Date of Award
1975
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
Committee Chair(s)
Ronald V. Canfield
Committee
Ronald V. Canfield
Committee
Rex L. Hurst
Committee
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.
Recommended Citation
Hattori, Stephen M., "An Evaluation of Bartlett's Chi-Square Approximation for the Determinant of a Matrix of Sample Zero-Order Correlation Coefficients" (1975). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 1210.
https://digitalcommons.usu.edu/gradreports/1210
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