STAT1751-1 : Statistical analysis for conservation biologists

Study Programmes 2015-2016

STAT1751-1

Statistical analysis for conservation biologists

Duration :

10h Th, 10h Pr

Number of credits :

Master in biology of organisms and ecology (120 ECTS)		2
Master in biology of organisms and ecology (120 ECTS)		2

Lecturer :

Gentiane Haesbroeck

Language(s) of instruction :

French language

Organisation and examination :

Teaching in the first semester, review in January

Units courses prerequisite and corequisite :

Prerequisite or corequisite units are presented within each program

Course contents :

The course is a general introduction to the methods used in multivariate statistics, i.e. when one studies several variables simultaneously. The course entails the following chapters:
- Graphical display of multivariate data
- Mean vector and dispersion matrix
- Principal component analysis
- Discriminant analysis
- Multiple regression and correlation

Learning outcomes of the course :

The methods of multivariate data analysis are taught based on a pragmatic approach. At the end of the course, the sudent should be capable of
- defining a multivariate problem,
- analysing the data,
- interpreting the results.
He/she should also be aware of the limitations of 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.
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 within two types of activities:
- Guided learning (by the professor and an assistant) in a computer room in order to reproduce, with the software R, the data analyses illustrated in theory;
- Personal homework (data analyses to perfom by oneself; corrections are provided later on)

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

The course counts 20 hours of face-to-face teaching. Each lecture lasts 2h30 and is decomposed into two parts: theory (ex-cathedra) and a practical in a computer room. The data analyses that cannot be finished during the practical have to be completed at home by the student.