Abstract
Principal components methods and factor analysis are popular tools for the dimension-reduction problem. These techniques can be used to obtain a smaller number of new variables. However, the new variables may include all or most of the original variables. In this study, two methods are given which will select the most informative subset of variables from the variables which are directly measured. The different approaches are compared in a concluding example.
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