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. Normal linear regression 6. Comparison of two groups with equal or unequal variances in a Normal model 7. Comparisons in a single classification in a model with a single factor 8. Comparisons along two classifications in a model with two factors 9. General linear model for a normal response 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 Poisson GLM 13. Poisson GLM with continuous and categorical explanatory variables 14. Poisson generalized linear mixed model, or Poisson GLMM 15. Comparing two groups in a Binomial regression, or logistic regression model 16. Binomial GLM with both continuous and categorical explanatory variables 17. Binomial generalized linear mixed model, or Binomial GLMM 18. Model goodness-of-fit and model selection 19. Non-GLMM hierarchical models: Site-occupancy species distribution model 20. Integrated models 21. Conclusions