Schedule of the individual two-day blocks:

Block 1 - Generalized Linear Models (GLM) and introduction to hierarchical designs

Day 1 - morning
Theory (cca 2 h) - Introduction to GLM, concept of deviance, link functions, etc., introduction to logistic regression
Exercise (approx. 1 h) - logistic regression, its assumptions, interpretation, construction of confidence intervals of the logistic curve

Day 1 - afternoon
Theory (approx. 1 h) - GLM with binomial and Poisson distribution, treatment of overdispersion
Exercises (approx. 3 h) - practical analyses using poisson and binomial GLM, interpretation of diagnostic graphs, detection and treatment of overdispersion

Day 2 - morning
Theory (approx. 1 h) - GLM with gamma distribution, other than canonical link-functions
Exercise (approx. 2 h) - practical exercises on GLM from the whole spectrum of variants discussed so far

Day 2 - afternoon
Theory (approx. 1 h) - Hierarchical data designs and hierarchical ANOVA (split-plot, hierarchical ANOVA s.s.)
Exercises (approx. 1.5 h) - Identification of individual layers of hierarchical designs, practical implementation of hierarchical ANOVs, auxiliary linear models for verification of assumptions
Theory (approx. 1.5 h) - Revision of the concept of random effect factors and introduction to linear models with mixed effects

1st classified homework: analysis of two data sets focused on GLM usage

Block 2 - Mixed Effect Models - Linear (LME) and Generalized Linear (GLMM)

Day 1 - morning
Theory (approx. 1 h) - LME - continuation, interpretation of LME, introduction to LME testing
Exercise (approx. 2 h) - LME with one random effect, introduction to testing of fixed effect factors, interpretation of LME results

Day 1 - afternoon
Theory (approx. 1 h) - LME - continuation, differences between random effect and mixed effect factor, testing of random effect factors in LME
Exercise (approx. 3 hours) - LME with multiple factors with random and mixed effects

Day 2 - morning
Theory (approx. 1 h) - construction of confidence intervals in LME - model profiling and other CI construction methods, expression of the amount of explained variability within LME (pseudo-R2)
Exercise (approx. 2 h) - construction of confidence intervals for LME, calculation of psuedo-R2

Day 2 - afternoon
Theory (approx. 1 h) - transition from LME to GLMM, common problems when working with mixed effect models and how to deal with them
Exercises (approx. 3 hours) - GLMM exercises and revision excersises for mixed effect models

2nd classified homework: analysis of two data sets with hierarchical design

Block 3 - Data with temporal, spatial or phylogenetic correlation between observations - Generalized Least Squares (GLS)

Day 1 - morning
Theory (approx. 1 h) - Introduction to GLS, possibility to use for heteroscedastic data, introduction to temporal and spatial autocorrelation of data, time series analyses, detection of spatial autocorrelation of data (semivariograms), functions useful for approximation of semivariogram
Exercise (approx. 2 h) - GLS with weights (heteroskedasticity), 1st-order autoregressive models, ARIMA models, spatial data autocorrelation

Day 1 - afternoon
Theory (approx. 1 h) - Introduction to work with phylogenetic data, models of character evolution, phylogenetically independent constants (PIC)
Exercise (approx. 3 hours) - recording and editing of phylogenesis data, mapping of characters to phylogenetic trees, analysis of data using PIC

Day 2 - morning
Theory (approx. 1 h) - Phylogenetic GLS (pGLS) and transformation of phylogenetic tree into a variance-covariance matrix, phylogenetic RMA (reduced major axis regression)
Exercise (approx. 2 h) - analysis of data sets with available data on phylogeny

Day 2 - afternoon
Theory (approx. 0.5 h) - Phylogenetic analysis of major components (phylPCA)
Exercise (approx. 1.5 h) - continuation of tasks from morning + phylPCA

Seminars (approx. 2 hours) - discussion of model tasks, focusing on the identification of the nature of data and selection of appropriate analytical techniques

3rd classified homework: analysis of two data sets with spatial, temporal or phylogenetic correlation of response variable