Faktör analiz skorlarına dayalı regresyon analizi ile Saanen oğlaklarının bazı vücut ölçülerinden vücut ağırlığının tahmini

Öz

Bu çalışmada, Saanen oğlaklarının bazı vücut ölçüleri kullanılarak en küçük kareler yöntemi ve faktör analizi skorlarına dayalı regresyon analizi kullanılarak vücut ağırlığının tahmini yapılmıştır. Bu amaçla toplam 90 oğlağın (90 günlük) canlı ağırlıkları ile cidago yüksekliği, sağrı yüksekliği, sağrı genişliği, göğüs genişliği, göğüs derinliği, göğüs çevresi ve vücut uzunluğu gibi vücut ölçüleri belirlenmiştir. Analizler sonucunda, en yüksek korelasyon cidago yüksekliği ile sağrı yüksekliği arasında olup bu korelasyonun istatistiksel olarak anlamlı olduğu bulunmuştur. En küçük kareler yöntemi ile yapılan analizde, cidago yüksekliği ve sağrı yüksekliği değişkenlerine ait varyans şişirme faktörü değerlerinin 10’un üzerinde olduğu tespit edilmiştir. Bu değişkenlerde çoklu doğrusallık sorunu olduğu için faktör analiz skorlarına dayalı regresyon analizi yapılmıştır. Veri setinin faktör analizi için uygunluğunu belirlemek amacıyla Kaiser-Meyer-Olkin (KMO) örnekleme yeterliliği ölçüsü ve Bartlett’in küresellik testi kullanılmıştır. İki faktörün özdeğerlerinin, varimax rotasyon sonuçlarına göre 1’den büyük olduğu belirlenmiştir. Çıkarılan ilk faktörün açıklanan varyansı %70.37, ikinci faktörün ise %17.36’dır. Faktör skorları ile elde edilen canlı ağırlık tahmin modeli BW=2.794FS1+1.072FS2 şeklinde bulunmuştur. Faktör analizi ile elde edilen faktör skorları çoklu doğrusallık sorununu ortadan kaldırmış; varyans şişirme faktörü değerlerinin 10’un altına düşmesiyle bu durum doğrulanmıştır. Böylece Saanen oğlaklarının vücut ağırlığının daha tutarlı ve güvenilir bir şekilde tahmin edilmesi sağlanmıştır.

Anahtar Kelimeler

• keçi
• faktör analizi
• Çoklu doğrusallık
• Regresyon

Estimation of body weight from some body measures of Saanen kids with regression analysis based on factor analysis scores

Abstract

The present study was conducted to estimate the body weight of Saanen kids by using some body measurements. The least squares method and regression analysis were employed, based on factor analysis scores. For this purpose, the body weight of a total of 90 kids (90 days old) and body measurements such as height at withers, rump height, rump width, chest width, chest depth, chest girth, and body length were determined. As a result of the analysis, the highest correlation was between height at wither and rump height, and this correlation was found to be statistically significant. In the analysis conducted with the least squares method, it was determined that the variance inflation factor values for the height at wither and rump height variables were above 10. Because there was a multicollinearity problem in these variables, regression analysis based on factor analysis scores was performed. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were used to determine the suitability of the data set for factor analysis. The eigenvalues of the two factors were determined to be greater than 1 based on the results of varimax rotation. The first factor extracted has an explained variance of 70.37% and the second factor of 17.36%. The body weight estimation model obtained with factor scores was found as BW=2.794FS1+1.072FS2. The factor scores obtained through factor analysis have eliminated the multicollinearity problem, as confirmed by the variance inflation factor values falling below 10. This allowed for a more consistent and reliable estimation of body weight in Saanen kids.

Keywords

• Goat
• Factor analysis
• Multicollinearity
• Regression

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