11_Hypothesis_Testing_Part_C.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aX6hvDeUJBZ"
      },
      "source": [
        "# **Introduction to Hypothesis Testing Part - 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVO9NGQAUJBg"
      },
      "source": [
        "## **Z-Test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJoXF6LvUJBh",
        "outputId": "b4eaa8bf-2265-4443-ca82-03c422cf3f3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Set the null hypothesis mean\n",
        "null_mean = 12\n",
        "\n",
        "# Calculate the sample size, mean, and standard deviation\n",
        "n = 25\n",
        "sample_mean = 11.8\n",
        "sample_std = 0.4\n",
        "\n",
        "# Calculate the Z-score\n",
        "z = (sample_mean - null_mean) / (sample_std / np.sqrt(n))\n",
        "\n",
        "# Calculate the p-value\n",
        "p_value = 2 * (1 - norm.cdf(abs(z)))\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "EUlpyfauUJBk",
        "outputId": "2f156135-931a-45d0-acc9-2c79cb8c5e6f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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/1qFDB02aNEnvvPOOunfvruXLl/sqbZIn6ajpvoEaOHCgJGn58uU1JjJLly6VpFr3d26I1NRU/fa3v5Uk3X333fWe750DHM4YG9p/je3vyiZMmKDmzZvrs88+05IlS+o8t/L8aO/c4c2bN1c7Lz8/v84vO5YvXy63213t9Q8//FDSobb3R5s2bXT66af7VgH3zgf3zg/38l7Te4/K9u3bp6+++kopKSlV5mjXpK7PvWrVKr/jDlRj+7579+667LLL9NFHHykjI0Ovv/56MMMDgKhHAg4AUWjFihW65JJLlJSUpFdffVWdOnVq9DW9C6Nt2rSp2rHBgwdr+PDheuWVV/Tkk0/W+P5vvvlGO3fulOSZI/zNN99UO6egoEAHDx5UYmJilfmhLVq0qDHhCFT79u114oknauPGjb6tuby++OILPffcc2rWrJnOPPPMoNzvnnvuUfv27fXvf/9bN910kwoLC6uds3v3bv3ud7/TCy+8EPYYr7nmGjmdTv3+97+vMXEtKSmpknA1tL/rkpmZqb/+9a+SpIkTJ+qdd96p8bzPP/9cxxxzTJX39erVS5988onWrl3re728vFx/+MMfamxjr/Xr1+vRRx+t8trrr7+ujz76SN27d/drG7LKvEPNZ8+erVdffVUtW7as1i8XXnihnE6nHnnkEf34449Vjt1xxx3Kz8/XhRdeWO9Wa4MHD5bD4dBzzz0nl8vlez0vL6/aYn2hMH78eHXr1k1///vftWjRohrP+eyzz3yx/fzzz9qwYUO1c/bu3avi4uIaF2cDgHjGHHAAiDIHDhzwrXh+1FFH6d1339W7775b6/mdO3eusr92bY4//nj95S9/0ZQpU3T22WcrMzNTWVlZuuaaayRJzz33nEaPHq3LLrtMf/3rXzVkyBBlZWVpy5YtWrNmjb799lt99tlnat26tbZu3aqBAwfq8MMP1xFHHKEOHTooPz9fb775pnJzc/W73/1OmZmZVe79wgsv6LTTTtORRx4pp9Op4447Tscdd1zA7fTYY4/p2GOP1U033aR3331XgwcP9u2x7XA4NG/evCoxNEabNm30/vvv64wzztADDzygp59+WieeeKI6duyokpISfffdd/rwww9VXFys1157Lewx9urVS08++aQuvfRS9e3bV2PHjlXPnj1VWlqqTZs2admyZWrVqpW+//5733sa0t/1mTRpkgoLC3XNNddo7NixGjBggIYOHapmzZppz549+uyzz/T111+rZcuWVd5300036bLLLtOxxx6rc845RykpKVq6dKlKS0vVv3//Kqt3VzZ27FjdcMMNevvtt9W/f3/fPuApKSl68skna1ygrS5jxoxR586dtWLFCkmeLzR+vcCYdx/2q6++WkceeaTOPfdctWrVSh999JE+++wz9erVS7Nmzar3Xm3bttWkSZM0f/58DRgwQOPGjVN+fr4WLVqk4447zrd4X6g4nU698sorOumkkzRu3DgNHTpUAwYMUFpamjZv3qyVK1dqw4YN2r59u9LS0vT111/rrLPO0lFHHaXevXurXbt22rVrl15//XWVlpb65oQDACqEeJszAECQefdH9vdPQ/bXnj17tunVq5dJSkoykqrtKZ2fn2/uvfdec+SRR5r09HSTkpJiOnfubE455RTzz3/+0xw8eNAY49kreubMmWbUqFGmXbt2JikpyWRnZ5sRI0aY5557zrjd7irX3bFjhzn//PNN69atjcPhMJLMjBkz6o23vj2Tt2zZYqZOnWo6duxonE6nadGihRk/frxZsWJFg6/lj+LiYvPEE0+Yk08+2WRnZxun02kyMjJMv379zLXXXmvWrFnTqBiNCWwfcK81a9aYiy++2HTs2NEkJSWZZs2amb59+5orrrjCvP/++9XO97e//bVp0yZz8803m4EDB5qmTZuaxMRE07JlSzNy5Ejz4IMPmv3791d7zxNPPGH69OljkpKSTJs2bcwVV1xhdu/eXec+4DNmzDCffvqpOf74401mZqbJyMgwJ554Yq1t6o977rnH9zP1/fff13reO++8Y0488USTlZVlkpKSTLdu3cxNN91k9u7dW+3cmj6DMZ79u2+88UaTk5Pj27/9vvvuM6WlpXXuA7506dJq16rrv+vK7fVrO3bsMLfccovp27evSU1NNenp6aZ79+7m7LPPNvPnzzelpaXGGGM2b95sbr31VjN06FDTpk0bk5SUZHJycszYsWPNokWLam0nAIhXljHGhC3bBwAACJEPP/xQo0aN0owZM/xeYBAAgHBiDjgAAAAAAGFAAg4AAAAAQBiQgAMAAAAAEAbMAQcAAAAAIAyogAMAAAAAEAYk4AAAAAAAhAEJOAAAAAAAYUACDgAAAABAGCTaHUAo7N27V2VlZXaHUadWrVpp165ddoeBetBP0YF+inxx20dFRWo1frwkadfrr0spKdVOKS4r1j0r7pEk3f6b25WcmBzWECWpqEgaP76VJOn113crJSX867NGQjtEi7j9eYoy9FPko4+iQzT0U2Jiopo1a+bfuSGOxRZlZWUqLS21O4xaWZYlyRMni9BHLvopOtBPkS+e+8gqLpZWr5YklRUXyyQkVDunqLRI//zqn5KkGwfcKIcJ/+C04mLLG6ZKSsqUkOAOewyR0A7RIJ5/nqIJ/RT56KPoEIv9xG83AAAAAADCgAQcAAAAAIAwIAEHAAAAACAMSMABAAAAAAiDmFyEDQAAAEBgiouLVVxcbHcYIVdYWKiSkhK7w0A9IqWfkpOTlZzc+B06SMABAAAASJIKCgpkWZYyMzN9K1DHKqfTGdE7J8EjEvrJGKPCwkIVFBQoPT29UdciAQcAwEZpzjRtnbLV7jBsRzsAkaGsrExNmza1OwwgoliWpbS0NO3fv7/R12IOOAAAAABJivmqN9AYwfj5IAEHAAAAACAMSMABALBRUVmRrlhyha5YcoWKyorsDsc2tAMAIB6QgAMAYCO3ceutn9/SWz+/Jbdx2x2ObWgHAJHg+uuv16WXXup7PmHCBN155502RhQcOTk5Wrx4cciu/+t2Q+1IwAEAAABEreuvv145OTn629/+VuX1xYsXKycnJ6T3XrBggXr37l3jsVAnvTWZPXu2TjzxxLDeU5LuvvtuPfjggwG9d/PmzcrJyVGHDh20ffv2Ksd27Nihtm3bKicnR5s3bw5GqLYjAQcAAAAQ1VJSUvToo49q3759docSl5o0adLo1fOzs7O1cOHCKq+99NJLatu2baOuG2lIwAEAAABUY4zkclm2/DGmYbEOGzZMrVq1qlYFr6ym6vDjjz+uIUOGBNI8DXLOOedo+vTpVV7bs2ePOnfurGXLlkmShgwZogcffFDTpk1T9+7dNWjQID311FNV3rN161Zdcskl6tGjhw477DBdeeWV2rVrlyRPNX7OnDlau3atcnJylJOTowULFvjem5eXp8suu0zdunXTscceq3fffbfKtb///ntdeOGF6tGjh/r3769rr71WeXl5vuNvvvmmjj/+eHXr1k19+/bVxIkT5XK5JFUfgl7XuXW1UeV4vZ/p3HPPrXZufbEuXbpUZ5xxhnr37q2+ffvqoosu0saNG33HvVX3RYsWacKECerWrZtOOOEErVq1qs4Yg4EEHAAAAEA1hYWWevRoa8ufwsKGbfeUkJCgP/7xj5o3b562bdsWohYJ3AUXXKDXXntNxcXFvtdefvllZWdna9iwYb7XHnvsMfXp00fvvPOOrr76at155536+OOPJUlut1uXXHKJ9u3bp5dfflnPP/+8Nm3apKuuukqSdPrpp+vKK6/UYYcdptWrV2v16tU6/fTTfdeeM2eOTjvtNC1ZskTHH3+8rrnmGu3du1eStH//fp177rnq27ev3n77bf373//W7t27deWVV0ryDAW/+uqrNXHiRH344YdauHChTj75ZJkavilpyLmVjRkzRvv379eKFSskSStWrND+/ft10kknVTmvvlglyeVy6YorrtCiRYu0YMECORwOXX755XK7q64xMmvWLE2dOlXvvvuuunbtqquvvlplZWV1xtlYiSG9OgAAAACEwcknn6w+ffpo9uzZmj17dtjum5+frx49etR5zsknn6zbb79d77zzji8pfvHFF3XuuedW2Vv6qKOO0jXXXCNJ6tatm1auXKnHH39cxx13nJYvX67vv/9en332mW9u+8MPP6xRo0bpq6++0oABA5Senq6EhAS1bt26WgznnnuuzjjjDEnSH//4R82dO1dfffWVRo0apXnz5qlfv3669dZbfefPnj1bRx11lH766Se5XC6VlZXplFNOUfv27SWp1rnvO3fu9PvcyhITE3XWWWfphRde0G9+8xu98MILOuuss5SYWDVlrS/Wbt26ady4cVXeM2fOHB1++OFat26devXq5Xt96tSpOuGEEyRJN954o0aNGqWNGzeqe/fu9cYbqIhKwF988cVq4/7btWunhx56yJ6AAAAAgDiVmmq0fv32+k8M0b0DMX36dJ177rmaOnVqkCOqXUZGRo2LrVWubKekpOjss8/WggULdPrpp+ubb77RDz/8UG2I+aBBg6o9f+KJJyRJ69evV7t27aosLNezZ081bdpU69ev14ABA+qMs3ISnJaWpszMTO3evVuStHbtWn366ac1fpHwyy+/aMSIERo2bJiOP/54jRgxQiNGjNC4ceOUlZVV7fw+ffr4fe6vnXfeeRo/frz++Mc/6s0339R//vOfaufUF2u3bt20YcMGPfDAA1q9erXy8vJ8le+tW7dWScArt4n3S4vdu3fHTwIuSR06dNAdd9zhe+5wMEoeABC7UhNTtX7yet/jeEU7AJHHsqS0tMASYbscffTRGjFihO6///5qc4cdDke1YdDBGG7scDjUpUuXes87//zzNWbMGG3btk0LFizQscce66sQh4PT6azy3LIsX2Lqcrl04okn6rbbbqv2vjZt2ighIUEvvPCCVq1apY8++kjz5s3TrFmz9Oabb6pjx45Vzm/Iub/Wu3dvde/eXdOmTVOPHj3Uq1cvff/991XOqS9WSZo8ebLat2+vP//5z8rOzpbb7dbo0aNVWlpa5fzK1XXvSIRfD1MPtojLbh0Oh7Kysnx/mjRpYndIAACEjGVZSnOmKc2ZVmUYYryhHQAEy2233ab33ntP//3vf6u83rx5c+3atatKEv6///0vbHH17t1b/fv313PPPadXX31V5513XrVzvvzyy2rPvZXeHj16aNu2bdq6davv+Lp167R//3717NlTkifJDiSB7Nevn3744Qd16NBBXbp0qfInLS1Nkuf/00cddZRuvPFGvfPOO3I6nXr77bdrvF5Dzv21iRMn6rPPPtPEiRMDijUvL08//fSTrrvuOg0fPlw9evTQ/v37G9wmoRJxFfDc3FxdeeWVcjqd6tmzpy644AK1bNmyxnNLS0urfIthWZZSU1N9jyOVN7ZIjhH0U7SgnyJfPPdR5c9sWZannBSBfh1nPPZVtIjnn6doQj/Zq3fv3jrzzDP15JNPVnl96NChmj59uh599FGNGzdOH3/8sZYuXaqMjIywxXb++efr9ttvV1pamsaOHVvt+MqVK/Xoo4/qpJNO0rJly/Tmm2/qmWeekSQNHz5cvXr10rXXXquZM2eqrKxMt912m4455hj1799fkmc08aZNm/Ttt9+qXbt2Sk9PV3Jycr1xTZ48Wc8995ymTZumadOmKSsrSxs3btTrr7+uBx54QF9//bWWL1+uESNGqGXLlvryyy+Vl5dX4zDwL7/80u9zazJp0iSddtpptRZi64s1KytLzZo107PPPqvWrVtr69atuv/++/26tz8a+3MdUQl4jx49NG3aNLVr10579+7VwoULdeedd2r27Nm+xLqyV199tcqc8S5dumjWrFlq1apVOMMOWHZ2tt0hwA/0U3SgnyJfXPZRQYHvYXZ2tpSeXu2U4rJiXfmmZ+XWf576TyUn1v8PpWDb/8tO3+Mmm39SyxFDwx5DJLRDNInLn6coFI39VFhYWG2ocqRzOByyLKtK3LfeeqveeOMNSYeGXvfp00ezZs3Sww8/rIceekinnnqqpk2bpvnz5/vO+fW1LMtSQkJCrW3iHcJc2/Ffv3fChAm66667dOaZZyozM7PKuZZl6aqrrtI333yjBx98UBkZGbr77rurbJ02f/583XbbbTrrrLPkcDg0evRo3Xfffb57jB8/XosXL9a5556r/fv3669//auv0l7T50hMTJTT6VSHDh301ltv6e6779YFF1ygkpIStW/fXqNHj1ZycrKaNWumFStWaO7cuTpw4IDat2+vmTNn+lYor9xu9Z1bWxt6Y3E6nTXmfv7GalmW/vWvf2n69Om+rdDuu+8+nXHGGb42+PU9K/dh5dd+LSkpqdH7klumvvXgbVRQUKBp06bp4osv1ujRo6sdr60CvmvXrpAvH98YlmUpOztbubm59S7HD/vQT9GBfop88dxHlsul7IqFXHJ//FGmYhhfZa5Sl7rP85zz4yU/Ks1Z/ZxQMsbo4Kx7dNhfH5UkfX/GScq8d7asZi3CGofd7RAt4vnnKZpEcz/t378/bqaAOp3OanOCQ23z5s0aOnSoFi1apMMPP7zKsSFDhujyyy/XlClTwhpTpLOjn2qTn5+vpk2bVns9MTHR7yJwRFXAfy09PV3t2rVTbm5ujce935DUJBr+Z2eMiYo44x39FB3op8gXl31U6fPW9vmNH+eEkln3rcz3aw694CqQ+73X5TjnkvDGYXM7RBvaKDrQT/AqLS3V3r179ec//1lHHnlkteQb0aOxP9MRtwhbZUVFRcrNzfVryXoAANBwZuWyGl8zIV4FFgDiycqVKzVw4EB99dVX+r//+z+7w4GNIqoC/swzz2jw4MFq2bKl9u7dqxdffFEOh6PKHnoAACA4jNst899Pq76Ymibt3S1t+EHq3rvmNwIAGmTo0KFVVi+vyRdffBGmaGCniErA8/Ly9PDDD+vAgQNq0qSJevXqpXvvvTdu5qEAABBW2zZJB/ZLyVm+l6ze/aU1S2V++EYWCTgAAEEVUQn49ddfb3cIAADEDfPjWs+DLoe2hrG69fIk4D9+Z1NUAADEroieAw4AAEKoIsm2uvbyvWR1q3j80/fMAwcAIMgiqgIOAEC8SU1M1ZoL1/geh5PZ8IMkyep62KEXczpLySlSYYGUu0Vq1zEssdjZDgAAhAsVcAAAbGRZllqktlCL1BayLCts9zVFLmlXxTaf7bsciichQcrp5Dlny8awxWNXOwAAEE4k4AAAxKOtmzx/N20mK7PqYqdWRQKurb+EOSgAAGIbCTgAADYqLi/WbZ/cpts+uU3F5cVhu6/xJtfeZLuy9p2rnhMGdrUDAIRaTk6OFi9ebHcYiBAk4AAA2KjcXa6n1z6tp9c+rXJ3efhuvM1TAbdqSMCtnM6eB2FMwG1rBwBRb8+ePfrjH/+oo446Sl26dNGAAQN0wQUXaOXKlXaHFhSbN29WTk6OOnTooO3bt1c5tmPHDnXs2FE5OTnavHmzTRGiIUjAAQCIQ6YiAa9xkbWcitd275ApphoNILJNmTJF3377rR566CEtW7ZM8+bN0zHHHKO9e/faHVpQZWdna+HChVVee+mll5SdnW1TRAgECTgAAPFop6eKYrXJqXbIymgipWV4nuzaXu04gDhhjCyXy5Y/MsavEPfv368vvvhC06dP17HHHqv27dtr4MCBuvbaazVmzBjfef/85z91/PHHq3v37ho8eLBuvfVWHTx40Hd8wYIF6t27t9577z0NHz5c3bp105QpU1RYWKgXX3xRQ4YMUZ8+fXTHHXeovPzQKJ0hQ4bowQcf1LRp09S9e3cNGjRITz31VJ0xb926VVdeeaV69+6tvn376pJLLvGren3OOedowYIFVV5bsGCBzjnnnGrnfv/997rwwgvVo0cP9e/fX9dee63y8vJ8x5cuXaozzjjDF8NFF12kjRs3+o57q+6LFi3ShAkT1K1bN51wwglatWpVvXGibiTgAADEGVNaKuXt8jxp07bmk9q08/y9kwQciFdWYaHa9uhhyx+rsNCvGNPT05Wenq7FixeruI4ROw6HQ3fffbeWLl2qhx56SJ988onuvvvuKucUFhbqySef1D/+8Q/9+9//1meffabLLrtMH3zwgebPn6+HH35Yzz77rN58880q73vsscfUp08fvfPOO7r66qt155136uOPP64xjtLSUk2aNEkZGRl65ZVX9Nprryk9PV2TJk1SSUlJnZ91zJgx2r9/v1asWCFJWrFihfbv368TTzyxynn79+/Xueeeq759++rtt9/Wv//9b+3evVtXXnml7xyXy6UrrrhCixYt0oIFC+RwOHT55ZfL7XZXudasWbM0depUvfvuu+ratauuvvpqlZWV1Rkn6sY+4AAAxJvduZ7qUkqqlJkl1fDvXKtVW5mf18ns3CY2BQMQqRITE/Xggw/q5ptv1rPPPqt+/frp6KOP1vjx49WnTx/feVOmTPE97tChg26++Wbdeuutuvfee32vl5aW6v7771fnzp0lSePGjdPLL7+sr7/+Wunp6erZs6eGDh2qTz/9VOPHj/e976ijjtI111wjSerWrZtWrlypxx9/XMcdd1y1eP/zn//I7XbrgQce8G25OGfOHPXu3VufffaZRowYUednPeuss/TCCy/oN7/5jV544QWdddZZSkysmtLNmzdP/fr106233up7bfbs2TrqqKP0008/qVu3bho3blyV98yZM0eHH3641q1bp169evlenzp1qk444QRJ0o033qhRo0Zp48aN6t69e61xom4k4AAAxBtvVbt129r33G7dtuq5AOKOSU3V9vXrbbu3v8aNG6fjjz9eK1as0H//+18tXbpU//jHP/SXv/xFEydOlCR9/PHH+tvf/qaffvpJBw4cUHl5uYqKilRYWKjUinulpqb6km9JatWqlTp06KD09HTfay1bttSePXuq3H/QoEHVnj/xxBM1xrp27Vpt3LhRPXv2rPJ6cXGxNm7cWGcCLknnnXeexo8frz/+8Y9688039Z///KdaRXrt2rX69NNP1aNHj2rv/+WXX9StWzdt2LBBDzzwgFavXq28vDxf5Xvr1q1VEvDevXv7Hrdu3VqStHv3bhLwRiABBwAgzpgd2yRJVut2tZ9UMTTdkIAD8cuyZNLS7I7CLykpKTruuON03HHH6fe//71uvPFGzZ49WxMnTtTmzZs1efJk/fa3v9Utt9yirKwsrVy5UjfccINKSkp8CbjT6axyTcuyqlWXLcuqNky7IQoKCnTEEUfokUceqXasRYsW9b6/d+/e6t69u6ZNm6YePXqoV69e+vbbb6uc43K5dOKJJ+q2226r9v42bdpIkiZPnqz27dvrz3/+s7Kzs+V2uzV69GiVlpZWOb/y5/d+YduYzw8ScAAAbJWSmKLPz/vc9zgsdud6/m7VptZTrFZtZaSwLcJmSzsAiFk9evTw7b29Zs0aud1uzZgxQw6HZwmsN954I2j3+vLLL6s9r6n6LEmHH3643njjDbVs2VKZmZkB3W/ixIm67bbbdP/999d4vF+/flq0aJE6dOhQ7QsEScrLy9NPP/2kv/zlLxoyZIgk+eaVI/RYhA0AABs5LIc6ZHZQh8wOcljh+bVs9lYMn2zeqvaTWlQc25cnUx76fbntaAcA0S8vL0/nnHOOXn75Za1du1abNm3SG2+8oX/84x866aSTJEmdO3dWaWmpnnzySf3yyy9auHCh5s+fH7QYVq5cqUcffVQ//fSTnnrqKb355pu67LLLajz3rLPOUrNmzXTJJZfoiy++0KZNm/Tpp5/qjjvu0LZt2/y636RJk/TNN9/oggsuqPH45MmTtW/fPk2bNk1fffWVNm7cqA8//FC///3vVV5erqysLDVr1kzPPvusfv75Zy1fvlwzZ84M+POjYaiAAwAQb/J2S5KsZnUk4E2aSYmJUlmZtG+P1KJ1mIIDAP+lp6fryCOP1OOPP65ffvlFpaWlateunS644AJde+21kqS+fftqxowZevTRR3X//ffr6KOP1q233qrrrrsuKDFceeWV+vrrrzVnzhxlZmZqxowZGjlyZI3npqam6pVXXtG9996ryy+/XAUFBcrOztawYcP8rognJiaqefPmtR7Pzs7Wa6+9pvvuu08XXHCBiouL1b59e40cOVIOh0OWZenRRx/VnXfeqeOPP15du3bVn/70J02YMCGQj48Gsozxc5O9KLJr165q8xciiWVZatu2rbZv364YbP6YQT9FB/op8sVzH1kul9pWDEPcvn59jXMpS8pLNGvVLEnSLYNvUVJCUsjjKv/Db6UD++W44yFZHbvK5bLUo4dnzvePP+YqNdUzv6/8tiukXbly3HSfrJ79QhqTHe0QjeL55ymaRHM/5efnq0mTJnaHERZOp7PROcOQIUN0+eWXV1llHcEVjH4Kltp+PpxOp1q1quNL7UoY4wUAgI3K3GV6bM1jemzNYypzh35vVVNaIh3Y73nSvGXdJ1cMUTfePcNDKNztAACAHUjAAQCIJ97530lJUnrdwx0t77DzPaFPwAEAiAfMAQcAIJ54E/CslrXvAe7lXYhtz87QxgQAUeqLL76wOwREGSrgAADEEbO3oppd3/Bz6dAQ9L27QxgRAADxgwQcAIB44lsBvUW9p1pZFavs7tsbyogAAIgbJOAAAMQT7xD0urYg82pakYDvzwtdPAAAxBEScAAA4ohvOLk/Q9CbNvP8fWC/TBkrkwMA0FgswgYAgI1SElP0wdkf+B6HXMWWYv4MQVdGEykhQSovl/L3+Ze0Byjs7QAAgA1IwAEAsJHDcuiw5oeF74beIeh+JNOWwyE1aSbt3S3t3xvSBDzs7QAAgA0Ygg4AQJwwJcXSwXzPk2Z+JtPeYej794QmKACw0aeffqqcnBzt37+/zvOGDBmixx9/PGj3nTBhgu68886gXa8ms2fP1oknnhjSe6DhSMABALBRSXmJZv93tmb/d7ZKyktCezNv9TspWUrL8O89FSuhmxCvhB7WdgAQc3bu3Knbb79dxxxzjLp06aLBgwfr4osv1rJly+p83+DBg7V69Wo1adJEkrRgwQL17t272nmLFi3ShRdeGJLYf+2xxx5Tnz59VFRUVO1YYWGhDjvsMM2dOzcssSD4SMABALBRmbtMc76cozlfzlGZO8QLne3zroDeUpZl+fUWy1cBD20CHtZ2ABBTNm/erJNPPlmffPKJbr/9di1ZskT//ve/NXToUE2fPr3W95WWliopKUmtW7eu9/+JLVq0UGpqarBDr9GECRPkcrn09ttvVzv25ptvqrS0VGeddVZYYkHwkYADABAnTP4+z4OmWf6/ia3IgLjnKnXV+qeorMjvcwvLCv06t6Fuu+02SdJbb72lcePGqVu3bjrssMN05ZVX6o033vCdl5OTo6efflqTJ09W9+7d9eCDD1YZgv7pp5/qD3/4g/Lz85WTk6OcnBzNnj1bUvUh6Pv379fNN9+s/v37q2vXrho9erTee+89SVJeXp6mTZumQYMGqVu3bjr++OP12muv+f15WrZsqRNPPFEvvPBCtWMLFizQSSedpGbNmunee+/VsGHD1K1bNx1zzDH685//rNLS0lqvW9Ow90svvVTXX3+973lxcbHuvvtuDRo0SN27d9epp56qTz/91Hd8y5Ytuvjii9WnTx91795do0aN0vvvv+/3