- Introduction to the general linear model - Multiple univariate regression (selection of variables, model validation, multicollinearity, outlier detection, inference concerning regression coefficients, error variance,...) - Univariate analysis of variance (one or more factors, balanced or non-balanced design, fixed, mixed or random effects model, inference concerning main effects, interactions, error variance,...) - Multivariate regression and multivariate analysis of variance
The contribution of this Teaching Unit to the development and
command of the skills and learning outcomes of the programme(s) can be
accessed at the end of this sheet, in the section entitled
“Programmes/courses offering this Teaching Unit”.
At the end of this learning unit, the student is able to :
By the end of this course the student will be familiar with the main linear models that are often encountered in statistics, and, by making use of computer packages, the student will be able to solve real data problems. The course stresses more the methodology, the interpretation, and the mechanisms behind linear models, and less the theoretical and mathematical aspects.
The course considers different aspects of general linear models (regression models and analysis of variance) :
- selection of covariates
- Ridge regression
- model validation
- inference concerning the parameters in the model (confidence intervals/hypothesis tests for regression coefficients, error variance,... prediction intervals,...)
- balanced or non-balanced designs
- fixed, mixed and random effects models
- multivariate linear models
The course consists of lectures, exercise sessions on computer, and an individual project on computer.
Syllabus du cours.
Références données au cours.
Title of the programme
Approfondissement en statistique et sciences des données
Minor in Statistics, Actuarial Sciences and Data Sciences
Master  in Mathematics
Master  in Agricultural Bioengineering
Master  in Data Science: Information Technology
Master  in Chemistry and Bioindustries
Master  in Data Science Engineering
Master  in Statistic: General
Master  in Statistic: Biostatistics
Master  in Forests and Natural Areas Engineering
Master  in Mathematical Engineering
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master  in Data Science : Statistic
Master  in Environmental Bioengineering
Master  in Biomedical Engineering