hypothesis_testing_exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "717c5740",
   "metadata": {},
   "source": [
    "## Soru türlerine özel test ve görselleştirme için yazmış olduğum fonksiyonlar en aşağıda bulunmaktadır."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31b160c",
   "metadata": {},
   "source": [
    "***\n",
    "#### 1) \n",
    "_Bir fabrikada üretilen halatların çekme gücünün ortalamasının 800 kg ve varyansınının 1600 $kg^2$ olduğu bilinmektedir. Teknik yönetim yeni bir yöntemle üretilen halatların çekme gücünün artırıldığını iddia etmektedir. Bunun için yeni yöntemle üretilen 36 halat denenmiş ve ortalama çekme gücü 820kg olarak bulunmuştur. Teknik yönetimin iddiasını %95 güven düzeyinde test ediniz._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08305930",
   "metadata": {},
   "source": [
    "Çözüm:\n",
    "\n",
    "$\\mu = 800$, $\\sigma^2 = 1600$, $\\bar{x} = 820$, n = 36, $\\alpha = 0.05$\n",
    "\n",
    "* Örneklem n > 30 iken _Merkezi Limit Teoremi_'nden dolayı dağılım normal dağılıma yakınsar, __z istatistiği__ kullanılabilir.\n",
    "\n",
    "$ H_0 : \\mu = 800 $ olduğu biliniyor.\n",
    "\n",
    "$H_1 : \\mu > 800 $ olduğu iddia ediliyor.\n",
    "\n",
    "\n",
    "$H_1$ hipotezi büyüklük içerdiğinden, __tek yanlı hipotez testi__ yapılır ve kritik nokta __pozitif__ taraftadır.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8e95ca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z value = 3.00000\n",
      "z alpha = 1.64485\n",
      "p value = 0.00135 <= 0.05 and H0 is rejected, H1 can be accepted.\n"
     ]
    }
   ],
   "source": [
    "mu = 800\n",
    "var = 1600\n",
    "xbar = 820\n",
    "n = 36\n",
    "significance = 0.05\n",
    "\n",
    "z = z_test(mu=mu, var=var, xbar=xbar, sample_size=n, significance=significance,one_tail=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37a586c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_dist_plot(z_value=z, significance=significance, two_tail=False, positive_tail=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4e73bc",
   "metadata": {},
   "source": [
    "* Test değeri $z_H  = 3$ ve $z_\\alpha = 1.645$ değerinden büyük olduğundan grafikte de görüldüğü üzere kritik bölgeye düşmektedir, $H_0$ __hipotezinin reddedilebileceğine işarettir.__\n",
    "* Aynı zamanda p değeri, önem değerinden küçük olduğu için, test istatistiğinin kritik bölgeye düştüğünü ve benzer şekilde $H_0$ __hipotezinin reddedilebileceğine kanıt oluşturur.__\n",
    "\n",
    "    Sonuç olarak $H_1$ __hipotezi kabul edilir__, %95 güven düzeyinde yeni yöntemle üretilen halatların çekme gücünün arttığı iddiası doğrulanmış olur.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd769d6",
   "metadata": {},
   "source": [
    "***\n",
    "#### 2) \n",
    "_Bir yurtta öğrenciler için çıkarılan yemeğin en az 1600 kalori sağlaması gerektiği yapılan sözleşmede yer almaktadır. Öğrenciler yemeklerin kalorisinin belirlenen alt sınırdan düşük olduğunu iddia etmektedirler. Bu hipotezi test etmek üzere 16 gün seçiliyor ve yemeklerin kalorisi ölçülüyor. Ortalaması 1500 kalori ve varyansı 64000 $kal^2$ olduğu tespit ediliyor. Buna göre %95 güven düzeyinde öğrencilerin iddiasını test ediniz._\n",
    "\n",
    "Çözüm:\n",
    "\n",
    "Kitle varyansı bilinmiyor. Örneklem hacmi > 30 ise kitlenin normal dağılıma yakınsadığı varsayılıp, örneklemin nokta tahminleri ile z istatistiği kullanılabilir.\n",
    "\n",
    "$s^2 = 6400$, $\\bar{x} = 1500$, n = 16, $\\alpha = 0.05$\n",
    "\n",
    "* Örneklem n < 30 olduğundan dağılımın normal dağılıma yakınsadığı söylenemez, __t istatistiği__ kullanılmalıdır.\n",
    "\n",
    "$ H_0 : \\mu = 1600 $ olması gerekiyor.\n",
    "\n",
    "$H_1 : \\mu < 1600 $ olduğu iddia ediliyor.\n",
    "\n",
    "\n",
    "$H_1$ hipotezi küçüklük içerdiğinden, __tek yanlı hipotez testi__ yapılır ve kritik nokta __negatif__ taraftadır.\n",
    "\n",
    "Test yaparken $H_0$ doğru olduğu varsayıldığı için, $\\mu$ = 1600 alabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4a6b478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t value = -5.00000\n",
      "t alpha = -1.75305\n",
      "p value = 0.00008 <= 0.05 and H0 is rejected, H1 can be accepted.\n"
     ]
    }
   ],
   "source": [
    "mu = 1600\n",
    "var = 6400\n",
    "xbar = 1500\n",
    "n = 16\n",
    "significance = 0.05\n",
    "\n",
    "t = t_test(mu=mu, var=var, xbar=xbar, sample_size=n, significance=significance,one_tail=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32c5049b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAPz222/49NNP1aevN2zYgKKiIrz00kvq17744otYunQpfvrpJ/U6QOXl5Vi7di38/f3Vs2Ha7JMMEwsgMknp6en47bffAFTN+ly5cgXbtm1DZmYmZs2apdFwfK/CwkJ06NABL774Inx9fWFnZ4dDhw7h9OnTGr/F9+vXD1u2bEFISAgGDBgAOzs7PPPMMw3K6+7ujsWLFyMlJQXdu3fHli1bEBsbi59++km9anPv3r0xaNAgzJ07F3fu3EHr1q2xefNmVFZWVtufNtmWLFmCkSNHYvDgwZg0aZL6MnhHR0ed3x/t4YcfxqJFi+Do6AgfHx8AVcXlAw88gKtXr9a45s29VAXhxx9/jJdffhktWrTAM8880+iZqi5dusDJyQmrV6+Gvb09bG1t4e/vf99erqCgIHz22WfqtWbMzDSXX3v66aexYcMGODo6olevXoiOjsahQ4dqvST/bm+++Sa2b9+OJ598EmPHjsX169fx22+/aTR5A8Drr7+OrVu3YsqUKYiIiMDQoUOhUCgQHx+PrVu34sCBA9Uu976brnqA6uLp6YmgoCD4+PjA2toax44dw+bNm+Hn51ft72PPnj0xfPhwjfWyvvzySwwZMgTDhw/HW2+9hZs3b2LZsmV44oknNAoof39/vPTSS5g7dy6ys7PRtWtXrF+/HikpKfj55581jlPffZKBkvISNKLGauhl8Pj/y3YFQRAdHBzE3r17i5MnTxZPnjxZ42tw16XW5eXl4ocffij6+vqK9vb2oq2trejr6yv+97//1XhNUVGR+Morr4hOTk4alwirLmfetm1btePUdhl87969xTNnzoiDBw8Wra2tRU9PT3HlypXVXn/9+nUxICBAtLKyEl1dXcV58+aJBw8erLbP2rLVdBm8KIrioUOHxKFDh4o2Njaig4OD+Mwzz4hXrlzRGKO6VDonJ0dje22X59fkzz//FAGII0eO1Nj+5ptvigDEn3/+udpr7v7eqHzxxRdi+/btRTMzM41jA6hx+YLaLie/1549e8RevXqJFhYW9b4k/tq1a+qft2PHjlV7Pi8vTwwODhadnZ1FOzs7MTAwUIyPj6+WqaafDVEUxWXLlont27cXraysxKFDh4pnzpypdhm8KIqiXC4XFy9eLPbu3Vu0srISW7VqJfbr10/8/PPP73uZeXN48803xV69eon29vZiixYtxK5du4offfSRKJPJqo0FUO39iaIoRkVFiUOGDBGtra3Ftm3bilOnTq3x9aWlpeIHH3wgurm5iVZWVuKAAQPEsLCwGnPVd59keARRbGDnHxEREZGB4q0wiIiIyOSwACIiIiKTwwKIiIiITA4LICIiIjI5LICIiIjI5LAAIiIiIpPDhRBroFQqkZGRAXt7+yZbVp+IiIh0SxRFFBYWwt3dvdqio/diAVSDjIyM+94ckIiIiPRTWloaOnToUOcYFkA1UN3zJS0tTeNmeURERKS/ZDIZPDw81J/jdWEBVAPVaS8HBwcWQERERAamPu0rbIImIiIik8MCiIiIiEwOCyAiIiIyOSyAiIiIyOSwACIiIiKTwwKIiIiITA4LICIiIjI5LICIiIjI5LAAIiIiIpPDAoiIiIhMDgsgIiIiMjksgIiIiMjksAAiIiIik8MCiIhIh0RRVP+/UlSiUlkpYRoiqg0LICIiHRIEQf3/ZoIZLMwsAAAlFSVSRSKiGlhIHYCIyJCJoghBEFBYXog1MWuw8eJGXMy6CKWoRLc23TCm5xhMHzgdbnZuAIAieRHsLO0kTk1Egnj3fC0BAGQyGRwdHVFQUAAHBwep4xCRntt/bT8m/zEZGYUZNT7vYOWArx//Gm/3exsAcLvkNtq0bNOcEYlMgjaf3zwFRkTUCIv/WYynNz1da/EDALJyGabsm4I3974JhVKBNi3bIKsoqxlTEtG9WAARETXQ/Ij5mHNoDkTUbyL953M/4/Vdr0OhVMDVzhWZhZlNnJCIasMCiIhIC6qugRUnV+A/R/+j9et/v/Q7Zh6YCQBwsXNBQVmBLuMRUT2xACIi0oIgCDhy4wjeP/B+g/ex8tRK/HT2J5gJZlCKSihFpQ4TElF9sAAiItJCZlEmgrYFQSEqGrWf9/56D5eyL6GVTSsk5SXpKB0R1RcLICKielCd+pr651RkFTe+gblcUY7Xdr4GuUKOrq27Il2W3uh9ElH9SV4ArVq1Cl5eXrC2toa/vz9OnTpV69jLly/jhRdegJeXFwRBwPLly+vc96JFiyAIAmbOnKnb0ERkcgRBwJZLW7AzfqfO9nk+6zwWHl0IALAyt+KpMKJmJGkBtGXLFoSEhGD+/PmIiYmBr68vAgMDkZ2dXeP4kpISdO7cGYsWLYKbm1ud+z59+jR+/PFHPPjgg00RnYhMTH5ZPqb/NV3n+/36n6+ReCcRzrbOiMuJ0/n+iahmkhZA33zzDSZPnozg4GD06tULq1evRsuWLfHLL7/UOH7AgAFYsmQJXn75ZVhZWdW636KiIrz66qtYs2YNWrVq1VTxicgEqE59fXn0S+SU5Oh8/+WKckzbPw0A8IDzAygqL9L5MYioOskKILlcjrNnzyIgIODfMGZmCAgIQHR0dKP2PXXqVIwaNUpj33UpLy+HTCbTeBARAVWnvq7dvobvTn7XZMc4cP0A9l/bDwszC1zPu95kxyGif0lWAOXm5kKhUMDV1VVju6urKzIzG7442ObNmxETE4PQ0NB6vyY0NBSOjo7qh4eHR4OPT0TGQzX789Ghj1ChrGjSY318+GMAgK+bL3JLcpv0WESkB03QupSWloYZM2Zg48aNsLa2rvfr5s6di4KCAvUjLS2tCVMSkaEQBAFnM85iV/yuJj9WbGYsNl/aDAC8TQZRM5CsAHJ2doa5uTmysjT/omdlZd23wbk2Z8+eRXZ2Nvr27QsLCwtYWFjgyJEj+P7772FhYQGFouZ1O6ysrODg4KDxICLTppr9+fzI5812zE8jPkWlshK9XXoju6jmi0GISDckK4AsLS3Rr18/hIeHq7cplUqEh4dj8ODBDdrnY489hosXLyI2Nlb96N+/P1599VXExsbC3NxcV/GJyMgJgoCYWzH4I+GPZjtm4p1EbLm0BQBws/Bmsx2XyBRZSHnwkJAQTJgwAf3798fAgQOxfPlyFBcXIzg4GAAwfvx4tG/fXt3PI5fLceXKFfX/p6enIzY2FnZ2dujatSvs7e3h7e2tcQxbW1u0adOm2nYiotqIoghBEPCfI9rf66uxFv+zGK8++Cp8XX1xp+QOWrds3ewZiEyBpAVQUFAQcnJy8NlnnyEzMxN+fn4ICwtTN0anpqbCzOzfSaqMjAz06dNH/fXSpUuxdOlSDB8+HJGRkc0dn4iMlCAIiM+Nx96re5v92BezL2Jfwj483f1pJOUlsQAiaiKCqDrRTWoymQyOjo4oKChgPxCRiVHN/kz9cyr+e+a/kmQY6jEUx944hvLKcihEBVq2aClJDiJDo83nt1FdBUZE1FiCIOB2yW2sO79Osgz/pP2DU+mnYGVhhUvZlyTLQWTMWAAREd1jTcwalFSUSJph5amVAID29u3BiXoi3WMBRER0lwpFBVacWiF1DGy9vBU5xTlo79AeF7MvSh2HyOiwACIiusveq3uRUZghdQyUK8rxv3P/AwDOABE1ARZARET4t8j48eyPEif51+ozq6FQKuDr5suFEYl0jAUQERGqmp+T85JxKOmQ1FHUUgtSsf/afgBViyQSke6wACIi+n+/nPsFIvTrdNMvsb8AALq26QqlqJQ4DZHxYAFERASgUlmJn8/9LHWMav5M+BM5xTlwsXXBhcwLUschMhosgIiIUFVo3Cq6JXWMaiqUFdhwYQMAQK6US5yGyHiwACIik6ZqfladatJHv5yrytbHrQ9kZTKJ0xAZBxZARGTSBEHAndI7+OvaX1JHqdXlnMs4nX4aLcxb4HLOZanjEBkFFkBEZPK2X9mOCmWF1DHqpLo1RyvrVtIGITISLICIyGSpTn/9ev5XiZPc39bLW1GprESPtj2QLkuXOg6RwWMBREQmS7X2z/G041JHua/cklz1GkXX865LnIbI8LEAIiKTtvnSZr1b+6c2v1/6HUDVDVKJqHFYABGRSVKd/lJdYm4IdsXtQlllGbq07sKVoYkaiQUQEZkkQRBw7tY5xOXGSR2l3grlhfgz4U8AQIZM+hu2EhkyFkBEZLJ2xO2QOoLWVKfBurTuwrvEEzUCCyAiMjmqwmHr5a0SJ9Hen9f+hKxchvYO7ZFwO0HqOEQGiwUQEZkcQRBwIesCrt25JnUUrZVVlmFfwj4AQFZRlsRpiAwXCyAiMkk743ZKHaHBVNk9nTx5GoyogVgAEZFJMeTTXyphiWEorSiFp5MnT4MRNRALICIyKYIg4ErOFYO6+utexRXFOHD9AAAgo5BXgxE1BAsgIjI5hnz6S0V1BVt7By6KSNQQLICIyGQYw+kvlX0J+1ChqED3Nt2RnJcsdRwig8MCiIhMhiAIuH7nOi5mX5Q6SqPll+XjcPJhAMCN/BsSpyEyPCyAiMikqC4hNwY746tO5bW1bStxEiLDwwKIiEyC6vTX7qu7pQ2iQ3vi90ApKtHbpTeyi7OljkNkUFgAEZFJEAQB+WX5OJZ6TOooOpNVnIWTN08CAK7mXpU4DZFhYQFERCbjr8S/UKmslDqGTu27VnVKr4V5C4mTEBkWFkBEZDL2xO+ROoLOqXqaHnR9EMXyYonTEBkOFkBEZBIqFBUISwyTOobOXci6gNSCVLRs0RIXswz/6jai5sICiIhMwtHUoygoL5A6RpNQzQKVKcokTkJkOFgAEZFJ2HfVeC5/v5eqAOrWuhuUolLiNESGgQUQERk11eXvexP2Spyk6USkRKCkogTtHdojPide6jhEBkHyAmjVqlXw8vKCtbU1/P39cerUqVrHXr58GS+88AK8vLwgCAKWL19ebUxoaCgGDBgAe3t7uLi4YPTo0bh6lZeHEpkqQRBwOfsykvKSpI7SZMoqy3Ao6RAAILM4U+I0RIZB0gJoy5YtCAkJwfz58xETEwNfX18EBgYiO7vmBb1KSkrQuXNnLFq0CG5ubjWOOXLkCKZOnYoTJ07g4MGDqKiowBNPPIHiYl4dQWSq/kr8S+oITe6PhD8AAG1bclVoovoQRNX8sAT8/f0xYMAArFy5EgCgVCrh4eGB6dOnY86cOXW+1svLCzNnzsTMmTPrHJeTkwMXFxccOXIEw4YNq1cumUwGR0dHFBQUwMHBoV6vISL9I4oiBEHAI+sfQWRKpNRxmpS7vTvSQ9KhFJW4XXKbt8cgk6TN57dkM0ByuRxnz55FQEDAv2HMzBAQEIDo6GidHaegoOqqj9atW9c6pry8HDKZTONBRIZPEAQUyYvwT+o/UkdpchmFGYi5FQMzwQxXb/O0P9H9SFYA5ebmQqFQwNXVVWO7q6srMjN1cw5bqVRi5syZGDp0KLy9vWsdFxoaCkdHR/XDw8NDJ8cnIukdTj6MCmWF1DGahTGuc0TUVCRvgm5KU6dOxaVLl7B58+Y6x82dOxcFBQXqR1paWjMlJKKmZkpFwYHrBwAAPZx7QKFUSJyGSL9ZSHVgZ2dnmJubIysrS2N7VlZWrQ3O2pg2bRr27duHo0ePokOHDnWOtbKygpWVVaOPSUT6Q9X/YwoN0CrRadGQlcvg3NIZFzIv4EG3B6WORKS3JJsBsrS0RL9+/RAeHq7eplQqER4ejsGDBzd4v6IoYtq0adi1axcOHz6MTp066SIuERkYQRAQnxuPlPwUqaM0mwplBcKTqv5NzS6p+WpaIqoi6SmwkJAQrFmzBuvXr0dcXBzeeecdFBcXIzg4GAAwfvx4zJ07Vz1eLpcjNjYWsbGxkMvlSE9PR2xsLBITE9Vjpk6dit9++w2bNm2Cvb09MjMzkZmZidLS0mZ/f0Qkrb+v/y11hGanOg3W2qb2Cz+ISOLL4AFg5cqVWLJkCTIzM+Hn54fvv/8e/v7+AIARI0bAy8sL69atAwCkpKTUOKMzfPhwREZGAqj6ra8ma9euxcSJE+uViZfBExk21emvJ397Ul0QmAovJy8kz0hGpbISBWUFaNOyjdSRiJqNNp/fkhdA+ogFEJHhK60oReuvW6Os0vRuEBo/NR4POD+AqBtReNjzYanjEDUbg1gHiIioKR25ccQkix8ACLtedeVbpbJS4iRE+osFEBEZJVO6/P1eBxKrTvt1ad0FnOQnqhkLICIyKqoPfFO6/P1eqtmvjo4dce32NanjEOklFkBEZFQEQUBKfgoSbidIHUUyJRUliLoRBQC4WXhT4jRE+okFEBEZHdVaOKZM1QdkZ2kncRIi/cQCiIiMzt9Jprf+z71UfUC92/Y22WZworqwACIio3M4+bDUESR3OecyMosyYWtpi0vZl6SOQ6R3WAARkVE5d+sccktypY6hF1SnAgvKCiROQqR/WAARkVHh7M+/DiUfAgCuBk1UAxZARGQUVJe/s//nX6oZIG8Xb+QWc1aM6G4sgIjIKAiCgPLKchxLPSZ1FL2RJktDwu0EWJhZIC43Tuo4RHqFBRARGY3jN4+jpKJE6hh65VBS1WmwckW5xEmI9AsLICIyGlz/pzpVAeTh4CFxEiL9wgKIiAyeqv/nYNJBiZPon8iUSChFJR5wfgApeSlSxyHSGyyAiMjgCYKA/LJ8nM04K3UUvZNXlqf+c0nOT5Y4DZH+YAFEREYhMiUSClEhdQy9pLocXhAEiZMQ6Q8WQERkFHj6q3aq3qhubbpBKSolTkOkH1gAEZFBU/X/qJp9qbp/0v5BWWUZ2tu3x9Xcq1LHIdILLICIyKAJgoC0gqr1bqhmZZVl6vWRMgozJE5DpB9YABGRwePtL+5PNUNm08JG4iRE+oEFEBEZPPb/3F94clUfUK+2vVChqJA4DZH0WAARkcFS9f+oPtypdjG3YpBXmgcnayfeFoMILICIyIAJgoD43HhkFmVKHUXvKUUljt44CgDIKc6ROA2R9FgAEZFBU32o0/1FpEQAAOws7SROQiQ9FkBEZNB4+qv+VAVQb5feKKsskzgNkbRYABGRQVL1/0SmREobxIBczLqI3JJc2Fna4VL2JanjEEmKBRARGSRBEHAl5wqyi7OljmIwRIg4knIEAJBXmidxGiJpsQAiIoOl+jCn+lOdBnO0dpQ4CZG0WAARkcFi/4/2VAWQt4s3iuRFEqchkg4LICIyWEducAZIW1dyriCrKAstW7TE5ezLUschkgwLICIySKqGXtKeqnFcVi6TNgiRhFgAEZFB4uxPw6lOg7WyaSVxEiLpsAAiIoOiuvydN0BtuLv7gDgLRKaKBRARGRRBEKAUlZwBaoSE2wnIKMyAtYU1rmRfkToOkSRYABGRwbmQdQF3Su9IHcOgqfuA5JwBItMkeQG0atUqeHl5wdraGv7+/jh16lStYy9fvowXXngBXl5eEAQBy5cvb/Q+icjw8P5fjac6DdbGpo3ESYikIWkBtGXLFoSEhGD+/PmIiYmBr68vAgMDkZ1d88quJSUl6Ny5MxYtWgQ3Nzed7JOIDIeq/4fr/zReRHJVAeTj6oP8snxpwxBJQNIC6JtvvsHkyZMRHByMXr16YfXq1WjZsiV++eWXGscPGDAAS5YswcsvvwwrKyud7JOIDIeq/4czQI13Pe860grSYGluiSs57AMi06N1AbR27VqUlJQ0+sByuRxnz55FQEDAv2HMzBAQEIDo6Ohm3Wd5eTlkMpnGg4j0U2xmLGcsdER1GqxYXixxEqLmp3UBNGfOHLi5uWHSpEk4fvx4gw+cm5sLhUIBV1dXje2urq7IzMxs1n2GhobC0dFR/fDw8GjQ8Ymo6fH+X7qjKoDa2raVOAlR89O6AEpPT8f69euRm5uLESNGoEePHli8eHGDixZ9MHfuXBQUFKgfaWlpUkcionuw/0f3VH1Avdv25t3hyeRoXQBZWFjg+eefx549e5CWlobJkydj48aN6NixI5599lns2bMHSqXyvvtxdnaGubk5srKyNLZnZWXV2uDcVPu0srKCg4ODxoOI9IsgCFAoFYhKjZI6itG4UXADyXnJaGHeAvG58VLHIWpWjWqCdnV1xUMPPYTBgwfDzMwMFy9exIQJE9ClSxdERkbW+VpLS0v069cP4eH//janVCoRHh6OwYMHNyhPU+yTiPRHzK0YrlysY6rTYCUVje/tJDIkDSqAsrKysHTpUvTu3RsjRoyATCbDvn37kJycjPT0dIwdOxYTJky4735CQkKwZs0arF+/HnFxcXjnnXdQXFyM4OBgAMD48eMxd+5c9Xi5XI7Y2FjExsZCLpcjPT0dsbGxSExMrPc+ichwcfVn3VMVQC62LhInIWpeFtq+4JlnnsGBAwfQvXt3TJ48GePHj0fr1q3Vz9va2mLWrFlYsmTJffcVFBSEnJwcfPbZZ8jMzISfnx/CwsLUTcypqakwM/u3RsvIyECfPn3UXy9duhRLly7F8OHD1TNO99snERkeURQhCALv/9UEVH1Avdr2Ql5pHm+QSiZDEFWdhfU0adIkvPnmm3WeUhJFEampqfD09Gx0QCnIZDI4OjqioKCA/UBEeqJSWYlWi1uhSF4kdRSjc236NXRt3RUn0k5gkMcgqeMQNZg2n99anwIbPnw4+vbtW227XC7Hr7/+CqCqWdFQix8i0k9nMs6w+Gki6j6gSvYBkenQugAKDg5GQUFBte2FhYXssyGiJsPVn5uO6saorrZsFSDToXUBpDoXf6+bN2/C0dFRJ6GIiFS4/k/TUxVAPZx7IL80X9IsRM2l3k3Qffr0gSAIEAQBjz32GCws/n2pQqFAcnIynnzyySYJSUSmSxAEVCgq8E/qP1JHMVoZhRlIuJ2A7m26I/52PAZ1YB8QGb96F0CjR48GAMTGxiIwMBB2dnbq5ywtLeHl5YUXXnhB5wGJiE5nnEZxBe9X1ZQiUiLQvU13lFWWSR2FqFnUuwCaP38+AMDLywtBQUGwtrZuslBERHfj/b+aXmRKJN7u9zb7gMhkaN0DNGHCBBY/RNQsVP0/h1O4/k9TU/UBPeD8APuAyCTUqwBq3bo1cnNzAQCtWrVC69ata30QEemKIAiQK+Q4nnZc6ihGL7MoE/G58TATzJBwJ0HqOERNrl6nwL799lvY29ur/7+mq8CIiJrCqfRTvE9VM4lIiUAP5x7sAyKTUK8C6O77ek2cOLGpshARVcP1f5pPRHIE3un/Du8LRiZB6x6gmJgYXLx4Uf31nj17MHr0aMybNw9yuVyn4YjIdKn7f3j/r2ajutlsD+ceKCirvuAtkTHRugB6++23kZBQdX44KSkJQUFBaNmyJbZt24bZs2frPCARmSZV/0/0zWipo5iM7OJsXM6+DABIuM0+IDJuWhdACQkJ8PPzAwBs27YNw4cPx6ZNm7Bu3Trs2LFD1/mIyISx/6f5Rd6IBAD2AZHRa9CtMJRKJQDg0KFDeOqppwAAHh4e6ivFiIh0gf0/zS8iuerGqG52bhInIWpaWhdA/fv3x8KFC7FhwwYcOXIEo0aNAgAkJyfD1ZULaBFR4/H+X9JR9QF1a9MNsjKZxGmImo7WBdDy5csRExODadOm4eOPP0bXrl0BANu3b8eQIUN0HpCITI+6/yeN/T/NLbckFxezqi50uXbnmsRpiJpOvW+FofLggw9qXAWmsmTJEpibm+skFBHRyZsnUVpZKnUMkxR5IxI+rj7sAyKjpvUMkIpcLsfNmzeRmpqK1NRUZGdn49atW7rMRkQm7Ggq+3+kwj4gMgVazwAlJCRg0qRJOH5cc2l6URQhCAIUCoXOwhGR6VH9W8L1f6Sjaj7v0roLZGUyOFg7SJyISPe0LoCCg4NhYWGBffv2oV27drwtBhHpFPt/pHe79DbOZ56Hr5svEu8koq97X6kjEemc1gVQbGwszp49ix49ejRFHiIi9v/ogYiUCPi6+aJMwT4gMk5a9wD16tWL6/0QUZPi+j/Si0yJBMA+IDJeWhdAixcvxuzZsxEZGYnbt29DJpNpPIiIGkp9/68U9v9I7eiNo1CKSnRu1RmF5YVSxyHSOa1PgQUEBAAAHnvsMY3tbIImosZi/4/+yCvLw/nM8+jTrg+u3bmGvu3YB0TGResCKCIioilyEBEBYP+PPolIiUCfdn0gr5RLHYVI57QugIYPH94UOYiIAHD9H30SmRKJkMEhcLNnHxAZnwYthBgVFYXXXnsNQ4YMQXp6OgBgw4YNOHbsmE7DEZHpUPf/cP0fvaHqA/Jy8mIfEBkdrQugHTt2IDAwEDY2NoiJiUF5eTkAoKCgAF999ZXOAxKRaWD/j/4pKC/AuVvnAACJdxIlTkOkW1oXQAsXLsTq1auxZs0atGjRQr196NChiImJ0Wk4IjIt7P/RPxEpVX2fcgX7gMi4aF0AXb16FcOGDau23dHREfn5+brIREQm6siNI1JHoHuo1gNqZ99O2iBEOqZ1AeTm5obExOpToceOHUPnzp11EoqITAv7f/RXVGoUFEoFOjp2hKyMa72R8dC6AJo8eTJmzJiBkydPQhAEZGRkYOPGjfjggw/wzjvvNEVGIjJygiCgvLIcJ26ekDoK3UNWLkPMrar2hut51yVOQ6Q7Wl8GP2fOHCiVSjz22GMoKSnBsGHDYGVlhQ8++ADTp09vioxEZAJOprP/R19FpERgQPsB7AMio6L1DJAgCPj4449x584dXLp0CSdOnEBOTg6++OKLpshHRCaC9//SX6pG6PYO7SVOQqQ7Ws8AAVXn62UyGVxdXdGrVy9dZyIiE6K6jQ77f/TXsdRjqFRWooNDBxSUFcDR2lHqSESNptUMUGZmJsaPH49WrVrB1dUVLi4uaNWqFd544w1kZWU1KMCqVavg5eUFa2tr+Pv749SpU3WO37ZtG3r06AFra2v4+Phg//79Gs8XFRVh2rRp6NChA2xsbNCrVy+sXr26QdmIqOmx/0f/FcmLcCbjDAAgKS9J4jREulHvGSCZTIYhQ4agqKgIwcHB6NGjB0RRxJUrV/D777/j2LFjiImJgZ2dXb0PvmXLFoSEhGD16tXw9/fH8uXLERgYiKtXr8LFxaXa+OPHj2PcuHEIDQ3F008/jU2bNmH06NGIiYmBt7c3ACAkJASHDx/Gb7/9Bi8vL/z9999499134e7ujmeffbbe2Yio+bD/R/9FpkRiUIdBqFBWSB2FSCcEUXX96X188cUX+PXXX3H8+HG0bdtW47ns7GwMHToUwcHBmDdvXr0P7u/vjwEDBmDlypUAAKVSCQ8PD0yfPh1z5sypNj4oKAjFxcXYt2+fetugQYPg5+ennuXx9vZGUFAQPv30U/WYfv36YeTIkVi4cGG9cslkMjg6OqKgoAAODg71fj9E1DALjy7EpxGf3n8gSeaJLk/gwGsHkC5LZy8Q6S1tPr/rfQrszz//xLx586oVPwDg4uKCuXPn4o8//qh3SLlcjrNnzyIgIODfMGZmCAgIQHR0zUvhR0dHa4wHgMDAQI3xQ4YMwd69e5Geng5RFBEREYGEhAQ88cQTtWYpLy+HTCbTeBBR0+P6P4bjn9R/UKGoQHuH9sgvy5c6DlGj1bsASkhIwJAhQ2p9fsiQIbh69Wq9D5ybmwuFQgFXV1eN7a6ursjMzKzxNZmZmfcdv2LFCvTq1QsdOnSApaUlnnzySaxatarG1atVQkND4ejoqH54eHjU+30QUcOx/8dwFFcU43TGaQDA9TtcD4gMX70LIJlMBicnp1qfd3Jy0ouZkxUrVuDEiRPYu3cvzp49i2XLlmHq1Kk4dOhQra+ZO3cuCgoK1I+0tLRmTExk2o6nHWf/j4FQ3RajUlkpbRAiHah3E7QoijAzq71eEgQB9WwnAgA4OzvD3Ny82tVjWVlZcHNzq/E1bm5udY4vLS3FvHnzsGvXLowaNQoA8OCDDyI2NhZLly6tdvpMxcrKClZWVvXOTkS6o/pQJf0XkRKBeQ/Pg4cjZ8nJ8NV7BkgURXTv3h2tW7eu8dGjRw+tDmxpaYl+/fohPDxcvU2pVCI8PByDBw+u8TWDBw/WGA8ABw8eVI+vqKhARUVFtULN3NwcSqVSq3xE1LRUvzAdSq59dpb0y/G045Ar5HC3d8ftkttSxyFqlHrPAK1du1bnBw8JCcGECRPQv39/DBw4EMuXL0dxcTGCg4MBAOPHj0f79u0RGhoKAJgxYwaGDx+OZcuWYdSoUdi8eTPOnDmDn376CQDg4OCA4cOH48MPP4SNjQ08PT1x5MgR/Prrr/jmm290np+IGk4QBBTJi3A6/bTUUaieSipKcCr9FB7q+BCS8pLQpmUbqSMRNVi9C6AJEybo/OBBQUHIycnBZ599hszMTPj5+SEsLEzd6JyamqoxmzNkyBBs2rQJn3zyCebNm4du3bph9+7d6jWAAGDz5s2YO3cuXn31Vdy5cweenp748ssvMWXKFJ3nJ6LGiboRxXVlDExkSiQe6vgQlCJn1cmw1XsdIFPCdYCImsfsg7Ox5PgSqWOQFh7t9CjCx4fjVuEttLNvJ3UcIg1Nsg4QEZGuqPt/ktj/Y2ii06JRXlmOdvbtkFlU85IlRIaABRARNTtBEHCn9A7OZ52XOgppqbSyFCfTTwIAUvJTpA1D1AgsgIhIEkdSjrCPxEBFpERIHYGo0bQugCIi+INPRI13OIW3vzBUqrWbvJy8tFr/jUifaF0APfnkk+jSpQsWLlzIFZOJSGvs/zF80WnRKKssg5udG27Kbkodh6hBtC6A0tPTMW3aNGzfvh2dO3dGYGAgtm7dCrlc3hT5iMjICIKAW4W3EJ8bL3UUaqByxb/3b7tRcEPiNEQNo3UB5OzsjPfffx+xsbE4efIkunfvjnfffRfu7u547733cP48mxqJqG7sITF8qu+hmcBWUjJMjfrJ7du3L+bOnYtp06ahqKgIv/zyC/r164eHH34Yly9f1lVGIjIyh5PZ/2PoVH1AXVp1YR8QGaQGFUAVFRXYvn07nnrqKXh6euLAgQNYuXIlsrKykJiYCE9PT7z00ku6zkpEBk71QRmeHH6fkaTvTt48idKKUrjauSLxTqLUcYi0pnUBNH36dLRr1w5vv/02unfvjnPnziE6OhpvvvkmbG1t4eXlhaVLlyI+nuf3iUiTIAhIzkvm+jFGoFxRjn/S/gEApMvSJU5DpL163wtM5cqVK1ixYgXGjBkDKyurGsc4OzvzcnkiqhH7f4zHoaRDCOgcAOsW1lJHIdKa1jNA8+fPx0svvVSt+KmsrMTRo0cBABYWFhg+fLhuEhKRUeHl78bjYNJBAECvtr1QqayUOA2RdrQugB555BHcuXOn2vaCggI88sgjOglFRMZH1f/DGSDjEZsZi9slt+Fg5cBlDcjgaF0AiaIIQRCqbb99+zZsbW11EoqIjI8gCLiSc4U30DQiSlGpbmi/U1L9F2MifVbvHqAxY8YAqPpHbOLEiRqnwBQKBS5cuIAhQ4boPiERGY2IZM7+GJtDSYcwtvdYOFo7Sh2FSCv1LoAcHat+uEVRhL29PWxsbNTPWVpaYtCgQZg8ebLuExKR0Qi7HiZ1BNIxVU9Xr7a9UFJRgpYtWkqciKh+6l0ArV27FgDg5eWFDz74gKe7iEgrFYoK9eJ5ZDyS85Nx/c51dGndBRezLqKve1+pIxHVS4OuAmPxQ0TaOnHzBIrkRVLHoCZwKLlqFqi4oljiJET1V68ZoL59+yI8PBytWrVCnz59amyCVomJidFZOCIyHlz92XgdvH4Qb/d7G252blJHIaq3ehVAzz33nLrpefTo0U2Zh4iMjOrK0QPXD0gdhZpIREoElKIS3dp0w+2S22jTso3UkYjuSxB5F7tqZDIZHB0dUVBQAAcHB6njEBm8/LJ8OH/tDIWokDoKNZFTb57CgPYDcOLmCQzqMEjqOGSitPn8btTd4ImI6iMyJZLFj5FT9QEpRaXESYjqp16nwFq1alVn38/dalolmohM28HrB6WOQE3s4PWDmPvQXHRu1bnWBXOJ9Em9CqDly5c3cQwiMkbs/zEdx9OOo7SiFG52bkjOS0anVp2kjkRUp3oVQBMmTGjqHERkhARBQHJeMq7nXZc6CjWxckU5olKj8ESXJ5AmS2MBRHqvXgWQTCZTNxPJZLI6x7JpmIjuxsvfTcfBpIN4ossTsLawljoK0X3Vuwfo1q1bcHFxgZOTU43ndlVT3QoFGx2J6F88/WU6VLfF6OncE3KFHJbmlhInIqpdvQqgw4cPo3Xr1gCAiAjezJCI6kcpKnE4+bDUMaiZnM88j5ziHLS1bYvYzFj4uflJHYmoVvUqgIYPH17j/xMR1eVsxlncKeWVoaZChIi/r/+NVx98Ffll+VLHIapTvW+Gere8vDz8/PPPiIuLAwD06tULwcHB6lkiIiKA/T+mKOx6GF598FW42LpIHYWoTlovhHj06FF4eXnh+++/R15eHvLy8vD999+jU6dOOHr0aFNkJCIDo1pgnv0/pufv638DAHq17YXbJbclTkNUO60LoKlTpyIoKAjJycnYuXMndu7ciaSkJLz88suYOnVqU2QkIgMjCAIKywvxT+o/UkehZpZdnI2zGWcBANfuXJM4DVHttC6AEhMTMWvWLJibm6u3mZubIyQkBImJiToNR0SG63DyYVQoK6SOQRIIux4G4N+ZQCJ9pHUB1LdvX3Xvz93i4uLg6+urk1BEZPj+SvxL6ggkkbDEqgKoe5vuLIJIb9WrCfrChQvq/3/vvfcwY8YMJCYmYtCgqjv+njhxAqtWrcKiRYuaJiURGQzVmmAsgEzXiZsnUFBWgDYt2yAhNwHdnbtLHYmomnrNAPn5+aFPnz7w8/PDuHHjkJaWhtmzZ2PYsGEYNmwYZs+ejRs3buCVV17ROsCqVavg5eUFa2tr+Pv749SpU3WO37ZtG3r06AFra2v4+Phg//791cbExcXh2WefhaOjI2xtbTFgwACkpqZqnY2ItCcIAq7kXEFqAf/OmapKZSUOJlXdADe7JFviNEQ1q9cMUHJycpMcfMuWLQgJCcHq1avh7++P5cuXIzAwEFevXoWLS/VLKI8fP45x48YhNDQUTz/9NDZt2oTRo0cjJiYG3t7eAIDr16/joYcewqRJk/D555/DwcEBly9fhrU1l2Ynai4HEnn1l6kLSwzDi71ehJO1k9RRiGokiBKeoPX398eAAQOwcuVKAIBSqYSHhwemT5+OOXPmVBsfFBSE4uJi7Nu3T71t0KBB8PPzw+rVqwEAL7/8Mlq0aIENGzY0OJdMJoOjoyMKCgp4bzMiLahOfwX8GsA1gExcB4cOSHs/DQqlAiUVJbC3spc6EpkAbT6/tW6CVrly5QrCwsKwd+9ejUd9yeVynD17FgEBAf+GMTNDQEAAoqOja3xNdHS0xngACAwMVI9XKpX4888/0b17dwQGBsLFxQX+/v7YvXu39m+QiLQmCAKK5cWISo2SOgpJ7KbsJi5lX4K5mTnicqtfOEMkNa1Xgk5KSsLzzz+PixcvQhAEdYe/6gap9b0Zam5uLhQKBVxdXTW2u7q6Ij4+vsbXZGZm1jg+MzMTAJCdnY2ioiIsWrQICxcuxOLFixEWFoYxY8YgIiKi1tt4lJeXo7y8XP31/e54T0S1i0iJgFwhlzoG6YGwxDB4u3ijQsHlEEj/aD0DNGPGDHTq1AnZ2dlo2bIlLl++jKNHj6J///6IjIxsgoj1p1QqAQDPPfcc3n//ffj5+WHOnDl4+umn1afIahIaGgpHR0f1w8PDo7kiExkdXv1FKqrL4bu07sLL4UnvaF0ARUdH4z//+Q+cnZ1hZmYGMzMzPPTQQwgNDcV7771X7/04OzvD3NwcWVlZGtuzsrLg5uZW42vc3NzqHO/s7AwLCwv06tVLY0zPnj3rvAps7ty5KCgoUD/S0tLq/T6IqIrqA27/tepXZpJpikqNQrG8GG52bried13qOEQatC6AFAoF7O2rmtmcnZ2RkZEBAPD09MTVq1frvR9LS0v069cP4eH/NkoqlUqEh4dj8ODBNb5m8ODBGuMB4ODBg+rxlpaWGDBgQLUcCQkJ8PT0rDWLlZUVHBwcNB5EpB1BEBCfG4+U/BSpo5CekCvkOJx8GACQLkuXOA2RJq17gLy9vXH+/Hl06tQJ/v7++Prrr2FpaYmffvoJnTt31mpfISEhmDBhAvr374+BAwdi+fLlKC4uRnBwMABg/PjxaN++PUJDQwFUnX4bPnw4li1bhlGjRmHz5s04c+YMfvrpJ/U+P/zwQwQFBWHYsGF45JFHEBYWhj/++EPy03NEpkB1I0wilbDrYXjmgWd4OTzpHa0LoE8++QTFxcUAgP/85z94+umn8fDDD6NNmzbYsmWLVvsKCgpCTk4OPvvsM2RmZsLPzw9hYWHqRufU1FSYmf07STVkyBBs2rQJn3zyCebNm4du3bph9+7d6jWAAOD555/H6tWr1afkHnjgAezYsQMPPfSQtm+ViOpJdfn7n9f+lDoK6Zl9Cfuw6qlV8HbxhqxMBgdrzrCTftDJOkB37txBq1at1FeCGTquA0SkvWJ5Mdp83QblivL7DyaTcn7KeTzo+iBO3jwJ/w7+UschI9Ys6wABQFpaGtLS0tC6dWujKX6IqGEOJR1i8UM12pdQtXitCF4JRvpD6wKosrISn376KRwdHeHl5QUvLy84Ojrik08+QUUF13ogMlV/JPwhdQTSU6qfjZ7OPaEUlRKnIaqidQ/Q9OnTsXPnTnz99dfqq6+io6OxYMEC3L59Gz/88IPOQxKR/lP9lk90r1Ppp5BTnIO2tm1xJecKerXtdf8XETUxrQugTZs2YfPmzRg5cqR624MPPggPDw+MGzeOBRCRCTp58ySyirPuP5BMklJU4s9rf2Ki30TkleZJHYcIQANOgVlZWcHLy6va9k6dOsHS0lIXmYjIwPDqL7of1Qyhu4O7xEmIqmhdAE2bNg1ffPGFxr2zysvL8eWXX2LatGk6DUdE+k11Eemeq3skTkL67u/rf0OukKOTUydkFmVKHYeofqfAxowZo/H1oUOH0KFDB/j6+gIAzp8/D7lcjscee0z3CYlIbwmCgNSCVFzIuiB1FNJzhfJCHEk5gse7PI7rd67Dza7mWx4RNZd6FUCOjo4aX7/wwgsaX/PmoUSmi/f+ovrad20fHu/yOGwtbaWOQqSbhRCNDRdCJLo/1erPIzeOVN/1m6gunVt1xvX3rqNCUYFyRTnsLO2kjkRGplkWQszJycGxY8dw7Ngx5OTkNHQ3RGSgBEFAkbwIEckRUkchA5GUl4QrOVfQwrwFLmdfljoOmTitC6Di4mK88cYbaNeuHYYNG4Zhw4bB3d0dkyZNQklJSVNkJCI9xdWfSVuqRRG5ICJJTesCKCQkBEeOHMEff/yB/Px85OfnY8+ePThy5AhmzZrVFBmJSE/9cZWrP5N29sRXXTHYq20vVCorJU5DpkzrHiBnZ2ds374dI0aM0NgeERGBsWPHGsXpMPYAEd2fQqlAu2XtkFNi+H/nqfkIEJAeko529u1wIesCHnR9UOpIZESatAeopKQErq6u1ba7uLjwFBiRCYlKjWLxQ1oTIWL31d0AgGJ5sbRhyKRpXQANHjwY8+fPR1lZmXpbaWkpPv/8c/W9wYjI+O2O3y11BDJQu+J2AQC6tu4KXohMUtH6XmDLly/Hk08+WW0hRGtraxw4cEDnAYlIv6guf98Zt1PqKGSgIlMikV+Wj7a2bZF4OxFd23SVOhKZIK1ngHx8fHDt2jWEhobCz88Pfn5+WLRoEa5du4bevXs3RUYi0iOCIOB0+mmkydKkjkIGqkJZoW6gzy7JljgNmSqtZoAqKirQo0cP7Nu3D5MnT26qTESk53j6ixprV/wuvO77Ojo6dpQ6CpkorWaAWrRoodH7Q0SmRdWvsSNuh8RJyNAduH4ApRWl6ODQAakFqVLHIROk9SmwqVOnYvHixais5PoNRKZGEARcybmCq7evSh2FDFxJRYn6FippBTydSs1P6ybo06dPIzw8HH///Td8fHxga6t5U7udO9kYSWTMePqLdGVX/C483/N5uNpVX1qFqKlpXQA5OTlVuxs8ERk/1dVfPP1FurIvYR8qlZXo2rorbhXeQjv7dlJHIhOidQG0du3apshBRHpOEATcyL+BmFsxUkchI5FXloeI5Ag83uVxXM+7zgKImlW9e4CUSiUWL16MoUOHYsCAAZgzZw5KS0ubMhsR6Zld8bukjkBGZtuVbQAAl5YuEichU1PvAujLL7/EvHnzYGdnh/bt2+O7777D1KlTmzIbEekJ1dVfv1/6XeIkZGx2xu1EpbIS3Z27I6soS+o4ZELqXQD9+uuv+O9//4sDBw5g9+7d+OOPP7Bx40YolcqmzEdEekAQBCTnJeNU+impo5CRuV16G4eSDgEAkvKSJE5DpqTeBVBqaiqeeuop9dcBAQEQBAEZGRlNEoyI9Mv2uO1SRyAjteXyFgCAix1Pg1HzqXcBVFlZCWtra41tLVq0QEVFhc5DEZH+UJ3+2nRxk8RJyFjtjt8NuUKOLq26ILMoU+o4ZCLqfRWYKIqYOHEirKys1NvKysowZcoUjbWAuA4QkXERBAEJtxMQmxkrdRQyUvll+TiQeADPPPAMUvJT4GbnJnUkMgH1LoAmTJhQbdtrr72m0zBEpJ9UV+oQNZUtl7fgmQeeQTs7XgpPzaPeBRDX/yEyParFD3+/yKu/qGntvboXZZVl8HTyRLosHe0d2ksdiYyc1vcCIyLTIQgCLmdfxuWcy1JHISNXKC/E/mv7AQA3Cm5InIZMAQsgIqoTT39Rc1FdDebh4CFxEjIFLICIqEZc/JCa276EfSiSF8HD0QPX71yXOg4ZORZARFQjQRBw8uZJJNxOkDoKmYiSihLsjKu6kji3JFfiNGTs9KIAWrVqFby8vGBtbQ1/f3+cOlX3arPbtm1Djx49YG1tDR8fH+zfv7/WsVOmTIEgCFi+fLmOUxMZv40XN0odgUzMhgsbAAAPOD8Apcg7DVDTkbwA2rJlC0JCQjB//nzExMTA19cXgYGByM7OrnH88ePHMW7cOEyaNAnnzp3D6NGjMXr0aFy6dKna2F27duHEiRNwd3dv6rdBZHQqFBVc/JCa3eHkw0iXpcPJ2gnxOfFSxyEjJnkB9M0332Dy5MkIDg5Gr169sHr1arRs2RK//PJLjeO/++47PPnkk/jwww/Rs2dPfPHFF+jbty9WrlypMS49PR3Tp0/Hxo0b0aJFi+Z4K0RGJSwxDLdLb0sdg0yMUlRi06WqwrtMUSZxGjJmkhZAcrkcZ8+eRUBAgHqbmZkZAgICEB0dXeNroqOjNcYDQGBgoMZ4pVKJ119/HR9++CF69+7dNOGJjJzqVARRc9twvupnz9vFG2WVLIKoaUhaAOXm5kKhUMDV1VVju6urKzIza74fTGZm5n3HL168GBYWFnjvvffqlaO8vBwymUzjQWTK8svysffqXqljkIm6mH0RsZmxsDS3xJWcK1LHISMl+SkwXTt79iy+++47rFu3DoIg1Os1oaGhcHR0VD88PLgGBZm2bZe3oVxRLnUMMmGqGUgbCxuJk5CxkrQAcnZ2hrm5ObKysjS2Z2Vlwc2t5pvhubm51Tk+KioK2dnZ6NixIywsLGBhYYEbN25g1qxZ8PLyqnGfc+fORUFBgfqRlpbW+DdHZIBUa//8euFXiZOQqdt0cRMUSgV6tu2J3GJeEk+6J2kBZGlpiX79+iE8PFy9TalUIjw8HIMHD67xNYMHD9YYDwAHDx5Uj3/99ddx4cIFxMbGqh/u7u748MMPceDAgRr3aWVlBQcHB40HkSkSBAHJecn4J/UfqaOQicssysShpEMAgMS8RInTkDGq981Qm0pISAgmTJiA/v37Y+DAgVi+fDmKi4sRHBwMABg/fjzat2+P0NBQAMCMGTMwfPhwLFu2DKNGjcLmzZtx5swZ/PTTTwCANm3aoE2bNhrHaNGiBdzc3PDAAw8075sjMkDrz6+HCFHqGET4JfYXBHYNhJeTl/rGvES6InkBFBQUhJycHHz22WfIzMyEn58fwsLC1I3OqampMDP7d6JqyJAh2LRpEz755BPMmzcP3bp1w+7du+Ht7S3VWyAyGkpRiZ/P/Sx1DCIAwO743bhdchtudm64dvsaurXpJnUkMiKCqDrpT2oymQyOjo4oKCjg6TAyKfuv7ceoTaOkjkGk9k3gN3h/0Ps4d+sc+rTrI3Uc0nPafH4b3VVgRKQ91e9BP8dw9of0y/9i/gegak2gkooSidOQMWEBREQQBAFZRVn4I+EPqaMQabiScwXH046jhXkLxOXESR2HjAgLICICULXuSoWyQuoYRNWoZoFcbF0kTkLGhAUQkYlTnf5aE7NG4iRENdt6eSsKywvh4eiBG/k3pI5DRoIFEJGJEwQBR28cRcLtBKmjENWouKIYv1/6HQCQU5IjcRoyFiyAiEyYevbnLGd/SL+pZih9XHxQWlEqcRoyBiyAiEyYIAjILs7G1itbpY5CVKczGWdwKv0UrCyseINU0gkWQEQm7udzP0OukEsdg+i+Vp5aCQBo79Be4iRkDFgAEZkwhVKB/57+r9QxiOpl6+WtyCnOgZudGxLv8P5g1DgsgIhM2N6re3FTdlPqGET1Uq4oV/cCcdaSGosFEJEp+v/m5xWnVkgchEg7q8+shkKpQK+2vXCn5I7UcciAsQAiMkWCgPisK4hIiZA6CZFW0mRp2Ht1LwAgOT9Z4jRkyFgAEZmon0+y94cM08rTVc3QPZx7oELB1cupYVgAEZmiO3ew6ew6qVMQNcjh5MO4lH0Jtpa2uJx9Weo4ZKBYABGZop9+QrG8WOoURA229PhSAIC7g7t6QU8ibbAAIjI1cjnw3XdSpyBqlE0XNyFdlg4XWxfexoUahAUQkanZuBHIzJQ6BVGjVCgr8P2p7wEAVhZWEqchQ8QCiMhUqE4TLF0qbQ4iHfnxzI8oLC+El5MX0grSpI5DBoYFEJGpEATgr7+AK7yPEhmHgvIC/BTzEwCgSF4kcRoyNCyAiEwBZ3/ISH134jtUKivRs21PZBdlSx2HDAgLICJTIAhAdDRw+LDUSYh0Kk2Whk0XNwEAsktYAFH9sQAiMnaq2Z+FC6XNQdREvoz6EkpRCW8Xb+SV5kkdhwwECyAiYycIwJkzwP79UichahIJtxOw+dJmAEBGYYbEachQsAAiMmaq2Z+vvpI2B1ETW3h0IZSiEr1deqOgrEDqOGQAWAARGTNBAC5cAHbvljoJUZOKy43DtsvbAICXxFO9sAAiMlZ3z/7wVgFkAhZGVfW59XLpxVkgui8WQETGShCA8+eBrVulTkLULC5lX8KOKztgJpghTcZZIKobCyAiY6Sa8fn4Y87+kEn5NOJTKJQKeLt4407pHanjkB5jAURkjAQBOHYM+PNPqZMQNau43DisP78eAHC75LbEaUifsQAiMjaqGZ9586TNQSSR+ZHzUVZZhm5tuiGrKEvqOKSnWAARGRtBqJr5iYqSOgmRJG7KbmLlqZUAgLLKMonTkL5iAURkbJTKqt4fIhMWeiwUBWUF8HTyREpeitRxSA+xACIyNuvXV139RWTC7pTewaJ/FgEAbC1tIfJiALoHCyAiY1JYCMydK3UKIr3wbfS3SMpLQlvbtrh255rUcUjPsAAiMgaq325DQ4EsNn0SAUC5ohyz/p4FAPB09IRcIZc4EekTFkBExkAQgKQk4JtvpE5CpFd2x+/GoaRDsLKwQkp+itRxSI/oRQG0atUqeHl5wdraGv7+/jh16lSd47dt24YePXrA2toaPj4+2H/XXa4rKirw0UcfwcfHB7a2tnB3d8f48eORkcE7BJORUs3+zJ4NlJdLm4VID80Im4FKZSW6t+mO3OJcqeOQnpC8ANqyZQtCQkIwf/58xMTEwNfXF4GBgcjOzq5x/PHjxzFu3DhMmjQJ586dw+jRozF69GhcunQJAFBSUoKYmBh8+umniImJwc6dO3H16lU8++yzzfm2iJqPIABhYcCOHVInIdJLV3KuYNXpVQAAuZKnwaiKIErcGu/v748BAwZg5cqqNRuUSiU8PDwwffp0zJkzp9r4oKAgFBcXY9++feptgwYNgp+fH1avXl3jMU6fPo2BAwfixo0b6Nix430zyWQyODo6oqCgAA4ODg18Z0TNpLQU6N0bSE7W6mVOc4AC6ybKRKRnHKwcEDc1Du727kjKS0LnVp2ljkRNQJvPb0lngORyOc6ePYuAgAD1NjMzMwQEBCA6OrrG10RHR2uMB4DAwMBaxwNAQUEBBEGAk5NTjc+Xl5dDJpNpPIj0nup3ly++0Lr4ITI1snIZpu2fBgDwcPBAWQUXSDR1khZAubm5UCgUcHV11dju6uqKzMzMGl+TmZmp1fiysjJ89NFHGDduXK3VYGhoKBwdHdUPDw+PBrwbomYmCMDFi8CSJVInITIIu+J3YVfcLrQwb4HskprbLMh0SN4D1JQqKiowduxYiKKIH374odZxc+fORUFBgfqRlpbWjCmJGmHKFKCyUuoURAZj2l/TUFBWgI6OHZFWwH/rTZmkBZCzszPMzc2Rdc+6JVlZWXBzc6vxNW5ubvUaryp+bty4gYMHD9Z5LtDKygoODg4aDyK9pTr19d13wPHj0mYhMjAZhRmYE17VX+rc0plrA5kwSQsgS0tL9OvXD+Hh4eptSqUS4eHhGDx4cI2vGTx4sMZ4ADh48KDGeFXxc+3aNRw6dAht2rRpmjdAJAVBAOLjgRouEiCi+/vxzI84eP0gbFrYILuYp8JMleSnwEJCQrBmzRqsX78ecXFxeOedd1BcXIzg4GAAwPjx4zH3rqX9Z8yYgbCwMCxbtgzx8fFYsGABzpw5g2nTqprbKioq8OKLL+LMmTPYuHEjFAoFMjMzkZmZCbmclT4ZgcpKYMIEoIxNnEQNIUJE8J5g5JXmoYNDB9wsuCl1JJKAhdQBgoKCkJOTg88++wyZmZnw8/NDWFiYutE5NTUVZmb/1mlDhgzBpk2b8Mknn2DevHno1q0bdu/eDW9vbwBAeno69u7dCwDw8/PTOFZERARGjBjRLO+LSOdEsWr2Z/Fi4D6LhRJR3dIL0/HOn+9g84ub4WbvhpKKErRs0VLqWNSMJF8HSB9xHSDSW6dPA0OHAhUVjd4V1wEiAjaO2YhXfF5BdnE2XGxdpI5DjWQw6wARkRYKCoCgIJ0UP0RUZer+qUjOS4aLrQuvCjMxLICIDMWbb3LBQyIdyy/Lx0vbXkJ5ZTk8HD3YFG1CWAAR6TPVGepVq4Dt26XNQmSkzt46i/cPvA8AaGXdCqUVpRInoubAAohInwlCVd/PrFlSJyEyaj+c+QG/X/wdLcxboKyyDGyPNX4sgIj02a1bwOjRQHm51EmIjN5b+97CxayLaGXTiqfCTAALICJ9VV4OjBkDZGRInYTIJBTJi/DM788guzgbrnauyCyq+R6TZBxYABHpqylTgBMnpE5BZFJuFNzA81ueR3llOdzs3DgTZMRYABHpE1XfQWgosG6dpFGITNXxtON48483AQAuti7IL8uXNhA1CRZARPpCtdLzL78A8+ZJnYbIpP124Td8fPhjAICTtROK5cUSJyJdYwFEpA9Uxc8ffwBvvSV1GiIC8FXUV/j2xLcAACsLK5RV8P57xoQFEJHUVMXPsWNVKz0rFFInIqL/N+vALPx6/ldYmFnAzMwM5ZW8ItNYsAAikpKq+PnnH2DkSKCUC7AR6RMRIibtnYQdV3bA0twSZoIZ5Aq51LFIB1gAEUlFVfxER1cVP0VFUiciohpUKivx8o6XseXSFrQwbwEzgTNBxoAFEJEU7i5+nnwSKCyUOhER1aFSWYlXd76KjRc2wsLMAhZmFrxlhoFjAUTU3FTFz/79QEAAIJNJnYiI6kEhKjB+93j8L+Z/MDczh00LGxTJOXNrqFgAETU3QQB++w147jmgpETqNESkBaWoxOQ/JmN+5HwAgJ2lHQrKCiRORQ3BAoiouS1ZAowfD1RWSp2EiBroP0f+gzf2vIFKZSUcrR1ZBBkgFkBEzaWsDJgwAZg9+98Vn4nIYK2NXYunNj6FO6V34GjtiGJ5Me8ib0BYABE1JdU/hhkZwPDhwK+/SpuHiHTqYNJB9P+pP2IzY2FraQulqOQVYgaCBRBRUxIEIDwc6NcPOHVK6jRE1ASS85Mx5Och2HhhI8zNzGFlYcXmaAPAAoioqVRWAp98AjzxBJCZKXUaImpCpZWleG3XawjeE4zC8kLYWdqhrLIMSqVS6mhUCxZARE0hKanqlNeXXwL8B5DIZKyLXQe/H/0QnRYNawtrmJmZ8UaqeooFEJEuqHp9lEpg+XLAxwc4flzSSEQkjaS8JDy89mHMDZ+L0opS2FraokJRgUolr/zUJyyAiHRBEIDLl4GhQ4H33+f6PkQmTiEqsOjYInj/4I2D1w+ihXkLrh6tZ1gAETVWXl5V0ePnB5w4IXUaItIjSXlJeOK3J/DqzldxU3YTNi1sAICFkB5gAUTUUJWVwKpVQNeuVae9uLAhEdVi08VN6L6iOz4+/DEKywvVhVBZZZnEyUwXCyAibSkUVev59OgBTJsG3LkjdSIiMgCllaX4KuordF3RFd+f/B6lFaWwtrAGwEJICiyAiOqrshLYuBHo1atqRefr16VOREQGKLs4GzPCZqDTd52w5PgSFMmL1IWQXCHnatLNhAUQUU3u/gcoLw9YuhTo3Bl47TUgIUG6XERkNLKKszD74Gx4LffCpxGfIl2WDktzSwiCAKWo5FVjTYwFEJHK3UWPIADnzgHTpwMeHsCHHwJpadJlIyKjdbv0NhYeXQiv77zw4tYXEZkSCTPBDBZmFgCASmUllCLXE9M1C6kDEElKFKuKHaDqv7m5wKZNwC+/AOfPS5uNiExKpbISO+J2YEfcDnRr3Q2vPfgaXnvwNXRu1VljjJlgBjOB8xeNJYg82ViNTCaDo6MjCgoK4ODgIHUc0rW7ix6gqujZswfYsQM4dAioqJAuWzNymgMUWEudgojuZ6jHUIzzGYdnuz8LD0cPjecqlZXqmSLS7vObBVANmqoAEkURJf+/QF7Lli0h3P0hTE3n3oIHAOLiqoqdnTuBqKiqK7tMDAsgIsPTt11fPPfAc3jugefg6+ar8ZxSVEIpKk26IGIB1EhNVQAVFxfDzs4OAFBUVARbW1ud7Zv+X03FDlB1M9KICODgwarHzZvNn03PsAAiMmxtW7bFI50ewaOdHsWjXo+iW5tu1cYolAoIgmAyp8y0+fw23TKRDFtthY4gVJ3Cio0FTp0C/vkHiI4GUlKaOyERUZPKKcnB1stbsfXyVgBAe/v28O/gD//2/hjUYRD6tesHW8uaf9GuVFbCXDA36TMRelESrlq1Cl5eXrC2toa/vz9OnTpV5/ht27ahR48esLa2ho+PD/bv36/xvCiK+Oyzz9CuXTvY2NggICAA165da8q3QE2hrslJ1V/a7OyqGZ1vv61am6dPH8DeHhg4sGqRwt9/Z/FDRCYhvTAdO+N24qNDH2H4uuFwXOSIB394EK/ufBWhx0KxL2EfkvOSAQAWZha1Fj+qU2nGTvIZoC1btiAkJASrV6+Gv78/li9fjsDAQFy9ehUuLi7Vxh8/fhzjxo1DaGgonn76aWzatAmjR49GTEwMvL29AQBff/01vv/+e6xfvx6dOnXCp59+isDAQFy5cgXW1pzzl1RtMzc1UY3LzQUSE4GkJODatapHYmLV4/btpstKRGTAFKICF7Mv4mL2RY3tdpZ26OncE11ad0HnVp3RtVVX9f93cOig1ekyVaFkiKfYJO8B8vf3x4ABA7By5UoAgFKphIeHB6ZPn445c+ZUGx8UFITi4mLs27dPvW3QoEHw8/PD6tWrIYoi3N3dMWvWLHzwwQcAgIKCAri6umLdunV4+eWX75uJPUDNQKmsKl5Uj6wsID29aq2d9PSqHp30dCAjAyjlTQObAnuAiOheVuZWcLd3h7u9O9o7tEd7+/Zwt3dHB4cOaG/fHs4tndGmZRu0tmnd6GbrYnlxrafoGspgeoDkcjnOnj2LuXPnqreZmZkhICAA0dHRNb4mOjoaISEhGtsCAwOxe/duAEBycjIyMzMREBCgft7R0RH+/v6Ijo6usQAqLy9HeXm5+muZTNaYt2UaKiqqVkiWyYDCwn//W9P/y2RVszh3P/Lzq4ogkozc8H5hI6ImVq4oR3J+MpLzk+scJ0CAo7Uj2ti0URdFzi2d0cq6Feyt7GFvaQ8HK4d//3vXNgcrB9ha2uJA4gG80OuFZnpn1UlaAOXm5kKhUMDV1VVju6urK+Lj42t8TWZmZo3jMzMz1c+rttU25l6hoaH4/PPPG/QeTNaKFcCsWVKnoEZwLwKut5Y6BREZIhEi8svykV+Wj+t5Dbsv4uS+k023ANIXc+fO1ZhVkslk8PDwqOMVhPffB156qe5GZdJrscoy3HGyqn9PFhGRDrnbu0t6fEkLIGdnZ5ibmyMrK0tje1ZWFtzc3Gp8jZubW53jVf/NyspCu3btNMb4+fnVuE8rKytYWVk19G2YJkGoukcWGSy7/38QEZkiSbsALC0t0a9fP4SHh6u3KZVKhIeHY/DgwTW+ZvDgwRrjAeDgwYPq8Z06dYKbm5vGGJlMhpMnT9a6TyIiIjItkrdBhoSEYM2aNVi/fj3i4uLwzjvvoLi4GMHBwQCA8ePHazRJz5gxA2FhYVi2bBni4+OxYMECnDlzBtOmTQMACIKAmTNnYuHChdi7dy8uXryI8ePHw93dHaNHj5biLdZpwYIFKCsr0+k+161bp5fvdcSIEejUqRP8/Pzg5+eHb7/9ttax+/btQ48ePdCtWzeMGTNGozG9oc8RERGpiXpgxYoVYseOHUVLS0tx4MCB4okTJ9TPDR8+XJwwYYLG+K1bt4rdu3cXLS0txd69e4t//vmnxvNKpVL89NNPRVdXV9HKykp87LHHxKtXr9Y7T0FBgQhALCgoaNT7updSqRSLiorEoqIiUalUiqIoigDEvLw8nR5n7dq14nPPPafTferC8OHDxV27dt13XGFhoeji4iLGxcWJoiiKU6dOFT/44INGPUdERMZPm89vvSiA9E1TFUD3evvtt0UAore3t+jr6ytmZWVpPP/bb7+Jo0aNUn+tVCrFTp06ibGxseKtW7fEESNGiH379hV79eolTp06VVQoFKIoahZAERERoq+vr3ofFy9eFD09PdVfh4WFiUOHDhX79u0rDhgwQDx8+HCTvd/6FkBbt24VAwMD1V9fvnxZbN++faOeIyIi46fN57fkp8BM2erVqwEAUVFRiI2Nrbby9ZgxY3DixAn15fuRkZFo1aoVfH194eTkhD/++ANnz57FhQsXkJKSgq1bt2p1/KSkJCxYsAD79+/H2bNnsWnTJrzyyisaayKpLFmyRH3q6t7Hrl276n3MOXPmwMfHB0FBQUhKSqpxTGpqKjw9PdVfe3l54datW6isrGzwc0RERHfjZfB6zMbGBi+88AI2bNiADz/8EOvWrVP3RimVSnz00Uc4duwYRFFEdnY2vL2967XStUpYWBgSExMxbNgw9TYzMzOkpqaiWzfNuwp/+OGH+PDDDxv1fjZs2AAPDw+IoohVq1bh6aefxpUrVxq1TyIioobgDJAeyc/PV8+qPP/88wCAN954A2vXrkVRURH27duHV155BQDwzTffIDs7GydPnsSFCxfwyiuv1NhMbWFhAYVCof767jGiKOLxxx9HbGys+pGenl6t+AG0nwE6dOiQ+vkvv/wSANRrKwmCgGnTpiEpKQm3a7iXV8eOHXHjxg311ykpKWjXrh0sLCwa/BwREZGGpj4fZ4iaqwdIFEXR3t5eTElJqXNMz549xbffflscO3aseltISIg4bdo0URRF8datW2KHDh3EGTNmiKKo2QOUnp4u2tvbi9nZ2aIoiuJ7772n7gG6du2a2LZtW/H8+fPq/Z48eVJH70xTRUWFmJmZqf56+/btYseOHWscK5PJxLZt22o0M8+aNatRzxERkfHT5vObvxpLbNasWXj88cfRsmVL/P3339X6gAAgODgYs2fPxl9//aXeNmPGDLz44ovo3bs33N3dNe59djd3d3fMnj0bAwcOhKurK0aOHKl+rmvXrti0aRPefvttlJSUQC6Xo0+fPti0aZPO32d5eTlGjRqF8vJymJmZwdnZGXv37lU//9lnn8Hd3R1TpkyBvb09/ve//2H06NGorKyEt7c31q9fDwANfo6IiOhukt8NXh811d3giYiIqOlo8/nNHiAiIiIyOSyAiIiIyOSwACIiIiKTwyboGqjaongfKSIiIsOh+tyuT3szC6AaFBYWAvh33RoiIiIyHIWFhXB0dKxzDK8Cq4FSqURGRgbs7e0hCILUcfSSTCaDh4cH0tLSeKWcHuD3Q7/w+6Ff+P3QL035/RBFEYWFhXB3d4eZWd1dPpwBqoGZmRk6dOggdQyD4ODgwH9Q9Ai/H/qF3w/9wu+Hfmmq78f9Zn5U2ARNREREJocFEBEREZkcFkDUIFZWVpg/fz6srKykjkLg90Pf8PuhX/j90C/68v1gEzQRERGZHM4AERERkclhAUREREQmhwUQERERmRwWQERERGRyWACRzpSXl8PPzw+CICA2NlbqOCYpJSUFkyZNQqdOnWBjY4MuXbpg/vz5kMvlUkczKatWrYKXlxesra3h7++PU6dOSR3JJIWGhmLAgAGwt7eHi4sLRo8ejatXr0odi/7fokWLIAgCZs6cKcnxWQCRzsyePRvu7u5SxzBp8fHxUCqV+PHHH3H58mV8++23WL16NebNmyd1NJOxZcsWhISEYP78+YiJiYGvry8CAwORnZ0tdTSTc+TIEUydOhUnTpzAwYMHUVFRgSeeeALFxcVSRzN5p0+fxo8//ogHH3xQsgy8DJ504q+//kJISAh27NiB3r1749y5c/Dz85M6FgFYsmQJfvjhByQlJUkdxST4+/tjwIABWLlyJYCqewt6eHhg+vTpmDNnjsTpTFtOTg5cXFxw5MgRDBs2TOo4JquoqAh9+/bFf//7XyxcuBB+fn5Yvnx5s+fgDBA1WlZWFiZPnowNGzagZcuWUsehexQUFKB169ZSxzAJcrkcZ8+eRUBAgHqbmZkZAgICEB0dLWEyAqr+LgDg3weJTZ06FaNGjdL4eyIF3gyVGkUURUycOBFTpkxB//79kZKSInUkuktiYiJWrFiBpUuXSh3FJOTm5kKhUMDV1VVju6urK+Lj4yVKRUDVTNzMmTMxdOhQeHt7Sx3HZG3evBkxMTE4ffq01FE4A0Q1mzNnDgRBqPMRHx+PFStWoLCwEHPnzpU6slGr7/fjbunp6XjyySfx0ksvYfLkyRIlJ9IPU6dOxaVLl7B582apo5istLQ0zJgxAxs3boS1tbXUcdgDRDXLycnB7du36xzTuXNnjB07Fn/88QcEQVBvVygUMDc3x6uvvor169c3dVSTUN/vh6WlJQAgIyMDI0aMwKBBg7Bu3TqYmfF3neYgl8vRsmVLbN++HaNHj1ZvnzBhAvLz87Fnzx7pwpmwadOmYc+ePTh69Cg6deokdRyTtXv3bjz//PMwNzdXb1MoFBAEAWZmZigvL9d4rqmxAKJGSU1NhUwmU3+dkZGBwMBAbN++Hf7+/ujQoYOE6UxTeno6HnnkEfTr1w+//fZbs/6DQlVN0AMHDsSKFSsAVJ166dixI6ZNm8Ym6GYmiiKmT5+OXbt2ITIyEt26dZM6kkkrLCzEjRs3NLYFBwejR48e+Oijj5r91CR7gKhROnbsqPG1nZ0dAKBLly4sfiSQnp6OESNGwNPTE0uXLkVOTo76OTc3NwmTmY6QkBBMmDAB/fv3x8CBA7F8+XIUFxcjODhY6mgmZ+rUqdi0aRP27NkDe3t7ZGZmAgAcHR1hY2MjcTrTY29vX63IsbW1RZs2bSTpy2IBRGREDh48iMTERCQmJlYrQDnZ2zyCgoKQk5ODzz77DJmZmfDz80NYWFi1xmhqej/88AMAYMSIERrb165di4kTJzZ/INIrPAVGREREJoedkURERGRyWAARERGRyWEBRERERCaHBRARERGZHBZAREREZHJYABEREZHJYQFEREREJocFEBGZLC8vLyxfvlzqGEQkARZARCSp+93lfsGCBdVe4+PjgylTptS4vw0bNsDKygq5ublNnJyIDBkLICKS1K1bt9SP5cuXw8HBQWPbBx98UO01kyZNwubNm1FaWlrtubVr1+LZZ5+Fs7Nzc8QnIgPFAoiIJOXm5qZ+ODo6QhAEjW2qG+ze7bXXXkNpaSl27NihsT05ORmRkZGYNGkSrl+/jueeew6urq6ws7PDgAEDcOjQoVpzpKSkQBAExMbGqrfl5+dDEARERkaqt126dAkjR46EnZ0dXF1d8frrr2vMNm3fvh0+Pj6wsbFBmzZtEBAQgOLi4ob/ARFRk2ABREQGx9nZGc899xx++eUXje3r1q1Dhw4d8MQTT6CoqAhPPfUUwsPDce7cOTz55JN45plnkJqa2uDj5ufn49FHH0WfPn1w5swZhIWFISsrC2PHjgVQNZs1btw4vPHGG4iLi0NkZCTGjBnDG9ES6SHeDZ6IDNKkSZMwcuRIJCcno1OnThBFEevXr8eECRNgZmYGX19f+Pr6qsd/8cUX2LVrF/bu3Ytp06Y16JgrV65Enz598NVXX6m3/fLLL/Dw8EBCQgKKiopQWVmJMWPGwNPTE0BVvxIR6R/OABGRQXr88cfRoUMHrF27FgAQHh6O1NRUBAcHAwCKiorwwQcfoGfPnnBycoKdnR3i4uIaNQN0/vx5REREwM7OTv3o0aMHAOD69evw9fXFY489Bh8fH7z00ktYs2YN8vLyGv9miUjnWAARkUEyMzPDxIkTsX79eiiVSqxduxaPPPIIOnfuDAD44IMPsGvXLnz11VeIiopCbGwsfHx8IJfLa90fAI3TVRUVFRpjioqK8MwzzyA2Nlbjce3aNQwbNgzm5uY4ePAg/vrrL/Tq1QsrVqzAAw88gOTk5Cb6UyCihmIBREQGKzg4GGlpadi5cyd27dqFSZMmqZ/7559/MHHiRDz//PPw8fGBm5sbUlJSat1X27ZtAVT18ajc3RANAH379sXly5fh5eWFrl27ajxsbW0BVF3WP3ToUHz++ec4d+4cLC0tsWvXLt29aSLSCRZARGSwOnXqhEcffRRvvfUWrKysMGbMGPVz3bp1w86dOxEbG4vz58/jlVdegVKprHVfNjY2GDRoEBYtWoS4uDgcOXIEn3zyicaYqVOn4s6dOxg3bhxOnz6N69ev48CBAwgODoZCocDJkyfx1Vdf4cyZM0hNTcXOnTuRk5ODnj17NtmfARE1DAsgIjJokyZNQl5eHl555RVYW1urt3/zzTdo1aoVhgwZgmeeeQaBgYHo27dvnfv65ZdfUFlZiX79+mHmzJlYuHChxvPu7u74559/oFAo8MQTT8DHxwczZ86Ek5MTzMzM4ODggKNHj+Kpp55C9+7d8cknn2DZsmUYOXJkk7x3Imo4QeT1mURERGRiOANEREREJocFEBEREZkcFkBERERkclgAERERkclhAUREREQmhwUQERERmRwWQERERGRyWAARERGRyWEBRERERCaHBRARERGZHBZAREREZHJYABEREZHJ+T+uRU4WWJn+9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_dist_plot(t_value=t, dof= n-1, significance=significance ,two_tail=False, positive_tail=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81e5915",
   "metadata": {},
   "source": [
    "* Test değeri $t_H  = - 5$ ve $t_\\alpha = -1.75$ değerinden küçük olduğundan grafikte de görüldüğü üzere kritik bölgeye düşmektedir, $H_0$ __hipotezinin reddedilebileceğine işarettir.__\n",
    "* Aynı zamanda p değeri, önem değerinden küçük olduğu için, test istatistiğinin kritik bölgeye düştüğünü ve benzer şekilde $H_0$ __hipotezinin reddedilebileceğine kanıt oluşturur.__\n",
    "\n",
    "    Sonuç olarak $H_1$ __hipotezi kabul edilir__, %95 güven düzeyinde öğrencilerin iddiası doğrulanmış olur.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30922bf2",
   "metadata": {},
   "source": [
    "***\n",
    "#### 3) \n",
    "_Bir çay firması aylık çay tüketimi üzerine bir araştırma yapmaktadır. Bu amaçla 10 hane rastgele olarak seçilmiş ve aylık çay tüketimleri (kg) sorulmuş ve yanıtlar aşağıdaki gibi belirlenmiştir_:\n",
    "> 2.8 , 3.5 , 3.3 , 1.4 , 5.7 , 4.4 , 6.7 , 6.9 , 7.1 , 7.4\n",
    "\n",
    "_Hane başına aylık çay tüketiminin ortalama olarak 4 kg olup olmadığını %90 güven düzeyinde test ediniz._\n",
    "\n",
    "Çözüm:\n",
    "\n",
    "Kitle varyansı, ortalaması bilinmiyor. Örneklem hacmi > 30 ise kitlenin normal dağılıma yakınsadığı varsayılıp, örneklemin nokta tahminleri ile z istatistiği kullanılabilir.\n",
    "\n",
    "n = 10, $\\alpha = 0.1$ sahip olunan bilgiler.\n",
    "\n",
    "* Örneklem n < 30 olduğundan dağılımın normal dağılıma yakınsadığı söylenemez, __t istatistiği__ kullanılmalıdır.\n",
    "* Ayrıca verilen örneklemin ortalaması ve varyansı hesaplanmalıdır.\n",
    "\n",
    "$ H_0 : \\mu = 4 $ ortalamanın 4 kg olması.\n",
    "\n",
    "$H_1 : \\mu \\neq 4 $ ortalamanın 4 kg olmaması.\n",
    "\n",
    "\n",
    "$H_1$ hipotezi eşitsizlik içerdiğinden, __iki yanlı hipotez testi__ yapılır ve kritik nokta __her iki__ taraftadır.\n",
    "\n",
    "Test yaparken $H_0$ doğru olduğu varsayıldığı için, $\\mu$ = 4 alabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05e098ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t value = 1.37322\n",
      "t alpha = 1.83311\n",
      "p value = 0.20292 > 0.1 and H0 can not be rejected.\n"
     ]
    }
   ],
   "source": [
    "veri = [2.8 , 3.5 , 3.3 , 1.4 , 5.7 , 4.4 , 6.7 , 6.9 , 7.1 , 7.4]\n",
    "mu= 4\n",
    "xbar = np.mean(veri)\n",
    "var = np.var(veri,ddof=1) #örneklem varyansı hesaplarken payda n-1 olmalı.\n",
    "n= 10\n",
    "significance= 0.1\n",
    "\n",
    "t = t_test(mu=mu, var=var, xbar=xbar, sample_size=n, significance=significance,one_tail=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f267eb15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_dist_plot(t_value= t, dof= n-1, significance=significance,two_tail=True,positive_tail=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74adabb0",
   "metadata": {},
   "source": [
    "* Test değeri $t_H  = 1.375$, $t_\\alpha =\\pm1.75$ değerleri arasında olduğundan grafikte de görüldüğü üzere kabul bölgesine düşmektedir, $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığına işarettir.__\n",
    "* Aynı zamanda p değeri, önem değerinden büyük olduğu için, test istatistiğinin kabul bölgesine düştüğünü ve benzer şekilde $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığını gösterir.__\n",
    "\n",
    "    Sonuç olarak $H_0$ __hipotezi reddedilemez__, %90 güven düzeyinde çay tüketiminin aylık ortalama 4 kg olduğu söylenebilir.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254a331b",
   "metadata": {},
   "source": [
    "***\n",
    "#### 4) \n",
    "_Bir sınıftaki kız ve erkek öğrencilerin istatistik genel sınavından aldığı puanlar aşağıdaki gibidir_: \n",
    "\n",
    "| Kız | 81  | 67  | 90  | 78  | 81  | 80  | 76  |     |     |\n",
    "| --- |:---:| ---:| --- | --- | --- | --- | --- | --- | --- |\n",
    "\n",
    "| Erkek | 72  | 69  | 98  | 66  | 85  | 76  | 79  |   80  |   77  |\n",
    "| --- |:---:| ---:| --- | --- | --- | --- | --- | --- | --- |\n",
    "\n",
    "_%99 güven düzeyinde istatistik dersinde kız öğrencilerin erkek öğrencilerden daha başarılı olduğu iddiasını test ediniz._\n",
    "\n",
    "Çözüm:\n",
    "\n",
    "Örneklemlerin ortalamaları arasındaki fark için, örneklem hacmi > 30 ise kitlenin normal dağılıma yakınsadığı varsayılıp, örneklemin nokta tahminleri ile z istatistiği kullanılabilir.\n",
    "\n",
    "$n_k$ = 7, $n_e$ = 9, $\\alpha = 0.1$ sahip olunan bilgiler.\n",
    "\n",
    "* Örneklem hacimleri küçük(n < 30) olduğundan dağılımların normal dağılıma yakınsadığı söylenemez, __t istatistiği__ kullanılmalıdır\n",
    "* Kız ve Erkek öğrenciler aynı sınıfta olduğundan, yani aynı kitleden çekilen iki örneklem olduklarından, kitle varyansları eşittir. Bu durumda örneklem varyansları ile __ortak varyans__ hesaplanılan yöntem tercih edilmelidir.\n",
    "* Ayrıca her örneklemin ortalaması ve varyansı hesaplanmalıdır, örneklem istatistikleri kullanılacaktır.\n",
    "\n",
    "$ H_0 : \\mu_k = \\mu_e $ kız ve erkeklerin başarılarının eşit olması.\n",
    "\n",
    "$H_1 : \\mu_k > \\mu_e $ kızların erkeklerden daha başarılı olduğu iddiası.\n",
    "\n",
    "\n",
    "$H_1$ hipotezi büyüklük içerdiğinden, __tek yanlı hipotez testi__ yapılır ve kritik nokta __pozitif__ taraftadır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df76af7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t value = 0.23432\n",
      "t alpha = 2.62449\n",
      "p value = 0.40906 > 0.01 and H0 can not be rejected.\n"
     ]
    }
   ],
   "source": [
    "kız = [81, 67, 90, 78, 81, 80, 76]\n",
    "erkek = [72, 69, 98, 66, 85, 76, 79, 80, 77]\n",
    "\n",
    "xbar_kız = np.mean(kız)\n",
    "var_kız = np.var(kız,ddof=1)\n",
    "\n",
    "xbar_erkek = np.mean(erkek)\n",
    "var_erkek = np.var(erkek,ddof=1)\n",
    "\n",
    "significance= 0.01\n",
    "\n",
    "t = t_test_diff(xbar_1= xbar_kız, xbar_2= xbar_erkek, \n",
    "                var_1= var_kız, var_2= var_erkek, \n",
    "                sample_size_1= len(kız), sample_size_2= len(erkek),\n",
    "                significance=significance, one_tail=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23a9c694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_dist_plot(t_value= t, dof= len(kız) + len(erkek) - 2, significance= significance,two_tail=False, positive_tail=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec5ad11",
   "metadata": {},
   "source": [
    "* Test değeri $t_H  = 0.234$ ve $t_\\alpha =2.624$ değerinden küçük olduğu için grafikte de görüldüğü üzere kabul bölgesine düşmektedir, $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığına işarettir.__\n",
    "* Aynı zamanda p değeri, önem değerinden büyük olduğu için, test istatistiğinin kabul bölgesine düştüğünü ve benzer şekilde $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığını gösterir.__\n",
    "\n",
    "    Sonuç olarak $H_0$ __hipotezi reddedilemez__, %99 güven düzeyinde kız ve erkek grupların sınav başarısının eşit olabileceği söylenebilir.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ede36f9",
   "metadata": {},
   "source": [
    "***\n",
    "#### 5) \n",
    "_Bir televizyon üreticisi geçmiş deneyimlerinden yola çıkarak, ürettiği televizyonların ilk iki yıl içerisinde %10'unun herhangi bir nedenle arızalandığını iddia etmektedir. Bu iddiasını doğrulamak için iki yıl önce üretilen televizyonlardan rastgele olarak 50 tanesini seçmiş ve bunlardan 9 tanesinin herhangi bir nedenle arızalandığını görmüştür. %95 güven düzeyinde televizyon üreticisinin iddiasının doğru olup olmadığını test ediniz._\n",
    "\n",
    "Çözüm:\n",
    "\n",
    "p = $10\\div100 = 0.1$, $\\hat{p} = 9 \\div 50 = 0.18$,$n$ = 50, $\\alpha = 0.05$ sahip olunan bilgiler.\n",
    "\n",
    "* $n.p \\geq 5$ ve $nq \\geq 5$ olduğundan kitle için __z istatistiği__ kullanılabilir.\n",
    "\n",
    "$ H_0 : p = 0.1 $ televizyonların %10'unun bozulması.\n",
    "\n",
    "$H_1 : p \\neq 0.1 $ televizyonların %10'unun bozulmaması.\n",
    "\n",
    "\n",
    "$H_1$ hipotezi eşitsizlik içerdiğinden, __iki yanlı hipotez testi__ yapılır ve kritik nokta __her iki__ taraftadır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7562e1ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z value = 1.88562\n",
      "z alpha = 1.95996\n",
      "p value = 0.05935 > 0.05 and H0 can not be rejected.\n"
     ]
    }
   ],
   "source": [
    "p = 0.1\n",
    "p_hat = 9/50\n",
    "sample_size = 50\n",
    "significance = 0.05\n",
    "\n",
    "z = z_test_ratio(p= p , p_hat= p_hat, sample_size= sample_size, significance= significance, one_tail=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc7a993d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_dist_plot(z_value= z, significance= significance, two_tail=True,positive_tail=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82fb128",
   "metadata": {},
   "source": [
    "* Test değeri $z_H  = 1.885$, $t_\\alpha = \\pm1.96$ değerleri arasında olduğu için grafikte de görüldüğü üzere kabul bölgesine düşmektedir, $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığına işarettir.__\n",
