Offering a planned approach for determining cause and effect, DOE Simplified: Practical Tools for Effective Experimentation, Third Edition integrates the authors decades of combined experience in providing training, consulting, and computational tools to industrial experimenters. Supplying readers with the statistical means to analyze how numerous variables interact, it is ideal for those seeking breakthroughs in product quality and process efficiency via systematic experimentation.Following in the footsteps of its bestselling predecessors, this edition incorporates a lively approach to learning the fundamentals of the design of experiments (DOE). It lightens up the inherently dry complexities with interesting sidebars and amusing anecdotes.The book explains simple methods for collecting and displaying data and presents comparative experiments for testing hypotheses. Discussing how to block the sources of variation from your analysis, it looks at two-level factorial designs and covers analysis of variance. It also details a four-step planning process for designing and executing experiments that takes statistical power into consideration.This edition includes a major revision of the software that accompanies the book (via download) and sets the stage for introducing experiment designs where the randomization of one or more hard-to-change factors can be restricted. Along these lines, it includes a new chapter on split plots and adds coverage of a number of recent developments in the design and analysis of experiments.Readers have access to case studies, problems, practice experiments, a glossary of terms, and a glossary of statistical symbols, as well as a series of dynamic online lectures that cover the first several chapters of the book.
Autorentext
Patrick J. Whitcomb
Mark J. Anderson
Inhalt
Preface
Introduction
1 Basic Statistics for DOE
2 Simple Comparative Experiments
3 Two-Level Factorial Design
4 Dealing with Nonnormality via Response Transformations
5 Fractional Factorial
6 Getting the Most from Minimal-Run Designs
7 General Multilevel Categoric Factorials
8 Response Surface Methods for Optimization.
9 Mixture Design
10 Back to the Basics: The Keys to Good DOE.
11 Split-Plot Designs to Accommodate Hard-to-Change Factors
12 Practice Experiments
Appendix 1
Appendix 2
Glossary
Statistical Symbols
Recommended Readings
Textbooks
Case Study Articles
About the Authors
About the Software.