CHAPTER 17 More of a Good Thing: Multiple Regression 233

Chapter 17

More of a Good Thing:

Multiple Regression

C

hapter 15 introduces the general concepts of correlation and regression, two

related techniques for detecting and characterizing the relationship between

two or more variables. Chapter 16 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 formula relating the pre-

dictor to the outcome, known as the model, 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, because it is a relatively simple way to do a regres-

sion. It’s good for beginners to learn!

This chapter extends simple straight-line regression to more than one

predictor — to what’s called the ordinary multiple linear regression model, or more

simply, multiple regression.

IN THIS CHAPTER»

» ^{Understanding what multiple}

regression is»

» ^{Preparing your data and interpreting}

the output»

» ^{Understanding how interactions and}

collinearity affect regression analysis»

» ^{Estimating the number of}

participants you need for a multiple

regression analysis