{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a5ff1be-2da3-4ce2-9a6c-9f171f1ff6c1",
   "metadata": {},
   "source": [
    "# DS 122 Homework 1\n",
    "\n",
    "### You can download the python notebook for this pdf from https://drive.google.com/file/d/1T7EBmSNzn2JP99nqHMduJH-wiIEPXTtG/view?usp=sharing\n",
    "\n",
    "**Due Sep., 20th**\n",
    "\n",
    "**Full credit is 87 points (With Bonus Question: 92 Points)**\n",
    "\n",
    "**Name:**\n",
    "\n",
    "**BUID:**\n",
    "\n",
    "Most homeworks will involve “analytical” questions, and many will involve “computational” questions. This homework involves analytical and computational questions.\n",
    "\n",
    "**NOTE**\n",
    "\n",
    "- It is advised not to use CHAT GPT or any other LLM to complete the homeworks and try the questions on your own unless otherwise stated in the question.\n",
    "\n",
    "- Try to answer the questions in detail, In case you do not get the correct answer, we will take into consideration the steps (process you take to solve the question) which will help you in getting partial points.\n",
    "\n",
    "- Please note unless otherwise mentioned, leaving the answer in fractional format is completely alright. Do not worry about accuracy of the decimal answers.\n",
    "\n",
    "- Coding questions might seem a little daunting at first but if you go through them you will notice that a lot of answers are directly available in the notebook. It is more for your understanding than for testing, if you are unable to find a solution at first please try reading up the documentation and your class notes. We are always available during our office hours in case you have doubts regarding a topic.\n",
    "\n",
    "- To make things clear to the grader, you MUST draw a box around your answer. Questions whose answers are not boxed will lose points. To put a box around your answer in LATEX use \\fbox{} or \\boxed{}.\n",
    "\n",
    "**SUBMISSION GUIDELINES**\n",
    "\n",
    "- You are free to write your solutions to math problems on paper and upload scanned copies as PDF. If you wish to type your solutions, I would suggest using Latex to write mathematical equations, you can use https://www.overleaf.com/ to create free latex documents.\n",
    "\n",
    "- For coding questions, please edit the jupyter notebook itself in the space provided to input your answer. You can choose to create a new cell to enter your code so as to not lose the sample output. \n",
    "\n",
    "- Final submissions should contain both your code (Jupyter Notebook) as well as mathematical files (Scanned or Typed PDF). You can select more than one file while uploading during submission. Please try to use the following naming convention for your submissions **{FirstName}\\_\\{LastName}\\_\\{BUID}\\_\\{analytical/computational}.zip**\n",
    "\n",
    "- Steps to Submit\n",
    "    - Write your answers to the mathematical questions on a paper or on Latex Editor\n",
    "    - If you wrote them on a paper, scan them as pdf or else save the pdf from the Latex Editor\n",
    "    - Download the Python notebook and complete the coding questions\n",
    "    - Copy content from the question cell to a new cell and write your answer\n",
    "    - Press Shift + Enter to run the cell and see if the output matches the sample output\n",
    "    - Once you have completed all the coding questions save the notebook with the prescribed name\n",
    "    - Submit both the files on gradescope (PDF and Jupyter Notebook)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1af25b2-fa7b-4b1c-9ec2-dd56053dc4ff",
   "metadata": {},
   "source": [
    "## Analytical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3115967f-032b-446d-a293-97be8d40e862",
   "metadata": {},
   "source": [
    "**Problem A - 5 points**\n",
    "\n",
    "Consider a wooden cube with painted faces that is sawed up into 27 smaller equal-sized cubes. If one of these small cubes is chosen at random, what is the probability that it has exactly 3 painted faces?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7433e86-0b4a-4007-a91a-d6f078204601",
   "metadata": {},
   "source": [
    "**Problem B - 5 points**\n",
    "\n",
    "In a penalty shootout, two footballers, Player X and Player Y, are known for their precision. Player X has an 85% probability of scoring a goal, while Player Y has a 80% probability. If both players take a shot one after the other, what is the probability that at least one of them scores?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a93a8c-e82e-4ffa-937b-140d00de7761",
