{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CDS-102: Lab 8 Workbook\n", "## Helena Gray\n", "### March 23, 2017" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below imports the tidyverse package which will be used for analysis. It then reads in a comma separate value file into a variable named 'newcomb'." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading tidyverse: ggplot2\n", "Loading tidyverse: tibble\n", "Loading tidyverse: tidyr\n", "Loading tidyverse: readr\n", "Loading tidyverse: purrr\n", "Loading tidyverse: dplyr\n", "Conflicts with tidy packages ---------------------------------------------------\n", "filter(): dplyr, stats\n", "lag(): dplyr, stats\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
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29\\\\\n", "\t 53 & 34\\\\\n", "\t 54 & -2\\\\\n", "\t 55 & 25\\\\\n", "\t 56 & 19\\\\\n", "\t 57 & 36\\\\\n", "\t 58 & 29\\\\\n", "\t 59 & 30\\\\\n", "\t 60 & 22\\\\\n", "\t 61 & 28\\\\\n", "\t 62 & 33\\\\\n", "\t 63 & 39\\\\\n", "\t 64 & 25\\\\\n", "\t 65 & 16\\\\\n", "\t 66 & 23\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "X | x | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 28 | \n", "| 2 | -44 | \n", "| 3 | 29 | \n", "| 4 | 30 | \n", "| 5 | 24 | \n", "| 6 | 28 | \n", "| 7 | 37 | \n", "| 8 | 32 | \n", "| 9 | 36 | \n", "| 10 | 27 | \n", "| 11 | 26 | \n", "| 12 | 28 | \n", "| 13 | 29 | \n", "| 14 | 26 | \n", "| 15 | 27 | \n", "| 16 | 22 | \n", "| 17 | 23 | \n", "| 18 | 20 | \n", "| 19 | 25 | \n", "| 20 | 25 | \n", "| 21 | 36 | \n", "| 22 | 23 | \n", "| 23 | 31 | \n", "| 24 | 32 | \n", "| 25 | 24 | \n", "| 26 | 27 | \n", "| 27 | 33 | \n", "| 28 | 16 | \n", "| 29 | 24 | \n", "| 30 | 29 | \n", "| ⋮ | ⋮ | \n", "| 37 | 32 | \n", "| 38 | 25 | \n", "| 39 | 28 | \n", "| 40 | 24 | \n", "| 41 | 40 | \n", "| 42 | 21 | \n", "| 43 | 31 | \n", "| 44 | 32 | \n", "| 45 | 28 | \n", "| 46 | 26 | \n", "| 47 | 30 | \n", "| 48 | 27 | \n", "| 49 | 26 | \n", "| 50 | 24 | \n", "| 51 | 32 | \n", "| 52 | 29 | \n", "| 53 | 34 | \n", "| 54 | -2 | \n", "| 55 | 25 | \n", "| 56 | 19 | \n", "| 57 | 36 | \n", "| 58 | 29 | \n", "| 59 | 30 | \n", "| 60 | 22 | \n", "| 61 | 28 | \n", "| 62 | 33 | \n", "| 63 | 39 | \n", "| 64 | 25 | \n", "| 65 | 16 | \n", "| 66 | 23 | \n", "\n", "\n" ], "text/plain": [ " X x \n", "1 1 28\n", "2 2 -44\n", "3 3 29\n", "4 4 30\n", "5 5 24\n", "6 6 28\n", "7 7 37\n", "8 8 32\n", "9 9 36\n", "10 10 27\n", "11 11 26\n", "12 12 28\n", "13 13 29\n", "14 14 26\n", "15 15 27\n", "16 16 22\n", "17 17 23\n", "18 18 20\n", "19 19 25\n", "20 20 25\n", "21 21 36\n", "22 22 23\n", "23 23 31\n", "24 24 32\n", "25 25 24\n", "26 26 27\n", "27 27 33\n", "28 28 16\n", "29 29 24\n", "30 30 29\n", "⋮ ⋮ ⋮ \n", "37 37 32 \n", "38 38 25 \n", "39 39 28 \n", "40 40 24 \n", "41 41 40 \n", "42 42 21 \n", "43 43 31 \n", "44 44 32 \n", "45 45 28 \n", "46 46 26 \n", "47 47 30 \n", "48 48 27 \n", "49 49 26 \n", "50 50 24 \n", "51 51 32 \n", "52 52 29 \n", "53 53 34 \n", "54 54 -2 \n", "55 55 25 \n", "56 56 19 \n", "57 57 36 \n", "58 58 29 \n", "59 59 30 \n", "60 60 22 \n", "61 61 28 \n", "62 62 33 \n", "63 63 39 \n", "64 64 25 \n", "65 65 16 \n", "66 66 23 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Run this code block to load the Tidyverse package\n", ".libPaths(new = \"~/Rlibs\")\n", "library(tidyverse)\n", "newcomb<-read.csv(\"newcomb.csv\")\n", "newcomb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 1 ###\n", "\n", "The code below plots a histogram of the dataset using ggplot2." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "newcomb.ggplot<-ggplot(data=newcomb) + geom_histogram(binwidth = 1, mapping = aes(x=x), alpha=.5)\n", "ggsave(\"newcomb.ggplot.png\", plot = newcomb.ggplot, device=\"png\", scale=1, width=5, height=4)\n", "newcomb.ggplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 2 ###\n", "\n", "A variable is created called 'newcomb_1' which filters out all the values of 'x' under 0." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "newcomb_1<-filter(newcomb, x>0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 3 ###\n", "\n", "The code below gives summary statistics for the filtered data set 'newcomb' and the unfiltered dataset 'newcomb_1'." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
