Design and Analysis of Experiments
Date: August 4-8, 2014 | Tuition: $3,300 | Continuing Education Units (CEUs): 3.0
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Planning Experiments, Doing Experiments, and Analyzing Experimental Data
This program is planned for those interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering, or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield.
Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional factorial designs are discussed in greater detail. These are designs in which two or more factors are varied simultaneously; the experimenter wishes to study not only the effect of each factor, but also how the effect of one factor changes as the levels of other factors change. The latter is generally referred to as an interaction effect among factors.
The fractional factorial design has been chosen for extra-detailed study in view of its considerable record of success over the last thirty years. It has been found to allow cost reduction, increase efficiency of experimentation, and often reveal the essential nature of a process. In addition, it is readily understood by those who are conducting the experiments, as well as those to whom the results are reported.
The program will be elementary in terms of mathematics. The course includes a review of the modest probability and statistics background necessary for conducting and analyzing scientific experimentation. With this background, we first discuss the logic of hypothesis testing and, in particular, the statistical techniques generally referred to as Analysis of Variance. A variety of software packages are illustrated, including Excel, SPSS, JMP, and other more specialized packages.
Throughout the program we emphasize applications, using real examples from the areas mentioned above, including such relatively new areas as experimentation in the social and economic sciences.
We discuss Taguchi methods and compare and contrast them with more traditional techniques. These methods, originating in Japan, have engendered significant interest in the United States.
All participants receive a copy of the text, Experimental Design: with applications in management, engineering and the sciences, Duxbury Press, 2002, co-authored by Paul D. Berger and Robert E. Maurer, in addition to extensive PowerPoint-style notes.
Fundamentals: Core concepts, understandings and tools (60%)
Latest Developments: Recent advances and future trends (15%)
Industry Applications: Linking theory and real-world (25%)
Lecture: Delivery of material in a lecture format (90%)
Discussion generated from questions from the students (10%)
Introductory: Appropriate for a general audience (50%)
Specialized: Assumes interest in, and a small amount of general familiarity with, experimentation (50%)
- Describe how to design experiments, carry them out, and analyze the data they yield.
- Understand the process of designing an experiment including factorial and fractional factorial designs.
- Examine how a factorial design allows cost reduction, increases efficiency of experimentation, and reveals the essential nature of a process; and discuss its advantages to those who conduct the experiments as well as those to whom the results are reported.
- Investigate the logic of hypothesis testing, including analysis of variance and the detailed analysis of experimental data.
- Formulate understanding of the subject using real examples, including experimentation in the social and economic sciences.
- Introduce Taguchi methods, and compare and contrast them with more traditional techniques.
- Learn the technique of regression analysis, and how it compares and contrasts with other techniques studied in the course.
- Understand the role of response surface methodology and its basic underpinnings.
- Gain an understanding of how the analysis of experimental design data is carried out using the most common software packages.
- Be able to apply what you have learned immediately upon return to your company.
WHO SHOULD ATTEND
This course is appropriate for anyone interested in designing, conducting, and analyzing experiments in the biological, chemical, economic, engineering, industrial, medical, physical, psychological, or social sciences.
Applicants need only have interest in experimentation. No previous training in probability and statistics is required, but any experience in these areas will be useful.
