STAT201-Group17-ProjectReport.ipynb

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      "Warning message in system(\"timedatectl\", intern = TRUE):\n",
      "“running command 'timedatectl' had status 1”\n"
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   "source": [
    "# install.packages(\"tidymodels\")\n",
    "# install.packages(\"infer\")\n",
    "suppressPackageStartupMessages(library(tidyverse))\n",
    "suppressPackageStartupMessages(library(tidymodels))\n",
    "suppressPackageStartupMessages(library(repr))\n",
    "suppressPackageStartupMessages(library(infer))\n",
    "suppressPackageStartupMessages(library(GGally))\n",
    "options(repr.matrix.max.rows = 6)\n",
    "options(repr.plot.width = 6, repr.plot.height = 6)\n",
    "set.seed(100)"
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   "source": [
    "# Climate Change: Study temperature and precipitation in historical vs contemporary weather records   \n",
    "**STAT 201  Project Proposal - Group 17  \n",
    "Date:         April 09, 2022**"
   ]
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    "## Introduction\n",
    "\n",
    "Climate change refers to long-term shifts in temperatures and weather patterns (United Nations, n.d.-b). Although the main cause for climate change varies from the rising concentration of greenhouse gases in the atmosphere to periodical change in solar activities and remains controversial, the effects of climate change are more universally observed. United Nations (n.d.-a) summarizes the effects of climate change as: hotter temperatures, more severe storms, increased drought, etc. We intend to obtain data from a Canadian weather station and study the presence of these effects of climate change by comparing historical data with the present.  \n",
    "\n",
    "The Canadian Climate and Weather Database provides weather records from numerous weather stations across Canada. Each weather station contains weather records in hourly, daily, and monthly formats. In order to obtain a meaningful and relatively large time span, the group queried stations with data available between 1950 and 2022. The query returned 8796 weather stations, from which, we randomly selected **ABBOTSFORD A** weather station in BC to use for this study (Historical Data, 2021, November 25).  \n",
    "\n",
    "We intend to choose daily temperature and precipitation as the random variables separated into two categories - historical vs contemporary. For temperature, the variable is the daily temperature, and we want to study the parameter mean. We believe this can aid us to study the claimed effect of hotter temperatures. For precipitation, the variable is yearly maximum rainfall. We also study the parameter mean to evaluate the claimed effect of increased drought (United Nations, n.d.-a). Thus, this project aims to answer:\n",
    "\n",
    "> Are there significant differences in the weather record of the 1950s and the 2000s in terms of temperature and rainfall? \n"
   ]
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   "source": [
    "## Overview"
   ]
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    "Table 1 summarizes the abbreviation used in this report: \n",
    "\n",
    "| Abbreviation    | Parameter \n",
    "| :----------: | :---------- \n",
    "| $\\mu_{ct}$ | mean of the contemporary daily mean temperature | \n",
    "| $\\mu_{ht}$ | mean of the historical daily mean temperature | \n",
    "| $\\mu_{cr}$ | mean of the contemporary yearly maximum rainfall | \n",
    "| $\\mu_{ht}$ | mean of the historical yearly maximum rainfall | \n",
    "| $\\bar{x}_{ct}$ | sample mean of the contemporary daily mean temperature | \n",
    "| $\\bar{x}_{ht}$ | sample mean of the historical daily mean temperature | \n",
    "| $\\bar{x}_{cr}$ | sample mean of the contemporary yearly maximum rainfall | \n",
    "| $\\bar{x}_{ht}$ | sample mean of the historical yearly maximum rainfall | \n",
    "\n",
    "<center><i>Table 1. Table of abbreviation.</i>  "
   ]
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    "The first hypothesis we test is:  \n",
    "**Test Statistic**: The difference in sample mean $\\bar{x}_{ct} - \\bar{x}_{ht}$  \n",
    "**Null Hypothesis**: The difference in population mean of the contemporary daily mean temperature and the historical daily mean temperature is zero.   \n",
    "**Alternative Hypothesis**: The difference in population mean of the contemporary daily mean temperature and the historical daily mean temperature is not zero.   \n",
    "  \n",
    "$$H_0^{1}: \\mu_{ct} - \\mu_{ht} = 0$$  \n",
    "  \n",
    "$$H_A^{1}: \\mu_{ct} - \\mu_{ht} \\neq 0$$\n",
    "\n",
    "The second hypothesis we test is:  \n",
    "**Test Statistic**: The difference in sample mean  $\\bar{x}_{cr} - \\bar{x}_{hr}$  \n",
    "**Null Hypothesis**: The difference in population mean of the contemporary maximum yearly rainfall and the historical maximum yearly rainfall is zero.     \n",
    "**Alternative Hypothesis**: The difference in population mean of the contemporary maximum yearly rainfall and the historical maximum yearly rainfall is not zero.    \n",
    "  \n",
    "$$H_0^{2}: \\mu_{cr} - \\mu_{hr} = 0$$  \n",
    "  \n",
    "$$H_A^{2}: \\mu_{cr} - \\mu_{hr} \\neq 0$$\n",
    "\n",
    "We test the hypotheses in two ways: bootstrap simulation and asymptotic two-sample t-test and choose a significant value of $\\alpha$ = 0.05 as a standard value in the statistics field. "
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    "## Methods\n",
    "\n",
    "### Data Importing, Cleaning, and Wrangling\n",
    "  \n",
    "We first download and import daily data from 1950 to 1969 and 2000 to 2019."
   ]
  },
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   "source": [
    "climate_1950 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1950_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1951 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1951_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1952 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1952_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1953 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1953_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1954 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1954_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1955 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1955_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1956 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1956_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1957 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1957_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1958 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1958_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1959 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1959_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1960 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1960_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1961 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1961_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1962 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1962_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1963 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1963_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1964 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1964_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1965 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1965_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1966 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1966_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1967 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1967_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1968 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1968_P1D.csv\", show_col_types = FALSE)\n",
    "climate_1969 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_1969_P1D.csv\", show_col_types = FALSE)\n",
    "\n",
    "climate_2000 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2000_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2001 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2001_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2002 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2002_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2003 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2003_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2004 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2004_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2005 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2005_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2006 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2006_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2007 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2007_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2008 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2008_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2009 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2009_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2010 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2010_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2011 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2011_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2012 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100030_2012_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2012_2 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2012_P1D_2.csv\", show_col_types = FALSE)\n",
