2. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 3 Association: Contingency, Correlation, and Regression Section 3.1 The Association Between Two Categorical Variables

3. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 3 Response variable (Dependent Variable)  The outcome variable on which comparisons are made. Explanatory variable (Independent variable)  When the explanatory variable is categorical, it defines the groups to be compared with respect to values on the response variable.  When the explanatory variable is quantitative, it defines the change in different numerical values to be compared with respect to the values for the response variable. Example: Response/Explanatory  Survival status/ Smoking status  Carbon dioxide(CO 2 )Level/Amount of gasoline use for cars  College GPA/Number of hours a week spent studying Response and Explanatory Variables

4. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 4 The main purpose of data analysis with two variables is to investigate whether there is an association and to describe that association. An association exists between two variables if a particular value for one variable is more likely to occur with certain values of the other variable. Association Between Two Variables

5. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 5 A Contingency Table:  Displays two categorical variables  The rows list the categories of one variable  The columns list the categories of the other variable  Entries in the table are frequencies Contingency Tables

6. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 6 Table 3.1 Frequencies for Food Type and Pesticide Status. The row totals and the column totals are the frequencies for the categories of each variable. The counts inside the table give information about the association. Contingency Tables

7. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 7 These proportions are called conditional proportions because their formation is conditional on (in this example) food type. Calculate Proportions and Conditional Proportions Table 3.2 Conditional Proportions on Pesticide Status, for Two Food Types. These conditional proportions (using two decimal places) treat pesticide status as the response variable. The sample size n in a row shows the total on which the conditional proportions in that row were based.

8. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 8 Questions: 1. What proportion of organic foods contain pesticides? 2. What proportion of conventionally grown foods contain pesticides? 3. What proportion of all sampled items contain pesticides? Calculate Proportions and Conditional Proportions

9. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 9 Using side by side bar charts to show conditional proportions allows for easy comparison of the explanatory variable with respect to the response variable. Figure 3.2 Conditional Proportions on Pesticide Status, Given the Food Type. For a particular pesticide status category, the side-by-side bars compare the two food types. Question: Comparing the bars, how would you describe the difference between organic and conventionally grown foods in the conditional proportion with pesticide residues present? Calculate Proportions and Conditional Proportions

10. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 10 If there was no association between organic and conventional foods, then the proportions for the response variable categories would be the same for each food type. Figure 3.3 Hypothetical Conditional Proportions on Pesticide Status, Given Food Type, Showing No Association. Question: What’s the difference between Figures 3.2 and 3.3 in the pattern shown by the bars in the graph? Calculate Proportions and Conditional Proportions