The main purpose of the sheep production is to improve profitability of yield traits such as meat, milk and wool obtained per animal. In this respect, selection is a remarkable tool for achieving genetic improvement and attaining better qualified offspring as to the quantitative traits. In obtaining of superior offspring according to a quantitative trait like live weight, the conservation of indigenous genetic sources and the detection of the breed standards, animal breeders take into account indirect selection criteria with the help of high genetic correlation coefficients between live weight and morphological traits. Moreover, the prediction of live body weight from some zoometrical (morphological) characteristics measured simply in farm animals is an important subject for developing prosperous animal breeding systems and in practice, regulating management conditions [1; 2]. A simple way to find out appropriate feed amount, medicinal dose and price of an animal farm is to predict live body weight from effective morphological traits. The predictive accuracy depends on choosing powerful statistical approaches. Among those, there is multiple linear regression, which leads analysts to make biased parameter estimates with multicollinearity problem occurring as an outcome of very strong Pearson correlation coefficients between morphological traits as predictors of body weight [3]. A good alternative is, in general, to use Ridge Regression Analysis instead. However, Ridge regression can produce unreliable outcomes [4]. More effective alternatives to remove multicollinearity problem are available, such as using scores of factor analysis and principal component analysis for multiple regression analysis technique [5; 6]. Predictors are exposed to factor or principal component analysis as one of multivariate analysis techniques and new uncorrelated predictors are used to predict the body weight without multicollinearity problem [6].