Study of variability

"General linear models with 1 or more random explanatory variables: from the translation of 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 seen through regression and 1-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and cross-factor ANOVA, multiple regression (notion of parameter and effects, and interaction)

incorporation of the dependence of explanatory random variables, confounding of 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 from an experimental protocol or collected in natural populations (with dependency, co-linearity, spatial or temporal structure).

Hypothesis testing mastered, cf UE DESINF (or equivalent)

100% continuous assessment