In this study, principal component regression (PCR) was applied to predict the albumen index in Tinted Coral chicken eggs. Initially, multiple linear regression analysis was performed to examine the relationships among independent variables, and a strong correlation revealed a multicollinearity problem. To address this issue, the independent variables were reduced into principal components (PC1–PC3) representing egg weight, dimensional traits, shape, geometric structure and egg shell. PCR results indicated that the albumen index was particularly influenced by morphological and dimensional characteristics. The model predictions were consistent with the observed values, and low VIF values along with high tolerance values confirmed that the multicollinearity problem was successfully resolved. The findings highlighted that egg Weight and dimensional traits were the most influential factors in predicting the albumen index. In conclusion, the PCR approach allows reliable modeling of the albumen index in egg data with multicollinearity problem.
Gök et al. (Fri,) studied this question.