Introduction to the R working environment.

- 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.

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