Date of Award:
Master of Science (MS)
Mathematics and Statistics
Department name when degree awarded
Donald V. Sisson
Rex L. Hurst
Sixteen "model building" and "model selection" procedures commonly encountered in industry, all of which were initially alleged to be capable of identifying the best model from the collection of 2k possible linear models corresponding to a given set of k predictors in a multiple linear regression analysis, were individually summarized and subsequently evaluated by considering their comparative advantages and limitations from both a theoretical and a practical standpoint. It was found that none of the proposed procedures were absolutely infallible and that several were actually unsuitable. However, it was also found that most of these techniques could still be profitably employed by the analyst, and specific directional guidelines were recommended for their implementation in a proper analysis. Furthermore, the specific role of the analyst in a multiple linear regression application was clearly defined in a practical sense.
Jensen, David L., "Selecting the Best Linear Model From a Subset of All Possible Models for a Given Set of Predictors in a Multiple Linear Regression Analysis" (1972). All Graduate Theses and Dissertations. 6871.
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