Abstract:
Multicollinearity often causes a huge explanatory problem in multiple linear
regression analysis. In presence of multicollinearity the ordinary least squares (OLS) estimators
are inaccurately estimated. In this paper the multicollinearity was detected by using observing
correlation matrix, variance influence factor (VIF), and eigenvalues of the correlation matrix. The
simulation multicollinearity data were generated using MINITAB software and make comparison
between methods of principal component regression (PCR) and the OLS methods. According to
the results of this study, we found that PCR method facilitates to solve the multicollinearity
problem.
