- Variable types in R.
- Statistical populations and samples through working examples.
- Measurements of central tendency and variability.
- Precision, accuracy and bias.
- Hypothesis testing: Falsability, Type-I and II errors and statistical power.
- Correlation and simple regression.
- P-value vs. effect magnitude.
- Linear Models: Residuals, assumptions and interpretation.
- Explained vs. unexplained variance of a model (the coefficient of determination).
- Building functions in R.
- Introduction to graphics in R.
- The concept of partial effect: Partial regression and correlation.
- General Linear Models (GLM).
- Curve fitting in linear models and General Additive Models (GAMs).
- The problem of spatial autocorrelation in ecology and evolution.
- Multicolinearity: When is there a problem?
- Additive vs. multiplicative effects: Checking and plotting interactions.
- Introduction to General and Generalized Linear Mixed Models (GLMM).
- Fixed vs. Random effects and implications for analysis: Main R functions.
- Introduction to Bayesian statistics: The function MCMCglmm.
- Practical examples in evolutionary ecology:
The study of natural selection.
Applications of linear models for quantitative genetics.
- Student’s case studies.Please feel free to distribute this information between your colleagues if you consider it appropriate.
With best regards