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，通过回归分析探讨了白色火鸡（Big-6）的胴体各部位重量（腿重、胸重、翅重、背重、肌胃重、心脏重及脚重）对其全胴体重的影响。为此，本研究共选用30只17周龄的火鸡胴体，其中公火鸡与母火鸡各15只。本研究测定的指标包括全胴体重（CW）、腿重（TW）、胸重（BRW）、翅重（WW）、背重（BW）、肌胃重（GW）、心脏重（HW）及脚重（FW）。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，成功消除了多重共线性问题。此外，该岭回归模型的决定系数R²=0.988。通过因子分析得分得到的两个变量的特征值均大于1，因此该模型可通过2个公因子进行解释；两个公因子解释的方差占总方差的88.80%。回归方程具有统计学显著性（p<0.01）。该回归方程中，通过因子分析得分得到的2个公因子作为自变量，以标准化全胴体重作为因变量。在基于因子分析得分构建的回归模型中，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全胴体重的预测，但岭回归法的决定系数更高，可更好地解释火鸡全胴体重的变化。