Multivariate Multiple Regression Analysis Based on Principal Component Scores to Study Relationships between Some Pre- and Post-slaughter Traits of Broilers

Authors : Mehmet MENDES

Pages : 0-0

Doi:10.1501/Tarimbil_0000001158

View : 18 | Download : 9

Publication Date : 2011-03-06

Article Type : Research Paper

Abstract :The main purpose of this study is to show that how can we use multivariate multiple linear regression analysis insert ignore into journalissuearticles values(MMLR); based on principal component scores to investigate relations between two data sets insert ignore into journalissuearticles values(i.e.pre- and postslaughter traits of Ross 308 broiler chickens);. Principal component analysis insert ignore into journalissuearticles values(PCA); was applied to predictor variables to avoid multicolinearity problem. According to results of the PCA, out of 7 principal components only the first three components insert ignore into journalissuearticles values(PC1, PC2, and PC3); with eigenvalue greater than 1 were selected insert ignore into journalissuearticles values(explained 89.45 % of the variation); for MMLR analysis. Then, the first three principal component scores were used as predictor variables in MMLR. The results of MMLR analysis showed that shank width, breast circumference and body weight had a similar linear effect on predicting the post-slaughter traits insert ignore into journalissuearticles values(P=0.746);. As a result, since the animals had high value of shank width, breast circumference and body weight, it might be probable that their post-slaughter traits namely heart weight, liver weight, gizzard weight and hot carcass weight were also expected to be high.
Keywords : Multivariate regression analysis, Principal component analysis, Canonical correlation