13-Installing and Using R in Jupyter.ipynb

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    "# Installing and Using R in Jupyter\n",
    "\n",
    "While Jupyter is most commonly associated with Python, it is truly a general purpose data analysis framework which can be used with a variety of other languages. iPython kernels exist for almost every language, including R, Julia, MATLAB, ROOT, and even compiled languages such as C/C++. The complete list of kernels can be found <a href=\"https://github.com/jupyter/jupyter/wiki/Jupyter-kernels\">here</a>.\n",
    "\n",
    "In this notebook, we demonstrate how to install R in Jupyter, and provide some sample statistical analyses.\n",
    "\n",
    "## Installation\n",
    "\n",
    "### Install with Anaconda (recommended)\n",
    "\n",
    "In order to install the R-Essentials package for Jupyter, you must have a distribution of R already installed locally. You can download R from <a href=\"https://www.r-project.org/\">the R project website.</a>\n",
    "\n",
    "In Bash, we can easily install the R kernel into the current environment with a single command:\n",
    "\n",
    "        [username@host directory]$ conda install -c r r-essentials\n",
    "\n",
    "Alternatively, you can create a new environment just for R:\n",
    "\n",
    "        [username@host directory]$ conda create -n my-r-env -c r r-essentials\n",
    "\n",
    "Both of these commands will also work on MS-DOS if \"conda\" is a path variable.\n",
    "\n",
    "### Install from R \n",
    "\n",
    "To install from the R interpreter, run the following commands:\n",
    "\n",
    "        install.packages('devtools')\n",
    "        devtools::install_github('IRkernel/IRkernel') #or devtools::install_local('IRkernel-master.tar.gz')\n",
    "        IRkernel::installspec()  # to register the kernel in the current R installation\n"
   ]
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    "## Sample R Code\n",
    "\n",
    "In the following cells, I demonstrate a few basic statistical analyses using R inside Jupyter, including descriptive statistics, linear regression analysis, and distribution comparison. \n",
    "\n",
    "### Descriptive Statistics"
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      "text/plain": [
       "Plot with title “Histogram of data_vector”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clear the environment\n",
    "rm(list=ls()) \n",
    "\n",
    "# Let's make some data.\n",
    "\n",
    "data_vector <- c(15,23,37,82,51,75,28,96,11,3,76,61,53,21,49,83)\n",
    "\n",
    "# Define a function for SEM.\n",
    "\n",
    "std_err_mean <- function(dataset){\n",
    "    stdev <- sd(dataset)\n",
    "    n <- NROW(dataset)\n",
    "    stderr <- stdev/sqrt(n)\n",
    "    return(stderr)\n",
    "}\n",
    "\n",
    "# Some basic stats.\n",
    "\n",
    "mean(data_vector)\n",
    "sd(data_vector)\n",
    "std_err_mean(data_vector)\n",
    "NROW(data_vector)\n",
    "\n",
    "# Create a histogram\n",
    "options(repr.plot.width=4, repr.plot.height=4) # resize the figure.\n",
    "hist(data_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data vector we created contains 16 points, as returned from NROW(), and has a mean of $47.75 \\pm 29.17$. The histogram of the data is shown above.\n",
    "\n",
    "### Linear Regression Analysis\n",
    "\n",
    "To do some linear regression, we will use the \"cars\" example data set that is native to R, which includes the speed and distance traveled of a car. This means that are two columns, each having 50 measurements. First, let's confirm this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "50"
      ],
      "text/latex": [
       "50"
      ],
      "text/markdown": [
       "50"
      ],
      "text/plain": [
       "[1] 50"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "2"
      ],
      "text/latex": [
       "2"
      ],
      "text/markdown": [
       "2"
      ],
      "text/plain": [
       "[1] 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm(list=ls()) \n",
    "nrow(cars)\n",
