MATH2901 is a Mathematics Level II course.
Units of credit: 6
Prerequisites: MATH1231 or MATH1241 or MATH1251 or DPST1014 (or, in program 3653, MATH1131 or MATH1141).
Exclusions: Students who have studied another introductory statistics course that has a theoretical focus: MATH2089, MATH2099, MATH2859, MATH2801, BEES2041, ECON3209, CVEN2002
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
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.
MATH2901 (alternatively MATH2801) is a compulsory course for Statistics majors.
If you are currently enrolled in MATH2901, you can log into UNSW Moodle for this course.
This course is an introduction to the theoretical underpinnings of statistics, essential knowledge for anyone considering a career in quantitative modelling or data analysis. Students will learn probability and distribution theory on which modern statistical practice is founded, and how to apply it to answer important practical questions raised in medical research, ecology, the media and more.
As for MATH2801 but in greater depth. This course provides an introduction to the theoretical underpinning of statistics; it covers fundamental results from probability and distribution theory and shows how to apply the theory to the analysis of data. Topics include: Random variables, univariate and bivariate distributions. Transformations of random variables. Convergence of random variables, the sampling distribution and the Central Limit Theorem. Estimation and inference including moment and likelihood estimation, interval estimation, and hypothesis testing.