Data Analysis & Experimental Methods for Civil and Environmental Engineering

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.

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.

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.

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.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

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.

Repeat Information

Repeat type: Internal & External