Course Title: Linear Models and Experimental Design

Part A: Course Overview

Course Title: Linear Models and Experimental Design

Credit Points: 12.00

Important Information:

 

 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2203

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 1 2016

MATH2203

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023

Course Coordinator: Assoc Prof Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

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

Course Coordinator Location: 015.04.020

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Pre-requisite: MATH2201 Basic Statistical Methodologies 

 


Course Description

The first part of this course provides an overview of linear models, including simple linear regression, multiple linear regression, logistic regression and model building based on these techniques. The second part provides an introduction to the concepts and various techniques of experimental design, using analysis of variance and related techniques.


Objectives/Learning Outcomes/Capability Development

This course contributes to the development of the following Program Learning Outcomes for BP083 Bachelor of Science (Applied Mathematics and Statistics):

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

  • The ability to use the appropriate and relevant, fundamental and applied mathematical and statistical 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.
  • The ability to understand the balance between the complexity and accuracy of the statistical models used.


On successful completion of this course, you should be able to

  1. Select and apply appropriate regression techniques to address research questions and hypotheses;
  2. Apply experimental design techniques in real world problems and in research;
  3. Argue the necessity of experimental design to the task of collecting valid and relevant data in order to draw the correct statistical evidence to support a hypothesis.


Overview of Learning Activities

Key concepts of linear models and design of experiments will be extensively covered in this course. These will be explained and elucidated with relevant class and computer laboratory examples. The assignments and labs will test your understanding of class materials.


Overview of Learning Resources

Material for this course will be provided by the lecturer. Other learning resources are the prescribed texts and references which can be accessed online and from the libraries.

The computer laboratories contain computers with state-of-the-art software such as SAS, Rstudio, MINITAB etc. which will be employed to analyse data and solve problems. A Library Guide is available at:

http://rmit.libguides.com/mathstats

 


Overview of Assessment

Note: This course has no hurdle assessments

 

Assessment Task 1- Mathematical and Statistical Assessments

Weighting 30% 

Addresses CLOs 1-3 

Assessment Task 2- Lab  Assignments

Weighting 30% 

Addresses CLOs 1-3

Assessment Task 3- Online Summative and Authentic Assessment

Weighting 40% 

Addresses CLOs 1-3