Clayton Second semester 2008 (Day)
Probability models. Random variables and their distributions. Multivariate distributions. Conditional distributions and conditional expectations. Generating functions. The Law of Large Numbers and the Central Limit Theorem. Introduction to stochastic processes (random walk, finite state Markov chain, branching processes). Application to statistical models. Sampling distribution. Mathematical principles of inference.
On completion of this unit, students will understand basic concepts in statistical modelling and applied probability; apply statistical techniques to practical problems in areas of science and industry; understand the mathematical principles underlying statistical inference using the normal, binomial, t, F and chi-square distributions.
Examination(3 hours): 70%
Continuous assessment: 30%
Three 1-hour lectures and one 2-hour support class per week
MTH1030 or equivalent
MTH3222, STA2022, STA3022