Students completing this course should be able to:
- Select appropriate methods of multivariate data analysis, given multivariate data and study objectives;
- Write SAS and/or Minitab programs to carry out multivariate data analyses;
- Interpret results of multivariate data analyses.
This graduate level course covers the following topics:
- Working with multivariate data and its graphical display
- Measures of central tendency, variance and association of multivariate data
- Interpreting the meaning of linear combination of random variables
- Understanding the multivariate normal distribution and how it is used
- Understanding the properties of sample mean vectors and correlation in multivariate data contexts
- Understanding the role that partial correlation may play in multivariate contexts
- Understanding how data reduction techniques can be used to generate more meaningful interpretation
- Using principal component analysis
- Using factor analysis
- Using canonical correlation analysis
- Using cluster analysis
- Understanding the implications involved in making inferences in multivariate contexts
- Using Hotelling's T-square in various multivariate contexts
- Using discriminant analysis
- Using MANOVA
- Using repeated measures analysis
Dr. Stephen Rathbun was the original author to develop materials for this course. Dr. Andrew Wiesner has added to and updated the focus of this course. Dr. Srabashi Basu, Dr. Scott Roths and Dr. Megan Romer have recently adapted the online course materials for STAT 505.
SAS is the recommended software and shall be used for all in-class demonstrations of statistical analyses, homework assignments, and exams. See the Statisitical Software page for more information.
SAS will be supported and sample programs will be supplied but you will be required to do some programing on your own. Due to different software applications, software versions and platforms there may be issues with running code. Students must be proactive in seeking advice and help from appropriate sources including documentation resources, other students, the teaching assistant, instructor or helpdesk.
Statistical software SAS involves programming. Students should already feel comfortable using SAS at a basic level, be a quick learner of software packages, or able to figure out how to do the required analyses in another package of their choice. Students who have no experience with programming or are anxious about being able to manipulate software code are strongly encouraged to take the one-credit course in SAS in order to establish this foundation before taking STAT 505.
Johnson, R.A., and Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. 6th ed. Prentice Hall, New York.