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Briefly, this approach takes one feature out and measures its relative importance and contribution to the model.
The correlated contribution to the model output uncertainty was <10% for the set of parameters investigated.
Based on that information we determine reduced-order mathematical models of the original eighteen-order model by eliminating state variables that have negligible contribution to the model dynamics.
If the overall model is deemed statistically significant, statisticians will usually conduct hypothesis tests on the individual parameters to determine if each independent variable makes a significant contribution to the model.
Thus, there is consistent lack of contribution to the model by respondent characteristics.
Further, none of the indicators of travel behavior makes a significant contribution to the model.
In contrast, language background did not make a significant contribution to the model.
To the limited extent interactions were able to be estimated, their relative contribution to the model was small.
The predictors as well as the knot location, having the most contribution to the model, are selected first.
When controlling for all relevant factors in the linear regression model, high problem perception also makes a significant contribution to the model.
Once these two variables had been entered into the model, none of the remaining independent variables made sufficient additional contribution to the model to meet the entry criterion.
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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