meanmaxminrangevarsd
27.75 40 16 24 25.841275.083431
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " mean & max & min & range & var & sd\\\\\n", "\\hline\n", "\t 27.75 & 40 & 16 & 24 & 25.84127 & 5.083431\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "mean | max | min | range | var | sd | \n", "|---|\n", "| 27.75 | 40 | 16 | 24 | 25.84127 | 5.083431 | \n", "\n", "\n" ], "text/plain": [ " mean max min range var sd \n", "1 27.75 40 16 24 25.84127 5.083431" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
meanmaxminrangevarsd
26.2121240 -44 84 115.462 10.74532
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " mean & max & min & range & var & sd\\\\\n", "\\hline\n", "\t 26.21212 & 40 & -44 & 84 & 115.462 & 10.74532\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "mean | max | min | range | var | sd | \n", "|---|\n", "| 26.21212 | 40 | -44 | 84 | 115.462 | 10.74532 | \n", "\n", "\n" ], "text/plain": [ " mean max min range var sd \n", "1 26.21212 40 -44 84 115.462 10.74532" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stat.table.filtered<-summarise(newcomb_1,\n", " mean=mean(x), max=max(x),min=min(x),range=max(x)-min(x),var=var(x), sd=sd(x))\n", "stat.table.filtered\n", "\n", "stat.table<-summarise(newcomb,\n", " mean=mean(x), max=max(x),min=min(x),range=max(x)-min(x),var=var(x), sd=sd(x))\n", "stat.table\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 4 ###\n", "\n", "The code below uses the dnorm() function to calculate the probability distribution for the filtered and unfiltered datasets.\n", "It takes the 'x' values (from the distribution) as input and returns the probability (taken from the bell curve) as output." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "newcomb_1_pdf<-dnorm(x=newcomb_1$x , mean = 27.75 , sd = 5.083431)\n", "\n", "newcomb_pdf<-dnorm(x=newcomb$x , mean = 26.21212 , sd = 10.745)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 5 ###\n", "\n", "The code below converts the histogram (unfiltered dataset only) into a probability mass function using\n", "the ggplot_build() function and stores the PMF values in a tibble." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.ggplot.full <- ggplot_build(newcomb.ggplot)\n", "data.ggplot.table <- data.ggplot.full$data[[1]]\n", "histogram.table <- tibble(x = data.ggplot.table$x, density = data.ggplot.table$density, frequency = data.ggplot.table$count)\n", "#data.ggplot.table\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 6 ###\n", "\n", "A plot is created that overlays the normal distribution models on top of the PMF to see how\n", "well that they agree." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+21QgjTV1816tvX9O23eodqSCgcAADNGfbsaXTddZZPPhFCBHr2PLFxo+/WW/UO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\n7xeKoneiU1E4ACB+GH/6yZGTI3k8isNRtmhRqG1bvRMhViSp/IU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JguV/2AAapr6YSawYML9+6l3Wh7Yg0ce/funT179vbt29t8trKysnfv3vGrCgCQ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\naJrqctBp5VOmVI0YISI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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "options(repr.plot.width = 6, repr.plot.height = 4)\n", "data.ggplot_1<-ggplot(data=histogram.table) + geom_col(mapping = aes(x=x, y=density), alpha=.5) + stat_function(fun=dnorm, args=list(mean=mean(newcomb$x),sd=sd(newcomb$x)), color= \"red\")\n", "ggsave(\"data.ggplot_1.png\", plot = data.ggplot_1, device=\"png\", scale=1, width=5, height=4)\n", "data.ggplot_1\n", "\n", "\n", "\n", "options(repr.plot.width = 6, repr.plot.height = 4)\n", "data.ggplot_2<-ggplot(data=histogram.table) + geom_col(mapping = aes(x=x, y=density), alpha=.5) + stat_function(fun=dnorm, args=list(mean=mean(newcomb_1$x),sd=sd(newcomb_1$x)), color= \"red\")\n", "ggsave(\"data.ggplot_2.png\", plot = data.ggplot_2, device=\"png\", scale=1, width=5, height=4)\n", "data.ggplot_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 7 ###\n", "The code below creates a cumulative distribution function generator for both the unfiltered and filtered\n", "datasets using the ecdf() function. Two tibbles are created, each with two\n", "columns. In each tibble the first column is t that contains values running from -45 to 45\n", "in increments of 0.1, and the second column is cdf containing values of the cdf generator\n", "evaluated at the same values of t." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "newcomb_1_range_vector <- seq(-45,45.1)\n", "newcomb_1_cdf_generator <- ecdf(newcomb_1$x)\n", "newcomb_1_cdf <- tibble(score = newcomb_1_range_vector, CDF = newcomb_1_cdf_generator(newcomb_1_range_vector))\n", "\n", "newcomb_range_vector <- seq(-45,45.1)\n", "newcomb_cdf_generator <- ecdf(newcomb$x)\n", "newcomb_cdf <- tibble(score = newcomb_range_vector, CDF = newcomb_cdf_generator(newcomb_range_vector))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 8 ###\n", "\n", "The code below creates the corresponding CDF for the normal distribution models (both unfiltered and\n", "filtered) using the qnorm() function. The two CDFs are evaluated in the range from 0 to 1 in\n", "increments of 0.01. The results are stored in two tibbles, each with two columns (this includes t,\n", "which is just the 0 to 1 range in increments of 0.01, and the CDF values)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "newcomb_1_percentiles <- seq(0, 1, 0.01)\n", "newcomb_1_cdf_model <- tibble(CDF = newcomb_1_percentiles,\n", " score = qnorm(p = newcomb_1_percentiles, mean = 27.75, sd = 5.08))\n", "\n", "\n", "newcomb_percentiles <- seq(0, 1, 0.01)\n", "newcomb_cdf_model <- tibble(CDF = newcomb_percentiles,\n", " score = qnorm(p = newcomb_percentiles, mean = 26.21, sd = 10.74))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 9###\n", "\n", "The code below visually compares the CDFs of models and datasets to estimate the quality of agreement. 2 plots are created that overlay the two model CDFs on top of the unfiltered and filtered datasets CDFs.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Kezo6Mj2MTpjTiHwAEAAHEqxozmRGQQTTBKRLghNs4hcAAAQJyqiDkCtOP8eXET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KagJgQOAADwRzQvj/nlF/l1hNBQbuRI\nrzwqpbCw8MknnywrKxN7WJZdv359jx49PN84EEKo0JAbmv2c3W6nDY+0jQXDMAzDOBwOXxei\nIL1eTwhxOBxa+lhWw7IsIUT6FUpjGIZxvseqqipf16IgnU7H8zxf7+mnGh2WZRmGEQTBh8cc\n286d3K+/yqxAIyJM48dTD9KGXq93HnDsdvvDDz/87bffSpeuW7du8uTJbm/cH+h0Okopz/Pq\nHHN4npeZD01TZzhU+z/1CZ1Op9fr7Xa7rwtRCqVUDBwa/jkajUbn0c3XhSiFZVkxcGg7ODoc\nDg1/ATAYDM7A4avPqnDjRh1pIzqa/dOfqoxG4m6FzmOO3W4XBOGZZ56pljZmzZr1xBNPNPZf\nVecXAJ7n1XkjgiA0ocBRWVnp6yqUYjKZWJbV8BtkGCYwMJAQYrfbG/svuQznH2MN/xwNBoPz\nAROVlZUaDhwmk8lut9tsNl8XohSGYfR6vSAIvvqssseOydxFKQQGVg0fTgghHpRHKQ0MDLRa\nrTt27Ni8ebN00YMPPrhgwQIN/J4aDAZnOFbtvQQHB7tahNtiAQDAvzBXrzLZ2TIr8L17Ey89\nyuTs2bPVbkuJj4/fsGGD87sBeBECBwAA+Bfmhx9klgohIXzXrl7ZUV5e3hNPPGG1WsWesLCw\nf/7zn5hyQwkIHAAA4EeYy5dpfr7MCny/fsQbpx94np8wYcKNGzf+t2uGWbt2bfv27T3fONSE\nwAEAAH5DEJiUFLkVzGa+Uyev7GrFihVHjhyR9sybN2/w4MFe2TjUhMABAAD+grlwgRYXy6zg\nuOsuN2b0qum777578803pT2DBg16/vnnPd8yuILAAQAAfoHyPHvypMwKQvPmgjeud+Tn51eb\nUbRNmzZr165lGPxNVBD+cwEAwC/QjAxSXi6zAnfXXZ7vRRCEZ599NltyF4xer9+wYUOzhj94\nBRoEgQMAAPwCvXZNZqnQpo0QE+P5Xt57773Dhw9Le1555ZU+ffp4vmWQh8ABAAB+gebkyCzl\n+vXzfBfnz59fvHixtOfhhx+eNm2a51uGOiFwAACAH3A4aFGRq4V8bKzQvLmHe6isrJw2bZp0\nIuOYmJh3331Xww/h8isIHAAA4Hs0N5e4ngtfaNPG810sWbLk0qVLYpNhmK1bt5rNZs+3DPWB\nwAEAAL4nfz3F89Mbx44d27Rpk7Rn9uzZDz74oIebhfpD4AAAAN+TCxyUClFRnmy8pKTk2Wef\nlT5NMCEhodojVEBpCBwAAOB7TG6uq0WC2Ux0Hj3b/K9//evNmzfFpslkWrt2rV6v92Sb0FAI\nHAAA4GPUaiVlZa6Weng95dChQx9//LG0Z9GiRfHx8Z5sE9yAwAEAAD5GZR9G70ngKCkpeeGF\nF6Q9999//+TJk93eILgNgQMAAHzN9fUU4lngmD9//q1bt8RmaGgo7oP1FQQOAADwMbkRozqd\nEB7u3maPHDmya9cuac/ixYtbt27t3tbAQwgcAADgY3IjRqOiiFvPVCsrK6t2MWXAgAETJkxw\nY1PgFQgcAADgS7S0lFitrpa6fT1l6dKlWVlZYjMkJGT58uW4mOJDCBwAAOBLdUz5FR3txjZT\nUlK2bt0q7Vm4cGGMN579Bm5D4AAAAF/y+hyjdrt97ty5PM+LPf369XvqqafcKQ68B4EDAAB8\nSiZwmExCaGhDt7dmzRrpM1MMBsOKFSsYtwaCgBfhBwAAAL7D80x+vsuFDb+ecvXq1eXLl0t7\n5s6di2m+/AECBwAA+AwtLCQOh6ulblxPmT9/vlUyBDUuLm727NluFgdehcABAAA+Iz+AgzQw\ncHz22WdHjhz538YpXb58ucFgcK828C4EDgAA8BkqP8doQy6pVFRULFy4UNozduzYu+++283K\nwNsQOAAAwGdkznAIISFCQED9N/XWW29JHwlrNptfffVVT2oD70LgAAAAH3E4aFGRq4UNGsBx\n6dKlDRs2SHsWLlxoNpvdrw28DYEDAAB8g+blEclsGdU06HrK/Pnzq6qqxGavXr3Gjx/vUXHg\nbQgcAADgG96a8mv//v3ffPON2GRZ9h//+Acm3vA3+HkAAIBvyAUOSoWoqPpsxGq1vvLKK9Ke\nJ598skePHh7WBl6HwAEAAL4hN2LUbCZ6fX02snr16szMTLEZHh7+8ssve6E48DYEDgAA8AFq\ntdKyMldL6zmA49atW6tWrZL2vPzyyxgr6p8QOAAAwAfqmIGjfgM4XnvtNYvFIja7du2Kh7T5\nLQQOAADwAZqRIbO0Pmc4Tp069cknn0h7li1bxrKsp5WBMhA4AABAbbS0lLlwweVilhUiIuS3\nIAjCwoULBUEQe4YOHXrvvfd6q0LwOgQOAABQG5OSIjMDBx8VReq6qfWzzz5LTU0VmwaDYdGi\nRV6rDxSAwAEAAOoqKGDS0+VWaNtWfgN2u33p0qXSnunTp7dr187jykBBCBwAAKAqXUoKkVwK\nqU6v5++4Q34LycnJV69eFZuRkZFz5szxUnWgFJ0K+xAEYffu3V999VVZWVn37t1nzJgREhIi\nXeHEiRPLli2r9qro6Ojk5OSDBw+uX79e2p+UlNS5c2fFiwYAAAXQ3Fz6668yK3AJCYLJJLNC\nUVHRihUrpD0vvPBCtT8r4IfUCBx79uzZuXPn1KlTzWbz9u3bFy9enJSURCkVV4iPj3/ppZek\nL/noo4/i4+MJIdnZ2XFxcY8++qi4qFWrVirUDAAASmC//15uscnEJyTIb2HFihXFxcViMz4+\n/sknn/RKbaAoxQMHz/P79+8fM2bMkCFDCCHR0dGzZ89OT0+XnqWIiIiQDi0+efKk1WqdMmUK\nISQnJ6dz584YeAwAoAE0M5PeuCGzAterFzEYZFbIzMzctGmTtGfRokU6nRpfnsFDio/hyMzM\nzM/P79Onj7MZGxsbHR19+vRpV+tXVlauXr161qxZAQEBhJDs7OwWLVpYLJbc3FxB5pofAAD4\nPTYlRW5xUFCdozeWLVtmt9vF5t133+38Ngv+T/FU6DzxFRkZ6WxSSiMjI6Vnw6rZt29f27Zt\nu3fvTggRBCE7O/vo0aObN28WBCEoKGjixInSz1ZFRcVrr70mNgcMGPDAAw8o9EZ8jmVZlmU1\nfJ1SvMoWEBBgNBp9W4xynF/FNPxzFB/RGRwc7NtKFMUwTEBAgEH2u3ij5vygMgzjxc8ql55e\nlZ0ts4K+f39TeLjMCj/++OOnn34qNimlb775ptsVOo85wcHBGv4265wGTa/Xq3PMkf+fVDxw\nlJaWEkKcpyucAgMDnZ01lZWV7d27d8mSJWLTZrO1a9du/vz5JpPpwIEDa9asad68ecJvV/js\ndvuXX34pvrxDhw4a/kPlpPk3SAjR1++JTY1aU5gMUfOfVZ1Op/kz+ZRSr/0cBaH8P/+RWc40\naxbYuzeR/dV49dVXecnsHWPGjLnvvvs8rEvDqVHk/L6qwo44jpNZqvhvizNVVVZWij/UyspK\n8YRHNYcPH27VqlWnTp2czdDQ0L1794pLx44dm5qaevz4cTFwGAyGgQMHiiu0bdvWZrMp8S78\ngfMTIz2XqDGUUueHpKqqinc9I1Bj5/wT5XA4fF2IUhiGcUZGDf8yEkIMBgPHcfKH10ZNp9Ox\nLCsIgreOOdzFi5zMw+gJYfv3tzkcxPWvxjfffPOvf/1LbOr1+kWLFnnyMXMec+x2u4bPcOj1\neoZhOI5T55gjCIJMslE8cISHhxNCCgsLw8LCnD0FBQXOKybVCIJw+PDhoUOHutoUpTQmJkZ6\nOSYoKOiNN94QmxaLpcz1swcbO5PJZDKZNPwGGYZxPuOxsrJSw7nKeaGhvLzc14UoxWAwOANH\neXm5ho/j4eHhlZWVGg5VQUFBAQEBPM9765jDnj4tM2ZQMJstbdoQ2X397W9/kzafeOKJ5s2b\ne1IepTQiIqK8vFzD33DCwsIYhqmqqlLtmGNyfUuz4oNGY2JizGZzWlqas5mTk5Odnd2zZ8+a\na168ePHWrVvS82MpKSkTJky4efOmsykIQkZGRtu6ZqADAAC/Qq1WRv7mlLvvJpK5Emr64osv\npBOZBwQEzJ0712v1gSoUP8PBsuywYcN27drlTB7JycmdOnVy3hN75MiRvLy88ePHO9c8ffp0\nixYtwiUjhhISEoKCgt56661Ro0aZzebDhw/n5eUNGzZM6ZoBAMCL6OXLMk9OEVq0EGRnJed5\n/vXXX5f2TJs2rXn9nl8P/kONEU+jR492OBybNm2qqKjo3r37zJkznWODU1JSrly5IgaOtLS0\nrl27Sl9oMBiWLFmyadOmdevWEUK6dOmSlJQUUdcjBAEAwK8wly/LLOV795Z/+Z49e37++Wex\n2axZs1mzZnmnMlAR1dJFVovFYrFYfF2FUpxjOGTuKG7sxDEcpaWlGMPReBkMhtDQUEJIQUGB\nlg4v1YSHh1ssFs2P4eA4rqioyNNtlZXpt293tVAwmRyTJsk8G5bjuPvuu+/KlStiz4IFC559\n9llPq/ptDEdhYaG2x3Do9Xqr1araMcfVTSEED28DAABFyT8YVoiPl38S/YcffihNG1FRUVOn\nTvVacaAiBA4AAFCQ/PUU4bd5EGplt9uXL18u7XnuuecCAwO9UxmoC4EDAACUQouKaEGBy8Uh\nIXyLFjIv37FjR2ZmptiMiYl56qmnvFgeqAmBAwAAlEIvXZJZysue3rBare+88460Z+7cuU1h\nYlCtQuAAAAClMJLhFzXx8fEyS7dt25YtefZKu3btxo4d67XKQHUIHAAAoAianU1LSlwtFcLD\nBdfTHFit1nfffVfa8/zzzzeFBy1pGAIHAAAogsrfn9K5s8zSLVu25Obmis0OHTo8+uijXqsM\nfAGBAwAAFCAIzC+/yCzn4+JcLaqsrFy1apW054UXXtD8s3k1D4EDAAC8j2ZmUtczMQotWgi/\nPdGzpq1bt+bl5YnN+Pj4UaNGebk+UB0CBwAAeJ/b029YrdbVq1dLe+bNmyfz0HNoLBA4AADA\nyyjHMRkZrhdTvmNHVwu3bt1abfTGyJEjvVse+AQCBwAAeBm9do24ftCM0KaN4GK2UJvNVu30\nxvPPP4/TG9qAwAEAAF5G5YeLup5+Y8eOHTk5OWKzY8eOo0eP9mZl4DsIHAAA4GX01i1XiwSW\n5du3r3WR3W6vdnrjueeew+kNzUDgAAAAb6JWKy0rc7m4XTtiNNa6ZOfOnVlZWZIV22HuDS1B\n4AAAAG+ikmsiNfFt2tTa73A4Vq5cKe2ZM2cO5t7QEgQOAADwKtnAIbh4POzu3buvX78uNtu0\nafPYY495uTDwKQQOAADwJrkzHDqdEB5es5vn+WpPTpk9ezaenKIxCBwAAOBNVDKLRjVCVBRh\navm7c+DAgXTJg1eio6PHjRunSHHgOwgcAADgNbS0lFqtrpYKzZvX0ikIK1askPY8/fTTJpPJ\n+8WBTyFwAACA18iPGBWio2t2Hj169KeffhKbZrP5qaee8n5l4GsIHAAA4DV1BI7aznBUO72R\nmJgYFBTk5bLADyBwAACA98gEDpNJCA2t1nfy5MkTJ06IzcDAwEmTJilUGvgWAgcAAHiJIDD5\n+a4W8rVdT1m+fLm0OXnyZLPZ7P3CwA8gcAAAgHfQggLicLhaWvN6yoULF7788kuxaTQap02b\nplRx4GsIHAAA4B3yAzhIjcCxatUqQRDE5vjx45vXNsgDtAGBAwAAvENmBg5S4xaV69ev7927\nV2yyLPv0008rVRn4AQQOAADwDpkzHEJIiBAQIO1Zs2aNQ3L9ZcSIEW3btlWwOPA1BA4AAPAG\nh4MWFblaWG0AR35+/gcffCA2KaWzZ89WsDbwAwgcAADgBTQvj/C8q6XVrqe89957VsmEpAMG\nDLjjjjsULA78AAIHAAB4Qf2n/LJYLFu2bJEunTVrllJlgd9A4AAAAC+QCxyUClFRYuuf//xn\nkeTiS8+ePfv3769obeAPEDgAAMAL5EaMhoeT354173A41q1bJ12K0RtNBAIHAAB4ilqttKzM\n1VLp9ZS9e/dmZmaKzbZt2z7yyCPKFgf+AYEDAAA8Vf8BHGvWrJEumj17NsuySpUF/gSBAwAA\nPFa/wHHs2LFz586J/VFRUWPHjlW2MPAbCBwAAOApmTlGBZYVfnse29q1a30JPW8AACAASURB\nVKWLpkyZYjQala0M/AYCBwAAeEoucERFEYYhhPz888/Hjh0T+wMCAiZOnKh8aeAvEDgAAMAj\ntKyMVla6XPzb9ZQ1a9ZIH9U2YcIEPIm+SUHgAAAAj9QxYjQ6mhBy69atPXv2iJ0sy06fPl3x\nysCf6HxdgDcxDGMymXxdhVL0er223yCl1PkP5zv1bTHKcQ7I1/DPUaf771HFZDJJv85qDKVU\nr9eLH1rtcX5QKaX1+awKBQUupzQnxBAbS0ymLVu22O12sXP48OGdO3f2QqEecP74jEajhj+o\nzmMpy7LqHHPk/ye1Fjg0PP6IYRhKqYbfoEiv14t/tLTHeRzX8M9RDIsGg8G3lSjKGTg0fD+n\nGDjq81m1uR7AQQMCjM2bV1RUbNu2Tdo/d+5cP/ktaCKBQ53/bd71w3SIxgKHw+GwWCy+rkIp\nJpPJZDKVlJT4uhClMAzjvKBrsVik34Q0Jjg4mBBSXl7u60KUYjAYQkNDCSGlpaUaPo6Hh4db\nLBabzebrQpQSFBQUEBDA83x9jjl614GDj4oqKSnZuHFjYWGh2NmvX78uXbr4/GhGKY2IiCgt\nLZX/M9mohYWF6fV6u92u2jFHJtlo9sQ1AACowWolkue+ViNERXEct2HDBmnnzJkzlS8L/A4C\nBwAAuI8pLpZZyjdrdvDgwevXr4s9HTp0eOihh5SvC/wOAgcAAHhANnCQ8PBqj2pLTEzU8Khw\nkIGfOgAAuI/KBo6UK1dSU1PFZnh4+Lhx45QvCvwRAgcAALhPJnAIJtP6zZulPU899VRgYKDy\nRYE/QuAAAAD3yQSOTJ4/ePCg2NTr9ZMmTVKlKPBHCBwAAOAuQRBcB471333ncDjE5qhRo1q1\naqVKWeCPEDgAAMBNtLycclyti8pttvePHpX2YC7zJg6BAwAA3CRzPeX9tLTSigqxed9993Xr\n1k2VosBPIXAAAICbXAUOjuc3pKRIe2bMmKFKReC/EDgAAMBdLgLHwUuXMiRzmXfo0GHgwIFq\n1QR+CoEDAADcRIuKau1fe+KEtInJvoAgcAAAgNtqvaTy482b31+7JjbDwsIw2RcQBA4AAHAT\nx5HankG67venN5588klM9gUEgQMAANxDi4uJIFTrzC4r23P+vNjU6XR/+ctf1K0L/BQCBwAA\nuKPW6ymbT560S2bmeOSRR2JiYlQsCvwXAgcAALijZuCwOhybT56U9mCyLxAhcAAAgDtqBo6P\nz57Nl0z2lZCQ0KdPH3WLAv+FwAEAAG6pETjW//CDtInTGyCFwAEAAO6odobj24yM8zk5YrNl\ny5bDhw9XvSjwXwgcAADQYNRqJVartGfd709vTJo0Sa/Xq1sU+DUEDgAAaLBqpzeuFRUdSk8X\nm0a9/oknnlC9KPBrCBwAANBwvw8cG1JSOJ4Xm2OGDo2MjFS9JvBrCBwAANBg0jMc5Tbb+2lp\n0qWJeDYs1IDAAQAADSYNHP/88cdSyXiO+zt27Nqzpy+KAr+GwAEAAA0mPidWEIT3UlKki6YN\nGeKLisDfIXAAAEADCYJQUuL855dXrlwpKBCXtAsPH9y/v4/KAr8mFzhiYmJee+011UoBAIBG\ngZaX098emLLh93fDJvbty5jNvigK/J1c4MjKyir5LcM66XS6F198UeGSAADAr4nXU64UFHz9\nyy9if7DR+HjPnkKzZj6qC/xawy6pcBzHS258AgCAJkgcMbr+xAle8oT6cT16hJpMBIEDaoMx\nHAAA0EDFxYSQEqv1wzNnxD5KaWLfvoRSEhbmu8rAfyFwAABAwzjPcLyfllZht4udD3bsGB8Z\nKYSECCzru9LAfyFwAABAw9DiYr7m3bD9+hFCMIADXEHgAACAhuA4Ul5+KD396m9DRwkhHSMi\nHoyLI4SQ8HCfFQb+TSe/+NSpU++88458DyFkzpw5Xq4LAAD8Ei0uJoJQy92wlBKc4QDX6ggc\nx44dO3bsmHwPQeAAAGgyaHHxxdzc4xkZYk+I0TghIcH5bwQOcEUucOzZs0e1OgAAoFGgxcUb\nUlIEyd2wj/fsGWw0Ov+NwAGuyAWOkSNHqlYHAAA0CqVZWR+dPSs2KaWT+/T5b0OnI8HBvikL\n/B4GjQIAQANsO3DAIrkbdnB8fFxEhPPfOL0BMuoYw+FUVVV16tSpjIyMX3/9lRDSoUOH9u3b\n9+rVS6/XK1weAAD4EY7jNh89Ku1J7NtX/DcCB8ioI3A4HI7333//tddec0YNqQ4dOixcuPDx\nxx/X6erYiCAIu3fv/uqrr8rKyrp37z5jxoyQkJBq6xw8eHD9+vXSnqSkpM6dO9fntQAAoI5D\n+/Zd+/3dsAM6dhSbCBwgQy4r8Dz/5z//+dNPPw0NDZ0zZ06PHj1iYmIopZmZmWfPnt20adOk\nSZMOHDjw0UcfMYzcpZk9e/bs3Llz6tSpZrN5+/btixcvTkpKopRK18nOzo6Li3v00UfFnlat\nWtXztQAAoI5NmzZJm9P79WOkB2Q8JxZckwscmzdv/vTTTwcNGvTxxx+H1Zgb/9VXX/3Tn/70\nySefbNu2bdKkSa42wvP8/v37x4wZM2TIEEJIdHT07Nmz09PTO3fuLF0tJyenc+fO9957rxuv\nBQAAFVy8ePHb1FSxGWoyjevRQ7oC/9tgDoCa5M5MJCcnm83mnTt31kwbhJDQ0NAPP/wwIiIi\nOTlZZiOZmZn5+fl9fhvDHBsbGx0dffr06WqrZWdnt2jRwmKx5Obmindb1fO1AACggvfee8/V\n3bCEEMKyeGwbyJA7w3H+/Pn+/fubXZ8iM5vNd91117fffiuzkeLiYkJIZGSks0kpjYyMLP7t\n0cZOgiBkZ2cfPXp08+bNgiAEBQVNnDhxyJAhdb7Wbrd/8803YjMmJqZ169YyxTRqOp2OUmqU\n/npri3ilTK/Xa/iqGcuyhBAN/xzFQV1Go1H6x0ljKKV1Dl9r1JwfVOkxp6Sk5JNPPhFXoJT+\npXdv6Uuo2WwMDFSzSK8wGAwa/qA6BzywLKvOMUf+f1LuF6a8vLzOixddunQ5cOCAzAqlpaWE\nkICAALEnMDDQ2SkqKyuz2Wzt2rWbP3++yWQ6cODAmjVrmjdvXlZWJv/aioqKl19+WWwmJiYm\nJibKF9zYNYUxs9KfuFY1hTu8grU+H0NT+KAyDCMec9avX2+xWMRFQyR3wzrpWrQIbIQHKM1/\nUAkher1enWMOx3EyS+tI6GxdTxmuM+M7P6yVlZUGg8HZU1lZKZ60cAoNDd27d6/YHDt2bGpq\n6vHjx//whz/U+VoAAFAaz/Pr1q2T9iT261dtHbZ5cxUrgsZH8VOC4eHhhJDCwkJxIEhBQUH3\n7t1lXkIpjYmJKS4urvO14eHhJ0+eFJsWiyU/P9/rb8FPmEwmk8lU7WqUljAM47x+V1paapdM\nK6Qxzq9T5eXlvi5EKQaDITQ0lBBSUFCg4TPV4eHhFovFZrP5uhClBAUFBQQEcBxXVFRECDl4\n8GCG5OEpHSMiBnToUO0lFSZTWaM6AlNKIyIiCgsLeZ73dS1KCQsL0+v1VqtVtWOOzEmBOgJH\nWlra6tWrZVY4deqU/BZiYmLMZnNaWlr79u0JITk5OdnZ2T179pSuk5KSsnLlyqSkJOetsIIg\nZGRk9OrVqz6vBQAApVW7G3Zav341B1rxuCcWZNUROL7++uuvv/7akx2wLDts2LBdu3Y500Ny\ncnKnTp2cQ0OOHDmSl5c3fvz4hISEoKCgt956a9SoUWaz+fDhw3l5ecOGDZN5LQAAqOPixYvS\nmwNCTabxv78blhBCDAbSCAdwgJrkAsfHH3/slX2MHj3a4XBs2rSpoqKie/fuM2fOdEbjlJSU\nK1eujB8/3mAwLFmyZNOmTc5rhF26dElKSoqIiJB5LQAAqCM5OVl6dWx8QkJwjVsecHoD6kS1\ndJHVYrFIB1FrDMZwaAPGcGhD0xnDcfXq1e7du4uHVkpp6qxZcTUm+OLvuIP7wx9UL9MjGMOh\nBJkxHHhaLAAAuLRjxw75u2GdBJzhgLrUK3BcvXr1xx9/FJuXL1+eN2/egQMHNJzuAQCA47gt\nW7ZIe2reDeskYFJzqEsdgSM7O3vIkCHt27eXzpNRUlKyfPnyoUOH9unT5+zZswpXCAAAvvH5\n559fv35dbMbVdjesE85wQJ3kAkdJSUnv3r0PHz48fPjwRx55ROzv0aPHl19+OXXq1HPnzv3f\n//1fYWGh8nUCAIDaqk2LMP2uu2ofth8UREwmlWqCRksucPzjH//IysrauHHjvn37+vbtK/br\n9foHH3xw48aNu3btKigoWLZsmfJ1AgCAqn766adjx46JzZrPhhXhIbFQH3KB44svvujRo8eU\nKVNcrTBmzJi77rrr+PHjChQGAAC+tGrVKmlzQkJC0G9PmagOgQPqQS5wXL58uVevXvLzXvTo\n0ePy5cvergoAAHypqKjon//8p9hkGGaq5Dx3NRgxCvUhFzh0Ol1lZaX864uLizV8qz0AQNO0\nadMm6d2wg/v16+B6WCgCB9SHXODo0qXL999/L5MneJ5PSUmJi4tToDAAAPANjuOSk5OlPYn/\n938u16ZUCA9XvCZo/OQCx/jx469evbp06VJXK7z55psZGRmPPvqoAoUBAIBvfPHFF9K7YTt1\n6vRA69auVhaaNSMsq0pd0LjJBY7p06cnJCS88sor06dPv3r1qnRRfn7+3LlzFyxY0LZt22ee\neUbZGgEAQEUbN26UNhOnTmWKilytjBk4oJ7kHt5mMBgOHTr0+OOPb9iwITk5uUOHDu3btzca\njb/++uvly5ftdnvPnj0/+ugj57MhAABAA86fP//999+LzWbNmj320ENk926XL3D97AwAqTpm\nGo2Ojj58+PDRo0cfeughu91+5MiRzz//PDc3t2/fvtu3bz958mR8fLw6hQIAgAo2bNggbU6Y\nMCFQ9qGYeE4s1JPcGQ7RAw888MADDxBCrFarzWYLCwtTtigAAPCFgoKCTz/9VGyyLDt58mQm\nJ0fuNTjDAfXTsKfFmkwmpA0AAK3aunWr9Kmcw4cPb9OmDcnPd/kCnU4ICVGjMmj88Hh6AAAg\nhBC73b5161Zpz+zZswkh1PUDswSzmchODgkgQuAAAABCCPnss8+ys7PFZo8ePe6//37K87Sk\nxNVLMOUX1B8CBwAAEELIe++9J20+++yzhBBSWEh43tVLEDig/hA4AACApKamnj59WmxGRkaO\nGzeOEEILCmRehcAB9YfAAQAA1Sf7mjJlislkInUFDjwnFuoPgQMAoKm7efPmgQMHxKbBYJg6\ndep/G64Dh2AyCQEBStcGmoHAAQDQ1G3evLmqqkpsDh8+vGXLls5/M65vUcEMHNAgCBwAAE1a\nZWXljh07pD3i6Q2hooKUl7t6IZ6iAg2CwAEA0KTt2rWrUHIao2/fvnfeeafz3/zNmzIvFHCG\nAxoCgQMAoOkSBKHacNFp06aJ/+bkA0d0tFJlgRYhcAAANF1Hjx5NT08XmzExMY888ojY5LKy\nXL5SpxPCwxWtDTQGgQMAoOlav369tDl58mSd7n8P9eRv3XL1QiEqijD4CwINgI8LAEATlZ6e\nfuzYMbEZGBj4+OOPi02+sFCorHT1WqF5c0VrA+1B4AAAaKI2bNggCILYHDt2bLNmzcSm3PUU\nBA5oOAQOAICmqLCwcNeuXWKTYZjExETpCnWMGEXggAZC4AAAaIq2bt1qtVrF5qBBgzp27Chd\nweH6DIdgMgkhIQoWB1qEwAEA0OTY7fbNmzdLe6R3wxJCCM/LjRjF6Q1oOAQOAIAmZ8+ePTk5\nOWLz9ttv79+/v3QFIT9fcDhcvRwzcIAbEDgAAJqcanfDTp8+vdoKguvTG4QQgjMc0HAIHAAA\nTcu333577tw5sRkdHT169Ohq68gHDpzhADcgcAAANC01J/syGAzV1pF5iooQGoqn0oMbEDgA\nAJqQy5cvHzlyRGyaTKaJEydWX8nhEFw/lR6nN8A9CBwAAE3IunXrqk32Za7xlHmam0t43tUW\ncIsKuAeBAwCgqSgoKPj444/FZs3Jvpyo5AaWmhA4wD0IHAAATcWWLVukk30NHDgwPj6+5mo0\nN9flJigVoqKUqA00T1f3Ko0HpZRlWV9XoRRKqebfoPMfDMNo/m1q+A0yvz1BlGVZ6al77Wl0\nH1SbzVZtsq9Zs2bV+hYYmcAREcEajV6vzSfEX0bx4KM9zrem2t8O+V95TQUOnU4XHh7u6yqU\npfk3SAgJDg72dQmKM2rlkC1D+hgwTQoKCgoKCvJ1FQ2wcePGvLw8sdmrV69hw4bVXE2wWEpL\nS11txBAbG6Cto1BYWJivS1Cc0WhU55jDcZzMUk0FjqqqKpnfk8bOaDSaTKaSkhJfF6IUhmGc\ncaq0tLSqqsrX5SglODhYEISKigpfF6IUg8EQEhJCCCksLNTwGY5mzZpVVlbabDZfF1JfPM+/\n9dZb0p5p06YVFBTUXJO5dk3mWrs1LMxS26saI0qp2WwuKiriXY+QbezCwsJ0Op3ValXtmBMR\nEeFqkaYCB6nrfI4GaPgNSt+a5t+m5t+g8x8afpuksb3BQ4cOXb58WWzGxMQMGzas1vqF7GyZ\n7fDR0Y3oXddH4/o5NpT099G3lRAMGgUAaArWrFkjbU6bNk2nq/0Lp9wtKjqdUOMeWoB6QuAA\nANC4tLS0EydOiM2QkJDx48e7WlnmFhUhKoow+KsBbsJHBwBA41atWiVtPvXUU6GhobWuSUtL\nqeS+2WowAwd4AoEDAEDLMjIyDh48KDYNBsPUqVNdrVzHlF+Y1Bw8gMABAKBl69atk96sOHr0\n6FatWrlaWW7KL5zhAM8gcAAAaFZ+fv6HH34oNimlM2fOlFlf7gyHySS4uBADUB8IHAAAmrVx\n40bpXOaDBg267bbbXK7N81QyM1j1hbieAp5B4AAA0CaLxbJt2zZpz6xZs2TWp0VFxOFwtRQD\nOMBDCBwAANq0bdu2wsJCsdmzZ8+7775bZn35EaMEAzjAMwgcAAAaVFVVtWHDBmnPM888I/8S\nPJUeFIXAAQCgQbt3787KyhKbHTt2fOSRR+RfIhM4hJAQISDAa8VBk4TAAQCgNYIgrF69Wtrz\n9NNPM/KThDoctKjI5QZxegM8hsABAKA1X3zxxaVLl8RmdHT0Y489Jv8SmpdHXD80FSNGwXMI\nHAAAWlNtLvMZM2YYjUb5l2AABygNgQMAQFP+/e9/nzx5Umw2a9Zs4sSJdb5KLnBQKkRFeaM0\naNIQOAAANGXlypXS5qRJk4KDg+t8ldyI0fBwotd7oTJo2hA4AAC0Iy0t7dixY2IzICAgMTGx\nzldRq5WWlblaiusp4BUIHAAA2lHt9Mbjjz8eGRlZ56swgANUgMABAKARFy9e/OKLL8SmwWB4\n+umn6/VKBA5QHgIHAIBGrFy5kpfc2jpmzJjWrVvX54UyT6UXWFYwm71QHDR5CBwAAFpw7dq1\nvXv3ik2WZeucy1wkFziiooj8jGEA9YOPEQCAFrzzzjsOybNehw8f3rFjx/q8kJaV0cpKl4tx\nPQW8BIEDAKDRu3Hjxq5du8QmpXTOnDn1fG0dI0Yxxyh4CQIHAECjt2rVKrvdLjaHDBnStWvX\ner4Wt6iAOhA4AAAat5ycnA8++EDaM3fu3Pq/XCZw0IAAISzM/coAJBA4AAAat5UrV1qtVrH5\n4IMP9uzZs74vFgSal+dqIdOqlYe1AYgQOAAAGrGcnJwdO3ZIe+