   "metadata": {},
   "source": [
    "**Problem C - 5 points**\n",
    "\n",
    "In a carnival game, players throw balls at a wall with five differently shaped targets (Please note that a throw will either hit one of the shapes or miss completely, there is no other possiblity). The probabilities of **not hitting** each of these shapes are:\n",
    "\n",
    "- Star: 0.68\n",
    "- Triangle: 0.95\n",
    "- Square: 0.90\n",
    "- Circle: 0.80\n",
    "- Pentagon: 0.81\n",
    "\n",
    "Given that the **outcomes for each shape are independent**, what is the probability that a player's shot does not hit any of the shapes?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f557b0-f187-4847-8733-f48c37d30145",
   "metadata": {},
   "source": [
    "**Problem D - 30 points (35 with Bonus)**\n",
    "\n",
    "**Premise:**\n",
    "\n",
    "You and your friend are sitting in the CDS building waiting for your next class. Your friend is bored and suggests playing a game where you roll two six-sided dice, You win if the sum of the two dice equals 7.\n",
    "\n",
    "**Part a - 5 points** \n",
    "\n",
    "If you decide to play the game just once, what is the probability that you win?\n",
    "\n",
    "**Part b - 5 points**\n",
    "\n",
    "If you play the game three times in a row and don't remember your previous rolls, what is the probability you win at least once?\n",
    "\n",
    "**Part c - 5 points**\n",
    "\n",
    "If you play 4 games, what is the probability you win exactly twice, given the probability of winning a single game as found in Part a?\n",
    "\n",
    "**Part d - 5 points**\n",
    "\n",
    "What is the expected number of games you will play before you win?\n",
    "\n",
    "**Part e - 10 points**\n",
    "\n",
    "Your friend now proposes a twist. If either dice shows a 1, you automatically lose, regardless of the sum. Calculate the probability of winning in this new scenario for a single game.\n",
    "\n",
    "**Part f - 5 points (Bonus Question - Optional)**\n",
    "\n",
    "You and your friend decide to further tweak the rules. Now, if the first dice shows a 1, you lose, but if the second dice shows a 6, you automatically win, regardless of the sum. Find the probability that you win the game in this new scenario.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b85b38-bda7-4876-b871-7dcceaf34b30",
   "metadata": {},
   "source": [
    "**Problem E - 10 points**\n",
    "\n",
    "Imagine you're Batman and the overall rate of individuals having committed a crime (let's denote this as event A) in Gotham is P(A)\n",
    "\n",
    "There's a certain marker, a unique tattoo (let's denote this as event B), which has been found common among many criminals. You observed that among the individuals with this tattoo, the probability of them having committed a crime is P(A | B) and this is higher than P(A)\n",
    "\n",
    "P(A) = 0.4\n",
    "\n",
    "P(B) = 0.3\n",
    "\n",
    "P(A | B) = 0.5\n",
    "\n",
    "Using the given probabilities and the definition of conditional probability, determine the exact value of P(B|A). Compare it to the value of P(B), is there any relation you find between P(B|A) and P(B) (Greater than, Lesser than, Equal to)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b612a28-f15f-48ff-9ceb-92c5eefe76dd",
   "metadata": {},
   "source": [
    "**Problem G - 5 points**\n",
    "\n",
    "Consider a radioactive source emitting alpha particles at\n",
    "          an average rate of 4 particles per second. What is the probability that in a particular one-second interval, less than two particles are emitted?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb453bb-c759-4f1f-9469-ecebaddecd5a",
   "metadata": {},
   "source": [
    "**Problem H - 5 Points**\n",
    "\n",
    "Consider the following joint probability table for two random variables, \\(X\\) and \\(Y\\):\n",
    "\n",
    "|         | Y = 1 | Y = 2 |\n",
    "|---------|-------|-------|\n",
    "| X = 1   | 0.2   | 0.3   |\n",
    "| X = 2   | 0.1   | 0.4   |\n",
    "\n",
    "What is the marginal probability \\(P(Y = 1)\\)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f039bb78-5200-4bce-bdd3-f04f68fe344e",
   "metadata": {},
   "source": [
    "## Computational\n",
    "\n",
    "- Add your answers in the same cell as the code or add another cell by copy pasting the existing cell\n",
