Course Title: Introduction to Probability and Statistics

Part A: Course Overview

Course Title: Introduction to Probability and Statistics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


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


City Campus


171H School of Science


Sem 1 2017,
Sem 1 2018,
Sem 1 2019

Course Coordinator: Prof Lewi Stone

Course Coordinator Phone: +(61 3) 9925 1728

Course Coordinator Email:

Course Coordinator Location: 8.9.21

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

This course provides a broad introduction to statistical techniques and data analysis using statistical packages. It is aimed at students who need a basic background in statistics and its application.  Topics areas include: summarising univariate and bi-variate data, fitting the regression line to bivariate data, using computer packages to analyse univariate and bivariate data, discrete and continuous random variables, binomial and normal distributions and generating random data using statistical packages.

Objectives/Learning Outcomes/Capability Development

 This course contributes to Program Learning Outcomes in various applied science programs. In particular it promotes knowledge, skills and their application in the following domains:

Personal and professional awareness

  • The ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence:



  • synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems

On completion of this course you will be able to:

  1. Construct appropriate graphical displays of data (stem and leaf plots, boxplots, etc) and explain the role of such displays in data analysis;
  2. Assess the nature of random variables and probability distributions (including binomial, Poisson, normal ) through direct calculation and computer simulation;
  3.  Perform basic statistical inference tasks using software (estimation and confidence intervals)
  4. Discriminate between univariate and bivariate data and fit a regression line to bivariate data.
  5. Select and use appropriate computer packages to analyse univariate and bivariate data, discrete and continuous random variables, binomial and normal distributions, and generate random data.
  6. Specify the calculations involved in such tasks and be cognisant of assumptions necessary for the validity of results (residual analysis, normality tests).

Overview of Learning Activities

  • Attendance at lectures where syllabus material will be presented and explained, and the subject will be illustrated with demonstrations and examples. While not compulsory, you will find that regular attendance is necessary as lectures form an important aspect of the learning experience.
  • Completion of tutorial questions and laboratory projects designed to give further practice in the application of theory and procedures and to provide feedback on your progress and understanding;
  • Assessment through a mixture of class demos, class tests, lab quizzes and a final exam.

If you are experiencing difficulty in understanding lecture material you may seek help from your lecturer or tutors. 


Total study hours

Three hours of lectures. One hour tutorial.

Overview of Learning Resources

You will be able to access course information and learning materials through Canvas and may be provided with copies of additional materials in class. Lists of relevant reference texts, and resources in the library will be provided. You will also use computer laboratory equipment and computer software within the School during project and assignment work

Overview of Assessment

 Note that:

 ☒This course has no hurdle requirements.

Assessment Task 1:  Class Demonstrations

Weighting 10%

This assessment task supports CLOs 1-6

Assessment Task 2: Class Tests

Weighting 20%

This assessment task supports CLO 1-6

Assessment Task 3: Lab Quizzes

Weighting 20%

This assessment task supports CLO 1-6

Assessment Task 4: Final Exam

Weighting 50% 

This assessment supports CLO 1-6