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Language of instruction
French, English
Teaching content
QUANTITATIVE METHODS
Training officer(s)
M.BUISINE
Stakeholder(s)
Matthieu Buisine
Jennifer Amar
Iuliana Matei
Marijn Verschelde
Arnaud Rys
Thomas Baudin
Mamoudou Camara
Jennifer Amar
Iuliana Matei
Marijn Verschelde
Arnaud Rys
Thomas Baudin
Mamoudou Camara
Présentation
Prerequisite
Basic knowledge of Excel (graphs, formulas…)
Basic statistical knowledge: scatter Plots, mean, standard deviation, linear correlation
Reading a statistical table (Standard Normal, Student and Fisher Tables)
Inferential Statistics: hypothesis testing, confidence interval on the mean.
Basic statistical knowledge: scatter Plots, mean, standard deviation, linear correlation
Reading a statistical table (Standard Normal, Student and Fisher Tables)
Inferential Statistics: hypothesis testing, confidence interval on the mean.
Goal
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 dependant variable, evaluate assumptions of the regression analysis, understand advantages and drawbacks of the Ordinaly Least Squares method.
- Identify outliers or influencial points, use a dummy variable.
- Use a statistical software or an Excel statistical package.
- Build a relevant model: being able to linearize a model, select the most relevant variables, understand multicolinearity.
- Assess the model quality using the R², and the Fisher Test
- 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 dependant variable, evaluate assumptions of the regression analysis, understand advantages and drawbacks of the Ordinaly Least Squares method.
- Identify outliers or influencial points, use a dummy variable.
- Use a statistical software or an Excel statistical package.
- Build a relevant model: being able to linearize a model, select the most relevant variables, understand multicolinearity.
- Assess the model quality using the R², and the Fisher Test
Presentation
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
Forms of instruction
Chapitre I - La Régression à une variable: les bases de l'économétrie: rappels sur l'échantillonage, représentations graphiques, limites de la corrélation linéaire, linéarisation, juger la qualité d'un modèle: SCE et R², hypothèses du modèle, inférence Statistique sur les paramètres estimés, intervalles de confiance sur la prévision
Chapitre II - La Régression à plusieurs variables: modèle de Régression multiple, significativité globale d'un modèle, R² Ajusté, Test de Fisher, Hypothèses du modèle multiple, Inférence sur les coefficients estimés, Variables dummy
Chapitre III - Conception de modèles: Linéarisation de formes diverses (quadratiques…). Introduction aux formes logistiques. Critères de choix d'un bon modèle: VIF, Cp, AIC…
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Face to face | |||
| Interactive class | 16,00 | ||
| Independent work | |||
| Reference manual 's readings | 6,00 | ||
| Independent study | |||
| Estimated personal workload | 6,00 | ||
| Group Project | 10,00 | ||
| Overall student workload | 38,00 | ||
Evaluation
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 |
|---|---|---|---|
| Others | |||
| Group Project | 0,00 | 1 | 40,00 |
| Final Exam | |||
| Written exam | 1,50 | 1 | 40,00 |
| Continuous assessment | |||
| QCM | 1,00 | 1 | 20,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Basic Business Statistics, 12/E (Mark L. Berenson, David M. Levine, Timothy C. Krehbiel), Pearson, 2011 -
Internet resources