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MATH2931 is a Mathematics Level II course; it is the higher version of MATH2831 Linear Models. 

Units of credit:Ìý6

Prerequisites: MATH2901 or MATH2801(DN)

Exclusions:Ìý²Ñ´¡°Õ±á2831

Cycle of offering: Term 3 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: °Õ³ó±ðÌýCourse Outline (pdf) contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

¹úÃñ²ÊƱ Plagiarism Policy

The University requires all students to be aware of its .

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

°Õ³ó±ðÌý contains up-to-date timetabling information.

MATH2931 (alternatively MATH2831) is a compulsory course for Statistics majors.

If you are currently enrolled in MATH2931, you can log into  for this course.

This course introduces students to statistical model building using the important class of linear models. Topics covered in the course include how to estimate parameters in linear models, how to compare models using hypothesis testing, how to select a good model or models when prediction of the response is the goal, and how to detect violations of model assumptions and observations which have undue influence on decisions of interest.

Concepts are illustrated with applications from finance, economics, medicine, environmental science and engineering. Linear models are a fundamental component of statistical practice and the course is a solid background for more advanced statistical courses.

This course gives an understanding of the fundamentals of regression modelling. This is essential for anyone contemplating a professional statistician career, or for students majoring in mathematics and statistics who are considering higher study. The components of the course (lectures, tutorials, assignments, tests and exam) will improve the research, enquiry and analytical thinking abilities of students. It will also extend their capacity and motivation for intellectual development. Essential computing skills in relation to statistical analysis of data will be developed.

This course covers multiple linear regression models and examples along with graphical methods for regression analysis. It also covers multi-variate normal distribution, quadratic forms (distributions and independence), Gauss-Markov theorem, hypothesis testing, model selection, analysis of residuals, influence diagnostics and analysis of variance.

Where content is in common with MATH2831, this course aims to give students a deeper level of understanding.