRF2CFJACUkAKSAEpIAWkgBRopoCgv5k6MY/98pe/dFqkgWxANiAbkA3IBmQD3bKBmIiiYlKgooCgvyJF8g0q9uPHj11fX5/7xS9+4X7+859rkQayAdmAbEA2IBuQDWS2AbiiEVs8evTIJxuTk4vOKLMCgv4Mdx/op+L97Gc/cz/96U+1SAPZgGxANiAbkA3IBtpiAw8fPqz6nu+//96xsF/QnwHeSnyqoD/DzbcsP8D/3XffaZEGsgHZgGxANiAbkA10xAb+93//13+voD8DuJX8VEF/BgMw6KflTWXUIg1kA7IB2YBsQDYgG2iHDVgyMfpdgv4M4FbyUwX9GQzAoF+Zfj3lMOestWxBNiAbkA3IBjphAwb/gv4M4FbyUwX9GQxA0C/H3gnHru+UXckGZAOyAdlA1AaAfvYB/UwgwrhC/UmBJAoI+pOoFSlr0B9274lWUn2W45YNyAZkA7IB2YBsIIsNWJaf76B3AdAPg+hPCiRRQNCfRK1IWUG/nHgWJ65zZT+yAdmAbEA2EMcGBP0RANPHVAoI+lPJ9sNJgn456zjOWmVkJ7IB2YBsQDaQxQYE/RlgTadWFBD0V6RIviHolxPP4sR1ruxHNiAbkA3IBuLYgKA/OaPpjFoFBP21msTeI+iXs47jrFVGdiIbkA3IBmQDWWxA0B8bzVSwiQKC/ibitDok6JcTz+LEda7sRzYgG5ANyAbi2ICgvxWR6XgcBQT9cVRqUEbQL2cdx1mrjOxENiAbkA3IBrLYgKC/AYhpdyIFBP2J5KouLOiXE8/ixHWu7Ec2IBuQDcgG4tiAoL+av/QpnQKC/nS6+bME/XLWcZy1yshOZAOyAdmAbCCLDQj6M8CaTq0oIOivSJF8Q9AvJ57Fietc2Y9sQDYgG5ANxLEBQX9yRtMZtQoI+ms1ib1H0C9nHcdZq4zsRDYgG5ANyAay2ICgPzaaqWATBQT9TcRpdUjQLyeexYnrXNmPbEA2IBuQDbSyAQN+W//0pz91fX19DgbRnxRIooCgP4lakbK//OUv3aNHj9z333/vrDK2qrw6LgcvG5ANyAZkA7IB2UBcGzC++Pbbbz1rCPojMKaPsRUQ9MeWqragoF9OO67TVjnZimxANiAbkA2ksQFBfy1/aU86BQT96XTzZwn65cDTOHCdI7uRDcgGZAOygbg2IOjPAGo6tUoBQX+VHMk+CPrltOM6bZWTrcgGZAOyAdlAGhsQ9CdjM5VurICgv7E2LY8I+uXA0zhwnSO7kQ3IBmQDsoG4NiDob4ljKhBTAUF/TKHqFRP0y2nHddoqJ1uRDcgGZAOygTQ2IOivR2Dal0YBQX8a1X51jqBfDjyNA9c5shvZgGxANiAbiGsDgv4MoKZTqxQQ9FfJkeyDoF9OO67TVjnZimxANiAbkA2ksQFBfzI2U+nGCgj6G2vT8oigXw48jQPXObIb2YBsQDYgG4hrA4L+ljimAjEVEPTHFKpeMUG/nHZcp61yshXZgGxANiAbSGMDgv56BKZ9aRQQ9KdR7VfnCPrlwNM4cJ0ju5ENyAZkA7KBuDZg0G9rvZE3A7iV/FRBfwYDEPTLacd12ionW5ENyAZkA7KBtDZgwM9a0J8B3Ep+qqA/gwEI+uXA0zpwnSfbkQ3IBmQDsoG4NmDQT3lBfwZwK/mpgv4MBiDol8OO67BVTrYiG5ANyAZkA2ltQNCfAdZ0akUBQX9FiuQbgn458LQOXOfJdmQDsgHZgGwgrg0I+pMzms6oVUDQX6tJ7D2CfjnsuA5b5WQrsgHZgGxANpDWBgT9sdFMBZsoIOhvIk6rQ4J+OfC0DlznyXZkA7IB2YBsIK4NCPpbEZmOx1FA0B9HpQZlBP1y2HEdtsrJVmQDsgHZgGwgrQ0I+huAmHYnUkDQn0iu6sKCfjnwtA5c58l2ZAOyAdmAbCCuDQj6q/lLn9IpIOhPp5s/S9Avhx3XYaucbEU2IBuQDcgG0tqAoD8DrOnUigKC/ooUyTcE/XLgaR24zpPtyAZkA7IB2UBcGxD0J2c0nVGrgKC/VpPYewT9cthxHbbKyVZkA7IB2YBsIK0NCPpjo5kKNlFA0N9EnFaHBP1y4GkduM6T7ZgNWDC3te3XWjYiG5ANmA2E/kFv5G1FZzreSAFBfyNlYuwX9Mshm0PWWrbQygYsaNdbf/vtt46FY+H3fP/991Wfw2Pals3JBspjA+Y3uOeC/hiApiJ1FRD015Ul3k5Bf3kcroKr7nVaGzBwD4GefQRuOxZ+d7gv3A7LaFv2KBsolw0I+uNxmUo1V0DQ31yfpkcF/eVyugqyut9JbMDAPoR7Avc333zjvv7668rCZ7L8fHe9c5L8T5WVjcoGimkDgv6mOKaDMRUQ9McUql4xQX8xnauCpu5ru2yAQA3g379/3929e9fduHHDXblyxV28eNGdP3/eXbhwwV26dMldu3bN3b592927d8+Xt64+nN+u36LvkV3LBvJrA4L+ehSmfUkVEPQnVSwoL+jPrwNV8NO967QNAPrA/OnTp92RI0fc3r173Xvvvee2bNni1q9f79asWePWrVvnNm/e7LZt2+Z2797tDh8+7E6cOOEbA7du3fJPBQT+stVO26q+v/dtTNAfwJc2Uysg6E8tnXNAf19fn38kH1ZIOdDed6C6R7pH7bYBuumQzb98+bL7/PPPPcS/9dZbbtWqVW7p0qVu/vz5btasWW7atGlu8uTJbsKECW7SpEluypQpbsaMGW7u3Llu8eLF7qWXXvINgh07driDBw+6M2fOuOvXr7sHDx5UDfRVf3/ZcLttWN/XmzZlfGFrDeTNAG4lP1XQn8EAQui3x/Fymr3pNHVfdF/aZQMWePk+tum+89VXX/luO8ePH3cffvihe+ONNzzAA/SjRo1yQ4YMcU8//bT7n//5H/fkk0/65Sc/+YmzhX1PPfWUGzBggBs8eLAbO3asmzlzplu2bJmj4bB//3537tw5R/afJwg2BqBd16TvUf2QDfSuDZjPMc4Q9GcAt5KfKujPYACC/t51kgpgujedsgELwGTaAf6bN2+6L774wr3//vvu1Vdf9Vn70aNHe3gH5A3sf/zjHzuW6Odwnx1jzbk0AMaPH+8WLlzoNmzY4Lv/8CSBvv/8DoI/C9eqzL9svlM2r+/tX9synyPozwBsOtUrIOjPYAiC/v51hApE0r/bNgBgA9d05blz547vr79r1y7fHYfuO3TZGTZsmM/Yk9UPIb7Vtj0BiK4HDhzonxZMnz7dvfzyy27r1q3u008/rXT5QQOgoNta6P+p/skGumMDgv4MoKZTqxQQ9FfJkeyDoL87Dk+BRTr3ig08fPjQd60hu0+//Xfeecf3waeP/vDhw30XHoN2g3w+N9oOy9p2uLbzaEAMGjTIjRkzxvf9p/vQJ5984mcBomuRZQB7RSf9DtVZ2UD7bEDQn4zNVLqxAoL+xtq0PCLob59TU4CQlnmwAaCfwbrMsEN3mzlz5vgsPF1xrL8+oF4P3NlnEM825eud0+h820+XHwYAL1++3H388cd+ph+ePKCfuvioHuWhHuk3JrNTQX9LHFOBmAoI+mMKVa9YPejXY/ZkzkzOX3r1ug0YSNN//+rVq35Q7erVq/0sPCNGjKh05QlBv1PbgD8NBcB/3LhxbsGCBX6g76lTp3xjBC35vfabe11b/T7Vf9lAaxsQ9NcjMO1Lo4CgP41qvzqnEfQL/Fs7MTl6aZQHGwCeqc9k93mZ1kcffeRWrFjhp9ykuw0Z/k4Bfr3vDZ8U8L9pdMyePdvP9c/MQczuw+8V+Kt+5aF+6TfGs1NBfwZQ06lVCgj6q+RI9kHQH89hybFLpzzagIEzfeZ5wRYv1mIWHWbmIdtu3W3qwXk39lkDgKw/U4Nu3LjRHTt2zM/sY3pbA8A+a626KBvInw0I+pOxmUo3VkDQ31iblkcE/flzngp4umdxbIBZegi0zNBz9OhRt2nTJp9Rt8G6BtzAvfXL7wboR/8Hv4P/z1OHqVOnutdff90dOnTI3bhxw88wxLVyHXGuWWVUN2QDvWkDgv6WOKYCMRUQ9McUql4xQX9vOkgFLt2XrDbAbDi8BRfgX7duXQX4wwx/f2f6aQCEjQ8y/rzt97XXXnMHDhxwly5dqnmLb1ZddL7qlmyg+zYg6K9HYNqXRgFBfxrVfnWOoL/7zk8BR5p32gYIsEzJeeTIEQ/8M2bMcEOHDq3qu9+f2X3L9tdrdAD+EydOdCtXrnR79+51165dq2T8O62bvl91UzbQGRsw6Lc1TyL7+vrc48ePMxCMTi2jAoL+FHcd2OeP9aNHj/ygOauMtpbz64zzk67Std02YH33bdAub7vl5Vdr1651vBAL4A8z/AbdvbIG/sO3/fIyL6b0XA842NAAACAASURBVLVqlZ/LnwYMU3pyfaF2+Krws7ZVt2QDvWsDxhasBf0pwE2neAUE/SkMQdDfu45RQUv3JokNhMDPeczSw6Bd68PP23WZJSfsRtMrsG+/I4R+4J/9Q4YMcc8++6zjJV48saCPfxTyo5+T6Kayqmeyge7agEE/ugv6U4CbThH0p7UBQX93nZ2Ci/TulA2E0M88/BcuXPCz9MyfP9+NHDmyMki3l6EfyLffZ2v2Meh45syZflYfpvOkQSPQV13qVF3S93bWtgT9aYlN54UKKNMfqhFzW9DfWeem4CF9u2UDBv28aZeBu7t37/bTco4aNSo3wG8Z/3AN/PPZBvfy5OLkyZN+YC+DlAX/qmPdqmP6P+2xNUF/TEBTsaYKCPqbylP/oKC/PU5MwUA69rcNAP0EU+bi379/v3vllVfcmDFj/Ft2LWtu6xCqe33bfjNjERiTMG/ePP/m3jNnzvhpSLlmrr2/9df/lw+QDcSzAUF/fR7T3mQKCPqT6eVLC/rjOSk5c+nUyzZgwH/79m134sQJt3r1aj8A1t6ya+DMutchP/x90ZmF+MyTC7osvfvuu+7s2bPu/v37FegHJnr5Pum3yY/IBn5434bVVfXpTwFuOsUrIOjPYAjAv2bvUUBSQMqfDQD8dOkBfr/44gu3YcMG/1bbp59+ut/ftBsCfLu2Af8RI0a4RYsWuT179viuTNgtXX1YzIbDbKLt0zp/9q17Vrx7FtZNQX8GcCv5qYL+DAYg6C+eY1WwLO49BfRt4T4zcBfg37Ztmx/wykw97YLsXvsenlYMGDDAjR071r+19/Dhw35gL1N5ogW6yPaLa/u6t/m/t4L+DLCmUysKCPorUiTfEPTn35EqGJbrHhrcAvxXrlxxH3zwgVuyZIkD+MmG560rT9zGhV0XTzJmzZrlpyQ9deqUo2sTMGG6qD6Uqz7ofufnfgv6kzOazqhVQNBfq0nsPYL+/DhMBTfdK4Nb1rdu3XKHDh3yA3fHjRvnM/zMcW9wHBem81KO6+L6nnjiCd/AYWDvO++84/v38zKysH6gT/hZ26o7soH+twFBf2w0U8EmCgj6m4jT6pCgv/8doYKR7kFcG7CuPbyh9ujRo/7FVTNmzHCDBg3yQFxU4KdhwrUZ+JPtp6HDE46PPvrInT9/3k/lafrE1VPlVPdkA92zAUF/KyLT8TgKCPrjqNSgjKC/ew5PwUVaZ7EBA1oG7n7++ee+ewsvrmJwK916ipzlD59GWMOG+fufeeYZt3LlSrd3714/sJf+/ermo3qWpZ7p3M7Zj6C/AYhpdyIFBP2J5KouLOjvnINT8JC2aW2gXvcUYJb9ly9fdtu3b3ezZ892Q4YMqfTjNxgOAblo2zRu7JqYlpQnHDzpWLdunW8I3blzp2omn7T66zzVXdlA+21A0F/NX/qUTgFBfzrd/FmC/vY7NgULaZrVBiw4hvDPNi/gOnjwoHv55ZfdyJEjSwX8BvusrYFDI2D48OG+AfTmm286XtxF/35l+1UHs9ZBnd9+GzK/hraasjMDuJX8VEF/BgMQ9LffsSlYSNOsNhAGR/uuu3fv+uk5165d66ZNm+ZsPn4D4BCKi7jNdYaLXSMZ/9GjR7sFCxa4nTt3ukuXLvknIqab1qqPsoHesIHQrwn6M4BbyU8V9GcwAEF/bzhDBSXdh2Y2QLC8cOGC27Fjh5+Pf+jQoRUANvgtw7oe9LOP+fsB/1WrVjnm7yfbj2bNNNUx1TnZQHdtQNCfAdZ0akUBQX9FiuQbgv7uOj0FGekd1wZs4C4ZMbr17N+/37+Nlu4sRZ6Pv1XjxcCfcmEff558TJ8+3dHN59y5c342n7haq5zqpWyg8zYg6E/OaDqjVgFBf60msfcA/X19fb4PrFVIW8sJdt4JSmNpXM8GDPipiw8ePHDHjx/303OOHz/eZ7VbgXHZjtMQ4JppEC1atMi/sOzatWvOZvOxQdDoWU9v7VM9lA103gaof99++61/CqfuPbExTQUjCgj6I4Ik+RhCv1VGQX/nnZ8CjDRuZgMG/bx1F3jdtm2bmzNnjgf+MLtdNrhvdr2AP9owjeeKFSt8Q4m39aKzoF/1rVl907Hu2AeMYZwh6E9CaiobKiDoD9VIuC3o746zU1CRzkltgKDI4F2y/C+99JLvs24Z7WbwW9ZjaMO7Cpi/n2k83333XT8OAsgA+tFfmX7Vw6T1UOXbZzOC/oSApuJ1FRD015Ul3k5Bf/scmoKDtGyXDQCpdE2hbzpZ/vCtu2WF+lbXbdDPbD6jRo3y05oyDuLWrVuV7KKgX3W0XXVU35PclgT98bhMpZorIOhvrk/To4L+5I5Lzl6addoGyPLfuHHD7du3zy1dutRDLF1XyGS3gt8yHwf8WQYOHOinNWVQ7+nTpx1vMbZuBZ2+d/p++QfZQH0bEPQ3xTEdjKmAoD+mUPWKCfrrOyc5benSXzZAlv/hw4d+Tv4NGza4cePG+Tn5AX5B/5MNGz00imy8A2sG9dJg2rVrl7t586Z/ctJf91T/V/5ENvCdb3hb41t9+usRmfbFUUDQH0elBmUE/QpGCkb9ZwPhoHlgn4XBu9evX/d90ufPn++z1mTvLYtd5kx+kmtnCs9Jkyb5ufsZF8GTE7pMyd77z96lfbm1V6a/AYhpdyIFBP2J5KouLOgvtxNWEO7f+2/Qb33NCYoA/yeffOL7pJPlB/aTwK7K/vA0AN2GDBniX2bGuIhTp075gdGy+f61eelfXv0F/dX8pU/pFBD0p9PNnyXoL68DVvDt/3tv0E8w5H4wW8/nn3/uVq9e7SZPnuyn6LQuPdZ1RVDfuItPqI3pNmLECLdkyRK3c+dOd/nyZT+Dj+lujS3Vhf6vC7oHxb8Hgv4MsKZTKwoI+itSJN8Q9Bff0SqY9v49Bj4JiAw63bJlix+EOnToUJ/hV6Y/HuSHwG/baDdgwADHS81WrVrln6DwdmN18+n9OiG/Vbx7JOhPzmg6o1YBQX+tJrH3CPqL51gVLPN3TwmGd+7c8YNOX3jhBQfwM/WkgD898AP+6McTEubu5+VmNKjI9jObj+pJ/uqJ7lm+75mgPzaaqWATBQT9TcRpdUjQn28nqiCY7/tng3cfPHjgzp8/79avX++79RiwsgZa1bUnG/yjH+Mjli1b5j799FM/mw91B/1Zq5tPvuuR/GA+7p890WSt2Xta0ZmON1JA0N9ImRj7gf5Hjx5VvaaeCqkgmA8nqmCX7/tk0MkLpPbu3ev7ng8bNkwz9TyZDfKte4+tyfgPGjTId5vavHmzO3PmjJ8lyfRXPcp3PdL9y8f9M7YQ9MeAMxVpqICgv6E0rQ8I+vPhLBXUinmfgE6y/GfPnq0M3mWqSXXraT/00wAYOXKkW7hwoduzZ4+fJUn1qpj1Sve1N++rQT/3R5n+1nymEvUVEPTX1yXWXkF/bzpHBa3y3Bem6CTLP2vWLN+XX8DfXuAH9tGUhUG9Y8eO9d2oTpw44Qf06qlmeeqa/Gr/3mtBfywsU6EWCgj6WwjU7LCgv3+doIJQufVnYBsvjlqzZo3PQmvwbvuB37r4sKZvP09S5s2b5z744AM/RSr3QPWw3PVQ978791/Q34zGdCyuAoL+uErVKSfo746zU1CRzlEbYNrIa9euue3bt/ssP1loy0qHoKrt9jYEyPgzqHflypWObD+zJinbr/oZrZ/63H6bEPTXgTDtSqyAoD+xZL8+QdDffsemYCFNW9kAwY8XcTGTzMsvv+x4gZR1QakH+ZrBp33gj84DBw70DS0aXBcvXvSDelvdMx1XvZYNZLMBQf+v2Utb6RUQ9KfXzgn6szkxBQHpF8cGGLBrC4Hv66+/dleuXHEbN250U6dO9Rl+3iBbD/wN+FnXaxBoX/IGATqPHj3avfjii+7o0aM+2899DKEkzn1VGdV/2UB8GwjrlwbyZgC3kp8q6M9gAIL++A5Lzl1apbUBA347nyk6yfIvWbLEjRo1qi7sA/Mh8Av6k8N9owYR0M8LuyZNmuS2bdvmZ08K+/YDJ3avtFa9lw20xwYE/RlgTadWFBD0V6RIviHob48zU1CQjnFtgL78TNG5detW/yIuupoAoVFAjQK/oL+90M+gad58TPeq/fv3Vw3qFfSrPsetzyoX31YE/ckZTWfUKiDor9Uk9h5Bf3yHJecurdphAwwc3bdvn3vhhRccL+IC9qPdeuoBv6C/PdAfas3g6enTpzte2HXhwgX/zoR23GN9h3yFbKDWBgT9sdFMBZsoIOhvIk6rQ4L+WsckZy1N2m0D9F+liw99+ZkxZv369T7LT7aZvvxxsvyC/vZAf7SRRfcq+vaT7eedCTyJ4f4r2y8/0G4/UPbvE/S3IjIdj6OAoD+OSg3KCPoV2MoeiLpx/QA/fcZv3rzp3n//fbdo0aLKi7jUtad9MB9tPDX7bBl/uleR7achdurUKd/NB5sQ9Ms3dsM3lOl/CPobgJh2J1JA0J9IrurCgn4FtjIFnf681q+++sqdPn3azw8/YcIEP0i3GZTqWOcbA9bgYspUXti1a9cuP6uSgF9+sT99RVH/t6C/mr/0KZ0Cgv50uvmzBP0KbkUNMP15XQaNdOt5+PChz/