Linear Mixed-Effects Models

Linear Mixed-Effects Models are statistical tools used to analyze data that involve both fixed effects (predictors of primary interest, consistent across all observations) and random effects (variability due to groups or subjects). They help account for data that has multiple levels or grouped structures, such as measurements from different individuals or locations. By combining fixed and random effects, these models provide more accurate estimates and insights into relationships within complex data, allowing researchers to understand overall trends while accounting for individual differences or other grouping factors.