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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    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
    Course Lecturer(s)
    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

    The students who succeeded in this course;

    • will be able to develop single and multi factorial designs
    • will be able to conduct full and fractional factorial designs
    • will be able to compare different experimental designs
    • will be able to apply blocking on experiments
    • 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 Weigthing
    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
    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
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     

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