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Multivariate Statistical Methods
Multivariate statistical methods extract information from datasets which consist of variables measured on a number of experimental units. These methods are 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 R, which is a powerful tool when dealing with large multivariate datasets. SAS will also be briefly explained. Special attention will be given to practical applications and the interpretation of the results.
The courses will:introduce multiple and multivariate regressionintroduce principal component analysis (PCA) and factor analysis (FA)introduce discriminant analysis (DA) and clustering methodsintroduce the use of the statistical analysis software R (and SAS) for basic multivariate analysesgive you experience in writing scientific and technical reportsYou will be able to:choose appropriate method for analysis of your datasetuse appropriate R function (or SAS procedures) to perform multivariate analysesbe able to interpret the analysis results in such a way that a non-user of statistics can understandwrite a scientific and technical report.
Subject to approval of the Head of School.
Students must attend one activity from each section.
Heyang (Thomas) Li
Assignments give you practice in analysing data and presenting results in a written report. You will be expected to use R (or 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 course includes a project report and a presentation on a method not covered in the course.
Everitt, Brian. , Dunn, G;
Applied multivariate data analysis;
Arnold ;Oxford University Press, 2001.
Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H;
The elements of statistical learning : data mining, inference, and prediction;
Springer, 2009 (2001 or 2009 editions suitable).
Johnson, Richard Arnold. , Wichern, Dean W;
Applied multivariate statistical analysis;
Prentice Hall, 2002.
School of Mathematics and Statistics Postgraduate Handbook
General information for students
Domestic fee $1,000.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics.