STA3030S - Statistical Inference & Modelling

Course Outline:

This course forms part of the third-year major in Applied Statistics. The aim of the course is to provide students with the main intellectual and practical skills required in the use of inferential statistics and statistical modelling. The course consists of 4 modules: The simulation module introduces students to the use of computer simulation and data re-sampling techniques (bootstrap) to investigate the following problems: one and two sample tests of means and variances; one and two way analysis of variances; moments and other properties of distributions; theory of distributions derived from normal distribution. The Bayesian module introduces students to decision theory and Bayesian inference. The generalized linear models module introduces students to the exponential family of distributions and extends linear and logistic regression models to models for other non-normal response variables. The machine learning module cover a basic introduction to statistical learning paradigms, applications of regression and classification trees, and a primer on feedforward neural networks and backpropagation. Students will use the R programming language.