Learning Objectives

1. State the regression equation
2. Define "regression coefficient"
3. Define "beta weight"
4. Explaine what R is and how it is related to r
5. Explain why a regression weight is called a "partial slope"
6. Explain why the sum of squares explained in a multiple regression model is usually less than the sum of the sums of squares in simple regression
7. Define R² in terms of proportion explained
8. Test R² for significance
9. Test the difference between a complete and reduced model for significance
10. State the assumptions of multiple regression and specify which aspects of the analysis require assumptions