STAT463-22S1 (C) Semester One 2022

Multivariate Statistical Methods

15 points

Start Date: Monday, 21 February 2022
End Date: Sunday, 26 June 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 6 March 2022
  • Without academic penalty (including no fee refund): Sunday, 15 May 2022


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.

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
  • 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.
    • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

      Biculturally competent and confident

      Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.

      Engaged with the community

      Students will have observed and understood a culture within a community by reflecting on their own performance and experiences within that community.

      Globally aware

      Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.


Subject to approval of the Head of School.

Course Coordinator

Jennifer Brown


Assessment Due Date Percentage 
Assignments (x4) 40%
Project Report and presentation 10%
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 $1,017.00

* 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

  • STAT463-22S1 (C) Semester One 2022
  • STAT463-22S1 (D) Semester One 2022 (Distance)