Encyclopedia of Clinical Neuropsychology

2018 Edition
| Editors: Jeffrey S. Kreutzer, John DeLuca, Bruce Caplan

Multiple Regression

  • Michael FranzenEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-57111-9_1218


Multiple regression is an extension of the general linear model to include multiple predictors (Allison 1999). The regression of each predictor on the criterion is calculated in a way that only unique variance, separate from the variance between the other predictors and the criterion, is measured. This can be especially useful when there may be correlations among the predictors creating overlapping shares of variance with the criterion. These regression weights are then combined in a linear equation that maximizes the relation of the sum of regression formulae to the criterion. When the linear combination is forced through the origin, standardized beta weights for each of the predictors reflect relative amounts of shared variance.

Current Knowledge

Multiple regression can be used in an exploratory fashion to find the best prediction of the criterion or in hypothesis testing in which the relation of a specific predictor to the criterion is tested as to whether that predictor...

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  1. Allison, P. (1999). Multiple regression: A primer. Thousand Oaks: Pine Forge Press.Google Scholar
  2. Pedhazur, E. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). New York: Harcourt Brace College Publishers.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Allegheny General HospitalPittsburghUSA