    "climate_2013 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2013_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2014 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2014_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2015 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2015_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2016 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2016_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2017 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2017_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2018 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2018_P1D.csv\", show_col_types = FALSE)\n",
    "climate_2019 <- read_csv(\"https://raw.githubusercontent.com/yxing6/STAT201-GroupProject-Climate/main/Data/en_climate_daily_BC_1100031_2019_P1D.csv\", show_col_types = FALSE)\n"
   ]
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    "Next, we combine the yearly data and mutate a column to indicate the recorded time. The data in 1950-1969 are labeled as `historical` and 2000-2019 as `contemporary`. We further combine these two data frames to serve as the raw sample for this study.\n",
    "      \n",
    "The raw sample contains 31 variables of weather measurements in which we decide to use the following as our possible parameters: \n",
    "- `Year`: the year of the weather record \n",
    "- `Max Temp`: the maximum temperature of the day in degrees Celsius.\n",
    "- `Min Temp`: the minimum temperature of the day in degrees Celsius.\n",
    "- `Mean Temp`: the average temperature of the day in degrees Celsius.\n",
    "- `Total Rain`: the total amount of rain of the day measured in millimetres.\n",
    "- `Total Precip`: the total amount of precipitation of the day measured in millimetres. \n",
    "\n",
    "The following codes demonstrate the steps of data cleaning and wrangling:"
   ]
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 14512 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Year</th><th scope=col>Max_Temp</th><th scope=col>Min_Temp</th><th scope=col>Mean_Temp</th><th scope=col>Total_Rain</th><th scope=col>Total_Precip</th><th scope=col>Category</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><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1950</td><td> -2.2</td><td> -9.4</td><td> -5.8</td><td>0</td><td>2.5</td><td>historical</td></tr>\n",
       "\t<tr><td>1950</td><td>-11.1</td><td>-15.6</td><td>-13.4</td><td>0</td><td>0.0</td><td>historical</td></tr>\n",
       "\t<tr><td>1950</td><td>-11.1</td><td>-17.2</td><td>-14.2</td><td>0</td><td>3.8</td><td>historical</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>2019</td><td> 9.6</td><td>3.3</td><td>6.5</td><td> 0.0</td><td> 0.0</td><td>contemporary</td></tr>\n",
       "\t<tr><td>2019</td><td> 8.1</td><td>4.3</td><td>6.2</td><td> 3.4</td><td> 3.4</td><td>contemporary</td></tr>\n",
       "\t<tr><td>2019</td><td>11.1</td><td>6.0</td><td>8.6</td><td>39.9</td><td>39.9</td><td>contemporary</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 14512 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " Year & Max\\_Temp & Min\\_Temp & Mean\\_Temp & Total\\_Rain & Total\\_Precip & Category\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 1950 &  -2.2 &  -9.4 &  -5.8 & 0 & 2.5 & historical\\\\\n",
       "\t 1950 & -11.1 & -15.6 & -13.4 & 0 & 0.0 & historical\\\\\n",
       "\t 1950 & -11.1 & -17.2 & -14.2 & 0 & 3.8 & historical\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 2019 &  9.6 & 3.3 & 6.5 &  0.0 &  0.0 & contemporary\\\\\n",
       "\t 2019 &  8.1 & 4.3 & 6.2 &  3.4 &  3.4 & contemporary\\\\\n",
       "\t 2019 & 11.1 & 6.0 & 8.6 & 39.9 & 39.9 & contemporary\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 14512 × 7\n",
       "\n",
       "| Year &lt;dbl&gt; | Max_Temp &lt;dbl&gt; | Min_Temp &lt;dbl&gt; | Mean_Temp &lt;dbl&gt; | Total_Rain &lt;dbl&gt; | Total_Precip &lt;dbl&gt; | Category &lt;fct&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1950 |  -2.2 |  -9.4 |  -5.8 | 0 | 2.5 | historical |\n",
       "| 1950 | -11.1 | -15.6 | -13.4 | 0 | 0.0 | historical |\n",
       "| 1950 | -11.1 | -17.2 | -14.2 | 0 | 3.8 | historical |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 2019 |  9.6 | 3.3 | 6.5 |  0.0 |  0.0 | contemporary |\n",
       "| 2019 |  8.1 | 4.3 | 6.2 |  3.4 |  3.4 | contemporary |\n",
       "| 2019 | 11.1 | 6.0 | 8.6 | 39.9 | 39.9 | contemporary |\n",
       "\n"
      ],
      "text/plain": [
       "      Year Max_Temp Min_Temp Mean_Temp Total_Rain Total_Precip Category    \n",
       "1     1950  -2.2     -9.4     -5.8     0          2.5          historical  \n",
       "2     1950 -11.1    -15.6    -13.4     0          0.0          historical  \n",
       "3     1950 -11.1    -17.2    -14.2     0          3.8          historical  \n",
       "⋮     ⋮    ⋮        ⋮        ⋮         ⋮          ⋮            ⋮           \n",
       "14510 2019  9.6     3.3      6.5        0.0        0.0         contemporary\n",
       "14511 2019  8.1     4.3      6.2        3.4        3.4         contemporary\n",
       "14512 2019 11.1     6.0      8.6       39.9       39.9         contemporary"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "climate_historical <- \n",
    "    rbind(climate_1950, climate_1951, climate_1952, climate_1953, climate_1954, climate_1955, climate_1956, climate_1957, climate_1958, climate_1959, \n",
    "          climate_1960, climate_1961, climate_1962, climate_1963, climate_1964, climate_1965, climate_1966, climate_1967, climate_1968, climate_1969) %>%\n",
    "    mutate(Category = as.factor(\"historical\"))\n",
    "\n",
    "climate_contemporary <- \n",
    "    rbind(climate_2000, climate_2001, climate_2002, climate_2003, climate_2004, climate_2005, climate_2006, climate_2007, climate_2008, climate_2009,\n",
    "          climate_2010, climate_2011, climate_2012, climate_2012_2, climate_2013, climate_2014, climate_2015, climate_2016, climate_2017, climate_2018, climate_2019) %>%\n",
    "    mutate(Category = as.factor(\"contemporary\"))\n",
    "\n",
    "climate_merge <- rbind(climate_historical, climate_contemporary) \n",
    "\n",
    "climate_sample <- climate_merge %>%\n",
    "    select(Year, \"Max Temp (°C)\", \"Min Temp (°C)\", \n",
    "           \"Mean Temp (°C)\", \"Total Rain (mm)\", \"Total Precip (mm)\", Category) %>%\n",
    "    rename(\"Max_Temp\" = \"Max Temp (°C)\",\n",
    "           \"Min_Temp\" = \"Min Temp (°C)\",\n",
    "           \"Mean_Temp\" = \"Mean Temp (°C)\",\n",
    "           \"Total_Rain\" = \"Total Rain (mm)\",\n",
    "           \"Total_Precip\" = \"Total Precip (mm)\") %>%\n",
    "    na.omit()\n",
    "\n",
    "climate_sample"
   ]
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   "id": "81fbbd9b-4ffb-40d3-b55f-97842704bf40",
   "metadata": {},
   "source": [
    "*Table 2. Table of sample weather record at Abbotsford A weather station.*"
   ]
  },
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   "metadata": {},
   "source": [
    "## Temperature"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "The United Nations (n.d.-a) states that the temperature in the recent decade is higher than last century. Is there enough evidence to support this claim? Can we see this claimed difference in `Mean_Temp` between historical and contemporary data in the sample weather records from the Abbotsford A weather station? We first visualize the categorical difference in `Mean_Temp` with a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "086f7940-0d09-473c-b872-fc1e83abd93a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FXKh6aMuIRMfprynF1FioyMbNmy5Y8/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\nnJycXFxcrly54ufnp6ur6+rqypRnZ2d7enqmp6c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FxdXadNm3bmzJnKysoDBw40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\nAKpDUAEAAACgOv8HtABA3k2aMOgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 540
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 9, repr.plot.height = 6)\n",
    "\n",
    "station_temp <- climate_sample %>%\n",
    "    filter(!is.na(Mean_Temp)) %>%\n",
    "    select(Mean_Temp, Category)\n",
    "\n",
    "temp_boxplot <- station_temp %>%\n",
    "    ggplot(aes(x = Category, y = Mean_Temp, fill = Category)) +\n",
    "    geom_boxplot() +\n",
    "    scale_fill_manual(values = c(\"#4682B4\", \"#F8766D\")) +\n",
    "    labs(x = \"\", y = \"Daily Mean Temperature (°C)\", fill = \"Temporal Category\") +\n",
    "    ggtitle(\"Boxplot of Temperatures\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "temp_boxplot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e63eb7-6300-41fa-84cb-daa2590a5372",
   "metadata": {},
   "source": [
    "*Figure 1. Boxplot of daily mean temperature at Abbotsford A weather station*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b819d3-5826-4d98-9ca5-d4f142d19a40",
   "metadata": {},
   "source": [
    "From this boxplot, we can observe that there is a small difference in the median of daily `Mean_Temp` between the historical and contemporary weather records. However, is there a ***real*** difference in the mean of the daily temperature? We answer the question by performing a hypothesis test:    \n",
    "  \n",
    "$$H_0^{1}: \\mu_{ct} - \\mu_{ht} = 0$$  \n",
    "  \n",
    "$$H_A^{1}: \\mu_{ct} - \\mu_{ht} \\neq 0$$\n",
    "  \n",
    "We begin by visualizing the sample distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f749e453-3d03-422e-9e4f-cda25d8fd711",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 14, repr.plot.height = 6)\n",
    "\n",
    "station_temp_sample_distribution <- station_temp %>%\n",
    "    ggplot(aes(x = Mean_Temp, fill = Category, color = \"grey\")) +\n",
    "    geom_histogram(binwidth = 1, color = \"lightgrey\") +\n",
    "    facet_grid(. ~ Category) +\n",
    "    scale_fill_manual(values = c(\"#4682B4\", \"#F8766D\")) +\n",
