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Course Description Course Name : Statistical Quality Control Course Code : EM-546 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. RIZVAN EROL Learning Outcomes of the Course : Uses Statistical Process Control(SPC) methods for process monitoring and improvement. Selects and applies an appropriate control chart type for a selected process or product quality characteristics. Analyzes capability of a process or a measurement system. 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 modern statistical methods for quality control and improvement. Basic knowledge of statistical concepts such as random variables and distributions, probability and hypothesis testing is required. Course Contents : Introduction to quality concepts. fundamental statistics required for quality, statistical methods for quality improvement, data analysis, pareto analysis, cause-and-effect diagrams, histograms and scatter diagrams, control charts and sampling methods, review of statistical packages for quality (SPSS, MINITAB, S-plus, Statistica). 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 Control Charts for Variables reading the related textbook chapter lecturing, discussion 2 Control Charts for Variables reading the related textbook chapter lecturing, discussion 3 Control Charts for Attributes reading the related textbook chapter lecturing, discussion 4 Control Charts for Attributes reading the related textbook chapter lecturing, discussion 5 Process and Measurement System Capability reading the related textbook chapter lecturing, discussion 6 CUSUM and EWMA Control Charts reading the related textbook chapter lecturing, discussion 7 CUSUM and EWMA Control Charts reading the related textbook chapter lecturing, discussion 8 Midterm Exam prepare for the exam written exam 9 Process Design and Improvement with Designed Experiments reading the related textbook chapter lecturing, discussion 10 Process Design and Improvement with Designed Experiments reading the related textbook chapter lecturing, discussion 11 Lot-by-Lot Acceptance Sampling for Attributes reading the related textbook chapter lecturing, discussion 12 Lot-by-Lot Acceptance Sampling for Attributes reading the related textbook chapter lecturing, discussion 13 Other Acceptance Sampling Techniques reading the related textbook chapter lecturing, discussion 14 Other Acceptance Sampling Techniques 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., 2008, Introduction To Statistical Quality Control (6th edition), John Wiley& Sons, Inc.  CHANDRA, M. J., 2001, Statistical Quality Control, CRC Press. Required Course Material(s)   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. 5 3 Work in multi-disciplinary teams and take a leading role and responsibility. 4 4 Identify, gather and use necessary information and data. 3 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. 3 8 Analyze Industrial Engineering problems, develop innovative methods to solve the problems. 3 9 Have the ability to propose new and/or original ideas and methods in developing innovative solutions for designing systems, components or processes. 3 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. 2 12 Use a foreign language in verbal and written communication at least B2 level of European Language Portfolio. 2 13 Present his/her research findings systematically and clearly in oral and written forms in national and international platforms. 2 14 Understand social and environmental implications of engineering practice. 3 15 Consider social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. 3 * 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