Study of variability

General linear models with one or more random explanatory variables: from translating the figure that answers the biological question to the statistical model, i.e., taking into account numerous effects and knowing how to interpret them.

General properties viewed through regression and one-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and crossed factor ANOVA, multiple regression (concept of parameters and effects, and interaction)

incorporation of the dependence of explanatory random variables, confounding effects (quantitative for multiple regression, and unbalanced designs for ANOVAs)

Construction of linear models with one or more explanatory variables appropriate to the structure of biological data obtained from an experimental protocol or collected in natural populations (with dependence, collinearity, spatial or temporal structure).

Hypothesis testing mastered, see DESINF course (or equivalent)

Continuous assessment: 100%