Module overview
Given the importance of data analytics, this module provides students with a systematic and comprehensive understanding of the fundamentals of applied statistical modelling. It shows how statistical analysis can be used to solve civil and environmental engineering problems, using real-world case studies whenever possible. Exploratory data analysis, hypothesis testing, and regression analysis are main topics covered in this module. The main focus will be on developing regression models. Students will gain hands-on experience in using statistical software.
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Compose a research paper in engineering
- Communicate project’s output both orally and in writing effectively
- Use creativity and innovation in problem solving
- Display initiative and personal responsibility within a team
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Critically assess the fit of statistical models including linear, logistic and autoregression models.
- Critically analyse and reflect upon the appropriateness of parametric and non-parametric inference.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Develop an appropriate study design for an engineering case study taking into account practical limitations.
- Apply knowledge of statistical analysis to assess a hypothesis by selecting appropriate statistical tests and by correctly interpreting the results of these tests.
- Propose an appropriate statistical model for a given dataset and interpret the goodness of fit.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Apply best practice in data management, anonymization and archiving, in line with current ethical considerations.
- Formulate suitable research questions
- Critically evaluate statistical analysis
Syllabus
The course will endeavour to cover the following topics:
(1) Study design
(2) Descriptive and exploratory data analysis
(3) Hypothesis testing
(4) Statistical methods for independent data, e.g., multiple linear regression and logistic regression
(5) Statistical methods for dependent data, e.g., time series analysis
(6) Data management, copyright, anonymization and archiving
Learning and Teaching
Teaching and learning methods
- In person and online recorded lectures introduce the theory and techniques
- Computer practical sessions introduce statistical software
- Tutorial sessions are available throughout the module for any students wanting additional support
- Practical coursework enables students to follow the whole process through from initial question to formal analysis and report presentation
Type | Hours |
---|---|
Wider reading or practice | 24 |
Lecture | 19 |
Practical classes and workshops | 9 |
Completion of assessment task | 70 |
Tutorial | 8 |
Preparation for scheduled sessions | 10 |
Follow-up work | 10 |
Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Individual Presentation | 30% |
Continuous Assessment | 70% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Set Task | 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 |
---|---|
Set Task | 100% |
Repeat Information
Repeat type: Internal & External