COURSE OBJECTIVES:

1. Introduce the students to additional biostatistical methods and the role they can play in decision making for public health majors.

3. Enable students to determine how to select an appropriate statistical application.

4. Enable students to interpret the results of a statistical application.

1. Analyze data using the following designs: Completely Randomized Design, Randomized Complete Block, Repeated Measures

2. Perform multiple comparison tests on means.

3. Construct least squares estimates of slope and intercept in simple linear regression.

4. Test for significant fit of the regression equation.

5. Construct prediction and confidence intervals using the regression model.

6. Calculate the correlation coefficient.

7. Use Fisher's Z transformation to test hypotheses and construct confidence intervals for the correlation coefficient.

8. Interpret regression coefficients in multiple linear regression.

9. Use Analysis of Variance to test for significant fit of the multiple regression equation.

10. Perform partial F-tests to compare model components.

11. Define multiple and partial correlation coefficients.

12. Define interaction.

13. Define dummy variables.

14 Use dummy variables to test for parallelism and coincidence of two or more regression lines.

15. Perform Forward, Backward, and Stepwise regression model building procedures.

16. Determine a fitted logistic regression model with both binary and continuous predictors.

17. Test for significant fit of the logistic regression equation.

18. Compute predicted odds ratios and confidence intervals using the logistic prediction equation.