IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference by Darren George & Paul Mallery

Author:Darren George & Paul Mallery
Language: eng
Format: mobi, epub, pdf
ISBN: 9781134793402
Publisher: Taylor and Francis
Published: 2016-03-21T23:00:00+00:00

Chapter 16

Multiple Regression Analysis

MULTIPLE REGRESSION is the natural extension of simple linear regression presented in Chapter 15. In simple regression, we measured the amount of influence one variable (the independent or predictor variable) had on a second variable (the dependent or criterion variable). We also computed the constant and coefficient for a regression equation designed to predict the values of the dependent variable, based on the values of the independent variable. While simple regression shows the influence of one variable on another, multiple regression analysis shows the influence of two or more variables on a designated dependent variable.

Another way to consider regression analysis (simple or multiple) is from the viewpoint of a slope-intercept form of an equation. When a simple correlation between two variables is computed, the intercept and slope of the regression line (or line of best fit) may be requested. This line is based on the regression equation mentioned in the previous chapter (pages 191–193) with the y-intercept determined by the constant value and the slope determined by the coefficient of the independent variable. We describe here a simple regression equation as a vehicle for introducing multiple regression analysis. To assist in this process we present a new example based on a file called helping1.sav. This data file is related to a study of helping behavior; it is real data derived from a sample of 81 subjects.

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