Date of Award

1975

Degree Type

Report

Degree Name

Master of Science (MS)

Department

Mathematics and Statistics

First Advisor

Ronald V. Canfield

Second Advisor

Rex L. Hurst

Third Advisor

Elwin G. Eastman

Abstract

The single equation least-squares regression model has been extensively studied by economists and statisticians alike in order to determine the problems which arise when particular assumptions are violated. Much literature is available in terms of the properties and limitations of the model. However, on the multicollinearity problem, there has been little research, and consequently, limited literature is available when the problem is encountered. Farrar & Glauber (1967) present a collection of techniques to use in order to detect or diagnose the occurrence of multicollinearity within a regression analysis. They attempt to define multicollinearity in terms of departures from a hypothesized statistical condition, and then fashion a series of hierarchical measures for its presence, severity, and location in a data set. Since the problem is of a statistical rather than of a mathematical nature, the question of existence or nonexistence is ignored and the focus is on the severity of the problem.

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