Probability and statistical inference

Duration

50h Th, 15h Pr

Number of credits

Lecturer

Cédric Heuchenne

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course is divided into two parts. The first one introduces random quantities on the basis of basic probability concepts and the second one describes statistical inference in a general way. In the first part, we explain how some phenomena can be described by random variables (discrete, continuous, uni- and multivariate) and their standard quantities. Next, usual discrete and continuous laws for random variables and vectors are developed. In the second part, the concept of sample taken from a population is introduced. We focus on the fact that a statistic constructed from the sample is a basis for statistical inference. Its distribution enables to evaluate precision of pointwise estimators, to build confidence intervals and to control error risk when testing hypothesis.

Learning outcomes of the learning unit

This course is a prerequisite for other courses teached later in the program. Indeed, a number of quantitative methods in management is based on concepts introduced in this course. Thus, the student should be able to understand and handle probability and statistics basic concepts.


Quality and Performance Control : The course will allow students to plan and implement the performance and quality control in a company, an organization or a project - using the appropriate analytical tools Adaptability : The course will allow students to adapt their managerial practice to the needs of a fast-evolving world - showing curiosity and a scientific precision of academic level
 
Intended key learning outcomes of the program
Being able to use accounting, mathematical, statistical and IT tools to solve a management problem
Moreover, this course can be considered as a toolbox to further understand and handle other intended key learning outcomes, especially,

• knowing and understanding the basic theories and principles governing the main functions of a firm: marketing, HRM, finance and supply chain management

• knowing and understanding the main theories, their sequence and structure, in terms of political economics, micro and macro economics and industrial economics.

Prerequisite knowledge and skills

Basic probability, Kolmogorov's axioms, conditional probabilities. This content is included in the course: STAT0003-1 Descriptive Statistics.

Planned learning activities and teaching methods

15h exercises.

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

A1. Classes (+ videos support).
A1. Study and comprehension of the course.
A2. Supervised exercises (on site or distance teaching). A teaching assistant supervises students solving exercises individually during programmed sessions. Supplementary exercises are placed at the disposal of students.

Organisational adjustments related to the current health context

Yellow, orange or red codes: distance exam on a platform (Lolaexamens), multiple choices questions.
 
Exam modalities
The exam consists of 4 to 5 pages of multiple-choice questions. Each page corresponds to a general statement with a maximum of 15 items (denoted from a. to o.): 1 to 15 boxes must therefore be checked. Among these 15 items, there are for example 5 groups of 3 check boxes (denoted by a., b., c.). This second notation (to the right of the first) makes it possible to distinguish sub-questions which all have the same form: if, for example, a single numerical value corresponding to 3 boxes is requested, we know that at most one of these 3 boxes should be ticked. You are asked to tick only the boxes corresponding to propositions which are always true.
Regarding the points allocation system, if a page for which the maximum number of points is 5, contains 15 items, if exactly 5 boxes are correct and if you tick them without any other, you get all the points for the page, i.e. 5 points. For any wrong box checked, you lose 0.5 point. The total for the page cannot be below 0. Likewise, if exactly 6 boxes are correct, ticking them without any other leads to the maximum number of points, i.e. 5 points. There are in this case, 9 boxes which should not be checked; you lose 100/9 = 11.11% of the points, or 0.556 points out of 5, for any wrong box checked. Again, it is not possible to go under 0.
You can change your answers and navigate anywhere in the exam during the exam time. We remind you that cheating is unnecessary: there are at least 300^4 questionnaires which are too different; comparing your answers is useless. Trying to collaborate would only waste precious time without leading you to good answers (the differences between two questions (from two different questionnaires) are sometimes very small but often completely change the answer).

Recommended or required readings

Learning material (for the exam): syllabus, slides used during the theoretical lectures, videos and exercises lists.

Advised books (optional):
-Droesbeke J-J. (1997), Eléments de Statistique, 3e édition, Editions de l'Université de Bruxelles/Editions Ellipses.
-Lecoutre J-P. (1998), Statistique et Probabilités, Dunod.
-Saporta G. (1990), Probabilités, Analyse des Données et Statistique, Editions Technip.

Assessment methods and criteria

Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.

Written exams in the first and second sessions (about the whole course -50% of the final mark about theoretical concepts and 50% exercices-).
Student evaluation is strictly individual.

Work placement(s)

Organizational remarks

Teaching language: French

Contacts

Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, N1, local 309, email: C.Heuchenne@ulg.ac.be

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