introduction to analysis of variance -- Regression -- Models, parameters and GLMs -- Using more than one explanatory variable -- Designing experiments -- keeping it simple -- Combining continuous and categorical variables -- Interactions -- getting more complex -- Checking the models I: independence -- Checking the models II: the other three asumptions -- Model selection I: principles of model choice and designed experiments -- Model selection II: datasets with several explanatory variables -- Random effects -- Categorical data -- What lies beyond? -- Answers to exercises -- meaning of p-values and confidence intervals -- Analytical results about variances and sample means -- Probability distributions