Probability and statistical inference

Duration

35h Th, 30h Pr

Number of credits

Lecturer

Célia Paquay

Language(s) of instruction

French language

Organisation and examination

All year long, with partial in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

1st part : Probability Theory
- Bases: Random situations, events and probability - Conditioning, probability trees and independance - 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)

Learning outcomes of the learning unit

- Allow to understand probability calculus and to modelize random situations - Provide probabilistic basics useful for statistical inference and operational research  - Allow to use principles and basic methods of statistical inference (estimation and tests)
In a general way, this course will allow to reach the following learning objectives :
- Strategy : The course will allow students to demonstrate scientific precision and a critical mind in the analysis of a complex situation. - Implementation : The course will train the student to capitalize on the characteristics of a more and more digitalized world when confronted with a complex situation. - Adaptability : The course will encourage students to be creative, self sufficient and full of entrepreneurial spirit in their studies as well as in their professional life.

Prerequisite knowledge and skills

- Descriptive statistics
- Elements of differential and integral calculus

Planned learning activities and teaching methods

The sessions are mainly based on the presentation of the theoretical frame by the teacher and the practice of exercices and applications by the students. The teacher expects the students to actively participate during the sessions, for instance via Wooclap or other surveys.
Some sessions will be dedicated to practical exercises.
The teacher uses Excel and Geogebra to provide illustrations of several concepts of the course. Students are invited to learn to use those softwares, but  this learning is not assessed.

Mode of delivery (face to face, distance learning, hybrid learning)

Ex-cathedra sessions where the presentation of theoretical concepts and the resolution of exercises by the students will be mixed. Some sessions will be particularly dedicated to exercises in order to allow students to face the subject and ask their questions to the teacher.

Recommended or required readings

Preliminary notes 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.

Assessment methods and criteria

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )


Additional information:

1st and 2nd session : individual written closed book exam with open questions for the two parts "Probability" and "Inferential Statistics". A scientific (non graphical) calculator is alload. The form and distribution tables will be provided during the exam.
If both grades are (strictly) greater than 7/20, the global grade will be the weighted average of the partial grades (40% for probability and 60% for inferential statistics). Otherwise, it will be the smallest of the values.
In case of a resit, an exemption will be given for the possible passed part (at least 10/20). In case of a resit failure, no partial exemption will be given for next year.  
In case of an orange or red code which would prevent the students to be on campus, the exam would remain a written exam, but online. It would be an individual written exam with open questions to hand in within three hours via eCampus. Considering that it wouldn't be possible to check the access of the students to their note, the questions would be adapted.

Work placement(s)

Organizational remarks

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