ZwOLsAEAED+8CXhmU//fU1EBN/tIwIF41eOpHrUeG91htOaPne97fsSzR1RLtL2OaXuMFp660Pd8yAtDlFdU/f8tW6ds9Tu2vXv3aunSpbrllluUlpZW7XjTplX/fzdnzhzddtttmjlzplJSUvTTTz/5jg0ePFgzZ87UAw88oI8//liSlJ6eXu2abrdbF154oQoKCvTII4+oU6dOWrdunRISEiR5ktgjjjhC06ZNU2Zmpt5//3397ne/U6dOnTRw4EC/Ptd5552niy++WFu2bFH79u0lSb/88os+//xzPffcc77YHnzwQWVnZ+u7777TzTffrIyMDE2bNs2ve9Tk9ttv17p16/Too4+qTZs2Wrx4sS688EItWbJEXbt21W233abS0lK9/PLLSktL07p162psI9SOBBwAgHhRkYBbTbL8fouV1VxGCvkQdAAIxMaNG2WMUffu3f06/4wzztDEiRMlSU6ns0oCnpSUpMzMTFmWpdatW9d6jWXLlumrr77Shx9+qG7dukmSOnXq5Dvetm1bTZ061ff80ksv1Ycffqg33njD7wR85MiRatOmjRYsWKAbbrhBkvTiiy+qXbt2GjZsmCRVqVx36NBBGzZs0Ouvvx5wAr5161YtWLBAK1asUHZ2tiRp6tSpWrp0qRYsWKBbb71V27Zt0ymnnOKbJ1/5c8M/JOAAAMSLAxWr/DYgAT80BJ0EHIhX6yevr/WYw6o6o3XNhWtqPffX86y/OO+LxgUmyRjToPP79+/f6Hv+73//U9u2bX3J96+Vl5frr3/9q958803l5uaqpKREJSUlDZpDnpCQoHPOOUcvvvii/vCHP8gYo5deekkTJ06Uw+Fp89dff11PPvmkfvnlFxUUFKi8vFwZGX4usFmD7777TuXl5Ro+fHiV10tKStSsmWc01KWXXqpbb71VH330kYYPH65TTjlFffr0Cfie8YgEHACAOOGbA96gBLxiEbb8fTLuclmOhGCHBSDCpTmrD+0O97m16dKliyzL0o8//ujfPWsYpt5QKSkpdR7/xz/+oblz52rmzJnq1auX0tLSNGPGjDrnZ9fkvPPO09/+9jctX75cxhht27bNV71ftWqVrr32Wt1www0aOXKkMjMz9frrr+tf//pXrderaaG5yjEVFBQoISFBb7/9tm84vZd3mPkFF1ygESNG6P3339fHH3+sv/3tb7rzzjt16aWXNuizxTMWYQMAIF54h6BnZvn/niZNJcshGbeUX/c+uQAQbs2aNdPIkSP11FNPyeWqvoBbfft7/1pSUpLKy8vrPKd3797avn17leHrla1cuVInnXSSzj77bPXt21edOnXShg0bGhSHJHXu3FlHH320FixYoAULFmj48OG++eCrVq1S+/btdd111/kWgtu6te658y1atNCOHTt8z8vLy/XDDz/4nvfr10/l5eXas2ePunTpUuVP5SH5OTk5uuiii/TEE0/oyiuv9M1Jh39IwAEAsFFyQrLeOuMtvXXGW0pOSA7tzQKogFuOhEPnh3AYeljbAUBMuffee+V2uzVu3Di99dZb2rBhg9avX6+5c+fq9NNPb9C12rdvr4KCAi1btkx5eXkqLKy+oNwxxxyjIUOG6IorrtDHH3+sTZs26YMPPtDSpUslearyH3/8sVauXKn169frlltu0e7duwP6bOeff77efvttLV68WOedd57vdW/C/frrr2vjxo2aO3dujduWVXbsscfq/fff15IlS/Tjjz/q1ltvVX5+vu94t27ddNZZZ+m6667TokWLtGnTJq1evVqPPPKIlixZIkm688479eGHH2rTpk365ptv9Mknn/g9/x4eJOAAANgowZGgAa0GaECrAUoI4fBuY4x0YJ/nSUOGoEuHhqGHcCX0cLUDgNjTqVMnLV68WEOHDtXdd9+t448/Xuedd56WL1+u+++/v0HXOuqoo/Tb3/5WV111lQ4//HA9+uijNZ73+OOPq3///po2bZpGjRqle++911c5v+6663T44Ydr0qRJmjBhglq1aqWTTjopoM92yimnKCkpSSkpKRo7dqzv9TFjxmjKlCmaPn26xowZo1WrVlVZlK0m5513ns455xxdd911Ovvss9WxY0cNHTq0yjlz5szRhAkTdPfdd+u4447TZZddpq+//lo5OTmSPCvAT58+XSNHjtSkSZPUtWtX3XfffQF9tnhlmYauXBAFdu3a1eA5FuFkWZbatm2r7du3N3jhCIQP/RQd6KfIF899ZLlcatvDs33P9vXrZYIw9zBQpsgl97We6onjby/KSj40h9HlstSjR1tJ0o8/5io11V3lveV/vVv6ZpWsi66RY/iY8AWNauL55ymaRHM/5efnq0mTJnaHERZOpzOicwZ4RFI/1fbz4XQ61apVK7+uwSJsAADYqKS8RHO/nStJuqzfZUpKSArNjbzDz5OSqyTf/rAym3q2IjsQujngYWsHAABsRAIOAICNytxlumfFPZKki/tcHPoEvKHDzyUps+Lb/gP5dZ/XCGFrBwAAbMQccAAA4kF+AHuAe2U29fx9kFXQAQBoDBJwAADiQEB7gHtleBJwE8Ih6AAAxAMScAAA4oFvD/CmDX6rFYYh6AAAxAMScAAA4kGgW5BJlYagk4AD8cDtdtd/EhBngvVzQQIOAEAcaNwQ9IoKOHPAgZiXlpamAwcOkIQDlbjdbh04cEBpQdhOlFXQAQCIBxWLsFmNWQW9pESmuKjB25gBiB6JiYlKT0/XwYMH7Q4l5JKSklRSUmJ3GKhHpPRTenq6EhMbnz6TgAMAYKPkhGS9NO4l3+OQ8VbAM7Ma/t7kVCnRKZWVevYCD0ECHrZ2AFCvxMRENWnSxO4wQsqyLLVt21bbt2+XMcbucFCLWOwnEnAAAGyU4EjQ0HZDQ38j7wrmTQJYhM2yPPPA9+72LMTWsk2QgwtjOwAAYCPmgAMAEONMWZlUWOB5ktHwBFzSoWHozAMHACBgVMABALBRqbtU//7u35KkSb0nyelwBv8mrgOevy1LSk8P7BoVC7GZA/myghRWZWFpBwAAbEYCDgCAjUrLSzX90+mSpHN7nhuaxPNgRQKemi7LkRDQJayMpjJSyCrgYWkHAABsxhB0AABinXf/7oxGLKrkHYJ+gL3AAQAIFAk4AACxzlsBz8gM/BqZFXPHDzAHHACAQJGAAwAQ40xBRQKe3pgEvGIO+EEq4AAABIoEHACAWFdRAbcaMQTdyqACDgBAY5GAAwAQ63xzwBtRAfcm7yTgAAAEjAQcAIBYV1CRgDdqCHpFBdw7nxwAADQY25ABAGCjpIQkPX3S077HoWB8i7AFYRX0wgKZslJZicHdJiwc7QAAgN1IwAEAsFGiI1EndDwhtDepGILemDngSsuQLIdk3J7rZbUIUnAeYWkHAABsxhB0AABiXUHjtyGzHA4pPb3iegVBCAoAgPhDBRwAABuVukv1yo+vSJLO6n6WnI7gDu2WdGjedmPmgEtSWqbnWgXBnwcelnYAAMBmJOAAANiotLxUf/joD5Kk07qcFvTE07jdUsFBz5PGDEGXpPQMz9+uECTgIW4HAAAiAUPQAQCIZYUFnnnbUuO2IZN8FXTjTegBAECDkIADABDLvMPPk1MbvXK55a2Ah2AIOgAA8YAEHACAWFaxAnqjq9/SoSHsVMABAAgICTgAALEsGHuAe6VlVL0mAABoEBJwAABimCmoqIA3dgX0ytdgCDoAAAEhAQcAIJZVVKutYFTAK+aAGxdD0AEACATbkAEAYKOkhCQ9dvxjvsdB561WB2EOuJWeKVP5mkEU8nYAACACkIADAGCjREeiTut6WuhucDCYQ9C9q6AHvwIe8nYAACACMAQdAIAYZrzVauaAAwBgOyrgAADYqMxdprc3vi1JOrnzyUp0BPlXs6vA87e3et0Y3msUFcqUlclKDF6sIW8HAAAiAL/dAACwUUl5iaa+P1WStH7y+uAnnhXDxa1gJOBp6ZJlScZIroNSk6zGX7NCyNsBAIAIwBB0AABimXfF8rTGJ+CWI0FKTfc8CcE8cAAAYh0JOAAAsSyICbikSgux5QfnegAAxBEScAAAYpRxl1eaA54enIv6FmKjAg4AQEORgAMAEKsKXYceB7kCblgJHQCABiMBBwAgVnmr1MkpshKdQbmkRQUcAICAkYADABCrgj3/W6o0B5wKOAAADcUeHwAA2MiZ4NScEXN8j4PKW6VOC9L8bylkc8BD2g4AAEQIEnAAAGzkdDg1sefEkFzbeCvg3qQ5GHwJeHAr4KFsBwAAIgVD0AEAiFUFoRiC7knADXPAAQBoMCrgAADYqMxdpg+3fChJGtl+pBIdQfzVXFEBt4K1BZkkKy1DRgp6BTyk7QAAQITgtxsAADYqKS/Rxe9cLElaP3l9SBLwoFbAvfPJCwuCd02FuB0AAIgQDEEHACBWhWIIuvdaruAm4AAAxAMScAAAYlRIFmGrVAE3xgTvugAAxAEScAAAYlUotiHzXqu8XCopDt51AQCIAyTgAADEKt8ibEEcgp6ULCUkVFyfYegAADRExCbgr732ms4991w99dRTdocCAEB08ibIQZwDblmWlFpRBXexFRkAAA0RkQn4jz/+qPfee0+dOnWyOxQAAKKXd6uwYM4Blw4NQ6cCDgBAg0TcHh9FRUV65JFHdOWVV+qVV16xOxwAAELKmeDUvUPv9T0OFlNeLhUVep4EcxV0qVIFPHgJeKjaAQCASBJxCfgTTzyhgQMH6ogjjqg3AS8tLVVpaanvuWVZSk1N9T2OVN7YIjlG0E/Rgn6KfPHcR5U/s2VZUg1tkJSQpEv6XRL8m1fap9tKz6iz/X8dZ319ZaVnyFTcI1j9GrJ2iDHx/PMUTeinyEcfRYdY7KeISsA/+eQT/fzzz7r//vv9Ov/VV1/VwoULfc+7dOmiWbNmqVWrVqEKMaiys7PtDgF+oJ+iA/0U+eKyjwoOJcHZ2dlSehBXI69HqbtUuZKs1HS1a9++znMrhak2bdrUG+bu5i1VKKmJM0GZbds2OlY0XFz+PEUh+iny0UfRIZb6KWIS8N27d+upp57S7bffrqSkJL/ec+aZZ+rUU0/1Pfd+M7Jr1y6VlZWFJM5gsCxL2dnZys3NZQ/VCEY/RQf6KfLFcx9ZLpe8/2TIzc2VSUurdk65u1xf5H4hSRqSPUQJjoSg3Nts/Nnzd2qatm/fXue5LpclVUS6Y8cOpaa66zy/vCLG/NztOljPtf0VqnaINfH88xRN6KfIRx9Fh2jpp8TERL+LwBGTgG/YsEH79+/XLbfc4nvN7Xbru+++0+LFi/Xcc8/J4ai6ZpzT6ZTTWfM8sUjuIC9jTFTEGe/op+hAP0W+uOyjSp+3ts9fVFakCW9OkCStn7xeac7qSXpAty7I9zxIz6i33Ssf9qufUj0xGtfBoPVpqNohVsXlz1MUop8iH30UHWKpnyImAT/88MP1wAMPVHntH//4h9q1a6fx48dXS74BAEDtTAi2IPPxLsJWwDZkAAA0RMQk4KmpqerYsWOV15KTk5WZmVntdQAAUI9Cl+fv1BBUkiuSelPINmQAADQEZWUAAGJRRXJshSQBZx9wAAACETEV8JrcdddddocAAEB08lXAg7/yupVWsQ0ZCTgAAA1CBRwAgFjkHR4eygo4Q9ABAGgQEnAAAGJRCCvgDEEHACAwET0EHQCAWJfoSNTtv7nd9zhYTCgXYUs9lIAbY2RZVqMvGap2AAAgkvAbDgAAGyUlJOmq/lcF/8K+IeghrIAbt1RUGJQkP2TtAABABGEIOgAAscjlqYCHZBV0Z5KUWPEdPsPQAQDwGwk4AAA2KneX66tdX+mrXV+p3F0evAuHcBE2y7J8e4Gr8GBQrhmydgAAIIIwBB0AABsVlxdr3GvjJEnrJ69XmiNICbN3DnhaCIage6+bvy9oFfCQtQMAABGECjgAADHGGCMVhXARNqnKQmwAAMA/JOAAAMSa4iLJ7fY8DsUibJKvsm5IwAEA8BsJOAAAscZb/XY4pKTkkNzC8s4BdwVnDjgAAPGABBwAgFjj2wM8PSh7dNeIIegAADQYCTgAALHGFboV0H3SKxLwQhJwAAD8RQIOAECsKQzxAmySlOodgk4CDgCAv9iGDAAAGyU6EvWHI//gexwMJhwJeJAXYQtFOwAAEGn4DQcAgI2SEpJ0w6AbgntR77DwUK2ALh3aXzxIQ9BD0g4AAEQYhqADABBrKirgVggr4JZvETZWQQcAwF9UwAEAsJHbuLV+73pJUo9mPeSwgvDdeGEYFmFLC+4q6CFpBwAAIgwJOAAANioqK9Lol0dLktZPXq80ZxCS5krbkIVMkBPwkLQDAAARhq+XAQCINWGpgFesgl7kknG7Q3cfAABiCAk4AAAxxoSjAu69tjFSkSt09wEAIIaQgAMAEGvCsA2Z5XRKSUmeJ+wFDgCAX0jAAQCINRVD0K1QVsAlKbViGDoJOAAAfiEBBwAg1oShAi4p6HuBAwAQ60jAAQCINb5F2EJcAfcm4AXsBQ4AgD/YhgwAABslOhI19YipvseNZYwJXwW84vqm0CWrkZcKdjsAABCJ+A0HAICNkhKSdMeQO4J3wZISqbzc8zgttAm4lZouIwVlCHrQ2wEAgAjEEHQAAGKJNxm2HFJyamjv5a2wsw0ZAAB+oQIOAICN3MatrQe3SpJyMnLksBr53bhv+HmqLKuxA8PrkZJW9Z6NEPR2AAAgApGAAwBgo6KyIh39wtGSpPWT1yvN2chh4+FagE06VAEPQgIe9HYAACAC8fUyAACxJFwLsEmHknz2AQcAwC8k4AAAxBJfBTwcCfihVdABAED9SMABAIghvmQ4DEPQLe8q60FYBR0AgHhAAg4AQCypSIatcA5BpwIOAIBfSMABAIglYayAsw0ZAAANQwIOAEAsCecibEHchgwAgHjANmQAANgowZGgi/tc7HvcaK5wbkNWcY/iIpnyclkJgccf9HYAACACkYADAGCj5IRk3XfsfUG7ngnrNmSV7lHkktIzA75UsNsBAIBIxBB0AABiSRi3IbMSE6WkJM8T9gIHAKBeJOAAANjIGKM9hXu0p3CPjDGNv2BFBdxKC8MQdCloK6EHvR0AAIhAJOAAANiosKxQRzx7hI549ggVlhUG4YLhq4BXuU8jE/CgtwMAABGIBBwAgFgSzm3IKt+HrcgAAKgXCTgAADHCGBPebcgkKSXVc+9C5oADAFAfEnAAAGJFaYlUXuZ5HO4KOHuBAwBQLxJwAABihXcYuGVJySlhuaXlrbSzCjoAAPUiAQcAIFa4KhLwlDRZjjD9iqcCDgCA30jAAQCIFeGe/135XiTgAADUK9HuAAAAiGcJjgSd0+Mc3+NGCfcWZJKU5k3AGzcEPajtAABAhCIBBwDARskJyXpo5EPBuZgtFXDPEHRT1Li9u4PaDgAARCiGoAMAECN8W4GFawV0VVqEjW3IAACoFxVwAABsZIxRYZmnepyamCrLsgK/WEUF3ApnBTwlOHPAg9oOAABEKCrgAADYqLCsUD2e6qEeT/XwJaCBX8yGOeC+VdAbVwEPajsAABChSMABAIgVvjng4RuCziroAAD4jwQcAIBYYecq6EWFMm53+O4LAEAUIgEHACBGGFsq4BX3MkZq5EroAADEOhJwAABihQ3bkFnOJCmxYk3XIoahAwBQFxJwAABihcszBN0KZwVcqrQQGwk4AAB1IQEHACBW2FABlySlpFbcn73AAQCoC/uAAwBgI4fl0Lgu43yPG8WbAKeFOQEPQgU8qO0AAECEIgEHAMBGKYkp+tcJ/wrOxexYhE3yVdyNq0BWgJcIajsAABCh+IoZAIAYYEpLpbJSz5NwD0FnDjgAAH4hAQcAIBZUnn/tnZMdJpY34ScBBwCgTiTgAADYyFXqUs7jOcp5PEeu0kYksN7kNyVVliMhOMH5K62iAt6IbciC1g4AAEQwEnAAAGKBtwIe7vnf0qEh76yCDgBAnUjAAQCIBXZtQSZJKQxBBwDAHyTgAADEAl8F3IYE3LsKOgk4AAB1CigB//3vf69XXnlFu3btCnY8AAAgAMauLcgq35Mh6AAA1CmgfcBbtGihl156SS+++KIOO+wwHXfccTrmmGOUlmbDt+4AAMCX/Fo2VMCt1DQZSXJRAQcAoC4BJeC333679u3bp+XLl+uTTz7Rv/71Lz355JM68sgjddxxx2ngwIFKTAzo0gAAIBAuOyvgLMIGAIA/As6Ss7KydOqpp+rUU0/Vtm3b9PHHH+uTTz7RihUrlJ6erqFDh2r48OE67LDDghkvAAAxxWE5NLrDaN/jgNm5CJtvG7LCgC8RtHYAACCCBaVM3a5dO5133nkaO3as5s2bp88//1zvvfee3nvvPbVp00annHKKxowZI4eDX6gAAFSWkpii+WPnN/5CEbAImwoLZIyRZVkNvkTQ2gEAgAjW6AS8qKhIK1as0LJly/Ttt99Kko488kiNGDFCiYmJWrJkiebNm6dNmzbpiiuuaHTAAACgOt8ibGk2DEH3bkPmdkslxVJySvhjAAAgCgSUgLvdbn311VdatmyZVq1apZKSEnXt2lUXXXSRjj32WDVp0sR37uDBg/Xcc8/pnXfeIQEHACBU7KyAJ6dIDocnAS8sIAEHAKAWASXgU6ZM0cGDB9W8eXOdfPLJOu6449S+fftaz+/UqZOKiooCDhIAgFjlKnXpiGePkCStuXCN0pwBJtAVFXDLhkXYLMvyVMFdBz1xZLVo8DWC1g4AAESwgBJw72rn/fr182ue17HHHqtjjz02kFsBABDzCssCX7zs0EVsXITNe1/XQckV+EroQWkHAAAiWECroo0aNUqdOnWqNfnOz8/X2rVrGxUYAABoAN8QdBvmgFe+byF7gQMAUJuAEvCZM2dqzZo1tR7/9ttvNXPmzICDAgAADWR3BTyt4r5FJOAAANQmJPuClZaWsuUYAABhYspKpdISzxObK+CGCjgAALXyew747t27tXPnTt/zrVu31jjM3OVyacmSJWrVqlVwIgQAAHUrrDR3OjXVlhCslFQZ6dBQeAAAUI3fCfjSpUu1cOFC3/NXXnlFr7zySo3nOhwOTZkypfHRAQCA+nmT3uRUWY4Ee2JgDjgAAPXyOwE/5phj1KFDB0nSgw8+qJNPPlm9evWqco5lWUpOTlbnzp2VlZUV1EABAIhFlmXpmLbH+B4HxO7535XvHWACHpR2AAAgwvmdgLdv39631/dVV12lPn36qHXr1iELDACAeJCamKqFpy6s/8S6+FZAtzMBr6iAB7gNWVDaAQCACBfQPuAjR44MchgAACBgEVQBZxE2AABq51cC/uijj8qyLF155ZVyOBx69NFH632PZVm66qqrGh0gAACom4mICjjbkAEAUB+/EvD//e9/sixLbrdbDodD//vf/+p9D/O3AACon6vUpSEvDJEkfXHeF0pzBpBEV1SdLbu2IJNkpaVXrIIeWAIelHYAACDC+ZWA//3vf6/zOQAACFxeUV7jLhBJFfBGbEPW6HYAACDCOewOAAAANJJvDrh9FXClNG4VdAAA4kFAi7AVFhaqoKBALVu29L2Wl5en9957T6WlpTr66KPVvXv3oAUJAADqEBGLsHn3AQ+8Ag4AQKwLKAH/5z//qV27dunee++VJLlcLk2fPl15eXmyLEtvv/22brvtNvXt27dB13333Xf17rvvateuXZI8W59NmDBBAwcODCRMAADig3frLzsr4N7kv6xMprREljPJvlgAAIhQAQ1B/+GHH3TkkUf6ni9btkx79+7Vn/70J82bN08dO3bUK6+80uDrNm/eXBdccIH+7//+T/fff7/69eunP//5z9q8eXMgYQIAEBdMJFTAU1Il7wKsVMEBAKhRQAl4fn6+mjdv7nu+atUq9erVSz179lRqaqpGjBihjRs3Nvi6gwcP1pFHHqm2bduqXbt2Ov/885WSkqL169cHEiYAAPGhIuG10uxLwC2Hw5OES5KLeeAAANQkoCHo6enp2rdvnySppKRE33//vc4880zfcYfDoZKSkkYF5na79dlnn6m4uFg9e/as8ZzS0lKVlpb6nluWpdTUVN/jSOWNLZJjBP0ULeinyBfPfVT5M1uWdahCXInD4VD/Vv19jwNqJ18FPCOg9/86zoD7KjVNKnTJKnI1+BpBaYc4EM8/T9GEfop89FF0iMV+CigB79mzp959913l5OToq6++UklJiY466ijf8e3bt1epkDfEpk2bNH36dJWWliolJUU33nij2rdvX+O5r776qhYuXOh73qVLF82aNUutWrUK6N7hlp2dbXcI8AP9FB3op8gXl31UcGgodnZ2tpRe8xztr6Z91ajbbC0pkltSq44dldS2bYPfXylMtWnTprYw65Wb2VSlebvVPC1VKQHE0dh2iCdx+fMUheinyEcfRYdY6qeAEvALL7xQ99xzj2bPni1JOvXUU9WhQwdJnsr1559/rv79+wcUULt27fSXv/xFLpdLn3/+uf7+979r5syZNSbhZ555pk499VTfc+83I7t27VJZWVlA9w8Hy7KUnZ2t3NxcGWPsDge1oJ+iA/0U+eK5jyyXS95/MuTm5sqEaIi4++ABSdLugkJZ27c3+P0ulyVVRLpjxw6lproDiqOsYuG1PVs2y9GmQ0DXQN3i+ecpmtBPkY8+ig7R0k+JiYl+F4EDSsCzs7P10EMPacuWLUpLS1Pr1q19x4qLi3XppZeqU6dOgVxaiYmJvm84unbtqp9++kmLFi3SFVdcUe1cp9Mpp9NZ43UiuYO8jDFREWe8o5+iA/0U+eKyjyp93lB9flNWJpUUex6npFa5p9/XMJUfNyLOir3ATWFB/PV1mMXlz1MUop8iH30UHWKpnwJKwCVPoty5c+dqr6emplYZjt5Ybre7yjxvAABiSWFZoUa+NFKS9OE5Hyo1MbVhFyiqtOBZio2roEuyUtNkpIBWQW90OwAAEAUCTsAlacuWLdqxY4cKCmr+pnvEiBENut5zzz2nAQMGqGXLlioqKtLy5cu1du1aTZ8+vTFhAgAQsYwx2nJwi+9xg3kXYEtKlpXYqF/rjefdhzyAVdAb3Q4AAESBgH5T5+bm6pFHHtGPP/5Y53kNTcD379+vv//979q7d6/S0tLUqVMnTZ8+XUcccUQgYQIAEPu81ebUAFdOCybvPuSFbEMGAEBNAkrAH3/8cW3atEmTJ09W7969lR7ocqm/ctVVVwXlOgAAxA3fFmT2Dj+vEkMAQ9ABAIgHASXg3n2/Tz755GDHAwAAGsJXAY+ABDzN84W8oQIOAECNHIG8qUmTJkoL0VYqAADAf8Y73zqShqAXkYADAFCTgBLwE088UcuWLZPbHdg+oQAAIEgqqs1WBFTALe+XAFTAAQCoUUBD0Nu2bSu3262bbrpJo0aNUosWLeRwVM/lhwwZ0ugAAQCIZZZlqWdWT9/jBvMOQU+LoAp4AHPAG90OAABEgYAS8Iceesj3eP78+bWet2DBgkAuDwBA3EhNTNXSc5YGfoFIWoQtJfBV0BvdDgAARIGAEvAZM2YEOw4AABCISFqEjVXQAQCoU0AJeJ8+fYIdBwAACERhBC3C5h0GX1IiU1YmKzGgf2YAABCzAlqEzau0tFTr1q3TypUrlZ+fH6yYAACIG4VlhRr10iiNemmUCssKG/x+E0kV8JRKMTRwGHpj2wEAgGgQ8FfTixYt0ksvvSRXxfYnd9xxh/r166f8/Hz9/ve/16RJkzR69OigBQoAQCwyxmjdvnW+xw3mWwXd/gq4lZAgJadIxUWeYeiZTfx+b6PbAQCAKBBQBXzp0qV6+umnNWDAAF111VVVjjVp0kR9+/bVp59+GpQAAQBAHSJpETap0jxwtiIDAODXAkrA33zzTQ0ePFjXXXedBg0aVO14165dtXnz5kYHBwAA6uEbgm5/BVzSoTiKSMABAPi1gBLw3NxcDRw4sNbjGRkZOnjwYMBBAQAAP0VsBZyV0AEA+LWAEvC0tLQ6F13bsmWLsrKyAo0JAAD4wbjLPfOtpcipgFcsxGZcVMABAPi1gBLwgQMH6v3331dBQfVvtzdv3qz333+/xqHpAAAgiAorrRYeIRVwizngAADUKqBV0M877zxNnz5dN9xwgy/R/vDDD/XBBx/oiy++ULNmzTRhwoSgBgoAQCyyLEvtM9r7HjeId5h3UlLk7Lnt3Qu8gUPQG9UOAABEiYB+Wzdv3lz/93//p+eff9632vmyZcuUkpKiY489VpMmTVKTJv5vPQIAQLxKTUzVF+d/EdibffO/I2T4uRTwKuiNagcAAKJEwF+XN23aVFOnTtXUqVOVn58vt9utJk2ayOEIaFQ7AABoKN8K6JEx/FwSi7ABAFCHoIxXo9oNAIANvFXmlEhKwL1D0JkDDgDArzU4Ad+3b58WL16sNWvWaMeOHSosLFRqaqqys7PVv39/nXTSSWratGkoYgUAIOYUlhXq7DfOliS9fNrLSk1M9fu9JoIr4KaB+4A3ph0AAIgWDUrAv/32W82ZM0cFBQVKSkpS27ZtlZKSoqKiIm3atEk//vij3nnnHd10003q1atXqGIGACBmGGP09e6vfY8bJALngFup6TJSgyvgjWoHAACihN8JeH5+vh588EElJCTod7/7nY4++mglJCT4jpeXl+uzzz7T008/rdmzZ2vOnDnKzMwMSdAAAECSy1MBtyKwAu6NDQAAHOL3imnvv/++CgsLdccdd+jYY4+tknxLUkJCgoYNG6bp06fr4MGDev/994MeLAAAqCQCK+CBroIOAEA88DsBX7NmjQYNGqSOHTvWeV7nzp01ePBgff31140ODgAA1MGXgEdgBZwEHACAavxOwLds2aLDDjvMr3MPO+wwbdmyJeCgAACAH7yLsKVFUgJeUY0vLpRxl9sbCwAAEcbvBNzlcvm93VhmZqZcLr75BgAglEwkD0GXpMJC++IAACAC+b0IW1lZmRwO//J1h8OhsrKygIMCACCeNE9pHtgbCyNvETYr0Sk5k6TSEk986Rl+vzfgdgAAIEo0aBuyn376SU6n06/zAABA/dKcafrmt98E9uZIrIBLnip4aUmD5oE3qh0AAIgSDUrAFy1apEWLFoUqFgAA0BCRuAib5PlCIH8fC7EBAPArfifgM2bMCGUcAACgobyLsEViBVwiAQcA4Ff8TsD79OkTyjgAAIhLhWWF+u3i30qS5o+dr9TEVL/eZ9zlUlHFImcRVwH3xGMKC2T5+ZZA2wEAgGjSoCHoAAAguIwx+mz7Z77HfiuqtMJ4DFTAA24HAACiiN/bkAEAgAjiTW4TnbL8WCA1nHyrsnuHyAMAAEkk4AAARCff/O8IG34uHarIMwccAIAqSMABAIhGrgjdgkyqNASdCjgAAJWRgAMAEI0idQsyiQo4AAC1CCgBX79+fbDjAAAADWC81eW0yK2AGxJwAACqCGgV9Ntvv13Z2dkaPny4hg8frjZt2gQ7LgAA4kZAW25FcAXcSk2XkRpcAWfrMQBArAsoAb/22mu1bNkyvfzyy3rppZfUs2dPDR8+XEOHDlVGRkawYwQAIGalOdP04yU/NvyNFRVwKwITcKVWJNINmAMecDsAABBFAkrAhw0bpmHDhik/P1+ffvqpli9frrlz5+rpp59W//79ddxxx2nw4MFKTGSbcQAAQqIwkhdhYw44AAA1aVSG3KRJE40dO1Zjx45Vbm6uli9fruXLl+vBBx9UWlqajj76aI0YMUK9evUKVrwAAECK8G3IvKugk4ADAFBZ0ErUSUlJSk5OltPplCRZlqVVq1bpgw8+UNeuXXX11Verffv2wbodAAAxoaisSFOWTJEkPX7C40pJTPHvjdFQAS9yybjdshz1r/kacDsAABBFGpWAFxYW6vPPP9fy5cu1du1aWZalAQMGaMKECRo0aJAcDodWrFihZ555Ro8++qjuu+++YMUNAEBMcBu3Ptj8ge+xv0wEL8Lmi8kYqbjIrxgDbQcAAKJJQAn4ypUrtWzZMn355ZcqLS1Vt27ddPHFF+vYY49VZmZmlXOPPvpoHTx4UHPnzg1KwAAAQJUWYYvACrgzSUpIlMrLPHFG4pcEAADYIKAE/IEHHlCLFi00btw4jRgxQu3atavz/M6dO2v48OEBBQgAAGoQwRVwy7I8cR3MZx44AACVBJSA33nnnerbt6/f53fv3l3du3cP5FYAAKAm3kXY0iKwAi6RgAMAUIP6V0WpwUcffaT169fXevzHH3/Uo48+GnBQAACgHi7vKuiRmoCzFRkAAL8WcAK+Y8eOWo/v3LlTH330UcBBAQCA2hl3uVRU6HkSyRVwScZbqQcAAIEl4PXJy8tTUlJSKC4NAAAKCw89jsA54JLYCxwAgBr4PQd85cqVWrlype/5kiVLtGbNmmrnuVwuffPNN8z5BgDAD2nONG2dsrVhb/JWlZOSZCU6gx9UEFipaTLSoVjrEVA7AAAQZfxOwLds2aLPP//c93z9+vXasGFDlXMsy1JycrJ69+6tiy66KHhRAgCAQyJ9/rfEHHAAAGrgdwJ+5pln6swzz5QkTZw4UVdddZWGDRsWssAAAEAtIngLMh/fEHTmgAMA4BXQNmQLFiwIdhwAAMSlorIi/e7D30mS/jryr0pJTKn/TYWxVwEPqB0AAIgyIVmEDQAA+Mdt3Hrr57f01s9vyW3cfr3HRMUQdO8q6P4l4IG0AwAA0cavCvjEiRNlWZaeffZZJSYmauLEifW+x7IsvfDCC40OEAAA/EpFUmtF6hZkEnPAAQCogV8J+Nlnny3LsuRwOKo8BwAANig86Pk7gueAW6mpDVoFHQCAeOBXAn7uuefW+RwAAISRt6pMBRwAgKjCHHAAAKJNFM0BJwEHAOAQvyrgH330UUAXHzFiREDvAwAAtTPeYd1RUQEvkDGGqWsAAMjPBPzRRx8N6OIk4AAAhICvAh65c8B9sbndUkmxlMy2YgAA+JWA/+1vfwt1HAAAxKXUxFStn7ze99gv3lXQUzNCFVbjJadIDocnAS8sqDcBD6gdAACIMn4l4K1atQp1HAAAxCXLspTmbGAlOwoq4JZlSSlpkuug5wuDrBb1nt/gdgAAIMqwCBsAANEmGuaASyzEBgDAr/hVAZ85c6Ysy9L06dOVkJCgmTNn1vsey7J05513NjpAAABiWXF5sW5ZdoskadbwWUpOSK7/TYVRsAq61KCtyAJqBwAAooxfFXBjjIwxVZ778x4AAFC3cne5Xlr/kl5a/5LK3eX1nm9KS6SyMs+TCB6CLklKrZjL7f3CoA4NbQcAAKKRXxXwu+66q87nAAAgTLzJrGVJKRG+WFlFBdwUusQmZAAAMAccAIDo4l2ALSVNliOyf41bvjng9VfAAQCIB35VwGvz3//+V6tXr9auXbskeVZLHzhwoAYNGhSU4AAAwK9451NH+gJsUoPmgAMAEA8CSsALCgr0wAMPaO3atXI4HGrWrJkkac2aNXrvvffUu3dv3XTTTUpPj4J/HAAAEE2iYAsyH1ZBBwCgioAS8Hnz5um7777TpEmTNGbMGKWkpEiSioqK9O677+q5557TvHnzdM011wQ1WAAA4l60bEEmHaqAuxiCDgCAFGACvnLlSo0ZM0ann356lddTUlJ0+umna/fu3froo4+CEiAAADjEuKJkCzLJVwE3RVTAAQCQAkzAExMT1a5du1qPt2vXTomJjZpeDgBAXEhNTNWaC9f4HterYji3FUUJuD9D0BvcDgAARKGAlk8dMmSIPv/8c7nd7mrHysvL9dlnn+noo49udHAAAMQ6y7LUIrWFWqS2kGX5sVlXFM0Bt9L8H4Le4HYAACAK+VWm3rBhQ5Xnw4cP15NPPqnbb79dJ5xwgrKzsyVJ27dv15IlS1RWVqbhw4cHP1oAAOJdNM4BZxsyAAAk+ZmA33rrrbUe++mnn2p8fcaMGVqwYEFgUQEAECeKy4s18/OZkqQZR89QckJy3W8ojKI54OkZnr/9qIA3uB0AAIhCfiXgV111VajjAAAgLpW7y/X02qclSbf/5nYpoe7zTTTuA+4qkDGmzqHlDW0HAACikV8J+MiRI0McBgAA8IvroCTJioI54L4vCYxbKiqMinnrAACEUkCLsAEAAJt4K+BRMATdSkqWEp2eJ+wFDgBAYNuQSVJJSYm++OIL/fzzz3K5XNVWRLcsi6HrAAAEWzQNQZc8cebvkwoPSmpldzQAANgqoAR8165dmjlzpnbt2qW0tDS5XC5lZGT4EvHMzEylpKQEO1YAABBF25BJOpSAF1ABBwAgoCHo8+fPl8vl0r333quHH35YkvT73/9ezzzzjCZNmqSkpCRNnz49qIECABDvjNstFUVbBbxiJfTCg/bGAQBABAgoAf/f//6nMWPGqHv37nI4PJcwxsjpdOr0009Xv3799NRTTwUzTgAAUFQoGeN5HAVzwCX5vigwzAEHACCwIejFxcVq3bq1JCk1NVWS5HK5fMd79uyp+fPnByE8AABiW0piij4/73Pf4zp5538nOmU5k0IcWXBYqekykm/19to0qB0AAIhSAVXAW7ZsqT179kiSEhIS1Lx5c61fv953fMuWLUpKio5/GAAAYCeH5VCHzA7qkNlBDqueX8veYdzRMv9bktIrhqDXUwFvUDsAABClAqqA9+vXT6tWrdI555wjybNP+GuvvaaDBw/KGKOPP/5YI0aMCGqgAADEPe9oM++86mjgHSrPEHQAAAJLwM844wz9+OOPKi0tldPp1Jlnnqm9e/fqiy++kMPh0LBhw3TRRRcFO1YAAGJOSXmJZq2aJUm6ZfAtSkqoYwRZYZStgC4dWiyuniHoDWoHAACiVEAJeMuWLdWyZUvf86SkJE2dOlVTp04NWmAAAMSDMneZHlvzmCTphiNvqDPx9C1kFi0roEt+L8LWkHYAACBaBZSAV2aMUX5+viSpSZMmsiyr0UEBAIAaVFSRrSgagm6lZXgWYStkCDoAAAEn4Fu2bNGCBQv09ddfq7i4WJKUnJys/v3765xzzlHHjh2DFiQAAJBUUDGMO4oS8END0EnAAQAIKAH/7rvvdN9998kYo8GDB6tdu3aSpG3btmnVqlX66quvdNttt6l3794Nuu6rr76qFStWaOvWrUpKSlLPnj114YUX+q4PAEBc886jTo+iIeip3lXQ654DDgBAPAgoAX/66afVtGlT3XXXXVXmgkvS7t27NWPGDD3zzDO6//77G3TdtWvX6qSTTlK3bt1UXl6u559/Xvfcc4/mzJmjlBT2BAUAxDlXFFbA06mAAwDgFdBGm5s3b9aYMWOqJd+SZ4G2MWPGaPPmzQ2+7vTp0zVy5Eh16NBBnTt31tVXX63du3drw4YNgYQJAEBMicpF2LwV8KJCmfJye2MBAMBmAVXAW7VqpbKyslqPl5WVqUWLFgEH5eWq2O80I6Pmb/pLS0tVWlrqe25ZllJTU32PI5U3tkiOEfRTtKCfIl8891Hlz2xZllRDG/z6nDrbqSIBt9Izg9qeDYqhoSp9WWAVuWRlNAl/DDEknn+eogn9FPnoo+gQi/0UUAI+YcIEPf300zryyCPVuXPnKsd+/vlnLV68WBdffHGjAnO73Xrqqad02GGH1bqg26uvvqqFCxf6nnfp0kWzZs1Sq1atGnXvcMnOzrY7BPiBfooO9FPki8s+Kjg07Do7O7vGudtu49a3V30rSerSqoscVu2D03JLilQqqUX7jkpp2zYUYapNmzZBn2K+JTVNptCl1hnpSqwl7oa0A+L05ykK0U+Rjz6KDrHUT34l4E8++WS115o2bapbbrlFhx12mK9Btm/frnXr1qljx45av369hg0bFnBgc+fO1ebNm3X33XfXes6ZZ56pU0891ffc+83Irl276qzQ282yLGVnZys3N1fGGLvDQS3op+hAP0W+eO4jy+WS958Mubm5MmlpNZ7XXM0lSTtyd9R5vbL8fZKkvKISWdu3BytMuVyWVBHpjh07lJrqDtq1JcmkpEmFLu3c+LMsJdR6nr/tEM/i+ecpmtBPkY8+ig7R0k+JiYl+F4H9SsDfeeedWo/98MMP+uGHH6q8tmnTJm3atEmXXHKJX0H82ty5c/Xll19q5syZdQ5ldzqdcjqdNR6L5A7yMsZERZzxjn6KDvRT5IvLPqr0eYPy+Su2ITNp6VWu3ViVLxWSfkpLl/bulik4ENS441lc/jxFIfop8tFH0SGW+smvBHzBggWhjkOSp2GffPJJrVixQnfddZdat24dlvsCAGCXkvISPfLVI5Kkawdcq6SEpBrPM2WlUkmx50k0rYIuSakVY9oLa18J3d92AAAgmgU0BzxU5s6dq+XLl+vmm29Wamqq9u3bJ0lKS0tTUhK/iAEAsafMXaY5X86RJF11xFW1J57eLcgsS0qteSh7xEr3fGFgXAWqbRkdv9sBAIAo1qgEfOfOnVq9erV27dolybM6+sCBAwOuXL/77ruSpLvuuqvK69OmTdPIkSMbEyoAANHNuwVZSposR3QtUGalpstIh75EAAAgTgWcgD/zzDNatGhRtbH4lmXplFNO0UUXXdTga7744ouBhgMAQGyrmP8dVXuAe3ljdtU+BB0AgHgQUAL+xhtv6K233tKQIUN02mmnKScnR5K0detWvfXWW3rrrbfUvHnzKiuUAwCARvAmr+lRNv9bOjRnnQQcABDnAkrA33//fQ0aNEh/+MMfqrzeo0cPXX/99SopKdGSJUtIwAEACBLjHb4dbQuwSVTAAQCoENAksl27dmnAgAG1Hh8wYIBvXjgAAAiCGEjADXPAAQBxLqAEvEmTJtq4cWOtxzdu3KgmTZoEGhMAAPi1ijngVhQOQbe8XxrUsQ0ZAADxIKAh6Mccc4wWLVqk1q1ba+zYsUpJSZEkFRUVafHixfrggw90yimnBDVQAABiUXJCst464y3f41p5q8epsbkIm9/tAABAFAsoAZ84caI2btyo559/XgsWLFDz5s0lSXl5eXK73erbt68mTpwY1EABAIhFCY4EDWg1oP4TvclrNK6C7v3SoI4h6H63AwAAUSygBDw5OVl33nmnVq5cqdWrV2v37t2SpP79++vII4/UoEGDZFlWUAMFACCe+eZPR+EQdBZhAwDAo8EJeHFxsR555BENGTJEw4cP11FHHRWKuAAAiAsl5SWa++1cSdJl/S5TUkJSzSf6KuDRmIBXxFxaIlNaIstZ/TP63Q4AAESxBi/ClpycrG+++UbFxcWhiAcAgLhS5i7TPSvu0T0r7lGZu6z2E72LsEVjAp6SKnlHxtVSBfe7HQAAiGIBrYLeq1cvrVu3LtixAACA2kTxEHTL4ZBS0zxPGIYOAIhjASXgl156qb7//nu98MIL2rNnT7BjAgAAvxbNq6BLh4ahsxc4ACCOBbQI20033aTy8nK9+uqrevXVV5WQkCCn01ntvKeffrrRAQIAEO9MeblUVOh5EoUVcEksxAYAgAJMwIcMGcIq5wAAhEthpaQ1yivgxnVQ/AsCABCvAkrAr7766mDHAQAAauMdtp2cKisxoF/d9vNW7gsO2BsHAAA2atBv8ZKSEq1atUo7d+5UZmamjjzySDVr1ixUsQEAAEkqqKiAp0dp9VuSlZ4pI/lWcwcAIB75nYDv379ft99+u3bu3Ol7LSkpSTfddJOOOOKIkAQHAECsS05I1kvjXvI9rpG3Ah6NW5B5pWd6/q6lAu5XOwAAEOX8TsBffvll7dq1S+PGjVO/fv2Um5url19+WY8//rgeeeSRUMYIAEDMSnAkaGi7oXWeY3wJePRWwOtLwP1pBwAAop3fCfjXX3+t4447ThdddJHvtaysLD388MPatm2b2rVrF5IAAQCIewWxUAGvWISNIegAgDjm9z7gu3fvVq9evaq85n2+b9++oAYFAEC8KHWX6qn/PaWn/veUSt2lNZ9UsQq6FcUJuFVPBdyvdgAAIMr5XQEvKytTUlJSlde8e3+73e7gRgUAQJwoLS/V9E+nS5LO7XmunA5n9ZNiqAJe2yJsfrUDAABRrkGroO/cuVMbNmzwPXe5XJKk7du3Ky0trdr5Xbt2bWR4AADAtwhbFK+CXt8ccAAA4kGDEvAFCxZowYIF1V5/4oknaj0fAAA0jompVdAPyrjdshx+z4IDACBm+J2AX3XVVaGMAwAA1MZVsQ94VK+CXvHlgXFLRa7o/jIBAIAA+Z2Ajxw5MoRhAACAWlXMm47qRdicSVJSslRS7Pk8UfxZAAAIFOO/AACIdLEwBF1iHjgAIO6RgAMAEOm8K4dnZNobR2PVsxI6AACxrkGLsAEAgOBKSkjS0yc97Xv8a6a83LcPuK+CHK0q4jcFB2T96lB97QAAQCwgAQcAwEaJjkSd0PGE2k9wVaoWx8wQ9OoV8HrbAQCAGMAQdAAAItnBivnSaemyEhLsjaWRLN8Q9Hx7AwEAwCZUwAEAsFGpu1Sv/PiKJOms7mfJ6XBWPcG7YFm0Dz+X6qyA19sOAADEABJwAABsVFpeqj989AdJ0mldTovxBNxbAa++Cnq97QAAQAxgCDoAABHM+BLwKJ//LVVahI1V0AEA8YkEHACASFYxB9xKb2JzII1n1VEBBwAgHpCAAwAQybzV4piogFd8iUACDgCIUyTgAABEMu+K4RmxNAecIegAgPhEAg4AQCTzbkMWA0PQD62CfkDGGHtjAQDABiTgAABEMOOKpSHoFZ/B7ZaKCu2NBQAAG7ANGQAANkpKSNJjxz/me1zNQc8QdCsGhqBbScmSM0kqLfHMA09N8x2rtx0AAIgBJOAAANgo0ZGo07qeVvsJvkXYoj8Bl+Spgu/L83yulm18L9fbDgAAxACGoAMAEMl8+4DHSgLunQeeb28cAADYgAo4AAA2KnOX6e2Nb0uSTu58shIdh341m9JSqbjI8yQGhqBL8iXgpuCgrEov19UOAADECn67AQBgo5LyEk19f6okaf3k9VUTT2/123JIKWk1vDsK+bYiq7oXeJ3tAABAjGAIOgAAkarg0AroliM2fmVbviHo7AUOAIg/sfHbHACAWOSdJx0r87+lWivgAADEAxJwAAAi1cGKJDVW5n9LlRZhowIOAIg/JOAAAEQo460Sp2XYG0gw+RZhowIOAIg/JOAAAESqiiTViqEK+KE54CTgAID4QwIOAECk8i3CFjsJuG8O+EEScABA/GGPDwAAbORMcGrOiDm+x1UcrFiELaNJmKMKoYyaK+B1tgMAADGCBBwAABs5HU5N7DmxxmPGm4BnxlIC3tTz98EDMm63b3u1utoBAIBYwRB0AAAi1YH9kiQrFivgxi25WAkdABBfSMABALBRmbtMSzYt0ZJNS1TmLqt60LcNWdPwBxYiVqJTSk33PPFW+FVPOwAAECMYgg4AgI1Kykt08TsXS5LWT16vREelX80VFfCYGoIuearghQXSgXwp2/NSne0AAECMoAIOAEAEMuXlh4Zox9IQdOnQ56lUAQcAIB6QgAMAEIm8q4Rb1qGtu2JFpmdIvSEBBwDEGRJwAAAi0YGK5DQ9Q5Yjwd5Ygsy3qJx3iD0AAHGCBBwAgEgUi3uAezEEHQAQp0jAAQCIRAcrqsMxtAK6TyYJOAAgPpGAAwAQgcyB2K+A+z4jAABxgj0+AACwkTPBqXuH3ut77FNRHbZibQsyeeaAG6lKBbzWdgAAIIaQgAMAYCOnw6nJfSdXPxDLc8ArVkGvvAhbre0AAEAMYQg6AACRKA6GoOvgAXvjAAAgzKiAAwBgo3J3ub7I/UKSNCR7iBIqthzz7ZGdGcOLsBUXypSWyHIm1doOAADEEhJwAABsVFxerHPeOkeStH7yeqU50jwHKlZBt2KxAp6aLjkcktvtqfQ3b1l7OwAAEEMYgg4AQCSK4TnglmWxFzgAIC6RgAMAEGGMMYfmgMfgKuiSDg2tP7i/7vMAAIghJOAAAESakmKptMTzOAYr4JLYCxwAEJdIwAEAiDTeYdmJTik5xd5YQiUj0/M3K6EDAOIICTgAAJHGuz92ZlPPfOkYZDEEHQAQh0jAAQCINL4EPEaHn0tSRkUCnk8CDgCIH2xDBgCAjRIdibr9N7f7HkuS8SalTbJsiioMKj6bObBPUs3tAABArOE3HAAANkpKSNJV/a+q+mL+PkmVhmnHIKtJUxnJ91lrbAcAAGIMQ9ABAIg0FUlpTFfAM7M8f3s/KwAAcYAKOAAANip3l+ubPd9Ikg5vcbgSHAnxkYB7P1vFfPca2wEAgBhDAg4AgI2Ky4s17rVxkqT1k9crzZHmmxcdFwl4UaFMSbGKrfJq7QAAQKxhCDoAAJHGNwc8y9YwQio1TUqsqAMwDB0AECdIwAEAiDRxMATdsqxqw9ABAIh1JOAAAEQQ4y6XDh7wPInhBFwSC7EBAOIOCTgAAJHk4AHJuCXLkjKa2B1NaHn3AicBBwDECRJwAAAiiTcZTc+UlRDbK4FbTSr2OScBBwDECRJwAAAiSRzM//ZhDjgAIM6wDRkAADZKdCTqD0f+wffYxGMCnr+vWjsAABCL+A0HAICNkhKSdMOgG3zP3RXVYCuzqV0hhU/FImwmf1+1dgAAIBYxBB0AgEgSRxVwq1IFHACAeEAFHAAAG7mNW+v3rpck9WjWI64S8ENzwPdVaweHRY0AABB7SMABALBRUVmRRr88WpK0fvJ6JcdjAn7wgAqLDlZphzRnmn1xAQAQIny9DABAJPHNAc+yN45wSM+QvJXuAlZCBwDEvoiqgK9du1b/+c9/9PPPP2vv3r268cYb9Zvf/MbusAAACJ84qoBbjgQps4nnM+fn2x0OAAAhF1EV8OLiYnXu3FmXXXaZ3aEAABB2xu2OqwRcktSkmedvFmIDAMSBiKqADxw4UAMHDrQ7DAAA7OE6IJWXeR43zbI1lLDJaiZt+Vkmf6/dkQAAEHIRlYA3VGlpqUpLS33PLctSamqq73Gk8sYWyTGCfooW9FPki+c+qvyZLcuSamiDKufs3+d5kNlUDmdSqMOrOQbLCmtfWVktZCRZ+w8l4OGOIZrE889TNKGfIh99FB1isZ+iOgF/9dVXtXDhQt/zLl26aNasWWrVqpWNUfkvOzvb7hDgB/opOtBPkS8u+6igwPcwOztbSk+vfkrJoXOyHFKhJGfLNspu2zYcEXpiOBSC2rRpU1OYIbO/fUflS0otK/G9lp2drfSkMAYRheLy5ykK0U+Rjz6KDrHUT1GdgJ955pk69dRTfc+934zs2rVLZWVldoVVL8uylJ2drdzcXBlj7A4HtaCfogP9FPniuY8sl0vefzLk5ubKpFXfWqukvERTj5gqSTqw6RclSipLz9D27dvDFqfLZUkVke7YsUOpqe6w3dud4JQklWzfpqmDPO2we+du5SewKFtN4vnnKZrQT5GPPooO0dJPiYmJfheBozoBdzqdcjqdNR6L5A7yMsZERZzxjn6KDvRT5IvLPqr0eWv7/E6HU3cMuUOS5H7rRRlJato8rG1V+VZh76emzSVJzn37dceQB6rEgdrF5c9TFKKfIh99FB1iqZ8iahV0AADi2r48z98VSWlcaFqxCvr+PHvjAAAgDCKqAl5UVKTc3Fzf8507d2rjxo3KyMhQy5YtbYwMAIDQcBu3th7cKknK3rfH8814Vhwl4BWf1b0/T1v3/yLL4VBORo4cFjUCAEDsiagE/KefftLMmTN9z5955hlJ0ogRI3T11VfbFRYAACFTVFako184WpL0/f5zlCbJiqcEvGIf8CJTqmNeHCpJWj95vdKc1efLAwAQ7SIqAe/bt69efPFFu8MAAMAe+d4h6M3sjSOMrMREKbOpVMAQdABA7GN8FwAAkSJ/n+fveKqAS/E15x0AENdIwAEAiBTl5Z6/m2TZGkbYZcVPxR8AEN9IwAEAiCSZTWUl1rzFZqyyqIADAOIECTgAAJEkHpPReBtyDwCIWyTgAABEkngcjh2PXzoAAOJSRK2CDgBAvElwJOjiPhfLbPxRCcYVl8OxrazmSjCWLirsIWvQUCU4EuwOCQCAkCABBwDARskJybrv2Pvk3vgPGfN2fFaDmzZTsnHoni1dlfC7++yOBgCAkGEIOgAAEcDs3eN50CwOE/CsFp6/9+fJuMvtjQUAgBAiAQcAwEbGGO0p3KM9+7bIyMhq3srukMIvq5mMw9IeR7H27PhJxhi7IwIAICRIwAEAsFFhWaGOePYIDWzzhgodbql5S7tDCjvLkaDCZlkaeOQa9X9zlArLCu0OCQCAkCABBwAgkjSLwwq4JDWLvy8eAADxhwQcAIBIkZwqpaXbHYUtrDis/AMA4g8JOAAAkaJZC1mWZXcU9vAuxAYAQAwjAQcAIFLEcxLaLI4/OwAgbpCAAwAQIeJ5GLbFHHAAQBwgAQcAIFJQAQcAIKYl2h0AAADxLMGRoAmlPaR9eUro1cbucGyT0CJbE3Z5knBHebnktDkgAABCgAQcAAAbJScka05uX2n7ZjnObGt3OLZJbtJCc7b2lEpK5Mg/IKVk2h0SAABBxxB0AABsZIyR8nZ5nsTxPGjLsg7tgZ63295gAAAIERJwAABsZAoOylVaIJejXCaO50EbY+Rq3lQuR7nce3baHQ4AACHBEHQAAGxUuHuzeg3+SpK03uFWmr3h2KawrFC9MudLg6Uf9mxTht0BAQAQAlTAAQCw0949dkcQefYyBB0AEJtIwAEAsJFhuHU1xjsnHgCAGEMCDgCAnXaTgFeze4fdEQAAEBIk4AAA2MjsIdmsZu9umfJyu6MAACDoSMABALAT1d7qysuZBw4AiEkk4AAA2MQYIzEHvGa7cu2OAACAoGMbMgAA7HJgvxxFxTolr5msI4+Rw4rf78UdlkPjuoyTWb9WDmPJ7N4hy+6gAAAIMhJwAADssitXKcahx/KGKOHEx+2OxlYpiSn61wn/knvnYzJmERVwAEBMit+v2gEAsJnxzv9u2cbeQCKJty2YGw8AiEEk4AAA2KWiymu1IgH3slpmS5IMFXAAQAwiAQcAwC67c+VylKtD6Z+V83iOXKUuuyOyjavUpZzHc9T+vxPkcpRLu0nAAQCxhwQcAACbmF0Ms67VwQMyrgK7owAAIKhIwAEAsAtV3pqlN/H8zTxwAECMIQEHAMAGprRE2rvH7jAik3chNuaBAwBiDAk4AAB22LFNMkZKTbc7kohjtW4rSTK5W2yOBACA4CIBBwDADt7ksk2OvXFEojbtPH/nbrU3DgAAgowEHAAAG3iru5Y32YSPle35UoIKOAAg1iTaHQAAAHFpu6e668juoNGO0Z7HVvx+L+6wHBrdoaIdsjt4XszdImOMLMuyMTIAAIKHBBwAABuYHZ4EPLVtZ80fON/maOyXkpii+WM97WBKS+V2OKSiQmn/Ximruc3RAQAQHPH7VTsAADYxxhyaA57d3t5gIpDldEotsz1PGIYOAIghJOAAAITb3j1ScZGUkCC1yrY7msjU1vPFBPPAAQCxhAQcAIBw8yaVrbJVaErUfV53dZ/XXa5Sl71x2chV6qrSDt6F2FgJHQAQS5gDDgBAmJlfDT8vLCu0MZrIUaUdKtrGbKcCDgCIHVTAAQAIt4qqrsUe4LXyVcB3UAEHAMQOEnAAAMLMbNvkedCWBdhq5V2cbs9OmaL4HZoPAIgtJOAAAISRMUbaslGSZLXvYm8wEczKaCJltfA82fKLvcEAABAkJOAAAITT3j1SwQHJ4ZDadbA7msjWwfMFhdnys82BAAAQHCTgAACE0+aKZDK7vSxnkr2xRDirfWfPg80k4ACA