STAT315-24S1 (C) Semester One 2024

Multivariable Statistical Methods and Applications

15 points

Start Date: Monday, 19 February 2024
End Date: Sunday, 23 June 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 3 March 2024
  • Without academic penalty (including no fee refund): Sunday, 12 May 2024


Detailed study of multivariate methods. Application of multivariate methods, test statistics and distributions.

STAT315 and STAT463 are courses in data modelling. The emphasis is on real world examples, and how you can use models to understand patterns and relationships in data. Multivariable statistical methods are used to extract information from data where there are multiple variables measured, for example in biology, health, engineering, finance, economics, and marketing.

In the course we will cover the application of common statistical methods used to fit models to data including multiple regression and dimension reduction methods like principal component analysis, factor analysis and clustering methods, generalised linear models (GLMs), mixed models (LMM and GLMM), generalised additive models (GAMs), and multiple comparison tests.

The course will introduce and use 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

  • Students from the courses STAT315 and STAT463 will be able to:

  •            choose appropriate method when analysing data
  •            define the problem in a statistical framework
  •            fit a multivariable regression model to data and interpret the results
  •            understand and apply other dimension reduction methods such as principle component analysis (PCA), factor analysis (FA) and clustering
  •            use a generalised linear model (GLM) with data that does not follow a normal distribution
  •            identify when a mixed-model should be used and apply this to linear and generalised linear models
  •            use a generalised additive model (GAM) with non-linear data
  •            undertake appropriate statistical tests when multiple comparisons are used
  •            use the statistical analysis software R
  •            write scientific and technical reports
    • 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.


15 points from STAT200-299 and a further 15 points from DATA200-299 or STAT200-299.

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 09:00 - 10:00 Meremere 526
19 Feb - 31 Mar
22 Apr - 2 Jun
Lecture B
Activity Day Time Location Weeks
01 Wednesday 09:00 - 10:00 Beatrice Tinsley 111
19 Feb - 31 Mar
22 Apr - 2 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 09:00 - 10:00 Jack Erskine 442 Computer Lab
19 Feb - 31 Mar
29 Apr - 2 Jun
02 Thursday 10:00 - 11:00 Jack Erskine 442 Computer Lab
19 Feb - 31 Mar
29 Apr - 2 Jun

Course Coordinator

John Holmes


Alasdair Noble


Assessment Due Date Percentage 
Assignments (x4) 40%
Final examination 60%

Assignments (x6)                          60%
Final examination                          40%

Indicative Fees

Domestic fee $847.00

International fee $4,988.00

* 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 STAT315 Occurrences

  • STAT315-24S1 (C) Semester One 2024