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