Comparison Of Least Squares, Ridge Regression And Principal Component Approaches In The Presence Of Multicollinearity In Regression Analysis

dc.contributor.author	Cankaya, S.
dc.contributor.author	Eker, S.
dc.contributor.author	Abaci, S.H.
dc.date.accessioned	2021-12-08T13:47:24Z
dc.date.available	2021-12-08T13:47:24Z
dc.date.issued	2019
dc.identifier.issn	2148-127X
dc.identifier.uri	https://app.trdizin.gov.tr/makale/TXpVME9ERXhNUT09/comparison-of-least-squares-ridge-regression-and-principal-component-approaches-in-the-presence-of-multicollinearity-in-regression-analysis
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/18429
dc.description.abstract	The aim of this study was to compare estimation methods: least squares method (LS), ridge regression (RR), Principal component regression (PCR) to estimate the parameters of multiple regression model in situations when the underlying assumptions of leas
dc.language.iso	English
dc.title	Comparison Of Least Squares, Ridge Regression And Principal Component Approaches In The Presence Of Multicollinearity In Regression Analysis
dc.relation.journal	Türk Tarım - Gıda Bilim Ve Teknoloji Dergisi
dc.identifier.doi	10.24925/Turkishjaf.v7i8.1166-1172.2515
dc.relation.volume	7
dc.relation.issue	8
dc.identifier.issue	8
dc.identifier.startpage	1166
dc.identifier.endpage	1172
dc.identifier.volume	7

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