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

• Autor(a) principal: Çelik,Ş
• Data de Publicação: 2018
• Outros Autores: Şengül,T, Söğüt,B, Inci,H, Şengül,AY, Kayaokay,A, Ayaşan,T
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Poultry Science (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2018000200273
• topic: Carcass weight; Carcass parts; factor analysis score based regression; ridge regression; white turkeys

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.