2MAq6AAAhJG3mmtVVHcty9IxbY/xPY5XNbaDtwJOAg4AiBEk4AAAhJM3maxILlMTU7Xw1IU2BhQZamoHq0MXGUna+ouMu1yWI8GW2AAACBaGoAMAEEbeai4LsPmhdVspKUkqKZZ2brc7GgAAGo0EHACAMDHFRdKuikSyQ2dbY4kGliNByuksSTKbN9oaCwAAwUACDgBAuGzZKBkjNW0mq0kzSZKr1KXD5x+uw+cfLldp/O53XVs7+BZiYyV0AEAMYA44AABhYjZt8DzwJpUV8orywh9MBKqxHTp0lSSZTT+FORoAAIKPCjgAAOGy4QdJktXlMJsDiR5Wlx6eBxvWybjd9gYDAEAjkYADABAmZsP3kiSrGwm439p3kZxJkuugtGOb3dEAANAoJOAAAISBObD/0EreVMD9ZiUmSp26Szr0BQYAANGKBBwAgHDYuN7zd3Z7WekZ9sYSZXwjBn4iAQcARDcScAAAwsBsWCeJ4eeBsLr2kiSZijn0AABEK1ZBBwAgDMxGTwKuimTSy7Is9W/Z3/c4XtXZDt0q2mzbJhlXgay09DBHBwBAcJCAAwAQDr94ttGyulVNwFMTU7XozEV2RBRR6moHq2kzqUVrac9OaeM6qc/AMEcHAEBwMAQdAIBwKC6S0tKlth3sjiQqWd17S5LMD/+zORIAAAJHAg4AQLj0OkKWg1+9Aek9QJJk1q62Nw4AABqBfwUAABAmVkUSWVlhWaGGPD9EQ54fosKywvAHFSHqawerzwDPg19+lCk4EN7gAAAIEhJwAADCxOpbfe6yMUZbDm7RloNbZIyxIarIUF87WM1aeIbvGyN9v8aGCAEAaDwScAAAwqFlG1mtsu2OIqp5q+Bm7Ve2xgEAQKBIwAEACAOr1xF2hxD1fAn4/1bH9WgBAED0IgEHACBEKieJJOBB0LOflJDo2Y5s+2a7owEAoMFIwAEACJUtPx963Lu/fXHECCslVfJWwf/7qb3BAAAQABJwAABCxHz5ue+xlZJqYySxwxp8rCTJ/PcTmyMBAKDhEu0OAACAWGSMkfmy/iqtZVnqmdXT9zhe+dsO1oAhMgmJ0tZfZLZvltW2Q7hCBACg0UjAAQAIhc0bpN076j0tNTFVS89ZGoaAIpu/7WClZXiGoX+zSmbVJ7JOOy/0wQEAECQMQQcAIATM5x/aHULMsgZVDEP/4iNWQwcARBUScAAAgsyUFMt88r7dYcQsa9AxUkqqtGOr9P0au8MBAMBvJOAAAASZWblcch2Umreq99zCskKNemmURr00SoVlhWGILjI1pB2slDRZx4ySJLk/XBSO8AAACAoScAAAgsxUJIXW8BPrP9cYrdu3Tuv2rYvr4dQNbQdrxCmeB199IZO3O8TRAQAQHCTgAAAEkVn3P2njeikxUdbRo+wOJ2ZZOR2lnn0lt1vmgzfsDgcAAL+QgAMAECTGGLlfmy9JsoaeIKtJlr0BxTjHSWdJkszSt2T25dkcDQAA9SMBBwAgWP63Wlq/Vkp0yjp1ot3RxL7DB0vdekklJTKLXrQ7GgAA6kUCDgBAEJiyMrlfflqSZI06RVazFjZHFPssy5LjzN9KkszH78ps32JzRAAA1I0EHACAIDCLX5a2/CylZcg6eYLd4cQN67DDPZXw8jK5n3pYxl1ud0gAANSKBBwAgEYyW36WeXOBJMk6/wpZmU39fq9lWWqf0V7tM9rLsqxQhRjxGtMOjguv8uwLvuEHmfdeD1GEAAA0XqLdAQAAEM1M/j65/36fVF4m9f+NrCEjGvT+1MRUfXH+FyGKLno0ph2s5q1knXuZzDN/k3nlGZkOXWT1GRjkCAEAaDwq4AAABMgUFcr96H3S7h1Sq2w5Lr42rqvYdrKGnSjr6JGS2y33Y7Nktvxsd0gAAFRDAg4AQADMgf1yz75d+ul7KS1djmvvbNDQcwSXZVmyLrpW6tFHKnTJ/ZfbPHuyAwAQQUjAAQBoIPPDN3Lfe4O0cb2UkSnHdXfJats+oGsVlhXqlFdP0SmvnqLCssIgRxo9gtEOltMpx9W3S917S64CuR+8Q+53X2VhNgBAxIjIOeCLFy/WG2+8oX379qlTp0669NJL1b17d7vDAgDEObMrV+aNF2Q++8DzQqtsOX53p6zswJJvSTLG6OvdX/sex6tgtYOVniHH7++We+4c6cvPZF6aJ7NyuRxnXij1HsAUAQCArSIuAf/000/1zDPPaMqUKerRo4feeust3XvvvXrooYfUtClD+wAA4WUO5sv8b7XMFx9J334pGbckyRo+Rta5l8pKSbM5QvyalZQsx9Q/yix/T2bBXGnjerkfnCHldJI1dLSsvoOkdh1IxgEAYRdxCfibb76p448/XqNGjZIkTZkyRV9++aWWLl2qM844w97gAAAxxZSVSSVFUnGxVOSS9u6R2bdH2rtH2vqLzOafpR1bpcoV2b4D5Rg/SVaXnvYFjnpZliVr+BiZI46SeXuhzMfvePr0pXkyL82TmmRJnXvIattBatNOVlZzz2uZTaWUNCkpSUpIJEkHAARVRCXgZWVl2rBhQ5VE2+Fw6PDDD9e6deuqnV9aWqrS0lLfc8uylJqa6nscqdyv/1u5a1aqrKysYW8MeEhegO9rzEjIqIm17jduT0hUWXkN/RTo5wtRnHW/NUr6ohFDTrclOFRe7m7YzRs10jfQtomS/26CHaclbXU45Ha7aznHhv++3W5P4u3v/4dzOsvqf5QcQ4+XlZ3j920q/y6yLEuq4XfTr8+x4/dXLMdgZTWXzr9C5vQLZFZ8LLP6M5kf10r5+6Q1K2XWrJRUy3+FlkNKSq5IxhM8z62K1y3L87fDOvS4lj4OttzExIb/GyLqRe6/62pkVeqn+J1ZEtnoo+hgSTubNpP1hz/ZHUnQRFQCnp+fL7fbraysrCqvZ2Vladu2bdXOf/XVV7Vw4ULf8y5dumjWrFlq1apVqENtlLyiAhX88pPdYcAP8fbPm2jF8kqRz213APVxJMhKTVNCi1YVf1rLmdNJzq49ldTtMCU0axHYdQsKfA+zs7Ol9PTqp5RUPSc9qfo5oVYpTLVp06amMEMfQzjaoXsP6YLLZEpLVPzDtyrd+JPKNv+ssu1bVL5vj9z79qp8f96hL2aMWyou9PyJIKX1n4IIQD9FPvooOpTm71VOdrbdYQRNRCXgDXXmmWfq1FNP9T33flu+a9euyP5m+LiT1WrkycrLywvfF24BfyMf+DfOgd8yzLHW9jbLUovmLbQnb08tVbZA7xfm9zVGpPRFne+x1LJlS+3eU1s/BfuGjXxr2Ns0zP/d1FLhbdGypfbs3l334lrhjNVySMnJUnKKlJwqJR4aalxe8afEe25RibR9e2C3cbnk/SdDbm6uTFr1+eKuUpfvcW5urtKc4Z9T7nJZUkWkO3bsUGpq+L8yCXs7tGjr+TNomO8lS1KCMZ4EvLRYKimRSoql0hKpvNyTkBtT9Y/b+5pbxh36drMsS82bN/f8GyJeFu2Lws9pWZaaN2umvL1746efogx9FB0sy1LzVq08v0MjuJ8SExP9LgJHVALepEkTORwO7du3r8rr+/btq1YVlySn0ymn01njtSK5g6y2HZTStq2s7duj8pdKvLAsS8kV/RTJ/z3FO8uylNS2rawM+iliVfSRktMj+v95Ifnvp9I1jTE13sMYo+Ypzes8J9Qq39K+GOxvB5/ERM+f1IZV4cPxFZJlWfwbIgrQT5GPPooO3n4yMfTv8YhKwBMTE9W1a1d9++23+s1vfiNJcrvd+vbbbzV27FibowMAIPjSnGn65rff2B2G7WgHAEA8iKgEXJJOPfVU/f3vf1fXrl3VvXt3LVq0SMXFxRo5cqTdoQEAAAAAELCIS8CHDh2q/Px8vfjii9q3b586d+6s2267rcYh6AAAAAAARIuIS8AlaezYsQw5BwDEhcKyQv128W8lSfPHzldqYqrNEdmDdgAAxIOITMABAIgXxhh9tv0z3+N4RTsAAOKBw+4AAAAAAACIByTgAAAAAACEAQk4AAAAAABhQAIOAAAAAEAYkIADAAAAABAGrIIOAPj/9u48qOrr/v/4E2SRRbi4ImpQRMWoGKyauMWIWjUatW7BpXFqpK7TaRJbWkNiQtURjUuS6iRxWqd0nCo1ikuNjUs1g1rXBDWaEFT0Z1EBEUFW8d7fHw7365ULKN5F4PWYYQzns/A+953Dh/c9n8+54mT6yK0H9DqIiEhdpwJcRETEibzdvUn7VZqzw3A6vQ4iIlIf6BZ0EREREREREQdQAS4iIiIiIiLiACrARUREnKi4rJhf7vklv9zzS4rLip0djtPodRARkfpAz4CLiIg4kdFk5MD/O2D+7/pKr4OIiNQHmgEXERERERERcQAV4CIiIiIiIiIOoAJcRERERERExAFUgIuIiIiIiIg4gApwEREREREREQeok6ugu7nVjm7VljjrO+WpdlCenn31MkeenhARAYCbpye4u1fcxcWTiMAH+3h6eOLuVnEfe3soTDw83HB3Nzk+hmfgdahN6uV4qoWUp2efclQ7POt5epL4XEwmk+OvsiIiIiIiIiL1jG5Bd4KioiJiYmIoKipydihSBeWpdlCenn3KUe2gPNUOylPtoDw9+5Sj2qEu5kkFuBOYTCYuX76Mbj54tilPtYPy9OxTjmoH5al2UJ5qB+Xp2acc1Q51MU8qwEVEREREREQcQAW4iIiIiIiIiAOoAHcCd3d3JkyYgLuV1XDl2aE81Q7K07NPOaodlKfaQXmqHZSnZ59yVDvUxTxpFXQRERERERERB9AMuIiIiIiIiIgDqAAXERERERERcQAV4CIiIiIiIiIOoAJcRERERERExAHcnB1AXXD+/Hl27NjB5cuXuX37NgsWLKB3797m7SaTicTERPbv309BQQFhYWHMnDmTli1bVnnePXv2sHPnTnJzcwkODmbGjBmEhobauzt1kj1ylJiYyJYtWyzagoKCWLNmjb26UedVl6djx46xd+9eLl26xN27d1m+fDlt27at9rxHjx5l8+bNZGVlERgYyNSpU+nRo4cde1K32SNPBw8eZN26dRZt7u7ubNy40R5dqPOqylFZWRmbNm3i22+/JTMzE29vb7p168aUKVNo3LhxlefVdcm27JEnXZtsr7rfeYmJiRw5coRbt27h5uZGSEgIUVFRdOjQocrzajzZjj1ypLFke9Xl6WFffPEF+/btY/r06YwcObLK89a2saQZcBsoKSmhbdu2vPnmm1a3b9++na+++oro6GiWLl2Kp6cnS5YsobS0tNJzHjlyhISEBCZMmEB8fDzBwcEsWbKEO3fu2KsbdZo9cgTQpk0bvvjiC/NXXFycPcKvN6rLU0lJCWFhYUydOvWxz/njjz/y8ccfExkZSXx8PL169WLFihVcvXrVVmHXO/bIE4CXl5fFeFq7dq0twq2XqspRaWkply9fZvz48cTHx/POO++QkZHB8uXLqzynrku2Z488ga5Ntlbd77ygoCBmzJjBRx99RFxcHM2aNWPx4sXk5eVVek6NJ9uyR45AY8nWqstTuePHj/PTTz8REBBQ7Tlr41jSDLgNREREEBERYXWbyWRi9+7djBs3jl69egEwf/58oqOjOXHiBP369bN63K5duxg8eDCDBg0CIDo6mtOnT/Of//yHsWPH2qUfdZk9cgTg6uqKwWCwR8j1UlV5Anj55ZcByMzMfOxz7t69mxdeeIHRo0cDEBUVxdmzZ9mzZw+//vWvny7gesoeeQJwcXHReLKRqnLk7e3Ne++9Z9E2Y8YMFi5cSHZ2Nk2bNrV6nK5LtmePPIGuTbZW3e+8/v37W3z/xhtvcODAAa5cuUK3bt2sHqPxZFv2yBFoLNladXkCyMnJ4a9//Svvvvsuy5Ytq/actXEsqQC3s8zMTHJzcwkPDze3eXt7ExoaSmpqqtXirqysjEuXLln8T+Pq6kq3bt1ITU11RNj1Sk1yVO7GjRvMmjULd3d3OnbsyJQpU6r8o0gcLzU1lVGjRlm0de/enRMnTjgpIqlMcXExc+fOxWQy0a5dOyZPnkybNm2cHVa9UFhYiIuLC97e3la367r0bKguT+V0bXKesrIy9u3bh7e3N8HBwZXuo/HkPI+To3IaS45lNBr59NNPGT169GNd/2vrWFIBbme5ubkA+Pv7W7T7+/ubtz0qLy8Po9FY4R03g8FARkaGHaKs32qSI4AOHTowd+5cgoKCuH37Nlu2bOH9999n5cqVeHl52TFieRK5ublPnFtxvKCgIObMmUNwcDCFhYXs2LGD2NhYVq1aRZMmTZwdXp1WWlrKxo0b6devX6WFna5Lzvc4eQJdm5zl1KlTrFmzhtLSUgwGA7Gxsfj5+VndV+PJOZ4kR6Cx5Azbt2+nQYMGjBgx4rH2r61jSQW4SA09fAtNcHCw+Rf10aNHiYyMdGJkIrVPx44d6dixo8X3b731Fnv37iUqKsqJkdVtZWVlrF69GoCZM2c6ORqpzJPkSdcm5+jSpQsrVqwgLy+P/fv3s3r1apYuXVrhDWBxnifNkcaSY126dIndu3cTHx+Pi4uLs8OxKy3CZmfl78g8uhDAnTt3Kn2mxM/PD1dX1wozdLm5uXoOxQ5qkiNrfHx8CAoK4saNGzaMTp6WwWB46tyK47m5udGuXTuNJzsqL+qys7OJjY2tclZV1yXneZI8WaNrk2M0bNiQwMBAOnbsyJw5c2jQoAEHDhywuq/Gk3M8SY6s0ViyrwsXLpCXl8fcuXOJiooiKiqKrKwsEhISmDdvntVjautYUgFuZ82bN8dgMHD27FlzW2FhIWlpaRazPQ8r/3iEc+fOmduMRiPnzp2r9BipuZrkyJri4mJu3LjxTA/4+qhjx44WuQU4c+ZMtR8PI85lNBq5evXqY62AKk+uvKi7ceMG7733Ho0aNapyf12XnONJ82SNrk3OYTKZuHfvntVtGk/PhqpyZI3Gkn29/PLLrFixguXLl5u/AgICGD16NO+++67VY2rrWNIt6DZQPiDLZWZmkp6ejq+vL02bNuXVV19l69attGzZkubNm7Np0yYCAgLMK24DxMXF0bt3b4YPHw7AqFGjWLt2LSEhIYSGhrJ7925KSkp45ZVXHN29OsEeOUpISKBnz540bdqU27dvk5iYiKura4WVNuXxVZenu3fvkp2dTU5ODoD5+R6DwWC+IP75z3+mcePGTJkyBYBXX32VDz74gJ07d9KjRw8OHz7MxYsXtQL6U7BHnrZs2UKHDh0IDAykoKCAHTt2kJWVxeDBgx3buTqiqhwZDAZWrVrF5cuXiYmJwWg0mmcPfH19cXN78KeBrkv2Z4886dpke1XlydfXl61bt9KzZ08CAgLIz89nz5495OTk0KdPH/MxGk/2ZY8caSzZXnV/Pzz6JqObmxsGg4GgoCBzW10YSyrAbeDixYt8+OGH5u8TEhIAGDhwIPPmzWPMmDGUlJTw+eefU1hYSFhYGAsXLsTDw8N8zM2bNy0+i7Bv377k5eWRmJhIbm4ubdu2ZeHChXrXrYbskaOcnBw+/vhj8vPz8fPzIywsjCVLllS5oIdUrbo8nTx5knXr1pm3r1mzBoAJEyYwadIkALKzsy2eHerUqRO/+c1v2LRpE//4xz9o2bIlv/vd73juuecc0KO6yR55unv3Lp9//jm5ubn4+PgQEhLC4sWLad26tQN6VPdUlaOJEydy8uRJAH7/+99bHLdo0SK6dOkC6LrkCPbIk65NtldVnqKjo8nIyGDlypXk5+fTqFEj2rdvz4cffmixirPGk33ZI0caS7ZX3d8Pj6MujCUXk8lkcnYQIiIiIiIiInWdngEXERERERERcQAV4CIiIiIiIiIOoAJcRERERERExAFUgIuIiIiIiIg4gApwEREREREREQdQAS4iIiIiIiLiACrARURERERERBxABbiIiIiIiIiIA6gAFxERsZHMzEwmTZrEwYMHnR1KjR05coRf/epXFBcXOzsUp1izZg2rVq1ydhgiIlJHqQAXEZF6KT4+nmnTplFUVFTpPp988gmTJ08mPz/fgZE5j9Fo5J///CcjRoygYcOGzg7HKcaMGcOxY8dIT093digiIlIHqQAXEZF6acCAAZSWlnL8+HGr20tKSjhx4gQvvPACjRo1cnB0znHy5EkyMjIYMmSIs0Nxmnbt2tG+fXt27drl7FBERKQOUgEuIiL1Us+ePfHy8iI5Odnq9hMnTlBSUsKAAQMcHJnzHDx4kE6dOtG4cWNnh+JUffr04dixY/X2NnwREbEfN2cHICIi4gweHh707t2b5ORk7ty5g7+/v8X25ORkvLy86NmzJwA3b95k48aNnD17lnv37hEcHMz48ePp0aNHlT/ngw8+sPi33Nq1azl//jxr164FHjw/Pn/+fKZNm4aHhwe7du0iNzeXsLAwZs+eTZMmTfjyyy/Zt28f+fn5dO/enblz5+Lr62tx3m+//ZZt27Zx+fJlXFxc6Ny5M9OmTaNNmzZVxllaWsp3333HL37xiwrb7t+/z7Zt2zh06BC3bt0iICCAfv36MXHiRNzd3c37TZo0iQkTJjBp0iSL4+fNm8fzzz/PvHnzgAeF/rp164iLi+PYsWN88803lJaWEh4ezqxZs/Dz86vQp6SkJHOfgoKCGDlyJP379zfv89NPP5GYmEhqair379+nffv2TJ48mbCwMPM+RUVFbN68mRMnTnD79m28vb0JDg5m6tSphISEmPcLDw/n73//O2fOnKF3795Vvm4iIiJPQjPgIiJSbw0YMID79+9z9OhRi/a7d++SkpJCr1698PDwIDc3l9jYWFJSUhg2bBhRUVGUlpYSHx9f6S3sNZWcnMzXX3/N8OHDGTVqFOfPn2f16tVs2rSJlJQUxowZw5AhQzh16hQJCQkWx37zzTcsW7aMhg0bMnXqVMaPH8+1a9d4//33yczMrPLnXrp0ibKyMtq1a1dh22effUZiYiLt2rVj+vTpdO7cmaSkJNasWfNUfd2wYQNXrlxh4sSJDB06lFOnTvGXv/zFYp+DBw+ybNky7t69y9ixY5kyZQrBwcF899135n3OnTvHokWLKCoqYuLEiUyePJnCwkLi4uJIS0sz77d+/Xq+/vprXnzxRWbOnMlrr72Gh4cH//vf/yx+ZuvWrfHw8OCHH354qv6JiIg8SjPgIiJSb3Xt2pWAgACSk5MZPny4uf3o0aPcv3/ffPt5UlISd+7cIS4uzjyjOmTIEBYsWMDf/vY3evbsiaurbd7TzsnJ4ZNPPsHb2xt4sDBaUlISpaWlLFu2jAYNGgCQl5dHcnIy0dHRuLu7U1xczIYNG4iMjGTWrFnm8w0cOJDf/va3bNu2zaL9URkZGQA0b97coj09PZ1Dhw4RGRnJ7NmzARg2bBj+/v7s3LmTc+fO0bVr1xr11dfXl9jYWFxcXAAwmUx89dVXFBYW4u3tTWFhIRs2bCA0NJRFixbh4eFhPtZkMpn/Xb9+PV26dGHhwoXmcw0dOpS3336bTZs2ERsbC8Dp06cZPHgwb7zxhvk8Y8aMqRBXgwYNaNKkSYXCXERE5GlpBlxEROotV1dX+vbtS2pqqsUMcXJyMv7+/nTr1g14cAt0aGioxe3MDRs2ZMiQIWRlZXHt2jWbxfTSSy+Zi2+ADh06AA9m68uL7/L2srIycnJyADhz5gwFBQX069ePvLw885erqysdOnTg+++/r/Lnlq/0bu2WdoBRo0ZZtL/22mvAg6K2poYMGWIumAE6d+6M0WgkKyvL3KeioiLGjBljUXwD5uPS09O5fv06/fv3Jz8/39zv4uJiunbtyoULFzAajQD4+PiQlpZmfs2q4uvrS15eXo37JiIiYo1mwEVEpF4bMGAA//rXv0hOTmbcuHHcunWLH374gREjRphntbOzs82F8MNatWpl3v7cc8/ZJJ6mTZtafF9ejFfWXlBQAMD169cBiIuLs3peLy+vx/r55TPL5bKysnBxcSEwMNCi3WAw4OPjQ3Z29mOd15pH++Tj4wP8X59u3LgBUOVrW97v8mfprSksLMTX15epU6eydu1a5syZQ0hICBEREQwcOJAWLVpUOMZkMlm8OSAiImILKsBFRKReCwkJoVWrVhw+fJhx48Zx+PBhTCaTxQJfT8PFxaVCUQuYZ2UfVdmt7JW1P3wrNsD8+fMxGAwV9nt49tya8o9aKygooEmTJhW2P00x+qR9tfZ6VaZ832nTptG2bVur+5R/pnnfvn3p3Lkzx48fJyUlhZ07d7J9+3YWLFhARESExTEFBQUV3nQQERF5WirARUSk3uvfvz+bN2/mypUrJCcn07JlS0JDQ83bmzZtan5G+mHlzwg/OpP7MB8fH27evFmh/Wlmjq0pn8X19/cnPDz8iY8PCgoCHqzG/vCMc7NmzTCZTFy/fp3WrVub23NzcykoKLDou4+PD4WFhRbnLSsr4/bt208cD2AugK9evVppMVzeb29v78fqd0BAAMOGDWPYsGHcuXOHmJgYtm7dalGA379/n+zsbH72s5/VKG4REZHK6BlwERGp98oXW9u8eTPp6ekVZr8jIiJIS0sjNTXV3FZcXMz+/ftp1qyZRWH6qBYtWpCRkWHxPHF6errNV9ju3r07Xl5ebNu2jbKysgrbq3ueOSQkBDc3Ny5evGjRXl6Y7t6926J9165dABYfwxYYGMiFCxcs9tu3b1+lM+DVCQ8Px8vLy7wI3cPKZ75DQkJo0aIFO3futPq53eX9NhqNFd4c8Pf3JyAgoMLrde3aNe7du0enTp1qFLeIiEhlNAMuIiL1XvPmzenUqRMnT54E/q8gLzd27FgOHz7M0qVLGTFiBL6+vhw6dIjMzEzeeeedKldAHzRoELt27WLJkiUMGjSIvLw89u7dS5s2bSgqKrJZH7y9vYmOjubTTz8lJiaGfv364efnR3Z2NqdPn6ZTp068+eablR7v4eFBeHg4Z8+e5fXXXze3t23bloEDB7Jv3z4KCgp4/vnnSUtL49ChQ/Tq1ctiBfTIyEjWr1/PRx99RHh4OFeuXCElJcV8e3tN+jR9+nQ+++wz/vjHP9K/f398fHy4cuUKJSUlzJ8/H1dXV2bPns3SpUt5++23eeWVV2jcuDE5OTl8//33eHl58Yc//IGioiJmz57NSy+9RHBwMA0bNuTs2bNcvHjRYlV0eLD4m6enZ43uJBAREamKCnAREREe3Ib+448/EhoaanXBscWLF7Nx40b27NlDaWkpwcHBxMTEWMwAW9O6dWvmz59PYmIiCQkJ5u+Tk5M5f/68zfsQEBBAUlISO3bs4N69ezRu3JjOnTszaNCgao+PjIxk5cqVZGdnW9xaPnv2bFq0aMHBgwc5fvw4BoOBsWPHMnHiRIvjBw8eTGZmJgcOHCAlJYWwsDBiY2P505/+VOM+RUZG4ufnx/bt2/nyyy9p0KABrVq1YuTIkeZ9unTpwpIlS9iyZQv//ve/KS4uxmAwEBoaytChQwHw9PRk2LBhpKSkcPz4cYxGI4GBgcycOZOf//znFj/zv//9L717937shetEREQel4vpSVY6ERERkTrLaDTy1ltv0adPH6KiopwdjlOkp6cTExNDfHx8pYu6iYiI1JSeARcRERHgwarkr7/+unkWuT5KSkrixRdfVPEtIiJ2oRlwEREREREREQfQDLiIiIiIiIiIA6gAFxEREREREXEAFeAiIiIiIiIiDqACXERERERERMQBVICLiIiIiIiIOIAKcBEREREREREHUAEuIiIiIiIi4gAqwEVEREREREQcQAW4iIiIiIiIiAOoABcRERERERFxgP8PClm+DU32SJkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set the x-axis limits\n",
        "x_min = 10\n",
        "x_max = 14\n",
        "\n",
        "# Create the x-axis values\n",
        "x = np.linspace(x_min, x_max, 1000)\n",
        "\n",
        "# Calculate the y-axis values for the normal distribution\n",
        "y = norm.pdf(x, null_mean, sample_std / np.sqrt(n))\n",
        "\n",
        "# Calculate the critical values for the test\n",
        "z_critical = norm.ppf(alpha / 2)\n",
        "x_left = null_mean + z_critical * sample_std / np.sqrt(n)\n",
        "x_right = null_mean - z_critical * sample_std / np.sqrt(n)\n",
        "\n",
        "# style and size\n",
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(12,7))\n",
        "\n",
        "# Plot the normal distribution and the critical values\n",
        "plt.plot(x, y)\n",
        "plt.axvline(x=null_mean, color='b', label='Null Hypothesis Mean')\n",
        "plt.axvline(x=sample_mean, color='r', label='Sample Mean')\n",
        "plt.axvline(x=x_left, linestyle='--', color='g', label='Critical Values')\n",
