Graduate Course Proposal Form Submission Detail - ESI6246
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Approved by SCNS
Submission Type: New
Course Change Information (for course changes only):
Comments: For MSIE, PhD in IE, MSEM - REQUIRED. GC appd 2/10/15. To USF Sys 2/27/15. Nmbr 6245 approved as 6246. Effective 4/1/15
- Department and Contact Information
Tracking Number Date & Time Submitted 5106 2014-10-15 Department College Budget Account Number Industrial and Management Systems Engineering EN 210300 Contact Person Phone Hui Yang 45579 email@example.com
- Course Information
Prefix Number Full Title ESI 6246 Advanced Statistical Design Models Is the course title variable? N Is a permit required for registration? N Are the credit hours variable? N Is this course repeatable? Y If repeatable, how many times? 1 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) R - Regular Abbreviated Title (30 characters maximum) Adv Statistical Design Models Course Online? Percentage Online C - Face-to-face (0% online) 0
Introduces theory and applications in the design & analysis of experiments. Students learn skills and techniques to develop successful experiments that can lead to reduced development lead time, enhanced process performance, and improved product quality.
A. Please briefly explain why it is necessary and/or desirable to add this course.
Replacing Selected Topics with Permanent number; already listed in program
B. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service?
anticipated enrollment = 7-20 students each year
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
Yes, 3 or more times
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
PhD in IE or other math/statistics related field to teach the course.
- Other Course Information
(1) learn the design of efficient and effective experiments to investigate real-world problems;
(2) learn the analysis of experimental results to improve industrial products and processes;
(3) learn the statistical design methods and their applications to system optimization, robustness, and treatment comparison in a number of fields.
B. Learning Outcomes
After successful completion of this course, students will be able to:
-understand basic statistical experiment designs
-use designs of experiments to solve real-world problems
-use software to find the best experimental designs
- analyze and interpret experimental results for process optimization.
C. Major Topics
1) Basic concepts for experimental design and introductory regression analysis (Chapter 1)
2) Experiments with a single factor and analysis of variance (Chapter 2)
3) Experiments with more than one factor, blocking, Latin squares, analysis of variance and covariance, random effects models, other analysis techniques (Chapter 3)
4) Full factorial experiments at two levels, comparison with “one-factor-at-a-time” plans, analysis of location and dispersion, choice of optimal blocking schemes (Chapter 4)
5) Fractional factorial experiments at two levels, maximum resolution and minimum aberration for choosing optimal designs, choice of optimal blocking schemes (Chapter 5)
6) Response surface methodology for process optimization and improvement (Chapter 10)
Experiments: Planning, Analysis and Parameter Design Optimization; Jeff Wu and Mike Hamada, 2nd edition, 2009, John Wiley.
E. Course Readings, Online Resources, and Other Purchases
F. Student Expectations/Requirements and Grading Policy
2 Exams - 35 pts each (Approx 25.9% each)
Quizzes/homework - 30 pts (Approx 22.2 %)
1 Comprehensive Final Exam - 35 pts (approx 25.9%)
Grading scale will be used: A: 90+; B: 80+; C: 70+; D: 60+, F:
G. Assignments, Exams and Tests
The top two scores from the three exams will be added to the total quiz/homework score to obtain the total grade for the course (out of a total of 100 pts). No make-up exams unless previous arrangements have been made. Students will be expected to attend class and prepare assignments. Habitual failure to do so will result in a reduced grade. An incomplete grade will only be given when a student misses a portion of the semester because of illness or accident. Cheating on examinations, plagiarism and other forms of academic dishonesty are serious offenses and may subject the student to penalties ranging from failing grades to dismissal.
H. Attendance Policy
Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
I. Policy on Make-up Work
Exams must be taken on the scheduled exam dates. Students are required to arrange with the instructor in advance for a make-up exam in the event of extenuating circumstances that prevent them from taking the exam as scheduled. In the event of an unforeseen emergency that prevents the student from taking the exam as scheduled, the student must provide documentation to the instructor before a make-up exam can be arranged.
J. Program This Course Supports
MSIE and PhD programs in IMSE department
- Course Concurrence Information
MS in Engineering Management, as well as graduate programs in engineering, health, science, and business.