Study programme 2019-2020Français
Probability and Statistics
Programme component of Bachelor's in Engineering à la Faculty of Engineering

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
Teacher(s)
UI-B2-IRCIVI-003-MCompulsory UEGILLIS NicolasF151 - Mathématique et Recherche opérationnelle
  • DAYE Pierre
  • GILLIS Nicolas

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français242400044.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-005Probability1212000Q250.00%
I-MARO-007Statistics1212000Q250.00%
Programme component
Prérequis
Prérequis

Objectives of Programme's Learning Outcomes

  • Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
    • Identify, describe and explain basic scientific and mathematical principles
    • Identify, describe and explain the basic principles of engineering particularly in their specialising field
    • Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines

Learning Outcomes of UE

- Understand and explain the basic concepts of probability and some important results (law of large numbers, Central Limit Theorem);- Solve simple applications implementing these concepts and results in a number of concrete situations (gambling, election polls, queues, fault diagnosis, ...); - Construct an interval containing almost certainly the value of a parameter of the distribution of a variable al- Testing the assumptions made on the value of parameters associated with random variables, and other assumptions such as independence, fit and homogénéit- Establish a linear regression model to an explanatory variable.

Content of UE

notions of probability: random variables and distribution; classical distributions; vectors al independence, correlation, normal distribution; laws of large numbers; central limit theorem, characteristic function, approximation to the binomial distribution by the normal distribution and the Poisson distribution; modeling real situations by probability (eg, queues, Google PageRank).

Notions of statistics: basic descriptive statistics in one and two dimensions (sample frequency, graphics); correlation; regression; independence; estimate average intervals, variance, proportion; regression coefficient; maximum likelihood method; hypothesis testing (population mean, proportion, variance, comparison of means and variances, fit, independence); linear regression model (in one variable).

Prior Experience

Calculus. 

Type of Assessment for UE in Q2

  • Written examination

Q2 UE Assessment Comments

50% probability, 50% statistics 

Type of Assessment for UE in Q3

  • Written examination

Q3 UE Assessment Comments

50% probability, 50% statistics 

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-005
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Démonstrations
I-MARO-007
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Démonstrations

Mode of delivery

AAMode of delivery
I-MARO-005
  • Face to face
I-MARO-007
  • Face to face

Required Reading

AARequired Reading
I-MARO-005Note de cours - Note de cours de Probabilités - Nicolas Gillis et Marc Pirlot
I-MARO-007Note de cours - Statistiques - Nicolas Gillis et Marc Pirlot

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-005Not applicable
I-MARO-007Not applicable

Recommended Reading

AARecommended Reading
I-MARO-005Notes d'exercices - Exercices - Jacques Teghem
I-MARO-007

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-005Sans objet
I-MARO-007Sans objet

Other Recommended Reading

AAOther Recommended Reading
I-MARO-005Not applicable
I-MARO-007Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
I-MARO-005Authorized
I-MARO-007Authorized
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 13/07/2020
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be