KC7047 - Applied Engineering Statistics

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 open book time-constrained examination (50% of the module mark) and a group assignment assessed via 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. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:
1. Identify and quantify statistical trends and time behaviour of engineering data and systems.

Intellectual / Professional skills & abilities:
2. Develop, test and adapt statistical models, using various approaches and to critically appraise model suitability.
3. Postulate and implement linear and nonlinear models for engineering systems using problem-solving skills.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
4. Manage time and resources effectively
5. Work effectively both individually and as a member of a team

How will I be assessed?

1. Examination (50%) – MLOS 1, 2, 3, 4, 5
2. Presentation (50%) – MLOS 1, 2, 4, 5
1. Seminar problems – MLOS 2, 4

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.





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 H602

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 2023

Fee Information

Module Information

All information on this course page is accurate at the time of viewing.

Our Campus based courses starting in 2022 and 2023 will be delivered on-campus with supporting online learning content. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to adjust the delivery of our education accordingly to ensure the health and safety of our students and staff.

On-campus contact time is subject to increase or decrease in line with any additional restrictions, which may be imposed by the Government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors. This could potentially mean increased or fully online delivery, should such restrictions on in-person contact time be required.


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