An approximate test for comparing independent regression models with unequal error variances

The usual F statistic for comparing two independent regression equations is commonly used by practitioners. This test, however, presumes the equality of the error variance of the two populations. For applications where this assumption is not valid, an approximate test based on the same statistic is proposed that improves the Toyoda (1974) approximation by using Satterthwaite's (1946) approximation not just for the numerator but also for the denominator of the usual F statistic. The unconditional significance level of this approximate test is computed for a variety of design configurations. The power of the approximate test relative to an upper bound is also considered.