Mixed-Effects Model

A mixed-effects model is a statistical tool used to analyze data that involves both fixed effects (predictors that have consistent impacts across all data points) and random effects (variables that introduce variability across groups or subjects). It helps disentangle the overall patterns from group-specific differences, making it useful when data is collected from different locations, times, or subjects. This approach provides more accurate insights by accounting for both the general trends and the unique variations within subsets of the data.