R-exams.xml

<?xml version="1.0" encoding="UTF-8"?>
<quiz>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 1</text>
</category>
</question>


<question type="multichoice">
<name>
<text> R1 Q1 : swisscapital </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the seat of the federal authorities in Switzerland (i.e., the de facto capital)?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>There is no de jure capital but the de facto capital and seat of the federal authorities is Bern.</p>
<ol type = "a">
<li> False </li>
<li> False </li>
<li> False </li>
<li> True </li>
<li> False </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>true</single>
<answernumbering>abc</answernumbering>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Geneva
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
St. Gallen
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Lausanne
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="100" format="html">
<text><![CDATA[<p>
Bern
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Basel
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R2 Q1 : swisscapital </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the seat of the federal authorities in Switzerland (i.e., the de facto capital)?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>There is no de jure capital but the de facto capital and seat of the federal authorities is Bern.</p>
<ol type = "a">
<li> False </li>
<li> False </li>
<li> False </li>
<li> True </li>
<li> False </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>true</single>
<answernumbering>abc</answernumbering>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Basel
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
St. Gallen
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Vaduz
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="100" format="html">
<text><![CDATA[<p>
Bern
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Lausanne
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R3 Q1 : swisscapital </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the seat of the federal authorities in Switzerland (i.e., the de facto capital)?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>There is no de jure capital but the de facto capital and seat of the federal authorities is Bern.</p>
<ol type = "a">
<li> False </li>
<li> True </li>
<li> False </li>
<li> False </li>
<li> False </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>true</single>
<answernumbering>abc</answernumbering>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Lausanne
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="100" format="html">
<text><![CDATA[<p>
Bern
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Geneva
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
St. Gallen
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
Basel
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False
</p>]]></text>
</feedback>
</answer>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 2</text>
</category>
</question>


<question type="numerical">
<name>
<text> R1 Q2 : deriv </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the derivative of <span class="math inline">\(f(x) = x^{3} e^{3.6 x}\)</span>, evaluated at <span class="math inline">\(x = 0.72\)</span>?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the product rule for <span class="math inline">\(f(x) = g(x) \cdot h(x)\)</span>, where <span class="math inline">\(g(x) := x^{3}\)</span> and <span class="math inline">\(h(x) := e^{3.6 x}\)</span>, we obtain
<span class="math display">\[
\begin{aligned}
f&#39;(x) &amp;= [g(x) \cdot h(x)]&#39; = g&#39;(x) \cdot h(x) + g(x) \cdot h&#39;(x) \\
      &amp;= 3 x^{3 - 1} \cdot e^{3.6 x} + x^{3} \cdot e^{3.6 x} \cdot 3.6 \\
      &amp;= e^{3.6 x} \cdot(3 x^2 + 3.6 x^{3}) \\
      &amp;= e^{3.6 x} \cdot x^2 \cdot (3 + 3.6 x).
\end{aligned}
\]</span>
Evaluated at <span class="math inline">\(x = 0.72\)</span>, the answer is
<span class="math display">\[ e^{3.6 \cdot 0.72} \cdot 0.72^2 \cdot (3 + 3.6 \cdot 0.72) = 38.718939. \]</span>
Thus, rounded to two digits we have <span class="math inline">\(f&#39;(0.72) = 38.72\)</span>.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<answer fraction="100" format="moodle_auto_format">
<text>38.72</text>
<tolerance>0.01</tolerance>
</answer>
</question>


<question type="numerical">
<name>
<text> R2 Q2 : deriv </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the derivative of <span class="math inline">\(f(x) = x^{5} e^{2.6 x}\)</span>, evaluated at <span class="math inline">\(x = 0.53\)</span>?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the product rule for <span class="math inline">\(f(x) = g(x) \cdot h(x)\)</span>, where <span class="math inline">\(g(x) := x^{5}\)</span> and <span class="math inline">\(h(x) := e^{2.6 x}\)</span>, we obtain
<span class="math display">\[
\begin{aligned}
f&#39;(x) &amp;= [g(x) \cdot h(x)]&#39; = g&#39;(x) \cdot h(x) + g(x) \cdot h&#39;(x) \\
      &amp;= 5 x^{5 - 1} \cdot e^{2.6 x} + x^{5} \cdot e^{2.6 x} \cdot 2.6 \\
      &amp;= e^{2.6 x} \cdot(5 x^4 + 2.6 x^{5}) \\
      &amp;= e^{2.6 x} \cdot x^4 \cdot (5 + 2.6 x).
\end{aligned}
\]</span>
Evaluated at <span class="math inline">\(x = 0.53\)</span>, the answer is
<span class="math display">\[ e^{2.6 \cdot 0.53} \cdot 0.53^4 \cdot (5 + 2.6 \cdot 0.53) = 1.996392. \]</span>
Thus, rounded to two digits we have <span class="math inline">\(f&#39;(0.53) = 2.00\)</span>.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<answer fraction="100" format="moodle_auto_format">
<text>2</text>
<tolerance>0.01</tolerance>
</answer>
</question>


