Statnote 25: Stepwise multiple regression

Anthony Hilton, Richard A. Armstrong

Research output: Contribution to specialist publicationArticle

Abstract

An investigator may also wish to select a small subset of the X variables which give the best prediction of the Y variable. In this case, the question is how many variables should the regression equation include? One method would be to calculate the regression of Y on every subset of the X variables and choose the subset that gives the smallest mean square deviation from the regression. Most investigators, however, prefer to use a ‘stepwise multiple regression’ procedure. There are two forms of this analysis called the ‘step-up’ (or ‘forward’) method and the ‘step-down’ (or ‘backward’) method. This Statnote illustrates the use of stepwise multiple regression with reference to the scenario introduced in Statnote 24, viz., the influence of climatic variables on the growth of the crustose lichen Rhizocarpon geographicum (L.)DC.
LanguageEnglish
Pages40-43
Number of pages4
Volume12
Specialist publicationMicrobiologist
Publication statusPublished - Jun 2011

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Keywords

  • variables
  • regression equation
  • stepwise multiple regression procedure
  • stepwise multiple regression
  • influence of climatic variables
  • growth
  • crustose lichen Rhizocarpon geographicum (L.)DC

Cite this

Hilton, A., & Armstrong, R. A. (2011). Statnote 25: Stepwise multiple regression. Microbiologist, 12, 40-43.
Hilton, Anthony ; Armstrong, Richard A. / Statnote 25: Stepwise multiple regression. In: Microbiologist. 2011 ; Vol. 12. pp. 40-43.
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Hilton, A & Armstrong, RA 2011, 'Statnote 25: Stepwise multiple regression' Microbiologist, vol. 12, pp. 40-43.

Statnote 25: Stepwise multiple regression. / Hilton, Anthony; Armstrong, Richard A.

In: Microbiologist, Vol. 12, 06.2011, p. 40-43.

Research output: Contribution to specialist publicationArticle

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