The applicability domains were discussed with the Williams graphs in Figures 4 and 5 of the MLR and MNLR models, respectively, in which the standardized residuals and the leverage values ([h.sub.i]) are plotted.
The Williams plot for the MNLR model is shown in Figure 5.
Validation of MNLR model is done by dividing the dataset into the training and the test set; the external validation of several correlation coefficients is PIC50 = 0.7 for MNLR for the whole test.
The consistency and reliability of the MLR, MNLR, and PLS model are validated using the cross-validation technique with a good correlation being obtained with cross-validation Rcv = 0.86.
MLR, MNLR, and ANN are generated using the SPSS 19.0 statistical package .
We performed in this work 100-y-randomization tests for the MLR and MNLR models.
MNLR model is a primal, useful technique which has been applied in all fields of engineering knowledge.
The eight important factors that affected faulting are used in multivariate nonlinear regression (MNLR) model and artificial neural network (ANN) model.
It also serves to select the descriptors that are used as input parameters in multiple nonlinear regressions (MNLR) and the multiple linear regression.
The results obtained for 3D-QSAR using MLR, MNLR, ANN, CV, and Y-randomization are represented in Tables 3 and 4.
The objective of this study was therefore to train, cross-validate, independently validate (test) and compare 28 ANNs and the best-fit MNLR
model in predicting tool flank wear.
In order to predict the correlation between these nine electronic descriptors of the sixteen studied molecules with their toxicity towards monocytes [pIC.sub.50] (Table 2), we evolved a quantitative models by the multiple linear regression (MLR), the Multiple Non Linear Regression (MNLR
) and the artificial neural network (ANN).