Multivariate descriptive statistical analysis

This is a well-written and interesting book about techniques for displaying multi- variate data. Although the examples are applications to socioeconomic research, it is claimed that the methods can also be applied to the social sciences, medicine, biology, and geography. The primary focus is on correspondence analysis, with other techniques such as canonical correlation, discriminant analysis, and cluster analysis discussed in this context. One could conclude from the absence of exercises that the book is not intended as a text, but it certainly could be used for a class if supplemented with problems. The main prerequisite is linear algebra, but some calculus is used, too, including matrix derivatives and Lagrange multipliers. The style is informal, with techniques presented often in terms of the analysis of a particular data set, and there are no theorems presented as such. There are, however, some mathematical derivation. This is a clear, carefully written discussion of correspondence analysis, a methodology which deserves to be more widely known.