Module Title 
Research Practice & Methodology

Module Code 
MM533

School 
School of Mechanical and Manufacturing Engineering

Online Module Resources

Level 
5

Credit Rating 
7.5

Prerequisite 
None

Corequisite 
None


Module Aims

Module Coordinators: Dr. Jeremiah, Dr. Joseph Stokes Dr. John Geraghty
Module Aims:
The aim of this module is introduce and improve the specific skills needed for research (reading, critical analysis of literature, writing and communication) and to understand and apply data analysis to experimental/theoretical research problems (methodology, statistics, experimental design and advice about how to do research). This will not only benefit students when doing their MSc project but also in monitoring results in industry.

Learning Outcomes

Learning Outcomes:
On completion of this module, the student will be able to :
Summarise numerical data
Solve probability problems involving the applications of the laws of probability and common probability distributions
Identify simple linear and multiple linear regression and correlation.
Design and analyse single and two factor experiments
Develop writing skills and conduct and report on previous literature
Critically analyse publications
Identify ethical problems
Communication research ideas

Indicative Time Allowances


Hours

Lectures 
36

Tutorials 
24

Laboratories 

Seminars 

Independent Learning Time 
52.5



Total 
112.5

Placements 

Assignments 


NOTE

Assume that a 7.5 credit module load represents approximately 112.5 hours' work, which includes all teaching, incourse assignments, laboratory work or other specialised training and an estimated private learning time associated with the module.

Indicative Syllabus

Indicative Syllabus:
The syllabus will investigate the following areas:
¿ Summary Statistics and Probability: Mean and standard deviation. Definition of probability. Laws of probability. Continuous random variables and the normal distribution.
¿ Inference: The Central Limit Theorem. Confidence intervals for the mean with variance both known and unknown. Testing for normality. Confidence intervals for the variance. Confidence intervals for proportions. Tests of hypotheses for a single sample.
¿ Inference for two samples: inference for the difference in means for two Normal distributions, variance both known and unknown.
¿ Regression: Simple linear regression and correlation. Multiple linear regression
¿ Design and analysis of single factor experiments: Designing engineering experiments. The completely randomised singlefactor experiment. The Random Effects Model. Randomised complete block design.
¿ Design of experiments with two factors.
¿ Writing Skills; How to write a thesis and Conference/Journal papers
¿ Utilisation of Word Long Document and Excel
¿ Citation and Referencing
¿ Literature search
¿ How to critically analyse publications
¿ Impact Factors
¿ Ethical considerations
¿ Case Studies
¿ Communication: Presentation Skills and Dissemination of Research

Assessment  Continuous Assessment  50%  Examination Weight  50% 

Indicative Reading List

Indicative Reading List:
1. Helstrom, Carl W. Probability and stochastic processes for engineers
2. Miller, Irwin.: Probability and statistics for engineers
3. Clarke, A.B.: Probability and random processes : a first course
4. Meyer, S.L.: Data analysis for scientists and engineers
5. Hamming, R.W.: The art of probability for scientists and engineers
6. Walpole, R.E. : Probability and statistics for engineers and scientists
7. Montgomery, D.C. : Applied statistics and probability for engineers
8. Murray, R.,: How to write a Thesis
9. Moore, N.: How to do research : the complete guide to designing and managing research projects
10. Kerr, A.W.: Doing statistics with SPSS
11. Puri, B.K.,: SPSS in practice : an illustrated guide


Programme or List of Programmes

CAMC  GCert CA Mechanical & Manufacturing Eng 
CAMG  GDip C.A. Mechanical & Manufacturing Eng 
CAMM  MSc. C.A. Mechanical & Manufacturing Eng 
IFPCME  PG International Foundation Cert:ME 
Archives:  