    "* Aynı zamanda p değeri, önem değerinden büyük olduğu için, test istatistiğinin kabul bölgesine düştüğünü ve benzer şekilde $H_0$ hipotezinin reddedilmesi için __yeterli kanıt olmadığını gösterir.__\n",
    "\n",
    "    Sonuç olarak $H_0$ __hipotezi reddedilemez__, %95 güven düzeyinde üretilen televizyonların %10'unun bozulacağı söylenebilir.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c0a0af",
   "metadata": {},
   "source": [
    "***\n",
    "#### 6) \n",
    "_Reklamların izleyiciler üzerinde etkili olup olmadığını araştırmak için yapılan bir çalışmada 80 kişiye komedi programı, diğer 80 kişiye de futbol maçı izlettirilmiştir. Komedi programını izleyenlerin 56'sının, futbol maçı izleyenlerin 38'inin reklamlarda gördüğü spagetti sosu markalarını hatırladıkları saptanmıştır. Buna göre %99 güven düzeyinde spagetti sosu reklamının komedi programı izlenirken daha etkili olduğu iddiasını araştırınız._\n",
    "\n",
    "Çözüm:\n",
    "\n",
    "$p_k$ = komedi, $p_f$ = futbol olsun.\n",
    "\n",
    "$ H_0 : p_k = p_f $ yani arada fark olmaması olsun.\n",
    "\n",
    "$H_1 : p_k > p_f $ komedi izlerken akılda kalma oranının daha fazla olması iddiası olsun.\n",
    "\n",
    "$n_k$ = 80, $n_f$ = 80, $\\alpha = 0.01$\n",
    "\n",
    "ve\n",
    "\n",
    "$\\hat{p} = (56+38) \\div (80+80) = 0.587$,\n",
    "\n",
    "$\\hat{p_k} = 56 \\div 80 = 0.7$,\n",
    "\n",
    "$\\hat{p_f} = 38 \\div 80 = 0.475$\n",
    "\n",
    "olarak elde edilir.\n",
    "\n",
    "* Örneklem büyüklükleri yeterince büyük olduğundan, kitle oranlarını karşılaştırmak için __z istatistiği__ kullanılabilir.\n",
    "\n",
    "\n",
    "\n",
    "$H_1$ hipotezi büyüklük içerdiğinden, __tek yanlı hipotez testi__ yapılır ve kritik nokta __pozitif__ taraftadır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0dd7738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z value = 2.89066\n",
      "z alpha = 2.32635\n",
      "p value = 0.00192 <= 0.01 and H0 is rejected, H1 can be accepted.\n"
     ]
    }
   ],
   "source": [
    "p_hat= (56+38)/(80+80)\n",
    "p_komedi = 56/80\n",
    "p_futbol = 38/80\n",
    "n_1 = n_2 = 80\n",
    "significance= 0.01\n",
    "\n",
    "z = z_test_ratio_diff(p_hat= p_hat, p_hat_1= p_komedi, p_hat_2= p_futbol, \n",
    "                  sample_size_1= n_1, sample_size_2= n_2,\n",
    "                  significance= significance, one_tail=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7b697cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_dist_plot(z_value= z, significance= significance, two_tail= False, positive_tail= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0385456e",
   "metadata": {},
   "source": [
    "* Test değeri $z_H  = 2.89$ ve $t_\\alpha = 2.326$ değerinden büyük olduğu için grafikte de görüldüğü üzere red bölgesine düşmektedir, $H_0$ __hipotezinin reddedilmesi için yeterli kanıttır.__\n",
    "* Aynı zamanda p değeri, önem değerinden küçük olduğu için, test istatistiğinin red bölgesine düştüğünü ve benzer şekilde $H_0$ __hipotezinin reddedilmesi için yeterli kanıt olduğunu gösterir.__\n",
    "\n",
    "    Sonuç olarak $H_1$ __hipotezi kabul edilir__, %99 güven düzeyinde, spagetti sosu reklamının komedi programı izlenirken daha etkili olduğu iddiasının doğru olduğu söylenebilir.\n",
    "    \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a69af55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "def z_dist_plot(z_value, significance, two_tail=False, positive_tail=True):\n",
    "    \n",
    "    if two_tail == False:\n",
    "        \n",
    "        alpha = round(stats.norm.ppf(1-significance),4)\n",
    "    \n",
    "        x = np.linspace(-4, 4, 10000)\n",
    "\n",
    "        plt.plot(x, 1/np.sqrt(2*np.pi) * np.exp(-x**2/2),color='white')\n",
    "\n",
    "        if positive_tail:\n",
    "\n",
    "            plt.fill_between(x[x<=alpha], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[x<=alpha]**2/2), color='green')\n",
    "            plt.fill_between(x[x>=alpha], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[x>=alpha]**2/2), color='red')\n",
    "            plt.axvline(x=z_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "            plt.text(z_value+0.01, -0.015, f\"z-value = {z_value:.3f}\", ha='left', fontsize=8)\n",
    "            \n",
    "        else:\n",
    "\n",
    "            plt.fill_between(x[x<=-alpha], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[x<=-alpha]**2/2), color='red')\n",
    "            plt.fill_between(x[x>=-alpha], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[x>=-alpha]**2/2), color='green')\n",
    "            plt.axvline(x=z_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "            plt.text(z_value+0.01, -0.015, f\"z-value = {z_value:.3f}\", ha='left', fontsize=8,color='black')\n",
    "\n",
    "    else:\n",
    "        \n",
    "        alpha = round(stats.norm.ppf(1-significance/2),4)\n",
    "    \n",
    "        x = np.linspace(-4, 4, 10000)\n",
    "\n",
    "        plt.plot(x, 1/np.sqrt(2*np.pi) * np.exp(-x**2/2),color='white')\n",
    "\n",
    "        plt.fill_between(x[(x<=-alpha)], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[(x<=-alpha)]**2/2), color='red')\n",
    "        plt.fill_between(x[(x>=alpha)], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[(x>=alpha)]**2/2), color='red')\n",
    "        plt.fill_between(x[(x>=-alpha) & (x<=alpha)], 0, 1/np.sqrt(2*np.pi) * np.exp(-x[(x>=-alpha) & (x<=alpha)]**2/2), color='green')\n",
    "        plt.axvline(x=z_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "        plt.text(z_value+0.01, -0.015, f\"z-value = {z_value:.3f}\", ha='left', fontsize=8)   \n",
    "\n",
    "    plt.xlabel('Z Values')\n",
    "    plt.ylabel('Probability Density')\n",
    "    plt.title(f'Standard Normal Distribution with z-value = {z_value:.3f}')\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "def t_dist_plot(t_value, dof, significance, two_tail=False, positive_tail=True):\n",
    "    \n",
    "    if two_tail == False:\n",
    "        \n",
    "        alpha = round(stats.t.ppf(1-significance, dof),4)\n",
    "    \n",
    "        x = np.linspace(-4, 4, 10000)\n",
    "\n",
    "        plt.plot(x, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x ** 2) / dof)) ** (-(dof+1)/2)),color='white')\n",
    "\n",
    "        if positive_tail:\n",
    "\n",
    "            plt.fill_between(x[x<=alpha], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x<=alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='green')\n",
    "            plt.fill_between(x[x>=alpha], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x>=alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='red')\n",
    "            plt.axvline(x=t_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "            plt.text(t_value+0.05, -0.005, f\"t-value = {t_value:.3f}\", ha='left', fontsize=8)\n",
    "            \n",
    "        else:\n",
    "\n",
    "            plt.fill_between(x[x<=-alpha], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x<=-alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='red')\n",
    "            plt.fill_between(x[x>=-alpha], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x>=-alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='green')\n",
    "            plt.axvline(x=t_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "            plt.text(t_value+0.05, -0.005, f\"t-value = {t_value:.3f}\", ha='left', fontsize=8)\n",
    "\n",
    "    else:\n",
    "        \n",