<question type="numerical">
<name>
<text> R3 Q2 : deriv </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the derivative of <span class="math inline">\(f(x) = x^{7} e^{2 x}\)</span>, evaluated at <span class="math inline">\(x = 0.64\)</span>?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the product rule for <span class="math inline">\(f(x) = g(x) \cdot h(x)\)</span>, where <span class="math inline">\(g(x) := x^{7}\)</span> and <span class="math inline">\(h(x) := e^{2 x}\)</span>, we obtain
<span class="math display">\[
\begin{aligned}
f&#39;(x) &amp;= [g(x) \cdot h(x)]&#39; = g&#39;(x) \cdot h(x) + g(x) \cdot h&#39;(x) \\
      &amp;= 7 x^{7 - 1} \cdot e^{2 x} + x^{7} \cdot e^{2 x} \cdot 2 \\
      &amp;= e^{2 x} \cdot(7 x^6 + 2 x^{7}) \\
      &amp;= e^{2 x} \cdot x^6 \cdot (7 + 2 x).
\end{aligned}
\]</span>
Evaluated at <span class="math inline">\(x = 0.64\)</span>, the answer is
<span class="math display">\[ e^{2 \cdot 0.64} \cdot 0.64^6 \cdot (7 + 2 \cdot 0.64) = 2.046478. \]</span>
Thus, rounded to two digits we have <span class="math inline">\(f&#39;(0.64) = 2.05\)</span>.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<answer fraction="100" format="moodle_auto_format">
<text>2.05</text>
<tolerance>0.01</tolerance>
</answer>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 3</text>
</category>
</question>


<question type="multichoice">
<name>
<text> R1 Q3 : ttest </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>The waiting time (in minutes) at the cashier of two supermarket
chains with different cashier systems is compared. The following
statistical test was performed:</p>
<pre><code>
    Two Sample t-test

data:  Waiting by Supermarket
t = -2.0525, df = 120, p-value = 0.02115
alternative hypothesis: true difference in means between group Sparag and group Consumo is less than 0
95 percent confidence interval:
       -Inf -0.2212139
sample estimates:
 mean in group Sparag mean in group Consumo 
             4.331325              5.481321 </code></pre>
<p>Which of the following statements are correct? (Significance level 5%)</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> True. The absolute value of the test statistic is equal to 2.052. </li>
<li> True. The test aims at showing that the difference of means is smaller than 0. </li>
<li> False. The p-value is equal to 0.0211. </li>
<li> False. The test aims at showing that the alternative that the waiting time is shorter at Sparag than at Consumo. </li>
<li> True. The test result is significant (<span class="math inline">\(p &lt; 0.05\)</span>) and hence the alternative is shown, that the difference of means are smaller than 0. </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The absolute value of the test statistic is larger than 1.96.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The absolute value of the test statistic is equal to 2.052.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
A one-sided alternative was tested.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The test aims at showing that the difference of means is smaller than 0.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The p-value is larger than 0.05.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The p-value is equal to 0.0211.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is longer at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test aims at showing that the alternative that the waiting time is shorter at Sparag than at Consumo.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is shorter at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The test result is significant (<span class="math inline">\(p &lt; 0.05\)</span>) and hence the alternative is shown, that the difference of means are smaller than 0.
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R2 Q3 : ttest </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>The waiting time (in minutes) at the cashier of two supermarket
chains with different cashier systems is compared. The following
statistical test was performed:</p>
<pre><code>
    Two Sample t-test

