Multivariate Data and Multivariate Analysis -- Looking at Multivariate Data -- Principal Components Analysis -- Exploratory Factor Analysis -- Multidimensional Scaling and Correspondence Analysis -- Cluster Analysis -- Grouped Multivariate Data: Multivariate Analysis of Variance and Discriminant Function Analysis -- Multiple Regression and Canonical Correlation -- Analysis of Repeated Measures Data.

Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined simultaneously to get the most informative results. This requires the use of one or other of the many methods of multivariate analysis, and the use of a suitable software package such as S-PLUS or R. In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R and S-PLUS code is given for each analysis in the book, with any differences between the two highlighted. A website with all the datasets and code used in the book can be found at http://biostatistics.iop.kcl.ac.uk/publications/everitt/. Graduate students, and advanced undergraduates on applied statistics courses, especially those in the social sciences, will find this book invaluable in their work, and it will also be useful to researchers outside of statistics who need to deal with the complexities of multivariate data in their work. Brian Everitt is Emeritus Professor of Statistics, King’s College, London.