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STAT8005 - Statistics for Engineering

Title:Statistics for Engineering
Long Title:Statistics for Engineering
Module Code:STAT8005
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2019/20 ( September 2019 )
Module Delivered in 5 programme(s)
Module Coordinator: David Goulding
Module Author: David Goulding
Module Description: This module will apply statistics and probability distributions to modern day engineering problems. It will develop graphical visualisation methods, probability theory and distributions. The module will develop knowledge, skill and competence of sampling theory and hypothesis testing.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Use the methods of descriptive statistics
LO2 Apply probability and probability models to engineering problems
LO3 Compute and interpret point and interval estimates of population parameters
LO4 Conduct a variety of statistical tests of hypothesis
LO5 Perform correlation and regression analysis
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 MTU 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

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.

No requirements listed

Module Content & Assessment

Indicative Content
Descriptive statistics
Graphical and numerical methods of data description
Probability and probability distributions.
Review of laws of probability and of Binomial, Poisson, Normal and Exponential distributions. Means and variances of random variables. Distribution of sums and differences of random variables.
Sampling Theory.
Sampling distributions of means, proportions, differences of means, differences of proportions, variances and ratios of variances. The Central Limit Theorem. Concept of standard error. Application to SPC charts.
Statistical Inference.
Estimation, point estimates and confidence intervals. Necessary sample size. Significance tests: null and alternative hypotheses, test statistic, level of significance, p-value. Z-tests, t-tests, F-tests, chi-square tests, paired comparisons, parametric versus non-parametric tests.
Regression and Correlation
Simple linear regression, method of least squares, coefficient of determination, confidence intervals and prediction intervals, correlations coefficient, significance tests in regression and correlation
Statistical software.
Application of a package such as MINITAB to the different aspects of statistical analysis dealt with in this course.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Other Written Test 1,2 35.0 Week 6
Other Written Test 1,2,3,4,5 35.0 Sem End
Other Laboratory Assessment 1,2,3,4,5 30.0 Week 12
No End of Module Formal Examination
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 No Description 3.0 Every Week 3.00
Lab No Description 1.0 Every Second Week 0.50
Tutorial No Description 1.0 Every Second Week 0.50
Independent & Directed Learning (Non-contact) No Description 3.0 Every Week 3.00
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
This module has no Part Time workload.

Module Resources

Recommended Book Resources
  • Douglas C. Montgomery, George C. Runger. 2018, Applied Statistics and Probability for Engineers, 7th Edition Ed., John Wiley & Sons, Hoboken,NJ [ISBN: 9781119400363]
Supplementary Book Resources
  • Richard L. Scheaffer, Madhuri S. Mulekar, James T. McClave. 2011, Probability & Statistics for Engineers, 5th Edition Ed., Brooks/Cole Cengage Learning Boston, MA [ISBN: 9780538735902]
  • Jay L. Devore. 2015, Probability and statistics for engineering and the sciences, 9th Edition Ed., Cengage Learning Florence [ISBN: 9781305251809]
  • Sheldon M. Ross 2014, Introduction to Probability and Statistics for Engineers and Scientists, 5th Edition Ed., Academic Press [ISBN: 9780123948113]
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 6 Mandatory
CR_EMECH_8 Bachelor of Engineering (Honours) in Mechanical Engineering 6 Mandatory
CR_CSTRU_8 Bachelor of Engineering (Honours) in Structural Engineering 6 Mandatory
CR_CCEEE_9 Master of Engineering in Civil Engineering (Environment and Energy) 6 Mandatory
CR_CSTEN_9 Master of Engineering in Structural Engineering 6 Mandatory

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