Number of credits
|Master in business engineering (120 ECTS)||5 crédits|
|Master in business engineering (120 ECTS) (Industrial Business Engineering)||5 crédits|
Language(s) of instruction
Organisation and examination
Teaching in the first semester, review in January
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
In this course, the methods studied in basic statistical courses are adapted to analyzing useful applied issues in Economics and Management: variance analysis (comparison of several averages); inter-variable relation modelling (linear models); nonparametric tests. Students will also be introduced, through simple examples, to the maximum likelihood estimation method, which is particularly useful in more complex models used in Econometrics. Finally, multivariate analysis and statistical process control will be introduced, topics especially interesting for students which are oriented to quantitative methods and supply chain management.
Learning outcomes of the learning unit
The aim of this course is to give an overview of statistical problems met in the fields of Economics and Management. By the end of the course, students should be able to understand and solve those problems in practice (by example, explaining variances, modelling relations between variables or using most important statistical tools to manage industrial processes). This course is also a prerequisite for quantitative courses which students will take later in their programme. More precisely, from its different teaching methods, this course contributes to the following Intended Learning Outcomes addressed by the program of master in Business Engineering :
- Strengthening knowledge and understanding of basic management disciplines in order to use them to perform a rigorous analysis of a management situation and provide pertinent solutions
- Capacity to research autonomously and methodically the information needed to solve a complex, transversal management problem, to perform a rigorous analysis of it and to suggest pertinent solutions
- Understanding and being capable of using modelization methods when seeking a solution for a concrete management problem
- Developing a critical sense (arguing)
- Developing a transversal, global vision
- Professional capacity for written communication
Prerequisite knowledge and skills
Basic course in probability (cumulative distribution function, density, distribution, mean, variance, usual discrete and continuous univariate laws, multivariate normal) and statistical inference (estimation , confidence intervals, hypothesis tests). Equivalent to the content of the course: Probability and statistical inference STAT1208-1
Planned learning activities and teaching methods
Mode of delivery (face to face, distance learning, hybrid learning)
A1. 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 (possibly distance) software applications: the professor presents the software to the students during the teaching sessions, and gives exercises (related to the project -see hereunder-) to them. Each student is expected to solve those exercises, aside from the teaching sessions (with the possible help from the professor).
A5. Supervised real data analysis project: the professor submits to the students a real-life problem, for which several questions need to be solved. Thanks to A1 and A2 above-mentioned, and under the supervision of the professor, each group of students gives solutions to the problem. The teacher interacts with the students during meetings, and comments each obtained result, as well as each used method. He encourages the students to choose and correctly assess their procedures, to understand the limitations of those, as well as to interpret, discuss and detail their results. The overall aim of the process is for the students to be able to propose final solutions based on a firm and accurate argumentation.
A4. Redaction of a project report: critical synthesis, relevant analysis and adequate presentation of the results are required.
Recommended or required readings
The videos (sofware and theoretical concepts), the reference books improving comprehension (protected by authors rights) and the statements of exercises (including documents to help using software) and project will be placed at the disposal of students (see the campus lola). Videos, defined reference books parts, exercises on software and solution of the project correspond to the material of the exam.
James G., Witten D., Hastie T. and Tibshirani R. (2013), An Introduction to Statistical Learning with Applications in R, Springer.
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.
Assessment methods and criteria
Exam(s) in session
Written work / report
The evaluation is divided into three parts: the class performance (10% about participation of students -presentation, questions-), the computational project treating a "real life" problem with methods displayed during the theoretical lectures (40%, project achieved by groups, common evaluation) and a written exam during January first session about the whole course (50%, individual evaluation).
Relative weighting of individual assessment : 60%
Teaching language: English
Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, N1, local 309, C.Heuchenne@ulg.ac.be
videos, documents to help using software, exercises on software