%0 Journal Article
%T Applications of Principal Component Analysis to Prediction of Fat-Tail and Carcass Weight Traits in Makooei Lambs
%J Iranian Journal of animal Science
%I پردیس کشاورزی و منابع طبیعی دانشگاه تهران
%Z 2008-4773
%A Mokhber, Mahdi
%A Moradi Shahr Babak, Hosein
%A Khelt Abadi Farahani, Amirhosein
%D 2013
%\ 12/22/2013
%V 44
%N 4
%P 347-354
%! Applications of Principal Component Analysis to Prediction of Fat-Tail and Carcass Weight Traits in Makooei Lambs
%K Makooei lambs
%K multicollinearty
%K principal components analysis
%R 10.22059/ijas.2013.50378
%X The objective followed in the present study was to survey the relationship between 18 body trait measurements (live weight, height at wither, paunch girth, neck diameter, body length, girth around the body, width of fat tail at above, below and midpoint of fat tail, fat tail length lowers right and left sides, fat tail gap length, fat tail depth at the above, below, and midpoint, and girth around fat tail at the above, mid and down point ) and the traits of fat tail weight and carcass (weight with and without fat tail) in Makooei Sheep and to Predict these traits, performance though multivariate Linear Regression Method Based on Principal Component Analysis. Sex showed significant effects on all the measured traits expect fat tail, depth at above, midpoint and down point, fat tail length towards right side plus gap length, width of fat tail at the above and below point of tail. Means and standard error for each trait were evaluated by sex. Multicollinearity was detected through a survey of the relationship among these traits, variance inflation factor and tolerance value. Principal component analysis was employed to resolve multicollinearity problem among independent variables and for a clearer explanation of the results. R2 range for different regression models varied between 0.973 for carcass weight without tail in male sex and 0.561 for carcass weight with fat tail in female sex of Makooei lambs.
%U https://ijas.ut.ac.ir/article_50378_ad403b8709700d34d18009967532ebe7.pdf