STAT463-21S1 (C) Semester One 2021

Multivariate Statistical Methods

15 points

Start Date: Monday, 22 February 2021
End Date: Sunday, 27 June 2021
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 7 March 2021
  • Without academic penalty (including no fee refund): Friday, 14 May 2021


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.

Learning Outcomes

  • The courses will:
  • introduce multiple and multivariate regression
  • introduce principal component analysis (PCA) and factor analysis (FA)
  • introduce discriminant analysis (DA) and clustering methods
  • introduce the use of the statistical analysis software R (and SAS) for basic multivariate analyses
  • give you experience in writing scientific and technical reports

    You will be able to:
  • choose appropriate method for analysis of your dataset
  • use appropriate R function (or SAS procedures) to perform multivariate analyses
  • 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.


Subject to approval of the Head of School.

Timetable 2021

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 13:00 - 14:00 Link 309 Lecture Theatre
22 Feb - 4 Apr
26 Apr - 6 Jun
Lecture B
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 Jack Erskine 101
22 Feb - 28 Mar
26 Apr - 6 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 13:00 - 14:00 Jack Erskine 442 Computer Lab
22 Feb - 4 Apr
26 Apr - 6 Jun
02 Wednesday 14:00 - 15:00 Jack Erskine 442 Computer Lab
22 Feb - 4 Apr
26 Apr - 6 Jun

Course Coordinator / Lecturer

Heyang (Thomas) Li

Contact Person

Jennifer Brown


Assessment Due Date Percentage 
Assignments (x4) 20%
Project Report and presentation 30%
Final Examination 50%

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.

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 $1,000.00

* 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

  • STAT463-21S1 (C) Semester One 2021