    "    labs(x = \"Daily temperature (°C)\", y = \"Frequence\") +\n",
    "    ggtitle(\"Sample Distribution of Daily Mean Temperature\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "station_temp_sample_distribution   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eedcd645-5cdb-442f-86f5-27dc472e161d",
   "metadata": {},
   "source": [
    "*Figure 2. Sample distribution of daily mean temperature at Abbotsford A weather station.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b300c2cd-9340-4699-8228-5bf8af1a6082",
   "metadata": {},
   "source": [
    "### Temperature - Bootstrap Simulation\n",
    "The first method we use to test this hypothesis is the simulation based permutation with the `infer` package. We first calculate the observed test statistic, then generate a simulation from the null distribution. We further obtain the P-value. The following codes demonstrate the steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a81dbc0-9533-451d-9177-2ca9deabd1e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Please be cautious in reporting a p-value of 0. This result is an approximation based on the number of `reps` chosen in the `generate()` step. See `?get_p_value()` for more information.”\n"
     ]
    },
    {
     "data": {
      "image/png": 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q3bJZdcEp7q2muvvSZNmpSdnV39+ddyEwpqseNXU/oLS/r16zdp0qTTTz89/Yp6\naWnpQw89dOmll6b+2aZNmzFjxqTPoU7WdQR/3wBovKroDpHybrrppsp6GqzsZHB2dvZDDz1U\nflbpt719+OGH6aNmzZoVjnrttdcqKyZsc8MNN1TWJnzg5MQTTywz6uWXX06vM5FI7Lbbbvvs\ns0/47sQddthh2bJl4d1i1157bWWTz5o1K33U1KlT05fGzjvvfOKJJ5500knhhakOHTr8+9//\nLioqSu/M/dBDDz3ttNM++uij1Ex+9atfpS/b7bffvnPnzr17997s0kv55S9/mf7X7b777scf\nf/wJJ5zQr1+/9DXYv3//VatWlZk2ffm//PLLlS3bZDIZPsJ36qmnVtGsCkcddVRFW81mZGdn\nP/HEE+XnVvslH76CLyU3N7d3796HHHLIgQceuPvuu6c63kxp3rz5jBkzytdw/fXXp8+hbdu2\n/fv3Hzx4cM+ePdPvW8vKyvrLX/5SfvLXX389PYHEYrFu3brtvffe4aLOysr617/+FfZA85vf\n/KbGy/zcc8+tTvv0/Jxy1llnlW9WfztUFaMy+nszMmLEiNTrPSpUxQ5Yy02o6h2/mvtmFeWl\nnwGZMWNG6pp2IpHYd999TzrppCFDhoRXuVMmTJhQfv61XNeb/TObzO8bAFsFgTBj77zzTjVP\nuCYSiRNPPPHzzz+vcD4NHgiTyeTNN99cWY7t2bPnokWLkslkeEPXlVdemT5t1Qep99xzT2Wv\nQu7Wrdtnn32WanbJJZeUGfvmm2+mRi1YsKD8w0W9evXa7NIL3X777VX0ApKVlXX++ecXFhaW\nn7CRB8L8/Px33nmnshnWfsk//vjjm+1kv2/fvu+9915lNTz55JNV9GoTBMFuu+0Wfl15kyZN\nateuXYUTtmrV6oUXXkgmk2EnGRdccEGNl3k1A2FpaWn//v3Ty6gwECbrbYfawoFw4MCB06ZN\nq3qeVe+AtdmEqt7xax8I02/yXLZs2Zw5c1IvmSyvWbNm48ePr+wrarOuN/tnNpnfNwC2Cl5M\nn7EDDjjgtddeW7BgweTJk995553PP/988eLF69atKyoqat68eevWrbfbbru+ffvut99+w4YN\nK3OyubG54oorjj766Pvuu+/tt99esmRJYWFh27Zt+/bte84555x22mmpW7/Crkqqfld4GWPG\njDn00EPvvffe6dOnL168uLi4uEOHDn379v35z39+1llnhVcJbr311uzs7D/96fNEzkYAACAA\nSURBVE/ff/99+/bt9913365du6ZG7bzzzjNnzrzyyitnzJixZs2aFi1adO/e/YQTTqh+DWPH\njj3rrLMmTpw4bdq0Tz75JNUJZ4cOHbp163b44Yeffvrp3bp1q/7cGkosFmvZsmWHDh169Oix\n3377DR06tMwzV2XUfsmfffbZw4cPf+aZZ6ZOnfrJJ58sXry4oKAgHo+3adNmp5122meffYYO\nHTp48ODKYmcQBGeeeeawYcOeeeaZV1555cMPP1yxYkVBQUGrVq26dOmSug5z3HHHpbauCg0b\nNiw/P/+hhx6aPHny119/vXr16o4dO3bt2vWkk04aOXJk6l67MDFW/ShvnYjFYg888MC+++6b\n/sKPCtXfDlV/YrFY+/btt9lmm1122WXQoEFHHXVU7969aznP2mxCtd/xq5b+uvnWrVt36tRp\n7ty5zz777LPPPvvFF198//33eXl5P/vZz4YMGXL++edX8RNRy3Xt9w2AxiOWrOcncAAAAGic\ndCoDAAAQUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgA\nABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARFRWQxcAAACw\nFVudSJQmk+U/b1dauuWLyZRAuHkrV65s6BIao+bNm5eWlhYVFTV0IdSlvLy8li1brlu3zppt\nYpo1axYEwfr16xu6EOpSbm5uq1atCgoKrNkmJi8vLx6PFxYWNnQh1KWcnJzWrVvbYZue3Nzc\nrKys0mQyKBcIk40mRyQSiXbt2lU21i2jAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABEl\nEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAA\nRJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAI\nAAAQUQIhAABARGU1dAFAncnLy4vFYrWZQ1ZWVhAE2dnZtZxPaWnphg0bajMHAAC2AIEQmo5m\nzZolEonazyc3Nzc3N7c2cygpKREIAQAaP4EQmpSioqKHH364YWv45S9/mbrSCABAI+egDZqU\nTZs2zZo1q2FrOPfccwVCAICtgk5lAAAAIkogBAAAiCiBEAAAIKIEQgAAgIgSCAEAACJKIAQA\nAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAAiCiB\nEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQAAAg\nogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCiBEIA\nAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAAgIgS\nCAEAACJKIAQAAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAA\nIiqroQvYCrRq1aqhS2iMEolEEATZ2dkNXQj/Ix5vLKd44vG4HadRSe2wWVl+85uU1C6fm5tr\nzTYxiUQiFouldluaDDtsUxWPx+Px+IYgSJYbFdtKcoQtcvOKiooauoTGKDc3N5lMbty4saEL\n4X80nnyeTCbtOI1Kbm5uEAQbNmxo6EKoS9nZ2dnZ2Zs2bfJT3MTk5OTE43G/ok1MaoctLi72\nU9zEZGdnV3H6ppHsyPF4PHUkUCGBcPM2bdrU0CU0RtnZ2aWlpRZOo5JMlj851TCSyaRto1FJ\nnZC2UpqY1AUHP8VNTyKR8Cva9MRisSAISkpKrNkmJh6Px2KxCo/Ako3mf96q7zhoLDeYAQAA\nsIUJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmE\nAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAAR\nJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIA\nAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRA\nCAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQ\nUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEA\nAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARGU1dAHQFLRp0yY7O7uhqwAAgMwIhFAHYrFY\nEAQLFy5s2DK6du3asAUAALB1EQihbiSTySuvvLJha3jyyScbtgAAALYuniEEAACIKIEQAAAg\nogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCiBEIA\nAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAAgIgS\nCAEAACJKIAQAAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQAAAgorIauoD/UVxcPG3a\ntBkzZixcuLCwsLB58+Zdu3bNz88/8sgjs7OzyzQuLS2dPn36G2+88fXXXxcUFLRq1Wr33Xc/\n5phj+vXrV37OGTUGAACIiMYSCFetWnXNNdcsXLgwCIJYLNa6des1a9bMnTt37ty5r7zyyo03\n3timTZuw8aZNm2655ZbZs2cHQZCbm9uuXbvVq1e/++6777777kknnXTOOeekzzmjxgAAANHR\nKAJhMpm8+eabFy5cmJeXd+655x522GE5OTlFRUVTpkyZMGHCokWLHn744bFjx4btn3766dmz\nZ+fk5IwePfrggw9OJBIbN26cPHnyhAkTXnzxxV133XXgwIE1awwAABAdjeIZwjlz5sybNy8I\nggsvvPCoo47KyckJgiAvL2/YsGHHHXdcEAT/+te/ioqKUo3Xrl370ksvBUFwzjnnHHbYYYlE\nIgiCnJycYcOGHXPMMUEQPPnkk8lksgaNAQAAIqVRBMJ169b16tWrW7duBx54YJlR++yzTxAE\nxcXFy5cvT33y9ttvFxcXN2/e/MgjjyzT+IQTTgiC4Pvvv//ss89q0BgAACBSGsUto/n5+fn5\n+RWOisViqYHUZcMgCD7//PMgCHr16pWVVbb47bbbrmPHjitXrvz888979uyZaWMAAIBIaRRX\nCKuQ6gxmu+2269y5c+qTRYsWBUGw/fbbV9i+S5cuQRCkOqfJtDEAAECkNIorhJWZP3/+P/7x\njyAIzjrrrPDDtWvXBkHQtm3bCidp165dEARr1qypQePQkiVLwgcLW7RokXrykDJisVg8Hrdw\nqIxto1FJ3W1hpTQx8Xg8CIJYLGbNNjHxeNxqbXpSO6xjp6YntcNWOCrWaP7nTW1+lWm8gXDh\nwoXXXnttcXHxEUcckf5s4fr164MgyM3NrXCq1J2lhYWFNWgcGjZsWHFxcWr45JNPvuyyy2rz\nhzRtzZs3b+gSGgu9E6VLJBKpEy40KnbYJql58+bWbJPUrFmzhi6ButesWTNrtkkqCoIKDwQb\nyeFQaWlpFWMbaSB8//33b7/99qKiooEDB44ePbr6E6YOyiuL6dVsPGjQoHCp7b777hs2bKh+\nAdGRlZWVTCZLSkoaupBGIXzGlZRkMrlx48aGroL/kTpDaYdtYuLxeHZ2dnFxsTXbxCQSiVgs\nFp6bpmmwwzZVqR22srGNJ0dUdoUsaJyBcNKkSRMnTkwmk0OHDj377LPLLOLmzZuvW7eusoWb\n+jw8V5pR49DNN9+c/s+VK1fW6O9o4po3b15aWhq+DiTi2rZt20huCWgkSktLUzds00ikTkin\nbpqgycjNzc3Ozt6wYYM128Tk5eXF4/HydzCxVcvJybHDNkm5ublZWVkVXh5M/v/PrzW4RCKx\n1QTCjRs3jhs3bsaMGTk5OaNGjRo0aFD5Nq1bt16+fPmqVasqnMOPP/4YpD00mFFjAACASGlE\nvYxu3LjxxhtvnDFjRrt27W655ZYK02AQBDvttFMQBN9++235Uclk8rvvvguCoFu3bjVoDAAA\nECmNJRAWFxfffPPNH3300fbbb3/XXXftuuuulbXs3bt3EASffvpp+SeU5s+fv3r16iAI+vTp\nU4PGAAAAkdJYAuETTzzx73//u1OnTjfddFOHDh2qaHnggQfm5eUVFRVNmTKlzKhJkyYFQdC9\ne/euXbvWoDEAAECkNIpAuGDBgpdffjkIglGjRrVv377qxnl5eaecckoQBE8++eS0adNSPTUV\nFhY+/vjjM2fODILgnHPOqVljAACASGkUncpMnjw59QaI2267rbI2w4cPHz58eGp42LBh33zz\nzVtvvfWHP/zhwQcfbNWq1apVq0pKSmKx2HnnnZe6TTSUUWMAAIDoaBSBMHwtRBU9LG/atCkc\njsfjF1988QEHHDB16tSvvvpq1apVbdu27dmz50knnVT+4cOMGgMAAERHowiEl1566aWXXprp\nVPn5+fn5+fXRGAAAIAoaxTOEAAAAbHkCIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBE\nCYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAA\nABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAEZXV\n0AUATU08Ho/FYtnZ2Q1dSJBMJouLixu6CgCAxksgBOpYbm5uLBZr06ZNQxcSbNq0afXq1Q1d\nBQBA4yUQAnVv3bp106dPb8ACYrHYMccc04AFAABsFQRCoO6tXbv2qaeeasAC4vG4QAgAsFk6\nlQEAAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAA\niCiBEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCiBEIAAICIEggBAAAiSiAEAACIKIEQ\nAAAgogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCi\nBEIAAICIEggBAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAAiCiBEAAAIKIEQgAA\ngIgSCAEAACJKIAQAAIgogRAAACCiBEIAAICIEggBAAAiqr4CYWlpaXFxcWlpaT3NHwAAgFrK\nIBAOGTJkyJAhS5curU7jm2++OTs7+7jjjqtpYQAAANSvrOo3ffXVV4MgKCgoqE7jn/3sZ0EQ\nzJkzp2ZlAQAAUN/q65bRL774IgiCH374oZ7mDwAAQC1t5grhrbfeWuaTBx98sEOHDlVMUlxc\n/OWXXz7zzDNBELRp06aW9QEAAFBPNhMIr7jiijKf3HHHHdWfe35+fsYVAQAAsEVs5pbR888/\nf6+99srKyuBRw9Aee+xxzz331KgqAAAA6t1mkt4DDzwQBEFhYeEHH3xw8MEHB0EwduzYqm8Z\nDYKgbdu23bt3P+ywwxKJRF0VCgAAQN2q1qW/5s2bDxw4MDV8/vnnd+/evT5LAgAAYEvI4F7Q\na665JgiC9u3b11sxAAAAbDkZBMJrr7223soAAABgS6uv9xACAADQyNWk+9CffvrpP//5z/Ll\nywsLC5PJZNWNzz777JrUBQAAQD3LLBAuWrTooosuevnll0tKSqo5iUAIAADQOGUQCJcvX56f\nn7948eL6qwYAAIAtJoNAeMcdd4RpsE+fPr17927Tpo03DQIAAGylMgiEU6ZMCYKgVatWL7/8\n8iGHHFJvJQEAALAlZNDL6MKFC4Mg+PWvfy0NAgAANAEZBMINGzYEQbDPPvvUWzEAAABsORnc\nMtqpU6clS5ZkZdXkTRVbtTZt2jR0CY1RPB4PgiA3N7ehC2kUPEzbOGVlZdl/U1I7bE5OTkMX\nQl1Krda8vDxrtomJx+OxWCw7O7uhC6Eu2WGbqtQOuyEIyr+LL9ZockTVbwrMIN0NGjToT3/6\n07x582pd0lZm3bp1DV1CY5SXl5dMJlPXjWnVqpVM2AiVlJTYf1Nyc3NjsVhRUVFDF0JdysnJ\nadGixYYNG/wUNzG5ubnxeHz9+vUNXQh1KTs7u2XLlhs3bvRT3MTk5OQkEonK8lYjOQ6Jx+NV\nnInIIBBedNFFTz/99COPPDJmzJhIXReq/ksXIyWZTJaWllo4NGbJZNImmpJMJi2Npqe0tDSw\nnTdFqTVrtTYxqRPHjp2antLS0tTl3/KSQVC6NazuDJ4h3GeffcaNG/fll1+eeuqpa9asqb+a\nAAAA2AIyuEJYUlJy9tlnt2rVasyYMd27dz/zzDP79+/fqVOnqp8qPOigg2pdJAAAAHUvg0BY\nJvjdfffd1Zmq6kcYAQAAaCgZ3DIKAABAU5LBFcJDDjkkLy8vKysrkUjEYrH6qwkAAIAtIINA\n+NZbb9VbGQAAAGxpbhkFAACIKIEQAAAgogRCAACAiMrgGcJ33nkno1lv2LChoKDgmGOOybAk\nAAAAtoQMAuGAAQNq8AXeQwgAANA4uWUUAAAgojK4QnjsscdWMba4uHj58uVz587dtGlT69at\nR44c2aJFi1atWtW6QgAAAOpFBoFw8uTJm22zdu3ahx9++Oqrr37//ff/+te/brfddrWoDQAA\ngHpUx7eMtmrV6uKLL3711Vc/+OCDIUOGFBQU1O38AQAAqCv18gxhfn7+GWecMWfOnEcffbQ+\n5g8AAEDt1VenMkOGDAmCYMKECfU0fwAAAGqpvgJhmzZtgiCYN29ePc0fAACAWqqvQLh48eIg\nCDZu3FhP8wcAAKCW6iUQlpSUPPHEE0EQdOjQoT7mDwAAQO1l8NqJ7777ruoGJSUla9asmTt3\n7v333z9z5swgCPbdd99aVQcAAEC9ySAQ/uxnP8t07qNGjcp0EgAAALaM+nqGMB6P33jjjUcf\nfXQ9zR8