Basic Statistics_Level-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0598e25c",
   "metadata": {},
   "source": [
    "# Q7) Calculate Mean, Median, Mode, Variance, Standard Deviation, Range & comment about the values / draw inferences, for the given dataset\n",
    "-\tFor Points, Score, Weight\n",
    "Find Mean, Median, Mode, Variance, Standard Deviation, and Range and also Comment about the values/ Draw some inferences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4210658f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:19:47.948260Z",
     "start_time": "2023-08-28T12:19:45.128053Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af907f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:19:56.420828Z",
     "start_time": "2023-08-28T12:19:56.391905Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('Q7.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d8ae89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:08.682205Z",
     "start_time": "2023-08-28T12:20:08.670238Z"
    }
   },
   "outputs": [],
   "source": [
    "numeric_columns = ['Points', 'Score', 'Weigh']\n",
    "numeric_data = data[numeric_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1816e30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:18.834932Z",
     "start_time": "2023-08-28T12:20:18.824960Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_values = numeric_data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a4d625e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:28.577173Z",
     "start_time": "2023-08-28T12:20:28.556226Z"
    }
   },
   "outputs": [],
   "source": [
    "median_values = numeric_data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adbe7ef6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:37.084911Z",
     "start_time": "2023-08-28T12:20:37.065962Z"
    }
   },
   "outputs": [],
   "source": [
    "mode_values = numeric_data.mode().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb5df034",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:49.666232Z",
     "start_time": "2023-08-28T12:20:49.653256Z"
    }
   },
   "outputs": [],
   "source": [
    "variance_values = numeric_data.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "817dbe8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:20:58.576262Z",
     "start_time": "2023-08-28T12:20:58.553299Z"
    }
   },
   "outputs": [],
   "source": [
    "std_deviation_values = numeric_data.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17b8d017",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:21:06.383693Z",
     "start_time": "2023-08-28T12:21:06.374706Z"
    }
   },
   "outputs": [],
   "source": [
    "range_values = numeric_data.max() - numeric_data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6655bc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:21:15.331230Z",
     "start_time": "2023-08-28T12:21:15.302308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Values:\n",
      " Points     3.596563\n",
      "Score      3.217250\n",
      "Weigh     17.848750\n",
      "dtype: float64\n",
      "\n",
      "Median Values:\n",
      " Points     3.695\n",
      "Score      3.325\n",
      "Weigh     17.710\n",
      "dtype: float64\n",
      "\n",
      "Mode Values:\n",
      " Points     3.07\n",
      "Score      3.44\n",
      "Weigh     17.02\n",
      "Name: 0, dtype: float64\n",
      "\n",
      "Variance Values:\n",
      " Points    0.285881\n",
      "Score     0.957379\n",
      "Weigh     3.193166\n",
      "dtype: float64\n",
      "\n",
      "Standard Deviation Values:\n",
      " Points    0.534679\n",
      "Score     0.978457\n",
      "Weigh     1.786943\n",
      "dtype: float64\n",
      "\n",
      "Range Values:\n",
      " Points    2.170\n",
      "Score     3.911\n",
      "Weigh     8.400\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Values:\\n\", mean_values)\n",
    "print(\"\\nMedian Values:\\n\", median_values)\n",
    "print(\"\\nMode Values:\\n\", mode_values)\n",
    "print(\"\\nVariance Values:\\n\", variance_values)\n",
    "print(\"\\nStandard Deviation Values:\\n\", std_deviation_values)\n",
    "print(\"\\nRange Values:\\n\", range_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc0c93b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:21:52.406022Z",
     "start_time": "2023-08-28T12:21:52.385079Z"
    }
   },
   "outputs": [],
   "source": [
    "statistical_summary = pd.DataFrame({\n",
    "    'Mean': mean_values,\n",
    "    'Median': median_values,\n",
    "    'Mode': mode_values,\n",
    "    'Variance': variance_values,\n",
    "    'Standard Deviation': std_deviation_values,\n",
    "    'Range': range_values\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df57cf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:22:07.866353Z",
     "start_time": "2023-08-28T12:22:07.842419Z"
    }
   },
   "source": [
    "print(statistical_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3e45043",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:22:22.533356Z",
     "start_time": "2023-08-28T12:22:21.962126Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[numeric_columns].hist(figsize=(10, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a0c9ad3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:22:33.663465Z",
     "start_time": "2023-08-28T12:22:33.445242Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Points'], data['Score'])\n",
    "plt.xlabel('Points')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Scatter Plot: Points vs Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95937793",
   "metadata": {},
   "source": [
    "# Q9)Calculate Skewness, Kurtosis & draw inferences on the following data\n",
    "      Cars speed and distance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc4bb23b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:37:10.610584Z",
     "start_time": "2023-08-28T12:37:10.336916Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import skew, kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3611178",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:37:10.642499Z",
     "start_time": "2023-08-28T12:37:10.617568Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Q9_a.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de5c9f87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:37:15.668610Z",
     "start_time": "2023-08-28T12:37:15.639698Z"
    }
   },
   "outputs": [],
   "source": [
    "speed_skew = skew(data['speed'])\n",
    "dist_skew = skew(data['dist'])\n",
    "\n",
    "speed_kurt = kurtosis(data['speed'])\n",
    "dist_kurt = kurtosis(data['dist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db0048e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:37:32.549420Z",
     "start_time": "2023-08-28T12:37:31.902484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter Plot of Speed vs Distance')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.scatterplot(x='speed', y='dist', data=data)\n",
    "plt.title('Scatter Plot of Speed vs Distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc15c96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:39:42.099488Z",
     "start_time": "2023-08-28T12:39:41.834418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CACYikgkDmIhIJgxgIiKZyBrAu3btwsyZM6HT6SBJEjZt2mR7zmg04plnnkFKSgp8fHyg0+lw7733oqioSL6CiYjsSNYAbmhoQGpqKtasWXPJc42NjTh06BAWL16MQ4cOYcOGDcjKysKsWbNkqJSIyP485Dz41KlTMXXq1Haf02q12Lp1a5ttb775JkaPHo28vDzExsb2RYlERL1G1gDuqtraWkiShICAgMvuYzAYYDAYbF/r9fo+qMy+8vLyUFFR0efHzcjI6PNjErkzpwng5uZmPPvss7jrrrvg7+9/2f1WrFiBZcuW9WFl9pWXl4fBSUloamyUrYb6+nrZjk3kTpwigI1GI2bPng2LxYK33nqrw30XLVqEhQsX2r7W6/WIiYnp7RLtpqKiAk2Njbj7mVcRHpvQp8fO2LcT3330Bpqbm/v0uETuyuED2Gg04vbbb0d2dja2b9/e4dUvAGg0Gmg0mj6qrveExyYgemBynx6zNO9cnx6PyN05dAC3hu+ZM2eQnp6O4OBguUsiIrIbWQO4vr4eZ8+etX2dnZ2NI0eOICgoCDqdDr///e9x6NAhfP311zCbzSgpKQEABAUFQa1Wy1U2EZFdyBrABw4cwMSJE21ft7bdzp07F0uXLsXmzZsBAMOHD2/zuvT0dKSlpfVVmUREvULWAE5LS4MQ4rLPd/QcEZGz41wQREQyYQATEcmEAUxEJBMGMBGRTBjAREQyYQATEcnEoe+Eo95T3diCzJI6FNc2o8lohpdKCYEQeARHy10akdtgALsZg8mMn89U4GSRHpeOsg6F7v63kGFoxkCDCT4a/ngQ9Sb+hrmR6sYWbD5ShJomIwAgLtgbCaG+8NV4oN5gwuHT51At+aHM7I1P/5OHaSkRiA70lrlqItfFNmA3Ud3Qgi8PFqCmyQhfjQd+f1U0bh4ehZQoLeJDfJASpUUyClD84RPwkYxoMpqx6XARzpZxbmCi3sIAdgNNLWZsOlKIhhYzgn3UuHN0DKICvdrdt6X0HEZ4liMh1AdmIfDdiWLkVDT0ccVE7oEB7OIsQuDbE8XQN5ug9VLh1qui4K3uuOVJKQHTUiIxKNwXFgF8c7wY5XWGDl9DRF3HAHZxR/JqUFDdBJVSwoxhkVcM31YKScKUIRGIDfKGySLw9bEiNBvNvVwtkXthALuw6sYW7D5fCQCYMDAUIb5dWylEqZAwdWgEtF4q6JtNSM8s640yidwWA9hFCSGwM7McZotAXJA3knUdL+V0OZ4qJW4aGgFJArJK63GmtM7OlRK5Lwawi8quaEBuVSOUkoTrEkMhSVK33yvC3xMj4wIBAOmZ5WhsMdmrTCK3xgB2QRYh8O9z1qaHEbEBCPTu+fJNY+KDEeKrRpPRjB2Z5T1+PyJiALukrJI6VDW0QOOhsF259pRSYe2UkyTgTFk9Cqob7fK+RO6MAexihBDYl1MFALg6LhAaldJu7x3qp0FKlBYAsCurAhYuGUXUIwxgF5NT2YjqRiPUSgVSowPs/v7XxAdD46FAeb0Bp4r1dn9/InfCAHYxh/OqAQBDo/yh9rD/6fVSKzE6PggAsOdcJQwmjg0m6i4GsAsprzMgv7oJEtArV7+tUqMDEOClQmOLGYfzanrtOESujgHsQg7nW69+B4T5wt9L1WvHUSokjE0IvnDMGhh4hxxRtzCAXUSDwYSsEuvMZSNiA3r9eAPDfBHso0aLyYJD+TW9fjwiV8QAdhGnS+pgFgLh/hpEatuf6cyeJEnCmAttwUfyajhPBFE3MIBdgBACGRdGJCTrtH123AFhvgjxVaPFbGFbMFE3MIBdQHmdAZUNLVAqJAwK8+2z41qvgq1twUfyazgigqiLGMAuoHU8bkKIj11vvOiMhFAfBHlbr4JPFHJcMFFXMICdnNkikHlhhrKkyO7NeNYTkiThqrgAANZRGGYL744j6iwGsJPLqWxAs9ECb7USsUHyLKCZGOEHH7USDQYzMks4XSVRZzGAnVxr51tShD8Uiu5POdkTHgoFhl8Y+nYwrxqCc0QQdQoD2Im1mCzIqbTOSpYY4SdrLSlRWqiVClQ1tNhqIqKOMYCdWG5lA8wWAa2XCiG+PZ/ztyc0HkoMjbK2QR/ljRlEncIAdmJny6x3vg0I8+3Rihf2MuzC/BO5VY2obmiRtxgiJ8AAdlImswXZlQ0AgAGhfTf2tyNaLxXiQ3wAAEcLauQthsgJMICdVF5VI4xmAV+NB8L9u7bacW9KjbbeiZdRXIcWk0XmaogcGwPYSZ0tv9D8EOoYzQ+tYoO8EeitQovZYhuhQUTtYwA7IbNF4Hy5tfkhIcxH5mrakiTJNhfx0YIaDkkj6gAD2AkV1jTBYLLAS6WELqD3Zz7rqqRIf6iVClQ3GpFXxSFpRJfDAHZCORc63+JDfKBwoOaHVmoPBZIireOSjxbUylwNkeNiADuhnAprAPcLlufW485IjQkAAGRXNEDfZJS3GCIHxQB2MrVNRlQ3GiFJkG3uh84I9FYjJsjaPHKyiJ1xRO1hADuZ1uaHSK1nn0892VUpFyaHP1lUCwtnSSO6BAPYyeRemGehX7BjjX5oT/9QX3iplGhoMdtuGiGiXzCAnYjJbEF+lfMEsFIhYYjOOj/EiUJ2xhH9mqwBvGvXLsycORM6nQ6SJGHTpk1tnhdCYOnSpdDpdPDy8kJaWhpOnjwpT7EOoLCmCSaLgI9GKfvkO52VfCGAcyoboW9mZxzRxWQN4IaGBqSmpmLNmjXtPr9y5UqsWrUKa9aswf79+xEREYHJkyejrs49J/3Ouaj5wZHufutIoLca0YHsjCNqj4ecB586dSqmTp3a7nNCCKxevRrPP/88br31VgDARx99hPDwcHz22Wd46KGH+rJUh5B7oR01zoGHn7UnJUqLguomnCrSY0y/INkmjidyNA7bBpydnY2SkhJMmTLFtk2j0eC6667D7t27ZaxMHvXNJlQ3Wv+Ejwl0rgDuH+oDL5US9QaTbRQHETlwAJeUlAAAwsPD22wPDw+3Pdceg8EAvV7f5uEK8qutzQ9hfhp4Ovjws1/zUPxyZ9wJNkMQ2ThsALf6dVunEKLD9s8VK1ZAq9XaHjExMb1dYp9oDeAYB775oiNDo6xjgnMqGlDHzjgiAA4cwBEREQBwydVuWVnZJVfFF1u0aBFqa2ttj/z8/F6tsy8IIZBf1QQAiAl0vMl3OiPQW43oAC8IsDOOqJXDBnB8fDwiIiKwdetW27aWlhbs3LkT48aNu+zrNBoN/P392zycXU2TEfUGE5SS5JCzn3VW61XwySI9LJymkkjeURD19fU4e/as7evs7GwcOXIEQUFBiI2NxYIFC7B8+XIMHDgQAwcOxPLly+Ht7Y277rpLxqr7XuvNF5FaT6iUDvtv5hUlhPnAM0uBeoMJuZWNtuWLiNyVrAF84MABTJw40fb1woULAQBz587Fhx9+iP/6r/9CU1MT5s2bh+rqaowZMwY//PAD/PzkXYK9r9maH5y0/beVh0KBwRH+OJJfg5NFtQxgcnuyBnBaWlqHKyZIkoSlS5di6dKlfVeUgxFCoMDWAee8zQ+tknXWAM6uaECDwQQfjaw/gkSyct6/Z91Eeb0BzSYL1EoFwv085S6nx0J8NYjw94RFABkl7Iwj98YAdnCF1