Statistical Consultancy and Data Presentation - MAST6012

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2023 to 2024
Spring Term 6 15 (7.5) Diana Cole checkmark-circle


This is a practical module to develop the skills required by a professional statistician (report writing, consultancy, presentation, wider appreciation of assumptions underlying methods, selection and application of analysis method, researching methods).
Software: R, SPSS and Excel (where appropriate/possible). Report writing in Word. PowerPoint for presentations.
• Presentation of data
• Report writing and presentation skills
• Hypothesis testing: formulating questions, converting to hypotheses, parametric and non-parametric methods and their assumptions, selection of appropriate method,
application and reporting. Use of resources to explore and apply additional tests. Parametric and non-parametric tests include, but are not limited to, t-tests, likelihood
ratio tests, score tests, Wald test, chi-squared tests, Mann Whitney U-test, Wilcoxon signed rank test, McNemar's test.
• Linear and Generalised Linear Models: simple linear and multiple regression, ANOVA and ANCOVA, understanding the limitations of linear regression, generalised linear
models, selecting the appropriate distribution for the data set, understanding the difference between fixed and random effects, fitting models with random effects, model
• Consultancy skills: group work exercise(s)


Contact hours

Total contact hours: 36
Private study hours: 114
Total study hours: 150

Method of assessment

50% examination, 50% coursework

Indicative reading

Chatfield, C. (1995). Problem Solving: a Statistician's Guide. Second edition. London: Chapman & Hall.
Cox, D.R. & Snell, E.J. (1981). Applied Statistics: Principles and Examples. London: Chapman & Hall.
Dobson, A.J. & Barnett, A. (2008). An Introduction to Generalized Linear Models. Third edition. London: Chapman & Hall.
Hand, D.J. & Everitt, B.S. (1987). The Statistical Consultant in Action.
Sprent, P. & Smeeton, N.C. (2007). Applied Nonparametric Statistical Methods. Fourth edition. London: Chapman & Hall.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes:
On successfully completing the module students will be able to:
1 demonstrate systematic understanding of and a reasonable level of skill in the professional skills required by a practising statistician;
2 demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of
the material in the following areas: data presentation, hypothesis testing, linear and generalised linear models;
3 apply key aspects of practical data analysis and reporting in well-defined contexts, showing judgement in the selection and application of tools and techniques;
4 show judgement in the selection and application of statistical analysis techniques using a range of statistical software, e.g. R, SPSS and Excel.

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 competent use of information technology skills such as word-processing and spreadsheet use, 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 demonstrate the acquisition of the study skills needed for continuing professional development;
10 give an oral presentation;
11 work effectively as a member of a team.


  1. Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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