Matrix Based Introduction to Multivariate Data Analysis Part 1. Elementary Statistics with Matrices 1 Introduction to Matrix Operations 2 Intra variable Statistics 3 Inter variable Statistics Part 2. Least Squares Procedures 4 Regression Analysis 5 Principal Component Analysis (Part 1) 6 Principal Component Analysis 2 (Part 2) 7 Cluster Analysis Part 3. Maximum Likelihood Procedures 8 Maximum Likelihood and Normal Distributions 9 Path Analysis 10 Confirmatory Factor Analysis 11 Structural Equation Modeling 12 Exploratory Factor Analysis Part 4. Miscellaneous Procedures 13 Rotation Techniques 14 Canonical Correlation and Multiple Correspondence Analyses 15 Discriminant Analysis 16 Multidimensional Scaling Appendices A1 Geometric Understanding of Matrices and Vectors A2 Decomposition of Sums of Squares A3 Singular Value Decomposition (SVD) A4 Matrix Computation Using SVD A5 Supplements for Probability Densities and Likelihoods A6 Iterative Algorithms References Index. 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.