# Linear models

lstat2120  2019-2020  Louvain-la-Neuve

Linear models
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
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.

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”.
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
Approfondissement en statistique et sciences des données

Minor in Statistics, Actuarial Sciences and Data Sciences

Master [120] in Mathematics

Master [120] in Agricultural Bioengineering

Master [120] in Data Science: Information Technology

Master [120] in Chemistry and Bioindustries

Master [120] in Data Science Engineering

Master [120] in Statistic: General

Master [120] in Statistic: Biostatistics

Master [120] in Forests and Natural Areas Engineering

Master [120] in Mathematical Engineering

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

Master [120] in Data Science : Statistic

Master [120] in Environmental Bioengineering

Master [120] in Biomedical Engineering