project-proposal-group6.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "019c4198-5631-4928-8a7f-2a76080bf8e2",
   "metadata": {},
   "source": [
    "<H3>PROJECT PROPOSAL</H3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd961fe-fead-4746-97e3-25cf5c2e1bbe",
   "metadata": {},
   "source": [
    "<h2> Relationship Between Gender & Mean Age of Football Olympic Athletes</h2>\n",
    "<h6>Group 06 :  Alejandro Solano <b>Romero</b>, Apram <b>Ahuja</b>, Aron <b>Bahram</b> , Ruhani <b>Kaur</b> </h6>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf95a15-c77f-4e82-b4ac-16bab770aa05",
   "metadata": {},
   "source": [
    "<h3>👋 INTRODUCTION</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44984b89-6094-4300-9d7b-6537f7874b12",
   "metadata": {},
   "source": [
    "The aim of this project is to analyze the historical data of Olympic athletes and their performance in various events. The data source for this project is the Kaggle dataset <a href = \"https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results\">120 years of Olympic history: athletes and results</a>, which was obtained from the <a href = \" https://www.sports-reference.com/\">Sports Reference</a>.\n",
    "\n",
    "The dataset consists of 271116 records, each representing an individual athlete who participated in an Olympic event. The dataset has 15 attributes, ```ID```, `Name`, `Sex`, `Age`, `Height`, `Weight`, `Team`, `NOC`, `Games`, `Year`, `Season`, `City`, `Sport`, `Event` & `Medal`. The dataset covers the period from 1896 to 2016 and includes both summer and winter Olympics.\n",
    "\n",
    "<h5>Research Question</h5>\n",
    "<hr/>\n",
    "We'll be investigating the question <b>whether gender has any effect on the mean age of Olympic athletes in Football from the year 1900 onwards.</b>\n",
    "\n",
    "Our parameter of interest would be the <u>difference of mean age in male and female football olympic athletes</u> from 1900 onwards. As well as, the scale parameter that is useful in answering this question would be the <u>standard deviation of age in male and female olympic athletes<u/>.\n",
    "\n",
    "We were interested in this research question as, the study by Bongard, V. shows negative relationship between age and athletes performance in olympics. Other study states that the decline in performance due to age were more in females football athletes, due to delayed maturation that has been observed in higher-skilled football athletes and differences within the sport in the relative age effect. These results inspire us to dig more into detail for this inferential question.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa88c37d-df8f-45fd-8f03-a48b2119df9c",
   "metadata": {},
   "source": [
    "<h2>🧑‍💻 PRELIMENARY RESULTS</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc0da3b-ff3c-42a0-b356-720f924a5b21",
   "metadata": {},
   "source": [
    "We downloaded the dataset from : \n",
    "https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results \n",
    "\n",
    "It was then saved in the ```data/``` in our main github repository\n",
    "\n",
    "We installed & loaded all the libraries that we might need to pre-process and visualise the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4233a2b-959c-4f17-8b8d-8bd3e8f7d4b1",
   "metadata": {},
   "source": [
    "<h5>Install packages on R kernel</h5>\n",
    "\n",
    "If these packages are not installed on R kernel then, Run the code cell below once to install packages before loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7af9a6ec-4c05-4b99-85f1-de52369656b6",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/jupyter/R/x86_64-pc-linux-gnu-library/4.3’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Installing package into ‘/home/jupyter/R/x86_64-pc-linux-gnu-library/4.3’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Skipping install of 'datateachr' from a github remote, the SHA1 (78d391f4) has not changed since last install.\n",
      "  Use `force = TRUE` to force installation\n",
      "\n",
      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.3     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n",
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      "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.2     \u001b[32m✔\u001b[39m \u001b[34mtidyr    \u001b[39m 1.3.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mpurrr    \u001b[39m 1.0.1     \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
      "Skipping install of 'taxyvr' from a github remote, the SHA1 (227a8d8f) has not changed since last install.\n",
      "  Use `force = TRUE` to force installation\n",
      "\n",
      "Installing package into ‘/home/jupyter/R/x86_64-pc-linux-gnu-library/4.3’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages('cowplot')\n",
    "install.packages(\"devtools\")\n",
    "devtools::install_github(\"UBC-MDS/datateachr\")\n",
    "library(datateachr)\n",
    "library(tidyverse)\n",
    "devtools::install_github(\"UBC-MDS/taxyvr\", build_vignettes = TRUE)\n",
    "library(taxyvr)\n",
    "install.packages('infer', dependencies = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ff0ed7-7653-43eb-9955-b51e380821f6",
   "metadata": {},
   "source": [
    "<h5>Loading necessary libraries</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2be539b-5012-4a0c-aab3-3be4e819c41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘cowplot’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:lubridate’:\n",
      "\n",
      "    stamp\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(cowplot)\n",
    "library(datateachr)\n",
    "library(dplyr)\n",
    "library(digest)\n",
    "library(infer)\n",
    "library(repr)\n",
    "library(taxyvr)\n",
    "library(tidyverse)\n",
    "library(ggplot2)\n",
    "options(repr.matrix.max.rows = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb22dd40-ed2a-4680-a8c1-05e176ba05a4",
   "metadata": {},
   "source": [
    "<H3>Dataset</H3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3279719-1523-44e8-ab29-390a56ef8abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m271116\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m15\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (10): Name, Sex, Team, NOC, Games, Season, City, Sport, Event, Medal\n",
      "\u001b[32mdbl\u001b[39m  (5): ID, Age, Height, Weight, Year\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 15</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>ID</th><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Height</th><th scope=col>Weight</th><th scope=col>Team</th><th scope=col>NOC</th><th scope=col>Games</th><th scope=col>Year</th><th scope=col>Season</th><th scope=col>City</th><th scope=col>Sport</th><th scope=col>Event</th><th scope=col>Medal</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>A Dijiang               </td><td>M</td><td>24</td><td>180</td><td>80</td><td>China         </td><td>CHN</td><td>1992 Summer</td><td>1992</td><td>Summer</td><td>Barcelona</td><td>Basketball   </td><td>Basketball Men's Basketball       </td><td>NA  </td></tr>\n",
       "\t<tr><td>2</td><td>A Lamusi                </td><td>M</td><td>23</td><td>170</td><td>60</td><td>China         </td><td>CHN</td><td>2012 Summer</td><td>2012</td><td>Summer</td><td>London   </td><td>Judo         </td><td>Judo Men's Extra-Lightweight      </td><td>NA  </td></tr>\n",
       "\t<tr><td>3</td><td>Gunnar Nielsen Aaby     </td><td>M</td><td>24</td><td> NA</td><td>NA</td><td>Denmark       </td><td>DEN</td><td>1920 Summer</td><td>1920</td><td>Summer</td><td>Antwerpen</td><td>Football     </td><td>Football Men's Football           </td><td>NA  </td></tr>\n",
       "\t<tr><td>4</td><td>Edgar Lindenau Aabye    </td><td>M</td><td>34</td><td> NA</td><td>NA</td><td>Denmark/Sweden</td><td>DEN</td><td>1900 Summer</td><td>1900</td><td>Summer</td><td>Paris    </td><td>Tug-Of-War   </td><td>Tug-Of-War Men's Tug-Of-War       </td><td>Gold</td></tr>\n",
       "\t<tr><td>5</td><td>Christine Jacoba Aaftink</td><td>F</td><td>21</td><td>185</td><td>82</td><td>Netherlands   </td><td>NED</td><td>1988 Winter</td><td>1988</td><td>Winter</td><td>Calgary  </td><td>Speed Skating</td><td>Speed Skating Women's 500 metres  </td><td>NA  </td></tr>\n",
       "\t<tr><td>5</td><td>Christine Jacoba Aaftink</td><td>F</td><td>21</td><td>185</td><td>82</td><td>Netherlands   </td><td>NED</td><td>1988 Winter</td><td>1988</td><td>Winter</td><td>Calgary  </td><td>Speed Skating</td><td>Speed Skating Women's 1,000 metres</td><td>NA  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 15\n",
       "\\begin{tabular}{lllllllllllllll}\n",
       " ID & Name & Sex & Age & Height & Weight & Team & NOC & Games & Year & Season & City & Sport & Event & Medal\\\\\n",
       " <dbl> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & A Dijiang                & M & 24 & 180 & 80 & China          & CHN & 1992 Summer & 1992 & Summer & Barcelona & Basketball    & Basketball Men's Basketball        & NA  \\\\\n",
       "\t 2 & A Lamusi                 & M & 23 & 170 & 60 & China          & CHN & 2012 Summer & 2012 & Summer & London    & Judo          & Judo Men's Extra-Lightweight       & NA  \\\\\n",
       "\t 3 & Gunnar Nielsen Aaby      & M & 24 &  NA & NA & Denmark        & DEN & 1920 Summer & 1920 & Summer & Antwerpen & Football      & Football Men's Football            & NA  \\\\\n",
       "\t 4 & Edgar Lindenau Aabye     & M & 34 &  NA & NA & Denmark/Sweden & DEN & 1900 Summer & 1900 & Summer & Paris     & Tug-Of-War    & Tug-Of-War Men's Tug-Of-War        & Gold\\\\\n",
       "\t 5 & Christine Jacoba Aaftink & F & 21 & 185 & 82 & Netherlands    & NED & 1988 Winter & 1988 & Winter & Calgary   & Speed Skating & Speed Skating Women's 500 metres   & NA  \\\\\n",
       "\t 5 & Christine Jacoba Aaftink & F & 21 & 185 & 82 & Netherlands    & NED & 1988 Winter & 1988 & Winter & Calgary   & Speed Skating & Speed Skating Women's 1,000 metres & NA  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 15\n",
       "\n",
       "| ID &lt;dbl&gt; | Name &lt;chr&gt; | Sex &lt;chr&gt; | Age &lt;dbl&gt; | Height &lt;dbl&gt; | Weight &lt;dbl&gt; | Team &lt;chr&gt; | NOC &lt;chr&gt; | Games &lt;chr&gt; | Year &lt;dbl&gt; | Season &lt;chr&gt; | City &lt;chr&gt; | Sport &lt;chr&gt; | Event &lt;chr&gt; | Medal &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | A Dijiang                | M | 24 | 180 | 80 | China          | CHN | 1992 Summer | 1992 | Summer | Barcelona | Basketball    | Basketball Men's Basketball        | NA   |\n",
       "| 2 | A Lamusi                 | M | 23 | 170 | 60 | China          | CHN | 2012 Summer | 2012 | Summer | London    | Judo          | Judo Men's Extra-Lightweight       | NA   |\n",
       "| 3 | Gunnar Nielsen Aaby      | M | 24 |  NA | NA | Denmark        | DEN | 1920 Summer | 1920 | Summer | Antwerpen | Football      | Football Men's Football            | NA   |\n",
       "| 4 | Edgar Lindenau Aabye     | M | 34 |  NA | NA | Denmark/Sweden | DEN | 1900 Summer | 1900 | Summer | Paris     | Tug-Of-War    | Tug-Of-War Men's Tug-Of-War        | Gold |\n",
       "| 5 | Christine Jacoba Aaftink | F | 21 | 185 | 82 | Netherlands    | NED | 1988 Winter | 1988 | Winter | Calgary   | Speed Skating | Speed Skating Women's 500 metres   | NA   |\n",
       "| 5 | Christine Jacoba Aaftink | F | 21 | 185 | 82 | Netherlands    | NED | 1988 Winter | 1988 | Winter | Calgary   | Speed Skating | Speed Skating Women's 1,000 metres | NA   |\n",
       "\n"
      ],
      "text/plain": [
       "  ID Name                     Sex Age Height Weight Team           NOC\n",
       "1 1  A Dijiang                M   24  180    80     China          CHN\n",
       "2 2  A Lamusi                 M   23  170    60     China          CHN\n",
       "3 3  Gunnar Nielsen Aaby      M   24   NA    NA     Denmark        DEN\n",
       "4 4  Edgar Lindenau Aabye     M   34   NA    NA     Denmark/Sweden DEN\n",
       "5 5  Christine Jacoba Aaftink F   21  185    82     Netherlands    NED\n",
       "6 5  Christine Jacoba Aaftink F   21  185    82     Netherlands    NED\n",
       "  Games       Year Season City      Sport        \n",
       "1 1992 Summer 1992 Summer Barcelona Basketball   \n",
       "2 2012 Summer 2012 Summer London    Judo         \n",
       "3 1920 Summer 1920 Summer Antwerpen Football     \n",
       "4 1900 Summer 1900 Summer Paris     Tug-Of-War   \n",
       "5 1988 Winter 1988 Winter Calgary   Speed Skating\n",
       "6 1988 Winter 1988 Winter Calgary   Speed Skating\n",
       "  Event                              Medal\n",
       "1 Basketball Men's Basketball        NA   \n",
       "2 Judo Men's Extra-Lightweight       NA   \n",
       "3 Football Men's Football            NA   \n",
       "4 Tug-Of-War Men's Tug-Of-War        Gold \n",
       "5 Speed Skating Women's 500 metres   NA   \n",
       "6 Speed Skating Women's 1,000 metres NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read_csv(\"data/athlete_events.csv\")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8924bf47-0a3e-4a07-a48f-20617a5dbe96",
