**Instructor:**Professor Jiayang Sun, Office: Yost 326, Phone: 368-0630, e-mail: jsun at case edu,**Office Hrs**: TTh 4-4:45pm (subject to change)**Teaching Assistant**: Vinay Bhandaru, email: vxb18 at case edu**Office Hrs**: MW 11am-1pm @Yost 322, 216-368-1498- Other TAs/Tutors - call stat dept 368-6941 to confirm
**Lab Administrator:**- Office Hrs: 12-1 MTWTh, and 1-3 W (subject to change) at Yost 335, Phone 368-0417
- e-mail: help at stat case edu or stats-help at case edu
**Class:**2:45-4:00PM on TTH at Yost 101**Course Description:**- Stat 326/426
introduces classical and modern techniques for modeling, analyzing and mining
multivariate data. The general outline is:
- Introduction
- Graphical Methods for Multivariate Data
- One Multivariate Sample, Multivariate ANOVA and Multivariate Regression
- Dimension Reduction Techniques: principal components, correspondence analysis and projection pursuit
- Classification and Clustering: multidimensional scaling, discriminant and cluster analysis, and classification and regression trees (CART)
- Analysis of Covariance Structures/Latent Variable Models: principle components (revisit), factor analysis and covariance structure models (time permitting)
- Other Data Mining Techniques: nearest neighbor, support vector machines, EM algorithms, boosting and bagging, ... (time permitting)

*sun.case.edu/~jiayang/426/* -
**Prerequisite:**Stat 325/425. **Computing:**Splus (or R) and some SAS, Xgobi/Ggobi packages will be used. Read a news report on R from New York Times**References:****Recommended Text:**- Johnson, R. A., Wichern, D. W. (2008), Applied Multivariate Statistical Analysis, Sixth edition, Prentice Hall, ISBN-10: 0131877151

**Other References**(most are reserved in the Kevin-Smith library):- Everitt, B. and Dunn, G. (2001), Applied Multivariate Data Analysis, Second Edition, New York : Oxford University Press, ISBN: 0195209370 (1. Link in amazon. 2. Code and Data Sets. 3. Wiley's print-on-demand link)
- Everitt, Brian S. (2007), An R and S-Plus Companion to Multivariate Analysis, Springer-Verlag, ISBN: 978-1-85233-882-4
- Hardle, W. and Simir, L. (2007), Applied Multivariate Statistical Analysis, Springer, ISBN: 978-3-540-72243-4
- Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer-Verlag, ISBN: 978-0-387-31073-2
- Hastie, T., Tibshirani, R. and Friedman J. (2009), The Elements of Statistical Learning: data mining, inference and prediction, Springer- Verlag, Second edition.
- Yasunori Fujikoshi, Vladimir V. Ulyanov, Ryoichi Shimizu (2010), Multivariate Statistics : High-Dimensional and Large-Sample Approximations, Wiley.
- Breiman, Friedman, Olshen and Stone (1984), Classification and regression trees, Second Edition, The Wadsworth statistics/probability series, ISBN: 053498054
- Sun, J. (1998), Projection Pursuit,
*Encyclopedia of Statistical Sciences*(updated volumes), Vol. 2, pp 554-560, Wiley, (Edited by: Samuel Kotz, Campbell Read, David Banks, and Norman Johnson.) - Hair, Black, Babin, Anderson and Tatham (2010), Multivariate Data
Analysis, Seventh Edition, Prentice Hall, ISBN-10: 0-13-813263-1
ISBN-13: 978-0-13-813263-7.

- Some S/Splus/R, SAS and E-books.

**Final Examination:**12:30-3:30PM on May 3, 2012**Grading Policy:**-
**Undergraduate**:- The assessment is based on
*homework assignments*(50%) and a*final examination*(50%). **Graduate:**-
The assessment is based on
*homework assignments*(45%), an*oral presentation*(10%) and a*final examination*(45%). A graduate student's presentation can be based on his/her ongoing research project to which some multivariate data analysis may be useful, or on an interesting topic/article approved by the instructor. **Notes:***No late homework!**Minimum score to pass the course is 60, out of a 100 scale.*