X-Git-Url: https://code.communitydata.science/stats_class_2020.git/blobdiff_plain/d237971ec0285f91ed3a286b6cb136e5493bb3d4..d3c229da53f72ea2c4374b9985d80a18c91c4e24:/psets/pset1-worked_solution.rmd

diff --git a/psets/pset1-worked_solution.rmd b/psets/pset1-worked_solution.rmd
index ef425ba..06b227e 100644
--- 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