Offering an applications-oriented approach which focuses on the use of each technique rather than its mathematical derivation, this textbook introduces a six-step framework for organising and discussing multivariate data analysis techniques.
KEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-oriented introduction...
The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field.
Thus PARC is an empirical but general approach to the analysis of multivariate data, empirical in the sense that few fundamental assumptions or models are needed to perform the analysis.
"This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource."--BOOK JACKET.
Multivariate Data Analysis
Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation.
Texts in Statistical Science Series Series Editors C. Chatfield , University of Bath , UK Jim Lindsey , University of ... Jr. Computer - Aided Multivariate Analysis , Third Edition A.A. Afifi and V.A. Clark A Course in Categorical Data ...
Designed for the non-statistician, this applications-oriented introduction to multivariate analysis greatly reduces the amount of statistical notation and terminology used, while focusing instead on the fundamental concepts that affect the...
This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis.