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The use of Stein estimation in multiple linear regression is considered. Tables and graphs are presented that compare the prediction mean squared errors of positive-part James-Stein, preliminary-test, reduced, and full-model least squares estimates. The appropriateness of using Stein contraction on possibly extraneous variables is emphasized, and a procedure is presented for evaluating the likely savings in using Stein estimation on the problem at hand. An example is given.
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