Geometric Concepts of Data Manipulation. Fundamentals of Data Manipulation. Principal Components Analysis. Factor Analysis. Confirmatory Factor Analysis. Cluster Analysis. Two-Group Discriminant Analysis. Multiple-Group Discriminant Analysis. Logistic Regression. Multivariate Analysis of Variance. Assumptions. Canonical Correlation. Covariance Structure Models. Statistical Tables. References. Tables, Figures, and Exhibits. Index.

FINALLY! YOUR STUDENTS DON'T HAVE TO BE MATHEMATICIANS TO LEARN MULTIVARIATE TECHNIQUES! Multivariate techniques are fundamental to research analysis—but their mathematical derivations are often overwhelming to students. This exciting new book takes a gentle approach that introduces students to the various multivariate techniques used in business without intimidation: The author uses geometry to illustrate concepts so students understand the various techniques and their application. Matrix algebra and proofs can also be used by referring to the appendices of the chapters.Each technique is discussed analytically using a small data set and hand calculations. This gives students a clearer understanding of the technique.Computer outputs from SAS and SPSS are annotated and discussed. Students learn how to interpret the results form these two popular statistical packages.The chapter-ending pedagogy includes exercises with data sets that reinforce concepts. INSTRUCTOR'S MANUAL: 978-0-471-13061-1 Also available with this text is a carefully developed teaching tool for instructors. It offers detailed answers to all end of chapter exercises, including computer output for questions which require students to perform data analysis. An additional set of examination questions with answers are also provided in the manual.