Table of contents
About this book
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.
Statistics Multivariate Analysis Data Analysis Matrices Vectors
- Book Title Matrix-Based Introduction to Multivariate Data Analysis
- DOI https://doi.org/10.1007/978-981-10-2341-5
- Copyright Information Springer Nature Singapore Pte Ltd. 2016
- Publisher Name Springer, Singapore
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Hardcover ISBN 978-981-10-2340-8
- Softcover ISBN 978-981-10-9595-5
- eBook ISBN 978-981-10-2341-5
- Edition Number 1
- Number of Pages XIII, 301
- Number of Illustrations 47 b/w illustrations, 8 illustrations in colour
Statistical Theory and Methods
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Statistics for Social Sciences, Humanities, Law
Statistics and Computing/Statistics Programs
Statistics for Business, Management, Economics, Finance, Insurance
Probability and Statistics in Computer Science
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