Practical Design of Experiments
This two-day course will enable the participant to plan the most efficient experiment or test which will result in a statistically defensible conclusion of the test objectives. It will show how properly designed tests are easily analyzed and prepared for presentation in a report or paper. Examples and exercises related to various NASA satellite programs will be included.
Many companies are reporting significant savings and increased productivity from their engineering, process control and R&D professionals. These companies apply statistical methods and statistically-designed experiments to their critical manufacturing processes, product designs, and laboratory experiments. Multifactor experimentation will be shown as increasing efficiencies, improving product quality, and decreasing costs. This first course in experimental design will start you into statistical planning before you actually start taking data and will guide you to perform hands-on analysis of your results immediately after completing the last experimental run. You will learn how to design practical full factorial and fractional factorial experiments. You will learn how to systematically manipulate many variables simultaneously to discover the few major factors affecting performance and to develop a mathematical model of the actual instruments. You will perform statistical analysis using the modern statistical software called JMP from SAS Institute. At the end of this course, participants will be able to design experiments and analyze them on their own desktop computers.
The students will receive instructor’s workbook Classic Design Of Experiments in addition to a full set of course notes.
What you will learn:
- How to design full and fractional factorial experiments.
- Gather data from hands-on experiments while simultaneously manipulating many variables.
- Analyze statistical significant testing from hands-on exercises.
- Acquire a working knowledge of the statistical software JMP.
- Survey of Statistical Concepts.
- Introduction to Design of Experiments.
- Designing Full and Fractional Factorials.
- Hands-on Exercise: Statapult Distance Experiment using full factorial.
- Data preparation and analysis of Experimental Data.
- Verification of Model: Collect data, analyze mean and standard deviation.
- Hands-on Experiment: One-Half Fractional Factorial, verify prediction.
- Hands-on Experiment: One-Fourth Fractional Factorial, verify prediction.
- Screening Experiments (Trebuchet).
- Advanced designs, Methods of Steepest Ascent, Central Composite Design.
- Some recent uses of DOE.
REGISTRATION: There is no obligation or payment required to enter the Registration for an actively scheduled course. We understand that you may need approvals but please register as early as possible or contact us so we know of your interest in this course offering.
SCHEDULING: If this course is not on the current schedule of open enrollment courses and you are interested in attending this or another course as an open enrollment, please contact us at (410)956-8805 or firstname.lastname@example.org. Please indicate the course name, number of students who wish to participate. and a preferred time frame. ATI typically schedules open enrollment courses with a 3-5 month lead-time. To express your interest in an open enrollment course not on our current schedule, please email us at email@example.com.
Dr. Manny Uy is a member of the Principal Professional Staff at The Johns Hopkins University Applied Physics Laboratory (JHU/APL). Previously, he was with General Electric Company, where he practiced Design of Experiments on many manufacturing processes and product development projects. He is currently working on space environmental monitors, reliability and failure analysis, and testing of modern instruments for Homeland Security. He earned a Ph.D. in physical chemistry from Case-Western Reserve University and was a postdoctoral fellow at Rice University and the Free University of Brussels. He has published over 150 papers and holds over 10 patents. At the JHU/APL, he has continued to teach courses in the Design and Analysis of Experiments and in Data Mining and Experimental Analysis using SAS/JMP.
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