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Analyse statistique multivariée
Course teacher(s)
Catherine DEHON (Coordinator)ECTS credits
5
Language(s) of instruction
french
Course content
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Background mathematics
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Principal components analysis (PCA)
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Robust statistics and detection of outliers
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Correspondence analysis
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Multiple correspondence analysis
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Canonical correlation analysis
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Discriminant analysis
Objectives (and/or specific learning outcomes)
At the end of the course, the students will be able to :
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Describe information contained in large datasets
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Understand mechanisms under multivariate statistical methods
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Use in practice multivariate statistical software
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To solve questions using real datasets
Teaching methods and learning activities
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Theory : 24h ex-cathedra class
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Exercises: 12h in computer room
Contribution to the teaching profile
The course contributes to the development of the following skills (Business Engineering) :
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Critically analyse situations based on a scientific managerial approach to develop innovative ideas.
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Devise strategies by developing innovative approaches and practical solutions to drive progress.
The course contributes to the development of the following skills (Economics) :
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Use data mining and management techniques as well as financial modeling to develop decision, evaluation or management tools
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Solve complex problems arising in economic, financial and public policy contexts to transfer knowledge in realistic solutions to operationalize solutions.
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Provide economic and financial recommendations and analyses at each stage of the process to keep stakeholders fully informed.
References, bibliography, and recommended reading
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Johnson, R. A., Wichern, D. W. (2002), Applied Multivariate Statistical Analysis, Prentice Hall, New-york.
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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
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Written exam: 13 points on theoretical and practical questions
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Compulsory project in group (from 2 to 5 students) on real dataset with presentation: 7 points
Mark calculation method (including weighting of intermediary marks)
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Written exam: 13 points on theoretical and practical questions
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Compulsory project in group (from 2 to 5 students) on real dataset with presentation: 7 points
Language(s) of evaluation
- english