# Linear models

lstat2120  2020-2021  Louvain-la-Neuve

Linear models
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 7.5 h
Q1
Teacher(s)
Language
English
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
 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.
Bibliography
Syllabus du cours.
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
Aims
Master  in Data Science : Statistic

Master  in Mathematics

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

Minor in Statistics, Actuarial Sciences and Data Sciences

Master  in Mathematical Engineering

Master  in Data Science Engineering

Master  in Chemistry and Bioindustries

Master  in Data Science: Information Technology

Approfondissement en statistique et sciences des données

Master  in Statistic: General

Master  in Statistic: Biostatistics

Master  in Biomedical Engineering 