STAT463-17S1 (C) Semester One 2017

Multivariate Statistical Methods

15 points

Details:
Start Date: Monday, 20 February 2017
End Date: Sunday, 25 June 2017
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 5 March 2017
  • Without academic penalty (including no fee refund): Sunday, 21 May 2017

Description

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 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.

Learning Outcomes

  • 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 reports

    You 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.

Prerequisites

Subject to approval of the Head of School.

Course Coordinator

Daniel Gerhard

Lecturer

Elena Moltchanova

Assessment

Assessment Due Date Percentage 
Assignments (x4) 30%
Project Report 25%
Oral Presentation 5%
Final Examination 40%


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 course includes a project report and a presentation on a method not covered in the course.

Textbooks / Resources

Recommended Reading

Everitt, Brian. , Dunn, G; Applied multivariate data analysis ; 2nd ed; Arnold ;Oxford University Press, 2001.

Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H; The elements of statistical learning : data mining, inference, and prediction ; 2nd ed; Springer, 2009 (2001 or 2009 editions suitable).

Johnson, Richard Arnold. , Wichern, Dean W; Applied multivariate statistical analysis ; 5th ed; Prentice Hall, 2002.

Indicative Fees

Domestic fee $932.00

* 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 .

All STAT463 Occurrences

  • STAT463-17S1 (C) Semester One 2017
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