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These graphs show data from a study of New Zealand sea lion pups at three breeding colonies on the Auckland Islands. The points are the estimated number of pups that day (with standard error bars). A fourth-order polynomial was fitted to the data at each site in order to estimate the date of peak pup-production. Parametric bootstrapping was used to provide an estimate of the uncertainty in these dates. The “peak-dates” were 13th, 14th and 3rd January for Sandy Bay, Dundas Island and Pebble Point respectively (with standard errors of 0.5, 1.8 and 0.8). We could probably come up with similar estimates of the dates “by eye” but providing an idea of their uncertainty would be much harder.

STAT501 Statistical Modelling for Research

First Semester, 18 points
The aim of this paper is to provide postgraduate students with many of the important statistical tools that they require in their research. Students will gain experience in using modern statistical software (R and WinBugs).

Paper details

We cover the basics of probability through to the fitting of complex models. There will be an emphasis on the practical analysis of real data.

Potential students

Postgraduate research students (outside the Department of Mathematics and Statistics) who have taken at least a first-course in statistics and want to become familiar with modern methods of analysis and software.

Main topics

  • Probability and Distributions
  • Fitting Models to Data
  • Normal linear models
  • Generalised linear models
  • Model-selection
  • Model-checking
  • Data-collection

Prerequisites

Ideally STAT 110/115 or equivalent. Enrolment in a research-based postgraduate programme.

Required text

None

Some references

Online access to all of these is available via the University Library
  • Gelman and Hill "Data Analysis Using Regression and Hierarchical Models"
  • Maindonald and Braun "Data Analysis and Graphics Using R"
  • Zuur, Ieno and Meesters "A Beginner's Guide to R"

Lecturer

David Fletcher, room 219 (dfletcher@maths.otago.ac.nz)

Lectures

Thursday 2-5pm, starting in Room 241 and finishing in B21 (computer laboratory), in the Department of Mathematics and Statistics.

Internal Assessment

There will be four assignments, each contributing 5% towards the fi nal mark, and a project worth 50%.

Exam format

2-hour exam

Final mark

The final mark F is calculated from:
F = E + A
where E (exam mark) is out of 30, A (internal assessment) is out of 70.


Plagiarism

Students should make sure that all submitted work is their own. “Plagiarism is a form of dishonest practice. Plagiarism is defined as copying or paraphrasing another’s work and presenting it as one’s own” (University of Otago Calendar). In practice this means that plagiarism includes any attempt in any piece of submitted work (e.g. an assignment or test) to present as one’s own work the work of another (whether of another student or a published authority). Any student found to be responsible for plagiarism in any piece of work submitted for assessment shall be subject to the University’s dishonest practice regulations which may result in various penalties, including forfeiture of marks for the piece of work submitted, a zero grade for the paper, or in extreme cases exclusion from the University. The University of Otago reserves the right to use plagiarism detection tools.

While we strive to keep details as accurate and up-to-date as possible, information given here should be regarded as provisional. Individual lecturers will confirm teaching and assessment methods.