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STA3300 Experimental Design

Semester 1, 2022 Online
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Student contribution band : Band 1
Grading basis : Graded
Version produced : 18 May 2022


Examiner: Shahjahan Khan


Pre-requisite: STA2300 or STA1003 or equivalent or approval of examiner


The proper design, implementation and analysis of results of experiments are of vital importance in many disciplines. The validity and reliability of research findings can be severely compromised if a poor design or experimental procedure is followed. This course introduces principles of good design in experiments and discusses different methods of analysis of planned experiments which require the use of an appropriate statistical package. This course has relevance to all students involved in or planning to be involved in experimental projects, especially students in the general science and engineering disciplines. Previous statistical knowledge to the level of STA2300 Data Analysis only is assumed.

This course covers principles of design such as randomisation, replication, factorial arrangement and blocking. The emphasis is on general principles of design and analysis of experimental data rather than in describing the details of particular design layouts. Consideration is given to checking of assumptions and quality of data, robustness, prior and posterior analysis, contrasts, confounding, covariates, error control and reduction, and interpretation of results. Practical experience is gained in designing, carrying out, analysing and writing up the report from the results of an experimental study. Methods of analysis and different models are discussed and practised mainly using the SPSS software package.

Course learning outcomes

On completion of this course students will be able to:

  1. Identify and examine the principles and assumptions of a variety of experimental designs.
  2. Evaluate the quality of data including underlying statistical assumptions.
  3. Apply and analyse various statistical models appropriate for experimental data sets using a statistical package.
  4. Communicate experimental analysis and results using appropriate statistical terminology for a wider audience.
  5. Independently develop and conduct an experiment and appropriately report results.


Description Weighting(%)
1. Data Screening - introduction to a computer package - exploratory and preliminary analysis - descriptive and graphical tools - transformations. 10.00
2. Inference - hypothesis testing and p values - estimation and confidence intervals - comparative experiments, independent and dependent samples - linear regression, dummy variables, model assumptions. 10.00
3. Introduction to experimentation - observational v experimental studies - causality and association - validity - some design principles. 10.00
4. Completely randomised designs with one factor - experimental procedure - principle of randomisation - modelling the data - analysis of variance and interpretation - descriptive techniques - residual analysis - nonparametric techniques. 15.00
5. Analytic comparisons - contrasts, simple and complex - planned and unplanned comparisons - multiple comparisons and error rates - Newman-Keuls range tests. 15.00
6. Balanced factorial experiments - principles of factorial arrangement- descriptive techniques- main and interaction effects- multiway analysis of variance- estimation of effects- model fitting. 15.00
7. Blocking- principle of error reduction- single and multifactor arrangements- random and fixed effects - calculation of expected mean squares - components of variance. 15.00
8. Regression Analysis - analysis and interpretation- analysis of covariance- trend analysis. 10.00

Text and materials required to be purchased or accessed

IBM SPSS STATISTICS BASE GRAD PACK VERSION 26.0 (SPSS Version 20.0 or later is acceptable) (Note: All students have access to this software in campus computer laboratories or via USQ Turbo.Net and are not required to purchase SPSS).

All additional study material will be provided on the course StudyDesk.

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Weighting (%) Course learning outcomes
Assignments Written Problem Solving 1 No 30 1,2,3,4
Assignments Written Problem Solving 2 No 30 1,2,3,4
Assignments Written Report No 40 1,2,3,4,5
Date printed 18 May 2022