Statistics - MA306

Location Term Level Credits (ECTS) Current Convenor 2017-18 2018-19
Canterbury Spring
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4 15 (7.5) DR AF Laurence


MAST4006 (Mathematical Methods 1), MAST4009 (Probability)





Increasingly data are collected to inform future decisions, varying from which websites people access on a regular basis to how patients respond to new drugs, to how the stock market responds to global events, or to how animals move around their local habitat. Therefore, most professionals will need to extract useful information from data and to manage and present data in their working lives.  This module explores some of the basic concepts of statistics, from data summarisation to the main methods of statistical inference. The techniques that are discussed can be used in their own right for simple statistical analyses, but serve as an important foundation for later, more advanced, modules. The statistical computing package R is used throughout the module for data analysis. The syllabus includes: an introduction to R and investigating data sets, sampling and sampling distributions, point and interval estimation, hypothesis testing, association between variables.


This module appears in:

Contact hours


Method of assessment

Examination (80%) and coursework (20%).

Preliminary reading

J. Devore and R. Peck. Introductory Statistics. (West 1990)
F. Daly et al. Elements of Statistics. (The Open University 1995)
G.M. Clarke and D. Cooke. A Basic Course in Statistics. (5th edition. Arnold. 2004)
D.V. Lindley and W.F. Scott. New Cambridge Statistical Tables (2nd edition. C.U.P. 1995)
J. Verzani. Using R for Introductory Statistics (2nd edition, CRC Press, 2014)

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes
On successfully completing the module students will be able to:
1 demonstrate knowledge of the underlying concepts and principles associated with statistics
2 demonstrate the capability to make sound judgements in accordance with the basic theories and concepts in the following areas, whilst demonstrating a reasonable level of skill in calculation and manipulation of the material: graphical and numerical summaries of data using R, point estimation, including maximum likelihood estimation for discrete data, interval estimation, hypothesis testing, association between variables.
3 apply the underlying concepts and principles associated with introductory statistics in several well-defined contexts, showing an ability to evaluate the appropriateness of different approaches to solving problems in this area
4 make appropriate use of the statistical computer package R

The intended generic learning outcomes.
On successfully completing the module students will be able to:

1 manage their own learning and make use of appropriate resources.
2 understand logical arguments, identifying the assumptions made and the conclusions drawn
3 communicate straightforward arguments and conclusions reasonably accurately and clearly
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working
5 solve problems relating to qualitative and quantitative information
6 make use of information technology skills such as R, online resources (moodle), internet communication.
7 communicate technical and non-technical material competently.
8 demonstrate an increased level of skill in numeracy and computation
9 give an oral presentation
10 work in small groups

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