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

Theoretical courses (using videos) including problems to be solved by the students

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

Blended learning


Additional information:

A1. Classes (+ videos support).

A1. Study and comprehension of the course.

A2. Supervised exercises (on site or distance teaching). Supplementary exercises are placed at the disposal of students.

Recommended or required readings

Learning material (for the exam):
syllabus, 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.

Exam(s) in session

Any session

- Remote

written exam ( multiple-choice questionnaire )


Additional information:

Written exams in the first and second sessions (theoretical concepts and their applications in particular through exercises will be mainly assessed-).

Student evaluation is strictly individual.

Work placement(s)

Organisational remarks and main changes to the course

Teaching language: French

Contacts

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

Association of one or more MOOCs

Items online