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Graphical Methods for the Design of Experiments

  • Russell R. Barton

Part of the Lecture Notes in Statistics book series (LNS, volume 143)

Table of contents

  1. Front Matter
    Pages i-x
  2. Russell R. Barton
    Pages 1-16
  3. Russell R. Barton
    Pages 17-53
  4. Russell R. Barton
    Pages 93-131
  5. Russell R. Barton
    Pages 133-159
  6. Russell R. Barton
    Pages 161-181
  7. Back Matter
    Pages 183-196

About this book

Introduction

Graphical methods have played an important role in the statistical analysis of experimental data, but have not been used as extensively for experiment design, at least as it is presented in most design of experiments texts. Yet graphical methods are particularly attractive for the design of experiments because they exploit our creative right-brain capabilities. Creative activity is clearly important in any kind of design, certainly for the design ofan experiment. The experimenter must somehow select a set of run conditions that will meet the goals for a particular experiment in a cost-efficient way. Graphical Methods for Experiment Design goes beyond graphical methods for choosing run conditions for an experiment. It looks at the entire pre-experiment planning process, and presents in one place a collection of graphical methods for defining experiment goals, identifying and classifying variables, for choosing a model, for developing a design, and for assessing the adequacy of a design for estimating the unknown coefficients in the proposed statistical model. In addition, tools for developing a design also provide a platform for viewing the results of the experiment, a platform that provides insights that cannot be obtained by examination ofregression coefficients. These techniques can be applied to a variety of situations, including experimental runs of computer simulation models. Factorial and fractional-factorial designs are the focus of the graphical representations, although mixture experiments and experiments involving random effects and blocking are designed and represented in similar ways.

Keywords

Microsoft Access Statistica design education form framework graphs management manufacturing engineering pattern patterns set simulation statistical model statistics

Authors and affiliations

  • Russell R. Barton
    • 1
  1. 1.Department of Industrial and Management Systems EngineeringPennsylvania State UniversityUniversity ParkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-1398-7
  • Copyright Information Springer-Verlag New York, Inc. 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94750-1
  • Online ISBN 978-1-4612-1398-7
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site