Hafner Christian ;
- 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
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 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”.
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.
- The student should have followed basis courses in probability, statistics and matrix algebra.
- Basic knowledge of SAS is required.
The evaluation consists of :
- an oral exam, which consists mainly of questions related to methodology, comprehension and interpretation of the course
- a project on computer, which consists of the analysis of real data
The course notes will be distributed during the first lecture.
Professor : Ingrid Van Keilegom, phone : 010/47 43 30, e-mail : firstname.lastname@example.org
Arnold, S.F. (1981). The theory of linear models and multivariate analysis, Wiley, New York.
Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W. (1996). Applied linear statistical models. McGraw-Hill, Boston.
Master  in data Science: Statistic
Minor in Statistics and data sciences
Master  in Chemistry and Bioindustries
Master  in Agricultural Bioengineering
Master  in Environmental Bioengineering
Master  in Forests and Natural Areas Engineering
Master  in Data Science Engineering
Master  in data Science: Information technology
Master  in Mathematical Engineering
Master  in Mathematics
Master  in Biomedical Engineering
Master  in Statistic: Biostatistics
Master  in Statistic: General