This module is not currently running in 2024 to 2025.
Nonparametric Methods: This part of the module comprises approximately 10 lectures on nonparametric methods, showing how they are applied in practice for testing goodness of fit to a distribution, including tests of normality, for testing randomness of a sequence, and for comparing two samples. Practical Statistics: There is no fixed syllabus for this component of the course. Students gain experience of practical data analysis through a series of assessments that confront them with unfamiliar data, which may require the use of techniques introduced in any of the other core modules of the Programme. Statistical Computing: At the start of the module, students are introduced to, and gain experience of, the document preparation system LaTeX, which enables the production of high-quality mathematical documents. Then there are sessions in which students learn the statistical package R, using a mixture of lectures and hands-on computing workshops. The initial aim is for students to gain familiarity with importing and manipulating data, producing graphs and tables, and running standard statistical analyses. The later parts of the module focus on the use of R as a programming language, introducing basic programming mechanisms such as loops, conditional statements and functions. This provides students with the means to develop their own code to undertake non-routine types of analysis if these are not already available in R.
55 workshops and lectures
70% Project, 30% Coursework
We will not follow a single text and the course will be heavily based on the lecture notes. Useful books include the following:
Chatfield, C. (1995) Problem–solving: a statistician’s guide. London, Chapman and Hall.
Crawley M. J. (2009) The R Book , Wiley
Cox, D.R. and Snell E.J. (1987) Applied Statistics: Principles and Examples (Chapman Hall statistics text series).
Conover, W. J. (1999) Practical Nonparametric Statistics, 3rd ed. New York, Wiley.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes. On successful completion of this module, students;
- will be proficient in LaTeX for document preparation and the statistical package R;
- will be able to select suitable techniques to analyse data in a sensible way and interpret the results appropriately;
- will be able to provide clear and competent reports on statistical analyses,
- will be able to use suitable nonparametric methods to analyse data.
The intended generic learning outcomes. On successful completion of this module, students
- will be able to plan and implement the analysis of unfamiliar material in a professional way;
- will be able to use information technology effectively for advanced data analysis including data retrieval;
- will be able to use scientific word processing software, such as LaTeX, to present reports on statistical analyses.
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