From 0694a8f02701f84270c3f14fa1d7cc6bd99e49d0 Mon Sep 17 00:00:00 2001
From: Neil Smith <neil.git@njae.me.uk>
Date: Sat, 24 Feb 2018 22:26:29 +0000
Subject: [PATCH] Moved small solution to solutions book

---
 section5.1.ipynb          | 63 +++++++++++----------------------------
 section5.1solutions.ipynb | 39 ++++++++++++++++++++++++
 2 files changed, 56 insertions(+), 46 deletions(-)

diff --git a/section5.1.ipynb b/section5.1.ipynb
index 5a8ef2e..864aff1 100644
--- a/section5.1.ipynb
+++ b/section5.1.ipynb
@@ -63,7 +63,7 @@
    "outputs": [],
    "source": [
     "rubber <- read.csv('rubber.csv')\n",
-    "rubber"
+    "head(rubber)"
    ]
   },
   {
@@ -108,17 +108,6 @@
     "# print(cbind(af,PctExp=afss/sum(afss)*100))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": null,
@@ -138,9 +127,13 @@
    },
    "source": [
     "### Exercise 5.1\n",
-    "Now repeat the for the regression of abrasion loss on tensile strength.\n",
+    "Now repeat the for the regression of abrasion loss on tensile strength. Create the model, look at how well it does, and generate the regression scatterplots.\n",
+    "\n",
+    "Place the new regression model in a variable called `fit.s`. \n",
     "\n",
-    "Enter your solution in the cell below."
+    "Enter your solution in the cell below.\n",
+    "\n",
+    "The solution is in the [Section 5.1 solutions](section5.1solutions.ipynb) notebook."
    ]
   },
   {
@@ -152,38 +145,6 @@
     "# Your solution here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "### Solution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true,
-    "solution2": "hidden"
-   },
-   "outputs": [],
-   "source": [
-    "fit.s <- lm(loss ~ strength, data = rubber)\n",
-    "summary(fit.s)\n",
-    "anova(fit.s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ggplotRegression(fit.s)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -247,6 +208,16 @@
     "Associated with each estimated parameter, GenStat gives a standard error (details of the calculation of which need not concern us now) and hence a _t_-statistic (estimate divided by standard error) to be compared with the distribution on `d.f. (Residual)`=27 degrees of freedom. GenStat makes the comparison and gives _p_ values, which in this case are all very small, suggesting strong evidence for the non-zeroness (and hence presence) of each parameter, $\\alpha$, $\\beta_1$ and $\\beta_2$, individually."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In case you've not completed exercise 5.1\n",
+    "fit.s <- lm(loss ~ strength, data = rubber)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/section5.1solutions.ipynb b/section5.1solutions.ipynb
index 1858975..c6cf696 100644
--- a/section5.1solutions.ipynb
+++ b/section5.1solutions.ipynb
@@ -45,6 +45,45 @@
     "source('plot_extensions.R')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rubber <- read.csv('rubber.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true,
+    "solution2": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "fit.s <- lm(loss ~ strength, data = rubber)\n",
+    "summary(fit.s)\n",
+    "anova(fit.s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ggplotRegression(fit.s)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
-- 
2.34.1