Registry

Module Specifications

Archived Version 2009 - 2010

Module Title	Probability and Engineering Statistics
Module Code	MM382
School	School of Mechanical and Manufacturing Engineering
Online Module Resources
Module Co-ordinator	Dr Jeremiah Murphy	Office Number	S365
Level	3	Credit Rating	5
Pre-requisite	None
Co-requisite	None

Module Aims

To introduce the main concepts of statistic and probability theory and to give the students a working knowldge of the practical application of these theories in the field of engineering

Learning Outcomes

On completion of this module, the student will be able to

· Be able to summarise numerical data (PO1, PO2, PO6)

· Be able to solve probability problems involving the applications of the laws of probability and common probability distributions (PO1, PO2)

· Be able to test simple statistical hypotheses and to communicate conclusions (PO1, PO2, PO6)

· Be able to identify the key elements of SQC and TQM (PO1, PO2)

· Be able to identify when the basic quality control charts are appropriate, be able to construct the same and be able to communicate conclusions (PO1, PO2, PO6)

Indicative Time Allowances
	Hours
Lectures	24
Tutorials	12
Laboratories	0
Seminars	0
Independent Learning Time	39

Total	75
Placements
Assignments

NOTE

Assume that a 5 credit module load represents approximately 75 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

· Variables and graphs· Frequency Distribution· Mean, Median, Mode and other measures of central tendency· Standard deviation and other measures of dispersion· Moments, Skewness and Kurtosis· Elementary Probability theory· Queuing theory· The Binomial, Normal and Poisson Distributions· Elementary Sampling Theory· Statistical decision theory, tests of hypothesis and significance· Small sampling theory· Curve fitting· Correlation theory· Introduction to relevant software packages (e.g. SPSS, Matlab etc.)

Assessment
Continuous Assessment	20%	Examination Weight	80%

Indicative Reading List

· Helstrom, Carl W. : Probability and stochastic processes for engineers· Miller, Irwin. : Probability and statistics for engineers· Clarke, A.B. : Probability and random processes : a first course· Meyer, S.L. : Data analysis for scientists and engineers· Hamming, R.W. : The art of probability for scientists and engineers· Walpole, R.E. : Probability and statistics for engineers and scientists· Montgomery, D.C. : Applied statistics and probability for engineers

Science & Mathematics	Discipline - specific Technology	Information and Communications Technology	Design and Development	Engineering Practice	Social and Business Context
4	1	0	0	1	2

Contribution to Programme Outcomes:

Knowledge and Its Application:

Problem Solving:

Design:

Ethical Practice:

Effective Work and Learning:

Effective Communication:

3

3

0

0

0

1

Teaching & Learning Strategies/Assessment Methodology:

Traditional mid-term tests (2), terminal examination, problem sheets, tutorials and lectures.