# Discrete data analysis.

lstat2100  2021-2022  Louvain-la-Neuve

Discrete data analysis.
5.00 credits
30.0 h + 7.5 h
Q2
Teacher(s)
El Ghouch Anouar;
Language
French
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
Each student is provided a data set to be analyzed by the taught techniques. This analysis is the object of a report orally presented by the student to the Professors. During this presentation, the Professors may question the student on the matter of the course.
Other information
Prerequisites : Elementary courses in Probability and Statistics
Teaching materials
• transparents sur moodle
Faculty or entity
LSBA

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Statistics: General

Master [120] in Statistics: Biostatistics

Master [120] in Economics: General

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Mathematical Engineering

Master [120] in Data Science : Statistic