    "        alpha = round(stats.t.ppf(1-significance/2, dof),4)\n",
    "    \n",
    "        x = np.linspace(-4, 4, 10000)\n",
    "\n",
    "        plt.plot(x, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x ** 2) / dof)) ** (-(dof+1)/2)),color='white')\n",
    "\n",
    "        plt.fill_between(x[(x<=-alpha)], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x<=-alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='red')\n",
    "        plt.fill_between(x[(x>=alpha)], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[x>=alpha] ** 2) / dof)) ** (-(dof+1)/2)), color='red')\n",
    "        plt.fill_between(x[(x>=-alpha) & (x<=alpha)], 0, (1 / (np.sqrt(dof) * np.sqrt(np.pi))) * ((1 + ((x[(x>=-alpha) & (x<=alpha)] ** 2) / dof)) ** (-(dof+1)/2)), color='green')\n",
    "        plt.axvline(x=t_value, ymin=0.035 ,ymax=0.06, color='black', linestyle='-')\n",
    "        plt.text(t_value+0.05, -0.005, f\"t-value = {t_value:.3f}\", ha='left', fontsize=8)   \n",
    "\n",
    "    plt.xlabel('T Values')\n",
    "    plt.ylabel('Probability Density')\n",
    "    plt.title(f'T Distribution with t-value = {t_value:.3f}')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def t_test(mu, var, xbar, sample_size, significance, one_tail=True):\n",
    "    \n",
    "    t_value = (xbar - mu) / np.sqrt(var/sample_size)\n",
    "\n",
    "    dof = sample_size - 1\n",
    "        \n",
    "    if one_tail & (t_value >= 0):\n",
    "        \n",
    "        p_value = 1 - stats.t.cdf(t_value, dof)\n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance,dof)*(-1)\n",
    "        \n",
    "    elif one_tail & (t_value < 0):\n",
    "\n",
    "        p_value = stats.t.cdf(t_value, dof) \n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance,dof)\n",
    "        \n",
    "    if one_tail == False:\n",
    "        \n",
    "        p_value = 2*(1 - stats.t.cdf(np.abs(t_value),dof))\n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance/2,dof)*(-1)\n",
    "    \n",
    "    if p_value > significance:\n",
    "        print(f't value = {t_value:.5f}')\n",
    "        print(f't alpha = {t_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} > {significance} and H0 can not be rejected.')\n",
    "    else:\n",
    "        print(f't value = {t_value:.5f}')\n",
    "        print(f't alpha = {t_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} <= {significance} and H0 is rejected, H1 can be accepted.')\n",
    "    \n",
    "    return t_value\n",
    "\n",
    "\n",
    "def t_test_diff(xbar_1, xbar_2, var_1, var_2, sample_size_1, sample_size_2, significance, one_tail=True):\n",
    "    \n",
    "    common_var = (((sample_size_1 - 1)*var_1) + ((sample_size_2 - 1)*var_2)) / (sample_size_1 + sample_size_2 - 2)\n",
    "    \n",
    "    t_value = (xbar_1 - xbar_2) / (np.sqrt(common_var)*np.sqrt((1/sample_size_1) + (1/sample_size_2)))\n",
    "\n",
    "    dof = sample_size_1 + sample_size_2 - 2\n",
    "        \n",
    "    if one_tail & (t_value >= 0):\n",
    "        \n",
    "        p_value = 1 - stats.t.cdf(t_value, dof)\n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance,dof)*(-1)\n",
    "        \n",
    "    elif one_tail & (t_value < 0):\n",
    "\n",
    "        p_value = stats.t.cdf(t_value, dof) \n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance,dof)\n",
    "        \n",
    "    if one_tail == False:\n",
    "        \n",
    "        p_value = 2*(1 - stats.t.cdf(np.abs(t_value)))\n",
    "        \n",
    "        t_alpha = stats.t.ppf(significance/2,dof)*(-1)\n",
    "    \n",
    "    if p_value > significance:\n",
    "        print(f't value = {t_value:.5f}')\n",
    "        print(f't alpha = {t_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} > {significance} and H0 can not be rejected.')\n",
    "    else:\n",
    "        print(f't value = {t_value:.5f}')\n",
    "        print(f't alpha = {t_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} <= {significance} and H0 is rejected, H1 can be accepted.')\n",
    "    \n",
    "    return t_value\n",
    "\n",
    "\n",
    "def z_test(mu, var, xbar, sample_size, significance, one_tail=True):\n",
    "    \n",
    "    z_value = (xbar - mu) / np.sqrt(var/sample_size)\n",
    "    \n",
    "    if one_tail & (z_value >= 0):\n",
    "        \n",
    "        p_value = 1 - stats.norm.cdf(z_value)\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)*(-1)\n",
    "        \n",
    "    elif one_tail & (z_value < 0):\n",
    "\n",
    "        p_value = stats.norm.cdf(z_value) \n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)\n",
    "        \n",
    "    if one_tail == False:\n",
    "        \n",
    "        p_value = 2*(1 - stats.norm.cdf(np.abs(z_value)))\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance/2)*(-1)\n",
    "    \n",
    "    if p_value > significance:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} > {significance} and H0 can not be rejected.')\n",
    "    else:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} <= {significance} and H0 is rejected, H1 can be accepted.')\n",
    "    \n",
    "    return z_value\n",
    "\n",
    "\n",
    "\n",
    "def z_test_ratio(p, p_hat, sample_size, significance, one_tail=True):\n",
    "    \n",
    "    z_value = (p_hat - p) / np.sqrt(p*(1-p)/sample_size)\n",
    "    \n",
    "    if one_tail & (z_value >= 0):\n",
    "        \n",
    "        p_value = 1 - stats.norm.cdf(z_value)\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)*(-1)\n",
    "        \n",
    "    elif one_tail & (z_value < 0):\n",
    "\n",
    "        p_value = stats.norm.cdf(z_value) \n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)\n",
    "        \n",
    "    if one_tail == False:\n",
    "        \n",
    "        p_value = 2*(1 - stats.norm.cdf(np.abs(z_value)))\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance/2)*(-1)\n",
    "    \n",
    "    if p_value > significance:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} > {significance} and H0 can not be rejected.')\n",
    "    else:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} <= {significance} and H0 is rejected, H1 can be accepted.')\n",
    "    \n",
    "    return z_value\n",
    "\n",
    "\n",
    "\n",
    "def z_test_ratio_diff(p_hat, p_hat_1, p_hat_2, sample_size_1, sample_size_2, significance, one_tail=True):\n",
    "    \n",
    "    z_value = (p_hat_1 - p_hat_2) / np.sqrt(p_hat*(1-p_hat)*((1/sample_size_1)+(1/sample_size_2)))\n",
    "    \n",
    "    if one_tail & (z_value >= 0):\n",
    "        \n",
    "        p_value = 1 - stats.norm.cdf(z_value)\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)*(-1)\n",
    "        \n",
    "    elif one_tail & (z_value < 0):\n",
    "\n",
    "        p_value = stats.norm.cdf(z_value) \n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance)\n",
    "        \n",
    "    if one_tail == False:\n",
    "        \n",
    "        p_value = 2*(1 - stats.norm.cdf(np.abs(z_value)))\n",
    "        \n",
    "        z_alpha = stats.norm.ppf(significance/2)*(-1)\n",
    "    \n",
    "    if p_value > significance:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} > {significance} and H0 can not be rejected.')\n",
    "    else:\n",
    "        print(f'z value = {z_value:.5f}')\n",
    "        print(f'z alpha = {z_alpha:.5f}')\n",
    "        print(f'p value = {p_value:.5f} <= {significance} and H0 is rejected, H1 can be accepted.')\n",
    "    \n",
    "    return z_value\n"
   ]
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