AAIBayuAKYa9evapuEIvF8vLyOnXq1K9fv9NPP71nz561qw0AAIB6lEEgnDt3bv3V\nAQAAwBZWX7eMAgAA0MgJhAAAABGVwS2j5SWTybVr165ZsyYIgrZt27Zs2bKOqgIAAKDe1SQQ\nfv/99xMmTJgyZcpHH32USoMp7du333fffYcNG3bmmWe2aNGi7ooEAACg7mV8y+j48eO7d+9+\n+eWX//Of/0xPg0EQ/Pjjj1OnTr3gggu6d+/+yiuv1F2RAAAA1L3MAuE999wzevTogoKC9A+b\nNWvWrFmz9E++//774447bsqUKXVQIAAAAPUjg0D4zTffXH755anhoUOH/uUvf1mwYEFJSUlh\nYWFhYWFxcfGXX375pz/9afDgwUEQlJSUjBw5cu3atfVSNQAAALWWQSB88MEHN2zYkJ2d/dJL\nL73wwgsnn3zyzjvvHI//vzkkEonu3bufccYZr7322iOPPBKLxX744YeHH364fsoGAACgtjII\nhG+88UYQBOedd94JJ5xQdctzzz33tNNOC4LAk4QAAACNVgaBcMGCBUEQHH/88dVpPHz48CAI\nPvnkk5qVBQAAQH3LIBCuWrUqCILtttuuOo27du0aBMEPP/xQs7IAAACobxkEwlRXotXsJ6ao\nqCgIgpycnJqVBQAAQH3LIBCmrg3OmjWrOo1Tzbp06VKzsgAAAKhvGQTCgw46KAiCcePGrVix\nouqWy5cvv+eee8JJAAAAaIQyCISnn356EARLliw5+OCDX3/99QrblJaWTpkyJT8/f/HixUEQ\njBw5sk6qBAAAoM5lVb/poEGDjjvuuMmTJ3/++eeDBw/u2rXr/vvvv/POO7ds2TKZTK5du3b+\n/PnvvPPO0qVLU+2HDx9+8MEH10/ZAAAA1FYGgTAIgqeffvroo4+eOXNmEASLFi1atGhRZS2P\nOOKICRMm1LY6AAAA6k0Gt4wGQdCqVavp06ffddddO+20U2Vtdtttt/vvv//VV19t3rx5basD\nAACg3mR2hTAIgkQi8dvf/vaiiy76z3/+M3v27G+++Wb16tWxWKxNmzY77rjj/vvv37t371gs\nVh+1AgAAUIcyDoQpsVhsr7322muvveq2GgAAALaYzG4ZBQAAoMmoSSBctGjRDTfc8MUXX5Qf\nNW7cuKuuumrBggW1LgwAAID6lVkgTCaT1157bffu3a+++uovv/yyfIOPP/74pptu6tGjx3XX\nXVdHFQIAAFAvMnuG8PLLL7/ttttSwytXrqys2aZNm6699toNGzbcfPPNtaoOAACAepPBFcIP\nP/zw9ttvD4IgKyvr7LPP3nfffcu3ueSSS6688spmzZoFQXDrrbfOmTOnrgoFAACgbmUQCMeP\nH59MJrOysl577bXHH3+8V69e5dvsscceN91005tvvpmVlZVMJu+77766KxUAAIC6lEEgfOut\nt4IgGDly5KGHHlp1ywMOOOD0008PJwEAAKARyiAQLl68OAiC/v37V6dxqllqEgAAABqhDAJh\nPB4PgqBVq1bVady8efNwEgAAABqhDAJbly5dgiCo8PWD5X300UdBEGy77bY1KwsAAID6lkEg\nHDhwYBAEjz/+eEFBQdUtFy1a9MQTTwRBMGDAgFrUBgAAQD3KIBCeeeaZQRAsXLjwiCOOmDt3\nboVtksnkSy+9dNBBB/3000/hJAAAADRCGbyY/rDDDjvjjDOeeuqpWbNm9enTp2/fvv369evS\npUuLFi2KiopWrFixbNmyWbNmLVu2LNX+hBNOOOqoo+qnbAAAAGorg0AYBMH48eO/++676dOn\nB0EwZ86cKt47f9hhhz311FO1rQ4AAIB6k1kvoK1bt3799dfvu+++XXbZpbI2u++++4MPPjht\n2rSWLVvWujwAAADqS2ZXCIMgSCQSo0ePHj169Jw5c2bPnr1w4cK1a9fG4/E2bdrssssue++9\nd8+ePeujUAAAAOpWxoEw1Ldv3759+9ZhKQAAAGxJXhwPAAAQUQIhAABARAmEAAAAESUQAgAA\nRJRACAAAEFECIQAAQEQJhAAAABElEAIAAERUzV9MD41Bbm5uIpFo6CqCeNy5FQAAtj4CIVu3\n3NzcnJychq4iCIIgmUw2dAkAAJAZgZCmYNy4cUVFRQ1YwMUXX5yVZW8CAGAr4xCWpmDu3LkF\nBQUNWEBpaWkDfjsAANSMB58AAAAiSiAEAACIKIEQAAAgogRCAACAiBIIAQAAIkogBAAAiCiB\nEAAAIKIEQgAAgIgSCAEAACJKIAQAAIgogRAAACCiBEIAAICIymroAspauXLlvffe++GHHwZB\n8Oc//7lFixYVNistLZ0+ffobb7zx9ddfFxQUtGrVavfddz/mmGP69etXy8YAAAAR0bgC4bRp\n0x555JHCwsKqm23atOmWW26ZPXt2EAS5ubnt2rVbvXr1u+++++6775500knnnHNOjRsDAABE\nR2MJhKtWrbr33ntnz57dokWLwYMHT5s2rYrGTz/99OzZs3NyckaPHn3wwQcnEomNGzdOnjx5\nwoQJL7744q677jpw4MCaNQYAAIiOxvIM4YwZM2bPnt2nT5977713wIABVbRcu3btSy+9FATB\nOeecc9hhhyUSiSAIcnJyhg0bdswxxwRB8OSTTyaTyRo0BgAAiJTGEgizs7P/z//5PzfeeGPH\njh2rbvn2228XFxc3b978yCOPLDPqhBNOCILg+++//+yzz2rQGAAAIFIaSyAcMmTI0KFDY7HY\nZlt+/vnnQRD06tUrK6vs/a7bbbddKk+m2mTaGAAAIFIaSyCsThRMWbRoURAE22+/fYVju3Tp\nEgTBwoULa9AYAAAgUhpLpzLVt3bt2iAI2rZtW+HYdu3aBUGwZs2aGjQOff755+GDhe3atcvL\ny6uDupuceDweBEH5S69bWPVPJRBBsViswTfRRqKR7LDUrdSD8fF43JptYuLxuNXa9KR22EQi\nYc02MYlEIh6Px4KgfK8ksUbzP2/qMKAyjaLEjKxfvz4Igtzc3ArH5uTkBEEQvrgio8ahs88+\nu7i4ODV88sknX3bZZXVQdxPVvHnzhi4BKpWVlVXZ+aBoatasWUOXQN1r1qyZNdskOR/dJOXl\n5VmzTdL6Sj5vJMchpaWlVYzd+gJh1VJX9qp51aiyxiNHjiwpKUkN9+nTJ5UqKSMrKyuZTIYL\nqqHk5OSkTrlBeclkctOmTQ1dRZBMJqv+Id4CUmcow1NdNA2JRCInJ2fTpk3WbBOTSCRisZjV\n2sTYYZuq1BXCysY2nhxRxanDrS8QNm/efN26dRs2bKhwbOrz8LJVRo1Do0aNSv/nypUra1lz\nk9S8efPS0tKioqKGLSORSAiElJf6aY7FYqkbARrWxo0bCwoKGraG1H8Djee/JepEbm5uTk7O\nxo0brdkmJi8vLx6Pl7+Dia1aTk6OHbZJys3NzcrKqvAtdskgaPADgJREItGkAmHr1q2XL1++\natWqCsf++OOPQdrF2YwaA03MypUr33zzzQYsIC8v7/jjj2/AAgAAqrb1BcKddtrpq6+++vbb\nb8uPSiaT3333XRAE3bp1q0FjoIn58ccf//rXvzZgAa1btxYIAYDGrLG8dqL6evfuHQTBp59+\nunHjxjKj5s+fv3r16iAI+vTpU4PGAAAAkbL1BcIDDzwwLy+vqKhoypQpZUZNmjQpCILu3bt3\n7dq1Bo0BAAAiZesLhHl5eaecckoQBE8++eS0adNSvVwWFhY+/vjjM2fODILgnHPOqVljAACA\nSGkszxCOHDkyvKsz7J/93HPPDRuceOKJI0aMSA0PGzbsm2++eeutt/7w0EpvHwAAIABJREFU\nhz88+OCDrVq1WrVqVUlJSSwWO++881K3iYYyagwAABAdjSUQFhQUlH9dWHqHy+kPAcbj8Ysv\nvviAAw6YOnXqV199tWrVqrZt2/bs2fOkk07addddy8wko8YAAADR0VgCYeqJvozk5+fn5+fX\nR2MAAIAo2PqeIQQAAKBOCIQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRA\nCAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQ\nUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEA\nAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJ\nhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRACAABElEAIAAAQUQIhAABARAmEAAAA\nESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEAIAAESUQAgAABBRAiEAAEBECYQAAAARJRAC\nAABElEAIAAAQUQIhAABARAmEAAAAESUQAgAARJRACAAAEFECIQAAQEQJhAAAABElEALw/7V3\n5/Ex3fvjxz9nZjIzQjSJ0BBBS1BLQ7n22KoosRTdUK1Y2ltVLmnRPu6t+63L7X5L269yUVVL\nCWJpueUGpbZGU1T0FlU7pZKQTZaZ3x+fX8+d72ySWTIx5/X8w+M4n3M+5z1zPpM57/l8zucA\nAACNIiEEAAAAAI0iIQQAAAAAjSIhBAAAAACNIiEEAAAAAI0iIQQAAAAAjSIhBAAAAACNIiEE\nAAAAAI0iIQQAAAAAjSIhBAAAAACNIiEEAAAAAI0iIQQAAAAAjSIhBAAAAACNIiEEAAAAAI0i\nIQQAAAAAjSIhBAAAAACNMgQ6AAAIWtWqVRNCGI3GyMjIwEaiKEpJSUlBQUFgwwAAAJUNCSEA\n+IuiKEKIW7du5eTkBDAMvV5fo0YNGQwAAIAtEkIA8K9jx469/fbbAQwgJibmrbfeCmAAAACg\n0uIeQgAAAADQKBJCAAAAANAoEkIAAAAA0CgSQgAAAADQKBJCAAAAANAoEkIAAAAA0CgSQgAA\nAADQKBJCAAAAANAoEkIAAAAA0ChDoAO4A0RERAQ6hMpIURQhRJUqVQIbhk7HjxpAmej1ev6a\nBRn177DZbA50LPAlRVEURTGZTIEOBL7EBzZYyQ/sLSGsjkWVJo+wWh2j+y8SwtvLzs4OdAiV\nUZUqVaxWa2FhYWDDqF69ekhISGBjAO4IpaWlOTk5gY4CvmQymapVq1ZQUBDwP8XwLZPJpNfr\n8/PzAx0IfMloNIaFhRUWFhYUFAQ6FviS/MA6zbeslSaP0Ov14eHhrkpJCG/PfUqtZVarNeBv\nTsADAO4gfF6CjHpCObPBpzJ8w8K35AnlzAYf9ye0kpxu92Ew3A4AAAAANIqEEAAAAAA0ioQQ\nAAAAADSKhBAAAAAANIqEEAAAAAA0ioQQAAAAADSKhBAAAAAANIqEEAAAAAA0ioQQAAAAADSK\nhBAAAAAANIqEEAAAAAA0ioQQAAAAADSKhBAAAAAANIqEEAAAAAA0ioQQAAAAADSKhBAAAAAA\nNMoQ6ABwpzIajXq9PjQ0NLBhKIoS2AAAAACAOxcJITynKEpOTo7FYglgDBEREQE8OgAAAHBH\nIyGEV/76179euXIlgAF8/PHHYWFhAQwAAAAAuHNxDyEAAAAAaBQJIQAAAABoFAkhAAAAAGgU\nCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAA\naBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAA\nAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkh\nAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgU\nCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAA\naBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAA\nAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkh\nAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgU\nCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaJQh0AEAAPwrKipKCGEwGCIjIwMdiygs\nLMzPzw90FAAA4P8jIQSAIKfT6YQQhYWFubm5AQzDYDCEh4crihLAGAAAgB0SQgDQhIMHD86f\nPz+AATRp0uS1114LYAAAAMAR9xACAAAAgEaREAIAAACARjFk9M6jKEpluAmnMsQA4A4ib2VU\nFEUuBJbVarVarYGOAgCAwNNEQmixWHbt2pWWlnb69Om8vLywsLAmTZr069evdevWgQ7NE6Gh\noVWqVAl0FABQPnXr1hVCmM1ms9kc6FhEXl5eQUFBoKMAACDwgj8hLC4unjNnTnp6uhDCZDJF\nRETk5OQcOHDgwIEDgwcPTkpKCnSAHvnpTHZ2dklJSdn3OGPycQgtW7Y0Go0+rhRAsLty5cr5\n8+cDGEB4eHjDhg0DGAAAAJVK8CeEK1asSE9PNxqNEyZM6Nq1q16vLyoq2rx589KlS1NTU+Pi\n4hISEgIdoydOnjx548aNsm//z6+3+TaA999/v2bNmr6tE0DQS09PX758eQADaNOmzdSpUwMY\nAAAAlUrgb+Twq5s3b27YsEEIkZSU1KNHD71eL4QwGo1Dhgzp16+fEGLZsmXcRgIAAABAm4I8\nIdyzZ09JSUloaGjv3r3tigYOHCiEuHz58vHjxwMRGgAAAAAEWJAnhD/++KMQonnz5gaD/eDY\n2rVrR0VFqdsAAAAAgNYEeUJ45swZIURMTIzT0jp16gghfvnll4oMCQAAAAAqiSCfVObmzZtC\niPDwcKelERERQgjHqVnS0tIsFotcrlu3rqt8MlDknZARERHlmrq9ffv2vg3DZDIJIVq1apWd\nne3bmstF9v22adPm1q1bAQxDPlfN529yeSmKYjAYKkMYZrM5sGHIj0lYWFhgw4iMjBRChIeH\nBzaMe+65RwgRFRUV2DAaNGgghKhdu3Zgw7j33nuFEAaDQf4Ru6OFhISIYHktsGUwGBRF4bQG\nGXnFwgc2+BgMBr1erwjhOCuJ8vsFc8C5f364EtxTqgwfPjw3N/fZZ5/t37+/Y+m8efO2bdvW\ntGnTN99803Z9hw4d1Cc6PProo9OmTauIWMvlpzPl3qVxfT/EAQAAAGhdtl4vfu9P+i9FCXdc\nGQgWi0X2XjgV5D2E7slk2DFjfv7559U8uUmTJnl5eRUdmVt6vV7XoHa5dysq8m0Y8oeu0tLS\nwP6moNPpFEUpLS0NYAzBFIZOpzMYDCUlJRYv/n7p9Xqr1epNDT5BGLbkB7ZcDy/1OUVRdDpd\nZXg3hBClpaWVIQwvya6GoqKi4uLiQMcCXwoJCVEUpcjXX9wILL1ebzab+cAGH4PBoNPpQlw8\nDa6S5BGKooSGhroqDfKEMDQ0NDc319VgQrne8d0ZNWqU7X+vXbvmp/DuaKGhoRaLpbCwMNCB\nwJfMZnO1atUKCws5s0GmSpUqQoiCgoJABwJfMplMJpOpuLiYMxtkrFarTqfjtAYZo9FoNpv5\nwAYfk8lkMBgq+WnV6/VuEsIgn1SmevXqQoisrCynpdevXxeu7zAEAAAAgOAW5AmhnMPg3Llz\njkVWq/X8+fNCiIYNG1ZwVAAAAABQGQR5QtiiRQshRGZmpuNA/FOnTuXk5AghWrZsGYDIAAAA\nACDQgjwh7NSpk9lsLiws/PLLL+2K1q5dK4Ro1KhR/fpMvwkAAABAi4I8ITSbzY899pgQYtmy\nZdu3b5ezL+bn5y9ZsuSbb74RQiQlJQU4RAAAAAAIkCCfZVQIMWTIkLNnz+7cuXPu3Lkff/xx\nWFhYVlZWaWmpoihjx46VY0oBAAAAQIOCPyHU6XRTpkxp3779V199dfLkyaysrPDw8GbNmg0e\nPDguLi7Q0QEAAABAwAR/Qih17ty5c+fOgY4CAAAAACqRIL+HEAAAAADgCgkhAAAAAGgUCSEA\nAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJ\nIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABo\nlGK1WgMdA4DKIiMjY8uWLf3794+Pjw90LABu49ixYxs2bOjdu3fbtm0DHQuA2/jpp59SUlK6\nd+/eqVOnQMcC/B/0EAL4r9OnT69bt+7MmTOBDgTA7Z09e3bdunWnTp0KdCAAbu/ixYvr1q37\n6aefAh0IYI+EEAAAAAA0ioQQAAAAADSKhBAAAAAANIpJZQAAAABAo+ghBAAAAACNIiEEAAAA\nAI0iIQQAAAAAjTIEOgAAfmexWHbt2pWWlnb69Om8vLywsLAmTZr069evdevWFVYDgDLy8uP2\n4osv/vLLL65Ku3fvPmXKFJ/FCkAIIcS1a9fmzZuXkZEhhFi5cmXVqlXLvi/fsAg4JpUBglxx\ncfGcOXPS09OFECaTKSwsLCcnp7i4WAgxePDgpKSkCqgBQBl5/3EbM2bM1atXTSaTXq93LE1I\nSJgwYYLPwwa0bPv27f/85z/z8/Plf8uVEPINi8qAHkIgyK1YsSI9Pd1oNE6YMKFr1656vb6o\nqGjz5s1Lly5NTU2Ni4tLSEjwdw0Aysj7j1tubq4Q4qWXXmrXrl2FhAxoV1ZW1rx589LT06tW\nrdqrV6/t27eXtwa+YVEZcA8hEMxu3ry5YcMGIURSUlKPHj1kj4HRaBwyZEi/fv2EEMuWLXM/\nTMD7GgCUkfcfN4vFUlBQIIQo14g1AJ7ZvXt3enp6y5Yt582b17Fjx/LuzjcsKgkSQiCY7dmz\np6SkJDQ0tHfv3nZFAwcOFEJcvnz5+PHjfq0BQBl5/3GT3YNCiGrVqvkpSACqkJCQ0aNHz5o1\nKyoqyoPd+YZFJUFCCASzH3/8UQjRvHlzg8F+fHjt2rXlF5jcxn81ACgj7z9ueXl5coEeQqAC\n9O3b95FHHlEUxbPd+YZFJcE9hEAwO3PmjBAiJibGaWmdOnWuXbvmZkJCn9QAoIy8/7ipPYQl\nJSWff/754cOHs7KyjEZj3bp1u3Tp0qFDB4+vXAE48vIDxTcsKgkSQiCY3bx5UwgRHh7utDQi\nIkIIcePGDb/WAKCMvP+4qQnh5MmT1TkPhRCnT5/evXt3y5YtZ8yYwWhSoJLgGxaVBENGgWAm\np5cwmUxOS41GoxDC9qrRHzUAKCPvP25qQlijRo2XXnpp2bJl69at+/DDD3v27CmEOHr06Dvv\nvOPjoAF4im9YVBL0EALaJecu82bEi/c1ACijsnzcmjZt+sorr+h0ulatWsmrSSFEbGzs5MmT\nIyMjU1JSDh06dPTo0ZYtW1ZExAC8wDcsKgw9hEAwCw0NFULcunXLaalcL7fxXw0Aysj7j1vN\nmjU7dOjQrl07NRtUPfHEE3Kw6P79+30TLgDv8A2LSoKEEAhm1atXF0JkZWU5Lb1+/bpwffeC\nr2oAUEZ+/bgZjcYGDRoIIa5evephfAB8im9YVBIkhEAwk9d/586dcyyyWq3nz58XQjRs2NCv\nNQAoI39/3EpKSoQQjhPcAwgIvmFRSZAQAsGsRYsWQojMzMyioiK7olOnTuXk5Agh3N9N5H0N\nAMrI+4/bvn37UlJSDhw44FhUVFQk5693Ncc9gArGNywqCRJCIJh16tTJbDYXFhZ++eWXdkVr\n164VQjRq1Kh+/fp+rQFAGXn/cdu3b9+nn366YMECx5kJ16xZU1hYKIRo3769T6MG4CG+YVFJ\nkBACwcxsNj/22GNCiGXLlm3fvr20tFQIkZ+fv2TJkm+++UYIkZSUZLv9xo0bk5OTZ8yY4XEN\nADzm/Qc2MTFRUZSrV6/OnDnz1KlTcmVBQcHatWtTUlKEEAkJCY0aNaqwVwRAxTcsKi1FzmkL\nIFhZLJZ//OMfO3fuFEKYTKawsLCsrKzS0lJFUcaOHTtgwADbjRcuXLhp06aQkBD526QHNQDw\nhvcf2H/961/z58+XV5ZhYWEmk0nWIIRo27btyy+/bDabK/QlAcFr1KhR6mhPi8UiO+Ft5wUd\nNGjQk08+KZf5hkWlxZ3lQJDT6XRTpkxp3779V199dfLkyaysrPDw8GbNmg0ePDguLq5iagBQ\nRt5/3Pr06dOsWbPNmzcfOXLk2rVrBQUFd911V+PGjR988MF27drxTDPAh/Ly8oqLi+1W2g7Y\ndrw50A7fsKgM6CEEAAAAAI3iHkIAAAAA0CgSQgAAAADQKBJCAAAAANAoEkIAAAAA0CgSQgAA\nAADQKBJCAAAAANAoEkIAAAAA0CgSQgB+16VLF0VRFEX58ccf7YpWrlzZqVOn6tWrh4SE1KxZ\nc8eOHWUpgg91795dnp0ffvgh0LEAQtAmg8LKlSvlSXz99dcr+NALFiyQh3777bcr+NDAHYqE\nEIBL27dvV5yRGVrjxo0HDRo0e/bs9PR0z+pfsGDB8OHD9+3bd/PmzZKSkmvXruXk5Ny2CABQ\nme3bt2/06NFCiEcfffTPf/6zur6oqGjOnDnNmjUzmUy1a9ceM2bM2bNn3VdVUlLyxRdfTJo0\nqW3btrGxsWazuUqVKtHR0QkJCVOmTNm1a5fjLuPHj58wYYIQYtq0aRs2bPDpKwOClBUAXNi2\nbVsZ/5I88MADKSkprup55pln4uPj4+PjT58+bbv+vvvuk7t369btk08+Wb169S+//HLbIvhW\nt27d5Ft99OjRCjvos88+K4SYM2dOhR0Rd5CAtEn4SnZ2dv369YUQ9erVy87OVteXlpb26tVL\nCFG9evVOnTrFxsYKIWrUqOHmLH/yySf33HOP+2+fVq1a7d69227HwsLC5s2bCyEiIiLOnTvn\nr5cKBAtDeZJHABpVo0aNF154Qf1vSUlJVlbWuXPnDh48eOXKFSHEd999N2zYsCeeeGLhwoXV\nqlWz233JkiWOdVqt1lOnTgkhjEZjampqeHh4WYoQHA4cOBDoEAD4xaRJk86cOSOEWLRo0V13\n3aWuf/fdd7dv3z5o0KBPP/20evXqQogPPvhg4sSJo0aN+u677+wqKSgoSEpKWrVqlbrmnnvu\nadOmTa1ataxW66VLl/bv33/58mUhxPfff9+tW7d333130qRJ6sYmk+nTTz/9wx/+kJWVNXr0\n6LL/uAloVIATUgCVmPol2qRJE1fbfP3114MHD1b/pDz44IOFhYVlqTw3N1fuUrdu3bIXwecq\nvjcmLy/PYDAIegjhAj2Ed659+/YpiiKEGDBggF1RgwYNQkNDbfsMrVZr//79hRCHDx+2XWmx\nWPr166d+rQwYMCAjI8OuttLS0k2bNqljSYQQy5Yts9smKSlJFq1du9ZHrw8ITtxDCMArCQkJ\n69evX758eUhIiBDi3//+t+3PtG5YrVa5oNfry16EIHDo0KGSkpJARwHA915++WX5B3z27Nl2\nRefOnYuLi7PtMxRCtGnTRgghexRVc+bM+fLLL4UQiqK8++67GzdubNWqlV1tOp0uMTHx22+/\nfeihh+Sa559//tdff7Xd5vXXXzcajUKI6dOnWywW718dEKxICAH4wPDhwxcuXCiX//nPf9rN\nDWg3y+j06dMVRQkLC5OlZ86cUaeradq0qaui1NRU2zqPHDny4osvxsfHh4eHm0ymmJiYrl27\nvvnmm7/99pvTCBMSEhRF0el0Vqs1Nzd30qRJtWrVMplMs2bNstuyvDWrMyKWlpYKIQ4fPpyU\nlBQbG2s0GsPCwlq2bDlt2jS7yxQ7u3btGjduXOPGjcPCwqpWrdq4cePx48d///33bnYpb5Bl\nIX/X37Rp0+DBg+vVq2cymaKiohISEubNm1dcXOyTYGbOnKkoSteuXeV/Z8yYId+6Pn36REdH\nK4pSvXp1+TbaGT58uNyyR48eTmNo0qSJoihmszk/P9+z2Dx+UZL3zcCp0tLSFStWDB06tGHD\nhtWqVTMYDOHh4a1atXrhhRccR9mp8vPz58+fn5iYWK9evapVq8pZoBISEmbNmnX16tWyxL91\n69aHH344NjY2NDS0UaNGY8eOPXHihLrx7t27hw0bdu+995pMppo1ayYmJu7cudOxztatW8s6\nZftJTU1NTEysW7euyWSqVatWQkLC/PnzPfhpwIeN3x+v2ss4vT935Wp7njUwV7799tvdu3cL\nIXr37t2iRQu70tjY2JMnT964ccN2ZUZGhhCiXr166prr16+ryeTUqVP/9Kc/uTli1apVV69e\nXbNmTSGEyWTau3evbWmdOnUee+wxIcSJEyc2b95c3pcDaEhgOygBVGZlGTJqq2fPnnL7kSNH\n2q7v3LmzXH/8+HGr1Tpt2jRXf5GaNGniqmj9+vWytlu3bj333HOuNouMjFyzZo1jbHIyAyFE\nXl6ebVIxdepUdRvPau7Tp4/c4ObNm//7v//rtFczJibG6aQ4OTk5gwYNcno4nU43Y8YMx108\nC9INdXheZmamnOvFUevWra9fv+59MK+99prTLfv06TNixAi5fPDgQccD1a5dW5aazWbHMckX\nLlyQpQ8++KD3b1TFNwNXLly40Lp1a1eRCCH+9Kc/Oe717bffyuk6nKpRo0ZaWpqb+G/cuDFj\nxgynL1wO4HT8DUUIodPpPv/8c7s6O3ToIEuvXr06fvx4p/G0a9cuKyvLbkdXQ0Z93vj98aq9\nidPLc1fetudZA3NDziwqhHD6At944w0hxKBBg3JycuSaefPmCSFatWplu5n6mIq6deveunWr\nLMdNS0tLS0srKSlxLJIJqhCiX79+5XotgKaQEAJwqbwJ4datW9ULl9LSUnW9XUJ47dq1EydO\nHD58WL1SOfG7M2fOuCrKzc2VtT3++OOyNDo6etasWVu2bDl06NCGDRuSkpLklZBer9+4caNd\nbA8//LDcS85wYzKZunTp0qtXr3feeUfdxrOa5T0wQoilS5cKIRo2bDh79uz169evWbNm+vTp\nam/n4MGD7XYsKSnp3r27LG3QoMHMmTNXrFjx8ccfJyUlyVvshBAzZ86028uzIN1QL75lot64\nceM5c+asW7fu888/nzhxoslkkqX9+/d33Le8wfz2228nTpxITk6WeyUnJ8uTe/HiRfnuCSHe\neustu6McO3ZMCKHT6eR8RTt37rTb4LPPPnPc1+M3qoKbgRtdunSRe7Vp02bu3Llbt25NS0tb\ntWrVH//4R3Xqprlz59ru8uuvv0ZFRdnu9cUXX6SlpS1evPiBBx6Q68PCws6fP+8q/g8//FAI\n0bNnz0WLFm3cuPG9995Te2/69u27du1aIcQf/vCHjz76aOPGjQsWLFAzipo1axYVFTmN/y9/\n+YsQ4v7773/77bdTU1NXrlz5/PPPy7F8TpuWq4TQ543fH6/a4zi9P3flbXseNDA3iouLIyMj\nhRBms1n9c22rtLRU5q62s4zefffddme5ffv28tCzZs0q46HdKC0tjY6OFkIYjUbHnx4ASCSE\nAFwqb0JYUFBQpUoVuYttP49dQijdvHlTrqxfv75dPW6Kli1bJovi4+OvXbtmV7p582Z5vVWn\nTp2bN2/aFg0YMEDu2L59+7Zt2168eNFXNQ8cOFDueNdddw0cONCu/0odVKbX6+0uRz766CNZ\n1KFDB7vrpx07dsic0GAw2P6u73GQbqgX3waDYeDAgXZXt3v27JF3hwoh9uzZY1vkcTBz5syR\nO9pOKnPp0iW50nEuCtmN0Lx5c9kF7ZgkjxkzRu6rXll6HFvFNwNX1J9FWrdu7dgpmpmZKS/0\no6OjLRaLun7mzJlyr65du9rtZbFYhg0bJkuTk5PtKlTjr169ul3X9M8//yx/F1AUpWbNmk8+\n+aTtzz25ublqp9a2bdtsd7RtWsOGDSsuLrZ7T9QfPuySfKcJoT8avz9etcdxen/uytX2PGtg\nbuzZs0dW+NBDD7naprS0dO7cuU2bNjUajdHR0ePHj//tt99sN8jNzVVbxaFDh8py3NsaNWqU\nrNDNs5EAjSMhBOBSeRNCq9Xatm1bucu6devUlT5MCOWccoqiHDt2zGkA6rRyS5YssV2vjsw0\nGo1Oh+15X3NUVJQ6FMpWy5YtnV713nvvvXL9kSNHHPd65plnZOn//M//eB+kG+rFd/Xq1R2v\nX61W69ixY+UGEyZMsF3vcTBOE0Lr729UeHi47ZW31Wp95JFHhBBJSUkvv/yyEKJHjx52B5Lv\nZExMjPexVXwzcEWdcP+VV15xusHixYv/8pe/LF68uKCgQF35xhtv9O3bt1WrVk47ytQ7rFq3\nbm1XpMYfFxfnOPROHZRYrVo1x4R26tSpstS2v91q07TCwsLsrvslNZN/7rnnnO5omxD6o/H7\n41V7HKf3565cbc+zBubGW2+9JSv885//XJbtnTp69KisxGg0Oh0C6gHZ/Suc5dIAJCaVAeBL\nNWrUkAsezJ9xW//5z3+OHz8uhOjUqVOzZs2cbvPUU0/JhU2bNjndYMCAAfKhyT6veeTIkfLh\nWnbUyRVs35MjR478/PPPslS9XLOVnJy8ZMmSTZs2yUkRfBWkG4MHD1ZPn60hQ4bIhV27dqkr\n/RFM7969hRDZ2dnqRaEQwmKxyON27NhRjiXbt2/frVu31A3Onj1UutxpAAAPcElEQVQr30n1\n8t3j2Cq+GbgRGhoqF2zfDVujR4/+61//Onr0aLPZrK58+eWXt2zZkpGRoXaJ21Ln6L948aKr\n4w4fPtzxJrSmTZvKhf79+zs+F1QtvXbtmtM6hwwZIgcTOq6XC7ZNyyl/N35fvWpv4vT+3JWr\n7XnWwNw4cuSIXHCcEbTs1Bl3IiMjfTXLtBqP2ikKwA4JIQBfqlq1qlywm+zRJ7755hu54DSD\nkuQk5sLm6sROQkKCn2ru2LGj0/XqNOu270l6erpdtXaaN2/+zDPPJCYmqhPt+CRIN9Qbiuzc\nf//9cuGnn35SpwD1RzAyIRT/Nz34/vvvr1+/LoTo1q2bPHeFhYW2z7VPS0uTC3379vUytopv\nBm507txZDsDetGnTyJEjPTihUnFxcU5OTnZ2dnZ2tppIFxYWutpevV3NlnofWnx8vJvSgoIC\np3V26tTJ6Xr1TrxTp045nV1W5e/G76tX7ds4y3vuytX2fNXAVL/88otcaNCggceVqA+hVb9K\nvHfPPffIBbuHWwBQkRAC8CX1912nfU1eUn8anz9/vuKC+gP52bNnnVYiJxjwR821atVyul79\nndv6+/MVhc3FU506dVy+YD8E6UajRo2crq9Tp45OpxNCFBUV5eTk+C+Yrl27yr6Ir7/+Wl0p\n8706derExcXVrFlTpse20/3v2LFDCKHX69XHkXkcW8U3AzciIyM//PBD+c4vX748Pj6+adOm\nf/zjH1evXu2qI061Y8eOMWPGtGjRIioqymQyhYeHR0REREREOG38jsd1E7z7UlcvLS4uzun6\nu+++W21aWVlZbqLyd+P31av2Pk5vzl252p43Dcwp9Tbgsv9Nc6TmrtnZ2R5XYic6Olq+A246\nVwGNIyEE4EvqN646XZ4Pub9ktFNUVFRUVOS4Xj6xyh81q3MhlIV6n2TZfwj3SZBuOB1sJoRQ\nFEUdM6b+fu+PYMxms+wDdEwI1elY5TMMbbsQZULYvn17dVCfx7FVfDNwb/To0du2bVO71/7z\nn//Mnz//8ccfv/vuu3v06PH55587Pms7Nzd3yJAhPXv2XLx48bFjx+Sde+U6qPv4PRvFp3am\n2dHpdOo0VHl5eW5q8Hfj99Wr9iZOf587Rx40MDd80rmnfnFkZWW5bxJlpyiKbGb+GLcCBAef\nfW8BwKVLl9TnODsdZOUl+WO2EOLpp59W51xxw+llnDrZvc9rLhf1iG4GgLnaxU9BurmaVBTF\nLgY/BdO7d+9t27ZdvXr1+PHj9913X0lJiZy60DYhXLhw4b59+4qKioxG46lTp86dOydsxot6\nE1vFN4Pb6tmzZ8+ePQ8ePLhx48atW7dmZGRYLBaLxbJz586dO3fOnTt3/fr1tv1CY8aMWb9+\nvRAiLCwsOTk5MTExJiYmMjJSThVbWFioJmAVSZ2o1pGa87h/MyvhqXHKmzgDcu7K28DcUAe1\nqs+q8UDDhg1DQ0Pz8/MtFsvevXvVbn8vySdhWCyW4uJiN60R0CwSQgA+k5qaKheaNWsWExPj\n8/rV0UQ1atRQM4RKXvNtj3j16tXy7uKnINUf+O1YrVb1Lin16WR+CqZ3794vvfSSEGLXrl33\n3XffwYMHZVeqegg5/2RBQcGBAwcSEhJk96CwmVHGm9gqvhmUUbt27dq1azdr1qzr16+npaWt\nW7cuJSWluLh47969jz/+uPom/PDDD6tXrxZChIaGfvPNN463sbm/T89/XDUti8Wi/iDivlup\n0p4aOx7HGdhzV8YG5p6aB966dcvp725lERIS0qFDBzkuICUlpewJYX5+vjpNjiPZzHQ6Hdkg\n4BRDRgH4RklJyTvvvCOXhw4d6o9DqM9pUPshK3/Ntz3ilStXyruLn4J0defVpUuX5MixqlWr\nqte7fgrm/vvvr127tvh91Ki8LoyJiVFvQouNjZWTxMrbCOWlalRUlPq8E29iq/hmUF6RkZHD\nhg1bsWJFRkbG3XffLYTYuXOnOsL2X//6l1x44oknnE5qcvr06QoL1ZarpnXlyhXZtEJDQyMi\nItzUUPlPjeRxnJXk3LlvYO6pKb2XQz0fffRRubBs2TL1vkT3Dh06FB0dPXHiRKctTf1Jy03G\nCGgcCSEA33j11VdPnTolhAgLC5s8ebI/DtGuXTu5sHv37vLeIxSoml1RZzXct2+f0zuFjh8/\nPnbs2LFjx77//vsVE+S3337rdL06K33Tpk3VsaP+C6ZXr17i94RQ5nt2PS22txHKfx966CF1\nqJ43sVV8M/BY8+bNJ0yYIJfV+SHVq2f1EQV21D78CqbOqWtHfQyAOpWuK3fKqfE4zsp27pw2\nMPfkTznC67lbRo0aJWfxKSgoGDNmzG1vpMzLy3vmmWdu3rz5wQcfqI8ctHX58mXZv6pGCMAO\nCSEAH3jnnXfUpxK/9tprTifl816jRo3kE6Wys7M/+eQTp9vs3LkzLi5u8uTJrh6uVcE1u9Ks\nWTN5EXzlypWNGzc6bvDZZ58tWrRo0aJF6phSfwe5evVq2+f7qeR9TUKIBx98UF3pk2BKSkoc\nV8qHT1y4cOHEiRNyEn+nCeH+/ftPnjx54cIF8X9vIPQmtopvBq5YLJZXXnmlT58+w4cPd7WN\n2iGj3lqmjtmTD+qwc/Hixffee08uO33n/cdV01KTHNum5VTlOTXueRxnBZ87zxqYe+rTJtQp\nlD0TGhr65ptvyuUtW7aMGjWquLjY1cbXr1/v1avXDz/8IAN49dVXHbdRO1e9eR4GENxICAF4\nJSMjIzExMTk5Wf6O+9hjj02dOtV/h0tOTpYLL7300qFDh+xKT58+PWbMmJMnT77//vturiEq\nuGZXpkyZIhdeeOEFuwdkpaeny+s/g8EwevToignywoULM2bMsFuZkZEhr2sVRbG7dvQ4GHU6\nUKfD6nr37i37IefNmyezCKcJYV5e3kcffSQDUx9g6H1sFd8MnNLpdHv27Pnqq69Wrlz56aef\nOm6Qn5+vrlefPqcONdywYYNd2nD+/PmHH364Xr16chbHvLy8cs2H6SWnTSs9PX3JkiVCCEVR\nRo4cedtKKsmpuS3P4qzgc+dZA3NPfWCp989/HzNmzIgRI+TyZ5999sADD3zxxRd2d1GWlpau\nXbu2Xbt2+/fvF0KEhYWtWbPG6VTJajxqhADsMKkMgNu7du3azJkzbdfk5eVduXJl7969cpio\nNHbs2A8++MCvkYwYMSI1NTUlJeXGjRudO3ceN25cnz59IiIiLl++vHv37sWLF8s5SMaPH+/0\nSdMBqdmVsWPHrlq1aseOHefPn2/VqtXo0aPj4+PldCnLly+XF4uvvvpqw4YN/RqkevX5wgsv\nvPfee5mZmaNHj27UqNGtW7e+/vrrv//97zIre+qpp+xmjvU4GPWBh6tWrYqNjW3cuPH58+en\nT58uh33WqlXr/vvvP3z48NKlS4UQMTExdg9IbNy4cXR09OXLlxctWiSEiI+Pd3xEm8exVXwz\ncOVvf/tbjx49SktLn3766eXLlw8aNCg2NrZatWrZ2dkZGRnLli2T/TCPPvpos2bN5C6JiYmR\nkZHXr1/PzMzs06dPcnJyvXr1rly5snXr1vnz5xcVFR08eHDChAly4tYZM2ZMmDAhIiKibt26\nfn0hQojnnnvOtmkVFBTs2LHjzTfflIMqR44cWZZJiSvPqfFHnBV/7jxoYO6peaPM0Ly0dOnS\nsLCw+fPnCyF++OEH+f507Nixdu3aBoPh4sWL+/fv//XXX+XG0dHRa9eutb2R2NaBAwfkQocO\nHbwPDAhOVgBwYdu2bWX8S9KkSZNVq1a5qqdz585ys+PHj6sr1Qfx1a9f3257N0VWq7WoqGjc\nuHHqzWx2dDrdpEmTSkpK7PYaNGiQ3GD37t2u4vRTzeqtOEuWLHF8pQMHDnR6OEVRZsyY4asg\n3VAvki5fvjxq1Cin1Xbv3j0vL89XwZSUlDjeKFVcXKxuICcalUaMGOF4XHXaCSHE9OnTnb4u\nj9+oim8GrqxcuVKd1tWpoUOH2p2XDRs2OJ3g8a677tq5c6fVarX7yWbatGl28cubWu289tpr\nsnThwoWOpWvWrJGlkyZNsl0vp4QVQmRmZj755JNOX0KPHj0cm5a649GjR23X+7zx++NVexyn\n9+euvG3PgwbmRlFRkZwZqEqVKmXfy72UlBR1QilXb+bTTz99+fJlVzVYLBZ562BISEhWVpZP\nogKCD0NGAXjCYDBER0e3bt164sSJmzdvzszMfPzxxyvm0CEhIQsWLPjuu+8mTpzYsmXL8PBw\ng8EQHh7+wAMPTJ48+fDhw//4xz88ewqZ/2p2pVq1ahs2bNiyZcuIESPuvffeqlWrVqlSpVGj\nRuPGjcvIyJg9e3YFBKk+rDkiImLp0qVr165NTEysW7eu0WisUaNGt27dFi5c+O9//9vpBH2e\nBaPX67du3frII4/UrFnTZDLFxMT07dvXdlYY22dIOJ27X00YhMMNhF7G5s2OPvfEE0+cPHly\n9uzZPXv2jImJMZlMer0+PDw8Pj7+2Wef3b17d0pKit15GThw4P79+5988sk6deoYDIbQ0ND4\n+PjXX3/9xIkT8k179tlnZ8yYERsbazKZ4uLi5A1v/ma1WlesWJGSkjJw4EDZtKKiohISEhYs\nWLB9+/ayz/1YeU6NP+Ks+HPnQQNz/6plalpQULBlyxYvY5OGDh2amZm5ZcuWF198sU2bNnXr\n1jWZTGazOSYmpnfv3n/7299+/vnnTz75RE6I6tTevXvlhD29evVSB6sDsKNYbzd9EwAAQHl1\n795dTgN79OjRFi1aBDoc+N2BAwfkiIM+ffps3bo10OEIIcTTTz8tb4ZMTU1Vu1IB2KGHEAAA\nAN5q3759ly5dhBBfffVVZmZmoMMRly5dWrVqlRCiUaNGAwYMCHQ4QOVFQggAAAAfkE+MsFqt\nTp8AUcFee+01OXHR3//+d9tB6QDs8PEAAACAD3Ts2PGpp54SQqSmpqalpQUwku+//37x4sVC\niJ49ew4dOjSAkQCVHwkhAAAAfGPu3Ln16tUTQiQlJd24cSMgMdy6dWvUqFGlpaXh4eHySaoA\n3CAhBAAAgG+Eh4evWrXKZDKdOXNm3LhxAYkhOTn56NGjiqIsWbIkNjY2IDEAdxASQgAAAPhM\nx44d5XDN1atXv/766xV89IULF8pnNr7xxhuDBw+u4KMDdyIeOwEAAAAAGkUPIQAAAABoFAkh\nAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGgUCSEAAAAAaBQJIQAAAABoFAkhAAAAAGjU\n/wM3svNk3CZ7EQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(100)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "\n",
