A User's Guide to Principal Components / Edition 1

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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.
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Editorial Reviews

Describes principal component analysis, a multivariate technique used in statistics and applied probability, especially in quality control applications. Among the topics are the extension to p variables, scaling input data, inferential procedures, operations with group data, and vector interpretation. Skims lightly over theory and computational techniques to focus on practical aspects of data reduction and interpretation. Annotation c. Book News, Inc., Portland, OR booknews.com
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Product Details

Table of Contents

Introduction 1
1 Getting Started 4
2 PCA With More Than Two Variables 26
3 Scaling of Data 63
4 Inferential Procedures 80
5 Putting It All Together - Hearing Loss I 105
6 Operations with Group Data 123
7 Vector Interpretation I: Simplifications and Inferential Techniques 142
8 Vector Interpretation II: Rotation 155
9 A Case History - Hearing Loss II 173
10 Singular Value Decomposition: Multidimensional Scaling I 189
11 Distance Models: Multidimensional Scaling II 233
12 Linear Models I: Regression; PCA of Predictor Variables 263
13 Linear Models II: Analysis of Variance; PCA of Response Variables 301
14 Other Applications of PCA 319
15 Flatland: Special Procedures for Two Dimensions 342
16 Odds and Ends 350
17 What is Factor Analysis Anyhow? 388
18 Other Competitors 424
Conclusion 435
App. A Matrix Properties 437
App. B Matrix Algebra Associated with Principal Component Analysis 446
App. C Computational Methods 450
App. D A Directory of Symbols and Definitions for PCA 456
App. E: Some Classic Examples 460
App. F Data Sets Used in This Book 464
App. G: Tables 475
Bibliography 497
Author Index 551
Subject Index 563
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