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Chapter 8: Multiple Regression Models

Chapter 8: Multiple Regression Models

pp. 137-173

Authors

, Deakin University, Victoria, , University of Melbourne
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Extract

Multiple predictors can all be continuous, or they can be mixtures of continuous and categorical. A common biological situation is a substantial number of continuous predictors, and fitting these models is commonly labeled multiple regression. We might also mix continuous and categorical predictors, and these have been called analyses of covariance. We show how these two analyses are closely related and how to fit and interpret these models. This chapter introduces the complication of correlated predictors (collinearity) and describes ways of detecting and dealing with the problem. This chapter also introduces measures of influence and leverage as part of checking assumptions.

Keywords

  • collinearity
  • centering
  • partial regression slope
  • standardizing
  • heterogenous slopes
  • interactions
  • analysis of covariance
  • polynomial

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