This module presents a parallel development of statistical theory and methods, building on the introductory material in prerequisites Applicable Mathematics 1 and 2. Topics covered include classical linear models, with an introduction to the analysis of variance; basic methods for handling discrete data; further work in the theory of probability distributions; some aspects of the theory of estimation and hypothesis testing. In the computing sessions, the MINITAB package is used to illustrate the application of statistical methods to the analysis of some typical data sets.
Learning Outcomes
By the end of this module the student will be able to: demonstrate a basic understanding of the principles of statistical inference; handle simple problems in the distributional calculus; identify standard situations to which statistical models apply; build statistical models and use them for the analysis of data; demonstrate some fluency with a standard statistical package.
Assessment
22506-04 : Ass Sem 1 : Class Test (10%)
22506-06 : Exam : Exam (Centrally Timetabled) - Written Unseen (80%)
22506-07 : Ass Sem 2 : Coursework (10%)
Assessment Methods & Exceptions
80% based on a 1.5 hour unseen examination (in a joint sitting covering other optional modules.)
20% based on course work and/or class tests.