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Design of Experiments—Factorial Designs

  • Richard M. Heiberger
  • Burt Holland
Part of the Springer Texts in Statistics book series (STS)

Abstract

Designs are often described by the number of factors. Chapter 6, “OneWay Analysis of Variance” , discusses designs with one factor. Chapter 12, “Two-Way Analysis of Variance” , discusses designs with two factors. More generally, we speak of “three-way” or “higher-way” designs and talk about main effects (one factor), two-way interactions (two factors), three-way interactions, four-way interactions, and so forth. Factors can have crossed or nested relationships. A factor can be fixed or random. When interaction is significant, its nature must be carefully investigated. If higher-order interactions, meaning those involving more than two factors, can be assumed to be negligible, it is often possible to design experiments that require observations on only a fraction of all possible treatment combinations.

Keywords

Simple Effect ANOVA Table Conditional Test Concomitant Variable Model Formula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Richard M. Heiberger
    • 1
  • Burt Holland
    • 1
  1. 1.Department of StatisticsTemple UniversityPhiladelphiaUSA

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