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We conducted bivariate analyses for all remaining explanatory variables, and only those with a p value of <0.25 from chi-square testing were considered in subsequent model building.
Significant predictors (p <.05) were included in subsequent model building.
The measured expression ratio distribution for technical noise (SD = 0.11) was used in subsequent model computations.
This resulted in a total of four candidate covariates per category that were considered in subsequent model building.
Coupled with biological variability, it would be additionally challenging to be clear about model limitations in subsequent model usage.
In subsequent model testing, age was a confounder for availability and age and employment band were confounders for participation among men.
Similar(50)
Interactions identified during the modeling process were evaluated in subsequent models; no interaction remained in the final model.
However, such influence lost statistical significance when adding key variables in subsequent models.
Although many improvements were made in subsequent models, this model lays the foundation for the rest of the RRAM models by accurately taking into consideration and explaining the non-linear dopant drift effects [3, 46].
Thus, those variables were omitted in subsequent models.
Note also that when exploring the first model, I provide definitions of most parameters and analytical methods used in subsequent models.
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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