Use of nonlinear regression in predicting body weight of female Saburai goat

Abstract

Abstract This study aimed to evaluate the use of nonlinear regression in predicting body weight of female Saburai goats using body measurements. Thirty three female Saburai goat aged 1-3 years were used in this study. Body measurements including chest girth (CG), body length (BL), shoulder height (SH), hip height (HH), and hip width (HW) were regressed on body weight (BW) using nonlinear regression (exponential and polynomial regression). The prediction accuracy of nonlinear regression was compared to linear regression by evaluating their determination coefficient (R2), adjusted R2, residual standard error (RSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Correlation and regression analysis between body measurements and body weight was done with the help of the R program using nls function and “ggplot2” package. The findings indicate that among the various linear and nonlinear regression models that make use of individual body measurements, the most efficient model for forecasting the BW of female Saburai goats is the linear regression model that employs CG as the measurement. This model is followed in terms of performance by the quadratic polynomial regression model using CG and the exponential regression model using CG, as determined by the best selection criteria. However, when considering a combination of body measurements and employing stepwise regression analysis for selecting parameters, the optimal predictor for BW is identified as a combination of CG, BL, SH, and HW. Additionally, for a more simplified model that still maintains statistical significance in its coefficient regression, the most suitable regression model for predicting the BW of female Saburai goats is one that combines CG and SH. The regression equation representing this model can be expressed as Ŷ = -65.9639 + 1.0160CG + 0.4342SH.