Tweaked to have separate script for boilerplate
[ou-jupyter-r-demo.git] / section5.1.ipynb
index 5b433be07941f4140207ab6a1463741760e5baf2..3539ef0c4d8c861ad764538a19e64fa521f06783 100644 (file)
@@ -9,23 +9,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true
-   },
+   "metadata": {},
    "source": [
     "### Imports and defintions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {
-    "hidden": true
+    "init_cell": true
    },
    "outputs": [],
    "source": [
     "library(tidyverse)\n",
-    "# library(cowplot)\n",
     "library(repr)\n",
     "library(ggfortify)\n",
     "\n",
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {
-    "hidden": true
+    "init_cell": true
    },
    "outputs": [],
    "source": [
-    "# Multiple plot function\n",
-    "#\n",
-    "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n",
-    "# - cols:   Number of columns in layout\n",
-    "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n",
-    "#\n",
-    "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n",
-    "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n",
-    "# 3 will go all the way across the bottom.\n",
-    "#\n",
-    "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n",
-    "  library(grid)\n",
-    "\n",
-    "  # Make a list from the ... arguments and plotlist\n",
-    "  plots <- c(list(...), plotlist)\n",
-    "\n",
-    "  numPlots = length(plots)\n",
-    "\n",
-    "  # If layout is NULL, then use 'cols' to determine layout\n",
-    "  if (is.null(layout)) {\n",
-    "    # Make the panel\n",
-    "    # ncol: Number of columns of plots\n",
-    "    # nrow: Number of rows needed, calculated from # of cols\n",
-    "    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n",
-    "                    ncol = cols, nrow = ceiling(numPlots/cols))\n",
-    "  }\n",
-    "\n",
-    " if (numPlots==1) {\n",
-    "    print(plots[[1]])\n",
-    "\n",
-    "  } else {\n",
-    "    # Set up the page\n",
-    "    grid.newpage()\n",
-    "    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n",
-    "\n",
-    "    # Make each plot, in the correct location\n",
-    "    for (i in 1:numPlots) {\n",
-    "      # Get the i,j matrix positions of the regions that contain this subplot\n",
-    "      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n",
-    "\n",
-    "      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n",
-    "                                      layout.pos.col = matchidx$col))\n",
