IJAPM 2015 Vol.5(2): 105-114 ISSN: 2010-362X
doi: 10.17706/ijapm.2015.5.2.105-114
doi: 10.17706/ijapm.2015.5.2.105-114
Selected Model Systematic Sequence via Variance Inflationary Factor
Zainodin H. J., Khuneswari G., Noraini A., Haider F. A. A.
Abstract—Literature reviews revealed that multicollinearity always exists when model a deals with several
independent variables. This phenomenon can cause the t statistic and the related probability-value to give a
misleading impression of the importance of the independent variables. There are two approaches in
tackling this issue. The common approach is correlation-coefficient based and the other is variance-based.
Many softwares in the market have highlighted this phenomenon and offer options in minimising the effect.
Currently, the variance-based approach is widely available in the software market. This is because it does
not depend on the type of dependent variables. This variance-based approach via Variance Inflation Factor
(VIF) quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides
an index that measures how much the variance (the square of the estimate's standard deviation) of an
estimated regression coefficient is increased because of collinearity. Thus, here, a novel approach is
revealed in detailing the procedures to remove several variables due to multicollinearity effects. Ultimately,
the insignificant variables are eliminated. It is found that when a very stringent criterion is set for
multicollinearity, the process of elimination of variables becomes smooth and easy besides shortening the
number of iteration.
Index Terms—Hierarchically multiple regression models, insignificant effects, multicollinearity effects, selected model, variance inflation factor.
Zainodin H. J., Noraini A., Haider F. A. A. are with Universiti Malaysia Sabah, Faculty of Science and Natural Resources, 88400 Kota Kinabalu, Sabah, Malaysia.
Khuneswari G. is with University of Glasgow, School of Mathematics and Statistics, G12 8QQ Glasgow, United Kingdom.
Index Terms—Hierarchically multiple regression models, insignificant effects, multicollinearity effects, selected model, variance inflation factor.
Zainodin H. J., Noraini A., Haider F. A. A. are with Universiti Malaysia Sabah, Faculty of Science and Natural Resources, 88400 Kota Kinabalu, Sabah, Malaysia.
Khuneswari G. is with University of Glasgow, School of Mathematics and Statistics, G12 8QQ Glasgow, United Kingdom.
Cite: Zainodin H. J., Khuneswari G., Noraini A., Haider F. A. A., "Selected Model Systematic Sequence via Variance Inflationary Factor," International Journal of Applied Physics and Mathematics vol. 5, no. 2, pp. 105-114, 2015.
General Information
ISSN: 2010-362X (Online)
Abbreviated Title: Int. J. Appl. Phys. Math.
Frequency: Quarterly
DOI: 10.17706/IJAPM
Editor-in-Chief: Prof. Haydar Akca
Abstracting/ Indexing: INSPEC(IET), CNKI, Google Scholar, EBSCO, Chemical Abstracts Services (CAS), etc.
E-mail: ijapm@iap.org
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