What will I learn on this module?
In this module, you will develop and apply the statistical techniques required for the analysis and modelling of engineering systems.
In the first half of the Semester the module is delivered through a series of lectures, with accompanying seminars, on requisite material, followed in the second half by assessed independent and group work associated with two case studies, assessed either by a PowerPoint or poster presentation.
You will receive on-going formative feedback during seminars in the first half of the semester, with both written and verbal feedback of their assessed work in the second half.
The two statistical modelling case studies will focus on regression analysis and time series, which are commonly required in engineering disciplines.
Outline Syllabus
Mathematical modelling
Modelling techniques, development, appraisal and modification. (20%)
Statistical methods
Generalised linear and non linear models. Curvilinear and non linear regression models. Analysis of variance and linear logistic model. Testing of model suitability. (40%)
Operational research and time series
Time series characteristics. Trends, moving averages and stationarity. Autocorrelation and tests of randomness. Queuing theory and its application. (40%)
Use of appropriate statistical software (e.g. R).
How will I learn on this module?
A wide range of learning and teaching approaches are used in this module. The module will be delivered using a combination of lectures, seminars and practical sessions in which you will be able to obtain help with set problems and formal assessments associated with the module. Lectures will allow you to experience and understand the formalism of advanced statistical techniques and include relevant examples. You will have the opportunity to enhance your understanding of the subject through seminars which promote independent learning and tackle relevant problems. Formative feedback will be provided for problems in seminars and you will have the opportunity to problem solve within peer groups. The statistical rigour and group work associated with this module naturally increases employability and is a highly transferrable skill.
Summative assessment is composed of an individual assignment (50% of the module mark) and a group assignment in the format of a formal presentation incorporating peer assessment (50% of the module mark). The two assessments in combination will require you to analyse and solve problems associated with the module and assess all Module Learning Outcomes.
How will I be supported academically on this module?
In addition to direct contact with the module team during lectures and seminars, you are encouraged to develop your statistical prowess by making direct contact with the module team either via email or the open door policy operated throughout the programme. During the module you will also be regularly referred to supporting resources including relevant texts and appropriate multimedia materials.
References to these resources will be made available through the e-learning portal and in lectures and seminars.
What will I be expected to read on this module?
All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.
What will I be expected to achieve?
Knowledge & Understanding:
1. Identify and quantify statistical trends and time behaviour of engineering data and systems (M1, M2)
Intellectual / Professional skills & abilities:
2. Develop, test and adapt statistical models, using various approaches and to critically appraise model suitability. (M3)
3. Postulate and implement linear and nonlinear models for engineering systems using problem-solving skills. (M1, M2, M3)
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
4. Manage time and resources effectively (M12)
5. Work effectively both individually and as a member of a team (M12, M17)
How will I be assessed?
SUMMATIVE
- coursework (CW): This component is an Individual assignment based upon the use of mathematical and statistical analysis to solve complex engineering problems coursework (50%) – Assesses MLO1,MLO3,MLO4
- presentation (PRE): This component is a group based project based on the application of mathematical and statistical analysis to solve complex engineering problems coursework (50%) – Assesses MLO2,MLO5
FORMATIVE
- Seminar problems
Feedback is provided to students individually and in a plenary format both written and verbally to help students improve and promote dialogue around each assessment component.
Pre-requisite(s)
None
Co-requisite(s)
None
Module abstract
Applied Engineering Statistics is a module that showcases how statistical techniques can be used to solve engineering-based problems of both a regression and time series nature. Employability skills are enhanced via the use of a group-based assessment, culminating in a presentation. Skills in using statistical software will be developed through use of the open-source software R. Upon completion of the module you should be equipped to analyse data, interpret findings and form coherent inferences. The module serves as useful preparation for employment in a practical engineering environment and provides a good grounding for quantitative study as part of postgraduate study.
Course info
UCAS Code H605
Credits 20
Level of Study Undergraduate
Mode of Study 4 years Full Time or 5 years with a placement (sandwich)/study abroad
Department Mathematics, Physics and Electrical Engineering
Location City Campus, Northumbria University
City Newcastle
Start September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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