-    "    }\n",
-    "  }\n",
-    "}"
+    "source('plot_extensions.R')"
    ]
   },
   {
@@ -99,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
     "# print(cbind(af,PctExp=afss/sum(afss)*100))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ggplot(rubber, aes(x=hardness, y=loss)) + \n",
+    "#     geom_point() +\n",
+    "#     stat_smooth(method = \"lm\", col = \"red\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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GPPHEE0Kh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\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9q9/fwan3zyzt/+5n/0qDwzU/b778xm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\nQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplotRegression(fit)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Your solution here"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = loss ~ strength, data = rubber)\n",
+       "\n",
+       "Residuals:\n",
+       "     Min       1Q   Median       3Q      Max \n",
+       "-155.640  -59.919    2.795   61.221  183.285 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)    \n",
+       "(Intercept) 305.2248    79.9962   3.815 0.000688 ***\n",
+       "strength     -0.7192     0.4347  -1.654 0.109232    \n",
+       "---\n",
+       "Signif. codes:  0 â€˜***’ 0.001 â€˜**’ 0.01 â€˜*’ 0.05 â€˜.’ 0.1 â€˜ â€™ 1\n",
+       "\n",
+       "Residual standard error: 85.56 on 28 degrees of freedom\n",
+       "Multiple R-squared:  0.08904,\tAdjusted R-squared:  0.0565 \n",
+       "F-statistic: 2.737 on 1 and 28 DF,  p-value: 0.1092\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Df</th><th scope=col>Sum Sq</th><th scope=col>Mean Sq</th><th scope=col>F value</th><th scope=col>Pr(&gt;F)</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>strength</th><td> 1       </td><td> 20034.77</td><td>20034.772</td><td>2.736769 </td><td>0.1092317</td></tr>\n",
+       "\t<tr><th scope=row>Residuals</th><td>28       </td><td>204976.59</td><td> 7320.593</td><td>      NA </td><td>       NA</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n",
+       "\\hline\n",
+       "\tstrength &  1        &  20034.77 & 20034.772 & 2.736769  & 0.1092317\\\\\n",
+       "\tResiduals & 28        & 204976.59 &  7320.593 &       NA  &        NA\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n",
+       "|---|---|\n",
+       "| strength |  1        |  20034.77 | 20034.772 | 2.736769  | 0.1092317 | \n",
