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An Analysis of Rates of Change in Community Per Capita Income by Discriminant Analysis

Published online by Cambridge University Press:  28 April 2015

Steve Murray*
Affiliation:
Department of Agricultural Economics, Oklahoma State University, Stillwater

Extract

Statistical methods for estimation, hypothesis testing, and confidence statements are based typically on exact specification of the response variates. In the applied sciences another kind of multivariate problem is common in which an observation must be assigned in some optimal fashion to one of several populations. Classification rules based on an index called the linear discriminant function provide a method for such assignment.

Use of the linear discriminant function is relatively new to regional economics. Previously it has been used in such disciplines as botany to classify a new specimen as belonging to one of several recognized species of a flower, in educational psychology to develop rules for admitting applicants to college programs, in routine banking to aid credit officers in evaluating loan applications, and in agricultural economics to determine producer plans for changes in hog marketings and to identify factors associated with watershed development.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 1978

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References

[1]Beale, Calvin. “Quantitative Dimensions of Decline and Stability Among Rural Communities,” in Vining, Larry R., ed., Communities Left Behind: Alternatives for Development, Ames: The Iowa State University Press, 1974, pp. 321.Google Scholar
[2]Chiang, Alpha C.Fundamental Methods of Mathematical Economics, 2nd ed., New York: McGraw-Hill Book Co., 1974.Google Scholar
[3]Cox, P. Thomas and Badger, Daniel D.. “Factors Contributing to Success of Upstream Watershed Development in Oklahoma,” Oklahoma State University Processed Series P-578, November 1967.Google Scholar
[4]Fisher, R. A.The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, Volume 7, 1936, pp. 179188.CrossRefGoogle Scholar
[5]Johnson, R. Bruce and Hagan, Albert R.. “Agricultural Loan Evaluation with Discriminant Analysis,” Southern Journal of Agricultural Economics, Volume 5, No. 2, December 1973, pp. 5762.Google Scholar
[6]Morrison, Donald F.Multivariate Statistical Methods, 2nd ed., New York: McGraw-Hill Book Co., 1976.Google Scholar
[7]Raikes, Ronald and Trampel, Michael. “An Analysis of Producers' Year-to-Year Changes in Slaughter Hog Marketings,” Southern Journal of Agricultural Economics, Volume 8, No. 2, December 1976, pp. 95101.Google Scholar
[8]Sexton, Dennis W. and Goldman, Roy D.. “High School Transcript as a Set of ‘Nonreactive’ Measures for Predicting College Success and Major Field,” Journal of Educational Psychology, Volume 67, No. 1, February 1975, pp. 3037.CrossRefGoogle Scholar
[9]Titner, Gerhard. Econometrics, New York: John Wiley and Sons, Inc., 1952.Google Scholar
[10]Tweeten, Luther and Brinkman, George. Micropolitan Development, Ames: The Iowa State University Press, 1976.Google Scholar
[11]Willis, T. W. and Associates. Ozarks Regional Commission Community Development Profile System: Uses and Applications, Farmville, North Carolina, 1970.Google Scholar