Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/1297
Title: Principal component regression for solving multicollinearity problem
Authors: Alibuhtto, M.C
Peiris, T.S.G
Keywords: Linear Regression
Multicollinearity
Variance Influence Factor
Simulation
Issue Date: 2015
Publisher: South Eastern University of Sri- Lanka, Oluvil, Sri- Lanka
Citation: Proceedings of 5th International Symposium 2015 on " Emerging Trends and Challenges in Multidisciplinary Resaearch, pp. 179-182
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.
URI: http://ir.lib.seu.ac.lk/handle/123456789/1297
Appears in Collections:5th International Symposium - 2015

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