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As these variables were important predictors of risk in our model, these correlations suggest that total bird capacity may be a useful proxy for risk.
A compound symmetry structure was used to model these correlations.
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Properly accounting for and modelling these correlations is essential in deriving reliable valuations for decision support.
The substitution of the complex first-principle MEA unit model with a compact blackbox model applying these correlations reduces the optimization time of the overall process considerably without penalizing the overall power plant model quality.
However, the correlations between these moderators and FSIQ turned out to be low to such an extent (see Table 1) that explicit modeling of these correlations was deemed redundant.
Of course, the diversity of the correlations for drag force and minimum bubble size and the sensitivity of the model predictions to these correlations may suggest the necessity to clarify further the essential and robust results in the current model and to reduce the uncertainties involved.
But correlation network methods can only be applied to model the correlations between numeric variables.
The GRNN model significantly outperformed all these correlations.
From these correlations, models that have overpredicted earthquakes as seen in small δ1 and large δ2 are likely not to be rejected in the L-test.
We used multivariate models to assess whether these correlations were independently associated with changes in steatosis.
If dramatic changes in climate occur, perhaps due to a regime shift, leading to changes in these correlations, the model predictions may be in error, although the approach taken here is conservative because current responses are divided among the correlated predictors.
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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