Checking date: 15/07/2023

Course: 2023/2024

Statistics and Data Science II
Master in Computational Social Science (Plan: 472 - Estudio: 375)

Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


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
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
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.

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