Discrete data analysis.

Teacher(s)

Adam Cécile (compensates El Ghouch Anouar); El Ghouch Anouar;

Language

French

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2020	Logiciels et programmation statistique de base
LSTAT2120	Linear models

Main themes

- Multinomial Distribution : marginal and conditional distributions and asymptotic properties - Two ways Contingency Tables : Independance and Homogeneity, measures of association and particular tests (Fisher, Mac Nemar, etc.). - Multiple ways Contingency Tables : Mutual, Partial and Conditional Independencies. - Log-linear Models. - Conditional Models - Generalized Linear Models - Logit and Probit Models - Multinomial Discriminant Analysis - Selection of explanatory variables

Learning outcomes

At the end of this learning unit, the student is able to :
1	The student will be able to use the basic techniques of Discrete Data Analysis and to apply these to real data using statistical softwares

Content

Content - Multinomial Distribution : marginal and conditional distributions and asymptotic properties - Two ways Contingency Tables : Independance and Homogeneity, measures of association and particular tests (Fisher, Mac Nemar, etc.). - Multiple ways Contingency Tables : Mutual, Partial and Conditional Independencies. - Log-linear Models. - Conditional Models - Generalized Linear Models - Logit and Probit Models - Multinomial Discriminant Analysis - Selection of explanatory variables Methods The course is concentrated on the first ten weeks. The following 4 weeks are devoted to the realization by each student of an empirical study of suitable data.

Evaluation methods

During the exam session: computer-assisted written exam.

Other information

Prerequisites : Elementary courses in Probability and Statistics

Teaching materials

• transparents sur moodle

Faculty or entity

LSBA