Introduction to Multiple Regression (1 of 3)

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In multiple regression, more than one variable is used to predict the criterion. For example, a college admissions officer wishing to predict the future grades of college applicants might use three variables (High School GPA, SAT, and Quality of letters of recommendation) to predict college GPA. The applicants with the highest predicted college GPA would be admitted. The prediction method would be developed based on students already attending college and then used on subsequent classes. Predicted scores from multiple regression are linear combinations of the predictor variables. Therefore, the general form of a prediction equation from multiple regression is:

Y' = b₁X₁ + b₂X₂ + ... + b_kX_k + A

where Y' is the predicted score, X₁ is the score on the first predictor variable, X₂ is the score on the second, etc. The Y intercept is A. The regression coefficients (b₁, b₂, etc.) are analogous to the slope in simple regression.