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Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/1297
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dc.contributor.authorAlibuhtto, M.C
dc.contributor.authorPeiris, T.S.G
dc.date.accessioned2016-02-02T06:50:18Z
dc.date.available2016-02-02T06:50:18Z
dc.date.issued2015
dc.identifier.citationProceedings of 5th International Symposium 2015 on " Emerging Trends and Challenges in Multidisciplinary Resaearch, pp. 179-182en_US
dc.identifier.urihttp://ir.lib.seu.ac.lk/handle/123456789/1297
dc.description.abstractMulticollinearity 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.isoen_USen_US
dc.publisherSouth Eastern University of Sri- Lanka, Oluvil, Sri- Lankaen_US
dc.subjectLinear Regressionen_US
dc.subjectMulticollinearityen_US
dc.subjectVariance Influence Factoren_US
dc.subjectSimulationen_US
dc.titlePrincipal component regression for solving multicollinearity problemen_US
dc.typeConference paperen_US
Appears in Collections:5th International Symposium - 2015

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