M.SC. in Computer Engineering (Without Thesis)

IE 544 | Course Introduction and Application Information

Course Name

Experimental Design in Engineering

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 544	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Ahmet Sermet Anagün
Course Lecturer(s)	Prof. Dr. Ahmet Sermet Anagün
Assistant(s)	-

Course Objectives

To teach general statistical analysis, design of engineering experiments and projects using theory of least squares, analysis of variance, randomized blocks, factorial experiments, and associated topics as well as engineering experimental design and analysis using software packages.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to develop single and multi factorial designs
2	will be able to conduct full and fractional factorial designs
3	will be able to compare different experimental designs
4	will be able to apply blocking on experiments
5	will be able to do regression modeling

Course Description

This course covers: basic statistical concepts; design of experiments methodology; simple comparative experiments; single and multi-factor experiments; randomized blocks; Latin square designs; full factorial designs; fractional factorial designs; regression models; response surface methodology.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction	Textbook, Chapter 1
2	Simple Comparative Experiments	Textbook, Chapter 2
3	Experiments with a Single Factor: The Analysis of Variance	Textbook, Chapter 2
4	Experiments with a Single Factor: The Analysis of Variance	Textbook, Chapter 2
5	Randomized Blocks, Latin Squares, and Related Designs	Textbook, Chapter 2
6	Introduction to Factorial Designs	Textbook, Chapter 4
7	Midterm	Textbook, Chapter 4
8	The 2k Factorial Design	Textbook, Chapter 4
9	The 2k Factorial Design	Textbook, Chapter 5
10	Blocking and Confounding in the 2k Factorial Design	Textbook, Chapter 5
11	Two-level Fractional Factorial Designs	Textbook, Chapter 6
12	Three-Level and Mixed-Level Factorial Designs	Textbook, Chapter 6
13	Fitting Regression Models	Textbook, Chapter 7
14	Fitting Regression Models	Textbook, Chapter 7
15	Response Surface Methods and Designs	Textbook, Chapter 8
16	Review of the Semester	Textbook, Chapter 8

Course Notes/Textbooks	Douglas C. Montgomery, 2009. Design and Analysis of Experiments, 7th Ed., John Wiley & Sons, Inc., NJ, USA.
Suggested Readings/Materials	Course notes and Slayts

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application	2	20
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	3	15
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	35
Total

Weighting of Semester Activities on the Final Grade		65
Weighting of End-of-Semester Activities on the Final Grade		35
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16	1	16
Study Hours Out of Class	15	5	75
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	5	5	25
Presentation / Jury			0
Project	1	-	0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	21	21
Final Exam	1	50	50
		Total	235

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	X
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	-	X
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-