3975 (v.4) Applied Statistics 302

Area:

Department of Mathematics and Statistics

Credits:

25.0

Contact Hours:

4.0

Lecture:

3 x 1 Hours Weekly

Practical:

1 x 1 Hours Weekly

Prerequisite(s):

8128 (v.6) Linear Algebra 202 or any previous version
AND
8393 (v.8) Statistical Methods 201 or any previous version
AND
302315 (v.2) Mathematical Statistics 202 or any previous version

Syllabus:

Introduction to multivariate statistical analysis. Review of matrix algebra. Random vectors, mean vectors, covariance and correlation matrices. Multivariate normal distribution and its properties. Random samples from the multivariate normal and sampling distributions thereof. Inference for multivariate normal parameters, maximum likelihood estimation, GLM and inferences for GLMS, one sample and two sample test of hypotheses on the mean vector, comparison of several mean vectors - multivariate analysis ofvariance (MANOVA), discriminant analysis. Analysis of multivariate data using statistical software like R and SAS.

Unit Outcomes:

On successful completion of this unit students will have gained the knowledge to explain the concept of multivariate statistical analysis, compute the mean vectors and covariance and correlation matrices for multivariate data, explain the multivariate normal distribution and its properties, describe the random sampling from multivariate normal distribution and find MLE for its parameters, describe generalized linear models and calculate maximum likelihood estimators and inferences from GLM, perform one-sample and two sample tests of hypotheses on the mean vector and comparison of several mean vectors (MANOVA), and describe the methods of Principal Components Analysis and Discriminant Analysis.

Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.

Unit References:

Anderson, T. W., 1984, 'An Introduction to Multivariate Statistical Analysis', Wiley, New York. Chatfield, C. and Collins A. J., 1980, 'Introduction to Multivariate Analysis', Chapman and Hall. Flury, B. and Riedwyl, H., 1988, 'Multivariate Statistics - A Practical Approach', Chapman and Hall. Dillon, W. R. and Goldstein, M., 1984, 'Multivariate Analysis - Methods and Applications, Wiley. Hair, J. F., et al, 1998, 'Multivariate Data Analysis', 5th Edition, Prentice Hall. Krzanowski, W. J., 1988, 'Principles of Multivariate Analysis - A Users Perspective', Clarendon Press. Manley, B., 1986, 'Multivariate Statistical Methods - A Primer', Chapman and Hall. Mardia, K. V., Kent, J. T. and Bibby, J. M., 1979, 'Multivariate Analysis', Academic Press. Morrison, D. F., 1976, 'Multivariate Statistical Methods, 3rd Edition, McGraw Hill. Rao, C. R., 1973, 'Linear Statistical Inference and its Applications', 2nd Edition, Wiley. Rencher, C., 1995, 'Methods of Multivariate Analysis', Wiley. 'SAS Institute Users Guide',1990, SAS, Carey. Venebles, W. N. and Ripley, B. D., 1997, 'Modern Applied Statistics with S-plus', 2nd Edition, Springer, New York. McCullagh, P. and Nedler, J. A., 1989, 'Generalised Linear Models', 2nd Edition, Chapman and Hall, London.

Unit Texts:

Johnson, R. A. and Wichern, D., 2002, 'Applied Multivariate Statistical Analysis', 5th Edition, Prentice-Hall, New York.

Unit Assessment Breakdown:

Assignment 1 10%. Mid-Semester Test 10%. Assignment 2 10%. Final Examination 70%.

Year	Location	Period	Internal	Area External	Central External
2004	Bentley Campus	Semester 2	Y