Linear Models and Linear Mixed Models with R (Module 2)

day 1: LM (linear regression, multiple regression, ANOVA, ANCOVA, least-square method, parametrisation, interactions, tests (marginal and sequential), model selection, model assumptions, predictions); day 2: LME (linear mixed models, maximum likelihood, restricted maximum likelihood, random and fixed effects, likelihood ratio test / bootstrap, random slopes-random intercept models, depending on participants further model types); day 3 LME (Bayesian way of fitting a linear model, model matrix, simulating posterior distributions of model parameters, predictions, posterior probabilities of hypotheses, preparing data for work on own data); day 4: projects (work on own data and presentations) Requirements Modul 1, basic knowledge in statistics