TITLE:
Choosing Appropriate Regression Model in the Presence of Multicolinearity
AUTHORS:
Maruf A. Raheem, Nse S. Udoh, Aramide T. Gbolahan
KEYWORDS:
Multicollinearity, Adequacy, Regression coefficients, Variance Inflation Factor (VIF), Mean Square Error
JOURNAL NAME:
Open Journal of Statistics,
Vol.9 No.2,
April
1,
2019
ABSTRACT: 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.