Abstract
The problems of subset selection and variable analysis in linear regression are reviewed. The underlying theory, computational techniques and selection criteria are discussed. Alternatives to least squares, including ridge and principal component regression, are considered. These biased estimation procedures are related and contrasted with least squares. An example on biased estimators for air pollution data is given.

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