Overview of Multivariate Data Analysis Techniques

Objective

The objective of this course is to acquaint students with the basic ideas, applicability, and methods of multivariate data analysis. After an introductory overview of fundamental concepts, students will learn four multivariate analysis methods.

Description

The course will be laboratory-based. Use of the MINITAB™ statistical software package will be demonstrated using data from case studies. Guidelines for interpreting MINITAB™ output both geometrically and analytically will be presented. Students are encouraged to bring their own data analysis problems to the class for discussion.

Topics covered

         Scales of data measurement

1                     Nominal

2                     Ordinal

3                     Interval

4                     Ratio

         Regression and its limitations

         Multivariate analysis methods

1                     Principal components analysis

2                     Factor analysis

3                     Cluster analysis

4                     Discriminant analysis

         Summary

Prerequisite

Work experience using basic statistics and/or one basic statistics course (which included the topic of regression) is recommended.

Course material

Sharma, Subhash. 1996. Applied Multivariate Techniques. New York, New York: John Wiley & Sons, Incorporated.

Supplemental notes prepared by the instructor.

Access to Web site prepared by the instructor.

References

Hair, Joseph F., Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black.  1998.  Multivariate Data Analysis, 5th edition.  Upper Saddle River, New Jersey:  Prentice-Hall, Incorporated.

Manly, Bryan F. J.  1998.  Multivariate Statistical Methods A Primer, 2nd edition.  Boca Raton, Florida:  Chapman & Hall/CRC

Instructor

Edward J. Williams, Adjunct Lecturer of Industrial and Systems Engineering, and of Management and Information Science

Target audience

This course is intended for analysts, engineers, designers, and managers involved in the collection and interpretation of multivariate observational and/or experimental data.