Actualités et agenda
Nos formations
Établissement
Langue d'enseignement
English
Matières
QUANTITATIVE METHODS
Responsable(s)
M.BUISINE
Intervenant(s)
Matthieu Buisine
Jennifer Amar
Iuliana Matei
Uyanga Turmunkh
Thomas Baudin
Ferdi Fouad
Balazs Kotosz
Jennifer Amar
Iuliana Matei
Uyanga Turmunkh
Thomas Baudin
Ferdi Fouad
Balazs Kotosz
Présentation
Prérequis
Basic probabilities
Descriptive probabilities
Inferential Statistics (confidence intervals, hypotheses testing, normal and Student distributions)
Descriptive probabilities
Inferential Statistics (confidence intervals, hypotheses testing, normal and Student distributions)
Objectifs
At the end of the course, the student should be able to:
- Understand how econometrics are used in each functional area of business, select a relevant research question or thesis statement and choose a relevant model.
- Use the simple or multiple regression analysis to predict the value of a dependent variable, evaluate assumptions of the regression analysis and understand advantages and drawbacks of the Ordinary Least Squares method.
- Identify outliers or influential points, use a dummy variable.
- Use statistical software or an Excel statistical package.
- Build a relevant model: being able to linearize a model, select the most relevant variables and understand multicolinearity.
- Assess model quality using R², Fisher Test. Check OLS assumptions.
- Understand how econometrics are used in each functional area of business, select a relevant research question or thesis statement and choose a relevant model.
- Use the simple or multiple regression analysis to predict the value of a dependent variable, evaluate assumptions of the regression analysis and understand advantages and drawbacks of the Ordinary Least Squares method.
- Identify outliers or influential points, use a dummy variable.
- Use statistical software or an Excel statistical package.
- Build a relevant model: being able to linearize a model, select the most relevant variables and understand multicolinearity.
- Assess model quality using R², Fisher Test. Check OLS assumptions.
Présentation
Chapter I Simple Linear Regression: basics on sampling, graphs, correlation and linearizing, the OLS, assess model quality: SCE, R², hypothesis of the SLR, checking assumptions using graphs, inference about the slope, confidence Intervals on the forecasted value
Chapter II: Multiple Linear Regression: the multiple regression model, F Test for overall significance, multiple Regression Assumptions, inference about the slope, Dummy variables
Chapter III: Multiple Regression Model Building: quadratic Regression Model, introduction to Logistic Models, model Building: stepwise, best subset, VIF…
Chapter II: Multiple Linear Regression: the multiple regression model, F Test for overall significance, multiple Regression Assumptions, inference about the slope, Dummy variables
Chapter III: Multiple Regression Model Building: quadratic Regression Model, introduction to Logistic Models, model Building: stepwise, best subset, VIF…
Modalités
Organisation
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours interactif | 16,00 | ||
| Autoformation | |||
| Lecture du manuel de référence | 6,00 | ||
| Travail personnel | |||
| Group Project | 10,00 | ||
| Charge de travail personnel indicative | 15,00 | ||
| Overall student workload | 47,00 | ||
Évaluation
Interractive lectures and Tutorial format. Lecture's understanding is assessed thanks to two MCQs
A final group project allows students to use all course materials with real life data sets.
A final group project allows students to use all course materials with real life data sets.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Contrôle continu | |||
| QCM | 1,00 | 1 | 20,00 |
| Autres | |||
| Projet Collectif | 0,00 | 1 | 40,00 |
| Examen (final) | |||
| Examen écrit | 1,50 | 1 | 40,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliographie
Basic Business Statistics, 12/E (Mark L. Berenson, David M. Levine, Timothy C. Krehbiel), Pearson, 2011 -
Ressources Internet