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IESEG School of Management ( IÉSEG )

Code Cours :



Niveau Année de formation Période Langue d'enseignement 
Professeur(s) responsable(s)M.Verschelde
Intervenant(s)M.Verschelde, Thomas BAUDIN

Pré requis

- Applying and analyzing linear regression models.
- Evaluating the assumptions of regression analysis and knowing what to do if the assumptions are violated.
- Creating a linear equation of a line using real data
- Explaining the meaning of the regression coefficients
- Making inferences about the slope and correlation coefficient

Objectifs du cours

At the end of the course, the student should be able to :
- Understand, identify and avoid common pitfalls when running a Linear Regression
- Recognize that we cannot use Linear Regression when the dependent variable is a dummy
- Comprehend, apply and interpret discrete choice models
- Compare the quality of different discrete choice models
- Develop and defend a project that concerns a quantitative analysis from scratch
- Evaluate the value of an existing analysis that concerns a quantitative analysis

Contenu du cours

1. Introduction: the art of econometrics
2. Beyond OLS: The linear probability model and likelihood maximization
3. The Logit model
4. Model fit and various tests

Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Independent work
Reference manual 's readings6,00  
Independent study
Group Project20,00  
Individual Project10,00  
Face to face
Interactive class6,00  
Charge de travail globale de l'étudiant50,00  

Méthodes pédagogiques

  • Tutorial
  • Presentation
  • Research
  • Project work
  • Interactive class
  • Case study
  • Coaching


• A classical final exam
• Involvement in-class and in the final project
• The project results
Respectively 30%, 35% and 35% of the final mark

Type de ContrôleDuréeNombrePondération
Continuous assessment
Final Exam
Written exam2,00130,00
Group Project0,00135,00
TOTAL     100,00

* Informations non contractuelles et pouvant être soumises à modification
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