Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been added All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields.
The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
Annotation Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new featuresA new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets Front Matter....Pages I-XVII Front Matter....Pages 1-1 Comparison of Batches....Pages 3-46 Front Matter....Pages 47-47 A Short Excursion into Matrix Algebra....Pages 49-71 Moving to Higher Dimensions....Pages 73-106 Multivariate Distributions....Pages 107-165 Theory of the Multinormal....Pages 167-181 Theory of Estimation....Pages 183-192 Hypothesis Testing....Pages 193-226 Front Matter....Pages 227-227 Regression Models....Pages 229-253 Decomposition of Data Matrices by Factors....Pages 255-267 Principal Components Analysis....Pages 269-305 Factor Analysis....Pages 307-330 Cluster Analysis....Pages 331-349 Discriminant Analysis....Pages 351-366 Correspondence Analysis....Pages 367-384 Canonical Correlation Analysis....Pages 385-395 Multidimensional Scaling....Pages 397-412 Conjoint Measurement Analysis....Pages 413-425 Applications in Finance....Pages 427-438 Computationally Intensive Techniques....Pages 439-490 Front Matter....Pages 491-491 Appendix A: Symbols and Notations....Pages 493-495 Front Matter....Pages 491-491 Appendix B: Data....Pages 497-507 Back Matter....Pages 509-516 Presenting the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians, this third edition includes new features such as a chapter on regression models. All the numerical examples are reproduced in MATLAB or R. By Wolfgang Karl Härdle, Léopold Simar.
