ENG2031 : Mathematical Modelling & Statistical Methods For Engineering
ENG2031 : Mathematical Modelling & Statistical Methods For Engineering
- Offered for Year: 2024/25
- Module Leader(s): Dr David Swailes
- Lecturer: Dr Otti Croze, Dr Magda Carr, Dr Aleksandra Svalova, Dr John Appleby
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
| Code | Title |
|---|---|
| ENG1001 | Engineering Mathematics I |
Pre Requisite Comment
ENG1001 Engineering Mathematics I.
English Language to IELTS 6.0 or Pearsons 54 or equivalent.
Satisfactory progression or admissions requirement for entry to Stage 2 of engineering undergraduate programme. Basic knowledge of statistics from A level mathematics or equivalent.
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
Mathematics: to extend students' knowledge, understanding and application of modelling methods used in Engineering.
Statistics: to provide students with a fundamental understanding of the basic statistical techniques (summary statistics, probability distributions, interval estimation and regression analysis) routinely used in the engineering industries.
Outline Of Syllabus
Mathematics:
A series of modelling case studies are presented utilising simple mathematics, with an emphasis on the formulation and interpretation of mathematics rather than methods.
Statistics:
Introduction: descriptive statistics
Probability: continuous distributions, normal distribution
Statistical interference: sampling distributions and confidence intervals - one sample problems (mean, standard deviation, paired comparisons) and Regression analysis
Learning Outcomes
Intended Knowledge Outcomes
Mathematics: techniques for mathematical modelling in engineering, using a range of simple solution methods (emphasis is on skills rather than knowledge)
Statistics: to develop the students' understanding of fundamental statistical techniques enabling them to present, describe and interpret data in an appropriate and statistically robust manner.
To develop the students' ability to implement the statistical techniques using statistical software. To develop the ability to identify the appropriate tools to use in the statistical analysis of industrial data.
To develop the ability to understand the fundamental statistical techniques (summary statistics, normal distribution, interval estimation, regression analysis) and how they relate to the baseline discipline.
Intended Skill Outcomes
Mathematical Modelling:
To acquire competence to
- analyse and formulate a problem mathematically
- devise an appropriate strategy to solve the problem
- identify and implement a suitable solution method (using computing aids as appropriate)
- interpret and communicate results effectively and draw appropriate conclusions within the context of standard mathematical methods for engineers.
Statistics:
The students will be able to present, describe and interpret data in an appropriate and statistically robust manner in an industrial context using the knowledge on statistical theory and techniques acquired.
The students will be able to apply the appropriate assumptions when performing statistical inference, hypothesis testing and regression.
The students will be able to use appropriate software for simple statistical analysis through custom or in-build functions (MS Excel).
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 1:30 | 1:30 | Exam (Statistics) |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Exam revision (Statistics) |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | In person lectures (Mathematical Modelling) |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | In person lectures (Statistics) |
| Guided Independent Study | Assessment preparation and completion | 1 | 10:00 | 10:00 | Case study report (Mathematical Modelling) |
| Structured Guided Learning | Lecture materials | 5 | 1:00 | 5:00 | Case study support material (Mathematical Modelling) |
| Scheduled Learning And Teaching Activities | Practical | 1 | 1:00 | 1:00 | In-person computer practical (Statistics) |
| Structured Guided Learning | Academic skills activities | 1 | 1:00 | 1:00 | Excel walkthrough videos (Statistics) |
| Structured Guided Learning | Academic skills activities | 1 | 7:00 | 7:00 | Tutorial questions (Statistics) |
| Scheduled Learning And Teaching Activities | Small group teaching | 5 | 1:00 | 5:00 | In-person drop-in tutorials (Statistics) |
| Guided Independent Study | Independent study | 1 | 13:30 | 13:30 | Review course material (Statistics) |
| Guided Independent Study | Independent study | 1 | 25:00 | 25:00 | Case study research (Mathematical Modelling) |
| Total | 100:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CME1027 | Data Analysis in Process Industries |
Teaching Rationale And Relationship
Mathematical Modelling: The emphasis is on formulation and application, so ‘lectures’ will be interactive. Tutorial and on-line support will be to encourage students’ own initiatives in developing and using models.
Statistics: In-person lectures convey the statistical concepts and theory and their application in engineering. Tutorial questions will be supplied for students' self-study. Drop-in tutorials will be used to address student queries and aid understanding.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Digital Examination | 90 | 2 | A | 45 | NUMBAS Statistics exam, in person |
Exam Pairings
| Module Code | Module Title | Semester | Comment |
|---|---|---|---|
| Data Analysis in Process Industries | 2 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Case study | 2 | M | 50 | modelling report |
| Prob solv exercises | 2 | M | 5 | Statistics in-course NUMBAS assessment |
Assessment Rationale And Relationship
The modelling case study report in Semester 2 permits a more open-ended assessment appropriate for developing and communicating ideas. The written statistics assessment in Semester 2 is appropriate for presenting data-intensive questions and testing the application of statistical techniques on these.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- ENG2031's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- ENG2031's past Exam Papers
General Notes
N/A
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