The course starts with basic statistics and progresses towards more advanced concepts and methods in statistical modelling.

The following topics are covered:

• Basic statistical concepts and terminology such as sampling, variation, probability, modelling, inference etc. Introduction to statistical thinking.
• Data manipulation, graphics, use of the R environment etc. for descriptive and exploratory data analysis.
• Fundamentals of study design.
• Null Hypothesis Scientific Testing.
• Presentation of statistical results (e.g., graphics).
• Linear and Generalized Linear Models, as a foundation for many statistical methods used in ecology and forestry.
• More advanced concepts used in modern statistics (e.g., mixed effects models (random slopes and intercepts), co-linearity, added variable plots, parsimony, likelihood, model selection, AIC, randomization, and bootstrap methods).