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      <text>36.1.5.5 Compute median, IQR</text>
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      <text><![CDATA[<p class="HELM_exercise">Exercise</p>
<p>Find the median, lower quartile, upper quartile, and inter-quartile range (IQR) for the data set:
\[{@S@}\]</p>
<p>Median = [[input:ans1]] [[validation:ans1]] [[feedback:prt1]]</p>
<p>Lower quartile = [[input:ans2]] [[validation:ans2]] [[feedback:prt2]]</p>
<p>Upper quartile = [[input:ans3]] [[validation:ans3]] [[feedback:prt3]]</p>
<p>Inter-quartile range (IQR) = upper quartile \(-\) lower quartile [[input:ans4]] [[validation:ans4]] [[feedback:prt4]]</p>]]></text>
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    <generalfeedback format="html">
      <text><![CDATA[<p>We first sort \({@S@}\) into increasing order to obtain \[{@L@},\] then allocate rank to the observations.</p>
<p>The number of observations is \(n=9\) so \(r_{2}=5\) and the median is the observation with rank 5, so median \(= {@ta1@}\).</p>
<p>\(r_{1}=2.75\) and the lower quartile is the average of the observations with ranks 2 and 3: 
\(\dfrac{{@L[2]@}+{@L[3]@}}{2}={@ta2@}\).</p>
<p>\(r_{3}=7.25\) and the upper quartile is the average of the observations with ranks 7 and 8: 
\(\dfrac{{@L[7]@}+{@L[8]@}}{2}={@ta3@}\).</p>
<p>IQR \(= {@ta3@} - {@ta2@} = {@ta4@}\).</p>]]></text>
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      <text>S:[0.7+rand(2),1.2+rand(2),2.4+rand(2),2.5+rand(2),3.0+rand(2),3.1+rand(2),3.5+rand(2),4.1+rand(2),5.0+rand(2)];
L:sort(S);
ta1:L[5];
ta2:float((L[2]+L[3])/2);
ta3:float((L[7]+L[8])/2);
ta4:ta3-ta2;</text>
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      <text>\[{@L@} \] 
Median = {@ta1@}, Lower quartile ={@ta2@}, Upper quartile = {@ta3@},  IQR = {@ta4@}</text>
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