Module Details
| Module Code: |
STAT8004 |
| Title: |
Stats & Experimental Design
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Long Title:
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Stats & Experimental Design
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 1 - 2016/17 ( September 2016 ) |
| Field of Study: |
4620 - Statistics
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| Module Description: |
This module aims to develop skills in the application of the methods of probability and statistics in engineering and science. The module will allow the student to acquire knowledge, skill and competence in the areas of probability, statistical models, sampling theory, hypothesis testing, design of experiments and regression analysis.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Apply probability models to engineering problems |
| LO2 |
Calculate point and interval estimates of population parameters |
| LO3 |
Formulate and carry out statistical tests of hypothesis, including analysis of variance procedures. |
| LO4 |
Choose an experimental design appropriate to a given problem and perform the analysis on the resultant data. |
| LO5 |
Perform regression analysis, both simple and multiple cases. |
| Dependencies |
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).
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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.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
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.
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| No requirements listed |
| Indicative Content |
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Probability models
Detailed treatment of the standard probability models including Binomial Poisson, Normal, and their application to Engineering problems
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Statistical Inference.
Sampling distributions. Estimation and significance testing; procedures involving normal, t, F and chi-square distributions. Analysis of variance.
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Design of experiments
Introduction to the concepts and terminology of experimental design, along with some analysis of designed experiments
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Regression Analysis.
Least squares. The simple linear model. Analysis of residuals, coefficient of determination. Analysis of variance for regression. Confidence limits for prediction. Introduction to multiple regression; model selection procedures.
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Statistical software
Application of a software package such as Minitab to the different statistical methods covered by this course.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 30.00% |
| End of Module Formal Examination | 70.00% |
Assessments
| End of Module Formal Examination |
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| 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.
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The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Lecture |
Every Week |
3.00 |
3 |
| Lab |
Contact |
Analysis using Statistical Software |
Every Second Week |
0.50 |
1 |
| Tutorial |
Contact |
Review of lecture content, tutorial sheets |
Every Second Week |
0.50 |
1 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Review of lecture content, completion of exercises |
Every Week |
3.00 |
3 |
| Total Hours |
8.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Lecture |
Every Week |
3.00 |
3 |
| Lab |
Contact |
Analysis using Statistical Software |
Every Second Week |
0.50 |
1 |
| Tutorial |
Contact |
Review of lecture content, tutorial sheets |
Every Second Week |
0.50 |
1 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Review of lecture content, completion of exercises |
Every Week |
3.00 |
3 |
| Total Hours |
8.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
|
Module Resources
|
| Recommended Book Resources |
|---|
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Douglas C. Montgomery, George C. Runger. (2011), Applied Statistics and Probability for Engineers, 6th Edition International Student Version, Sixth Edition. Jon Wiley&Sons, Hoboken, NJ, [ISBN: 9781118744].
| | Supplementary Book Resources |
|---|
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Richard L. Scheaffer, Madhuri S. Mulekar, James T. McClave. (2011), Probability & Statistics for Engineers, Fifth Edition. Brooks/Cole Cengage Learning, Boston, [ISBN: 9780538735902].
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Jay Devore. (2014), Probability and Statistics for Engineeering and the Sciences, Ninth Edition. Brooks Cole, [ISBN: 978130525180].
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Sheldon M. Ross. (2014), Introduction to probability and statistics for engineers and scientists, Fifth Edition. Elsevier, [ISBN: 9780123948113].
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Douglas C. Montgomery. (2008), Introduction to statistical quality control, Sixth Edition. John Wiley & Sons, Hoboken, N.J., [ISBN: 9780470233979].
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Jiju Antony. (2014), Design of Experiments for Engineers and Scientists, Second Edition. Elsevier, [ISBN: 9780080994178].
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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