The importance of logistic regression implementations in the Turkish livestock sector and logistic regression implementations /fields

ArticleAçık Erişim

Özet

Logistic regression analysis is one of the mostly preferred regression methods that can beimplemented in modelling binary dependent variables. Logistic regression is a mathematicalmodelling approach used to define the relationship between such independent variables as X1,X2, , Xn and Y binary dependent variable which is coded as 0 or 1 for two possible categories.The independent variables may be continuous, discrete, binary or a combination of them. In thispaper, logistic regression models are researched. Maximum likelihood methods may be used toestimate the parameters of the logistic model. The interpretations of coefficients are made withodds rate values. In other words, in this paper, the logistic regression analysis has been reviewedthat can define the relationship between the binary result variable and independent variablescomprising of both continuous and discrete variables. Shortly, the applicability of logisticregression in the livestock has been researched.

Anahtar Kelimeler

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/19202Resmi atıflarda kullanılan kalıcı adres
Tam Kayıt
AlanDeğerDil
dc.contributor.authorYİĞİTER, Şule Yüksel
dc.contributor.authorKORKMAZ, MURAT
dc.contributor.authorGüney, Selami
dc.date.accessioned2026-10-18T21:03:50.284040818Z
dc.date.available2026-10-18T21:03:50.284040818Z
dc.date.issued2012
dc.description.abstractLogistic regression analysis is one of the mostly preferred regression methods that can beimplemented in modelling binary dependent variables. Logistic regression is a mathematicalmodelling approach used to define the relationship between such independent variables as X1,X2, , Xn and Y binary dependent variable which is coded as 0 or 1 for two possible categories.The independent variables may be continuous, discrete, binary or a combination of them. In thispaper, logistic regression models are researched. Maximum likelihood methods may be used toestimate the parameters of the logistic model. The interpretations of coefficients are made withodds rate values. In other words, in this paper, the logistic regression analysis has been reviewedthat can define the relationship between the binary result variable and independent variablescomprising of both continuous and discrete variables. Shortly, the applicability of logisticregression in the livestock has been researched.
dc.identifier.endpage36
dc.identifier.issn1300-6819
dc.identifier.issue2
dc.identifier.startpage25
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/19202
dc.identifier.volume16
dc.language.isoen
dc.relation.ispartofHarran Üniversitesi Ziraat Fakültesi Dergisi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectZiraat Mühendisliği
dc.subjectİstatistik ve Olasılık
dc.titleThe importance of logistic regression implementations in the Turkish livestock sector and logistic regression implementations /fields
dc.typeArticle
dspace.entity.typePublication
rdl.citation.trdizin7
rdl.departmentTanımlanmamış Kurum
rdl.departmentERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ
rdl.source.indexTRDIZIN