2023-2024 / STAT0750-1

Multivariate statistical analysis (software R)


10h Th, 10h Pr

Number of credits

 Bachelor in biology3 crédits 
 Bachelor in geography : general2 crédits 


Arnout Van Messem

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course is a general introduction to the most commonly used methods in multivariate statistics (i.e. when one studies several variables simultaneously) in biology. The course entails the following topics:

  • Graphical display and statistical summary of multivariate data
  • Multivariate exploratory techniques: principal component analysis, clustering, principal coordinates analysis
  • Multiple regression and generalized linear models

Learning outcomes of the learning unit

The methods of multivariate data analysis are taught based on a pragmatic approach. At the end of the course, the student should be able to

  • define a multivariate problem,
  • understand the working of the taught methods,
  • analyse the data using the statistical software R, and
  • interprete the results.
At the same time, the student should also be aware of the limitations of the application of the taught methods.




Prerequisite knowledge and skills

The students must have followed a basic course on descriptive and inferential statistics. The concepts of normal distribution, confidence intervals and hypothesis tests are considered as known. Moreover, a basic knowledge of the software R is expected.

The methods are presented without emphasizing the mathematical justifications. Nevertheless, the students must be comfortable with the following mathematical concepts: basic linear algebra (vectors, matrices, including the notions of determinant and inverses), as well as linear, exponential and logarithmic functions.


Planned learning activities and teaching methods

Next to the ex-cathedra courses focusing on the theoretical approach of the techniques, the students will be asked to apply the techniques following the learning process described below:

  • Preparation at home : go through the script provided for the practical session.
  • Practical session : solve the problems under the supervision of the assistant/student-supervisor.
After the practical session, a detailed correction sheet for all exercises will be made available. 



Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Additional information:





Recommended or required readings

There are no lecture notes but the slides that are used for the lectures will be available on eCampus in advance. The scripts related to the software package R and the statement of the data analyses to be performed (as well as their correction and an explanatory video) will be posted on eCampus.

The following textbook (available on-line through the website of the libraries of ULiège) will be used for most parts of the course (PCA, association measures and principal coordinates analysis, multiple regression and generalized regression):
A.F. Zuur, E.N. Ieno and G.M. Smith, Analysing ecological data, Springer serie (Statistics for Biology and Health)




Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )

Additional information:

The examination consists in the analysis of data using the software package R. The focus will lie on the interpretation of the results and the appropriate use of the techniques but attention will also be paid to the use of the software package R and the understanding of the used methods.

During the exam, the students may either use their own laptop or a computer of the computer room of the Mathematics Department. The exam is an open book examination.




Work placement(s)

Organisational remarks and main changes to the course

The course is organised according to the time slots indicated on Celcat. A detailed planning will be provided through eCampus.


Professeur: Arnout Van Messem

Assistant: Carole Baum


Association of one or more MOOCs