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Regressions are commonly misinterpreted

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  • David C. Hoaglin

Abstract

Much literature misinterprets results of fitting multivariable models for linear regression, logistic regression, and other generalized linear models, as well as for survival, longitudinal, and hierarchical regressions. For the leading case of multiple regression, regression coefficients can be accurately interpreted via the added-variable plot. However, a common interpretation does not reflect the way regression methods actually work. Additional support for the correct in- terpretation comes from examining regression coefficients in multivariate normal distributions and from the geometry of least squares. To properly implement mul- tivariable models, one must be cautious when calculating predictions that average over other variables, as in the Stata command margins. Copyright 2016 by StataCorp LP.

Suggested Citation

  • David C. Hoaglin, 2016. "Regressions are commonly misinterpreted," Stata Journal, StataCorp LP, vol. 16(1), pages 5-22, March.
    • Handle: RePEc:tsj:stataj:v:16:y:2016:i:1:p:5-22
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    Citations

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    Cited by:

    1. Bornmann, Lutz & Adams, Jonathan & Leydesdorff, Loet, 2018. "The negative effects of citing with a national orientation in terms of recognition: National and international citations in natural-sciences papers from Germany, the Netherlands, and the UK," Journal of Informetrics, Elsevier, vol. 12(3), pages 931-949.
    2. Pillai N., Vijayamohanan, 2016. "How Do You Interpret Your Regression Coefficients?," MPRA Paper 76867, University Library of Munich, Germany.
    3. Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.

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