Detection of Multicollinearity Using Min-Max and Point-Coordinates Approach
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 640-643
Received: Dec. 14, 2015;
Accepted: Dec. 23, 2015;
Published: Jan. 23, 2016
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Umeh Edith Uzoma, Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Awopeju Kabir Abidemi, Department of Statistics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
Ajibade F. Bright, Department of General Studies, Petroleum Training Institute, Effurun, Delta State, Nigeria
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Multicollinearity is one of the problems or challenges of modeling or multiple regression usually encountered by Economists and Statisticians. It is a situation where by some of the independent variables in the formulated model are significantly or highly related/correlated. In the past, methods such as Variance Inflation Factor, Eigenvalue and Product moment correlation have been used by researchers to detect multicollinearity in models such as financial models, fluctuation of market price model, determination of factors responsible for survival of man and market model, etc. The shortfalls of these methods include rigorous computation which discourages researchers from testing for multicollinearity, even when necessary. This paper presents moderate and easy algorithm of the detection of multicollinearity among variables no matter their numbers. Using Min-Max approach with the principle of parallelism of coordinates, we are able to present an algorithm for the detection of multicollinearity with appropriate illustrative examples.
Variance Inflation Factor, Matrix, Eigen Values, Characteristics Root, Range, Gradient
To cite this article
Umeh Edith Uzoma,
Awopeju Kabir Abidemi,
Ajibade F. Bright,
Detection of Multicollinearity Using Min-Max and Point-Coordinates Approach, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 6,
2015, pp. 640-643.
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brien, R. M. (2007). "A Caution Regarding Rules of Thumb for Variance Inflation Factors". Quality & Quantity 41(5): 673. doi: 10.1007/s11135-006-9018-6.
Belsley, D. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression. New York: Wiley. ISBN0-471-52889-7.
Draper, N. R., Smith, H. (2003). Applied regression analysis, 3rd edition, Wiley, New York.
Gujrati, D. N. (2004). Basic econometrics 4th edition, Tata McGraw-Hill, New Delhi.
Hawking, R. R. and Pendleton, O. J. (1983). “The regression dilemma”, Commun. Stat.-Theo. Meth, 12,497-527.
Jim F. (2013). “What Are the Effects of Multicollinearity and When Can Ignore Them?” http://blog.minitab.com/blog/adventures-in-statistics/what-are-the-effects-of-multicollinearity-and-when-can-i-ignore-them.Assessed:17th, Dec., 2015.
Johnston, J. and Dinardo, J. (1997) Econometric methods, 4th edition, McGraw-Hill, Singapore.
Kennedy, P. E. (2002), “More on Venn Diagrams for Regression,” Journal of Statistics Education [Online], 10(1). (www.amstat.org/publications/jse/v10n1/kennedy.html).
Kumar, T. K. (1975). "Multicollinearity in Regression Analysis". Review of Economics and Statistics 57(3): 365–366. JSTOR 1923925.
Kock, N.; Lynn, G. S. (2012). "Lateral collinearity and misleading results invariance-based SEM: An illustration and recommendations". Journal of the Association for Information Systems 13(7): 546–580.
Math Centre (2009); Equations of Straight Lines. www.mathcentre.ac.uk/resources/uploaded/mc-ty-strtlines-2009-1.pdf. Assessed: 10/12/2015.
Montgomery, D. C., Peck, E. A., Vining, G. G. (2001). Introduction to linear regression analysis, 3rd edition, Wiley, New York.
Vaughan, T. S., and Berry, K. E. (2005): “Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity”. Journal of Statistics Education. Vol.13, Number 1.
Wetherill, G. B., Duncombe, P., Kenward, M., Kollerstrom, J. (1986). Regression analysis with application, 1st edition, Chapman and Hall, New York.