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: 36
Private study hours: 114
Total study hours: 150
Method of assessment
50% examination, 50% coursework
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages
See the library reading list for this module (Canterbury)
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, including ethical considerations;
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.
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Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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