Course: 2022/2023

Regression Methods

(13711)

Requirements (Subjects that are assumed to be known)

Probability I-II
Statistical Inference Methods I-II
Linear Algebra
Calculus II

Knowledge of least squares techniques in a linear regression model.
Learning of statistical regression software.

Skills and learning outcomes

Description of contents: programme

The course is an introduction to the foundations of linear regression analysis.
1. Simple linear regression model. Ordinary least squares estimation.
2. Inference problems in the simple linear regression model. Analysis of variance and testing methods.
3. Multiple linear regression model.
4. Inference problems in the multiple regression model. Matrix notation, estimation and testing techniques.
5. Analysis of residuals and model diagnosis.
6. Introduction to generalized linear models.

Learning activities and methodology

Competences will be acquired by students both trough theory classes and the resolution of assigned homework. There will also be practical classes of exercises. There will be also a final collective session to clarify doubts and revise material.

Assessment System

- % end-of-term-examination 40
- % of continuous assessment (assigments, laboratory, practicals...) 60

Basic Bibliography

- KUTNER, M. H., NETER, J, NACHSTEIM, C. J. and WASSERMAN, W.. Applied Linear Statistical Models, 5th Edition. . McGraw-Hill Higher Education. 2004

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