Probability and Statistics

Undergraduate | 2026

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Area/Catalogue
STAT X100
Course ID icon
Course ID
208187
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
1
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

This course provides an introduction to statistical theory, application and communication. Topics covered will include: probability, random variables and distributions; inference, including hypothesis testing for means and proportions; and linear regression including diagnostics, model checking, and multiple linear regression with continuous predictors, factors, and interaction terms. In this course students will develop their: mathematical understanding of these topics; their ability to implement their ideas in the statistical software R; and their skill in communicating their results to both statistical and non-specialist audiences. This course encourages the development of skills for all mathematicians across a broad range of program outcomes including communication, professional practice, data literacy, ethics and integrity.

  • Probability
  • Inference
  • Regression

Course learning outcomes

  • Apply laws of probability and properties of random variables, including expected value and variance of linear combinations of random variables to a variety of problems
  • Choose and perform appropriate hypothesis tests in a variety of scenarios including one sample and two sample tests for means and proportions
  • Evaluate the assumptions of linear regression models fit to data and interpret the outputs from such a model fit including coefficients, measures of model-fit, and predictions
  • Describe data and data analysis to both statistical and generalist audiences
  • Analyse data using the statistical software R including fitting linear regression models to data, making predictions using such models, computing probabilities, applying hypothesis tests and producing graphics to visualise and investigate data

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A