        "plt.axvline(x=x_right, linestyle='--', color='g')\n",
        "plt.legend()\n",
        "plt.xlabel('Volume (ounces)')\n",
        "plt.ylabel('Probability Density')\n",
        "plt.title('Z-test for Coffee Cup Volumes')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7yS1roGuUJBk"
      },
      "source": [
        "`Z-test` on the tips Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "oDg9Td5XUJBl",
        "outputId": "b6cba6cb-6d97-443b-9ec1-ce2ee4f949de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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LoAcdAAAAAAALoEAHAAAAAMACKNABAAAAALAACnQAAAAAACzA7u0A3nLixAm5XC5vx0AFynZl68mNT0qSHjn/EVWxVynV9qdOSf3715QkLV58TCEhpb9/YnkzBKRTp1Szf39J0tHFi6WQEC8HCiw1a9bU0aNHvR0DqBS0dwQS2jsCiS+0d7vdrujo6L9frxKyWJLL5ZLT6fR2DFSgU85T+u93/5Uk3X/e/bKZpRsgkp1taMuWvOmcHJeCgtyVniEQGdnZyj/xruxsmUFBXk4UOAzDkJT395AHesDf0d4RSGjvCCT+1t6pHgAAAAAAsAAKdAAAAAAALIACHQAAAAAAC6BABwAAAADAAgL2JnEAAAAASs80TaWnp/vFDbngH7KyspSTk+PtGKpSpYqqVCnfU5wo0AEAAACUWHp6uqpUqaLg4GBvRwEkSQ6Hw+tP6DJNU1lZWcrIyFB4eHiZ90OBDr8R5gjTgVsOBHwGAACAs8k0TYpz4C8Mw1BYWJjS0tLKtR+uQQcAAAAAoALkP5e9rCjQAQAAAACwAAp0+I1TrlO6deWtunXlrTrlOhWwGQAAAAD4Jgp0+A236dbHez/Wx3s/ltt0B2wGAAAAnD133323brrpJs/rQYMG6dFHH/VioooRFxenTz755Kzt/6/nDUWjQAcAAADg1+6++27FxcXppZdeKjD/k08+UVxc3Fk99ty5c9WyZcsil53torgoU6ZM0aWXXlqpx5Skxx9/XM8991yZtv3tt98UFxenevXq6dChQwWWHT58WLVr11ZcXJx+++23iojqVZa6i7vb7da8efO0du1apaamqnr16urevbuuvvpqz8X2pmlq3rx5WrVqlTIyMtSiRQslJSWpdu3aXk4PAAAAwKpCQkI0ffp0DR8+XFFRUd6OE3CqVatW7n3ExsZq/vz5uvPOOz3zPvjgA9WuXVu///57ufdvBZbqQV+0aJFWrFihm2++Wc8995yGDRumJUuWaPny5Z51Fi9erOXLl+uWW27RU089pSpVqmjSpEmWeDA9AAAAEEhMU8rMNLzyY5qly3rRRRepZs2ahXrRT1dU7/Lrr7+uzp07l+X0lMo111yjcePGFZh3/PhxNWzYUGvXrpUkde7cWc8995xGjx6tpk2bqkOHDpo5c2aBbQ4cOKAbb7xRzZo1U/PmzTVq1CgdPXpUUl5v/tSpU7Vt2zbFxcUpLi5Oc+fO9WybkpKim2++WU2aNFHXrl312WefFdj3L7/8ouHDh6tZs2ZKSEjQnXfeqZSUFM/ypUuXqmfPnmrSpInOPfdcDRkyRJmZmZIKD3E/07pnOken581/T4MHDy607t9lXb16ta666iq1bNlS5557rm644Qbt27fPszy/137ZsmUaNGiQmjRpol69eunbb789Y8byslSBvmPHDnXs2FHt27dXTEyMLrjgArVt21a7du2SlNd7vmzZMg0cOFCdOnVSgwYNdMcdd+jEiRPatGmTl9MDAAAAgSUry1CzZrW98pOVVbrHWQUFBemhhx7SW2+9pYMHD56lM1J2Q4cO1aJFi5Sdne2Z9+GHHyo2NlYXXXSRZ96rr76qVq1a6dNPP9WYMWP06KOP6ssvv5SUNyL5xhtvVGpqqj788EO999572r9/v26//XZJ0pVXXqlRo0apefPm2rJli7Zs2aIrr7zSs++pU6fqH//4h1auXKmePXt6ai1JSktL0+DBg3Xuuedq+fLlevfdd3Xs2DGNGjVKUt5Q8zFjxmjIkCFas2aN5s+fryuuuEJmEd+klGbd01122WVKS0vTxo0bJUkbN25UWlqaevfuXWC9v8sqSZmZmbr11lu1bNkyzZ07VzabTUlJSXK7C95HavLkybrtttv02WefqXHjxhozZoxcLtcZc5aHpYa4x8fHa9WqVTp48KDq1Kmjffv2afv27brhhhskSUeOHFFqaqratm3r2SYsLExNmzbVjh071LVr10L7dDqdcjqdnteGYSg0NNQzDf9x+udpGEapP9/ybl9R+wg0fz1n4pxVmvxzTztFIKC9I5DQ3ot3xRVXqFWrVpoyZYqmTJlSacc9efKkmjVrdsZ1rrjiCj3yyCP69NNPPUXzvHnzNHjw4AKfZadOnXTHHXdIkpo0aaJNmzbp9ddf18UXX6x169bpl19+0ddff+25tn7atGm65JJL9N133+m8885TeHi4goKCFBMTUyjD4MGDddVVV0mSHnroIc2YMUPfffedLrnkEr311ltq3bq1Hn74Yc/6U6ZMUadOnbR7925lZmbK5XKpT58+qlu3riQVe+39kSNHSrzu6ex2uwYOHKj3339f559/vt5//30NHDhQdnvBsvbvsjZp0kR9+/YtsM3UqVPVpk0b7dixQy1atPDMv+2229SrVy9J0v33369LLrlE+/btU9OmTYvNWZ7fPUsV6FdddZWysrJ0zz33yGazye1269prr1W3bt0kSampqZKkyMjIAttFRkZ6lv3VwoULNX/+fM/rRo0aafLkyapZs+ZZeQ/wnoycDM90bGyswoPDS7f9n5urVq1aCi/d5hWSISBlFDxnZTrxKJfY2FhvRwAqDe0dgeRstfesrCw5HA5JUrVq0t69R8/Kcf5OWJi9xN/r22w2GYYhh8OhCRMmaODAgbrjjjsUFBQkSZ73c/p6+YKCggrM++s6hmEoKCiowDans9vtioiI0MqVKwstu+CCCzzbOhwOXXPNNZo3b56uvvpq/fDDD9q+fbveeeedAsfq1KlTgWOdf/75eu211+RwOLRnzx7FxcWpYcOGnuXnnnuuIiMjtWfPHnXq1KnI95ivTZs2nvmRkZGqWrWqTpw4IYfDoV9++UXr168v8ouGAwcOKDExUd26dVPPnj11ySWXKDExUf/4xz881/ufftyEhIQzrlvUOcz/7/Dhw9W3b1898sgjWrp0qZYtW+bp0bbb7SXK2qJFC+3Zs0eTJ0/W//73P6WkpHh6zg8fPqw2bdp4jnn6Ocn/0iM1NbXYzzs4OLhc90ezVIH+9ddfa926dfrnP/+pevXqad++fZo5c6aio6OVmJhYpn0OGDBA/fr187zO/zbj6NGjZ3VoAiqfaZradWPe5RBpx9J00jhZqu0zMw1Jef+QHT58WKGhpX9MWnkzBCIjM1P5//uQnJwsMyzMq3kCiWEYio2NzTvvpb2QD/AxtHcEkrPd3nNycgqMUA0OrvBDlEhp/lfe7XbLNE05nU517NhR3bt31xNPPOG5dvn09+N2uwu8zs7O9mz7131Jef//l5ubW2CbgjldstlsqlevXpHLT992yJAhuuyyy/Trr7/q3XffVdeuXRUbG1vgWH/Nl5ub68lz+nRxx/lr/tMZhlFo9HH+iOT09HRdeumlGjt2bKHtatWqJbfbrffee0/ffvutvvjiC73xxht66qmntHTpUtWvX7/Qcc+0blHnMP+/LVq0UNOmTXXrrbeqWbNmatq0qX755RfP8pJkdTqdGjZsmOrWratnnnlGsbGxcrvd6tGjh7KysuR0OgvUivmZ8+f99XfgdDk5OYXuNC/lfXlQkk5iSxXo77zzjvr37+8Zql6/fn0dPXpUixYtUmJioucblbS0NEVHR3u2S0tLK/At0enyv40qCv9A+59Qe6hnurSf7+mrm6ZZ5vZRngwB6bRzVJ7zjrLjvCOQ0N4RSGjvxRs7dqwuu+wyNWnSpMD86tWr6+jRozJN09Ox99NPP1VarpYtWyohIUFz5szRwoULNWnSpELrbN68udDr/J7iZs2a6eDBgzpw4ICnt3fHjh1KS0tTfHy8pLz66K/XWZdE69attWzZMtWrV6/QkPJ8+T38nTp10j333KPzzz9fy5cvL3Dtd1nW/ashQ4Zo7Nixevrpp8uUNSUlRbt379Z//vMfzw0A869rrwjl+b2z1E3isrOzZbMVjGSz2TxvMCYmRlFRUdq6datneWZmpnbt2uVpcAAAAABwJi1bttSAAQP05ptvFph/4YUX6vjx45o+fbpnNO/q1asrNdt1112nl19+WZJ0+eWXF1q+adMmTZ8+Xbt379bMmTO1dOlS3XzzzZKkbt26qUWLFrrzzju1detWbdmyRXfddZe6dOmihIQESVK9evW0f/9+/fjjj0pJSSlwU7ozGTlypFJTUzV69Gh999132rdvn9asWaN77rlHubm52rx5s1544QV9//33OnDggJYtW6aUlJQih5mXZt2iDBs2TFu3btXQoUPLlDUqKkrR0dF65513tHfvXq1bt04TJ04s0bHPNksV6B06dNCCBQu0efNmHTlyRBs3btTSpUvVqVMnSXnfsvTp00cLFizQt99+q/379+ull15SdHS0Zx0EruzcbN295m7dveZuZeeW7A+NP2YAAADA3/vXv/5VqCe5WbNmeuqppzRz5kxdeuml2rJlS4l6dCvSVVddpaCgIPXv318hISGFlo8aNUrff/+9evfurWnTpmnChAmey4ENw9Bbb72lyMhIDRw4UNdee63q16+vV155xbN9nz59lJiYqMGDB6tNmzZatGhRiXLFxsZq0aJFcrvdGjp0qHr27KkJEyaoWrVqstlsqlq1qjZs2KDrr79e3bp10zPPPKNHH31UPXr0KLSv0qxbFLvdrurVqxfbk/93WW02m6ZPn66tW7eqZ8+eeuyxx/TII4+U6Nhnm2FaaNxLVlaW5s6d67ldfvXq1dW1a1cNGjTIc/JN09S8efO0cuVKZWZmqkWLFrr55ptVp06dUh3r6NGjxV43AN+U6cxUs5l537rtHLlTYY7SXcucmZn3mBBJ2rUruUzXoJc3QyAyMjNV+/+/LT20cyfXoFciwzBUu3ZtHTp0iCGQ8Hu0dwSSs93eT548qWrVqlX4fpHnt99+04UXXqhly5apTZs2BZZ17txZSUlJuuWWW7yUzpocDodlarvifj8cDofvXYMeGhqqkSNHauTIkcWuYxiGhgwZoiFDhlReMPgN95I5xS/Ldki6S5KUu3Su3FVK/0vuNnP+nF42T27DS3dNqSS2K4seVgQAAIDScTqdOnHihJ555hm1b9++UHGOwGCpIe4AAAAAEIg2bdqkdu3a6bvvvtO///1vb8eBl1iqBx0AAAAAAtGFF16oAwcOnHGdDRs2VFIaeAs96AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABXANOvxGqD1UPwz/wTPtlQxy6LuaEzzTAAAAAFBSFOjwG4Zh6JzQc7yfwYjwagYAAAAAvokh7gAAAAAAWAAFOvxGdm62xn41VmO/Gqvs3GzvZDBdGndygcadXKBs0+WVDAAAAPAtcXFx+uSTT7wdAxZAgQ6/kevO1axtszRr2yzlunO9k0Fuzc76WrOzvlau3F7JAAAAgIKOHz+uhx56SJ06dVKjRo103nnnaejQodq0aZO3o1WI3377TXFxcapXr54OHTpUYNnhw4dVv359xcXF6bfffvNSQpQU16ADAAAA8Gu33HKLcnJy9Pzzz6tBgwY6evSo1q1bpxMnTng7WoWKjY3V/Pnzdeedd3rmffDBB4qNjdWBAwe8mAwlRQ86AAAAgLIxTRmZmV75kWmWKGJaWpo2bNigcePGqWvXrqpbt67atWunO++8U5dddplnvf/+97/q2bOnmjZtqo4dO+rhhx9WRkaGZ/ncuXPVsmVLrVixQt26dVOTJk10yy23KCsrS/PmzVPnzp3VqlUrjR8/Xrm5f47m7Ny5s5577jmNHj1aTZs2VYcOHTRz5swzZj5w4IBGjRqlli1b6txzz9WNN95Yot7va665RnPnzi0wb+7cubrmmmsKrfvLL79o+PDhatasmRISEnTnnXcqJSXFs3z16tW66qqrPBluuOEG7du3z7M8v9d+2bJlGjRokJo0aaJevXrp22+//ducKB496AAAAADKxMjKUu1mzbxy7EM7d8oMC/vb9cLDwxUeHq5PPvlE7du3V5UqVYpcz2az6fHHH1f9+vX166+/auzYsXryySf19NNPe9bJysrSm2++qVdeeUXp6elKSkrSzTffrGrVquntt9/Wr7/+qltvvVUdO3ZU//79Pdu9+uqruvPOO3Xffffpiy++0KOPPqrGjRvr4osvLpTD6XRq2LBh6tChgxYsWCC73a5p06Zp2LBhWrlypYKDg4t9r5dddpnefvttbdy4Ueeff742btyotLQ0XXrppXr++ec966WlpWnw4MG67rrr9Nhjj+nUqVOaNGmSRo0apQ8++ECSlJmZqVtvvVUtW7ZURkaGnn32WSUlJemzzz6TzfZnP+/kyZM1fvx4NWrUSJMnT9aYMWP01VdfyW6n1CwLzhoAAAAAv2W32/Xcc8/pgQce0DvvvKPWrVvrggsuUP/+/dWqVSvPerfccotnul69enrggQf00EMPFSjQnU6nnn76aTVs2FCS1LdvX3344Yf6/vvvFR4ervj4eF144YVav359gQK9U6dOuuOOOyRJTZo00aZNm/T6668XWaAvWbJEbrdbzz77rAzDkCRNnTpVLVu21Ndff63u3buf8b0OHDhQ77//vs4//3y9//77GjhwYKFi+a233lLr1q318MMPe+ZNmTJFnTp10u7du9WkSRP17du3wDZTp05VmzZttGPHDrVo0cIz/7bbblOvXr0kSffff78uueQS7du3T02bNi02J4pHgQ4AAACgTMzQUB3audNrxy6pvn37qmfPntq4caP+97//afXq1XrllVf0n//8R0OGDJEkffnll3rppZe0e/du/fHHH8rNzdWpU6eUlZWl0P8/VmhoqKc4l6SaNWuqXr16Cg8P98yrUaOGjh8/XuD4HTp0KPT6jTfeKDLrtm3btG/fPsXHxxeYn52drX379p2xQJeka6+9Vv3799dDDz2kpUuXasmSJXK5Cj5daNu2bVq/fr2aFTH64ddff1WTJk20Z88ePfvss9qyZYtSUlLkdufdAPnAgQMFCvSWLVt6pmNiYiRJx44do0AvIwp0AAAAAGVjGCUaZm4FISEhuvjii3XxxRfrnnvu0f33368pU6ZoyJAh+u233zRy5Ehdf/31evDBBxUVFaVNmzbpvvvuU05OjqdAdzgcBfZpGEah3mnDMDzFbFlkZGSobdu2evHFFwstO+ecc/52+5YtW6pp06YaPXq0mjVrphYtWujHH38ssE5mZqYuvfRSjR07ttD2tWrVkiSNHDlSdevW1TPPPKPY2Fi53W716NFDTqezwPqnv//8Hv/yvP9AR4EOvxFiD9E3137jmfZKBtm1vsbDnmkAAABYU7NmzTzPHv/hhx/kdrs1YcIEz/XVH330UYUda/PmzYVeF9V7LUlt2rTRRx99pBo1aqhq1aplOt6QIUM0duzYAsPzT9e6dWstW7ZM9erVK/Ja8ZSUFO3evVv/+c9/1LlzZ0nSxo0by5QFpcNd3OE3bIZN9arWU72q9WQzvNO0bYZN9YKqq15Qda9lAAAAwJ9SUlJ0zTXX6MMPP9S2bdu0f/9+ffTRR3rllVfUu3dvSVLDhg3ldDr15ptv6tdff9X8+fP19ttvV1iGTZs2afr06dq9e7dmzpyppUuX6uabby5y3YEDByo6Olo33nijNmzYoP3792v9+vUaP368Dh48WKLjDRs2TFu3btXQoUOLXD5y5EilpqZq9OjR+u6777Rv3z6tWbNG99xzj3JzcxUVFaXo6Gi988472rt3r9atW6eJEyeW+f2j5OjiAwAAAOC3wsPD1b59e73++uv69ddf5XQ6VadOHQ0dOtTzvPBzzz1XEyZM0PTp0/X000/rggsu0MMPP6y77rqrQjKMGjVK33//vaZOnaqqVatqwoQJSkxMLHLd0NBQLViwQJMmTVJSUpIyMjIUGxuriy66qMQ96na7XdWrVy92eWxsrBYtWqSnnnpKQ4cOVXZ2turWravExETZbDYZhqHp06fr0UcfVc+ePdW4cWM98cQTGjRoUFnePkrBMM0SPkDQzxw9erTQ9RPwbTm5OZr87WRJ0oMdH1RwUOFHULiXzCl2+8xsh5rfnfdHePvz0xRWpfTtI8d06Zn0vKFSD0RcrmDDv78Ds11Z9LeypWFkZnoez1LSx6WgYhiGodq1a+vQoUMK0H8KEEBo7wgkZ7u9nzx5UtWqVavw/fqrzp07KykpqcBd4lGxHA6HZWq74n4/HA6Hatas+bfbMwYXfsPldunVH17Vqz+8Kpfb9fcbnI0Mcuu/mV/ov5lfyCVujgEAAACg5CjQAQAAAACwAP8efwsAAAAAXrRhwwZvR4APoQcdAAAAAAALoEAHAAAAAMACKNABAAAAALAACnQAAAAAACyAm8TBb4TYQ/T51Z97pr2SQXatPOc+zzQAAAAAlBQVBPyGzbCpefXm3s9gj/VqBgAAAAC+iSHuAAAAAFCM9evXKy4uTmlpaWdcr3Pnznr99dcr7LiDBg3So48+WmH7K8qUKVN06aWXntVjoHQo0OE3cnJzNOV/UzTlf1OUk5vjnQymS1PTP9PU9M+UY7q8kgEAAACFHTlyRI888oi6dOmiRo0aqWPHjhoxYoTWrl17xu06duyoLVu2qFq1apKkuXPnqmXLloXWW7ZsmYYPH35Wsv/Vq6++qlatWunUqVOFlmVlZal58+aaMWNGpWRBxaJAh99wuV2aunmqpm6eKpfbO8WxS249l7FCz2WskEtur2QAAABAQb/99puuuOIKffXVV3rkkUe0cuVKvfvuu7rwwgs1bty4YrdzOp0KDg5WTEyMDMM44zHOOecchYaGVnT0Ig0aNEiZmZlavnx5oWVLly6V0+nUwIEDKyULKhYFOgAAAIByyXRmFvtzynWqxOtmubJKtG5pjR07VpL08ccfq2/fvmrSpImaN2+uUaNG6aOPPvKsFxcXp1mzZmnkyJFq2rSpXnjhhQJD3NevX697771XJ0+eVFxcnOLi4jRlyhRJhYe4p6Wl6YEHHlBCQoIaN26sHj16aMWKFZKklJQUjR49Wh06dFCTJk3Us2dPLVq0qMTvp0aNGrr00kv1/vvvF1o2d+5c9e7dW9HR0Zo0aZIuuugiNWnSRF26dNEzzzwjp9NZ7H6LGlZ/00036e677/a8zs7O1uOPP64OHTqoadOm6tevn9avX+9Z/vvvv2vEiBFq1aqVmjZtqksuuUSrVq0q8XsLdNwkDgAAAEC5NJvZrNhlPer10NuXv+153fadtoUK8XxdanfR/H7zPa87v99ZKadSCq134JYDJc524sQJrV69Wg8++KDCwsIKLY+MjCzweurUqRo7dqwmTpwou92uX3/91bOsY8eOmjhxop599ll9+eWXkqTw8PBC+3S73Ro+fLgyMjL04osvqkGDBtqxY4eCgoIk5RW5bdu21ejRo1W1alWtWrVK//znP9WgQQO1a9euRO/r2muv1YgRI/T777+rbt26kqRff/1V33zzjebMmePJ9txzzyk2NlY///yzHnjgAUVERGj06NElOkZRHnnkEe3YsUPTp09XrVq19Mknn2j48OFauXKlGjdurLFjx8rpdOrDDz9UWFiYduzYUeQ5QtEo0AEAAAD4rX379sk0TTVt2rRE61911VUaMmSI5/XpBXpwcLCqVq0qwzAUExNT7D7Wrl2r7777TmvWrFGTJk0kSQ0aNPAsr127tm677TbP65tuuklr1qzRRx99VOICPTExUbVq1dLcuXN13315j/mdN2+e6tSpo4suukiSCvR816tXT3v27NHixYvLXKAfOHBAc+fO1caNGxUbm/fkottuu02rV6/W3Llz9fDDD+vgwYPq06eP5zr90983/h4FOgAAAIBy2TlyZ7HLbEbBq2p/GP5Dsev+9TrvDdduKF8wSaZplmr9hISEch/zp59+Uu3atT3F+V/l5ubqhRde0NKlS5WcnKycnBzl5OSU6hr2oKAgXXPNNZo3b57uvfdemaapDz74QEOGDJHNlnfOFy9erDfffFO//vqrMjIylJubq4iIiDK/r59//lm5ubnq1q1bgfk5OTmKjo6WlPdlw8MPP6wvvvhC3bp1U58+fdSqVasyHzPQUKADAAAAKJcwR+Gh45W9bnEaNWokwzC0a9eukh2ziGHwpRUSEnLG5a+88opmzJihiRMnqkWLFgoLC9OECRPOeH14Ua699lq99NJLWrdunUzT1MGDBz29/99++63uvPNO3XfffUpMTFTVqlW1ePFivfbaa8Xur6gb4Z2eKSMjQ0FBQVq+fLlnuH6+/GHsQ4cOVffu3bVq1Sp9+eWXeumll/Too4/qpptuKtV7C1TcJA4AAACA34qOjlZiYqJmzpypzMzCN5j7u+eb/1VwcLByc3PPuE7Lli116NAh7d69u8jlmzZtUu/evXX11Vfr3HPPVYMGDbRnz55S5ZCkhg0b6oILLtDcuXM1d+5cdevWzXM9+rfffqu6devqrrvu8tyo7sCBM1+7f8455+jw4cOe17m5udq+fbvndevWrZWbm6vjx4+rUaNGBX5OH/IfFxenG264QW+88YZGjRrluSYef48CHX6jSlAVfXzVx/r4qo9VJaiKdzLIro+q/1MfVf+nqjBABQAAwBImTZokt9utvn376uOPP9aePXu0c+dOzZgxQ1deeWWp9lW3bl1lZGRo7dq1SklJUVZW4RvedenSRZ07d9att96qL7/8Uvv379fnn3+u1atXS8rr1f/yyy+1adMm7dy5Uw8++KCOHTtWpvd23XXXafny5frkk0907bXXeubnF+SLFy/Wvn37NGPGjCIfy3a6rl27atWqVVq5cqV27dqlhx9+WCdPnvQsb9KkiQYOHKi77rpLy5Yt0/79+7Vlyxa9+OKLWrlypSTp0Ucf1Zo1a7R//35t3bpVX331VYmv/wdD3OFHgmxBOq/med7NYNh0nqOeVzMAAACgoAYNGuiTTz7RCy+8oMcff1xHjhxR9erV1bZtWz399NOl2lenTp10/fXX6/bbb9eJEyd07733em7SdrrXX39dTzzxhEaPHq2srCw1bNhQDz/8sCTprrvu0v79+zVs2DCFhoZq2LBh6t27t/74449Sv7c+ffrokUcekc1m0+WXX+6Zf9lll+mWW27RuHHjlJOTo549e+ruu+/W1KlTi93Xtddeq23btumuu+6S3W7XLbfcogsvvLDAOlOnTtW0adP0+OOPKzk5WdWrV1f79u3Vq1cvSXl3sB83bpwOHTqkiIgIJSYm6rHHHiv1+wpUhlnauyb4iaNHj5b6Gg/4PveS4ofXZGY71PzuuyRJ25+fprAqtI+/Y7tyaLn3YWRmqnazvEezHNq5U2YFXPeFkjEMQ7Vr19ahQ4dKfQMdwNfQ3hFIznZ7P3nypKpVq1bh+wXKyuFwWKa2K+73w+FwqGbNmn+7PT3o8Bs5uTma8eMMSdLNrW9WcFBw5WcwXXozc50k6aawixRs8CsGAAAAoGSoHuA3XG6Xntz4pCRpRKsRXinQXXJrUvrHkqQbwi5U5ScAAAAA4Ku4SRwAAAAAABZAgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAABKhachAIW53e5y74MCHQAAAECJValSRVlZWd6OAViK2+3WH3/8obByPjKYu7gDAAAAKLEqVaooIyNDaWlpMgzD23EABQcHKycnx9sxFB4eLru9fCU2BTr8RpWgKvqg7weeaa9kkF3zom/zTAMAAPij8PBwb0cAJEmGYah27do6dOiQX1x6QQUBvxFkC9KFdS70bgbDpi7BTbyaAQAAAIBv4hp0AAAAAAAsgB50+A2n26l3f35XkjSs5TA5bI7Kz2Dm6t2sb/IyhF4ghxFU6RkAAAAA+CYKdPgNZ65T49aPkyQNjh/snQJduRr/x6K8DKGd5BAFOgAAAICSsVSBPmbMGB09erTQ/Msuu0xJSUnKycnR7NmztX79ejmdTiUkJCgpKUlRUVGVHxYAAAAAgApkqQL96aefLvBw9/379+vJJ59Uly5dJEmzZs3S5s2bde+99yosLEwzZszQlClT9MQTT3grMgAAAAAAFcJSN4mrVq2aoqKiPD+bN29WrVq11KpVK2VmZurzzz/XiBEj1Lp1azVu3FijR4/W9u3btWPHDm9HBwAAAACgXCzVg346l8ultWvXqm/fvjIMQ3v27FFubq7atGnjWScuLk41atTQjh07FB8fX+R+nE6nnE6n57VhGAoNDfVMw3+c/nkahlHM51uyzzxv04poH/7dxirid+