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Because linear and log lead specifications produce large differences in predictions only as BPb approaches zero, data sets including substantial numbers of very low BPb levels are required to notice, appreciate, and test for adequacy of alternative specifications.
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Sample sizes were then tested for adequacy in terms of precision at specified levels of confidence for each specific disease combination.
The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models.
In this paper, the models were tested for adequacy using analysis of variance, the predicted errors were calculated, the effects of joining process parameters were determined, and the optimal conditions were identified.
That indicates that neither element had been tested for adequacy.
The model was tested for adequacy by ANOVA (Table 4).
All specimens were tested for adequacy (KG) using the 2002 ASCCP guidelines.
The final model was subjected to Hosmer and Lemeshow testing for adequacy of the data; collinearity was also tested to ensure its absence between dependent variables.
Feasibility tests using the Kaiser Meyer Olkin (KMO) test for sampling adequacy and Bartlett's sphericity test indicate the suitability of the variables for factor analysis, which can be found in Table 3.
To compare a more complex model a to a simpler model b, we employed the information-theoretic evidence ratio (ER = AIC c weight of model a ÷ AIC c weight of model b) to quantify the relative support of a versus b, and used the per cent deviance explained (%DE) to determine structural goodness-of-fit of model a (test for model adequacy).
Various models such as linear, two factor interaction, quadratic, and cubic models were tested for the adequacy.
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CEO of Professional Science Editing for Scientists @ prosciediting.com