Course Title: Regression Analysis

Part A: Course Overview

Course Title: Regression Analysis

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 2 2006,
Sem 1 2010,
Sem 1 2011,
Sem 1 2013,
Sem 1 2015


City Campus


171H School of Science


Sem 1 2017,
Sem 1 2019,
Sem 1 2021

Course Coordinator: Assoc Prof Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

Course Coordinator Email:

Course Coordinator Location: 015.04.020

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

 MATH1324 Introduction to Statistics

Course Description

This course covers multiple linear regression, classical estimation and testing methods and residual analysis. Various statistical packages will be used for practical application of the key concepts.

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:

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical 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 balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.


  • 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. Define generalised linear regression models
  2. Formulate generalised linear regression models and appreciate their limitations
  3. Estimate and validate generalised linear regression models and interpret the results obtained.

Overview of Learning Activities

There will be a combination of lectorials to cover theoretical concepts and  lab sessions to apply theory to practice using various software packages.

Overview of Learning Resources

A list of recommended textbooks for this course is provided on Canvas. 

All course materials, including lecture notes, lab exercises, practical exercises, and assessment tasks will be posted on Canvas.

The statistical packages Minitab and R can be accessed from the school computer labs, as well as  through the RMIT MyDesktop system from anywhere and anytime.  

Library Subject Guide for Mathematics & Statistics 

Overview of Assessment

This course has no hurdle requirements. 

Assessment tasks

Take home discipline specific formative Assignments 
Weighting: 50%  
This assessment task supports CLOs 1, 2, and 3 


Data Analysis Project 
Weighting 50% 
This assessment task supports CLO 1, 2, and 3