Appendix 1—figure 9. Residuals errors for each linear regression model.

Gray line represent the regression model, the points over the line are the predicted values $\widehat{y_i}$ (see Equation (31)) by the models, and the dots filled with a color gradient are the real values $y_i$. The errors between the predicted and real values, $ϵ=|\widehat{y_i}-y_i|$, are represented as a gradient of colors as follows (from lowest/coldest to highest/warmest). For each model, the RSE and $R^2$ (R-squared) were included, note that this statistics are the same that were presented in Appendix 1—table 6. (A) $NSP5\approx 0.8667\times NSP2$, (B) $NSP4\approx 0.9912\times NSP2$, (C) ${\displaystyle VP1=1.1506\times NSP2}$, (D) $VP2\approx 1.1244\times NSP2$, (E) $VP6\approx 1.1806\times NSP2$, (F) $VP4\approx 1.3887\times NSP2$, (G) $\text{VP7-Tri}\approx 1.9417\times NSP2$, (H) ${\displaystyle \text{VP7-Mon}\approx 1.9407\times NSP2}$.