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MTH2232 · Mathematical statistics

Official Handbook

2026 Handbook6 credit pointsLevel 2School of Mathematics

Last checked: 23 Aug 2026 UTC

Overview

This unit is a rigorous introduction to the theory of mathematical statistics and more specifically of statistical inference. It provides the mathematical theory underlying the methods and concepts used in statistics, such as estimation and hypothesis testing. This unit will cover a variety topics including: properties of a random sample, principles of data reduction, point estimation (including maximum likelihood estimation), hypothesis testing, interval estimation, the analysis of variance and linear regression.

Areas of study: Applied mathematics Financial and insurance mathematics Mathematical statistics Mathematics

Offerings

CampusTeaching periodMode
ClaytonSecond semesterTeaching activities are on-campus (ON-CAMPUS)

Assessment

The Handbook lists an examination for this unit.

#AssessmentTypeWeightHurdle
1Continuous assessmentProject50%
2Final assessment - Exam (3 hours and 10 minutes)Examination50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

prerequisite

  • MTH2222 — Mathematics of uncertainty

corequisite

  • MTH2010 — Multivariable calculus
  • MTH2015 — Multivariable calculus (advanced)
  • MTH2021 — Linear algebra with applications
  • MTH2025 — Linear algebra (advanced)
  • MTH2040 — Mathematical modelling
  • ENG2005 — Advanced engineering mathematics
  • MTH2019 — Multivariate mathematics for data science

Joined by OR.

Learning outcomes

  1. Demonstrate understanding of basic concepts in statistical inference, and in particular point and confidence estimation and hypothesis testing;
  2. Use point and confidence estimation and hypothesis testing in a variety of contexts including analysis of variance and linear regression;
  3. Demonstrate advanced skills in the effective use of statistical software;
  4. Demonstrate advanced skills in the written and oral presentation of mathematical and statistical arguments.

Workload

• Three 1-hour seminars; • One 2-hour applied class (in weeks 2-12) and • 7 hours of independent study per week.

ActivityDuration
Seminars36 hours
Applied sessions22 hours

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