Part A: Course Overview

Course Title: Regression Analysis

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1312

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

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

MATH1312

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2017,
Sem 1 2019,
Sem 1 2021,
Sem 1 2023,
Sem 1 2024,
Sem 1 2025

Course Coordinator: Professor Stelios Georgiou

Course Coordinator Phone: +61 3 9925

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

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed following course/s before you commence this course.

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.


Course Description

This course covers multiple linear regression, classical estimation and testing methods and residual analysis. R statistical package 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.

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 balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

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. 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

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 

The statistical software  R is available for free download and use. It is also accessible from the University computer labs, as well as through the RMIT MyDesktop system anywhere and anytime. 

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks

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

Assessment Task 2: Data Analysis Project 
Weighting 50% 
This assessment task supports CLOs 1, 2 & 3 

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.