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Module

ENG2031 : Mathematical Modelling & Statistical Methods For Engineering

  • Offered for Year: 2024/25
  • Module Leader(s): Dr David Swailes
  • Lecturer: Dr John Appleby, Dr Otti Croze, Dr Magda Carr, Dr Aleksandra Svalova
  • 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

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

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials51:005:00Case study support material (Mathematical Modelling)
Guided Independent StudyAssessment preparation and completion11:301:30Exam (Statistics)
Guided Independent StudyAssessment preparation and completion110:0010:00Exam revision (Statistics)
Scheduled Learning And Teaching ActivitiesLecture101:0010:00In person lectures (Mathematical Modelling)
Scheduled Learning And Teaching ActivitiesLecture111:0011:00In person lectures (Statistics)
Guided Independent StudyAssessment preparation and completion110:0010:00Case study report (Mathematical Modelling)
Structured Guided LearningAcademic skills activities17:007:00Tutorial questions (Statistics)
Scheduled Learning And Teaching ActivitiesPractical11:001:00In-person computer practical (Statistics)
Structured Guided LearningAcademic skills activities11:001:00Excel walkthrough videos (Statistics)
Scheduled Learning And Teaching ActivitiesSmall group teaching51:005:00In-person drop-in tutorials (Statistics)
Guided Independent StudyIndependent study125:0025:00Case study research (Mathematical Modelling)
Guided Independent StudyIndependent study113:3013:30Review course material (Statistics)
Total100:00
Jointly Taught With
Code Title
CME1027Data 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.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Digital Examination902A45Statistics NUMBAS exam (hybrid format), in person
Exam Pairings
Module Code Module Title Semester Comment
Data Analysis in Process Industries2N/A
Other Assessment
Description Semester When Set Percentage Comment
Case study2M50modelling report
Prob solv exercises2M5Statistics 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.

Reading Lists

Timetable