Regressions are commonly misinterpreted
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 interpretation comes from examining regression coefficients in
multivariate normal distributions and from the geometry of least squares. To
properly implement multivariable models, one must be cautious when
calculating predictions that average over other variables, as in the Stata
command margins.
View all articles by this author:
David C. Hoaglin
View all articles with these keywords:
regression models, added-variable plot, multivariate normal distribution, geometry of least squares, margins command
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
