Sri Pujilestari, - and Nurkaromah Dwidayati, - and Sugiman, - (2017) Pemilihan Model Regresi Linier Berganda Terbaik pada Kasus Multikolinieritas Berdasarkan Metode Principal Component Analysis (PCA) dan Metode Stepwise. Unnes Journal of Mathematics, 6 (1). pp. 70-80. ISSN 2460-5859

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Abstract

One of the issues on the assumption of multiple linier regression model is frequent correlation between the independent variables in a multiple linier regression model known as multicollinierity. If there is multicollinierity then the conclusions are not appropriate. In this study, the method that been used to find the best model in the case of multicollinierity are stepwise method and the method of Principal Component Analysis (PCA) The main objective of this research is looking for the best model using Stepwise Method and Principal Component Analysis (PCA) method. The results obtained to search for the best model in the case of data multicollinierity stock returns in LQ 45 in BEI period from July to December 2015 by using Principal Component Analysis (PCA) Y = 11.992 + 2.179 F1with value R 2 adjusted by 0,050 and the value of S 2 amounted to 63.049, while the results obtained by the stepwise method is Y = 4.891 + 0.144 X6+ 7,804X2value R 2 adjusted by 0.191 and the value of S 2 amounted to 53.678. From these results it can be concluded that the Stepwise method is more suitable to find the best model on a case of multicollinierity.

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