Duration
35h Th, 25h Pr
Number of credits
| Bachelor in economics and business management | 5 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
All year long, with partial in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
1st part : Probability Theory
- Random variables and probability distribution
- Typical probability distributions (discrete and continuous)
- Multivariate r.v.
- Functions of r.v.
2nd part : Statistical inference
- Principles of inferential statistic: object, variables, observations, population and sample, sampling and sampling distribution
- Point estimation (estimators : properties and construction)
- Confidence interval estimation
- Statistical tests (principle and power, conformity, independence, several samples and populations)
- Use of R software for hypotheses testing
Learning outcomes of the learning unit
This course aims to:
- enable the student to understand the calculation of probabilities and to model random phenomena
- provide the necessary probabilistic bases for inferential statistics
- master the basic principles and methods of inferential statistics (estimating and testing hypotheses) and to know how to apply these in concrete contexts and to interpret the results.
- The course will allow the student to show critical thinking and scientific rigor in the analysis of a complex situation.
- The course will encourage the student to be autonomous and an entrepreneur in his learning.
Prerequisite knowledge and skills
- Probability theory
- Basic algebra: order of operations, fractions, distributive property, use of parentheses, remarkable products, use of summation symbol
- Function of variables: knowing how to graph linear functions, use of the correct vocabulary (abscissa, ordinate, sloped,...)
- Properties of the exponential and logarithmic functions
- Descriptive statistics: among others, definitions and properties of mean, variance, Chebychev's theorem; for bivariate series, definitions and properties of covariance, linear correlation; the principle of variance decomposition
- Elements of differential and integral calculus: knowing how to integrate polynomials, knowing how to perform partial derivatives (finding the maximum of a function with several variables)
- Basic operations in R
Planned learning activities and teaching methods
Face-to-face sessions alternate between the instructor presenting theoretical concepts and students working on examples, exercises, and applications. In order to respond effectively to questions, the instructor, teaching assistant, and student monitors will circulate among the students during the work sessions. The instructor expects students to participate actively during the sessions, for example through the use of Wooclap or other surveys during the session.
E-learning on the statistical tools of the R software is organized.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Face-to-face course
Additional information:
Ex-cathedra sessions where the presentation of theoretical concepts and the resolution of exercises by the students will be mixed.
Course materials and recommended or required readings
Course notes, syllabus with additional exercises and slides per chapter available on Lola.
References used to write down the notes
[1] Catherine Dehon, Jean-Jacques Droesbeke, and Catherine Vermandele. Eléments
de statistique : 6e édition revue et augmentée. Editions de l'Université de
Bruxelles/Editions Ellipses, 2015.
[2] Gentiane Haesbroeck. Probabilité et statistique I, 2007. Course notes at Sciences
Faculty, University of Liege.
[3] Bernard Lejeune. Probabilités et inférence statistique, 2005. Course notes at HEC-
Liege.
[4] Brigitte Tribout. Statistique pour économistes et gestionnaires. Pearson Education
France, 2013.
[5]Newbold, P., Carlson, W. L., & Thorne, B. M. (2013). Statistics for business and economics (Global ed. ed.). Harlow: Pearson.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Further information:
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Further information:
The first session consists of a partial examination in January for the "Probability" part and in June for the "Statistical Inference" part.
1st and 2nd session: individual written exam with open-ended questions and closed course, covering both the "Probability" and "Statistical Inference" sections. A non-graphic calculator is permitted. The form and tables of distributions will be provided at the time of the exam.
In addition to the written exam, in June, a practical exam on the R software will be organized during the session in the HEC computer rooms.
If the marks for the two written exams (probability and statistical inference) are strictly above 7/20, the overall mark will be equal to the weighted average of the partial marks (40% for probability, 50% for statistical inference and 10% for software). Otherwise, it will be equal to the lowest of the partial marks.
In the event of a second session, a partial exemption will automatically be granted for the part passed (at least 10/20). If the course is not passed overall in the second session, no partial exemption will be granted for the following academic year.
Work placement(s)
Organisational remarks and main changes to the course
Where conditions allow, course sessions are recorded via the podcast system and made available to students online. No commitment is made as to the availability of videos of all courses.
Contacts
Professor
Célia Paquay
HEC- Management School of the University of Liege (building N1)
e-mail : cpaquay@uliege.be
Office: N1 - 308
Teaching Assistant
Emeline Leloup
HEC- Management School of the University of Liege (building N1)
email: emeline.leloup@uliege.be
Office: N1 - 310