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

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 Aims

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.

Course Description

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.