Applied Multivariate Statistical Analysis
4th Edition
Published on 28. April 1998
Book
Hardback
799 pages
978-0-13-834194-7 (ISBN)
Description
This market leading text is appropriate for courses that teach statistical methods for describing and analyzing multivariate data in depts. of statistics, math, marketing, and in the biological, physical, and social sciences. The 4th edition makes more extensive use of SAS and SPSS output with an emphasis on interpretation. Features include additional exercises, data sets, and graphics to illustrate points. Various techniques such as MANOVA and Discriminate Analysis, Correspondence Analysis, and Biplots are integrated more thoroughly.
More details
Edition
4th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 243 mm
Width: 196 mm
Thickness: 35 mm
Weight
1421 gr
ISBN-13
978-0-13-834194-7 (9780138341947)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
12/2001
5th Edition
Pearson
€100.51
Article is exhausted; no reprint
Previous edition
Richard A. Johnson | Dean W. Wichern
Applied Multivariate Statistical Analysis
Book
03/1992
3rd Edition
Pearson Education (US)
€34.61
Article exhausted; check different version
Content
*(NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References)*I. GETTING STARTED*Aspects of Multivariate Analysis*Matrix Algebra and Random Vectors*Sample Geometry and Random Sampling*The Multivariate Normal Distribution*II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS*Inferences about a Mean Vector*Comparisons of Several Multivariate Means*Multivariate Linear Regression Models*III. ANALYSIS OF COVARIANCE STRUCTURE*Principal Components*Factor Analysis and Inference for Structured Covariance Matrices*Canonical Correlation Analysis*IV. CLASSIFICATION AND GROUPING TECHNIQUES*Discrimination and Classification*Clustering, Distance Methods and Ordination*Appendix*Data Index*Subject Index