Gjergji Josifi In the linear mixed effects model, the random effects are implicitly assumed to be uncorrelated with the predictor variables. However, it's important to look for potential correlations between the random effects and predictor variables, particularly in cases where such correlations are suspected or theoretically plausible. Try to conduct residual diagnostics and sensitivity analyses to assess the validity of this assumption in reality.

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Gjergji Josifi The assumption that the unobserved variables (random effects) should not be correlated with the observed variables is a key assumption in random effects regression. Linear mixed-effects models offer more flexibility. The correlation between random effects and predictors in mixed effects models depends on the specific structure of the model and the inclusion of random slopes. If the variance of the slope is significant, it suggests that the relationship between XitX_{it}Xit and YitY_{it}Yit is not constant but varies across entities.

Also take closer look at the fixed and random effect through slopes values.