CHAPTER 14

Building Multiple Regression Models

LEARNING OBJECTIVES

This chapter presents several advanced topics in multiple regression analysis, enabling you to:

  1. Generalize linear regression models as polynomial regression models using model transformation and Tukey's ladder of transformation, accounting for possible interaction among the independent variables.
  2. Examine the role of indicator, or dummy, variables as predictors or independent variables in multiple regression analysis.
  3. Use all possible regressions, stepwise regression, forward selection, and backward elimination search procedures to develop regression models that account for the most variation in the dependent variable and are parsimonious.
  4. Recognize when multicollinearity is present, understanding general techniques for preventing and controlling it.
  5. Explain when to use logistic regression, and interpret its results.

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image Determining Compensation for CEOs

image Chief executive officers for large companies receive widely varying amounts of compensation for their work. Why is the range so wide? What are some of the variables that seem to contribute to the diversity of CEO compensation packages?

As a starting place, one might examine ...

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