    "- Outputs from the answer key have been left as they are for your reference. My personal suggestion would be to create a new cell with the same code copied and make sure that the output coming is the same. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b58f42-cc0e-4211-bf19-e23c9192b782",
   "metadata": {},
   "source": [
    "**Problem I - 0 points**\n",
    "\n",
    "Install Python 3 and NumPy package on the computer you will use for this\n",
    "  course.    Read the document ``Getting \n",
    "  Started with Python'' on Piazza.\n",
    "  https://numpy.org/install/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f910e5f-7d3e-4df7-8242-14c867597abb",
   "metadata": {},
   "source": [
    "**Problem J - 2 points**\n",
    "\n",
    "Verify that NumPy is installed correctly:  \n",
    "\n",
    "execute \n",
    "\n",
    "$$\\texttt{import numpy as np;}$$\n",
    "$$\\texttt{A = np.array([1, 2, 3])};$$\n",
    "$$\\texttt{print(np.sum(A))}$$\n",
    "\n",
    "Cut and paste the input and output from your Python interpreter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ea5244-9828-4e4a-8a7f-fe20de9d2829",
   "metadata": {},
   "source": [
    "**Problem K - 10 points**\n",
    "\n",
    "Now we will use some of the skills we have learned to examine the attritubtes of professional baseball players. Read in ’hwk01 mlb.csv’ (https://drive.google.com/file/d/1AioLAfFQF7cXig7MdlBZ_bnAbkjpwCcP/view?usp=sharing) and fill in the code to compute a CDF of the players ages using no built-in functions of NumPy other than sort. Plot the resulting CDF.\n",
    "\n",
    "Complete the blanks wherever given as TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba588294-30a8-4af3-96ee-b964d59dcad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import _____ as pd #TODO Import pandas with the alias pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07768af-897a-470b-90cf-c7f245c83fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading the dataset\n",
    "df = pd.read_csv('_________') #TODO, read the dataset by completing the function read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e63b225-5e3e-4f49-a555-4f7acb800211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Position</th>\n",
       "      <th>Height(inches)</th>\n",
       "      <th>Weight(pounds)</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adam_Donachie</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>74</td>\n",
       "      <td>180.0</td>\n",
       "      <td>22.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul_Bako</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>74</td>\n",
       "      <td>215.0</td>\n",
       "      <td>34.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ramon_Hernandez</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>72</td>\n",
       "      <td>210.0</td>\n",
       "      <td>30.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin_Millar</td>\n",
       "      <td>BAL</td>\n",
       "      <td>First_Baseman</td>\n",
       "      <td>72</td>\n",
       "      <td>210.0</td>\n",
       "      <td>35.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris_Gomez</td>\n",
       "      <td>BAL</td>\n",
       "      <td>First_Baseman</td>\n",
       "      <td>73</td>\n",
       "      <td>188.0</td>\n",
       "      <td>35.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brian_Roberts</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Second_Baseman</td>\n",
       "      <td>69</td>\n",
       "      <td>176.0</td>\n",
       "      <td>29.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Miguel_Tejada</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Shortstop</td>\n",
       "      <td>69</td>\n",
       "      <td>209.0</td>\n",
       "      <td>30.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Melvin_Mora</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Third_Baseman</td>\n",
       "      <td>71</td>\n",
       "      <td>200.0</td>\n",
       "      <td>35.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Aubrey_Huff</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Third_Baseman</td>\n",
       "      <td>76</td>\n",
       "      <td>231.0</td>\n",
       "      <td>30.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adam_Stern</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Outfielder</td>\n",
       "      <td>71</td>\n",
       "      <td>180.0</td>\n",
