**Regression equations** are calculations used to predict a person’s score on one variable when that person’s score on another variable is already known. They are essentially “prediction equations” that are based on known information about the relationship between the two variables. For example, after discovering that seating pattern and exam score are related, a regression equation may be calculated that predicts anyone’s exam score based only on information about where the person sits in the class. The general form of a regression equation is:

where *Y* is the score we wish to predict, *X* is the known score, *a* is a constant, and *b* is a weighting adjustment factor that is multiplied by *X* (it is the slope of the line created with this equation). In our seating–exam score example, the following regression equation is calculated from the data:

Thus, if we know a person’s score on *X* (seating), we can insert that into the equation and predict what that person’s exam score (*Y*) will be. If the person’s *X* score is 2 (by sitting in the second row), we can predict that *Y* = 99 + (−16), or that the person’s exam score will be 83. Through the use of regression equations such as these, colleges can use SAT scores to predict college grades.

When researchers are interested in predicting some future behavior (called the **criterion variable**) on the basis of a person’s score on some other variable (called the **predictor variable**), it is first necessary to demonstrate that there is a reasonably high correlation between the criterion and predictor variables. The regression equation then provides the method for making predictions on the basis of the predictor variable score only.

Thus far we have focused on the correlation between two variables at a time. Researchers recognize that a number of different variables may be related to a given behavior (this is the same point noted above in the discussion of factors Page 258that contribute to weight). A technique called **multiple correlation** is used to combine a number of predictor variables to increase the accuracy of prediction of a given criterion or outcome variable.

A multiple correlation (symbolized as *R* to distinguish it from the simple *r*) is the correlation between a combined set of predictor variables and a single criterion variable. Taking all of the predictor variables into account usually permits greater accuracy of prediction than if any single predictor is considered alone. For example, applicants to graduate school in psychology could be evaluated on a combined set of predictor variables using multiple correlation. The predictor variables might be (1) college grades, (2) scores on the Graduate Record Exam Aptitude Test, (3) scores on the Graduate Record Exam Psychology Test, and (4) favorability of letters of recommendation. No one of these factors is a perfect predictor of success in graduate school, but this combination of variables can yield a more accurate prediction. The multiple correlation is usually higher than the correlation between any one of the predictor variables and the criterion or outcome variable.

In actual practice, predictions would be made with an extension of the regression equation technique discussed previously. A multiple regression equation can be calculated that takes the following form:

where *Y* is the criterion variable, *X*1 to *Xn* are the predictor variables, *a* is a constant, and *b*1 to *bn* are weights that are multiplied by scores on the predictor variables. For example, a regression equation for graduate school admissions would be:

Researchers use multiple regression to study basic research topics. For example, Ajzen and Fishbein (1980) developed a model called the “theory of reasoned action” that uses multiple correlation and regression to predict specific behavioral intentions (e.g., to attend church on Sunday, buy a certain product, or join an alcohol recovery program) on the basis of two predictor variables. These are (1) attitude toward the behavior and (2) perceived normative pressure to engage in the behavior. Attitude is one’s own evaluation of the behavior, and normative pressure comes from other people such as parents and friends. In one study, Codd and Cohen (2003) found that the multiple correlation between college students’ intention to seek help for alcohol problems and Page 259the combined predictors of attitude and norm was .35. The regression equation was as follows:

This equation is somewhat different from those described previously. In basic research, you are not interested in predicting an exact score (such as an exam score or GPA), and so the mathematical calculations can assume that all variables are measured on the same scale. When this is done, the weighting factor reflects the magnitude of the correlation between the criterion variable and each predictor variable. In the help-seeking example, the weight for the attitude predictor is somewhat higher than the weight for the norm predictor; this shows that, in this case, attitudes are more important as a predictor of intention than are norms. However, for other behaviors, attitudes may be less important than norms.

It is also possible to visualize the regression equation. In the help-seeking example, the relationships among variables could be diagrammed as follows:

You should note that the squared multiple correlation coefficient (*R*2) is interpreted in much the same way as the squared correlation coefficient (*r*2). That is, *R*2 tells you the percentage of variability in the criterion variable that is accounted for by the combined set of predictor variables. Again, this value will be higher than that of any of the single predictors by themselves.

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