Si presentano i metodi avanzati della statistica e diverse loro applicazioni.

Lo studente imparerà come i metodi statistici che ha studiato in teoria vengono applicati in pratica, mettendone a frutto tutte le potenzialità metodologiche.

Una preparazione equivalente a 15 crediti di Probabilità e Statistica Matematica.

Modelli lineari e applicazioni. Modelli lineari generalizzati e applicazioni. Principi di sperimantazione clinica e biomedica. Sopravvivenza e affidabilità, parametrica e non parametrica. Analisi discriminante. Clusterizzazione. Introduzione alle reti bayesiane. Dati qualitativi. R, OPeBUGS e altro software specializzato.

Esercitazioni in forma tradizionale completeranno le lezioni e un software statistico appropriato sarà usato in laboratorio informatico.

- An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani http://faculty.marshall.usc.edu/gareth-james/ISL/ - The BUGS Book: A Practical Introduction to Bayesian Analysis di David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter. Chapman & Hall. - Categorical Data Analysis di Alan Agresti. Wiley - Statistical analysis of designed experiments di Ajit C. Tamhane. Wiley - Foundations of Linear and Generalized Linear Models by Alan Agresti

Modalità di esame: Prova orale obbligatoria;

At the end of the course, a list of case studies seen in the course will be provided, together with explanations, references and software. During the oral exam each student will be assigned at random two case studies from the list, which he/she will have to discuss thoroughly. The student will comment on the specific aspects of the case study, on its methodological foundations and on the software used. The student will have to be able to connect the specific details of the case studies to general principles and methodologies. Finally, the professor may ask the student one or more extra questions regarding any topic covered in the course.

Modalità di esame: Prova orale obbligatoria;

At the end of the course, a list of case studies seen in the course will be provided, together with explanations, references and software. During the oral exam each student will be assigned at random two case studies from the list, which he/she will have to discuss thoroughly. The student will comment on the specific aspects of the case study, on its methodological foundations and on the software used. The student will have to be able to connect the specific details of the case studies to general principles and methodologies. Finally, the professor may ask the student one or more extra questions regarding any topic covered in the course.