A User's Guide to Principal Components

Jackson, J. Edward

ISBN 10: 0471622672 / ISBN 13: 9780471622673

Published by Wiley & Sons, Incorporated, John, 1991

About this Item

Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Seller Inventory # 6003092-6

Bibliographic Details

• Title: A User's Guide to Principal Components
• Publisher: Wiley & Sons, Incorporated, John
• Publication Date: 1991
• Language: English
• ISBN 10: 0471622672
• ISBN 13: 9780471622673
• Binding: Hardcover
• Condition: Good
• Weight: 2 pounds

About this title

Synopsis

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

From the Publisher

Principal component analysis is a multivariate technique in which a number of related variables are transformed to a (usually smaller) set of uncorrelated variables. This text is designed for practitioners of principal component analysis. Among the topics explored are extension to p variables, scaling input data, inferential procedures, operations with group data and vector interpretation. Dealing with the ``how-to-do-it'' as well as the ``why-it-works,'' it avoids getting bogged down in theoretical matters and computational techniques focusing instead on practical aspects of data reduction and interpretation.