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For ISD based predictions elastoplastic models gave the smallest Dev Total, especially DP.
Also, the nonlinear regression models gave the best results within a field.
For responses related to photo-oxidation (Microscal unit/mercury lamp, carboxylic acid carbonyl growth was monitored by IR), linear models gave the best data fit, thus indicating negligible interaction between the three variables.
As shown in Tables 2, 3, 4, and 5, the MPLE models gave the lowest (q_{ext}^{2}) (0.572–0.596) and the highest RMSE (0.729 0.754) and MAE (0.558 0.580) values for the test set, suggesting that they had the worst prediction capabilities.
Both forward and backward models gave the same final model that includes 5 terms with nominal significance of p< = 0.05 (Table 2).
However, repeated measures ANOVA models gave the same results for the main effects.
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Fleetwood followed suit this year with several smaller models, giving the movement a greater foothold.
The decoding aims to find the optimal path through the models given the observed acoustic information.
This subsection addresses the question: Which of the developed regression models gives the most accurate predictions?
Intersecting the estimations of the two models gives the first stage results of detection.
A minority of interviewees suggested alternative roles or models given the current workforce shortages.
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CEO of Professional Science Editing for Scientists @ prosciediting.com