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

Yıl 2025, Sayı: Advanced Online Publication, 798 - 807

Melis Çelik Güney , Özge Uyguner , Murat Durmuş , Nazan Koluman

https://doi.org/10.37908/mkutbd.1750767

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

Öz

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.

Anahtar Kelimeler

Goat , Factor analysis , Multicollinearity , Regression

Etik Beyan

Ethical approval was not required for this study.

Kaynakça

• Akdağ, F., Pir, H., & Teke, B. (2011). Comparison of growth traits in Saanen and Saanen × Hair crossbred (F1) kids. Hayvansal Üretim, 52, 33-38.
• Awwad, F.A., Dawoud, I., & Abonazel, M. (2022). Development of robust Ozkale–Kaçiranlar and Yang–Chang estimators for regression models in the presence of multicollinearity and outliers. Concurrency and Computation: Practice and Experience, 34, e6779. https://doi.org/10.1002/cpe.6779
• Bartlett, M.S. (1950). Tests of significance in factor analysis. British Journal of Statistical Psychology, 3, 77–85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x
• Bolacalı, M., & Küçük, M. (2012). Various body measurements of Saanen kids. Journal of the Faculty of Veterinary Medicine, University of Yüzüncü Yıl, 23, 23-28.
• Chan, J.Y.L., Leow, S.M.H., Bea, K.T., Cheng, W.K., Phoong, S.W., Hong, Z.W., & Chen, Y.L. (2022). Mitigating the multicollinearity problem and its machine learning approach: A review. Mathematics, 10, 1283. https://doi.org/10.3390/math10081283
• Çankaya, S., Altop, A., Kul, E., & Erener, G. (2009). Faktör analiz skorları kullanarak Karakaya kuzularında canlı ağırlık tahmini. Anadolu Tarım Bilimleri Dergisi, 24, 98-102.
• Çelik, S., Şengül, T., Söğüt, B., İnci, H., Şengül, A. Y., Kayaokay, A., & Ayaşan, T. (2018). Analysis of variables affecting carcass weight of white turkeys by regression analysis based on factor analysis scores and ridge regression. Brazilian Journal of Poultry Science, 20, 273-280. https://doi.org/10.1590/1806-9061-2017-0574
• Daikwo, S.I., Dike, U.A., & Onaleye, J.K. (2014). Use of factor scores in multiple regression model for predicting the live weight of native chickens using body measurements. Journal of Biology, Agriculture and Healthcare, 4, 76-80.
• Daskıran, I., Keskin, S., & Bingöl, M. (2017). Usability of the factor analysis scores in multiple linear regression analyses for the prediction of daily milk yield in Norduz goats. Journal of Agricultural Science and Technology, 19, 1507-1515.
• Engindeniz, S., & Uçar, K. (2014). Süt keçisi yetiştiriciliğinin ekonomik yönleri ve yatırım özellikleri. GTHB Türktarım Dergisi, 219, 78-83.
• Eyduran, E., Karakuş, K., Karakuş, S., & Cengiz, F. (2009). Usage of factor scores for determining relationships among body weight and some body measurements. Bulgarian Journal of Agricultural Science, 15, 374-378.
• Eyduran, E., Topal, M., & Sonmez, A.Y. (2010). Use of factor scores in multiple regression analysis for estimation of body weight by several body measurements in brown trouts (Salmo trutta fario). International Journal of Agriculture and Biology, 12, 611-615.
• Eyduran, E., Topal, M., Sonmez, A. Y., & Keskin, S. (2012). Carcass weight estimation from some morphological traits of Capoeta capoeta capoeta (Güldenstädt, 1772) using factor scores in multiple regression analysis. Pakistan Journal of Statistics, 28, 159-165.
• Han, D., & Ren, X. (2020). Financial risk assessment based on factor analysis model. Journal of Physics: Conference Series, 1616 (1), 012056. https://doi.org/10.1088/1742-6596/1616/1/012056
• Hair, J.F., Babin, B.J., Anderson, R.E., & Black, W.C. (1998). Multivariate data analysis. Macmillan Publishing Company.
• Ifeanyichukwu, U. (2012). Use of factor scores for determining the relationship between body measurements and semen traits of cocks. Open Journal of Animal Sciences, 2, 41-44. https://doi.org/10.4236/ojas.2012.21006
• Jolliffe, I.T. (2002). Principal component analysis. Springer.
• Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200. https://doi.org/10.1007/BF02289233
• Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151. https://doi.org/10.1177/001316446002000116
• Kaiser, H. F. (1970). Second generation little jiffy. Psychometrika, 35, 401–416.
• Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
• Kayaalp, G.T., Çelik Güney, M., & Cebeci, Z. (2015). Çoklu doğrusal regresyon modelinde değişken seçiminin zootekniğe uygulanışı. Çukurova Üniversitesi Ziraat Fakültesi Dergisi, 30, 1-8.
• Kesenkaş, H., Dinkçi, N., Kınık, O., Gönç, S., & Ender, G. (2010). Saanen keçisi sütünün genel özellikleri. Akademik Gıda, 8, 45-49.
• Keskin, S., Daskıran, I., & Kor, A. (2007a). Factor analysis scores in a multiple linear regression model for the prediction of carcass weight in Akkeci kids. Journal of Applied Animal Research, 31, 201-204. https://doi.org/10.1080/09712119.2007.9706664
