2023-2024 / MATH0067-1

Introduction to statistics and probability


20h Th, 25h Pr, 15h Proj.

Number of credits

 Bachelor of Science (BSc) in Architectural Engineering2 crédits 


Vincent Denoël

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course covers superficially four aspects of statistics and probabilities: - data collection (survey, inventory, experiment) - descriptive statistics (1-D and 2-D, including linear regression) - elementary probability distributions - inductive statistics (sampling distributions and hypotheses testing)

Learning outcomes of the learning unit

Data Collection - understand the difference between survey and experimentation, as well as their specificities - use properly the vocabulary - set up formally an inventory, a survey, an experiment
Descriptives Statiststics - know the different indicators of position and distribution - understand the notions of correlation and stochastic independence - choose adequately the tools for a graphical representation and apply the concepts with Matlab - summarize data to a set of indicators - use (least-squares) linear regression and nonlinear regression
Probability - classification of probability distributions - importance of the normal distribution and the central limit theorem - probabilistic modeling with random variables - algebraic operations and other transformations of random variables
Inferential Statistics - understand consequences of the choice of a sampling strategy - formalize and test hypotheses

Prerequisite knowledge and skills

- Calculus

Planned learning activities and teaching methods

The course target the different job opportunities that an architect-engineer may face: - Design office (descriptive statistics, data interpretation,...) - Administration (statistics, survey, inventories,...) - Research or PhD thesis (experimentation, hypothesis testing,...)
The lectures are a mix between ex cathedra theory and exercises.
The course also comporises a personal project to develop.

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

Lectures take place on Tuesday morning, 8:30 till 12:30. Attendance is compulsory

Recommended or required readings

Pierre Dagnelie, Statistique théorique et appliquée, de Boeck, 2nd édition, 1998.

A written examination concerning theoretical as well as pratical aspects.  The final mark is obtained as an arithmetic mean of the marks obtained for each question. The final mark is unique and globalized for both theory and exercises.
Additional tests are organized during the year and allow to obtain bonus points (the test is not penalizing in case it is missed or failed).
Organisation de l'examen de janvier 2021:
si « Évaluation en présentiel possible » : Examen écrit avec questions à réponses ouvertes longues
si « Évaluation à distance imposée par la situation sanitaire » : Examen écrit à distance de type questions à réponses ouvertes longues

Work placement(s)

Organisational remarks and main changes to the course


Vincent Denoël 04/366.29.30 v.denoel@ulg.ac.be

Association of one or more MOOCs

Items online

Lecture slides
click here to download (open access)