2019-2020 / STAT0723-2

Linear models

Duration

30h Th, 10h Pr, 20h Mon. WS

Number of credits

 Master in mathematics (120 ECTS)8 crédits 
 Master in mathematics (60 ECTS)8 crédits 

Lecturer

N...

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course concerns linear models : multiple linear regression, ANOVA, and an introduction to some extensions.

Learning outcomes of the learning unit

The course includes the following chapters:
 
Multiple linear regression

  • Model description
  • Statistical properties of the estimators
  • Inference and prediction
  • Validation and adaptation of the model
  • Variables selection
  • Extensions
 
ANOVA
  • Anova I
  • Anova II
  • Extensions
There is  a focus on the implementation of these techniques on real datasets.
 
 

Prerequisite knowledge and skills

Courses of Statistics and Probability and Algebra included in the program of Bachelor in Mathematics. Use of a statistical software.
 
 

Planned learning activities and teaching methods

The course is organised as a set of lectures, as well as some practical work in the computer room (use of statistical softwares).  It also includes a project.

Mode of delivery (face-to-face ; distance-learning)

The course is organized face-to-face. Il also includes a project.

Recommended or required readings

Reference (non mandatory) : Neter, J., Kutner, M.H., Nachtsheim, C.J. et Wasserman, W. (1996), Applied linear statistical models. McGraw-Hill, Boston.
 
The course is mainly given at the blackboard.  Additionnal material will be provided via myULg.

Assessment methods and criteria

  •  A project of data analysis, including a report and an oral presentation
  •  An examination, during the assesment period, which includes the use of statistical software
Criteria: correct use of methods and tools; understanding the methods; use of statistical software for linear models (including R and SAS); implementation of the methods on real data; interpretation of statistical results in context; communication of statistical results. 

Work placement(s)

Organizational remarks

The course is organized face-to-face, with mathematical details given at the blackboard. Il also includes a project.
 
The course is only given on even academic years (2018-2019 is the next year).

Contacts

Catherine.timmermans@ulg.ac.be

Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session

Teaching methods implemented : distance-learning

Assessment subjects

Assessment methods

Contacts

Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session

Assessment subjects

Assessment methods

Contacts