Aims

- to develop some control of the most classical multivariate tools by showing the similarities and the differences between these approaches
- to introduce the minimum of mathematical elements required to complete the students formation

Main themes

ACP, factor analysis, multiple linear regressions, cluster analysis, structural equations models

Content and teaching methods

- to develop some control of the most classical multivariate tools by showing the similarities and the differences between these approaches
- to introduce the minimum of mathematical elements required to complete the students formation
At the end of the course the students should be able to implement with actual data the most classical tools to treat multidimensional data. The course will thus be centred on a good understanding of the methods and of their use, including the control of an appropriate software.

Content : introduction to multivariate methods, bitmap notations and basic properties of stochastic vectors, multiple and descriptive tools, classification, regression models, including ANOVA and ANCOVA, discriminant analysis.

Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

The teaching includes practical work with treatment of actual data.

Other credits in programs