In 2002, an article entitled “Four assumptions of multiple regression that researchers should always test” by
Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times
(as of August 2013), and it is one of the first results displayed in a Google search for “regression
assumptions”. While Osborne and Waters’ efforts in raising awareness of the need to check assumptions
when using regression are laudable, we note that the original article contained at least two fairly important
misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the
assumption of normally distributed variables; and secondly, that measurement errors necessarily cause
underestimation of simple regression coefficients. In this article, we clarify that multiple regression models
estimated using ordinary least squares require the assumption of normally distributed errors in order for
trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or
predictor variables. Secondly, we point out that regression coefficients in simple regression models will be
biased (toward zero) estimates of the relationships between variables of interest when measurement error is
uncorrelated across those variables, but that when correlated measurement error is present, regression
coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of
the assumptions of multiple regression when using ordinary least squares.
In 2002, an article entitled “Four assumptions of multiple regression that researchers should always test” byOsborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times(as of August 2013), and it is one of the first results displayed in a Google search for “regressionassumptions”. While Osborne and Waters’ efforts in raising awareness of the need to check assumptionswhen using regression are laudable, we note that the original article contained at least two fairly importantmisconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires theassumption of normally distributed variables; and secondly, that measurement errors necessarily causeunderestimation of simple regression coefficients. In this article, we clarify that multiple regression modelsestimated using ordinary least squares require the assumption of normally distributed errors in order fortrustworthy inferences, at least in small samples, but not the assumption of normally distributed response orpredictor variables. Secondly, we point out that regression coefficients in simple regression models will bebiased (toward zero) estimates of the relationships between variables of interest when measurement error isuncorrelated across those variables, but that when correlated measurement error is present, regressioncoefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary ofthe assumptions of multiple regression when using ordinary least squares.
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