MATH2831 Linear Models

 MATH2831 is a Mathematics Level II course. See the course overview below.

Units of credit: 6

Prerequisites: MATH2801 or MATH2901.

Exclusions: MATH2931, BIOS2041, BEES2041.

Cycle of offering: Term 3

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

More information: The Course Outline (pdf) contains information about course objectives, assessment, course materials and the syllabus.

The Online Handbook entry contains up-to-date timetabling information.

The higher version of this course, MATH2931 Higher Linear Models, is offered yearly in Semester 2.

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

If you are currently enrolled in MATH2831, 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 sound understanding of linear model regression analysis. There is an emphasis in this course on how to formulate and analyse linear statistical models and also how to develop software to assess the performance of such models. As such students will become proficient not only in the application of the statistical theory but also in the implementation of these models to solve real statistical problems.