Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP

  • 1h 48m
  • Andrew Karl, Heath Rushing, James Wisnowski
  • SAS Institute
  • 2013

With a growing number of scientists and engineers using JMP software for design of experiments, there is a need for an example-driven book that supports the most widely used textbook on the subject, Design and Analysis of Experiments by Douglas C. Montgomery. Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP meets this need and demonstrates all of the examples from the Montgomery text using JMP. In addition to scientists and engineers, undergraduate and graduate students will benefit greatly from this book.

While users need to learn the theory, they also need to learn how to implement this theory efficiently on their academic projects and industry problems. In this first book of its kind using JMP software, Rushing, Karl and Wisnowski demonstrate how to design and analyze experiments for improving the quality, efficiency, and performance of working systems using JMP.

Topics include JMP software, two-sample t-test, ANOVA, regression, design of experiments, blocking, factorial designs, fractional-factorial designs, central composite designs, Box-Behnken designs, split-plot designs, optimal designs, mixture designs, and 2 k factorial designs. JMP platforms used include Custom Design, Screening Design, Response Surface Design, Mixture Design, Distribution, Fit Y by X, Matched Pairs, Fit Model, and Profiler.

With JMP software, Montgomery’s textbook, and Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP, users will be able to fit the design to the problem, instead of fitting the problem to the design.

About the Authors

Heath Rushing, Principal Consultant and co-founder of Adsurgo, LLC, an analytics consulting company that specializes in commercial and government training. Heath is a former professor from the Air Force Academy. He holds an M.S.degree in Operations Research from the Air Force Institute of Technology and has used JMP since 2001. After teaching at the Academy, Heath was a quality engineer and Six Sigma Black Belt in both biopharmaceutical manufacturing and Research and Development, where he used JMP to design and deliver training material in Six Sigma, Statistical Process Control (SPC), Design of Experiments (DOE), and Measurement Systems Analysis (MSA). In addition, Heath has been a symposium speaker at both national and international pharma and medical device conferences. Heath is an American Society of Quality (ASQ) Certified Quality Engineer and teaches JMP courses regularly, including a course he recently developed on Quality by Design (QbD) using JMP.

Jim Wisnowski is co-founder and principal at Adsurgo, LLC, an analytics consulting company that specializes in commercial and government training. He has a Ph.D. in Industrial Engineering from Arizona State University and has published numerous journal articles and textbook chapters. He was an award-winning professor and statistics chair while at the United States Air Force Academy. Jim retired from the Air Force, where he held various analytical and leadership positions throughout the Department of Defense in training, test and evaluation, human resources, logistics, systems engineering, and acquisition.

Andrew T. Karl is a senior management consultant for Adsurgo, LLC, developing and teaching courses on a variety of statistical topics for the U.S. Department of Defense, Fortune 500 corporations, and international clients. He received his B.A. in mathematics from the University of Notre Dame and his Ph.D. in statistics from Arizona State University. Dr. Karl's research interests focus on computation and applications of non-nested linear and nonlinear mixed models, including value-added problems. Additionally, he frequently works with problems from data mining, text mining, and experiment design.

In this Book

  • About This Book
  • Introduction
  • Simple Comparative Experiments
  • Experiments with a Single Factor—The Analysis of Variance
  • Randomized Blocks, Latin Squares, and Related Designs 4
  • Introduction to Factorial Designs
  • The 2k Factorial Design
  • Blocking and Confounding in the 2k Factorial Design
  • Two-Level Fractional Factorial Designs
  • Three-Level and Mixed-Level Factorial and Fractional Factorial Designs
  • Fitting Regression Models
  • Response Surface Methods and Designs
  • Robust Parameter Design and Process Robustness Studies
  • Experiments with Random Factors
  • Nested and Split-Plot Designs
  • Other Design and Analysis Topics
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