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.

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.