Table of contents for A user's guide to principal components / J. Edward Jackson.

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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.