# Discrete data analysis.

lstat2100  2022-2023  Louvain-la-Neuve

Discrete data analysis.
5.00 credits
30.0 h + 7.5 h
Q2
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

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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic

Master [120] in Statistics: Biostatistics

Master [120] in Statistics: General

Master [120] in Mathematical Engineering

Master [120] in Economics: General

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