The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Course Name: Statistical design of experiments
Course Code: IEN 436
I. Basic Course Information
Program(s) on which the course is given: Industrial:
Department offering the course: Mechanical
Academic level: 5th level
Semester in which course is offered : Varies
Course pre-requisite(s): Mathematics 3 (IEN 334)
Credit Hours: 3.0
Contact Hours Through:
Lecture
2.0
Tutorial*
1.0
Practical*
0.0
Total
3.0
Approval date of course specification: September 2014
II. Overall Aims of Course
- Introduction: Basic definitions, the effective design of an experiment, basic principles
(Replication – Randomization – Local control). Analysis of variance: design with One
Source of Variation, Randomizwd Block designs, (Two way classifications), the Latine
– Square design, the Graeco – Latine Square design, after ANOVA Analysis. Full –
Factorial Experiments: Design and analysis of full – factorial experiments, orthogonal
linear contrasts, 2k Factorial Design, Blocking a replicated 2k factorial design,
confounding in the 2k factorial design (two blocks – four blocks, 2p blocks, Partial
confounding. Two level Fractional Factorial Design: Introduction, the one – half
Fractional Factorial Design, the one – quarter fraction of the factorial design 2k design,
general of the 2k – p fractional factorial design. Fitting regression model: Introduction,
estimation of the parameters in linear regression models. Response Surface Methods
RSM: introduction, the method of steepest ascent, design for fitting the first order
model, design for fitting the second order model, blocking in response surface designs,
mixture experiments. Experiments with Random Factors: introduction, the random
effects model, the two factors mixed model. Nested and Split – Plot Designs: the two
stage nested design, the general m – stage nested design.
III. Program ILOs covered by course
Program Intended Learning Outcomes (By Code)
Knowledge &
Intellectual Skills
Professional Skills
Understanding
K.14
I.1
P.2
General
Skills
G.f
1
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
IV. Intended Learning Outcomes of Course (ILOs)
a. Knowledge and Understanding
On completing the course, students should be able to:
k. 1
The constraints within which the effective design of an experiment.
b. Intellectual/Cognitive Skills
On completing the course, students should be able to:
i.1 Select appropriate mathematical and computer-based methods to Plot
Designs: the two stage nested design, the general m – stage nested design.
c. Practical/Professional Skills
On completing the course, students should be able to:
p.1
professionally merges the engineering knowledge, understanding
Experiments with Random Factors
d. General and Transferable Skills
On completing the course, students should be able to:
g.1
Effectively manage the method of steepest ascent, design for fitting the first
order model.
V. Course Matrix Contents
Main Topics / Chapters
1-
2-
3-
4-
5-
6-
Introduction: Basic
definitions, the effective
design of an experiment,
basic principles (Replication
– Randomization – Local
control).
Analysis of variance: design
with One Source of Variation,
Randomizwd Block designs,,
(Two way classifications), the
Latine – Square design, the
Graeco – Latine Square
design
Blocking a replicated 2k
factorial design, confounding
in the 2k factorial design (two
blocks – four blocks, 2p
blocks, Partial confounding.
Two level Fractional
Factorial Design:
Introduction, the one – half
Fractional Factorial Design
Two level Fractional
Factorial Design:
Duration
(Weeks)
Course ILOs Covered by Topic
(By ILO Code)
K&U
I.S.
P.S.
G.S.
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
2
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
7-
8-
9-
10-
11-
12-
13-
Introduction, the one – half
Fractional Factorial Design,
the one – quarter fraction of
the factorial design 2k design,
general of the 2k – p
fractional factorial design.
Fitting regression model:
Introduction, estimation of
the parameters in linear
regression models. Response
Surface Methods RSM:
introduction, the method of
steepest ascent
Fitting regression model:
Introduction, estimation of
the parameters in linear
regression models. Response
Surface Methods RSM:
introduction, the method of
steepest ascent
design for fitting the first
order model, design for fitting
the second order model,
blocking in response surface
designs, mixture experiments.
design for fitting the first
order model, design for fitting
the second order model,
blocking in response surface
designs, mixture experiments.
