Overview of Multivariate
Data Analysis Techniques
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.
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.
1
Nominal
2
Ordinal
3
Interval
4
Ratio
·
Regression and its
limitations
·
Multivariate analysis
methods
1
Principal components
analysis
·
Summary
Work experience using basic
statistics and/or one basic statistics course (which included the topic of
regression) is recommended.
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.
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
Edward J. Williams, Adjunct Lecturer of Industrial and
Systems Engineering, and of Management and Information Science
This course is intended for
analysts, engineers, designers, and managers involved in the collection and
interpretation of multivariate observational and/or experimental data.