Multivariate Statistical Analysis
[INGE1222]
[30h + 15h] 4 credits
This course is taught in the 2d semester.
Teacher(s): Johan Segers
Language: French
Level: First
Aims
Main themes
In this part, students are taught how matrix notation facilitates treatment of multidimensional data and basic properties of random vectors. They will also learn that the basic (uni-and bivariate) descriptive tools have both their uses and limitations.
Part 2: Techniques of multivariate data analysis.
In this part, students learn about basic dimension reduction techniques for continuous and qualitative variables (principal components, correspondence analysis). Basic classification techniques are also presented. A wide range of examples is given to illustrate these methods and show when they should be used.
Part 3: Multivariate analysis models.
In this part, students see how to model inter-variable relations: linear models (including variance and variance-covariance analysis) which make it possible to use explanatory variables to explain response variable variation. Models adapted to categorical response variable are also introduced, log-linear models for contingency tables, the logit model and discrimination analysis models. Here too, a wide range of examples is given to illustrate these methods and show when they should be used.
Content and teaching methods
Course content:
Introduction to multivariate methods, matrix notations and basic properties of random vectors, basic descriptive tools, principal component analysis, simple and multiple correspondence analysis, classification, regression models, including ANOVA and ANCOVA, categorical variable models, discrimination analysis.
Method:
The course comprises:
- lectures (the teacher introduces concepts on the basis of concrete applications and abstracts from this),
- computer-based practical exercise sessions, using software to analyse authentic data
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)