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| MQGE0005-2 | Quantitative Methods in Management
- Partim Operations Research
- Partim Statistics
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| Duration : | Partim Operations Research : 12h Th
Partim Statistics : 12h Th
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| Credits/ECTS : |
| Master in Management Sciences, in-depth approach, 1st year | | Premier quadrimestre | | 3 |
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| Master in Management Sciences, didactic approach, 1st year | | Premier quadrimestre | | 3 |
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| Master in Management Sciences, professional Focus in Entrepreneurship, 1st year | | Premier quadrimestre | | 3 |
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| Master in Management Sciences, professional Focus, 1st year | | Premier quadrimestre | | 3 |
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| Master in Management Sciences, professional Focus (Full english), 1st year | | Premier quadrimestre | | 3 |
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| Master en sciences de gestion, à finalité spécialisée en management, 1st year | | Premier quadrimestre | | 3 |
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| Master in Management Sciences | | Premier quadrimestre | | 3 |
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| Master in Management Sciences (Full english) | | Premier quadrimestre | | 3 |
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| Holder(s) : | Partim Operations Research : Yasemin Arda
Partim Statistics : Cédric Heuchenne
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| Language : | Langue anglaise |
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| Course contents : |
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| Operations research (OR) is a discipline that aims to solve complex real world decision problems using scientific approaches. Application areas of this discipline are various: transportation, production systems, telecommunication, administration, etc. The course gives an introduction to the most popular mathematical models and methods of operations research: linear programming, network models (minimal spanning tree problems and shortest path problems), project scheduling. |
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| In this course, the methods studied in basic statistical courses are adapted to analyzing useful applied issues in Economics and Management (comprehension of a situation and its evolution, support for decision-making...).
Covered contents will be first variance analysis (comparison of several averages) and inter-variable relation modelling (linear models). Next, we will develop some nonparametric tests (goodness-of-fit and independence). Finally, students will be introduced to the maximum likelihood estimation method and some basic concepts in time series and multivariate analysis. |
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| Course objective : |
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| C1. Acquire a basic knowledge about the mathematical models of real world decision problems and the fundamental methods of operations research.
P2. To be able to solve and interpret correctly the solutions of simple OR problems.
P3. To be able to recognize the situations where OR techniques can be used as decision making tools and to interpret correctly the conclusions which can be derived using these techniques. |
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| C1. To acquire an overview of statistical problems met in the fields of Economics and Management.
P2. To be able to solve and interpret solutions of practical simple problems related to the theoretical part of the course.
P3. To be able to recognize situations where studied methods can be applied and what are their limitations in such particular situations. |
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| Prerequisites : |
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| Basic notions of mathematics and statistics |
| | Partim Statistics |
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| Probability and statistical inference STAT0067-1 |
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| Workshops : |
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| Organization : |
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| Methodology used:
A1. Lectures
A1. Readings
A1. Discussions
A1. Software presentations
General planning of the course:
Semester: First semester
Number of lectures: 6 lectures of 2 hours
Start date: 12/11/2008
Finish Date: 17/12/2008 |
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| Used methodology
A1. Ex-cathedra classes: theoretical introduction and applications (quick overview of lessons of previous years, presentation of various methods, interpretation of their solutions, examples)
A1. Study and comprehension of the course material
A2. Supervised software applications: the professor presents the software to the students during the teaching sessions, and gives them exercises. Each student is expected to solve those exercises, aside from the teaching sessions (with the possible help from the professor).
A3. Supervised real data analysis: the teacher submits to the students a particular problem, whose analysis requires the use of the procedures studied in the course. A small work, suggesting some possible solutions to the problem, has to be handed in by each group of students.
Overview of the course agenda
The course starts on 17/11/2008 and ends on 15/12/2008. The statistical part of the course consists of 6 two hours lessons. The real data analysis is presented during the last class, and the corresponding student work has to be handed in for the week preceding the beginning of the evaluations.
Decomposition of the student workload
A1 Ex-cathedra course (9h)
A2 Study (12h)
A2 Software applications (3h)
A3 Real data analysis (10h)
Exam (2h) |
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| Written notes : |
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| Documents that can be found on the virtual campus Lol@:
1. Syllabus
2. PowerPoint slides used during the lectures
3. Exercises |
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| The syllabus, the slides of the course and the statements of exercises and real data analysis will be placed at the disposal of students (also online on lol@).
Advised books:
Course materials: References: Wonnacott R.J. and Wonnacott T.H. (1990), Introductory Statistics for Business and Economics, New York, John Wiley & Sons (ISBN : 047161517X)
Simar, L. (2003), Statistique en Economie et Gestion, manuscript 248 pages, Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve |
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| Assessment : |
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| E1/E2/E3. Written exam |
| | Partim Statistics |
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| E3. Real data analysis work, to be handed in for the week preceding the evaluations. This work adds or takes away a maximum of 2 points to the final grade.
E1/E2/E3. Final written exam (during the weeks dedicated to the evaluations), covering the complete course material (50% dedicated to theory, 30% to applications and 20% to questions concerning the real data analysis). |
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| Contacts : |
| | Partim Operations Research |
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| Yasemin ARDA
Building B31, Room 2.50
yasemin.arda@ulg.ac.be
Phone: +32 (0)4 366 31 97 |
| | Partim Statistics |
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| Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, B31, local 2.53, email: C.Heuchenne@ulg.ac.be |
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