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Individual identity was controlled by modeling this factor as a random effect.
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Abramowitz and Hill et al try to model this factor in one or another, while Fair does not.
Although provider effects are an important determinant of diabetes control, we did not have sufficient power to model this factor in our analyses.
In vision models, this factor is sometimes referred to as point spread function of retinal cells [120].
Also responsiveness is an important factor that is considerable for the managers; with the provided model, this factor increases and absolutely customers are more satisfied.
In the predictive model, this factor had the highest risk ratio, with p < 0.001.
If the univariate estimate of the hazard ratio (HR) for CPM changed more than 10% if a potential confounder was added to the model, this factor was accepted as a confounder.
Interestingly, the addition of the individual covariate describing the intensity of use of AFS greatly improved the models without this factor (models 7 12, Table 1).
The existing M/G(n)/C/C model and D/D/1/C model ignore this factor and result in underestimation of width.
We also compared the AIC of both models and found that the score of the model without intergroup encounters was lower (775.0) than the score of the model including this factor (814.9).
They then tried to model the factors driving this pattern.
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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