Course

Syllabus

The Organization of Data: Arrays, Descriptive Statistics, Two-Dimensional Scatter Plots, Distance. Random Vectors and Matrices: Mean Vectors and Covariance Matrices, Geometry of the Sample, Random Samples, Sample Mean and Covariance Matrix and Correlation Matrix, Generalized Variance.The Multivariate Normal Distribution: Multivariate Normal Density and its Properties, The Sampling Distribution of sample mean X ? and S.Principal Component Analysis: Principal Components for Covariance Matrices, Sample Variation by Principal Components. Factor Analysis: The Orthogonal Factor Model, The Principal Component Method.Discrimination and Classification: Separation and Classification for Two Populations, Classification with Two Multivariate Normal Populations, Classification with Several Populations.

Text Books / References

Text Books:

1. Johnson, R.A., and Wichern, D.W. (2007). Applied Multivariate Statistical Analysis. 6th Edition, Prentice Hall, New York.
2. T. W. Anderson (2003), An Introduction to Multivariate Statistical Analysis, 3rd Edition, John Wiley and Sons, New Jersey, USA.

References:

1. Morrison (1990). Multivariate Statistical Methods, McGraw-Hill,
2. Hair, Joseph F.; Black, William C.; Babin, Barry J.; Anderson, Rolph E. Multivariate data analysis, 7th edition, Pearson