dbmB12Ap8vcQZYcZe0YPVmo55px5NYYwA6usMYawFFOOvysPYPC/KBSSqhpMqKoplnucohkwwB2YEKIXwLYiYef/ZraQ4FB4daO1JNFnKaS3BcD2IFVNbSg2WiBh0JCmAu0/15s6IXVMs6U1cNgNMtcDZE8GMAOrPXqN1LrCaWLtP+2CvfXINhHDZNFILPUPacXJWIAOzBXbH5oJUmSbbJ23ppM7ooB7KCEcM0OuIsNjvSHUpJQVmdAdYtrXeETdQYD2EE1mIAGgxkKCYjwd63231ZeKiUSwqx3w+XU80eR3A9/6h1UhcF6asL9PeHhxPM/XEnyhc64/AYFJA+NzNUQ9S3X/c12chUG65/k0S7a/NAqJtAL/p4eMAoJ3omXn+WOyBUxgB1U6xWwTuvaAWztjLNeBfsOm3KFvYlcCwPYASm8tWgwWa+AI7Wu2f57MetyRQKesSkoqjPJXQ5Rn2EAOyCNLhEAEOyjhsbJ1n/rDj9PFSI8rXNC/Hi+UeZqiPoOA9gBaaKSAAARbnD126qfr/VuuPScJhjNFpmrIeobDGAHpNENBuAezQ+tIr0EzPXVqDVYsC2jTO5yiPoEA9jBmCwC6shBAIBIF++Au5hCAupP/AgA+OcB519IlagzGMAOJrvGCIVKA7VCINBbJXc5far+mHUB1h2ZZSiubZK5GqLexwB2MFkVRgBAkFpAktzr9lxTdRGSQ9WwCOB/DxTIXQ5Rr2MAO5jTlS0AgGCNe3ZE3dDf2uzyxYF8WCxcLYNcGwPYwWReCOAgjXuGzzVRXvDz9EBBdRN2n6uUuxyiXsUAdiDFtU2oaLRAWMwIUrtnAGs8JNwyIgoAsH5/nszVEPUuBrADOZRbAwBoKcuGhxufmdtHxgAAfjhZiqqGFpmrIeo9bvxr7ngO5lYDAAyFp2WuRF5Do7QYGuWPFrMFGw8Xyl0OUa9hADuQQ3nWAG4pcu8ABoA7RsUCAL7Yn8el68llMYAdRLPRbFsh2FCYIXM18puVqoOnSoGs0noczq+RuxyiXsEAdhAnCmthNAsEeCpgqi2VuxzZab1UmJYSCQD4Yh/vjCPXxAB2EK3tv4nB7nX3W0dmX2iG+OpYEeoNnKaSXA8D2EEcufBn9qBgtbyFOJBR/QLRP8QHjS1mfH20SO5yiOyOAewgjrYGcBCvgFtJkoQ7RlmHpH3BCXrIBTGAHUCZvhlFtc1QSED/QAbwxW69KhoeCgmH82qQWVIndzlEdsUAdgBHC6yjHwaG+cFLxVNysVA/DW5ICgcAfLGfV8HkWvjb7gBamx9SY7TyFuKg7hhtbYbYcLgABpNZ5mqI7IcB7ACOFtQAAFJjAmStw1FNGBiKSK0nahqN+OEkh+iR62AAy8xiEbYREMMZwO1SKiTcdmF+CDZDkCthAMssu7IBdc0meKoUGBTuJ3c5Duu2q6MhScDPZyuQX8WVk8k1MIBl1tr+O1SnhUrJ03E5MUHeGD8gBADXjCPXwd94mf3SARcgax3OoHVM8L8OFMDEpevJBTCAZXbkwhA0BvCVTR4SjkBvFUr0zdh1plzucoh6jAEsI4PJjIwiPQBgeHSAvMU4AY2HErdeFQ0AWM8JesgFMIBldLq4Di1mCwK9VYgJ8pK7HKfQ2gyx7XQZyuqaZa6GqGcYwDK6ePyvuy1B312Dwv1wVWwAzBaBDYe4WgY5NwawjI7k1QDg+N+umm1bLSOfq2WQU2MAy+gI74DrlunDIuGjViK7ogH7sqvkLoeo2xjAMqltMuJ8eQMAIJUdcF3io/HArOE6ALwzjpwbA1gmxy8MP4sJ8kKQDydh76rWRTu/OV6M2iajzNUQdY9DB7DJZMKf//xnxMfHw8vLC/3798cLL7wAi8X5B+EfK6wBAAzj1W+3pEZrMTjCDwaTBZuPsDOOnJNDB/Arr7yCdevWYc2aNcjIyMDKlSvx6quv4s0335S7tB47UWi9Ah4WxSkou+Pi1TLWsxmCnJRDB/CePXvw29/+FtOnT0e/fv3w+9//HlOmTMGBAwfkLq3Hjl1ogkiJZgB31y0joqD2UOBkkd72DxqRM/GQu4COjB8/HuvWrUNWVhYGDRqEo0eP4ueff8bq1asv+xqDwQCDwWD7Wq/X90GlXVPd0IKC6iYAwFBeAbeRkZHRpf1HR6rxc34z3vz2EB66uvvfy5CQEMTGxnb79UTd4dAB/Mwzz6C2thaDBw+GUqmE2WzGSy+9hDvvvPOyr1mxYgWWLVvWh1V23fELV2vxIT7w9+QacACgr7LO7TBnzpwuvc4zLhXhs1/Cd6fK8fd5UyFMhiu/qB1e3t44nZHBEKY+5dAB/MUXX+CTTz7BZ599huTkZBw5cgQLFiyATqfD3Llz233NokWLsHDhQtvXer0eMTExfVVyp7QGcAqvfm2a6q1/qUx/6HkkDru6068TAvi+SKBR44NbX/oCcb5d76AtzTuHT195GhUVFQxg6lMOHcBPP/00nn32WcyePRsAkJKSgtzcXKxYseKyAazRaKDRaPqyzC47duEGjGFs/71EsC4O0QOTu/SaVFUV9pyvRJHFF9cOdKx/bIk64tCdcI2NjVAo2paoVCqdfhjaiULr1R7bf+1jSKQ/JABFNc2obmiRuxyiTnPoAJ45cyZeeuklfPPNN8jJycHGjRuxatUq3HLLLXKX1m0V9QYU1jRBkoBknb/c5bgEX08P9AvxAQCcLHK8Tleiy3HoJog333wTixcvxrx581BWVgadToeHHnoIf/nLX+Qurdta23/7h/jAjx1wdjNU54/sigacKtbjmoQgeCgc+tqCCICDB7Cfnx9Wr17d4bAzZ3OigB1wvaFfsA98NR6oN5hwrqwBiRFc4JQcHy8T+tix1hEQvAXZrhQKCUMvNOkc500Z5CQYwH2sdRIejoCwv2SdFpIEFNY0obK+e+OBifoSA7gPldU1o0TfDIVk7bkn+/L19ED/C51xvAomZ8AA7kOt8xUkhPrCR+PQze9Oq7VtPaOkDkYuXU8OjgHchzgBT++LDfKG1kuFFpMFWaV1cpdD1CEGcB+ytf9yBESvkSQJQ6PYGUfOgQHch2xzQPAKuFcNifSHUpJQqjegVM+l68lxMYD7SKm+GWV1hgsdcAzg3uSt9sCAMF8A4DzB5NC6FcD9+/dHZWXlJdtramrQv3//HhflilrbfweF+8FLrZS5GtfX2hmXWVoHg8ksczVE7etWAOfk5MBsvvSH2mAwoLCQ63O1h1NQ9i1dgCeCfNQwmgVOl7AzjhxTl8ZCbd682fb/W7ZsgVb7S5iYzWZs27YN/fr1s1txruT4hSko2f7bNyRJQkqUFjuzynG8oBbDorSQJEnusoja6FIA33zzzQCsP9y/no9XpVKhX79+eO211+xWnKsQQvAKWAZJEX7499kKVDa0oLi2GboAL7lLImqjSwHcOg9vfHw89u/fj5CQkF4pytWU6JtRUd8CD4WEJN4B12c0KiUGhfvhVLEexwtrGcDkcLrVBpydnc3w7YLWDriB4X7wVLEDri+1NvmcKatHUws748ixdPt+2G3btmHbtm0oKyu7ZIWK999/v8eFuRLegCGfcD8Nwvw0KKszIKNYj6viAuUuicimW1fAy5Ytw5QpU7Bt2zZUVFSgurq6zYPa4g0Y8mntjAOs50EIIXNFRL/o1hXwunXr8OGHH+Kee+6xdz0uhx1w8hsU7oefzlSgpsmI/OomxAZ5y10SEYBuXgG3tLRg3Lhx9q7FJRXWNKGqoQUqpYTBkVylQQ5qD4Xte8/5IciRdCuAH3jgAXz22Wf2rsUltd4KmxjhB40HO+Dk0vrXx/nyejQYTDJXQ2TVrSaI5uZmvPvuu/jxxx8xbNgwqFRtF5dctWqVXYpzBce4BpxDCPHVIFLrieLaZpworMWY/sFyl0TUvQA+duwYhg8fDgA4ceJEm+d4t1Fbv7T/BshbCGFYtBbFtc04XlSLkf2CoFTwZ5Xk1a0ATk9Pt3cdLokdcI5lQJgvdmVVoMFgxvnyegwMZ5s8yYvTUfaiguom1DQaoVJKGBThK3c5bs9DobD9Q3i0gJ1xJL9uXQFPnDixw6aG7du3d7sgV9J69Ts4wp8dcA5iaJQ/9udWobCmCRX1BoT4auQuidxYtwK4tf23ldFoxJEjR3DixIlLJulxZ60BPJTNDw7Dz1OFhBBfnC2vx7GCWlw/OEzuksiNdSuAX3/99Xa3L126FPX19T0qyJWcYPuvQxoWrcXZ8nqcLtHj2gEcDUHysWsb8Jw5czgPxAVCCNsQtGG8BdmhRAd62SZrzyjmZO0kH7sG8J49e+Dp6WnPt3RaBdVNqG0yQq1UYBB72x2KJEm2fxSPFtSA00OQXLrVBHHrrbe2+VoIgeLiYhw4cACLFy+2S2HO7vhFd8CpPTjYxNEkRfhj99lK1DQaUebD8cAkj24F8MVLEQGAQqFAYmIiXnjhBUyZMsUuhTk72x1wbH5wSGoPBZIi/XC0oBbn6jlCheTRrQD+4IMP7F2Hy2EHnOMbFh2AowW1KG6SoPQPlbscckPdnpAdAA4ePIiMjAxIkoQhQ4ZgxIgR9qrLqfEOOOcQ5KNGTJAX8qua4Dd8mtzlkBvqVgCXlZVh9uzZ2LFjBwICAiCEQG1tLSZOnIj169cjNNS9rybyq9gB5yxSowOQX9UE39QpaDGzN476Vrd6hx577DHo9XqcPHkSVVVVqK6uxokTJ6DX6/H444/bu0anY7sDLpIdcI4uPtgH3koBpbcW/85vkrsccjPdSofvv/8eb7/9NpKSkmzbhgwZgrVr1+K7776zW3HO6lhhDQDeAecMFAoJ8b7WxTq/O9soczXkbroVwBaL5ZI5gAFApVJdskCnO2rtgOMinM4h3tcCYTLibJURR/Jr5C6H3Ei3Avj666/HE088gaKiItu2wsJCPPnkk5g0aZLdinNGQgjbKsi8AnYOGiXQcPonAMDHe3LkLYbcSrcCeM2aNairq0O/fv2QkJCAAQMGID4+HnV1dXjzzTftXaNTyatqhL7ZxA44J1N36GsAwNdHi1FeZ5C5GnIX3RoFERMTg0OHDmHr1q04ffo0hBAYMmQIbrjhBnvX53RaO+CS2AHnVFqKszAwSIUzVUZ8+p9cLLhhkNwlkRvoUkJs374dQ4YMgV6vBwBMnjwZjz32GB5//HGMGjUKycnJ+Omnn3qlUGfBKSid18xBPgCAT/bmwmAyy1wNuYMuBfDq1avx4IMPwt/f/5LntFotHnroIbdfkPM4F+F0WtdEeyJS64mK+hZsPlJ05RcQ9VCXAvjo0aO46aabLvv8lClTcPDgwR4X5aza3AHHOSCcjodCwr1j+wEA3v93DgSnSaNe1qUALi0tbXf4WSsPDw+Ul5f3uChnlVvZiLpmE9Qe7IBzVneOjoGXSomMYj32nq+SuxxycV0K4KioKBw/fvyyzx87dgyRkZE9LspZ2TrgIvygUrIDzhkFeKvxu6ujAADv/ZwtczXk6rqUEtOmTcNf/vIXNDc3X/JcU1MTlixZghkzZtitOGdzgs0PLuH/XRsPANh2uhQ5FQ0yV0OurEsB/Oc//xlVVVUYNGgQVq5cif/7v//D5s2b8corryAxMRFVVVV4/vnn7VpgYWEh5syZg+DgYHh7e2P48OEO2858jB1wLiEh1BcTE0MhBPDh7hy5yyEX1qVxwOHh4di9ezceeeQRLFq0yNZJIUkSbrzxRrz11lsIDw+3W3HV1dW49tprMXHiRHz33XcICwvDuXPnEBAQYLdj2IsQAieKOATNVfxhfDzSM8vxrwP5WDhlEPw9L9/3QdRdXb4RIy4uDt9++y2qq6tx9uxZCCEwcOBABAYG2r24V155BTExMW0mgO/Xr5/dj2MP7IBzLeMHhGBQuC+ySuvxz/35eOA3/eUuiVxQt3uKAgMDMWrUKIwePbpXwhcANm/ejJEjR+K2225DWFgYRowYgb///e+9cqyeOma7A86fHXAuQJIk/OFCW/AH/86BycxJpsj+erQiRm87f/483n77bSxcuBDPPfcc9u3bh8cffxwajQb33ntvu68xGAwwGH65l7/1rr2uyMvLQ0VFRZdes+2o9TgR6hYcOnSoy8dslZGR0e3Xkn3dPCIKr27JRGFNE745XozfDo+SuyRyMQ4dwBaLBSNHjsTy5csBACNGjMDJkyfx9ttvXzaAV6xYgWXLlnX7mHl5eRiclISmxq7NDRs++yV4xqXi0zdX4N3jW7t9/Fb19fU9fg/qGU+VEveN64fXtmbhnZ3nMStVB0niCspkPw4dwJGRkRgyZEibbUlJSfjyyy8v+5pFixZh4cKFtq/1ej1iYmI6fcyKigo0NTbi7mdeRXhsQqdeIwSwuUAFkwDu+MM8BKgf6fTxfi1j305899Eb7Q71o753z9g4vLXjHE4V6/HTmQpMGOTey22RfTl0AF977bXIzMxssy0rKwtxcXGXfY1Go4FGo+nxscNjExA9MLlT+1Y3tsCUnwulQkJSUhKUiu5fJZXmnev2a8n+ArzVmD06Bh/8Owfv7DrHACa7cujeoieffBJ79+7F8uXLcfbsWXz22Wd49913MX/+fLlLa6NMb21zDvFV9yh8yTE98Jv+UCok/PtspW2yJSJ7cOgAHjVqFDZu3IjPP/8cQ4cOxV//+lesXr0ad999t9yltVFWZ20uCPfzlLkS6g1RAV6YlaoDAKzbxb9QyH4cugkCAGbMmOHwtze3XgGH+fe86YMc0x8n9MfGw4X47ngxcisbEBfsI3dJ5AIc+grYGQghUHZhCZswXgG7rKRIf6QlhsIigP/5iZP0kH0wgHuoptGIFrMFSoWEIB+13OVQL3pognVUzD8P5KOinuvGUc8xgHuoRG9t/w3z07ADzsVd0z8IqdFaGEwWfMxJesgOGMA9VHohgMP92fzg6iRJwsPXWa+CP9qTi7pmo8wVkbNjAPdQ6YUOuHB2wLmFKckR6B/qg9omI/6xN1fucsjJMYB7wGwRKK9vDWBeAbsDpULCoxMHALB2xjW2mGSuiJwZA7gHKusNMFsENB4KBHhxvlh3MStVh7hgb1Q1tODTvXlyl0NOjAHcA780P3hykhY34qFUYP6Fq+B3dp1HU4tZ5orIWTGAe6DE1gHH9l93c8uIKEQHeqGi3oDP9/EqmLqHAdwDpXUcAeGuVEoF5qVZr4LX7TyHZiOvgqnrGMDdZDRbUFXfAoAB7K5+d3UUdFpPlNUZ8M8D+XKXQ06IAdxNZXoDBAAfjRK+GoefUoN6gcZDiUfSrOOC16af5VUwdRkDuJtamx8iePXr1m4fFYOoAC+U6g34xx6OC6auYQB3U2nthVuQGcBuTeOhxBOTBgIA3tpxlnfHUZcwgLup9MIMaOF+HAHh7m69Kgr9Q3xQ3WjE+z/nyF0OOREGcDc0tZhR22S90mEHHHkoFXhy8iAAwP/8dB7VDS0yV0TOggHcDa3tvwFeKniqlDJXQ45gekokkiL9UWcwcdUM6jQGcDfYZkDT8uqXrBQKCX+aYr0K/mh3Dsr0XNWarowB3A22W5DZ/ksXuX5wGEbEBqDZaMHqbWfkLoecAAO4i4QQnAOY2iVJEhZNTQIArN+Xh6zSOpkrIkfHAO6iOoMJjS1mSJJ1FQyii42OD8KNyeGwCGDFtxlyl0MOjgHcRa1XvyE+Gngo+e2jSz1z02B4KCSkZ5bj5zMVcpdDDowJ0kVcAYOupH+oL+ZcEwcAePGbUzBbhMwVkaNiAHcR23+pM56YNBD+nh44XVKHLw8VyF0OOSgGcBcIIVCm5xJEdGWBPmo8dr31FuW/bcnk0kXULgZwF1Q1tKDFbIGHQkKwj1rucsjB3TsuDjFBXiirM2DN9rNyl0MOiAHcBSUXNT8oFFyCiDqm8VBi8fQhAIC//3Qe58vrZa6IHA0DuAuKL8yAFsE74KiTJg8JR1piKIxmgaVfnYIQ7JCjXzCAu6DkQgBHMoCpkyRJwpKZyVArFdiVVY4fTpXKXRI5EAZwJxlMZlRemOWKk7BTV8SH+ODBCfEAgBe+OsVVlMmGAdxJreN//T094MMliKiL5k8cAJ3WE4U1TXh7BzvkyIoB3EklbP+lHvBWe2DxDGuH3Lqd53G2jB1yxADutOLaJgBsfqDuu2loBNISQ9FituC5Dcdh4R1ybo8B3AlCCNsQtEitl8zVkLOSJAkv3jwU3mol9uVUYf1+LmXv7hjAnVDTZESz0QKlQkIoZ0CjHogO9MZTUxIBACu+y+DE7W6OAdwJre2/YX4aKHkDBvXQfeP6ITVai7pmE5ZsPil3OSQjBnAn2Drg2P5LdqBUSFhx6zAoFRK+O1GCLSdL5C6JZMIA7oRiPW/AIPsaovPHHyf0BwD8edMJrqTsphjAV2A0W1BRbx0DzCFoZE9PTBqIhFAflNcZ2BThphjAV1CmN0AIwEejhC9vwCA78lQp8drtw6GQgM1Hi/Dd8WK5S6I+xgC+govH/0oSO+DIvobHBOCRtAQAwPObTtj+2iL3wAC+Ao7/pd72+KSBGBzhh6qGFvx54wnOmOZGGMAdEEKgqIa3IFPv0ngo8drtqfBQSPj+ZAk2Hy2SuyTqIwzgDtQ0GdFkNEMpSQjnDRjUi5J1Wjw+ybqE0V/+76Rt7UFybQzgDhRfuPoN8+cS9NT7HklLQEqUFrVNRjz75TE2RbgBpkoHii50wOkC2P5LvU+lVOC121Oh9lAgPbMcn+/jXBGuzqkCeMWKFZAkCQsWLOiT4xXVXAhgtv9SHxkU7of/utE6V8Rfvz6FnIoGmSui3uQ0Abx//368++67GDZsWJ8cz2AGqhuNAIBIXgFTH/rDtfEY2z8YTUYzFv7zCExmi9wlUS9xigCur6/H3Xffjb///e8IDAzsk2NWGqxjfoN81PBSKfvkmEQAoFBI+NvtqfDTeOBQXg3W7Twnd0nUS5zi1q758+dj+vTpuOGGG/Diiy92uK/BYIDB8Mtgdr1e361jVhis/zax+cF9ZGRkyHJcg8EAjebSUTb/L9UH/72vFq9vzUK4pQoJQSq7HzskJASxsbF2f1/qHIcP4PXr1+PgwYM4cOBAp/ZfsWIFli1b1uPjtl4BswPO9emrygEAc+bMkakCCUD7Ix5CfvssfAaPxxPrD6HkowUQJvtO2uPl7Y3TGRkMYZk4dADn5+fjiSeewA8//ABPz85diS5atAgLFy60fa3X6xETE9Ol40oealS3MIDdRVO99a+k6Q89j8RhV/fpsTP27cR3H71x2WMbzMCPxQIIicX1S/+F1ED7rahcmncOn77yNCoqKhjAMnHoAD548CDKyspw9dW//GCazWbs2rULa9asgcFggFLZtn1Wo9G0++dcV6gjBkJAgo9aCX9Ph/4WkR0F6+IQPTC5T49ZmnfuisdWhjTg/44W4WydEikDYhEb5N2XJVIvcuhOuEmTJuH48eM4cuSI7TFy5EjcfffdOHLkyCXhay+aaOvqtboAL07AQ7LrF+KDlCgtAGDrqVIYjPa7CiZ5OfTlnZ+fH4YOHdpmm4+PD4KDgy/Zbk8XBzCRI/jNwBDkVTWitsmI9Kxy3JQcIXdJZAcOfQUsB4sQ8IxKAsAVMMhxqJQK3JgcDglAZkkdskrr5C6J7MChr4Dbs2PHjl59//xaExSevlBKAqG+nICHHEek1guj+gVhX04Vtp8ug07rBV/2UTg1XgH/SkaFdZhPsEZAwRWQycGMjg9CmJ8GBpMFP2aUcsIeJ8cA/pXTtgDm7Z/keJQKCTcmR0CpkJBb1YhjhbVyl0Q9wAD+lTnD/FG++VVEezOAyTEF+agxfkAIAODnMxVcUdmJMYB/JcRbicaMnfC3/12fRHaTGq1FTJAXTBaBLadKYLawKcIZMYCJnJAkSZicFA6NhwKlegP251TJXRJ1AwOYyEn5eaowMTEMALAvp8q2gCw5DwYwkRNLjPDDoDBfCAFsOVkCI+cOdioMYCInN3FwGHw0StQ0GvHvsxVyl0NdwAAmcnKeKiUmJ4UDAI4W1CK3kssYOQsGMJELiAv2QWr0hQl7MkrRzAl7nAIDmMhFXDsgBIHeKjQYzEg/XSZ3OdQJDGAiF6FSKjAlOQKSBGSV1SOzhBP2ODoGMJELifD3xOh+QQCA9Mwy1DebZK6IOsIAJnIxo/oFIdzfOmHPjiw2RTgyBjCRi1EqJEwaHA6FBJwrb8D58nq5S6LLYAATuaBQPw1GxAYCANIzy9Fi4g0ajogBTOSixsQHQeulQr3BhD3nK+Uuh9rBACZyUSqlAhMTQwEAR/JrOFeEA2IAE7mwuGAfJEb4AQC2Z5TBwmkrHQoDmMjFTRgYAk8PBcrrDTicXyN3OXQRBjCRi/NWe2D8QOsKGnvPV0LfZJS5ImrFACZyA0Mi/REVYF1BY9eZcrnLoQsYwERuQJIkpCWGQrowNpgzpjkGBjCRmwjx1WB4dAAAYEdWOczsj5MdA5jIjYzpHwRvtXXy9rN1/PWXG88AkRvReChtS9pn1Cqh9AuWuSL3xgAmcjODI/yg03rCLCQETrxf7nLcGgOYyM1YO+TCAAj4JE3A8TKD3CW5LQYwkRsK9dMgwdc6Qc//HNJzNWWZMICJ3NSQADPMDTXI15vw8Z5cuctxSwxgIjelVgA1uz4GAKz+MQuV9WyK6GsMYCI3Vn/8R8QHeKCu2YS//ZApdzluhwFM5M6EBQ+MsC5nv35/Pk4U1spckHthABO5uaRQNWal6iAEsHTzSQjBW+T6CgOYiLBo2mB4qZQ4kFuNzUeL5C7HbTCAiQiRWi/MS0sAALz83Wk0tnA5+77AACYiAMCDE/ojOtALxbXNWLfjnNzluAUGMBEBADxVSvx5ehIA4J1d55Ff1ShzRa6PAUxENjcmR2BcQjAMJguWf5shdzkujwFMRDaSJOEvM4dAIQHfnSjB7nMVcpfk0hjARNTG4Ah/zLkmDgCwbPMpmDhPRK9hABPRJRZOHoQAbxUyS+vw2b48uctxWQxgIrpEgLcaT00eBAB47YcsVDe0yFyRa2IAE1G77hwdi8ERfqhtMuL1H7PkLsclMYCJqF0eSgWWzEwGAHyyNxenS/QyV+R6GMBEdFljE4IxLSUCFmHtkOM8Efbl0AG8YsUKjBo1Cn5+fggLC8PNN9+MzExOmUfUl56blgSNhwJ7zlfi+xMlcpfjUhw6gHfu3In58+dj79692Lp1K0wmE6ZMmYKGhga5SyNyG9GB3njoOus8ES9+k4Fmo1nmilyHh9wFdOT7779v8/UHH3yAsLAwHDx4EBMmTJCpKiL38/B1/fGvA/korGnCu7vO4/FJA+UuySU4dAD/Wm2tdbLooKCgy+5jMBhgMPyytIpez44Doo5kZHTuluM7kzyxam8z1mzPwmBNDUK8lT06bkhICGJjY3v0Hs7OaQJYCIGFCxdi/PjxGDp06GX3W7FiBZYtW9aHlRE5J31VOQBgzpw5nX5N+F0vAzFDcffKf6Liq7/16Phe3t44nZHh1iHsNAH86KOP4tixY/j555873G/RokVYuHCh7Wu9Xo+YmJjeLo/I6TTVW/86nP7Q80gcdnWnXlPTImFbiYDPkDRMS7sWIZ7dGxVRmncOn77yNCoqKhjAju6xxx7D5s2bsWvXLkRHR3e4r0ajgUaj6aPKiJxfsC4O0QOTO7VvNIBSRSlOFOlxqskXs4fGQCFJvVugC3PoURBCCDz66KPYsGEDtm/fjvj4eLlLInJ7YxOCofZQoLzOgFNF7GPpCYcO4Pnz5+OTTz7BZ599Bj8/P5SUlKCkpARNTU1yl0bktrzVHrgm3toRvvtcJQwcltZtDh3Ab7/9Nmpra5GWlobIyEjb44svvpC7NCK3Niw6AEHeajQZzdibXSV3OU7LoduAedsjkWNSKiRMGBSCTUeKcKygBilRWgT5qOUuy+k49BUwETmuuGAf9A/xgUUA20+X8YKpGxjARNRt1w0KhUopobCmCScK2SHXVQxgIuo2fy8VxiWEAAB+PluBumajzBU5FwYwEfXIsGgtIrWeaDFbkJ5ZzqaILmAAE1GPKCQJkwaHQSlJyK5oQFZpvdwlOQ0GMBH1WLCvBqPiAwEAO7PK0dhikrki58AAJiK7GBkXhGBf69jgbRkcFdEZDGAisgulQsKNQyKglCScr2jgqIhOYAATkd2E+mkwbkAwAGDXmXJUcTn7DjGAiciuRsQEIDbIGyaLwPcnSmCyWOQuyWExgInIriRJwuQh4fBUKVBeb8Cec5Vyl+SwGMBEZHe+Gg/ckBQOADiUV4Pz5Rya1h4GMBH1ioRQX6RGawEAW06WorqR7cG/xgAmol7zm4Gh0F24S+7rY8VoMbE9+GIMYCLqNUqFhGkpkfBRK1HV0ILvT5bAwvHBNgxgIupVPhoPTB8WCaXCeqvyrqxyMIOtGMBE1OsitV64cYi1U+5oQS3O1DF6AAYwEfWRgeF+GD/AOnXl8RoP+A6bLHNF8mMAE1GfuSo2ACNiAgAAQTc9hvScRnkLkhkDmIj6jCRJ+M3AEPT3NUOSFFi7vxb/e7BA7rJk49CLchKR65EkCcMDzTj681b4Db8Jf/rXURzNPI+bE30gSVKf1RESEoLY2Ng+O157GMBE1OfqqstRtWUtLC2N0I6+Ff84Voe173+K6u3/A4i+GSvs5e2N0xkZsoYwA5iI+lxTvR6AwLWDIiEFmHC8xgP+I2ch4doZGB1igqeyd49fmncOn77yNCoqKhjAROSegnVxGH51EmJK67A1oxTlBgV2VnjjpuQIRAV6yV1er2MnHBHJbmC4H2aPikWgtwr1BhP+91ABdmaWw2h27VuXGcBE5BCCfNSYPSoWyTp/AMCRghp8sjcXZ0rrXHZ5IwYwETkMtYcCNySF4+bhOvhqPKBvNuHbEyX418ECFFQ3ulwQM4CJyOHEBfvg3rFxGBMfBA+FhOLaZnx5qBD/PFCAs2X1MFtcI4jZCUdEDkmlVOCa/sEYqtNiX04VThXrUaJvxjfHi+GtVmJIpD8GhfshxFfdp+OH7YkBTEQOzdfTA9cPDsOY+CAcya/BySI9GlvMOJBbjQO51fD39ED/UF8khPogwt8THkrn+cOeAUxETsFH44FrB4Tgmv7ByK5owOkSPXIrG6FvNuFIfg2O5NdAqZAQ4e+JqAAvRAV6IVLrCZUDBzIDmIicilIhYUCYLwaE+cJotiCvqhHnyuuRW9mIxhYzCmuaUFjTBOQAkgSE+GoQ4e+JCK0nIvw9Eeitkvsj2DCAichpqZQKJIT6IiHUF0II1DQaUVjThIKaJhRWN6HeYEJ5nQHldQYcL6wFAGg8FNB6eMA39SaZq2cAE5GLkCQJgT5qBPqoMTRKCyEE6gwmlNY2o0TfjJLaZpTVGWAwWVBmUkAdniB3yQxgInJNkiTB31MFf08VBob7AQDMFoHKBgMyzmTj+1M7APxB1hoZwETkNpQKCWF+nmjxs8BQcFLucngjBhGRXBjAREQyYQATEcmEAUxEJBMGMBGRTBjAREQyYQATEcmEAUxEJBMGMBGRTJwigN966y3Ex8fD09MTV199NX766Se5SyIi6jGHD+AvvvgCCxYswPPPP4/Dhw/jN7/5DaZOnYq8vDy5SyMi6hGHD+BVq1bh/vvvxwMPPICkpCSsXr0aMTExePvtt+UujYioRxw6gFtaWnDw4EFMmTKlzfYpU6Zg9+7dMlVFRGQfDj0bWkVFBcxmM8LDw9tsDw8PR0lJSbuvMRgMMBgMtq9ra62TMOv1+k4ds76+HgBQcOYkDE2N3Sm7R0rzzgEASnKycM7Hm8fmsXnsXlBekA3A+vvemWxo3UcIO6/GLBxYYWGhACB2797dZvuLL74oEhMT233NkiVLBAA++OCDD7s/8vPz7ZpxDn0FHBISAqVSecnVbllZ2SVXxa0WLVqEhQsX2r62WCyoqqpCcHBwu0tX6/V6xMTEID8/H/7+/vb9ADLhZ3IOrvaZXO3zAL98pry8PEiSBJ1OZ9f3d+gAVqvVuPrqq7F161bccssttu1bt27Fb3/723Zfo9FooNFo2mwLCAi44rH8/f1d5oemFT+Tc3C1z+RqnwcAtFptr3wmhw5gAFi4cCHuuecejBw5EmPHjsW7776LvLw8PPzww3KXRkTUIw4fwHfccQcqKyvxwgsvoLi4GEOHDsW3336LuLg4uUsjIuoRhw9gAJg3bx7mzZvXK++t0WiwZMmSS5otnBk/k3Nwtc/kap8H6P3PJAlh73EVRETUGQ59IwYRkStjABMRyYQBTEQkE7cPYGed6nLFihUYNWoU/Pz8EBYWhptvvhmZmZlt9rnvvvsgSVKbxzXXXCNTxVe2dOnSS+qNiIiwPS+EwNKlS6HT6eDl5YW0tDScPHlSxoqvrF+/fpd8JkmSMH/+fADOcY527dqFmTNnQqfTQZIkbNq0qc3znTkvBoMBjz32GEJCQuDj44NZs2ahoKCgDz/FLzr6PEajEc888wxSUlLg4+MDnU6He++9F0VFRW3eIy0t7ZLzNnv27C7X4tYB7MxTXe7cuRPz58/H3r17sXXrVphMJkyZMgUNDQ1t9rvppptQXFxse3z77bcyVdw5ycnJbeo9fvy47bmVK1di1apVWLNmDfbv34+IiAhMnjwZdXV1Mlbcsf3797f5PFu3bgUA3HbbbbZ9HP0cNTQ0IDU1FWvWrGn3+c6clwULFmDjxo1Yv349fv75Z9TX12PGjBkwm8199TFsOvo8jY2NOHToEBYvXoxDhw5hw4YNyMrKwqxZsy7Z98EHH2xz3t55552uF2PXG5udzOjRo8XDDz/cZtvgwYPFs88+K1NF3VdWViYAiJ07d9q2zZ07V/z2t7+Vr6guWrJkiUhNTW33OYvFIiIiIsTLL79s29bc3Cy0Wq1Yt25dH1XYc0888YRISEgQFotFCOF85wiA2Lhxo+3rzpyXmpoaoVKpxPr16237FBYWCoVCIb7//vs+q709v/487dm3b58AIHJzc23brrvuOvHEE0/0+PhuewXsalNdts76FhQU1Gb7jh07EBYWhkGDBuHBBx9EWVmZHOV12pkzZ6DT6RAfH4/Zs2fj/PnzAIDs7GyUlJS0OV8ajQbXXXed05yvlpYWfPLJJ/jDH/7QZl4SZztHF+vMeTl48CCMRmObfXQ6HYYOHeoU5662thaSJF0ypcGnn36KkJAQJCcn409/+lO3/hJzihsxekN3prp0VEIILFy4EOPHj8fQoUNt26dOnYrbbrsNcXFxyM7OxuLFi3H99dfj4MGDDjlYfsyYMfj4448xaNAglJaW4sUXX8S4ceNw8uRJ2zlp73zl5ubKUW6Xbdq0CTU1Nbjvvvts25ztHP1aZ85LSUkJ1Go1AgMDL9nH0X/Xmpub8eyzz+Kuu+5qMxfE3Xffjfj4eERERODEiRNYtGgRjh49amti6iy3DeBWv54hTQjR7qxpjuzRRx/FsWPH8PPPP7fZfscdd9j+f+jQoRg5ciTi4uLwzTff4NZbb+3rMq9o6tSptv9PSUnB2LFjkZCQgI8++sjWMeXM5+u9997D1KlT28yo5Wzn6HK6c14c/dwZjUbMnj0bFosFb731VpvnHnzwQdv/Dx06FAMHDsTIkSNx6NAhXHXVVZ0+hts2QXRnqktH9Nhjj2Hz5s1IT09HdHR0h/tGRkYiLi4OZ86c6aPqesbHxwcpKSk4c+aMbTSEs56v3Nxc/Pjjj3jggQc63M/ZzlFnzktERARaWlpQXV192X0cjdFoxO23347s7Gxs3br1ijOhXXXVVVCpVF0+b24bwBdPdXmxrVu3Yty4cTJV1XlCCDz66KPYsGEDtm/fjvj4+Cu+prKyEvn5+YiMjOyDCnvOYDAgIyMDkZGRtj/3Lj5fLS0t2Llzp1Ocrw8++ABhYWGYPn16h/s52znqzHm5+uqroVKp2uxTXFyMEydOOOS5aw3fM2fO4Mcff0RwcPAVX3Py5EkYjcaun7ced+M5sfXr1wuVSiXee+89cerUKbFgwQLh4+MjcnJy5C7tih555BGh1WrFjh07RHFxse3R2NgohBCirq5OPPXUU2L37t0iOztbpKeni7Fjx4qoqCih1+tlrr59Tz31lNixY4c4f/682Lt3r5gxY4bw8/OznY+XX35ZaLVasWHDBnH8+HFx5513isjISIf9PK3MZrOIjY0VzzzzTJvtznKO6urqxOHDh8Xhw4cFALFq1Spx+PBh26iAzpyXhx9+WERHR4sff/xRHDp0SFx//fUiNTVVmEwmh/o8RqNRzJo1S0RHR4sjR460+d0yGAxCCCHOnj0rli1bJvbv3y+ys7PFN998IwYPHixGjBjR5c/j1gEshBBr164VcXFxQq1Wi6uuuqrNMC5HhsssmfLBBx8IIYRobGwUU6ZMEaGhoUKlUonY2Fgxd+5ckZeXJ2/hHbjjjjtEZGSkUKlUQqfTiVtvvVWcPHnS9rzFYhFLliwRERERQqPRiAkTJojjx4/LWHHnbNmyRQAQmZmZbbY7yzlKT09v92dt7ty5QojOnZempibx6KOPiqCgIOHl5SVmzJgh2+fs6PNkZ2df9ncrPT1dCCFEXl6emDBhgggKChJqtVokJCSIxx9/XFRWVna5Fs6GRkQkE7dtAyYikhsDmIhIJgxgIiKZMICJiGTCACYikgkDmIhIJgxgIiKZMICJiGTCACa3kpaWhgULFgCwLhe0evVqWesh98YAJre1f/9+/PGPf+zUvgxr6g1uPx8wua/Q0FC5SyA3xytgclkNDQ2499574evri8jISLz22mttnv/1Ve3SpUsRGxsLjUYDnU6Hxx9/HIC12SI3NxdPPvmkbQVcIntgAJPLevrpp5Geno6NGzfihx9+wI4dO3Dw4MF29/3f//1fvP7663jnnXdw5swZbNq0CSkpKQCADRs2IDo6Gi+88IJtBVwie2ATBLmk+vp6vPfee/j4448xefJkAMBHH3102VVD8vLyEBERgRtuuAEqlQqxsbEYPXo0AOtCp0qlEn5+frYVIIjsgVfA5JLOnTuHlpYWjB071rYtKCgIiYmJ7e5/2223oampCf3798eDDz6IjRs3wmQy9VW55KYYwOSSujrNdUxMDDIzM7F27Vp4eXlh3rx5mDBhAoxGYy9VSMQAJhc1YMAAqFQq7N2717aturoaWVlZl32Nl5cXZs2ahf/+7//Gjh07sGfPHhw/fhyAdQ1Bs9nc63WTe2EbMLkkX19f3H///Xj66acRHByM8PBwPP/881Ao2r/m+PDDD2E2mzFmzBh4e3vjH//4B7y8vBAXFwfAOmJi165dmD17NjQaDUJCQvry45CLYgCTy3r11VdRX1+PWbNmwc/PD0899RRqa2vb3TcgIAAvv/wyFi5cCLPZjJSUFHz11Ve2FXFfeOEFPPTQQ0hISIDBYOhyEwdRe7gmHBGRTNgGTEQkEwYwEZFMGMBERDJhABMRyYQBTEQkEwYwEZFMGMBERDJhABMRyYQBTEQkEwYwEZFMGMBERDJhABMRyeT/A4AlmjDyS42aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(data['dist'], kde=True)\n",
    "plt.title('Histogram of Distance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b3b24cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:38:03.491350Z",
     "start_time": "2023-08-28T12:38:03.472401Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of Speed: -0.11395477012828319\n",
      "Skewness of Distance: 0.7824835173114966\n",
      "Kurtosis of Speed: -0.5771474239437371\n",
      "Kurtosis of Distance: 0.24801865717051808\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness of Speed:\", speed_skew)\n",
    "print(\"Skewness of Distance:\", dist_skew)\n",
    "print(\"Kurtosis of Speed:\", speed_kurt)\n",
    "print(\"Kurtosis of Distance:\", dist_kurt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfd1600",
   "metadata": {},
   "source": [
    "# Q10) Draw inferences about the following boxplot & histogram-SP and Weight(WT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c356e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:54:17.105467Z",
     "start_time": "2023-08-28T12:54:17.093526Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a7595a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:54:26.156723Z",
     "start_time": "2023-08-28T12:54:26.141763Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('Q9_b.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aabf58c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:54:33.094267Z",
     "start_time": "2023-08-28T12:54:33.071325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          SP         WT\n",
       "0           1  104.185353  28.762059\n",
       "1           2  105.461264  30.466833\n",
       "2           3  105.461264  30.193597\n",
       "3           4  113.461264  30.632114\n",
       "4           5  104.461264  29.889149"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef94e70f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:54:46.930533Z",
     "start_time": "2023-08-28T12:54:46.757183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=data, y='SP')\n",
    "plt.title('Boxplot of Systolic Blood Pressure (SP)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39dcd4d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T12:54:56.075312Z",
     "start_time": "2023-08-28T12:54:55.819791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=data, x='WT', bins=15, kde=True)\n",
    "plt.title('Histogram of Weight (WT)')\n",
    "plt.xlabel('Weight')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e353993",
   "metadata": {},
   "source": [
    "# Q11)  Suppose we want to estimate the average weight of an adult male in  Mexico. We draw a random sample of 2,000 men from a population of 3,000,000 men and weigh them. We find that the average person in our sample weighs 200 pounds, and the standard deviation of the sample is 30 pounds. Calculate 94%,98%,96% confidence interval?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f539455",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:05:15.463966Z",
     "start_time": "2023-08-28T13:05:15.448010Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f81593",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:05:23.427893Z",
     "start_time": "2023-08-28T13:05:23.391991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198.738325292158, 201.261674707842)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.interval(0.94,200,30/(2000**0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6870843",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:05:30.315152Z",
     "start_time": "2023-08-28T13:05:30.300189Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198.43943840429978, 201.56056159570022)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.interval(0.98,200,30/(2000**0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "067fb18c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:05:39.912333Z",
     "start_time": "2023-08-28T13:05:39.895379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198.62230334813333, 201.37769665186667)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.interval(0.96,200,30/(2000**0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da32672",
   "metadata": {},
   "source": [
    "# Q12)  Below are the scores obtained by a student in tests \n",