• Keskin, S., Kor, A., & Karaca, S. (2007b). Use of factor analysis scores in multiple linear regression model for determining relationships between milk yield and some udder traits in goats. Journal of Applied Animal Research, 31, 185-188. https://doi.org/10.1080/09712119.2007.9706660
• Achary, V.M.M., Ram, B., Manna, M., Datta, D., Bhatt, A., Reddy, M.K., & Agrawal, P.K. (2017). Phosphite: A novel P fertilizer for weed management and pathogen control. Plant Biotechnology Journal, 15, 1493-1508. https://doi.org/10.1111/pbi.12803
• Koluman, N., Durmuş, M., & Güngör, İ. (2024). Improving reproduction and growth characteristics of indigenous goats in smallholding farming system. Ciência Rural, 54, 20230028. https://doi.org/10.1590/0103-8478cr20230028
• Liu, K., Huang, K., Sfarra, S., Yang, J., Liu, Y., & Yao, Y. (2021). Factor analysis thermography for defect detection of panel paintings. Quantitative InfraRed Thermography Journal, 25-37. https://doi.org/10.1080/17686733.2021.2019658
• Malomane, D.K., Norris, D., Banga, C.B., & Ngambi, J.W. (2014). Use of factor scores for predicting body weight from linear body measurements in three South African indigenous chicken breeds. Tropical Animal Health and Production, 46, 331-335. https://doi.org/10.1007/s11250-013-0492-2
• Montgomery, D.C., Peck, E.A., & Vining, G.G. (2001). Introduction to linear regression analysis (3rd ed.). John Wiley & Sons.
• Ogah, D.M., Alaga, A.A., & Momoh, M.O. (2009). Use of factor analysis scores in multiple regression model for estimation of body weight from some body measurements in Muscovy duck. International Journal of Poultry Science, 8, 1107-1111.
• Önder, H., & Abacı, S.H. (2015). Path analysis for body measurements on body weight of Saanen kids. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 21, 351-354. https://doi.org/10.9775/kvfd.2014.12500
• Önder, H., Şen, U., Takma, C., Ocak, S., & Abacı, S.H. (2015). Genetic parameter estimates for growth traits in Saanen kids. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 21, 799-804. https://doi.org/10.9775/kvfd.2015.13407
• Önk, K., Sarı, M., & Gürcan, İ.S. (2018). Estimation of live weights at the beginning and the end of grazing in Tuj lambs via scores of factor analysis. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 65, 261-266. https://doi.org/10.1501/vetfak_0000002855
• Öztürkler, Y. (2015). Koyun ve keçilerde kısa süreli östrus senkronizasyonu. Türkiye Klinikleri Reproduction and Artificial Insemination - Special Topics, 1, 9-19.
• Rohe, K., & Zeng, M. (2020). Vintage factor analysis with varimax performs statistical inference.
• Sangun, L., Cankaya, S., Kayaalp, G.T., & Akar, M. (2009). Use of factor analysis scores in multiple regression model for estimation of body weight from some body measurements in lizard fish. Journal of Animal and Veterinary Advances, 8, 47-50.
• Sharma, S. (1996). Applied multivariate techniques. John Wiley & Sons.
• Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Allyn and Bacon.
• Tahtalı, Y. (2019). Use of factor scores in multiple regression analysis for estimation of body weight by certain body measurements in Romanov lambs. PeerJ, 1, e7434. https://doi.org/10.7717/peerj.7434
• Teke, B., Akdağ, F., & Arslan, S. (2011). Halk elinde yetiştirilen Saanen keçilerinde bazı dölverimi, büyüme ve davranış özellikleri. İstanbul Üniversitesi Veteriner Fakültesi Dergisi, 37, 1-8.
• Tırınk, C., Abacı, S.H., & Önder, H. (2020). Comparison of ridge regression and least squares methods in the presence of multicollinearity for body measurements in Saanen kids. Journal of the Institute of Science and Technology, 10, 1429-1437. https://doi.org/10.21597/jist.671662
• Tolunay, A., & Ayhan, V. (2010). Türkiye’de kıl keçisi yetiştiriciliğinde orman kaynaklarından yararlanmada mevcut durum, darboğazlar ve çözüm önerileri. Ulusal Keçicilik Kongresi, 92-96.
• Topal, M., Eyduran, E., Yaganoğlu, A.M., Sönmez, A.Y., & Keskin, S. (2010). Çoklu doğrusal bağlantı durumunda ridge ve temel bileşenler regresyon analiz yöntemlerinin kullanımı. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 41, 53-57.
• Weisberg, S. (2005). Applied linear regression. John Wiley & Sons.
• Yakubu, A., Idahor, K.O., & Agade, Y.I. (2009). Using factor scores in multiple linear regression model for predicting the carcass weight of broiler chickens using body measurements. Revista UDO Agricola, 9, 963-967.
• Yakubu, A., Okunsebor, S.A., Kigbu, A.A., Sotolu, A.O., & Imgbian, T.D. (2012). Use of factor scores for predicting body weight from some morphometric measurements of two fish species in Nigeria. Journal of Agricultural Science, 4, 60-64. https://doi.org/10.5539/jas.v4n8p60
• Yılmaz, F., Bayyurt, L., Abacı, S.H., & Tahtalı, Y. (2020). Comparison of least squares and some bias estimators in multicollinearity. Turkish Journal of Agriculture - Food Science and Technology, 8, 793-799. https://doi.org/10.24925/turjaf.v8i3.793-799.3405
• Yong, A.G., & Pearce, S.A. (2013). Beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9, 79-94. https://doi.org/10.20982/tqmp.09.2.p07