    "obs_diff_in_temp <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "null_diff_in_temp <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    hypothesize(null = \"independence\") %>%\n",
    "    generate(reps = 1000, type = \"permute\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_temp_plot <- null_diff_in_temp %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_p_value(obs_stat = obs_diff_in_temp, direction = \"both\") +\n",
    "    xlab(\"Difference between sample means (°C)\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "diff_in_temp_plot\n",
    "\n",
    "p_value <- null_diff_in_temp %>%\n",
    "    get_p_value(obs_stat = obs_diff_in_temp, direction = \"both\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1a7752-c5ab-471f-8c8c-868ae1073354",
   "metadata": {},
   "source": [
    "*Figure 3. Figure of simulation distribution and P-value of the daily temperature hypothesis test.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ebfdae-70d5-42da-a420-2caac674943c",
   "metadata": {},
   "source": [
    "Notice that the red line in the above graph indicates the observed difference in temperature, which is far out of the range of values generated by simulation under the null hypothesis, suggesting that the null hypothesis might possibly be rejected. To further prove this, we notice that the simulation returned a p-value of 0; however, we should never report a p-value of 0 because this suggests that making a Type I error is impossible. Since we used 1000 reps to calculate the sample, we conclude:  \n",
    ">**The p-value is < 0.001, we reject the null hypothesis $H_0^1$ in favor of $H_A^1$ at the 5% significant level.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538a9e92-baa5-4987-af62-575b462d681d",
   "metadata": {},
   "source": [
    "We further compute the 95% confidence interval of this observed test statistic using the bootstrap method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79dd45b6-a396-4661-a320-67f05bb9f059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "set.seed(100)\n",
    "diff_in_temp_bootstrap_dist <- station_temp %>%\n",
    "    specify(formula = Mean_Temp ~ Category) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_temp_ci <- \n",
    "     diff_in_temp_bootstrap_dist %>% \n",
    "     get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "diff_in_temp_ci_plot <- diff_in_temp_bootstrap_dist %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_ci(diff_in_temp_ci) +\n",
    "    xlab(\"Difference between sample means (°C)\") +\n",
    "    scale_x_continuous(limits = c(-0.3, 2.5)) +\n",
    "    geom_vline(xintercept = 0, size = 2, linetype = \"dashed\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "\n",
    "diff_in_temp_ci_plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab98c92-b4b3-4c8a-8874-f14917f3f812",
   "metadata": {},
   "source": [
    "*Figure 4. Figure of simulated bootstrap distribution of confidence interval of the sample statistic.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d245d2e-9393-4767-a74c-98a0c6476645",
   "metadata": {},
   "source": [
    "Notice that the value 0 is not included in our confidence interval, again suggesting that $\\mu_{ct}$ and $\\mu_{ht}$ are truly different.  \n",
    "\n",
    "Table 3 summarizes the value we obtained for this test statistic.  95% confidence interval. \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |  p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:|\n",
    "| $\\bar{x}_{ct} - \\bar{x}_{ht}$  |1.258 | <0.001|1.048|1.465|\n",
    "\n",
    "<center><i>Table 3. Table of the estimates for test statistic - difference in mean temperature by simulation.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b516bc2-152f-436f-b562-9e09236fb1f4",
   "metadata": {},
   "source": [
    "### Temperature - Asymptotic Two Samples T-Test\n",
    "We further investigate the difference between mean temperatures using the asymptotic method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "370f843d-a5b6-40b2-b589-2b14f8bb14f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_contemporary <- station_temp %>%\n",
    "    filter(Category == \"contemporary\")\n",
    "\n",
    "temp_historical <- station_temp %>%\n",
    "    filter(Category == \"historical\")\n",
    "\n",
    "temp_t_test <- tidy(\n",
    "    t.test(temp_contemporary$Mean_Temp, temp_historical$Mean_Temp, alternative = \"two.sided\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ad75e2-5a05-4a7e-a46c-00b4997716d2",
   "metadata": {},
   "source": [
    "Table 4 summarizes the result of the t-test. We can see that the estimates of the difference in mean temperature between historical and contemporary data obtained from the two-sample t-test are very similar to the result we obtained from bootstrap simulation. The 95% confidence interval for the observed test statistic does not include 0 as well. \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |  p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:|\n",
    "|  $\\bar{x}_{ct} - \\bar{x}_{ht}$  |1.258 | <0.001|1.055|1.461|\n",
    "\n",
    "<center><i>Table 4. Table of the estimates for test statistic - difference in mean temperature by asymptotic.</i>\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2025404a-2331-4d8d-b6b5-de1a1fd24c69",
   "metadata": {},
   "source": [
    "Looking at the 95% confidence interval and the p-value for the observed test statistic from both bootstrap and t-test, there is enough evidence to conclude that the difference between historical and contemporary temperature is not zero. We further demonstrate the parameter difference between the two categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61d0281-4c04-4036-b5d1-3f3e17a50f87",
   "metadata": {},
   "source": [
    "### Temperature - Bootstrapping Simulation for Point Estimates\n",
    "\n",
    "From our hypothesis testing, we conclude that there is a difference between the two group's mean temperature. Now we are looking at the distribution of each group and we aim to explore how much they different by.  \n",
    "We use a bootstrap distribution to estimate the standard error in the point estimate and generated a 95% confidence interval for each point estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63aabc65-3a67-4942-88e7-a1c1c6d8d3a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "options(repr.plot.width = 12, repr.plot.height = 5)\n",
    "set.seed(100)\n",
    "\n",
    "temp_historical_mean <- temp_historical %>%\n",
    "    summarize(mean = mean(Mean_Temp)) %>%\n",
    "    pull()\n",
    "\n",
    "temp_historical_bootstrap <- temp_historical %>%\n",
    "    specify(response = Mean_Temp) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"mean\")\n",
    "\n",
    "temp_historical_percentile_ci <- temp_historical_bootstrap %>%\n",
    "    get_ci(level = 0.95, type = \"percentile\")\n",
    "\n",
    "temp_historical_percentile_ci_plot <- temp_historical_bootstrap %>%\n",
    "    visualize(binwidth = 0.05) +\n",
    "    shade_ci(endpoints = temp_historical_percentile_ci, fill = \"#4682B4\", color = \"#4682B4\") +\n",
    "    labs(title = \"\", x = \"Sample mean of historical daily temperature (°C)\") +\n",
    "    geom_vline(xintercept = temp_historical_mean, size = 2, linetype = \"dashed\") +\n",
    "    scale_x_continuous(limits = c(9, 11)) +\n",
    "    theme(text = element_text(size = 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1aaca163-d0b6-4369-a00d-1e28bed83ca4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing non-finite values (stat_bin).”\n",
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”\n"
     ]
    },
    {
     "data": {
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WQOzGPx2travRuT5I/nJ1TauPo976uW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\nvXs3Jkkyx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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 720
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GB/yis4rrvuuuXLl3d0dOy9cNKkSZMmTdp7ycsvv3z++eevXr16FAICAAAAHEgZ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\nAAAgegoOAAAAIHoKDgAAACB6Cg4AAAAgegoOAAAAIHoKDgAAACB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CvbLEdmPtr3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\nWg8dHAAAAAAAAACg9f4fWwP0tF9HbkoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 12, repr.plot.height = 5)\n",
    "set.seed(100)\n",
    "\n",
    "temp_contemporary_mean <- temp_contemporary %>%\n",
    "    summarize(mean = mean(Mean_Temp)) %>%\n",
    "    pull()\n",
    "\n",
    "temp_contemporary_bootstrap <- temp_contemporary %>%\n",
    "    specify(response = Mean_Temp) %>%\n",
    "    generate(reps = 1000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"mean\")\n",
    "\n",
    "temp_contemporary_percentile_ci <- temp_contemporary_bootstrap %>%\n",
    "    get_ci(level = 0.95, type = \"percentile\")\n",
    "\n",
    "temp_contemporary_percentile_ci_plot <- temp_contemporary_bootstrap %>%\n",