    "34,36,36,38,38,39,39,40,40,41,41,41,41,42,42,45,49,56\n",
    "1)\tFind mean, median, variance, standard deviation.\n",
    "2)\tWhat can we say about the student marks? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "535e7044",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:09:13.464036Z",
     "start_time": "2023-08-28T13:09:13.456060Z"
    }
   },
   "outputs": [],
   "source": [
    "scores = [34, 36, 36, 38, 38, 39, 39, 40, 40, 41, 41, 41, 41, 42, 42, 45, 49, 56]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a79d15e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:09:25.546189Z",
     "start_time": "2023-08-28T13:09:25.528239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[34, 36, 36, 38, 38, 39, 39, 40, 40, 41, 41, 41, 41, 42, 42, 45, 49, 56]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c083ba7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:09:41.415937Z",
     "start_time": "2023-08-28T13:09:41.402955Z"
    }
   },
   "outputs": [],
   "source": [
    "mean = sum(scores) / len(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df40ff51",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:09:50.120952Z",
     "start_time": "2023-08-28T13:09:50.111978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 41.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean:\", mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329b1464",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:02.997487Z",
     "start_time": "2023-08-28T13:10:02.985520Z"
    }
   },
   "outputs": [],
   "source": [
    "sorted_scores = sorted(scores)\n",
    "n = len(sorted_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf2dd48d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:10.602254Z",
     "start_time": "2023-08-28T13:10:10.595273Z"
    }
   },
   "outputs": [],
   "source": [
    "median = sorted_scores[n // 2] if n % 2 != 0 else (sorted_scores[(n // 2) - 1] + sorted_scores[n // 2]) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8340aa3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:21.002845Z",
     "start_time": "2023-08-28T13:10:20.991876Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 40.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Median:\", median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bafdb24b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:32.451523Z",
     "start_time": "2023-08-28T13:10:32.430566Z"
    }
   },
   "outputs": [],
   "source": [
    "squared_deviations = [(x - mean)**2 for x in scores]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "930119b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:39.146825Z",
     "start_time": "2023-08-28T13:10:39.126874Z"
    }
   },
   "outputs": [],
   "source": [
    "variance = sum(squared_deviations) / (len(scores) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c3b9d02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:44.898397Z",
     "start_time": "2023-08-28T13:10:44.879452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance: 25.529411764705884\n"
     ]
    }
   ],
   "source": [
    "print(\"Variance:\", variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed2017b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:51.993223Z",
     "start_time": "2023-08-28T13:10:51.975272Z"
    }
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc0dc32",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:10:59.358521Z",
     "start_time": "2023-08-28T13:10:59.342565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation: 5.05266382858645\n"
     ]
    }
   ],
   "source": [
    "std_deviation = math.sqrt(variance)\n",
    "print(\"Standard Deviation:\", std_deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05c04d4",
   "metadata": {},
   "source": [
    "# Q 20) Calculate probability from the given dataset for the below cases\n",
    "\n",
    "Data _set: Cars.csv\n",
    "Calculate the probability of MPG  of Cars for the below cases.\n",
    "       MPG <- Cars$MPG\n",
    "a.\tP(MPG>38)\n",
    "b.\tP(MPG<40)\n",
    "c.    P (20<MPG<50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2101348b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:34:56.651877Z",
     "start_time": "2023-08-28T13:34:56.638913Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9d251a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:03.802192Z",
     "start_time": "2023-08-28T13:35:03.782118Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('Cars.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d16d74d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:13.259652Z",
     "start_time": "2023-08-28T13:35:13.250678Z"
    }
   },
   "outputs": [],
   "source": [
    "MPG = data['MPG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4b3d5d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:21.267543Z",
     "start_time": "2023-08-28T13:35:21.255586Z"
    }
   },
   "outputs": [],
   "source": [
    "prob_mpg_gt_38 = len(MPG[MPG > 38]) / len(MPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeead1e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:28.934663Z",
     "start_time": "2023-08-28T13:35:28.924689Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability (MPG > 38): 0.4074074074074074\n"
     ]
    }
   ],
   "source": [
    "print(\"Probability (MPG > 38):\", prob_mpg_gt_38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a8635bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:37.992842Z",
     "start_time": "2023-08-28T13:35:37.971905Z"
    }
   },
   "outputs": [],
   "source": [
    "prob_mpg_lt_40 = len(MPG[MPG < 40]) / len(MPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "326475ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:35:49.463046Z",
     "start_time": "2023-08-28T13:35:49.454069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability (MPG < 40): 0.7530864197530864\n"
     ]
    }
   ],
   "source": [
    "print(\"Probability (MPG < 40):\", prob_mpg_lt_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce142ce9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:36:00.311643Z",
     "start_time": "2023-08-28T13:36:00.300673Z"
    }
   },
   "outputs": [],
   "source": [
    "prob_mpg_between_20_50 = len(MPG[(MPG > 20) & (MPG < 50)]) / len(MPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c719f068",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:36:06.773512Z",
     "start_time": "2023-08-28T13:36:06.754562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability (20 < MPG < 50): 0.8518518518518519\n"
     ]
    }
   ],
   "source": [
    "print(\"Probability (20 < MPG < 50):\", prob_mpg_between_20_50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7701d1",
   "metadata": {},
   "source": [
    "# Q 21) Check whether the data follows normal distribution\n",
    "a)\tCheck whether the MPG of Cars follows Normal Distribution \n",
    "        Dataset: Cars.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc32ffc6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:38:41.153517Z",
     "start_time": "2023-08-28T13:38:40.929637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(MPG, bins=20, edgecolor='k')\n",
    "plt.title('Histogram of MPG')\n",
    "plt.xlabel('MPG')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67cd4461",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:38:49.945113Z",
     "start_time": "2023-08-28T13:38:49.736665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ii4tJKVEzKEmSJEkF3eTJ0L07zJ0bXJ94YrDMrkWLmJWUlAQ9e8KKFX+PJSQEXcgTE2NWVra59E6SJEkqqFasgCuugDZtgpBUqRI880ywPynGIaljx/QhCYJjmzp2DF7P7wxKkiRJUkGzfTsMHgzHHBMcHlusGNx2GyxeDLfcEtP1beFwMJMUiWR+LXWsV6/gvvzMoCRJkiQVJB98AA0bwn33wZYtcOqpMGsWPPlkMKMUY1OnZp5J2l0kAsuXB/flZwYlSZIkqSD4+We48EI4/3z46aegQcP//V+QOI4/PtbVpUlOztn7YsWgJEmSJOVnmzfD/fdD/frw/vtQvDjceScsXAhXXw2hUKwrTCc+PmfvixW73kmSJEn5USQCb70Fd9wRrFUDaNsWnngCjj02trXtRcuWQXe7lSuz3qcUCgWvt2yZ97VFwxklSZIkKb+ZNy84HPayy4KQdPjhQau4jz/O1yEJgj4So0YFv8842ZV6PXJk/j9PyaAkSZIk5RcbNsDtt8Nxx8GkSVCqFPTvD/Pnw8UX57tldnuSmBhMhtWsmX48ISEYLwjnKLn0TpIkSYq1lBQYPx7uuQdWrw7GOnSAESOgdu1YVrbfEhOhffug10RycrAnqWXL/D+TlMqgJEmSJMXSzJnQvTvMmBFcH3NMsA+pXbvY1pUD4uKgdetYV7F/XHonSZIkxcKaNXDzzXDyyUFIKlcOhg6FH34oFCGpoHNGSZIkScpLu3bBs88GLb/Xrw/GrroqCEk1asS0NP3NoCRJkiTllalToVu3YNYIgqYNY8bAaafFti5l4tI7SZIkKbf99lswa3T66UFIOuQQePJJmDXLkJRPOaMkSZIk5ZYdO4JDgx56CP76K2jvfdNN8MgjUKVKrKvTXhiUJEmSpNzw8cfQowcsWhRcn3JKsMyuSZPY1qVscemdJEmSlJOWLAkOhz3nnCAkVasG48bBV18ZkgoQZ5QkSZKknLBlCwwZEnSv27YtOESoRw/o3x8qVox1dYqSQUmSJEk6EJEITJgAvXvDr78GY2eeGRwaW79+bGvTfjMoSZIkSftrwYJg1mjixOC6Vi0YPhwuuSRo3KACyz1KkiRJUrQ2boS77oJGjYKQVLJkcIDs/PnQsaMhqRBwRkmSJEnKrkgE/vnPICStWhWMXXghjBgBRx4Z29qUowxKkiRJUnZ89x107x50rwM46igYNQrOPTe2dSlXuPROkiRJ2pu1a+G226Bp0yAklS0LgwfDnDmGpELMGSVJkiQpK+EwvPAC3HcfrFsXjF1xBQwbBgkJsa1Nuc6gJEmSJGU0bVqwzO6//w2uGzaE0aOhdeuYlqW849I7SZIkKdWqVdC5M5x6ahCSKlYM9iF9950hqYhxRkmSJEnauTOYMRowADZtCtp7X389DBoEhx4a6+oUAwYlSZIkFW2ffhocGjt/fnB90kkwZgycfHJs61JMGZQkSZJUNP36K9xxB7z9dnBdtSo8+ih06QLFYrdDJRyGqVMhORni46FlS4iLi1k5RZZBSZIkSUXL1q1B57pHHw1+HxcHXbvCwIFw8MExLS0pCXr2hBUr/h5LSAi2SSUmxq6uoshmDpIkSSoaIhH497+hQQPo3z8ISa1aBY0aRo3KFyGpY8f0IQlg5cpgPCkpNnUVVQYlSZIkFX6LFsF550GHDrBkCdSsCa+/DpMmQaNGsa6OcDiYSYpEMr+WOtarV3Cf8oZL7yRJkhQzObEfZ6/P+OsvePhhGD486GxXokSwL+m++6BcuVyvLbumTs08k7S7SASWLw/us0t53ojpjNKAAQMIhULpvqpXr572eiQSYcCAAdSoUYPSpUvTunVr5s2bF8OKJUmSlFOSkqB2bWjTBjp1Cn6tXTu6JWZ7fMbbEXj1VTjmGBgyJAhJ554Lc+cGLb/3EZJyorZoJCfn7H06cDFfetegQQOSk5PTvubMmZP22tChQxk+fDhjxozh22+/pXr16rRt25ZNmzbFsGJJkiQdqJzYj7OnZ1Ra8QOVO7aGq66C336DI46Ad9+F//wHjjoqT2qLVnx8zt6nAxfzoFS8eHGqV6+e9lW1alUgmE0aOXIkffv2JTExkYYNG/Lyyy+zZcsWXn311RhXLUmSpP2VE/txsnrGwfzJE3Tnv5xAK75ga6g0KQ8+DPPmwYUXBofI5kFt+6Nly6C73Z5KDIWgVq3gPuWNmAelxYsXU6NGDerUqcMVV1zBL7/8AsCSJUtYtWoV7dq1S7u3ZMmStGrVimnTpu3xedu3b2fjxo3pviRJkpR/RLMfJzvPKEaYG3meRRxNd8YQRwpvcinHRBbwRcu+UKpUnta2P+LigsZ7kDkspV6PHOl5SnkppkGpWbNmjB8/no8//pjnn3+eVatW0aJFC9auXcuqVasAqFatWrr3VKtWLe21rAwePJiKFSumfdWqVStXvwdJkiRFJyf246S+djJfM4NTeJ6bqcoa5lGfM/iMy3mT5RwW9Z6eWO4VSkyEt94KGvLtLiEhGPccpbwV06535557btrvGzVqRPPmzTnyyCN5+eWXOeWUUwAIZYjUkUgk09ju+vTpQ+/evdOuN27caFiSJEnKR3JiP85hJX/nRfpwPWMB2EAFBjCAMXRjFwdF/Vk5WduBSEyE9u3zrtue9ixftQcvW7YsjRo1YvHixXTo0AGAVatWEb/bT+Lq1aszzTLtrmTJkpQsWTK3S5UkSdJ+St2Ps3Jl1nuBQqHg9Sz34+zcCU89RYsHHuBUgi0WY+lCHwbzO9Wz94zcqi2HxMXZAjw/iPkepd1t376d+fPnEx8fT506dahevToTJ05Me33Hjh1MmTKFFi1axLBKSZIkHYj93o8zaRKccAL06kVo40b+PLIJLZjGDaGxmULSHp+RW7Wp0IlpULrzzjuZMmUKS5Ys4euvv6Zjx45s3LiRzp07EwqF6NWrF4MGDWLChAnMnTuXLl26UKZMGTp16hTLsiVJknSAotqPs3w5XH45nHFG0MGucmV49lkOWfg1d77dPMf39LhXSAChSCSrScW8ccUVV/DFF1+wZs0aqlatyimnnMJDDz1E/fr1gWA/0sCBA3n22Wf5888/adasGU8++SQNGzbM9mds3LiRihUrsmHDBipUqJBb34okSZL2Qzi8l/0427fD44/DI4/Ali1QrBjceis8+CBUqpS9Z+RWbSqQoskGMQ1KecGgJEmSVAD95z/BgUY//xxcn3YajB4Nxx8f07JUsEWTDfLVHiVJkiQVcT/9BBdcEHz9/HMwlfPKK/DFF4Yk5SmDkiRJkmJv82bo2xcaNAhmk4oXh7vugoUL4aqrMndWkHJZvmoPLkmSpCImEoF//QvuuANWrAjG2rULWs8de2xsa1ORZlCSJElSbMydCz16BG2/AWrXhhEjghNXnUFSjLn0TpIkSXlr/Xro1SvYczRpEpQqBQMGwI8/QocOhiTlC84oSZIkKW+kpMDLL8O998Lq1cHYxRfD8OHBbJKUjxiUJEmSlPu+/Ra6d4evvw6ujz0WnngC2raNbV3SHrj0TpIkSbnnjz/gppugWbMgJJUrB8OGwfffG5KUrzmjJEmSpJy3axc88wz06xfsSQK45hoYMiQ4G0nK5wxKkiRJyllffAHdusGcOcH18cfDmDFw6qkxLUuKhkvvJEmSlDNWroROnaBVqyAkVaoETz8NM2caklTgOKMkSZKkA7N9e3D+0cMPw+bNQXvvW24JritXjnV10n4xKEmSJGn/ffRRcGjs4sXBdfPmwTK7E0+MbV3SAXLpnSRJkqL3yy/Qvj2ce24QkqpVg/Hj4auvDEkqFAxKkiRJyr4tW+CBB6B+fXj3XSheHHr3hkWLgq52oVCsK5RyhEvvJEmStG+RCLz9NtxxByxbFoyddVZwaGy9erGtTcoFBiVJkiTt3Y8/BvuQPvssuD7sMBg+HBITnUFSoeXSO0mSJGVt48ZgBum444KQVLJksOxu/ny45BJDkgo1Z5QkSZKUXkoKvPIK3H03/P57MNa+fTCLdMQRsa1NyiMGJUmSJP3tv/+Fbt1g+vTg+uijYdQoOOec2NYl5TGDkiRJkmDtWujbF557DiIRwqXKMqdDPzZcfzunnVGCuH28PRyGqVMhORni46FlS4jb15ukfMw9SpIkSUVZOAxPPx3MHD37LEQiTCjdicO2LeSE1++hdbsS1K4NSUl7fkRSEtSuDW3aQKdOwa/7eo+U3xmUJEmSiqqvvoKmTeG222DdOtYf3phWTCFx6z/5jZppt61cCR07Zh18kpKC11asSD++t/dIBYFBSZIkqahJTg4Ohz3tNJg9Gw4+mJRRozl+1yy+4PRMt0ciwa+9egUTUKnCYejZ8+/Xs/MeqaAwKEmSJBUVO3bAY48Fy+xeeSVo733jjbBoEV807savK/e8fT0SgeXLg31IqaZOzTyTtK/3SAWFzRwkSZKKgokTg0NjFywIrps1g9Gj4aSTgGCSKTt2v29/3iMVFM4oSZIkFWZLlwaHw7ZrF4SkqlXhpZdg2rS0kARBp7rs2P2+/XmPVFAYlCRJkgqjrVth4ECoVy/oqBAXF2woWrQIrrsOiqX/z8CWLSEhIViNl5VQCGrVCu47kPdIBYVBSZIkqTCJROCdd6B+fRgwALZtg9atg6YNI0fCwQdn+ba4uOBcWcgcfFKvR45MfzbS/rxHKigMSpIkSflQOAyTJ8NrrwW/ZtU5LtM9Py6Ec86Biy8OltwlJMAbb8DnnxOu13Cfz0tMhLfegpo1048nJATjiYk58x6pIAhFIlk1dCw8Nm7cSMWKFdmwYQMVKlSIdTmSJEn7lJQUrJLbvaNcQkIwe5MaPHa/pxyb6MdD9GIkJdgJJUrAnXfCffdB2bLZet7uwuGgU11ycrC/qGXLfc8K7c97pLwWTTYwKEmSJOUjqQe4ZvwvtNSlbG+9Ffwa3BOhE68yjLuoQdBa7n3O56AxIzm7a91sP89ZHxUVBqXdGJQkSVJBEQ5D7dp7PpsoFPp7iVvlFbMZTXda8iUAP3EkPRnFh6HzSUiAJUuC+/b1vNR7nf1RURBNNvAcJUmSpHwiOwe4bl6xjofoxz94hjhS2EwZHqEvw+nNdkpBhkNes3sgbOvWOfqtSAWeQUmSJCmf2NvBrMUIcwMvMoj7qMJaAF7ncu5iGCuoFdWzDuReqagwKEmSJOUTezqY9RSmM5ruNGUWAHNoSA+eYDJton7Wgd4rFRUGJUmSpHwi9QDXlSuDZXHVWMWj3EsXXgZgAxV4vOJDjC93G8t+Kw5Z7DRP3XeUesjr7s/b172S/uY5SpIkSflE6gGuxSM7uZ0RLOSYtJA0lus4hkUc/1IPhj8R/L/ufR3y6oGw0v4zKEmSJOUjiRU/Y03C8QynNxXZyLc0pRkz6F/rJZ56uxqJidEd8uqBsNL+sT24JElSlPZ1uOp+vb5yGdxxR9pBSZEqVVjUZTD/Pf564msWy/IA12gOefVAWMn24JIkSbkmKQl69kzfdjshIVjilpgY/esl2cZDFR7j9m2DKL5jKxQrBrfdRujBBznmkEM4Zi+1xMVlv613NPdKckZJkiQp25KSoGPHzI0RUvf73HknPPZYdl+PcCHvMYLbOZJfAFhTryVVXhsNxx2Xq9+HVFRFkw0MSpIkSdkQDkPt2ns/wDUuLrhvX6/XZTGj6Ml5fAjASmpwN8OYmnAlS5aGXBIn5ZJosoHNHCRJkrJh6tS9hyTYe0gCKBX+i0H0YS4NOY8P2cFBPMo9HMsCXqUTy1eEmDo152qWtP/coyRJkpQNyckH8u4Il/MGj3EnCawE4EPOoRcjWZRhF9KBfY6knGJQkiRJyob4+P17X0PmMJrutGYKAL9Qh16M5D0uBEKZ7t/fz5GUs1x6J0mSlA0tWwbd6zIe3Lq7uLi/X6/IekbRg+84gdZMYSul6B96kIbM4z0uImNICoWgVq3gcyTFnkFJkiQpG+LighbfkDkshULBV+/eEIqkcD0vsYij6cFoihPmLS6hHgvYemc/toVKZ/l+gJEjPdtIyi8MSpIkSdmUmBicB1uzZvrxhIRgfGjHb/jjqOa8yA0cyh/8SD3OYiK9a73F8LcPZ+jQvb8/MTHvvhdJe2d7cEmSpCiFw0EXvOTkYE9Ry2NWE9fvPnjxRQAi5cvz89UDmNm8O9VrHUTLlulnijK9v6UzSVJeiCYb2MxBkiQpg30Fmbg4aN0a2LULnn4aOvSDDRuCF6+9ltCQIdStXp26e3h+2vsl5VsGJUmSpN0kJUHPnunPTEpICPYnpVsaN2UKdO8Oc+YE1yecAGPGQIsWeVqvpNzhHiVJkqT/SUqCjh0zHyy7cmUwnpRE8OKVVwZTQnPmQKVK8Mwz8O23hiSpEHFGSZIkiWC5Xc+ekNXu7UgESrKdRdePILLrYUKbNwet6m65BR5+GCpXzvuCJeUqg5IkSRLBnqSMM0mpzuUDRtKLozcsDgZatAiW2Z1wQt4VKClPufROkiSJoHFDRkfwM//mIj7gfI5mMclUZ/qt4+HLLw1JUiHnjJIkSSqSMna2O/TQv18rzRb6MJi7GEYptrOT4oykFw/Rj3cvqwChPT9XUuFgUJIkSQXe3tp5Z/Xav/+dubNdzZpQuVKEM9a9xWPcwWEsB+AT2tKTUSwM1SMhIXi/pMLPoCRJkgq0vbXzhsyvVa4Ma9dmfs7BK+cxih6cyecALOVwbmcE79CBUCiYQho50oNhpaLCoCRJkgqs1HbeGTvVrVwJl1yS9XsyhqQKbGAAA+jOaIoTZiuleLr8Pdy/6W62UgYIgtfIkRnOUZJUqBmUJElSgbSvdt77EiKFaxnPEO6hGqsBmEAHejOcFybU4YO4rJfySSoaDEqSJKnA2H2/0e+/77md976cyCzG0I3mzABgIUfTgyf4hLMBWL06OFNWUtFlUJIkSQVCVnuRolWZNTxCX27ieYoRYRPleIh+jKQXOymRdl98fA4ULKlAMyhJkqR8b097kbIrjl3cwrM8RD8q8ScAr3AVdzOUZGqk3RcKYWc7SYBBSZIk5XN724uUHacxldF053i+B2A2x9Gd0XxJ+jT0v8Z2draTBECxWBcgSZK0N1OnRr/cLhSCeH7j/7iaqZzO8XzPOg7hNp7kJGbyJS2pXDn9exIS4K237GwnKeCMkiRJyteSk6O7/yB20L/CKO7e/iAHbfuLFEI8z0305RHWUoVatYJZo/bt93xIrSQZlCRJUr6W3cYKI0bAcas+ptmrPSmzfGEweMopREaN4ZgtTRidRSBq3TpXSpZUCBiUJElSvtayZbAsbuXKrPcphULQvPoSek7uTejf7wSD1arBkCFwzTXEFStG67wsWFKh4B4lSZKUr8XFwahRwe9TGy6kKs1W+kcG8MXa+kFIiouD22+HhQuhc2co5n/qSNo//tNDkiTle4mJQaOFmjVTRyJ0YAIL4urTn4HE7dgGZ5wB338Pw4dDxYqxLFdSIWBQkiRJBUJiIixdCl+/vIDfGp3DBBI5LLwUatWCf/0LPv0UGjSIdZmSCgn3KEmSpIJh40biHnqIk0eOhF27oEQJuOsu6NMHypaNdXWSChmDkiRJyt8iEfjnP4NQtGpVMHbBBUGbu7p1Y1ubpELLoCRJkvKv776D7t3hq6+C67p1g84O550X27okFXruUZIkSfnP2rVw663QtGkQksqUgUGDYO5cQ5KkPOGMkiRJyj/CYXj+eejbF9atC8YuvxyGDQuaNkhSHjEoSZKk/GHaNOjWLVhuB9CwITzxBLRpE9u6JBVJLr2TJEmxtWpVcDjsqacGIalixWAf0nffGZIkxYwzSpIkKTZ27oTRo2HAANi0KRi74QYYNIhw5UOZOhWSkyE+Hlq2hLi4mFYrqYgxKEmSpLz36afQowfMnx9cn3QSjBkDJ59MUhL07AkrVvx9e0JCMMmUmBibciUVPS69kyRJeefXX6FjR2jbNghJVavCCy/AjBlpIaljx/QhCWDlymA8KSk2ZUsqegxKkiQp923bBg89BPXqwdtvQ7FiwYzSokXBcrtixQiHg5mkSCTz21PHevUKGuNJUm5z6Z0kSco9kQi8916QcJYsCcZOPz3Ym9S4MeEwTJ0c7EX6/ffMM0kZH7V8OUydCq1b537pkoq2fDOjNHjwYEKhEL169Uobi0QiDBgwgBo1alC6dGlat27NvHnzYlekJEnKvkWLgsNh27cPQlLNmvDaazB5MjRuTFIS1K4dNLbr1Aluvz17j01Ozs2iJSmQL4LSt99+y3PPPUfjxo3TjQ8dOpThw4czZswYvv32W6pXr07btm3ZlNoZR5Ik5T9//QX33hucg/TRR3DQQcH1ggVwxRUQCu1xL1J2xMfnfMmSlFHMg9Jff/3FVVddxfPPP88hhxySNh6JRBg5ciR9+/YlMTGRhg0b8vLLL7NlyxZeffXVGFYsSZKyFInAq6/CMcfAkCFB++9zz4V582DwYChXDmCve5H2JhSCWrWCVuGSlNtiHpS6du3K+eefz1lnnZVufMmSJaxatYp27dqljZUsWZJWrVoxbdq0PT5v+/btbNy4Md2XJEnKZT/8EGwcuuoq+O03OOIIePdd+M9/4Kij0t06dWr0M0mhUPDryJGepyQpb8S0mcPrr7/OrFmzmDlzZqbXVq1aBUC1atXSjVerVo1ff/11j88cPHgwAwcOzNlCJUlS1v78Ex54AJ56ClJSoHRp6NsX7rgDSpVKuy0cJu0A2R9/jP5jEhKCkOQ5SpLySsyC0vLly+nZsyeffPIJpXb7B2lGodT/hfQ/kUgk09ju+vTpQ+/evdOuN27cSK1atQ68YEmSCpndw0t8fLCkLS4u63HIMHZqCqGxL7Hr7j6U2LAGgJSOl1Ls8ccI1zws3b1r1gSNGqKdRRoxAqpVS1+bJOWVmAWlWbNmsXr1apo0aZI2Fg6H+eKLLxgzZgwLFy4Egpml+N12ba5evTrTLNPuSpYsScmSJXOvcEmSCoGkpGCf0O7hJSEBrrwyaEy3+3jlysGva9cGv57M1zwT140TwjMpAcyjPt0ZzeIZZ3DlmMzvj1YoFNTSvbvhSFLsRB2Uli9fTigUIiEhAYBvvvmGV199lfr163PzzTdn+zlnnnkmc+bMSTd23XXXceyxx3LPPfdwxBFHUL16dSZOnMgJJ5wAwI4dO5gyZQpDhgyJtmxJkvQ/qR3nMjZTWLEChg3LfH9qQDqU3xlMH65nLIRhAxUYwADG0I1dHAR7eH803IskKb+IOih16tSJm2++mWuuuYZVq1bRtm1bGjRowCuvvMKqVat44IEHsvWc8uXL07Bhw3RjZcuWpXLlymnjvXr1YtCgQRx11FEcddRRDBo0iDJlytCpU6doy5YkSexfx7k4dtGVJxlIfw5mAwBj6UIfBvM71XO0PvciScovog5Kc+fO5eSTTwbgzTffpGHDhnz11Vd88skn/OMf/8h2UMqOu+++m61bt3Lbbbfx559/0qxZMz755BPKly+fY58hSVJREm3HuVZMZjTdacRcAGZxIt0Ywwya51hN998P9eu7F0lS/hJ1UNq5c2faHqBPP/2Uiy66CIBjjz2W5AM8Knvy5MnprkOhEAMGDGDAgAEH9FxJkoq61AYNb7+dvfsTWM5j3MnlvAnAGipzH4N4kRtIIWeTzJlnBp3FJSk/ifocpQYNGvDMM88wdepUJk6cyDnnnAPAb7/9RuXU3Z6SJCnfSEqC2rWhTRsYM2bv95ZgO30YxAKO5XLeJEwxnuQ2jmYRz3NzjoYkD5CVlJ9FPaM0ZMgQLr74YoYNG0bnzp057rjjAHj33XfTluRJkqT8YU+NG7JyHv9hJL04ip8A+JJT6cYYvuf4HK/Lpg2S8rtQJBLNds5AOBxm48aNHHLIIWljS5cupUyZMhx66KE5WuCB2rhxIxUrVmTDhg1UqFAh1uVIkpRnwuFgJmlfe5KO5CdGcDsX8j4AvxHP3Qzln1wF7PnswgNRq5ZNGyTlvWiywX6doxSJRJg1axY///wznTp1onz58pQoUYIyZcrsV8GSJCmz7Bz82qIFTJuW9T2ffbb3kFSGzdzHIO7kMUqygx0cxLSTevGPlf1Y+NvfjZMynqO0p7FateCKKzKfo1SrFjz+OFStmvlwW0nKr6KeUfr1118555xzWLZsGdu3b2fRokUcccQR9OrVi23btvHMM8/kVq37xRklSVJ+k50AtGYN3H773g9+hSBshMN7vyezCJfyLx7nDmoRfMC6pu2o+PITxNU/Jlv17WkstZ6sxiUp1qLJBlEHpQ4dOlC+fHlefPFFKleuzPfff88RRxzBlClTuPHGG1m8ePEBFZ/TDEqSpPwkKSk4x2hfASi3NGAuT9CDM5gEwBJqs/nB4TS8v8PfG4ckqZDK1aV3X375JV999RUlSpRIN3744YezcuXKaB8nSVKRsafGCnkRkCqwgQEMoDujKU6YrZRiCPfyz5p3s+C+0rm1FUmSCqyog1JKSgrh3ef4/2fFihUeBCtJ0h6Ew8FMUvQtlA5MiBQ68zKPci/VWA1AEhdzB8P5NVSbt55wWZwkZSXqc5Tatm3LyJEj065DoRB//fUX/fv357zzzsvJ2iRJKnDCYZg8OWhoMHlycB0Ow+jR++4+l9OaMJNptGAs11ON1SzgGNrxMZeQRLhWbd56y65zkrQnUe9R+u2332jTpg1xcXEsXryYpk2bsnjxYqpUqcIXX3xhe3BJUpEV6/1HqarwB4O4jxt4kWJE2EQ5BtKfJ+jBPfeX4MwzbbAgqWjK1WYOAFu3buW1117jv//9LykpKZx44olcddVVlC5der+Lzi0GJUlSXojmYNfcEscubuFZHuZ+DmE9AP/H1dzDEFaFapCQAEuWGJAkFV25HpQKEoOSJCm3Zfdg19x0GlMZQzeO4wcAvuN4ujOarzgtrZmdS+0kFXW52vVu/Pjxe3392muvjfaRkiQVaFOn5k1IyvKQ12IrGZxyN1fxKgB/hg5hUJlHGL75ZlIIpo4SEmDkSEOSJEUj6qDUs2fPdNc7d+5ky5YtlChRgjJlyhiUJElFTnLygb0/ywBUCx5/HKpWzfqQ19+X76DplyM54p8PEtq8mUgoRPIFN1Pt+Yd5tEoVzvfAV0k6IFEHpT///DPT2OLFi7n11lu56667cqQoSZIKkvj4/X/viBHQvXvw+6nZDDett30ED/eERYuCgebNCY0eTY0mTf6+p/X+1yRJysE9SjNnzuTqq69mwYIFOfG4HOMeJUlSbkvdo7RyZfabOYRCRN9cYckSuP12+Pe/g+tq1WDoULj6aigW9YkfklTkRJMNcuyfqnFxcfz222859ThJkgqMuDgYNSr4fWrjhL1JvWfkyGyGpC1b4IEHoF69ICQVLw69e8PChXDttYYkScoFUS+9e/fdd9NdRyIRkpOTGTNmDKeeemqOFSZJUkGSmBh0lcvOOUrZbq4QiQR9x3v3hmXLgrEzzwxOr61XLyfLlyRlEPXSu2IZ/q9VKBSiatWqnHHGGTz++OPEH8hC7Vzg0jtJUm4JhzPvK4Lsje1zJunHH6FHD/jss+D6sMNg+PAgXWVn2kqSlEmutgdPSUnZ78IkSSoskpIyzx4lJARL8LKaKcp2c4WNG2HgQHjiCdi1C0qWhLvvhnvvhTJlcqJ0SVI2uKhZkqQoJSVBx46Zz05auTIYT0raj4empMD48XD00cHM0a5dcOGFwczSgw8akiQpj2Vr6V3v3r2z/cDhw4cfUEE5zaV3kqSclNrhbk8HzO5XN7v//he6dYPp04Pro44KpqbOPTcnSpYk/U+OL7377rvvsvXBIddMS5IKualT9xySIOi/sHx5cN8+l9utXQv33w/PPhu8sWxZ6NcPevUKltxJkmImW0Fp0qRJuV2HJEn5yp4aNaT2VtiX5OR9PPy554KQtG5dMHbllTBsGNSseUB1S5JyRtTNHCRJKuyyatSQVZvvvdljE9ivvgqW2c2eHVw3ahS0+27Van/LlSTlgv0KSt9++y3/+te/WLZsGTt27Ej3WtJ+7WCVJCl/SG3UkHEHb3YDUuoepdQZqDTJyUH3uldeCa4PPhgeegj+8Y/gAFlJUr4Sdde7119/nVNPPZUff/yRCRMmsHPnTn788Uc+//xzKlasmBs1SpKUJ8LhYCYpuhMG/5a6VXfkyN0aOezYAY89BsccE4SkUAhuvBEWLQpmlgxJkpQvRR2UBg0axIgRI3j//fcpUaIEo0aNYv78+Vx22WUcdthhuVGjJEl5Yl+NGvYlIQHeemu3c5QmToTjjoO77oJNm+Dkk+Hrr+H556Fq1RypWZKUO6IOSj///DPnn38+ACVLlmTz5s2EQiFuv/12nnvuuRwvUJKkvLLXBgz7cP/9QUvwxETg11/hkkugXTtYsCAIRS+9FLT/PumkHKtXkpR7og5KlSpVYtOmTQDUrFmTuXPnArB+/Xq2bNmSs9VJkpSH9tiAIRvOPBPidmwNDoc99thgs1NcXLCWb9EiuO46KOY575JUUES9MLply5ZMnDiRRo0acdlll9GzZ08+//xzJk6cyJlnnpkbNUqSlOvC4eCrUqW/O3ZnRygECTUjtPzzXajfC5YuDV5o3TroZtewYS5UK0nKbdkOSrNnz+b4449nzJgxbNu2DYA+ffpw0EEH8eWXX5KYmEi/fv1yrVBJknJLVu3AsyMUgqMjC5lSpSdxiR8HgwkJ8PjjcOmlf3d3kCQVOKFIJHu9fYoVK8YJJ5zAjTfeSKdOnQpMh7uNGzdSsWJFNmzYQIUKFWJdjiQpH9j9MNnFi2HAgH13ust4jlI5NjG0/MPcsnUExXbthBIl4I474L77oFy5XK1fkrR/oskG2V4s/dVXX3HiiSdy7733Eh8fz9VXX82kSZMOuFhJkvJSUhLUrg1t2kCnTtC//95DUqVK8Omn8PvvwdekzyN8dds/WVP5GG7dNDQISeedB3PnwqBBhiRJKiSyPaOUauvWrbz55puMHTuWqVOnUrt2ba6//no6d+5MQkJCbtW535xRkqTCa/eZofj4vw953dPYv/8dnHEUrUmTgi1HfP89dO8ePAzgyCODB15wwYF/M5KkXBdNNog6KO3u559/ZuzYsYwfP57k5GTatm3LBx98sL+PyxUGJUkqPDIumXv++fT7ijIuj9vTWLTeem4dl3z/ADz9NKSkQOnS0LdvsNSuVKn9f7AkKU/lWVAC+Ouvv/jnP//Jfffdx/r16wmHwwfyuBxnUJKkwmF/Gy4ciGKEuZ6XeKpCHw7a+L+kdeml8Nhj4CHrklTgRJMNom4PnmrKlCm89NJLvP3228TFxXHZZZdxww037O/jJEnao6Qk6Nhx3w0XclIzZjCGbjRlFmwEGjSAJ56AM87IuyIkSTETVVBavnw548aNY9y4cSxZsoQWLVowevRoLrvsMsqWLZtbNUqSirBwOJhJyquQdCi/8yj3ch3jANhZpgIHDXoQbrsNDjoob4qQJMVctoNS27ZtmTRpElWrVuXaa6/l+uuv55hjjsnN2iRJYurUvFluV5yddOVJBtKfimwEYOkZ11H71cFQrVruFyBJyleyHZRKly7N22+/zQUXXEBcXFxu1iRJKoIydrBr0QKmTYO33879zz6Dz3mC7jTgRwA2HtOUsi+NoXaLZrn/4ZKkfCnbQendd9/NzTokSUVYVo0a4uKC8JSbarGMx7iTy/hXMFClCgwaRIUbboBi2T5qUJJUCO13MwdJknLCnho15GZIKsk27uBx+hd/hBK7thIpVozQbbfBgw/CIYfk3gdLkgoMg5IkKWZyulFDds5ROp/3eTKuJ4eHf4FdQMuWhEaPhuOOy5kiJEmFgkFJkpSj9rTXKPW6ZcvgvqlT4bPPDqxRQ0IC3HQTHHVU5mdn/LyZry0m4fFe1Jz9AYSBGjVg2DC48koIhQ7oe5YkFT4GJUnSfssYitasgdtv3/teo6xmfaLVrRtcckkQgrLqL9S69W4XmzfDI4/Q7PHHYceOoMX37bfD/fdD+fL7X4QkqVDLVlCKppHDRRddtN/FSJIKjqwaMGQl416jAwlIqS65JEMYykokAm++CXfe+XeRZ58No0aBx1tIkvYhW0GpQ4cO6a5DoRCR3RaUh3ZbshDO7RZFkqSY21MDhtwWCgXL7VKX0+3RnDnQowdMnhxc16kDI0bARRe5zE6SlC3Z6n2akpKS9vXJJ59w/PHH8+GHH7J+/Xo2bNjABx98wIknnshHH32U2/VKkmIspxswZFdqvhk5MuvldgCsXx8Ud8IJQUgqVSroZDdvHrRvb0iSJGVb1HuUevXqxTPPPMNpp52WNnb22WdTpkwZbr75ZubPn5+jBUqS8pepUw+sAUN2ZdzblJAQhKTExCxuTkmBcePg3nvhjz+CsUsugccfh8MPz/1iJUmFTtRB6eeff6ZixYqZxitWrMjSpUtzoiZJUj6WnJy7z7//fjjzzKy75WU5k/Ttt0F3h2++Ca7r1YMnnoCzzsrdQiVJhVooEolu8cTpp5/OQQcdxCuvvEJ8fDwAq1at4pprrmHHjh1MmTIlVwrdXxs3bqRixYps2LCBChUqxLocSSrwJk+GNm1y/rmp+4+WLNnL0rrd/fEH9OkDL70UrAMsXx4GDIDu3YPOdpIkZRBNNsjWHqXdvfTSS6xevZrDDz+cunXrUrduXQ477DCSk5N58cUX97toSVLB0LJlEGhycrtPtvYfpdq1C0aPhqOPhhdfDELSNdfAwoXQu7chSZKUI6Jeele3bl1++OEHJk6cyIIFC4hEItSvX5+zzjorXfc7SVLhFBcXdNju2DEIOPtal5Cdc5T2uv9od1OmBDNGc+YE1yecAGPGBOv0JEnKQVEvvdvdtm3bKFmyZL4OSC69k6TckdU5SrVqBf0Tqlb9e29RVnuNIP1BtXvcf5RqxQq46y54/fXgulIlGDQIbrwxm+v0JEmKLhtEHZRSUlJ45JFHeOaZZ/j9999ZtGgRRxxxBP369aN27drccMMNB1R8TjMoSVLuCYejDDzR2r49OP/o4Ydh8+ZgCuuWW4Lr1KkpSZKyKVf3KD388MOMGzeOoUOHUqJEibTxRo0a8cILL0RfrSSpwIqLg9at4corg19zNCR9+CE0ahQ0bNi8OZiamjULnn7akCRJynVRB6Xx48fz3HPPcdVVVxG3278RGzduzIIFC3K0OElSEfTzz3DRRXDeebB4MVSvDuPHw5dfBnuSJEnKA1EHpZUrV1K3bt1M4ykpKezcuTNHipIkFUFbtkC/ftCgAbz3HhQvDnfeGXSzu+aanG2zJ0nSPkTd9a5BgwZMnTqVwzOcdP6vf/2LE/w/fZKkaEUi8PbbcMcdsGxZMNa2bdBar1692NYmSSqyog5K/fv355prrmHlypWkpKSQlJTEwoULGT9+PO+//35u1ChJKqx+/DFo9/3558H14YcHzRs6dHAGSZIUU1Evvbvwwgt54403+OCDDwiFQjzwwAPMnz+f9957j7Zt2+ZGjZKkwmbDhuBw2OOOC0JSyZLwwANBcLr4YkOSJCnmoppR2rVrF4888gjXX389U6ZMya2aJEmFVUoK/N//wT33wO+/B2Pt28Pw4XDEEbGtTZKk3UQ1o1S8eHGGDRtGePcj1iVJyo5Zs+DUU6FLlyAkHX00fPQRvPOOIUmSlO9EvfTurLPOYvLkyblQiiSpUFqzJjgk9qSTYMYMKFcOhgyBOXPg7LNjXZ0kSVmKupnDueeeS58+fZg7dy5NmjShbNmy6V6/6KKLcqw4SVIBFg7Ds8/C/ffDn38GY1ddBUOHQo0asa1NkqR9CEUikUg0byhWbM+TUKFQKN8ty9u4cSMVK1Zkw4YNVKhQIdblSFLR8OWXQTe72bOD68aNYcwYaNkypmVJkoq2aLJB1DNKKSkp+12YJKmQS06Gu++GV14Jrg8+GB5+OFh6Vzzqf+VIkhQzUe9R2t22bdtyqg5JUkG2YwcMGxY0aHjllaC99403wqJF0LWrIUmSVOBEHZTC4TAPPfQQNWvWpFy5cvzyyy8A9OvXjxdffDHHC5Qk5XOffBIsrbv7bvjrL2jWDL75Bp5/HqpWjXV1kiTtl6iD0iOPPMK4ceMYOnQoJUqUSBtv1KgRL7zwQo4WJ0nKx5YsCQ6HPftsWLgQDj0Uxo6FadOgadNYVydJ0gGJOiiNHz+e5557jquuuoq4uLi08caNG7NgwYIcLU6SlA9t3QoDBkD9+sEZSHFxcPvtwTK7Ll1gL01/JEkqKKJeNL5y5Urq1q2baTwlJYWdO3fmSFGSpPwnvCvC/MHvUPuJ2ym35tdgsE0bGD0aGjSIbXGSJOWwqINSgwYNmDp1Kocffni68X/961+ccMIJOVaYJCnvhMMwdWrQtC4+Hlq0CFbQpV5v/34Bpe/pwenbJwKwjFoMqvQ47bp2JLFBKMbVS5KU86IOSv379+eaa65h5cqVpKSkkJSUxMKFCxk/fjzvv/9+btQoScpFSUnQsyesWPH3WFxcEJ7Ks5F+PEQvRnIQu9hOCYZxF4Ppw9Y/y/LcpfDWW5CYGLv6JUnKDVEfOAvw8ccfM2jQIGbNmkVKSgonnngiDzzwAO3atcuNGg+IB85K0p4lJUHHjpD53wQRruKfDOVuapAMwLtcyO2M4BeOTLsrFIKEhKCvw27bViVJypeiyQb7FZQKEoOSJGUtHIbatdPPJAEcx2zG0I3T+AqAxdSlJ6P4kPP2+KxJk6B169yrVZKknBBNNrA1kSQVUVOnpg9Jh7COJ7mNWTThNL5iM2XowyAaMnevIQmCvUySJBUm2dqjdMghhxAKZW+z7rp16w6oIElS3kgNN8UIcyMv8Ah9qcJaAN7gMu7kMVZQK1vPio/PrSolSYqNbAWlkSNHpv1+7dq1PPzww5x99tk0b94cgOnTp/Pxxx/Tr1+/XClSkpTz4uPhFKYzhm404b8AzKEhPXiCybTJ1jNS9yi1bJmblUqSlPei3qN0ySWX0KZNG7p165ZufMyYMXz66ae88847OVnfAXOPkiRlYdUqUu65l2LjXwZgPRV5gAd5itsIZ7MhaupCA7veSZIKilzdo/Txxx9zzjnnZBo/++yz+fTTT6N9nCQpL+3cCcOHw9FHp4WksVzHMSxiND2yHZIgmEkyJEmSCquoz1GqXLkyEyZM4K677ko3/s4771C5cuUcK0ySlMM++wx69IAffwRg7REnsfSO0ZQ/tBklbgeyOEcpVa1a8PjjULXq34fQtmxpS3BJUuEVdVAaOHAgN9xwA5MnT07bozRjxgw++ugjXnjhhRwvUJJ0gH79Fe64A95+G4C1xapwd8qjjP3lOiJdi5GQEEwy7R6CWrSAadMMRZKkoivqpXddunRh2rRpHHzwwSQlJfH2229TsWJFvvrqK7p06RLVs55++mkaN25MhQoVqFChAs2bN+fDDz9Mez0SiTBgwABq1KhB6dKlad26NfPmzYu2ZEkqmrZtg4cegnr14O23iRQrxmi6UzdlES9xA5H//Stg5Uq4/HJYtw6uvDI4D6lEieDX1GtDkiSpqImqmcPOnTu5+eab6devH0ccccQBf/h7771HXFwcdevWBeDll19m2LBhfPfddzRo0IAhQ4bwyCOPMG7cOI4++mgefvhhvvjiCxYuXEj58uWz9Rk2c5BU5EQi8N570KsXLFkSDLU8nXYLR/Pp6sZZviW1e92SJYYiSVLhFU02iLrr3cEHH8x///vfHAlKWalUqRLDhg3j+uuvp0aNGvTq1Yt77rkHgO3bt1OtWjWGDBnCLbfckq3nGZQkFSmLFkHPnvDRR8F1jRrw+ONMrnY5bc7Y93l4kyYFM0iSJBVGudr17uKLL86VFuDhcJjXX3+dzZs307x5c5YsWcKqVato165d2j0lS5akVatWTJs2bY/P2b59Oxs3bkz3JUkFQTgMkyfDa68Fv4bDWY9lee+Gv0i5+15SGjSEjz4ipfhBpNx9L+EfFzK5+hW8nZS9Q8NTD6GVJKmoi7qZQ926dXnooYeYNm0aTZo0oWzZsule79GjR1TPmzNnDs2bN2fbtm2UK1eOCRMmUL9+/bQwVK1atXT3V6tWjV9//XWPzxs8eDADBw6MqgZJirWkpGAiaMVunedSG4muXfv3WEJCsG/otddS741wBa9zTOhO4iO/AfAB59Jr10jWvXg0vJj+/fsSH3/A34okSYVC1Evv6tSps+eHhUL88ssvURWwY8cOli1bxvr163n77bd54YUXmDJlCuvXr+fUU0/lt99+I363f3PfdNNNLF++nI9Sl5VksH37drZv3552vXHjRmrVquXSO0n5VlISdOwYbC2KRiN+YDTdacUXAPxCHXoyive5AMjeDFIq9yhJkoqCaJbeRT2jtOR/G4NzSokSJdKaOTRt2pRvv/2WUaNGpe1LWrVqVbqgtHr16kyzTLsrWbIkJUuWzNEaJSm3hMPBTFI0Ielg/mQg/enKk8SRwhZKM4j7eIw72U6pqGsI/S9TjRxpSJIkKVXUe5RSrVmzhrXRrOfIpkgkwvbt26lTpw7Vq1dn4sSJaa/t2LGDKVOm0KJFixz/XEmKhalT0y+325sQKdzACyziaHowmjhS+BcdOZYFPML9+xWSIJhJeustSEzcr7dLklQoRRWU1q9fT9euXalSpQrVqlXj0EMPpUqVKnTr1o3169dH/eH33XcfU6dOZenSpcyZM4e+ffsyefJkrrrqKkKhEL169WLQoEFMmDCBuXPn0qVLF8qUKUOnTp2i/ixJyo+y2zzhJL5hBqfwAjdRlTXMoz5n8imX8S+Wc9h+fXa3bkGXuyVLDEmSJGWU7aV369ato3nz5qxcuZKrrrqKevXqEYlEmD9/PuPGjeOzzz5j2rRpHHLIIdn+8N9//51rrrmG5ORkKlasSOPGjfnoo49o27YtAHfffTdbt27ltttu488//6RZs2Z88skn2T5DSZLyu301T6jKah7lXq5nLAAbKU9/BjKGbuzioAP67EsusRW4JEl7ku1mDr169eKzzz7j008/zbRHKLWN95lnnsmIESNypdD95TlKkvKbcDhYcpecDIceCl26wMqV6fcpxbGL23iKB3mAg9kAwDg6cy+P8jvVD+jzbdwgSSqqcuUcpXfeeYfHHnssy0YK1atXZ+jQoUyYMCH6aiWpCElKgtq1oU0b6NQJzjoLtm4NQlJqU4VWTOY7TuAJenIwG5jFiTRnGtcxLkdCEti4QZKkfcl2UEpOTqZBgwZ7fL1hw4asWrUqR4qSpMIotQ14xuYN69YFvzaouILXuILJtKERc1kbqkzvss9yMt8wg+YA1KoFd90VzAjtrnLlv89d2tuYjRskScqebO9RqlKlCkuXLiUh47+d/2fJkiVUzvhvZEkSsPc24AdFtnMHw7l/w8OUYQspoWIkX/QPqj//EMMqVeKi/y3Ti4+Hli2DmaDBg/9evpc6DtkbcyZJkqR9y/YepRtuuIGffvqJiRMnUqJEiXSvbd++nbPPPpsjjzySF198MVcK3V/uUZKUH0yeHCy3y+g8/sNIenEUPwGwoeGpVPy/MXD88XlanyRJRUGuHDg7cOBAmjZtylFHHUXXrl059thjAfjxxx956qmn2L59O//3f/93YJVLUiGVsQ34EfzMSHpxIe8D8Bvx3MUwLujTiSuPD8WgQkmStLtsB6WEhASmT5/ObbfdRp8+fUidiAqFQrRt25YxY8ZQq1atXCtUkgqy1DbgZdhMHwZzF8MoyQ52UpwR3M5D9OMvynNTjdjWKUmSAtleere7P//8k8WLFwNQt25dKlWqlOOF5RSX3knKD8K7InSt9hb3rbuDw1gOwCe0pQdPsJBjbdktSVIeyJWld7s75JBDOPnkk/erOEkqcubNI657d55ZNwmApRzO7YzgHToAIVt2S5KUD2W7PbgkKUobNpDS83YijY+DSZMIlyjFj5f2p23N+bzDxUCQkGzZLUlS/rNfM0qSpL1ISYHx49nW6x5KbVgNwAQ6cPuOEYSn12b4CKha1ZbdkiTlZwYlScpJM2dC9+4wYwalgAUcQw+eYCLtAAithMsvD2aQrrwytqVKkqQ9c+mdJOWENWvg5pvh5JNhxgz+CpXjTobRmB/SQhL8feBsr17BIbSSJCl/MihJ0oHYtQuefBKOOgqefx4iEVa1vZqjIwt5nDvZSYlMb4lEYPlymDo1BvVKkqRscemdJO2vqVOhWzf44QcA/jryOKZfNYYvUk4jeeK+357xEFpJkpR/GJQkKVq//QZ33QWvvgrAjnKHMLD4wzz68y2kPJj9rgyph9BKkqT8x6AkSdm1Y0dw2NGDD8LmzRAK8Uvbm2n2ycOsoUq2H5N6uGzLlrlXqiRJOjDuUZKk7Pj4Y2jUCO65JwhJzZsT/nomrX58JuqQBB4uK0lSfmdQkqS9WbIEOnSAc86BRYugWjV4+WX48kumbj6RFSuie5yHy0qSVDC49E6SsrJlCwwZEnxt3w7Fi0OPHoT7PsDUHyqS/Ab8+GP2HnX//VC/vofLSpJUkBiUJGl3kQhMmAC33w7LlgVjZ54JTzxB0oL69DyOqGeRzjwTWrfO8UolSVIucumdJKWaPx/atYNLLglCUq1a8K9/wcSJJC2oT8eO0YWkUCh4hE0bJEkqeAxKkrRxI9x5JzRuDJ9+CiVLBuvlFiyAjh0Jp4To2TOYbMoumzZIklSwufROUtGVkgKvvBJ0slu1Khi76CIYMQKOOCLttqlTo19ul5AQhCSbNkiSVDAZlCQVTf/9L3TvDtOmBddHHQWjRsG552a6NTk5e4+0aYMkSYWHQUlS0bJ2bZBonn02WEtXtiz06we9egVL7rIQH5+9R9u0QZKkwsM9SpKKhnAYnnkGjj46+DUSgSuvhIULg6V3ewhJEMwOJST8ve8oI5s2SJJU+BiUJBV+X30FJ50Et94K69ZBo0YweTK8+irUrLnPt8fFBavyIHNYsmmDJEmFk0FJUuGVnAzXXgunnQbffQcVK8ITTwT7k1q1iupRiYnw1luZc1VCQjBu0wZJkgoX9yhJKnx27AgC0YMPwqZNwbTPDTfAoEFQtep+PzYxEdq3D7rgJSfbtEGSpMLMoCSpcJk4EXr0CM5AAjj5ZBgzJlh6lwPi4mzYIElSUeDSO0mFw6+/wiWXQLt2QUiqWhVeegmmT8+xkCRJkooOg5Kkgm3r1mCJ3bHHQlJSMOXTowcsWgTXXQfF/MecJEmKnkvvJBVMkQi8+25w/tHSpcFY69bB3qRGjWJYmCRJKgz8X62SCp6FC+Hcc6FDhyAkJSTA66/D558bkiRJUo4wKEkqODZtCg6HbdQIPv4YSpSAPn1g/ny4/PI9nwgrSZIUJZfeScr/IhF47TW46y747bdg7LzzglNejzoqpqVJkqTCyaAkKX/7/nvo3j04vAjgyCODgHTBBTEtS5IkFW4uvZOUP61bB926wYknBiGpdGl4+GGYO9eQJEmScp0zSpLyl3A4OP+oTx9YuzYYu/RSeOwxOOyw2NYmSZKKDIOSpPxjxoxgFmnWrOC6fv2g3feZZ+Z5KeFwMJGVnAzx8dCyZXBEkyRJKhoMSpJi7/ff4d57Ydy44LpCBRg4ELp2hYMOytWPzioQ/fvf0LMnrFjx930JCTBqFCQm5mo5kiQpnzAoSYqdnTvhySehf3/YuDEY69IFHn0UqlXL9Y9PSsociCpX/nvF3+5WroSOHeGttwxLkiQVBTZzkBQbn38OJ5wAt98ehKQmTWDaNBg7Ns9CUseO6UMSZB2SIOhQDtCrVzALJUmSCjeDkqS8tWwZXHZZsO9o3rxgCufZZ+Hrr6F58zwpIRwOZpJSw092RSKwfPnfncolSVLh5dI7SXlj2zZ4/HEYNAi2bIFixeDWW+HBB6FSpTwtZerUzDNJ0UhOzrlaJElS/mRQkpT73n8/WLP288/B9WmnwZgxcNxxMSnnQINOfHzO1CFJkvIvl95Jyj2LF8P558OFFwYhKT4e/vlP+OKLmIUk2P+gEwpBrVpBZzxJklS4GZQk5bzNm6FvX2jYED74IGjxfdddsHAhdOoUJI4YatkyaPcdTRmp944c6XlKkiQVBQYlSTknEoE33oBjjw32Iu3YAe3awZw5MHQolC8f6wqBIOiMGhX8PmNYSr2uXDn9eEKCrcElSSpK3KMkKWfMnQvdu8PkycF17dowYgS0b5+jM0hZHRCbOsOzp9eyGk9MDIJPVgfLjhwZlL2nz5EkSYWfQUnSgVm/HgYMCJozhMNQqhT06RMstStdOkc/KqsDYhMS/p4dyuq1K6+E117L+j2JiXsPRK1b52j5kiSpAAlFItGeJFKwbNy4kYoVK7JhwwYqVKgQ63KkwiMlBcaNg3vvhT/+CMYSE4MW4LVr5/jHpR4Qm/GfWKFQ9OchpU5wuZROkqSiJZpsYFCSFL1vv4Vu3eCbb4LrY4+FJ56Atm33+da9LZ3b0+sQZK8DOfsoo1AomFlassQldZIkFRXRZAOX3kkC9h1gAMKr/mD1DX2o/uFLhCIRIuXLE+rfP9ibVKLEPp+zt6VziYl7fv2mm3I2JEEwC7V8eVCrS+wkSVJGBiVJ+www7NrF7H88Te2XHiA+sh6A8VzDqHJD6FsnnsQS+34OZL10buXKYPzOO+Gxx7J+vX//HP120znQw2clSVLh5NI7qYjb294fgMkDp3DcC92puGwOAN9xPN0YwzROTbfXB/a+h6hyZVi7ds91pHany2uTJjmjJElSUeEepd0YlKQ9C4f3vPenJisYxl1cyesArKUSfXmE57mJFP5ekxcKQc2awe9zenlcbnKPkiRJRY97lCRly9SpmcNNCbZzOyO4n4cpx2ZSCPEst3A/D7OOypmeEYnkbUDK2OVu9+vsdsBLnQkbOdKQJEmSslYs1gVIip2M+3PO5iPm0IhH6UM5NvMVLWjKTG7j6SxDUl4bOPDv2atUCQnw9tvBV8bXatUKjnNKSMj8HluDS5KkvXHpnVSETZ4MbdpAHX5hBLfTnncBWEU17mIYr3A1EMqTWuLigqOZsvon0u7L5GDPXfX21HEvOx39JElS4ecepd0YlKQ9C2/awpiag7ll0zBKsZ2dFGcUPXmQB9hEhXT7j1au3HOIyc49lSrBunXBdcalc/B317s9ve4MkCRJOlDRZAOX3klFUSQCb71FXINj6bnpYUqxnYmcRWN+4C4eSwtJELT2Tm3vHcowuRTNPc89F4SdrJbOvfUWDB2699cNSZIkKS85oyQVNT/+GBwQ+/nnwfXhhzPjsuFc+urFrFj5d8qpVStodpAaULI6I2l/7tnXMjiXyUmSpNzi0rvdGJSk/9mwIeiGMHo07NoFJUvCPfcEX2XKZCug5NQ9kiRJsWBQ2o1BSUVeSgr83/8Fgej334OxDh1g+HCoUyempUmSJOUlz1GSFJg1C7p1gxkzguujj4YnnoCzz45tXZIkSfmczRykwmjNGrjlFjjppCAklS0LQ4bAnDmGJEmSpGxwRkkqTMJhePZZuP9++PPPYKxTp6ClXMZ2cpIkSdojg5JUWHz5ZdDNbvbs4LpxYxgzJuimIEmSpKi49E4q6JKT4ZprgkA0ezYcfHAQkGbNMiRJkiTtJ2eUpIJqx47glNcHH4S//gpOdr3xRnjkEahaNdbVSZIkFWgGJakgmjgxWGa3cGFw3axZMIvUtGmmWz3XSJIkKXouvZMKkqVLITER2rULQtKhh8LYsTBtWpYhKSkJateGNm2Cng5t2gTXSUl5XbgkSVLBYlCSCoKtW2HgQKhXDyZMCKaEevWCRYugSxcolvlv5aQk6NgRVqxIP75yZTBuWJIkSdozg5KUn0UiQTCqXx8GDIBt24Jpoe+/hxEjoGLFLN8WDkPPnsHbs3okBDkrHM61yiVJkgo0g5KUXy1YAOecEyy1W7oUEhLgzTfhs8+gQYO9vnXq1MwzSbuLRGD58uA+SZIkZWZQkvKbTZvgrrugUSP45BMoUQL69g2C06WXBt3t9iE5OXsfld37JEmSihq73kn5RSQC//wn3H333wnmgguCJXZ160b1qPj4nL1PkiSpqHFGScoPZs+G008PDo5NTg6C0fvvw3vvRR2SIGgBnpCw58mnUAhq1fI8WkmSpD0xKEmxtG4ddO0KTZrAl19CmTIwaBDMnQvnn7/fj42LC86ihcxhKfV65EjPU5IkSdoTg5IUC+EwPPssHH00PPUUpKTA5ZcH+5D69IGSJQ/4IxIT4a23oGbN9OMJCcF4YuIBf4QkSVKh5R4lKa9Nnw7dusF//xtcN2wITzwRtP3OYYmJ0L590N0uOTnYk9SypTNJkiRJ+2JQkvLKqlVw773w8svBdcWK8OCDcNttUDz3/laMi4PWrXPt8ZIkSYWSQUnKbTt3wpgxwYGxGzcGY9dfD4MHw6GHxrQ0SZIkZS2me5QGDx7MSSedRPny5Tn00EPp0KEDCxcuTHdPJBJhwIAB1KhRg9KlS9O6dWvmzZsXo4qlKH32GRx/PPTuHYSkpk1hxgx48cX9DknhMEyeDK+9FvwaDudkwZIkSYIYB6UpU6bQtWtXZsyYwcSJE9m1axft2rVj8+bNafcMHTqU4cOHM2bMGL799luqV69O27Zt2bRpUwwrl/Zh2bLgcNizzoIff4QqVeD55+Hrr6FZs/1+bFIS1K4dbGfq1Cn4tXbtYFySJEk5JxSJRCKxLiLVH3/8waGHHsqUKVM4/fTTiUQi1KhRg169enHPPfcAsH37dqpVq8aQIUO45ZZb9vnMjRs3UrFiRTZs2ECFChVy+1tQUbdtGzz2WNDie+tWKFYsaP89cCAcckim28Ph7DdaSEqCjh2Dc2l3l9ru2052kiRJexdNNshX7cE3bNgAQKVKlQBYsmQJq1atol27dmn3lCxZklatWjFt2rQsn7F9+3Y2btyY7kvKE++/Dw0aQL9+QUg6/XT47rugo10WISma2aFwGHr2zByS4O+xXr1chidJkpRT8k1QikQi9O7dm9NOO42GDRsCsGrVKgCqVauW7t5q1aqlvZbR4MGDqVixYtpXrVq1crdwafHi4HDYCy+EX36BGjXg1VeDDUSNG2f5ltTZoRUr0o+vXBmMZwxLU6dmvnd3kQgsXx7cJ0mSpAOXb4JSt27d+OGHH3jttdcyvRZKXVv0P5FIJNNYqj59+rBhw4a0r+XLl+dKvRJ//QX33Recg/TBB3DQQXDPPbBwIVx55d9r4jLYn9mh5OTslZTd+yRJkrR3+aI9ePfu3Xn33Xf54osvSEhISBuvXr06EMwsxcfHp42vXr060yxTqpIlS1KyZMncLVhFWyQCb7wBd94ZTAEBkbPP4ZurRvFL8aOJn7n3vUbRzA6lnn+024//XmX