Analysis of Variables Affecting Carcass Weight of White Turkeys by Regression Analysis Based on Factor Analysis Scores and Ridge Regression

ABSTRACT In this study, the influence of carcass parts weights (thigh, breast, wing, back weight, gizzard, heart, and feet) on whole carcass weight of white turkeys (Big-6) was analyzed by regression analysis based on ridge regression and factor analysis scores. For this purpose, a total of 30 turkey carcasses of 15 males and 15 females with 17 weeks of age, were used. To determine the carcass weight (CW), thigh weight (TW), breast weight (BRW), wing weight (WW), back weight (BW), gizzard weight (GW), heart weight (HW), and feet weight (FW) were used. In the ridge regression model, since the Variance Inflation Factor (VIF) values of the variables were less than 10, the multicollinearity problem was eliminated. Furthermore, R2=0.988 was obtained in the ridge regression model. Since the eigenvalues of the two variables predicted by factor analysis scores were greater than 1, the model can be explained by two factors. The variance explained by two factors constitutes 88.80% of the total variance. The regression equation was statistically significant (p<0.01). In the regression equation, two factors obtained by using factor analysis scores were independent variables and standardized carcass weight was considered as dependent variable. In the regression model created by factor analysis scores, the Variance Inflation Factor values were 1 and R2=0.966. Both regression models were found to be suitable for predicting carcass weight of turkeys. However, the ridge regression method, which presented higher R2 value, has been shown to better explain the carcass weight.

摘要 本研究基于岭回归与因子分析得分的回归分析方法，探究了白色火鸡（Big-6）的胴体各部位重量（腿肉、胸肉、翅肉、背肉、肌胃、心脏及爪部重量）对其全胴体重量的影响。本次研究共纳入30份17周龄的火鸡胴体样本，公火鸡与母火鸡各15只。本研究测定了胴体重量（Carcass Weight, CW）、腿肉重量（Thigh Weight, TW）、胸肉重量（Breast Weight, BRW）、翅肉重量（Wing Weight, WW）、背肉重量（Back Weight, BW）、肌胃重量（Gizzard Weight, GW）、心脏重量（Heart Weight, HW）及爪部重量（Feet Weight, FW），用于后续建模分析。在岭回归模型中，各变量的方差膨胀因子（Variance Inflation Factor, VIF）值均小于10，多重共线性问题得以消除。此外，该岭回归模型的决定系数R²=0.988。通过因子分析得分提取的两个公共因子的特征值均大于1，因此该模型可通过两个公共因子进行解释，两个公共因子解释的总方差占比达88.80%。所得回归方程具有统计学显著性（p<0.01）。该回归方程以因子分析得分得到的两个公共因子作为自变量，以标准化胴体重量作为因变量。在基于因子分析得分构建的回归模型中，各变量的方差膨胀因子值均为1，决定系数R²=0.966。两种回归模型均适用于火鸡胴体重量的预测，但岭回归方法的决定系数更高，可更优地解释火鸡胴体重量的变异情况。