1. Section 4-3 Relations in Categorical Data
Statistics 101
Section 4-3
Relations in Categorical Data

2. Categorical Data Typically grouped into categories
Examples are age, education, etc.

3. Example 4.19 p 241

4. Marginal Distributions
On the previous example – education and age are called marginal distributions because their totals appear at the right and bottom margins of the two-way table.
Percents are more informative than counts.

5. Bar-graph of years of schooling completed among people aged 25 years and over

6. How common is college education?
What do you think?

7. What do you see

8. Conditional Distributions
Useful when the column variable is the explanatory variable.
There is a conditional distribution of the row variable for each column in the table

9. Example of Conditional Distribution

11. Simpson’s Paradox
The reversal of the direction of a comparison or an association when data from several groups are combined to form a single group.

12. To understand this paradox
Read through example 4.13 on p 249 entitled ‘patient outcomes in hospitals’