    "ncol(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the cars data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MrsFCK2LYs0/BkCVR8DXohqnoUK51uc\n93RHvwFdgr6F3ob6aekqj015ZuFfCUhAAkNPIK1kbYQJZJwiM/RmLGEQB50x4ofR2qhfXdDb\nc+xcrDMjTQISkMAgE7ALepLOTtVbwKfCKe/8fh0t2ITZzYS9Dj2A/g9thjQJSEACEigncDVR\nJ6O8glQ2CWtD4n6EtBEmkJbvZSity8zKewdqZmn53oOSLtod9dJsAfeStseSgAQmSiDzVvKB\no0tRZj0vizLnJk43Q37deg2pUpOw4FR5y/jqPug69OYWtV2KuHRZ64BbQDJKAhKQQI3A3Cz9\nEIeXQ8cEOulyfwW55YMcvTRbwL2k7bEkIIHJJJAvCM6PejGHpVIt4Kq/htR4keUjHO3sgnYJ\njJeABCQggWcJZHjv1me3XOmYQCctwo4zM6EEJCABCUhAAp0R0AF3xslUEpCABCQggUklUPUu\n6IytzjYOYuewz7nj2M9dJCABCUhAAh0RqLoD/ggU8orRWG13dtABj5Wa6SUgAQlIoGMCVXfA\nm0DiGLQmOg4dhDqxqzpJZBoJSEACEpCABMoJzEDUeehRtEp5sr7G+BpSX/F7cAlIYEgIVOo1\npFGYhBXH+/7axfXDIbnILKYEJCABCVScwCg44JzCfI7yiygTsnr9kQ0OqUlAAhKQgAQkMIgE\n7IIexLNimSQggUEjYBf0oJ0RyyMBCUhAAhIYNgKj0gU9bOfF8kpAAhKQQMUJ6IArfoKtngQk\nIAEJDCYBHfBgnhdLJQEJSEACFSegA674CbZ6EpCABCQwmAR0wIN5XiyVBCQgAQlUnIAOuOIn\n2OpJQAISkMBgEtABD+Z5sVQSkIAEJFBxAjrgip9gqycBCUhAAoNJQAc8mOfFUklAAhKQQMUJ\n6IArfoKtngQkIAEJDCYBHfBgnhdLJQEJSEACFSegA674CbZ6EpCABCQwmAR0wIN5XiyVBCQg\nAQlUnIAOuOIn2OpJQAISkMBgEtABD+Z5sVQSkIAEJFBxAjrgip9gqycBCUhAAoNJQAc8mOfF\nUklAAhKQQMUJ6IArfoKtngQkIAEJDCYBHfBgnhdLJQEJSEACFSegA674CbZ6EpCABCQwmAR0\nwIN5XiyVBCQgAQlUnIAOuOIn2OpJQAISkMBgEtABD+Z5sVQSkIAEJFBxAtNXvH79qt4SHPgC\n9MIOC+B56BCUySQgAQlUhYA3/u6cyevJdlvUqQPeiLQf605RzFUCEpCABAaRgA64O2flKbL9\n/Riynn8MaU0qAQlIQAIVIOAYcAVOolWQgAQkIIHhI6ADHr5zZoklIAEJSKACBHTAFTiJVkEC\nEpCABIaPgA54+M6ZJZaABCQggQoQ0AFX4CRaBQlIQAISGD4COuDhO2eWWAISkIAEKkDA15Aq\ncBKtggQkIIE+EpiNYy+GHkBTkdYhAVvAHYIymQQkIAEJ/A+BRdk6Ct2JLkHXoX+hdyKtAwK2\ngDuAZBIJSEACEvgfAkuxdQ76J9oCxQlPh16PDkTLoF2RJoGBJ7A9JfwvmnngS2oBJSABCUwz\nzVlAOA0dih5GuX9Fl6E90JNoTTTZls/75jjdyHuyy9o2P7ug2yIygQQkIAEJ1BF4GevroOVR\nWrpboYXQiuh49Fl0JfoQ0loQsAu6BRyjJCABCUjgeQRWIuRRlNbuJugxFLsFfQGdh45B+hcg\ntDJbwK3oGCcBCUhAAo0E5iNgBpT/4FY43/o0x7FxFZqrPtD15xPQAT+fiSESkIAEJFBO4Ila\n1OzlSZ520Bmr1VoQ0AG3gGOUBCQgAQk8j8BthKQLen805/Nip5nm44TlFaWk01oQsI++BRyj\nJCABCUjgeQQuICRd0DOiv6O90V9Rupy3RVuiq1FmSmsSGHgCvoY08KfIAkpAAnUEfst6nO7X\nUT6+8RT6D8os6D3R4+ilaLKtUq8hTTYc8xsfAR3w+Li5lwQk0B8CL+awl6Nr0A7olSgf4fg5\nyhjx+1E3TAfcDaojnqcOeMQvAKsvgSEkMBtl/ha6BWXCVWZE/wGti7plOuBukR3hfHXAI3zy\nrboEKkAgX/HrxaTeSjlgJ2FV4Mq3ChKQgAT6TODBPh9/KA/fiyeWoQRjoSUgAQlIQALdJKAD\n7iZd85aABCQgAQmUENABl4AxWAISkIAEukZgYXL+Aco/bcgHO85FO6KM8Y6M6YBH5lRbUQlI\nQAIDQSCzpC9Fr0I/Rvly1h/Rl9GZqNUnLonWJDC5BJwFPbk8zU0CBYEVWDkIXYHywYhj0SZI\n6w+BuTnsnehHaNqGIuSfPPwD/bohvH6zUrOg6yvmev8I6ID7x94jV5fA+6havsj0e/RRlI9D\n5EMRCUvLS+s9gS9xyKtR2Rs4qxOXd4qXQc1MB9yMimETIqADnhA+d5bA8wjky0xxtDs8L2aa\nadYk7AEUp6z1lsDJHG6vNoecSnwenppZpRzwKI4B5793LI6WRQuhvECuSUAC1SLwRaqT7xX/\ntEm1MuFnV7QLGsV7YBMkPQt6EUfKw08rS/xMrRIYN1wEVqG4B6LbUbo3GpWxofxrrXlQP8wW\ncD+oe8wqE7iXyr21RQXz8J37wHIt0hg1+QR+Qpb/1yLbNJDyScsNStJUqgVcUsdKBedJt3C4\n17N+DjoBHYlOQuejf6OkyeSAbVCvTQfca+Ier+oEchPfsEUlZyEuv/nVWqQxavIJrEGW+c9J\nZQ52P+KuQWVjxDpg4AyLbUVB8yOLo121RaEzG289dAFK+rVQL00H3EvaHmsUCFxKJb/YoqKv\nIS5jxHO0SGNUdwh8m2wfRHn9KP/QIfYS9Av0CFoHlZkOuIzMAIYfRpnSvTxDh2VL98f9KN0k\nvTQdcC9pe6xRILAzlcwHHhZoUtkphJ2NftMkzqDeEPgYh0mPYxo8D9eWF7HM5LlWpgNuRWfA\n4vIUfOgYy/Rn0v9ujPtMNLkOeKIE3X+sBNLj81N0KjoGfQRlgkxVLDfq/JbTnbkxmg7FVkan\noVvQwkjrH4E8CKVLOudnmQ6LoQPuENQgJDuFQlyBcqI7saIF3G6afCd5jSWNDngstEw7EQJx\nRD9DT6Lj0FfRfuhWlN6i5VBVLG84ZHLlEyitrPRupcWV+8JiSBs+AjrgITpn21LW/OCOR3nS\nKrOMAa+LMiErP9a1US9NB9xL2qN9rD2pfrr+Grv64qzikK9DWa+SzU1l0sraAi1RpYqNYF10\nwEN00uNYd0IZ8I8jvgmdh05ER9SWeSfwFpT4TMr4BOq16YB7TXw0j/diqv0oektJ9fPu5Y3o\nUyXxBkug3wQq5YCn7zfNLh8/TvV7KE/2efLPuFdjS/ghwuKAv4v2QbkBTdTi+HOsKR1mVKVu\nvw6rbLI+EHg1x8z1ng9UNLPE/Qq9Hu3dLIFhEpDA5BGougMuSF3Lyta1jUx7nx3NiPJhjvvQ\nZFu6uf4P5WlNk8CgEJiLguSaf6pFgW4lbv0W8UZJQAKTRGAUP8OWiRhp5V6N4nzjJFdCkznu\nFYef/NIC7kSZgapJoNsEruMAi6FZWhzoZcRNbRFvlAQkIIExEXg7qX+EPodeUtszN6F0t8Uh\np6s6s0LzInhax7227TlgyjCZDwG9roPHG3wCM1DEDLd8paSo+W08jMrGiEt2M1gCPSOQBlPu\nlWv27IgeaNwE0sLP+G9OWKF7WF8UHVALO5VlXlX4S237LJYZw+2l6YB7SXu0j7Ul1c9M/11Q\nhmEKy1sAU1E+09rr659Dds1yw87w0w/Qfii9TZkVrQ0nAR3wEJ23D1LWON4/oM1RfnzpHk73\nc8bB3orqLTelpC/Gi+vjurmuA+4mXfNuJJDr/nb0ALoQTUX5PfwMVeljHBlaugblHzMci45E\nN9S2beUDYghNBzxEJy0Toe5C9U/6eRcwTjavIjVaWsz5gf64MaLL2zrgLgM2++cRmImQN6Gd\n0QfQkqhKtiCVuQ0dgTLxsrDpWPkiehy9GmnDRUAHPETn6zLKelRDeTPOmvHevRrCi80zWPld\nsdGjpQ64R6A9zMgQyEN0vi1c9qZHvvf+15GhUZ2KVsoBp8VXZUtrdgNU3wLehO3Ue3nUaPmx\nroqmNka4LQEJDBWBN1LafVHGu5tZxoRXQYs0izRMAr0gUHUHnAlYc6J0RWfyyRfQ99HfURzx\nNqiwsMjErMyO/hPSJCCB4SUwP0W/rkXxp9bikk6TgAS6QCBONZMvMuZbKJNP5kM/rYWdz/Jo\ndHNt+xSWvTa7oHtN3ONVnUDe9c/Ydpm9jIjcExYuS2D4QBKoVBf0QBLuQqHS+s2Y74fRQrX8\n52B5CLoD5Yf4EEq31ItQr00H3GviHq/qBH5IBf+GppRUNL1dmQGuDRcBHfBwna+2pU0reQmU\n2ZH9Mh1wv8h73KoSSNfyregolIftwjLPY1