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

Abstract

KeywordsCarcass weight, Carcass parts, factor analysis score based regression, ridge regression, white turkeys. ABSTRACTIn 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, R 2 =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 R 2 =0.966. Both regression models were found to be suitable for predicting carcass weight of turkeys. However, the ridge regression method, which presented higher R 2 value, has been shown to better explain the carcass weight.

Use of factor scores in multiple regression analysis for estimation of body weight by certain body measurements in Romanov Lambs

“…The study was carried out both to predict the body weight using the factor analysis scores that were calculated using certain body measurements of the Romanov lambs and multiple regression model and to solve the multicollinearity between the relevant body measurements. Similar studies were also carried out on sheep and goat breeding (Keskin, Daskiran & Kor, 2007; Cankaya et al, 2006; Cankaya et al, 2009; Onk, Sarı & Gurcan, 2018; Eyduran, Karakus & Karakus, 2009; Khan et al, 2014; Yakubu, 2009; Daskiran, Keskin & Bingol, 2017; Merkhan, 2014), poultry breeding (Celik et al, 2018; Pimentel et al, 2007; Ogah, Alaga & Momah, 2009) and aquaculture (Eyduran, Topal & Sonmez, 2010; Sangun et al, 2009).…”

“…If the null hypothesis is rejected according to results of the Bartlett’s test, the factor analysis is continued (Sharma, 1996). Obtaining a value below 0.5 with the KMO test indicates that the relationship between the variable pairs cannot be explained by other variables (Celik et al, 2018).…”

Stepwise Method and Factor Scoring in Multiple Regression Analysis of Cashmere Production in Liaoning Cashmere Goats

“…In factor analysis, the Kaiser–Meyer–Olkin (KMO) and Bartlett were used to test the separability of the correlation matrix to the factors [ 20 ]. Obtained by the KMO test greater than the threshold of 0.5, indicating that the relationship between variables can be explained by other variables [ 21 ]. Considering factor loadings, a varimax rotation was used, and factor coefficients were used to obtain the selected factor scores [ 13 , 22 ].…”

Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep

“…Previous studies have been carried out using the factor scores from factor analysis (latent variables) in the multiple regression equations (Çelik et al, 2018;Önk et al, 2018;Tahtali, 2019;Tariq et al, 2012). The coefficients of determination found in their study ranged from 0.754 to 0.966, showing to be suitable for use as independent variables in regression analysis.…”