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.