In section 2, we started to talk about associations between two variables.  In particular, we were intersted in the association between gender and college at William Paterson.  What do I mean by association?  Consider two variables -- call them X and Y.  If X is associated with Y, then knowing X will allow you to predict Y with better than chance accuracy.  If X is NOT associated with Y, then knowing X won't enable you to predict Y any better than you could have without knowing Y. 
In the example in class, X was gender, and Y was academic college.  We had data on William Paterson students (including some of you).  Information that would allow ready identication has been stripped away.  We built a table called a "crosstab" and examined the gender distribution of each college at William Paterson.

We saw that gender was in fact related to academic college.  We talked about this in class.

Your homework for Thursday is to build a table and a chart, just as we did in class.  And do so using the dataset on WP students.  Your challenge, however, will be to pick another variables or variables on which to build a table.  For example, you could find another variable that you think might be related to gender, and then create a table (as we did) showing the relationship between gender and this other variable.  Good luck.  We'll be looking at more of associations like these on Thursday.

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