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Regression (Statistical Analysis)

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Encyclopedia of Personality and Individual Differences

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Synonyms

Linear regression

Definition

(Linear) regression analysis is a statistical model to predict expected scores of a (metric) criterion variable relying on a linear (additive) combination of predictor variables.

Introduction

In statistical modeling, regression analysis is used to estimate the relation between one criterion variable and one or more predictor variable(s). The resulting regression function can be used to calculate expected scores of the criterion variable given the scores on the predictor variables and to calculate the expected differences in the criterion variable depending on differences in the predicting variables (Tabachnick and Fidell 2007).

Simple Linear Regression

In simple linear regression analysis, the scores of one criterion variable (y i ) are predicted by one predictor variable (x i ). The function is a linear combination of an intercept (β 0) and a slope parameter times the score on the predictor variable (β 1 x i) as well as a residual term (e ...

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References

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Author information

Authors and Affiliations

  1. Department of Psychology, Bielefeld University, Bielefeld, Germany

    Fridtjof W. Nussbeck, Peter Fuchs & Anne Hagemann

Authors
  1. Fridtjof W. Nussbeck
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  2. Peter Fuchs
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  3. Anne Hagemann
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Corresponding author

Correspondence to Fridtjof W. Nussbeck .

Editor information

Editors and Affiliations

  1. Oakland University, Rochester, USA

    Virgil Zeigler-Hill

  2. Oakland University, Rochester, USA

    Todd K. Shackelford

Section Editor information

  1. Humboldt University, Germany, Berlin, Germany

    Matthias Ziegler

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Nussbeck, F.W., Fuchs, P., Hagemann, A. (2017). Regression (Statistical Analysis). In: Zeigler-Hill, V., Shackelford, T. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-28099-8_1347-1

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  • DOI: https://doi.org/10.1007/978-3-319-28099-8_1347-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28099-8

  • Online ISBN: 978-3-319-28099-8

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