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

Course Coordinator: Dr Stelios Georgiou

Course Coordinator Phone: +61 3 9925 3158

Course Coordinator Email:

Course Coordinator Location: 8.9.74

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities


MATH1322 Introduction to Statistical Computing

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 lectures 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 Blackboard.

All course materials, including lecture notes, lab exercises, practical exercises, assignments will be posted on Blackboard LMS.

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

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task1: Assignments
Weighting 30%
This assessment task supports CLOs 1, 2, and 3

Assessment Task 2: Project
Weighting 20%
This assessment task supports CLO 1, 2, and 3

Assessment 3: Final Exam
Weighting 50% 
This assessment supports CLOs 1, 2, and 3