+       "| Residuals | 28        | 204976.59 |  7320.593 |       NA  |        NA | \n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "          Df Sum Sq    Mean Sq   F value  Pr(>F)   \n",
+       "strength   1  20034.77 20034.772 2.736769 0.1092317\n",
+       "Residuals 28 204976.59  7320.593       NA        NA"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fit <- lm(loss ~ strength, data = rubber)\n",
+    "summary(fit)\n",
+    "anova(fit)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "metadata": {
     "solution2": "hidden"
    },
     "anova(fit)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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OgQMAAAAE\nh8ABAAAAgkPgAAAAAMEhcAAAAIDgEDgAAABAcAgcAAAAIDgEDgAAABAcAgcAAAAIDoEDAAAA\nBIfAAQAAAIJD4AAAAADBIXAAAACA4BA4AAAAQHDiELxHXV3dunXrjhw5IhKJRowYccsttyQn\nJxNCnE7nhg0bduzY4XA4iouLlyxZIpFIfLQDAABAlBJ8hMNut69evVomk61evXr58uWnT59+\n5pln2F+tW7eutLR06dKld95552+//fbqq6/6bgcAAIAoJXjgqKysbGxs/POf/zxkyJDi4uIF\nCxaUl5dbLBaz2fz1118vXry4uLi4qKho2bJlpaWlra2t3tqF7icAAAAIR/BTKkOGDPnwww/l\ncrnFYmloaNi+ffvQoUPlcvmRI0csFsuoUaPYpxUWFjqdzoqKCoVC4bF99OjRbIvZbF67di23\n/DFjxnC/EpRIJFKpVCF4o7CgKCqG104sFhNCFAoFwzDh7osgxGIxwzAikSjcHREETdOEEIlE\nEqt/ohRF0TQdq2tHOrZgDK+gSCSK7cMLIUQulwdS2+ByuXwtKmid8oKmablcTgh57LHHDh8+\nrFarn332WUJIc3OzWCzm/gTFYrFarT579qxSqfTYzi3QYrFs2LCBeyiTyS644AKh14JdEYVC\nEYI3CpfYXjtCCPt3CFFKLBazB75YFfM7YGyvYKzGfY5UKg3kaU6n08dvQ7cDP/TQQ2az+auv\nvvrrX/+6Zs0ahmEoinJ7jtPp9NbO/azVav/9739zDzUaTUtLi3DdJoRQFBUXF+dwONrb2wV9\nozDSarUGgyHcvRCKUqmUSqUGg8F3+o5ecrnc5XLZbLZwd0QQ7LcOi8VisVjC3RdBsMMbbW1t\n4e6IULRaLUVRMXxmXKVSmc3mWD28yGQyhUJhNBrtdrvfJzMMk5CQ4O23ggeO6urqM2fOFBUV\naTQajUZzww03bN68+cCBA4mJiXa73Ww2s7HX6XS2t7cnJycrlUqP7dwCRSLR8OHDuYcmk8lk\nMgm6CmwAYhjG4XAI+kbhFcNrxw51Op1O3+k7erlcLpfLFatbMOZ3QJqmY3jtSMcOGNsrGMOH\nF+7q0b5vwVAUjb744ovcljCZTDabTSwW63Q6mUx24MABtv3w4cM0TQ8aNMhbu9D9BAAAAOEI\nPsJRVFS0Zs2aV1555aqrrrLb7e+//35aWtp5550nk8mmTp26fv36pKQkiqLWrl174YUXskMx\n3toBAAAgSlEhKKwtLy9fv359ZWWlTCYrKChYtGhRSkoKIcTpdK5bt27nzp0ulyIirIAAACAA\nSURBVGvcuHGLFy/mhm48tnsUmlMqSUlJdrs9hs9BJiYm8itzY4xGo5HJZM3NzbE65qlUKl0u\nV6yWOEgkkri4OLPZbDQaw90XQdA0rdVqha5FC6OEhASKomL4CKPVao1GY6weXhQKhUqlMhgM\nAVaJ8Usg3IQicAgKgSMoEDiiGgJHVEPgiHYIHHw+AgfupQIAAACCQ+AAAAAAwSFwAAAAgOAQ\nOAAAAEBwCBwAAAAgOAQOAAAAEBwCB8QChmHMZnO4ewEAAF4hcEB0O3369PLly9lJ8ceNG7dp\n06Zw9wgAADyI5ds9Q8yz2+033HDDr7/+yj6sqKi44447CCHXXHNNWPsFAADuMMIBUezTTz/l\n0gbn0UcfjdX7RAMARC8EDohiZWVl3RtPnTp16tSp0HcGAAB8QODwqrKysrKyMty9AF80Gk33\nRpqm1Wp16DsDAAA+IHD4wcaOsrKyP/74I9x9AXdXXHGFTCZza7z00ktVKlVY+gMAAN4gcPRA\nZYdwdwTOycvLW716tVQq5VoGDx78wgsvhLFLAADgEa5S6Q0ucwwaNCi8PYFbbrllwoQJn3/+\n+enTpwsKCmbPns3PHwAAECEQOPqEP9qB8BEuubm5ubm54e4FAAD4gsARNBj2AAAA8AaBI/iQ\nPAAAANwgcAgIyQMAAICFwBEKKPUAAIB+DoEj1DDsAQAA/RACR9ggeQAAQP+BwBF+OOECAAAx\nD4EjsvTbYY8DBw588sknTU1Nw4YNW7hwYWJiYrh7BAAAwYTAEaH6VfJYv379/fffzz187bXX\nNm/enJ+fH8YuAQBAcCFwRLqYP+FSVVX1yCOP8FtaWlqWLVu2bdu2cHUJAACCDoEjmsTksMe3\n335rsVjcGsvKyqqqqrKzs8PRIwAACD4EjqgUS8mje9pgmc3mEPcEAACEg8AR3WLghEthYWH3\nRq1WO3jw4NB3BgAABEKHuwMQNJUdwt2Rnpk4ceKMGTPcGp944gncZR4AIJZghCMGRd2wx+uv\nv56Xl7dp06aGhoa8vLw777xz5syZ4e4UAAAEEwJHjIuKag+5XH7//ffzr4wFAIAYg8DRX0RF\n8gAAgFiFwNHvRN0JFwAAiAEIHP0aFz4wlTgAAAgKgQMIIeTIkSNGo5H9GcMeAAAQdAgc4A7V\nHgAAEHQIHOAVkgcAAAQLAgf4hzpTAADoIwQO6BkMewAAQC8gcEAvYdgDAAACh8ABQYBhjz5y\nOp01NTUMw+j1epFIFO7uAAAEH27eBsFUyRPuvkSNL774YsyYMcXFxePGjRs9evSWLVvC3SMA\ngOBD4AChIHkEYt++fYsXL66vr2cfNjQ0LFu2bM+ePeHtFQBA0FEMw4S7D31is9kEGoI+cuQI\n9zNN0wzDRPtn5QNN0y6XKwRvlJeXF4J3cUPTNEVRTqcz9G/t1/z58zdt2uTWeNVVV3366aeB\nLyS2/z4pimJXMDR/omERsh0wLNhDdGTugEHRH3ZAl8sVyAq6XC6JROLtt1Ffw+FwONra2oRY\nstls5n5WqVQul8tisQjxRpFAqVTy11c4v/32G/dzyAo+1Gq1TCYzGAwReEz/448/ujeWl5e3\ntLQEvhClUul0Oq1Wa/D6FUEkEolWq7VYLCaTKdx9EQRN0xqNprW1NdwdEUp8fDxN0z36k44u\nWq3WaDTGaqJSKBRKpdJoNNpstkCen5SU5O1XUR84CCEC5UpusRRFCfpGESL0a1dRUcH+ELLk\nEYFbMDk5uXvjgAEDetRV9skRuHZBwa1XbK9grK4dK4YHAEjH2sXqCnJ/n31fQdRwQPj151LT\nG2+8sXvjggULQt8TAABBIXBAZOlvyeOqq65asWKFVCrlWpYvXz537twwdgkAQAixcEoFYlL/\nmVjsr3/963XXXbdr1y6Xy1VSUjJ48OBw9wgAIPgQOCAKxHz4GDRoUEyuFwAAB4EDogxmNQUA\niEYIHBCtYn7YAwAgliBwQCxA+AAAiHAIHBBrcM4FACACIXBAzOKSh1wuHzFiRHg7AwDQzyFw\nQL9QVlZmMpnYqc0x8gEAEHoIHNDvoOADACD0EDigX0PBBwBAaCBwABCCYQ8AAIEhcAC4Q/gA\nAAg6BA4AXxA+AACCAoEDIFAIHwAAvYbAAdAbCB8QabZt27Z169bW1taCgoJFixap1epw9wig\nCwQOgL5C+ICwe+yxx1577TX2548++ujtt9/+4osv0tLSwtsrAD463B0AiCmVPOHuC/QX27dv\n59IG68SJE/fee2+4+gPgEUY4AISCkQ8Ija+++qp743fffWez2aRSaej7A+ARAgdAKCB8gHCs\nVmv3RqfT6XA4EDggciBwAIQawgcE16hRo7o35ubmKpXK0HcGwBsEDoBwciv1QP6AXpg7d+67\n7777yy+/8BufeeaZcPUHwCMEDoAIgsEP6AWxWPzee+89//zzW7dubWlpGTly5P333z9+/Hgf\nL6moqPjxxx/b2tpGjx49adKkkHUV+jMEDoAIhfABgYuLi3viiSeeeOKJQJ781ltvrV692maz\nsQ+nTJmyceNGVHuA0BA4oH8xGAzHjh0zm80ZGRnZ2dnh7k6gcOYFgmXPnj2rVq3it3z//fdP\nPfXUY489FqYeQX+BwOEdwxCKCncnIJj27t378ccfcyX9w4cPv+mmm8Ti6NsLMPgBvfbxxx93\nb3z//fcROEBo0XeoDRGGGX3ppU6NxpqRYcnJceblWTMyXMnJ1vR0pJAo1djYuGnTJrvdzrWU\nlZV99tlnV199dRh71XeVlZVSqZRhGHbVkD/At7Nnz3ZvbGlpYRiGwsENhITA4Rnd1CQ2GMQG\ng6y+Xrt7N9fuVKksWVkWnc6q11t0OvZnJ+5ZEA327t3LTxusn3/+eebMmbF0nMXgB/g2dOhQ\nj42xtBdAZELg8Ex