- Docente: Giuliano Galimberti
- Credits: 10
- SSD: SECS-S/01
- Language: Italian
- Moduli: Giuliano Galimberti (Modulo 1) Linda Altieri (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Statistical Sciences (cod. 8875)
Also valid for Second cycle degree programme (LM) in Statistical Sciences (cod. 8875)
Learning outcomes
Students will learn the basic notions to define statistical models.
In particular, students will be able to:
- estimate parameters, test hypotesis about them and build confidence intervals for generalized linear models,
- choose the most suitable model for the specific problem at hand.
Course contents
PART 1 - 40 hours - common for all students
- Statistical models: introduction.
- Revision of linear regression models.
- Generalized linear models. Exponential families, linear predictor, link functions. Maximum likelihood estimators. Goodness of fit: the deviance of a model. Residual analysis. Inference on the parameters: likelihood ratio statistic.
- Poisson regression for count data.
- Logistic regression for categorical data.
PART 2 - 20 hours - for students from Modelli Statistici C.A.
- Linear mixed models: basic concepts. Fixed and random effects. Variance-covariance matrix structures. maximum likelihood and restricted maximum likelihood estimators. Residual analysis. Goodness of fit of a linear mixed model. Inference about the parameters: confidence intervals and hypothesis testing.
PART 2 - 20 hours - for students from Modelli Statistici per le scienze biologiche e sociali
- Linear mixed models: basic concepts. Fixed and random effects. Variance-covariance matrix structures. maximum likelihood and restricted maximum likelihood estimators. Residual analysis. Goodness of fit of a linear mixed model. Inference about the parameters: confidence intervals and hypothesis testing.
- Case studiies
Readings/Bibliography
Dobson, A. J. (2002) An Introduction to Generalized Linear Models. Second Edition. Chapman & Hall/CRC.
West, B. T., Welch, K. B. and Galecki, A. T. (2007) Linear Mixed Models. A Practical Guide Using Statistical Software. Chapman & Hall/CRC.
Everitt, B. S., Hothorn, T. (2006) A Handbook of Statistical Analysis Using R. Chapman & Hall/CRC.
Handsouts.
Azzalini, A. (2001) Inferenza Statistica. Una Presentazione Basata sul Concetto di Verosimiglianza. Seconda Edizione. Springer-Verlag.Teaching methods
Lectures
Tutorial sessions in computer laboratory
Assessment methods
Modelli statistici C.A.
Written exam
Oral exam (optional)
Modelli statistici per le scienze biologiche e sociali
Written exam (21 points)
practical exam (11 points)
Final Mark: sum of the points obtained in the written exam and in the practical exam. Non-integer final marks are rounded down to the next small integer. Final marks larger that 30 are rounded down to 30. Final marks equal to 32 are considered 30 cum laude.
Office hours
See the website of Giuliano Galimberti
See the website of Linda Altieri