Module overview
This module concerns the design of experimental or observational studies aimed at enabling the understanding, analyses and interpretation of data and to deliver datasets from which robust, statistically defendable conclusions can be drawn. Essentially the module takes the student through the steps of (i) experiment design, (ii) data analysis and visualisation methods and (iii) data archiving. Engineering and energy case studies with large datasets will be used to enable the student to explore this topic from a real world perspective.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically evaluate statistical analysis.
- Apply best practice in data management, anonymization and archiving, in line with current ethical considerations.
- Formulate a research question and associated set(s) of hypothesis.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Communicate project’s output both orally and in writing.
- Display initiative and personal responsibility within a team.
- Compose a research paper in engineering.
- As our participants are Post Graduate students who will have already demonstrated a wide range of skills, the list below is included more for the completeness of the module description.
- Develop and apply deductive and inductive research approaches.
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Critically analyse and reflect upon the appropriateness of parametric and non-parametric inference.
- Critically assess the fit of statistical models including linear, logistic and autoregression models.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Propose an appropriate statistical model for a given dataset and interpret the goodness of fit.
- Apply knowledge of statistical analysis to assess a hypothesis by selecting appropriate statistical tests and by correctly interpreting the results of these tests.
- Develop an appropriate experimental research design for an engineering case study taking into account practical limitations.
Syllabus
The course will endeavour to cover the following topics
- Experimental and observational study design, including: power calculations, sample selection & recruitment, and data collection method selection;
- Descriptive and exploratory data analysis, including: measures of central tendency, histograms, density distributions, box plots and correlograms;
- Statistical inference of parametric and non-parametric data, including: tests, confidence intervals and hypothesis testing.
- Statistical models of independent data, including: simple and multiple linear regression; logistic regression, and factorial models. Principles of model selection, Goodness-of-fit. Sensitivity analysis.
- Statistical models of dependent data, including: time series analysis (stationarity, trend and seasonality), autocorrelation and autoregression models.
- Data management, copyright, anonymization and archiving including open, public and restricted/licensed access data models.
Learning and Teaching
Teaching and learning methods
Teaching methods consist of four hours per week, comprising of lectures and of supporting tutorials and practical classes. The classes will require a small amount of preparation and provide material to be reviewed and/or developed through independent study. Students will be expected to demonstrate additional independent learning via their coursework assignments.
Type | Hours |
---|---|
Completion of assessment task | 28 |
Practical classes and workshops | 18 |
Preparation for scheduled sessions | 6 |
Lecture | 20 |
Follow-up work | 12 |
Revision | 32 |
Wider reading or practice | 24 |
Tutorial | 10 |
Total study time | 150 |
Resources & Reading list
Textbooks
Shumway, R.H. and Stoffer, D.S (2011). Time series analysis and its applications with R examples.
Field, A., Miles, J. and Field, Z. (2012). Discovering Statistics Using R. SAGE Publications Ltd.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Coursework assignment(s) | 40% |
Examination | 60% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat Information
Repeat type: Internal & External