A User's Guide to Principal Components (eBook)

J. Edward Jackson received his Ph. D. from Virginia Tech in 1947. His first position was as a statistician for Eastman Kodak for whom he worked 37 years. He was self-employed from 1985-91. He and his wife, Suzanne, continue to live in New York.

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.