Course Title: Design and Analysis of Experiments

Part A: Course Overview

Course Title: Design and Analysis of Experiments

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1302

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2006,
Sem 2 2014,
Summer2016

Course Coordinator: Dr. Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

Course Coordinator Email: stelios.georgiou@rmit.edu.au


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.

Problem-solving

  • 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.

Communication

  • 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 attend lectures 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 Blackboard learning system.

Assessment will be through a mixture of class exercises, assignments and an exam. While attendance at lectures is not compulsory, you will find that regular attendance is necessary as lectures are an important aspect of the learning experience.

 


Overview of Learning Resources

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

A library guide is available at http://rmit.libguides.com/mathstats


 


Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks:

Assessment Task 1:  Class Exercises
Weighting 25%
This assessment task supports CLOs 1,2 & 3

Assessment Task 2: Assignments
Weighting 25%
This assessment task supports CLO 1,2 &3

Assessment Task 3: Final Exam
Weighting 50% 
This assessment supports CLO 1,2 & 3