Duration
30h Th, 10h Pr, 20h Mon. WS
Number of credits
| Master in mathematics (120 ECTS) (Even years, not organized in 2023-2024) | 8 crédits | |||
| Master in mathematics (60 ECTS) (Even years, not organized in 2023-2024) | 8 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
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 generalized linear models. Depending on the time constraints, models used in more specific situations might be considered (eg: survival analysis, quantile regression).
Learning outcomes of the learning unit
The student should be able to estimate, validate and interpret a mulitple linear model and an analysis of variance. He·She will be able to recognize situations when the usual hypotheses are violated.
Prerequisite knowledge and skills
Courses of Statistics and Probability and linear algebra included in the program of Bachelor in Mathematics. Some basics of R are also recommanded.
Planned learning activities and teaching methods
The learning activities consist in ex-cathedra sessions of theory, home-sessions of exercises and some applications of the software R.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Additional information:
ATTENTION: this course is only organised every two years (see the section "organisational remaraks").
The practical organisation depends on the number of students.
If there are at least 3 students registered for the course, the lectures will be given in a face-to-face way.
If the number of students is smaller than 3, the theoretical lectures will be given by means of a guided reading with some discussion organised face-to-face or in a virtual class.
In all cases, the exercises will have to be done at home. The resolutions or the encountered problems will be discussed from time to time during a lecture or a practical.
Some practicals (data analyses with R) will also be organised face-to-face.
Recommended or required readings
The slides will be put on eCampus and for each chapter, some referece books will be suggested. For some topics, partial lecture notes will be provided.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Written work / report
Additional information:
The mak of the course will be based on two parts:
- Written exam on theory and exercises
- A personal data analysis (with a written report and an oral defense)
Work placement(s)
Organisational remarks and main changes to the course
The course is only given on even academic years (22-23, 24-25,...). Therefore, it will not be taught this academic year 2023-2024.
There is no assistant in charge of this course.
Contacts
G. Haesbroeck: G.Haesbroeck@uliege.be