Possibly, marketing managers may make some effort and enquire advice from marketing research experts. It is significant for the researched data to emanate a precise diverse course of action as well as the alternative chances for success (Erevelles, Sunil, Nobuyuki Fukawa, and Linda Swayne, 899). To sustain the objective of marketing research, marketing management must have enough confidence in the results to be formulated to meet the risky decisions concerning those outcomes (Graue, 8). However, it will be essential for the marketing researchers to apply the scientific method, which is thus able to interpret an individual’s prejudices, opinions, and notions into explicit hypotheses/proposals which are empirically tested. Alternative clarification of phenomena interest or events receives equal deliberation.
The possible problem of collinearity and multiple regressions is the possibility for independent variable value to change while other variables remain constant. Linking an independent variable creates a room for indicating adjustments in one variable related to other variables (Erevelles, Sunil, Nobuyuki Fukawa, and Linda Swayne, 900). The change of one variable regardless of the other is determined with how stronger the correlation is. Since the independent variable changes, it is hard for the model to predict the relationship of every independent variable.
Multicollinearity may result in the following two major type problems:
The primary objective for the regression model and the severity of the multicollinearity determines its possible to reduce errors. The following are essential ways to minimize the problems of regression model:
The rate of the multicollinearity determines the increase of different mistakes. We can moderate multicollinearity to reduce errors. It mainly affects a specific linked independent variable (Graue, 14). The researcher may minimize the problems of multicollinearity by using the experimental variables and various control variables. The researcher can easily interpret the experimented variable swiftly without any issues when the high multicollinearity is present only in the control variables.
Though the p-values and the coefficients are being affected most by the high multicollinearity, it has no adverse influence on the predictions, the goodness of fit statistics as well as the precision of the projections (Erevelles, Sunil, Nobuyuki Fukawa, and Linda Swayne, 901).
Erevelles, Sunil, Nobuyuki Fukawa, and Linda Swayne. “Big Data consumer analytics and the transformation of marketing.” Journal of Business Research 69.2 (2016): 897-904.
Graue, Carolin. “Qualitative data analysis.” International Journal of Sales, Retailing & Marketing 4.9 (2015): 5-14.
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