Among the subjects to be discussed are
- The logic of complete two-level factorial designs
- Detailed discussion of interaction among studied factors
- Large versus small experiments
- Simultaneous study of several factors versus study of one factor at a time
- Fractional experimental designs; construction and examples
- The application of hypothesis testing to analyzing experiments
- The important role of orthogonality in modern experimental design
- Single degree-of-freedom analysis; pinpointing sources of variability
- The trade-off between interaction and replication
- Response surface experimentation
- Yates' forward algorithm
- The reliability of estimates in factorial designs
- The usage of software in design and analysis of experiments
- Latin and Graeco-Latin squares as fractional designs; examples
- Designs with all studied factors at three levels
- The role of fractional designs in response surface experimentation
- Taguchi designs
- Incomplete study of many factors versus intensive study of a few factors
- Multivariate linear regression models
- The book and journal literature on experimental design
- Session 1 - 9:00 - 10:00am
Introduction to Experimental Design
- Session 2 - 10:30 - 12:00 noon
- Session 3 - 1:00 - 3:00pm
ANOVA I, Assumptions, Software
- Session 4 - 3:30 - 5:00pm
Multiple Comparison Testing
- Session 5 - 9:00 - 10:00am
ANOVA II, Interaction Effects
- Session 6 - 10:30 - 12:00 noon
Latin Squares and Graeco-Latin Squares
- Session 7 - 1:00 - 3:00pm
- Session 8 - 3:30 - 5:00pm
2K Designs (continued)
- Session 9 - 9:00 - 10:00am
- Session 10 - 10:30 - 12:00 noon
Confounding/Blocking Designs (continued)
- Session 11 - 1:00 - 3:00pm
2k-p Fractional-Factorial Designs
- Session 12 - 3:30 - 5:00pm
2k-p Fractional-Factorial Designs (continued)
- Session 13 - 9:00 - 10:00am
- Session 14 - 10:30 - 12:00 noon
Taguchi Designs (continued)
- Session 15 - 1:00 - 3:00pm
Orthogonality and Orthogonal contrasts
- Session 16 - 3:30 - 5:00pm
3K Factorial Designs
- Session 17 - 9:00 - 10:00am
Regression Analysis I
- Session 18 - 10:30 - 12:00 noon
Regression Analysis II
- Session 19 - 1:00 - 3:00pm
Regression Analysis III & Introduction to Response Surface Modeling
- Session 20 - 3:30 - 5:00pm
Response Surface Modeling (continued), Literature Review, Course Summary
Course schedule and registration times
Class runs 9:00 am - 5:00 pm every day.
Registration is on Monday morning from 8:15 - 8:45 am.
Experiment Director, Science Applications International Corporation
“Very knowledgeable professor who in almost every instance provided real-world examples to illustrate lessons. I enjoyed the opportunity to be in a classroom setting and found the material germane to my job and learned new methods that I will incorporate into our technical approach.”
Process Engineer, Bayer Films Americas
“Overall, this course was excellent. The knowledge I gained from the course I don't think I could get from anywhere [else].”
Product Manager, Vertex Pharmaceuticals
“Professor Berger was very engaging and he clearly has a lot of relevant knowledge regarding the complications and pitfalls of DOE application to real-world problems. The material he covered was material that I can instantly apply to my job function. He did an excellent job of covering just enough mathematical/statistical principles to maintain the rigor of his statements without bogging the class down in theoretical discussions. He really focused well on practical applications of the techniques covered in the class.”
research statistician, poet llc
“Definitely exceeded my high expectations and I look forward to communicating what I learned with my company internally on Monday!”
quality engineer, emd millipore
“Professor Berger was able to explain the material in a way that I could understand, not only using practical examples, but also answering questions/problems I brought from work. The material is condensed into five days only, but by being dynamic, fun, and using interesting examples, I was able to continuously pay attention and understand the topic. Every single topic covered was explained with examples.”
The program is under the direction of Professor Paul D. Berger, who for many years co-directed and taught the course with Professor Harold Freeman of the Economics Department, MIT. Classes will be conducted by Professor Berger.
The program has been an offering with MIT Professional Education since Professor Freeman first developed, directed, and taught the program over 60 years ago in what was then called the MIT Summer Session. Since that first offering, the program has been continually updated to reflect new developments in the field of experimental design, data analysis and related computer software.
About the Presenter
Paul D. Berger
Professor Berger has been teaching in the program for over 35 years, since he was pursuing graduate studies as Professor Freeman's student and teaching assistant. He has also taught in MIT's Sloan Fellows Program and Management of Technology Program, as well as a wide range of in-house industrial programs in experimental design, quality control, and Taguchi methods. He has extensive consulting experience in the area of design of experiments, and is the principal author of a textbook, Experimental Design, with applications in Management, Engineering, and the Sciences, published by Duxbury Press, which is used at several colleges and universities.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please contact the Short Programs office for further details.
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