There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient for assessing the appropriateness of the fitted model. Here, an easy-to-use test of the assumption of equal variance (i.e., homoscedasticity) as well as model specification is provided. Given the importance of the equal-variance assumption (i.e., if uncorrected, "severe" violations preclude the use of statistical inference and "moderate" violations result in a loss of statistical power) and given the fact that, if uncorrected, a misspecified or underspecified model could invalidate an entire study, the test developed by Halbert White in 1980 is recommended for supplementing a graphic residual analysis when teaching regression modeling to business students at both the undergraduate and graduate levels. Using this confirmatory approach to supplement a traditional residual analysis has value because students often find that graphic displays are too subjective for determining what constitutes "severe" from "moderate" departures from the equal variance assumption or for assessing patterns in plots that might indicate model misspecification or underspecification.