Scheda insegnamento

Anno Accademico 2019/2020

Conoscenze e abilità da conseguire

By the end of the course the student gains an appreciation of the types of problems and questions arising with multivariate data. In particular the student should be able: - to apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, factor analysis, canonical variates - to apply and interpret methods for cluster analysis and discrimination - to interpret the output of R procedures for multivariate statistics


Multivariate data and derived matrices. Dimension reduction methods: principal component analysis, factor analysis. Cluster analysis. Discriminant analysis


Handnotes on IOL

In Italian

-Appunti di analisi statistica multivariata, S.Mignani e A.Montanari, Esculapio

In English

http://en.bookfi.org [http://en.bookfi.org/]

-Applied Multivariate Data Analysis. B.S. Everitt and G.G. Dunn, 2001, Edward

Arnold. Second Edition.

-A First Course in Multivariate Statistics. B. Flury, 1997, Springer Verlag.

-Introduction to Multivariate Analysis. C. Chatfield and A.J. Collins, 1980, Chapman and Hall.

-Principles of Multivariate Analysis: A User's Perspective. W.J. Krzanowski, 1988, Oxford University Press.

-Multivariate Analysis. K.V. Mardia, J.T. Kent and J. Bibby, 1979, Academic Press.

-Modern Applied Statistics with S. Venables, W.N. and Ripley, B.D. 2002. 4th Edition. Springer Verlag, New York. Very useful for Splus and R

-The Elements of Statistical Learning. Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer Verlag, New York. Some very advanced material as well as that covered in this course (but available for free as a .pdf download on http://www.stat.



Handontes on IOL

Metodi didattici

lectures and practicals

Modalità di verifica dell'apprendimento

written exam 

Orario di ricevimento

Consulta il sito web di Angela Montanari