Experiments with Random
Factors: introduction, the
random effects model, the
two factors mixed model.
Nested and Split – Plot
Designs: the two stage nested
design, the general m – stage
nested design
Experiments with Random
Factors: introduction, the
random effects model, the
two factors mixed model.
Nested and Split – Plot
Designs: the two stage nested
design, the general m – stage
nested design
Experiments with Random
Factors: introduction, the
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
1
K.1
I.1
P.1
G.1
3
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
random effects model, the
two factors mixed model.
Nested and Split – Plot
Designs: the two stage nested
design, the general m – stage
nested design
Experiments with Random
Factors: introduction, the
random effects model, the
two factors mixed model.
14Nested and Split – Plot
Designs: the two stage nested
design, the general m – stage
nested design
Net Teaching Weeks
1
K.1
I.1
P.1
G.1
14
VI. Course Weekly Detailed Topics / hours / ILOs
Week
No.
1
2
3
4
5
6
7
8
9
Sub-Topics
Total
Hours
Introduction: Basic definitions, the effective
design of an experiment, basic principles
2
(Replication – Randomization – Local
control).
Analysis of variance: design with One
Source of Variation, Randomizwd Block
3
designs,,
(Two way classifications), the Latine –
Square design, the Graeco – Latine Square
3
design
Two level Fractional Factorial Design:
Introduction, the one – half Fractional
3
Factorial Design
Two level Fractional Factorial Design:
Introduction, the one – half Fractional
Factorial Design, the one – quarter fraction
3
of the factorial design 2k design, general of
the 2k – p fractional factorial design.
characteristics of a good random numbers,
sources of random numbers, random
3
numbers generation methods
Midterm Exam
Fitting regression model: Introduction,
estimation of the parameters in linear
regression models. Response Surface
Methods RSM: introduction, the method of
steepest ascent
Fitting regression model: Introduction,
3
Contact Hours
Theoretical
Practical
Hours
Hours*
2
2
1
2
1
2
1
2
1
2
1
2
1
2
1
4
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
10
11
12
13
14
15
estimation of the parameters in linear
regression models. Response Surface
Methods RSM: introduction, the method of
steepest ascent
design for fitting the first order model,
design for fitting the second order model,
blocking in response surface designs,
mixture experiments.
Analyzing Input Data and tests: Goodness
of fit tests, randomness test, nonparametric
test implementation. Verification:
Intrroduction
Experiments with Random Factors:
introduction, the random effects model, the
two factors mixed model. Nested and Split
– Plot Designs: the two stage nested design,
the general m – stage nested design
Experiments with Random Factors:
introduction, the random effects model, the
two factors mixed model. Nested and Split
– Plot Designs: the two stage nested design,
the general m – stage nested design
Experiments with Random Factors:
introduction, the random effects model, the
two factors mixed model. Nested and Split
– Plot Designs: the two stage nested design,
the general m – stage nested design
Final Exam
Total Teaching Hours
3
2
1
3
2
1
3
2
1
3
2
1
3
2
1
38
26
12
Teaching/Learning
Method
Lectures & Seminars
Tutorials
Computer lab Sessions
Practical lab Work
Reading Materials
Web-site Searches
Research & Reporting
Problem Solving /
Problem-based Learning
Projects
Independent Work
Group Work
Selected
Method
VII. Teaching and Learning Methods
1
1
Course ILOs Covered by Method (By ILO Code)
K&U
K.1
K.1
Intellectual
Skills
I.1
I.1
Professional
Skills
P.1
P.1
General
Skills
G.1
G.1
1
5
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Case Studies
Presentations
Simulation Analysis
Others (Specify):
Selected
Method
VIII. Assessment Methods, Schedule and Grade Distribution
Course ILOs Covered by Method
(By ILO Code)
Assessment
Method
K&U
I.S.
P.S.
G.S.
Midterm Exam
Final Exam
Quizzes
Course Work
Report Writing
Case Study
Analysis
Oral
Presentations
Practical
Group Project
Individual
Project
7
15
1
K.1
K.1
K.1
I.1
I.1
I.1
P.1
P.1
P.1
Assessment
Weight /
Percentage
Week
No.
G.1
G.1
G.1
Others (Specify):
IX. List of References

Essential Text Books

Course notes

Recommended books 
Periodicals, Web sites, 
etc …
Cox D. R. and N. Reid, The Theory of the Design of Experiments,
Boca Raton, FL: Chapman and Hall/CRC, 2000.
Lectures
X. Facilities required for teaching and learning
 White Board
Course coordinator: Dr. Mahmoud
Head of Department: Dr .Mahmoud Mohamed
Date: September 2014
6