LTZ5ws/+zZs31ffgNOwX3n4b6ZxtwHsv3jx493b7zxhjt27Ji7f/++v2f9aUP63/LNRbMBQX8GWNOpFQUE/RUpkm8I+hVYihZYeul66NYD+DNF54EDB/xMMWPHjnVPP/103Rl7msGpjnWuccB4CabwnDt3rtuxY4e7dOmSXtj1nXxjL/mSIvwWQX9yRtMZtQoI+ms1ib1H0K/AVoRg0svXQKCjWw8zxEyePNlnla0/uUC+cyCfRFvuB+DPC7tWrFjhDh486Pv22xObXrYv/Tb58LzYgKA/NpqpYBMFBP1NxGl1SNCvgJGXgJGH32mQSIaf38vg3QcPHridO3e6xYsX+2wyMKquPb0B+2HDAOgfNGiQmzlzZmUKT7r55MHu9Bvlx/NgA4L+VkSm43EUEPTHUalBGUG/gkUegkUefyPgD/BfvnzZrVmzxr/9FbBUlr/3gN/gn/tD96ulS5e6Q4cOuRs3bvhZfEJYyaMt6jfLz/eCDYT1iG6PfX197vHjxw3oRLulQH0FBP31dYm1V9CvYNALwaBov8H68t++fdsdPnzYLVy4sGqKTmX6ew/8rTFG3/5p06b5NyafO3fOz9tvsMK6aLaq61EM6JYNWD3i/wn6YyGaCtVRQNBfR5S4uwT9cvjdcvhl+T8APzP2cL0XL150b7/9dk2WX9Dfm9BvL0pjCk/emEzf/nv37lWBvsBfPrMsvqzd1ynoj0tmKtdMAUF/M3VaHBP0K4C127GX/fsM+u/cueM++eQTn+UHIq0Lia3pSmLbWvdWI4ApPCdOnOi2bNnimGqVsRllt2tdv2JFVhsQ9LcAMh2OpYCgP5ZM9QsJ+uXIszpynV9tQ9a15+zZs+7NN990Y8aMcQMGDKgZvCvo7y3Q537YPWFNN58XX3zR7d+/3/FiNYCFeyt7r7Z36SE94tqAoL8+h2lvMgUE/cn0qiot6JfDjuuwVS6+rXzzzTduz549bsmSJe6pp55ydBuxriOW1TfAtM9a928jIHo/uF9Msbpu3To/GPvrr7/2/ZBVD+LXA2klrUIbEPRX4Zc+pFRA0J9SOE4D+hlBTwbLKqStw8qqbTlv2UC1DVBPQk3oAsI+m7Fn/fr1vi+/Ab/14wcubRHo9y/oN9Of+zVs2DC3YMEC/2I1ZvJRpr+6DoT2r21p08oG8I/mJzWQNwO4lfxUQX8GAwih3yqjoF/Ou5Xz1vFaG6H+sNy8edNDIvPyDx061Gf4DfiBTAP+aGa5GYDqWPcbB9wzntLQt3/Tpk3uzJkzfiYf2X6t7UsTaRLHBsxHwhiC/gzgVvJTBf0ZDEDQL2cdx1mrTK2dRLO+BDK6gJw/f96R5QcWgfVGwC/o7z7IJ2k8cd9Yhg8f7rtpMSibKVij9111o7ZuSBNpUs8GBP0ZYE2nVhQQ9FekSL4h6Jdzruecta+1XQD5UZ3I8jPNI1l+ZuwJ+/GHGX7bTgKhKtvdRoJBP2/pnTRpktu2bZufyafefY/agT63rj/SqHwaCfqTM5rOqFVA0F+rSew9gv7yOV4F2/bfc0CQhZc5AYcMAAUWLctvkB9dC+S7C/JJ9LZ79/TTT/ts/6uvvupftMaYDeBF9aj99UiaFltTQX9sNFPBJgoI+puI0+qQoL/YTlZBtDv3l2DGtI50AVm+fLkfAEp/cIPMKOzbZzuude/Bv0E/9wrwnz17tm/QXb9+3Q/WVt3qTt2SzsXRWdDfish0PI4Cgv44KjUoI+gvjkNVcOzuvbTsPrrTl58sv83YEwK/gL73gD7JPQH6Kc/7FpYtW+az/XTjUjef7tY3+bf86y3obwBi2p1IAUF/IrmqCwv68+9IFQz75x4CfQzqJJCR/f3www/99I5DhgypdOtJApcq23uNg/CJDN21pk+fXpnJ5/79+1VdfNQI6J96KP+XH90F/dX8pU/pFBD0p9PNnyXoz4/DVHDrvXv18OFDd+/ePff555+7V155xT3zzDN+Ss6wa4hgvvdgPsk9sUw/54wcOdI37Hjx2rVr13y232bzEfT3Xv2Uz+yteyLozwBrOrWigKC/IkXyDUF/bzlFBal83Q/mmr58+bJ7//33fRY4zPKHWeIkkKmyvddIMPCnbz/g//rrr7ujR4/6pzzYgIG/6m++6q/uV3fvl6A/OaPpjFoFBP21msTeI+jvrtNTkCmG3kCede05cuSIW7FihRs1apR/mRNZfoNEAN6y/oL53oP5JPfE7uOAAQPc3Llz3bvvvuu7dX3zzTe+m48y/cWo2/LRnbuPgv7YaKaCTRQQ9DcRp9WhEPoJWuEi59c55ydt862tQf+tW7c8/M2YMcMNHDiwarYeA0qDRfusdT7hn/to93L8+PFu1apV7sSJE+7u3bsa1Ptdvuuz/HF37h98Afiz1ht5W9GZjjdSQNDfSJkY+4H+R48e+axlCPxsyxF2xxFK5/zobHXEsvxffPGFe+211/yLuMLsvsA+n2Af574B/gzqJdv/wQcfuKtXrzqy/diE6nJ+6rLuVffvlflPtBf0xwA0FamrgKC/rizxdgr6u+/4FGyKoTnz8u/cudMP7LQXcVkmOA48qkx+GwY08CZMmOCz/adOnXLM5APEqG4Xo27rPnbmPgr643GZSjVXQNDfXJ+mRwX9nXFuChrF1NW69TAv/6VLl9zq1avdpEmTKn35Bf35BfkkjTDu89ChQ/0Lu/bu3ev79mtAbzHrvHx5++6roL8pjulgTAUE/TGFqldM0N8+h6bgUHwtDfp5OdOhQ4fcnDlzPPwlAUaVzX/DAOjnBWyjR4/28/afPn26avpO+YLi+wLd4+T3WNBfj8K0L6kCgv6kigXlBf3JHZecfXk1s6BFX/61a9e6cePGVbL8gvn8w3zcewj0szB4e/HixW737t0a0KvBvOre1cIGzH8SQ9WnPwAxbSZSQNCfSK7qwoL+8gKsGi/J7z1B686dOx7yyPIPHjzYw9+Pf/zjysw9ccFR5fLbSAD4ueesJ06c6DZs2ODOnTvn6PalepW8Xkmzcmgm6K/mL31Kp4CgP51u/ixBfzmcrYJqtvtMsEJDpps7e/asW79+fWXGHuBP0J9fgE/T+DLof+KJJ/xMPmT76dt/+/btyvSdZjOqe9nqnvQrjn6C/gywplMrCgj6K1Ik3xD0F8ehKjh29l4SsJixZ9euXW7RokWOt7MCjAAgSxp41Dn5bSwY+LOePHmy7+5Fg5CZfDR9Z2fronxdPvUV9CdnNJ1Rq4Cgv1aT2HsE/fl0ngp63b1vQNy9e/d8lt9m7DFgF/DnF9ztHqZdG/iPGDHCT9164MABP5OP6md366f0zofegv7YaKaCTRQQ9DcRp9UhQX8+nKWCWv/dJ5ux58svv3R79uzxL2ViusYoKOrlXOWEf8B/wIABft7+t956yzGTDy/rUp3tvzor7XtTe0F/KyLT8TgKCPrjqNSgjKC/N52jglbv3BfrqnH+/Hm3efNmPy8/XXvCDD/AL+gvH/SbDXDvhw0b5pYuXer279/vB3ub3QA6qs+9U591L/rvXgj6G4CYdidSQNCfSK4fCgP7/An6+88BKvj0lvZhQLJ7A7g9fPjQd+05fPiwW7JkiRs5cmQV4Av4ywf74VMewN+y/dOmTXNbtmxxNBAN9m1tNqV1b9V73Y/u3Y/Qx2rKzhTgplO8AoL+FIYg6O+eo1NQyY/WYVDivlnXHt6++8477/jpGQcNGlTp2mPAryx/ecEf4LfZm+jbv2LFCv/iNgb0CvjzU/flpzt/r0L/KuhPAW46RdCf1gYE/Z13cAoi+dLYumPYfSMosf3gwQPHAM1ly5b5efl5EyuZXgF/eUE/mum3Ofuxieeee843EK9fv+779kftyuxL63z5B92v7PdL0J+W2HReqIAy/aEaMbcF/dkdmIJAcTUE1IB+gJ+XLq1bt85NnTq1CvSV3Rf0G/xbFx/WY8aM8X37Dx48qJl8WryhVT60uD603r0V9McENBVrqoCgv6k89Q8K+svlbOs5YO2rbwPWpYc12dqPPvrIzZs3zzFjj3XjsEy/QZ/WagAY+A8cONDP279x40Z38uRJP2+/6lr9uiZdyqWLoL8+j2lvMgUE/cn08qUF/eVytgqu8e432X3r1sPbd5l+8eWXX3Zjx471WX4DO9YC/fKCvt3/el282MdMPvPnz/cNxqtXr2r2HmX8ZQPffefHuAD+xCP8bF9fn3v8+HEKgtEpZVZA0J/h7gP/VDyymtYKt7VAMR4oSqdi6GTBiPtJfbhy5Yr74IMP3JQpU3yWH8g32BPwlxf449x75u0fN26cf0vv8ePHfd9+sy97kiS/UQy/ofsY/z5SB0imsBb0ZwC3kp8q6M9gACH0W2WkQlqAkkOL79CkVb61ito9sMbbd8naMi+/oF+gHwf4aRiS7WeWp4ULF7oPP/zQffXVVx525CPy7SN0/7LdPxjDOEPQnwHcSn6qoD+DAQj6szkxBYHi6GcNXda8UXXXrl1uwYIFzmbriQN8KqOGgTUOgf8JEya4NWvWOLr4MCgcf2GNS7M3+ZDi+BDdy+b3UtCfAdZ0akUBQX9FiuQbgv7mTkpOvFz6kH0C+JmXf+3atW78+PHq0vOkQD5uYy6c0Qno5ynRokWL/Lz9N2/erAC/ZTvlX8rlX8p+vwX9yRlNZ9QqIOiv1ST2HkG/gk7ZA1F4/fS3vnPnjtuzZ4/P8tNFA3hjiQt+KqdGgtkM3cImTZrkNm3a5M6ePVuZt9+y/aHtaVu+uOg2IOiPjWYq2EQBQX8TcVodEvQr0BQ90CS5PoISWf7XX3/dPfPMMxXgN4gT0Avo49iA2QtTvDLVKzP5fPLJJ+7u3bvu4cOHlW4+SWxTZeWr824Dgv5WRKbjcRQQ9MdRqUEZQb8CSd4DSTt+P5lXy/IfOnTIzZo1yw0ZMkQZfnXtyfyEh5l8Ro4c6TZv3uyz/dhZO2xW3yHfnTcbEPQ3ADHtTqSAoD+RXNWFBf0KHHkLHJ38vWfOnPFwNmrUKD+AV916lNmPk9lvVoZ+/nTzeeGFF9zevXvdvXv3NJOP5u0vZcNP0F/NX/qUTgFBfzrd/FmCfkF/JyE6D99tmdf79+97KGPgJZAm4BfwN4P5JMfo5sP7HnhL7+XLlysz+eShfug3Kka0ywYE/RlgTadWFBD0V6RIviHol0Nvl0PPy/cY5NuUidbHmr78GzZs8H35bZrOcDaWJJCnsmowRG2Avv3PP/+8+/TTT93t27dLmenNi4/Q7+xMXBT0J2c0nVGrgKC/VpPYewT9nXFuChr50pWXJzHQcunSpZW370ahTZ8F8mltgKdGPD2aPn2627p1q7t48aL7+uuvBf7q5lMqGxD0x0YzFWyigKC/iTitDoXQb9PI2Vrgmi9w1f2Kd78sw296AV90uWCgJVBmWf60gKfz1DiI2oB1FRszZox76aWX3JEjR3y2P2qLZpNax6vL0ilfOmHvgD9rvZG3FZ3peCMFBP2NlGmyH9jnj/WjR4/8zCVUxHCRQ82XQ9X9Sna/sHU0Y17+Y8eOuYULF7oRI0aoL79m7Mk8Y08U+u0zM0LRt//dd991Fy5c8PP2m89V/U1Wf6VX/vQKbV3Q3wTQdKipAoL+pvLUPyjoz5/DVJBr/z3j7bvM2LNlyxb/EqWBAwcK+gX9bYd+GxtCFx+m71y5cqV/Sy8z+Vjjk/odbqu+t7++S9P+1VTQX5/HtDeZAoL+ZHr50oL+/nV+Cj79q78N5r1586bbvXu3W7JkiRs+fLgzOLPMrNbqqtMOGwjtinn7eQ8Effs1k0//+gH54e7qL+hPAWs6pUYBQX+NJK13CPq76+wUXHpLb6CfAPTFF1+4tWvXuvHjx/uBlgZ4IaTZPq3VAEhrA1F7ooHJoHEGj9PwtH7O1hiVv+gtf6H70Z77IehvzWYq0VoBQX9rjWpKCPrb48QUDPKrIzP20Ld63rx5bvDgwb5Lhw24jEJaWtjTeWooRG3AZvKZOnWqW7dunTt16pTDFunjLOjPrz9RLGh97wT9NSimHSkUEPSnEE3Q39pByYkXUyPAir7U9OVfsWKFGzdunO/WA4wJ+gXpUUhv92ezMQaN0+DcuXOnu3r1qod+gX8xfY5iyQ/3VdCfAtZ0So0Cgv4aSeLvAP41e48CTdGDkgUb1kD/tWvX3EcffeRmzJjhGLzLG1PbDXf6PjUgmtkAg3pHjx7tVq9e7T7//HPf3UzQL19cZF9sfphr1Ow98TlNJasVEPRX65Hok6BfQabIQcauzYINa+blP3nypIetsWPH+nn5LfvaDNJ0TBDfbhsYNGiQmzt3rm+A2lt61cVHPtn8VtHW5oe5LkF/iOGAbAAAIABJREFUIlRT4UABQX8gRtJNQb8CTNECS6vrYeDkrl273Jw5c/T2XU3P2W9PeGhoMnaEQeTr16/33c0ePHhQqje0tqqrOl6s+CToT0poKl9PAUF/PVVi7hP0F8upKkjWv59kTy2DSl/+jRs3+q4VvH1XWX5l8NudwY/zfdgdCy/seuGFF9y+ffv8i+KYyUf1uH49li751kXQHxPMVKypAoL+pvI0Pyjoz7cTVRCMd/9C6N+7d6+fl5/50oEzQb+gPw6kt7uMQT/fO23aNLdp0yZ35coV3/1M9TpevZZO+dJJ0N+cx3Q0ngKC/ng61S0l6M+X01SQS3e/6D/K23e//PJL98Ybb7iJEyf2W7eOdsOjvi+/jRbAn0HkvKV38eLF7uDBg37efnsqpfqerr5Lt97UTdBfF8O0M6ECgv6EgoXFBf296RwVtNpzXwyegH4GSh46dMgPnGRefmX48wvLRWnoGPQzkw8NUbL9dD8DjswHhNu2T+v2+Afp2F0dBf0hfWk7rQKC/rTKOecE/d11egoy/aM3webcuXNuzZo1fl5+oFHQL+jv78aDvQQOW6QhOn/+fEf3szt37lSgXz6jf3yGdG+/7oL+DLCmUysKCPorUiTfEPS337EpWPSOpmT6yfLfuHHDz9jz7LPP+oGTQJagX9Df39Bv/58uPjQAmEKWmXxOnz7tGNAbjkWRX+kdv6J7ke5eCPqTM5rOqFVA0F+rSew9gv50zktOPx+6GTR98cUXbt26dX6KTpuxR9Av6Dfo7u810M9CNx/69jOlLG+Nxs/QaJW/yYe/0X1qfp8E/bHRTAWbKCDobyJOq0OC/uZOSk48v/pYgCFjynSIS5Ys8WBlWX5Bv6C/v2Gf/x928XniiSfcpEmTfAP16tWrfiYfG5ciX5RfX6R798O9M5+MHjRm+/r63OPHj1thio5LgSoFBP1VciT7APRT8QgsViFtLUelIJNXGzBQ4u27zNjDvPyTJ0+ugv5eAD79BjU8Qui3mXyef/55d+TIET+TD/44r/VQv1sxJLQBbJkkDGtBfzJWU+lfKyDo/7UWibdC6LfKKOiXow4ddR63rRF769YtD0+8/Gj48OG+H78y/ALtXm1sYZsDBw5006dPd++88447f/685u3/Tv44jz643m+GMYwzBP2JcU0n/EoBQX8GUxD0K6DUc8553Qfs2/LgwQM/Y8+bb77pX35Ef+lehT39LjVEsAFrkI4aNcotW7bMHT582N29e7fyNum81kv9bsUZbEDQnwHWdGpFAUF/RYrkG4J+OeMiBWSA/+HDh365du2a2717t5s3b14lyy+4Flz3qg1YNx9+H9l+5u1/++23fcPVsqNFqqu6lvLFHkF/ckbTGbUKCPprNYm9R9BfPsdb5GBrWX768n/22Wd+Xv4xY8b4WVEsi9qr0KffpQaJ2QANgEGDBrmlS5f6huvNmzfVzUfdfHI/tkPQHxvNVLCJAoL+JuK0OiToF/QXqREA9H/zzTeOmU/oE/3cc885pug0mNJaYJ0HG6CByqBeZvJZvXq1O3HihF7YJegX9LcCGh0vhQKC/gy3WdAv6C8K9DMAHej/6quv3IEDB9xLL73k6BsddpvIA/DpN5a7YQLwG/QPGTLEzZo1y3fzuXDhgu8TbfP2Y+9Fqbu6jnLEIWX6M8CaTq0oIOivSJF8Q9BfDmdbhqBqWf6LFy/6t5pOmzbNDRgwQFn+J8sN0XlsRBn402DlLb0vvviiH9R7+/btqumVy1CvdY3FiVGC/uSMpjNqFRD012oSe4+gvzgOtczBEeBnAC9TdB48eNAtWLDAjRgxQsAv4M+tDVi2f/DgwW7KlCm+u9q5c+cqUx6Wub7r2vMZtwT9sdFMBZsoIOhvIk6rQ4L+fDpPBb1f3zcDfqD/7NmzbtOmTW7cuHHK8gv4cwn8ZPetSxrgzzbgT3e1/fv3u/v37/uXG8kH/NoHSIt8aCHob0VkOh5HAUF/HJUalBH058NZKqg1vk9APwsz9uzbt88tXLjQDR06tAJOeezeod+sLkkG/tgC23RX27x5s7t06ZIfrI7Nyy809gvSpve0EfQ3ADHtTqSAoD+RXNWFQ+i3N/HaWk6z95ym7kntPQF+sFnm5QeKlOUXMBeh0RRCPxl/3ijNFJ6HDh2qvLBL4F/rD+Qje1cT/DTgz1pv5K1mMX2Kr4CgP75WNSWB/kePHlUNDhP0967TLHtAwzajGhA8CCS8vfSFF17w3XpCYCoCAOoa1JDBpunbT8OWBi71QNAvXx31h738OWQLQX8NjmlHTAUE/TGFqldM0K+g0ctBotVvI3AQSK5fv+5hCChifnMyowJlgXKRbAC7HjZsmJs/f37VTD6t6oiOy8f3ig0I+utRmPYlVUDQn1SxoLygXwGhVwJCmt/B4N07d+64I0eO+Bl7gKIf/ehHAn8N4i1co++JJ57wL5pjCs+33nrLnT592vftr/f0K01d0jmKBZ22AUF/AF/aTK2AoD+1dM4J+uXoO+3o2/n9UcChewPTGG7cuNG/iItsqDL9yvAXKcNv18LTK5aBAwe6xYsXu927d/sGL13b2lnH9F2KCZ2yAUF/BljTqRUFBP0VKZJvCPrl4Dvl4Dv9vQQQ5uXftWuXmzNnjp/W0MBI3XsE/gbLRVmbbdO3f8KECW7dunXuzJkz7sGDB4L+7+THO+1v2/H9gv7kjKYzahUQ9NdqEnuPoF/Boh3OvNvfQfD45ptv3MmTJ90bb7zhs/xPPfVUpS+/oF/QXxTYt+sw6OfzkCFDKtn+mzdv+oHs3a6D+n+KHUltQNAfG81UsIkCgv4m4rQ6JOiX407quPuzvM1WAvDTl//DDz908+bN8/OYG+hr5h4Bv4FykdYh9HNdkyZNcmvXrvXd2+7du+cHtBtUse7Peqr/rbhSzwbMPjmm2Xta0ZmON1JA0N9ImRj7Bf1yzvWccy/uA/gZuMv67t27fiDjypUr3fjx46v68Qv6Bf1Fgv1618K4FebtX7BggTtw4ID78ssvPfT3Yr3Vb1KMMRsQ9MeAMhVpqYCgv6VEjQsI+uWQzSH3+hrYtzn5L1y44N599103a9Ys39VBg3cF+vXguIj7yPhj7wMGDKj07f/ss898Q1gZfvnzXvbjgv7GLKYj8RUQ9MfXqqakoF9BopeDRPS3Af506zl48KB/EdeoUaOquvYUEfJ0TWrQhDZg0M968ODBbvbs2e6dd95xNITp9hatM/osH98rNiDor0Ew7UihgKA/hWh2iqBfAaFXAkKr32Hde4Ab5ikfM2aMz3aGQKRtAXJRbSDs00+mn4XB6zR8ly9f7rv5fPXVV+rmo5l8erbhJ+g38tI6iwKC/gzqCfoF/a1guxeOA/wEDKDm448/dkuWLKkAPzBUVNDTdakRU88GwgYA3XxmzJjhNmzY4Af1UkeoswZYtu6FeqzfUO54E9qiBvJmALeSnyroz2AAgv5yO+FeDsJhgAD6maGEeclfe+0198wzz/jpOQX8guJ6UFymfdSBESNG+EG9vLPi8uXLlTf1Wh1i3ct1Xb+tHHHI7JH7LejPAG4lP1XQn8EABP3lcLZ5C6phcLDfzgwlH3zwgXvuuefcoEGDqmbsKRPk6VrV0AltAOh/+umn3bhx49yrr77qPv30Uz+ol4YydUfALx9vPrS/16FfF/RnALeSnyroz2AAgn4FhP4OBI3+fwgrdFlghpKlS5f6vvxMy6kZewS/IfyWfZsXds2cOdPt2LHDXbp0SZl99e3vORsQ9GeANZ1aUUDQX5Ei+YagX9DfCLp7ZT8ZSyBm+/btfk5++jDbQMayg56uXw0fbIBsvw3qXbVqlc/2f/311x76LOPfK/VZv6O8MUfQn5zRdEatAoL+Wk1i7xH0l9cB93LwjYLK4cOH3csvv+zBBsCxLD8Zf72MS+Bb9sYPdcLAn3dXbN261d26dcv37be6FAJXL9d9/bbixqTQBtW9JzamqWBEAUF/RJAkHwX9xXWweQ6egApBgXnHr1y54jZu3OgmT57sZ+kxwAH0BP0C/rIDv2X6qRdsM4Un3eCOHj3qwR/QyrMv0G8vTowS9CehM5VtpICgv5EyMfYL+ovjUIsUHIF+Fl7EtW/fPrdo0SL/IqIQ+A36BX0Cf9nAD118qB90f5syZYrbvHmzO336tLt//76vS0XyD7qWfMYtQX8MKFORlgoI+ltK1LiAoD+fzrPoQQ/g//bbb/1bRlevXl13ik516xHsC/arbcAaxUOHDvVv6mUKz+vXr/unZtSpovsNXV9vxzNBf2MW05H4Cgj642tVU1LQ39tOsqxBDEBhik6gZdq0aZUsv0GegL8a9kwXraUL4M8UnsOHD3dr1qxxx48f9w1o6pTAX/6+P2OKoL8GwbQjhQKC/hSi2SmCfgWB/gwC9f43geHBgwe+TzIv4iJrCcwCM4JaQa1sIJ4N0DCeO3eun8Lzxo0bfnxMvfqmfYoB3bIBQb+Rl9ZZFBD0Z1BP0C+H3y2HH/f/MNXg1atX/Qwk06dP94N1w9l6Quiz7gzhPm3Hg0LpVAyd6j31ol5QZ0aPHu2WL1/us/3M5kOXubj1UOUUG9ptAwb9rDV7TwZwK/mpgv4MBgD09/X1+ce+BISwUra7wuv7FESiNmBdDmzN8bt377qDBw/6GUh44ZABDGsAxxZBazGgVfex/ffR6gyDenlh17Zt29zZs2fdvXv3vK/Hz1tdDLdtn9by1Z2wAWzNOEPQnwHcSn6qoD+DAQj65dw74dzTfidv3j1z5ozbsGGDmzp1qgd8AIYFOBTwtx8QBd3F1NTqzJgxY3wDeu/evf4JmiV20tZRnaeYkdYGAH5BfwZg06leAUF/BkMQ9MuBp3Xg7T6PYMCbdz/66CPfF5mBiNatx8C0XlcGO6Z1MeFV9zXdfbXG8qBBg9zEiRN9Q/rYsWM+209da3f91fcplrSyAUF/BljTqRUFBP0VKZJvCPrlqFs56k4et64FdO+hW8+BAwfcSy+95EaMGFHJ8lt2X8CfDv4EzeXUzaCfegP4z58/323fvt1Pg2tz99PFopP1W9+t+BLagKA/OaPpjFoFBP21msTeI+iXUw6dcn9t8+bdkydPunXr1rkJEyb4KQcNWoBWA38BbDkBVvc9232n/owdO9a9+OKL7uOPP/bdfKhzgn75/276fEF/bDRTwSYKCPqbiNPqkKBfTr+bTr/e/wI+mFKQLOScOXP8G0VD4Bf0ZwM+AXM59QsbytQn5u6fNGmSW7lypfvss8/c7du3Bf3fyf/X88md2ifob0VkOh5HAUF/HJUalBH0y+l3ysHH+V7r1nPixAm3ePFi/0IhA37WIfADMQLYcgKs7nvy+x6FfuoT3XwA/61bt7pz586pa4+gv6s2IOhvAGLanUgBQX8iuaoLC/oF/XHgvFNlmJP/9OnTbvPmzX6wIVMMAngG/CH0C/ySg580K69m9aCffcOGDXPPP/+8f9v1zZs3NXe/wL9r4C/or+YvfUqngKA/nW7+LEG/oL9TQB/ne7/88ku3c+dO362HOfkNUqPQb/u1Li/E6t4nu/f1oJ96RTcfxs2sWbPGv7SLaXJtQH2cOqsyihlpbUDQnwHWdGpFAUF/RYrkG4J+OfC0DjzreQQAphCkj/HgwYMbZviBFwFfMuCTXtKrkQ1QnwYOHOhmz57t3nnnHT+o98GDB13L9mb1Gzo/vzFL0J+c0XRGrQKC/lpNYu8R9OfXgeY5+NGt59atW/5NobNmzaoAfzTDL+AXvDaCV+1PZxtWx5jNh+lxjxw54ujmk2d/ot+ejzgm6I+NZirYRAFBfxNxWh0Kod/e1GhrOdJ8ONK83SfsC8g4evSon0Jw1KhRNS/hEtClAzrpJt1a2QDQz0vvGNQ7bdo09/bbb/u3YNMQZ2A9S958in5vPmIVvh/wZ810sX19fe7x48etMEXHpUCVAoL+KjmSfQD6Hz165B29wb6t5Ujz4UjzcJ/MpljTlYDBuxs3bvSDd8nmAyGtYEXHBbSygew2APQ/8cQTfrA8g3qXLFni9uzZ4xvigBiLwF++vxNxxeIA3y3oT8ZqKv1rBQT9v9Yi8ZagX869E849+p32WJf9165d84N3n3vuOTd06FBl+Z/MDnKCYWkY1wYs009D+6mnnvIv7WJQL+NreFMvYCboV1yI+vB2fBb0J0Y0nVBHAUF/HVHi7hL0y7m3w5m3+g6cPS/hunPnjjtw4IBbsWKFGzFiROXNu3GBReUEt7KBbDYA9NuClkyTy6DeLVu2+Ln7mc2H+qyuPooNrfx60uOC/rhkpnLNFBD0N1OnxTFBvxx7UsedpjzOHpigWw9ZxYkTJ6o7jzL8soEesAEy/iNHjnSLFi3yc/dfvnzZN9Ctm4+y/ooRaXx+vXME/S2ATIdjKSDojyVT/UKCfjn0es653fvox3/x4kW3Y8cON3PmTJ9dbNSPXzP2ZMvkKhMu/VrZQFjHyPozd/+4cePcK6+84g4dOuRn1sIHWLZf4K840Y6YIOivz2Ham0wBQX8yvapKC/rlzNvhzJt9B44e4OclXLwJdPTo0T7DC/RbNwPWrUBFxwWzsoHO2QAvx2M2n02bNlVe2sVYnGjdpj5H9+mz4kgcGxD0V+GXPqRUQNCfUjhOE/TLWcdx1mnKkB3EydOtZ//+/b4fP9lEXgwE5Av6OwdwgmNpm9QGGNTLwPqFCxe67du3uwsXLrh79+75OhyCfridxi/onPLGHEF/BljTqRUFBP0VKZJvCPrL64A7HXyBfub+Pnv2rFu7dq3vx083AmAkCv1JAUXlBbWygfbZgD1po9vP+PHj3bJly3xD/fr1635e9U77Cn1/OeKQoD85o+mMWgUE/bWaxN4j6C+Hs+12UGUQIA7+yy+/dO+9956bN2+eo/sAUAFghNAveGsfvElLaZnGBqxOcu7gwYPd1KlT3erVq91nn33mbt++re483ylOtCOGCPpjo5kKNlFA0N9EnFaHBP1y5u1w5uF3kOEH+m/duuUOHz7s37o7duzYSp99AwzWaQBF5whsZQPttQGrk+hKw5yXdk2fPt1t3brVz7jFEzuALazn2lbsSGoDgv5WRKbjcRQQ9MdRqUEZQb8cd1LH3ay8AT9lPv/8c58tZOAu/YWtD7+Arb3AJj2lZ7tsIJzVh654ixcv9gPwb9686afxbFb3dUyxpJUNCPobgJh2J1JA0J9Irh8KA/v8CfrlqFs56mbHo9k/oJ8ZPy5duuTeeustN3ny5MoLuJTZF5y2C071PZ23JRoAPKFbvnx5ZRpP6na0zjfzDzqm+BLagKA/BazplBoFBP01krTeIeiXMw6dcTu2DfjJCu7atcu/7IeZesjwswjUOg9q0lgat9MGyPZPmTLFvfHGG+7EiRO+fz/gRl1naYff0HeUJxYJ+luzmUq0VkDQ31qjmhKC/vI42k4GVQv85szv3LnjTp486afnJEso2BeEthNC9V3dtSeezjGwl/n7mcaTmbi++eabKuin7uNjzBd00t/ou/MdtyxOcB8Z99XX1+ceP35cwyfaIQWaKSDob6ZOg2OC/nw7z14Lfjjzu3fvumPHjrl169a5CRMm+LfuqktPdyFNUCy922kD1F8b2LtkyRL3wQcfuCtXrvipeEOA6zV/pN/Tm/EttBlBfwM40+6WCgj6W0pUW0DQ35tOMU/BCgfO76WfLy/g+uKLL9zbb7/tZs6c6bOD7YQPfZdgVjbQfRsA+lkYiP/MM8+4V1991R08eNDduHHDPXjwQHP4ayrPRF28BP21LKY9yRUQ9CfXzA/g5TTg/9GjR/7RrFVIW+cJQPVb+68Rw1s7eXvnu+++6/vx81ZPsoPK8ncf0gTG0rydNmDQz5rxOc8++6zbuHGj79/P2B2m8jTfa0kA+6x1//nkXtXe2IK1Mv0pwE2neAUE/RkMAeinXx39MW1mBquYveo49Lu6G0ysr24Y1NmH0+ZeXLt2ze3evds9//zzbsyYMRXgF/QLQNsJoPqu/rEnA38a8qNGjXJz5sxxW7ZsccePH3eM4cEP4A9C/yAf3V0fnRe9sRHjDEF/BnAr+amC/gwGIOiXc04TMAz6eQHXoUOH/MBdHv8PGDCg8sZdQX//QJrgWLp3ygbI9vPejfnz5/uufKdOnfLdfAT8iiNx4gjAL+jPAGw61Ssg6M9gCIJ+OetWzrpeQGff/fv3fbZv/fr1fuAuQADo20u4BP2Cz07Bp763+7Zl9ZlpPEeMGOFf3EWXvsuXLzu6+NXzE618i46XK/4I+jPAmk6tKCDor0iRfEPQXy6nmzXIWoYf4Gf6vg0bNrjp06f7gX6AmIEBa9sWoHUf0KS5NO+UDVi9JuPPjD579uzxM/owlWdW/6Lzix2PBP3JGU1n1Cog6K/VJPYeQX+xnWy7gyj9MJm148yZM35A36xZsxwDdw0EQtCoty88rm2BqWwgfzZAvWahK9/48eN9174DBw74GX3a7W/0fcWKT4L+2Gimgk0UEPQ3EafVIUF/sZxqJ4Ik2X0Wvhunff78ebdjxw6f4Qf4ATcDfAb7sYQwF/0cHtN2/qBP90z3zMCfLn0TJ070b+w9cuSIf1eHMv6KKY3ikKC/FZHpeBwFBP1xVGpQRtAvB93IQdt+g36COdP00Y937ty5vkuPBX9AMNwWGAoMZQPFtoGwvk+ePNmtXr3aMbCXGX3Uv19xxeJHuBb0NwAx7U6kgKA/kVzVhQX9cs6hU663DfQD/F9++aWfmvOFF15wI0eO9JBvg3bJ5ocQIOArNvDp/ur+mg1Q74cMGeLn8N+8ebP7/PPPPfjX8yXaV+54I+iv5i99SqeAoD+dbv4sQX+5nXCcIEzWjgw/j+9XrFjh38wJ5AP8Bv0GAFoLBmUD5bIBoJ97Pnz4cD+H//bt293p06f97F5AXhwfozLliEOC/gywplMrCgj6K1Ik3xD0l8PZpg2qAD8Dd3ls/9Zbb3ngZwBfFPYt8Av4ygV8ut/lvN/RcTrU/6eeespn/HkS+OGHH/oZfZjlK63v0XnFi02C/uSMpjNqFRD012oSe4+gv3iOtZ3BEuAna8cbOGfPnu0GDRrks3qW5Y/CvyCwnBCo+17O+x429mkIkBDgJX3Lli1zu3btchcvXvQZ/3b6JH1XfmOWoD82mqlgEwUE/U3EaXUohH6yuuEi55pf55r13uGcedvuyZMnfYZ/3rx5/vF92HffgD8M/IK/csKf7rvuOzaAfyAxMGnSJPfyyy+7nTt3+ul9v/rqKz/zV1a/pPPzHZPgC2ILa6Z/7uvrc48fP26FKTouBaoUEPRXyRHvA7DPH+tHjx75KRlD4GdbDjbfDjbL/bt79647duyYn4t/xowZlbn47bE+oC/YF+gJ9mUDoQ2YXyDjP2bMGEdXH2b7OnfunAP8La7YOouP0rn5i0/GGNw7QX88VlOpWgUE/bWatNwj6M+fw+x0kMMJM1MPXXo+++wzP/c2U/ENHjzYZ/AE+QK8EPC0LXuI2oAlBdhPH39m+Zo/f757++23fTdBkgmd9mP6/t6NbYL+lmimAjEUEPTHEClaRNDfu46xP4PW7du3/ZR7b7zxhn/5Fi/fIYAL+AV4UcDTZ9lE1AYM+i3jD/iPHj3a0T1w27Zt7sSJE/4FXnTx6E8/p//dP/FP0B8lMX1Oo4CgP4Vqgv7+cXq9HGzIwh0/ftxt2LDBkeEH+K3ffjS467OATzYgG4jagEG/7Qf+AX+m8yTjD/gzMQC+RuBfvhgk6E8BazqlRgFBf40krXcI+svncJs1OHDGvFRn/fr1fhAeg/EsgFvWzrL99tkCu9aCP9mAbMBsAL8R9R18NvDfunWrO3v2rO/j38wn6VjxYpSgvzWbqURrBQT9rTVqWAL410De4jnXMGCGjjbczzZv2uXFW8zDT4Z/5syZfvYNC9oWyMO1oF+AF9qDtmUPzWzA/AUZ/7Fjx7pFixb5wb1k/O/du6duPt8VO/6EMSeMRRrI2xDLdKCFAoL+FgI1OyzoL77DDR0tA3Vxwuz7+uuv3fXr1/0sPbx4C+AfMmSI779vgbpZMNcxwZ5sQDYQxwbMnzCrD+BvL/C6cOGCnzgAfxTCobaLGZfCWCTob0ZmOtZMAUF/M3VaHBP0F9O5WtAE8i2gsm3QD/DfuHHDHTlyxG3evNk999xz/vE7AZx+/BbICda2rbUATzYgG0hjA6EfAfwZ3PvSSy+5jz76qOrNvearzH9pXaz4JOhvAWQ6HEsBQX8smeoXEvQXy6k2CpLAvk3JeefOHT9v9v79+917773n1q5d6xYsWFCB/jCoh8E63K9twZ9sQDaQxgboOmhv7n3++efdO++848cT0c2Q7ob4sDBB0cinaX/+Ypegvz6HaW8yBQT9yfSqKi3oz5/jTBrsLIDicHlBzpkzZ9y+ffvcjh073Pvvv+/n0F62bJkbNWpUTVZf0C+wSwN2Okd2E7UBYN/GCuFXnn76ae9z5syZ4zZt2uQOHTrks/72Ei97KpnU36l878Y0QX8VfulDSgUE/SmF4zRBf+86yHYFLzL8fBfBlMFzO3fu9FPn7dq1y8+b/emnn/ps/7hx4yr9+aMBW58FcbIB2UBaGwDyDfpZ89mmA+bzxIkT3fLly93HH3/s6OfPAF9Bf/Fik6A/A6zp1IoCgv6KFMk3BP3FcKw407CREH7mDbuXL1/2/ffJ7AP7vHH36tWrvl8/U3XyMi4G2CmzL7BLC3Y6T7aT1AbwNyxMEUzSYe7cue7NN9/0/oluiPgx4D/0Z6Gf03a+4hf30e6lBvIm5zWd8YMCgv4MliDoz5fTbBTkQmdqGTL6x5LdP3/+vH90/uGHHzqWY8eOuS+//NK/HOf+/ft+uk5Bv4AtKbCpvGymHTZgGX+m9Bw6dKibPXu2nz6YxASTDTDpgIFiI/+n/fmIY2GcEvRnALeSnyroz2B01K9UAAAgAElEQVQAgv58OMskQQ3oB/jJlF28eNHt3r3b99//4IMP3MmTJ/28/LwNk3I8BWCO/jVr1rgxY8Yo0/+kQK4dIKfvkB3FtQHL9rPmHKYNnjFjhu9yyOxi165d837K3uCL3wqXJL5RZfs33gn6M8CaTq0oIOivSJF8Q9Dfv06wXUGIIEjmhMB4+/Ztn90/evSoB35g/8CBA/4tmHfv3vVlLGiS6T9x4oR7/fXXBf0C/pqB3HHBTeUE+WltIIR+tunjT8Z/8uTJ7pVXXvGTDViygqx/u3ymvqf7sU/Qn5zRdEatAoL+Wk1i7xH0d9/xdSrYkN3nZVtA/J49e3xXHubBZlYMuvgA/DhdA34aCYJ+wVpaWNN5sp2sNhAFfvvM9w4cONBNmDDBLVmypDK7D08u6bIYZv075U/1ve2PjYL+2Gimgk0UEPQ3EafVIUF/+x1bt4MFAZBuOjwGP378uH/hDXNfM2jXpsED7oH9qNPlPGX6BW9Z4U3ny4aS2kAI+OG57KefPwtZ/+HDh/vuPq+99pqfeYwZyHiaid/Dp3Xb3+r/pY+ZFn9Yq09/KzrT8UYKCPobKRNjP9Df19fnnSdONKyUcm7pnVs7tYsGNrtHBvFk8Jl7f+/evf5lW8A+03Ay9R39+nkCQFmcbPi72Af08+hcffoFbSF4aVv20As2QAMA8Gd2H2YXmz9/fmWQL081zbfhy8Il9HPa7o04xn0gdhlnCPpjAJqK1FVA0F9Xlng7Bf294xAbBacwmLFNORwnj7kvXbrkp7dj7n1m5gH8ydwzOw/ZfZws5e07wv/BPkG/4K4X4E6/QXbYygZ4mdfIkSN91n/lypWOsUr4ulu3bvkZfvBnDx8+rLx5PPR12u6NOEfcEvTHYzOVaqyAoL+xNi2PCPp7wxk2C0oG7KxxmLy4hqnszp496z755BMf/LZv314ZrEtjANi38wz8o/+D44J+wVYr2NJx2Ugv2ABZf37HgAED/KQDlvU/fPiwn6WMJ574OPN7rM3n2T77rHX/xD1Bf0skU4EYCgj6Y4jUqIigv3+cX5KgY91yAHRg/4svvnD79+/303Du2LHDz9DDvps3b1bmtK4H+mEQ5P/zWdAvoOsFoNNvkB22sgEbA2BrBvqOHz/eLV682G3dutU/8cQ/4tPCpIf52no+0Y5p3Z04KOhvRGLan0QBQX8StSJlBf3dcXZpgwqBisz+lStX/CBdpt7kVfXMyrNv3z7H23Tp4kOWi/6t0f8D2NNoMOC3NeUE/QKtVqCl47KRXrEBg30b5Mtnsv6jRo1ys2bNcitWrPDwz9PPc+fOVcYzRX2iPvdfzBP0RwBMH1MpIOhPJdsPJwn6+88BNgs+ADzddMhcMUiXWXjos//ee+954LfA1mgWC4DeFkG/wK1XwE2/Q7aY1gYM+qNrvo+s/+jRo31//+XLl7tt27b5yQxIiNhkBs38rY51Jw4K+jPAmk6tKCDor0iRfEPQ3x1nFwYVsvf1HjVbFp5j9oItYJ8uPG+//bZfA/v05WfwGg6U7zW4j7u230J5de8RhKWFMJ0n2+kFG6ARQPafNb9n2LBhbsqUKe7FF1/0PpOnofhLEimNfK/5RK07Gw8F/ckZTWfUKiDor9Uk9h5Bf2edXBhEoqAfQjrHmG2HzD4v0mJwGl14mG+fWSqAffrtMxd/2JWHLL5l8sPva7Ztv0nQL2jrBWjTb5AdZrWBMPvPFJ9Dhgxx48aNc3PnznWrV6/28/ufOnXK+8/oy73MV5pftDX7bVvr9sRJQX9sNFPBJgoI+puI0+qQoL89zixOUIhCPw6QTDuPn69eveq78Rw9etRPu0lXHubbZyrOzz77rNJvP5rdF/QLmLICk86XDRXFBgz+uZ6nnnrKDR061E2ePNm/1XfDhg3enx47dszP9sPEB82mNRb0tz82CvpbEZmOx1FA0B9HpQZlBP3td2z1GgD2WNnWPGomY88AXeaa3r17t++vzywUW7Zs8YN1gf3Lly9XAlO9jJSgX8BWFGDTdciWs9oAWX4Wvifs9mPTfM6ZM8cxxz9dJo8cOeKTKWT+8cf4V+b5D2E/3K7n17UvWfwU9DcAMe1OpICgP5Fc1YUF/cmcVlonD+x//fXXPqsPyAP6TLtJJp/BuXTjYVYe3qTLa+bJ/NOvn0wUjjL6lCD8HdYYiLu2cymvPv0CraygpfNlQ71iA2Gm38CffWzzci+6/fBm3xkzZvjsP11/eKJqT1PxufhpfGTUn5rf1Dp9zBT0V/OXPqVTQNCfTjd/Vgj9loW2tZxbOueGfmSOmGqTAWT0w2cWCWbhofsOsA/g04Vn165d/jNZJwbo8iZdzrNBZ3HvQTRANfts3ynoF6z1Cqzpd8gW22EDUejnO22frdk3aNAgN2LECDdhwgQ3e/ZsP93nm2++WelOyZSfJF7w39HEC37TxlGZL9U6XqwkNloSCw37+vrc48ePMxCMTi2jAoL+DHcd6H/06JHPahjs27psjqwZKNuxqCamFWtAncw53XboLwroM3PEwYMHffcdMkp032E6ORucS0OAspyPE+zma+QF/QKtdoCWvkN2lBcbAPxtnv9wzZSfPAFgvv9XXnnFvfXWW74BQDKGBsD169f9U1r8O74a3xmNBfaZ47atdXVjAG1MH0F/BnAr+amC/gwGIOj/4TGuZW5w5mzbZ+vjadBvx3DmZCzIyvNIGHCn2w4z7AD5e/fu9bPvbN++3bGQ1SfDT6Yf0Cf7zwBeyyLZ/+tmkBD0C9byAmv6nbLVTtoA4wDo/jN48GA3cuRI/wTg2WefdYsWLXKvvvqq4ykAT2Xplomf5wkAYwGsK1Dovw1qLWaEPj2E3nB/WbbD60czZfozwFuJTxX0Z7j5gv7avpvmwEOnDeCT5cHRA/lkfsjkMw0c2SDm0wfqCQwAPn31AX8aABw/efKknzGC82zgWKPgYPs7HQgE/QKpToKUvlv2lUcbCBsAw4cPd+PHj3fTp0/3DYBVq1b5BgBPbZlGGb9OHCCJQ+IH3x4+DcDHdtqP5+n7Bf0ZYE2nVhQQ9FekSL4h6P/h8aM5I9YAPhkcnDeZeBw5mR0yPGTpyfaQsT9w4EAlm89AXGaEoNsOM/FYH30CAd19LBDgoK0xYf+TfWx323kL+gVleYQy/WbZbTdswLoCsaYhQBcgGwdAN6CXX37ZMQ3ou+++68dokfjhSS8zslkDgNhBDCGeWF92iwHd9ve98P/CmKdMf3Je0xk/KCDoz2AJZYd+g3z64+Oc6XJDNh7HzUuycOJAPg6d7D0O3rrssA3kW1af+Z85hxdsWdcdc3IG9ba2DJAdt8/ddMyCfsFTN+BJ/0N21us2EA7yDbejv5u5/+kGRAOApwC8AGzSpElu5syZ7vnnn3dvvPGGjxF79uzxMYOYwAQNDAomJpD8Af7x8+b7LSYAwfWeMnczJnT6f9k1838E/RnAreSnCvozGEA3oN8qenRtDsacnn2OroHTqEM052hlcaSAOxkVWyxTD8zT956MOxl7ZsgB6nksC6TjlMng86iWqdsAfLL4dM+huw4OnMW2OWZddjj34sWL/vv4bv5PNKtjv7GX1qYpGnHdTF03ZsyYykwX0WBXhs8E+zJcZzuvkQxouLTzu/P6XaEebHMdZbetetdv2uTxPnM9/H5rADAV6KhRo9zEiRMdYwHmz5/vGwHLly/3DQHevUKXIOKIdfk8fvy4n56ZOERMIn7QMODpAAuxBP9MPCG2hU8K4sQSfHy4hHE23I5+V9o4Hf2eep/tuzkm6M8AbiU/VdCfwQA6Cf04HCq3VXScli3mxKKgbsAeriljS7jfut7gKMmu4zjpW0lWBainOw5AfuHCBQ/31jUHsGc+fJwvAL9v3z6/pk8+oE/3nHCh7ybdecj4A/l8L/8Pp8zvMgdqDZN6zq7X9nFvGKSMniH0M6NFvQCdx8Cc9Dc3u26ONTue9H8VsXyeIa5d9wMNogvfXWb7sWsP1+3Suz++h+sIZ/4JtznGb7J3AtAdiDEBU6dOdc8995wfF/Diiy86GgMkWjZv3uy7hTKFM3GImERsIt7QKOBJM7MH0TAgrhF3rHFA3CORRQPB1sQk4qI1FvDv0Zhrn8OYFDYOwv1sh3E8eizpZ2MBzhP0ZwC3kp8q6M9gAJ2AfoNgnIVtU8kN8HFIOCcWHBf9H1lwaHStAd5Z23YI8Tg/A3kAnIG0PEK17DyOE2AnK0+3G7IrLLwAi4V+9yxs24BbyuNsgXocLFDP/+T/42BxojjKeo7RQD88lpdtoJ97QmBZu3atz/QL+mu7YoSwou0fGj8GOgZABjtl18caPlE9rF6ZPgar9rkM66gG4TWH9hTuz9t29Br5/MQTT1QWrgcbsUYBMwVZF6Fp06b5bkK8N2DBggXuhRde8DMH4ZvtSQFxzZJQxD0aBkwLjQ8nqcWTa+KjxTBiqTUUaBhYo4DGAL6fuGYATjxg+dnPfla1sI84lxTw65UX9GeANZ1aUUDQX5Ei+UanoB9YBqDJWJC5OHz4sAdrHBbZdbLqADqPO8lyAOhAOH3kDdKt/zyDZJnbnjnu3377bb/gBJlLmanUyJaEC/s5TlnOswG2/B8An2y+ZfeBfRoM/M7QedKg4PfjRHlSgCNlsUaHfbZjrPO2cC0ECu4BmSceTxOUAJJotrIMn6PXbWCmdW1DyOwh1IZ94eeyb1tdQodwm8/19CuLXmhhcFz0a+Za7XqB/9AOsAHgf8CAAf5lYUwXOnToUDds2DA/XoAGAe8O4AViU6ZM8TMIMXZgzpw5vlHAdKKLFy/2y9KlS/3AYt4xsHLlSv8UYd26dW7jxo0+ThIHLdFlcdBiIbHY4iCxkFgdLjQsiHc0GMIkXj2ob7VP0J+c0XRGrQKC/lpNYu/pBPSTPQAoAXzA28B806ZNjgVHxKwHLOvXr/dZZrIZDIJas2aNX3j0+frrrzumSGPBkUUX9r/22mvewXEe32GOjv/D/7XGgjk9HJ5BP87OFhoiOL7Q+bHNNVhDhTK22D5rvNj35GFNg4ffybXg+GkkEUAmT57sgwyPo1meeeaZUi3R6yYDR0OI/roWgNln5cq+BkgYzIg2jAcpux5cv9mHaWP6sN+OlVEnu3bW2Aq6mDZoZZqYTr28tt8aZ23XTVmuM3pd7Isuo0eP9rMEoQ/b9h22xi/TEGAAMT6bhe5DM2bM8E8K6EY0d+5cP65g4cKFvlHAUwO6FS1btsw3Dph5iESPLTQWeB9BvYV4TczgiYE98W4F942OC/pjo5kKNlFA0N9EnFaH2g39VGoeG5Ih51EkIA/AG8TbNmtAneMG6wA7Cw0BGgQ4GxYAnkw+EB9m8e3ttjabTnRt3Xj4DhoHNBr4Lsv+c9yeIrC289nPwmfKhsfss5XhmP0OjuVhsd/L9aEpuvBImaCA07fGFJqVaYleN1oQLMmyMTCPgEkZLau8nWAvAAbakGmULj8kIdABmAK8qFeAF3Us1MfqVbivyNvh9aMNNmMZ6xUrVlT0oVyvL9wnu3/RdfRY+Jltu7ZwO9SG7ZdeesknYejiA5zbccvic274vfwG+15bUxad8VnUU2YWIrGDTVJnGWxMI4EuRTQaaEAwCJmFBkW4cA5xly5DQH+Wrj6C/lZEpuNxFBD0x1GpQZlOQT999RlMe/r0ad/vnsGi9L+PLvRFpIyt2a630A2l3kI3nOhCt5ywaw5ZbQNcHl/y/VaG77T/V+/7ORbuj34Oz7fv6cU1v9N0suvhM4Oa6U5FcPjoo4/8+IjoPSrTZ7NT7iFdvkwbuothR6EWVhbbZb99DssUZZtrs4Vroj8x3cJosPM0jwY+3ePCMtFrR6eiL1wzY4N4EkjigiQGiQD0MvuwdVSfon6O2gT1isQDyRjWfEazol5/kuuiDlGv0AXQxufQvYbvSGI36Ml3cS4L9oevp9uOPaXmSS/fb91s+b/hwlNgFmImsYIBwzYGoFEmv9V+QX8DENPuRAoI+hPJVV243dBPpccxsIQDdxm822hhUFF4jIGz7Vj4Tr4b0MB5MUYA2KdBwvfbYGKbIq3Ia7vWcI0+DFgmCPDkheDA24aj9yO8N0XfNrvDdhnIzdSsgC3BkKdXdtzWZmOs2VdUfcJrwz6wExqP1CmggToWDhQMdaB8mRbqGN0b6UONPtQv9EJDdAi1DHUq+rbZAOO9AE7GcAGddBtBs6Jff5zrow5Rr6hTNKTxOcQrtLPzTUf7XG+NjdVbbKYf/k+4YJ/1Fu4V5fgfxHSgvRXYNzsu6K/mL31Kp4CgP51u/qxOQL85BmaxsRkBWq0p266ZcMzp8J0///nP/SAkshWAP08fcJpWpkzrqMNllgYcPRkgsm4EG7ThXuVlBqJ2/07swWyRYEd2jS5Q2A8NJI6H9t3u/9/L30f9xDawGxpFPO6nTjFQn4F+lgXkGqxemZa9fF3t/G3ow7UDsowfYiHLSr3i/1jdCvVp5//vxe8yu+HaWQBIfA5gi+1Qz6hT1m2kF6+hW7+JekWDEV1oLOJzsB20sd8Q6hnut+PN1uG5dj8araPfbTabZR2NQX19fe7x48cZCEanllEBQX+Gu94J6I86i2ZOyI7hSMLtLI7FvsccHNOBkjGhywZZE4OTLP8jj+eaLqxNGwIw8MagZaZ3QxuOt/N+5FUrMmUEXbL9PCGigRRqWJbt0BbsmrET6hIZfoCfjGBYLrznZk/hviJtc33UJ1u4NjKqaEO3DCAOmGO/aWHrIunQ7Fq4XuASjQxsSTIw45llkTnfoDC6bvbdeT2GJtGF66YuoYs9lQ59stlYFvuJ/s9Gnzuhq91XvptrEfRngLcSnyroz3DzOwH95kSyOA1zDtF1q+/kf+NMLMBQngCMIwXaCDj2+1i3+r6iHA91scwOGVuuD7gF+C0bGepTJo0soNoa20Mbsrb2iN20o4zphIa2XUS9uCa7Xq4VXVgb+FO/qFdWpshacN3Rhes1u7BrN22wG7qu2H6zreh3FPkz127XzTb2gyZk+LEdsyc04HiRtQivzWwiXHMcP4w25nPYZ/HM6hia2RJ+Z69vh7+ZaxH0Z4C3Ep8q6M9w8zsB/WkcT+j4zTHUWzf7bnOeOBOCLoEFYAP42eZc4ASHyn57CZg51/A3NPs/eTuGLlwbGTUaPvYiNNZ8BmzDMuiBRuxHx7xdb9LfyzVaoMVWsA0W9AFK+D70w3asAWllsCvTCA2T/u+8lLf6ZPZjoG92gzZoUTbbCW3Drh9tsCeOGazxGe3Mbqze5eX+p/2d2A1+x+oN18029cnqC37GINe0s+OUsSXtb+jV88wnc+3owsI2tsIxFrbRhGOh7WBr1DnKdPP67J6l/Z92XZwv6M8AbiU/VdCfwQB6BfrTOhE7zwIDTgUnyaN1Bl8yaw/9I+nLj5NkP9NtMlvNkiVL/NRnvBCMQZsct++x7837muvBuXJ99BFlSjemoeTaWTPdGy9GA3Dp/sQ7DJg1At2Y6YGGkQWXbgeYTmlvgcuuh0DLtTOuAbuwF94wZScDMeniQ9ClDDPVMI0e0+Ax5Sx9k2ko8Fvtezv1u7v9vVwPGgFldDWgCxh1BdtgkCEzhFDXsBFmCmGmI2aqYQArZa9du1aB327/9m79P6AWGOP66b+PNtQn+vFjM9Qd7MMGhDN1ItObMtUvXX84n8VssVu/u1v/B7/CBAHUI6ZdZlYafIzVKboW0ref42iCDTGOJlqniqSP+WT8DjZAnbKpqNmmXqEb9Y6ucwx4xk9jN6wpQ3dVwD9vuvB77TcL+jOAW8lPFfRnMICiQD9BjKwIQQToAG6BM5wpAZggA5ww9SLzQ48YMcK/JIY5tAEVAKWI0G/BHShhhh5e7MJLXnghzFNPPeXf/sj8zQxU5Tgwy/zONAqYP5tAjHZoy3eZw7bvzfsa4KLPNYGUuatNH3tLJvOJA/ssNBaZ05oX5jCvNUGYBqUBSt61iP5+4MTAg3pjL87jpXnYBrMaUbcANvaZ7dBwYq5xGt00tqPfW5TP2A5PNoB83jeCv0EH5khHK6aixB9R99CLOdCxHeZH530hwF0Rod8aizQIAXxAFZ+LTeBvWbAZBoFTp0g+oBkLDW6mOaXRCNRiK3xfUWzGrgM/St0h0WDvAMGf4IuJXcxqBNjTkMQX20u90I6GE40B6qZ9X17Wgv4MsKZTKwoI+itSJN8oCvTjTAB3nCRgBtTzynKyJAyKAvhxsryYhNef/+hHP/LgC+ACJ4AbAZgAU6Qggy40Zsjak7kGbHnZCuv//M//dP/xH//h34bJDDUEFmAWmCMQsU05wJasJYGF72NdFI3QhgGFBFJepMR1z5s3zz399NNeGzQg82jB91/+5V/8MV5qA+jRGABOiqJHCA9cE/edLDXaUJ8AewCWbWwD3dCMOsealwYBKTQqgT2ehFCvwu8twjbaYDs0iIF97AQN0AbARRvAFu1YDxo0yD3xxBO+kQ3Yml9CG6tTRdCFayCDizY0+NADv4I/BmZnzZrltcCe0IG6xnH8MPYyffp0v/CCRvy5+eQiaIPNsHC/AXaeGhKPaAjim2kUYUdoxWfem4Jt8fnHP/6x14kGNb6IJwFonDdduHazd2X6k/OazvhBAUF/BksoAvTjROg3SvcVMiV/93d/58GMzInNPU82l4w+oPvnf/7n7ic/+Yl/SyGZKIAfB8RijjlvzrTR7zVtyDiSWaQBRDAmsw+YACoEYAIy2W0aBGSRyMABtjSQyPKyj/9hQRitGv3PXt8f3mPsBnDjMTsvoKGrBhk2IARAYY1m6DNw4ED3x3/8xz4Ik8k14Od6w+/s9euP8/u4Hu4xdYPuF2PGjPHXDdACctgMgAacAPg8IQFiaFhjO8ALZeiukcduCHE04ukXfoXrpJE4ePBgby+ArEE/4EZD6fd///fdP//zP/ssP/uoj9henP+TtzJMEMC10VjGLvC1vNUa/0xCYciQIe6//uu/PMgOGzbMa8XTEhITgD9PRMj+0xXTsv1506DZ78Vu8DMA/Pjx4309QhuefHDdJGKoU7yRd+jQoe7f/u3f3JNPPumfLNq003ltSAv6M8CaTq0oIOivSJF8owjQjwMETnhkTuD9vd/7Pfev//qvPjNCNomuG0AuAEKQ+Yu/+Av3h3/4h46sLY0EsnF8R1Ghn4wQj9rpisA2WvH4mEAMnJCh5bEx4EKfY8oRXKZOner+6Z/+yYMdAdhAvwiAa9fAfUcbMtqACkGJBg56YDt0DyPjSOZx1KhR7k//9E/9QjDmjavAWx4zbs2ghGMG/UAXjUW6pNBgpkENgNBYBFDoEjZy5EgPKdQzGkg8OaP8hAkTvEY2XqbV/8zbcWyHjCsa0CDENsjIAnIAHU858DuA7D/+4z+6P/qjP3L/8A//4OsVyQbOzds1t/q91B8GL5PJxm6Afp5wsAZs6dePvfzt3/6tX2gQ4H84RiOB7i5ktvFLNAKwP/M7rf53Ho5Tr8xuyOID99QTGtb4ZLo4Uc/QyJ6C0FgkUQX8E6+wHWsw8n15uG77jYL+5IymM2oVEPTXahJ7T96hH6cHdAFfPB4FUv/gD/7ABxqCB914yE6ShaSPLZlJHqcSdH73d3/XZ7LpQwoI45j4vrw5UnOojdahoyUgk51kwCHZWwIuoE9WH+gHVggovDETsKOBRHcNnpjYFJ+N/k/e9tt9Nn2AC2yJPtgEYuyJAEsjCD0IyARj4O4v//IvvR0BdRznO/J2/c1+r9UDAIUnQ2gBfPy///f/3G//9m/7DCRPQQBeGtLUKbodUBaA4ymJdVPA3mxMSLP/mbdj3HPgliw1dem3fuu3/ALI0s2JBhBPjRj0TD0ju/0nf/In7q/+6q/8kyMbyJu36272e9GEeoQP4ckZ3VdoJNK9iQY0NkMDCR14aoYm+Be6EPLkjKeKHKfhRN0D+vFZzf5nno5ZA4ZBvDwt5Dp58kHCimsH6i1hhb+xJx8kqEhm4aPZZ12f7PvyooH5WrMTTdkZG9VUMFBA0B+IkXQT6KfiEeQJ8GGl7HVHwm/G6fG76ZcN3P/N3/yNz5SQJSFTi7Mk+wa04GgBNAAWWCHgkD0ho21vo+X7QhjsdQ1a/T67FsqxTQDl+oESYI2uGgQRstgEZz5jAwRhoJ/sJLrSYOLtxnyP2Uir/5234+iDjZB1o1sTDURAxK6ZpwE0COgChV392Z/9mc9cArV5u9Y4v9eAgow0Yz6wj9/8zd90v/Ebv+HrDTqQrQTSsB8y/IydIUML7LGfri2Ar0E/thPnf/d6GasDXBcZfbr4AGVo89d//de+Ic2MR0ArZdCQbk9A3u/8zu/47k9087GnS/Z9RdAHH8N10IXFGoU86cDPArgkEtCK5My///u/+24raIheTBxA3cOeeNpIfcQOi6BLaNNcD2M90ATI56krC3WGhAL7uX6LVwwQJ47xFICnIOjFE0qro+F39/I2sdo4g98u6E9KbCqPAoL+DHZQJOgnm0YQ4VEyDpVMEQMOcZas6btNJpdATJaNPv9kKOl/DMyRtTMnilMuSqDhmkL4JyNLowjoB+asDzZQR+MIEAHc6LMNwKAT+limv0jahAESjQBUMpLACV0NsBn0s2wjgZZGAU9GgBeydcCK2U34fXnf5pq4XrppAO9ACfWFTDYNZuyFfdQvMt1ktMn081QEMBk+fLjPaFu9Q4+i1CmuBV8CzGMvPNnAZsjEog3b7Cfbb9fN00i6hHGcJ0kMgsYXGQTl3V74/dQh8zX4EeCUa6YRyIKtkEigEUBXJ7r+kJwhycCTAcZd0XWOhANPjPIItnHuI/WA66WRgy8msULdIn79/d//vc/4U+/4LmwEHXkKSxm6zlHXaFTF+V+9VEbQnwHWdGpFAUF/RYrkG3mGfpwZAQZHQj9sHh3zGJksCcBPH1EcJZkjgghOlGwtwRqHCdQRrAlGNAKKDFq9YSQAACAASURBVP0GpWTO6FcLrKET2VkCLoGXKTwZPGazIAFudI+iPzswbODbS0Gk3b+FqQJp+GAv9FE3ewE+CL7YDsGahhDZfmwIsDN92/17+vP7uCbqCXUJW8A26ArHUyK67vCUjJlXaATQN5nGI2M/6O5CgxENaVSiocG+rfvzutrxv/E7ZPABeRpEXC/wRkaWbP5///d/+zpF9y/qHABM1pbsPl3ESEKgF3aFTRVJF/Q18OfaeVpGXaLO0ChiXAj+h2w+gI//xQ/xBJL6hH7YWjQR04771kvfgT/Br1JfeKpIXWIQL+PSqGf4FewD22Gb7j/YDdpQJ6mbebMbQX9yRtMZtQoI+ms1ib0n79CPE8fx0SeffvtkYAEQHCSBhD6SZN3scSmBhcwskA+8MGCTmVnIUOKQDN74zrw51GYBjevi+ggeNI7INNKth4BM9xT0IgCTRSLLT3aOAMNTEwIMGU3Tptn/yfMx7jfZV2wFkKNLE4GZgag0GFmwE0COhgF6EbDp0pLn627027nfNAABNuyFvtc8JUIX6hUNbBrNZPQBFrpyYCvYEvWQgb7UQaDF/kdR6hTacF34ErK0QCz1ikYjtoM2wCxgT/KB7oM0iMhkA7X01yaTTaLBoN9A2bTK65rrCBf8DnbEFLfMzMNTId6lgv+lsYRt4bupS3QXw+9gSzZIvii6RO8n18W9x78wxoq6BPDjW6g32BazYRGr8Mk0BKiDaMM+GgTR7+z1z4L+2Gimgk0UEPQ3EafVobxCfxTKcZ4EV7LYgCv9jcm8EWABe4IJC8cIKjhPytA9A4dK8LVAheOMfn+vO9NGv8+uA0ghM0mjBxhDB7qpEHAIHkCJZSwBOrpKkZlDT4Iv55o+RQzC6ATgk2kEWAFXGkhkZwm8BGLADttBG7TiCQiBGdtppH+e92Mz2Ab1CqClPlGXGPeBBjwdAtSAOWyGpwEMCmfhM2BHJhNtzWbYzrMm9tu5Hq6FpxgAK9lXGov4GHRBJ2yIxjO2Y1OYsh99gDpLNPA9RdEFfexeA3j4F2ZHo6sT186TV7pecu00HqlDPBHCntCFBhRPS+giRn00XWxt+hdhje3gj7Efrp2YZPGImGQJCBpGxCrsiilP8dskufDJedNB0N+KyHQ8jgKC/jgqNShTFOjH+eFEARSCLSBCJoksPmBCkKWrgTlXstzMmEBmzh6TWrDKmyNt9nsNKLg2ggSQSj9SYJ4MP8CKIybAEkzYTyYJ+KUrAtlJyoRBt6g6oQF2AqjRhxZ7Yh9PQ8iyAbIs2BVAa9m2UJtm9yJvx4B+bIOnPEwnSL0COqg3gAoDDemvTzcMbAVgQSfqGfrQDYruHXm77iS/l2QDdsD1Wr2xrDV1B43wRcCsNRR5WkJD2p6AFNV+0Ibr5MkYPge7wMcA/Fwz4EqDGl8D/FPvKEN5axDZvSiiRjSIaPiYP+aFbcQvdLFuP4A+C0+GqH/EK/w22uZRE0F/AxDT7kQKCPoTyVVdOK/Qb8HA1gaiOEMghT7+AC7BA3AjY8k2gMLCMZyuZfjte4q4JjiwGNzTHQVYoxFggQP9gBA0IeBwHGDjHCtTRG3smrhGbIcGIKCCvdi1Yz8EWgIymTn0ITCbLmZ79tm+M+9rrsuujXpi2lC3rO5wjVy31S90wnZ4QoJ+edeg0e83Xez6qSv4l2jdwU7wNWS76R6GPmjFeeF3NPo/edsfXhP3H1/D9fMkkW18DGODWCjLZ+wK7Vjw3eyzuhR+X960aPV7uVaeJtI4pM5Qx2hocx4+hy5R2AzxCv3QCdvBT7X67l49jk2YX+VaNXtPNY/pUzwFBP3xdKpbqijQb06OYIFTBGhZDDxsPwHFFnOeRQ4soS5ogB5cNwufWShjGrAffVibc7bvKMPart/sBl1Y2E9QJuiijx0vgyZmJ6xNH9ZmO2hgx9jPEupjtlVkrbhe/A22YX4HTdg2uwHkrG5xLNSvSNrYdQF12ALXz7WjBcesTnHN6EYZjrGwbdrYukjahNcSamPXbXWFazfbQTs0RCvTxLbD78vDNr/bfrugvy6SaWcMBQT9MURqVKRo0B86PhwojsUcKccs4ETX4XlF3baA0ez6Ql0oxznNyhfxWKgTelhWsojXmuSa0MLqk9lJ9PxG+6PlyvA5tCPTjeu2/UWsW3ZtXGdoC42u1cq3WpfBXsJrNO3qrSln4Byek4dtQX8jEtP+JAoI+pOoFSlbVOi3IIPTxBlGgwr7QoeaB4fZjt8Y6lDv+9AkCij1yhV9X2g/Uei3Y0XXoN71WZ3BRliiZcy+bH/0s+0v6tr04frMTqJ1yo7Z8SJpEb0m8yXR/eE1cyxcQjAM94fnFGkb+4heT2gzZlO2pmwzPaPf1Uufw3uLbah7TwTI9DGWAoL+WDLVL1Q06K/nQM1B1jtmDrHZMStTpHX0eqMaRY8X6dobXUt4zaYHZcP90e3wc6Pvzfv+UIsk18J5rZYk39dLZRtdF78RmwgbRGWwkfDecL22hPsbbZuWpl1Z9IpqxGdrJEWPoRH6hFo10rOX9wv663OY9iZTQNCfTK+q0kD/o0ePvJM2h2LrXnYe+m3f1WSHpIk0kQ30vw1EgU33pP/vSR7ugdkN6zz83jS/0diCtTL9VSimDwkUEPQnECtaVNCvgJTGeesc2Y1sQDYgG5ANJLEBg37OEfRHaUyf4yog6I+rVJ1ygn457SROW2VlL7IB2YBsQDaQxgYE/XUgTLsSKyDoTyzZr08Q9Mt5p3HeOkd2IxuQDcgGZANJbEDQ/2v20lZ6BQT96bVzgn457SROW2VlL7IB2YBsQDaQxgYE/RlgTadWFBD0V6RIviHol/NO47x1juxGNiAbkA3IBpLYgKA/OaPpjFoFBP21msTeI+iX007itFVW9iIbkA3IBmQDaWxA0B8bzVSwiQKC/ibitDok6JfzTuO8dY7sRjYgG5ANyAaS2ICgvxWR6XgcBQT9cVRqUEbQL6edxGmrrOxFNiAbkA3IBtLYgKC/AYhpdyIFBP2J5KouLOiX807jvHWO7EY2IBuQDcgGktiAoL+av/QpnQKC/nS6+bME/XLaSZy2yspeZAOyAdmAbCCNDQj6M8CaTq0oIOivSJF8Q9Av553Geesc2Y1sQDYgG5ANJLEBQX9yRtMZtQoI+ms1ib1H0C+nncRpq6zsRTYgG5ANyAbS2ICgPzaaqWATBQT9TcRpdUjQL+edxnnrHNmNbEA2IBuQDSSxAUF/KyLT8TgKCPrjqNSgjKBfTjuJ01ZZ2YtsQDYgG5ANpLEBQX8DENPuRAoI+hPJVV1Y0C/nncZ56xzZjWxANiAbkA0ksQFBfzV/6VM6BQT96XTzZwn65bSTOG2Vlb3IBmQDsgHZQBobEPRngDWdWlFA0F+RIvmGQf9Pf/pTRyUOK2WaSq1zFAxkA7IB2YBsQDYgG4jaQMgXDx8+dH19fe7x48fJwUVnlFoBQX+G2w/0U+l+/vOfOwP/aEXVZzlv2YBsQDYgG5ANyAbaYQPff/+9+9nPfuYePXok6M/Ab2U9VdCf4c4b9P/iF7/w4A/8a5EGsgHZgGxANiAbkA10wgbgDZb/+7//czCI/qRAEgUE/UnUqlOWSkfl0yINZAOyAdmAbEA2IBvohg2oa08dIPv/7d0FkDVH9QVw3F2Du7u7E9wJBAmBYEFDgECwIEGDB3cSAsHdggYIFtzd3d1t/vWbf91UZzLP9tt9377s6aqpeTuvp/v26Z6dc2+f7pdLMxEI6Z8JUTIEgSAQBIJAEAgCQSAIBIHVRiCkf7X7L9YHgSAQBIJAEAgCQSAIBIGZCIT0z4QoGYJAEAgCQSAIBIEgEASCwGojENK/2v0X64NAEAgCQSAIBIEgEASCwEwEQvpnQpQMQSAIBIEgEASCQBAIAkFgtREI6V/t/ov1QSAIBIEgEASCQBAIAkFgJgIh/TMhSoYgEASCQBAIAkEgCASBILDaCIT0r3b/xfogEASCQBAIAkEgCASBIDATgZD+mRAlQxAIAkEgCASBIBAEgkAQWG0EQvpXu/9ifRAIAkEgCASBIBAEgkAQmIlASP9MiJIhCASBIBAEgkAQCAJBIAisNgIh/avdf7E+CASBIBAEgkAQCAJBIAjMRCCkfyZEyRAEgkAQCAJBIAgEgSAQBFYbgZD+1e6/WB8EgkAQCAJBIAgEgSAQBGYiENI/E6JkCAJBIAgEgSAQBIJAEAgCq41ASP9q91+sDwJBIAgEgSAQBIJAEAgCMxEI6Z8JUTIEgSAQBIJAEAgCQSAIBIHVRiCkf7X7L9YHgSAQBIJAEAgCQSAIBIGZCIT0z4QoGYJAEAgCQSAIBIEgEASCwGojENK/2v0X64NAEAgCQSAIBIEgEASCwEwEQvpnQpQMQSAIBIEgEASCQBAIAkFgtREI6V/t/ov1QSAIBIEgEASCQBAIAkFgJgIh/TMhSoYgEASCQBAIAkEgCASBILDaCIT0r3b/xfogEASCQBAIAkEgCASBIDATgZD+mRAlQxAIAkEgCASBIBAEgkAQWG0EQvpXu/9ifRAIAkEgCASBIBAEgkAQmIlASP9MiJIhCASBIBAEgkAQCAJBIAisNgIh/avdf7E+CASBIBAEgkAQCAJBIAjMRCCkfyZEyRAEgkAQCAJBIAgEgSAQBFYbgZD+1e6/WB8EgkAQCAJBIAgEgSAQBGYiENI/E6JkCAJBIAgEgSAQBIJAEAgCq41ASP9q91+sDwJBIAgEgSAQBIJAEAgCMxEI6Z8JUTIEgSAQBIJAEAgCQSAIBIHVRiCkf7X7L9YHgSAQBIJAEAgCQSAIBIGZCIT0z4QoGYJAEAgCQSAIBIEgEASCwGojENK/2v0X64NAEAgCQSAIBIEgEASCwEwEQvpnQpQMQSAIBIEgEASCQBAIAkFgtREI6V/t/ov1QSAIBIEgEASCQBAIAkFgJgIh/TMhSoYgEASCQBAIAkEgCASBILDaCIT0r3b/xfogEASCQBAIAkEgCASBIDATgZD+mRAlQxAIAkEgCASBIBAEgkAQWG0EQvpXu/9ifRAIAkEgCASBIBAEgkAQmIlASP9MiJIhCASBIBAEgkAQCAJBIAisNgIh/avdf7E+CASBIBAEgkAQCAJBIAjMRCCkfyZEyRAEgkAQCAJBIAgEgSAQBFYbgZD+1e6/WB8EgkAQCAJBIAgEgSAQBGYiENI/E6JkCAJBIAgEgSAQBIJAEAgCq41ASP9q91+sDwJBIAgEgSAQBIJAEAgCMxEI6Z8JUTIEgSAQBIJAEAgCQSAIBIHVRiCkf7X7L9ZvAAL/+9//ZpY6Kc+k6zML3A4ZVsnW7QDPulW5Vpzdt9Z718v4sfrLruF37d9tnuHn9bJtkXLKhmn3tPa3+SZdb/PkcxAIAkFgFRAI6V+FXtoCNtZLuc7bu8l//vOfu+9///vdJz/5ye5jH/tY98UvfrH73e9+1/3rX/+aaNqQHAz/bm+c9p18hcOsfG2Z836usufNP2++KneSzZOuL1K+vPrg97//ffflL3+5+/jHP9594hOf6L7zne90f/jDH7r//ve/8xa3tHyFy7wVVv46z3vfeuer+uvclj92bZHv27yLfFbveqSyf73KWw+bUkYQCAJBYKMRCOnfaIRT/kwE2hdwfZ550wZm+M9//tN9+9vf7l7zmtd0D3vYw7oHPehB3f7779997Wtf6zgDY2lIHqodw+t1L3I66bv23vpc923rucqbVPe2lN+WPVb+8Jq/4VDH8PvWlsrr2l//+tfuW9/6VveCF7yge+hDH9o95CEP6Q444IDeCfDdZkuT2jWp3fK3xzLaU/gObW3tGPtukm3DvJPyLXp9Pcpt2zTtOWSbvJP6aVHbkz8IBIEgsL0RCOnf3j2Q+pdOcIaQt0TC5z/96U/dm970pu5mN7tZd6Yznak74xnP2F3vetfr3vve93a/+tWvhrdPJO8yFqlo66gCXBu7Xt+v4ll72jZXG4ft9Pe///3v7u9//3tP4v/xj390nK1pqcoQ0Rfhv/Wtb92d85zn7Ptnxx137A488MDupz/96abCtGwetgtG2qz9//znP4dfL/VvNsKeLbNmsqpvN9rASbitd73qmVaXMcqRhI3PSUEgCASBVUYgpH+Ve+9oZjvi8be//a37+te/3n34wx/u3vGOd3Rvf/vbjzj87XjnO9/ZHXLIId1HPvKRXnbz85//vH8prwccbPjmN7/ZPf7xj+/Ofe5zdyc4wQm6M5/5zN1tb3vb7tOf/nT3xz/+8UjVFGn49a9/3X3uc5/r3vzmN3evf/3rezsPP/zw3oGYhyjJ85Of/KRvkzJe97rX9e0mK/rtb397pDq39Q/ETpnse8973tPbWtjOOsPeUfl8/uAHP9jbjhhJhUlLprTP92ZQ9K32vexlL+ue//znd895znO6F77whd2rXvWq7l3velf32c9+tvvFL35xFAJa5Snnq1/9ane3u92tO9e5ztUd//jH7/to77337qU+lW9bcVrP+9lsnGrbu9/97r6tL37xi/v2v+hFL+oOOuig7q1vfWsvJSMr8xxsVDuUbayRrsHbjNbLX/7y3hY2HXzwwX3/spXNrTPW2uSzstirT5VVz2uNj7Fz5WnP8sHlC1/4Qu9YV50cIs/Wpz71qf5794yVOc815fuf8bOf/ax3uIZjVZ3GnWfOc+E5hstzn/vcflaJU+nZ1FZyMs6AcZ0UBIJAEFgVBEL6V6WntoCdpDPf+MY3egJ4v/vdr9tll12629/+9kcc/nbsuuuu3Z3vfOduzz337B73uMf1hOmjH/1o98Mf/rAnlmt9ESMxyBkiq44TnehE3XGOc5zukpe8ZLfPPvt0P/jBD0ajsu6jL0dgRZ9vfvObd3e84x27xz72sb0Dgxy0ZGnYlexFrpA+bdp55537Mu5yl7v0bUMw1jNxXL7yla/02MHxDne4w9wHXNr8/mbzBz7wgZ4wtXZWm0W0kUxrIxCoPfbYo2+f6PxVr3rV7spXvnJ39atfvbvRjW7U3elOd+oe+chHdm984xt7ORXsigBWefAS0X/Sk57UXf7yl++Od7zj9cTfzMwrX/nKnozVPa092+MzO5BWBPrVr35196hHParbbbfd+rZe85rX7Nuv7WaSbnOb23R77bVX95KXvKT7zGc+05Nf0eVq97bYDzMEHW5mSV7xilf0sijj9KY3vWmnL652tat1bLrhDW/Y9/GjH/3o7g1veEP33e9+t5e1De1Q5o9//ON+VuwBD3hA33f1vNazOnauPO1ZPmPxec97Xvf5z3++d/jU95vf/KbzbHuW4OYeedsxOM9n93ieSMG03/ocSR3GGFxgzgHi8LPlFre4RY/LVa5ylX586iPPt/HLSbWWRN9u75mabRkXuTcIBIGthUBI/9bq703VWi/clkj86Ec/6iNr17rWtbpTnOIU3bGOdazR49jHPnZP9E5ykpN0pzvd6brzne983e1ud7tOxBQJQdzXkhAsEfAik8c85jH7em5yk5t0r33ta/uo/Vi5yA/pD8JaNrONs4BEsEmq9rZtdl3kXRTS/Sc96Ul7R0M5ZznLWTrES/RzPZNI59ve9rbuUpe6VAfLsnktZ/ezc7/99usdH1gMk/qQeLMl5z//+fs2Vl0wrsM1BP7Upz51T7aUWRHVKrMwJMGCmTJPfvKT92246EUv2mv8jSMEVxpiXeVsxLlsq7L9zZHlEJmFuOxlL9ud9rSn7WePtLXa7Vx4GPcXu9jF+vzvf//7jzRTtC1