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Unit information: Introduction to Environmental Statistics using MATLAB in 2021/22

Unit name Introduction to Environmental Statistics using MATLAB
Unit code CENGM0023
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Laura Dickinson
Open unit status Not open




School/department Department of Civil Engineering
Faculty Faculty of Engineering


In this unit students will develop the statistical skills that are important in the water and environmental sciences. The aim is that students will acquire a comprehensive understanding of key statistical methods for data description, analysis and presentation within the MATLAB computing environment.

The unit will outline various statistical concepts that relate to data populations, including central tendency and variability and will introduce both discrete and continuous distributions. There will be a focus on probability theory and the statistics of hydrological extremes, including methods to deal with data that do not follow a normal distribution.

The unit will also consider regression, objective functions, time series analysis and the use of statistics for data visualisation. Maximum likelihood estimation, the method of moments, L-moments, probability weighted moments and basic principles of spectral analysis will also be introduced.

Intended learning outcomes

At the end of this module, the successful student will be able to:

M2.1 summarise environmental data using standard statistical techniques, using both hand calculations and MATLAB;

M2.2 demonstrate how statistical distributions may be manipulated to model environmental data;

M2.3 construct easily-interpretable presentations of environmental data, for example, by using MATLAB to draw clear, labelled graphs;

M2.4 describe how probability theory is applied to support hydrological design;

M2.5 analyse environmental time series, by interpreting graphical presentations of data and by employing standard statistical techniques;

M2.6 explain why fundamental assumptions force scientists to tailor specific techniques for data analysis;

M2.7 identify the criteria/circumstances for which it is NOT appropriate to use a given statistical technique;

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, which may include lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Details

This unit will be assessed by coursework (a report/workbook will be submitted to show competency in computational statistics). Formative assessment/feedback will also be provided throughout the unit from tutorial exercises, to help students prepare for the summative assessments.


If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. CENGM0023).