2.  Some variables are inherently categorical, for example:  Sex  Race  Occupation  Other categorical variables are created by grouping values of a quantitative variable into classes.  To analyze categorical data, we will use counts or percents.

3.  Two way tables are used to describe two categorical variables.  Each table will contain a row and column variable.

4.  The distribution of a categorical variable just says how often each outcome occurred.  If the row and column totals are missing in a two way table the first thing to do is to calculate them.  Marginal distributions appear to the right and bottom of two-way tables

5.  No single graph (scatterplot) portrays the form of the relationship between categorical variables, and no single numerical measure (correlation) summarizes the strength of an association.  To describe relationships among categorical variable, calculate appropriate percents from the counts given.

6.  Refers to the reversal of the direction of a comparison or an association when data from several groups are combined to form a single group.