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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
Authors
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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Abstract
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.
Keywords
Variance Inflation Factor, Matrix, Eigen Values, Characteristics Root, Range, Gradient
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. doi: 10.11648/j.ajtas.20150406.36
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