data:  Waiting by Supermarket
t = -1.9156, df = 117, p-value = 0.05786
alternative hypothesis: true difference in means between group Sparag and group Consumo is not equal to 0
95 percent confidence interval:
 -2.74815480  0.04574051
sample estimates:
 mean in group Sparag mean in group Consumo 
             5.925846              7.277053 </code></pre>
<p>Which of the following statements are correct? (Significance level 5%)</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> False. The absolute value of the test statistic is equal to 1.916. </li>
<li> False. The test aims at showing that the difference of means is unequal to 0. </li>
<li> True. The p-value is equal to 0.0579. </li>
<li> False. The test result is not significant (<span class="math inline">\(p \ge 0.05\)</span>). </li>
<li> False. The test result ist not significant (<span class="math inline">\(p \ge 0.05\)</span>). </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
The absolute value of the test statistic is larger than 1.96.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The absolute value of the test statistic is equal to 1.916.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
A one-sided alternative was tested.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test aims at showing that the difference of means is unequal to 0.
</p>]]></text>
</feedback>
</answer>
<answer fraction="100" format="html">
<text><![CDATA[<p>
The p-value is larger than 0.05.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The p-value is equal to 0.0579.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is longer at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test result is not significant (<span class="math inline">\(p \ge 0.05\)</span>).
</p>]]></text>
</feedback>
</answer>
<answer fraction="-25" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is shorter at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test result ist not significant (<span class="math inline">\(p \ge 0.05\)</span>).
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R3 Q3 : ttest </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>The waiting time (in minutes) at the cashier of two supermarket
chains with different cashier systems is compared. The following
statistical test was performed:</p>
<pre><code>
    Two Sample t-test

data:  Waiting by Supermarket
t = 1.336, df = 135, p-value = 0.0919
alternative hypothesis: true difference in means between group Sparag and group Consumo is greater than 0
95 percent confidence interval:
 -0.2010577        Inf
sample estimates:
 mean in group Sparag mean in group Consumo 
             7.067593              6.228852 </code></pre>
<p>Which of the following statements are correct? (Significance level 5%)</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> False. The absolute value of the test statistic is equal to 1.336. </li>
<li> True. The test aims at showing that the difference of means is larger than 0. </li>
<li> True. The p-value is equal to 0.0919. </li>
<li> False. The test result is not significant (<span class="math inline">\(p \ge 0.05\)</span>). </li>
<li> False. The test aims at showing that the waiting time at Sparag is longer than at Consumo. The test result ist not significant (<span class="math inline">\(p \ge 0.05\)</span>). </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="-33.33333" format="html">
<text><![CDATA[<p>
The absolute value of the test statistic is larger than 1.96.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The absolute value of the test statistic is equal to 1.336.
</p>]]></text>
</feedback>
</answer>
<answer fraction="50" format="html">
<text><![CDATA[<p>
A one-sided alternative was tested.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The test aims at showing that the difference of means is larger than 0.
</p>]]></text>
</feedback>
</answer>
<answer fraction="50" format="html">
<text><![CDATA[<p>
The p-value is larger than 0.05.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The p-value is equal to 0.0919.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-33.33333" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is longer at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test result is not significant (<span class="math inline">\(p \ge 0.05\)</span>).
</p>]]></text>
</feedback>
</answer>
<answer fraction="-33.33333" format="html">
<text><![CDATA[<p>
The test shows that the waiting time is shorter at Sparag than at Consumo.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The test aims at showing that the waiting time at Sparag is longer than at Consumo. The test result ist not significant (<span class="math inline">\(p \ge 0.05\)</span>).
</p>]]></text>
</feedback>
</answer>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 4</text>
</category>
</question>


