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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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