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A User's Guide to Principal Components [Paperback]

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  • Category: Books (Mathematics)
  • Author:  Jackson, J. Edward
  • Author:  Jackson, J. Edward
  • ISBN-10:  0471471348
  • ISBN-10:  0471471348
  • ISBN-13:  9780471471349
  • ISBN-13:  9780471471349
  • Publisher:  Wiley-Interscience
  • Publisher:  Wiley-Interscience
  • Pages:  592
  • Pages:  592
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-May-2003
  • Pub Date:  01-May-2003
  • SKU:  0471471348-11-MPOD
  • SKU:  0471471348-11-MPOD
  • Item ID: 100707352
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jan 18 to Jan 20
  • Notes: Brand New Book. Order Now.
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

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;lĂ-

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