# Inference for overall population parameters with multilevel models

I have a dataset that it is clearly need a multilevel model approach -observations from different regions-. However, I am not interesting in population parameters of regions, but overall parameters for covariates and intercept.

Is applying multilevel model in the described case worth doing? I know that if I acount for inner class correlations, I would have more realistic variance estimates...

E.g., for a linear mixed model using lme4: model <- lmer(Dependent ~ Predictor1 + Predictor2 + (1|Region), data = data) for a simple model without interactions.