Author(s)

Maruf A. Raheem^1*, Nse S. Udoh², Aramide T. Gbolahan³

¹Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK.
²Department of Mathematics & Statistics, University of Uyo, Uyo, Nigeria.
³Department of Computer Science, Sheffield Hallam University, Sheffield, UK.

This work is geared towards detecting and solving the problem of multicolinearity in regression analysis. As such, Variance Inflation Factor (VIF) and the Condition Index (CI) were used as measures of such detection. Ridge Regression (RR) and the Principal Component Regression (PCR) were the two other approaches used in modeling apart from the conventional simple linear regression. For the purpose of comparing the two methods, simulated data were used. Our task is to ascertain the effectiveness of each of the methods based on their respective mean square errors. From the result, we found that Ridge Regression (RR) method is better than principal component regression when multicollinearity exists among the predictors.

Multicollinearity, Adequacy, Regression coefficients, Variance Inflation Factor (VIF), Mean Square Error

Raheem, M. , Udoh, N. and Gbolahan, A. (2019) Choosing Appropriate Regression Model in the Presence of Multicolinearity. Open Journal of Statistics, 9, 159-168. doi: 10.4236/ojs.2019.92012.