Comparison of Ridge Regression and Least Squares Methods in the Presence of Multicollinearity for Body Measurements in Saanen Kids

Yıl 2020, , 1429 - 1437, 01.06.2020

Cem Tırınk , Samet Hasan Abacı , Hasan Onder

https://doi.org/10.21597/jist.671662

Cited By: 2

Öz

Least square (LS) method is a common method used to estimate the coefficients in multiple regression models. The least square multiple regression models produce biased regression coefficients when the multicollinearity is encountered in the studied data sets. Multicollinearity problem can be solved by using some methods. As one of the methods, Ridge Regression (RR) is a biased estimation method that enables to obtain models having more reliable coefficient of determination (R2). This study was conducted on 40 Saanen kids in order to determine some morphological measurements (withers height, rump height, body length, chest width, chest girth and chest depth) affecting body weight. In this study, usability of ridge regression method in the presence of multicollinearity was evaluated. Variance Inflation Factor (VIF) values higher than 10 were detected for withers height and rump height. Coefficient of determination (R2) was obtained as 0.88 from LS method and R2 was obtained 0.875 with k=0.0136 from RR method. As a result, the model obtained from RR is more reliable than that obtained from LS.

Anahtar Kelimeler

Least squares, multiple regression, Multicollinearity, Ridge Regression

Teşekkür

This research was presented as an oral presentation at the International 8th Balkan Animal Science Conference (BALNIMALCON) held on 6-8 September 2017 in Prizren.

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