Statnote 25: Stepwise multiple regression

Anthony Hilton, Richard A. Armstrong

Research output: Contribution to specialist publication or newspaperArticle


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.
Original languageEnglish
Number of pages4
Specialist publicationMicrobiologist
Publication statusPublished - Jun 2011


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


Dive into the research topics of 'Statnote 25: Stepwise multiple regression'. Together they form a unique fingerprint.

Cite this