Multivariate Data Analysis, 7th International Edition

Editorial Reviews

Multivariate is a central tool whenever several variables need to be considered at the same time. The present book explains a powerful and versatile way to analyse data tables, suitable also for researchers without formal in .
The of multivariate data requires the extension of standard univariate statistical models and methods but also introduces new problems. Initial attention is given to Data Mining techniques such as summarising and displaying high dimensional data and to ways of reducing multivariate problems to more manageable univariate ones. This is followed by routine generalisations of standard distributions and statistical tests before consideration of new strategies for constructing hypothesis tests. Finally, problems specific to multivariate data such as discrimination and classification (use in diagnosis problems for example) are studied. Most of these methods can be implemented in standard computer packages.

This book shows that multivariate analysis are:

  • Design for capability (also known as capability-based design)
  • Inverse design, where any variable can be treated as an independent variable
  • Analysis of Alternatives (A0A), the selection of concepts to fulfill a customer need
  • Analysis of concepts with respect to changing scenarios
  • Identification of critical design drivers and correlations across hierarchical levels.

Table of Contents

Chapter 1. Overview of Multivariate Methods
Chapter 2. Examining Your Data
Chapter 3. Exploratory Factor Analysis
Chapter 4. Multiple Regression Analysis
Chapter 5. Multiple Discriminant Analysis
Chapter 6. Logistic Regression: Regression with a Binary Dependent Variable
Chapter 7. Conjoint Analysis
Chapter 8. Cluster Analysis
Chapter 9. Multidimensional Scaling
Chapter 10. Analyzing Nominal Data with Correspondence Analysis
Chapter 11. Structural Equations Modeling Overview
Chapter 12. Confirmatory Factor Analysis
Chapter 13. Structural Equations Models
Chapter 14. Manova and Glm

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