eUWdB591kbLgKVcsBlMwSH65RMrLR5//G6iWXh\n3hKQwIARiPPdEGV46QZ0JnoErYUyKXMrdBbSJNA3AjrgvqH3wBKQQJcJ/IP8X44yI3pNlNbT\n7iit4nuQJoG+EtAB9xW/B5eABLpMIF3Nv6mpy4cyewmMjYAOeGy8TC0BCUhAAs8RiA/JWPrS\n6H6Urv5bkCaBoSHgJKyhOVUWVAISqBHYkGXmzzyGrkJ5oyQfPslXyGZA3bBKTcLKDGBNAhKQ\ngAQkMBYCryXxSSjfWZgXLVtbbsoy39vPOHuV/qsW1dGqSsAWcFXPrPWSQPUI5JXN/JepfDeh\nmaU7Ot9VyEzzyTZbwJNN1PwkIAEJDCiBlSlXHMlGaNYBLWOvi7UGB1wcfbXkwFcTfhjatiTe\n4BoBu6C9FCQgAQk8n0BeW7oU5T8mZUzzeHQb+grq50d7OHzfbSlKkIlWd7YoycXEJZ3WgoAO\nuAUcoyQggZEksDa1Ph1dgBZFGePM/xR+P/ooOgiNst1H5edErR5E5iE+6TQJDDwBx4AH/hRZ\nwBEhkEbJlSj/rKWZrUJgZv1u3CxyRMLifPNVsbeW1DevJmVW9G4l8RMJrtQY8FhBLMkOc7fY\nKRfv+ijjJlrnBHTAnbMypQS6SSCfqsyrNGn1llnGN48oi+xzeP7PeV4PyhfAujkL+evkn/8s\n13ivn0LYISjd9a18BdHjspF2wNeCrNVTTf6T0H/RvuNCO7o76YBH99xb88Ei8F6Kc12bIu1E\nfMaGB8nSIk+rM/fftNCz/BfKZzi7Yel+/iXKl8byMPIZ9E2UY96KVkfdsEo54HQVtLKliVyv\nLkFmAa6KMhbSaGn9Fk9DdzdGui0BCUhgCAj8hzJmvDetxzixZjYHgUk3KPZ2CnIYymtBaf3e\niBZEH0f5ZxQ7oMket36SPN+FctwsM1P8fvQzlAbYvUibIIFciLegXIidKhfmakjrnIAt4M5Z\nmVIC3SSQf2OYLuiNSg6Slt/l6Ksl8b0OnosDxtl9tuTAHyH8QbRASfywBY9UCzhPNG9AGVeI\n7Y3OQr/NRoM9xfZD6K/ohoY4NyUgAQkMA4F0n/4UHYBei/6FCksvX1qZ86EfFoEtlksSl9by\nVHQ36oa9lUzjYL9bkvl+hKfLfGuU+7c2QATadUGnqHGoUWx1dCY6JhuaBCQggQoS+BR1Whxd\njA5Bf0eZUBQntijKuGq+e1xmSbcHigOOpXFyMvokuhpNpqVx9Bf0ZEmm6bk8DxWNqJJkBveD\nQJ7oxmK5gJo53zjyfAu0m7PuxlJO00pAAhIYL4G8YpOevw+ipdDn0NvQKehlKL2AZfZFIn6O\nfomy72xoPZR7ZBzlCmgyLWWdpU2GiU86rQIE3kId9q+rx+as34fypJXx4k2QNjYCjgGPjZep\nJTCIBFahUGmJNpt5nMbJUSit6clsqGxGfhn6mxc1szkIzP05E7WqYJUaAx7rCXkTO8TRPoxy\nEc2O7kXpYvk9yonOdp78BtXmpGCLo7TYF0Izo36bDrjfZ8DjS2DiBPYli7SSy2xBIuKg1yxL\nMI7w9GL+DeX+m9dA6y3O6rfoSjSlPmKI10faAedEX4tWrJ3Ad7OMQ/52bTtjHtneubY9KIs8\nmR6IbkcpX6My0SKt+nlQP0wH3A/qHlMCk0vgz2S3S5ssryJ+hzZpxhqd+27uyxlf/jTaEu2E\nLkc3oZeiqlilHHDGJTq1PGkth/ZGl9R22rS2zLtmsVwEV6BVszEgtivl+EqtLDewPBfdjfK6\nVFrwc6FFUX4U6V7fER2ONAlIQAJjIfA4iWdos8OMxCfdZFruu7nnfgq9B6VnL7O5j0d7obuQ\nNuQE4qzScvx4rR7TsYwjy8mNcy4sF0PGOgbB8nJ4ynwSygVaZulOXw9dgJJ+LdRLswXcS9oe\nSwLdIfB1sk0vYe4nzWxFAnN/qVKLtFk9uxlWqRbwWEHF2f66ttPGLHMxHVbbziJdvQn7VjYG\nwFK2dC+3eyotijonK/ejnxQBPVrqgHsE2sM8j0AerMfSE/a8DAx4lsDCrKVn7cvPhjy3Es55\nwP/dc0GujYPASDvgfQAWB/sndCd6Cq2PYrno8kL4k2hQnvAupSyHorHYn0nc6x+JDngsZ8i0\nEyUwFxnkt3wHyu85X376I1oHaRMj8EZ2zys/J6C3odegT6Cp6B+oX/NMOHQlbKQdcMYvfoEy\nCzoTmj6KCssP+CH0ziJgAJaZkZgx6SkdlqVoAe/VYfrJSqYDniyS5tOOQGbiplcoE3Teg1ZG\nG6KfoyfQdkibGIEV2P1XKEN0aZBcg76C2r2vSxKtDYGRdsAFm0BoHOfI+MasRYIBWW5LOfKE\nn8kIa7QoU+qyLjof5Sa0Nuql6YB7SXu0j5WH0vTyzNQEw4cJewwt3STOoPERaLxPji8X9yoI\n6IALEkOwzMW/E0rXeBzxTeg8dCI6orY8l+UtKPGZnZjuol6bDrjXxEfzeMtT7VznL2tR/bOJ\n+36LeKMk0E8ClXLA07chmS7ZKShdKWkZzo0y+7mdxeFF/bbcbL6HjkN7osx0bmwJp9s8Dvi7\naB90I5qozUEGX0G5WDqxQRkz76SsphleAq+g6Lm+L2tRhZOIywRLTQIS6DKBdg74dI6/EsoP\n90J0AVoCtbPdSRAHNCiWV6O2rhVmNpazo4xnZxz7PjTZFq6Z6JKHl06sWXdgJ/uZRgJjIZDX\nBTMm2coysbL+tcJWaY2TgAQmQKCdA/4jeV+N7qkdI0/H89bWWy0ywWNQLa8ZRbG05l+CMqM7\nn9CcLEt+7xpDZtuTNg85mgS6SeBvZL44yjWfiUHNbEMCk06TgAQkMGECeWDIe70H1+WUFvB+\nKK8LpJs6rYJL0M6oHxYHnHLM3I+De8yRIvBnapvvBjfrndmG8Aw1vRxpEhhEApUaAx5EwJNZ\npheT2U0ozu2MWsa58VxUC4vjPR0dha6vhcUx97oLTgcMdK0nBJbkKJnzkBn/m6NF0WooE6/i\nfHdEVbJXUZn8vnMfyJDTqehtqJu2BJnnof9f6C70F/RJ1OkHgUiqlRColAOevqSSRXAmJi1e\nbIxh+SvS/noM6buV9ItkvBD6Avpe7SAfY7kqOgDtim5FsZzYvVBuQMegPyBNAlUjkPkQq6Nv\novxOX4Ri6XZ+IzoxGxWxj1OP/O6PRbkXpMdrPfRztBnaDuXhfDJtAzL7LboU5X4Sp78iyvEz\nD2Uj1I15J2SrVY3A36lQLtBWeqAh/iG24/AGwfKKUW449S3aONd7ULMuuKS7AX0L9dJsAfeS\ntscqCKRFltbaXEVAhZbrUpe06NOt3mgrE5B7wKcbIya4neGu5PsdNG1DXonLl7Dy0KONn0Cl\nWsDtMMxGgjnrlCfne9Hv0BooM4ljs6B0Z12JjkPtWtYk6YldwFGObjjSUWxf3BBWv3keG3HS\nvTQdcC9pe6xRIJCW/KEtKvpR4tI6zUTMybLdyehyVP/AX5/3K9lIY2ap+kDXx0RgpBxwI5nT\nCPgTKrtoFyPuIfRhNAiWcZjMeJ67rjDplnoYzVMXVqzOz0qemncrAnq01AH3CLSHmTCB+cgh\n3dcXorwhcTJ6FypzOkT1xdLNu2WLI+e3Hme4fIs0Y43K+PLX2+yUsej3tEnT6+jlOGDulZmI\nehX6DXo9GkSrlAMey48m3VVrobQgnyw5M9cTnm7rdUriex18IAdMuVOmdEnFfoYuQhmjXhAV\nlm6pM1EccK9bwEUZXEpgkAnk958W3qYoN+kM1cQJ74tOQi9Cg2LpnftPi8IUcZNZ5nbHTHFy\n3KQbFNuWgqRHcFl0ENobpYFyAvoR0gaEQFq9d6E9W5QnaW5B326RptdR7+WAuaCeQrnQcpEd\nUNt+jGVuKLehPA0nTVqjvTZbwL0m7vHGSiC9SHegtJQaH9wzjnwd2h8NiuWhe7cWhdmIuPz+\nM8w2WZb7ShxXmaXXLQ/4RWOgLF2vwlfhQGGwY5MD5mEr83vSYzhIVqkW8FjBHs4OOSlrNtkx\nLc39UBzZoFxgRTHnY+Ub6AaUH0DKWK88lR6BXo76YTrgflD3mGMhsCuJ0z1ZNvy0AXFPooXQ\nIFgcRxoMizUpTFqg6UI/rEncRIJexc55iA+LZnYwgVegMobN9ulmWHozj21xgE8Ql8ZJ4wNX\ni126HjXSDjjdtDejOK/TULoovo5+jjK2kfA8BQ/SCaM4/2O5+HOTeCWKw50D9dt0wP0+Ax6/\nHYH83vdsk+h24rduk6ZX0ZkIegq6EW2