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Linear models [ LSTAT2120 ]


5.0 crédits ECTS  22.5 h + 7.5 h   1q 

Teacher(s) Hafner Christian ;
Language French
Place
of the course
Louvain-la-Neuve
Main themes - 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
Aims 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.
Content The course considers different aspects of general linear models (regression models and analysis of variance) : - selection of covariates - multicollinearity - 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 Teaching methods The course consists of lectures, exercise sessions on computer, and an individual project on computer.
Other information Prerequisites - The student should have followed basis courses in probability, statistics and matrix algebra. - Basic knowledge of SAS is required. Evaluation 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 Teaching materials The course notes will be distributed during the first lecture. Others Professor : Ingrid Van Keilegom, phone : 010/47 43 30, e-mail : vankeilegom@stat.ucl.ac.be References 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.
Cycle et année
d'étude
> Master [120] in Statistics: General
> Master [120] in Statistics: Biostatistics
> Master [120] in Agricultural Bioengineering
> Master [120] in Environmental Bioengineering
> Master [120] in Forests and Natural Areas Engineering
> Master [120] in Chemistry and Bio-industries
> Master [120] in Mathematics
> Certificat universitaire en statistique
> Master [120] in Mathematical Engineering
Faculty or entity
in charge
> LSBA


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