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Multivariate Statistical Methods
STAT315 and STAT463 are courses in multivariate statistical methods. Multivariate statistical methods extract information from datasets which consist of variables measured on a number of experimental units. Due to the large memory capacity available and with the advent of computing power, these methods are now widely applied in a variety of fields, including bioinformatics, epidemiology, finance and marketing. The course will cover the theory and application of various multivariate statistical methods, namely: multiple regression, principal component analysis, factor analysis, discriminant analysis, and clustering methods. It will also introduce the statistical analysis software SAS, which is a powerful tool when dealing with large multivariate datasets. R-syntax will also be briefly explained. Special attention will be given to practical applications and the interpretation of the results.For a full list of 2012 Honours courses, please refer to the Department of Mathematics and Statistics Honours Booklet Mathematics and Statistics Honours Booklet
The courses will: introduce multiple and multivariate regression introduce principle component analysis (PCA) and factor analysis (FA) introduce discriminant analysis (DA) and clustering methods. introduce the use of statistical analysis software SAS give you experience in writing scientific and technical reportsYou will be able to: choose appropriate method for analysis of your dataset use SAS procedures to perform the analysis be able to interpret the analysis results in such a way that a non-user of statistics can understand write a scientific and technical report.
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Subject to approval of the Head of School.
Assignments give you practice in analysing data and presenting results in a written report. You will be expected to use SAS for analysis. The assignments provide an opportunity for you to learn not only statistical modeling techniques, but to develop your scientific writing skills.The STAT463 course includes a project report and a presentation on a method not covered in the course.
Recommended Reading:Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction (2001 or 2009) Springer, Ch 1-7 Johnson, R.A. and Wichern, D.W. (2002). Applied Multivariate Statistical Analysis. Fifth Edition. Prentice Hall.Everitt, B. and Dunn, G. (2001). Applied Multivariate Data Analysis. Second Edition. Hodder Arnold.
Mathematics and Statistics Honours Booklet
Domestic fee $788.00
International Postgraduate fees
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Mathematics and Statistics.