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4 different procedures are commonly employed with sample data to reduce a set of predictor variables. In the present study these procedures were repeatedly applied to computer‐simulated samples to provide comparative data pertaining to two questions: (a) Which procedure can be expected to produce an equation that yields the most accurate predictions for the population? (b) Which procedure is most likely to identify the optimal set of independent variables? The samples were drawn from 12, mathematically defined, multivariate normal populations. Each population consisted of 1 criterion and 10 predictor variables. Five or fewer independent variables constituted the optimal set in each case. With respect to both questions small differences among the procedures were observed. However, the forward selection and stepwise procedures consistently produced more favorable results than the 2 backward elimination procedures. The question of the number of sampling units to use is discussed.
Halinski et al. (Tue,) studied this question.
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