<question type="multichoice">
<name>
<text> R1 Q4 : boxplots </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>In the following figure the distributions of a variable
given by two samples (A and B) are represented by parallel boxplots.
Which of the following statements are correct? <em>(Comment: The
statements are either about correct or clearly wrong.)</em><br />
<img src="@@PLUGINFILE@@/boxplot-1.png" width="500" /></p>
</p>]]></text>
<file name="boxplot-1.png" 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</file>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> False. Distribution A has on average higher values than distribution B. </li>
<li> True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box. </li>
<li> False. The interquartile range in sample A is <em>not</em> clearly bigger than in B. </li>
<li> True. The skewness of both distributions is similar, both are about symmetric. </li>
<li> True. Distribution A is about symmetric. </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The location of both distributions is about the same.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. Distribution A has on average higher values than distribution B.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
Both distributions contain no outliers.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The spread in sample A is clearly bigger than in B.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The interquartile range in sample A is <em>not</em> clearly bigger than in B.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The skewness of both samples is similar.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The skewness of both distributions is similar, both are about symmetric.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
Distribution A is about symmetric.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. Distribution A is about symmetric.
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R2 Q4 : boxplots </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>In the following figure the distributions of a variable
given by two samples (A and B) are represented by parallel boxplots.
Which of the following statements are correct? <em>(Comment: The
statements are either about correct or clearly wrong.)</em><br />
<img src="@@PLUGINFILE@@/boxplot-1.png" width="500" /></p>
</p>]]></text>
<file name="boxplot-1.png" 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</file>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> False. Distribution B has on average higher values than distribution A. </li>
<li> True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box. </li>
<li> True. The interquartile range in sample A is clearly bigger than in B. </li>
<li> True. The skewness of both distributions is similar, both are about symmetric. </li>
<li> False. Distribution B is about symmetric. </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The location of both distributions is about the same.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. Distribution B has on average higher values than distribution A.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
Both distributions contain no outliers.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The spread in sample A is clearly bigger than in B.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The interquartile range in sample A is clearly bigger than in B.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The skewness of both samples is similar.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The skewness of both distributions is similar, both are about symmetric.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
Distribution B is left-skewed.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. Distribution B is about symmetric.
</p>]]></text>
</feedback>
</answer>
</question>


<question type="multichoice">
<name>
<text> R3 Q4 : boxplots </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>In the following figure the distributions of a variable
given by two samples (A and B) are represented by parallel boxplots.
Which of the following statements are correct? <em>(Comment: The
statements are either about correct or clearly wrong.)</em><br />
<img src="@@PLUGINFILE@@/boxplot-1.png" width="500" /></p>
</p>]]></text>
<file name="boxplot-1.png" 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</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<ol type = "a">
<li> False. Distribution A has on average higher values than distribution B. </li>
<li> True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box. </li>
<li> False. The interquartile range in sample A is <em>not</em> clearly bigger than in B. </li>
<li> True. The skewness of both distributions is similar, both are about symmetric. </li>
<li> True. Distribution B is about symmetric. </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<shuffleanswers>false</shuffleanswers>
<single>false</single>
<answernumbering>abc</answernumbering>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The location of both distributions is about the same.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. Distribution A has on average higher values than distribution B.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
Both distributions contain no outliers.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. Both distributions have no observations which deviate more than 1.5 times the interquartile range from the box.
</p>]]></text>
</feedback>
</answer>
<answer fraction="-50" format="html">
<text><![CDATA[<p>
The spread in sample A is clearly bigger than in B.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
False. The interquartile range in sample A is <em>not</em> clearly bigger than in B.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
The skewness of both samples is similar.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. The skewness of both distributions is similar, both are about symmetric.
</p>]]></text>
</feedback>
</answer>
<answer fraction="33.33333" format="html">
<text><![CDATA[<p>
Distribution B is about symmetric.
</p>]]></text>
<feedback format="html">
<text><![CDATA[<p>
True. Distribution B is about symmetric.
</p>]]></text>
</feedback>
</answer>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 5</text>
</category>
</question>


<question type="shortanswer">
<name>
<text> R1 Q5 : function </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the name of the R function for least-squares regression?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><code>lm</code> is the R function for least-squares regression.
See <code>?lm</code> for the corresponding manual page.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<usecase>0</usecase>
<answer fraction="100" format="moodle_auto_format">
<text>
lm
</text>
</answer>
</question>


<question type="shortanswer">
<name>
<text> R2 Q5 : function </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the name of the R function for extracting the estimated covariance matrix from a fitted (generalized) linear model object?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><code>vcov</code> is the R function for extracting the estimated covariance matrix from a fitted (generalized) linear model object.
See <code>?vcov</code> for the corresponding manual page.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<usecase>0</usecase>
<answer fraction="100" format="moodle_auto_format">
<text>
vcov
</text>
</answer>
</question>


<question type="shortanswer">
<name>
<text> R3 Q5 : function </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>What is the name of the R function for Poisson regression?</p>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><code>glm</code> is the R function for Poisson regression.
See <code>?glm</code> for the corresponding manual page.</p>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>1</defaultgrade>
<usecase>0</usecase>
<answer fraction="100" format="moodle_auto_format">
<text>
glm
</text>
</answer>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 6</text>
</category>
</question>


