This study aims to predict albumen height accurately and reliably by using the external quality traits of eggs obtained from Atak-S hens. In this context, the model includes egg weight, egg width, egg length, shape index, Haugh unit, shell weight, and shell thickness as independent variables. To evaluate the predictive performance of the model, the study applies multiple linear regression analysis and finds a high level of explanatory power (R² = 0.96; r = 0.98). However, strong correlations among variables and high variance inflation factor (VIF) values reveal a serious multicollinearity problem in the model.To address this issue, the study applies Ridge regression analysis and finds the model statistically significant (F = 767.93, p 0.001). Ridge regression maintains high explanatory power (R² = 0.96, r = 0.97) while reducing all VIF values to acceptable levels and increasing tolerance values (TV).In conclusion, Ridge regression provides a more reliable and stable approach for predicting albumen height by effectively handling multicollinearity in the dataset. These findings contribute to quality assessment and improving production efficiency in poultry science.
Mustafa Şahin (Tue,) studied this question.
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