Study Programmes 2016-2017
| STAT0201-3 | ||||||||
| Multivariate statistics | ||||||||
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Duration :
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| 30h Th, 10h Pr, 20h Mon. WS | ||||||||
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Number of credits :
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Lecturer :
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| Gentiane Haesbroeck | ||||||||
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Language(s) of instruction :
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| French language | ||||||||
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Organisation and examination :
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| Teaching in the first semester, examination in June | ||||||||
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Units courses prerequisite and corequisite :
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| Prerequisite or corequisite units are presented within each program | ||||||||
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Learning unit contents :
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| The theoretical course is subdivided as follows:
Part I: - Random vectors, multivariate distributions, mean vector, disperson matrix and correlation matrix - The multivariate normal distribution and its properties - Hotelling T² test for comparing two mean vectors Part II: - Principal component analysis - Clustering - Discriminant analysis |
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Learning outcomes of the learning unit :
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| The student will gain sufficient knowledge to be able to select the appropriate multivariate technique to reduce the dimension of the problem or construct classification rules,... | ||||||||
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Prerequisite knowledge and skills :
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| Probability and inferential statistics courses are required for this course. | ||||||||
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Planned learning activities and teaching methods :
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| Practicals include: - solving theoretical problems in multivariate statistics - using the statistical package R | ||||||||
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Mode of delivery (face-to-face ; distance-learning) :
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| The course is officially scheduled in the first quarter of the academic year, on uneven years (it is not taught in 2016-2017). Depending on the number of enrolled students for that course, lectures will be taught face-to-face (at least 3 students are required) or reading material will be distributed and discussed on a regular basis. | ||||||||
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Recommended or required readings :
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| There are no lecture notes. Textbooks are:
- Multivariate statistical inference and applications, Alvin C. RENCHER. - Applied multivariate statistical analysis, Richard A. Johnson, Dean W. Wichern. |
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Assessment methods and criteria :
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| The final grade is a weighted mean computed on the grades obtained at two exams taking place in January:
1) a written exam on theory and exercises 2) a data analysis to be performed in the computed room on the same day as the written exam. |
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Work placement(s) :
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Organizational remarks :
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| This course is only taught every other year (on uneven years: 2017-2018, 2019-2020). | ||||||||
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Contacts :
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| Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be | ||||||||