Dr. Marc Kéry is a senior scientist at the Swiss Ornithological Institute, a non-profit NGO with about 200 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the universities of Basel and Zürich, Switzerland. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, USA, he moved into animal population ecology and during the last 25 years has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and applied statistics. He has published more than 150 peer-reviewed journal articles and six textbooks on applied statistical modeling. He has taught more than 60 one-week workshops all over the world to biologists and wildlife managers about the concepts and practice of modern statistical analysis in their fields, something which goes together with his books, which target the same audiences.

1. Introduction
2. Introduction to statistical inference
3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models
4. Introduction to general-purpose model-fitting engines and the model of the mean
5. Simple linear regression with Normal errors
6. Comparison of two groups
7. Comparisons among multiple groups
8. Comparisons in two classifications or with two categorical covariates
9. General linear model with continuous and categorical explanatory variables
10. Linear mixed-effects model
11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression
12. Overdispersion, zero-inflation and offsets in a GLM
13. Poisson regression with both continuous and categorical explanatory variables
14. Poisson mixed-effects model or Poisson GLMM
15. Comparing two groups in a Binomial regression
16. Binomial GLM with both continuous and categorical explanatory variables
17. Binomial mixed-effects model or Binomial GLMM
18. Model building, model checking and model selection
19. General hierarchical models: Site-occupancy species distribution model (SDM)
20. Integrated models
21. Conclusion