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MATH2831 is a Mathematics Level II course. 

Units of credit:Ìý6

Prerequisites: MATH2801 or MATH2901

Exclusions:Ìý²Ñ´¡°Õ±á2931

Cycle of offering: Term 3

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

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

Important additional information as of 2023

ÁñÁ«¹ÙÍø 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.

The higher version of this course, , is offered yearly in Term 3

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

If you are currently enrolled in MATH2831, 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, which is essential for anyone contemplating a career as a professional statistician or higher study in statistics for students majoring in mathematics and statistics. The various components of the course (lectures, tutorials, assignments, tests, exam)  will improve the research, enquiry and analytical thinking abilities of the students, as well as their capacity and motivation for intellectual development. Essential computing skills in relation to statistical analysis of data will also be developed.

Multiple linear regression models and examples. Graphical methods for regression analysis. Multi-variate normal distribution. Quadratic forms (distributions and independence), Gauss-Markov theorem. Hypothesis testing. Model selection. Analysis of residuals. Influence diagnostics. Analysis of variance.