# 85195 - Multivariate Statistics

### Course Unit Page

• Teacher Angela Montanari

• Credits 6

• SSD SECS-S/01

• Language English

• Campus of Bologna

• Degree Programme Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)

Also valid for First cycle degree programme (L) in Statistical Sciences (cod. 8873)

## 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 Virtuale

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

Practicals

Handnotes on Virtuale

## Teaching methods

lectures and practicals

## Assessment methods

written exam

The exam lasts 1 and half our and involves four exercises, one for each of the main topics dealt with during the course.

Past exams highlighting how grades are assigned will be made available on virtuale and solved in class

## Office hours

See the website of Angela Montanari