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A User's Guide to Principal Components / Edition 1

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Overview

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

From the Reviews of A User’s Guide to Principal Components

"The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA."
–Technometrics

"I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results."
–Mathematical Geology

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Editorial Reviews

Booknews
Describes principal component analysis, a multivariate technique used in statistics and applied probability, especially in quality control applications. Among the topics are the extension to p variables, scaling input data, inferential procedures, operations with group data, and vector interpretation. Skims lightly over theory and computational techniques to focus on practical aspects of data reduction and interpretation. Annotation c. Book News, Inc., Portland, OR booknews.com
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Product Details

Table of Contents

Preface.

Introduction.

1. Getting Started.

2. PCA with More Than Two Variables.

3. Scaling of Data.

4. Inferential Procedures.

5. Putting It All Together—Hearing Loss I.

6. Operations with Group Data.

7. Vector Interpretation I : Simplifications and Inferential Techniques.

8. Vector Interpretation II: Rotation.

9. A Case History—Hearing Loss II.

10. Singular Value Decomposition: Multidimensional Scaling I.

11. Distance Models: Multidimensional Scaling II.

12. Linear Models I : Regression; PCA of Predictor Variables.

13. Linear Models II: Analysis of Variance; PCA of Response Variables.

14. Other Applications of PCA.

15. Flatland: Special Procedures for Two Dimensions.

16. Odds and Ends.

17. What is Factor Analysis Anyhow?

18. Other Competitors.

Conclusion.

Appendix A. Matrix Properties.

Appendix B. Matrix Algebra Associated with Principal Component Analysis.

Appendix C. Computational Methods.

Appendix D. A Directory of Symbols and Definitions for PCA.

Appendix E. Some Classic Examples.

Appendix F. Data Sets Used in This Book.

Appendix G. Tables.

Bibliography.

Author Index.

Subject Index.

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