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.

part 1|3 pages

Preparation for Analysis

chapter Chapter 1|16 pages

What is multivariate analysis?

chapter Chapter 2|6 pages

Characterizing data for analysis

chapter Chapter 3|19 pages

Preparing for data analysis

chapter Chapter 4|21 pages

Data visualization

chapter Chapter 5|15 pages

Data screening and transformations

chapter Chapter 6|9 pages

Selecting appropriate analyses

part 2|1 pages

Regression Analysis

chapter Chapter 7|28 pages

Simple regression and correlation

chapter Chapter 8|29 pages

Multiple regression and correlation

chapter Chapter 9|25 pages

Variable selection in regression

chapter Chapter 10|23 pages

Special regression topics

chapter Chapter 11|22 pages

Discriminant analysis

chapter Chapter 12|38 pages

Logistic regression

chapter Chapter 13|25 pages

Regression analysis with survival data

chapter Chapter 14|16 pages

Principal components analysis

chapter Chapter 15|19 pages

Factor analysis

chapter Chapter 16|21 pages

Cluster analysis

chapter Chapter 17|22 pages

Log-linear analysis

chapter Chapter 18|27 pages

Correlated outcomes regression