Use of Factor Scores in Multiple Regression for Predicting Live Weight in Indigenous Savak Akkaraman Lambs
Use of Factor Scores in Multiple Regression for Predicting Live Weight in Indigenous Savak Akkaraman Lambs
Adile Tatliyer1,*, Sinan Bas1 and Serdar Yagci2
ABSTRACT
The objective of this study is to predict live body weight from 12 different morphological traits measured from Savak Akkaraman lambs at three different periods (average 11 days after birth, weaning and at the beginning of grazing) using factor scores in multiple linear regressions to remove multicollinearity problem. Morphological data obtained from 159 lambs were subjected to Kaiser-Meyer-Olkin (KMO) and Bartlett’s Sphericity tests to ascertain the suitability of factor score analysis. A result showed that implementation of factor analysis was appropriate for the studied data set at each period. At each period, factors whose eigenvalues which is the characteristic roots of a square matrix with p×p were higher than 1 were elected by applying the Varimax rotation in explanatory factor analysis. In the first period of average 11 days after birth, 2 factor scores (FS) were used as new latent predictors in order to predict live body weight in multiple linear regression model. The 2FS at the first period, 3 FS at the second period, 4 FS at the third period accounted for 80, 45 and 57 % of the total variability in live weight, respectively. The achieved results revealed that using of body measurements could allow breeders to conduct better breeding programs. Also, it is recommendable that periods in wide variability should be taken into consideration in selection program.
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