F4nRzX0rj4Dw0FeXBfLItvX8PouNQmP0FnYjORAlf\nAw2KpUej1fkqxsn71TBpxmmkHXCA5KSchB5GcbiFprKeJyZt7AR0wGNn5h69JRBH8pk2h7ya\n+A+0SdPL6PTKfQflXpWWaXq/0ko/GuU+1g1bgUzvRkVPW7HMcf+A4kAGxcJl4xaFCb/c39Oy\nHxQbeQdcnIi0JJdHr0UvLgJdjouADnhc2NyphwQO5Vi/anG8jBE/jtZrkaZfUTNz4LXRa9C8\nXSzErOSdFvev0UxodbQhyhh57pU3ox+gQbGMk+/SojBhlgeHQbq/64BbnDCjxkdABzw+bu7V\nOwJxJHGwcSrNLDOhr0Z5MB9V+zQVvwGlu7uZvZ7AtIgXbBbZh7CdOObtaIEmx855PB0d3ySu\nn0E64H7Sr+ixdcAVPbEVq9bPqE+6V7dFRVfqQqwfgB5F3Wr9Tkveb0KHoIzrZs7Jm1HCB8lO\npjDfbVGglDeTmrZpkaaXUTmHZ6E8OL0OZYw8ltZ66hLnvDgaJKuUAy6ADxJgyyIBCQwmgTwo\nfh/9FN2PcoNOl+taKENRZ6LJttnJ8A/ocJQxyQvQFPRLlElOgzCJkmI8bSlrJjaVWcZT70JJ\nNwj2GIXIGPCfUOb15JymfJehtOLXRFORJoFKE8iNLT/OjFVpEhh0ArNQwI3QVijvknazJfo7\n8s+7+oujeluUjUtRHMegWMbJj2hRmHB7BIXdoNk8FOgNKD0Lywxa4erK80LWc6/Mw4EmgUkh\noAOeFIxmUjEC61CfjJkuV1KvpQl/HL26JL7XwZtwwLQqVyw58NcJvwmlJa+Nj0ClHPD042Pg\nXhKQgAS6TiDdo2ejK0uOlLHLdHsX3aglyXoWnNb40egUdBCaE6WLPE4369uhjGVnvFyTwMD8\n1yJPhQQkIIFGAnm16d+NgQ3biU+6QbHPU5DzUJZpDedd29lQuumPQSciTQJPE7AF7IUgAQkM\nKoGpFGztNoXLjN20OgfB0j2aMetr0IboZSgt4GtR4jI+/AWUrmhNAhIYEAKOAQ/IibAYA0Xg\nJZQmY8Cbl5QqY66JX7YkvtfBO3DAO1G6m5vZtgQ+hOZqFmlYRwTyIOMkrI5QmahTAjrgTkmZ\nrp7AFDbehvZGP0S5jgbptRyKM2Hbgxzyesw7UDHbOsvMwL4PfRN1yxYh40+hfGTkGyit2lZ2\nPJE/bpFgOuLuQW9tkcao1gR0wK35GDsOAjrgcUAb8V0y0zaTkOKETkDphs146F2orMVI1NBZ\nnO0u6BGU+p2Dbqlt78aycMqsTqrtTG455lUon+A8FWXy1BloPtTMMvb7uWYRdWGXsf6Rum1X\nx0ZABzw2XqbugIAOuANIJnmWwIKs5SMYR6L6jzqkRfwVlMk/a6EqWZzee1DGULdD86Nu2Q5k\nHOf7roYDLMb2+eivKKwb7TcEHNgYWLcd5/EA2qIuzNWxEdABj42XqTsgoAPuAJJJniWwH2v5\nItT0z4b878rBbKY1po2dwEzscjfasWTXuQnPOG+cdKNtTcB/ULqum1nyTBf0LM0iDeuIgA64\nI0wmGgsBHfBYaJk2XbHbtcCwEnH/Rd1sJbY4/FBHZWJXJkrN2KIW+xD3f03iX0DYn9Dl6OWo\nsHSTfwClCztLbfwEKuWAy56gx4/HPSUggW4SyE0+3bF5taXMrqtFLMDy1rJEhjclEGZ5wEkX\ndJmF/fpNIp8i7I3o5+hi9FeUb0PHGaflnHHlVl3URGujREAHPEpn27pWgUBu8hn/XQLlK1DN\nbPFaoM63GZ3WYXG+ccIzoLRYm1nYJ10zy6S4fO1qdfQ6lFnpx6Fj0W1Ik8BIE5iT2i+OlkUL\noUH4Bwh2QXMitI4J7E/KTAbKay3NLK2svzSLMKwtgdwPMk770ZKUeYc3rdoPlcQb3F0CleqC\n7i6qwcl9FYqSm1JaDhkba9S/CMtNbR7UD9MB94P68B5zYYoeJ3AomrWuGunR+jJ6HK1bFz7q\nq6sCIA71k2gD1K7n78OkeRi9A9VbuJ+D/o7iCLTeE9AB9575hI64K3sXDvd61vMDynuTR6KT\nUFoS6U5Kmsxu3Ab12nTAvSY+/MfLQ2XGItNa+y3K9XxjbftNLLVnupL/CIh021+B/obyitaV\n6BWolX2OyHRB573dw9DJKOPCZ6MFkNYfAjrg/nAf11G3Yq841jjaPAWX2bRErIcuQEnf63co\ndcBA18ZMIOOUeWD8AdoPpeWWLlLtmX+AEEcbh7lMHZBMhjoEPYDqZyqz+TxbnJDPop+i76BN\nUO4VWv8I6ID7x37MRz6MPdK9nBtVJ5bx4fvRTzpJPIlpdMCTCNOsJACBPVB++/Vd9PVgjmMj\nrWNtuAhUygG3GwsZrlPz/NKuSNC5KF1JnVi68y5BmZylSUACk08gXef5XWac+jyUbvROLB/I\nWB8tiDJkdAZ6EJXZO4jYB6Wl28zioDP8NC/K3BBNAhKYZAKnkF/GfqZ0mG/RAt6rw/STlcwW\n8GSRNJ9BJbAsBYvDzRBP5mLcWls/mmW7bvMPkiYPxw+ha1Ac770oXe5l9gQRmXBVZnHoKcsr\nyhIYPpAEKtUCHkjCk1iobckrP7Lj0Rot8s24TmaN5ok4P9y1US9NB9xL2h6r1wSW4ICZ4Jjf\nYdYLi/PLxKiL0cxFYMMyH6/I5KePoxlrcTOwzKzmh9HnamGNi7Rq8/svsyWJyL1hqbIEhg8k\nAR3wQJ6W5oWKY90J5Yk5P7abUJ7CT0RH1JbnsrwFJT7dYp9AvTYdcK+Je7xeEjiWg52Gpmty\n0PQ6XY92axK3CGFxvu9sEpegdDM/hhZHjfZLAv7YGFi3/TXWr6nbdnU4COiAh+M8/U8p87Qb\nh3sziqOtV5zz1SizHPOD74fpgPtB3WP2gsAcHCS9Suu3ONgnictvsNE+RcBVjYEN25exnZnK\njbYsAfltfxPl85319nY28rC9VX2g60NBoFIOuOqTsIorKhM9tq5tzMZydpTurHRT3Ycm2xYn\nwz+jKR1mXHStdZjcZBIYGgKLU9K0fP/eosSJy0NyHOVTdelewnqr/ZI08UnXaHHcW6Jf15Z5\n9z+t6fXQmihd10chTQJ9IzAqDrge8P1sRIXNw0reDfwnqv/xF/HjWd7ITh9DeVrrxF5Log92\nktA0EhgyAsVvLb+xsofdxOXf+DX+/pI+LdlW9mIiM4TUzDIJc2m0A1oL5YH4Lyi/tcuRJgEJ\n9JnAtzh+uqTn6mM57ILuI3wP3XUC13KEXVsc5bfEHdMkfkPCHkWLNYlL0MIordqNs6GNBAG7\noIfoNOd9w7LZlUU1FqqtvIJl8bSeFuxNRQKXEqgogTiwpVBampeiJ1E3LM73Z+hC9H8NB/g0\n25uhNRrCs/lHdB5KN3LSZCZ1YXlgThfyRej3RWAXlrk/pIs794ZLULcYkbUmgWoRyPhQ/YSr\nTtd36zEGW8A9Bj7ih3s59T8D5feQbt8sb0MfQt2y3ck4x8pYbJzul1Cc68PobajM5iMiTvYu\ntA/6JPo+ijPOK0wLoG7Y8mR6OqpndDvbH+3GwcyzYwK2gDtG1f+EP6EI30MzouPRFajRXkPA\nK9EPUG4GsbOfWfhXApUjsAo1OhOdilZGafnOhd6FvosyGeqzaLJtdzJM6zfjr29HmYV8DtoG\nXYvKLA8Gr0LvRW9C66F/o13QQegxNNm2EhmGUR5Swist3zDaFmXIKr0Gn0KaBCTQhsDLiL8Y\nPYQ+jqZF9eYYcD0N16tMINd+nMlhJZXMZMB0sWbC0qhaGKVl/SvUeK8Ik/VRGOVBQOs9gUq1\ngHuPrz9HnIHDfgc9hU5BGdcpTAdckHBZEFiMlcwfyCtrg25TKOBGaHM0e5vCpiUZ59Gq2/Y4\n4tOyHFVbnYrnPpHx8TI7mohflEUa3lUClXLAL+gqqsHJPDMpM+6UWZUvRel2ewfSJFBPIN2h\n16CpKL0md6HcbOOQB83ieE9Gj6BMQjoe3YPSwq1/wGTzWUtv0HUoXbhlluGXpBtVS91vQK0m\nYY46o1G9Nia93qPigAtwp7GSls0f0BHocDQn0iSwKwgORoeiZVDeTX09ejG6AC2NBsXSNRpH\n+jqUrtLM4M+D5d5oWXQ1auaE8yA6M2pliY9TH1ULo5naVH7UGbXBY7QE2hPIpJP70H9rmqv9\nLl1LsX2tDO1ujl0rwIhnnEl46XbcrAmH6Qg7EZ3TJK5fQb/kwCnvJk0KsChhcSLp5Wm0JQjI\n9f6qxojadhx7Wv5fL4kfheDwC9t1