Prediction of chicken gender before putting eggs in incubator using logistic regression model

Document Type : Original Article

Authors

1 Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Clinical Sciences, Faculty of Veterinary Medicine, Baft branch, Islamic Azad University, Baft, Iran

3 Department of Clinical Sciences, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

This paper propose several regression models to predict the probability of hatch a cockerel from egg before it is even putted in an incubator. The prediction of egg’s gender is based on the minor and major diameters of eggs as the explanatory variables which are simply measureable. A binomial logistic regression model is fitted to the observed length of minor and major diameters for 60 eggs to predict their genders. In this study we achieve a simple statistical classifier model to classify eggs into male and female classes. In other words, we found that the more pointed eggs would be cockerel with high probability and the more spherical eggs would be pullet with high probability. After introducing the thin index for eggs, a simple linear regression model and also another binomial logistic regression model are fitted based on the computed thin index data. On the basis of goodness-of-fit criteria AIC, BIC, chi-square test and so the difference between null deviance and residual deviance, the best model is determined among three fitted regression models. All estimated criteria implied that the proposed logistic regression with considering thin index as the explanatory variable, is the best fitted model to the observed data for the egg gender prediction. Moreover, the regression analyses on the physical shape variables are provided for each three fitted model. The advantages and merits of the proposed logistic regression model are simplicity, cheep, fast and finally the proposed method has economical benefits for the user.

Keywords

Veterinary and Comparative Biomedical Research
Volume 1, Issue 2
August 2024
Pages 8-18

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