Chapter 19
More of a Good Thing: Multiple Regression
In This Chapter
Understanding what multiple regression is
Preparing your data for a multiple regression and interpreting the output
Understanding how synergy and collinearity affect regression analysis
Estimating the number of subjects you need for a multiple regression analysis
Chapter 17 introduces the general concepts of correlation and regression, two related techniques for detecting and characterizing the relationship between two or more variables. Chapter 18 describes the simplest kind of regression — fitting a straight line to a set of data consisting of one independent variable (the predictor) and one dependent variable (the outcome). The model (the formula relating the predictor to the outcome) is of the form Y = a + bX, where Y is the outcome, X is the predictor, and a and b are parameters (also called regression coefficients). This kind of regression is usually the only one you encounter in an introductory statistics course, but it’s just the tip of the regression iceberg.
This chapter extends simple straight-line regression ...
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