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Journals / 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
Pages
1429–1437
Publication Date
2020-06-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Least square (LS) method is a common method used to estimate the coefficients in multiple regression models.
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Key Findings & Data: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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Conclusion & Impact:As a result, the model obtained from RR is more reliable than that obtained from LS.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Least squares · multiple regression · multicollinearity

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.

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.

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Cite This Article

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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References (4)

  1. Akcay A, Sariozkan A, 2015. Estimation of income with using Ridge Regression analysis in layer hen industry. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 62, 69-74, 2015. Albayrak AS, 2005. An alternative bias estimation technique and an application of the least-squares technique in multiple linear connections. Zonguldak Karaelmas University Journal Social Sciences1: 105-126. Alpar R, 2011. Uygulamalı çok değişkenli istatistiksel yöntemler. 3. Baskı. Kızılay/Ankara. Detay Yayımcılık. ISBN:978-605-5437-42-8 Anderson B, 1998. Scandinavian evidence on growth and age structure, ESPE 1997 Conference at Uppsala University. Ari A, Onder H, 2013. Regression Models Used for Different Data Structures. Anadolu Journal of Agricultural Sciences, 2013,28(3):168-174. doi: 10.7161/anajas.2013.28.3.168. Aytekin I, Eyduran E, Karadas K, Aksahan R, Keskin I, 2018. Prediction of Fattening Final Live Weight from some Body Measurements and Fattening Period in Young Bulls of Crossbred and Exotic Breeds using MARS Data Mining Algorithm. Pakistan Journal of Zoology, vol. 50(1), pp 189-195, 2018.
  2. Cankaya S, Eker S, Abaci, SH, 2019.
  3. Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis. Turkish Journal of Agriculture-Food Science and Technology, 7(8): 1166-1172. DOI: 10.24925/turjaf.v7i8.1166-1172.2515 Ergüneş E, 2004. The examing least square method and Ridge regression method by comparison. Cukurova University Graduate School of Natural and Applied Sciences, Master Thesis (Printed). Eyduran E, Zaborski D, Waheed A, Celik S, Karadas K, Grzesiak W, 2017. Comparison of the predictive Capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the indigenous beetal goat of Pakistan. Pakistan Journal of Zoology, 49: 257-265. https://doi.org/10.17582/journal.pjz/2017.49.1.257.265. Gujarati DN, 1995. Basic econometrics, 3rd ed. McGraw-Hill, New York, USA. Hair JF Jr, Black WC, Babin BJ, Anderson RE, (2014). Multivariate Data Analysis (7th edition), pp. 200, ISBN 10: 1-292-02190-X, Pearson Education Limited -England. Hoerl AE, Kennard R, 1970. Ridge regression: Biased estimation for non-orthogonal problems. Technometrics, 12: 55-67. https://doi.org/10.1080/00401706.1970.10488635 IBM Corp. Released 2012.
  4. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. Kurtuluş M, 2001. A study on ridge regression.