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STAT8002 - Mathematics for Engineers

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Title:Mathematics for Engineers
Long Title:Mathematics for Engineers
Module Code:STAT8002
 
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2016/17 ( September 2016 )
Module Delivered in 2 programme(s)
Module Coordinator: David Goulding
Module Author: AINE NI SHE
Module Description: Design and analysis of experiments (DOE), topics from the field of Operations Research along with some partial differential equations and their applications.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 evaluate the possible experimental designs for a given problem, select a suitable design and analyse the resultant data.
LO2 formulate, solve and interpret the solution to linear programming problems.
LO3 conduct sensitivity analysis on the solution to a LP problem.
LO4 formulate and solve special category LP problems such as transportation problems.
LO5 solve partial differential equations relevant to mechanical engineering.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

Statistics for Engineering 301
 

Module Content & Assessment

Indicative Content
Analysis of Variance
ANOVA fundamentals; sums of squares, degrees of freedom, F-ratios.
Design and analysis of experiments
The need for experimental design, independent and dependent variables, factors, levels, treatment, error, randomisation, confounding, replication, one-way and two-way analysis of variance, factorial and fractional factorial designs, Taguchi concepts.
Introduction to Linear Programming
Assumptions underlying the L.P. model, formulating L.P. problems, graphical representation and solution by graphical means.
The Simplex Method.
Representing L.P. problems as a set of linear equations, use of slack and artificial variables, basic solutions, Simplex tableau format, Simplex routine, M-technique, two-phase method, infeasible problems, unbounded problems.
Duality and Sensitivity Analysis
Primal and dual problems, formulating the dual, relationships between solutions to primal and dual, Dual Simplex method, complementary slackness, sensitivity analysis.
Special category L.P. problems
Formulation and solution of special category problems such as transportation problems and assignment problems.
Partial differential equations
Half Range Fourier series. One dimensional heat flow. One dimensional wave equation. Laplace's equation.
Assessment Breakdown%
Course Work20.00%
End of Module Formal Examination80.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Short Answer Questions In class test 1,2,3 10.0 Week 7
Short Answer Questions In-class test 4,5 10.0 Week 12
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 80.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture 3.0 Every Week 3.00
Tutorial Tutorial 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Independent learning 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture 3.0 Every Week 3.00
Independent & Directed Learning (Non-contact) Independent learning 3.0 Every Week 3.00
Tutorial Tutorial 1.0 Every Week 1.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Douglas C. Montgomery 2012, Design and Analysis of Experiments, 8th Ed., Wiley [ISBN: 978-111814692]
  • E Kreyszig 2011, Advanced Engineering Mathematics, 10th Ed., Wiley [ISBN: 978-047064613]
Supplementary Book Resources
  • Jiju Antony 2003, Design of Experiments for Engineers and Scientists, Butterworth-Heinemann [ISBN: 0750647094]
  • Hamdy A. Taha 2010, Operations Research, an introduction, 9th Ed., Prentice Hall [ISBN: 978-013255593]
  • D.G. Zill & R.Cullen 2000, Advanced Engineering Mathematics, 2nd Ed., Mac Millian [ISBN: 0-333-6574-4]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_EBIOM_8 Bachelor of Engineering (Honours) in Biomedical Engineering 7 Mandatory
CR_EMECH_8 Bachelor of Engineering (Honours) in Mechanical Engineering 7 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie