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The significance of the model parameters was discussed.
ANOVA was performed to find the significance of the model.
Finally, the statistical significance of the model was proved.
Four generated GQSAR models were selected based on the statistical significance of the model.
The engineering significance of the model is presented in terms of operational and design considerations.
Numerical results also show the capabilities and significance of the model in engine crankshaft design.
A low p-value of <0.0001 validated the significance of the model.
Analysis of variance was employed to estimate the statistical parameters and evaluate the statistical significance of the model.
The R2ext for the test set was found to be 0.882, which confirms the statistical significance of the model.
The adequately high R2 value (0.947) and F score 19.11 indicated the statistical significance of the model.
Finally, we used a nonparametric testing approach to test for the statistical significance of the model by generating 1,000 surrogate data sets under the null hypothesis of r(predicted, observed) (ref. 68).
More suggestions(15)
emphasis of the model
salience of the model
scope of the model
proportions of the model
values of the model
weights of the model
significance of the models
merits of the model
magnitudes of the model
significant of the model
significance of the action
significance of the situation
nature of the model
significance of the agreement
significance of the evidence
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