tQUxF5Dked73rXbuLX/ziPW7wrrrLHn8f97jH7WVT17nOdXoibmZlKP3xvIh63/e+9+2fw7asKnPa2dhpjxOe8IQ9qRZN13/GkiAAx569bJpW3qTvaoxz4s973vP2Do8xUokzz7nx/F360pfu29LW1eKiLOXoo3vd617dhz70oZ74V1k5B4EgEAQ2MwIh/Zu5d7aQbV7wos8WzV7wghfsie8xjnGMnhh5mdeLu32xu4ZoINhnO9vZuhvc4Abd05/+9D66XgSliNg8hEkE3NS+iOLZz3723oYddtihE8V0XcR6LCmbtMei0tOc5jQ9OWEbWdDDH/7wPoI4dp9r9OnuFT288IUv3LcTybCOQOQasSU5WM+kPFKMy13uckfgDOvCu0hOneu79vv22hnOcIbuCU94QvelL33pSCRbH4ioiipzyuChr8yewAdZh6+2+qwv1Ylwnf70p++jzk984hN77IvEFw76ggzMQmtETpnuudWtbtUddthhfYS48s7T95V3Pc9mN8hO7nnPe/Zk8pSnPGXfRraSjXFuOK3O/ja2tR3xRz6NHbIWkehFUo1593iuYCWK/axnPaufYYAXvNmBaJ/qVKfqznrWs/aH8UsuxRY2cehE/i2URo5bLDkSnEezPSc72cn6Pm3HRTteaiw51zPsc+V37cQnPnEfxTfTxnHnVIj6m0ni2MtT+aedlWt8teXLr/wLXehCvSPOETULY3ySlZldsz4E9jUO4eMzHJRXddbY5WRy5sxEsLfFpv28SN8lbxAIAkFgIxEI6d9IdFP23Ah4aSJrps8tnvWC98L1Ir7iFa/YE/rrX//63XWve91+qv0Sl7hEnw9pKSKBfMpjEW4R5SJA87yEkTT3XuMa1+hJDPKjHmSJzGCSZETZovl2kxHdR2wRBMSKROAtb3nLUUgBYJAxUfynPOUpfRTYfdqNQGmnehGtSc7G3OAOMtruEgm8973v3bf1Sle6UlcHqc20Q4QTzkWA2IswPvOZz+xJeFsVzERCSSXOcY5z9PcgTIguUrvTTjv1kou73/3ufb+LhHMCkC3liorrC6SMdKv6Uh36Qjto0ElSEDMHuY+1AiQp1V/tfa19G/VZfcgkXTiZGmeuZnAQf8RTBJ0sheTEbAWJD4Jd41l+Y5nUZ6irn2V3tdfZc2UdxbOf/eye8NdMAwLsOSNdISsSrb/Pfe7Tk1/OIPLv+XNwyhBvfdnKjTh1Iv1mo6597Wv3ZdU4cp40jq5whSv0Mz4Itr42ljg7nEIOtrUGHAp1WV/DkWcjW6eVW/X5f2EWixOo3Bqrxh3c9YsZPXWYSSL5ETSQ1zPL4Xe/cUVyRv6kXv+L9I+xqUzPKUmU8aaP2lku2CcFgSAQBDYbAiH9m61HtpA9RU402UuYph3JQ0y8gEUgaY4RH/pZB5Ih39Oe9rReF45wVhQOQXEvEkJDLbV1TINWPhIGel6RUC92JNwLn9wAgZz2IkfMSXwQOOQCKUBCL3KRi/TbfVZksbUHUbA4EElBgDgvdQ9Jg0g2GQtyMqv+aW0bfoesIcwilJwcko86yJGGB2mThbfO9PbIUEVROUZIrPtLxlS42+LU7IfvkTv3IJv6+KlPfWrfl/JoJ6L35Cc/ude2VxTaPYjVAx/4wH4BJhKIWLX9AHNOhXzKt7D3wQ9+cL/oGuEtW9p7hnisx99t+ew0/uj3L3CBCxxB5NnIsREd5ghaB6LtxrVxYKwh4kVSRbcRcfmGMx3z2Mwmu03pu1ve8pb9jAKM9BkCa+0JR5XG/Xvf+17vKB166KG9VAvx56DI7/kqB+Qvf/nLEc6UvuBcW+BOvmV8jI0d1+vwPRkeh5PUi1RGe40LDptF3cZRjXezb9pv4X6VX2WNnbXVrITnhxMAc+VrhxlEY0l5nleYcn7JetTP0eK0+5vD7f+MZ8TY1D/GGccMfsr0/4YD/IhHPKKf5Sonc56+SZ4gEASCwPZAIKR/e6CeOo+CgEiunTJEkpE+L1aRP7IREgcyGIcIKiJDn+wFL0qMZHsBI+rucw0ZbFNLytrr9RlBFMn0wke8kASyi/vf//79i9/908pAgMiTEFdOA6KEtIqYipIiF8i7JK/PnAnSiIp4IvxIKxKBFLLHwkbEvIife7claYMyEFPE/5e//GUfpeSATDoQOwfHBVm3+LainaLAZjMs0tU/lZA10iQRYP0DT44cImbNBLJvJgCJdLiXhAoJ5QTBvu4hLxFNVSa722RsWGRdZEx0F3kmEdG+ZSf2cWC1g92IpLFgHMBN1BrJhju9v7azk8bcd/AyhuFr/JsNMQO2lrZwGBFysw2cz4p6n+c85+klbKQ5fu9A2Qiwg13scw9nTR+wBSH3LA4XrhrH+mWecWR8GUPI9D3ucY9+Vsdzyy795znhdLQL3+EJJ2PFGFRGjcex8ap8u1JxuBB45SPoxpNx6pljq2cA8bdmwNoRsjCzDBwGTpj/R/7PwMbYNAvIyZDPGNOnDoEB0jx1Dsfmssde6gsCQSAIzEIgpH8WQvl+KQgggQilyL0pdJE3kUCOgBfwMCGvZAt20eAoVEQPQSEFQJoXSUiFKCQJQel6RUNb2co00q8uJISGWySb44IwIR1IqG0QS5uNRHACECvRR6QEgbBFKCfAAs6KLiKO2oPEiKbS/yM9ovWz7Blrv3vWch+CpF5R9JYMkuk85jGP6SPF5dSol2zCTj2cGH0JB2RJpBWx42SVA1P2IMDIkwWSotwwgSHttPYbB+xok3pIfCz0NAYcxoNrpFHLTkiivqXjN361gTMnwm/9ARI+RuBFiTkqFtkiqIiwWSsyL2MK9osm7TfuLIxXFls4IcjvS1/60n68DvFkh51sRNxJV2qGhkTJDAXyWzMo7Km+m8c24wORVradcET52VSyG6SazTUu5ilzmIdTwtkkE6xZQG0gKeK0mNEo+5F09nCqbM8pqq/+ek7bsrVTv1pnYUzrH/9rOGj6mqMU0t8ils9BIAhsRgRC+jdjr2xBm5AhpAhp85ImLSAxEOX2YpaGBAN5os9FjBBK5NmLmJTBS3xaUlYd8iEDIq2iq0ga0q5+RFv0cJ6ELJBr1P7x7HEgoaL3pSMuGVHtSILYcjTILdgtn6g3qZEIMXJEc0yPvO+++/akBgHmPCBSQ1xaW6uN0/K0+etz3Vd/c4qQIxrnVooE0so9vwAAIABJREFUa6RUtLdNIv+2nyypjjOdNMeqxVM9LclDKsm5EM7St9P5I8MkM6K+bYIV6YctJ5FHJEx+Ei8O0jJSYeVsHHFUOX7GMFJrbJKGsJP98g2Ta54B48T6B2OQvA0OpFNjju+wjPq77FEeZwlJVZ5xxLE0riy6npQ8V8ahXavcZ3yaHRDRRopLaqQefTfWnmHZ8iDkiLVtNz1nng3PunFBNsaBGzohw3Im/a18DktF+RF+tiPnxqs1C35PYmgr+5H1MSda3jrUKzDxjGc8o3fUazbGGCXB4qSH9E/qnVwPAkFgsyAQ0r9ZemIL2+HF6oUsYuYljWSQjYiQuz4klAUV+YLvb3zjGx+xuHTeSH+9zJ0lJAjhMl2PqNHqqp/EpiKzlbfqb8++QzqQY7MDZgyK9CtLtB7Z5xSQEIj8InXaqs1Iogio2QIERF5SoZISIBmIM+mQKKmIO4fEQsdJxH/YxtbeRT+TQdhJh0wEUXMgpxw1My5DsuZHlOyN3van3YisyRDRrzS0EYnXp3ZT4fDAULs5F0iz79ukXhFY+UlE2CWabSyZMWkdimn915a5yGflVx36gTTEDA5yre2Ip762BoVDUOSybXd95ujZjx7BRiaRVVInux+Rs82blAcXEW/OhnHmuYCnsUMWV470WJmcSTIYmHsejFGzXnT47CjS796yfayc+t5ZdJ3ThtybuTGePWekdBw6faVeWCpz0QRXToXZJc8S/KrNHCfOphmMNpXtdfad+j3HZUdrC/v9j6hIv7EGW8+iBfnuSwoCQSAIbGYEQvo3c+9sAdu8XJEl+2SbkveiRvS8WOl+EbohoSxYEBf6YC91hAY5ISOxIwqCOCvVy96Z5GT33XfvSaNy1I+MeJmXHGCe8hAi22HamYWsRXuQULMGpEgcAusGEDqk0MwGIk16IDIrWsgeBIXmGpklrSD5QJKQJVIN1+i9kSgkztaGtMpFKtla7Ztl96zvRaetkTATIYquf5BZMywcGM5XS3jUS1ZiVqD6k7OCDCO2bGxTa6fZEo4M0l7RYHWJ5CuTzEX+SsYPhwQhJQlCxOBqLIlWF57s42yIVFt7gcAZWw6fFzmMCY4OZ9DYLHuQTnVa9KqP2GJcGgvkZnBqyWS1u87WbYjCa0eRfrKURUm/Othm1yMOMeKuH8wWwdWv0rbEvbCsM/LNodTfZgc8D5wY0fI20i9/2V73TjpzeKzLQMhhYiwb+7Cy/sHi3eG4mFTW8Lr21oyPhf8CBtoLQ7MIZseMu+FzPGa7a20fqcu1cqJsPesZYL9xaVzX70m4LykIBIEgsJkRCOnfzL2zBWxDykTyvfhpjxEMByJMomGhYb1MvXzbhByKjiNJCDaShUjY65/Gdp6kTISQnpjWuaLzdOsieLZ+rPrnKU97kFCyClv/cUKQUBFN5VuoiShoo+9o+hFWEeJWuoKgIFjsooOHDdJGMoL4u1+bkTG6f04DZwMZRYBaMjqP3cM8cCm8ETYRVHWVIyPCyS5yDaklSjCgqUfw2AlTUiYzFyQcrYMwrBfxY79tFNXnXv1qy0S/eMwZKrvqXjjB28JNWLuHk8AG5JZtziQyHCRrRyz+dXCaFjncg5jTxCOSNQvEFuOR/MPYQbSRQm2wkwynQrsL17FzRfqNjyL92u1XhrWxTUMM2u/gb8bIM1UyKbjYSQgBNqs1bUxrE0mR372osWZ9goWu7Q+fqbPa0dbfflaPsWhscqo5r8Y9Z8KMhn4racy0NrVlDj/rW46cWSfjDCE3Bsz8cAKM0eEMkTLmrU8bjDvjzxqWmv0wA+V5Puigg/pZBvlm4TG0PX8HgSAQBJaJQEj/MtFOXUdBQJRf9BZpRdqQRC9tkXE717S/nNne7AUrmi8aj+i7B5EgOUFQkcs2TXrBFylRF5KKLDhECO0yQ2O+SFIeJ0Z01t7rRRCQLkSZrYiUOjgCFu6SEIkSu1diq88IMAJG6iNSjHCSZ9Ari2Q6lGsmwcwEkkZ+QFpDhoH8rTW15IXcxuJa+BbOCKQof2nNKz+7RevtC0+aoj8ddn+h1Ze/bWfdV3b6jsODnCLPcHI/icz+++/fR4Td0yaETH12x0GW3aM+TgZc4Uh2ZTbH7AiyKdpMOjXpsD5AtH7sQCY5YbT7nMJK1jFwhNyL2HLQROqRQqR92Na6z9l3diKyUJSzyGFwvzZxVNrtUNv7xj7rd+MWJpwGmLCHI+03IUjMpiUEWV95HvQ3/K1LYRus9Q979dW0NqkDIYeRses3L6q8coI9w2bslCPNKm/Mbtia1fC81YwfmxFyRN3MxbzPQtVf9qjP+PGrymYelc8JVX79fgGnwv+xwmPMxlwLAkEgCGwGBEL6N0MvbGEbkALRfGQJcfUyFU1Gbi3iJVlpkxerlzgpDBJS0d16CVt8aHGvPJXaF3hdqzMyIMKO4F/mMpfpCaOXOrKFMMy7a0qRBeUqk4SCnIUUADlnH8JTRF2U0PZ/iDPSgzRUau3VXrIUZMvCVBFYkWnkVVSzZCRIorpco8Uu2Q/JCMI7L+kpG5xFpovAw6PawenwewRI7nBhqnpsq4pwc5y024F8ImbIqDZNStqOZJFMIIkIvEOElS6eE9jioxxjBC6kIpws+RFcDhAnA0lF/unGOUZwQoJhNungmMkzdui7kmuZWWEPrDhmpDxs0Ne15SZplPorDe133TXOnzFjDYh69S0JlSj58DmossbO+sC4NX6tLUH62WPWwSwY5wLGw1TPlnHm9xWMpeo/TpQ1HdpRY5XNdQzL8jc79JeZClIvUX59w/H1Ww/2wodfleeeaeUN66hnw9oFswjwhpv2cpz0v1mEmmEZ3l9/t3W2n31v/HNazNYYw8qHCfLP8TPuOB1scW+l9nNdyzkIBIEgsL0RCOnf3j2wxeunc7bjxm677dZHXZEC5IAUxhaGSBsCj7giMiL4yJEFj6QLCISXsCi0aDBiQl7jRS/NevkiP6LpIpF08spCBO2UI1qP5AyJwFiXDfNwZEoSU5ITbXPQ83NWSJNIQhZJCDUiT3NNzsDJEXUXXUZ2OCwi/2RDyD/yXLIfDlbhMk+dCA+8W3298uEsYjxGdhA4/WQbT8S7SCOnwboNO/fMQ/oR/HLCYMaBUKfxMOxTkWltNGaMB/lF8/3GAny1oxxFunQRbISWUzHtkGd4iHgjwHag0QdILUzV4UenOKuIIQeJJAaJR6LHFi+3fQATuyBZeCvCD2eRZLp6DqR+nzexByZmIsicONH6wayGxelmHuDCkTC+nTljrpnVQcZJxkTj3actCK7xzMlrx9Bw3Lc2eq4s3EbI9Uc5vXCxJz/nw/MvTSunvm/L9tlYYzO5lX7iqKkDbsYCJ8z/jVmprbv9zGkhbSOx47RwFuGhf41HjpFZuLZvqy7lJAWBIBAENhsCIf2brUe2mD3ICXLvJS2CirAVOUG6ER569/e97339IkkReYSLjr8Iv3vIevwoF+10G+WfBSfSzwb6/dLacyCQHi97pAEha4nOrDJ9LzJPlsGRQEbY6ECgRFzpo5HBKrfIxti56vNd2UInjXSLInN0kH87rHAw1IH8aAfipi107CQ/yOm0VPXLg7xrA5kER4j9MOdM6A9EtM3vHnYh5mZhkLsijSK7SG0b8R6zQ3n6ZEj6OUkcmDHSj1yS7tghiUNV48FWikXKlKkv5TPjQO9uYTWivsjhPmSahEoEGAlGPrUL+WwdFbMT+mYotRpiBgf9aocZ2MJaH4pWk38NnYYx3NprytLPZsosaPZc6Qfj0HiwONxMjB169AnnhZxHZFx+BJrjYSy5jwPCLs8DIsx+qdpR59YGnzmn+pEDyhHVJjaYpdIHntMqj82LJvdzYIz9WmtSzr9ZDo532Tqp7Pb79rP8yudMtjtDea7MwHDOPHvDma6qZ1hWXc85CASBILA9EQjp357op+4+8mvBJT1uEUsvcATcziN2y3BwCkTfRdjIS0gokBEveSSa3hbJQQARvHkTkorcijwizUiOsm0vKVI4Rmynle1lL6IuwkmyJOqIKBTpVz57LVRFiiq5bxZRqDx1JlsRpUVsOU5+Z8CMiQg7olckS4QSlkgeZ0Qaq6vK9T0M2acNSJuy2E5nX7IZ2A3LYhNnRPvKiXIv0s/GeUg/IqgtZhTUCTukf1KkX7SYjIO0xroJ+Uk9/F6CXY18r23IOSkVx8EMBtK+6OE+JN6MhQgvW/W3NsOKM6p+0WBtRqYtqh3DuwevkVFZV8LBMl4cHEazFWauCuu6Z9pZXewigSNLUY4xrR88Y54fDolZCePC2S8ly2e9grHTEn6OHoeao9eS83a8tPaomyN94IEH9s8tR8yzqhxjU99aTCxf4VLntpyxz5VP+eRl+hjm9YyZ9eLscf79L5DqnrHy6vs2D7usy7F7lkX2Agr+z+hTAQmOEWfD7Ii8SUEgCASBVUEgpH9VeupoaKcXLQJFqoPU1Yvb2UuWblZUzRlxcA0Z8T0Sg9iRWhThn7Todxp0CJv7vNztjoNkikqKRoryFXGYVkZ9pz2IJRIusm4RJwemCH+dESvbitphSH7JvS3xqDLb86Q8iJioJJJHgsE5QvSrPmeEjpwIya362rLrWtlQMzCIqz6AC+JGHiJKi4QWAax7lOH6GOknqUGikO5pSVmI1JD0I6mu6au2PmWJtoqG2wq1dON08f4mIZn0Ow/T7FjkO06F3WMQUIQaVggux1EUHZbTEsw4BiRR2ul+BNNCaDNbnJQil8O2j5VbeTgmxrCxJnrvGaqZIH1pbLLT4TOyL4+/fc8OkXnPGSmZ7U2rbPX63P5d1+DNCSNtKlmPen1mC3me8bqWpD5YcBrMIggCeF7rf4Ln1hoCz+1aMOPscowt3CUF85sCsFA+B94aC46cxdDKH7Z/LW3KPUEgCASBZSEQ0r8spFPPURAgbRHFRA4qQopo1CEyOTy8gBF++U272yIR2V1E89waQgaBCItkIuPqRn6Ubfp+EXKiPewwc2FRack0qj1FwhErUWxyiYp8jxGosnPWd+pFPOFghxukH+mt+tQvwmo3F5HqsTQkL359lONSi171gzUDZDvI36SEwIqADuU91l/Q9JtVGdbVluU7ZGpI+uFFa67sYRJxt90lyQUHUbu133aNyGfbh4XlepzLDn0u6kx2pl54W2Nh5kF0vK2/7mnP5TTYIQkxdj8H16LjWldSDpbzLPwqr3FhJoiExzNGemQmBJGv5wqZ1a9w4xgi0WZzEH/fcQYQX89ZLVou21sM65q+4/T5PQqzd9qhPZ4phNnC15ptck+VUffPOssPL2OJbE17lK8N/ieQgLGzMJhVXvu9svUlwm9XMH1RMx7K57hynAUqajZx2B+LtqetP5+DQBAIAhuNQEj/RiOc8icigKzR69sJRoQUEfGSJQvxgrW7DS2xM0KFmJNQ0E77oR+LDr3gW0mPl+4iqaQZFqtWlFbkUJ12BVkk0o/M+LEwUiQOBAcFWSCPEbUVMUe4RHGRLL9DQH4yLU1rjwg33TLZDBkHHJE2hLOIHdIFT7IfkqNyMto6h3X4W1SerArp0y9IIMcBCW1JW5VTZZi5ELXWNiQSIXO/nU9IItxbeetcZTgjUcqwhSuyicAbExYC2xtf2cNkHPnBLG2sSL+orL8tEke6lcvBM144LbZZdCDrixzuUabZHAQR+UOs9YFFt/pYe/W/GSizOfppLFX73c85cL++c79yOC12ohI5r7zO9XlSmfW9MydM+ZwfhFt0nDyLbIgMxm4+ZhiQdGsbLE61aBdJZwc8aebNWGjvpFR1csrI4