• Total units 24 Units
  • Areas of interest Statistics
  • Minor code APST-MIN
  • Academic career Undergraduate


Statistics is concerned with the process of planning how to collect data, collection of the data and extracting information from data in the presence of uncertainty and variation. The process is guided by the underlying purpose of the investigation and the formulation of hypotheses and models. The minor covers the core components of applied statistical methods and provides a solid foundation for students undertaking courses of study which will require them to perform standard quantitative analyses.

Learning Goals:

Students who complete the Applied Statistics minor will be able to:

  1. Discuss the importance of proper collection and management of quantitative information to the scientific process, including appreciation of the importance of data accuracy, verifiability and context.
  2. Apply data analytic techniques that are appropriate to inquiry context, including demonstrating appreciation for underlying assumptions and data requirements for these techniques to be valid.
  3. Communicate the principles and results of data analyses using the language and conventions of the discipline.
  4. Apply a range of statistical testing and estimation techniques, including modern and computer based modelling, in appropriate contexts.

Other Information

Advice to Students:

First year courses: You should enrol in STAT1003 or STAT1008

Students should seek further course advice from the academic convener of the Applied Statistics minor.

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This minor requires the completion of 24 units, which must include:

12 units from completion of the following course(s):

Code Title Units
STAT2008 Regression Modelling 6
STAT3008 Applied Statistics 6

6 units from completion of the following course(s):

Code Title Units
STAT1003 Statistical Techniques OR 6
STAT1008 Quantitative Research Methods 6

6 units from completion of the following course(s):

Code Title Units
STAT3011 Graphical Data Analysis OR 6
STAT3015 Generalised Linear Modelling 6
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