LAUSR

| This research aimed to analyse the correlation and regression model and also to find the best regression model for predicting body weight of female Ettawa Grade (EG) goat using its body measurements. This research used 41 female EG goat aged 3–4 years. The method used was a survey with data collection by census, all female EG goats aged 3-4 years in research location were collected. Body weight (BW) data were regressed and correlated to body measurements (body length = BL, chest girth = CG and shoulder height = SH) using linear and multiple linear regression using R program. Pearson correlation was used to calculate correlation coefficient (r). Coefficient of determination (R2), adjusted R2, residual standard error (RSE), Akaike information criterion (AIC), Bayesian information criterion (BIC) and step wise regression analysis were used to analyse and find the best and parsimonious model for predicting BW. The results showed that body measurements had positive correlation with the BW, which CG had the highest correlation (0.838); followed by BL (0.744) and SH (0.543). Results also showed that CG was the best predictor for BW compared to BL and SH if using single predictor. Combination of CG and BL resulted in the fittest prediction of BW with model regression BW = -67.86 + 0.87*CG + 0.51*BL with the highest correlation coefficient (r = 0.87), R2 (0.76), adjusted R2 (0.75) and the lowest RSE (2.795), AIC (205.51) and BIC (212.36). The results of this study suggested that CG and BL could be used as predictor for body weight and as indicator of indirect selection to improve genetic merit in body weight of EG goat.