Application of linear, quadratic and cubic regression models to predict body weight from different body measurements in domestic cats
2011
Erat, S. (Kirikkale Univ. (Turkey). Dept. of Animal Breeding and Husbandry)
The aims of this study were to predict body weight (BW) from different body measurements and to determine the best regression model for domestic cats. For this aims, a total of 48 adult Turkish cats (20 females & 8 males Turkish Angora; 13 females & 7 males Turkish Van) were used. In the study, wither height (WH), body length (BL) and head circumference (HC) were assumed as independent variables, whereas body weight was used as dependent variable. Linear, quadratic and cubic effects of the independent variables were included in the assumed model as Y= b0 + b1X + b2X2 + b3X3 + e. Where Y = body weight; b0 = the intercept; X = independent variables, (WH, BL, or HC); b1, b2 and b3 = regression coefficients and e = random error. Conceptual predictive (Cp) and Akaike information criterion (AIC) were used to determine the most suitable model among the assumed models. The model that has the smallest Cp and AIC values is the best model. The R2 values from the regression indicate the BL (R2 = 0.50) to be moderately related to the BW. Neither the quadratic term nor the cubic term was significant for all body traits, whereas the linear term was highly significant (p less than 0.001) for all independent variables. Since the maximum number of independent variables is three, there were seven possible different models. It can be concluded that cat body weight was explained with the following model. (BW) = - 4.53 + 0.11 WH + 0.13 BL with p- values, less than 0.001, 0.0083, and less than 0.001 for the intercept, b1 and b2, respectively with R2 = 0.57.
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Эту запись предоставил National Agricultural Research Centre