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   "source": [
    "<h5> Cleaning and Wrangling into a tidy format</h5>\n",
    "<hr/>\n",
    "For our inferential study, we would look at data from year 1900 & on. As well as, we are accessing who won medals in the Olympics in the event Football Men's/Women's Football. \n",
    "\n",
    "1. We'll first filter our data to select only these columns and remove all the dataset with `NA` age values.\n",
    "2. Now, we'll impute the `NA` medal values with `No` specifying that they didn't won any medal. We won't be removing the examples with `NA` Medal values as it would lead to poor inference by removing the data of athletes who participated in Olympics but didn't win any medal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9cf1988-aaee-4d4f-b50a-74a514ea1214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Medal</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>M</td><td>24</td><td>NA    </td></tr>\n",
       "\t<tr><td>F</td><td>23</td><td>Bronze</td></tr>\n",
       "\t<tr><td>M</td><td>28</td><td>NA    </td></tr>\n",
       "\t<tr><td>M</td><td>26</td><td>NA    </td></tr>\n",
       "\t<tr><td>M</td><td>22</td><td>Gold  </td></tr>\n",
       "\t<tr><td>M</td><td>22</td><td>NA    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Sex & Age & Medal\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t M & 24 & NA    \\\\\n",
       "\t F & 23 & Bronze\\\\\n",
       "\t M & 28 & NA    \\\\\n",
       "\t M & 26 & NA    \\\\\n",
       "\t M & 22 & Gold  \\\\\n",
       "\t M & 22 & NA    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| Sex &lt;chr&gt; | Age &lt;dbl&gt; | Medal &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| M | 24 | NA     |\n",
       "| F | 23 | Bronze |\n",
       "| M | 28 | NA     |\n",
       "| M | 26 | NA     |\n",
       "| M | 22 | Gold   |\n",
       "| M | 22 | NA     |\n",
       "\n"
      ],
      "text/plain": [
       "  Sex Age Medal \n",
       "1 M   24  NA    \n",
       "2 F   23  Bronze\n",
       "3 M   28  NA    \n",
       "4 M   26  NA    \n",
       "5 M   22  Gold  \n",
       "6 M   22  NA    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<ol class=list-inline><li>6370</li><li>3</li></ol>\n"
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       "\\begin{enumerate*}\n",
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       "1. 6370\n",
       "2. 3\n",
       "\n",
       "\n"
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       "[1] 6370    3"
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       "4839"
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       "[1] 4839"
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   ],
   "source": [
    "football_data <- data %>% \n",
    "                        filter(Year >= 1900) %>%\n",
    "                        filter(!is.na(Age)) %>%\n",
    "                        filter(Sport == \"Football\" & (Event == \"Football Men's Football\" | Event == \"Football Women's Football\")) %>%\n",
    "                        select(Sex , Age, Medal) \n",
    "head(football_data)\n",
    "dim(football_data)\n",
    "sum(is.na(football_data))"
   ]
  },
  {
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   "execution_count": 5,
   "id": "f636b7e3-1030-406e-9759-9bd5d6942ade",
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      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Medal</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>M</td><td>24</td><td>No    </td></tr>\n",
       "\t<tr><td>F</td><td>23</td><td>Bronze</td></tr>\n",
       "\t<tr><td>M</td><td>28</td><td>No    </td></tr>\n",
       "\t<tr><td>M</td><td>26</td><td>No    </td></tr>\n",
       "\t<tr><td>M</td><td>22</td><td>Gold  </td></tr>\n",
       "\t<tr><td>M</td><td>22</td><td>No    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Sex & Age & Medal\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t M & 24 & No    \\\\\n",
       "\t F & 23 & Bronze\\\\\n",
       "\t M & 28 & No    \\\\\n",
       "\t M & 26 & No    \\\\\n",
       "\t M & 22 & Gold  \\\\\n",
       "\t M & 22 & No    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| Sex &lt;chr&gt; | Age &lt;dbl&gt; | Medal &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| M | 24 | No     |\n",
       "| F | 23 | Bronze |\n",
       "| M | 28 | No     |\n",
       "| M | 26 | No     |\n",
       "| M | 22 | Gold   |\n",
       "| M | 22 | No     |\n",
       "\n"
      ],
      "text/plain": [
       "  Sex Age Medal \n",
       "1 M   24  No    \n",
       "2 F   23  Bronze\n",
       "3 M   28  No    \n",
       "4 M   26  No    \n",
       "5 M   22  Gold  \n",
       "6 M   22  No    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "6370"
      ],
      "text/latex": [
       "6370"
      ],
      "text/markdown": [
       "6370"
      ],
      "text/plain": [
       "[1] 6370"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "football_data$Medal[is.na(football_data$Medal)]<- \"No\"\n",
    "head(football_data)\n",
    "nrow(football_data)\n",
    "sum(is.na(football_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd85cb0-742d-432b-81ce-8c1ecd913d93",
   "metadata": {},
   "source": [
    "<h5>Sample From the Population of Male/Female Olympic Football Athletes</h5>\n",
    "<hr/>\n",
    "\n",
    "Just like in actual world scenario, we take only one sample without replacement from population. Since, our population size is $6370$, taking a sample size which ~10% is better for inference i.e., sample size of $n = 600$, would be ideal for our research question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80ed4e65-a2e1-47b4-881e-65066514f3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Medal</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>M</td><td>22</td><td>Bronze</td></tr>\n",
       "\t<tr><td>F</td><td>21</td><td>Bronze</td></tr>\n",
       "\t<tr><td>M</td><td>24</td><td>Gold  </td></tr>\n",
       "\t<tr><td>M</td><td>26</td><td>No    </td></tr>\n",
       "\t<tr><td>M</td><td>34</td><td>No    </td></tr>\n",
       "\t<tr><td>M</td><td>20</td><td>No    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 3\n",
       "\\begin{tabular}{lll}\n",
       " Sex & Age & Medal\\\\\n",
       " <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t M & 22 & Bronze\\\\\n",
       "\t F & 21 & Bronze\\\\\n",
       "\t M & 24 & Gold  \\\\\n",
       "\t M & 26 & No    \\\\\n",
       "\t M & 34 & No    \\\\\n",
       "\t M & 20 & No    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 3\n",
       "\n",
       "| Sex &lt;chr&gt; | Age &lt;dbl&gt; | Medal &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| M | 22 | Bronze |\n",
       "| F | 21 | Bronze |\n",
       "| M | 24 | Gold   |\n",
       "| M | 26 | No     |\n",
       "| M | 34 | No     |\n",
       "| M | 20 | No     |\n",
       "\n"
      ],
      "text/plain": [
       "  Sex Age Medal \n",
       "1 M   22  Bronze\n",
       "2 F   21  Bronze\n",
       "3 M   24  Gold  \n",
       "4 M   26  No    \n",
       "5 M   34  No    \n",
       "6 M   20  No    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(1234) #for better reproducibility\n",
    "\n",
    "football_sample <- football_data %>%\n",
    "                  sample_n(size = 600)\n",
    "\n",
    "head(football_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85ee0914-c152-4a5e-afea-83861664855b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  15.00   21.00   23.00   23.45   26.00   38.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(football_sample$Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0b53a2-d0e9-4f01-8648-1067473e65cc",
   "metadata": {},
   "source": [
    "<h4> 📈 Plotting Relevant Data</h4>\n",
    "\n",
    "As we're interested in the difference in mean age for male and female football olympic athletes and their standard deviations i.e., we can look at the histograms of the age distribution for each gender. Additionally, box plot would be great to learn about the spread between these two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9ef7266-2c6b-4975-ab1f-20396f5686b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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bADAACQRGigC7i0ysrK/v37x8TENHRD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\nBMEOAABAEv8PoVyYqaeKQssAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filtering sample for female athletes and ploting the age distribution\n",
    "female_age_plot <- football_sample %>%\n",
    "    filter(Sex == 'F') %>%\n",
    "    ggplot(aes(x=Age, fill = Medal)) +\n",
    "    geom_histogram(bins = 10) +\n",
    "    labs(title = \"Female Football Athletes\",\n",
    "         x = \"Age (years)\") +\n",
    "    theme_bw()\n",
    "\n",
    "# filtering sample for male athletes and ploting the age distribution\n",
    "male_age_plot <- football_sample %>%\n",
    "    filter(Sex == 'M') %>%\n",
    "    ggplot(aes(x=Age, fill = Medal)) +\n",
    "    geom_histogram(bins = 10) +\n",
    "    labs(title = \"Male Football Athletes\",\n",
    "         x = \"Age (years)\") +\n",
    "    theme_bw()\n",
    "\n",
    "# comparing the distribution plots between the two genders\n",
    "age_comparison <- plot_grid(female_age_plot, male_age_plot, rel_widths = c(1, 1), rel_heights = c(1, 1))\n",
    "age_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96d005d3-2375-4141-83da-0069a56cfada",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“\u001b[1m\u001b[22mThe `<scale>` argument of `guides()` cannot be `FALSE`. Use \"none\" instead as\n",
      "of ggplot2 3.3.4.”\n"
     ]
    },
    {
     "data": {
      "image/png": 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UVERISFhYWFhfXv378YUp2wbu8qnj3z0aNHRn+ACSEOHz4sDbi5uclPLJA/1AUd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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot <- football_sample %>% \n",
    "            ggplot() +\n",
    "            geom_boxplot(aes(Sex , Age, fill = \"Blue\")) +\n",
    "            theme(text = element_text(size = 18)) +\n",
    "            ggtitle(\"Boxplot Dist Ages of Olympic Football Athletes\") + \n",
    "            xlab(\"Sex of Football Athletes\") + \n",
    "            ylab(\"Age (in yrs)\") + \n",
    "            guides(fill = FALSE)\n",
    "boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a1244b-9278-496e-9392-301d3f0ac213",
   "metadata": {},
   "source": [
    "<h4>🕵️ Observed Statistics</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7addff4b-961b-4b23-946b-afde6832655a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Sex                 Age           Medal          \n",
       " Length:507         Min.   :15.00   Length:507        \n",
       " Class :character   1st Qu.:21.00   Class :character  \n",
       " Mode  :character   Median :23.00   Mode  :character  \n",
       "                    Mean   :23.17                     \n",
       "                    3rd Qu.:25.00                     \n",
       "                    Max.   :36.00                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "3.20718976974806"
      ],
      "text/latex": [
       "3.20718976974806"
      ],
      "text/markdown": [
       "3.20718976974806"
      ],
      "text/plain": [
       "[1] 3.20719"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "football_sample_men <- football_sample %>%\n",
    "                        filter(Sex == \"M\")\n",
    "summary(football_sample_men)\n",
    "sd_men_obs <- sd(football_sample_men$Age) #observed sample stnd deviation for male athletes\n",
    "sd_men_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41e8c3c3-98f6-40de-894e-4cbadfc14723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Sex                 Age           Medal          \n",
       " Length:93          Min.   :18.00   Length:93         \n",
       " Class :character   1st Qu.:22.00   Class :character  \n",
       " Mode  :character   Median :25.00   Mode  :character  \n",
       "                    Mean   :24.97                     \n",
       "                    3rd Qu.:28.00                     \n",
       "                    Max.   :38.00                     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "4.3199527294697"
      ],
      "text/latex": [
       "4.3199527294697"
      ],
      "text/markdown": [
       "4.3199527294697"
      ],
      "text/plain": [
       "[1] 4.319953"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "football_sample_female <- football_sample %>%\n",
    "                        filter(Sex == \"F\")\n",
    "summary(football_sample_female)\n",
    "sd_female_obs <- sd(football_sample_female$Age) #observed sample stnd deviation for female athletes\n",
