Handbook 1996 : Faculty of Science (Volume 4 page 237)
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619-230 Sampling, Simulation and Resampling

Note: Special requirement: An electronic calculator of an approved type (refer to Department of Statistics Office).

Credit points: 16.5

Coordinator: Prof TC Brown

Prerequisite: Statistics 619-100 and 617-141 or 617-142 (Before 1996: 619-100 and 617-160)

Contact: 39 lectures (three a week) and 39 tutorial/practice class hours

Timetable: Second semester

Objectives:

Students completing this course

• should comprehend:
• the concepts of randomness, sampling and simulation;
• the choice of an efficient sampling method
• should have developed the skills:
• to administer and analyse a simple sample survey;
• to select a random sample;
• to simulate sampling from a population;
• to use resampling as an inference tool.
• should appreciate:
• the breadth of application of simulation techniques to diverse problems in operations research, probability and statistics;
• the principles of sampling methods;
• the usefulness of resampling methods in statistical inference.

Content:

Sampling Samples and their properties. Using population characteristics to improve sampling efficiency: stratified sampling. Sampling from a finite population: finite population correction. Simulation Uniform number generators. Tests for randomness. Generating numbers from other distributions. Simulating random samples. Dependent variables. Monte Carlo methods. Resampling Simulation testing: randomisation tests, Monte Carlo tests. Bootstrap methods: sampling and bootstrap distributions; parametric bootstrap.

Assessment:

Up to 3 hours end-of semester written examination; up to 50 pages of assignments may be assessed.

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Handbook 1996 : Faculty of Science (Volume 4 page 237)

Status:          Official 1996
Date created:    Oct  9 1995
Last modified:   Oct  9 1995
Authorised by:   Academic Registrar
Email enquiries: Course_Information@registrar.unimelb.edu.au

Copyright © University of Melbourne 1995,1996.