TÜRKİYEDE HAYVANCILIK SEKTÖRÜNDE LOJİSTİK REGRESYON UYGULAMALARININ ÖNEMİ LOJİSTİK REGRESYON UYGULAMALARI/ALANLARI

Year 2012, Volume: 16 Issue: 2, 25 - 36, 20.02.2014

Murat Korkmaz Selami Güney , Şule Yiğiter

Abstract

Lojistik regresyon analizi ikili bağımlı değişkenleri modellemek için uygulanabilen en çok tercih edilen regresyon metotlarından biridir. Lojistik regresyon X1, X2, …, Xn gibi bağımsız
değişkenleri ile iki olası kategori için O veya 1 gibi kodlanmış Y ikili bağımlı değişkeni arasındaki ilişkiyi tanımlamak için kullanılan matematiksel modelleme yaklaşımıdır. Burada bağımsız değişkenler sürekli, kesikli, ikili veya bunların karışımı olabilir. Bu çalışmada lojistik regresyon modelleri araştırmaktadır. En çok olabilirlik metotları lojistik modelin parametrelerini tahmin etmek için kullanılır. Katsayıların yorumu odds oran değerleriyle yapılır. Bir başka deyişle bu araştırmada, ikili sonuç değişkeni ile hem sürekli hem de kesikli değişkenlerden oluşan bağımsız değişkenler kümesi arasındaki ilişkiyi tanımlayabilen lojistik regresyon analizi incelenmiştir. Kısaca, bu çalışmada lojistik regresyonun hayvancılıkta uygulanabilirliği ele alınmıştır.

Keywords

Lojistik Regresyon Analizi, İkili değişken, adımsal (ileriye, geriye doğru) lojistik regresyon, Hayvancılık

THE IMPORTANCE OF LOGISTIC REGRESSION IMPLEMENTATIONS IN THE TURKISH LIVESTOCK SECTOR AND LOGISTIC REGRESSION IMPLEMENTATIONS/FIELDS

Abstract

Logistic regression analysis is one of the mostly preferred regression methods that can be implemented in modelling binary dependent variables. Logistic regression is a mathematical
modelling 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 this paper, logistic regression models are researched. Maximum likelihood methods may be used to estimate the parameters of the logistic model. The interpretations of coefficients are made with odds rate values. In other words, in this paper, the logistic regression analysis has been reviewed that can define the relationship between the binary result variable and independent variables comprising of both continuous and discrete variables. Shortly, the applicability of logistic regression in the livestock has been researched. 

Keywords

Logistic Regression Analysis, Binary variable, Stepwise (onward and backward) logistic regression, livestock

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