    "sd_female_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2561d99-b30a-4a8f-b823-cd15eb81cd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>male_sd</th><th scope=col>female_sd</th><th scope=col>male_mean</th><th scope=col>female_mean</th><th scope=col>obs_mean_difference</th><th scope=col>obs_sd_difference</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>3.20719</td><td>4.319953</td><td>23.16765</td><td>24.96774</td><td>-1.800089</td><td>-1.112763</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " male\\_sd & female\\_sd & male\\_mean & female\\_mean & obs\\_mean\\_difference & obs\\_sd\\_difference\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 3.20719 & 4.319953 & 23.16765 & 24.96774 & -1.800089 & -1.112763\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 6\n",
       "\n",
       "| male_sd &lt;dbl&gt; | female_sd &lt;dbl&gt; | male_mean &lt;dbl&gt; | female_mean &lt;dbl&gt; | obs_mean_difference &lt;dbl&gt; | obs_sd_difference &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 3.20719 | 4.319953 | 23.16765 | 24.96774 | -1.800089 | -1.112763 |\n",
       "\n"
      ],
      "text/plain": [
       "  male_sd female_sd male_mean female_mean obs_mean_difference obs_sd_difference\n",
       "1 3.20719 4.319953  23.16765  24.96774    -1.800089           -1.112763        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "observed_stat <- data.frame(\n",
    "    male_sd = sd_men_obs,\n",
    "    female_sd = sd_female_obs,\n",
    "    male_mean = mean(football_sample_men$Age),\n",
    "    female_mean = mean(football_sample_female$Age),\n",
    "    obs_mean_difference = mean(football_sample_men$Age)-mean(football_sample_female$Age),\n",
    "    obs_sd_difference = sd_men_obs-sd_female_obs)\n",
    "observed_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9bfffa0-9d74-4c31-9987-a5af290c0747",
   "metadata": {},
   "source": [
    "The visualisations above do address our question as there seem to be some difference in the sample means of Male - Female as well as there are some evident differences in the spread of Male and Female respectively.\n",
    "\n",
    "These parameters across the groups (Male,Female) can be seen in the table above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e79df77-7967-4179-972f-f7041b2349f3",
   "metadata": {},
   "source": [
    "<h5>Confidence Interval Plots</h5>\n",
    "\n",
    "We're exploring the Confidence Interval for the parameters like mean and standard deviation for the groups we're interested in (Male and Female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a01d54b8-e24e-4a8f-8c8b-e91fdd84ec6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A infer: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>stat</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>23.10454</td></tr>\n",
       "\t<tr><td>2</td><td>23.23274</td></tr>\n",
       "\t<tr><td>3</td><td>23.39448</td></tr>\n",
       "\t<tr><td>4</td><td>23.12623</td></tr>\n",
       "\t<tr><td>5</td><td>23.14201</td></tr>\n",
       "\t<tr><td>6</td><td>23.03550</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A infer: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " replicate & stat\\\\\n",
       " <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 23.10454\\\\\n",
       "\t 2 & 23.23274\\\\\n",
       "\t 3 & 23.39448\\\\\n",
       "\t 4 & 23.12623\\\\\n",
       "\t 5 & 23.14201\\\\\n",
       "\t 6 & 23.03550\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A infer: 6 × 2\n",
       "\n",
       "| replicate &lt;int&gt; | stat &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1 | 23.10454 |\n",
       "| 2 | 23.23274 |\n",
       "| 3 | 23.39448 |\n",
       "| 4 | 23.12623 |\n",
       "| 5 | 23.14201 |\n",
       "| 6 | 23.03550 |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate stat    \n",
       "1 1         23.10454\n",
       "2 2         23.23274\n",
       "3 3         23.39448\n",
       "4 4         23.12623\n",
       "5 5         23.14201\n",
       "6 6         23.03550"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lower_ci</th><th scope=col>upper_ci</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>22.8856</td><td>23.45375</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lower\\_ci & upper\\_ci\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 22.8856 & 23.45375\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| lower_ci &lt;dbl&gt; | upper_ci &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 22.8856 | 23.45375 |\n",
       "\n"
      ],
      "text/plain": [
       "  lower_ci upper_ci\n",
       "1 22.8856  23.45375"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(423)\n",
    "# Bootstrapping for Estimating Standard Deviation  \n",
    "# TUTORIAL 04 : USE SUMMARISE\n",
    "football_male_resample_mean <- football_sample %>%\n",
    "                    filter(Sex == \"M\")%>%\n",
    "                    specify(response=Age)%>%\n",
    "                    generate(reps=1000, type = \"bootstrap\")%>%\n",
    "                    calculate(stat=\"mean\")\n",
    "football_male_ci_mean<- football_male_resample_mean %>% \n",
    "  get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "head(football_male_resample_mean)\n",
    "football_male_ci_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b365a7d9-b2ad-451b-bcf8-4e2224a21394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A infer: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>stat</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>24.96774</td></tr>\n",
       "\t<tr><td>2</td><td>24.36559</td></tr>\n",
       "\t<tr><td>3</td><td>24.50538</td></tr>\n",
       "\t<tr><td>4</td><td>25.04301</td></tr>\n",
       "\t<tr><td>5</td><td>24.86022</td></tr>\n",
       "\t<tr><td>6</td><td>24.83871</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A infer: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " replicate & stat\\\\\n",
       " <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 24.96774\\\\\n",
       "\t 2 & 24.36559\\\\\n",
       "\t 3 & 24.50538\\\\\n",
       "\t 4 & 25.04301\\\\\n",
       "\t 5 & 24.86022\\\\\n",
       "\t 6 & 24.83871\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A infer: 6 × 2\n",
       "\n",
       "| replicate &lt;int&gt; | stat &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1 | 24.96774 |\n",
       "| 2 | 24.36559 |\n",
       "| 3 | 24.50538 |\n",
       "| 4 | 25.04301 |\n",
       "| 5 | 24.86022 |\n",
       "| 6 | 24.83871 |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate stat    \n",
       "1 1         24.96774\n",
       "2 2         24.36559\n",
       "3 3         24.50538\n",
       "4 4         25.04301\n",
       "5 5         24.86022\n",
       "6 6         24.83871"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lower_ci</th><th scope=col>upper_ci</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>24.10726</td><td>25.77419</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lower\\_ci & upper\\_ci\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 24.10726 & 25.77419\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| lower_ci &lt;dbl&gt; | upper_ci &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 24.10726 | 25.77419 |\n",
       "\n"
      ],
      "text/plain": [
       "  lower_ci upper_ci\n",
       "1 24.10726 25.77419"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(404)\n",
    "football_female_resample_mean <- football_sample %>%\n",
    "                    filter(Sex == \"F\")%>%\n",
    "                    specify(response=Age)%>%\n",
    "                    generate(reps=1000, type = \"bootstrap\")%>%\n",
    "                    calculate(stat=\"mean\")\n",
    "football_female_ci_mean<- football_female_resample_mean %>% \n",
    "  get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "head(football_female_resample_mean)\n",
    "football_female_ci_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9249dffb-8d20-4217-88ec-37bc9db5bad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HLGzp+iKNOnTx88ePD+/fs3bdokatxH3W63NkEw0wTszmg0rly58tixYytXrhwx\nYkRubu6SJUu6du36+uuv11yn9o1THjk5OSNHjtyzZ09WVtb69evfe++9Dz74YNKkSf4zavu6\nN61sz7sY50u7Bla7oiVgnWFZwID8a9bWs/8yhibkzRqMQYMGCSG09yM2b97co0ePpKSk1NTU\nZs2aae9HbNmyRQhR6zsydTd9+vT//ve/zz777Ny5c1esWHHkyJGpU6cePXrUYDD4/yP0sNvt\nnkuIhBBJSUlfffVVQUHBTz/9dPLkySFDhrzyyivvvffeqlWr4uPjHQ5HSUmJ9k9U07x5c/+v\n2fTo0qWLEOLf//53OJcTtWH0O1+MfqFh9GuYo59ug52mffv2QogDBw4IIWw2m9VqdTgc2nvt\n3rRtk5ycHMw0NXTXqlWrSZMmbdiwoaCg4Pnnn3e73ZMmTaqsrAy+4IULFwohNm3apL2TcsMN\nN1x//fUB90vtENObVnMNO3ENqqqq1q9fL4S44YYbapis7gsYkP+ZAO8D6IADUw1XNvir42at\nWd++fS0Wy5dffllZWbl9+/ZrrrlGCCHLcmZmpja0ff755506ddJ2xUhr27btPffc89hjj02e\nPDklJeXYsWP5+fndunXzvrbG37lz54QQ3tMkJyd37NhRluX8/PwZM2bceeed3pc0eZMkqYYj\n6YEDBwoh1q5dq70b4k9V1UWLFmmvUIQXo1+QGP3Ov+r/h9GvYY5+Og922lWfnoitXb3x9ddf\ne09z+vTpgwcPxsTEaF8NEMw0/o4dO+b9YrNYLFlZWZmZmWfPntWuoAxGZWVlXl6ezWbTTgVr\nVFX98MMP/Sf2/pC5EKKkpOTAgQOKorRp0ybI7rzNnDkzLy/vkksuGTJkSMAJwrKA1fE5m11Z\nWXnw4EFZlrUDaIvFIvxOaO/YseO8ughtswbDYrFcddVVX3311ddff11RUaENbUKIq6++ev/+\n/fv37z9w4MD5vhMRggMHDrz44os//fSTd6P2Dp12+bnD4Rg+fHhaWpo2kHn89ttvBw4ckCTp\nsssu83/arKwss9nsueWO0WiMi4vz/ldUWFhYw3/T2267rUWLFocPH9Y+AOjv8ccff/jhh7V3\nuBBejH5BYvQLrXLB6NdQRz/dBju32718+fLPPvvMbDZ74ra2Bh9//PGqqirPlI8//rjT6bzj\njju0zB7MNNqLzXPUuHv37rZt244aNcp7lpKSkiNHjiiKol2/6TNLQGazOTExsXcWxWkAACAA\nSURBVLS0VLtGQQihqurChQu1z2r5HG999tln33zzjefhyy+/XFVVpX1j53mtqOPHj99xxx3L\nly+3Wq2vvPJKwNPy4VrA6nz22Wfbtm3zPHzhhRfKy8uvvfZabVm0oz3t+w60CX788ccXXnjB\n+xlq7T2YzRqyQYMGnTp16sUXX9QuMdEar776avX37z6tYWiry3rztnPnzvHjx0+fPt3zjV/b\ntm178skn7Xa79o0JRqNRu4x6+vTpDodDm6akpGTChAnl5eV//OMfmzdv7vOcr7322saNG1eu\nXOl97VGfPn08/4qOHj2an59fwxXiMTEx2mpZsmTJ2LFjPReeCyHy8/OnTJkyb968Zs2arV69\nuo6LD2+MfkFi9GP00+foV8evS6l32hcUxcbG/snLoEGDUlJShBCKonh/A5D2PY1CiEsuuWTW\nrFlz584dMGCAEKJz586eW5EEM82hQ4ckSTIajWPHjs3KylJV9fbbbxdCtG/ffsqUKQ8//LB2\nI2EhhOf+JP6zBLx3yr333iuE6NSp06OPPvroo4+mp6d36dLlo48+EkI0b9588eLFubm5Wvwf\nM2aM9mm4RYsWjR49WlEUo9G4ffv24FfUH/7wh549e2pXclx44YVff/219/Q+XzgUrgX0eVrt\n4YQJE5o1azZmzJhHHnnk1ltvVRTFbDZ/88032jQnT57UxriMjIy//vWvt956q81m077taciQ\nIdX17tNRMJs1YMHaUWBqamp1K9azbk0m0yWXXOJprKioMJvNJpPJZDKVlJRUtwaCXG+1llFZ\nWal96q1z585jx44dNmyYwWBQFOWtt97yTJObm9u6dWshRNu2bW+++eaRI0dqh5tdu3bVvurJ\n28mTJ5OSkm677Taf9g0bNgghZs2atWXLlr59+8bHx9f6rUtvvvmmtgVlWU5NTR00aNAll1yi\nfaiwTZs2e/bs8Z6Y77ELHqMfo1/A3hn9mvjop5Ng5yMuLu7SSy8dN27c7t27faZ3OBzZ2dmX\nX3651Wo1m81du3adM2fOmTNnzneaxYsXJyUlmc3myy+/XFVVl8u1YsWKzMzMpKQkRVHi4uKu\nvvrqNWvWuN3u6mYJuAeXl5c/+OCDHTp0MJvNbdq0mTx5svbCu+uuu2JjY1u2bLlnzx5taPv7\n3//+6aef9uvXz2az2Wy2fv36+dxQpdYVJctycnLygAEDli9f7rmXjofPKzBcC+jztDNmzBBC\nvPHGG9rrxGazxcbG9uvXb+vWrd5z7d27t3///lar1Waz/c///M+GDRu0q0Ouueaa6nr3v6lO\nrZs15KHN7XZrB+5Tp071bte+yMC7yICFBbPeginj7Nmzs2fPbt++vcViSUpKGjFixL///W+f\naU6cODFt2rSOHTuazWar1XrppZcuWLAg4K0P//znP7do0cIz7nt77rnntK/ovOqqqzz/gWpW\nWFi4cOHC9PT0pKQkg8GQkJDQt2/f1atXl5eX+0xJsAseox+jX8DeGf2a+OhX06V/aJjmzZv3\n2GOPLV++fOrUqfVdCwBED6MfUCvdXmMHAADQ1BDsAAAAdIJgBwAAoBMEOwAAAJ3gwxMAAAA6\nwRk7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0wlDfBSCQoiJx\n6JBvo9UqunWrj2rQgO3bJyoqfBs7dxZ2e31UA0TXTz+J4mLfxosvFklJ9VEN6s+hQ6KoyLex\nqe4JjfsLiov8N2T4WK1Wo9FYUlLidrsj10tAsZs3G/74R5/Gk5d0X/PxuxHtV1EURVGcTmf0\nF1lRFFVVo9+vLMsGg8Hlcrlcruh3Lcuy0+msy5NMvHpQ88NHfBrPbdzo7Nevhn7tdntVVVV5\neXldug6BLMsxMTHnzp2L3PPbdZpoCwsLI91FYmLi6dOnI91LCIxGY1xcXHl5uf+e0+zmm01b\ntvg0vvfU47tuuyla1Qmj0ehyuaI/dgXDbDYLISorK+u7kABkWVYUxeFwhOXZbr1zfIctX/g0\nlj7zTMWdd4bwbFar1Wq1FhcXV1VVhaO6MEtISCgpKYmPj69ugsZ9xk7bayNElmUhhMlkin72\n1br2JUmKokS0X0mStN61X6JJ+l30+9V+RnrdBuy67v0GXGFGo1Gp/qWhLbKiKBF9+VTXtSzL\n0e8XAJqOxh3sSktLI/fkdrtdUZRz585F/1AszuXyT3aqqobr4KY6BoNBO4cU/UU2GAyqqkb/\ntJmiKLIsu93uOp45C0FYDlgDHnWUl5c7qn9paJHO4XBE9OVTXdc2my1y/SqKYrFYIvTkANAo\n8OEJAAAAnSDYAQAA6ATBDgAAQCcIdgAAADrRuD88AQANxOnTp9esWbN79+6qqqr27dvffffd\nnTt3FkKUlpbm5OTs2bPH4XB06dIlKyurRYsWNbQDQF1wxg4AwuDRRx8tLCx85JFHsrOzk5KS\nFi5cWFFRIYTIzs4uKCiYP3/+U089ZbVaFy5cqH3qvLp2AKgLgh0A1FVJSUlycvKUKVPat2/f\nqlWr0aNHFxcX5+bmFhYW7tixY+LEie3atWvdunVWVlZeXt7evXura6/v5QDQ6PFWLADUld1u\nnzNnjufhqVOnZFlOSko6cOCA0Whs166d1m6z2VJSUg4ePFhWVhawPTU1tR6qB6AjBDsACKeS\nkpLly5ffeOONCQkJxcXFdrvd+5YqcXFxRUVFcXFxAds9Dzdv3rxv3z7td5PJNHr06EiXLUlS\nbGxspHsJgXYnHqPR6F9ewBu3yIpsMETvX5t2/5jAtwuqb9oOFs21ETztPjThqi3gXYvMZrMS\n0i6tVWWxWIxGY10ri4Ba79/TELc3ADRSx48fX7RoUc+ePceMGaO1VHejvJpvoPf1119v3LhR\n+91qtU6aNCm8dQYUExMThV5CYzAYAoSAQMFOkZWGGWXqS0NeG2ELdnKAV5PRaDTWYZc2mUx1\nqCiyCHYAEA27d+9esmTJbbfdNmzYMK0lPj6+uLhYVVVPjCsqKkpISKiu3fNUd9xxx+DBg7Xf\nZVn2PpkXIc2aNSsuLo50LyEwGAyxsbGVlZXah1G8xTqd/v/DnC5nNO/dbjAY3G53w/zgi9Fo\nlCSpYd7JXpZl7Q6WYXm2gOu/vLy8KqQXjtlstlgsZWVlkb6NZ2jsdntZWZnNZqtuAoIdAITB\n/v37n3zyyfvuu++KK67wNHbq1MnhcBw+fLhjx45CCO0TFd26dWvVqlXAds+M7du3b9++vedh\nYWFhpOuPwt2o68LtdvuXF/DfuepWoxmzVFVtsMFO/F5efVcRmCRJYastwE2zhcvlCm2X1t6B\ndTqdDfMVoapqzYG4IV4WAACNS1VVVXZ29vDhwy+++OLC31VUVCQmJmZkZKxYseLnn3/Oy8tb\ntmxZhw4dunfvXl17fS8HgEaPM3YAUFc//vhjfn7++vXr169f72mcNGnS0KFDp02blpOTs2DB\nApfL1aNHj3nz5mlvv1bXDgB1QbADgLpKTU195513Av7JarXOmDEj+HYAqAveigUAANAJgh0A\nAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBO\nEOwAAAB0gmAHAACgEwQ7AAAAnTDUdwFA7SRJquMzqKoalkoAIHIY61B3BDs0dAaDwWCo647q\ncDhcLldY6gGASGCsQ1gQ7NA4nD171uFwhDBjTEyMzWYLez0AEAmMdagjgh0ah59//vnMmTMh\nzNimTZtOnTqFvR4AiATGOtQRH54AAADQCYIdAACAThDsAAAAdIJgBwAAoBN8eAIRJ0lSMF/O\nJEmSLAc