| 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 |
| Pre-requisites |
N/A |
| Co-requisites |
N/A |
| School/department | School of Civil, Aerospace and Design 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.
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 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.
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).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
