typo

diff --git a/psets/pset1-worked_solution.html b/psets/pset1-worked_solution.html
index bd80eb7d73e5081691798f691416cce045ac8929..354e4e9413fa4ded79b18360966adc963f03f8a9 100644 (file)
--- a/psets/pset1-worked_solution.html
+++ b/psets/pset1-worked_solution.html
@@ -1803,7 +1803,7 @@ p + geom_histogram()</code></pre>
 <h2>Statistical questions</h2>
 <div id="sq1" class="section level3">
 <h3>SQ1</h3>
-<p>A compelling answer to this depends on the variable you chose. For the one I looked at in my example code (<code>poverty</code>) the data is somewhat right skewed, but not much. In this case, the mean and standard deviation should represent the central tendency and spread of the variable pretty well. If your variable was different (e.g., one of the population or income measures, it would probably be good to also examine and report the median and interquartile range. See <code>OpenIntro</code> chapter 2 for more on distinctions/reasons behind this.</p>
+<p>A compelling answer to this depends on the variable you chose. For the one I looked at in my example code (<code>poverty</code>) the data is somewhat right skewed, but not much. In this case, the mean and standard deviation should represent the central tendency and spread of the variable pretty well. If your variable was different (e.g., one of the population or income measures), it would probably be good to also examine and report the median and interquartile range. See <code>OpenIntro</code> chapter 2 for more on distinctions/reasons behind this.</p>
 </div>
 <div id="sq2" class="section level3">
 <h3>SQ2</h3>

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diff --git a/psets/pset1-worked_solution.rmd b/psets/pset1-worked_solution.rmd
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--- a/psets/pset1-worked_solution.rmd
+++ b/psets/pset1-worked_solution.rmd
@@ -201,7 +201,7 @@ Note that ggplot2 generates a warning about 5 "non-fininte values." In this case
 
 ### SQ1
 
-A compelling answer to this depends on the variable you chose. For the one I looked at in my example code (`poverty`) the data is somewhat right skewed, but not much. In this case, the mean and standard deviation should represent the central tendency and spread of the variable pretty well. If your variable was different (e.g., one of the population or income measures, it would probably be good to also examine and report the median and interquartile range. See `OpenIntro` chapter 2 for more on distinctions/reasons behind this.
+A compelling answer to this depends on the variable you chose. For the one I looked at in my example code (`poverty`) the data is somewhat right skewed, but not much. In this case, the mean and standard deviation should represent the central tendency and spread of the variable pretty well. If your variable was different (e.g., one of the population or income measures), it would probably be good to also examine and report the median and interquartile range. See `OpenIntro` chapter 2 for more on distinctions/reasons behind this.
 
 ### SQ2