       "      <td>27.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name Team        Position  Height(inches)  Weight(pounds)    Age\n",
       "0    Adam_Donachie  BAL         Catcher              74           180.0  22.99\n",
       "1        Paul_Bako  BAL         Catcher              74           215.0  34.69\n",
       "2  Ramon_Hernandez  BAL         Catcher              72           210.0  30.78\n",
       "3     Kevin_Millar  BAL   First_Baseman              72           210.0  35.43\n",
       "4      Chris_Gomez  BAL   First_Baseman              73           188.0  35.71\n",
       "5    Brian_Roberts  BAL  Second_Baseman              69           176.0  29.39\n",
       "6    Miguel_Tejada  BAL       Shortstop              69           209.0  30.77\n",
       "7      Melvin_Mora  BAL   Third_Baseman              71           200.0  35.07\n",
       "8      Aubrey_Huff  BAL   Third_Baseman              76           231.0  30.19\n",
       "9       Adam_Stern  BAL      Outfielder              71           180.0  27.05"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the top 10 elements of the dataframe using the head function\n",
    "df.________ #TODO, Show the top 10 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d45bf3a-4aba-407c-8822-0667551f37d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking out the column 'Age' and converting it to a list\n",
    "#this is advisable for a better code\n",
    "#Convert the values from column Age to a list\n",
    "ages = df['Age'].to_list() #TODO, Store the values of Ages from the dataset in the variable ages in the form of a list\n",
    "\n",
    "#sort the list of ages\n",
    "sorted_ages = np.sort(ages) #TODO, Use np.sort to sort the list of ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11a2f325-3316-4cb7-9903-9cf5c49196b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22.99, 34.69, 30.78, 35.43, 35.71, 29.39, 30.77, 35.07, 30.19]\n",
      "[20.9  21.46 21.52 21.58 21.78 21.85 21.9  22.02 22.06]\n"
     ]
    }
   ],
   "source": [
    "#printing the first 10 elements of the arrays\n",
    "#this step is not required; arrays printed for better understanding\n",
    "print(ages[0:9])\n",
    "print(sorted_ages[0:9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c448661-f3e9-40c3-9c61-292adbb0d071",
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a function for calculating the CDF\n",
    "#Complete the below code to calculate the CDF\n",
    "def calc_cdf(sorted_data):\n",
    "    #calculating proportional values\n",
    "    p = 1. * np.________(1,len(sorted_data)+1) / (len(sorted_data)) #TODO, calculate the proportional values from the data and return them\n",
    "    return ______"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "08b4742b-a1d3-41bc-956c-6f05998a58f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calling the function to calculate cdf of ages\n",
    "cdf_ages = calc_cdf(_________) #TODO, calculate cdf of ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea3bb4f3-3d9b-4c16-a52e-94378dd1b2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ePPe0fv36afz48UpOTq42/5///GfNmzdPe/fudU975ZVX9MILL+jgwYM1vkdpaalKS0vdzx0Oh6Kjo5Wfn6/Q0NBG/DSAb5uzbI9eWbpbJeUude/YWh9NGyl7cIDZsQC0EA6HQ3a7vU7f36btuSkrK1NaWpqSkpKqTE9KStLq1atrXGbEiBE6dOiQUlJSZBiGjh49qn//+9+69tpra32f5ORk2e129yM6OrpRPwfQEmw9nK8/ffmDSspd6hMWon9OGUaxAeCxTCs3ubm5cjqdCgsLqzI9LCxM2dnZNS4zYsQILVy4UBMmTJDNZlN4eLjatm2rV155pdb3mTVrlvLz892P2vbwAKjOMAzNWbZHP5u/RpLUNzxEX0wfpXB7kMnJAKB2pp9QbLFUHXvGMIxq087Yvn27HnzwQT3xxBNKS0vTF198oYyMDE2dOrXW3x8YGKjQ0NAqDwDnZhiGth9x6LcfbNGfvvxBp8qdGt69g/5x99Ba//8JAJ7CtEvBO3bsKD8/v2p7aXJycqrtzTkjOTlZI0eO1COPPCJJGjBggFq3bq1Ro0bpD3/4gyIiIpo8N+DrDp88pclvrdXunEL3tHsu7a5Hx/Wl2ADwCqbtubHZbEpISFBqamqV6ampqRoxYkSNyxQXF8tqrRrZz6/yKg0Tz4sGfEa506VH3t/sLjY/6dlRvxnbRw9f3YdiA8BrmHoTv5kzZ2rixIlKTEzU8OHD9frrryszM9N9mGnWrFk6fPiw3nnnHUnS9ddfrylTpmjevHm6+uqrlZWVpenTp2vIkCGKjIw086MAXs0wDK0/cEK/+2irfjhaIEn6zdg+uu/yniYnA4D6M7XcTJgwQXl5eXrmmWeUlZWl+Ph4paSkKCYmRpKUlZVV5Z43kydPVkFBgV599VX9+te/Vtu2bXXllVfqj3/8o1kfAfB6hmFoxuJN+njTEUlS+9Y2PZzUR7dewpWFALyTqfe5MUN9rpMHWoLP07N078INkqRbErtoxpjeirAHm5wKAKqqz/c3Y0sBLdxn6VmSKkf1fuHmgSanAYCGM/1ScADm2ZHl0H+3VJabicNiTE4DAI2DcgO0UEdOntKNcyvvBt4m0F+JMe1MTgQAjYNyA7RAFU6Xfv2vzTpV7lSfsBC996thslq51BuAb+CcG6AF+tOSH7RmX54C/a16+bZB6hvOyfUAfAd7boAW5r9bsvTa8n2SpGfHx1NsAPgcyg3Qgnyy6bCm/bPysu/bh3bVLYncywaA76HcAC3Isp05kqRLurXTE9fFmZwGAJoG5QZoIV5fsVefbq68C/H1AyMVFOBnciIAaBqcUAy0AOv2H9dzKTslSVf3D9MNg6JMTgQATYdyA7QAb63KkCRd2bezXpuYaHIaAGhalBvAh1U4XZqzbK8+35otSbr2ogiTEwFA0+OcG8CHvZ92SH/9apckqW2rAF0VF2ZyIgBoeuy5AXxUWYVLry7dI0maelkP3XtZD9mDA0xOBQBNj3ID+KjU7Ud1+OQpdWht0/SrenF1FIAWg8NSgI96c1XlXYiT+odRbAC0KJQbwAftPlqgDZknJUm3DelqbhgAaGaUG8DH/JBdoFtf/06SFNU2WPGRdpMTAUDz4pwbwIdk5Bbp5nmrVVBaoY5tbPrnlKGyWi1mxwKAZkW5AXzInGV7VFBaoZgOrbTwl0PVpV0rsyMBQLOj3AA+wOky9P/+u0P/TjskSXr2hniKDYAWi3IDeLmScqcefn+zPtuSJUm6+yexGtWro8mpAMA8lBvAizlKyvV/c77V3mNFkqSRPTvod9f2k8XCeTYAWi7KDeDF/vl9prvY3DUyVg+N7kWxAdDiUW4AL1Va4dSfvvxBkjR+UKSeuD7O5EQA4Bm4zw3gpZZsOyqny5AkPXF9f5PTAIDnoNwAXigjt0gPLNooSbr1kmi1b20zOREAeA7KDeBl0g6c0M3zVkuSOrS26Un22gBAFZxzA3iRLYdO6qbTxaZbh1b6x91DFWxjUEwA+DH23ABeoqCkXDfPXyNJCg7w0z/uHqro9tyoDwDORrkBvMTvP96qsgqXggKs+mjaCIoNANSCcgN4gT05Bfp40xFJ0oI7h6hveKjJiQDAc1FuAC/wr/WVY0bFRYRqWPcOJqcBAM/GCcWABzMMQx9sOKy/rdwnSXpwdE+TEwGA56PcAB7s5a9366WvdkuSBka3VVJcuMmJAMDzcVgK8FD5p8r1xsoMSdLV/cP0j7uHyGpl3CgAOB/23AAe6r21mSosrVCvzm005/bB8vfj3yIAUBf8tQQ8UGZesV5dukeSdHNCF4oNANQDfzEBD3O8qEz3LkxTQWmFJOn2oV1NTgQA3oVyA3iYh97bqG1HHJKkO0d2U0hQgMmJAMC7UG4AD7PvWJEkafKIbvrdtXEmpwEA70O5ATzIiaIyHT55SpL0y1Gx8uPqKACoN8oN4EHuW7hBkhTToZWi2gabnAYAvBPlBvAQ3+7J1Zp9eZKkicNiZLGw1wYALgTlBvAAB48Xa/riTZKk/7s4Snf/JNbcQADgxbiJH2CyX/9rsz7YUDkwps3fqseu6cdeGwBoAMoNYKIl27LdxSYxpp3uuayHOoUEmpwKALwb5QYw0X/TsyRJNwyK1Mu3XmxyGgDwDZxzA5hk19ECfbLpiCTppwMjTU4DAL6DcgOY5KWvdkmSrBbp0t6dTE4DAL6DcgOY4FSZU9/vOy5J+s3YvgpgYEwAaDT8RQVM8MrS3corKlMrmx8DYwJAI6PcAM2swunS3G/2SpKe/ml/hTIwJgA0KsoN0IxyC0t13Sur3M/HxoebmAYAfBOXggPNJCO3SOPnfKv8U+WSpLt/EqsQ9toAQKOj3ADNJDllh/JPlSu6fbD+dPNADevewexIAOCTTD8sNXfuXMXGxiooKEgJCQlauXLlOecvLS3V448/rpiYGAUGBqpHjx566623miktcGFyCkq0ZPtRSdIfbxpAsQGAJmTqnpvFixdr+vTpmjt3rkaOHKnXXntN48aN0/bt29W1a81XkNxyyy06evSo3nzzTfXs2VM5OTmqqKho5uRA/Tzy/hZJUnT7YI3o0dHkNADg2yyGYRhmvfnQoUM1ePBgzZs3zz2tX79+Gj9+vJKTk6vN/8UXX+jWW2/Vvn371L59+zq9R2lpqUpLS93PHQ6HoqOjlZ+fr9DQ0IZ/COA8svJPaeTzS+UypEfH9dXUy3qYHQkAvI7D4ZDdbq/T97dph6XKysqUlpampKSkKtOTkpK0evXqGpf59NNPlZiYqBdeeEFRUVHq3bu3Hn74YZ06darW90lOTpbdbnc/oqOjG/VzAOez4Nv9chlSpD1I91za3ew4AODzTDsslZubK6fTqbCwsCrTw8LClJ2dXeMy+/bt06pVqxQUFKSPPvpIubm5uu+++3T8+PFaz7uZNWuWZs6c6X5+Zs8N0BxcLkNfbqvcnu+9oqcsFovJiQDA95l+tdTZf+wNw6j1C8DlcslisWjhwoWy2+2SpBdffFE333yz5syZo+Dg4GrLBAYGKjAwsPGDA3WQlnlC+/OKFRRgZXBMAGgmph2W6tixo/z8/KrtpcnJyam2N+eMiIgIRUVFuYuNVHmOjmEYOnToUJPmBS7EVzsqr5AaGttB9mDuaQMAzcG0cmOz2ZSQkKDU1NQq01NTUzVixIgalxk5cqSOHDmiwsJC97Rdu3bJarWqS5cuTZoXqI/84nK9vmKv3l1zQJJ02xAOhQJAczH1PjczZ87UG2+8obfeeks7duzQjBkzlJmZqalTp0qqPF9m0qRJ7vlvv/12dejQQXfeeae2b9+uFStW6JFHHtFdd91V4yEpwAzlTpfGz/1Wz6XsVFGZUwkx7ZQUxzALANBcTD3nZsKECcrLy9MzzzyjrKwsxcfHKyUlRTExMZKkrKwsZWZmuudv06aNUlNT9cADDygxMVEdOnTQLbfcoj/84Q9mfQSgms+3Zisjt0iS9PyNF+mGQVGyWjmRGACai6n3uTFDfa6TB+rL5TJ05V++0f68Yk27ooceubqv2ZEAwCd4xX1uAF/0ytI92p9XLJufVb8YFmN2HABokSg3QCM5dKJYL329S5I0M6m3IuycBwYAZqDcAI3AMAw99el2GYYU27E1QywAgIkoN0Aj+N3HW933tOFmfQBgLsoN0EDHi8r03rqDkqRuHVrpVu5pAwCmMn34BcDbPf5RupwuQ1Ftg7Xs4csZPwoATMaeG6ABjpw8pc+3Vg4hcs9l3Sk2AOABKDfABSopd+qBRRslSe1b2/SLoVz6DQCegHIDXKBFazOVduCEWtv89OYdidyFGAA8BOUGuEBvrMyQJN2c0EUXd21nchoAwBmUG+ACrNt/XIdPnpIkje4XZnIaAMCPUW6AenK5DD3y/mZJ0rDu7XVp704mJwIA/BjlBqinOcsqx49qZfPTszfEmx0HAHAWyg1QDzuyHPpLauX4UXeO7KZeYSEmJwIAnI1yA9RRfnG5fv7G95Ike3CAHriyl8mJAAA1qVe5mTRpkgoKCtzPN2/erPLy8kYPBXgawzA0ecFaHS8qU/eOrfXVzMsUFOBndiwAQA3qVW4WLlyoU6dOuZ+PGjVKBw8ebPRQgKf51/qD2ph5UpL0xPVx6hQSaG4gAECt6lVuDMM453PAFxmGoT99+YMk6f4reuryPp1NTgQAOBfOuQHOobisQrf/7XvlFpZJkiYOZ4gFAPB09R4VfPv27crOrhwo0DAM7dy5U4WFhVXmGTBgQOOkA0z22eYsrdmXJ0l68vo4hYUGmZwIAHA+9S43o0ePrnI46rrrrpMkWSwWGYYhi8Uip9PZeAkBE/03PUuSdMfwGN05MtbkNACAuqhXucnIyGiqHIDHWbQ2U8t3HZMk3XJJtMlpAAB1Va9yExPD+QZoGdIOHNesD9MlSYOi26p/pN3kRACAuqr3YSlJ2r17tz755BPt379fFotFsbGxGj9+vLp3797Y+QBTLN+VK0nyt1q04M5LTE4DAKiPepeb5ORkPfHEE3K5XOrcubMMw9CxY8f06KOP6rnnntPDDz/cFDmBZlPudOn99ZX3b5p6WQ+1bWUzOREAoD7qdSn4smXL9Lvf/U6PP/64cnNzlZWVpezsbHe5efTRR7VixYqmygo0iw83HFJWfomsFmnKKPZGAoC3sRj1uBPfhAkT1LZtW7322ms1vv6rX/1KBQUFWrRoUaMFbGwOh0N2u135+fkKDQ01Ow48TI6jREOe+1qSdNfIWD1xfZzJiQAAUv2+v+u152bt2rWaOHFira9PnDhR3333XX1+JeAxXC5Dk95aK0my+Vk1eUQ3cwMBAC5IvcrN0aNH1a1bt1pfj42Ndd/gD/A2T3y6VTuzKweGffnWQeraoZXJiQAAF6Je5aakpEQ2W+0nVwYEBKisrKzBoYDmln+qXIvWVp5E/Ltr+2ncRREmJwIAXKh6Xy31xhtvqE2bNjW+VlBQ0OBAQHM7UVSm+xZukNNlyOZn1e1Du5odCQDQAPUqN127dtXf/va3884DeIu0Ayc06c3vVVRWOWRI8o0XqZXtgm7/BADwEPX6K75///4migE0v6OOEt00b7UkKaptsGZd01fXDYg0ORUAoKHqdc7N0qVLFRcXJ4fDUe21/Px89e/fXytXrmy0cEBTWrk71/3zO3cPodgAgI+oV7l56aWXNGXKlBqvL7fb7brnnnv04osvNlo4oCkdyCuSJF3dP0w9OtV8HhkAwPvUq9xs3rxZY8eOrfX1pKQkpaWlNTgU0Bw2HTwpSQyKCQA+pt73uQkICKj1dX9/fx07dqzBoYCmlpFbpFV7Kg9LjYkLMzkNAKAx1avcREVFKT09vdbXt2zZoogI7g8Cz7dqT64MQxoY3Vb9IhiGAwB8Sb3KzTXXXKMnnnhCJSUl1V47deqUnnzySV133XWNFg5oCuVOl15bvleSdHnvTianAQA0tnoNnHn06FENHjxYfn5+uv/++9WnTx9ZLBbt2LFDc+bMkdPp1IYNGxQW5rm7+Rk4Ex9tPKQZizdLkjY/mSR7cO2HWgEAnqE+39/1us9NWFiYVq9erXvvvVezZs3SmV5ksVh09dVXa+7cuR5dbIDDJ0/p4fe3SJKuHRBBsQEAH1TvW7HGxMQoJSVFJ06c0J49e2QYhnr16qV27do1RT6gUc3/Zq+crspS/tz/XWRyGgBAU7jg+8y3a9dOl1xySWNmAZpUbmGpFq+rHBzzpQmD2GsDAD6qXicUA97s2z25KnO61CkkUDcM4m7EAOCrKDdoEcoqXJr3TeUVUlf1C5PFYjE5EQCgqVBu0CI89Z9t2pldoJBAf913eQ+z4wAAmhDlBj7veFGZ3l9fea7NPZd1V3T7ViYnAgA0JcoNfJphGLpvYZrKnYai2gbrvst7mh0JANDEKDfwaV9szdZ3+47Lz2rRk9fHyWrlXBsA8HWUG/i0tAMnJEk/S+iipP7hJqcBADQHyg182pbD+ZKkdq1tJicBADQXyg181rGCUq3NOC5JGt23s8lpAADNhXIDn5WSniVJ6tW5jRK7tTc5DQCguVBu4JMMw3APtTAunnNtAKAlodzAJ72ydI+2ZzkkSVfFMVI9ALQklBv4HMMwNH955VALNyd00YAubc0NBABoVqaXm7lz5yo2NlZBQUFKSEjQypUr67Tct99+K39/fw0aNKhpA8Lr/PWr3Souc0qSnrw+zuQ0AIDmZmq5Wbx4saZPn67HH39cGzdu1KhRozRu3DhlZmaec7n8/HxNmjRJo0ePbqak8BZf7ziq2V/vliT9ZmwfhQQFmJwIANDcLIZhGGa9+dChQzV48GDNmzfPPa1fv34aP368kpOTa13u1ltvVa9eveTn56ePP/5YmzZtqvN7OhwO2e125efnKzQ0tCHx4WGOFZTqJ39cqtIKlwZGt9Un00aaHQkA0Ejq8/1t2p6bsrIypaWlKSkpqcr0pKQkrV69utbl3n77be3du1dPPvlknd6ntLRUDoejygO+adaH6SqtcKlNoL/+8rOBZscBAJjEtHKTm5srp9OpsLCqV7KEhYUpOzu7xmV2796tRx99VAsXLpS/v3+d3ic5OVl2u939iI6ObnB2eJ7s/BJ9teOoJGnxPcPUs3MbkxMBAMxi+gnFFkvVgQwNw6g2TZKcTqduv/12Pf300+rdu3edf/+sWbOUn5/vfhw8eLDBmeF5NmRWjiHVpV2w+kfaTU4DADBT3XZ/NIGOHTvKz8+v2l6anJycantzJKmgoEDr16/Xxo0bdf/990uSXC6XDMOQv7+/lixZoiuvvLLacoGBgQoMDGyaDwGP4HQZevvbDElSbMfWJqcBAJjNtD03NptNCQkJSk1NrTI9NTVVI0aMqDZ/aGio0tPTtWnTJvdj6tSp6tOnjzZt2qShQ4c2V3R4mJe+2qV1+0/IapFmjqn7Xj0AgG8ybc+NJM2cOVMTJ05UYmKihg8frtdff12ZmZmaOnWqpMpDSocPH9Y777wjq9Wq+Pj4Kst37txZQUFB1aaj5TAMQwu/r7x1wGPX9NPFXduZnAgAYDZTy82ECROUl5enZ555RllZWYqPj1dKSopiYmIkSVlZWee95w1atqU7c3S8qEx+VotuH9rV7DgAAA9g6n1uzMB9bnzLjXO/1YbMk0qKC9PrkxLNjgMAaCJecZ8boKGKSiu0IfOkJOm+K3qaGwYA4DEoN/Baf/xipyQpJNBfA6K4/BsAUIlyA69kGIZS0rMkSXf+JFZWa/V7IwEAWibKDbxSVn6JcgvLJEmTR3QzNwwAwKNQbuCV1uzNkyT1CQtR+9Y2k9MAADwJ5QZe6UBekSQpLpIr3gAAVVFu4HUMw9Dq03tuurZvZXIaAICnodzA63y1I0frD5yQzd/KjfsAANVQbuBVDMPQE59slST9YmiMwkKDTE4EAPA0lBt4lXe/O6Cs/BJZLNJDo3uZHQcA4IEoN/AaJ4rK9PtPtkmSbh7cRfZWASYnAgB4IsoNvIJhGHrk31vcz5+4Ps7ENAAAT0a5gVdYm3FcX+04KotFmnP7YIUEsdcGAFAzyg28wtqM45Kk0X0769oBESanAQB4MsoNvMLnW7MlSYOi25obBADg8Sg38Hg7shzanuWQJN0wKMrkNAAAT0e5gcd7+9sMSdKIHh0UzR2JAQDnQbmBR9uYeUIfbDgsSXrk6j4mpwEAeAPKDTzaE59sk9NlaExcmC7u2s7sOAAAL0C5gcdaufuY0g/nS5J+O7avyWkAAN6CcgOP9c6aA5Kkn/TsqJ6d25icBgDgLSg38EiZecVaujNHkjQzqbfJaQAA3oRyA4+07IccOV2G4qNCNZhzbQAA9UC5gcfJKyzVgtX7JUk9OnE4CgBQP5QbeJw3VmUoI7dIkjRlVHeT0wAAvA3lBh7n001HJEkzruqt+Ci7yWkAAN6GcgOPsmRbtg6fPCV/q0W3DY02Ow4AwAtRbuAxCksrdP8/N0qSJlwSrc4hQSYnAgB4I8oNPMbyH46pzOlSK5uffnUp59oAAC4M5QYeY9PBE5Kk0f3CFNOhtclpAADeinIDj1BQUu6+/PvKvp3MDQMA8GqUG3iEF1N3qdxpKMIepGsvijQ7DgDAi1Fu4BG+2JotSXrgyl6y+bNZAgAuHN8iMN2xglJl5ZdIkkb362xyGgCAt6PcwHT/TjskSeoXEaqwUC7/BgA0DOUGpso/Va531uyXJN00OMrcMAAAn0C5gak+23JEWfkl6hwSqAmXcEdiAEDDUW5gmlNlTv399OXfNwyKVEhQgLmBAAA+gXID09y7ME27jhbK32rRL4bFmB0HAOAjKDcwxXf78vTND8ckSb8d25c7EgMAGg3lBs2uwunSPf9IkyRd1a+zpjCOFACgEVFu0Oye+HSb8k+VS5KSbxxgchoAgK+h3KBZuVyGPt54WJI046re6hQSaHIiAICvodygWb237qCKy5wK9Lfqnss4HAUAaHyUGzQbl8vQX7/aJUmaOCxGQQF+JicCAPgiyg2azZbD+TpWUCpJmjGmt8lpAAC+inKDZvPppiOSpIFd7God6G9yGgCAr6LcoFnkFpZqweoMSdLdozjXBgDQdCg3aHKGYejBRRvlMqQ+YSEaFx9udiQAgA+j3KDJ/XC0QKv35kmSXpwwUAF+bHYAgKbDtwya3KrduZKkQdFt1T/SbnIaAICvo9ygSRmGoQWnR/4eExdmbhgAQItAuUGT2nusUIdOnJIk/Syxi8lpAAAtAeUGTeqDDZVDLfQOa6POIUEmpwEAtASUGzSpT06PIzVxWIzJSQAALQXlBk3mu315OpJfIkkaE8fl3wCA5mF6uZk7d65iY2MVFBSkhIQErVy5stZ5P/zwQ40ZM0adOnVSaGiohg8fri+//LIZ06KuDMPQb/69RZI0Lj5c4XYOSQEAmoep5Wbx4sWaPn26Hn/8cW3cuFGjRo3SuHHjlJmZWeP8K1as0JgxY5SSkqK0tDRdccUVuv7667Vx48ZmTo7zeX3FPmUeL5YkTRzOISkAQPOxGIZhmPXmQ4cO1eDBgzVv3jz3tH79+mn8+PFKTk6u0+/o37+/JkyYoCeeeKJO8zscDtntduXn5ys0NPSCcuPcSsqdGvj0EpVWuBQeGqQVv7lCNn/TdxICALxYfb6/TfvGKSsrU1pampKSkqpMT0pK0urVq+v0O1wulwoKCtS+ffta5yktLZXD4ajyQNN6+evdKq1wSZLenJxIsQEANCvTvnVyc3PldDoVFlb1xm5hYWHKzs6u0+/4y1/+oqKiIt1yyy21zpOcnCy73e5+REdHNyg3zm/1nso7Ek+7ogd3JAYANDvT/0ltsViqPDcMo9q0mixatEhPPfWUFi9erM6dO9c636xZs5Sfn+9+HDx4sMGZUbtyp0s7sgskcYUUAMAc/ma9cceOHeXn51dtL01OTk61vTlnW7x4se6++269//77uuqqq845b2BgoAIDAxucF3WTdbJEZRUu+Vstio/knCYAQPMzbc+NzWZTQkKCUlNTq0xPTU3ViBEjal1u0aJFmjx5sv75z3/q2muvbeqYqKcNmSckSUEBfvJn9G8AgAlM23MjSTNnztTEiROVmJio4cOH6/XXX1dmZqamTp0qqfKQ0uHDh/XOO+9Iqiw2kyZN0ssvv6xhw4a59/oEBwfLbufcDrOt339cv/9kqyQppkMrk9MAAFoqU8vNhAkTlJeXp2eeeUZZWVmKj49XSkqKYmIq74uSlZVV5Z43r732mioqKjRt2jRNmzbNPf2OO+7QggULmjs+fmT7EYd+8eb3Kil3qX9kqN65a4jZkQAALZSp97kxA/e5aRpJf12uXUcLdXHXtlr4y6FqZTO1NwMAfIxX3OcGvuMPn23XrqOFkqTZt15MsQEAmIpygwZZm3Fcb6zKkCQN695e0e051wYAYC7KDRrkvbWV50QN6dZeC+7kPBsAgPkoN7hgeYWl+s+WI5KkmUm9FRTgZ3IiAAAoN2iAQydOqdxpqHNIoIZ172B2HAAAJFFucIFKK5x6/ON0SZI9OMDkNAAA/A/lBhfkzrfXaevhyhHWJ1zCYKQAAM9BuUG9fbcvT6v35kmSHr+mn345qrvJiQAA+B/KDertH98dkCSNiw/XlEspNgAAz0K5Qb1syDyhL7ZWjul1c0IXk9MAAFAd5QZ1VlLu1NR/pMnpMjS2f7hG9wszOxIAANVQblBnz6XsUE5BqfysFj10VS+z4wAAUCMGAcJ5GYah+cv3aeH3lXcjnv+LBPWLYNBRAIBnotzgvJ75bLve/na/JOm2IV01Jo7DUQAAz0W5wTnlnyp3F5uHk3pr2hU9zQ0EAMB5cM4NzmnzwZOSpOAAP027oqcsFou5gQAAOA/KDc4pJT1LknR5n04UGwCAV6DcoFYZuUX6YMMhSdLkEd3MDQMAQB1RblCr332crnKnoUu6tdNQRv0GAHgJyg1qdPjkKX27p3L8qEfH9TM5DQAAdUe5QY3mLNsjSRrQxa6EmHYmpwEAoO4oN6gmO79E/zx9w74pjPgNAPAylBtUs3b/cUlSu1YBun5gpMlpAACoH8oNqnkpdZckadxFESYnAQCg/ig3qOLz9Cztyy2SxCEpAIB3otzA7VhBqX77wRZJ0nUDIhTbsbXJiQAAqD/KDdzeXJUhR0mFIu1Beu7Gi8yOAwDABaHcQJL0/vqDen3FXknSz4fFKDQowOREAABcGEYFh9bvP65H/r3F/fy6AZxIDADwXpQbaO43lXtsundsrY+mjZQ9mL02AADvRblpwQzD0POf79TSnTmSpJdvvZhiAwDwepxz04J9vjVbr63YJ6ly1O+LuthNTgQAQMNRblqoDzcc0n0LN0iSrr0oQk/9tL/JiQAAaBwclmqB3lyVoWc/2y5JCgqwUmwAAD6FctPCFJdV6A//rSw2I3t20NzbE2RvxXk2AADfQblpYdIP5cswKn9+566h8rNazA0EAEAj45ybFmTZDzl6YNFGSdKoXh0pNgAAn8SemxaitMKphxZtlKOkQpJ0c0IXkxMBANA0KDctxOJ1B93F5tP7R2pAl7bmBgIAoIlwWKoFKCl36oUvfpAk3XhxFMUGAODTKDctwPOf71RhaeVem2fHx5ucBgCApsVhKR9mGIae/WyHFqzeL0maelkPtQ7kf3IAgG9jz40Pe3/9Ib31bYYk6bYhXfXg6J4mJwIAoOnxz3gfVe506dnTN+ubNDxGz9zA4SgAQMvAnhsf9fqKfSo4fXXUzxKiTU4DAEDzodz4oK93HNWfvqy8OuqO4TGM9g0AaFE4LOVDlu86pj98tl27cwolVY4d9ei4fianAgCgeVFufMTqPbm646217ucDutj16m2DFWzzMzEVAADNj3LjA/YeK9Ttb3wvSWoT6K9/3ztcfcNDTU4FAIA5KDderKTcqb9+tUt/W7HPPe3T+0eqe6c2JqYCAMBclBsvtf2IQw+9t9F9fs2VfTtr+lW9KDYAgBaPcuOFvt+Xp1v/9p0MQ+oUEqjn/u8ijYkLMzsWAAAegXLjRQzD0K/f36wPNxyWJAX6W/Wf+3+icHuQyckAAPAc3OfGiyzdmeMuNoOi2+rL6ZdSbAAAOAt7brzEqt25mv7eJknSzxK66E8/G2huIAAAPBTlxoPtySnUil3HtDunQP9af0hOl6G+4SH6dVIfs6MBAOCxTD8sNXfuXMXGxiooKEgJCQlauXLlOedfvny5EhISFBQUpO7du2v+/PnNlLTpGYahg8eLtXTnUT2waKPG/HW5nvlsuxatPSiny9BlvTvp42kjORQFAMA5mLrnZvHixZo+fbrmzp2rkSNH6rXXXtO4ceO0fft2de3atdr8GRkZuuaaazRlyhS9++67+vbbb3XfffepU6dOuummm0z4BBeurMKlQyeKlVNQqqOOEm074lBKepYOnThVZb5RvTrqoii74iJDNbZ/uPz9TO+jAAB4NIthGIZZbz506FANHjxY8+bNc0/r16+fxo8fr+Tk5Grz//a3v9Wnn36qHTt2uKdNnTpVmzdv1po1a+r0ng6HQ3a7Xfn5+QoNbby7+DpdhnIKSlRS7lJJufP0w6WSCqdKz/x8evqmgyf11Y4cFZZWVPs9Nj+rundqrfgou+4aGau4SO40DABAfb6/TdtzU1ZWprS0ND366KNVpiclJWn16tU1LrNmzRolJSVVmXb11VfrzTffVHl5uQICAqotU1paqtLSUvdzh8PRCOmrO+oo0Yjnl9ZrmVY2P4WHBqlzaKC6tGul0X076/I+nRkPCgCABjCt3OTm5srpdCosrOrN58LCwpSdnV3jMtnZ2TXOX1FRodzcXEVERFRbJjk5WU8//XTjBa9FcICf/K0WBQX4KSjAevq/p3/2/9/PgQF+irQHaWx8uC6Obier1dLk2QAAaElMv1rKYqn65W4YRrVp55u/pulnzJo1SzNnznQ/dzgcio6OvtC4tWrX2qY9z13T6L8XAADUj2nlpmPHjvLz86u2lyYnJ6fa3pkzwsPDa5zf399fHTp0qHGZwMBABQYGNk5oAADg8Uy79MZmsykhIUGpqalVpqempmrEiBE1LjN8+PBq8y9ZskSJiYk1nm8DAABaHlOvK545c6beeOMNvfXWW9qxY4dmzJihzMxMTZ06VVLlIaVJkya55586daoOHDigmTNnaseOHXrrrbf05ptv6uGHHzbrIwAAAA9j6jk3EyZMUF5enp555hllZWUpPj5eKSkpiomJkSRlZWUpMzPTPX9sbKxSUlI0Y8YMzZkzR5GRkZo9e7bX3eMGAAA0HVPvc2OGprrPDQAAaDr1+f7mdrcAAMCnUG4AAIBPodwAAACfQrkBAAA+hXIDAAB8CuUGAAD4FMoNAADwKZQbAADgUyg3AADAp5g6/IIZztyQ2eFwmJwEAADU1Znv7boMrNDiyk1BQYEkKTo62uQkAACgvgoKCmS32885T4sbW8rlcunIkSMKCQmRxWKp9rrD4VB0dLQOHjzI2FMNwHpsHKzHhmMdNg7WY8OxDhvGMAwVFBQoMjJSVuu5z6ppcXturFarunTpct75QkND2fgaAeuxcbAeG4512DhYjw3HOrxw59tjcwYnFAMAAJ9CuQEAAD6FcnOWwMBAPfnkkwoMDDQ7ildjPTYO1mPDsQ4bB+ux4ViHzafFnVAMAAB8G3tuAACAT6HcAAAAn0K5AQAAPoVyAwAAfEqLLDfJycm65JJLFBISos6dO2v8+PH64YcfqsxjGIaeeuopRUZGKjg4WJdffrm2bdtmUmLPVJf1OHnyZFksliqPYcOGmZTYM82bN08DBgxw39hr+PDh+vzzz92vsy2e3/nWIdvhhUlOTpbFYtH06dPd09ge66emdcj22PRaZLlZvny5pk2bpu+++06pqamqqKhQUlKSioqK3PO88MILevHFF/Xqq69q3bp1Cg8P15gxY9xjU6Fu61GSxo4dq6ysLPcjJSXFpMSeqUuXLnr++ee1fv16rV+/XldeeaVuuOEG9xcG2+L5nW8dSmyH9bVu3Tq9/vrrGjBgQJXpbI91V9s6lNgem5wBIycnx5BkLF++3DAMw3C5XEZ4eLjx/PPPu+cpKSkx7Ha7MX/+fLNieryz16NhGMYdd9xh3HDDDeaF8lLt2rUz3njjDbbFBjizDg2D7bC+CgoKjF69ehmpqanGZZddZjz00EOGYfC3sT5qW4eGwfbYHFrknpuz5efnS5Lat28vScrIyFB2draSkpLc8wQGBuqyyy7T6tWrTcnoDc5ej2d888036ty5s3r37q0pU6YoJyfHjHhewel06r333lNRUZGGDx/OtngBzl6HZ7Ad1t20adN07bXX6qqrrqoyne2x7mpbh2ewPTatFjdw5tkMw9DMmTP1k5/8RPHx8ZKk7OxsSVJYWFiVecPCwnTgwIFmz+gNalqPkjRu3Dj97Gc/U0xMjDIyMvT73/9eV155pdLS0rhL54+kp6dr+PDhKikpUZs2bfTRRx8pLi7O/YXBtnh+ta1Die2wPt577z1t2LBB69atq/Yafxvr5lzrUGJ7bA4tvtzcf//92rJli1atWlXtNYvFUuW5YRjVpqFSbetxwoQJ7p/j4+OVmJiomJgY/fe//9WNN97Y3DE9Vp8+fbRp0yadPHlSH3zwge644w4tX77c/Trb4vnVtg7j4uLYDuvo4MGDeuihh7RkyRIFBQXVOh/bY+3qsg7ZHpteiz4s9cADD+jTTz/VsmXL1KVLF/f08PBwSf/7V8oZOTk51f7FgtrXY00iIiIUExOj3bt3N1M672Cz2dSzZ08lJiYqOTlZAwcO1Msvv8y2WA+1rcOasB3WLC0tTTk5OUpISJC/v7/8/f21fPlyzZ49W/7+/u5tju2xdudbh06ns9oybI+Nr0WWG8MwdP/99+vDDz/U0qVLFRsbW+X12NhYhYeHKzU11T2trKxMy5cv14gRI5o7rsc633qsSV5eng4ePKiIiIhmSOi9DMNQaWkp22IDnFmHNWE7rNno