07JjndqNRdeSI6sgRfqM9IcGi11t0OqtOdy6FZGUxEVyBVV9ff+bMmfj4\n+KysrP5zlGlvb+/eaLVaHQ6HRCIJfX9CAJUf0N0tt9yyYcOGxsZGfuODDz4Yrv5A/4HA4RmT\nkHDq6qvlNTXymhrJmTO+nyxpbpY0N2t+/72ziaatAweeiyAdYyG2tDSGDvPNa9ra2jZu3His\nI05lZmYuWLBgwIAB4e1VaHhczfj4+FhNG90hfwAhJDEx8cMPP/zLX/6yc+dOQkhqaurDDz98\nxRVXhLtfEPsohmF68TKn0/n555+7XK6LLrpIq9UGvVuBM5lMJpNJiCVzR2exyRR/6pS4ooI+\ndoyNIPLaWpGnr8u+MRKJJSPDotd3DoTo9fakpGB33Je33377jz/+4LekpaXdfffdcXFxRqMx\nlD0JJblcLhaLT5069fzzz7e0tPB/NW/evHHjxoWrY8HCr+HotYjNHxKJJC4uzmw2x+qfKE3T\nWq3W7S8zBAwGg9FoDMEdZRMSEiiK8lg7Ehu0Wq3RaHQ6neHuiCAUCoVKpTIYDNx11L4lJyd7\n+1WgIxxGo/Huu+/etm0b+8/VrFmztmzZQgjJycn5/vvvdTpdgMuJRk6VypKS4szLM5vNXKOk\nuVleXS2vqZGxEaSmRl5XR/ncHpTdrqi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QZCrDqdI3ZnCum1qL9KxWQyhaBoNObv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\nASznWhsY1T0PAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplotRegression(fit)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
     "anova(fit)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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JcFpkFgd3\n7Pjzzz9NLAR6/fvf/9aawuH06dPbtm1zVn0AAICxIHAwkcNih95JWtlsaPcy1dmzZ3ULz5w5\n4/iaAAAAw0HgYC4HxI4xY8boFo4dO9auL+pOqBm+tcD1FAAA0AVnRqaza+x45ZVXevbsqVnS\nt2/fl156yU4v535GjhxpYiEAAHRw0HjuGuw0jEUgEBw+fHjjxo3UpYGnnnrq2Wef5fF4Nn8h\nd/XJJ59cuHBB8x4WEyZMmDlzphOrBAAAzASBw2XYaRgLn89ftmzZsmXLbLvaDiI8PPz06dPf\nfffdlStXPDw84uPjMzIy9F5nAQCADg4Ch4tx+kRhQEtAQMAHH3zg7FoAAADTQR8OlwTTogMA\nAHAtEDhcGMQOAAAArgICh8uD2AEAAID5IHC4CYgdAAAAmAwCh1uBzAEAAICZIHC4G2jqAAAA\nwEAQONwTxA4AAACMAoHDnd27d6+goMDZtQAAAAAgcHQAd+7cgdYOAAAAzgWBo6OAiywAAACc\nCAJHxwKZAwAAgFNA4OhwoKkDAACA40Hg6KAgdgAAAHAkCBwdGsQOAAAAjgGBA0DsAAAAYHcQ\nOMDfIHYAAACwHwgc4AkQOwAAANgDBA6gB2QOAAAAtgWBA+gHTR0AAABsCAIHMARiBwAAAJuA\nwAGMg9gBAADAShA4gKkgdgAAALAYBA5gHogdAAAALACBA1gCYgcAAACzQOBoV8Cvv0r27MHb\n2pxdEeaCzAEAAMBEbGdXgKmam4M2bGA1N4d/9VV9fHzV7Nmyzp2dXScmojJHp06dnF0RAAAA\njAYtHPqxtm1jNTcjhFgtLZKcnD5z5vRcvFh89CimVju7akwEV1gAAAAYBi0c+mHV1QSXiysU\ndInn1aueV68qAgNrUlJqZsxQisVOrB4zQWsHAACA9kALh36qlStvHDxYvny5PCREs5xbVRW6\nfn3/hITOq1Z5XrnirOoxGbR2AAAA0AUtHO1S+fhUzJ9f8fTTXhcvSnJyfI8fxwiCWoQplX6H\nD/sdPtwWFVU7fXp1crJaJHJubZkGWjsAAABogsBhDI43xsU1xsXxHjyQ7Nwpyc1lNzTQC/kl\nJWHr1gVv3lw3bVp1aqoMvl+fBLEDAAAABS6pmEoeGlq+fPmV3Nx7H3/cGBenuYjV0hKwY0ef\n2bN7ZWT479+PqVTOqiQzwRUWAAAAjmjhKC8v37RpU2FhIYvF6tu37z/+8Q9/f3+EkFqt3rJl\ny5kzZ1QqVVxc3OLFizkcjoFyJiA5nPrx4+vHjxfeuhWwc6ffwYO4TEYv9Sgs7DsL9Q0AACAA\nSURBVLR6ddh339VOm1adlqYICnJiVRkFmjoAAKCDY61evdquL6BUKt98802JRPLiiy