WlT2r8R9u0W8Ud0j8EKyzjW8ZvcOYc69IrA4BzoKnY5m\nRf0yHXC/yD9z3J+xOKFFETLrNT/6zI4dBHuQQpzZoiBxDnEizXp30vNzGZoXNdqeBNyPFmyM\nmMTt9LotgNKzMFZ7ETssgrLspuUBJ29MzNfkIF8h7D8o5WhnmUOQseQp7RIa3zEBHXDHqEzY\nKQEdcKekupPuQrL9dJusMy747jZpehGdVmqca6vy5sMReWB4M2q0TNQ6H92Kvoy2QO9DeQiN\nY2/WqiZ4wpZXAfdAt6GULcp4+wdRO8t7yyegx1H2yzKvNK2IumF5GD8X5b3f3VDB6FTW8zbF\nG1Arex2Recc55ynljcM+BOXBQ5sYAR3wxPi5dxMCOuAmUHoYlJvtF9scLw5r6zZpehWdG3uc\nZ5mlpZ4bf7Mu9ewzA9oZ/RVlGCYPFwehpVE3LC3Wc9CNaAe0DFoBhfkDKMcus/WJyIPB8WgD\ntCR6LToWxRlmvRuWG/1O6CJUMDqY9WVRK/sgkU+gfVG6+tN7siW6AN2CUn5t/ARyXnJt2wU9\nfobu2UBAB9wApMeb+bLSOS2OuTpx+dEPys0zs52bjfEWVfg5K0+iONpBsO9SiKmoWZfuqoTH\nwb4TNdrMBPwb7dMYUdvem2Va1GmxDoItRyHSOn9vk8LEcfwend0kzqDOCeiAO2dlyg4J6IA7\nBNWlZGmlZOZvWjyNNgcBaSke0xjRx+2vcuy0gj/XpAwbEhbn+8cmcf0IykNAxpXzileZ7UXE\n+U0i30PYHSjd180seccBv79ZZB/C8iB3Vovj5gEu5y0PHdr4COiAx8fNvVoQ0AG3gNOjqHQv\nP4Z+g96E1kUfR1NRWptzo0GyP1OY3MwvQenK/QQ6HSUsrcY4p0GwjN+m92CeFoXZiLhHUca3\n6y0t39/WBzRZP4qwHzcJ70fQmRx01zYHzrj3B9qkMbqcQKUc8AvK62mMBEaKwBHUdi00BR2K\nzkBpEf8CZbzpLjRItg6F+TpaFO2BvofWQEeihVEc2iBYnG+s0bk+E/rM3yKuSFvEZbuIK8Ia\nl4lv3K8xTa+2U45299RBKm+vuHgcCQw0AVvAg3d6phu8IpWWKDf1QS1vWiz3oXeVlv6ZD4ic\n0yQ+++TBp+zDGJnclS7q7dAg2HcpxLktCrI0cXHSK7dIY1RrApVqAbeuqrG9IqAD7hVpj9MP\nAt/koDeiZl/nykzhh9HbUaPFwWa/n6BmLeF0Pd+CZkaDYC+hEOl5+HCTwmQc+zT0pyZxBnVO\nQAfcOStTdkhAB9whqAFNFufwfvQXFGeSFt/JaAPUzuYnwQ/RDSgzaK9HGfucD1XFMh59OroV\n7YhWQq9EX0N5lSiv7JRZhgUyiesUlPdvl0ebofB9AGWsfpBsOwrzBDoYvQa9DG2NLkY5txky\n0MZPQAc8fnbuWUJAB1wCZgiCp6OMR6M4iW+gTdCWKDfg3Ii/gMpsBSLilDLLOtfAhmgH9Hf0\nb5Sbd1UsN84voTxopBs2+gd6D2pny5DgKJSHm+z3CArz5dAgWh4KTkdPopT3HrQfajURjWit\nAwI64A4gmWRsBHTAY+M1SKl3oTB3oGbO4I2ExwnHsTZabiRXo1+h6Rsip7Cd2dhXoqxXzeak\nQrOMo1LhFCfWyGscWfVkl7T8X4zSQ6JNDgEd8ORwNJc6AjrgOhhDtBpHcDfK+Suzg4j4fZPI\ndxB2L5q9SVyC4qTSqn5rNjQJSOBpApVywO2mzHvOJTBKBJagsukq/CdK1/Cf0YdQWYsrrd44\nyuNQmSUu45iNlrA/ofsaI2rb6bY8C61ZEp/gjKMeiaaim9FJKK3udpZW9UfQ2Sj1vAplQtNi\nqJ0lTdJmn+ybPJJX8tQkIIExENABjwGWSStNYCNql49arIi+jz6B4gC/jv6IZkaNlqfxWMYk\nyyxxzZxTwlrtl/wy5lkcI9v19nE2zkGJ/wr6DLoR/Rr9FJVZun4zG/dr6AyUev4ArYrywZEN\nUJklLmmSNvtk3+SRvJLneLqV2U2TgAQk0D8C23PoTNZodpPvX6lG58gLUtW0RL/VpMoLE5ax\n2nQlN1ocTpzoZo0Rddt7sH5h3Xax+lFWMiFpuiKgYZlW981oh4bwbK6HMra8bTYaLK3i+1Ec\ndDP7BYFpvabO9TYtG99F6Rafrz6itp6wxCVN0tZb8kqeyVuTQDcJ5IEz98o1u3kQ8x4tAjrg\n/p7vzF6+GDU6lqJUr2YlM1oXKgLqlgezfglq9vC0NOFx7M2c6Ny1uM+zbGa7EHgPmrNJ5MmE\ntXJ2OxF/C2qsT7qPn0LroGb2AgIzMzkt2kZLWOKSppklz+SdY2gS6BYBHXC3yI5wvjrg/p78\njPXu1qYIdxL/tiZp5iIsrb848E1RWsWZ+fo+dDs6FpU5ra2IS0s2xz8fJZ/z0NnocbQlamYP\nErhFs4ha2CIs/4uWaUizDdsZt21lcbR/apIgYc0cc33S5J1jaBLoFoFKOeB0c2kSGHUCLwJA\nPurQyv5D5ExNEmQWdLrD9ka/RblBxNJd+320J0rLsJmdS+CNKN3GU+oSxPnegOKUGy3OPK+3\ntCpvEZd61VvKX8TVh9evJ75xv8R3wij7NmOU/TUJSKCBQNmTeUMyNyVQaQJXULtXtahhxjjT\nqry8JE2c8HYoLd9XoJXQvOgrKC3cZpbu4WNQHG32WxylDOnCzb7/Rr9Bjd3IceZpKbcqb+Ie\nQ/9C9ZZ6LoGajfEW6bJv0jVaO0bJM3mXMWrMz20JSGAECWRMbXG0LMqYXrOxO4J7anZB9xT3\n8w72WkLiKNOSbWa/JPBS1OgMm6XtNOwNJHwILVCyQ67NR9Drm8TvTFi6t5Om0WYk4EKUMjda\nHrjjIA9ujKhtr8vySbRek/iEJS5jvc0seSZvH+qb0TFssghUqgt6sqAMej6rUMADUW5aGRtr\nVFoK+6N5UD+sqg44QxzvRemaPRelRbctmg4Nmu1Lge5DO6BZaoVbjuWvUbqfV6uFTdYis4lP\napPZH4j/VpM0uQmdga5Db0Tpvs7DwVoonKeitEib2SsJfAgdiZapJZiV5YdQupDzelGZJS5p\nkjb7xJJH8sq4dPLWJNBNArn2c/8ue1ju5rHNexwEdmWfwuFez/o56ASUm0ZugOejf6OkuRP1\nYxJJFR1wHEBaYvegn6DPowPQ/SiTjjJ5aZAsDuzTKOVNN+/DKNdEHNpKaLLtp2R4WJtM4/x/\nXJImY7I/RI+itN6zTLl/ixZArWxVInPdp36pZ/ZLN/pOqJ0lTdLWM0peyVOTQLcJ6IC7TXgS\n89+KvHKTiaNtdYPIzXc9dAFK+rQkemlVc8DheRbKjbmxV2FBwi5GJ6NBtPzA83S9MVqqiwWM\ns7+yRf5heDX6ZIs0iZoNvRq9Ds2PxmIvIXHqmfqm3p1aPaPkoUmgVwR0wL0iPQnHSQsj3csz\ndJjXnKS7H/2kw/STlaxqDngTwDyCFikBtDThj6N1S+JHIThswujdJZV9H+FpneaBRZOABJ4h\nUCkHnDG6KtuKVC5diOme68TS/XgJWqiTxKYpJZDW2GnoxpIUadmdh5IuLeVRtLD5PDoArY+m\nQ+ktuAOle/ed6FPoFjRIlhvgtmhDNDvKA+5h6C9Ik4AExkCg6g44Y7uZPDMFPd4Bl7SA47T3\n7yCtScoJzEFUHEkry4S4pBtlO5jKfwC9F2XoI5OjZkLpfv4H+gUaJHsJhfkdmhcdj65AGR/P\nw9S+aEeUhwdNAhKQwNNP6rmx5WaxRgseueGlOzRjlpnQsjbqpW3PwVLOQXglajLqvQuZ/LVN\nRv8kvt34Zpsshj46kwEvR8ui16BtasvlWF6JjkWDYnkwuBplPkVavvWW3056j3arD3RdAl0g\nUKku6C7wGags41gza/NBFAd3E8rT+onoiNoyXdTp5kt8WsmfQL22qjngZQCYB5ktSkDG0bQa\nIy7ZrVLB61GbXG9xvs1seQLDcK1mkX0Iy+8iv5PiFa3GIryDgIxZz9UY4bYEJpGADngSYfYq\nqyU5UBzuzSiOtl5xznmy/w7KxJjJsleS0Tod6pukS5mq0gKmKtN8Ff0HbYcyvhnLUMAHUW7U\nn0WDZi+gQFuiH6Ffot1QHia6Yd8g04yTt7KziPxaqwQ9jDuZY+3d4ng5x/eiN7dIY5QEJkpA\nBzxRgn3efzaOH0e7NGrsSpusoi1FRmnd1Dv6Ttar5IDD8jMoTjjdk5ei+9D96GNo0CzXRN5b\nTnnT9XsgugilFRpHPNl2ABke2