## 3 Results
### Categorical Linear Regression (CATREG) Modelling Validation  
Tables 4-6 report a summary of the parameters computed to compare the CATREG models comprising Murciano-Granadina LAS scores to those comprising zoometric traditional assessment variables. Concretely, Tables 4 and 5 report a 0.632 bootstrap mean squared error of prediction (MSEP) to test for model cross validation in bucks and does, respectively. Table 6 shows a summary of the determination coefficients (R\({}^{2}\) and Adj. R\({}^{2}\)) to compare the explanatory variability. R\({}^{2}\) values were 0.779, 0.660 and 0.734 for primipara does, multipara does and bucks, respectively, when computed through the model using Murciano-Granadina zoometric traditional assessment. Values of R\({}^{2}\) slightly (0.859) to moderately/highly increased (0.883 and 0.813) for bucks, primipara and multipara does, respectively, for models which used LAS scores (Table 7). All models reported a highly statistically significant ability to predict the outcome of the variables measured when compared to the raw models exclusively consisting of the interception but excluding any predictor, as suggested by the values of MSPE and P \(<\) 0.001 for the ANOVA for regression analysis. Following the premises of information theory, several methods have been presented for the comparison of models with regard to their ability to explain or capture the variability observed in the data set being studied (Akaike information criterion (AIC) and corrected Akaike information criterion (AIC)) and the predictive potential (Bayesian information criterion (BIC)) of the model designed for the data being modeled. Additionally, the mean square residual or error (MSE) measures how close a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bucks** & \multicolumn{2}{c}{**Standardized Coefficients**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**F**} & **Sig.** \\ \hline
**LAS Scores** & **Beta** & **Bootstrap (1000) MSEP** & & & \\ \hline \hline
**Stature (Height to witness)** & 0.274 & 0.028 & 6.000 & 98.843 & 0.001 \\
**Chest width** & 0.212 & 0.027 & 4.000 & 61.434 & 0.001 \\
**Rump width** & 0.174 & 0.023 & 4.000 & 57.311 & 0.001 \\
**Angulosity** & 0.282 & 0.018 & 7.000 & 250.375 & 0.001 \\ \hline
**Bucks** & \multicolumn{2}{c}{**Standardized Coefficients**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**F**} & **Sig.** \\ \hline
**Measurements** & **Beta** & **Bootstrap (1000) MSEP** & & & \\ \hline \hline
**Stature (Height to witness)** in cm & 0.250 & 0.036 & 1.000 & 48.220 & 0.001 \\
**Chest Width in cm** & 0.236 & 0.036 & 1.000 & 42.081 & 0.001 \\
**Rump Width in cm** & \(-\)0.264 & 0.026 & 1.000 & 107.334 & 0.001 \\
**Angulosity in degrees** & 0.353 & 0.017 & 1.000 & 451.181 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(\beta\)-standardized coefficients to replace the Z score in each of the respective equations in Supplementary Table S5 and a 0.632 bootstrap mean squared error of prediction (MSEP) for the comparison of model validity between the two linear regression models using linear appraisal traits as opposed to Murciano-Granadina zoometric traditional assessment in bucks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gender** & **Method** & **Multiple R** & **R Square (R\({}^{2}\))** & **Adjusted R Square (Adj, R\({}^{2}\))** \\ \hline \hline
**Primipara does** & Measurements & 0.631 & 0.399 & 0.398 \\  & LAS Scores & 0.883 & 0.779 & 0.779 \\
**Multipara does** & Measurements & 0.630 & 0.397 & 0.397 \\
**LAS Scores** & 0.813 & 0.661 & 0.660 \\
**Bucks** & Measurements & 0.804 & 0.647 & 0.646 \\
**Bucks** & LAS Scores & 0.859 & 0.738 & 0.734 \\ \hline \hline \end{tabular}
\end{table}