Objectives : The purpose of this course is to introduce the students to how statistics is used in practice. By working-out practical examples, students will learn how to select and use appropriate statistical methodologies. We will use data produced in many different areas including among other biology, medicine, toxicology, etc. The course will present both statistical theory and practical analysis on real data sets. The R statistical software and several R packages will be used for implementing methods presented in the course and analyzing real data.

 

Taking previously the course " Statistics with R" (MAP536) is strongly encouraged.

Website:  sia.webpopix.org

Syllabus


             1. Statistical tests

                          • Parametric and non-parametric tests for comparing two groups,
                          • Power analysis,
                          • Equivalence testing,
                          • Multiple comparisons,
                          • Permutation tests,

             2. Regression models

                          • Linear and nonlinear regression models,
                          • Confidence and prediction intervals,
                          • Diagnostic plots,
                          • Model comparison,

             3. Mixed effects models

                          • The population approach,
                          • Linear mixed effects models,
                          • Nonlinear mixed effects models,
                          • Pharmacokinetics modelling,


             4. Mixture models

                          • k-means clustering,
                          • Finite mixture of Gaussian distributions,
                          • The EM algorithm for Gaussian mixtures,
                          • Supervised classification,


             5. Change point analysis

                          • Detection of a single change point,
                          • Detection of multiple change points,
                        

             6. Image restoration

                          • Markov random fields
                          • Gibbs sampler
                          • Simulated annealing


Langue du cours : Français

Credits ECTS : 4