STA308 Experimental Design and Analysis (8)

Proper experimental design is a prerequisite to the efficient and cost effective resolution of comparative quantitative research questions. This subject introduces experimental design and analysis by examples and by the study of the underlying linear model. Use of appropriate computer packages allows testing of assumptions and investigation of advanced topics. Extensions of the basic methodology are explored.


Session 1 (30)
On Campus
Wagga Wagga Campus
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA308. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Not available to students who have completed STA508.


STA201 or STA401 or QBM217

Subject Relationships

STA508 Similar content

Incompatible Subjects


Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to identify situations for which a linear model is appropriate;
  • be able to design, and check the design of, basic experiments;
  • be able to perform the analysis of such experiments using appropriate statistical protocols;
  • be able to communicate results and justify interpretations in terms of the original problem;
  • be able to critically test any assumptions underpinning the use of the linear model;
  • be able to perform design and analysis tasks on recognised software platforms.


This subject will cover the following topics:
  • Principles of experimental design.
  • Simple linear regression: review and extension.
  • Polynomial and multiple linear regression.
  • Principles of experimental design; including completely randomised designs, randomised block designs and Latin square designs.
  • Multiple comparison methods.
  • Diagnostic checking of the basic model.
  • The analysis of experiments with factorial treatment structures.
  • Analysis of variance.
  • Analysis of covariance.
  • Statistical software.


For further information about courses and subjects outlined in the CSU handbook please contact:

Current students

Future students

The information contained in the CSU Handbook was accurate at the date of publication: May 2019. The University reserves the right to vary the information at any time without notice.