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STAT-S401

Analyse statistique multivariée

academic year
2023-2024
2024-2025

Course teacher(s)

Catherine DEHON (Coordinator)

ECTS credits

5

Language(s) of instruction

french

Course content

  • Background mathematics

  • Principal components analysis (PCA)

  • Robust statistics and detection of outliers

  • Correspondence analysis

  • Multiple correspondence analysis

  • Canonical correlation analysis

  • Discriminant analysis

Objectives (and/or specific learning outcomes)

At the end of the course, the students will be able to :

  • Describe information contained in large datasets

  • Understand mechanisms under multivariate statistical methods

  • Use in practice multivariate statistical software

  • To solve questions using real datasets

Teaching methods and learning activities

  • Theory : 24h ex-cathedra class

  • Exercises: 12h in computer room

Contribution to the teaching profile

The course contributes to the development of the following skills (Business Engineering) :

  • Critically analyse situations based on a scientific managerial approach to develop innovative ideas.

  • Devise strategies by developing innovative approaches and practical solutions to drive progress.

The course contributes to the development of the following skills (Economics) :

  • Use data mining and management techniques as well as financial modeling to develop decision, evaluation or management tools

  • Solve complex problems arising in economic, financial and public policy contexts to transfer knowledge in realistic solutions to operationalize solutions.

  • Provide economic and financial recommendations and analyses at each stage of the process to keep stakeholders fully informed.

References, bibliography, and recommended reading

  • Johnson, R. A., Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, Prentice Hall, New-york.

  • Hardle, W., Simar, L. (2000), Applied Multivariate Statistical Analysis, Springer, Berlin.

Other information

Contacts

cdehon@ulb.ac.be

Evaluation

Method(s) of evaluation

  • Other

Other

  • Written exam: 13 points on theoretical and practical questions

  • Compulsory project in group (from 2 to 5 students) on real dataset with presentation: 7 points

Mark calculation method (including weighting of intermediary marks)

  • Written exam: 13 points on theoretical and practical questions

  • Compulsory project in group (from 2 to 5 students) on real dataset with presentation: 7 points

Language(s) of evaluation

  • english

Programmes