Principal component regression for solving multicollinearity problem

Alibuhtto, M.C; Peiris, T.S.G

dc.contributor.author	Alibuhtto, M.C
dc.contributor.author	Peiris, T.S.G
dc.date.accessioned	2016-02-02T06:50:18Z
dc.date.available	2016-02-02T06:50:18Z
dc.date.issued	2015
dc.identifier.citation	Proceedings of 5th International Symposium 2015 on " Emerging Trends and Challenges in Multidisciplinary Resaearch, pp. 179-182	en_US
dc.identifier.uri	http://ir.lib.seu.ac.lk/handle/123456789/1297
dc.description.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.	en_US
dc.language.iso	en_US	en_US
dc.publisher	South Eastern University of Sri- Lanka, Oluvil, Sri- Lanka	en_US
dc.subject	Linear Regression	en_US
dc.subject	Multicollinearity	en_US
dc.subject	Variance Influence Factor	en_US
dc.subject	Simulation	en_US
dc.title	Principal component regression for solving multicollinearity problem	en_US
dc.type	Conference paper	en_US