Explore innovative strategies for constructing and executing experiments—including factorial and fractional factorial designs—that can be applied across the physical, chemical, biological, medical, social, psychological, economic, engineering, and industrial sciences. Over the course of five days, you’ll enhance your ability to conduct cost-effective, efficient experiments, and analyze the data that they yield in order to derive maximal value for your organization.

Course Overview

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR As part of THE PROFESSIONAL CERTIFICATE PROGRAM IN BIOTECHNOLOGY & LIFE SCIENCES.

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 30 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 notes.

Participant Takeaways

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.

Program Outline

Class runs 9:00 am - 5:00 pm every day.

Day One

Session 1 - 9:00 - 10:00am
Introduction to Experimental Design
Session 2 - 10:30 - 12:00 noon
Hypothesis Testing
Session 3 - 1:00 - 3:00pm
ANOVA I, Assumptions, Software
Session 4 - 3:30 - 5:00pm
Multiple Comparison Testing

Day Two

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
2K Designs
Session 8 - 3:30 - 5:00pm
2K Designs (continued)

Day Three

Session 9 - 9:00 - 10:00am
Confounding/Blocking Designs
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)

Day Four

Session 13 - 9:00 - 10:00am
Taguchi Designs
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

Day Five

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

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