3PkmSJO1dTGeUIpEI3bp1Iykpic8//5w6deqke71OnTpUr16diRMnpo3t2LGDKVOm0KJFi7wuVzGWW22xo3runDnBZqIrrwxCUp06TL/33xw29wNOufbobHWi25/ZoZYtISFhj5NUhEJQq1ZwnyRJkg5cTINS165deeWVV3j11VcpX748q1atYtWqVWzduhUIltz16tWLQYMGMWHCBObOnUuXLl0oU6YMnTp1imXpymO51RY7289dvx569IATToApU6B0aXjwQd4Z9COnDrmIFSvTJ5g97TWC/ZsdiouDUaOC32cMS6nXI0fueRZLkiRJ0Ylpe/A97TMaO3YsXbp0AYJZp4EDB/Lss8/y559/0qxZM5588sm0hg/7Ynvwgi+32mJn67kdUmDsWOjTB/74I3ihY0d47DHCCYdTu/ael9GFQsEs0JIl6QNMOByEsZUrs96ntKf3pdbcs2f6z6xVKwhJtgaXJEnau2iyQb46Ryk3GJQKttRQEW0YyYnnnl/1G949rBuhmd8Gg/XqwejRcOaZQLBMr02bfX/WpEl/7zVKlRrSIH1Yyk74i+bsJUmSJP2twJ6jpIIht/YKZSW32mLv7blVWc3zkRt4b3WzICSVLw/Dh8P336eFJDiwTnSJiUEYqlkz/XhCwr5nyOLiguB15ZXBr4YkSZKknJcvut6p4Mhq6VdCQrB/JjeWfuVWW+ys7o9jF7fxFA/yAAcTHH68pOW11HlzCPyvA+PuDrQTXWIitG/v7JAkSVJ+ZFBStu1pT09q44L93Su0N7nVFjvj/a2YzGi604i5AMziRLozmkEPtqBO5owE/N2Jbl97jfbWiS51dkiSJEn5i0vvlC37c0hqTsitttipz01gBa9xBZNpQyPmspZK3MIzNOMbVtRqsc+QYyc6SZKkwsmgpGzJrb1C+5JbYSRu13Y+bDWYBRzDFbxBmGI8xa0cxWKeD91CSiguW889kL1GkiRJyr8MSsqW3NorlB05HkY++AAaNqThP++jLFv4psSpNGEWXXmKP6kU9XMTE2Hp0qC73auvBr8uWWJIkiRJKsjco6Rsya29QtmVI40Pfv45WB/4/vvBdXw8DBtGk8s7MfLL0AE1VHCvkSRJUuHiOUrKlgM5JDXmNm+GwYNh2DDYsQOKF4fbb4d+/YLW35IkSSoSPEdJOa5ANi6IROBf/woOin3kkSAktW0Lc+bA0KGGJEmSJO2RQUnZVqAaF8ybFxwOe9llQZeJww8P+pt//DEce2ysq5MkSVI+5x4lRSXfH5K6YQMMGACjRwfrBUuVgnvuCb5Kl451dZIkSSogDEqKWr5sXJCSAuPHB4Fo9epg7OKLYfjwYHOVJEmSFAWDkgq+mTOhe3eYMSO4PuYYeOIJaNcutnVJkiSpwHKPkgquNWvg5pvh5JODkFSuXNCk4YcfDEmSJEk6IM4oqeDZtQuefRbuvx/Wrw/GrroqCEk1asS0NEmSJBUOBiUVLFOnQrduwawRwHHHwZgxcNppsa1LkiRJhYpL71QwrFwZzBqdfnoQkg45BJ58EmbNMiRJkiQpxzmjpPxtx47gJNsHH4TNm4PTbW+6KThAtkqVWFcnSZKkQsqgpPzr44+hRw9YtCi4PuWUYJldkyaxrUuSJEmFnkvvlP8sWQIdOsA55wQhqVo1GDcOvvrKkCRJkqQ8YVBS/rFlC/TvD/Xqwb//HZxse/vtsHAhdO4MxfxxlSRJUt5w6Z1iLxKBCROCULRsWTB25pnBobH168e2NkmSJBVJBiXF1vz5wT6kTz8NrmvVguHD4ZJLgsYNkiRJUgy4lkmxsXEj3HknNG4chKSSJYMDZOfPh44dDUmSJEmKKWeUlLciEXjlFbj7bli1Khi78EIYMQKOPDK2tUmSJEn/Y1BS3vnuO+jWDaZNC66POgpGjYJzz41tXZIkSVIGLr1T7lu7Fm69NWjtPW0alC0LgwfDnDmGJEmSJOVLzigp94TD8Pzz0LcvrFsXjF1xBQwbBgkJsa1NkiRJ2guDknLHtGnBMrvvvguuGzaE0aOhdeuYliVJkiRlh0vvlLOSk+Haa+HUU4OQVLFicB7Sd98ZkiRJklRgOKOknLFzZxCIBg6ETZuC9t7XXw+DBsGhh8a6OkmSJCkqBiUduIkTg0NjFywIrk86CcaMgZNPjm1dkiRJ0n5y6Z3236+/BofDtmsXhKSqVeHFF2HGDEOSJEmSCjRnlBS9rVuDznWPPhr8Pi4OunYNlt0dfHCsq5MkSZIOmEFJ2ReJwLvvwu23w5IlwVirVkE3u0aNYlubJEmSlINceqfsWbgwOBy2Q4cgJNWsCa+9BpMmGZIkSZJU6BiUtHebNsE99wRh6OOPoUQJ6NMn2JN0xRVBdztJkiSpkHHpnbIWiQQzRnfdBb/9Foydey6MGgVHHRXb2iRJkqRcZlBSZj/8AN26wdSpwfURR8DIkXDBBc4gSZIkqUhw6Z3+9uef0L07nHBCEJJKl4aHH4Z58+DCCw1JkiRJKjKcURKEw/DSS3DffbBmTTB26aXw2GNw2GGxrU2SJEmKAYNSUff118Eyu5kzg+v69YN232ecEdu6JEmSpBhy6V1R9fvvcN11cMopQUiqUAFGjIDZsw1JkiRJKvKcUSpqdu6EJ5+E/v1h48ZgrEsXGDwYqlePaWmSJElSfmFQKkomTQqaNcybF1w3aRIss2vePLZ1SZIkSfmMS++KguXL4fLLgyV18+ZB5crw7LPB/iRDkiRJkpSJM0qF2bZt8PjjMGgQbNkCxYrBrbfCgw9CpUqxrk6SJEnKtwxKhdX770OvXvDzz8H1aacFy+yOPz6WVUmSJEkFgkvvCpvFi+H884MDYn/+GeLj4ZVX4IsvDEmSJElSNhmUCovNm4MDYxs2hA8+gIMOgrvugoUL4aqrIBSKdYWSJElSgeHSu4IuEoE334Q774QVK4Kxdu3giSfgmGNiW5skSZJUQBmUCrK5c4N235MnB9e1aweHxrZv7wySJEmSdABcelcQrV8fNGo4/vggJJUqBQMGwI8/QocOhiRJkiTpADmjVJCkpMC4cXDvvfDHH8HYxRfD8OHBbJIkSZKkHGFQKii+/Ra6dYNvvgmujz022IfUtm1s65IkSZIKIZfe5Xd//AE33gjNmgUhqVw5GDYMvv/ekCRJkiTlEmeU8qtdu+Dpp+GBB4I9SQBXXw1DhkCNGjEtTZIkSSrsDEr50RdfBMvs5swJro8/HkaPhtNOi2lZkiRJUlHh0rv8ZOVK6NQJWrUKQtIhh8BTT8HMmYYkSZIkKQ85o5QfbN8enH/08MOweXPQ3vvmm4PrKlViXZ0kSZJU5BiUYu3DD6FnT1i8OLhu3hzGjIETT4xtXZIkSVIR5tK7WPn5Z7joIjjvvCAkVasGL78MX35pSJIkSZJizKCU17ZsgX79oEEDeO89KF4ceveGhQvh2muhmH9JJEmSpFhz6V0eCe+KsOCRt6k95g7KrlkWDJ55ZnBobP36sS1OkiRJUjpOX+SBpCQ4tvY2yg/oTdk1y/iVw7i50lsk3TrRkCRJkiTlQ84o5bKkJOjYESKR0vRkFCfwHY9yL9v+LMMLl8Jbb0FiYqyrlCRJkrS7UCQSicS6iNy0ceNGKlasyIYNG6hQoUKefnY4DLVrw4oVWb8eCkFCAixZAnFxeVqaJEmSVOREkw1cepeLpk7dc0gCiERg+fLgPkmSJEn5h0EpFyUn5+x9kiRJkvKGQSkXxcfn7H2SJEmS8oZBKRe1bBnsQQqFsn49FIJatYL7JEmSJOUfBqVcFBcHo0YFv88YllKvR460kYMkSZKU3xiUclliYtACvGbN9OMJCbYGlyRJkvIrz1HKA4mJ0L590N0uOTnYk9SypTNJkiRJUn5lUMojcXHQunWsq5AkSZKUHS69kyRJkqQMDEqSJEmSlIFBSZIkSZIyMChJkiRJUgYGJUmSJEnKwKAkSZIkSRkYlCRJkiQpA4OSJEmSJGVgUJIkSZKkDGIalL744gsuvPBCatSoQSgU4p133kn3eiQSYcCAAdSoUYPSpUvTunVr5s2bF5tiJUmSJBUZMQ1Kmzdv5rjjjmPMmDFZvj506FCGDx/OmDFj+Pbbb6levTpt27Zl06ZNeVypJEmSpKKkeCw//Nxzz+Xcc8/N8rVIJMLIkSPp27cviYmJALz88stUq1aNV199lVtuuSUvS5UkSZJUhOTbPUpLlixh1apVtGvXLm2sZMmStGrVimnTpu3xfdu3b2fjxo3pviRJkiQpGjGdUdqbVatWAVCtWrV049WqVePXX3/d4/sGDx7MwIEDM40bmCRJkqSiLTUTRCKRfd6bb4NSqlAolO46EolkGttdnz596N27d9r1ypUrqV+/PrVq1cq1GiVJkiQVHJs2baJixYp7vSffBqXq1asDwcxSfHx82vjq1aszzTLtrmTJkpQsWTLtuly5cixfvpzy5cvvNWApZ2zcuJFatWqxfPlyKlSoEOtyVMD586Sc5M+Tcoo/S8pJ/jzlrUgkwqZNm6hRo8Y+7823QalOnTpUr16diRMncsIJJwCwY8cOpkyZwpAhQ7L9nGLFipGQkJBbZWoPKlSo4N/syjH+PCkn+fOknOLPknKSP095Z18zSaliGpT++usvfvrpp7TrJUuWMHv2bCpVqsRhhx1Gr169GDRoEEcddRRHHXUUgwYNokyZMnTq1CmGVUuSJEkq7GIalGbOnEmbNm3SrlP3FnXu3Jlx48Zx9913s3XrVm677Tb+/PNPmjVrxieffEL58uVjVbIkSZKkIiCmQal169Z77TgRCoUYMGAAAwYMyLuidEBKlixJ//790+0Tk/aXP0/KSf48Kaf4s6Sc5M9T/hWKZKc3niRJkiQVIfn2wFlJkiRJihWDkiRJkiRlYFCSJEmSpAwMSpIkSZKUgUFJuWLp0qXccMMN1KlTh9KlS3PkkUfSv39/duzYEevSVEA98sgjtGjRgjJlynDwwQfHuhwVME899RR16tShVKlSNGnShKlTp8a6JBVAX3zxBRdeeCE1atQgFArxzjvvxLokFVCDBw/mpJNOonz58hx66KF06NCBhQsXxrosZWBQUq5YsGABKSkpPPvss8ybN48RI0bwzDPPcN9998W6NBVQO3bs4NJLL+XWW2+NdSkqYN544w169epF3759+e6772jZsiXnnnsuy5Yti3VpKmA2b97Mcccdx5gxY2Jdigq4KVOm0LVrV2bMmMHEiRPZtWsX7dq1Y/PmzbEuTbuxPbjyzLBhw3j66af55ZdfYl2KCrBx48bRq1cv1q9fH+tSVEA0a9aME088kaeffjptrF69enTo0IHBgwfHsDIVZKFQiAkTJtChQ4dYl6JC4I8//uDQQw9lypQpnH766bEuR//jjJLyzIYNG6hUqVKsy5BUhOzYsYNZs2bRrl27dOPt2rVj2rRpMapKktLbsGEDgP+dlM8YlJQnfv75Z0aPHs0//vGPWJciqQhZs2YN4XCYatWqpRuvVq0aq1atilFVkvS3SCRC7969Oe2002jYsGGsy9FuDEqKyoABAwiFQnv9mjlzZrr3/Pbbb5xzzjlceuml3HjjjTGqXPnR/vw8SfsjFAqlu45EIpnGJCkWunXrxg8//MBrr70W61KUQfFYF6CCpVu3blxxxRV7vad27dppv//tt99o06YNzZs357nnnsvl6lTQRPvzJEWrSpUqxMXFZZo9Wr16daZZJknKa927d+fdd9/liy++ICEhIdblKAODkqJSpUoVqlSpkq17V65cSZs2bWjSpAljx46lWDEnMJVeND9P0v4oUaIETZo0YeLEiVx88cVp4xMnTqR9+/YxrExSURaJROjevTsTJkxg8uTJ1KlTJ9YlKQsGJeWK3377jdatW3PYYYfx2GOP8ccff6S9Vr169RhWpoJq2bJlrFu3jmXLlhEOh5k9ezYAdevWpVy5crEtTvla7969ueaaa2jatGna7PayZcvcM6mo/fXXX/z0009p10uWLGH27NlUqlSJww47LIaVqaDp2rUrr776Kv/+978pX7582qx3xYoVKV26dIyrUyrbgytXjBs3juuuuy7L1/yR0/7o0qULL7/8cqbxSZMm0bp167wvSAXKU089xdChQ0lOTqZhw4aMGDHCFryK2uTJk2nTpk2m8c6dOzNu3Li8L0gF1p72SI4dO5YuXbrkbTHaI4OSJEmSJGXgphFJkiRJysCgJEmSJEkZGJQkSZIkKQODkiRJkiRlYFCSJEmSpAwMSpIkSZKUgUFJkiRJkjIwKEmSJElSBgYlSSoili5dSigUYvbs2bEuJSq1a9dm5MiROfa81q1b06tXrxx7XiyEQiHeeecdoOD+dZWk/M6gJEmFQCgU2utXly5dYl3iPo0bN46DDz440/i3337LzTffnPcF5QMDBgzg+OOPzzSenJzMueeem/cFSVIRUjzWBUiSDlxycnLa79944w0eeOABFi5cmDZWunRp/vzzz1iURjgcJhQKUazY/v2/uapVq+ZwRQVf9erVY12CJBV6zihJUiFQvXr1tK+KFSsSCoUyjaX65ZdfaNOmDWXKlOG4445j+vTp6Z41bdo0Tj/9dEqXLk2tWrXo0aMHmzdvTnv9zz//5Nprr+WQQw6hTJkynHvuuSxevDjt9dSZoffff5/69etTsmRJfv31V3bs2MHdd99NzZo1KVu2LM2aNWPy5MkATJ48meuuu44NGzakzYINGDAAyLz0bv369dx8881Uq1aNUqVK0bBhQ95//30A1q5dy5VXXklCQgJlypShUaNGvPbaa1H/eT766KNUq1aN8uXLc8MNN3Dvvfemm9nJavlehw4d0s3cvfLKKzRt2pTy5ctTvXp1OnXqxOrVq9Nenzx5MqFQiM8++4ymTZtSpkwZWrRokRZwx40bx8CBA/n+++/T/kzGjRsHpF96l5Uff/yR8847j3LlylGtWjWuueYa1qxZk/b6W2+9RaNGjShdujSVK1fmrLPOSvfXWJJkUJKkIqdv377ceeedzJ49m6OPPporr7ySXbt2ATBnzhzOPvtsEhMT+eGHH3jjjTf48ssv6datW9r7u3TpwsyZM3n33XeZPn06kUiE8847j507d6bds2XLFgYPHswLL7zAvHnzOPTQQ7nuuuv46quveP311/nhhx+49NJLOeecc1i8eDEtWrRg5MiRVKhQgeTkZJKTk7nzzjsz1Z6SksK5557LtGnTeOWVV/jxxx959NFHiYuLA2Dbtm00adKE999/n7lz53LzzTdzzTXX8PXXX2f7z+fNN9+kf//+PPLII8ycOZP4+HieeuqpqP+cd+zYwUMPPcT333/PO++8w5IlS7JcAtm3b18ef/xxZs6cSfHixbn++usBuPzyy7njjjto0KBB2p/J5Zdfvs/PTU5OplWrVhx//PHMnDmTjz76iN9//53LLrss7fUrr7yS66+/nvnz5zN58mQSExOJRCJRf4+SVKhFJEmFytixYyMVK1bMNL5kyZIIEHnhhRfSxubNmxcBIvPnz49EIpHINddcE7n55pvTvW/q1KmRYsWKRbZu3RpZtGhRBIh89dVXaa+vWbMmUrp06cibb76Z9vlAZPbs2Wn3/PTTT5FQKBRZuXJlumefeeaZkT59+uy17sMPPzwyYsSISCQSiXz88ceRYsWKRRYuXJjtP4/zzjsvcscdd6Rdt2rVKtKzZ8893t+8efPIP/7xj3RjzZo1ixx33HF7fUb79u0jnTt33uNzv/nmmwgQ2bRpUyQSiUQmTZoUASKffvpp2j3/+c9/IkBk69atkUgkEunfv3+6z00FRCZMmBCJRP7+6/rdd99FIpFIpF+/fpF27dqlu3/58uURILJw4cLIrFmzIkBk6dKle6xVkhSJOKMkSUVM48aN034fHx8PkLYkbNasWYwbN45y5cqlfZ199tmkpKSwZMkS5s+fT/HixWnWrFnaMypXrswxxxzD/Pnz08ZKlCiR7nP++9//EolEOProo9M9e8qUKfz888/Zrn327NkkJCRw9NFHZ/l6OBzmkUceoXHjxlSuXJly5crxySefsGzZsmx/xvz582nevHm6sYzX2fHdd9/Rvn17Dj/8cMqXL0/r1q0BMtWyt78e+2PWrFlMmjQp3Z/zscceC8DPP//Mcccdx5lnnkmjRo249NJLef7552O2f02S8jObOUhSEXPQQQel/T4UCgHBkrbUX2+55RZ69OiR6X2HHXYYixYtyvKZkUgk7VkQNI/Y/TolJYW4uDhmzZqVtkwuVbly5bJde+nSpff6+uOPP86IESMYOXIkjRo1omzZsvTq1YsdO3Zk+zOyo1ixYpmWqu2+9HDz5s20a9eOdu3a8corr1C1alWWLVvG2WefnamWvf312B8pKSlceOGFDBkyJNNr8fHxxMXFMXHiRKZNm8Ynn3zC6NGj6du3L19//TV16tTZ78+VpMLGoCRJSnPiiScyb9486tatm+Xr9evXZ9euXXz99de0aNECCBooLFq0iHr16u3xuSeccALhcJjVq1fTsmXLLO8pUaIE4XB4r/U1btyYFStWsGjRoixnlaZOnUr79u25+uqrgSA0LF68eK+1ZVSvXj1mzJjBtddemzY2Y8aMdPdUrVo1XafBcDjM3LlzadOmDQALFixgzZo1PProo9SqVQuAmTNnZruGVNn5M8noxBNP5O2336Z27doUL571v+ZDoRCnnnoqp556Kg888ACHH344EyZMoHfv3lHXKEmFlUvvJElp7rnnHqZPn07Xrl2ZPXs2ixcv5t1336V79+4AHHXUUbRv356bbrqJL7/8ku+//56rr76amjVr0r59+z0+9+ijj+aqq67i2muvJSkpiSVLlvDtt98yZMgQPvjgAyDobvfXX3/x2WefsWbNGrZs2ZLpOa1ateL000/nkksuYeLEiSxZsoQPP/yQjz76CIC6deumzZbMnz+fW265hVWrVkX1Z9CzZ09eeuklXnrpJRYtWkT//v2ZN29eunvOOOMM/vOf//Cf//yHBQsWcNttt7F+/fq01w877DBKlCjB6NGj+eWXX3j33Xd56KGHoqoj9c9kyZIlzJ49mzVr1rB9+/Z9vqdr166sW7eOK6+8km+++YZffvmFTz75hOuvv55wOMzXX3/NoEGDmDlzJsuWLSMpKYk//vgjqjApSUWBQUmSlKZx48ZMmTKFxYsX07JlS0444QT69euXtncGYOzYsTRp0oQLLriA5s2bE4lE+OCDD9ItIcvK2LFjufbaa7njjjs45phjuOiii/j666/TZlxatGjBP/7xDy6//HKqVq3K0KFDs3zO22+/zUknncSVV15J/fr1ufvuu9NmXfr168eJJ57I2WefTevWralevTodOnSI6s/g8ssv54EHHuCee+6hSZMm/Prrr9x6663p7rn++uvp3Lkz1157La1ataJOnTpps0kQzDiNGzeOf/3rX9SvX59HH32Uxx57LKo6AC655BLOOecc2rRpQ9WqVbPV6rxGjRp89dVXhMNhzj77bBo2bEjPnj2pWLEixYoVo0KFCnzxxRecd955HH300dx///08/vjjHmArSRmEIhkXWUuSpHQGDBjAO++8w+zZs2NdiiQpjzijJEmSJEkZGJQkSZIkKQOX3kmSJElSBs4oSZIkSVIGBiVJkiRJysCgJEmSJEkZGJQkSZIkKQODkiRJkiRlYFCSJEmSpAwMSpIkSZKUgUFJkiRJkjL4f0wuJSYizfqJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "stats.probplot(MPG, plot=plt)\n",
    "plt.title('Q-Q Plot of MPG')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "695ac6da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:38:59.816129Z",
     "start_time": "2023-08-28T13:38:59.794188Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test:\n",
      "Test Statistic: 0.9779686331748962\n",
      "p-value: 0.17639249563217163\n",
      "Data follows a normal distribution (p > 0.05)\n"
     ]
    }
   ],
   "source": [
    "statistic, p_value = stats.shapiro(MPG)\n",
    "alpha = 0.05\n",
    "print(\"Shapiro-Wilk Test:\")\n",
    "print(\"Test Statistic:\", statistic)\n",
    "print(\"p-value:\", p_value)\n",
    "if p_value > alpha:\n",
    "    print(\"Data follows a normal distribution (p > 0.05)\")\n",
    "else:\n",
    "    print(\"Data does not follow a normal distribution (p <= 0.05)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5d103a",
   "metadata": {},
   "source": [
    "# b)\tCheck Whether the Adipose Tissue (AT) and Waist Circumference(Waist)  from wc-at data set  follows Normal Distribution \n",
