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Course Description Course Name : Design of Experiments for Engineers Course Code : EM-503 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. RIZVAN EROL Learning Outcomes of the Course : Understands and applies statistical design principles in experimental studies. Selects an appropriate experimental design for the objective and scope of an experimental study. Performs statistical analysis of observations of an experimental study. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The primary aim of this course is to study statistical experimental designs used commonly in engineering experiments and statistical analysis of exprimental data. This course will cover underlying theory of such designs and the use of an experimental design software for both building and analyzing engineering experiments. Course Contents : Simple comparative experiments. experiments with a single factor. analysis of variance. randomized blocks, latin squares and related designs. factorial designs. the 2k factorial design. blocking and confounding in the 2k factorial design. two-level fractional factorial designs. three-level and mixed-level factorial and fractional designs. factorial experiments with random factors. nested and split-plot designs. Language of Instruction : English Work Place : seminar room   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Strategy of experimentation; some typical applications of experimental design; basic principles and guidelines reading the related textbook chapter lecturing, discussion 2 Simple comparative experiments; basic statistical concepts; sampling and sampling distributions; central limit theorem; inferences about the difference reading the related textbook chapter lecturing, discussion 3 Experiments with a single factor; ANOVA; the fixed effects model; model adequacy checking reading the related textbook chapter lecturing, discussion 4 Practical interpretation of results; regression model; contrasts; computer analysis; random effects model reading the related textbook chapter lecturing, discussion 5 More about single factor experiments; choice of sample size; discovering dispersion effects; fitting response curves; regression approach to ANOVA reading the related textbook chapter lecturing, discussion 6 Randomized complete block design; statistical analysis; model adequacy checking; estimating model parameters reading the related textbook chapter lecturing, discussion 7 Latin square design; Graeco-latin square design; balanced incomplete block designs reading the related textbook chapter lecturing, discussion 8 Midterm Exam prepare for the exam written exam 9 Introduction to factorial designs; the advantage of factorials; two-factor factorial designs reading the related textbook chapter lecturing, discussion 10 General factorial design; fiiting response curves and surfaces; blocking in a factorial design; unbalanced data in a factorial design reading the related textbook chapter lecturing, discussion 11 2k factorial design; 22 design; 23 design; the general 2k design; model adequacy; parameter estimation; addition of center points reading the related textbook chapter lecturing, discussion 12 Blocking and confounding 2k in the factorial design; partial confounding reading the related textbook chapter lecturing, discussion 13 Two-level fractional factorial designs; one-half fractional designs; one-quarter fractional designs; design resolution reading the related textbook chapter lecturing, discussion 14 The general 2k-p fractional design; resolution III designs; resolution IV and V designs reading the related textbook chapter lecturing, discussion 15 Project Presentations prepare for the presentation presentations 16/17 Final Exam prepare for the exam written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  MONTGOMERY, D. C., 2010, Design and analysis of experiments (7th ed.), Wiley & Sons Inc., New York, NY. Required Course Material(s)  Design-Expert 8.01 Manual with Tutorials, 2010, DesignEase Inc.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 75     Homeworks/Projects/Others 5 25 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Understand, interpret and apply knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. 3 2 Acquire comprehensive knowledge about methods and tools of industrial engineering and their limitations. 4 3 Work in multi-disciplinary teams and take a leading role and responsibility. 3 4 Identify, gather and use necessary information and data. 5 5 Complete and apply the knowledge by using scarce and limited resources in a scientific way and integrate the knowledge into various disciplines. 3 6 Keep up with the recent changes and applications in the field of Industrial Engineering and analyze these innovations when necessary. 3 7 Work in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. 4 8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 4 9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 4 10 Design and perform analytical modeling and experimental research and analyze/solve complex matters emerged in this process. 4 11 Follow, study and learn new and developing applications of industrial engineering. 4 12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 3 13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 4 14 Understand social and environmental implications of engineering practice. 2 15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 5 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 5 7 35     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 10 10 Total Workload: 139 Total Workload / 25 (h): 5.56 ECTS Credit: 6