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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.

Background: Market studies on consumer preferences on product items had shown that consumer's behavioural patterns and intentions are sources of business profit level. In the advent wave of global businesses, the behavioural buying patterns of consumers have to be studied and analysed. Hence, this research illustrated the procedures in getting the best polynomial regression model of the consumer buying patterns on the demand for detergent that had included interaction variables. Methods: The hierarchically multiple polynomial regression models involved were up to the third-order polynomial and all the possible models were also considered. The possible models were reduced to several selected models using progressive removal of multicollinearity variables and elimination of insignificant variables. To enhance the understanding of the whole concept in this study, multiple polynomial regressions with eight selection criteria (8SC) had been explored and presented in the process of getting the best model from a set of selected models. Results: A numerical illustration on the demand of detergent had been included to get a clear picture of the process in getting the best polynomial order model. There were two single independent variables: the "price difference" between the price offered by the enterprise and the average industry price of competitors' similar detergents (in US$) and advertising expenditure (in US$). Conclusion: In conclusion, the best cubic model was obtained where the parameters involved in the model were estimated using ordinary least square method.