This lecture covers the linear mixed model, which includes fixed and random effects. It explains how to estimate parameters using maximum likelihood estimation and inference techniques. The model is applied to real data examples.
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Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
