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Given the exploratory nature of the last step, variables in the final step were entered in a stepwise fashion with backwards removal to identify an optimally fitting model.
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The purpose is not to establish optimally fitting models but to identify how the unmeasurable data of metabolites located in a focused pathway probably behave, which time-series data within the dataset may be inconsistent with the understanding of pathway, as well as which data within a large dataset are probably reliable and which, if any, are most likely imprecise.
Our fitting model is described by the following optimization problem.
She is also a fitting model represented by Ford Models.
So what might be a more fitting model?
A pure diffusion fitting model was employed.
The worst fitting model was (d), the unbalanced input noise model.
Ultimately, selecting a "best fitting" model remains somewhat subjective.
CFA indicated a poorly fitting model in this sample.
Our best fitting model is described in table 6.
In this case, the bad fitting model is rejected.
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