Mathematical Tools for Applied Multivariate Analysis
eBook - PDF

Mathematical Tools for Applied Multivariate Analysis

  1. 402 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Mathematical Tools for Applied Multivariate Analysis

About this book

Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.

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Yes, you can access Mathematical Tools for Applied Multivariate Analysis by Paul E. Green in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2014
Print ISBN
9780122975509
eBook ISBN
9781483214047
Topic
Mathematics
Subtopic
Applied Mathematics
Index
Mathematics

Table of contents

  1. Front Cover
  2. Mathematical Tools for Applied Multivariate Analysis
  3. Copyright Page
  4. Table of Contents
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. CHAPTER 1. The Nature of Multivariate Data Analysis
  9. CHAPTER 2. Vector and Matrix Operations for Multivariate Analysis
  10. CHAPTER 3. Vector and Matrix Concepts from a Geometric Viewpoint
  11. CHAPTER 4. Linear Transformations from a Geometric Viewpoint
  12. CHAPTER 5. Decomposition of Matrix Transformations: Eigenstructures and Quadratic Forms
  13. CHAPTER 6. Applying the Tools to Multivariate Data
  14. APPENDIX A: Symbolic Differentiation and Optimization of Multivariable Functions
  15. APPENDIX B: Linear Equations and Generalized inverses
  16. Answers to Numerical Problems
  17. References
  18. Index