Multivariate multiple regression analysis based on principal component scores to study relationships between some pre- and post-slaughter traits of broilers

Mendes Mehmet

doi:10.1501/tarimbil_0000001158

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.

Highlights

Information on the relations between two data or variable sets is quite important (Mendeş et al 2005)
It has observed that when the raw data of the study were used for the multivariate multiple linear regression analysis (MMLR), a multicollinearity problem had existed with high values of variance inflation factor (VIF>10.0; Table 1)
The loading of SHW (0.868), BRC (0.892) and BW (0.849) found to be similar to each other. These results suggest that these three pre-slaughter traits namely SHW, BRC and BW vary together

Summary

Introduction

Information on the relations between two data or variable sets (i.e. pre- and post-slaughter traits) is quite important (Mendeş et al 2005). Canonical correlation analysis (CCA) is commonly used to investigate the relations between two data sets (Akbaş & Takma 2005; Mendeş & Akkartal 2007; Sousa et al 2007). Multivariate multiple linear regression analysis (MMLR) can be used for this objective (Lutz & Eckert 1994). There are no studies in literature using MMLR. The MMLR is one of the alternatives of CCA. It will be useful to show how MMLR can be used in investigating relations between two data sets

Objectives

Methods

Results

Conclusion