# Matrix-Based Introduction to Multivariate Data Analysis

Textbook

1. Front Matter
Pages i-xiii
2. ### Elementary Statistics with Matrices

1. Front Matter
Pages 1-1
Pages 3-16
Pages 17-28
Pages 29-43
3. ### Least Squares Procedures

1. Front Matter
Pages 45-45
Pages 47-62
Pages 63-77
Pages 79-91
Pages 93-105
4. ### Maximum Likelihood Procedures

1. Front Matter
Pages 107-107
Pages 109-126
Pages 127-144
Pages 145-159
Pages 161-173
Pages 175-189
5. ### Miscellaneous Procedures

1. Front Matter
Pages 191-191
Pages 193-205
Pages 207-224
Pages 225-241
Pages 243-253
Pages E1-E1
7. Back Matter
Pages 255-301

### Introduction

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter.

This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra.

The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.

### Keywords

Statistics Multivariate Analysis Data Analysis Matrices Vectors

#### Authors and affiliations

• 1
1. 1.Graduate School of Human SciencesOsaka UniversityOsakaJapan

### Bibliographic information

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