I Descriptive Techniques -- 1 Comparison of Batches -- II Multivariate Random Variables -- 2 A Short Excursion into Matrix Algebra -- 3 Moving to Higher Dimensions -- 4 Multivariate Distributions -- 5 Theory of the Multinormal -- 6 Theory of Estimation -- 7 Hypothesis Testing -- III Multivariate Techniques -- 8 Decomposition of Data Matrices by Factors -- 9 Principal Components Analysis -- 10 Factor Analysis -- 11 Cluster Analysis -- 12 Discriminant Analysis -- 13 Correspondence Analysis -- 14 Canonical Correlation Analysis -- 15 Multidimensional Scaling -- 16 Conjoint Measurement Analysis -- 17 Applications in Finance -- 18 Highly Interactive, Computationally Intensive Techniques -- A Symbols and Notation -- B Data -- B.1 Boston Housing Data -- B.2 Swiss Bank Notes -- B.3 Car Data -- B.4 Classic Blue Pullovers Data -- B.5 U.S. Companies Data -- B.6 French Food Data -- B.7 Car Marks -- B.8 French Baccalauréat Frequencies -- B.9 Journaux Data -- B.10 U.S. Crime Data -- B.11 Plasma Data -- B.12 WAIS Data -- B.13 ANOVA Data -- B.14 Timebudget Data -- B.15 Geopol Data -- B.16 U.S. Health Data -- B.17 Vocabulary Data -- B.18 Athletic Records Data -- B.19 Unemployment Data -- B.20 Annual Population Data.

Most of the observable phenomena in the empirical sciences are of multivariate nature. This book presents the tools and concepts of multivariate data analysis with a strong focus on applications. The text is devided into three parts. The first part is devoted to graphical techniques describing the distributions of the involved variables. The second part deals with multivariate random variables and presents from a theoretical point of view distributions, estimators and tests for various practical situations. The last part covers multivariate techniques and introduces the reader into the wide basket of tools for multivariate data analysis. The text presents a wide range of examples and 228 exercises.