ibTI4nfr02aXkWHzc5tDnYV8Tu0SWO0BCZCoFIOePqJkBjC\nfdPCiQOImlme4uOg00JLF+l47V/sOANKF3gn9n4SVXHi117UK45sAzQ/Sg/EqaiMP1F9sRk5\nalp4d6A8PN2GCnsTK4ehtO72KQKbLOcm7O1oBZSHr3PR0egx1MyuJzCt7Vb2UiKPaJWgh3Ep\nb8pTZhkjXhQlnSYBCUjgaQLz8fdX6G6U1s3paG3UzFYmMC3VbrR4mh2vCNu+dtyqtYCL+g36\nMi3yON2yHpG06vLQMAtqZm8hMA76BpRW67G17WtYroiaWZzZk2ijZpGEbYrS+k5PzSBYHi4y\nXLN4SWG+RHgY5mFGk0C3CFSqBdwtSIOSb26YuSnGqab780qU1yRy49sTNZoOuJFI/7ancOg4\ntm+jvdF7UPH9YVYn1X5PbjlGmeVHH+ezWZMEryYsLd4vovSgFDYbK2m9xiktUAQ2LL/Ddrrn\nG1vChUP/RkP6fm5Oy8H/gK5Gr6grSM7Tp1EeFuKkNQl0k4AOuJt0Jznvr5BfnO/uqLh5r8b6\nxSjhjTddHTBQBsBWpAz/RA+gk9DxKN3DcWZlLUaixm1/Z89PtNk7wwrva5Lmr4T9pEl4gjLE\ncxH6cTaaWIZE9kDpps6D4pnoRvQo2h3F6Q2S5YH2MJTfTh5mz0J3obvRO5EmgW4T0AF3m/Ak\n5p8n9ty0cyOst3Q15maXG8ln6iJ0wHUw+rS6EMe9HaUrd466MszA+rfQI6i+BVaXZNyrJ7Ln\nD1vs/SLiHkavb0izCNu5hpZvCK/ffD8bN9UHNFlPnT+AvoySfkE0yJb6fhztgrZGsyFNAr0g\noAPuBeVJOkY+cvCbkrxy00hLOF3Sb6ul0QHXQPRxsT/H/gtqfGgqinQ4K3l4mkyL00tX8Hwl\nmaaL9U4UR1xvedUsDrgxvD7NBmyke1aTgAQmTkAHPHGGPcsh3Zf3orKJIWl5pOsvrZu1kQ4Y\nCH22tH7f1aIMaf3moenFLdKMNSrO/nz0N7R03c7pAt4epYv4PXXhxerCrMQBv6wIaLJMy/bG\nJuH1QZk9/CGUIZMs07LWJCCB5xPQAT+fycCGpOWSG+TeqKxbb1nictPPJK0voaTfDfXScpPP\ncUd9FvSUGoe1WsCfq5ZmhRZpxhM1Nzv9HqW1ejbKuPP16EH0EVRmFxJxQElk6hOnXta9/QLi\nvokeR9ei09B1KA5/T5R4TQISeI6ADvg5FgO/lpbvZSjO7Un0DtTM0vJNF2TSRbujXtr2HCzH\nHXUHHOaZ1LNNVkpsFcLDat6S+IkGr0MGu6Hvog+jsm4ql4RbAAAVrUlEQVRpop629fgbB7or\nqu82n4Pto9CtaH7UzL5HYOr7hobIN7Kd63GvhnA3JTDqBHTAQ3YFzEJ590HXoTe3KPtSxKXL\nOjf33VEvTQf8HO2DWf0zKmv9HUTcuc8lH4i1N1GKzAS+Bf0GnYDuR1ehl6NmtjyBeSjcsFkk\nYRvX4tNDo0lAAs8Q0AEP8ZVQdlOvr1LGGCe7e7M+/2brOuDnqCzOapzZz9BMqLDpWNkFpbWZ\nVuqg2ZwUaAeU7ua0nt+KpqAy+zIRF5ZF1sL/zvILbdIYLYFRIlApB1zfZTYKJ/GpDip5QQdp\nTNI9AlPJOq2/o1HGYE9HhdOdnfXMWE8LedAsXcY/HUOhFiVt3qVtZYlPOk0CEqgggU5ahBWs\ntlUacAJ/oXzLoM+gjJE+hDIemmGC36Iq2J1UYqE2FVmY+DvapDFaAhKQgAQmQMAu6AnAG9Jd\n043+BHppSfkzdpwx4leVxBssgVEkYBf0KJ71AapzxkLXREuidHuehe5F2nMEwiiOKy3msAmj\nsBokSzf6iSgt+jega1Bhaf0fg45F5xWBLiUgAQlIYPIJdNoCzqcQM5s7Y6IZH30A5SMi+YCD\nwwlAwDKr+FqU1mXB6BHW90BxzINks1GYzLxP+eKI90Zxuo+izKSeBWkSkMBzBCrVAn6uWq71\nk0AnDnhTChinkhm2+RhFbHr0dpRx0gPRqFseUPJw8j2UD2vEwmgrlLHUQ9Ag2iYU6ifoOLQf\nSj00CUjg+QR0wM9nYsgECbRzwDOQ/03omyXHyTeJ45xfXRI/CsFTqGRavN8pqexqhMc5v64k\n3mAJSGDwCeiAB/8cDV0J2zngvJaTruZWXZIZM8y7s6NqG1LxdN2mW7fMfk3Ez8siDZeABAae\nQKUcsOOGA3+9PV3Apfl7NfpPi+L+jbikG1VL3f+F8gWqMht1RmVcDJeABPpAQAfcB+jjOGSc\nSjHuW7Z74jMpa1RNRqN65q23BIaUgA54OE7cGRRzAbR+SXEzRvwWdFpJ/CgEh9E8aIOSyqbr\nKp+HHGVGJWgMloAEJDC6BNqNAYdMZjnnFaQls1FncSyHo0zSmrUufBRXM5M4E7Fe0lD5KWz/\nEt2CZm+Ic1MCEhgeArnf/RflWwhDb9MPfQ1GpwIfp6qZaHUpOrS2nI/l1ij/tCCvKY1yFzTV\nn+aTaGF0CQqjLPNvC9+B8nCyGboPaRKQgAQkIIGnCXTSAk7CaVGcSd4XjSM+E+2C5kDaMwTC\nKP+wIR+0CKN8BWtXlP9WpElAAsNNoFIt4OE+FdUpfacOuDo1tiYSkIAExk6gUg7YSVhjvwDc\nY+wE5maX5VFmamsSkIAEJAABHbCXQTcJZKLEn9Gd6LLa8nSWqyFNAhKQwEgT0AGP9OnvauXf\nSO55NSgfx3gFymSotdBt6Gz0OqRJQAISkIAE+kqgamPA6Wq+B+1WQnUvwuOIW31as2RXgyUg\ngREm4BjwCJ/8Qal6/q3e/CivHw2i5dWoB9CeJYXLzO30vrylJN5gCUhAApUnYBf0cJ3iBShu\n/uHCvejfKE7uTLQ+GiRbicLk9Z8nSgr1KOHnoqTTJCABCYwkAR3w8Jz2JSnqhShOazu0HHo1\nyj9pOBVthwbFnqQgU9oUJvFJp0lAAhKQgAT6RqCTMeBzKN3JKGMgjfZhAtKqXKoxok/b23Hc\nO1BZF/nsxN2P8m1mTQISkECnBCo1BtxppU3XXQLtHHBmET+FFm9RjPOJ+3aL+F5GxfHeiA5A\n+TJVvU3HxuEoLfd2reT6/VyXgAQkUCkHPIrfgs4nCdMCy38Qyv/XzXjqg2iQLe/N/hNNbVHI\nU4jLaz6DYA9RiK1QWuzLoH3RVJQW+o615YYsH0eaBCQggZEkMCpjwKtwdg9Et6O70XXoSnQT\nihP+F9ofzYO0ySFwHtmE+/UobLP9Q/QPtCq6GGkSkIAEJFBhAvkQ/39rijM4B52AjkQnoXTd\n/hslTb7YtA3qtVWtC7oZvxmbBRomAQlIYAwEKtUFPYZ6D2XSdIPGscbRptVVZhmnXA9dgJK+\n11257RwwRXr6wWFYJmGlvJoEJCCBySZQKQdc9THgN3H2r0VZZpZwmcXpnok2QmklvxulpTxe\nm40dv4Q6nWT08g4O9E7SnIVSrm+gdOXm847bofegD6B0pWsSkIAEJDAEBKrugFfkHJyLWjnf\n+tN0DxuXoIXqA8exnu7WvLebp7VOLP8sPtZqUlIeJFZHe6BDUD7j+BQ6G22AzkCaBCQgAQlI\nYCAIZGbwFajTlmhmSOf91HyruJe2JgdLK7xThz0daedHZe/ZEqVJQAISqByB3CNzr8w9Uxtw\nAttSvpys49EaLcqaMeB1USZk5fOJa6Ne2lgdcC/L5rEkIAEJDAqBSjngqndBH85Vk3HSdNtu\njm5GN6G7UFq6GaudCy2GFkBxvjujdOtqEpCABCQgga4RqLoDTuv3e+g4tCdaDzW2hPPRiFvQ\nd9E+6EbUL8vTXS8s5z2tfk0CEpBAvwmk4ZN7dSfWq3tkJ2WZcJqqO+ACUCYwbV3bSKs3X8LK\nRKl8mOM+1G8rJl/lvxtpEpCABCTQmsBjraOHI9ZW0OCcp8xw7nSy2ERK/Xp23hF9biKZVHzf\nfPozD2yfrng9J1K9Vdj53WiniWRS8X3zFsb70ScqXs+JVC+vYH4QvWYMmcT5XjSG9CaVwMAQ\n2IaSZCxcKyfwNqJuK482BgJvRplLoZUT2IKoQehhKy9h/2M2pQiD/i3+rlEalW9Bdw2gGUtA\nAhKQgATGQ0AHPB5q7iMBCUhAAhKYIAEd8AQBursEJCABCUhgPAR0wOOh5j4SkIAEJCCBCRLQ\nAU8QoLtLQAISkIAExkNABzweau4jAQlIQAISmCABHfAEAbq7BCQgAQlIYDwEdMDjoeY+EpCA\nBCQggQkS0AFPEKC7S0ACEpCABMZDQAc8HmrDvU++O12J76h28TTIqD1cGcmoPYH2KbyO2jMy\nRYUI5B9w5N8vauUEpiNq8fJoYyAgo/aXQRo4S7RPNtIpwmjJkSZg5SUgAQlIQAISkIAEJCAB\nCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQg\nAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk\nIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhIYJgLTDlNhLeuECCzK3mXn+2binphQ7sO9\n85so/tXospJq5H/froEWQJegpB01a8VoNmDMWQLkQcLvLImrQvBMVGIFlP+xnd/RP9B9qJmN\n6nXUKaNRvo6aXS+GVYTAvNTjvy20TEXqOZ5qbF/jsnPJzksTfkUtTcEwjnqRkvRVDG7HaN8G\nPgWnLA+vIpBand7N8jZUX9/72d6xFl+/GNXraCyMRu46mr7+CnG9sgRWrtXsjyzzhN5o9zQG\njMj2G6nnj1vUNT0GP0MLoXeh89Br0D7oz2h5lBZela0do9Q919d/0IHZaLCLGrarsvk6KnII\nuh59Ef0OvRZ9BOX6yG/qlyg2qtfRWBiF0yheR6m3VnECn6N+eUpfv+L17LR6c5PwUBQmj9SW\nzVrAH67FfZBlvW3PRvZtDK9PM+zrnTJ6ARWN8z192Cs8xvKnvrkGNmrY7xW18PSSFDaq19FY\nGI3qdVRcIy4rTOAI6vYUmrXCdRxL1c4ncW6ev0bpIst6MwecdHHQc6B6y1jVw+iC+sCKrXfK\naFnqHX57Vaz+raoTZ/EXFCebcd1Gu5KAzKko4kbxOhoro1G8jqaxC7rxp1PN7XTt/BO9EG2N\n5keXozNRHMmoWbpFv4TSJb9FSeWnEB5uV6F7G9JknC832ZVQ0j2OqmadMEqdwyiW9Guh1VD4\npLs+7KpoeZh9ZUnFZiQ8k/WmoifRqF5HY2EEppG8jlJvreIEZqJ+uRHcinJjTGulUJxy2Y2E\nqJGwOOBmLeB5a+Gnl1A4tRa/YEl8lYLLGKWO30Dhl2upuK6yzDW3Nxq1h/zdahy+xTLmdfQM\nh/q/jYwSN5LXUboJtGoTWJHq5TznNZGvoeXRy1Au+CXR79BcSPtfArPVNsteobm7Fj/z/+42\nclur1GqcB7xN0SK1ZWaO74Q+j0bF3kZFd0V5TW13FPM6eoZD8bcZo8R5HRWEXFaKQJ7A34HW\nblKrPKWntbJHk7hRCdqixqBxDHjhWvjRJSCOqcXnIabqVsYo9V4XvQ+l67XeMsyRrvtH0Cg8\npGxHPR9DeRB5KSrM66ggMc0027HajFFSeB2FgjZSBNISjgM+caRq/b+VLXMu6TrNONbp/5v8\n2a0/sRZ2cz8bUt2VMkbtanwUCcIoM4OrbGn1pp7XosZ36r2OnjnzrRg9k6L8b2Wvo1wc2ugS\nuKNW9dlGF0FpzTOL9XZU1j2f8IdQ4wQtgrQagapfX3m/9/toR3QB2hzdhupt1K+jThjV82q2\nXtnrKGODWrUJZBwus1G3blLN5WphVZ2t2qTKYwrKOGbGzF/csNc8bKebMTN/M9loVG1WKh4G\n56Bm95IqX1+p70EozvdY9GrU6HwJetpG9TrqlNEoX0fFNeKyogTeQr3SPZYvYOVptLCsn4wS\nt14ROILLVt2rb67x+WwDl8/Xwt/aEF7VzVaMLq2xyOSaesucg6fQqfWBFVr/MHXJb+cYVLzv\nW1a9Ub2OxsJoVK+jsmvG8IoQyM3hNJSbxenoXWhLdApK2AFolK2Vc8kT/OUordzMIN8Q7VHb\nzo13VKwVow2AED6ZLf5dFEZ5YHkA3YVWRFWzualQPjWZ308eMNICbqZZCI+N4nU0VkajeB09\nc3X4t/IE5qSG+6GMR+WmEeWG+Rk06tbKuYRNup9PQmnNFex+z3pm+Y6KtWOU14/q3wPOdXYW\nWqKigPJ97OJaaLXM766wUbuOxsNo1K6j4tpwOSIE8qrICmjxEanvZFYz41SroVFyvGPltwA7\nrIpmGuuOI5Te66j9yfY6as/IFBKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQ\ngAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIS\nkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEAC\nEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhA\nAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlI\nQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJ\nSEACEpCABCQgAQlIQAISkIAEJCCBZwlM9+yaKxKQgAR6S2AKh3sjmg3d3NtDezQJSEACEpDA\n6BKYk6r/Fx09ugis+SgTeMEoV966S0ACEpCABPpFQAfcL/IeVwISkIAERprA9CNdeysvgeEl\nMC1F3xi9Cs2CrkRnoH+iwtLFuwU6D92H3oCWRkl3OnoYNVruCZuhldEM6O/od6hZWoKfzu+1\nLJdFU9Gf0CWomS1D4KZoDnQauhRpEpCABCQggaEhkMlLJ6GMnz6BbqutP8ryY6iwFVlJmn1R\nJjndj65AT6F70XKo3pZk43yUfeKw76ytX84yeTXazgTkmMnvRpSyPIn2RHlAqLcfs5F8H0N3\n1NYPrC0dAwaEJgEJSEACg0/g3RQxzuzbaNZacZdneQtKSzUtzFjhgJP2l+iFCcRWR7ejtJpn\nQ7E4zAtQHOg7a9sspnkdugtdhYr9WZ1mc5R805peEMVSlsNRwt+DCvsAK0UZZqoFbsAy+Sb8\n6FqYCwlIQAISkMBAE0gLM47rNQ2l3Ijtj6L5auGFA76F7RlrYcUiLeXksX0t4B217XQ3N9pX\nCUjaD9ZFxHknbLW6sKzOjB5COWbRCk7r+Fb0IlRvO7KRPHTA9VRcHxkCTsIamVNtRStE4NRa\nXY5nme7lzVEc3ykoXb3pkq63Y9h4pD6A9ewbW+WZxdNjyVnN2Gwcd73SBR1LyzmWFnbGfK9G\nj6P6tEuxnZb0Aigt47nRwihlS+u83n5Vv+G6BCQgAQlIYBgIvJ9CZhw3LcgoDvYEtAYqLI4x\ncZ8pAuqW07GesduzamEnsizyKlueXkv7ig7SJo/10atrab/BstHSQk65bQE3knF7JAg4C3ok\nTrOVrCCBn1Gnw9CG6PVoE7QZyphttv+ECnthsVK3nJX1OMB0DceKFvK2rDe2oJ9OwJ9M4ooV\naX/P+l5PhzT/8w+Ci/HhtNAbLce3F66RitsSkIAEJDCwBF5CyTZtUrrPEZaW5/61uKIFfEht\nu37xKjaS9vO1wLRQs51XlRotXc5x9IvUIl7EMq3nC2vbjYu0wtNdHceflnbGhNMF3WhLEJBj\n2gJuJOO2BCQgAQkMJIHfUqo4rrR46y2t34R/rxZYOOA4wKIlWoua5jesxImuWQvIMtvnoDjN\nejuUjeT7trrAk2thjQ8CLyP8UXQxmoJif0DJe9Vs1FnKqQOuA+KqBCQgAQkMNoHXULy8LnQT\n2hNtjL6ArkHpHs4YbaxwwHFy16J3ozjMTH5K2C6o3g5iI+F/Rm9HW6Kfo4Qdh+ptWTYermk3\nlnH+n0UpwxOomLDF6tMt53+zvAt9BG2EfozyYJC0toCBoElAAhKQwHAQiIOciuIco7QwL0Pp\n/i2scMAHE3AkitNO2qkorxY1WsZjP4PqJ3cl37SW50eNthwBZ6Ii3+Sdh4L3oEaLQz4VPYaS\nLmPP66MHkA4YCJoEJCABCQwPgTjMhVG6dmdrUuzCAf+wFpc0yzRJ1yxoUQJXQs3ybUw/EwEr\no8VQY/d1Y9rZCVi6MdBtCUhAAhKQQJUINDrgKtXNukhg6AnkCVqTgAQkIAEJSKDHBHTAPQbu\n4STQQwIZa82YayY6aRKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJ\nSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCAB\nCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQg\nAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk\nIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE\nJCABCUhAAhKQgAQkIAEJSEACEpCABAaVwP8DusBsF+b1Z7gAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “cars”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(cars, main=\"cars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try