English translation for model all VIFs and usage examples from the best sources

In the present model, all VIFs were less than 3.

In all multivariable models, all VIF were less than 2, excluding significant multicollinearity.

In all models, VIFs were <1.3 for each of the predictors, and therefore the results of these multiple regression analyses are unlikely to be confounded by multicollinearity.

We then used Variance Inflation Factor (VIF) regression statistics to test for multi-collinearity in all models; VIFs ranged from 1.08 to 4.14 and as these values were less than 10 there was no evidence of multi-collinearity.

Multicolinearity between covariates was assessed in models by examining variance inflation factors (VIF); all VIFs were less than 30.

All VIFs in the four regression models were below 2, meaning that there is no severe multicollinearity problem in the data (Hair et al. 2009).

All VIFs were < 5 in the presented two-pollutant models.

The final models were also checked for multicollinearity using the Variance Inflation factor (VIF), but no multicollinearity was noted (all VIFs < 3.0).

All VIFs are below 2, indicating a lack of multicollinearity.

For BMI group, based on all VIFs being less than 3 and percentage increases in log likelihood as shown in Table  1, the BMI categories for the two previous surveys were included in the transition model.

We calculated variance inflation factors (VIFs) to test for possible collinearity among independent variables, but found no substantial collinearity (all VIFs were less than 6, and all except the VIFs for the household size variables were less than 3).