Instructor: Wen-Ping Hsieh
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<br>Reference
<br>Johnson, R.A. and Wichern, D.W. Applied Multivariate Statistical Analysis. 
<br>Sixth Edition, 2007.  Pearson Prentice Hall.  ISBN 0-13-187715-1.
<br>Brian Everitt, Torsten Hothorn. An introduction to applied multivariate 
<br>analysis with R, 2011. Springer.
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<br>Tentative Schedule
<br>‧Introduction
<br>‧Graphical displays, principal components, multidimensional scaling
<br>‧Multivariate normal distribution
<br>‧Extension of linear model to multivariate response: Hotelling T2 and MANOVA
<br>‧Factor analysis
<br>‧Cluster analysis: Kmeans, Hierarchical Clustering
<br>‧Correspondence analysis
<br>‧Canonical correlation analysis
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<br>ob<x>jectives of the course  
<br>Students taking this course will learn about the techniques of displaying and 
<br>analyzing multivariate data. We will introduce the theories underlying these 
<br>methods, and applications will be followed to make it clear. Demonstration 
<br>with R or SAS will be provided. Students can choose their preferred software 
<br>to solve the problems. 
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<br>Prerequisite
<br>Students are assumed to have knowledge of linear algebra, calculus, some basic 
<br>ideas of probability theory, statistical inference and linear models.
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