40qj7FzsBANBEEOwQcQaDQVGU+q4CAAD9I9ghSk6ePBnalzO1aNHCZDKFvR4AAPSH\nYIcoOXr06Llz50KY0W63E+wAAAgGH54AAADQCYIdAACAThDsAAAAdIJgBwAAoBMEOwAAAJ0g\n2AEAAOgEwQ4AAEAnCHYAAAA6QbADAADQCYIdAACAThDsAAAAdIJgBwAAoBMEOwAAAJ0g2AEA\nAOgEwQ4AAEAnCHYAAAA6YajvAoDIMpvNQghZliVJ8v+r1ijLssFQ02vB7Xa73e4IVQgAQLgQ\n7KBzMTExQghFUWqYRpZlWa7p7LXT6STYAQAaPoIdmoTc3NyioqIQZoyPj09JSQl7PQAARALB\nDk1CUVFRQUFBCDNKkkSwAwA0Fnx4AgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAH\nAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACg\nEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOGOq7ADQCBoNBlkM/BpAk\nKYzFAACA6hDsUDtZlusS7AAAQHQQ7BCsbdu2ORyOEGbs3bu31WoNez0AAMAHwQ7BcrlcLper\nvqsAAAQQGxsrhFAUxfMGi3YZjNFoDP5JXC6X2+2ORHmIGoIdAACNnhbg/C+bURQl+CdRVZVg\n19gR7AAA0ImDBw8WFhaGMGNycnLnzp3DXg+ij2AHAIBOOByOysrK0GYMezGoF3zUEQAAQCcI\ndgAAADpBsAMAANAJgh0AAIBOEOwAAAB0Ihqfis3Ly1u2bNmhQ4c2bNjgaZw2bdrRo0c9Dy0W\ny+uvvy6EKC0tzcnJ2bNnj8Ph6NKlS1ZWVosWLaJQJAAAQGMX8WC3devW1atX9+rV69ChQ97t\npaWlEydOTE9P1x56vlMxOzu7tLR0/vz5ZrN5/fr1CxcufPbZZ7lRKQAAQK0iHuwcDsfSpUsP\nHz78+eefe7eXlJS0bNkyKSnJu7GwsHDHjh3Lli1r166dECIrK+vOO+/cu3dvampqpOsEgAYr\nISEh0l3IshyFXkKg3RfLYrGYTCafP8mBbpZlMBrMZnM0KhNCCCFJUrhOPWhLWr8URTmvO1XU\nRRhXXcDnsVqtMSHt0tqGsNlsqqrWtbIIUBTFZrPVMEHEg13//v2FEIcPH/Zu1L5Bcfv27a++\n+mpJSUnHjh1Hjx594YUX/vTTT0ajUUt1QgibzZaSknLw4EGCHYCm7MyZM5HuIjExMQq9hMBo\nNMbFxVVUVJw7d87nT80cDt+sJ4TT4QztG3pDYzQaw3V/VYPBYDDU810DXC6X0+mMQkeyLCuK\nEq5vRQ64/svKyipC2qWtVqvVai0tLa2qqqpzaeGXkJBQWloaHx9f3QT1sw+VlZXFx8c7nc7J\nkycLIV577bU5c+Y8//zzxcXFdrvd+6glLi6uqKjI8/Cpp5764IMPtN9jYmI2bdoUuSK1Murl\nEFYKdBgqS7LFYolC7/6HxYjcgFvHg2NJCnCc2qxZM9G8ec0zWiyWaJ7V8JAkqXlttYWMe1wC\nQP0Eu7i4uFdeecXzcPbs2WPGjNm2bZuo7Vy0xWKx2+3a7zExMREdx2VZliSpXv5VyKoaaC2o\nUTgtLEmSfy8N4Q2C+hWhNR9wbdedqqpqjfutoij1cqtvbUeKXL8N830TAIimBnGv2JiYmOTk\n5MLCwvbt2xcXF6uq6kkSRUVF3ufM7rnnnnvuucfzMLRbHQfJbrebzeaioqLo//+Lczr9T9m5\nVTXS7y9o56UcDofPIptMpib++ZVIvD0RlnciVDXAzllSUuKo/g0IRVESEhIqKytLS0vr0nUI\ntEtDvM/Bh/35G+ZVYgAQNfXz3/rYsWPPPfec5z9lRUXFb7/91rJly06dOjkcDs8FecXFxbm5\nud26dauXIgEAABqXiJ+xO3PmjMvlKikpEb+fYLPZbImJidu3b3c6nbfeeqvL5XrllVdsNltm\nZqbZbM7IyFixYsW0adNMJtPq1as7dOjQvXv3SBcJAACgAxEPdrNmzSooKNB+Hzt2rBBi/Pjx\nw4cPX7Ro0UsvvTRjxgyj0dilS5cnnnhCu5R72rRpOTk5CxYscLlcPXr0mDdvHhd4AQAABCPi\nwW716tUB29u3b79o0SL/dqvVOmPGjAgXBQAAoENN+op4AAAAPSHYAQAA6ATBDgAAQCcIdgAA\nADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpB\nsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMA\nANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJ\ngh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0A\nAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBO\nEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwA\nAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0\ngmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBOEOwAAAB0wlDf\nBQCATuTl5S1btuzQoUMbNmzwNE6bNu3o0aOehxaL5fXXXxdClJaW5uTk7Nmzx+FwdOnSJSsr\nq0WLFtGvGYDOEOwAIAy2bt26evXqXr16HTp0yLu9tLR04sSJ6enp2kNZ/n/vk2RnZ5eWls6f\nP99sNq9fv37hwoXPPvus568AEBoGEQAIA4fDsXTpUk+A8ygpKWnZsmXS7xITE4UQhYWFO3bs\nmDhxYrt27Vq3bp2VlZWXl7d37976KByArnDGDgDCoH///kKIw4cPezc6HI7Kysrt27e/+uqr\nJSUlHTt2HD169IUXXvjTTz8ZjcZ27dppk9lstpSUlIMHD6amptZD6QB0hGAHAJFSVlYWHx/v\ndDonT54shHjttdfmzJnz/PPPFxcX2+12SZI8U8bFxRUVFXke/uc///FcmWcwGLTUGFGSJFks\nlkj3EgJFUbSf/uVpf/Ihy3LA9giRJElRFO9NGbKG8Ea8tjjR6SiMfQVc/0ajUYS0SxsMBiGE\nyWRqCFvEnyRJJpOphgkIdgAQKXFxca+88orn4ezZs8eMGbNt2zZRzb8ij/fee2/jxo3a71ar\ndfjw4RGtU2Oz2aLQS2hMJlOAf2aBYoGiKEajMRo1/e9Oo9xjhCiKEs1lCVdykuQAryaz2Wyu\nwy7dMI9zNDXXRrADgCiJiYlJTk4uLCxs3759cXGxqqqeeFdUVJSQkOCZ8k9/+lNGRob2u6Io\nJSUlka7NZrOVlpZGupcQKIpitVqrqqoqKyt9/hTjdPr/D3O5XA6HIzq1CSEURXG73aqqhuWp\n6v0UkcvlcrvdUehIkiRZll0uV1ieTXUHWP8VFRWOkF44JpPJbDaXl5c7nc46lxZ+sbGx5eXl\nsbGx1U1AsAOASDl27Ni7776blZWlvblTUVHx22+/tWzZslOnTg6H4/Dhwx07dhRCFBcX5+bm\nduvWzTNj9+7du3fv7nlYWFgY6VJjY2P9k1NDoJ1+c7lc/uWZA0UQt9sdrrgQDFmW3W53WMKQ\nlnXq/jx1oapqdNaeLMuSJIUt2AUK1k6nM7RdWjtn6XA4qqqq6lpZBFit1poPXQh2ABAGZ86c\ncblc2qk1LYfZbLbExMTt27c7nc5bb73V5XK98sorNpstMzPTbDZnZGSsWLFi2rRpJpNp9erV\nHTp08E5yABAagh0AhMGsWbMKCgq038eOHSuEGD9+/PDhwxctWvTSSy/NmDHDaDR26dLliSee\nMJvNQohp06bl5OQsWLDA5XL16NFj3rx5YbkAH0ATR7ADgDBYvXp1wPb27dsvWrTIv91qtc6Y\nMSPCRQFochriR3kBAAAQAoIdAACAThDsAAAAdIJgBwAAoBMEOwAAAJ0g2AEAAOgEwQ4AAEAn\nCHYAAAA6QbADAADQCYIdAACATjTuW4rZ7fbIPbnRaBRC2Gw2VVUj10tAiqL4N0qSpJUUOdqt\nKg0Gg88icwtLWZbDvvIlSar7Ng24aaxWq7v6l4Y2i9FojOjLp7quFUWJfr8A0HQ07mBXXl4e\nuSeXZVmW5YqKCrfbHbleAlLc7gCnUlXV5XJFtl9FEUK43W6fRdYiSES7buD810ndybIsSVId\nt2nAg47Kykpn9S8NRVFMJpPL5YroyycgWZatVmvk+pVl2Ww2R+jJAaBRaNzBzul0Ru7JtX/k\nTqcz+sEu4DlC9feSIkeWZRGZEKMDkVgniqLU+WkD7Coul6uGl4a2d7nd7oi+fAJSFEVV1cj1\nG/BUNwA0KVxjBwAAoBON+4wdAACou7i4OCGEwWAwGEIPBk6nM/pvBcAHwQ4AAAghREVFRWVl\nZQgzGgyG2NjYsNeDEBDsAACAEEL88ssvx48fD2HGhISEXr16hb0ehIBr7AAAAHSCYAcAAKAT\nBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsA\nAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsAAACd\nINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Y6rsAAAD0wGAwyHLop0skSQpjMWiy\nCHYAAISBJEl1CXZAWBDsAAAIm2+//fbcuXMhzHjFFVfExcWFvR40NRxbAAAA6ATBDgAAQCcI\ndgAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAA\nADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpB\nsAMAANAJgh0AAIBOGOq7AKBBs9lsQghFUWQ59KMgl8vlcrnCVxQAAIER7ICaKIoihJAkKbTZ\ntRndbnc4awIAoBoEO6B2//3vf48fPx7CjAkJCb169Qp7PQAABMQ1dgAAADpBsAMAANAJgh0A\nAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpBsAMAANAJgh0AAIBO\ncK9YAGjoEhISIt2FLMtR6CUEkiQJISwWi8lk8vmTbDT6T28wGsxmczQqE0IIIUmSLMue36PW\nb8NkMBgURQlyYu9VV0cBn8dqtcaEtEtr29Fms6mqWtfKIkBRFJvNVsMEBDsAaOjOnDkT6S4S\nExOj0EsIjEZjXFxcRUXFuXPnfP7UzOHwzXpCOB3OysrK6NQmhDAajS6Xy+12a78HH2t0yel0\nOp3OYKaUZVlRFIfDEZZ+tfXvo6ysrCKkXdpqtVqt1tLS0qqqqjqXFn4JCQmlpaXx8fHVTcBb\nsQAAADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAA\nADpBsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcIdgAAADpB\nsAMAANAJgh0AAIBOEOwAAAB0gmAHAACgEwQ7AAAAnSDYAQAA6ATBDgAAQCcMUegjLy9v2bJl\nhw4d2rBhg6extLQ0Jydnz549DoejS5cuWVlZLVq0qKEdAAAANYv4GbutW7fOnTs3JSXFpz07\nO7ugoGD+/PlPPfWU1WpduHCh2+2uoR0AAAA1i3iwczgcS5cuTU9P924sLCzcsWPHxIkT27Vr\n17p166ysrLy8vL1791bXHukiAQAAdCDib8X2799fCHH48GHvxp9++sloNLZr1057aLPZUlJS\nDh48WFZWFrA9NTVVa9m/f/+JEye03xVFSUtLi1zliqIIIcxmc/RPGcpygMAtCUkrKXIkSdJ6\n135BuMiy7L/tJEmSpLpuU0kE2FJGo1E2m2soRgihKIq5+mkiRJZlWZYj1y/7LQBE4xo7f8XF\nxXa73XsUjouLKyoqiouLC9juefjWW29t3LhR+91qtX755ZeRLjU2NjbSXQQQ6J+9JElGozEK\nnRsM9bNX6JgWaKr7U52eOlCUsVqtwm6veT6j0Rid3cmfvbbaQsZlGwBQb//Cqzu2rvmYe8CA\nARdddJH2u9FoPHfuXPgr+53ZbDYYDGVlZaqqRq6XgCwul3+yU4XqdDoj2q+WP1wul88iK4rC\nuZC6cLvd/plDO2NX1ywSaOesqKhwVf/SkGU5JibG6XRWVlbWqevzJ8uyyWSqqKiI0PNLkmS1\nWiP05ADQKNRPsIuPjy8uLlZV1RMXioqKEhISqmv3zJiZmZmZmel5WFhYGLkiDQaDwWCoqKiI\n/mkAk9sdINipEQ92BoNBC3Y+i8ybs3Xkdrv9t532/mwdt6kqAgS7yspKR3l5dbMoiqIFu/Lq\np4kQRVEMBkPk+lUUhWAHoImrn++x69Spk8Ph8Fx4V1xcnJub261bt+ra66VIAACAxiXiwe7M\nmTOFhYUlJSVCiMLCwsLCwoqKisTExIyMjBUrVvz888/at9x16NChe/fu1bVHukgAAAAdiPhb\nsbNmzSooKNB+Hzt2rBBi/Pjxw4cPnzZtWk5OzoIFC1wuV48ePebNm6e92VddOwAAAGoW8WC3\nevXqgO1Wq3XGjBnBtwMAAKBm3CsWAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcA\nAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKAT\nBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCYAcAAKATBDsA\nAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHSCRMcRLQAAIABJREFU\nYAcAAKATBDsAAACdINgBAADoBMEOAABAJwh2AAAAOkGwAwAA0AmCHQAAgE4Q7AAAAHQiqGCX\nlpb2448/+re/9dZb3bt3D3dJAFA/GOsANHZBBbudO3eeO3fOp9HpdO7bt+/w4cMRqAoA6gFj\nHYDGzlDznyVJ0n7p3bt3wAkuv/zyMFcEAFHHWAdAH2oJdrt27friiy+mT58+YsSIpKQk7z9J\nktS6desJEyZEsjwAiAbGOgD6UEuwS01NTU1Nff/995966qlOnTpFpyYAiDLGOgD6UEuw03z4\n4YeRrgMA6h1jHYDGLqgPTxQUFNx1110XXnihoiiSn0iXCADRwVgHoLEL6ozd1KlT//Wvf/Xr\n12/QoEEGQ1CzAECjU8exLi8vb9myZYcOHdqwYYOnsbS0NCcnZ8+ePQ6Ho0uXLllZWS1atKih\nHQDqIqiRa/PmzW+++eaIESMiXQ0A1KO6jHVbt25dvXp1r169Dh065N2enZ1dWlo6f/58s9m8\nfv36hQsXPvvss7IsV9cepkUB0EQFNYiUl5dnZmZGuhQAqF91GescDsfSpUvT09O9GwsLC3fs\n2DFx4sR27dq1bt06KysrLy9v79691bWHYyEANGlBnbG74oor9u3bd80110S4GACoT3UZ6/r3\n7y+E8Pke459++sloNLZr1057aLPZUlJSDh48WFZWFrA9NTVVa9m/f/+JEye03xVFSUtLC2mB\nzoMkSWazOdK9hEBRFO2nf3kBT3DKsqzNEh2SJMmyrF2CyYWYwa987brVcG2pgGveYDCEtktr\nVRmNxoa5QSVJMhqNNUwQVLBbtmzZ5MmTs7OzMzIywlQYADQ4YR/riouL7Xa797+HuLi4oqKi\nuLi4gO2eh2+99dbGjRu1361W65dffhmWempmt9uj0EtoTCaTyWTybQ10HaSiKDX/2ws73kD3\nkGX5vNZGuFadJAdIYBaLxVKHXTomJqYOFUVWzbUFFeymT5/+66+/ZmZmWq3W5ORkn78ePXo0\n5OIAoOGIxFhX3UF/zScDBgwYcNFFF2m/G41G/xudhZ3Vai0rK4t0LyFQFMVisTgcjqqqKp8/\nWVwu/xM+LrfL6XRGpzYhhCzLqqqqqirOP9boj9vtdrvdwUypnbELcuJaqW7Vv7GystIZ0gvH\naDSaTKaKigqXy1Xn0sIvJiamoqLCarVWN0FQwU6W5c6dO3fu3Dl8hQFAgxP2sS4+Pr64uFhV\nVU+MKyoqSkhIqK7dM2NmZqb31X6FhYXhKqk6MTEx5eXlke4lBEaj0WKxOJ1O//KMgYKd2+WO\nZrAzGo0ul0sLKFE+U9gAud3BrnztTdtwbSktWPtwOBwVIe3SkiSZTKaqqir/Y4mGwGKxVFVV\n1TXYReddAACoX2Ef6zp16uRwOA4fPtyxY0chRHFxcW5ubrdu3Vq1ahWwPby9A2iCmvRJYwAI\nlzNnzhQWFpaUlAghCgsLCwsLKyoqEhMTMzIyVqxY8fPPP2vfctehQ4fu3btX117fCwGg0Qvq\njJ3PLbG9VVVVFRcXh68eAKg3dRnrZs2aVVBQoP0+duxYIcT48eOHDx8+bdq0nJycBQsWuFyu\nHj16zJs3T3v7tbp2AKiLoILdVVdd5dPy66+/7t27t0OHDv369YtAVQBQD+oy1q1evTpgu9Vq\nnTFjRvDtQGOkfbGILMtB3rJF+5oYn4mD/+wFahDUBvC+PY5Hfn7+LbfcMmTIkHCXBAD1g7EO\nCI32ZTTn+7lgn2Dn+RgK6iL0G7+2bNny6aefzsrKGjp0aBgLAoAGhbEOCFJ+fv6vv/4awowx\nMTFdu3YNez1NU+jBTgiRkpKyf//+cJUCAA0TYx0QjIqKijNnzoQwo8PhCHsxTVbon4pVVXXN\nmjXNmzcPYzUA0NAw1gFoRII6Y9ezZ0+fFpfLlZ+fX1hYOHPmzAhUBQD1gLEOQGMX4luxRqPx\nsssuGzFiRFZWVngLAoCGg7EOQOMSVLDbtWtXpOsAgHrHWAegsTuPM3anTp365ptvTpw4Icty\nSkpKZmam3W6PXGUAUC8Y6wA0XkEFO7fbPXv27Geffdb7cyuxsbHz58+fNWtWxGoDgKhirAPQ\n2AUV7J5++umnn3565MiRw4YNa9WqldvtzsvLe/vtt2fPnn3BBReMHj060lUCQBQw1gFo7IIK\ndi+99NK999779NNPezdOnDhx0qRJf/vb3xjsAOgDYx2Axi6o77E7cuRIwK9cHzFixI8//hju\nkgCgfjDWAWjsggp2BoOhrKzMv93hcCiKEu6SAKB+MNYBaOyCCna9evV65plnqqqqvBsrKir+\n/ve/p6WlRaYwAIg2xjoAjV1Q19jNmTNn2LBhnTp1uuGGGy688EJVVXNzc9977738/PyPPvoo\n0iUCQHQw1gFo7IIKdjfccMPbb789Z86clStXehovvfTSF154YeDAgRGrDQCiirEOQGMX7BcU\n33jjjTfeeOOJEyfy8vIkSWrTps0FF1wQ0coAIPoY6wA0akFdYyeEyM/PX758eevWrXv37p2W\nlibL8sKFCwsKCiJaHABEGWMdgEYtqGB38ODBXr16zZw509NSVlY2f/781NTUI0eORKw2AIgq\nxjoAjV1Qwe6BBx6w2WxfffWVp+Xiiy/ev3+/zWbjNjsAdIOxDkBjF1Sw+/rrr+fOndu7d2/v\nxm7dus2aNeuTTz6JTGEAEG2MdQAau6CCXWlpqclk8m+32WwulyvcJQFA/WCsA9DYBfsFxevW\nrfMZ10pKSrKzs3v16hWZwgAg2hjrADR2QX3dycMPPzxkyJDOnTsPGTIkOTnZ7Xbn5uZu2rTp\n1KlT77//fqRLBIDoYKwD0NgFFewGDx780UcfzZkzZ8WKFZ7Gyy677OWXXx48eHDEagOAqGKs\nA9DYBfsFxYMGDRo0aNCpU6dOnDihKEqbNm3sdntEKwOA6GOsA9CoBRvsNM2bN2/evHmESgGA\nBoKxrmmS5WC/tN9DkiTPXJIkhbsi4LydX7ADAECvDAZDCNkuhFmAyCHYAQDw/x09ejS0b7e5\n6KKLjEZj2OsBzgvBDgCA/y83N9fhcIQwY6tWrQh2qHecQAYAANAJgh0AAIBOEOwAAAB0gmAH\nAACgEwQ7APi/7d17cFT1/f/xc9lbNrtsEu4QwXBLubSoTFXQ6rdaOr+RQrV0LMN1RMFIx4jW\nYlEcUGwZtQ4RodYMl5oiqDOV8dZRwVZ0NFUYEXAoiOEiAgoBks0m2fv5/XHqNk12w+Zkz57d\nT56Pv/acPZ/zeX92z37yyu7ZswAgCIIdAACAIPL7ciemXuZb37ksy1xMHGYw77jq/KC18MBO\ndG3q/gGgJ8vvYOfz+czbuaqqkiRZ8jORqi3J86LIssPhMLVf/e+i3W7XNO1/uua66t2jqmrH\nx1B/tLv5nCaNMoWFhVrql0biWTb15ZOqa0VRzOu33XELAD1Qfge7hoYG83bu9XqdTqff74/H\n4+b1kpQvGu14jcu4poXDYVP7tdlsNpstEom0G7LD4SDbdUcsFotGo+1WKoqiqqqx66AmJI0y\ngUAgkvqloapqcXFxOBwOBALd6doAVVU9Hk9jY6N5+y8uLjZp5wCQF/hrDQAAIAiCHQAAgCAI\ndgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAA\ngiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIm9UFIBvsdrssyxfdTN/G\nbrdrmtZxPQAAyHEEux5BlmVFSffdWVmWSXIAAOQjgl0P8o9//MNYw2uvvdbhcGS2GAAAkHGc\nYwcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAA\nIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIId\nAACAIAh2AAAAgrBZXQAA4CKKi4vN7kJRlCz0YoAsy5IkuVwuh8PR7i7Fbu+4vc1uczqd3ekL\nVlFVVVGMvN+UtJXb7S4wdEjrh4HH49E0zUBzs6mq6vF4OtmAYAcAua6hocHsLoqLi7PQiwF2\nu71Xr17BYLClpaXdXb2i0Y7JLhqJhkIhY305HA5jwQIZEYvFIpGIgYbxeLzjytbW1qChQ7qg\noMDtdjc3N4fDYQPNzVZUVNTc3Ozz+VJtQLADgFyXnXcOcvP9iURVHcvLzYKRIzRN684R0s3m\npuq8MP41AQAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAE\nQbADAAAQBMEOAABAEPykGGAiVVUlSZJlueMPUCqKknR9R7n8yzYAgJxCsANM5Ha7JUlSVVVP\neB05HI6L7sTwD2MDAHoagh1gOr/f7/f7DTS02+39+/fPeD0AAFER7ADTnT9//siRIwYaejwe\ngh0AIH18eQIAAEAQBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAA\nEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQBMEO\nAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQ\nBDsAAABBEOwAAAAEQbADAAAQBMEOAABAEAQ7AAAAQRDsAAAABEGwAwAAEATBDgAAQBAEOwAA\nAEEQ7AAAAARBsAMAABCEzaqOKysrjx07llh0uVwvv/yyJEmBQKC6unrfvn2RSKS8vLyioqJf\nv35WFQkAAJBHLAt2gUBg4cKFV199tb6oKP9577CqqioQCCxfvtzpdG7ZsuXRRx9ds2ZN4l4A\nAACkYllgampqGjBgQJ/vlJSUSJJUX1+/a9euhQsXlpWVDRo0qKKi4uTJk/v377eqSAAAgDxi\nzTt2kUgkFArV1tZu3ry5qalpxIgRc+fOHTx48OHDh+12e1lZmb6Zx+MpLS09dOjQ+PHjLakT\nAAAgj1gT7FpaWoqKiqLR6KJFiyRJ2rp169KlS5999lm/3+/1emVZTmzp8/kaGxsTi5s3b/7o\no4/02y6X6/HHHzevSFVVJUnq1auXpmnm9ZKUzZbkeVFk2eFwGNshn2XnNUVRUj31bV8sCYWF\nhZrPl2pvehOHw+FLvY15bDabef1m/6UKALnGmmDn8/lqamoSi0uWLJk3b56e2JL+oUo4evTo\nJ598ot92u912u93UOqUUGct0yR8EmXzWM8mynPp1kWS9zWaTLvbSUBTFqsPJvJdtPB43ac8A\nkC8s+/JEWwUFBX379q2vrx82bJjf79c0LfFnrLGxsbi4OLHlb3/723vuuUe/LcvyuXPnzKvK\n6/U6HI4LFy5k/69Fr0ik45++uBYPBoPGduhwOAiF+SsWi0UikaR3aVqSg9Pv90dSvzRUVS0q\nKgqFQoFAIGMlpkdV1cLCQr/fb97+i4qKTNo5AOQFa4Ld8ePHX3/99YqKCv39sGAwePbs2QED\nBowcOTISidTV1Y0YMUKSJL/ff+LEidGjRycaulwul8uVWKyvrzevSP1jHU3T+HwH+aXzg9bC\nAzvRtan7B4CezJpgV1JSUltbG41GZ8yYEYvFampqPB7PpEmTnE7nxIkT161bV1lZ6XA41q9f\nP3z48DFjxlhSJAAAyAK32y1Jkqqq+tntXaWofCT1X9YEO6/Xu3Llyk2bNi1evNhut5eXl69a\ntcrpdEqSVFlZWV1dvWLFilgsNnbs2GXLlnV+1h0AABBAKBRqbm420DDVySo9k2Xn2A0bNmzl\nypUd17vd7sWLF2e/HgAAYKFz584dPHjQQMPpTdk+YziX8e4lAACAIAh2AAAAgiDYAQAACIJg\nBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAg\nCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCBsVhcAACKrrKw8duxYYtHlcr388suS\nJAUCgerq6n379kUikfLy8oqKin79+llWJQBREOwAwESBQGDhwoVXX321vqgo//mcpKqqKhAI\nLF++3Ol0btmy5dFHH12zZk3iXgAwhkkEAEzU1NQ0YMCAPt8pKSmRJKm+vn7Xrl0LFy4sKysb\nNGhQRUXFyZMn9+/fb3WxAPIe79gBgFkikUgoFKqtrd28eXNTU9OIESPmzp07ePDgw4cP2+32\nsrIyfTOPx1NaWnro0KHx48fra44cOVJfX6/fVhRl5MiRZpcqy7Ldbje7FwNsNpskSYqidCwv\n6RucsiLzxickSVJV1dghrR8/NptN07RMF5UBsizrL4pUCHYAYJaWlpaioqJoNLpo0SJJkrZu\n3bp06dJnn33W7/d7vV5ZlhNb+ny+xsbGxOILL7zw6quv6rfdbvf777+fhWp9Pl8WejHG6XQ6\nnc72a5P9ebOpNofDkY2akNsKCgoKunFIu93uDBaTWZ3XRrADALP4fL6amprE4pIlS+bNm/fR\nRx9JktQ21XV0zTXXFBUV6bcdDkdra6updUqS5HK5gsGg2b0YoCiK0+mMRqORSKTdXY5YTO2w\nfSwei0ajxvpSVbXz5wV5JBKJRA29cGw2m91uD4fDsVgs41V1n8vlCoVCBQUFqTYg2AFAlhQU\nFPTt27e+vn7YsGF+v1/TtESMaGxsLC4uTmx5ww033HDDDYnFxMey5nE6nc3NzWb3YoDdbnc6\nnZFIpGN5arJgF4/FDQc7RVEIdsIIhUJBQ4e02+222+3BYDAcDme8qu5zOBydBztORAAAsxw/\nfnzt2rWJnBEMBs+ePTtgwICRI0dGIpG6ujp9vd/vP3HixOjRo62rFIAgeMcOAMxSUlJSW1sb\njUZnzJgRi8Vqamo8Hs+kSZOcTufEiRPXrVtXWVnpcDjWr18/fPjwMWPGWF0vgLxHsAMAs3i9\n3pUrV27atGnx4sV2u728vHzVqlX6lwAqKyurq6tXrFgRi8XGjh27bNkyPgQE0H0EOwAw0bBh\nw1auXNlxvdvtXrx4cfbrASA2gl3ekC/2NToAANDDEezyhiwrSS7jBAAA8B2CXR7RTp06Zazl\nwIEDebcPAADhEezyhqZpBw8eNNa2f//+qtrxek8AAEAoXMcOAABAEAQ7AAAAQRDsAAAABEGw\nAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQ/PIEAEAcNrvN8M9q89OLEADB\nDgAgDlmS4/G4sbb89CIEQLADAAhl586dxhpee+21Docjs8UAWcY5dgAAAIIg2AEAAAiCYAcA\nACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiC\nHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACA\nIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYA\nAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg\n2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAA\nCIJgBwAAIAiCHQAAgCBsVhfQLQUFBebtXFVVSZJcLpemaeb1kpSiELjxX7Is22zJX6qyJHdc\n6XQ6balfGvrRZbPZTH35pOpaURTz+pXlJI8GAPQo+R3sshC5NE3LfrAD2unSQdj5QavfZcmB\nneg6y/0CQM+R38EuGAyat3O73W6z2UKhUDweN6+XpJzxuJrlLpHDNE2LxWLJ75KShKRwOBxJ\n/dJQVdXtdsdiMVNfPqm6ttvt5vWrqmphYaFJOweAvMBHfgAAAILI73fsALHpp6Opqqqf8dmR\nLCf538zn80l9+iQWw+Gw3+83qUIAQE4h2AG5S/82QCQSSfXxZdLzBL755pvWo0clSVIUZejQ\noaZWCADIKQQ7INedPXv24MGDSe9KGvg2btz4+ZtvSpJUVFT0pz/9ydziAAC5hHPsAAAABEGw\nAwAAEATBDgAAQBAEOwAAAEEQ7AAAAARBsAMAABAEwQ4AAEAQXMcOAHKdz+czuwtZlrPQiwH6\nZbqdTqfN1v4Pls1ut6Ii5IeCggKnoUNaURRJkgoLC/Xf/sk1iqK43e5ONiDYAUCuCwQCZnfh\n8/my0IsBNpvN6/WGw+HW1tZ2d3miUZIdUgmFQiFDh7TL5SooKGhtbY1EIhmvqvt69eoVDAYd\nDkeqDQh2AJDrYrGYML10lf72iaZpHcvTNM2KipAf4vG4sUNaP64MN8+CpD8mmcA5dgAAAIIg\n2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCK5jBwDIFcXFxaqqdlxfUFCQ\n5GcAUl+jFeixCHYAgBwSi8W++uqrdLYc0Nqaiz/5BFiKYAcAyCHNzc0PPfRQOlv+7vDhH5hd\nDZBvOMcOAABAEAQ7AAAAQRDsAAAABEGwAwAAEARfnsgSh8Mhy3KaGysKgRsAAHQZwS5LvF5v\n+sFOSnYZJwAAgM4R7LKnsbHxzTffTGfLwZ9+er3Z1QAAAOEQ7LKnqanpjTfeSGfLK06eJNgB\nAJAOWUnygZjb7XYVFaW/k2g0GggEMleUZQh2AAAgj8lSkmCnKIpi64khpyeOGQAAiG39+vX/\nfO+9NDd+4YUXzKwlqwh2AABAQJqmpbNZF77amA+4rAYAAIAgeMcOEJbD4ZAkSVVVl8uVWKlf\nJbHdyk7E4/FwOGxShQCAzCLYAcLSo5uqqh6Pp91ddrvdbrens5NoNEqwA4B8QbADBHf06NG3\n3nrLWNu77rors8UAAExFsAMEd/78+Q8++MBAQ1mWCXYAkF/48gQAAIAgCHYAAACCINgBAAAI\ngmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcA\nACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCJvVBeQNr9fbneayLGeqEgAAgKQIdmmRZdnp\ndFpdBQAAQGcIdl3wxRdfrF271ljbNWvWZLYYAACAdgh2XRCNRuvr662uAgAAIDm+PAEAACAI\ngh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAA\ngCAIdgAAAIIg2AEAAAjCZnUBAAAAlpFlWZIkm83Wu3fvxKLX6+3STmKxWENDgxnldRXBDgAA\n9HThcPjMmTPG2g4aNEiPg7mAYAcAAHq648ePL1++3Fjb6urqgoKCzNZjGOfYAQAACIJgBwAA\nIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgl+eAJBc4vcT\n+/TpY3gnsVjswoULmSsKANAZgh2AzgSDwZMnTxpre+mll2a0FlycLMsOh8NYQ6fTqd+Ox+OR\nSMRYAYmdGJM7P7gJpM9ms7V9BRkTCoUyU0xG9gJAVCdOnBDj9xN7CEVRvF6vsbaJhpFIpLGx\n0dhOPB4P4Qw9jdPplGXZ8EtPkiRN0wh2AIDkjh079v777xtoaLPZZs6c2c3eGxoaXnvtNWNt\nZ8+e3c3eAUs0NTVt27bNWNuf//znvXr1ylQlBDsAEM3p06ffeustAw2dTmf3g10gEDDWuyRJ\ns2bN6mbvgCVaWloMH/Y33ngjwQ4AkHn6+Xl2u713797G9sDnsIC1CHYAgP8RCoXq6+uNtR08\neHBmiwHQJT0o2ClK1y7ap//fqbfif1DAgLYvosSNrr4S4/F4xgtD5+rq6h577DFjbbds2ZLZ\nYgB0Sc4Fu0AgUF1dvW/fvkgkUl5eXlFR0a9fv+7vVlXV4uJiAw2Lioq63zvQM7ndblmWS0pK\n2q5st9g5TdPOnTuX6bpygklzHYAeLueCXVVVVSAQWL58udPp3LJly6OPPrpmzZqu/oufyvnz\n548ePWqgoc1mGz9+fEZqAHqUUCj0+eefG2s7duzYbl4XKpeZOtcB6LFyK9jV19fv2rVr9erV\nZWVlkiRVVFTMmTNn//79mQpVhw4deuaZZww09Hq9zz33XEZqAHqUhoaGp556yljbJ598ctCg\nQZmtJ0eYPdcB6LFyK9gdPnzYbrfrM50kSR6Pp7S09NChQ4nJ7sKFCy0tLfptWZbTv/ap/n+w\ny+Xq37+/gcIKCwslSXI4HMaa62w2W5rNi5qbk63uwniTMtxcP1OqoKDAZjNywCQefGMnS+nN\nnU6nsfpVVZUkyeFwGGuuD9lwc7vdru/EWHP9K4qqqqZqriQ7+7O4uFg/0vQPPZ1Op7HjVn/k\n7XZ7dw57VVUNN9cffP0ZTEcevd3FXJeKLMuKoqTZPNVvbDDXGWie43Nd55K+9nv16pXmgSTY\nXCdrmmasDjO8/fbbL7744qZNmxJrli1bNnTo0AULFuiLK1eufPXVV/Xbbrfb2BU488C//iU9\n/LB+83jkP5eibii7dMcf/2BdTchF/+/u+zynvtFvD7V/96nlE09Il19uWU3Wicfj+ZLtmOsy\nY+lSafdu/WZiqty9aOGxG//PspJghWsfe3zAnr367f/OhPfeK910k2U1manzuS633rGTLvb9\n0zFjxjR/926W0+lM//c3ZFlOPwvrFEWRZTkej+vZV38QDX9Br0vNlauu0t5+W++39LvmQyRp\nnMm9txtygizL+npjvevNNU3r5L+ITipMp7nh3vW3BzRNSzU683pP81G9yHP3162Jm7G262Ox\nVM0vOuQu9N7F5sYOpGg0mv7GeXROXu7MdTpVVWOxmJTduS6d5p0vWDL8AAAPkklEQVQdsW2+\nutt2quzmZNWl5npt+gvc1MnKQHP9SIjFYp22M6v3dLZv+1B368j5Y1WiedKZ8KLa9p7qT2Ga\nzQ1Lc65zOByRSKSTuS63gl1RUZHf79c0LTHlNTY2tv026/Tp06dPn55YNHylpXR4vV6n09nQ\n0JD9qy14vd5gMGj4R7gNc7vdbrc7EAiEw+Esd11QUKBpWjAYzHK/TqfT6/W2tLS0trZmuWuH\nw+FwOAKBQJb71b8hHgqFLOna4/EY/hHSdPafL8Eup+Y6XUlJyYULF8zuxQC73e7z+YLBYHPy\nc1Qs5vF4wuFw9ufMdBQXF8uynLNPq8vlampqsrqQJCz8U5iO4uLi1tbWTua63PrYYuTIkZFI\npK6uTl/0+/0nTpwYPXq0tVUBQGYx1wEwSW4Fu5KSkokTJ65bt+7o0aMnT55cvXr18OHDx4wZ\nY3VdAJBJzHUATJJbH8VKklRZWVldXb1ixYpYLDZ27Nhly5bxqw8AxMNcB8AMORfs3G734sWL\nra4CAMzFXAfADLn1USwAAAAMI9gBAAAIgmAHAAAgCIIdAACAIAh2AAAAgiDYAQAACIJgBwAA\nIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgBAAAIgmAHAAAgCIId\nAACAIAh2AAAAgiDYAQAACIJgBwAAIAiCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAg\nCHYAAACCINgBAAAIwmZ1Ad1it9vN2/mXX37p9/tHjhxpai9JaZqmqmqWO5Uk6dSpU2fPnh06\ndGhhYWGWu5ZlWZbl7D/UjY2Nn3/+ef/+/Xv37p3lrhVF0TQt+0MOh8O1tbVFRUWDBg3KcteK\nosTjcfOGrCjC/qeaheMkFotl/2hMR0tLy4EDB/r06dOvXz+ra0lOUZTcfOg+++yzWCxWXl5u\ndSFJWDUBpuP06dNnzpwZMmSIx+OxupYkYrHYRRKChhSWLl06YcKEU6dOWV1I9jz33HMTJkz4\n8MMPrS4ke3bs2DFhwoSamhqrC8meurq6CRMmPPLII1YXAqRl165dEyZMWLt2rdWF5J9p06ZN\nnjzZ6iryz4YNGyZMmLBz506rCzFI2H9wAQAAehqCHQAAgCAIdgAAAIKQNU2zuoYctX///m+/\n/fbaa691uVxW15IlR44cOXLkyOWXX579bxJY5cyZM/v27Rs5cuTQoUOtriVLmpuba2trBw0a\nNGbMGKtrAS7u/Pnzn3766aWXXjpixAira8kzH374YSwWu+6666wuJM8cPXq0rq5u/Pjxffv2\ntboWIwh2AAAAguCjWAAAAEEQ7AAAAASR3xco7pLz589v3Lhx79694XB42LBht91226hRozpZ\nn7B///6HHnqo3d7uvPPOKVOmVFZWHjt2LLHS5XK9/PLL5g8lXamGduLEieeff/7f//63pmll\nZWVz5sz53ve+165tIBCorq7et29fJBIpLy+vqKjQrw6aan3u6M6oU7UV+IlONbTcf6IhqovO\nye++++7TTz/94IMPXn311VYVmYPydPqyXCfH29///vdt27adO3du8ODBc+fO/eEPf2htqWnq\nQefY3XfffQ6HY+HChQUFBVu2bNmzZ8/69etdLleq9YmGkUiksbExsXjmzJkVK1Y89dRTl1xy\nyfz583/xi18kJhdFUUpKSrI9sNSSDs1ms91xxx3jx4+/9dZbFUV56aWXPv74440bNxYUFLRt\n+9hjjwUCgTvvvNPpdG7ZsuXYsWNr1qxRFCXVeqvG2FF3Rp3qYBD4iU41tNx/oiGqzufkhoaG\nysrKlpaW+++/n2DXVp5OX5ZL9bi9++67NTU1d99995AhQ2pra998882qqiq32211vWmw8urI\nWeT3+//whz989dVX+uKZM2emTp36xRdfpFrfya6WLVu2ZcsW/fYvf/nLXbt2mVq5YamG1tDQ\n8Morr7S0tOjrv/7666lTp9bV1bVte/bs2WnTpiVWNjU13XzzzZ999lmq9dka08V1Z9SdHAyi\nPtFaiqHl/hMNUV10Tl61atWGDRvmzJlTW1trUY25KE+nL8t18rgtWLDg3XfftbQ6g3rKR7Fe\nr3fp0qWJxXPnzimK0qdPn1TrU+3ngw8+OH369PLlyyVJikQioVCotrZ28+bNTU1NI0aMmDt3\n7uDBg00dSPpSDc3n891yyy36yqamptdee620tPSSSy5p2/bw4cN2u72srExf9Hg8paWlhw4d\namlpSbp+/PjxWRnTxXVn1KnaCvxEpxpaqgMgd55oiKrzObm2traurm7x4sXvvfeeNfXlqjyd\nviyX6nE7d+7cN998I0lSZWXl6dOnhw4descdd3Q8lSU39cQPVpqamp555pmbb765uLg4nfUJ\n8Xh8y5YtM2bMsNlskiS1tLQUFRVFo9FFixY98MAD4XB46dKlzc3N2RhDF3UcWjwenz59+qxZ\ns06cOLFy5cp2v8Ts9/u9Xq8sy4k1Pp+vsbEx1frsjKKrujrqVG0FfqJTDS2/nmiIqt3xHAgE\n/vznP//617/uOdcWNSZPpy/LtX3czp07J0nSjh07lixZsnHjxvLy8kceeSRf5sAeF+y+/vrr\n+++/f9y4cfPmzUtnfVsffvhhMBj88Y9/rC/6fL6ampp777131KhRo0aNWrJkSTAY/Oijj8wd\nQNclHZqiKE8//fTvf//7Xr16Pfjgg4FAoF2rtn/U01mfa4yNOmlbgZ/oToaWL080RNXxeN6w\nYcMVV1xx2WWXWVtYjsvT6ctySefPX/3qV6WlpV6vd/78+bIs796928IK09ezgt3evXsfeOCB\nqVOn3nXXXW3/bqVa384///nPSZMmqaqa9N6CgoK+ffvW19dnvu5u6GRopaWl3//+95csWdLY\n2Lhz5862dxUVFfn9fq3NF2saGxuLi4tTrTd7FF1lbNQXbasT6YluJzG0fHmiIaqOx/Nnn332\n6aefzp8/3+rSclqeTl+W6/i46d8vKSws1DdQVbWkpOTChQtWVpm2HhTsDhw48Pjjj993330/\n+9nP0lnfTnNz8549e6688srEmuPHj69duzYajeqLwWDw7NmzAwYMMKN4Y5IObc+ePQsXLgyF\nQvqiLMv6J8ttjRw5MhKJ1NXV6Yt+v//EiROjR49Otd78oXSB4VGnaivwE51qaHnxRENUSY/n\n7du3Nzc3V1RUzJo1a9asWY2NjatXr161apWFdeaaPJ2+LJf0cSspKSkuLj548KC+GA6Hz549\n279/f4tq7Jqe8uWJcDhcVVU1bdq0oUOHJv5Z8Xg8iqIkXe9yubZv3x4MBqdOnaqv/PLLL2Ox\n2MCBAxP7LCkpqa2tjUajM2bMiMViNTU1Ho9n0qRJWR5aKqmGPHLkyGAwWFVVNXPmTLvd/vrr\nrweDwQkTJkiSlBhySUnJxIkT161bV1lZ6XA41q9fP3z48DFjxsiynHS9pQP9H90Zdaq2Yj/R\nSYfmdDpz/ImGqFIdzxUVFbfddltis3vvvXfu3LlXXXWVRWXmnDydviyX6nFzuVxTp0598cUX\nS0tLS0tLt27d6nK5uI5dbtm7d+/DDz/cbuWdd95ZWlqadP2UKVOefPJJv9+/cuVKfeV77723\nevXqv/3tb23f9jhy5MimTZv0rxCWl5cvWLAgdxJ9qiFPmTLl+PHjmzZtOnDggCzLQ4YMmT17\ntv5tx7ZDbmlpqa6u3rNnTywWGzt2bEVFhf5JXKr1OaI7o+6krcBPdKqh5fgTDVF1cjy3XTN3\n7txFixZxHbuEPJ2+LNfJ4xaPxzdv3rxjx45AIFBeXr5o0aJ2VxXIWT0l2AEAAAivB51jBwAA\nIDaCHQAAgCAIdgAAAIIg2AEAAAiCYAcAACAIgh0AAIAgCHYAAACCINgh1/n9fo/HI8vytm3b\nrK4FAMzCXIeMINgh173wwgvNzc3FxcXr16+3uhYAMAtzHTKCX55ArrviiiskSbr++uufeeaZ\nY8eOlZaWWl0RAGQecx0ygnfskNN27969Z8+eGTNmzJ49OxaL/eUvf2l7bzweX7FixSWXXOJy\nuSZMmLB9+/a7777b4XAkNti5c+fkyZN79erldruvuOKKjRs3ZnsAAJAG5jpkjAbksIULF6qq\nevLkSU3TfvCDH5SVlcXj8cS9v//97yVJuvXWW99+++3169cPHDjwyiuvLCws1O/dsWOHqqrX\nXXfd66+//s4771RUVEiS9Mc//tGakQBAasx1yBSCHXJXU1OT1+u96aab9MWqqipJkrZv364v\nxuPx/v37jxs3LjH9/etf/5IkKTHZXX755SNGjGhubk7scNq0aV6vt7W1NYuDAICLYK5DBvFR\nLHLXiy++2NTUNH/+fH1x9uzZDodjw4YN+uI333zz7bffTp48WZZlfc1VV101btw4/faZM2f2\n7NkzZcoURVGC37npppuampr279+f/bEAQCrMdcgggh1yV3V1tc/nmzRpUn19fX19vaZpP/3p\nT7dt23b+/HlJkr799ltJkgYOHNi2SXl5uX7j1KlTkiQ9/fTTBW3on1B8/fXX2R4JAKTGXIcM\nslldAJDc3r17d+3aJUnSoEGD2t3117/+9Z577gmFQpIkKcr//HOS+I9WN3/+/AULFrRrPmLE\niMyXCwCGMNchswh2yFHV1dWSJG3durVPnz5t18+bN2/Dhg333HNPSUmJ9N3/sgmHDh3SbwwZ\nMkSSpFgsdvXVV2epYgDoOuY6ZJjVJ/kBSbS0tPh8vokTJ3a863e/+50kSR9//HEkEvH5fOPG\njUvc9cknn0htTii+8sorfT7fhQsXEhs8//zzDz30UCQSMbt+AEgHcx0yjnPskIteeumlxsbG\n22+/veNd+vnF69evt9lst99+++eff37bbbe988471dXVt9566zXXXJPY8oknnmhpabn++utr\namreeeedhx9++I477jh58qTNxhvVAHICcx0yz+pkCSQxadKkwsLCpqampPded911Xq83EAgE\ng8G77767T58+hYWFP/rRjz7++OOZM2d6PJ7Elh988MHkyZO9Xq/dbh81atQTTzzBv7AAcgdz\nHTKOnxSDUH7yk58cOHBA/5oYAIiKuQ6p8FEs8lhVVdX06dOj0ai+2NDQsHv37ssuu8zaqgAg\ns5jrkD4+gEce69279yuvvHLLLbcsWLAgGAxWVVX5/f7f/OY3VtcFAJnEXIf0EeyQx+bMmSNJ\n0urVq2fOnKlp2mWXXfbGG2/ceOONVtcFAJnEXIf0cY4dAACAIDjHDgAAQBAEOwAAAEEQ7AAA\nAARBsAMAABAEwQ4AAEAQBDsAAABBEOwAAAAEQbADAAAQxP8HkY/QpefsHmQAAAAASUVORK5C\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_medal_vis_mean<-football_male_resample_mean%>%\n",
    "                    visualize()+\n",
    "                    ggtitle(\"Football Male Athlete's Mean Age \\nBootstrap Distribution with 95% CI\")+\n",
    "                    labs(x=\"Age\")+\n",
    "                    shade_ci(endpoints = football_male_ci_mean, color=\"red\")\n",
    "female_medal_vis_mean<-football_female_resample_mean%>%\n",
    "                    visualize()+\n",
    "                    ggtitle(\"Football Female Athlete's Mean Age \\n Bootstrap Distribution with 95% CI\")+\n",
    "                    labs(x=\"Age\")+\n",
    "                    shade_ci(endpoints = football_female_ci_mean, color=\"red\")\n",
    "bootstrap_vis_mean <- plot_grid(male_medal_vis_mean, female_medal_vis_mean, rel_widths = c(1, 1), rel_heights = c(1, 1))\n",
    "bootstrap_vis_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11b59dd7-0483-4327-bd8f-8142f25d19e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A infer: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>stat</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>3.288721</td></tr>\n",
       "\t<tr><td>2</td><td>3.121125</td></tr>\n",
       "\t<tr><td>3</td><td>3.400665</td></tr>\n",
       "\t<tr><td>4</td><td>3.167436</td></tr>\n",
       "\t<tr><td>5</td><td>3.253615</td></tr>\n",
       "\t<tr><td>6</td><td>3.221092</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A infer: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " replicate & stat\\\\\n",
       " <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 3.288721\\\\\n",
       "\t 2 & 3.121125\\\\\n",
       "\t 3 & 3.400665\\\\\n",
       "\t 4 & 3.167436\\\\\n",
       "\t 5 & 3.253615\\\\\n",
       "\t 6 & 3.221092\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A infer: 6 × 2\n",
       "\n",
       "| replicate &lt;int&gt; | stat &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1 | 3.288721 |\n",
       "| 2 | 3.121125 |\n",
       "| 3 | 3.400665 |\n",
       "| 4 | 3.167436 |\n",
       "| 5 | 3.253615 |\n",
       "| 6 | 3.221092 |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate stat    \n",
       "1 1         3.288721\n",
       "2 2         3.121125\n",
       "3 3         3.400665\n",
       "4 4         3.167436\n",
       "5 5         3.253615\n",
