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- Home
- Our school
- Study with us
- Our research
-
Student life & resources
Postgraduate research
- Info for new students
- Current research students
- Postgraduate conference
- Postgraduate events
- Postgraduate student awards
- Michael Tallis PhD Research Travel Award
- Information about research theses
- Past research students
- Resources
- Entry requirements
- PhD projects
- Obtaining funding
- Application & fee information
Student services
- Help for postgraduate students
- Thesis guidelines
- School assessment policies
- Computing information
- Mathematics Drop-in Centre
- Consultation
- Statistics Consultation Service
- Academic advice
- Enrolment variation
- Changing tutorials
- Illness or misadventure
- Application form for existing casual tutors
- ARC grants Head of School sign off
- Computing facilities
- Choosing your major
- Engage with us
- News & events
- Contact
Overview
MATH3821 is a Mathematics Level III course.
Units of credit: 6
Prerequisites: MATH2831 or MATH2931
Cycle of offering: Term 2
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: The course handout contains information about course objectives, assessment, course materials and the syllabus.
Important additional information as of 2023
UNSW Plagiarism Policy
The University requires all students to be aware of its policy on plagiarism.
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.
The Online Handbook entry contains up-to-date timetabling information.
If you are currently enrolled in MATH3821, you can log into UNSW Moodle for this course.
Course aims
The aim of MATH3821 is that at the end of session students should understand the concepts and techniques involved in the syllabus and be able to apply those concepts and techniques to the solution of appropriate problems. The R package will allow them to solve problems computationally.
Course description
Introduction to flexible and modern approaches to statistical modelling and statistical computing. Theory, applications and computation for linear models, generalised linear models, the Bayesian linear model, nonparametric regression using kernel smoothers and smoothing splines, nonparametric density estimation and bandwidth selection. Applications of simulation in statistical inference including Monte Carlo simulation, bootstrap methods, and Markov chain Monte Carlo. Statistical packages include R or Splus.