    "    visualize(binwidth = 0.05) +\n",
    "    shade_ci(endpoints = temp_contemporary_percentile_ci, fill = \"#F8766D\", color = \"#F8766D\") +\n",
    "    labs(title = \"\", x = \"Sample mean of contemporary daily temperature (°C)\") +\n",
    "    geom_vline(xintercept = temp_contemporary_mean, size = 2, linetype = \"dashed\") +\n",
    "    scale_x_continuous(limits = c(9, 11)) +\n",
    "    theme(text = element_text(size = 20))\n",
    "\n",
    "temp_historical_percentile_ci_plot\n",
    "temp_contemporary_percentile_ci_plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b091b9f-e88e-4ee9-8959-1952d6465cd2",
   "metadata": {},
   "source": [
    "*Figure 5. Bootstrap distribution of the sample means with 95% confidence interval.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa3e1b9-e65f-46f3-8ffc-a2fa39af724c",
   "metadata": {},
   "source": [
    "Table 5 summarizes the point estimate for each category and reports their 95% confidence interval. \n",
    "\n",
    "| Category    | Point Estimate $\\bar{x}$  |  Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:|\n",
    "| historical|9.53|9.39|9.68\n",
    "| contemporary|10.79|10.64|10.92\n",
    "\n",
    "<center><i>Table 5. Table of the estimates for daily mean temperature.</i>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66f7b43-4a25-4c6c-9302-c1b53eaba161",
   "metadata": {},
   "source": [
    "And we interpret this as:\n",
    "- If we repeated our sampling procedure a large number of times, we expect about 95% of the confidence intervals [9.39, 9.68] to capture the value of the population parameter $\\mu$ - the mean of daily temperature from 1950 to 1969.  \n",
    "- If we repeated our sampling procedure a large number of times, we expect about 95% of the confidence intervals [10.64, 10.92] to capture the value of the population parameter $\\mu$ - the mean of daily temperature from 2000 to 2019. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1f2cff-cd4b-4c73-b4d8-4806fd59234a",
   "metadata": {},
   "source": [
    "## Precipitation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8e3e3c-ec95-42ae-b45c-b606d2795942",
   "metadata": {},
   "source": [
    "The United Nations (n.d.-a) states that due to climate change, destructive storms have become more intense and more frequent in many regions. The reason for this is as temperature rises, more moisture evaporates, which leads to extreme rainfall and flooding, causing more destructive storms. Can we see this claimed difference in yearly extreme rainfall between historical and contemporary data at Abbotsford A weather station? We first subset the `Total_Rain` to obtain the highest yearly rainfall. The hypothesis we will be testing here is:  \n",
    "  \n",
    "$$H_0^{2}: \\mu_{cr} - \\mu_{hr} = 0$$  \n",
    "  \n",
    "$$H_A^{2}: \\mu_{cr} - \\mu_{hr} \\neq 0$$  \n",
    "\n",
    "We generate a box plot and its sample distribution to visualize the sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af1010fb-0b6d-4dd4-ae80-28d0809f0bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1QsjJ9EBQBeLyq1rv7WeB3t3btXHg3p9OnTjRs3ztt4kH9kZGSULVtW6rj8448/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Xr16NjY1t2LChm5vbpEmTJkyYUGIDSo3Y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\nmUzXJQA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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 540
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 9, repr.plot.height = 6)\n",
    "\n",
    "station_rain <- climate_sample %>%\n",
    "    select(Year, Total_Rain)\n",
    "\n",
    "station_rain_yearly_max <- station_rain %>%\n",
    "   group_by(Year) %>%\n",
    "   summarize(max_rain = max(Total_Rain)) %>%\n",
    "   mutate(Category = as.factor(\n",
    "       case_when(Year %in% c(2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019) ~ \"contemporary\",\n",
    "                 Year %in% c(1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969) ~ \"historical\")))\n",
    "\n",
    "rain_boxplot <- station_rain_yearly_max %>%\n",
    "    ggplot(aes(x = Category, y = max_rain, fill = Category)) +\n",
    "    geom_boxplot() +\n",
    "    scale_fill_manual(values = c(\"#F8766D\", \"#4682B4\")) +\n",
    "    labs(x = \"\", y = \"Yearly Max Rainfall(mm)\", fill = \"Temporal Category\") +\n",
    "    ggtitle(\"Boxplot of Yearly Max Rainfall\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "\n",
    "rain_boxplot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0a2349-bfdc-4af0-bc6f-706a388c9c3f",
   "metadata": {},
   "source": [
    "*Figure 6. Boxplot of Yearly Maximum Rainfall at Abbotsford A weather station*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41266478-7a6d-4ccc-9097-62e55ce882a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ueg1+sdbzsjGTBggNypxJYtW4YOHapshHL+/PmnnnpqxYoV999/v/wwi4PvBBkz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\nV6+2k+D44YcfEhISqlSpsmDBgnr16kkjc3Jy5s6du2/fvoULF65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u7cWejjffrppxs1amR2sl27do899tjx48fj4+N37do1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\nftJAD7NHZ+mtMJtXIoQ4fvz4mDFjEhIS/Pz8Nm3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JzJkzN2/efO7c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\ncSnFxcUPPvig0+mMi4ubO3duq1a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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 12, repr.plot.height = 6)\n",
    "\n",
    "station_temp_sample_distribution <- station_rain_yearly_max %>%\n",
    "    ggplot(aes(x = max_rain, fill = Category)) +\n",
    "    geom_histogram(binwidth = 5, color = \"lightgrey\") +\n",
    "    facet_grid(. ~ Category) +\n",
    "    scale_fill_manual(values = c(\"#F8766D\", \"#4682B4\")) +\n",
    "    labs(x = \"Maximum Yearly Rainfall(mm)\", y = \"Frequence\") +\n",
    "    ggtitle(\"Sample distribution of yearly max rainfall\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "\n",
    "station_temp_sample_distribution   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d04b81-6203-4b95-9306-28858c93d701",
   "metadata": {},
   "source": [
    "*Figure 7. Sample distribution of yearly maximum rainfall at Abbotsford A weather station.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d6c788-891c-4ba5-816c-6ad6271aed88",
   "metadata": {
    "tags": []
   },
   "source": [
    "It seems that rainfall data are actually similar between two groups, but our sample size is quite small. We need to increase the number of replication in the simulation method. \n",
    "### Extreme Rainfall - Bootstrap Simulation\n",
    "\n",
    "We first test by simulation. We calculate the observed test statistic, then generate a simulation from the null distribution. We further obtain the P-value. The following codes demonstrate the steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d007ed1d-4868-458b-bd5c-ab68f635488a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FhRdm2Ob3n5FnwSX1/fZs2aPfXUU+3atWvUqJE08KbE399/9+7dZWt4//33zdcQ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\nAAAUikAIAAAAAApFIAQAAAAAhSIQAgAAAIBCEQgBAAAAQKEIhAA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nTo22+/FUK8+OKL/fr1CwkJEUKEhoYOGTJkwIABQohd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\nAACAogiEAAAAAKAoAiEAAAAAKOr/ASamH5jDzEn4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(100)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "\n",
    "obs_diff_in_rainfall <- station_rain_yearly_max %>%\n",
    "    specify(formula = max_rain ~ Category) %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "null_diff_in_rainfall <- station_rain_yearly_max %>%\n",
    "    specify(formula = max_rain ~ Category) %>%\n",
    "    hypothesize(null = \"independence\") %>%\n",
    "    generate(reps = 10000, type = \"permute\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_rainfall_plot <- null_diff_in_rainfall %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_p_value(obs_stat = obs_diff_in_rainfall, direction = \"both\") +\n",
    "    xlab(\"Difference between sample means (mm)\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "diff_in_rainfall_plot\n",
    "\n",
    "p_value <- null_diff_in_rainfall %>%\n",
    "    get_p_value(obs_stat = obs_diff_in_rainfall, direction = \"both\")\n",
    "# p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3b1243-0855-4428-bd17-7901439ddb17",
   "metadata": {},
   "source": [
    "*Figure 8. Simulation distribution and P-value of the yearly maximum rainfall hypothesis test.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95402bc-e969-4b8d-9ac4-d4090d5c8e91",
   "metadata": {},
   "source": [
    "We obtain a p_value of 0.474, so we do not reject the null hypothesis at 5% significant level. \n",
    "We further compute the 95% confidence interval of this test statistic. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d11a2aa-e43e-4289-951d-9ba4585c8038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AACpFIAQAAAAAlSIQAgAAAIBKEQgBAAAAQKX0ga4AAACBcduAvrcN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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 10, repr.plot.height = 6)\n",
    "set.seed(100)\n",
    "\n",
    "diff_in_rainfall_bootstrap_dist <- station_rain_yearly_max %>%\n",
    "    specify(formula = max_rain ~ Category) %>%\n",
    "    generate(reps = 10000, type = \"bootstrap\") %>%\n",
    "    calculate(stat = \"diff in means\", order = c(\"contemporary\", \"historical\"))\n",
    "\n",
    "diff_in_rainfall_ci <- \n",
    "     diff_in_rainfall_bootstrap_dist %>% \n",
    "     get_confidence_interval(level = 0.95, type = \"percentile\")\n",
    "\n",
    "diff_in_rainfall_ci_plot <- diff_in_rainfall_bootstrap_dist %>%\n",
    "    visualize(bins = 10) +\n",
    "    shade_ci(diff_in_rainfall_ci) +\n",
    "    xlab(\"Difference between sample means (mm)\") +\n",