       "6 6         3.221092"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lower_ci</th><th scope=col>upper_ci</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2.969945</td><td>3.414191</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lower\\_ci & upper\\_ci\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2.969945 & 3.414191\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| lower_ci &lt;dbl&gt; | upper_ci &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 2.969945 | 3.414191 |\n",
       "\n"
      ],
      "text/plain": [
       "  lower_ci upper_ci\n",
       "1 2.969945 3.414191"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(123)\n",
    "# Bootstrapping for Estimating Standard Deviation  \n",
    "# TUTORIAL 04 : USE SUMMARISE\n",
    "football_male_resample_sd <- football_sample %>%\n",
    "                    filter(Sex == \"M\")%>%\n",
    "                    specify(response=Age)%>%\n",
    "                    generate(reps=1000, type = \"bootstrap\")%>%\n",
    "                    calculate(stat=\"sd\")\n",
    "football_male_ci_sd<- football_male_resample_sd %>% \n",
    "  get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "head(football_male_resample_sd)\n",
    "football_male_ci_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "822944b6-ab1a-4762-a157-7ba6f48b7f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A infer: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>replicate</th><th scope=col>stat</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>4.223036</td></tr>\n",
       "\t<tr><td>2</td><td>3.930497</td></tr>\n",
       "\t<tr><td>3</td><td>4.024324</td></tr>\n",
       "\t<tr><td>4</td><td>4.026153</td></tr>\n",
       "\t<tr><td>5</td><td>4.528260</td></tr>\n",
       "\t<tr><td>6</td><td>3.831112</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A infer: 6 × 2\n",
       "\\begin{tabular}{ll}\n",
       " replicate & stat\\\\\n",
       " <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 4.223036\\\\\n",
       "\t 2 & 3.930497\\\\\n",
       "\t 3 & 4.024324\\\\\n",
       "\t 4 & 4.026153\\\\\n",
       "\t 5 & 4.528260\\\\\n",
       "\t 6 & 3.831112\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A infer: 6 × 2\n",
       "\n",
       "| replicate &lt;int&gt; | stat &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 1 | 4.223036 |\n",
       "| 2 | 3.930497 |\n",
       "| 3 | 4.024324 |\n",
       "| 4 | 4.026153 |\n",
       "| 5 | 4.528260 |\n",
       "| 6 | 3.831112 |\n",
       "\n"
      ],
      "text/plain": [
       "  replicate stat    \n",
       "1 1         4.223036\n",
       "2 2         3.930497\n",
       "3 3         4.024324\n",
       "4 4         4.026153\n",
       "5 5         4.528260\n",
       "6 6         3.831112"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>lower_ci</th><th scope=col>upper_ci</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>3.683722</td><td>4.926247</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " lower\\_ci & upper\\_ci\\\\\n",
       " <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 3.683722 & 4.926247\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 2\n",
       "\n",
       "| lower_ci &lt;dbl&gt; | upper_ci &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| 3.683722 | 4.926247 |\n",
       "\n"
      ],
      "text/plain": [
       "  lower_ci upper_ci\n",
       "1 3.683722 4.926247"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(101)\n",
    "football_female_resample_sd <- football_sample %>%\n",
    "                    filter(Sex == \"F\")%>%\n",
    "                    specify(response=Age)%>%\n",
    "                    generate(reps=1000, type = \"bootstrap\")%>%\n",
    "                    calculate(stat=\"sd\")\n",
    "football_female_ci_sd<- football_female_resample_sd %>% \n",
    "  get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "head(football_female_resample_sd)\n",
    "football_female_ci_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02fece91-33dd-4295-92b7-4bc4c66b9cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bez2dysWTNVVU+dOlVpoz5ZrVatUZ/vhmsBferRo4f7y5iYmFatWqkXT3oIXZU2\na5cuXdzrJCcnC6+V71K3bt0rr7xy+/bt2k9F33///YULF7p169azZ89Dhw6dOXNGCJGTk3Pg\nwIEbb7yxSg8B9OiG9gtCfn5+RfVnzZoly/L06dNXrlyZk5Nz7ty5NWvW3HPPPdq+rN1uF0I0\nbdo0MzPzmWeeOX78+Pr16999992jR4/edddd586dGzlypBDCbDbfcccd69at27Zt25kzZx59\n9NHExMSBAwdmZ2c/9NBD9913X79+/ZYvX37llVcuWLDguuuuW7ZsWVFR0euvv15Rr7ThVANv\nu6AnRD+inx9Ev0sw+unkKd116tR59dVX3UscDseLL744efLkPn367N6922g0FhUVlZaWmkwm\n7UJod2lpaUKI3NzcQOr47MDixYsdDseqVau0IdKmTZv+/ftnZWU1adLEf8+12brbsGHDwoUL\nd+3aVVpa6mfCunXrepRoH0Lt4xSEs2fPiopPQQjjAnqrX7++R4nWDW1PK3RV2qz16tVzr6Dt\neasVP1K5T58+Cxcu/PHHHzt16rR161ZJknr16nXy5EkhxJdffjl48GDtJI/Az8xw75iLLMv+\nu9GpU6fnn39+6tSpo0ePdl3o9/zzz7/++us5OTlacJk6derUqVPdp7JYLCtWrPjwww+/++67\nI0eONGvWbPHixcePH+/Zs6cQIjU19Y033khKSho6dGhcXNyiRYuEED/88IPrhKEGDRrUq1fv\nu+++q6hX2k8h4dqO8InoJ4h+FSP6XYLRTydH7LwpijJp0qSbb775559/3rx5s/A7Rp1Op1Yh\nkDo+mzMajcuWLTtx4sSyZcsGDhx48uTJBQsWtGrV6s033/TfT+2OUy4rVqwYNGjQ3r17s7Ky\n1q9f//7773/44Ydjx471nlAb6+60brt+xagq7RxY7YwWn/0MywL65N1nbT17L2Nwgt6sgejT\np48QQvs9YsuWLW3btk1NTW3fvn1CQoL2e8TWrVuFEJX+IhO6SZMm/ec//3nhhRceffTRpUuX\nHjt2bMKECcePHzcYDN5fhC7x8fGuU4iEEKmpqV9//XVOTs7hw4fPnDnTt2/ftWvXvv/++8uX\nL09KSrLZbIWFhdqXqOayyy7zvs2mS8uWLYUQ//73v8O5nKgM0a+qiH7BIfrVzOin28ROk5GR\nIYQ4ePCgEMJqtVosFpvNpv3W7k7bNmlpaYHU8dNc/fr1x44du2HDhpycnJdeevrFzagAACAA\nSURBVMnpdI4dO7asrCzwDs+ePVsIsXnzZu2XlH79+t1yyy0+x6W2i+lO67OfQexHeXn5+vXr\nhRD9+vXzUy30BfTJ+0iA+w60z8Dk58wGbyFuVv969uwZExPz1VdflZWV7dix47rrrhNCyLLc\nrVs3LbR98cUXzZs314ZipDVu3PiBBx546qmnxo0bl56efuLEidOnT7du3dr93BpvFy5cEEK4\n10lLS2vWrJksy6dPn548efK9997rfkqTO0mS/OxJ9+7dWwixZs0a7dcQb6qqzpkzR/uEIryI\nfgEi+lW91/+H6Fczo5/OEzvtrE9Xiq2dvfHNN9+41zl37tyhQ4diY2O1WwMEUsfbiRMn3D9s\nMTExWVlZ3bp1O3/+vHYGZSDKysqys7OtVqt2KFijqupHH33kXdn9InMhRGFh4cGDBxVFadSo\nUYDNuZsyZUp2dna7du369u3rs0JYFrAiHkezy8rKDh06JMuytgMdExMjvA5o79y5s0pNBLdZ\nAxETE9OjR4+vv/76m2++KS0t1UKbEOLaa6/9+eeff/7554MHD1b1l4ggHDx48JVXXjl8+LB7\nofYLnXb6uc1mGzBgQKdOnbRA5vL7778fPHhQkqQrr7zSe7ZZWVlms9n1yB2j0ZiYmOj+VZSb\nm+vn2/Tuu++uU6fO0aNHtQsAvc2bN++JJ57QfuFCeBH9AkT0C67nguhXU6OfbhM7p9O5ZMmS\nzz//3Gw2u9JtbQ3OmzevvLzcVXPevHl2u33IkCFazh5IHe3D5tpr3LNnT+PGjYcOHeo+SWFh\n4bFjxxRF0c7f9JjEJ7PZnJKSUlRUpJ2jIIRQVXX27NnatVoe+1uff/75t99+63r56quvlpeX\na3fsrNKKOnXq1JAhQ5YsWWKxWNauXevzsHy4FrAin3/++fbt210vX3755ZKSkuuvv15bFm1v\nT7vfgVbhwIEDL7/8svscKm09kM0atD59+pw9e/aVV17RTjHRCq+99lr14r1P/YS2UNabu127\ndo0aNWrSpEmuO35t3779mWeeiY+P1+6YYDQatdOoJ02aZLPZtDqFhYWjR48uKSn505/+dNll\nl3nM84033ti4ceOyZcvczz3q3Lmz66vo+PHjp0+f9nOGeGxsrLZaFixYMGLECNeJ50KI06dP\njx8/fsaMGQkJCStXrgxx8eGO6Bcgoh/RT5/RL8TbpVQ77QZFcXFxf3bTp0+f9PR0IYSiKO53\nANLu0yiEaNeu3dSpUx999NEbb7xRCNGiRQvXo0gCqXPkyBFJkoxG44gRI7KyslRVveeee4QQ\nGRkZ48ePf+KJJ7QHCQshXM8n8Z7E57NTHnzwQSFE8+bN586dO3fu3C5durRs2fLjjz8WQlx2\n2WXz588/efKklv4PHz5cuxpuzpw5w4YNUxTFaDTu2LEj8BV16623dujQQTuTo2HDht988417\nfY8bDoVrAT1mq70cPXp0QkLC8OHDn3zyybvuuktRFLPZ/O2332p1zpw5o8W4rl27/vWvf73r\nrrusVqt2t6e+fftW1LpHQ4FsVp8d1vYC27dvX9GKda1bk8nUrl07V2FpaanZbDaZTCaTqbCw\nsKI1EOB6q7QbZWVl2lVvLVq0GDFiRP/+/Q0Gg6Io77zzjqvOyZMnGzRoIIRo3Ljx4MGDBw0a\npO1utmrVSrvVk7szZ86kpqbefffdHuUbNmwQQkydOnXr1q09e/ZMSkqq9K5Lb7/9trYFZVlu\n3759nz592rVrp11U2KhRo71797pX5j52gSP6Ef18tk70u8Sjn04SOw+JiYlXXHHFyJEj9+zZ\n41HfZrMtXry4Y8eOFovFbDa3atVq+vTpeXl5Va0zf/781NRUs9ncsWNHVVUdDsfSpUu7deuW\nmpqqKEpiYuK11167atUqp9NZ0SQ+R3BJScljjz3WtGlTs9ncqFGjcePGaR+8++67Ly4url69\nenv37tVC29///vfPPvusV69eVqvVarX26tXL44Eqla4oWZbT0tJuvPHGJUuWuJ6l4+LxCQzX\nAnrMdvLkyUKIt956S/ucWK3WuLi4Xr16bdu2zX2qffv23XDDDRaLxWq1/vGPf9ywYYN2dsh1\n111XUeveD9WpdLMGHdqcTqe24z5hwgT3cu1GBu6d9NmxQNZbIN04f/78tGnTMjIyYmJiUlNT\nBw4c+O9//9ujzq+//jpx4sRmzZqZzWaLxXLFFVfMmjXL56MPb7/99jp16rjivrsXX3xRu0Vn\njx49XN9A/uXm5s6ePbtLly6pqakGgyE5Oblnz54rV64sKSnxqEliFziiH9HPZ+tEv0s8+vk7\n9Q8104wZM5566qklS5ZMmDChuvsCANFD9AMqpdtz7AAAAC41JHYAAAA6QWIHAACgEyR2AAAA\nOsHFEwAAADrBETsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACd\nMFR3B3CRwyF27/ZR3qGDUJSo9wY1zLFjIi/PszA9XdStWx29Aarbnj3CbvcsbN1aWCzV0RvU\nGL//Ln75xbMwMVE0a1YdvaketfsGxbm5uUKImJgYq9VaVFRUWloa5Q6YzWaDwXDhwoXQZyUV\nFV3WpIl3+cIDP5TFx3sUyrJsMpkcDofNZgu96SqRJMloNJaXl0e5XSFETEyM0+mslqbNZnNZ\nWVn02zWZTLIsl5aW/ilrYuvNH3q8e2HOnJKsrAg1nZCQUFxcbPf+7oywuLi42NjY8+fPB9G0\noijJycmR6FW102JdhFgsFqfTGZ34mZSUZDAYQl+clJYt5XPnPApXfrzpTNvWrpeyLCuKEp0g\naTAYDAZDeXm50+mMQnNRi0iKohiNRrvdHp1QYDKZbDZbKGlJx3Vv9J3+hEdh+U03Fbz+unuJ\nLMspKSnl5eUFBQVBtxU4i8XicDjCuMn8xzp+igUAANAJEjsAAACdILEDAADQCRI7AAAAnSCx\nAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMA\nANAJQ3V3AAD04Ny5c6tWrdqzZ095eXlGRsb999/fokULIURRUdGKFSv27t1rs9latmyZlZVV\np04dP+UAEAqO2AFAGMydOzc3N/fJJ59cvHhxamrq7NmzS0tLhRCLFy/OycmZOXPms88+a7FY\nZs+e7XQ6/ZQDQChI7AAgVIWFhWlpaePHj8/IyKhfv/6wYcMKCgpOnjyZm5u7c+fOMWPGNGnS\npEGDBllZWdnZ2fv27auovLqXA0Ctx0+xABCq+Pj46dOnu16ePXtWluXU1NSDBw8ajcYmTZpo\n5VarNT09/dChQ8XFxT7L27dvXw29B6AjJHYAEE6FhYVLliy57bbbkpOTCwoK4uPjJUlyvZuY\nmJifn5+YmOiz3PXy/fff37Nnj/a/yWSaMGFC5DpsMBhUVTUYovF1IMuyEMJqtYY4H/dV52Iw\nGIxGo3sdSZLcSyJH64+2JqPTXDSXS5blqDUX4jhUFMW70GAweAw51/YKfSgGQhsY0VmHgsQO\nAMLo1KlTc+bM6dChw/Dhw7USnymIn3LNDz/8sHHjRu1/i8UyZcqU8PbTW9S+dYQQMTExoc7C\n19pTFNn7e93nN32EaGlrdLBcPsmyj4Ehy7LPIVdReYSE8SPm/3xcEjsACI89e/YsWLDg7rvv\n7t+/v1aSlJRUUFCgqqorjcvPz09OTq6o3DWrUaNG3X777dr/iqKcP38+ct2OiYlxOp3l5eWR\na8IlPj4+LIuTqKreX+A2m819KSRJUhTFbreH2FYgFEVRFMVms0XniJ3RaLTZbFFoSJZlg8Fg\nt9ujc2WP1lYoc7DbHd6FNpvtwv8OOUmSEhMTbTbbhQsXQmkuQGH/iMmynJCQUNG7JHYAEAY/\n//zzM88889BDD1199dWuwubNm9tstqNHjzZr1kwIoV1R0bp16/r16/ssd01Yv379+vXru17m\n5uZGrudOp9PpdEYnAdLyntDbUn0ldk6n6p5/yLKsqmp0MhLtmFbUmhOVHbMJF9eOR9SWS1XV\nUJJjn9Oqquox5FzbKzrD3ul0OhyOMLbl/7gmV8UCQKjKy8sXL148YMCAyy+/PPei0tLSlJSU\nrl27Ll269L///W92dvaiRYuaNm3apk2bisqrezkA1HocsQOAUB04cOD06dPr169fv369q3Ds\n2LGZmZkTJ05csWLFrFmzHA5H27ZtZ8yYoR0FqagcAEJBYgcAoWrfvv2mTZt8vmWxWCZPnhx4\nOQCEgp9iAQAAdILEDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ7jdCaIh9Bt0RedB\nPQBQjQIPlRXVJFSCxA4RZzQaQ39edXQeZAkA1cVgMBgMgX4pm81mn+U2m83h8PG8VFw6SOwQ\nJefOnQsu3MTFxVkslrD3BwBqoPPnz9tstiAmjI2NtVqtYe8Pah0SO0TJ4cOHL1y4EMSEGRkZ\njRs3Dnd3AKAm+u9//5uXlxfEhI0aNWrevHnY+4Nah4snAAAAdILEDgAAQCdI7AAAAHSCxA4A\nAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILEDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABA\nJ0jsAAAAdMJQ3R0AAAChMhgMQghJkmQ5+EM2qqqqqhq+TqEakNgBAFDrWa1WcTG9C5rdbrfb\n7WHqEaoHiR0AADrx+++/X7hwIYgJ4+Li0tLSwt4fRB+JHQAAOnHmzJmcnJwgJqxbty6JnT5w\n8QQAAIBOkNgBAADoBIkdAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgEiR0AAIBOkNgBAADoBIkd\nAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgEiR0AAIBOkNgBAADoBIkdAACATpDYAQAA6ASJHQAA\ngE6Q2AEAAOgEiR0AAIBOkNgBAADoBIkdAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgEiR0AAIBO\nkNgBAADoBIkdAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgEiR0AAIBOkNgBAADohKG6OxCSuLg4\nIYTBYBBCmM1mRVGi3AFFUWRZ1roRKqfTZ7FBMTgMnptJkiTtr8HrrUiTJKmq7cpyGPYftI1b\nLYtcve0KIQwGg/aPB5PJJIdl7PmiKEpsbKyzgmEZOUajUQhRLU0DgA7U7sTObrcLISRJMhqN\nDodDe1ld3QiR5HD4LFdVVVXViqby81ZEValdVVV95iXBtVgrFjk6TTudzsiNeaPRaLfbo59d\naRm8w+FwVPCJ8CP0YQYAtV3tTuzKysrExWhut9u1l9XVjRBJZWVWX+UOp49vOO0YmKqqQXz5\nhUiSJFmWq9RuWI7YaRlGtSyyEMJgMFRLu4qiSJLkcDh85nYRHfNms9lms0V/Z0k7MlpeXh5E\n09E/Zg8ANQ3n2AEAAOgEiR0AAIBOkNgBAADoBIkdAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgE\niR0AAIBOkNgBAADoBIkdAACATpDYAQAA6ASJHQAAgE6Q2AEAAOgEiR0AAIBOkNgBAADoBIkd\nAACAThiquwMAgEokJydHbuaSJAkhYmNjI9eEiyzLIhyLo83Hg8lkNJvN7iWSJPmsGXbaOjQa\njaHPpHopiqIoSiCFkSBJkslkCmUOBoOPrMZoNPocchWVh50sy6qqWiyWcM1QVVU/75LYAUBN\nl5eXF7mZWywWp9NZWloauSZckpKSDAZD6IuT4nR652vl5baysjLXS1mWFUWx2WwhthUIg8Fg\nMBhsNpvT6QxxJmHsVRAcDofdbne9VBTFaDR6FEaOyWSy2Wz+sxb/fPbTZrMV/O+Qk2U5JSXF\nZrMVFBQE3VbgLBaLw+FwH5whUhTFT0rKT7EAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAA\nADpBYgcAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADpBYgcAAKATJHYAAAA6\nQWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADphqO4OAJGlKIqiKCHORJKksHQGAICIIrGDzkmS\nJMscmQYAXBJI7HBJ2L9/f05OThAT1q1bt23btmHvDwAAkcCRDAAAAJ0gsQMAANAJEjsAAACd\nILEDAADQCS6eQE2XkJAghDAajUIIWZbNZnN19wgAgBqKxA41nXYXOqfTqapq0JMDAHApILFD\n7bB37968vLwgJrziiivS0tLC3h8AAGogzrEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMA\nANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQ\nCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkS\nOwAAAJ0gsQMAANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJEjsA\nAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJQxTayM7OXrRo0ZEjRzZs2OAq\nnDhx4vHjx10vY2Ji3nzzTSFEUVHRihUr9u7da7PZWrZsmZWVVadOnSh0EgAAoLaLeGK3bdu2\nlStXXnXVVUeOHHEvLyoqGjNmTJcuXbSXsvx/xw4XL15cVFQ0c+ZMs9m8fv362bNnv/DCC653\nAQAAUJGIJ0w2m23hwoWuBM6lsLCwXr16qRelpKQIIXJzc3fu3DlmzJgmTZo0aNAgKysrOzt7\n3759ke4kAACADkT8iN0NN9wghDh69Kh7oc1mKysr27Fjx2uvvVZYWNisWbNhw4Y1bNjw8OHD\nRqOxSZMmWjWr1Zqenn7o0KH27dtrJXl5ecXFxdr/kiTFxsaKi0f7ZFlWFCXSi+NBlmVJksLS\nrlTBTCRJkiSpwqkqfitCtBaj3261q8ZFrqjpiI55SZKq5TOlLayiKKqqVnVaDu0DQDTOsfNW\nXFyclJRkt9vHjRsnhHjjjTemT5/+0ksvFRQUxMfHu3+NJSYm5ufnu16++OKLGzdu1P63WCxf\nffWV6y2LxWKxWKK1BP8jJiYmDHMx+N4WZpNZmM0+31IUJfrfuxpzBV3SsWpcZLPZ7HNDx8bG\nxiYnR65do9EYuZn7Fx8fH8RUTqcz7D0BgNqlehK7xMTEtWvXul5OmzZt+PDh27dvF5UdF+nY\nsaPrG85kMpWWlgohFEUxGo02m83hcESy1z4oiiJJkt1uD8O8Skt9pocOh8PncmmHNKrlm0yW\n5Sq1qx3XjFx/oiP6o0tcXHUOh8Pn4Su73W4vLY1Q0yaTyW63R3+AGQwGg8FQXl4eXNPh2csC\ngFqrehI7D7GxsWlpabm5uRkZGQUFBaqquvKA/Pz8ZLdjEpmZmZmZma6Xubm5QoiYmBij0VhW\nVlYasS+5ipjNZoPBcOHChdBnJV244PMbyWa32Ww2j0LtNzKn0+n9VqRJkqSl0YFPYjQaq+vI\nYhhFf1ULIUwmkyRJNpvNZ5ZTVlZWUlQUoaYTEhKKi4vDs9NSFXFxcQaDIbimFUUhsQNwiaue\nU1JOnDjx4osvugJ3aWnp77//Xq9evebNm9tsNtcJeQUFBSdPnmzdunW1dBIAAKB2ifgRu7y8\nPIfDUVhYKC4eYLNarSkpKTt27LDb7XfddZfD4Vi7dq3Vau3WrZvZbO7atevSpUsnTpxoMplW\nrlzZtGnTNm3aRLqTAAAAOhDxxG7q1Kk5OTna/yNGjBBCjBo1asCAAXPmzFm9evXkyZONRmPL\nli2ffvpp7eT0iRMnrlixYtasWQ6Ho23btjNmzNDB6VkAAABREPHEbuXKlT7LMzIy5syZ411u\nsVgmT54c4U4BAADoELd9AgAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQiRrx5AkA0IHs\n7OxFixYdOXJkw4YNrsKJEyceP37c9TImJubNN98UQhQVFa1YsWLv3r02m61ly5ZZWVl16tSJ\nfp8B6AyJHQCEwbZt21auXHnVVVcdOXLEvbyoqGjMmDFdunTRXsry//1Osnjx4qKiopkzZ5rN\n5vXr18+ePfuFF15wvQsAwSGIAEAY2Gy2hQsXuhI4l8LCwnr16qVelJKSIoTIzc3duXPnmDFj\nmjRp0qBBg6ysrOzs7H379lVHxwHoCkfsACAMbrjhBiGE61HXGpvNVlZWtmPHjtdee62wsLBZ\ns2bDhg1r2LDh4cOHjUZjkyZNtGpWqzU9Pf3QoUPt27fXSn777bfz589r/yuKEtFfabXDhAZD\nNL4OtCcJhd6WzycSybLkfshTkiRJkqJzEFTrT4jN1ZDHLHmsQ+/CiNK2WiiT+yz0GHKu7RWd\nYR/2j5j/zUFiBwCRUlxcnJSUZLfbx40bJ4R44403pk+f/tJLLxUUFMTHx7t/CSUmJubn57te\nrly5cuPGjdr/Fovlq6++inRXLRZLpJtwSUpKCnUWvr6/jUajyWTyKPQuiRyj0Ri1tiLEZ/IR\nnexHE+L2MhgU70Kj0ehzyFVUHiFh/Ig5nU4/75LYAUCkJCYmrl271vVy2rRpw4cP3759u6js\n8EzHjh0V5f++okwmU2lpaeQ6aTAYVFV1OByRa8LFZDLJshz64phV1Xv1ORxO96XQjv34/woM\nF+1YndPpVFU1xJmEsVdB8FiEsCxX4LS2QpmD0+mjn06ns/x/h5wkSWaz2el0lpeXh9JcgCLx\nEYuJiamwuTA2AwDwIzY2Ni0tLTc3NyMjo6CgQFVVV3qXn5+fnJzsqpmZmZmZmel6mZubG7le\nWSwWp9MZ0dzRJSkpSZbloqKiEOdj8pXY2e12m83meinLsqIo7iWRYzAYZFm22+2h5CXaTMLY\nqyA4nU673e56qSiKlmy5F0aOyWSy2+2hJJE+kye73e4x5GRZNpvN3uURYrFYHA5HWVlZuGao\nKIqfxI6LJwAgUk6cOPHiiy+6vhRLS0t///33evXqNW/e3GazuU7IKygoOHnyZOvWrauvpwB0\ngiN2ABAGeXl5DoejsLBQXDzAZrVaU1JSduzYYbfb77rrLofDsXbtWqvV2q1bN7PZ3LVr16VL\nl06cONFkMq1cubJp06Zt2rSp7oUAUOuR2AFAGEydOjUnJ0f7f8SIEUKIUaNGDRgwYM6cOatX\nr548ebLRaGzZsuXTTz9tNpuFEBMnTlyxYsWsWbMcDkfbtm1nzJhRQy6KBFCrkdgBQBisXLnS\nZ3lGRsacOXO8yy0Wy+TJkyPcKQCXHM6xAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQ\nCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkS\nOwAAAJ0gsQMAANAJEjsAAACdMFR3BwAA0ANZlhVFCWXyMHYGlywSOwAAwkCWZZIzVDsSOwAA\nwmb37t0lJSVBTNiuXbv4+Piw9weXGhI7AADCpqysLLjEzul0hr0zuARx0BgAAEAnSOwAAAB0\ngsQOAABAJ0jsAAAAdILEDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILE\nDgAAQCdI7AAAAHSCxA4AAEAnDNXdAQAAUM2sVqsQQlEUWf7/R3wkSfIu9MPhcDgcjgj1EAEi\nsQMA4FKnKIp3oaqqqqoGMrkkSZIkOZ3OcPcLVUZiBwAAhBDi8OHDp06dCmLC5OTkq666Kuz9\nQRA4xw4AAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILEDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0\ngsQOAABAJ0jsAAAAdILEDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILE\nDgAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILEDgAAQCdI7AAAAHSCxA4A\nAEAnSOwAAAB0gsQOAABAJ0jsAAAAdMJQ3R0ISXJyshBCkiQhhMViiY2NjXIHJEmSJMlkMoVh\nXgbf28JsMguz2edbiqLIcjWk5pIkmSvoUkX1I9eZqKnSIoeLturMZrPPDR0bGxuTnByhpmVZ\nTkhIUFU1QvP3064QIrimo99bAKhpandil5eXJ4SIiYmxWq3FxcWlpaVR7oDZbDYYDBcuXAh9\nVlJR0WW+ysvKy8rKPBNHWZZNJpPD4bDZbKE3XSWSJBmNxvLy8sAnMRqNiqJErkvRUVZWFv1G\nTSaTLMtlZWVOp9P73ZKSkpK8vAg1nZCQUFxcbLfbIzT/isTFxcXGxhYUFATRtKIoyRHLdAGg\nVuCnWAAAAJ0gsQMAANAJEjsAAACdILEDAADQCRI7AAAAnSCxAwAA0AkSOwAAAJ0gsQMAANAJ\nEjsAAACdILEDAADQCRI7AAAAnajdz4oFgEuB1WqN3MwNBoOqqgZDNL4OZFkW4VgcSZK8Cw0G\ng9FodK+jPds6xLYC74/PXl1SZFkOZYVLkhTiOPT5XHKDweAx5LQt5V0eIdpHLDpDUZDYAUDN\nZ7PZIjdzSZJUVY1oEy7ad1vobZl9Faqq0+l0ul5KkiTLsntJ5Gj5hKqq5HahrHBZllVVVVU1\n6Dmoqo/WnU6nx5CTJMlsNnuXR4g2DsPYlv9hRmIHADVdWVlZ5GauKIrT6YxoEy6xsbEiHIsT\np6re32wOh9PhcLheyrIsSZJ7SeRoSWQUGqrhnE5nKCtcURSHwxFKYud0+pjWe3hrGytqw15b\nrjC25fPApAsDEQAAQCdI7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ0jsAAAAdILEDgAAQCdI\n7AAAAHSCxA4AAEAnSOwAAAB0gsQOAABAJ3hWLCqnKIrrkcOSJEmSZDBUYeTwCEUAAKKDxA6V\nUxTFIzmrUmIHAACig69nBGrv3r12uz2ICdu0aRMTExP2/gAAAA8kdghU+enyfQAAIABJREFU\nfn6+zWYLYkKn0xn2zgAAAG+c/AQAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAA\nADpBYgcAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADpBYgcAAKATJHYAAAA6\nQWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADpBYgcAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFi\nBwAAoBMkdgAAADpBYgcAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADpBYgcA\nAKAThuruAFCjWa1W7R+TyRT0TBwOh8PhCFOPAACoEIkd4I+iKEIIVVUlSQpuDpIkOZ3OsHYK\nAADfSOyAyh0+fPjUqVNBTJicnHzVVVeFvT8AAPjEOXYAAAA6QWIHAACgEyR2AAAAOkFiBwAA\noBMkdgAAADpBYgcAAKATJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADpBYgcAAKAT\nJHYAAAA6QWIHAACgEyR2AAAAOkFiBwAAoBMkdgAAADphiEIb2dnZixYtOnLkyIYNG1yFRUVF\nK1as2Lt3r81ma9myZVZWVp06dfyUAwAAwL+Ajth16tTpwIED3uXvvPNOmzZt/E+7bdu2Rx99\nND093aN88eLFOTk5M2fOfPbZZy0Wy+zZs51Op59yAIi0UGIdANQEASV2u3btunDhgkeh3W7/\n6aefjh496n9am822cOHCLl26uBfm5ubu3LlzzJgxTZo0adCgQVZWVnZ29r59+yoqr9IiAUBw\nQol1AFATVPJTrCRJ2j/XXHONzwodO3b0P4cbbrhBCOEREw8fPmw0Gps0aaK9tFqt6enphw4d\nKi4u9lnevn37yhYEAIIXeqwDgJqgksRu9+7dX3755aRJkwYOHJiamur+liRJDRo0GD16dBCt\nFhQUxMfHuyKpECIxMTE/Pz8xMdFnuevl9u3bDx8+rP1vNBoHDhwohDAYDNpL9wmjw2AwyLIc\nGxsb+qwkh8N3E4rBYfC9mSRJMlTwVnhFf8XqjCzLwW0pbc0bDAafm8BoNIpwjD2fFEUxm81G\nozFC86+ItqKCazqUgRqhWAcAUVbJl0379u3bt2//wQcfPPvss82bNw9jwxWFYP+h+fPPP9+4\ncaP2v8Viueeee1xvmc1ms9kcxh4GLjxffhWcSmgwVJjYybIsy1zXXAuEuKW0/QfvcpPJZIqL\nC6FflQjLHks0mw7lfNzIxToAiKaAjiJ89NFH4W01KSmpoKBAVVVXGpefn5+cnFxRuWvCIUOG\n3Hzzzdr/sixrB/NMJlNsbGxJSUl5eXl4+1kpo9GoKEppaWnos5KKihJ8lZeXl3svlyRJRqPR\n6XTa7fbQm65URYkFAuRwOBwVHJH1T1vz5eXlPlOW0tLSMrfj2eEVFxdXWloaXLdDERMTYzab\ni4qKgmhaluX4+PhQWg97rAOAKAsoscvJyZk2bdqnn356+vRp7y8YVVWr2mrz5s1tNtvRo0eb\nNWsmhCgoKDh58mTr1q3r16/vs9w1YUZGRkZGhutlbm6uEEJRFCGEw+Gw2WxV7UmIZFmWJCks\n7UoVzMSpOr3XuZZmqarKJcO1Qohbyul0+vyURXTMa7sN0dlzcGcymYQQwTWthYJQhD3WAUCU\nBZTYTZgw4b333uvVq1efPn2qeqpQXl6ew+EoLCwUF/Mwq9WakpLStWvXpUuXTpw40WQyrVy5\nsmnTpm3atJEkyWd5EAsGAFUVSqwDgJogoMi1ZcuWt99+W7tSoaqmTp2ak5Oj/T9ixAghxKhR\nowYMGDBx4sQVK1bMmjXL4XC0bdt2xowZ2s+vFZUDQKSFEusAoCYIKLErKSnp1q1bcA2sXLnS\nZ7nFYpk8eXLg5QAQaaHEOgCoCQJK7K6++uqffvrpuuuui3BnAKA6hRjreHwigGoX0KWOixYt\nevjhh3fs2BHp3gBANQol1vH4RAA1QUBH7CZNmvTbb79169bNYrGkpaV5vHv8+PHw9wsAoi6U\nWKc9PvHo0aNffPGFq1B7TOKiRYu0B+pkZWXde++9+/bta9iwoc9ynrIDIEQBJXayLLdo0aJF\nixaR7g0AVKNQYh2PTwRQEwSU2H311VeR7gcAVLuwx7qgH5+4ZMmSzz77TPs/Njb29ddfD2/H\n3MmyrKpqdB40ot2D0/2286HMx4PJZPR4/pAkSdG5ubq2KbmRu8FgCOV2kpIkaXeyDKUD3oVG\no9HnkKuoPOy0j5jFYgnXDP3fU5MbNQFABAX3+MTS0lLt9p9CCLvdHumMQZKk6NxYKqIJkM+l\n4IZZURbiCg95ct/z9Dnkopb3i3B/xPyfjxtQYufxSGx35eXlBQUFVe4UANQ8YY91QT8+cerU\nqVOnTnW91O7uHiEWi8XpdIbl0YiVSkpKMhgMZ8+eDXE+KU6n9xdyWVm5+1LIsqwoSnSeSGQw\nGAwGg9PpvMQP2oX4uBqTyWSz2UJ5xIvN5qP18vLygv8dcrIsp6SkRC2BsVgsDoejrKwsXDNU\nFMXPscaAErsePXp4lPz222/79u1r2rRpr169QuodANQYYY91QT8+EQCCE1Bi535PJpfTp0/f\neeedffv2DXeXAKB6hBLreHwigJog+HPs6tWr99xzz2VlZWVmZoaxQwBQowQY63h8IoCaIKSL\nJ9LT03/++edwdQUAaqZAYh2PTwRQEwR/mqeqqqtWrbrsssvC2BsAqGmIdQBqkYCO2HXo0MGj\nxOFwnD59Ojc3d8qUKRHoFQBUA2IdgNouyJ9ijUbjlVdeOXDgwKysrPB2CABqDmIdgNoloMRu\n9+7dke4HAFQ7Yh2A2q4KR+zOnj377bff/vrrr7Isp6end+vWLT4+PnI9A4BqQawDUHsFlNg5\nnc5p06a98MIL7rfwjouLmzlzpvu90QGgViPWAajtAkrsnnvuueeee27QoEH9+/evX7++0+nM\nzs5+9913p02bVrdu3WHDhkW6lwAQBcQ6ALVdQInd6tWrH3zwweeee869cMyYMWPHjv3b3/5G\nsAOgD8Q6ALVdQPexO3bsmM9brg8cOPDAgQPh7hIAVA9iHYDaLqDEzmAwFBcXe5fbbDZFUcLd\nJQCoHsQ6ALVdQIndVVdd9fzzz5eXl7sXlpaW/v3vf+/UqVNkOgYA0UasA1DbBXSO3fTp0/v3\n79+8efN+/fo1bNhQVdWTJ0/+v/buPEiK8o7/+NPdc+8MewBy7CLZlVvwwgPMoYkSS7xjNBYg\nRlTcmAKPqAHiFdDyp8ZyvUp/K4eiAppEyjMqkopGRcUKAQwEFQVhOXQX2JnZZXbn+v3RPybr\n7uzQ9PZMTz+8X39szfTM0/19tnue+cz0Ma+//vquXbveeuutfJcIAIXBWAfA6QwFu4kTJ770\n0kuzZ89+8sknMxPHjBnz1FNPnXnmmXmrDQAKirEOgNMZvUDxhRdeeOGFF+7YsaOhoUFRlEGD\nBvXr1y+vlcFCqmponzsAxjoAjmb0/X7Xrl2PPvrowIEDTzrppBNPPFFV1blz53777bd5LQ5W\ncblcnh4gF+LwwVgHwNEMfWO3adOm008/fc+ePTNmzNCntLa23nnnnU888cQHH3xQU1OTzwph\nmW+++cZcw8rKSk4JxOGAsQ6A0xkKdrNmzQoGg6+88kpmyuDBgzds2HD++effcsstf/3rX/NW\nHqz05ZdfmmvYv39/gh0OB4x1AJzO0C62Dz74YM6cOSeddFLHiSNHjrzllltWrFiRn8IAoNAY\n6wA4naFgF41GPR5P1+nBYDCZTFpdEgDYg7EOgNMZvUDxs88+22lci0QidXV1xx9/fH4KA4BC\nY6wD4HSGjrG74447zj777GHDhp199tl9+/ZNpVLbtm177bXXmpqa3njjjXyXCACFwVgHwOkM\nBbuzzjrrrbfemj179uOPP56ZeMwxxzz99NNnnXVW3moDgIJirAPgdEYvUDxhwoQJEyY0NTXt\n2LFD07RBgwaFQqG8VgYAhcdYB8DRjAY7Xe/evXv37p2nUgCgSDDWAXAoflEAAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQhMvuAgAAKBaKohSsFZAPBDsAAIQQwuPxqKr5HVk9aQtYhWAHAMD/7NmzJ5VK\nmWhYXl6uaZrl9QCHhGAHAMD//Oc//4nH4yYajhs3LhAIWF4PcEj43hgAAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEO\nAABAEi67C+gRl8slhFBVVf+r3y0kVVWtWq7SzUxURdU7+L0nK4r+t+tD2WeuKD0sDz1hfE1l\npapq1jWY121eURRN0/I08xz0f5S5fvXknwwAcnB2sPP7/UII/e3H4/EU/n1ID3Z6GT2VSGSd\nrGla135lgp0tb704VKYDh76iNU3LGuzcbrdqybaXjaZpXq83nU7naf7d0SOd1+t1u90FXjQA\nSMDZwS4SiQghfD5fMBiMxWKxWKzABXi9XpfL1dLS0vNZKdGoN9v0eCIej8c7TVRV1ePxpFKp\nrg9l5fF4+NLORslkMtFNcM9NX3HxeDyVSnV9tK2tbX8k0uPqsuvVq1dra6u5snuipKTE7/e3\ntLSYWLQeRvNRFQA4BXsuAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACTh7LNiAeBwkNeL\ndOoXSizMdUD10/N7vqxuruz4vQtGKorSw0tIwrjM1cd68g9XDujJHLJO7LTJZS4ZVpjNvieX\n58wxw+4Q7ACg2Flzscxu6JfDLEwA0peSp+6o6veu+qlHhEO62CeXhTJNTy09jGX6fHpy+cys\nm7GmaZ02ucwlQvP6ysrQO1Ww684S7ACg2EXydsFCIUQgEEilUoW5DmhZWZnL5ep5dyrS6a7x\nIZFIdLy0p6qqmqYZvNinjkt+9tC2bdu++eYbEw0DgcDxxx+fTqfj8XhPgl0ymew6MZFIdNrk\nVFWtqKjoOj1PAoFAMplsa2uzaoa5r9lJsAMAABYwHV/4pRkLcfABAACAJAh2AAAAkiDYAQAA\nSIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAH\nAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJ\ngh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAA\ngCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2\nAAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACS\nINgBAABIwmV3AYDMfD6fEEJVVbfbbaK5oihCCE3TLC4LACApgh2QR3qeU9UefTXew+YAgMMH\nwQ7Iux07dmzfvt1EQ7/fP2bMGMvrAQDIimAH5F17e3s0GrW7CgCA/NjFAwAAIAmCHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEjCZXcBAABYw+PxqOr3vrDQNM2uYgBb\nEOwAAFKJRqPmGpaUlCiKYm0xQIER7AAAUvnkk0/MNfzRj37k8XisLQYoMI6xAwAAkIRt39jN\nnDlzy5Ytmbs+n+/FF18UQkSj0fr6+nXr1sXj8eHDh9fW1h5xxBF2FQkAAOAgtgW7aDQ6ffr0\ncePG6Xczh7vW1dVFo9E777zT6/UuWbJk7ty5jzzySKeDYQEAANCVbYEpEon079+/zwEVFRVC\niMbGxtWrV0+fPr26unrgwIG1tbUNDQ3r16+3q0gAAAAHsecbu3g83tbWtmrVqueeey4SiQwZ\nMmTq1KmVlZVffPGF2+2urq7WnxYMBquqqjZt2nTsscfaUicAAICD2BPsWltby8rKEonEdddd\nJ4RYunTp7Nmzn3jiiXA4HAqFOp5tXlpa2tzcnLk7f/78d999V7/t8/meeOIJcWA3biAQ8Pl8\nBe2GEIqiKIridrstmFc3F1vyuD3pLmdp6f8iVVUNnsDFvmxHU1U16xr0+/3esrI8LVTTtFAo\nlE6n8zT/7ug9NbfowlcLAMXGnmBXWlq6ePHizN1bb731iiuu+PDDD8WByNKdnTt3bty4Ub8d\nCARcrv/V392bXwFYs1xX9nWRo196rLRg0Shu3a1oVVXVbjYbS9h4ZVdzi06lUpZX0nOcKAag\nkIriOnZ+v79v376NjY01NTXhcDidTmfexpqbm8vLyzPPvP3222+//fbM3cbGRiGEz+cLBoPR\naDQWixW4cq/X63K5Wlpaej4rJRrtnW16rC3WFuv8jaD+XV0ymYzH40Zm3vVq7HCQZDKZTCa7\nTm9padnf2Jinhfbq1au1tTWRSORp/t0pKSnx+/379u0zsWhN0zoOF0WCE8UAFJI9g8jWrVsf\ne+yxzMAdi8W+++67/v37Dx06NB6Pb968WZ8eDoe3bds2cuRIW4oEgJ7jRDEAhWTPN3YVFRWr\nVq1KJBKXXXZZMplcvHhxMBg89dRTvV7v+PHjH3/88ZkzZ3o8nvnz5x911FGjRo2ypUgA6CFO\nFANQYPYEu1AoNG/evEWLFt1www1ut3v48OH33nuv1+sVQsycObO+vv6uu+5KJpNHH330bbfd\nxmFkABzK9IliH3744RdffKHfdrvdF1xwQf6KdLvdHQ+AySt9X7Pf7+/hfLJWq2ka7xeOpihK\nDw/t1bIdzKBpWqdNTt9Ouk7PE7fbrWmahQda5N7ObTvGrqamZt68eV2nBwKBG264ofD1AIDl\nTJ8otnLlypdfflm/HQgEJk2alNc6hRCF/I3UkpKSns4i23/P5SLYOZ6rZyeEqVr2YJd1k+tu\nep7o315ZIveJYkVx8gQAHA6Mnyg2efLks846S7+tqmrHL/Ms5/P5UqlUe3t7/haREQwGNU3r\neXd6pdNdE1w8Hi/YV4/Ih3Q6nUgkenLdokQiy3lmiUSi5fubnKIovXr1isfjra2tppdlnM/n\nM36yoxGqqoZCoe4eJdgBQL5s3br11Vdfra2t1b+H6Hqi2JAhQ0S2E8Vqampqamoydxvzdvqz\nEMLtdqdSKQvfdXLQ37N7vqx0tmCXSqUJdk6XSqV6Euyytu26eet7RdPpdGE2e7fbbW2wy73D\nmmAHAPnCiWIACoxgBwD5woliAAqMYAcAecSJYgAKiaucAwAASIJgBwAAIAmCHQAAgCQIdgAA\nAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAknDZ\nXQAMcbm+t6YURdH/dpreHf35AABAbgQ7Z9A0rWs4U1VVVfnOFQDgbPp7maIoqqqm02m7y3E2\ngp1jJBKJzz77zFzbY445hggIAChOPp9Pv+F2u03PJJVKWVSOsxHsHCOVSu3Zs8dcWz4AAQCK\nXDgcbmxsNNFQ07TBgwdbXo9DEewAAID9otHoli1bTDT0eDwEuwx2zwEAAEiCYAcAACAJgh0A\nAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgB\nAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiC\nYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAA\nIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJl90FAADw/3m9XkVRDvq0rM/RNNVIW0BuBDsA\nQLEIBAKaph38edkCnMvlItgBBDsAQBHZv3//okWLcj9nWizm6zLxq81fBUaNVFUOMcJhjWAH\nACgi8Xj8/fffz/2cy+PxrsGusalpUDqdp6oAp+CTDQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJ\nZ588UVJSIoRwuVxCCK/Xa+gkeUtpmqaqql5Gbj6f7yDn4Xu92Rt6fYqv61HCOIwoipJ14/F4\nPKqBbc8cTdP8fn8qlcrT/LvjdruFELYsGgAk4Oxgl0gkhBCKorjd7mQyqd+1q4yDisfjDQ0N\n3T2qtrQcmW16NBqJKSIUCpmtDtJKpVL52+bdbncikSh8utI/niWTyWQyeahtuYYZADg72LW1\ntYkDo3kikdDv2lVGbsFgcPfu3XPmzOnuCb54fGG26f/615qWgP+nP/1pDwqEs6XT6XS2izjk\ndZv3er3xeLzwH5b0L+Db29tNLLrw39kDQLFxdrAD5KannO52xbrdbiUQOOhMksmkXZ95YJXy\n8vL8zVzfuvx+f/4WkaFfPThHd0jnME1VVX3M7MTtdmfd5LqbbjlVVdPpdMDAcG1Q1o/6GQQ7\noHh5PB4hhKqqWS+m7/F4PAZGivb2doKd0+3duzd/Mw8EAqlUKhaL5W8RGWVlZS6XK0d3CvNG\nCyl1d3RKPB4Pf3+TU1W1oqIiHo+Hw+ECFBYIBKz9gK1pWo5XCsEOKHZNTU379u3rOn3lypWr\nc44UJSUlM2bMyFtdAICiQ7ADil1bW1t7e3vX6Tt37ly3bl2OhmVlZXkrCgBQjLiOHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmX3QUAAACYp6qqEEJRFE3N8nWVpmmBQKDjFEVRuk5PJpNtbW15rrQQ\nCHYAAMDBXC6XEEJRFFUzFOyyTk8kEgQ7AACAohAOh7dvb+g6ffPmzS898ECOhoqi3HzzzXmr\nq9AIdgAAwPHa29tbWqJdp4fD4TVr1uRoqO+ZlQYnTwAAAEiCYAcAACAJgh0AAIAkCHYAAACS\nINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEA\nAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJg\nBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAg\nCYIdAACAJAh2AAAAkiDYAQAASMJldwEAAAA2U1XV7/ebbp5Op2OxmIX1mEawAwAAhy9FUYQQ\nqqqWlJSYnkkymSTYAQAAFIWdO3f++c9/Ntd2+vTpbrfb2npMI9gBAIDDXTQa/eijj8y1nTZt\nWvEEO06eAAAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASXO7EEEVRevfu3d2j\nPblWNZA/vXr1EkJ4PJ4+ffqYaO7xeIQQ8Xi8ubnZ4sogL6/Xm+NR/UqwOZ6jPwGAaQS7QxCL\nxXbu3GmubXV1tbXFAAa1trbu3r3bXNvq6mreaHFIgsHgQbeZUChUmGKAwxDB7hB89dVXd999\nt7m2S5YssbYYwKCNGzc++OCDJhoqivL8889bXg+kt2/fvldffdVc28mTJ1tbDHC4IdgBAKwU\njUb/9re/mWs7adIka4sBDjcEOwAAAPNKSkoURcl9NHPuIxDS6XRTU5MlxRDsAAAAeiQejzc0\nNJhrW1lZ6XJZlscIdgAAAD2yZ8+eOXPmmGv7wAMPDBw40KpKuI4dAACAJAh2AAAAkiDYAQAA\nSOJwOcZOUZRAIGB3FYCT6JeZVVW1pKTE9EzS6XRra6t1RSHv3G633SUAMO9wCXaqqvLDX4AJ\nPXztJJNJgp2DKIpSWlpqdxUAzDtcgp3us88+e/HFF000DAQCs2bNsrweoPht27btqaeeMtd2\n1qxZuX85FMWpsbHxo48+Mtf23HPPtbYYAIfk8Ap2kUjkyy+/NNGQXzbEYSsWi5l71QghUqmU\ntcWgML799lvTv4JIsAPsxckTAAAAkiDYAQAASIJgBwAAIImiO8YuGo3W19evW7cuHo8PHz68\ntrb2iCOOsLsoAGZ4vV5VVYPBoPEm+rU2AoFA5vi8aDSal+LslqexTlGUQz2L2e12p9NpVeVz\nPiCDogt2dXV10Wj0zjvv9Hq9S5YsmTt37iOPPMKIAziRy+VSFMXn8x1qQ4/Ho99Ip9OyBrs8\njXWqqpq7Zmfmfw7A0Yor2DU2Nq5evfqhhx6qrq4WQtTW1l5++eXr168/9thj7S4NgBlNTU33\n3XefubY333xz3759ra2nSOR7rNu4cePLL79soqHf77/++ustqQGALYor2H3xxRdut1sf6YQQ\nwWCwqqpq06ZNmcFu7969mYudZvY46J9xVVXVNK27OevP8fl8/fr1M1GYfuV9j8djrrnO5XLl\naO5tb8863efzpfx+YWoPSyemm+u/QOD3+10uMxtM5p9v7uIXenOv12uufn2r8Hg85prrXTbd\nXN+x6HK5zDXXv0TRNC3rth0MBnNvkBUVFUIIr9drbrvV//Nut7u8QpuQAAASPUlEQVQnm306\nnY7H46bbigNr0AgHfbVvbqwzQv8nxGKx3bt3myisAGNdboqiqKp60OZZ13XmoomMdSaaF8lY\nZ665vupVVXW7s3zxfNDt2ZKxTtM00831f75VY52iD51F4q233lq2bNmiRYsyU2677bbBgwdf\nc801+t158+ZlPoYGAoH33nvPhirzZP9+cf75mXtb4236jZefnR/nx9AOe+P+9HDVqo/124Pd\nBy75W1srLr7YtpqKTyqVckq2O6zHOkv88peiuVm/mRkt33nw3n0/GGxfTbBfzdvvnPB/F+q3\n/zdUjhsn5s2zraY8yD3WFdc3duLAB6bujBo1qqWlRb/t9Xrb2tqEEJqmuVyuRCKRTCZzzNZ4\nFs5K/yd2+hyWdaLx5t/j8Yg338zcqzpw47fZmusfatPptMHPhcbrzEpRFEVR9OYdbx9S83Q6\nbe5TRKa53oscazl/S1cU5VCXa8nSVVXVm6fu/j+Zid+r42BVmV71+gamvx5Nbzkmlq53OZVK\nZf5jiUTCeHMH/dCFibHO4GwPdazrtJp6OFzkbq7XluPVZHTpL7yQuZkZLX/9/eamBytzrxf9\npSqE6PlYd9DmmqZ1/R9aNdJ2bG78vaaHSxfZXvuH2lwIkbr0cnHp5fqUHENl137ldbM3OHOr\nxrriCnZlZWXhcDjzdiKEaG5uLi8vzzzh4osvvrjDVxSNjY1CCJ/PFwwGY7FYLBYrcMFer9fl\ncmXG34JxuVxlZWVtbW2FP65c07RgMNh84LNyIfXu3TuZTO7bt6/wi66oqNi7d2/hl1taWup2\nu5uamgq/6F69erW2th7SQGOJkpISv98fDodNLFrTNKcEO3NjXZ7o5yAXZvwsKytzuVyFeTW5\n3W6fzxeJRAqwLH27bW5uLsxLpmAjktfrDYVCra2t+/fvL8DiSktLo9GouU/Rh0RV1YqKing8\nHg6H870sIUQgEEgmk8Y/nh1U7rGuuHZbDB06NB6Pb968Wb8bDoe3bds2cuRIe6sCAGsx1gHI\nk+IKdhUVFePHj3/88ce//vrrhoaGhx566Kijjho1apTddQGAlRjrAORJce2KFULMnDmzvr7+\nrrvuSiaTRx999G233Zb7SBQAcCLGOgD5UHTBLhAI3HDDDXZXAQD5xVgHIB+Ka1csAAAATCPY\nAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABI\ngmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcA\nACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASEJJp9N212Be\nc3OzEGLv3r07d+4cMGBAeXl5gQtwuVyqqra3txd4uW1tbZs3by4vLx8wYECBF60oitfrjcVi\nBV6uEGLjxo1er7empqbwiw4EAq2trYVf7tdff71///6RI0cqilLgRft8vvb29lQqVeDl7t69\nu6mpqbq62u/3H2pbVVVDoVA+qrKdPtblidvtFkLE4/H8LSJD36RHjRpVgGVpmuZyudra2gqw\nrJ5styYUbESKRCLbtm3r169f7969C7A4n8/X1tZWgFiSSCQ+//zzUCg0aNCgfC9LCOHxeFKp\nVCKRsGqGBxnr0s63fPnysWPHLl++3O5CCufzzz8fO3bsPffcY3chBTV+/PhJkybZXUVBXXXV\nVWPHjk0kEnYXUjgPPvjg2LFj165da3chyIspU6accsopdldhPX27Xbdund2FWGzlypVjx459\n5pln7C7EYk1NTWPHjr3xxhvtLiQv2BULAAAgCYIdAACAJAh2AAAAknD2yRO6HTt2bNiwYdSo\nUQMHDrS7lgKJRqMfffRRZWXlyJEj7a6lcP7+97+XlJSccsopdhdSOJ9++um+ffvOOOOMwp88\nYZfPP//8m2++Oemkk0pLS+2uBdb75JNPotHoz372M7sLsZis2+233367bt26oUOHDh482O5a\nrNTe3v7ee+/17dv32GOPtbsW68kQ7AAAACDYFQsAACANgh0AAIAkXHYXcGj27NmzcOHCtWvX\ntre319TUXHnllcOGDev0nO3bty9atGjTpk2JRKK6uvryyy8vzMUw82Tbtm3PPPPMxo0b0+m0\n3p0RI0Z0ek40Gq2vr1+3bl08Hh8+fHhtbe0RRxxhS7WWMNJlI1uCgxjpcsbKlSsffvjhOXPm\njBs3rpBFWstgl994443ly5c3NTVVVlZOnTr1pJNOKnypMMHI+p05c+aWLVsyd30+34svvljQ\nKnsgx8vQ6QNyjq45cZUZqdnpq6wThx1jd9NNN3k8nunTp/v9/iVLlqxZs2b+/Pk+ny/zhHQ6\nfe211x5zzDHTpk3TNO0vf/nLyy+/vGDBAodejz6RSFx99dXHHnvspZdeqqrqCy+88PHHHy9c\nuLDTxc3vvvvuaDR67bXXer3eJUuWbNmy5ZFHHlFVR34da7DLB90SHMRgl3X79u2bOXNma2vr\nzTff7NxgZ7DLK1euXLx48YwZM4488shVq1a9/vrrdXV1gUDArrJhkMH1O23atF/84heZzVhV\n1YqKCjvqPWS5X4aOHpBzd82Jq8xIzY5eZV05qe5IJNK3b9/f/va3NTU1AwYMmDp1ajgc3rZt\nW8fnhMPhXbt2nXnmmYFAwOv1Tpw4MRaL7dy5066ae6ilpeWCCy6ora2trKwcMGDAJZdc0tLS\n0qk7jY2Nq1evnj59enV19cCBA2traxsaGtavX29XzT1kpMtGtgQHMdLljCeffPL00093ergx\n2OUXXnjhiiuuOPHEE4844ogLLrigvr7e6R0/TBhcv5FIpH///n0OKP6IkJHjZej0ATn3COPE\nVXbQmp2+yrpy0q7YUCg0e/bszN2mpiZVVfv06dPxOaWlpSNGjHjzzTcrKyvdbvebb77Zr1+/\nH/zgB4Wu1SKlpaUXXXSRfjsSibzyyitVVVWdftvuiy++cLvd1dXV+t1gMFhVVbVp0yaHnsVt\npMtGtgQHMdJl3apVqzZv3nzDDTf84x//KGiJVjPS5aampl27dgkhZs6cuXPnzsGDB1999dU5\n9lCjeBhZv/F4vK2tbdWqVc8991wkEhkyZMjUqVMrKyvtqPfQ5H4ZOnpAzt01J64yIzU7epVl\n5aRg11EkEnn00UcvvPDC8vLyTg/NmjXrjjvumDx5shCivLz8jjvu8Hg8dtRomVQqdckll8Tj\n8dGjR8+bN0//xe6McDgcCoU6XuestLQ0rz8ZXgC5u9xRji3BWQ7a5Wg0+uSTT954440O3ePc\nVe4uNzU1CSHeeeedW2+9tbS0dNmyZX/84x+ffPJJya4TJrHc67e1tbWsrCyRSFx33XVCiKVL\nl86ePfuJJ54oKSmxqV5DDvoydO6AfNCuOXGVGanZuausO07aFZuxffv2m2++efTo0VdccUWn\nhxKJxNy5c0eMGPHss88uW7bsvPPOu/POO/fu3WtLnVZRVfXhhx++5557evXqNWfOnGg02ukJ\n8l299qBd1uXYEhznoF1esGDBCSeccNxxx9lSXj4YWcu/+tWvqqqqQqHQtGnTFEX59NNPC18n\nzMm9fktLSxcvXnzjjTcOGzZs2LBht956aywW+/DDD+2q1iAjL0OHDsgH7ZoTV5nBmh26yrrj\nvGC3du3a3//+9+edd95vfvObritj/fr1X3/99dVXX11aWhoIBH75y196vd7333/fllItVFVV\nNWbMmFtvvbW5ufndd9/t+FBZWVk4HO54Ekxzc7PTv74SObusy70lOFGOLv/73//+17/+NW3a\nNLtqy5McXdYPhcl8sNY0raKiwukf0g43B30VZ/j9/r59+zY2NhasNhOMvAwdOiCbGGEcsco6\nyVqzQ1dZDg4Ldhs2bLjvvvtuuummc889N+sT0ul0Op1OpVKZKYlEolDVWW/NmjXTp09va2vT\n7yqK4nJ13ns+dOjQeDy+efNm/a5+GoFzf2rMSJeFgS3BQYx0ecWKFS0tLbW1tZMnT548eXJz\nc/NDDz107733FrxYaxjpckVFRXl5+X//+1/9bnt7+3fffdevX7+CFgpTjKzfrVu3PvbYY5nx\nORaLfffdd/379y9ooYfIyMvQoQOyka45cZUZqdmhqywHJx1j197eXldXd/755w8ePDiTuIPB\noM/nW7FiRSwWO++880aMGFFeXr5w4cJf//rXHo/ntddea2lpOfHEE+2t3LShQ4fGYrG6urpJ\nkya53e5XX301FouNHTtWCJHpckVFxfjx4x9//PGZM2d6PJ758+cfddRRzr10n5Eu59gSbK3d\nJCNdrq2tvfLKKzNNbrzxxqlTpzr3Z3ONdFlV1fPOO2/ZsmVVVVVVVVVLly71+Xxcx84RDA5c\nq1atSiQSl112WTKZXLx4cTAYPPXUU+2uPZccL0OnD8gGu+a4VZajZqevshycdB27tWvX3n77\n7Z0mXnvtteecc84DDzwQDofnzZsnhNi6deszzzzz+eefJ5PJI488csqUKWPGjLGjXmts3bp1\n0aJFGzZsUBRF745+qk7HLre2ttbX169ZsyaZTB599NG1tbWO/hr5oF3OsSXYUa8FjKzljqZO\nnXrdddc59zp2wliXU6nUc889984770Sj0eHDh1933XVZTxZGETKyfr/66qtFixbp5yQOHz78\nmmuucdY3sh1fhpINyN11zYmrrLuaJVtlHTkp2AEAACAHhx1jBwAAgO4Q7AAAACRBsAMAAJAE\nwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOxS4cDgeDQUVRli9fbnctAJAvjHWwBMEOxe75\n559vaWkpLy+fP3++3bUAQL4w1sES/PIEit0JJ5wghDjttNMeffTRLVu2VFVV2V0RAFiPsQ6W\n4Bs7FLVPP/10zZo1l1122ZQpU5LJ5NNPP93x0VQqdddddw0aNMjn840dO3bFihUzZszweDyZ\nJ7z77rsTJkzo1atXIBA44YQTFi5cWOgOAIABjHWwTBooYtOnT9c0raGhIZ1OH3PMMdXV1alU\nKvPoPffcI4S49NJL33rrrfnz5w8YMODkk08uKSnRH33nnXc0TfvJT37y6quvvv3227W1tUKI\nP/3pT/b0BAC6x1gHqxDsULwikUgoFJo4caJ+t66uTgixYsUK/W4qlerXr9/o0aMzw99HH30k\nhMgMdscff/yQIUNaWloyMzz//PNDodD+/fsL2AkAOAjGOliIXbEoXsuWLYtEItOmTdPvTpky\nxePxLFiwQL+7a9eu3bt3T5gwQVEUfcopp5wyevRo/fa33367Zs2ac845R1XV2AETJ06MRCLr\n168vfF8AoDuMdbAQwQ7Fq76+vrS09NRTT21sbGxsbEyn0z//+c+XL1++Z88eIcTu3buFEAMG\nDOjYZPjw4fqNHTt2CCEefvhhfwf6Hort27cXuicA0D3GOljIZXcBQHZr165dvXq1EGLgwIGd\nHnr22Wevv/76trY2IYSqfu/DSeYTrW7atGnXXHNNp+ZDhgyxvlwAMIWxDtYi2KFI1dfXCyGW\nLl3ap0+fjtOvuOKKBQsWXH/99RUVFeLAZ9mMTZs26TeOPPJIIUQymRw3blyBKgaAQ8dYB4vZ\nfZAfkEVra2tpaen48eO7PjRr1iwhxMcffxyPx0tLS0ePHp156JNPPhEdDig++eSTS0tL9+7d\nm3nCM88884c//CEej+e7fgAwgrEOluMYOxSjF154obm5+aqrrur6kH588fz5810u11VXXfXZ\nZ59deeWVb7/9dn19/aWXXvrDH/4w88z777+/tbX1tNNOW7x48dtvv3377bdfffXVDQ0NLhdf\nVAMoCox1sJ7dyRLI4tRTTy0pKYlEIlkf/clPfhIKhaLRaCwWmzFjRp8+fUpKSn784x9//PHH\nkyZNCgaDmWf+85//nDBhQigUcrvdw4YNu//++/kIC6B4MNbBcvykGKRy5plnbtiwQT9NDABk\nxViH7rArFg5WV1d38cUXJxIJ/e6+ffs+/fTT4447zt6qAMBajHUwjh3wcLDevXu/9NJLF110\n0TXXXBOLxerq6sLh8O9+9zu76wIAKzHWwTiCHRzs8ssvF0I89NBDkyZNSqfTxx133GuvvXbG\nGWfYXRcAWImxDsZxjB0AAIAkOMYOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAA\nAEkQ7AAAACRBsAMAAJDE/wMWTluQEjt7/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_medal_vis_sd<-football_male_resample_sd%>%\n",
    "                    visualize()+\n",
    "                    ggtitle(\"Football Male Athlete's SD of Age \\nBootstrap Distribution with 95% CI\")+\n",
    "                    labs(x=\"Age\")+\n",
    "                    shade_ci(endpoints = football_male_ci_sd, color=\"red\")\n",
    "female_medal_vis_sd<-football_female_resample_sd%>%\n",
    "                    visualize()+\n",
    "                    ggtitle(\"Football Female Athlete's SD of Age \\n Bootstrap Distribution with 95% CI\")+\n",
    "                    labs(x=\"Age\")+\n",
    "                    shade_ci(endpoints = football_female_ci_sd, color=\"red\")\n",
    "bootstrap_vis_sd <- plot_grid(male_medal_vis_sd, female_medal_vis_sd, rel_widths = c(1, 1), rel_heights = c(1, 1))\n",
    "bootstrap_vis_sd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744718a2-4e93-46ec-96fe-16ea9a728866",
   "metadata": {},
   "source": [
    "<H2>💡 Methods: Plan </H2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d26a834-8136-4658-83e1-f1673e39de1e",
   "metadata": {},
   "source": [
    "The \"good thing\" about this report is the sample population we are using for the argument, `olympic football athletes` is sampled at random and unbiased fashion. Separating sports competition by gender is often regarded as one of the most controversial topics in the history of sports, especially with the recent rise and push for gender equality. Therefore by using this report, it directly reflects those competing at the highest levels, hence, those who are directly impacted by decision of gender affect on sports. As shown above, we were able to calculate the point estimate mean age and standard deviation of age and parameters of interests, as well as produce graphs that help us visualize those point estimates that help us draw a relationship between the age and the olympic athletes in both genders.\n",
    "\n",
    "(This is done from the single sample we took from population, as we're assuming we don't have access to the population)\n",
    "\n",
    "We see some observed negative differences in Male and Female Athlete's mean & standard deviation of age. However, we know nothing about the reliability of these results. To address this gap, we will be conducting a hypothesis test and <b>calculating a two-tailed 95% confidence interval</b>. \n",
    "\n",
    "Initially, we'll be using bootstrapping method as it can gain a better approximation accuracy than asymptotic distribution (theoretical method). However, if we have enough time, we'll explore asymptotic method to validate our results as it highly depends on CLT and our dataset does satisy CLT conditions due to large sample size ($n= 600$).\n",
    "\n",
    "For both genders, the hypothesis tests will be:\n",
    "\n",
    "$$H_{0} : \\mu_{Age of Male Football Olymic Athletes} - \\mu_{Age of Female Football Olymic Athletes} = 0$$\n",
    "$$H_{A} : \\mu_{Age of Male Football Olymic Athletes} - \\mu_{Age of Female Football Olymic Athletes} \\not= 0$$\n",
    "\n",
    "Additionally, for the the scale parameter hypothesis test will be:\n",
    "\n",
    "$$H_{0} : \\sigma_{Age of Male Football Olymic Athletes} - \\sigma_{Age of Female Football Olymic Athletes} = 0$$\n",
    "$$H_{A} : \\sigma_{Age of Male Football Olymic Athletes} - \\sigma_{Age of Female Football Olymic Athletes} \\not= 0$$\n",
    "\n",
    "<h5>🫰 Expectations</h5>\n",
    "<hr/>\n",
    "We expect the p-value from the hypothesis test to be less than 0.05 ($ p < 0.05$), as this would indicate that the deviations from the null hypothesis to be statistically significant, allowing us to reject the null hypothesis. As for the confidence interval, the sample we are using is really large ($n = 600 > 50$). Therefore a 95% confidence interval should provide a fairly low margin of error, allowing us to prove that the estimates are reliable. \n",
    "\n",
    "This would help us conclude that age does in fact play a role in the performances of athletes for both genders. These findings could be used to argue that sport competitions should or shouldn’t be separated by both gender and age. \n",
    "\n",
    "<h5>🔮 Future Questions</h5>\n",
    "<hr/>\n",
    "Leading to some further questions :\n",
    "Is there a relationship between the Weight Class and Gender for Olympic Football Athletes? Does Gender affect the likeliness of Olympic Football Athletes winning a medal?"
   ]
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   "source": [
    "<H2>📚 References</H2>"
   ]
  },
  {
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   "source": [
    "- Bongard, V., McDermott, A. Y., Dallal, G. E., & Schaefer, E. J. (2007). Effects of age and gender on physical performance. AGE, 29(2-3), 77–85. https://doi.org/10.1007/s11357-007-9034-z\n",
    "\n",
    "- Barreira, J., Bueno, B., & Chiminazzo, J. G. C. (2021). Relative age effect and age of peak performance: an analysis of women’s football players in the Olympic games (1996-2016). In Motriz: Revista de Educação Física (Vol. 27). FapUNIFESP (SciELO). https://doi.org/10.1590/s1980-65742021006921"
   ]
  }
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