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A linear multivariable regression model (LMVR model) was fitted to the logarithmically transformed experimental data.
Finally, a non-linear second-order multivariable regression model is developed to predict pedestrian wind comfort in the lift-up area.
Table S5 Odds ratios for multivariable regression model 1 covariates.
Odds ratios for multivariable regression model 2 covariates.
A multivariable regression model was used to assess the independent informational content of these predictors.
The remaining (significant) factors were incorporated into a binary multivariable regression model for trauma patients.
This multivariable regression model accounted for 54.2%% of variance in the total number of visits.
Odds ratios for multivariable regression model 1 after addition of SBP.
Multivariable regression model was fit using the ordinary least squares approach.
This multivariable regression model accounted for 46.3%% of variance in the total treatment time.
Relationship between ARF etiology and hospital mortality was assessed using a multivariable regression model adjusting for confounders.
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