TESTING THE DISTRIBUTIONAL ASSUMPTIONS OF LEAST-SQUARES LINEAR-REGRESSION

The error terms in least squares linear regression are assumed to be n ormally distributed with equal variance (homoskedastic), and independe nt of one another. If any of these distributional assumptions are viol ated, several of the desirable properties of a least squares fit may n ot hold. A variety of statistical tests of the assumptions is availabl e. The following are recommended for reasons of ease of use and discri minating power: the K-2 test for testing for non-normality, either the Durbin-Watson test or the Q-test for testing for autocorrelation, and either Szroeter's or White's test for testing for heteroskedasticity. The assumptions should be tested in this order; violating one of the assumptions may Invalidate the results of subsequent tests. A microcom puter-based software package for least squares linear regression that incorporates the above tests is introduced.