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Language of instruction
English
Teaching content
QUANTITATIVE METHODS
This course occurs in the following program(s)
IESEG Degree - Programme Grande École
- Crédits ECTS: 2.00
Training officer(s)
M.Verschelde
Stakeholder(s)
Balazs KOTOZS
Wanda MIMRA
Uyanga TURMUNKH
Marijn VERSCHELDE
Wanda MIMRA
Uyanga TURMUNKH
Marijn VERSCHELDE
Présentation
Prerequisite
- 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
- 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
Goal
- Understand, identify and avoid common pitfalls when running a Linear Regression
- Recognize that caution is needed when we 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 a project that concerns a quantitative analysis from scratch
- Evaluate the value of an existing analysis that concerns a quantitative analysis
- Recognize that caution is needed when we 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 a project that concerns a quantitative analysis from scratch
- Evaluate the value of an existing analysis that concerns a quantitative analysis
Presentation
1. Introduction: the art of econometrics
2. The linear probability model
3. The Logit model
4. Model fit and various tests
2. The linear probability model
3. The Logit model
4. Model fit and various tests
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Autoformation | |||
| Recherche | 2,00 | ||
| Lecture du manuel de référence | 2,00 | ||
| Travail personnel | |||
| Individual Project | 30,00 | ||
| Présentiel | |||
| Cours interactif | 6,00 | ||
| Travaux dirigés | 4,00 | ||
| Coaching | 6,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
• A classical final exam
• Involvement in-class and in the final project
• The project results
Respectively 30%, 35% and 35% of the final mark
Collective feedback will be provided on www.ieseg-online.com.
• Involvement in-class and in the final project
• The project results
Respectively 30%, 35% and 35% of the final mark
Collective feedback will be provided on www.ieseg-online.com.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Contrôle continu | |||
| Participation | 16,00 | 1 | 35,00 |
| Examen (final) | |||
| Examen écrit | 2,00 | 1 | 30,00 |
| Autres | |||
| Projet Individuel | 0,00 | 1 | 35,00 |
| TOTAL | 100,00 | ||