initial commit for week 10 (linear models) material

[stats_class_2020.git] / psets / pset1-worked_solution.rmd

diff --git a/psets/pset1-worked_solution.rmd b/psets/pset1-worked_solution.rmd
index ef425ba65356daec5733eb7eb9a935fe01cd6839..06b227ef80309142efd5aae4abada923d7cb7dce 100644 (file)
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+++ 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
 
 
 ### 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
 
 
 ### SQ2