<question type="cloze">
<name>
<text> R1 Q6 : lm </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>Using the data provided in <a href="@@PLUGINFILE@@/regression.csv">regression.csv</a> estimate a linear regression of
<code>y</code> on <code>x</code> and answer the following questions.</p>
<ol type = "a">
<li> {1:MULTICHOICE:<code>x</code> and <code>y</code> are not significantly correlated~<code>y</code> increases significantly with <code>x</code>~=<code>y</code> decreases significantly with <code>x</code>} </li>
<li> Estimated slope with respect to <code>x</code>: {1:NUMERICAL:=-0.861:0.01} </li>
</ol>
</p>]]></text>
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</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><br />
<img src="@@PLUGINFILE@@/scatterplot-1.png" width="450" /></p>
<p>To replicate the analysis in R:</p>
<pre><code>## data
d &lt;- read.csv(&quot;regression.csv&quot;)
## regression
m &lt;- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)</code></pre>
</p>]]></text>
<file name="scatterplot-1.png" 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</generalfeedback>
<penalty>0</penalty>
<defaultgrade>2</defaultgrade>
</question>


<question type="cloze">
<name>
<text> R2 Q6 : lm </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>Using the data provided in <a href="@@PLUGINFILE@@/regression.csv">regression.csv</a> estimate a linear regression of
<code>y</code> on <code>x</code> and answer the following questions.</p>
<ol type = "a">
<li> {1:MULTICHOICE:<code>x</code> and <code>y</code> are not significantly correlated~=<code>y</code> increases significantly with <code>x</code>~<code>y</code> decreases significantly with <code>x</code>} </li>
<li> Estimated slope with respect to <code>x</code>: {1:NUMERICAL:=0.531:0.01} </li>
</ol>
</p>]]></text>
<file name="regression.csv" 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</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><br />
<img src="@@PLUGINFILE@@/scatterplot-1.png" width="450" /></p>
<p>To replicate the analysis in R:</p>
<pre><code>## data
d &lt;- read.csv(&quot;regression.csv&quot;)
## regression
m &lt;- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)</code></pre>
</p>]]></text>
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</file>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>2</defaultgrade>
</question>


<question type="cloze">
<name>
<text> R3 Q6 : lm </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>Using the data provided in <a href="@@PLUGINFILE@@/regression.csv">regression.csv</a> estimate a linear regression of
<code>y</code> on <code>x</code> and answer the following questions.</p>
<ol type = "a">
<li> {1:MULTICHOICE:=<code>x</code> and <code>y</code> are not significantly correlated~<code>y</code> increases significantly with <code>x</code>~<code>y</code> decreases significantly with <code>x</code>} </li>
<li> Estimated slope with respect to <code>x</code>: {1:NUMERICAL:=0.024:0.01} </li>
</ol>
</p>]]></text>
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</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p><br />
<img src="@@PLUGINFILE@@/scatterplot-1.png" width="450" /></p>
<p>To replicate the analysis in R:</p>
<pre><code>## data
d &lt;- read.csv(&quot;regression.csv&quot;)
## regression
m &lt;- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)</code></pre>
</p>]]></text>
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</file>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>2</defaultgrade>
</question>


<question type="category">
<category>
<text>$course$/R-exams/Exercise 7</text>
</category>
</question>