    "       Dataset: wc-at.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ff5907",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:40:37.372776Z",
     "start_time": "2023-08-28T13:40:37.354822Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('wc-at.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f74ac710",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:40:51.921436Z",
     "start_time": "2023-08-28T13:40:51.905475Z"
    }
   },
   "outputs": [],
   "source": [
    "AT = data['AT']\n",
    "Waist = data['Waist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcef647c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:41:00.579365Z",
     "start_time": "2023-08-28T13:41:00.344818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(AT, bins=20, edgecolor='k')\n",
    "plt.title('Histogram of Adipose Tissue (AT)')\n",
    "plt.xlabel('AT')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "585f66e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:41:13.568277Z",
     "start_time": "2023-08-28T13:41:13.343697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(Waist, bins=20, edgecolor='k')\n",
    "plt.title('Histogram of Waist Circumference (Waist)')\n",
    "plt.xlabel('Waist')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72011f0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:41:31.528654Z",
     "start_time": "2023-08-28T13:41:31.117543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "stats.probplot(AT, plot=plt)\n",
    "plt.title('Q-Q Plot of Adipose Tissue (AT)')\n",
    "plt.subplot(1, 2, 2)\n",
    "stats.probplot(Waist, plot=plt)\n",
    "plt.title('Q-Q Plot of Waist Circumference (Waist)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f13f89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:41:45.542703Z",
     "start_time": "2023-08-28T13:41:45.523751Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test for Adipose Tissue (AT):\n",
      "Test Statistic: 0.9523370862007141\n",
      "p-value: 0.0006539996829815209\n",
      "AT does not follow a normal distribution (p <= 0.05)\n"
     ]
    }
   ],
   "source": [
    "statistic_AT, p_value_AT = stats.shapiro(AT)\n",
    "alpha = 0.05\n",
    "print(\"Shapiro-Wilk Test for Adipose Tissue (AT):\")\n",
    "print(\"Test Statistic:\", statistic_AT)\n",
    "print(\"p-value:\", p_value_AT)\n",
    "if p_value_AT > alpha:\n",
    "    print(\"AT follows a normal distribution (p > 0.05)\")\n",
    "else:\n",
    "    print(\"AT does not follow a normal distribution (p <= 0.05)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf5c329",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:41:54.095179Z",
     "start_time": "2023-08-28T13:41:54.080216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk Test for Waist Circumference (Waist):\n",
      "Test Statistic: 0.9558579921722412\n",
      "p-value: 0.0011704873759299517\n",
      "Waist does not follow a normal distribution (p <= 0.05)\n"
     ]
    }
   ],
   "source": [
    "statistic_Waist, p_value_Waist = stats.shapiro(Waist)\n",
    "print(\"Shapiro-Wilk Test for Waist Circumference (Waist):\")\n",
    "print(\"Test Statistic:\", statistic_Waist)\n",
    "print(\"p-value:\", p_value_Waist)\n",
    "if p_value_Waist > alpha:\n",
    "    print(\"Waist follows a normal distribution (p > 0.05)\")\n",
    "else:\n",
    "    print(\"Waist does not follow a normal distribution (p <= 0.05)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024a5939",
   "metadata": {},
   "source": [
    "# Q 22) Calculate the Z scores of  90% confidence interval,94% confidence interval, 60% confidence interval "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb3c6e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:44:49.387178Z",
     "start_time": "2023-08-28T13:44:49.367234Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_90 = 0.90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e25c98c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:44:55.423411Z",
     "start_time": "2023-08-28T13:44:55.401467Z"
    }
   },
   "outputs": [],
   "source": [
    "z_score_90 = stats.norm.ppf(1 - (1 - confidence_level_90) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043705c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:01.308906Z",
     "start_time": "2023-08-28T13:45:01.297936Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score for 90% confidence interval: 1.6448536269514722\n"
     ]
    }
   ],
   "source": [
    "print(\"Z-score for 90% confidence interval:\", z_score_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94c45dc2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:07.668202Z",
     "start_time": "2023-08-28T13:45:07.649253Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_94 = 0.94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74e3f8f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:13.630780Z",
     "start_time": "2023-08-28T13:45:13.618812Z"
    }
   },
   "outputs": [],
   "source": [
    "z_score_94 = stats.norm.ppf(1 - (1 - confidence_level_94) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46256bcb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:20.072431Z",
     "start_time": "2023-08-28T13:45:20.054480Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score for 94% confidence interval: 1.8807936081512509\n"
     ]
    }
   ],
   "source": [
    "print(\"Z-score for 94% confidence interval:\", z_score_94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8609f6b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:29.182309Z",
     "start_time": "2023-08-28T13:45:29.175331Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_60 = 0.60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a950dba4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:39.379232Z",
     "start_time": "2023-08-28T13:45:39.365269Z"
    }
   },
   "outputs": [],
   "source": [
    "z_score_60 = stats.norm.ppf(1 - (1 - confidence_level_60) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ffa1546",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:45:47.988678Z",
     "start_time": "2023-08-28T13:45:47.968734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score for 60% confidence interval: 0.8416212335729143\n"
     ]
    }
   ],
   "source": [
    "print(\"Z-score for 60% confidence interval:\", z_score_60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e118d0",
   "metadata": {},
   "source": [
    "# Q 23) Calculate the t scores of 95% confidence interval, 96% confidence interval, 99% confidence interval for sample size of 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0f6f75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:03.330390Z",
     "start_time": "2023-08-28T13:47:03.310443Z"
    }
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d64c2a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:10.573460Z",
     "start_time": "2023-08-28T13:47:10.565481Z"
    }
   },
   "outputs": [],
   "source": [
    "sample_size = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ded5a0f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:17.230796Z",
     "start_time": "2023-08-28T13:47:17.216833Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_95 = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fce7f1c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:24.027760Z",
     "start_time": "2023-08-28T13:47:24.012798Z"
    }
   },
   "outputs": [],
   "source": [
    "t_score_95 = stats.t.ppf(1 - (1 - confidence_level_95) / 2, df=sample_size - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3dff612",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:41.325944Z",
     "start_time": "2023-08-28T13:47:41.304995Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-score for 95% confidence interval: 2.0638985616280205\n"
     ]
    }
   ],
   "source": [
    "print(\"t-score for 95% confidence interval:\", t_score_95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8e6785",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:50.506097Z",
     "start_time": "2023-08-28T13:47:50.489146Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_96 = 0.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e6c58d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:47:57.947844Z",
     "start_time": "2023-08-28T13:47:57.931887Z"
    }
   },
   "outputs": [],
   "source": [
    "t_score_96 = stats.t.ppf(1 - (1 - confidence_level_96) / 2, df=sample_size - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17b0a572",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:48:35.448479Z",
     "start_time": "2023-08-28T13:48:35.437512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-score for 96% confidence interval: 2.1715446760080677\n"
     ]
    }
   ],
   "source": [
    "print(\"t-score for 96% confidence interval:\", t_score_96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1411e419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:48:56.164864Z",
     "start_time": "2023-08-28T13:48:56.158879Z"
    }
   },
   "outputs": [],
   "source": [
    "confidence_level_99 = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7888433c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:48:58.939181Z",
     "start_time": "2023-08-28T13:48:58.926222Z"
    }
   },
   "outputs": [],
   "source": [
    "t_score_99 = stats.t.ppf(1 - (1 - confidence_level_99) / 2, df=sample_size - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "386a8f30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:49:08.832089Z",
     "start_time": "2023-08-28T13:49:08.826105Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-score for 99% confidence interval: 2.796939504772804\n"
     ]
    }
   ],
   "source": [
    "print(\"t-score for 99% confidence interval:\", t_score_99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef562d3a",
   "metadata": {},
   "source": [
    "# Q24 A Government company claims that an average light bulb lasts 270 days. A researcher randomly selects 18 bulbs for testing. The sampled bulbs last an average of 260 days, with a standard deviation of 90 days. If the CEO's claim were true, what is the probability that 18 randomly selected bulbs would have an average life of no more than 260 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09056a65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-28T13:55:26.984342Z",
     "start_time": "2023-08-28T13:55:26.966390Z"
    }
   },
   "outputs": [],
   "source": [
    "x_bar = 260\n",
    "pop_mean = 270"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf14972f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_value = (260-270)/(90/np.sqrt(18))\n",
    "t_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6199488e",
   "metadata": {},
   "outputs": [],
   "source": [
    "1-stats.t.cdf(abs(t_value),df = 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25726eed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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