|This course covers the basic theory and methods for analysis of multi-dimensional data. The ability to analyze multiple responses simultaneously opens up many applications. The topics covered in this course include descriptive statistics for multivariate data, basic properties of multivariate distributions such as normal and Student-t, multivariate linear regression, principal components analysis for dimension reduction, factor analysis and dynamic factor models, canonical correlation analysis, discrimination and classification, independent component models, dimension reduction, and simple multiple time series models. Data mining will be discussed if time permits. Applications in business and economics are emphasized. Software R will be used.|
|Textbook: R.A. Johnson and D.W. Wichern, Applied Multivariate Statistical Analysis, 6th ed. (Prentice-Hall). Reference book: T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning 2nd Edition, (Springer 2009).|
|Mid-term (35%), final exam (40%), and homework assignments (25%).|
|Business 41901 or 41902 or equivalent courses.
Description and/or course criteria last updated: 06/11
|Course Conditions and Course Related Items:|