Jackson E. A User's Guide to Principal Components

5th Edition. — Hoboken: CRC Press, 2011. — 508 p. — (Texts in Statistical Science Series). — ISBN: 978-1-4665-0324-3. This new version of the bestselling Computer-Aided Multivariate Analysis has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods, Practical...

2nd ed. — John Wiley & Sons Ltd., 2010. — 324 p. — ISBN: 9780470711170, 9781118887486, 0470711175. Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract...

N.-Y.: Wiley, 1991. — 456 p. The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters...

Pearson, 2014. — 775 p. — 6th ed. — ISBN: 1292024941, 1299960308, 9781292024943 For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable...

Springer, 2014. — 168 p. — ISBN: 3319024019. This is a text in methods of applied statistics for researchers who design and conduct experiments, perform statistical inference, and write technical reports. These research activities rely on an adequate knowledge of applied statistics. The reader both builds on basic statistics skills and learns to apply it to applicable scenarios...

John Wiley & Sons, Inc., 2002. — 732 p. — ISBN 0-471-41889-7. A textbook on multivariate statistical analysis covering a fairly standard set of topics: matrix algebra, the concept of multivariate data and the simplest methods for analyzing them, multivariate normal distribution, comparison of means for vectors, multivariate analysis of variance, comparison of covariance...