This unit develops basic computer-intensive statistical methods. Many of them find applications in scientific research and industry. Topics include: Monte-Carlo simulation; bootstrapping; regression computations which include collinearity diagnostic and models selection using cross-validation; alternatives to least squares; ridge regression, weighted least squares and logistic regression; maximum likelihood computations using iterative methods, such as Newton-Raphson and Fisher scoring; and applications of the maximum likelihood method. -- Course Website
Instructor: Dr Jun Ma
Prerequisites: 39cp including (STAT272(P) or STAT273(P) or STAT278(P))
