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.

+ Subject Availability Modes and Location

Session 1
InternalWagga Wagga Campus
DistanceWagga 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

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Enrolment restrictions

Not available to students who have completed STA508.
Prerequisite(s)Incompatible subject(s)Related subject(s)
STA201 or STA401 or QBM217STA508 STA508 Similar content

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.


The 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.


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.