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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 R, which is a powerful tool when dealing with large multivariate datasets. 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 give 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.
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 $1,017.00
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