updates after class

[stats_class_2019.git] / problem_sets / week_03 / ps3-worked_solution.html

diff --git a/problem_sets/week_03/ps3-worked_solution.html b/problem_sets/week_03/ps3-worked_solution.html
index 992712436ab3c5cbad4def19170d46591d960439..a909ded118ce0ebcff743e25e70233d7f6abb0a7 100644 (file)
--- a/problem_sets/week_03/ps3-worked_solution.html
+++ b/problem_sets/week_03/ps3-worked_solution.html
@@ -464,7 +464,7 @@ w2.data &lt;- log1p(w2.data)</code></pre>
 <pre><code>## [1] 9.643215 2.158358 1.396595 0.192623 1.752234 0.170634</code></pre>
 <p>Inspecting the first few values returned by <code>head()</code> gave you a clue. Rounded to six decimal places, the vectors match!</p>
 <p>I can create a table comparing the sorted rounded values to check this.</p>
 <pre><code>## [1] 9.643215 2.158358 1.396595 0.192623 1.752234 0.170634</code></pre>
 <p>Inspecting the first few values returned by <code>head()</code> gave you a clue. Rounded to six decimal places, the vectors match!</p>
 <p>I can create a table comparing the sorted rounded values to check this.</p>

-<pre class="r"><code>table(sort(round(w2.data, 6)) == sort(round(w3.data$x, 6)))</code></pre>
+<pre class="r"><code>table(round(w2.data,6) == round(w3.data$x,6))</code></pre>

 <pre><code>## 
 ## TRUE 
 ##   95</code></pre>
 <pre><code>## 
 ## TRUE 
 ##   95</code></pre>


@@ -546,7 +546,7 @@ head(w3.data)</code></pre>
 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 ##   -4.42    3.19    7.81    9.96   14.61   33.14       5</code></pre>
 <pre class="r"><code>### Run this line again to assign the new dataframe to p
 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 ##   -4.42    3.19    7.81    9.96   14.61   33.14       5</code></pre>
 <pre class="r"><code>### Run this line again to assign the new dataframe to p

-p &lt;- ggplot(w3.data, aes(x=x, y=y))
+p &lt;- ggplot(data=w3.data, mapping=aes(x=x, y=y))

 
 p + geom_point(aes(color=j, size=l, shape=k))</code></pre>
 <pre><code>## Warning: Using size for a discrete variable is not advised.</code></pre>
 
 p + geom_point(aes(color=j, size=l, shape=k))</code></pre>
 <pre><code>## Warning: Using size for a discrete variable is not advised.</code></pre>


@@ -641,7 +641,7 @@ l68/length(d)</code></pre>
 <ol start="4" style="list-style-type: lower-alpha">
 <li>Is random assignment to tents likely to ensure <span class="math inline">\(\leq1~arachnophobe\)</span> per tent?</li>
 </ol>
 <ol start="4" style="list-style-type: lower-alpha">
 <li>Is random assignment to tents likely to ensure <span class="math inline">\(\leq1~arachnophobe\)</span> per tent?</li>
 </ol>

-<p>Random assignment and the independence assumption means that the answer to part c is the inverse of the outcome we’re looking to avoid: <span class="math inline">\(P(\gt1~arachnophobes) = 1-P(\leq1~arachnophobe)\)</span>. So, <span class="math inline">\(P(\gt1~arachnophobes) = 1-0.84 = 0.16 = 16\%\)</span>. Those are the probabilities, but the interpretation really depends on how confident the camp counselor feels about a <span class="math inline">\(16\%\)</span> chance of having multiple arachnophobic campers in one of the tents.</p>
+<p>Random assignment and the independence assumption means that the answer to part c is the complement of the outcome we’re looking to avoid: <span class="math inline">\(P(\gt1~arachnophobes) = 1-P(\leq1~arachnophobe)\)</span>. So, <span class="math inline">\(P(\gt1~arachnophobes) = 1-0.84 = 0.16 = 16\%\)</span>. Those are the probabilities, but the interpretation really depends on how confident the camp counselor feels about a <span class="math inline">\(16\%\)</span> chance of having multiple arachnophobic campers in one of the tents.</p>

 </div>
 </div>
 
 </div>
 </div>