General information 
Course unit name: Generalized Linear Models
Course unit code: 361234
Academic year: 20212022
Coordinator: JORDI CORTES MARTINEZ
Department: Faculty of Economics and Business
Credits: 6
Single program: S
Estimated learning time 
Total number of hours 150 
Facetoface and/or online activities 
60 
 Lecture with practical component 
Facetoface and online 
30 

 ITbased class 
Facetoface and online 
30 
Supervised project 
40 
Independent learning 
50 
Learning objectives 
Referring to knowledge — Understand the processes of parameter estimation in a generalized linear model.
Referring to abilities, skills — Know how to estimate the parameters of a generalized linear model.

Teaching blocks 
1. Introduction to generalized linear models
1.1. Introduction: models in general and the need to extend linear models
1.2. Definition of the generalized linear model (GLM): Random component: family and parameters. Deterministic component: linear predictor and link function
1.3. GLMs with normal distribution: properties and examples
1.4. Common families of distribution in GLMs: Gaussian, gamma, inverse Gaussian, Bernoulli, binomial, Poisson, negative binomial
1.5. Parameter estimation
1.6. Quasilikelihood
2. GLMs: inference
2.1. Predictions. Measures of goodness of fit: deviance, generalized Pearson statistic
2.2. Estimation of the dispersion parameter ŋ when it is unknown
2.3. Residual analysis
2.4. Estimate properties and goodnessoffit tests of fitted models
2.5. Examples of GLMs with a continuous response variable
3. Binary response models
3.1. Logistic regression: binomial response
3.2. Interpretation of usual links (logit, probit and cloglog)
3.3. Estimation, inference and validation
3.4. Predictive capacity measures
3.5. Presentation of case studies
4. Count models
4.1. Poisson, quasiPoisson and negative binomial regression models
4.2. Diagnosis and treatment of overdispersion
4.3. Zeroinflated and zerotruncated models
4.4. Loglinear models: Modeling of contingency tables
4.5. Estimation, inference and validation
4.6. Presentation of case studies
Reading and study resources 
Consulteu la disponibilitat a CERCABIB
Book
McCULLAGH, Peter, et al. Generalized linear models. London [etc.]: Chapman & Hall, 1989
FOX, John. Applied Regression Analysis and Generalized Linear Models. Los Angeles [etc.]: SAGE, 2008
FOX, John, et al. An R Companion to Applied Regression. Thousand Oaks, Calif.: SAGE, 2011
DOBSON, Annette J. An Introduction to generalized linear models. Boca Raton: CRC Press / Chapman & Hall, 2008
FARAWAY, Julian James. Extending the Linear Model with R, Generalized Linear, Mixed Effects and Nonparametric Regression Models. Boca Raton (Mass.): Chapman & Hall/CRC, 2006
ZUUR, Alain F et al. Mixed Effects Models and Extensions in Ecology with R. New York (NY): Springer, 2009
Web page
The R Project for Statistical Computing