ivn5v4vaw0+eeev4UIBLR3BBLaOwKJv7V3yxboGzduVEZGhhITEyVJqampstvtCg8PL7BeZGSkUlNTi93PwoULNX/+fM/rRo0aafLkyapZs+bZiA0vysjJ8EzHxsYqPDi80DppVSOK3d7m+PPXITw8QuEhrlJnsLkd0pG86YiICIXbqpR6H74ksnbt8u8ko+DnpvDCnxvOrtjYWG9HACoN7R2BhPaOQOIv7d2yBfrq1at13nnnqXr16uXaz4ABA9SvXz/P6/xvVo4ePSqXq/QFGKwr05npmU5OTlaYI6zQOrl/pBe/ffafvw4ZGelyO0vfPjLNbM90enq63IbzDGv7vsxDh8q9DyMzU/l/TpOTk2WGFf7ccHYYhqHY2Ni8826a3o4DnFW0dwQS2jsCia+0d7vdXqJOYksW6EePHtUPP/yg+++/3zMvKipKLpdLGRkZBXrR09LSzngXd4fDIYej6MdtWfkDROk5bA7N6j3LM13051uyzzxv09K3j2AFaWbUTZ7psuzDl1TI79Bp+zBNk99LL+C8I5DQ3hFIaO8IJP7S3i1ZoK9evVqRkZFq3769Z17jxo0VFBSkrVu36oILLpAkHTx4UMeOHSv2+nMEFrvNrl71e3k3gxGknlVaejUDAAAAAN9kuQLd7XZrzZo16t69u4KCgjzzw8LC1KNHD82ePVsREREKCwvTm2++qfj4eAp0AAAAAIDPs1yBvnXrVh07dkyXXHJJoWUjRoyQYRiaMmWKXC6XEhISlJSU5IWUsCKn26kFuxZIkgY2HSiHrehLG85qBjNXC09tliQNCGkvhxH0N1sAAAAAQB7LFegJCQmaN29ekcuCg4OVlJREUY4iOXOduveLeyVJ/2j0D+8U6MrVfSfz2m+/kAQ5RIEOAAAAoGRs3g4AAAAAAAAo0AEAAAAAsAQKdAAAAAAALIACHQAAAAAAC6BABwAAAADAAijQAQAAAACwAMs9Zg0oq+CgYL3a81XPtFcyKEivRA73TAMAAABASVGgw2/YbXb9o/E/vJvBCFK/kASvZgAAAADgmxjiDgAAAACABdCDDr/hcru0fN9ySdIVDa+Q3Vb5zdtl5uqT7B8lSZdXaS27wTB3AAAAACVDgQ6/kZObo9tW3SZJ2jlyp1cK9Bzl6va0dyRJ22Mmyc516AAAAABKiCHuAAAAAABYAAU6AAAAAAAWQIEOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABbAY9bgNxxBDk3tPtUz7ZUMCtKUaoM90wAAAABQUhTo8BsOm0ND4od4N4MRpMGhnbyaAQAAAIBvokAHUGbuJXPKvQ8jO+fP/X08T2aV4HLv05fZrhzq7QgAAADwEgp0+A2X26U1v6+RJCXWTZTdVvnN22Xm6oucHZKk7sHxshsMcwcAAABQMhTo8Bs5uTka8ekISdLOkTu9UqDnKFcjU9+UJG2PmSQ716EDAAAAKCHu4g4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABVCgAwAAAABgATxmDX7DEeTQpAsneaa9kkFBeqLqVZ5pAAAAACgpCnT4DYfNoZHnjvRuBiNII8O6ejUDAAAAAN/EEHcAAAAAACyAHnT4jVx3rjYkb5AkdY7trCBb5Q8xzzXd2ujcK0k639FIQQbfgQEAAAAoGQp0+I3s3Gxd8/E1kqSdI3cqzBZW+Rnk0uATr0qStsdMUpiCKz0DAAAAAN9E9x4AAAAAABZAgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABfCYNfgNu82uR85/xDPtlQyyaVxEX880AAAAAJQUBTr8RnBQsG5PuN27GQy7bgtP9GoGAAAAAL6JLj4AAAAAACyAHnT4jVx3rrYe3ypJanNOGwXZgio/g+nWVteBvAz2OAUZfAcGAAAAoGQo0OE3snOz1XdR3vXfO0fuVJgtrPIzyKV/pLwgSdoeM0lhCq70DAAAAAB8E917AAAAAABYAAU6AAAAAAAWQIEOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABbAY9bgN+w2u+5tf69n2isZZNM94Zd6pgEAAACgpCjQ4TeCg4J1X4f7vJvBsOveiMu8mgEAAACAb6KLDwAAAAAAC6AHHX7Dbbq188ROSVKz6GayGZX//ZPbdGtn7pG8DEExXskAAAAAwDdZrkBPSUnRO++8o++++07Z2dmKjY3V6NGj1aRJE0mSaZqaN2+eVq1apYyMDLVo0UJJSUmqXbu2l5PD2065TqnHhz0kSTtH7lSYI6zyM8ilXsenSJK2x0xSmIIrPQMAAAAA32SpAj09PV3jx4/Xueeeq7Fjx6patWo6dOiQwsPDPessXrxYy5cv15gxYxQTE6O5c+dq0qRJmjp1qoKDKYYAAAAAAL7JUuNvFy9erHPOOUejR49W06ZNFRMTo4SEBMXGxkrK6z1ftmyZBg4cqE6dOqlBgwa64447dOLECW3atMnL6QEAAAAAKDtL9aB/++23SkhI0NSpU7Vt2zZVr15dl112mXr16iVJOnLkiFJTU9W2bVvPNmFhYWratKl27Nihrl27Ftqn0+mU0+n0vDYMQ6GhoZ5p+I/TP0/DMIr5fEv2medtWhHtgzZWeoF9zirz71L+sfhbiEBAe0cgob0jkPhbe7dUgX7kyBGtWLFCffv21YABA7R792699dZbstvtSkxMVGpqqiQpMjKywHaRkZGeZX+1cOFCzZ8/3/O6UaNGmjx5smrWrHm23ga8JCMnwzMdGxur8ODwQuukVY0odnub489fh/DwCIWHuEqdweZ2SHn3iFNERITCbVVKvY+A43B4JiMiIqSQwD5nkV64n0b+KCUgENDeEUho7wgk/tLeLVWgu91uNWnSREOHDpWUV0zv379fK1asUGJiYpn2OWDAAPXr18/zOv+blaNHj8rlKn0BBuvKdGZ6ppOTk4u8SVzuH+nFb5/9569DRka63M7St49MM9sznZ6eLrfhPMPakCQjO1v5X7mlp6fLdAb2Ocs8dKjSjmUYhmJjY5WcnCzTNCvtuIA30N4RSGjvCCS+0t7tdnuJOoktVaBHR0erbt26BebVrVtXGzZskCRFRUVJktLS0hQdHe1ZJy0tTQ0bNixynw6HQ47TeuhOZ+UPEKV3+udpmmYxn2/JPvO8TSuifdDGSi+wz5k3/i4V//sC+B/aOwIJ7R2BxF/au6UK9ObNm+vgwYMF5h08eNDzTUNMTIyioqK0detWT0GemZmpXbt26bLLLqvsuLAYu82u29re5pn2SgbZNCqsu2caAAAAAErKUgV63759NX78eC1YsEAXXnihdu3apVWrVunWW2+VlDd8oU+fPlqwYIFq166tmJgYvf/++4qOjlanTp28nB7eFhwUrPGdx3s3g2HXI1X7/f2KAAAAAPAXlirQmzZtqvvvv19z5szRhx9+qJiYGI0YMULdunXzrNO/f39lZ2frv//9rzIzM9WiRQuNHTuWZ6ADAAAAAHyapQp0SerQoYM6dOhQ7HLDMDRkyBANGTKkElPBF7hNtw6kH5AkxUXEyWZU/hBzt+nWAXdqXgZblFcyAAAAAPBNlivQgbI65TqlC96/QJK0c+TOIu/iftYzyKULjz0tSdoeM0lhYmQHAAAAgJKhew8AAAAAAAugQAcAAAAAwAIo0AEAAAAAsAAKdAAAAAAALIACHQAAAAAAC6BABwAAAADAAnjMGvxGkC1II1qN8Ex7JYNsuiG0i2caAAAAAEqKAh1+o0pQFT3V9SnvZjDsmlRtoFczAAAAAPBNdPEBAAAAAGAB9KDDb5imqZRTKZKk6iHVZRiGdzKYGXkZjHCvZAAAAADgmyjQ4TeyXFlq+05bSdLOkTsV5gir/Axy6ryjEyVJ22MmKUzBlZ4BAAAAgG9iiDsAAAAAABZAgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABfCYNfiNIFuQrml2jWfaKxlk06CQDp5pAAAAACgpCnT4jSpBVfR84vPezWDY9VzktV7NAAAAAMA30cUHAAAAAIAF0IMOv2GaprJcWZKkUHuoDMPwTgY58zLI4ZUMAAAAAHwTPejwG1muLDWb2UzNZjbzFOqVnkFONT8yTs2PjPMU6gAAAABQEhToAAAAAABYAAU6AAAAAAAWQIEOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABbAc9DhN2yGTX0b9fVMeyWDDPWt0tYzDQAAAAAlRYEOvxFiD9FrvV7zbgbDoVejrvdqBgAAAAC+iSHuAAAAAABYAAU6AAAAAAAWQIEOv5HpzFTc63GKez1Omc5M72Qwc1Tv8L9U7/C/lGnmeCUDAAAAAN9EgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABVCgAwAAAABgAXZvBwAqis2wqUe9Hp5pr2SQoR7BLTzTAAAAAFBSFOjwGyH2EL19+dvezWA4NCv6Zq9mAAAAAOCbGOIOAAAAAIAFUKADAAAAAGABFOjwG5nOTDV9q6mavtVUmc5M72QwcxR/eKziD49VppnjlQwAAAAAfBPXoMOvZLmyvB1BWXJ6OwIAAAAAH0QPOgAAAAAAFkCBDgAAAACABVCgAwAAAABgARToAAAAAABYAAU6AAAAAAAWwF3c4TcMw1CX2l08017JIEMXOBp7pgEAAACgpCjQ4TdC7aGa32++dzMYDn1Q/XavZgAAAADgmxjiDgAAAACABViqB33evHmaP79gD2idOnX0/PPPS5JycnI0e/ZsrV+/Xk6nUwkJCUpKSlJUVFTlhwUAAAAAoAJZqkCXpHr16mn8+PGe1zbbn538s2bN0ubNm3XvvfcqLCxMM2bM0JQpU/TEE094IyosJtOZqc7vd5Ykbbh2g8IcYZWfwcxRl6NPSZK+rjlWYUZwpWcAAAAA4JssN8TdZrMpKirK81OtWjVJUmZmpj7//HONGDFCrVu3VuPGjTV69Ght375dO3bs8HJqWEXKqRSlnErxbgYzQylmhlczAAAAAPA9lutBT05O1qhRo+RwOBQfH6+hQ4eqRo0a2rNnj3Jzc9WmTRvPunFxcapRo4Z27Nih+Pj4IvfndDrldDo9rw3DUGhoqGca/uP0z9MwjGI+35J95nmbVkT7oI2VXmCfs8r8u5R/LP4WIhDQ3hFIaO8IJP7W3i1VoDdr1kyjR49WnTp1dOLECc2fP1+PPvqopkyZotTUVNntdoWHhxfYJjIyUqmpqcXuc+HChQWua2/UqJEmT56smjVrnq23AS/JyPmz1zo2NlbhweGF1kmrGlHs9jbHn78O4eERCg9xlTqDze2QjuRNR0REKNxWpdT7CDgOh2cyIiJCCgnscxZZu3alHzM2NrbSjwl4C+0dgYT2jkDiL+3dUgV6u3btPNMNGjTwFOxff/21goPLdi3vgAED1K9fP8/r/G9Wjh49Kper9AUYrCvTmemZTk5OLvIa9Nw/0ovfPvvPX4eMjHS5naVvH5lmtmc6PT1dbsN5hrUhSUZ2tiL/fzo9PV2mM7DPWeahQ5V2LMMwFBsbq+TkZJmmWWnHBbyB9o5AQntHIPGV9m6320vUSWypAv2vwsPDVadOHSUnJ6tt27ZyuVzKyMgo0IuelpZ2xru4OxwOOU7roTudlT9AlN7pn6dpmsV8viX7zPM2rYj2QRsrvcA+Z974u1T87wvgf2jvCCS0dwQSf2nvlrtJ3OlOnTql5ORkRUVFqXHjxgoKCtLWrVs9yw8ePKhjx44Ve/05AAAAAAC+wlI96LNnz1bHjh1Vo0YNnThxQvPmzZPNZtNFF12ksLAw9ejRQ7Nnz1ZERITCwsL05ptvKj4+ngIdkvKGtyTUSPBMeyWDDLW11/VMAwAAAEBJWapAT0lJ0bRp0/THH3+oWrVqatGihSZNmuR51NqIESNkGIamTJkil8ulhIQEJSUleTk1rCLUHqplA5Z5N4Ph0Mfn3OXVDAAAAAB8k6UK9LvvvvuMy4ODg5WUlERRDgAAAADwO5a+Bh0AAAAAgEBBgQ6/keXKUuf3Oqvze52V5cryTgYzR12OPqUuR59SlpnjlQwAAAAAfJOlhrgD5WGapn5P/90z7ZUMkn53n/BMAwAAAEBJ0YMOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABZAgQ4AAAAAgAVwF3f4DcMwFB8V75n2SgZJ8UG1PNMAAAAAUFLlKtBPnDih6OjoisoClEuoPVSrr1nt3QxGsFbVuN+rGQAAAAD4pnIV6Lfffrtat26tiy++WOeff75CQkIqKhcAAAAAAAGlXNegDx48WCdOnNDLL7+sW265RS+88IK+++47ud3uisoHAAAAAEBAKFcP+sCBAzVw4EDt3btXa9eu1fr16/XVV1+pWrVq6tq1q7p166YmTZpUVFbgjLJcWeqzsI8kadmAZQq1h1Z+BjNH/Y6/IElaes4/FWoEV3oGAAAAAL6pQm4S16hRIzVq1EjXX3+9fvzxR61bt05r1qzR8uXLVadOHXXr1k0XX3yxatSoURGHA4pkmqZ2pO7wTHslg6QduYc90wAAAABQUhX6mDXDMNSyZUu1a9dOzZo1kyQlJyfrgw8+0B133KGpU6fqxIkTFXlIAAAAAAD8QoU9Zi2/53zDhg3KzMxU/fr1df311+uiiy5SUFCQVq9erYULF+qll17S+PHjK+qwAAAAAAD4hXIV6Pv27dO6dev01VdfKSUlRVFRUerRo4e6d++u+vXrF1j3yiuvVHBwsN5+++1yBQYAAAAAwB+Vq0B/8MEHFRwcrE6dOuniiy9W27ZtZbMVP2q+bt26io+PL88hAQAAAADwS+V+DvoFF1xQ4ueft27dWq1bty7PIQEAAAAA8EvlKtATExMrKAZQfoZhqG5EXc+0VzJIqmuL9kwDAAAAQEmVq0BftmyZtmzZonHjxhW5/KmnnlLHjh112WWXlecwQImE2kO14boN3s1gBOvrmmO9mgEAAACAbyrXY9ZWr16tuLi4YpfXrVtXK1euLM8hAAAAAAAICOUq0JOTk1W3bt1il9epU0eHDx8uzyEAAAAAAAgI5SrQ7Xa7UlNTi12emprqtWuBEXiyXFnqs7CP+izsoyxXlncymE71PT5NfY9PU5bp9EoGAAAAAL6pXAV6fHy81qxZo6yswsVQZmamVq9erWbNmpXnEECJmaap7499r++PfS/TNL2TQaZ+cP2uH1y/y5R3MgAAAADwTeW6SdygQYP02GOP6YEHHlCfPn1Ur149SdL+/fu1bNkypaam6q677qqQoAAAAAAA+LNyFejNmjXTgw8+qNdee00zZ84ssCwmJkYPPPCA4uPjy3MIAAAAAAACQrkKdElq27atXnjhBe3bt0/JycmSpNjYWDVq1IjrzwEAAAAAKKFyF+iSZLPZ1LhxYzVu3LgidgcAAAAAQMCpkAL9999/1+HDh5WRkVHkzbm6d+9eEYcBAAAAAMBvlatAT05O1osvvqhdu3adcT0KdFSW6iHVvR1B1Y1wb0cAAAAA4IPKVaC//vrr2r9/v0aOHKmWLVsqPJzCBN4T5gjT1uu3ejeDEazvYx7zagYAAAAAvqlcBfovv/yiAQMG6IorrqioPAAAAAAABCRbeTauVq2awsLCKioLAAAAAAABq1wF+qWXXqq1a9fK7XZXVB6gzLJcWRq0dJAGLR2kLFeWdzKYTl2T8oquSXlFWabTKxkAAAAA+KZyDXGvXbu23G63/vWvf+mSSy7ROeecI5utcM3fuXPn8hwGKBHTNPX1oa89017JIFPfOPd4pgEAAACgpMpVoD///POe6bfffrvY9ebOnVuewwAAAAAA4PfKVaBPmDChonIAAAAAABDQylWgt2rVqqJyAAAAAAAQ0MpVoOdzOp3au3ev0tLS1Lx5c1WrVq0idgsAAAAAQMAod4G+bNkyffDBB8rMzJQkjR8/Xq1bt9bJkyd1zz33aNiwYerRo0e5gwIAAAAA4M/K9Zi11atXa9asWTrvvPN0++23F1hWrVo1nXvuuVq/fn25AgKlEWoPVag91LsZ5FCoHF7NAAAAAMD3lKsHfenSperYsaPuuusu/fHHH4WWN27cWMuXLy/PIYASC3OEadeNu7ybwQjWjlpPeTUDAAAAAN9Urh705ORktWvXrtjlERERSk9PL88hAAAAAAAICOUq0MPCwnTy5Mlil//++++KiooqzyEAAAAAAAgI5SrQ27Vrp1WrVikjI6PQst9++02rVq1Shw4dynMIoMROuU7p+k+u1/WfXK9TrlPeyWA6NeLEDI04MUOnTKdXMgAAAADwTeW6Bv3aa6/VuHHjdN9993kK8TVr1ujzzz/Xhg0bFB0drUGDBlVIUODvuE23Pv/tc8+0VzLI1Oc5v3imAQAAAKCkylWgV69eXf/+97/13nvvee7WvnbtWoWEhKhr164aNmwYz0QHAAAAAKAEyv0c9MjISN1222267bbbdPLkSbndblWrVk02W7lGzwMAAAAAEFDKXaCfjt5yAAAAAADKplwF+vz580u0HtehAwAAAABwZuUq0D/44IMSrUeBXjbuJXO8HcGnuM2cP6eXzZPbCPZiGgAAAAAonXIV6HPnzi00z+1269ixY/rkk0/0888/a+zYseU5BAAAAAAAAaHC7+Rms9kUExOjG264QbVr19abb75Z0YcAihRmBOu3Wv/Rb7X+ozAv9Z5bIQMAAAAA33RWb7XesmVLbdmypUzbLlq0SIMHD9bMmTM983JycvTGG2/opptu0vXXX69nn31WqampFRMWAAAAAAAvOqsF+u7du2UYRqm327Vrl1asWKEGDRoUmD9r1iz973//07333quJEyfqxIkTmjJlSkXFBQAAAADAa8p1DfoXX3xR5PyMjAz9/PPP2rhxo3r06FGqfZ46dUovvviiRo0apQULFnjmZ2Zm6vPPP9ddd92l1q1bS5JGjx6te+65Rzt27FB8fHzZ3wj8winTqbvT3pckPR95rUIMR0BmAAAAAOCbylWgT58+vdhlVatWVf/+/Ut9B/c33nhD7dq1U9u2bQsU6Hv27FFubq7atGnjmRcXF6caNWqcsUB3Op1yOp2e14ZhKDQ01DNtbVbPZy1umfo4+wdJ0lQNUXnOX17TKP32FZkhcAX2OavMv0v5x7L+30Kg/GjvCCS0dwQSf2vv5SrQX3rppULzDMNQeHi4pwguja+++kp79+7V008/XWhZamqq7Ha7wsPDC8yPjIw843XoCxcuLPC89kaNGmny5MmqWbNmqfNVtrSqEd6O4FNsbod0JG86IiJC4bYqpdve8eevQ3h4hMJDXJWeISA5/hxlEBERIYUE9jmLrF270o8ZGxtb6ccEvIX2jkBCe0cg8Zf2Xq4CvSKL3GPHjmnmzJl65JFHFBxccXe/HjBggPr16+d5nf/NytGjR+Vylb4Aq0y5f6R7O4JPyTSzPdPp6elyG84zrF3E9tl//jpkZKTL7Sx9+yhvhkBkZGcr8v+n09PTZToD+5xlHjpUaccyDEOxsbFKTk6WaZqVdlzAG2jvCCS0dwQSX2nvdru9RPVzuQr0irRnzx6lpaXpwQcf9Mxzu936+eef9cknn2jcuHFyuVzKyMgo0IuelpamqKioYvfrcDjkcBR9HbCVP8A8Vs9ndWU/f3lNoyLOP59h6QX2OfPG3yXTNH3g7yFQMWjvCCS0dwQSf2nv5SrQhwwZUuptDMPQ+++/X2h+mzZt9OyzzxaY98orr6hOnTrq37+/atSooaCgIG3dulUXXHCBJOngwYM6duwYN4gDAAAAAPi8chXoV199tTZt2qTff/9dCQkJqlOnjiTpwIED+uGHH1SvXj116tSpRPsKDQ1V/fr1C8yrUqWKqlat6pnfo0cPzZ49WxEREQoLC9Obb76p+Ph4CnQAAAAAgM8rV4FevXp1nTx5UlOmTPEU5/l+//13Pf7444qOjlavXr3KFTLfiBEjZBiGpkyZIpfLpYSEBCUlJVXIvgEAAAAA8KZyFehLlixR7969CxXnklS3bl1dfvnlWrJkSZkL9Mcee6zA6+DgYCUlJVGUo0ihcmh7zCTPdKBmAAAAAOCbylWgHz9+XHZ78bsICgrS8ePHy3MIoMQMw1CYKu4JAL6aAQAAAIBvspVn4/r16+vTTz9VSkpKoWXHjx/XZ599Vui6cgAAAAAAUFi5etBHjBihSZMm6a677lKnTp08D4dPTk7Wpk2bZJqm7rzzzgoJCvydbNOlh07OlyT9u9ogVTEq/ymCVsgAAAAAwDeVq3po0aKFJk2apLlz52rTpk3KycmRlHeteEJCggYPHkwPOipNrtyaf+p/kqRJ1QYGbAYAAAAAvqnc3Xv169fXv/71L7ndbp08eVKSVK1aNdls5Ro9DwAAAABAQKmw8bc2m03BwcEKCQmhOAcAAAAAoJTKXUnv3r1bkyZN0vDhw3XTTTdp27ZtkqSTJ0/qmWee0U8//VTukAAAAAAA+LtyFejbt2/Xo48+quTkZHXr1k2maXqWVatWTZmZmVqxYkW5QwIAAAAA4O/KVaC/9957iouL09SpU3XdddcVWn7uuedq165d5TkEAAAAAAABoVwF+u7du5WYmCiHwyHDMAotr169ulJTU8tzCAAAAAAAAkK5bhIXFBRUYFj7X6WkpCgkJKQ8hwBKLFQOfVdzgmc6UDMAAAAA8E3l6kFv1qyZvvnmmyKXnTp1SmvWrFGrVq3KcwigxAzD0Dm2CJ1jiyhyREegZAAAAADgm8pVoA8ePFh79uzR008/rS1btkiS9u3bp1WrVumhhx7SyZMndfXVV1dIUAAAAAAA/Fm5hrg3a9ZMDz/8sF5//XW9/PLLkqS3335bklSrVi09/PDDatCgQflTAiWQbbr0+B9LJEmPVr1SVYxyNW+fzQAAAADAN5W5ejBNU1lZWYqPj9e0adO0b98+HTp0SKZpqlatWmrcuDFDfFGpcuXW7KyvJUnjqvYL2AwAAAAAfFOZC3SXy6WbbrpJ1113nfr376+GDRuqYcOGFRgNAAAAAIDAUeZr0B0Oh6KiouRwcKdqAAAAAADKq1w3iUtMTNQXX3whl8tVUXkAAAAAAAhI5bqDVf369bVp0ybde++9SkxMVM2aNRUcHFxovc6dO5fnMAAAAAAA+L1yFejTpk3zTM+dO7fY9c60DAAAAAAAlKFAnzNnjrp27aoGDRpowoQJZyMTAAAAAAABp9QF+uLFi1W/fn01aNBArVq10h9//KGkpCSNHz9erVu3PhsZgRIJkV3razzsmQ7UDAAAAAB8ExUE/IbNsKleUPWAzwAAAADAN5XrLu4AAAAAAKBi0IMOv5FjuvRM+ieSpAciLlewUfnN2woZAAAAAPimMlUPR44c0Z49eyRJmZmZkqRDhw4pLCysyPUbN25cxnhAybnk1n8zv5Ak3RtxmQo/8C8wMgAAAADwTWUq0OfOnVvo0WlvvPHGGdcHAAAAAADFK3WBfvvtt5+NHAAAAAAABLRSF+iJiYlnIQYAAAAAAIGNu7gDAAAAAGABFOgAAAAAAFgABToAAAAAABbAQ5rhN0Jk18pz7vNMB2oGAAAAAL6JCgJ+w2bY1NweG/AZAAAAAPgmhrgDAAAAAGAB9KDDb+SYLr2U8bkk6Y7wHgo2Kr95WyEDAAAAAN9E9QC/4ZJbz2WskCTdFp6o4ADNAAAAAMA3McQdAAAAAAALoEAHAAAAAMACKNABAAAAALAACnQAAAAAACyAAh0AAAAAAAugQAcAAAAAwAJ4zBr8RhXZ9VH1f3qmAzUDAAAAAN9EBQG/EWTYdJ6jXsBnAAAAAOCbGOIOAAAAAIAF0IMOv5FjuvRm5jpJ0k1hFynYqPzmbYUMAAAAAHwT1QP8hktuTUr/WJJ0Q9iFCg7QDAAAAAB8E0PcAQAAAACwAAp0AAAAAAAsgAIdAAAAAAALoEAHAAAAAMACKNABAAAAALAACnQAAAAAACzAUo9Z++yzz/TZZ5/p6NGjkqS6detq0KBBateunSQpJydHs2fP1vr16+V0OpWQkKCkpCRFRUV5MTWsoorsmhd9m2c6UDMAAAAA8E2WqiCqV6+uoUOHqnbt2jJNU1988YWeeeYZPfPMM6pXr55mzZqlzZs3695771VYWJhmzJihKVOm6IknnvB2dFhAkGFTl+AmAZ8BAAAAgG+y1BD3jh07qn379qpdu7bq1Kmj6667TiEhIdq5c6cyMzP1+eefa8SIEWrdurUaN26s0aNHa/v27dqxY4e3owMAAAAAUC6W6kE/ndvt1tdff63s7GzFx8drz549ys3NVZs2bTzrxMXFqUaNGtqxY4fi4+OL3I/T6ZTT6fS8NgxDoaGhnmlrs3o+a3GauXo36xtJ0rDQC+Qwgsq8r7ymUfrzX5EZAldgt/vK/LuUfyzr/y0Eyo/2jkBCe0cg8bf2brkCff/+/Ro3bpycTqdCQkJ0//33q27dutq3b5/sdrvCw8MLrB8ZGanU1NRi97dw4ULNnz/f87pRo0aaPHmyatasebbeQoVJqxrh7Qg+JcOdrfFHFkqSbqqZqHBblVJtb3P8+esQHh6h8BBXpWcISA6HZzIiIkIKCexzFlm7dqUfMzY2ttKPCXgL7R2BhPaOQOIv7d1yBXqdOnX0n//8R5mZmfrmm2/08ssva+LEiWXe34ABA9SvXz/P6/xvVo4ePSqXq/QFWGXK/SPd2xF8SqaZ7ZlOT0+X23CeYe0its/+89chIyNdbmfp20d5MwQiIztbkf8/nZ6eLtMZ2Ocs89ChSjuWYRiKjY1VcnKyTNOstOMC3kB7RyChvSOQ+Ep7t9vtJeoktlyBbrfbPd9+NG7cWLt379ayZct04YUXyuVyKSMjo0Avelpa2hnv4u5wOOQ4rYfudFb+APNYPZ/Vlf385TWNijj/fIalF9jnzBt/l0zT9IG/h0DFoL0jkNDeEUj8pb1b6iZxRXG73XI6nWrcuLGCgoK0detWz7KDBw/q2LFjxV5/DgAAAACAr7BUD/qcOXN03nnnqUaNGjp16pTWrVunbdu2ady4cQoLC1OPHj00e/ZsRUREKCwsTG+++abi4+Mp0AEAAAAAPs9SBXpaWppefvllnThxQmFhYWrQoIHGjRuntm3bSpJGjBghwzA0ZcoUuVwuJSQkKCkpycupAQAAAAAoP0sV6LfffvsZlwcHByspKYmiHAAAAADgdyxVoAPlEawgzYy6yTMdqBkAAAAA+CYKdPgNuxGknlVaBnwGAAAAAL7J8ndxBwAAAAAgENCDDr/hNHO18NRmSdKAkPZyGJU/xNwKGQAAAAD4Jgp0+A2ncnXfyXmSpH4hCXJ44RpwK2QAAAAA4JsY4g4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABVCgAwAAAABgATxmDX4jWEF6JXK4ZzpQMwAAAADwTRTo8Bt2I0j9QhICPgN8m3vJnEo8mqG0qhHK/SNdklmJx608tiuHejsCAABAiTHEHQAAAAAAC6AHHX7DZebqk+wfJUmXV2ktu1H5Q8ytkAEAAACAb6JAh9/IUa5uT3tHkrQ9ZpLsXrgG3AoZAAAAAPgmhrgDAAAAAGABFOgAAAAAAFgABToAAAAAABZAgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWACPWYPfcChIU6oN9kwHagYAAAAAvokCHX7DYQRpcGingM8AAAAAwDcxxB0AAAAAAAugBx1+w2Xm6oucHZKk7sHxshuVP8TcChkAAAAA+CYKdPiNHOVqZOqbkqTtMZNk98I14FbIAAAAAMA3McQdAAAAAAALoEAHAAAAAMACKNABAAAAALAACnQAAAAAACyAAh0AAAAAAAugQAcAAAAAwAJ4zBr8hkNBeqLqVZ7pQM0AAAAAwDdRoMNvOIwgjQzrGvAZAAAAAPgmhrgDAAAAAGAB9KDDb+Sabm107pUkne9opCCj8r9/skIGAAAAAL6JAh1+I1suDT7xqiRpe8wkhSk4IDMAAAAA8E107wEAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABVCgAwAAAABgARToAAAAAABYAI9Zg9+wy6ZxEX0904GaAQAAAIBvokCH3wg27LotPDHgMwAAAADwTXTxAQAAAABgAfSgw2/kmm5tdR2QJLWxxynIqPzvn6yQAQAAAIBvokCH38iWS/9IeUGStD1mksIUHJAZAAAAAPgmuvcAAAAAALAACnQAAAAAACyAAh0AAAAAAAugQAcAAAAAwAIo0AEAAAAAsABL3cV94cKF2rhxow4cOKDg4GDFx8dr+PDhqlOnjmednJwczZ49W+vXr5fT6VRCQoKSkpIUFRXlveAAAAAAAJSTpQr0bdu2qXfv3mrSpIlyc3P13nvv6cknn9TUqVMVEhIiSZo1a5Y2b96se++9V2FhYZoxY4amTJmiJ554wsvp4W122XRP+KWe6UDNAAAAAMA3WapAHzduXIHXY8aMUVJSkvbs2aNWrVopMzNTn3/+ue666y61bt1akjR69Gjdc8892rFjh+Lj470RGxYRbNh1b8RlAZ8BAAAAgG+yVIH+V5mZmZKkiIgISdKePXuUm5urNm3aeNaJi4tTjRo1ii3QnU6nnE6n57VhGAoNDfVMW5vV8/mvvKbB+fcOzntlyf8TaBiSafrnebf+33lUlvy2QJtAIKC9I5D4W3u3bIHudrs1c+ZMNW/eXPXr15ckpaamym63Kzw8vMC6kZGRSk1NLXI/Cxcu1Pz58z2vGzVqpMmTJ6tmzZpnLXtFSasa4e0IPsVturU9J1mS1Dw4VjajdEPMbY4/fx3CwyMUHuKq9AwByeHwTEZEREghVbwYJjDlfwnqjyJr1/Z2BFhMbGystyMAlYb2jkDiL+3dsgX6jBkz9Ntvv+nxxx8v134GDBigfv36eV7nf7Ny9OhRuVylL8AqU+4f6d6O4FMyzWx1OZLXXrbHTFKYUbpCLzP7z1+HjIx0uZ2lbx/lzRCIjOxsRf7/dHp6uszTRrzg7DKMvOI8PT1dpuntNGdH5qFD3o4AizAMQ7GxsUpOTpbprw0e+H+0dwQSX2nvdru9RJ3ElizQZ8yYoc2bN2vixIk655xzPPOjoqLkcrmUkZFRoBc9LS2t2Lu4OxwOOU7roTudlT/APFbPZ3VlP395TaMizj+fYelxzipL/rD2imvv1mP9v/OobKZp0i4QMGjvCCT+0t4tNf7WNE3NmDFDGzdu1KOPPqqYmJgCyxs3bqygoCBt3brVM+/gwYM6duwYN4gDAAAAAPg0S/Wgz5gxQ+vWrdMDDzyg0NBQz3XlYWFhCg4OVlhYmHr06KHZs2crIiJCYWFhevPNNxUfH0+BDgAAAADwaZYq0D/77DNJ0mOPPVZg/ujRo5WYmChJGjFihAzD0JQpU+RyuZSQkKCkpKRKTgoAAAAAQMWyVIE+b968v10nODhYSUlJFOUAgL/lXjLH2xH8ju3Kod6OAACA37LUNegAAAAAAAQqS/WgA+Vhl02jwrp7pgM1AwAAAADfRIEOvxFs2PVI1X5/v6KfZwAAAADgm+jiAwAAAADAAuhBh99wm24dcKdKkuJsUbIZlf/9kxUyAAAAAPBNFOjwG6fk0oXHnpYkbY+ZpDAFB2QGAAAAAL6J7j0AAAAAACyAAh0AAAAAAAugQAcAAAAAwAIo0AEAAAAAsAAKdAAAAAAALIACHQAAAAAAC+Axa/AbQbLphtAunulAzQAAAADAN1Ggw29UMeyaVG1gwGcAAAAA4Jvo4gMAAAAAwALoQYffME1TKWaGJKm6ES7DMAIyAwAAAADfRIEOv5Elp847OlGStD1mksIUHJAZAAAAAPgmhrgDAAAAAGABFOgAAAAAAFgABToAAAAAABZAgQ4AAAAAgAVQoAMAAAAAYAEU6AAAAAAAWACPWYPfCJJNg0I6eKYDNQMAAAAA30SBDr9RxbDruchrAz4DAAAAAN9EFx8AAAAAABZADzr8hmmaypJTkhQqhwzDCMgMAAAAAHwTPejwG1lyqvmRcWp+ZJynSA7EDAAAAAB8EwU6AAAAAAAWQIEOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABZAgQ4AAAAAgAXwHHT4DZsM9a3S1jMdqBkAAAAA+CYKdPiNEMOhV6OuD/gMAAAAAHwTQ9wBAAAAALAACnQAAAAAACyAAh1+I9PMUb3D/1K9w/9SppkTsBkAAAAA+CYKdAAAAAAALIACHQAAAAAAC6BABwAAAADAAijQAQAAAACwAAp0AAAAAAAsgAIdAAAAAAALsHs7AFBRbDLUI7iFZzpQMwAAAADwTRTo8BshhkOzom8O+AwAAAAAfBND3AEAAAAAsAAKdAAAAAAALIACHX4j08xR/OGxij88VplmTsBmAAAAAOCbuAYdfiVLTm9HsEQGAAAAAL6HHnQAAAAAACyAAh0AAAAAAAugQAcAAAAAwAIsdQ36tm3btGTJEu3du1cnTpzQ/fffr/PPP9+z3DRNzZs3T6tWrVJGRoZatGihpKQk1a5d24upAQAAAAAoP0v1oGdnZ6thw4a6+eabi1y+ePFiLV++XLfccoueeuopValSRZMmTVJODnfLBgAAAAD4Nkv1oLdr107t2rUrcplpmlq2bJkGDhyoTp06SZLuuOMO3XLLLdq0aZO6du1amVFhQYYMXeBo7JkO1AwAAAAAfJOlCvQzOXLkiFJTU9W2bVvPvLCwMDVt2lQ7duwotkB3Op1yOv987JVhGAoNDfVMW5vV81lLqBGsD6qPrpB95TWN0p//iswQuGj3lSX/T6BhSKbJeUfJWP/fzqLl5/bV/EBp0N4RSPytvftMgZ6amipJioyMLDA/MjLSs6woCxcu1Pz58z2vGzVqpMmTJ6tmzZpnI2aFSqsa4e0IAcXm+PPXITw8QuEhLi+mCSAOh2cyIiJCCqnixTCBKSKCvzUouUgfv+9LbGystyMAlYb2jkDiL+3dZwr0showYID69evneZ3/zcrRo0flclm7AMv9I93bEQJKZvafvw4ZGelyO63dPvyFkZ2t/K/d0tPTZZ424gVnl2HkFefp6ekyTW+nga/IPHTI2xHKxDAMxcbGKjk5WSYNHn6O9o5A4ivt3W63l6iT2GcK9KioKElSWlqaoqOjPfPT0tLUsGHDYrdzOBxynNZDdzorf4B5rJ7PWjLNHHU5+pQk6euaYxVmBJd5X3lNo/TnvyIzBC7afWXJH9Ze1vaOwGT9fzvPzDRNn38PQEnR3hFI/KW9W+ou7mcSExOjqKgobd261TMvMzNTu3btUnx8vBeTwUpSzAylmBkBnwEAAACA77FUD/qpU6eUnJzseX3kyBHt27dPERERqlGjhvr06aMFCxaodu3aiomJ0fvvv6/o6GjPXd0BAAAAAPBVlirQd+/erYkTJ3pez549W5LUvXt3jRkzRv3791d2drb++9//KjMzUy1atNDYsWMVHMwwYgAAAACAb7NUgX7uuedq3rx5xS43DENDhgzRkCFDKjEVAAAAAABnn89cgw4AAAAAgD+jQAcAAAAAwAIsNcQdKA9Dhtra63qmAzUDAAAAAN9EgQ6/EWo49PE5dwV8BgAAAAC+iSHuAAAAAABYAAU6AAAAAAAWQIEOv5Fl5qjL0afU5ehTyjJzAjYDAAAAAN/ENejwG6ak390nPNOBmgEAAACAb6IHHQAAAAAAC6BABwAAAADAAijQAQAAAACwAAp0AAAAAAAsgAIdAAAAAAAL4C7u8BuGpPigWp7pQM0AAAAAwDdRoMNvhBrBWlXj/oDPAAAAAMA3McQdAAAAAAALoEAHAAAAAMACKNDhN7LMHPU89qx6HntWWWZOwGYAAAAA4Ju4Bh1+w5S0I/ewZzpQMwAAAADwTfSgAwAAAABgARToAAAAAABYAAU6AAAAAAAWQIEOAAAAAIAFUKADAAAAAGAB3MUdfsOQVNcW7ZkO1AwAAAAAfBMFOvxGqBGsr2uODfgMAHA2uZfM8XaEMjKUVjVCuX+ky0oPwrRdOdTbEQAAFsIQdwAAAAAALIACHQAAAAAAC6BAh9/IMp3qe3ya+h6fpizTGbAZAAAAAPgmrkGH3zBl6gfX757pQM0AAAAAwDfRgw4AAAAAgAVQoAMAAAAAYAEMcQcAAPAS331snXXx6DoAvowedAAAAAAALIACHQAAAAAAC2CIO/xKdSPc2xEskQEAAACA76FAh98IM4L1fcxjAZ8BAAAAgG9iiDsAAAAAABZAgQ4AAAAAgAVQoMNvZJlOXZPyiq5JeUVZpjNgMwAAAADwTVyDDr9hytQ3zj2e6UDNAAAAAMA30YMOAAAAAIAFUKADAAAAAGABFOgAAAAAAFgABToAAAAAABZAgQ4AAAAAgAVwF3f4lVA5vB3BEhkAAAAA+B4KdPiNMCNYO2o9FfAZAAAAAPgmhrgDAAAAAGABFOgAAAAAAFgABTr8xinTqREnZmjEiRk6ZToDNgMAAAAA38Q16PAbbpn6POcXz3SgZgAAIJC5l8zxdgQLMJRWNUK5f6RL/P8I/J4h3Xaft0NUGHrQAQAAAACwAAp0AAAAAAAswCeHuH/yySf66KOPlJqaqgYNGuimm25S06ZNvR0LAAAAAIAy87ke9PXr12v27NkaNGiQJk+erAYNGmjSpElKS0vzdjQAAAAAAMrM5wr0pUuXqmfPnrrkkktUt25d3XLLLQoODtbq1au9HQ0AAAAAgDLzqSHuLpdLe/bs0VVXXeWZZ7PZ1KZNG+3YsaPIbZxOp5zOPx93ZRiGQkNDZbdb/627a9TydgSfUsXMUTtbu7zp6rXkMIJLt31OkNrlba6QmJpyOHIrPUMgMnJylH/iHTVryQzmnFUWw5BsoWEKDgmTyU1+4edo7wgktHcEEsPI+6/D4ZBp4QZf0vrT+lXqaU6ePCm3262oqKgC86OionTw4MEit1m4cKHmz5/ved21a1fdddddio6OPptRK8ag672dwOds1s3l235o/tTQM612VjMEpKF55yzGyzECVVVvBwAqEe0dgYT2jkBSo0YNb0eoED43xL20BgwYoJkzZ3p+brnllgI96sDpsrKy9OCDDyorK8vbUYCzjvaOQEJ7RyChvSOQ+Ft796ke9GrVqslmsyk1NbXA/NTU1EK96vkcDoccDsfZDwe/YJqm9u7da+nhMUBFob0jkNDeEUho7wgk/tbefaoH3W63q3Hjxvrxxx8989xut3788UfFx8d7MRkAAAAAAOXjUz3oktSvXz+9/PLLaty4sZo2baply5YpOztbiYmJ3o4GAAAAAECZ+VyBfuGFF+rkyZOaN2+eUlNT1bBhQ40dO7bYIe5AaTgcDg0aNIjLIhAQaO8IJLR3BBLaOwKJv7V3w/SXwfoAAAAAAPgwn7oGHQAAAAAAf0WBDgAAAACABVCgAwAAAABgARToAAAAAABYgM/dxR04GxYuXKiNGzfqwIEDCg4OVnx8vIYPH646dep4OxpwVi1atEhz5sxRnz59NHLkSG/HASpcSkqK3nnnHX333XfKzs5WbGysRo8erSZNmng7GlCh3G635s2bp7Vr1yo1NVXVq1dX9+7ddfXVV8swDG/HA8pl27ZtWrJkifbu3asTJ07o/vvv1/nnn+9Zbpqm5s2bp1WrVikjI0MtWrRQUlKSateu7cXUZUOBDijvl753795q0qSJcnNz9d577+nJJ5/U1KlTFRIS4u14wFmxa9curVixQg0aNPB2FOCsSE9P1/jx43Xuuedq7Nixqlatmg4dOqTw8HBvRwMq3KJFi7RixQqNGTNGdevW1Z49ezR9+nSFhYWpT58+3o4HlEt2drYaNmyoHj166Nlnny20fPHixVq+fLnGjBmjmJgYzZ07V5MmTdLUqVMVHBzshcRlR4EOSBo3blyB12PGjFFSUpL27NmjVq1aeSkVcPacOnVKL774okaNGqUFCxZ4Ow5wVixevFjnnHOORo8e7ZkXExPjxUTA2bNjxw517NhR7du3l5TX1tetW6ddu3Z5ORlQfu3atVO7du2KXGaappYtW6aBAweqU6dOkqQ77rhDt9xyizZt2qSuXbtWZtRy4xp0oAiZmZmSpIiICC8nAc6ON954Q+3atVPbtm29HQU4a7799ls1btxYU6dOVVJSkh544AGtXLnS27GAsyI+Pl4//vijDh48KEnat2+ftm/fXmxRA/iLI0eOKDU1tcD/04SFhalp06basWOHF5OVDT3owF+43W7NnDlTzZs3V/369b0dB6hwX331lfbu3aunn37a21GAs+rIkSNasWKF+vbtqwEDBmj37t166623ZLfblZiY6O14QIW66qqrlJWVpXvuuUc2m01ut1vXXnutunXr5u1owFmVmpoqSYqMjCwwPzIy0rPMl1CgA38xY8YM/fbbb3r88ce9HQWocMeOHdPMmTP1yCOP+Nw1WUBpud1uNWnSREOHDpUkNWrUSPv379eKFSso0OF3vv76a61bt07//Oc/Va9ePe3bt08zZ85UdHQ07R3wIRTowGlmzJihzZs3a+LEiTrnnHO8HQeocHv27FFaWpoefPBBzzy3262ff/5Zn3zyiebMmSObjauf4B+io6NVt27dAvPq1q2rDRs2eCkRcPa888476t+/v+d62/r16+vo0aNatGgRBTr8WlRUlCQpLS1N0dHRnvlpaWlq2LChd0KVAwU6oLybS7z55pvauHGjHnvsMW4iBL/Vpk2bQnc/feWVV1SnTh3179+f4hx+pXnz5p7rcfMdPHhQNWvW9FIi4OzJzs4u9DfcZrPJNE0vJQIqR0xMjKKiorR161ZPQZ6Zmaldu3bpsssu8264MqBAB5TXc75u3To98MADCg0N9VyvEhYWxjBg+JXQ0NBC91aoUqWKqlatyj0X4Hf69u2r8ePHa8GCBbrwwgu1a9curVq1Srfeequ3owEVrkOHDlqwYIFq1KihunXrat++fVq6dKkuueQSb0cDyu3UqVNKTk72vD5y5Ij27duniIgI1ahRQ3369NGCBQtUu3ZtxcTE6P3331d0dLTnru6+xDD5Wg3Q4MGDi5w/evRohoXB7z322GNq2LChRo4c6e0oQIX73//+pzlz5ig5OVkxMTHq27evevXq5e1YQIXLysrS3LlztXHjRqWlpal69erq2rWrBg0aJLudPjn4tp9++kkTJ04sNL979+4aM2aMTNPUvHnztHLlSmVmZqpFixa6+eabVadOHS+kLR8KdAAAAAAALICLDQEAAAAAsAAKdAAAAAAALIACHQAAAAAAC6BABwAAAADAAijQAQAAAACwAAp0AAAAAAAsgAIdAAAAAAALoEAHAAAAAMACKNABADjLXn75ZY0ZM8bbMQKG2+3WfffdpwULFhS5/KefftLLL79c7Pbjxo3TO++8c7biAQBQLLu3AwAA4IsGDx5covUmTJhwVnNs3rxZ//73vxUdHa1XXnlFNpt/ffe+efNm7dq1q8TnW5K++uorHTt2TJdffnmZjtm/f3+9+OKL6tevn6Kiosq0DwAAyoICHQCAMrjjjjsKvP7yyy/1ww8/FJofFxenUaNGyTTNs5Jj3bp1qlmzpo4ePaoff/xRbdu2PSvH8ZYtW7bo008/LVWBvmTJEnXt2lVhYWFFLs/NzVVubq7cbneRX2h07NhRoaGh+vTTTzVkyJAyZwcAoLQo0AEAKIOLL764wOudO3fqhx9+KDT/bDp16pQ2bdqkoUOHas2aNVq3bp3fFeiltXfvXv3666+64YYbCi37/PPPNX/+fB07dkyStH79esXFxem6665Tx44dPevZbDZdcMEF+vLLLzV48GAZhlFp+QEAgY0CHQCAs+zll1/Wtm3bPNc9HzlyRHfccYeGDx8um82mZcuWKS0tTU2bNtXNN9+s+vXrl2i/GzduVE5Ojrp06aKcnBwtWLBASUlJCg4OLrDe4MGD1bt3b7Vq1Urz5s3TkSNH1LBhQ40aNUr169fXihUrtGTJEqWkpKhZs2YaPXq0YmJiCuzj66+/1qJFi/T7778rJCRECQkJGj58uKpXr+5Z57HHHivw35K8/7CwMC1evFjHjx9XgwYNdPPNN6tp06ae7b744gvPe8g3b968Ys/Jpk2bZLfb1bJlywLzf/rpJ7366qtKSEhQjx499PPPP+vCCy/Uzz//rMOHDxfaT9u2bfXJJ59o3759atSoUbHHAwCgIlGgAwDgJV9++aWysrLUu3dvOZ1OLVu2TI8//rieffbZEl37vG7dOrVu3VpRUVHq2rWr5syZo//973/q0qVLoXV/+eUXffvtt+rdu7ckadGiRfr3v/+tK6+8Up999pl69+6t9PR0LVmyRK+88kqBa+fXrFmj6dOnq0mTJho6dKjS0tK0bNkybd++Xc8884zCw8PL9P6/+uorZWVlqVevXjIMQ4sXL9aUKVP04osvym6369JLL9WJEyeKvHSgONu3b1e9evVktxf8X5zNmzcrJCREDzzwgHbs2KHDhw+rZ8+e6tmzZ5H7ady4sWd/FOgAgMpCgQ4AgJckJyfrhRde8PRCn3feeRo7dqwWL16sESNGnHHbtLQ0bd26VUlJSZKkGjVqKD4+XmvXri2yQD948KCee+45T894RESEXnvtNS1YsEDTpk1TaGiopLw7oC9atEhHjhxRTEyMXC6X3n33XdWrV08TJ0709M63aNFC//73v/Xxxx+X6vrw0x07dkzTpk1TRESEJKlOnTp65pln9P3336tDhw6Kj49X7dq1S3XpwMGDBz098Kez2Wxyu91yOp0l2k/16tVlt9v1+++/l/wNAQBQTv51q1cAAHxIp06dCgwRb9q0qZo1a6YtW7b87bZfffWVDMNQ586dPfO6du2q7777Tunp6YXWb926dYFh6/lFbOfOnT3FuSQ1a9ZMUt4wdEnas2eP0tLS1Lt37wJD59u3b6+4uDht3ry5pG+3kC5duniKcymv6JdU5JDzkvrjjz+K7NHv1q2b3G63HnnkEX3xxRfKyMhQTk7OGfcVHh6ukydPljkLAAClRYEOAICX1K5du8h5R48e/dtt165dq6ZNmyo9PV3JyclKTk5Ww4YN5XK59M033xRav0aNGgVe59/h/Jxzzilyfn6Rn5+lTp06hfZZp06dEmUtzl8z5RfrGRkZZd5ncerXr69JkyapTp062rBhg7799luNHDlSU6dO9dw0rijcIA4AUJkY4g4AgI85dOiQdu/eLUn65z//WWj52rVr1atXrwLzins+ekU+N90wjCIfJ+d2u0t17PI8kq5q1apFjiCQpEaNGun+++/XTz/9pMWLF6tVq1ZasGCBDhw4oGeeeUZBQUEF1s/IyFDVqlXLnAUAgNKiQAcAwEsOHTpU5LyaNWuecbu1a9cqKChId955Z6Ei95dfftHy5ct17NixQj3UZZGf5eDBg2rdunWBZQcPHiyQNTw8vMjh6Wfqof47pe3BLmmvfmRkpK666irZ7XbNnj1bBw4cKHD3/JSUFLlcLtWtW7fUmQEAKCuGuAMA4CWbNm1SSkqK5/WuXbu0c+dOnXfeeWfcbt26dWrZsqUuvPBCXXDBBQV+rrzySs86FaFx48aKjIzUihUrCtxgbcuWLTpw4IDat2/vmVerVi0dPHiwwHXb+/bt0y+//FLm41epUkVSyYe9x8fHa//+/YVuBldcr7rL5ZKkQo+m27Nnj2d/AABUFnrQAQDwktjYWI0fP16XXXaZ5zFrVatWVf/+/YvdZufOnUpOTtbll19e5PLq1aurUaNGWrduna666qpyZ7Tb7Ro2bJimT5+uxx57TF27dlVqaqqWL1+umjVrqm/fvp51L7nkEi1dulSTJk3SJZdcopMnT2rFihWqV6+esrKyynT8/MedvfXWW0pISJDNZlPXrl2LXb9Tp0768MMPtW3bNiUkJHjmz549W+np6Tr//POVmZmplJQUzZ8/X4sXL1azZs1Uq1atAvv54YcfVKNGDR6xBgCoVBToAAB4ycUXXyybzaaPP/5YJ0+eVNOmTXXTTTcpOjq62G3Wrl0rSerQoUOx63To0EEffPCBfv31VzVo0KDcORMTExUcHKzFixfr3XffVZUqVdSpUycNHz68wB3T69atqzvuuEPz5s3T7NmzPa/XrVunbdu2lenYnTt31uWXX67169dr7dq1Mk3zjAV648aN1aBBA3399dcFCvTevXvr008/1YcffugZvn7w4EF17dpV1157bYGh9G63Wxs2bNAll1zCTeIAAJXKMMtzJxYAAFBqR44c0R133KHhw4d7hqSj4nz55ZeaMWOGpk+fXuQj13766SetWbNGY8aMKXL7jRs36oUXXtCLL754xi9LAACoaFyDDgAA/MpFF12kGjVq6NNPPy3T9osXL9bll19OcQ4AqHQMcQcAAH7FZrNpypQpxS6vWbOmOnXqVOzySZMmnY1YAAD8LQp0AAAQUGJiYhQTE+PtGAAAFMI16AAAAAAAWADXoAMAAAAAYAEU6AAAAAAAWAAFOgAAAAAAFkCBDgAAAACABVCgAwAAAABgARToAAAAAABYAAU6AAAAAAAWQIEOAAAAAIAF/B8aQm6gTMNOtAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the tips dataset from Seaborn\n",
        "tips = sns.load_dataset(\"tips\")\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Set the null hypothesis mean\n",
        "null_mean = 2.0\n",
        "\n",
        "# Extract the tip data\n",
        "tips_data = tips['tip']\n",
        "\n",
        "# Calculate the sample size, mean, and standard deviation\n",
        "n = len(tips_data)\n",
        "sample_mean = tips_data.mean()\n",
        "sample_std = tips_data.std(ddof=1)\n",
        "\n",
        "# Calculate the t-score and p-value using scipy.stats\n",
        "t_score, p_value = stats.ttest_1samp(tips_data, popmean=null_mean)\n",
        "\n",
        "# Calculate the critical values for the test\n",
        "t_critical = stats.t.ppf(alpha / 2, df=n-1)\n",
        "x_left = null_mean + t_critical * sample_std / np.sqrt(n)\n",
