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The final estimates were evaluated based on data fit, adequacy to initial estimates, diagnostic parameters and residual analysis.
VIF greater than 10 was used to assess collinearity among independent variables.[ 17] Model diagnostics were conducted to test the goodness of fit, adequacy and accuracy of the final model.
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Analysis of variance (ANOVA) has been performed to verify the fit and adequacy of the developed mathematical models.
To test the model fit and adequacy, the pseudo R2, also known as McFadden's likelihood ratio was used.
Looking beyond the usual measures of fit and adequacy, residual analysis revealed nothing out of the ordinary and tests of parameter coefficients significance all looked satisfactory.
Looking beyond the usual measures of fit and adequacy, residuals analysis revealed nothing out of the ordinary and all parameter estimates were significant (|t| > 2.5).
Model fit and adequacy measures were used to compare and select models.
If the variable improved the model fit and adequacy (based on the likelihood ratio criteria and the significance of the parameter) it was kept for the next step; otherwise, the variable was excluded.
This includes the test for significance of the regression model, test for significance on model coefficients and test for lack-of-fit model adequacy.
We used the posterior mean deviance as a Bayesian measure of fit or model adequacy as defined by Spiegelhalter et al. [20].
The second step is to test the X→M→Y model for adequacy of fit; assuming adequate fit, the model is X→Y, X→M, and M→Y are examined for significance.
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