## Materials and Methods
### Linear Regression Modelling Validation  
The main application of linear regression is the identification of linear relationships between variables in multivariate analysis. For instance, even if correlational analysis is frequently used to validate and compare scales which measure the same construct, regression analysis is still preferred to quantify the validity of the correlational analysis of large variable sets [18; 19]. Regression analysis have been proven to surpass the performance of correlational analysis between scale scores and its validity to quantify a certain construct. One of the advantages of regression analysis is that it provides a way to quantify a scale-construct association in meaningful units, which facilitates the issuance of a verdict on the validity of the relationship being tested. Additionally, regression analysis can quantify the underlying variability accounted for by a set of estimators/predictors for a certain trait in a population, while it simultaneously prevents confounding effects from distorting the validity of a judgment. This in turn increases the repeatability of the results obtained [20]. Thus, we performed CATREG analyses using the aforementioned general regression model as a reference. The first CATREG analysis aimed to compute the ability to describe the variability in the population regarding the final scores for zoometric traits, which were considered as a dependent variable (DV), and considered the actual scores for the complete set of 17 measurements directly taken from the Murciano-Granadina primipara and multipara does and bucks, which were taken as the independent variable (IV) in the model. Then, we performed a second regression analysis to evaluate the variability in the population for the final scores of zoometric traits, but instead using the 17 LAS variables (on a nine-point scale) routinely evaluated in Murciano-Granadina goats. Then, the comparison of the determination power or prediction efficiency of both regression models was used to validate the performance of CAPRIGRAN LAS. Regression analyses were separately performed for primipara and multipara does and bucks, as traits comprising the mammary system were excluded from the appraisals of males. Comparative regression models for validity testing contrast with the predictive regression models described in the previous section for primipara and multipara does and bucks as these comprise combinations of the 17 traits scored through LAS without including those identified as redundant. For model validity comparison, the variables of body depth, angulosity, bone quality, rear legs rear view and side view and mobility were excluded from does and bucks' comparative regression models. This decision was made based on the fact that, even during regular zoometric assessment,these zoonotic parameters may rely on descriptive hedonic measurements rather than providing actual direct measurements for a certain zoometric trait; thus, no comparison could be performed as they were always scored using a specific LAS. The designed models can be seen in Table S2. As the variables in our study comprised levels categorized following different criteria, we used standardized coefficients to interpret and compare their effects on our DV, as these are not unit-dependent. The stepwise linear regression model applied to the transformed variables resulted in the standardized and unstandardized coefficients being equal. Thus, unstandardized coefficients could be interpreted. As a rule, we assumed that the standardized results reported used full standardization (both DV and IVs were converted to standard scores) and that the Z formula was used for standardization.