0aOVnp6uTZs2uR+JiYn6+c9/rk2bNql79+5sj+dxvnXo5+dXbRm2xyZg2qnMJrr33nsNu91ufPPNN0ZWVpb7UVxc7J7n+eefN+x2u/Hhhx8a6enpxm233WZEREQYDofDxOSe5XzrsaCgwPj1r39trF692sjIyDCWLVtmDB8+3IiKimI9/sisWbOMFStWGBkZGcaWLVuMxx57zLBarcaSJUsMw2BbrItzrUO2w4Y5+0oftsf6+/E6ZHtsHi2y3Eiq8fH222+753G5XMaTTz5phIeHG4GBgcall15qpKenmxfaA51vPRYXFxtJSUlGp06djICAAKNr167GHXfcYWRmZpob3MPcddddRkxMjGGz2YxOnToZo0ePdhcbw2BbrItzrUO2w4Y5u9ywPdbfj9ch22PzsBiGYZizzwgAAKDxtchzbgAAgO+i3AAAAJ9CuQEAAD6FcgMAAHwK5QYAAPgUyg0AAPAplBsAAOBTKDcAAMCnUG4AAIBPodwA8DiTJ0+WxWKRxWJRQECAwsLCNGbMGL311ltyuVx1/j0LFixQ27Ztmy4oAI9EuQHgkcaOHausrCzt379fn3/+ua644go99NBDuu6661RRUWF2PAAejHIDwCMFBgYqPDxcUVFRGjx4sB577DF98skn+vzzz7VgwQJJ0osvvqiLLrpIrVu3VnR0tO677z4VFhZKkr755hvdeeedys/Pd+8FeuqppyRJ7777rhITExUSEqLw8HDdfvvtysnJMemTAmhslBsAXuPKK6/UwIED9eGHH0qSrFarZs+era1bt+rvf/+7li5dqt/85jeSpBEjRuill15SaGiosrKylJWVpYcffliSVFZWpmeffVabN2/Wxx9/rIyMDE2ePNmsjwWgkfmbHQAA6qNv377asmWLJGn69Onu6bGxsXr22Wd17733au7cubLZbLLb7bJYLAoPD6/yO+666y73z927d9fs2bM1ZMgQFRYWqk2bNs3yOQA0HfbcAPAqhmHIYrFIkpYtW6YxY8YoKipKISEhmjRpkvLy8lRUVHTO37Fx40bdcMMNiomJUUhIiC6//HJJUmZmZlPHB9AMKDcAvMqOHTsUGxurAwcO6JprrlF8fLw++OADpaWlac6cOZKk8vLyWpcvKipSUlKS2rRpo3fffVfr1q3TRx99JKnycBUA78dhKQBeY+nSpUpPT9eMGTO0fv16VVRU6C9/+Yus1sp/p/3rX/+qMr/NZpPT6awybefOncrNzdXzzz+v6OhoSdL69eub5wMAaBbsuQHgkUpLS5Wdna3Dhw9rw4YNeu6553TDDTfouuuu06RJk9SjRw9VVFTolVde0b59+/SPf/xD8+fPr/I7unXrpsLCQn399dfKzc1VcXGxunbtKpvN5l7u008/1bPPPmvSpwTQFCg3ADzSF198oYiICHXr1k1jx47VsmXLNHv2bH3yySfy8/PToEGD9OKLL+qPf/yj4uPjtXDhQiUnJ1f5HSNGjNDUqVM1YcIEderUSS+88II6deqkBQsW6P3331dcXJyef/55/fnPfzbpUwJoChbDMAyzQwAAADQW9twAAACfQrkBAAA+hXIDAAB8CuUGAAD4FMoNAADwKZQbAADgUyg3AADAp1BuAACAT6HcAAAAn0K5AQAAPoVyAwAAfMr/B+yNsULWsujJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the CDF\n",
    "#Complete the below code to plot the CDF\n",
    "plt._______(sorted_ages, cdf_ages) #TODO, Plot the cdf of ages\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('CDF')\n",
    "##Complete the below code to show the plot\n",
    "plt.__________ #TODO, Show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54ab587d-d0f3-4714-801a-873fc56381a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NOTE - a neat trick to cross check your answer is to use a built-in function\n",
    "cdf_check_ages = np.cumsum(sorted_ages)\n",
    "#plotting the CDF\n",
    "plt.plot(sorted_ages, cdf_check_ages)\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('CDF')\n",
    "plt.show()\n",
    "#The above is only for your referance and need not be completed, you have your own user defined function finding the cumulative sum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394dcbd3-83e5-4ae5-bb0c-eb6276825173",
   "metadata": {},
   "source": [
    "**Problem L - 10 points**\n",
    "\n",
    "Next we will consider the players heights and weights, which taken together are bivariate data. First, plot a scatterplot of the two variables against each other. Second, plot the CDFs of the marginals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54015d43-014f-4663-bd1b-bb11705555e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting scatterplot of heights and weights\n",
    "#Use the scatter function in plt to plot the scatter plot of Height and Weight\n",
    "plt.scatter(_________, _________) #TODO, complete the scatter function with the height and weight values from the dataset\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4df17ca2-42b3-4f75-9828-da9e2dd0bcb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Below we calculate the cdf_height and cdf_weight, enter the function name to complete the code\n",
    "cdf_height = calc_cdf(np.____(df['Height(inches)'])) #TODO, find the cdf of height\n",
    "cdf_weight = calc_cdf(np.sort(df[________________])) #TODO, find the cdf_ of weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a26216fd-0fdf-4032-a098-f271dd47db55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the CDF\n",
    "sorted_height = np.sort(df['Height(inches)'])\n",
    "plt.____(___________, cdf_height) #TODO, plot the cdf\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('CDF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de73d57c-dbfd-4c38-bacb-18108edd7f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the CDF\n",
    "sorted_weight = np.____(df['Weight(pounds)']) #TODO, Store the sorted heights\n",
    "plt.plot(sorted_weight, ________) #TODO, Plot the sorted weight\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('CDF')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}