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Module Specifications

Archived Version 2008 - 2009

Module Title Research Practice & Methodology
Module Code MM533
School School of Mechanical and Manufacturing Engineering

Online Module Resources

Module Co-ordinatorDr Joseph StokesOffice NumberS381
Level 5 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Module Aims
Module Coordinators: Dr. Jeremiah, Dr. Joseph Stokes Dr. John GeraghtyModule 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
Lectures 36
Tutorials 24
Independent Learning Time 52.5

Total 112.5
Assume that a 7.5 credit module load represents approximately 112.5 hours' work, which includes all teaching, in-course 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 single-factor 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
Continuous Assessment50% Examination Weight50%
Indicative Reading List
Indicative Reading List:1. Helstrom, Carl W. Probability and stochastic processes for engineers2. Miller, Irwin.: Probability and statistics for engineers3. Clarke, A.B.: Probability and random processes : a first course4. Meyer, S.L.: Data analysis for scientists and engineers5. Hamming, R.W.: The art of probability for scientists and engineers6. Walpole, R.E. : Probability and statistics for engineers and scientists7. Montgomery, D.C. : Applied statistics and probability for engineers8. Murray, R.,: How to write a Thesis9. Moore, N.: How to do research : the complete guide to designing and managing research projects10. Kerr, A.W.: Doing statistics with SPSS11. Puri, B.K.,: SPSS in practice : an illustrated guide
Programme or List of Programmes
CAMGGDip C.A. Mechanical & Manufacturing Eng
CAMMMSc. C.A. Mechanical & Manufacturing Eng
ECSAStudy Abroad (Engineering & Computing)
IFPCMEPG International Foundation Cert:ME