        "x_right = null_mean - t_critical * sample_std / np.sqrt(n)\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")\n",
        "\n",
        "# Set the x-axis limits\n",
        "x_min = 0\n",
        "x_max = tips_data.max() + 1\n",
        "\n",
        "# style and size\n",
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(12,7))\n",
        "\n",
        "# Create the histogram of tip amounts\n",
        "plt.hist(tips_data, bins=10, alpha=0.5)\n",
        "\n",
        "# Plot the null hypothesis mean and sample mean on the x-axis\n",
        "plt.axvline(x=null_mean, color='b', label='Null Hypothesis Mean')\n",
        "plt.axvline(x=sample_mean, color='r', label='Sample Mean')\n",
        "\n",
        "# Plot the critical values on the x-axis\n",
        "plt.axvline(x=x_left, linestyle='--', color='g', label='Critical Values')\n",
        "plt.axvline(x=x_right, linestyle='--', color='g')\n",
        "\n",
        "# Add a legend, axis labels, and a title\n",
        "plt.legend()\n",
        "plt.xlabel('Tip Amount ($)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('T-test for Tip Amounts')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPHs1rK7UJBl"
      },
      "source": [
        "## **T-Test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "iPmonNWHUJBm",
        "outputId": "483e1cc1-6e17-4393-b8a4-aa958997c4cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the Iris dataset from scikit-learn\n",
        "iris = load_iris()\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Set the null hypothesis mean\n",
        "null_mean = 5.0\n",
        "\n",
        "# Extract the sepal length data\n",
        "sepal_length = iris.data[:, 0]\n",
        "\n",
        "# Calculate the sample size, mean, and standard deviation\n",
        "n = len(sepal_length)\n",
        "sample_mean = sepal_length.mean()\n",
        "sample_std = sepal_length.std(ddof=1)\n",
        "\n",
        "# Calculate the t-score and p-value using scipy.stats\n",
        "t_score, p_value = stats.ttest_1samp(sepal_length, popmean=null_mean)\n",
        "\n",
        "# Calculate the critical values for the test\n",
        "t_critical = stats.t.ppf(alpha / 2, df=n-1)\n",
        "x_left = null_mean + t_critical * sample_std / np.sqrt(n)\n",
        "x_right = null_mean - t_critical * sample_std / np.sqrt(n)\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")\n",
        "\n",
        "# Set the x-axis limits\n",
        "x_min = 4\n",
        "x_max = 8\n",
        "\n",
        "# style and size\n",
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(12,7))\n",
        "\n",
        "# Create the histogram of sepal lengths\n",
        "plt.hist(sepal_length, bins=10, alpha=0.5)\n",
        "\n",
        "# Plot the null hypothesis mean and sample mean on the x-axis\n",
        "plt.axvline(x=null_mean, color='b', label='Null Hypothesis Mean')\n",
        "plt.axvline(x=sample_mean, color='r', label='Sample Mean')\n",
        "\n",
        "# Plot the critical values on the x-axis\n",
        "plt.axvline(x=x_left, linestyle='--', color='g', label='Critical Values')\n",
        "plt.axvline(x=x_right, linestyle='--', color='g')\n",
        "\n",
        "# Add a legend, axis labels, and a title\n",
        "plt.legend()\n",
        "plt.xlabel('Sepal Length (cm)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('T-test for Sepal Length')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yinz-4NtUJBn"
      },
      "source": [
        "## **ANOVA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZIHv73qUJBn"
      },
      "source": [
        "**On IRIS DataSet**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "8gxs7-BOUJBn",
        "outputId": "28124b4c-8b9c-4327-f84f-db69ebc98248"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the Iris dataset from scikit-learn\n",
        "iris = load_iris()\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Extract the sepal length data for each species\n",
        "setosa_sepal_length = iris.data[:50, 0]\n",
        "versicolor_sepal_length = iris.data[50:100, 0]\n",
        "virginica_sepal_length = iris.data[100:, 0]\n",
        "\n",
        "# Conduct the one-way ANOVA using scipy.stats\n",
        "f_value, p_value = stats.f_oneway(\n",
        "    setosa_sepal_length, versicolor_sepal_length, virginica_sepal_length)\n",
        "\n",
        "# Calculate the degrees of freedom\n",
        "df_between = 2\n",
        "df_within = len(setosa_sepal_length) + \\\n",
        "    len(versicolor_sepal_length) + len(virginica_sepal_length) - 3\n",
        "\n",
        "# Calculate the critical value for the F-distribution\n",
        "f_critical = stats.f.ppf(1 - alpha, dfn=df_between, dfd=df_within)\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")\n",
        "\n",
        "# Create the boxplot of sepal lengths for each species\n",
        "data = [setosa_sepal_length, versicolor_sepal_length, virginica_sepal_length]\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data)\n",
        "ax.set_xticklabels(['Setosa', 'Versicolor', 'Virginica'])\n",
        "ax.set_xlabel('Species')\n",
        "ax.set_ylabel('Sepal Length (cm)')\n",
        "ax.set_title('One-way ANOVA for Sepal Length')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAPctLcbUJBo"
      },
      "source": [
        "**On Tips DataSet**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "ZBn_7rHvUJBo",
        "outputId": "549064ca-d165-4c81-df70-058c32bfdacd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the Tips dataset from seaborn\n",
        "tips = sns.load_dataset('tips')\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Extract the data for each group\n",
        "group1 = tips[tips['day'] == 'Thur']['total_bill']\n",
        "group2 = tips[tips['day'] == 'Fri']['total_bill']\n",
        "group3 = tips[tips['day'] == 'Sat']['total_bill']\n",
        "group4 = tips[tips['day'] == 'Sun']['total_bill']\n",
        "\n",
        "# Conduct the one-way ANOVA using scipy.stats\n",
        "f_value, p_value = stats.f_oneway(group1, group2, group3, group4)\n",
        "\n",
        "# Calculate the degrees of freedom\n",
        "df_between = 3\n",
        "df_within = len(tips) - 4\n",
        "\n",
        "# Calculate the critical value for the F-distribution\n",
        "f_critical = stats.f.ppf(1 - alpha, dfn=df_between, dfd=df_within)\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")\n",
        "\n",
        "# Create the boxplot of total bill for each group\n",
        "data = [group1, group2, group3, group4]\n",
        "fig, ax = plt.subplots()\n",
        "ax.boxplot(data)\n",
        "ax.set_xticklabels(['Thur', 'Fri', 'Sat', 'Sun'])\n",
        "ax.set_xlabel('Day')\n",
        "ax.set_ylabel('Total Bill ($)')\n",
        "ax.set_title('One-way ANOVA for Total Bill')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDjHQSiEUJBp"
      },
      "source": [
        "## **Chi-Square tests** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOCJbOYwUJBp",
        "outputId": "f3fc4552-92a3-48fd-f191-9ba24be2002d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reject the null hypothesis\n",
            "Expected frequencies:\n",
            "[[16.73913043 18.26086957]\n",
            " [21.52173913 23.47826087]\n",
            " [16.73913043 18.26086957]]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import scipy.stats as stats\n",
        "\n",
        "# Create a contingency table\n",
        "data = {'Male': [20, 25, 10], 'Female': [15, 20, 25]}\n",
        "df = pd.DataFrame(data=data, index=['Math', 'Science', 'English'])\n",
        "\n",
        "# Conduct the Chi-Square test using scipy.stats\n",
        "chi2_stat, p_val, dof, ex = stats.chi2_contingency(df)\n",
        "\n",
        "# Set the significance level\n",
        "alpha = 0.05\n",
        "\n",
        "# Compare the p-value to the significance level and print the result\n",
        "if p_val < alpha:\n",
        "    print(\"Reject the null hypothesis\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis\")\n",
        "\n",
        "# Print the expected frequencies\n",
        "print(\"Expected frequencies:\")\n",
        "print(ex)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko1-oE1kUJBp"
      },
      "source": [
        "**On Iris DataSet**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "ajwagZ9FUJBp",
        "outputId": "e8b1035e-bcb2-4a8f-e5c6-ea01b81f0c27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class         Iris-setosa  Iris-versicolor  Iris-virginica\n",
            "petal_length                                              \n",
            "short                  50                0               0\n",
            "medium                  0               48               6\n",
            "long                    0                2              44\n",
            "Chi-Square test statistic: 256.52173913043475\n",
            "p-value: 2.561410449304364e-54\n",
            "Degrees of freedom: 4\n",
            "Expected frequencies:\n",
            " [[16.66666667 16.66666667 16.66666667]\n",
            " [18.         18.         18.        ]\n",
            " [15.33333333 15.33333333 15.33333333]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import chi2_contingency\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the iris dataset\n",
        "iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',\n",
        "                   header=None, names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])\n",
        "\n",
        "# Create a contingency table\n",
        "petal_length_bins = pd.cut(iris['petal_length'], bins=3, labels=['short', 'medium', 'long'])\n",
        "table = pd.crosstab(petal_length_bins, iris['class'])\n",
        "\n",
        "# Print the contingency table\n",
        "print(table)\n",
        "\n",
        "# Perform the Chi-Square test\n",
        "stat, p, dof, expected = chi2_contingency(table)\n",
        "\n",
        "# Print the test statistic, p-value, and degrees of freedom\n",
        "print('Chi-Square test statistic:', stat)\n",
        "print('p-value:', p)\n",
        "print('Degrees of freedom:', dof)\n",
        "print('Expected frequencies:\\n', expected)\n",
        "\n",
        "# Plot the observed counts\n",
        "observed_counts = np.array(table)\n",
        "labels = petal_length_bins.cat.categories\n",
        "\n",
        "\n",
        "\n",
        "plt.bar(labels, observed_counts[:, 0], color='r', label='setosa')\n",
        "plt.bar(labels, observed_counts[:, 1], bottom=observed_counts[:, 0], color='g', label='versicolor')\n",
        "plt.bar(labels, observed_counts[:, 2], bottom=observed_counts[:, 0]+observed_counts[:, 1], color='b', label='virginica')\n",
        "plt.xlabel('Petal length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Observed counts')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "W1QVScd0UJBq",
        "outputId": "890ae04e-3a82-4481-85fc-8df59c3cfacb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 'sepal length (cm)'\n",
            "\tChi2 statistic: 10.817820878494002\n",
            "\tP-value: 0.004476514990225768\n",
            "\n",
            "Feature 'sepal width (cm)'\n",
            "\tChi2 statistic: 3.7107283035324965\n",
            "\tP-value: 0.15639598043162514\n",
            "\n",
            "Feature 'petal length (cm)'\n",
            "\tChi2 statistic: 116.31261309207025\n",
            "\tP-value: 5.533972277193866e-26\n",
            "\n",
            "Feature 'petal width (cm)'\n",
            "\tChi2 statistic: 67.04836020011118\n",
            "\tP-value: 2.758249653003403e-15\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.feature_selection import chi2\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Load the iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Convert the target variable to categorical values\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)\n",
        "\n",
        "# Use chi-square test to get the chi2 statistics and p-values for each feature\n",
        "chi2_stat, p_values = chi2(X, y)\n",
        "\n",
        "# Print the results\n",
        "for i in range(len(iris.feature_names)):\n",
        "    print(f\"Feature '{iris.feature_names[i]}'\")\n",
        "    print(f\"\\tChi2 statistic: {chi2_stat[i]}\")\n",
        "    print(f\"\\tP-value: {p_values[i]}\\n\")\n",
        "\n",
        "\n",
        "# Convert X to a Pandas DataFrame\n",
        "df = pd.DataFrame(X, columns=iris.feature_names)\n",
        "\n",
        "# style and size\n",
        "plt.style.use('ggplot')\n",
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "# Calculate the correlation matrix and plot it as a heatmap\n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm')\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}