bNm1f/l9PCQiIZaloNi8AB\n3oPAAQDQiK1evVp6euP+++/v06dP/V8uP4ADZzjAixA4AAAaq7y8vG3btkl7nn/++QZtQWYG\nDoIzHOBVCBwAAI3VqlWrKiVTaNx333133313g7Ygc4aDCQujQUHuFwfwewgcAACNUm5u7tat\nW6U9DT29QRwOWljoaiFbv2nRAeoJgQMAoFF69913q53euPfeexu0BZqXRyTPXqkGgQO8C4ED\nAKDxycnJqTZ644UXXmjoRuRHjCJwgHchcAAAND7vvPOO9OaU/v3733PPPQ3diNyIUUrZFi3c\nqw2gVggcAACNTFZW1vbt26U9L774ohvbkZtjNCKCGo1ubBPAFQQOAIBG5u2335Y+OWXAgAF3\n3XVXg7ditdLSUlcLmZYt3asNwBUEDgCAxuTq1as7d+6U9rz00ktubIeRnYGDInCAtyFwAAA0\nJklJSVVVVWJz8ODBvXr1cmM78lN+IXCA1yFwAAA0GhcvXty9e7fYpJS+/PLLbm4rO9vVEoFl\naVSUm5sFcAGBAwCg0XjjjTc4jhObw4YN69atm3ubkjvDERVFGPx1AC/TqbAPQRB279791Vdf\nlZWVde/efcaMGSEhIdXWsVgsO3bsSElJKSsra9eu3YQJE3r06EEIOXjw4Pr166VrJiUlde7c\nWYWyAQD8Slpa2sGDB8Umy7Lujd4ghNCyMiqZNKwaITravc0CyFAjcOzZs2fnzp1Tp041m83b\nt29fvHhxUlISpVS6zoYNG3788ccpU6ZERER8+eWXixYtevPNNzt16pSdnR0XF/foo4+Ka7bC\nw4QAoElaunSpIAhi809/+lOnTp3c25T8lF9C8+bubRZAhuKBg+f5/fv3jxkzZsiQIYSQ6Ojo\n2bNnp6enS89SWK3Wo0ePzpkzp3///oSQ22677cqVK4cPH+7UqVNOTk7nzp0bOl8vAIDGHDt2\n7JtvvhGbBoPBvbk3nORHjCJwgBIUDxyZmZn5+fl9+vRxNmNjY6Ojo0+fPi0NHAUFBe3bt7/j\njjucTUqp2WwuKioihGRnZ3ft2tVisZSXl0dFRVU7LyIIQllZmdjkeb7aClrifGuaf4M1/60x\nTefnqOH36EQpVe09CoKwdOlSac+kSZNiY2Pd3qDcGQ6jkTRr9r81tftzFH8ZNf8eiX/8HBUP\nHMXFxYSQyMhIZ5NSGhkZ6ewUtW7deuXKlWIzMzPzp59+evzxxwVByM7OPnr06ObNmwVBCAoK\nmjhxovNMibjxQYMGic3ExMTExERl34+vRURE+LoExYWGhvq6BMUZm8Acjmaz2dclKCs4ODg4\nOFidfe3cufPMmTPSXS9evNj9o4EglObnCy4W6mJiwn7bMsuymj/mhIeH+7oExZlMJpPJpMKO\npCOaa1I8cJSWlhJCAgICxJ7AwMBSF9PbCYJw4sSJ1atXx8XF/fGPfywrK7PZbO3atZs/f77J\nZDpw4MCaNWuaN2+ekJCgdNkAAH7CbrcvWLBA2jN37tzmHlz14PLyBMlEpdXgmW2gEMUDh/OG\nlMrKSoPB4OyprKwUT3hI5efnr169+uzZsyNHjhw7dqzBYDAYDHv37hVXGDt2bGpq6vHjx8XA\nERQU9MYbb4grxMTESK+waIxer9fr9RaLxdeFKIVS6vy+WFlZ6XA4fF2OUpzfM6SP3dIYnU7n\n/IJRXl4uHeGoMUFBQTabTZ0P6oYNG3755RexGRUVNX36dE+OdYJkazXZw8OrysqMRqPBYOB5\nvqKiwu0d+b+QkBBtf1ADAwNZlq2qqlLnmCMIgswpasUDh/NsVWFhYVhYmLOnoKCge/fu1Vb7\n9ddf//a3v3Xo0GHt2rUtXDyikFIaExMjvRxjMBgGDhwoNi0Wi7b/Hut0OpvN5utClML8dt9/\nVVWV3fXXr8ZOr9cTQjT8cxQEwRk4bDabto/jDodDhZ9jWVmZ9GsVIWTu3Ll6vd6TXbNZWTKT\nbFSZzYLNptPpCCGCIGj4s+oc1mC323me93UtSjGZTCzLchznDz9Hxad2iYmJMZvNaWlpzmZO\nTk52dnbPnj2l63Act2zZsr59+y5ZskSaNlJSUiZMmHDz5k1nUxCEjIyMtm3bKl0zAICfePfd\ndwsKCsRm+/btn3zySQ+3KTdiNDhYCAz0cPsAtVL8DAfLssOGDdu1a5czeSQnJ3fq1Ml5i8qR\nI0fy8vLGjx9/9uzZ3NzckSNHpqamii80m80JCQlBQUFvvfXWqFGjzGbz4cOH8/Lyhg0bpnTN\nAAD+4NatWxs2bJD2LFiwQLw87R7KcbSw0NVSHlN+gWLUmPhr9OjRDodj06ZNFRUV3bt3nzlz\npvNEVkpKypUrV8aPH5+VlUUI2bhxo/RV99xzz8svv7xkyZJNmzatW7eOENKlS5ekpCTND5kG\nAHBaunRppWQ+0F69enn+jYvm5RHXVxAwAwcoh2rpIqu2x3A472uqdkexljAM47yRsrS0VMNj\nOJwDY8vLy31diFIMBoNz1FhBQYGWDi/VhIeHWywWRa+LnzlzZvDgwdLhBfv37+/Xr5+Hm2XO\nnGH//W9XSx0jRwqtWxNCgoKCAgICOI5zTomkSZTSiIiIwsJCDY/hCAsL0+v1VqtVtWNOrTeF\nOOHxPAAA/mjBggXSP4RDhw71PG0Q+TlGKRXwkFhQDAIHAIDf+eyzz06cOCE2DQbDK6+84pUt\ny4wYFcLDiWcDRABkIHAAAPgXm8326quvSnumTJnSvn17L2zaaqUlJa4W4iGxoCgEDgAA/7Ju\n3brMzEyxaTab586d65UtM3l5MksROEBRCBwAAH7k1q1b77zzjrTnpZdeEidO9BCeSg8+hMAB\nAOBHFi9eLJ1NvEuXLp7P9CWSG8DBMMT1/QUAnkPgAADwF6mpqZ9++qm0Z8mSJc5Zxr1D5haV\n6GiBwV8EUBA+XgAAfoHjuJdeekk6ecnQoUMHDBjgtR2UlVHXMxXxuCEWFIbAAQDgF7Zs2fLT\nTz+JTZPJtHjxYi9un5E5vUEIwQAOUBgCBwCA7+Xl5VV7KuysWbNiY2O9uAu5Kb8wYhSUh8AB\nAOB7S5YsKZHMkBEbG/vss896dxdyt6gYDIKXboQBcAWBAwDAx06cOPHRRx9Je5YtW2Yymby5\nD0GQOcMhREcTSr25O4AaEDgAAHzJbrfPmzdPOlZ08ODBQ4YM8e5emJwcUlXlaimup4AKEDgA\nAHxpzZo16enpYtNkMi1btszre6GXL8ss5THHKCgPgQMAwGeuXr264ynEWQAAHbhJREFUfPly\nac+8efPatm3r5d3wvFzgoJS0bOnlPQLUgMABAOAzL7zwgtVqFZtxcXEzZ870+l6YrCxaWelq\nKd+8uRAQ4PWdAlSDwAEA4BsffvjhsWPHxCalNCkpyaDAA+IZySWbmoROnby+R4CaEDgAAHwg\nLy9v0aJF0p5x48bdd999Xt8R5Tj6668uFzOMEBfn9Z0C1ITAAQDgA3/961+LiorEZnR0tHfn\nFRXRq1eJ3e5qKR8Tg+spoA4EDgAAte3fv3/fvn3Sntdff71Zs2ZK7Ev+egofH6/ETgFqQuAA\nAFBVYWHhiy++KO0ZNGjQ8OHDFdmZ3U6vXXO1UGBZoUMHRfYLUAMCBwCAqubPn5+Xlyc2w8LC\n3n77bYX2xfzyC+E4V0uFdu2IAmNUAWqFwAEAoJ4DBw7s3r1b2rN06dKWik2DwcjP94X7U0BF\nCBwAACrJy8ubN2+etOfBBx8cO3asQrujlZU0K8vlYoNB8PoMYwCuIXAAAKjkueeeKygoEJuh\noaHVphn1Lnr5MuF5V0v5jh0Jyyq3d4BqEDgAANTw/vvvHzp0SNrz97//vVWrVsrtEfengF9B\n4AAAUNzVq1cXLlwo7XnkkUeUu5hCCKGlpTQnx9VSISBAaN1aub0D1ITAAQCgrKqqqunTp5eX\nl4s9kZGRyt2Z4iQ/XFSIjycMjv+gKnzgAACU9eabb546dUras3z58sjISEV3iusp4G8QOAAA\nFPSf//zn3XfflfY89dRTDz/8sKI7pRkZpLDQ1VIhNFRo0ULRAgBqQuAAAFBKQUHBjBkzeMmt\nIp06dXrttdeU3asgsCdOyCzH6Q3wCQQOAABF8Dw/Y8aMW7duiT0Gg2HDhg0BCj8sjbl8mbo+\nvUEw3xf4CAIHAIAiVq5cefToUWnPwoUL77jjDmX3yvPMDz/ILBeio4nZrGwNALVB4AAA8L7v\nvvvuzTfflPY89NBD06ZNU3q/zIULtLRUZgWud2+lawCoFQIHAICX5ebmJiYmOhwOsad169Yr\nV66klCq6X8px7O9vh6lGiI4W2rdXtAYAVxA4AAC8yW63T5w4MUcy6ZZer09OTjYrfyGDnjlD\nJLN91MTdfbfSNQC4gsABAOBNCxYsSE1NlfYsXLiwtwoXMux2Ni1NZrnQpo0QE6N4GQAuIHAA\nAHjNBx98sGXLFmnPiBEjpk+frsKumbQ0YrXKrMD17atCGQCuIHAAAHjHDz/88MILL0h7brvt\nNhWGbhBCqNXKnjkjs4LQoQMm+wLfQuAAAPCCzMzMiRMn2u12sSc4ODg5OTkoKEiFvTMnT5Kq\nKpeLKXX066dCGQAyqCAIvq7Ba+x2O6PdxxExDEMp5TjO14UoSKfTEUI4jtPSx7Ia50dUOvWk\nxlBKWZYlhEjv0dAelmV5nhc/qGVlZffff/+5c+fEFRiG+fTTT//4xz+qUAxfVmZdu1Zw/R+u\n797dMGxYg7bJMAzDMIIgaP6Yo/kPKqWU53l1jjk8zxsMBldLdSpUoBqe56VfLzRGr9fr9Xqr\n7DXaRo1S6gwcdrtdw8c4o9FICLHZbL4uRCk6nc4ZOGw2m4aDY2BgYFVVlfNvlcPhGDdunDRt\nEEIWL1784IMPqvMLy3/9tUzaIAzD9+3b0EoMBoPBYBAEQfPHHG1/UAMCAliW5ThOnWOOIAhN\nKHBo+HeDEMKyrIbfIMMwzpPPVVVVGg6OzlCl4Z+jwWAwmUyEEKvVqu3jeFVVlfMgPm/evEOH\nDkmX/vnPf54+fbo6P2VaXKy7cEFmBf7226uMRvnxpDU5U6PmA0dQUJDNZtPwGUej0egMHKr9\nHENCQlwt0uwFCAAAFSxfvnz79u3Snj59+ixfvly1ApgffiAyfy9ZluvVS7ViAGQgcAAAuGnX\nrl1vvPGGtKdt27bvv/++zFll76L5+cyVKzIr8AkJRJVRqwB1QuAAAHDH119//dxzz0kvG5nN\n5p07d6owo6iI+f57ucVGI5eQoFYtAHVA4AAAaLBjx46NHTtWOtjIZDK9//77cXFxqtVAb91i\nrl+XWYHv2ZOYTKrVAyAPgQMAoGFOnz49atQo6Sg8hmHWrVvXp08fNctgT5yQWSqYTFz37qoV\nA1AnBA4AgAa4cOHC2LFjy8rKxB5K6T/+8Y+hQ4eqWQZz7Rq9eVNmBaFvX6LXq1YPQJ0QOAAA\n6uv8+fOjRo0qKiqSdi5YsGDixIkqV8LInt4gISH87berVQtAvSBwAADUy08//TR69OjCwkJp\n55w5c5555hmVK2GuXKH5+TIrcH37CtqddhkaKXwiAQDqdubMmZppY/LkyX/7299UroTeusUe\nPSqzghAeznfurFo9APWEwAEAUIfU1NRHH320uLhY2jlp0qRly5apXAnNytJ9/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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create Plot\n", "cdf.filtered<-ggplot(newcomb_1_cdf) +\n", "geom_line(mapping = aes(x = score, y = CDF), color = \"cyan4\", size = 2) +\n", "geom_line(data = newcomb_1_cdf_model, mapping = aes(x = score, y = CDF), color = \"black\", size = 1)\n", "ggsave(\"cdf.filtered.png\", plot = cdf.filtered, device=\"png\", scale=1, width=5, height=4)\n", "cdf.filtered\n", "# Create Plot\n", "cdf.unfiltered<-ggplot(newcomb_cdf) +\n", "geom_line(mapping = aes(x = score, y = CDF), color = \"lightcoral\", size = 2) +\n", "geom_line(data = newcomb_cdf_model, mapping = aes(x = score, y = CDF), color = \"black\", size = 1)\n", "ggsave(\"cdf.unfiltered.png\", plot = cdf.unfiltered, device=\"png\", scale=1, width=5, height=4)\n", "cdf.unfiltered" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lab Task 10 ###\n", "\n", "The code below calculates the confidence interval for the unfiltered and filtered datasets, and then prints out the two versions of the experimental result in the format mean ± confidence interval." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The confidence interval for the unfiltered dataset is 26.21212 +- 21.49065 \n", "The confidence interval for the unfiltered dataset is 27.75 +- 10.16686" ] } ], "source": [ "ci_filtered<- 2 * stat.table.filtered$sd\n", "ci<- 2 * stat.table$sd\n", "\n", "cat(\"The confidence interval for the unfiltered dataset is \", stat.table$mean, \"+-\",ci,\"\\n\")\n", "cat(\"The confidence interval for the unfiltered dataset is \", stat.table.filtered$mean, \"+-\",ci_filtered)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below performs a 1 sample t-test which can tell us the probability that the confidence interval obtained contains the true mean." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "\tOne Sample t-test\n", "\n", "data: newcomb$x\n", "t = -5.1471, df = 65, p-value = 2.648e-06\n", "alternative hypothesis: true mean is not equal to 33.02\n", "95 percent confidence interval:\n", " 23.57059 28.85365\n", "sample estimates:\n", "mean of x \n", " 26.21212 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t.test(x = newcomb$x, mu = 33.02)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R (SageMath)", "language": "r", "name": "ir-sage" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.4" } }, "nbformat": 4, "nbformat_minor": 0 }