<question type="cloze">
<name>
<text> R1 Q7 : fourfold2 </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>An industry-leading company seeks a qualified candidate for a management position.
A management consultancy carries out an assessment center which concludes in making
a positive or negative recommendation for each candidate: From previous assessments they know that
of those candidates that are actually eligible for the position (event <span class="math inline">\(E\)</span>) <span class="math inline">\(74\%\)</span>
get a positive recommendation (event <span class="math inline">\(R\)</span>). However, out of those candidates that are not eligible
<span class="math inline">\(79\%\)</span> get a negative recommendation. Overall, they know that only
<span class="math inline">\(6\%\)</span> of all job applicants are actually eligible.</p>
<p>What is the corresponding fourfold table of the joint probabilities? (Specify all entries in percent.)</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"> {1:NUMERICAL:=4.44:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=1.56:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=6:0.05~%0%689609:0}%</td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"> {1:NUMERICAL:=19.74:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=74.26:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=94:0.05~%0%689609:0}%</td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"> {1:NUMERICAL:=24.18:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=75.82:0.05~%0%689609:0}%</td>
<td align="center"> {1:NUMERICAL:=100:0.05~%0%689609:0}%</td>
</tr>
</tbody>
</table>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the information from the text, we can directly calculate the following joint probabilities:
<span class="math display">\[
\begin{aligned}
  P(E \cap R) &amp; =
    P(R | E) \cdot P(E) = 0.74 \cdot 0.06 = 0.0444 = 4.44\%\\
  P(\overline{E} \cap \overline{R}) &amp; =
    P(\overline{R} | \overline{E}) \cdot P(\overline{E}) = 0.79 \cdot 0.94 = 0.7426 = 74.26\%.
\end{aligned}
\]</span>
The remaining probabilities can then be found by calculating sums and differences in the fourfold table:</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"><strong>4.44</strong></td>
<td align="center"><em>1.56</em></td>
<td align="center"><strong>6.00</strong></td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"><em>19.74</em></td>
<td align="center"><strong>74.26</strong></td>
<td align="center"><em>94.00</em></td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"><em>24.18</em></td>
<td align="center"><em>75.82</em></td>
<td align="center"><strong>100.00</strong></td>
</tr>
</tbody>
</table>
<ol type = "a">
<li> <span class="math inline">\(P(E \cap R) = 4.44\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap R) = 19.74\%\)</span> </li>
<li> <span class="math inline">\(P(E \cap \overline{R}) = 1.56\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap \overline{R}) = 74.26\%\)</span> </li>
<li> <span class="math inline">\(P(R) = 24.18\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{R}) = 75.82\%\)</span> </li>
<li> <span class="math inline">\(P(E) = 6.00\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E}) = 94.00\%\)</span> </li>
<li> <span class="math inline">\(P(\Omega) = 100.00\%\)</span> </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>9</defaultgrade>
</question>


<question type="cloze">
<name>
<text> R2 Q7 : fourfold2 </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>An industry-leading company seeks a qualified candidate for a management position.
A management consultancy carries out an assessment center which concludes in making
a positive or negative recommendation for each candidate: From previous assessments they know that
of those candidates that are actually eligible for the position (event <span class="math inline">\(E\)</span>) <span class="math inline">\(72\%\)</span>
get a positive recommendation (event <span class="math inline">\(R\)</span>). However, out of those candidates that are not eligible
<span class="math inline">\(74\%\)</span> get a negative recommendation. Overall, they know that only
<span class="math inline">\(8\%\)</span> of all job applicants are actually eligible.</p>
<p>What is the corresponding fourfold table of the joint probabilities? (Specify all entries in percent.)</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"> {1:NUMERICAL:=5.76:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=2.24:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=8:0.05~%0%216905:0}%</td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"> {1:NUMERICAL:=23.92:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=68.08:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=92:0.05~%0%216905:0}%</td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"> {1:NUMERICAL:=29.68:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=70.32:0.05~%0%216905:0}%</td>
<td align="center"> {1:NUMERICAL:=100:0.05~%0%216905:0}%</td>
</tr>
</tbody>
</table>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the information from the text, we can directly calculate the following joint probabilities:
<span class="math display">\[
\begin{aligned}
  P(E \cap R) &amp; =
    P(R | E) \cdot P(E) = 0.72 \cdot 0.08 = 0.0576 = 5.76\%\\
  P(\overline{E} \cap \overline{R}) &amp; =
    P(\overline{R} | \overline{E}) \cdot P(\overline{E}) = 0.74 \cdot 0.92 = 0.6808 = 68.08\%.
\end{aligned}
\]</span>
The remaining probabilities can then be found by calculating sums and differences in the fourfold table:</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"><strong>5.76</strong></td>
<td align="center"><em>2.24</em></td>
<td align="center"><strong>8.00</strong></td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"><em>23.92</em></td>
<td align="center"><strong>68.08</strong></td>
<td align="center"><em>92.00</em></td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"><em>29.68</em></td>
<td align="center"><em>70.32</em></td>
<td align="center"><strong>100.00</strong></td>
</tr>
</tbody>
</table>
<ol type = "a">
<li> <span class="math inline">\(P(E \cap R) = 5.76\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap R) = 23.92\%\)</span> </li>
<li> <span class="math inline">\(P(E \cap \overline{R}) = 2.24\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap \overline{R}) = 68.08\%\)</span> </li>
<li> <span class="math inline">\(P(R) = 29.68\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{R}) = 70.32\%\)</span> </li>
<li> <span class="math inline">\(P(E) = 8.00\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E}) = 92.00\%\)</span> </li>
<li> <span class="math inline">\(P(\Omega) = 100.00\%\)</span> </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>9</defaultgrade>
</question>


