Prediction of Egg Weight from External Egg Traits of Guinea Fowl Using Multiple Linear Regression and Regression Tree Methods

ABSTRACT The study was done to predict egg weight from the external traits of the Guinea fowl egg using the statistical methods of multiple linear regression (MLR) and regression tree analysis (RTA). A total of 110 eggs from a flock of 23-week-old Guinea fowl were evaluated. Egg weight (EW) and external traits: eggshell weight (ESW), egg polar diameter (EPD), egg equatorial diameter (EED), egg shape index (ESI), and egg surface area (ESA) were measured. Descriptive statistics, Pearson correlation coefficients, and regression equations using the MLR were obtained; additionally, a RTA was done using the CHAID algorithm with the SPSS software (IBM ver. 22). EW presented positive correlations (p<0.0001) with ESA (r = 0.72), EPD (r = 0.65), and EED (r = 0.49). EW can be predicted through MLR using ESA as a predictor variable (R2 = 72%). Predictive accuracy improves when adding EPD and EED traits to the model (R2 = 75%). The RTA built a diagram using ESA, EED, and EPD as significant independent variables; of these, the most important variable was ESA (F = 50,295, df1 = 4, and df2 = 105; Adj. p<0.000) and the variation explained for EW was 74%. Likewise, the RTA showed that the highest egg weight (41.818 g) is obtained from eggs with a surface area > 59.03 cm2 and a polar diameter > 5.10 cm. The proposed statistical methods can be used to reliably predict the egg weight of Guinea fowl.摘要 本研究旨在利用多元线性回归（multiple linear regression, MLR）与回归树分析（regression tree analysis, RTA）两种统计方法，基于珍珠鸡蛋的外部性状预测蛋重。研究共评估了23周龄珍珠鸡种群所产的110枚蛋，测定的指标包括蛋重（egg weight, EW）、蛋壳重（eggshell weight, ESW）、蛋极径（egg polar diameter, EPD）、蛋赤道径（egg equatorial diameter, EED）、蛋形指数（egg shape index, ESI）与蛋表面积（egg surface area, ESA）等外部性状。本研究计算了描述性统计量、皮尔逊相关系数，并基于MLR构建了回归方程；此外，借助SPSS软件（IBM ver. 22）与CHAID算法开展了RTA。结果显示，EW与ESA（r=0.72）、EPD（r=0.65）及EED（r=0.49）均呈极显著正相关（p<0.0001）。仅以ESA作为预测变量时，通过MLR即可实现EW预测（R²=72%）；若在模型中加入EPD与EED，预测精度可进一步提升（R²=75%）。基于RTA构建的决策树以ESA、EED与EPD作为显著自变量，其中ESA为最重要的预测变量（F=50,295，自由度df₁=4，df₂=105；校正后p<0.0001），该变量可解释74%的EW变异。同时，RTA分析结果表明，当ESA＞59.03 cm²且EPD＞5.10 cm时，所产珍珠鸡蛋的EW最高，可达41.818 g。本研究所采用的两种统计方法可用于可靠预测珍珠鸡的蛋重。