Generally, generator states are modeled using multiple state random variables.
Using mixed-effects modeling with multiple independent variables, these studies have revealed that continuous measures of cross-linguistic similarity are indeed predictive of bilinguals' responses in L2 tasks.
In determining the sample size, we have taken into consideration multiple independent variables, using multiple regression modeling with a medium effect size (f) of 0.15, at 0.8 power and a significance level of 0.05.
Therefore, we employed regression modeling to quantitate the effects of multiple independent variables on dependent variables, including oxLDL.
Due to the presence of multiple independent variables, multiple regression modeling with a medium effect size (f2) of 0.15, at 0.8 power and a significance level of 0.05, was used to calculate the sample size.
Hierarchical multiple regression analyses will be used to investigate the relationships between one continuous dependent variable and multiple independent variables.
Relationships between multiple independent variables on the dependent variable(s) will be tested using multivariate regression analysis.
All tables display the association in such analyses including either a single independent variable ('univariable') or multiple independent variables ('multivariable') in linear or logistic regression models, as appropriate.
Using multiple regression (all independent variables in combination), none of the variables acted as predictors of reintubation (P = 0.25).
However, both methods will work for multiple independent variable models where the independent variables can be either categorical or quantitative.
The association between dependent variables (stunting and wasting) and independent variables was determined using multiple logistic regression modeling, which included all potential socioeconomic, and demographic confounders that were significant at P values < 0.05 in the bivariate analysis.