    "    geom_vline(xintercept = 0, size = 2, linetype = \"dashed\") +\n",
    "    theme(text = element_text(size = 20))\n",
    "diff_in_rainfall_ci_plot \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6a2bd3-de67-49d5-a543-cf9c1913b8b0",
   "metadata": {},
   "source": [
    "*Figure 9. Simulated bootstrap distribution of sample statistic and its confidence interval*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a590cf5-7925-41e6-a59b-19221cfc3833",
   "metadata": {},
   "source": [
    "Notice that the value 0 **is** included in our confidence interval, suggesting that there is no significant difference between $\\mu_{cr}$ and $\\mu_{hr}$. \n",
    "\n",
    "Table 6 summarizes the value we obtained for this test statistic.  \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |  p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:|\n",
    "| $\\bar{x}_{cr} \\;- \\;\\bar{x}_{hr}$  |3.505 | 0.474 |-6.005|13.008|\n",
    "\n",
    "<center><i>Table 6. Table of the estimates for test statistic - difference in yearly max rainfall by simulation.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45c74d5-6cb0-488e-9d4b-9b4205e76972",
   "metadata": {},
   "source": [
    "### Extreme Rainfall - Asymptotic Two Samples T-Test\n",
    "We further investigate the difference between the mean of maximum yearly rainfall using the asymptotic method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c17ed06-c538-4cf6-910f-3faf6c226bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rain_contemporary <- station_rain_yearly_max %>%\n",
    "    filter(Category == \"contemporary\") %>%\n",
    "    pull(max_rain)\n",
    "rain_historical <- station_rain_yearly_max %>%\n",
    "    filter(Category == \"historical\")%>%\n",
    "    pull(max_rain)\n",
    "\n",
    "Max_rain_t_test <- tidy(t.test(x = rain_contemporary, y = rain_historical, alternative = \"two.sided\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4755d597-e3c3-4f8c-bf41-23db5bce869a",
   "metadata": {},
   "source": [
    "| Test Statistic    | Observed Test Statistic |  p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:|\n",
    "| $\\bar{x}_{cr} \\;- \\;\\bar{x}_{hr}$|3.505 | 0.481 |-6.485|13.495|\n",
    "<center><i>Table 7. Table of the estimates for test statistic - difference in yearly max rainfall by asymptotic.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84a90b5-559f-4baf-83ef-2f0fb92a1221",
   "metadata": {},
   "source": [
    "As a result, we have confirmed that we do not have enough evidence to reject the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79d8f01-fc7b-446c-8bb9-42731e903cd9",
   "metadata": {},
   "source": [
    "## Results\n",
    "In the first part of this report, we test the:  \n",
    "**Null Hypothesis**: The difference in population mean of the contemporary daily mean temperature and the historical daily mean temperature is zero.  $H_0^{1}: \\mu_{ct} - \\mu_{ht} = 0$  \n",
    "**Alternative Hypothesis**: The difference in population mean of the contemporary daily mean temperature and the historical daily mean temperature is not zero. $H_A^{1}: \\mu_{ct} - \\mu_{ht} \\neq 0$  \n",
    "  \n",
    "Table 8 summarizes the p-value and the 95% confidence interval results by simulation and asymptotic for the temperature variable. \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |Method  | p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:| :---------:\n",
    "| $\\bar{x}_{ct} - \\bar{x}_{ht}$ | 1.258 |Simulation|<0.001|1.048|1.465|\n",
    "|  $\\bar{x}_{ct} - \\bar{x}_{ht}$ | 1.258 |Asymptotic |<0.001|1.055|1.461|\n",
    "\n",
    "<center><i>Table 8. Table of the estimates for test statistic - difference in mean temperature.</i>  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a04c53fd-2215-4c80-ad59-bc3f2489f8c3",
   "metadata": {},
   "source": [
    ">**The p-value is < 0.001, we reject the null hypothesis $H_0^1$ in favor of $H_A^1$ at the 5% significant level. There is a significant difference between $\\mu_{ct}$ and $\\mu_{ht}$.**  \n",
    "  \n",
    "  \n",
    "    \n",
    "In the second part of this report, we test the:  \n",
    "**Null Hypothesis**: The difference in population mean of the contemporary maximum yearly rainfall and the historical maximum yearly rainfall is zero. $H_0^{2}: \\mu_{cr} - \\mu_{hr} = 0$   \n",
    "**Alternative Hypothesis**: The difference in population mean of the contemporary maximum yearly rainfall and the historical maximum yearly rainfall is not zero. $H_A^{2}: \\mu_{cr} - \\mu_{hr} \\neq 0$\n",
    "\n",
    "Table 9 summarizes the p-value and the 95% confidence interval results by simulation and asymptotic for the rainfall variable. \n",
    "\n",
    "| Test Statistic    | Observed Test Statistic |Method  | p-value | Lower CI  |  Upper CI\n",
    "| :----------: | :----------: | :----------: |:----------:| :---------:| :---------:\n",
    "| $\\bar{x}_{cr} - \\bar{x}_{hr}$  |3.505 | Simulation | 0.474 |-6.005|13.008|\n",
    "|  $\\bar{x}_{ct} - \\bar{x}_{ht}$ | 3.505 |Asymptotic | 0.481 |-6.485|13.495|\n",
    "\n",
    "<center><i>Table 9. Table of the estimates for test statistic - difference in rainfall.</i> \n",
    "\n",
    "   >**The p-value is larger than 0.05, we cannot reject the null hypothesis $H_0^2$ at the 5% significant level. There is no significant difference between $\\mu_{cr}$ and $\\mu_{hr}$.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb3f68e-ed71-4e92-be73-c93a5230839f",
   "metadata": {},
   "source": [
    "we conclude: \n",
    "> Based on the first hypothesis test, we notice that there is barely any difference between p-value with bootstrap simulation test and asymptotic two samples t-test, and the 95% confidence intervals by the two test methods are approximately the same. As the sample size in the first test is large enough, the results of bootstrap simulation test and asymptotic two samples t-test are extremely close.  \n",
    "\n",
    "> According to the second hypothesis test, we notice that there is a small difference in p-values between the two test methods, which is possibly due to the small sample size. We notice that the 95% confidence interval by the asymptotic two samples t-test is wider than that by the bootstrap simulation test. We believe this is due to the simulation method applies a normal distribution whereas the t-test applies a t-distribution. A t-distribution has heavier tails that generate a wider confidence interval. However, there is not enough evidence to conclude which test method is better, because we don't have enough information on the population."
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    "## Discussion\n",
    " \n",
    "***Summary And Implications of Findings***\n",
    "\n",
    "This study serves as an empirical analysis of climate change for the Abbotsford region. According to the preliminary data visualization, we found out that there is a difference in mean temperature between historical and contemporary data sets.  We then performed hypothesis testing accordingly with a significance level of 0.05. Both simulation-based bootstrap and asymptotic t-test showed a similar result for mean temperature. We conclude that there is sufficient evidence that the true population parameter is significantly different from the specified value in which the observed test statistic of 1.258 showed that there is a rise in mean temperature. It may be a shred of evidence to support the statement that the climate is changing. It may impact how the municipalities plan to adjust their local agriculture industry facing the rising temperature in the future.\n",
    "\n",
    "However, the means were similar for yearly maximum precipitation although there were more extreme values in the contemporary dataset. For both simulation-based bootstrap and asymptotic t-test, the test is insignificant at the given significance level. Therefore, we failed to reject the null hypothesis, which indicates that there is insufficient evidence to conclude that there is a difference in the mean of maximum yearly rainfall between contemporary and historical datasets. The probability of finding a value more or as extreme as our observed test statistic of 3.505 is pretty high. From this finding, there is no significant change in maximum yearly rainfall, so we cannot conclude that there were more severe storms or droughts in recent years compared to the past years. \n",
    "  \n",
    "***Alignment With Expectation***\n",
    "  \n",
    "The results align with our hypothesis that there is a difference in mean temperature. We are now confident that the mean temperature keeps rising. The result for mean precipitation also align with the preliminary finding.\n",
    "\n",
    "***Suggestions For Future Study***\n",
    "  \n",
    "For future studies we would like to examine whether other stations may show similar/different results (eg. Can our study be generalized to a broader population outside of the Abbotsford region?). There are many factors other than temperature and precipitation that may be interesting to observe (eg. What other dataset do we need to include for such generalization to be valid and meaningful?)."
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    "## Reference\n",
    "  \n",
    "Historical Data. (2021, November 25), Government of Canada, weather, climate and hazards. https://climate.weather.gc.ca/historical_data/search_historic_data_e.html  \n",
    "  \n",
    "United Nations. (n.d.-a), Causes and effects of climate change. https://www.un.org/en/climatechange/science/causes-effects-climate-change\n",
    "\n",
    "\n",
    "United Nations. (n.d.-b), What is climate change? https://www.un.org/en/climatechange/what-is-climate-change\n",
    "\n"
   ]
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