Course Title: Design and Analysis of Experiments

Part A: Course Overview

Course Title: Design and Analysis of Experiments

Credit Points: 12.00

Important Information:

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Please read the Student website for additional requirements of in-person attendance: 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 2 2014,


City Campus


171H School of Science


Sem 1 2018,
Sem 1 2020,
Sem 1 2022

Course Coordinator: Dr. Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

Basic knowledge of statistics. Some knowledge of statistical packages such as MINITAB and SAS would be beneficial.

Course Description

This course deals with the concepts and techniques used in the design and analysis of experiments. The concepts and different models of an experimental design will be studied, leading to their statistical analysis based on linear models and appropriate graphical methods.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical knowledge, methodologies and modern computational tools.


  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the relationship between the purpose of a model and the appropriate level of complexity and accuracy.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

 On completion of this course you should be able to:

  1. Critically review basic concepts and models of experimental design.
  2. Analyse the results of a designed experiment in order to conduct the appropriate statistical analysis of the data.
  3. Interpret statistical results from an experiment and report them in non-technical language.

Overview of Learning Activities

You will review recorded lectures or attend lectorials each week and spend time outside class revising materials covered during that week’s lectures. Computer laboratory classes will also be held regularly. The learning experience outside formal tuition may be tested and supplemented by assignments and /or projects. The course is supported by the Canvas learning system.

Assessment will be a mixture of assignments and a end of semester  online timed  test. While attendance is not compulsory, you will find that regular attendance is necessary as classess  are an important aspect of the learning experience.

Overview of Learning Resources

Some basic course notes for this course will be available on Canvas. A recommended reading list will also be provided.

A library guide is available at

Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks:

Assessment Task 1: Take home Assignments Weighting 50%
This assessment task supports CLOs 1, 2 & 3

Assessment Task 2: On line summative tests
Weighting 50% 
This assessment supports CLOs 1, 2 & 3