Servizi per la didattica
PORTALE DELLA DIDATTICA

Modelli statistici

02NMRNG, 02NMRPF

A.A. 2020/21

Lingua dell'insegnamento

Italiano

Corsi di studio

Corso di Laurea Magistrale in Ingegneria Matematica - Torino
Corso di Laurea Magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi) - Torino/Trieste/Parigi

Mutua

04RLONG

Organizzazione dell'insegnamento
Didattica Ore
Lezioni 40
Esercitazioni in aula 20
Docenti
Docente Qualifica Settore h.Lez h.Es h.Lab h.Tut Anni incarico
Gasparini Mauro Professore Ordinario SECS-S/01 20 0 0 0 8
Collaboratori
Espandi

Didattica
SSD CFU Attivita' formative Ambiti disciplinari
SECS-S/01 6 D - A scelta dello studente A scelta dello studente
2020/21
Si presentano i metodi avanzati della statistica e diverse loro applicazioni.
The purpose of the course is to present advanced statistical methods, together with their applications, at an advanced undergraduate level.
Lo studente imparerą come i metodi statistici che ha studiato in teoria vengono applicati in pratica, mettendone a frutto tutte le potenzialitą metodologiche.
The student will learn how to apply in practice the statistical methods s/he has studied in theory, in order to use all of their methodological potentials.
Una preparazione equivalente a 15 crediti di Probabilitą e Statistica Matematica.
Previous education equivalent to 15 credits of Probability and Mathematical Statistics.
Modelli lineari e applicazioni. Modelli lineari generalizzati e applicazioni. Principi di sperimantazione clinica e biomedica. Sopravvivenza e affidabilitą, parametrica e non parametrica. Analisi discriminante. Clusterizzazione. Introduzione alle reti bayesiane. Dati qualitativi. R, OPeBUGS e altro software specializzato.
Linear models and their applications. Generalized linear models. Principles of clinical and biomedical experimentation. Parametric and nonparametric survival and reliability. Principal components Discriminant analysis. Introduction to Bayesian networks. Categorical data. R, OpenBUGS and other specialized software.
Esercitazioni in forma tradizionale completeranno le lezioni e un software statistico appropriato sarą usato in laboratorio informatico.
Traditional exercise sessions will complement lectures, whereas appropriate statistical software will be used in computer lab sessions.
- 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
- 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 by David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter. Chapman & Hall. - Categorical Data Analysis by Alan Agresti. Wiley - Statistical analysis of designed experiments by Ajit C. Tamhane. Wiley - Foundations of Linear and Generalized Linear Models by Alan Agresti
Modalitą 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.
Exam: Compulsory oral exam;
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.
Modalitą 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.
Exam: Compulsory oral exam;
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.
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