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Dergiler / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2020 / Cilt: 10 - Sayı: 2

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

Cem TIRINK
Samet Hasan ABACI
Hasan ONDER
Sayfa
1429–1437
Yayın Tarihi
2020-06-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

As a result, the model obtained from RR is more reliable than that obtained from LS.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Least square (LS) method is a common method used to estimate the coefficients in multiple regression models.
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Temel Bulgular & Veri: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).
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Sonuç & Katkı:As a result, the model obtained from RR is more reliable than that obtained from LS.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Least squares · multiple regression · multicollinearity

Özet

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.

Abstract

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.

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Bu Makaleye Atıf Yap

TIRINK, C., ABACI, S. H., ONDER, H. (2020). Comparison of Ridge Regression and Least Squares Methods in the Presence of Multicollinearity for Body Measurements in Saanen Kids. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 10 - Sayı: 2, 1429–1437. https://ajindex.com/articles/257252

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Kaynakça (4)

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