Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is pointing out the analogy between a common univariate statistical technique and the corresponding multivariate method. Many computer examples--drawing on SAS software --are used as demonstrations. Throughout the book, the computer is used as an adjunct to the presentation of a multivariate statistical method in an empirically oriented approach. Basically, the model adopted in this book is to first present the theory of a multivariate statistical method along with the basic mathematical computations necessary for the analysis of data. Subsequently, a real world problem is discussed and an example data set is provided for analysis. Throughout the presentation and discussion of a method, many references are made to the computer, output are explained, and exercises and examples with real data are included.
Students also learn how to compute each technique using SPSS software. New to the Sixth Edition Instructor ancillaries are now available with the sixth edition.
Students also learn how to compute each technique using SPSS software. New to the Sixth Edition Instructor ancillaries are now available with the sixth edition.
This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations.
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( b ) Obtain the same results for the case where the observations are weighted by the N * N diagonal matrix D. ANSWER See Bock and Jones ( 1968 , Appendix D ] .
This textbook is likely to become a useful reference for students in their future work." —Journal of the American Statistical Association "In this well-written and interesting book, Rencher has done a great job in presenting intuitive and ...
Friedl, A., Padouvas, E., Rotter, H., Varmuza, K.: Anal. Chim. Acta 544, 2005, 191–198. Prediction of heating values of biomass fuel from elemental composition. Furnival, G. M., Wilson, R. W.: Technometrics 16, 1974, 499–511.
Some elementary statistical concepts. Matrix algebra. Samples from the multivariate normal population. Tests of hypotheses on means. The multivariate analysis of variance. Classification by the linear discrimination function. Inferences from...
The tests are further elucidated throughout the text by real examples of analysis. Of particular value to students is the book's detailed discussion of how to utilize SPSS to run each test, read its output, interpret, and write the results.
The Third Edition features new or more extensive coverage of: Patterns of Dependence and Graphical Models–a new chapter Measures of correlation and tests of independence Reduced rank regression, including the limited-information maximum ...