fitting a line to this data set. We'll break the data frame into two, independent vectors, and then we'll use those vectors as a parameter into a linear regression tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = distance ~ speed)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-29.069  -9.525  -2.272   9.215  43.201 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -17.5791     6.7584  -2.601   0.0123 *  \n",
       "speed         3.9324     0.4155   9.464 1.49e-12 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 15.38 on 48 degrees of freedom\n",
       "Multiple R-squared:  0.6511,\tAdjusted R-squared:  0.6438 \n",
       "F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance <- cars$dist\n",
    "speed <- cars$speed\n",
    "\n",
    "fit <- lm(distance ~ speed)\n",
    "summary(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary of the fit computed above provides a wealth of information about the regression analysis, including the best-fit parameters, residual distribution, and statistical significance. We can now visualize the fit against our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k+EPUDDJgody8WgD5P2Ww8OaFhqEmh2OAE327i3V3QD/+YAnwUljbjQaNdTcRUu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\nK9cVvh6w+gz0KzoKJ+C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ZSI6x3nscLL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\n3rwN2IAN2EA5DTgBl/N991HbgA3Yg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      "text/plain": [
       "Plot with title “cars”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(speed, distance, main=\"cars\")\n",
    "abline(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Distributions\n",
    "\n",
    "In the few cells below, we generate two random distributions of data. We plot their histograms, and use paired, two-sided t-test to determine if the means are significantly different. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "-0.0965469994077768"
      ],
      "text/latex": [
       "-0.0965469994077768"
      ],
      "text/markdown": [
       "-0.0965469994077768"
      ],
      "text/plain": [
       "[1] -0.096547"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "-0.0542475884101106"
      ],
      "text/latex": [
       "-0.0542475884101106"
      ],
      "text/markdown": [
       "-0.0542475884101106"
      ],
      "text/plain": [
       "[1] -0.05424759"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1fytAfyzMQ8CCCAAAII9FegaAH2k81uvqQ8WfHTvm5LKv4rQ2cq45W6tH9rQ7ZU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\nYII=",
      "text/plain": [
       "Plot with title “Histogram of dist1”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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FZ8rbKb0kehIYAAAggggEAVgawFOF7MkxrwHy1YRtlK8f3hjZWrlEmKv+ZxZYWG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\n0hsoL85vmor6ENZc2nZ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      "text/plain": [
       "Plot with title “Histogram of dist2”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(121)\n",
    "\n",
    "dist1 <- rnorm(100)\n",
    "\n",
    "set.seed(391)\n",
    "dist2 <- rnorm(100)\n",
    "\n",
    "mean(dist1)\n",
    "mean(dist2)\n",
    "\n",
    "hist(dist1)\n",
    "hist(dist2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPaired t-test\n",
       "\n",
       "data:  dist1 and dist2\n",
       "t = -0.4923, df = 99, p-value = 0.6236\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3416082  0.2057942\n",
       "sample estimates:\n",
       "mean of the differences \n",
       "            -0.06790704 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's do a t-test to compare the difference in their means\n",
    "\n",
    "t.test(dist1, dist2, alternative=\"two.sided\", var.equal=F, paired=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the results of the t-test above, the difference of the means of our two distributions is not statistically significant ($t=-0.4924$, $df=99$, $p > .05$)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}