## STAT380 Statistical Computing

First Semester |

This course develops a working knowledge of several topics in modern statistical computing. We focus on a practical understanding of how and why such methods work rather than their analytic bases. A second aim is to learn a suitable language that allows computer implementation of the statistical ideas covered.

### Potential students

This paper is intended for students with some exposure to statistical techniques.

### Prerequisites

STAT 241 + 18 additional points of 200-level statistics

### Main topics

- Introduction to R
- Bootstrap and randomization procedures
- Optimization, numerical integration and maximum likelihood estimation
- Simulation and MCMC methods

### Required text

There is no prescribed textbook, but some texts that may be useful include:

- Peter Dalgaard,
*Introductory Statistics with R*, Springer, 2002. ISBN 0-387-95475-9. - Anthony Davison and D.V. Hinkley,
*Bootstrap Methods and their Application*, Cambridge University Press, 1997. - Geof H. Givens and Jennifer A. Hoeting,
*Computational Statistics*, Wiley, 2005. ISBN 0-471-46124-5. - William N. Venables and Brian D. Ripley,
*Modern Applied Statistics with S*, Fourth Edition, Springer, New York, 2002. ISBN 0-387-95457-0.

### Lecturers

Matthew Schofield, Peter Dillingham

### Lectures

Monday and Wednesday at 9:00 - 9:50 AM; room B21. These are compulsory.

### Tutorial/Practical

Tuesday at 9:00 - 9:50 AM; room B21. This is compulsory.

### Internal Assessment

The internal assessment will make up 40% of the final mark. Six assignments will make up a total of 25% of the final mark. A mid-term test will make up 15% of the final mark.

### Exam format

The final exam will be worth 60% of the final mark. A minimum score of 40% is required on the exam in order to pass the paper.