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The user can also search for a facility in the 3D model by entering its ID, utilities, or location.
We created our model by entering all covariates at once.
The result was compared with a backward logistic regression model by entering the same variables as the above model, and this led to a similar result.
Assuming a linear relationship, predicted median estimates were calculated based on the estimation of the linear model by entering the observed early quantile into the model.
The presence of effect-measure modification by age and sex was assessed for all variables in the final model by entering product terms.
P value for the trend was assessed for each model by entering the year of the survey as a continuous variable.
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Following Crawley (2013), we estimated minimum adequate models by entering all fixed effects and dropping them sequentially until only those that explained significant variation remained (see site variable effect results in Supplementary Table S4).
A final model was achieved by entering the variables retained in the backward selection model.
An overall multidimensional model was built by entering explanatory variables from each of the seven dimension models showing an association of p<0.20 with not working as independent variables.
Each model was elaborated by entering into a logistic regression the variables included in these models (Table 3).
A multivariable logistic regression model was created by entering simultaneously all the risk factor variables.
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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