Experimental Design and Analysis for Process Improvement Part 1: Basics

  • First Online: 27 August 2016

Cite this chapter

process improvement experimental design

  • Stephen B. Vardeman 6 &
  • J. Marcus Jobe 7  

Part of the book series: Springer Texts in Statistics ((STS))

3382 Accesses

The first four chapters of this book provide tools for bringing a process to physical stability and then characterizing its behavior. The question of what to do if the resulting picture of the process is not to one’s liking remains. This chapter and the next present tools for addressing this issue. That is, Chaps. 5 and 6 concern statistical methods that support intelligent process experimentation and can provide guidance in improvement efforts.

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Iowa State University, Ames, Iowa, USA

Stephen B. Vardeman

Farmer School of Business, Miami University, Oxford, Ohio, USA

J. Marcus Jobe

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Vardeman, S.B., Jobe, J.M. (2016). Experimental Design and Analysis for Process Improvement Part 1: Basics. In: Statistical Methods for Quality Assurance. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-79106-7_5

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DOI : https://doi.org/10.1007/978-0-387-79106-7_5

Published : 27 August 2016

Publisher Name : Springer, New York, NY

Print ISBN : 978-0-387-79105-0

Online ISBN : 978-0-387-79106-7

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    1. What is experimental design? [5.1.1.] 2. What are the uses of DOE? [5.1.2.] 3. What are the steps of DOE? [5.1.3.] 2. Assumptions [5.2.] 1. Is the measurement system capable? [5.2.1.] 2. Is the process stable? [5.2.2.] 3. Is there a simple model? [5.2.3.] 4. Are the model residuals well-behaved? [5.2.4.] 3. Choosing an experimental design [5 ...

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  4. EXPERIMENTAL DESIGN PROCESS IMPROVEMENT PART 1: BASICS

    EXPERIMENTAL DESIGN AND ANALYSIS FOR PROCESS IMPROVEMENT PART 1: BASICS. ysical stability and then characterizing its behavior. The question of what to do if the resulting p. cture of the process is not to one’s liking remains. This chapte.

  5. Fundamentals of Experimental Design: Guidelines for Designing ...

    Four basic tenets or pillars of experimental design— replication, randomization, blocking, and size of experimental units— can be used creatively, intelligently, and consciously to solve both real and perceived problems in comparative experiments.

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    Process Improvement. 1. Introduction. Definition of experimental design. Uses. Steps. 2. Assumptions. Measurement system capable. Process stable. Simple model. Residuals well-behaved. 3. Choosing an Experimental Design. Set objectives. Select process variables and levels. Select experimental design. Completely randomized designs.

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    Stephen B. Vardeman & J. Marcus Jobe. Part of the book series: Springer Texts in Statistics ( (STS)) 3379 Accesses. Abstract. The basic tools of experimental design and analysis provided in Chap. 5 form a foundation for effective multifactor experimentation.

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