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Model 2 is taken as final model which uses only financial variable.
Dataset statistics and refinement statistics as well as final model contents are summarized in Table 1.
The model with the lowest value of the Bayesian information criterion <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0016364.e001.PNG" class= inline-graphic"/> [9], where k is the number of parameters in the model and N is the number of observations, was selected as final model.
Individual-level variables included as final model covariates were selected using a backward variable selection technique (p for inclusion < 0.05).
In comparing the additive predictive effects of all 15 facets included in the initial prediction model (shown as Model 1) against the final set of facets remaining in the last step of each regression analysis (shown as Final model), the appropriate statistic to examine is the adjusted R, which can account for the unequal degrees of freedom.
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The 35 probes selected as final-model features are shown in Supplementary Table 1.
In order to ensure minimal bias in evaluating our results an exhaustive validation procedure was followed, both for model selection as well as for final model assessment.
Here as well, the final model form allows for the synthesis of finite order controllers.
Some authors have suggested that 1PL models, as compared to 2PL models, are unsuitable as a final model for describing data resulting from functional status items [ 21].
As RMS is almost converged, we used the fourth-generation model as our final model.
The model with the lowest modeller objective function and optimal DOPE-score was selected as the final model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com