Practical multivariate analysis

by Afifi, A. A., (Abdelmonem A.)

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Title Statements

Full title :Practical multivariate analysis / Abdelmonem Afifi, Susanne May, Robin A. Donatello, Virginia A. Clark.

Summary

This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computing, and exercises. Updated with a new chapter on data visualization.

Edition Statements

Publication Date:2020
Edition:"A Chapman & Hall Book"

Publication Statements

Publisher(s):CRC Press
Place of Publication:Boca Raton, FL
Language:eng
Publication Date:2020

The Physical Object

Description:1 online resource (xv, 409 pages)

Edition Identifiers

ISBN:9781351788915, 1351788914, 9781351788908, 1351788906, 9781315203737, 1315203731
oclc:on1124777411
LC Classification:QA278
Dewey Decimal:519.5/35

What is multivariate analysis? -- Characterizing data for analysis -- Preparing for data analysis -- Data Visualization -- Data screening and transformations -- Selecting appropriate analyses -- Simple regression and correlation -- Multiple regression and correlation -- Variable selection in regression -- Special regression topics -- Discriminant analysis -- Logistic regression -- Regression analysis with survival data -- Principal components analysis -- Factor Analysis -- Cluster analysis -- Log-linear analysis -- Correlated outcomes regression.

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