A User's Guide to Principal Components.

@article{Jolliffe1991AUG,
  title={A User's Guide to Principal Components.},
  author={Ian T. Jolliffe and J. Edward Jackson},
  journal={The Statistician},
  year={1991},
  volume={42},
  pages={76-77}
}
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… 

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Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using

[Principal components analysis].

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Only reduction data dimension could be justify the use of principal component analysis in identifying factors or components latent besides health indicators from Spanish regions, and its graphic output.

Common Principal Components and Related Multivariate Models

Preliminaries principal component analysis relationships between matrices common practical components proportional covariance matrices partial common components and common space analysis how

Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure.

TLDR
This work discusses the key algebraic similarities and differences between principal component analysis and factor analysis, and analyzes a number of theoretical and practical issues.