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We approach our discussion by examining various decisions in the research process and include a detailed discussion of specifying the model and obtaining narrative data.
This involved repeatedly (n = 2000) randomly reordering the model residuals, adding these to the fitted values from the original model, reapplying the model, and obtaining model predictions from the updated model.
Reducing the complexity of a model and obtaining an easier model interpretation and a possible improvement in prediction can be achieved by the variable selection.
Cumulative effects are modeled by including all lags to be accumulated in a single model, and obtaining an estimate of the weighted average of the lag effects.
A recent organ level study of human brain [ 28] discusses the challenges of both composing an organ level compartmentalized model and obtaining the data required to constrain it.
Thus, ABM allows for a strong interplay between constructing an explanatory model and obtaining empirical data (de Vries and Biesmeijer 1998).
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Building on this premise, we propose a model and obtain two main results.
We describe a simple computation of the worm-like chain model and obtain the corresponding force-versus-extension curve.
We derive analytical solutions for the basic model and obtain managerial insights from them.
Beside, the experimental verification of worked out forging model and obtained selected numerical results was done.
We analyze the fast evolutionary game model and obtain two stable equilibria.
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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