MATH2931 Higher Linear Models

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

Units of credit: 6

Prerequisites: MATH2901 or MATH2801(DN)

Exclusions: MATH2831, BIOS2041, BEES2041.

Cycle of offering: Yearly in Semester 2.

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

More information: This recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus.

The Online Handbook entry 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 UNSW Moodle for this course.

Course Overview

Statistics is about using probability models to make decisions from data in the face of uncertainty. This course gives an introduction to the process of building statistical models using an important class of models (linear models). In a linear model we try to predict or explain variation in a response variable in terms of related quantities (predictors). The relationship between the expected response and predictors is linear in unknown model parameters.

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 an undue influence on decisions of interest. Concepts are illustrated with applications from finance, economics, medicine, environmental science and engineering.

Students completing this course should have a rigorous understanding of linear model regression analysis. In addition to the emphasis placed on the formulation and analyse of linear statistical models and associated software analysis. This higher level course offers strong students the opportunity to develop a sound understanding of the proofs of certain results obtained and used throughout the course. This extension should place students wishing to major in statistics with a strong foundation for future regression courses.