<question type="cloze">
<name>
<text> R3 Q7 : fourfold2 </text>
</name>
<questiontext format="html">
<text><![CDATA[
<p>
<p>An industry-leading company seeks a qualified candidate for a management position.
A management consultancy carries out an assessment center which concludes in making
a positive or negative recommendation for each candidate: From previous assessments they know that
of those candidates that are actually eligible for the position (event <span class="math inline">\(E\)</span>) <span class="math inline">\(65\%\)</span>
get a positive recommendation (event <span class="math inline">\(R\)</span>). However, out of those candidates that are not eligible
<span class="math inline">\(75\%\)</span> get a negative recommendation. Overall, they know that only
<span class="math inline">\(10\%\)</span> of all job applicants are actually eligible.</p>
<p>What is the corresponding fourfold table of the joint probabilities? (Specify all entries in percent.)</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"> {1:NUMERICAL:=6.5:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=3.5:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=10:0.05~%0%124381:0}%</td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"> {1:NUMERICAL:=22.5:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=67.5:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=90:0.05~%0%124381:0}%</td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"> {1:NUMERICAL:=29:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=71:0.05~%0%124381:0}%</td>
<td align="center"> {1:NUMERICAL:=100:0.05~%0%124381:0}%</td>
</tr>
</tbody>
</table>
</p>]]></text>
</questiontext>
<generalfeedback format="html">
<text><![CDATA[<p>
<p>Using the information from the text, we can directly calculate the following joint probabilities:
<span class="math display">\[
\begin{aligned}
  P(E \cap R) &amp; =
    P(R | E) \cdot P(E) = 0.65 \cdot 0.1 = 0.065 = 6.5\%\\
  P(\overline{E} \cap \overline{R}) &amp; =
    P(\overline{R} | \overline{E}) \cdot P(\overline{E}) = 0.75 \cdot 0.9 = 0.675 = 67.5\%.
\end{aligned}
\]</span>
The remaining probabilities can then be found by calculating sums and differences in the fourfold table:</p>
<table>
<thead>
<tr class="header">
<th align="center"></th>
<th align="center"><span class="math inline">\(R\)</span></th>
<th align="center"><span class="math inline">\(\overline{R}\)</span></th>
<th align="center">sum</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><span class="math inline">\(E\)</span></td>
<td align="center"><strong>6.50</strong></td>
<td align="center"><em>3.50</em></td>
<td align="center"><strong>10.00</strong></td>
</tr>
<tr class="even">
<td align="center"><span class="math inline">\(\overline{E}\)</span></td>
<td align="center"><em>22.50</em></td>
<td align="center"><strong>67.50</strong></td>
<td align="center"><em>90.00</em></td>
</tr>
<tr class="odd">
<td align="center">sum</td>
<td align="center"><em>29.00</em></td>
<td align="center"><em>71.00</em></td>
<td align="center"><strong>100.00</strong></td>
</tr>
</tbody>
</table>
<ol type = "a">
<li> <span class="math inline">\(P(E \cap R) = 6.5\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap R) = 22.5\%\)</span> </li>
<li> <span class="math inline">\(P(E \cap \overline{R}) = 3.5\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E} \cap \overline{R}) = 67.5\%\)</span> </li>
<li> <span class="math inline">\(P(R) = 29.0\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{R}) = 71.0\%\)</span> </li>
<li> <span class="math inline">\(P(E) = 10.0\%\)</span> </li>
<li> <span class="math inline">\(P(\overline{E}) = 90.0\%\)</span> </li>
<li> <span class="math inline">\(P(\Omega) = 100.0\%\)</span> </li>
</ol>
</p>]]></text>
</generalfeedback>
<penalty>0</penalty>
<defaultgrade>9</defaultgrade>
</question>

</quiz>