Duration
10h Th, 10h Pr
Number of credits
| Bachelor in biology | 3 crédits | |||
| Bachelor in geography : general | 2 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 is a general introduction to the most often used methods in multivariate statistics (i.e. when one studies several variables simultaneously) in biology. The course entails the following chapters:
- 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 capable of
- defining a multivariate problem,
- understanding the working of the methods,
- analysing the data,
- interpreting the results.
The student should also be aware of the limitations of the application of the methods.
Prerequisite knowledge and skills
The students must have attended a basic course on descriptive and inferential statistics. The concepts of normal distribution, confidence interval and hypothesis tests are considered as known. Moreover, basic knowledge of the software R is expected.
The methods are presented without emphasizing the mathematical justifications. Nevertheless, the students must have the following background in mathematics: basic linear algebra (vectors, matrices, including the notions of determinant and inverses), linear, exponential and logarithmic functions.
Planned learning activities and teaching methods
Together with the ex-cathedra courses focusing on a theoretical approach, the students will be asked to apply the techniques following the learning process describred below:
- Personal preparation at home in order to get familiar with the script constructed by the professor and his assistants;
- Discussion on the script and the interpretation of the results
- Group discussion on data analyses
Mode of delivery (face to face, distance learning, hybrid learning)
The course counts 20 hours of face-to-face teaching, 10 of which are devoted to ex-cathedra lectures for the theory. During the 10 hours of practicals, the students will first be invited to ask all the questions they have on the scripts and the intepretation of the results. Then, they will discuss in small groups in order to analyse some data. A brief correction will be detailed at the end of the practical, before being put on line.
Recommended or required readings
There are no lecture notes but the slides that will be used for the lectures will be available on eCampus in advance. Also, the scripts related to the software package R and the statement of the data analyses to be performed (and their corrections) will be displayed on line.
The following textbook (available on-line from the web site 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 et G.M. Smith, Analysing ecological data, Springer serie (statistics for biology and health)
Assessment methods and criteria
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Additional information:
The examination consists in the analysis of some data with the software R. The focus in the marking will be on the interpretation of the results and the appropriate use of the techniques but some attention will also be given 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.
Work placement(s)
Organizational remarks
The course is organised on the time slots indicated on Celcat. Two groups will be constructed for the practical sessions. The students whose group has an available laptop will have the practical session in a clasic classroom while the others will be invited to work in the computer room of the Maths Departement.
Contacts
Professeur: Arnout Van Messem
Assistant: Carole Baum, Jimmy Keydener