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Visualization of evidence in regression with the QR decomposition

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  • W. Braun

Abstract

Graphical ANOVA is a simple and effective tool for visualizing evidence of differences between treatment means for data coming from factorial experiments. The purpose of the present article is to propose an analogous method for the visualization of the significance of regression, using the QR decomposition. Two graphical tests are proposed and compared with the classical $$F$$ F test, by simulation. It is found that when the number of candidate predictors is small relative to the sample size, the classical test has slightly higher power than the graphical tests. When the number of predictors is large, the graphical tests remain powerful. while the classical $$F$$ F test exhibits poor power properties. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • W. Braun, 2015. "Visualization of evidence in regression with the QR decomposition," Computational Statistics, Springer, vol. 30(4), pages 907-927, December.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:4:p:907-927
    DOI: 10.1007/s00180-015-0558-x
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    References listed on IDEAS

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    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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