Duration
30h Th, 15h Pr
Number of credits
| Bachelor in physics | 4 crédits |
Lecturer
N...
Substitute(s)
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course begins with a relatively thorough description of the different notions of probability essential to a proper understanding of inferential statistics: probability measures (continuous, discrete, conditional) random vectors, distributions and densities, expectations (both conditional and non-conditional), an overview of the most common probability laws and the most important theorems of convergence. We then continue with mathematical statistics : point estimation (method of moments, maximum likelihood, evaluation), interval estimation (CI for mean, variance, proportion ...) hypothesis testing (general and tests for mean and variance) and fitting.
Most illustrations are performed in R statistical software. (freely available via http://cran.r-project.org/)
All information is available on eCampus.
Learning outcomes of the learning unit
At the end of the course, students will be able to calculate and interpret the traditional indicators (both from the practical and theoretical standpoint) obtained during an empirical study of a data set. The student will understand the basic concepts of probability and be able to perform any basic calculation of risk. The student will know (both from the practical and theoretical standpoint) what probabilistic model is appropriate under what circumstances. The student will understand the basic principles of the point estimation and interval estimation, and he will understand the basic principles of a statistical test protocol. The student will know and be able to apply the classical parametric tests (adjustment, orientation, dispersion, ...), master the basics of regression, and will be able to study the propagation of errors. Finally the student will be able to set up a Monte Carlo protocol.
Prerequisite knowledge and skills
A good understanding of calculus is required.
Planned learning activities and teaching methods
Ex cathedra teaching and exercise sessions
Mode of delivery (face-to-face ; distance-learning)
Recommended or required readings
Course notes as well as exercise sets are available through eCampus. The notes are from Y. Swan who gives the course in 2018-2019.
Bibliography
- Albert A., Biostatistique, Les Editions de l'Université de Liège, 2005
- Bevington P.R. and Robinson D.K., Data Reduction and Error Analysis for the Physical Sciences, Third edition, McGraw-Hill, 2003
- Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA : Duxbury, 2002.
- Dehon, Catherine, Jean-Jacques Droesbeke, and Catherine Vermandele. Eléments de statistique : 5e edition revue et augmentée. 2008.
- Dehon, C., Probabilités et inférence statistique, Notes de cours de l'Université libre de Bruxelles, 2011
- Haesbroeck G., Probabilités et Statistique mathématique, Note de cours de l'Université de Liège, 2004
- Haesbroeck G., Statistique descriptive et notions de probabilité, Notes de cours de l'Université de Liège, 2013
- Paindaveine, D., Introduction aux probabilités, Notes de cours de l'Université libre de Bruxelles, 2018
- Ruwet, C., Statistique des données expérimentales de la physique, Notes de cours de l'Université de Liège, 2013
Assessment methods and criteria
The first and second session exams will contain theoretical questions (some of which are announced in class) and exercises inspired by those studied during the year. The theoretical and practical parts are separated: the first to be solved without notes, the second to be solved with the help of the student's own computer.
Work placement(s)
Organizational remarks
Contacts
Marie Ernst Département de Mathématique, Grande Traverse, 12, Sart Tilman, B-4000 Liège +32 4 366 94 02 m.ernst at uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
Assessment subjects
Assessment methods
Contacts
Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session
Assessment subjects
Same content as in Januar
Assessment methods
Distance written exam using ECampus.
See the official schedule for the timing. Different questions will be generated for each student.
Contacts
Béatrice Lahaye : beatrice.lahaye@uliege.be