Study Programmes 2015-2016
| SDOC0030-1 | ||
| Multivariate statistics | ||
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Duration :
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| 20h Th | ||
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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 second semester | ||
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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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Course contents :
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| The course is split into two parts: ex-cathedra lectures on some multivariate techniques (12h, see details below) and learning of the statistical software R (8h). The PhD students who already use a statistical software (other than R) in their research are not obliged to attend the R lectures (but the professor will not be able to show how to apply the methods in all the other potential softwares).
The "theory" lectures are devoted to the four following themes of multivariate statistics: Theme 1: mean vectors, covariance matrices, normal distribution and classical hypothesis tests (tests on the mean vectors, homoscedasticity test, normality test) Theme 2: Exploratory multivariate analysis by means of Principal Component Analysis and clustering Theme 3: discrimination Theme 4: Multiple regression and some of its generalizations The techniques are explained without insisting on the mathematical justifications. |
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Learning outcomes of the course :
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| At the end of the course, the PhD students are expected to be able to
- find out if one of the taught methods would be appropriate for analysing a multivariate data set in their own research field. - apply the appropriate technique. - interpret the results of the analyses. The PhD students should also be able to detect situations in which the techniques cannot be applied (due to some violations of the hypotheses like lack of normality or lack of independence). |
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Prerequisite knowledge and skills :
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| The students must have attended a basic course on descriptive and inferential statistics. The concepts of summary statistics, normal distribution and hypothesis tests are considered as known and will be exploited without further explanation.
The methods are presented without emphasizing the mathematical justifications. Nevertheless, the students must have some background in basic linear algebra (vectors, matrices, including the notions of determinant and inverses). |
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Planned learning activities and teaching methods :
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| 12h of ex-cathedra lectures
8h of guided learning in a computer room. |
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Mode of delivery (face-to-face ; distance-learning) :
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| This year, the course will be given face-to-face in one week according to the following calendar:
From Monday 25th of January 2016 to Thursday 28th of January 2016 (time periods: 9h-10h30; 11h-12h30; 13h30-14h30; 15h-16h). In the morning, the lectures will be held in the room 0/33 of the Institute of Mathematics (Building B37, Polytech Quarter) while the 2h-course of the afternoon will take place in the computer room of the same building. |
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Recommended or required readings :
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| There are no lecture notes but the slides used during the lectures will be available and sent by email on the 21st of January to all registered students. | ||
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Assessment methods and criteria :
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| Most students registered for this course of 3rd cycle will consider that the course is part of their PhD training. Depending on the constraints imposed by the corresponding PhD colleges, the following possibilites are offered to the students:
- Simple attendance form (either to the whole course or solely to the ex-cathedra part) - Evaluation based on a personal homework of data analysis (using either R or another statistical software). It is not the plain use of R which will be evaluated but mainly the good application of the techniques and the quality of the interpretation of the results. |
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Work placement(s) :
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Organizational remarks :
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| The course is included in the PhD training folder made by the Administration of Research and Development.
The students who wish to follow the course must register via the Administration of Research and Development. Deadline for registration is 20 January 2016. The PhD students who wish to officially include the course in their PhD training need also to register via MyULg-Doctorat. Some additionnal rermarks on the organisation: 1) An attendance form will be signed each half-day of the course in order to monitor the follow-up and in order to help the professor writte the potential attendance forms. 2) The learning of the statistical software R takes place in the computer room of the mathematics institute in order to offer exactly the same environment to all students. Informations about the downloading and installation of the free software R will be given but the professor will not be able to help solving technical problems on personal computers. 3) If a registered PhD student does not speak French, the course will be taught in English. |
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Contacts :
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| G.HAESBROECK, Institute of mathematics, Building B37, room 0/60, tel: 04/366-95-94, email: G.Haesbroeck@ulg.ac.be | ||