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 [more]

day 1: binominal model (repetition LM, logistic regression, binomial model, tests, model assumtions, overdispersion, predictions); day 2: poisson model (poisson model, tests, model assumptions, overdispersion, predictions, depending on participants: zero-inflation, mixture models); day 3 GLMM (including random effects, Bayesian way of fitting a model, glmer-function and MCMCglmm-finction, depending on participants: introduction to WinBUGS and further mixture models); day 4: projects (work on own data and presentations) Requirements Modul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]

Day 1: Binomial model (refreshing LM and LMM, introduction Bayesian data analysis, logistic regression, binomial model, tests, model assumptions, overdispersion, predictions); Day 2: Poisson model (tests, model assumptions, overdispersion, predictions, depending on participants wishes: zero-inflation, mixture models); Day 3: GLMM (including random effects, glmer-function and MCMCglmm-function, depending on participants wishes: introduction to WinBUGS and further mixture models); Day 4: work on own data and presentations [more]

day 1: 2-3 Short inputs depending on participants projects short presentation of participants projects day 2 and 3: guided work on own project day 3: presentations of projects prerequisite for participation basic knowledge in statistics, participation in at least 2 of the Modules 1 – 3. Participants bring their own data. They are requested to send a short description of their projects to the teachers at least one week before the start of the workshop. [more]

Day 1: Binomial model - refreshing LM and LMM - introduction Bayesian data analysis - logistic regression, binomial model - model assumptions, overdispersion - tests, predictions Day 2: Poisson model - Poisson model - model assumptions, overdispersion - tests, predictions - depending on participants wishes: zero-inflation Day 3: GLMM - including random effects - glmer-function - depending on participants wishes: introduction to WinBUGS and more complex models Day 4: projects - work on own data and presentationsPrerequisite for participationModul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]