Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this books shows that non-parametric regression can be successfully applied to system identification, and it highlights the achievements in doing so. With emphasis on Hammerstein, Wiener systems, and their multidimensional extensions, the authors show how to identify nonlinear subsystems and their characteristics when limited information exists. Algorithms using trigonometric, Legendre, Laguerre, and Hermite series are investigated, and the kernel algorithm, its semirecursive versions, and fully recursive modifications are covered. The theories of modern non-parametric regression, approximation, and orthogonal expansions, along with new approaches to system identification (including semiparametric identification), are provided. Detailed information about all tools used is provided in the appendices. This book is for researchers and practitioners in systems theory, signal processing, and communications and will appeal to researchers in fields like mechanics, economics, and biology, where experimental data are used to obtain models of systems "Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis for the non-statistician. The seventh edition incorporates pivotal advances in technology that will assist students in gaining a firm understanding of statistical and managerial principles so as to develop a "comfort zone" not only for the statistical, but also the practical issues involved."--BOOK JACKET. Overview of multivariate methods -- pt. I. Preparing to apply multivariate analysis. Examining your data ; Exploratory factor analysis -- pt. II. Dependence techniques. Multiple regression analysis ; Multiple discriminant analysis ; Logistic regression : regression with a binary dependent variable ; MANOVA and GLM ; Conjoint analysis -- pt. III. Interdependent techniques. Cluster analysis ; Multidimensional scaling ; Analyzing nominal data with correspondence analysis -- pt. IV. Moving beyond the basic techniques. Structural equations modeling overview ; Confirmatory factor analysis ; Testing structural equations models ; Advanced SEM topics and PLS
KEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
Preparing For a MV Analysis; Dependence Techniques; Interdependence Techniques; Moving Beyond the Basic Techniques
MARKET: Statistics and statistical research can provide managers with invaluable data. This textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace.
For graduate and upper-level undergraduate marketing research courses. For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. This text provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques. :Pearson New International Edition Offering an applications-oriented approach which focuses on the use of each technique rather than its mathematical derivation, this textbook introduces a six-step framework for organising and discussing multivariate data analysis techniques
