# 28178 - Multivariate Analysis

### Course Unit Page

• Teacher Angela Montanari

• Credits 6

• SSD SECS-S/01

• Language English

## Learning outcomes

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

## Course contents

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

stanford.edu/~tibs/ElemStatLearn/)

Practicals

Handnotes on IOL

## Teaching methods

lectures and practicals

## Assessment methods

written exam

see http://www2.stat.unibo.it/montanari/course3.htm for past exams

## Office hours

See the website of Angela Montanari