Course: 2024/2025

Statistics and Data Science II

(19141)

Requirements (Subjects that are assumed to be known)

Statistics and Data Science I (19140)

- Ability to estimate generalized linear regression models with cross-sectional data, as well as to understand and explain the statistical principles underlying the estimations.
- Ability to apply robustness tests to generalized linear regression model estimates.
- Ability to interpret the parameters of a generalized linear regression, obtain predictions and evaluate the goodness of fit.

Skills and learning outcomes

Description of contents: programme

1. Generalized Linear Models (GLM)
1.1. Regression models for categorical dependent and independent variables
1.2. Other models for other types of qualitative dependent variables: binary, ordered, multinomial, counting, etc.
2. Generalized Linear Mixed Models (GLMM)
3. Real Life Examples

Learning activities and methodology

Training Activities:
- Theoretical-practical classes
Teaching Methods:
- Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning.
- Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group.

Assessment System

Basic Bibliography

- Charles E. Mcculloch,John M. Neuhaus. Generalized Linear Mixed Models. Wiley. 2014
- G. James, D. Witten, T. Hastie and R. Tibshirani.. An Introduction to Statistical Learning with Applications in R. Springer. 2021
- Julian J. Faraway . Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis. 2016

The course syllabus may change due academic events or other reasons.

**More information: **Aula Global