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A user's guide to principal components [electronic resource] / J. Edward Jackson.

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Principal component analysis is a multivariate technique in which a number of related variables are transformed to a set of uncorrelated variables. This paperback reprint of a Wiley bestseller is designed for practitioners of principal component analysis.

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Bibliographic Details
Online Access: Full Text (via Wiley)
Main Author: Jackson, J. Edward
Format: Electronic eBook
Language:English
Published: New York : Wiley, ©1991.
Series:Wiley series in probability and mathematical statistics. Applied probability and statistics.
Subjects:
Principal components analysis.
Factor analysis.
Multivariate analysis.
Table of Contents:
  • Getting started
  • PCA with more than two variables
  • Scaling of data
  • Inferential procedures
  • Putting it all together
  • hearing loss I
  • Operations with group data
  • Vector interpretation I: simplifications and inferential techniques
  • Vector interpretation II: rotation
  • A case history-hearing loss II
  • Singular value decomposition: multidimensional scaling I
  • Distance models: multidimensional scaling II
  • Linear models I: regression; PCA of predictor Variables
  • Linear models II: analysis of variance; PCA of response variables
  • Other applications of PCA
  • Flatland: special procedures for two dimensions
  • Odds and ends
  • What is factor analysis anyhow?
  • Other competitors.

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