Department:Mathematics and Statistics
Course Convenor:Professor JR Whitehead
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Curriculum Design: Outline Syllabusback to top
Data collection and summary
Modelling discrete data
Modelling continuous data
Curriculum Design: Pre-requisites/Co-requisites/Exclusionsback to top
A-level Mathematics at A-grade or above, or equivalent.
Educational Aims: Subject Specific: Knowledge, Understanding and Skillsback to top
The module aims to enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems in which the recorded information is subject to systematic and random variations. Specifically, by the end of the module, students should be able to select and formulate appropriate probability models, to implement the associated statistical techniques, and to draw clear and informative statistical conclusions for a range of simple scientific problems.
The module starts with the description of examples of scientific investigations in which specific questions are of interest, but they are not straightforward to answer, as the available data are subject to systematic and random variations. A range of exploratory data analysis methods for gaining insight into the sources of variations will be introduced. Then a general strategy for the statistical treatment of such problems will be developed, involving aspects of modelling, investigation, and conclusions.
Educational Aims: General: Knowledge, Understanding and Skillsback to top
The module will encourage students to take a critial approach to the assessment of evidence and quantitative information that they enounter in the media and later in their professional lives.
Learning Outcomes: Subject Specific: Knowledge, Understanding and Skillsback to top
1. select appropriate probability models which have variations which are consistent with the mechanisms that generated the data,
2. fit the probability model to the data by estimating unknown features of the probability model,
3. assess whether the fitted probability model agrees with the data,
4. provide answers to scientific questions reflecting the uncertainty in the data.
Students will obtain experience in implementing this general strategy for statistical investigation by application to the motivating problems and by studying some standard statistical techniques. The module will be supported by statistical software, which students will learn about through LAB100, lectures and workshops. The strategic understanding and software experience developed in this module are skills used in all the subsequent statistical modules of the degree.
Learning Outcomes: General: Knowledge, Understanding and Skillsback to top
Students will gain practice in detecting the flaws and biases in claims made in media reports, and in discussing their importance.
Assessment: Details of Assessmentback to top
Assessment will be through:
(i) weekly coursework, aimed at testing and consolidating understanding of the basic elements of the course;
(ii) a test at the end of the module which assesses the students' understanding through short questions;
(iii) an examination in the Summer which assesses more fully the students' understanding and summative knowledge of the topics.
Curriculum Design: Select Bibliographyback to top
CLARKE, G. M. and COOKE, D. (1998) A Basic Course in Statistics, Fourth Edition. Arnold.
DALY, F., HAND, D. J., JONES, M. C., LUNN, A. D. and McCONWAY, K. J. (1995) Elements of Statistics.
LINDSEY, J.K. (1995) Introductory Systems, A Modelling Approach. Oxford Science Publications.