rTF88RxUA6Hw5788Ndfa02cCn1gTYzyjXPjo354zA5Nyi975q3HmOOQcsTN2JD6CS54VmFBAsXx4QzrzyL76lm0rnltSr4gEASCwHojENK/3oimvLkRQEDJD0SmkR0kGeFG9C00RDS8hP3KKTLthY68kFKQeYj4eflWWssLmAYdEUWASt7jZY+4W6w5jehUvc6cAzMDZEK2HkUWRPppjDkrttC0zWNFJpEhC0+tQxCRnUQchteREwtSzZAgmkgcGQtSJcqsTiQIWVM3ssJRqgir9g5TWwdSpc3087WvOuKNxO6yyy7dYYcdNroYuMpAgtlHlkKyhfQ72Ed6JApcfeaeuq9sggVShZjW7I82GSPaMCYPqlkOi15bTb9oLYKPNHPu7MRiFgax1U916Pt5DvlF4xFOuCP/pDtkNBY8izzDHclFGhFrzsikxdParr2IsEXFHENOludA262LcD/bKw3xquvteSwPG5BaTiIpFGfEInltIJVxnQ5eu2zz6RkwjrTD+OEYcSAmJX3KTo7l3nvv3a9b4fAqg4OLSHue5NHmtSRj1+8VsFFQwDNmbHGQrIUg6xGFrwSHMSzq+zrDxgyYhdRmiyxA1wfKN+49Q2aZ4MOGaeO3ysw5CASBILAZEQjp34y9skVsolUW/UUISV5E7chJaPzJNbxcEQ0HMjrtBT7vC34ILRLN+WgX8iJeFd2cpcdWL9sQAtFti3SRHaS+JB6cF6QJoUCiSnaDFCPHIpNFhMbaWHUgHCKpyBoZjwWFZhEQNCTFoW6LDS3M3GOPPXqnSVQaMR6mMczYghTSXpeDgsSyFfGZRKrK7iLt8oruI2Xaa5ZAv9picVpb9TUSjTDbvca92mfBKcePQzFMIvkwMTNh1xn3cOBE2pFmbXcfLbk1CXaw4Uis5TBGjVXrNTgQyKI+QTiRQ5jpe7MryC/td0vah7b7zmyX8ecefcgu0hhSEg5pSUmG967X3/oO7hwkDhGnVTs54NbMaCenoPptrF42IuTtD4Lpe1hxds0itGNnrIxp19holsfsG4e85GvGpvUKhXXrYI2N77YO37NbH3IorcEwe6AP6vnlyNHwkwD6XzFMykgKAkEgCKwKAiH9q9JTR0M7TZWTFRRRRu5Ep5GMWrCJ+Ds26uXqpS+yjTSIriIqSBf9MSLXymGKRNRZlyBtbCVHqR9CQkQQbyTQLAWyI8KNCCNARYTlIUERaZ0kAVGH70SDabM5FkgPEs1BIt8psm+XERFxUXKRZ4SXtAMRmkbYWmxFg213SE6jXARaJJWDwXFpZz4Kh/Z+n9Vl5xYYwkIZiCQSzqEYI081vBF4i29F7TlN7kWkSZeGC0CrXjaZZSHJIsVwjzFFx87hgZ9+pNWmk4c7UufQxnkP+bWHU0hGZftO/VKkX3s5HfIh8CL1ZmQ4a4VVtbPOiKzZLJIaOFdk3LggsdJ3FVmue9b7rD84F5wMY9ZzqJ2cLusSzApwfgvvYf3s0wf142T6DQaeIxIv41GfwmGtyb3s4Bx5Tjkl6tCXnOmS4hVWQ1uHf7NDmWYNyXZqXCiX48wx1Hbj2PNd/dCW035ea7tyXxAIAkFgmQiE9C8T7dR1JAQsPPWjWCXtQe5EqC3SG4voHunmdfpDlJ78g964NORIINKL1CHNbfKir4PDIAJoNxukAWlD6JFCC1dJlERIkQukiG7b7iL08YgpUkSmwLkQ3W4Tu0iGOEbIFKeCvMaCVlFO5ARBFElFgiwcJjux1oEEChFVb9k6D0GR3wwLKQaSzkaHSCp8OAQiwpWmlc1mRJ2NMOGgwIhGfdo6CTjAk/OCfLoX+bSTii1Ex/TgCL0x4weURKfZbAaEIyeKyzHTx5wg400f2JUI0SYLWeRwn+gvmQ9pGnsd7cJShJkDZrYCnghjYVXYOSOo+opNtS2kscPxNQPEQeJAzdN3bbmLfFa2fuU0Ibn63fhHfM02WMwtgt/2+7B82LLVb12YEao+FzW3IB4h59xsSzv0oy1NzTwY8zA2PthINoaYe2YqDfEe/u159P8Hzp4d45MTYfxwVEjDRP89v8ot26uc+rvqyzkIBIEgsAoIhPSvQi8dDW1EeCzERZ68vBE1L14LABGQaZFvcKzXSxepQiQRdFH4IrokR66JgI4l9SObou/IrYWjCCodsCivBZLkEiXtQKhpyhHR2qFFXfKKXoo4ttF4RMoMgNkB2n8kFiFBqBwwE43kJMFQRBLxEilvy2EnrAsv5/o8bBdiZi9/0hXESnsQITMLotGT7hu7Tm6jXWxGIp3tClPR8bF7RJwRexFyxEvdbKgfuUJOx2YJkG6aeA4UsgpX6xH23XffnpAjrBw0/WVNgtkXP7B1wAEH9LjBbt7DPQcffHDvvJCnIfTWGXBm4MTxQ0hrFofGXV+2Sdu1Q/TcrBYnggMIJ8+AaDunSX9I8hs/nCUYaK86PSPaNcTS33XU/e3fda0cS5jUL1KbOSotuzUMZkfU3aa2LGMNMeZscnRLEqYMf3MaPEPlNLh3kWTsupe0y+yHmRw4cXo5gxwNC3vZ0Zbdjnn1lc2uk3uRVCH8nvmaneFw+cVli/o56HBun6W2nEXakLxBIAgEgc2CQEj/ZumJLWSHFy8yjHxZtFtEzUsc0bC4EJmR6mW9UfAUqaDbFV0u0o90Ip+IujytLWxCBhADEWhEDQlBRixaFCXkDGhj3euMPKlHhBmZVReSKHosaiz6WEm9CCZSIg9SiEw6I/tmIhAe0UjOgRkJ9Q3JT+HnPCtxUpA/Doy2cC4QItdErBdJZC8cFtFe/VtRVIuOLehE5IY2kUDZHUjb4AgjbRaZt8iyJBZDO5Bv6wU4B2yGK8JJX45wV6TWmYOHPCPcDpgvcrjH/Ygj4q5f/W3MipSX86fN+pmWHXmsccB2fYQII/Z2pbL+QDsRZZFyeHMCjRcYqceMj+fFTju2Q7WlKad5SEyVLT98tXdSkocThExzzsxy1TadHD5rWvTFsPy2PLZpuzK0o8aNZ0G/77XXXkcsAB72dVvO8HObl2NjXQRnGSEvR1BdbPSczTOLoEzYKM9za3FytdmYqVlGv6DMIdC/9T9oaF/7d2trez2fg0AQCAKbEYGQ/s3YK0dzm7x8kQWESHQWSUB6yBpsPUmHXSTJS3UjX6xFBkS4RdSLjNOE23VH5HkYpUQcEG3kC9FEkN0nustxEAlGYIdJXXYgom2vNiOHFvdamEv/XQkGdtBBbpASkXI2iQKTjdi+ksa+1RvXvXUu7Gbh53ttFM0Xda6ZF1FVMx7DtQ1V/rSz/m13ZkLE4YRocZTsWsR+zgSMyVzMApAwlXyJg6P9pEU04cZNtanq9jcHg37frivGkb5ACNldxLzyb8SZM0LmheQaw9rKDv3FESBXQiT1qXGDrBv7tuQ0g1N72RsH9OlwE8n3DCDuovvGlO+MNxgaByLV8GvHp/tgZTxzrkTB4UseY22Js3vMpsF711137eVbNSNDaueZ5LCRQyl7bPy4BlsSGf1jFqfWl3gOzNhxSBHyepZhP1bWpD7RdrjCascddzxiFsG4sH7GLIJZhmr/pHLqOifHDJSxAkfjvJwtP0DGZrN7nBjrR4aHWRv3w9Szaowbk0lBIAgEgVVBIKR/VXrqaGSnCJpIpx/SoRdHakU5EUwv+G3Z5WOtMJE4mGVgC9Iomk6u4QUv6ldJ9NULXxSZnhhZQvIQnvoFVSR0UpTQvbYcRK60GelAPjgCIpBFkER6kRpSIDMgyuaEwAeBtOYB6ZqUkKs6JuWp68iVNQCi5bTlhYHtCun7kfNpUeMqx7lInXaITtuqlNQGUYMrCQWCaGGwmQqRVUTLrArNv7bWLAMi5prdj1qpS9suOCOxnIWKmLMfmRUFLntaG9f7MxsQQNpy5LTtV/3HmfSLtvrdmOf0IO3wNW44gLb6RDrJfTwb1bfGG9LOweQgaJvxJpJuRqndQhUBhYXF8fBwcBDhS/5k5kj5MCdhQe6Nc32jXNFu14wDhH9MllTYsc/skvLsrKRfjWWzBQi1scuW4bhZpD/MwHBOyOe0V/ki/fqZ/dN+A6HsrHNho+1mDOAOR4f+4lxaiG0myvat/hcMD9f1nZkR8jAzEPM6HGVHzkEgCASB7YlASP/2RH+L1u1FifwifV7mXuSILzmECCXSU6kleHVtI85ItohlaYZJHGjbRf3axbwi+LYZJR9B3BFZB0KmPaK5JCRDclN/I4fKLC0x0oFwIfV2zYENwszxEXkXKbY+QJ3In2glwiVPlTnEozCb9P0wP3sttkROi5yzCVk3o0BqtJakrWQv9WNH2gorZSOJIsL6H9H32RgwFuQjK7LAFqkU7R2Sx7JHHWZPLMZUhrL1IfkGh22Y5sVkeN/Y31WWs/4QBVYvAonIV1sRYbMX2onoI9cleWIv4s0JgLXnomY01Mnh5AyK8iuj8EF8d9999574GjNsMOPgtxtqjQmM4Siv8Ynskq3BnA1sVB7SS0LGBo6J2QhjQpsmJeNQ5JtzxVHUVv3GydHf+l3fsKs9JpU3vO4e2+Bal8F+JF0dbC7pUa1fqfKHZbR/m52zG5ItW7W17K0xCQdjH2bG4bRDf5IzWccBp6QgEASCwKogENK/Kj11NLITOSFBEK1DnEXwEBqks7YH1Nx6mTtvdCKTQbpJRLz4EQCLbO0YQkLABmTMlL9dckqCgoT4rC3kFEiafGV/nasNosK08/LXnv3aTwqC4BSREWVVL2JB+sIJQFwmkd8Wn8KtvTbtM3ImSs2RYQsihIgij+pu1xpMK6e+a9tKA0/KI0JqVgfhagmxz46K7iNUsDATYDZFNFW7h6nqqHUA9VsPyDRyTRplpqFNdU97bVs+D8szW0Knb0aGNl/0XrsQSuTemEKwi2j73m40Znk4dRzGdlaJbdrOqRHV50y4F16w9MvUnFVjgi1mBUiHamzJpz+djWmzUsgsOxz+tlsPZ9tvOrDBL0S36yCGbWQTZ8DvLRgzHGPlsEt7zI6ZUTAz0852KWesrDH8lW99im1n/U9gu/I5cxwXMxcWrWu3vGNlD+syhmtrVA69Mb6WA54wNCPgfwE7k4JAEAgCq4JASP+q9NTRyE4vSi9tkXWLNr3IacmRGOSWUyDVy3xQSa4/AAAIGklEQVT4At8IKBAdEXgRY4tIvdxFjkXvEV82IPMIGO02Ukdb7QeoEFRbNnIc2lR2VzvqO+QQwbKvPPLmENWm60f6i8wgTbBwrrKqjGnnRfIqB+kn/0CwtEl/sI2u2yxHSU2m1dl+N6xfew855JB+gaqF24gu50pUGMF3tu2ove9Fm8kq6PGR4EltrzqQVNuZcqAQfpFtsyZj26DWPa2t2/J5WB5bLSoWbSdXElXmjIiua1+1VdTdTI8fgyKlkd/sFtI+TPpfxNuzYb2I/kG0/XiayLX72OEwbjgBtPp+qAwO6oIvcu+Au/71Haw5CPpZ1L61ococtpF9iLYZCfv6i7qbEdJ3Ptvzng2Ljpm23Z4zzg6Srp3sdWg/h9uMCmdSmmTn0G7PlTHIIePcen4d7F7k0J+eVY6H8bkt7WzbnM9BIAgEgWUgENK/DJRTx5EQQE6QSfIL5AzhJOtB4BCfit7VefgCP1Jh6/gHckUDTU4gCiiCiaSK4EvsIHWhM2Yzfb1dZeiqaezntVMb7X1OUiMqStZhZxbtRxyr3evYtKlFsYf+mtNjdyHbL5KUWPSpr7Y1wUUdFqTqcw6PRaj0/rTu9OoWcKsbtmY4RLyHeA7/ZldFtpF90WDyGVs7HnrooaMzBNvalkn3l23O+pAkSSTYIl4ae7M42uqHqkhojHtEm6MIm7pf+T7XAX9OmQWzxhqi7d4ivkinvDVmONScLOswLKS1yw98Oap2wOEgGbdmX2CEvCPQVV+dJ7Wz7HMPx52za7wYx2aljJmyaVoZY9+pW9IWkiUzNRwiEi8aek4OidvY2Bgrr72mT+DiWTUbw6FwmE1Y5PCcch7YNksC1dafz0EgCASBzYBASP9m6IUtZoOXu5c6ooz0INKijCVtqZd/EZD6e6NhsgCXnEcEkCSCBEJEFklCNJARkVcOC5vNSiDvkxbVTrJbJBM5Q4LJUxwIifZXmze6rW352qVu2zNqE3u0cT1ITbVHm6vPRcPJdjgaHB2H6DBMq14kre5tba3PbBYN5kCYJaL5JqURVedQlOyl8i/zzG7jhO7d+Dau2KOdFsiSxrhu3BgH2uKeseQ6LMhTjDXORN2LXNe9dT+cORFIufwcKDiTlHFqkdV63tgHQ+VLyqhyxmxp86ibM2Lc1hiuvmPTWlLV76wMzxyZUI1J47OkR4uWrzxYw1w57F7r4bllG6xn4bWonckfBIJAENhIBEL6NxLdlL1NCBQJ2KZCFrjZdofkO2QKtOd02KL+IqWIGqKzSDo6EIJtbcOkPnRdFBvhdAwJVN03qX59gQySvZC86CvrAeyeI5JbspdF+mu98o7ZrH3aqc1DUlxt3Zb6x+pUnutVdzkJk+qZx45J9Uwqc5Hr89S/SHnJGwSCQBAIAkdGIKT/yHjkr+2MwEaSillNEyEVRbUDCy00Xb9FhDvvvHMv8REVrdTa2X6u73NeHIEifS2e7ee2RLMQpEIWuHLQ9JVdZGi2RdVFYqVJ97dlredn9Q1J/bTy5R+zsa6PfTetvEW+a8ue9HlY3rR8vqtjeN96/r2MOmbZuxlsmGVjvg8CQSAIDBEI6R8ikr+3GwJepNszIWuIPX0yyQgiSSdu0aPdYMgXJhG6IgHr2Yb1LGsarsuqZ5oNk75rbWs/k2lYsGpRpQW8+soCTQtJSVtKsjKp3PW+3trWlt1ebz+3eXye97t581WZw/zDv4d2bMvfyl6P8tejjGE7xsocuza8b+xv96313rHyci0IBIEgsCwEQvqXhXTqWQkEyC/s1mNBb0l8RJAtDq39y8caUkRgFcnAZra5ta0+k6mQW9lm0oyMbTBtwyjqb3G07yvvWF9txLVJ9U26PrRhvfMpX5nDcod/D+3I37MRGMN19l3JEQSCQBDY/giE9G//PogFmwgBkXwRfTvq2I4T8d9hhx16rbgdbej+pZY8tZ83UVOOtqZYdGx3Gjsr2ZPfFqu2PbVLjkWzbX8UQWuvHW2BScOCQBAIAkEgCExBIKR/Cjj5amsgMCSEdpmxReJ+++3X7bTTTv3C3j333PNIP1TV3tN+3hqIbd9W2sHFdpH77LNPv94C+bf9p20sfZcUBIJAEAgCQSAIHBWBkP6jYpIrWwyBIWkX7bedoS0W7QRjL3K7+gx/vGiLwbRpmssps5Ujku+3BfTR4Ycf3l8jzxr256YxPIYEgSAQBIJAENiOCIT0b0fwU/XmQGBIEutvZySytpOs66xuP2+OVmwtK+CvX+poW5++adHI5yAQBIJAEAgC/49ASH9GwpZHYBpJHH7n7zq2PHCbFIBhn21SM2NWEAgCQSAIBIGlIhDSv1S4U1kQCAJBIAgEgSAQBIJAEFg+AiH9y8c8NQaBIBAEgkAQCAJBIAgEgaUiENK/VLhTWRAIAkEgCASBIBAEgkAQWD4CIf3Lxzw1BoEgEASCQBAIAkEgCASBpSIQ0r9UuFNZEAgCQSAIBIEgEASCQBBYPgIh/cvHPDUGgSAQBIJAEAgCQSAIBIGlIhDSv1S4U1kQCAJBIAgEgSAQBIJAEFg+AiH9y8c8NQaBIBAEgkAQCAJBIAgEgaUiENK/VLhTWRAIAkEgCASBIBAEgkAQWD4CIf3Lxzw1BoEgEASCQBAIAkEgCASBpSIQ0r9UuFNZEAgCQSAIBIEgEASCQBBYPgIh/cvHPDUGgSAQBIJAEAgCQSAIBIGlIhDSv1S4U1kQCAJBIAgEgSAQBIJAEFg+AiH9y8c8NQaBIBAEgkAQCAJBIAgEgaUi8H8sHDUN4udXwwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:2b63e905-8cfb-45c1-b5ff-43128fca3f4d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08075665923377107"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.cdf(73,80,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13590512198327787"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.cdf(90,80,5) - norm.cdf(85,80,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hipotez Testleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Populasyonun parametreleri( $\\mu$ (Ortalama), $\\sigma$ (Standart Sapma), $\\sigma^2$ (Varyans), $p$ (Oran)) ile ilgili herhangi bir iddia atıldığında bu iddianın doğruluğu hipotez testi ile test edilir.\n",
    "* Hipoteztinin amacı populasyon parametreleriyle iligili ortaya atılmış bir iddianın doğruluğunu örneklem verilerini kullanarak belirlemektir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sıfır ve Alternatif Hipotez"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Populasyon üzerine bir **iddia** atılır.\n",
