Statistical Computing - STAT378
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.
| Credit Points: | 3 |
| When Offered: | TBD - Not offered in the current year; next offering is to be determined |
| Staff Contact(s): | Statistics Staff |
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| Assessed As: | Graded |
| Offered By: | Department of Mathematics and Statistics Faculty of Science and Engineering |
Course structures, including unit offerings, are subject to change.
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