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• Akbulut, Ö., Bayram, B. and Yanar, M (2001). Estimates of phenotypic and genetic parameters on birth weight of brown swiss and holstein friesian calves raised in semi entansif conditions. Lalahan Hay Arşt Derg, 41(2): 11-20.
• Aksakal V and Bayram B (2009). Estimates of genetic and phenotypic parameters for the birth weight of calves of Holstein friesian cattle reared organically. J Anim Vet Adv, 8(3): 568-572.
• Bakır G, Kaygısız A and Ulker H (2004). Estimates of genetic and phenotypic parameters for birth weight in Holstein Friesian cattle. Pakistan J Biol Sci, 7(7): 1221-1224.
• Bakır G, Keskin S and Mirtagioğlu H (2010). Determination of the effective factors for 305 days milk yield by regression tree (RT) method, J Anim Vet Adv, 9 (1): 55-59.
• Chang LY and Wang HW (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis and Prevention 38: 1019-1027.
• De’ath G and Fabricius KE (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 81(11): 3178-3192.
• Doğan N and Özdamar K (2003). CHAID analysis and an aplication related with family planning. Türkiye Klinikleri J. Med. Sci., 23: 392-397.
• Doğan, İ (2003). Investigation of the factors which are affecting the milk yield in Holstein by CHAID analysis. Ankara Univ Vet Fak Derg, 50: 65-70.
• Kaygısız, A. (1998). Estimates of genetic and phenotypic parameters for birth weight in brown swiss and simmental calves raised at Altındere State Farm. Turk J Vet Anim Sci, 22: 527-535.
• Kertz AF, Reutzel LF, Barton BA and Ely RL (1997). Body weight, body condition score and wither height of prepartum Holstein cows and birth weight and sex of calves by parity: A data base and summary. J Dairy Sci, 80: 525-529.
• Koçak, S., Tekerli, M., Ozbeyaz, C. and Yüceer, B (2007). Environmental and genetic effects on birth weight and survival rate in Holstein calves. Turk J Vet Anim Sci, 31(4): 241-246.
• Larsen DR and Speckman PL (2004) Multivariate regression trees for analysis of abundance data. Biometrics, 60: 543-549.
• Questier F, Put R, Coomans D, Walczak B and Vander HY (2004). The use of CART and multivariate regression trees for supervised and unsupervised feature selection. Chemometrics and Intelligent Laboratory Systems, 76: 45-54.
• SPSS 17. (2011). Statictical package for Social Sciences (SPSS) for Windows Release 17.0 SPSS Inc,
• Tilki M, İnal Ş, Tekin ME and Çolak M (2003). The Estimation of Phenotypic and Genetic Parameters for calf birth weight and gestation length of brown swiss cows reared at the Bahri Dağdaş International Agricultural Research Institute, Turk J Vet Anim Sci., 27, 1343-1348.
• Tilki M, Saatcı M and Çolak M (2008). Genetic parameters for direct and maternal effects and estimation of breeding values for birth weight in Brown Swiss cattle, Turk J Vet Anim Sci, 2008; 32(4): 287-292.
• Timofeev R (2004). Clasification and Regression Trees (CART) theory and applications. Center of Applied Statistics and Economics, Humboldt University, Unpublished Master Thesis, Berlin.
• Topal M, Aksakal V, Bayram B and Yağanoğlu AM (2010). An Analysis of the factors Affecting Birth Weight and Actual Milk yields in Swedish Red cattle using regression tree analysis. J Anim Plant Sci, 20(2): 63-69.
• Yohannes Y and Hoddinott J (1999). Classification and Regression Tress: An Introduction. International Food Policy research Institute, Washington, D.C. 20006 U.S.A.
• Zheng H, Chen L, Han X, Zhao X and Ma Y (2009). Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions. Agriculture, Ecosystems & Environment, 132: 98-105.