Checking date: 01/06/2021

Course: 2021/2022

Regression Models
(17758)
Study: Master in Statistics for Data Science (345)
EPI

Coordinating teacher: DURBAN REGUERA, MARIA LUZ

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:

Requirements (Subjects that are assumed to be known)
Probability Statistical Inference Programming in R
Objectives
SKIILS ACQUIRED: CG1 Ability to apply analysis techniques with the aim to adapt the information to real problems. CG2 Ability to identify the best stochastic model for each real problem, and to apply it for its analysis, design and solution. CE5 Apply advanced statistical foundation for the development and analysis of real problems that include the prediction of a response variable. KNOWLEDGE ACQUISITION 1) linear models 2) generalized linear models 3) generalized additive models
Skills and learning outcomes
Description of contents: programme
Regression Models 1) Linear regression: Estimation. Inference. Diagnostics. 2) Introduction to Generalized Linear Models: Exponetial family. Estimation. Inference. Diagnostics. 4) Logistic Regression, Multinomial, Ordinal, Poisson. 3) Generalized Additive Models: Smoothing Methods. Penalized Splines. Estimation. Smoothing parameter selection
Learning activities and methodology
Learning activities: Master classes Exercises Computer labs Projects Teaching methodologies: Presentations of the professor in class with computing and visual media, where the professor develops the mail concepts of the subject and provides bibliography supplementing the knowledge of students. Critical reading of texts recommended by the professor: manuals and/or academic papers, either for their posterior discussion in class, or for widening and consolidating the subject matter.
Assessment System
• % end-of-term-examination 50
• % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
• Annette J. Dobson, Adrian G. Barnett . An Introduction to Generalized Linear Models. CRC Press. 2018
• Julian J. Faraway. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press. 2016
• Michael H. Kutner, Chris J. Nachtsheim, John Neter. Applied Linear Regression Models. McGraw-Hill Higher Education. 2003
• P. McCullagh, John A. Nelder. Generalized Linear Models. CRC Press. 1989
• Simon Wood. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC Texts in Statistical Science. 2017

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