Course Title: Statistical Inference

Part A: Course Overview

Course Title: Statistical Inference

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2016


City Campus


171H School of Science


Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 1 2022,
Sem 1 2023

Course Coordinator: Professor Irene Hudson

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities

Pre-requisites -

MATH2200: Introduction to Probability and Statistics

MATH2201: Basic Statistical Methodologies

Course Description

This course deals with fundamental concepts and techniques of statistical inference including estimation and tests of simple and composite hypotheses. A brief revision will also be given of some basic topics in probability theory as well as single and multiple random variables. The impact that statistics has made and will continue to make in virtually all fields of scientific and other human endeavours is considered.

During this course you will develop a deeper understanding of the basis underlying modern statistical inference and equip yourself with a statistical tool kit which will enable you to apply your knowledge and skills to real world tasks. 

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for Bachelor of Science (Statistics): BP245:

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 challenges.


Knowledge and technical competence

  • The ability to use the 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 and accuracy  of the mathematical and  statistical models used, and the timeliness of the delivery of the solution.

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 the course, you will be able to:

  1. Apply various discrete and continuous univariate and multivariate probability distributions in modelling statistical processes.
  2. Elucidate the concepts of sampling distribution and how to apply them.
  3. Estimate unknown parameters of a given probability distribution using standard and non-standard estimation techniques.
  4. Conceptually map the theoretical basis of tests of simple and composite hypotheses.

Overview of Learning Activities

Key concepts in estimation and hypothesis testing will be explained with relevant examples in lectures, tutorials and online notes. The assignments and tutorials will also test and consolidate your  understanding of the topics covered in lectures. You will also have the opportunity to discuss your progress with teaching staff.

Overview of Learning Resources

You will have access to learning resources comprising the recommended references, a set of detailed course notes and other relevant materials such as extra notes, assignments, practical examples and their solutions. These are available online via the Canvas. You will also have access to RMIT Library online and other hardcopy resources.  A series of Class Practical sheets with detailed solutions on relevant text problems will also be provided on a weekly basis.

Library Subject Guide for Mathematics & Statistics

Overview of Assessment

Assessment Tasks:

Assessment Task 1: Applied maths problems
Weighting 30% 
This assessment task supports CLOs 1, 2, 3, 4 

Assessment Task 2: Applied maths problems 
Weighting 35% 
This assessment task supports CLOs 1, 2, 3, 4 

Assessment Task 3: In class timed online test 
Weighting 35% 
This assessment task supports CLOs 1, 2, 3, 4