Year:13/14
Department:Mathematics and Statistics
Level:Part I
Learning Hours:80
Credit Points:8
Weight:0.2
Course Convenor:Professor JR Whitehead
Status:Live
Assessment Rules
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Curriculum Design: Outline Syllabus
back to topData collection and summary
Modelling discrete data
Continuous distributions
Modelling continuous data
Curriculum Design: Pre-requisites/Co-requisites/Exclusions
back to topA-level Mathematics at A-grade or above, or equivalent.
MATH104 Probability
Educational Aims: Subject Specific: Knowledge, Understanding and Skills
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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 Skills
back to topThe 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 Skills
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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 Skills
back to topStudents will gain practice in detecting the flaws and biases in claims made in media reports, and in discussing their importance.
Assessment: Details of Assessment
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(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 Bibliography
back to topCLARKE, 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.
Addison Wesley.
LINDSEY, J.K. (1995) Introductory Systems, A Modelling Approach. Oxford Science Publications.
