Chapter 7. Design and Analysis of Industrial Experiments

  1. Shirley Coleman Technical Director2,
  2. Tony Greenfield3,
  3. Dave Stewardson2 and
  4. Douglas C. Montgomery4
  1. Timothy J. Robinson Associate Professor

Published Online: 3 MAR 2008

DOI: 10.1002/9780470997482.ch7

Statistical Practice in Business and Industry

Statistical Practice in Business and Industry

How to Cite

Robinson, T. J. (2008) Design and Analysis of Industrial Experiments, in Statistical Practice in Business and Industry (eds S. Coleman, T. Greenfield, D. Stewardson and D. C. Montgomery), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470997482.ch7

Editor Information

  1. 2

    Industrial Statistics Research Unit, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

  2. 3

    Greenfield Research, Little Hucklow, Buxton SK17 8RT, UK

  3. 4

    Regents' Professor of Industrial Engineering & Statistics, ASU Foundation Professor of Engineering, Department of Industrial Engineering Arizona State University Tempe, AZ 85287-5906, USA

Author Information

  1. Department of Statistics, University of Wyoming, Laramie, WY 82070, USA

Publication History

  1. Published Online: 3 MAR 2008
  2. Published Print: 7 MAR 2008

ISBN Information

Print ISBN: 9780470014974

Online ISBN: 9780470997482

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Keywords:

  • controllable factors and uncontrollable factors;
  • hard to change factors;
  • experimental design and treatment combination;
  • phase one - region seeking;
  • popular optimisation algorithm - steepest ascent/descent;
  • two-level factorial design;
  • fractional factorial designs;
  • sparsity-of-effects principle and ABC interaction;
  • path of steepest ascent (or descent);
  • second-order model - central composite design (CCD)

Summary

This chapter contains sections titled:

  • Introduction

  • Two-level factorial designs

  • Centre runs

  • Blocking in 2k factorial designs

  • Fractional factorial designs

  • Process improvement with steepest ascent

  • Optimal designs and computer-generated designs

  • Graphical techniques for comparing experimental designs

  • Industrial split-plot designs

  • Supersaturated designs

  • Experimental designs for quality improvement

  • The future

  • References