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

Abstract

The main purpose of this study is to show that how can we use multivariate multiple linear regression analysis (MMLR) based on principal component scores to investigate relations between two data sets (i.e.pre- and postslaughter traits of Ross 308 broiler chickens). Principal component analysis (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 (PC1, PC2, and PC3) with eigenvalue greater than 1 were selected (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 (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

References

1. Akbas Y & Takma Ç (2005). Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers. Czech Journal of Animal Science 50(4): 163-168