# Linear models

lstat2120  2023-2024  Louvain-la-Neuve

Linear models
5.00 credits
30.0 h + 7.5 h
Q1
Teacher(s)
Language
Prerequisites
English at the medium level and concepts and tools equivalent to those taught in the course LSTAT2014
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
Learning outcomes
 At the end of this learning unit, the student is able to : 1 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.
Teaching methods
The course consists of main lectures, which provide the main theoretical material but also give many practical examples, and exercise sessions that teach how to implement the methods in a common statistical programming package. The exercise sessions will also help the students to work on their individual projects.
Evaluation methods
A written exam (60%) and an individual project on a real data set (40%).
Bibliography
Références données au cours.
Teaching materials
• matériel sur moodle
Faculty or entity

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master  in Data Science : Statistic

Master  in Biomedical Engineering

Master  in Statistics: Biostatistics

Master  in Mathematics

Master  in Statistics: General

Master  in Chemistry and Bioindustries

Approfondissement en statistique et sciences des données

Master  in Mathematical Engineering

Master  in Data Science Engineering

Minor in Statistics, Actuarial Sciences and Data Sciences

Certificat d'université : Statistique et science des données (15/30 crédits)

Master  in Data Science: Information Technology 