    "* Bu iddia üzerinden iki adet hipotez oluşturulur. Bunlar $H_0$ Sıfır Hipotezi, $H_1$ Alternatif hipotezdir.\n",
    "* $H_1$ hipotezi\n",
    "    * Daima populasyon üzerine atılan iddiayı içermelidir.\n",
    "    * Örneklemden elde edilen değer ile populasyonun bilinen değeri arasında fark yoktur anlamı taşımaktadır.\n",
    "* $H_0$ hipotezi\n",
    "    * Daima iddia içerisinde geçen sayıya eşit olmalıdır.\n",
    "    * Örneklemden elde edilen değer ile populasyonun bilinen değeri arasında önemli bir fark vardır anlamı taşımaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "İddia : bir populasyonun ortalaması 5'ten küçüktür.\n",
    "* $H_0$ için $\\mu = 5$\n",
    "* $H_1$ için $\\mu < 5$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burada oluşturulan iddialar populasyon üzerinedir, bu sebeple $H_0$ ve $H_1$ hipotezlerinin karşısında mutlaka ve mutlaka populasyon parametrelerini göreceğiz.\n",
    "**Örneklem parametreleriyle kesinlikle hipotez oluşturulmaz**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hipotez testinde, bir hipotez ile onun karşıtı olan hipotezden hangisinin örneklemden elde edilen sonuçlar ile daha iyi bağdaştığı araştırılmaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hipotezlerin Kuruluşu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\mu_0$ soruda verilen değerdir.\n",
    "* 1.çeşit\n",
    "    * $H_0 : \\mu = \\mu_0$\n",
    "    * $H_1 : \\mu \\neq \\mu_0$\n",
    "    * Çift Taraflıdır\n",
    "* 2.çeşit\n",
    "    * $H_0 : \\mu = \\mu_0$\n",
    "    * $H_1 : \\mu > \\mu_0$\n",
    "    * Tek Taraflıdır\n",
    "* 3.çeşit\n",
    "    * $H_0 : \\mu = \\mu_0$\n",
    "    * $H_1 : \\mu < \\mu_0$\n",
    "    * Tek Taraflıdır"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Bir hastalıkta ortalama iyileşme süresinin 10 günden az olduğu iddia ediliyor.<br>\n",
    "$H_0 : \\mu = 10$<br>\n",
    "$H_1 : \\mu < 10$<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Anlamlılık Düzeyi (Significance Level)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Anlamlılık düzeyi bize hipotez testinin kalitesini göstermektedir.\n",
    "* $\\alpha$ ile temsil edilmektedir.\n",
    "* $\\alpha$ değeri genelde %5 olarak alınır. Bu değer arttıkça hipotezin kalitesi düşer ve zorlama olarak görülür. Bu değer küçüldükçe hipotezin doğruluk yüzdesi artar.\n",
    "* Bu değer kritik bölgeyi ve kritik değeri bulurken bize yardımcı olur.\n",
    "* %95 güven düzeyi deniyorsa $\\alpha$ değerimiz %5 olmaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kritik Bölge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Anlamlılık düzeyinden elde edilir.\n",
    "* Ret bölgesi olarak da söylenebilir.\n",
    "* $H_0$ hipotezinin reddedildiği bölgedir.\n",
    "* 3 çeşidi vardır. Bu farklılığı hipotez testinin kuruluşundan farklılık oluşturulur."
   ]
  },
  {
   "attachments": {
    "0c673704-601f-4cba-ae08-c2b252d4c8fb.png": {
     "image/png": 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"
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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\alpha = 0,05$\n",
    "* 1.çeşit:\n",
    "    * $H_0 : \\mu = 10$<br>\n",
    "    * $H_1 : \\mu < 10$<br>\n",
    "    * ![image.png](attachment:bdb7211f-1354-498d-a3d8-89bbde0eeca4.png)\n",
    "    \n",
    "* 2.çeşit:\n",
    "    * $H_0 : \\mu = 10$<br>\n",
    "    * $H_1 : \\mu > 10$<br>\n",
    "    * ![image.png](attachment:96470f4b-c592-4fff-8f24-82cc90619b69.png)\n",
    "    \n",
    "* 3.çeşit:\n",
    "    * $H_0 : \\mu = 10$<br>\n",
    "    * $H_1 : \\mu \\neq 10$<br>\n",
    "    * ![image.png](attachment:0c673704-601f-4cba-ae08-c2b252d4c8fb.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kritik Değer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hipotez testinde kritik bölgesini(ret bölgesi) kabul bölgesinden ayıran değere denir.\n",
    "* 4 adet örneklem dağılımından biri ile hesaplanır. Bunlar;\n",
    "    * $z, t, \\chi^2, F$ tablolarından bulunur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test İstatistiği"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hangi dağılım kullanılacağı belirlenmelidir.\n",
    "* 4 adet tablonun her birinde farklı bir formüle sahiptir.\n",
    "* Örnek olarak $z_{test} = \\dfrac{x-\\mu}{\\dfrac{\\sigma}{\\sqrt{n}}} $\n",
    "* Eğer $z_{test}$ değeri ret bölgesinin içine düşerse $H_0$ hipotezi ret edilir. $H_1$ hipotezi kabul edilir.\n",
    "* Eğer $z_{test}$ değeri kabul bölgesinin içine düşerse $H_0$ hipotezi not rejected olur. $H_1$ hipotezi hatalı, hpotezi yeni koşullarda tekrardan oluşturmak gerekir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Type I and II Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>$H_0$ Doğru</th>\n",
       "      <th>$H_0$ Yanlış</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$H_0$ Red</th>\n",
       "      <td>I.Tip Hata</td>\n",
       "      <td>Doğru Karar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$H_0$ Kabul</th>\n",
       "      <td>Doğru Karar</td>\n",
       "      <td>II.Tip Hata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             $H_0$ Doğru $H_0$ Yanlış\n",
       "$H_0$ Red     I.Tip Hata  Doğru Karar\n",
       "$H_0$ Kabul  Doğru Karar  II.Tip Hata"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame([[\"I.Tip Hata\",\"Doğru Karar\"],[\"Doğru Karar\",\"II.Tip Hata\"]],columns=[\"$H_0$ Doğru\",\"$H_0$ Yanlış\"], index=[\"$H_0$ Red\",\"$H_0$ Kabul\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Satırlar **Test Sonucu**, Sütünlar ise **Gerçek Durumu** temsil etmektedir.\n",
    "* P(1.Tip Hata) = $\\alpha$ yani 1.tip hatanın oluşma olasılığı anlamlılık düzeyine eşittir.\n",
    "* P(2.Tip Hata) = $\\beta$ yani $\\beta$ kabul bölgesine düşmüştür."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hipotez Testinin Gücü"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* $H_0$ hipotezi yanlış iken onun reddedilmesi olasılığı testin gücüdür.\n",
    "* Araştırmamızda **doğru karar** verme olasılığımızdır.\n",
    "* $1-\\beta$ ile hesaplanır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hipotez Testinin Uygulanma Adımları"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **1.adım :** Hipotezler oluşturulmalıdır. $H_0$ , $H_1$\n",
    "* **2.adım :** Anlamlılık düzeyi $\\alpha$ belirlenmeli ve hipotezlerin kuruluşuna göre ret bölgesi ve kabul bölgesi çizimi yapılmalıdır.\n",
    "* **3.adım :** Örneklem dağılımının belirlenmesi, hangi tabloyu kullanacağımızı belirleyeceğiz. $z, t, \\chi^2, F$\n",
    "* **4.adım :** Bu örneklem dağılımından Kritik Değer elde edilmeli ve dolayısıyla ret bölgesi belirlenmelidir.\n",
    "* **5.adım :** Test istatistiği hesaplanmalıdır. Her bir örneklem dağılımında farklı formüle sahiptir.\n",
    "* **6.adım :** Kritik değer ile test değeri karşılaştılarak $H_0$ reddedilememesi ya da reddedilmesi belirlenir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hipotez Testinde Örneklem Dağılımını Belirleyen Çeşitler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **1.çeşit :** Populasyon Ortalamasının Hipotez Testi $\\mu$\n",
    "    * Populasyonun varyansı biliniyorken $\\sigma^2$ $\\Rightarrow$ **z tablosu**\n",
    "    * Populasyonun varyansı bilinmiyorken ve n $\\geq$ 30 $\\Rightarrow$ **z tablosu**\n",
    "    * Populasyon varyansı bilinmiyorken ve n $\\leq$ 30 $\\Rightarrow$ **t tablosu**\n",
    "* **2.çeşit :** Populasyon Oranının Hipotez Testi $p$ $\\Rightarrow$ **z tablosu**\n",
    "* **3.çeşit :** Populasyon Varyansının Hipotez Testi $\\sigma^2$ $\\Rightarrow$ **$\\chi^2$ tablosu**\n",
    "* **4.çeşit :** Eşlendirilmiş Gözlemler için Ortalamanın Farkı(Md) Hipotez Testi $\\Rightarrow$ **t tablosu**\n",
    "* **5.çeşit :** Populasyon Ortalamalarının Farkı için Hipotez Testi $\\mu_1 - \\mu_2$ $\\Rightarrow$ **t tablosu**\n",
    "    * Populasyon varyansları biliniyorken ($\\sigma_1^2$,$\\sigma_2^2$) $\\Rightarrow$ **z tablosu**\n",
    "    * Populasyon varyansları bilinmiyorken (Varyanslar eşit kabul edilir) $\\Rightarrow$ **t tablosu**\n",
    "    * Populasyon varyansları bilinmiyorken (Varyanslar eşit kabul edilmemesi) $\\Rightarrow$ **t tablosu**\n",
    "* **6.çeşit :** İki Populasyon Oranının Hipotez Testi $p_1 - p_2$ $\\Rightarrow$ **z tablosu**\n",
    "* **7.çeşit :** İki Populasyon Varyansının Oranı için Hipotez Testi $\\dfrac{\\sigma_1^2}{\\sigma_2^2}$ $\\Rightarrow$ **F tablosu**"
   ]
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   "source": [
    "## P Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* İstatistikte, p değeri, bir istatistiksel modele bağlı olarak gözlemlenen örneklem sonuçlarının (bir test istatistiği) ne kadar aşırı olduğunu ölçmek için kullanılan bir fonksiyondur. \n",
    "* Eğer $p$ değeri $\\alpha$ değerinden küçük ise bu durumda $H_0$ hipotezini reddedebiliyoruz.\n",
    "* Genel olarak $\\alpha$değeri $0.05$ alındığı için $p<0.05$durumunda $H_0$ değeri ret edebiliriz."
   ]
  }
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