Registry

Module Specifications

Archived Version 2009 - 2010

Module Title	Research Practice & Methodology
Module Code	MM533
School	School of Mechanical and Manufacturing Engineering
Online Module Resources
Module Co-ordinator	Dr Joseph Stokes	Office Number	S381
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
	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, 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

Assessment
Continuous Assessment	50%	Examination Weight	50%

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

BSSA

Study Abroad (DCU Business School)

BSSAO

Study Abroad (DCU Business School)

CAMC

GCert CA Mechanical & Manufacturing Eng

CAMG

GDip C.A. Mechanical & Manufacturing Eng

CAMM

MSc. C.A. Mechanical & Manufacturing Eng

CAPD