Statistics and Probability

mqant1113  2022-2023  Mons

Statistics and Probability
6.00 credits
45.0 h + 20.0 h
Q2
Teacher(s)
Vrins Frédéric;
Language
French
Main themes
  • One-dimensional descriptive statistics: graphical representations, central tendency, dispersion.
  • Two-dimensional descriptive statistics: joint distribution, covariance, linear correlation, linear regression, non-linear fits.
  • Algebra of events and combinatorial analysis.
  • Basic rules of probability calculation: probability axioms, conditional probabilities, Bayes formula, decision trees.
  • Discrete and continuous random variables: density function, distribution function, mathematical expectation, variance.
  • Studies of the main probability distributions: Bernoulli, binomial, Poisson, uniform, normal.
  • Law of large numbers, central limit theorem, sampling.
Learning outcomes

At the end of this learning unit, the student is able to :

1 Given the « competencies referential » linked to the LSM Bachelor in Management and Business Engineering, this course mainly develops the following competencies:
  • 1.1. Demonstrate the ability to reason independently and adopt a considered and critical approach to knowledge (academic and common sense).
  • 2.3. Acquire a knowledge base in quantitative, IT and digital methods.
  • 3.2. Apply clear and structured analytical reasoning, conceptual frameworks and science-based models to describe and analyse a simple but concrete problem and offer a solution.
  • 3.4. Analyse and interpret results or proposals, and provide a well-argued critique, for a simple but concrete management problem.
At the end of the class, the student will be able to:
  • represent a random experiment using the probabilistic model.
  • demonstrate the basic properties associated with the concepts of probability, expectation, variance, covariance, ...
  • assess the probability of an event occurring in a simple random experiment.
  • calculate a series of indicators related to one or more random variables (expectation, variance, probability distribution, covariance, correlation).
  • apply the central limit theorem to estimate a probability, confidence interval, maximum margin of error, or minimum sample size.
 
Bibliography
  • Slides, syllabus et classeurs Excel
  • TRIBOUT B (2013). Statistique pour economistes et gestionnaires, 2eme ed, Pearson
  • WONNACOTT R., WONNACOTT R. (1995), Statistique, Economica, traduction de WONNACOTT R., WONNACOTT R. (1990) Introductory Statistics for Business and Economics, 4th ed., John Wiley & Sons.
Teaching materials
  • Slides, syllabus et classeurs Excel
Faculty or entity
CLSM


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Bachelor : Business Engineering
INGM1BA
6
Bachelor in Management
GESM1BA
6