/269fv\n4sWLp06dio+PRwj9/PPPeXl5S5cuHTZs2N69e4uLi4cNG2agvL31K5VKm1eby+XW1tYSjztt\n6Hldf3/pyJHVaWlKPz/ew4fsR4/oRSyZTHT1auCOHcJ791Q+PvLgYIRhNq+hKXAc53A4BEGo\nmTGaVyqVSqVSX19fc5+IYRifz29z/UnYeDwei8WSyWQkSTq7LlbhcDgkSapcvDEPx3GBQKBW\nqxUa49FclEAgkGn8+HFRXC6XzWa3tbUZOPe6BDabjWGYPb6bHAnDMKFQSBCEXC43/VlCobC9\nRXa/pFJcXFxZWfniiy927do1Li5u3rx5t2/fbmtrk8lkv//++6JFi+Li4mJjY5cuXXrq1KlH\njx61V27velpGLRJVzZ2bn5VV8OOP9ePHkywWvQhTq32PHu3xwgt909KCt25lNzY6sZ6MAsNY\nAACgA7L7JZWuXbvu2LGD+nlaUVGRl5fXrVs3Pp9fWFjY1tYWExNDPax///5qtbqoqEggEOgt\nHzBgAFWiUChyc3Pp9Xfr1s0eDfUsFovFYmEmt0zIBw8uHTy4oqrKPztbvHMnp66OXsQvKwtb\nty5k06aGqVNrZ82Sdetm89q2B8dxhBCGYcy5JkUrLy9HCHXt2tWUB2MYRjVy2LlSdkcdER6P\n5+otHGw229U3AT0+HCwWyw3eWu7xAWGxWOhxO4ez62IVDofjBkeE+gbEcdz0DTF8WrD7QaXr\nunr16ps3b3p6en766acIoYaGBjab7eHh8Xc92GxPT8/6+nqhUKi3nF5hS0vLRx99RP+7ZMmS\nvn372qPmlnxJR0TUv/xyw7JloiNHfLdtE168SC/BW1v9srL8srJksbH1c+c2xceTjgoBbDab\nsZ/esrIyhFDPnj1NebCnp6edq+Mg9Nvb1fF4PGdXwQaok4yza2ED7rEVyGCbvGvhcrnOroIN\nsFgs099ahi/fO+57aOXKlTKZ7PDhwytWrPjpp59IktRtP1Cr1e2V0397eHi89dZb9L/dunVr\nbm62eW15PJ5SqbT4OmLbuHE148bxSkr89uzxy8piNTXRiwSXL4devqz6+OP6GTNq09IUT04s\nZltUHw6VSsWQPhztuXLlCjLY2oFhmEAgaG1tdWCl7ILP57PZ7JaWFldvHuByuSRJuvolahzH\nhUKhSqVyg+5BHh4eLS0tzq6FtXg8HofDaW1tdfU+HFQLh6v3DcIwzMPDQ61Wm949iCRJUfuz\nUtk9cNy/f7+uri42NlYkEolEoqeffnr37t35+flisVipVMpkMoFAgBBSq9XNzc3+/v5CoVBv\nOb1CLpebkpJC/9va2mqP7yE2m61Wq63sE6cMDW1+/vnyjAy//fsDsrIEGh0X2PX1AZs3S7Zs\neTRyZHVa2qO4OHt0LGWxWFTnPpf4YigoKEDtjGRxm06j1C8euVzu6udTHMcJgnD1I8JisYRC\noVqtdvUNQQgJhUI32Ao2m83hcBQKhav3R0YI4Tju6keEChzmftINBA5HdBr96quv6F/Yra2t\nCoWCzWZHRETweLz8/Hyq/ObNmziOd+rUqb1ye9fTftQeHtUzZ17/7bfC9evrx48nNa5uYATh\nc/Jk9+XL+6alBf3vf9CxFMGkHQAA4KbsPixWLBbn5uaWl5f7+/tXVVVt2LABw7CMjAyBQNDQ\n0HDw4MGePXtKpdL169fHxMSMHTuWw+HoLW9v/c4aFmsBRUhIw4QJNUlJKl9ffmkpS+NKELux\n0fvcuaDt2wV37iglEltN4MG0YbEm0h096zYtHDAsllFgWCzTwLBYRrH5sFjMASe+27dvb968\nubi4mMfj9enTZ8GCBQEBAQghtVq9adOms2fPEgQxZMiQRYsW0RN/6S3Xy06XVDw9PQsKCux3\nPsXUap+TJwOysrwuXUI6h6ClV6/qtLT6+HjCuj5HLBZLIBAolUqz3i6MQjVuYRjm4+PToDGp\nvIvy8vLicrn19fWufj6lTkOuHgFZLJavr69cLm/S6GXlosRisWbnehfl6enJ5/OlUqmrZ1k+\nn4/juKt3O8MwzM/PT6lUmjUzhWYXCO0VuvovLRcNHDR+SUlAVpb//v0sna6vKm/v2oSE6pQU\neViYZSt3g8BB6dy5MwQORoHAwTQQOBgFAodecC8VJ2uLiip97bUrBw8Wr17d2r275iL2o0dB\nv/zSLy2tx7Jl4qNHMZe6LGJbxcXFhYWFzq4FAAAAyzF0eoaOhuBya6dOrZ061fPKlYDsbPGx\nYxh98Y8gvM6f9zp/Xh4cXJOaWpOQoDJ/anD3ADdkAQAA1wUtHMzSHBNT9MEHV3Nzy59/Xqvr\nKK+iImzdupiEhM7vvuv5eBRPBwQzowMAgCuCFg4mUvr6VixcWLFggdfFi5KcHN/jx7HHl/wx\nhcLvwAG/AwdkUVE1KSk1M2YQAoFza+sU0NoBAACuBQIHg+F4Y1xcY1wcr7xcsmuXZM8etlRK\nLxSUlER8+WXohg318fFVs2bJunRxYk2dBWIHAAC4Crik4gLkYWHly5Zd3bu3+O23W3r10lzE\nammR5OT0SU/vsWyZ74kTHbNjKVxhAQAA5oMWDpdB8Hi1CQm1CQkehYWSnBy/AwdwelAiSVId\nS5X+/rVTp1bPnKkIDHRqZR0NmjoAAIDhYB4O/Rw2D4fF2I2N/nv3BuzcySsr01pEcjgNY8ZU\np6W1DhrkHvNwmHXzNibHDjeYh4MgiOzs7MuXL+M4PmzYsGnTpunebdFVwDwcTAPzcDAKTPyl\nrcMGjr8RhPeffwZkZXnn5WE632GyLl0ePf107eTJMqbent5EFtwtlpmxw9UDh0KhSEtLO3v2\nLF0yZcqU//73vzjukhdnIXAwDQQORoGJv8CTcPzRsGF3vvjiWk5OxYIFWlN0CO7dC3r//Z4T\nJ0Z+9pmgg3V0gNGz9vDNN99opg2E0IEDBzZv3uys+gAAXAgEDjehCA4uf/HFK3v3Fr33XnPf\nvpqLWC0tAZmZfWbPjn7+efGRI5iL/3QwC8QO29q/f79u4YEDBxxfEwCAy3HtlnagheRy66ZM\nqZsyRVBSIsnOluzZg2vcQFJ06ZLo0iWln1/ttGnVqamK4GAnVtWRoEuprei9H6mrtxsDABwD\nWjjckywqqvTVV/MPHKhasUIeFaW5iFNXF7x1a7+UlK5vvul1/rzuvWrdFbR2WK9fv366hf37\n93d8TQAALgcChztTi0T18+ff2rXr1rp1DWPHkiwWvQhTq32PH++xbFnfWbMCt29nuX6nORNB\n7LDGypUrRSKRZolEInnllVecVR8AgAuBwNEBYFhjXNzdTz+9undv+bJlWlN08O/fj/jyy5gp\nUzqvXi28dctZdXQwiB2WiYyM3Ldv34QJE0QikY+Pz/Tp0/ft2yeRSJxdLwCAC4Bhsfq5zLBY\ng1gslu48HJhK5Xv8eEBWluivv3Sf0ty3b/XMmfXjxpFcrgNraoQFw2JN5OCOHa4+LJYmFAoJ\ngmijp55zTTAslmlgWCyj2HxYLHQa7XB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bf4gFB4PJ6zq2Abrv6TlUJdeTTxwYb7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\nl0gkWq1Wq9Xa/czOxDCMu7t7fX09Hz7xNAdFUe7u7kIfMUAIcXN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YMSY71goOAgcAAAiMi8UOQtMVvXpV9OolUSqDtmwJTk8XGU3Kc795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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ggplotRegression(fit)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {},
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "anaerobic <- read.csv('anaerob.csv')\n",
     "ggplot(anaerobic, aes(x=oxygen, y=ventil)) + geom_point()"
     "\n",
     "a. Using GenStat, perform the regression of expired ventilation (`ventil`) on oxygen uptake (`oxygen`). Are you at all surprised by how good this regression model seems?\n",
     "\n",
-    "b. Now form a new variable `oxy2`, say, by squaring oxygen. (Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of ventil on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1).\n",
+    "b. Now form a new variable `oxy2`, say, by squaring oxygen. (Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of `ventil` on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1).\n",
     "\n",
     "c. Make the usual residual plots and comment on the fit of the model again.\n",
     "\n",
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [