Exact(1)
Data were analyzed and assumed to have an insignificant term when P > 0.05 and significant term when P < 0.05.
Similar(59)
Insignificant terms are eliminated, giving rise to the so-called "exact" Hessian matrix.
The models based on data from the DOE were then optimized by eliminating insignificant terms.
The model then removes the statistically insignificant terms from the proxy equation.
The net-elastic regularization is successfully used for the estimation of the metamodel parameters, avoiding overfitting and eliminating insignificant terms.
Using backward elimination process, insignificant terms (p value > 0.05) have been eliminated from the reduced quadratic model.
The ANOVA table for MRR after dropping insignificant terms and interactions has been presented in Table 5.
The ANOVA table for surface roughness (SR) after dropping insignificant terms and interactions has been presented in Table 6.
However, before these insignificant terms are removed from the model, fitting error and precision must also be taken into consideration.
Also, the model has an R-squared value of 0.930, indicating a good data fit even without excluding the insignificant terms.
Some insignificant terms of the above model can be neglected based on the statistical analysis for the accurate prediction of response (Cao et al. 2014).
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