QLTY09032 2013 Advanced Experimental Design

General Details

Full Title
Advanced Experimental Design
Transcript Title
Experimental Design
N/A %
Subject Area
QLTY - Quality
MENG - Mech. and Electronic Eng.
09 - NFQ Level 9
05 - 05 Credits
Start Term
2013 - Full Academic Year 2013-14
End Term
2019 - Full Academic Year 2019-20
Programme Membership
SG_EPROJ_M09 201300 Master of Science in Project Management SG_SPROJ_O09 201700 Postgraduate Diploma in Science in Project Management

The student will be learn how to design conduct and analyse mixed level experiments, and interpret the data from these experiments.

Learning Outcomes

On completion of this module the learner will/should be able to;


Conduct two and three level fractional factorial experiments and analyse the resulting data.


Plan, conduct and analyse experiments using Response Surface Methodology (RSM).


Design and analyse mixed level experiments.


Analyse multiple response experiments and interpret the results.


Analyse and interpret data from experiments involving random effects models.


Formulate the expected means square rules to develop appropriate statistical models.


Use Minitab to design an experiment, analyse, interpret and evaluate the resulting data.

Module Assessment Strategies

Continuous Assessment

Project work - Design and analyse own experiment                                                                   20%.

Final Examination                 

One written paper of 2.5 hours duration on experimental desgign theory and analysis                  40%

One computer based exam of 2.5 hours duration involving data analysis and interpretation          40%

Indicative Syllabus

1.     Two Level Factorial Designs :  full factorial Designs. fractional factorial designs. Analysis of Residuals. Blocking in two level designs. Fold-over designs. repeat experiments vs. replicate experiments. Building the regression model and verification of the model.

 2.     More complex Two Level Factorial designs : Analysis of single replicate designs using probability plots and Lenth's method. Data transformations in a factorial design, variance stabilisation and Bob-Cox transformations. Analysis of multiple response experiments e.g. mean response and variability of response. Addition of centre points to a   design. Robust methods of experimental design such as Taguchi methods. Solution of static and dynamic problems using Taguchi methods.

 3.     Three Level Factorial Designs:  Full Factorial Designs, fractional factorial designs, Blocking in  and  designs, Analysis strategies for multiple responses.

 4.     Design and Analysis of Mixed Level Designs: Constructing mixed level designs using method of replacement. Analysis strategies for mixed level designs.

 5.     Response Surface Methods: Method of Steepest Ascent, verifying adequacy of first order model. Analysis of second order response surface. Locating the stationary point. Characterising the response surface. Ridge systems. Multiple response problem. Selecting designs for fitting response surfaces. Central composite designs. Box-Behnken designs. Face centred cube design. Blocking in Response surface designs. Introduction to mixture experiments. Evolutionary Operation.

 6.     Experiments with Random factors: The random effects model. Rules for Expected means squares. Repeatability and Reproducibility (R&R) studies using the random effects model. Estimation of variance components. Mixed models. Approximate F tests and Satterthwaite's method.

7.     Nested and Split Plot Designs: Crossed vs. Nested designs. Two stage nested design. Diagnostic checking and estimation of variance components. Staggered nested design. The general m-stage nested design. Analysis of Split-plot designs.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
20 %
End of Semester / Year Formal Exam
80 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Project Design and analyse own experiment Continuous Assessment UNKNOWN 20 % OnGoing 1,2,7

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam One final exam 2.5 hour theory paper. One computer based 2.5 hour exam involving data analysis and interpretation Final Exam UNKNOWN 80 % End of Term 1,2,3,4,5,6,7

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 3 Weekly 3.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Part Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Distance Learning Suite Lecture 3 Weekly 3.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Part Time Average Weekly Learner Contact Time 3.00 Hours

Module Resources

Non ISBN Literary Resources





Montgomery, Douglas

Design and Analysis of Experiments, 8th Edition

John Wiley & Sons


Box, Hunter & Hunter

Statistics for Experimenters,

John Wiley & Sons.


Draper, N. & Smith, H.,

Applied Regression Analysis,

John Wiley & Sons.


Montgomery, DC & Myers, R.

Response Surface Methodology: Process and Product Optimization using Design Experiments

John Wiley & Sons.


Montgomery, D.C, Peck, E.A. & Vining, G.G.

Introduction to Linear Regression Analysis. 5th Edition

John Wiley & Sons.


Wu, J. & Hamada, M.,

Experiments: Planning, Analysis and Parameter Design Optimization

John Wiley & Sons.


Other Resources


Additional Information