Checking date: 04/06/2021


Course: 2021/2022

Advanced Regression Methods
(14467)
Study: Bachelor in Statistics and Business (203)


Coordinating teacher: DURBAN REGUERA, MARIA LUZ

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Statistical inference I Statistical inference II Regression methods
Objectives
-Being able to identify and propose the correct model for a specific problem -Ability to manage computationally and analiticaly the models proposed and carry out the analysis of the resuts obtained. -Ability to model and analyze static and dynamic data -Ability to validate models and interpret the results -Ability to draw conclusions and write reports -Ability to work in multidisciplinar groups
Skills and learning outcomes
Description of contents: programme
1. Revision of linear models 1.2 Estimation 1.3 Inference 2. Introduction to generalized linear models 2.1 Exponential family 2.2 Components of a GLM 2.3 Estimation: Fisher Scoring Algorithm 2.4 Inference 2.5 Diagnostics 3. Models for binary data and proportions 3.1 Logistic regression 3.2 Parameter interpretation: Odds ratio 3.3 Validation: ROC curve 4. Models for count data 4.1 Poisson regression 4.2 Log-linear models 5. Generalized additive models 5.1 Smoothing techniques 5.2 Estimation and inference 6. Random effects models 6.1 Estimation 6.2 Inference 6.3 Models for repeated measures and longitudinal data
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • Dobson, A.. An introduction to generalized linear models. Chapman and Hall. 2001
  • Faraway, J.. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. hapman & Hall/CRC Texts in Statistical Science. 2016
  • McCulloch, C.. Generalized, Linear, and Mixed Models. Wiley Series in Probability and Statistics. 2001
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.