Discrete data analysis.

lstat2100  2019-2020  Louvain-la-Neuve

Discrete data analysis.
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 7.5 h
Q2
Teacher(s)
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
Aims

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
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Mathematical Engineering

Master [120] in Statistic: Biostatistics

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

Master [120] in Statistic: General

Master [120] in Economics: General

Master [120] in Data Science : Statistic