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One-Way Classification

In: Analysis of Variance for Random Models

Author

Listed:
  • Hardeo Sahai

    (Universidad Central del Caribe, Center for Addiction Studies School of Medicine)

  • Mario Miguel Ojeda

    (Universidad Veracruzana, Económico Administrativa)

Abstract

In this chapter, we consider the random effect model involving only a single factor or variable in an experimental study involving a comparison of a set of treatments, where each of the treatments can be randomly assigned to experimental units. Such a layout is commonly known as the one-way classification or the completely randomized design. The one-way classification is the simplest and most useful model in statistics. In a one-way random effects model, treatments, groups, or levels of a factor are regarded to be a random sample from a large population. It is the simplest nontrivial and widely used variance component model. Moreover, the statistical concepts and tools developed to handle a one-way random model can be adapted to provide solutions to more complex models. Models involving two or more factors will be considered in succeeding chapters.

Suggested Citation

  • Hardeo Sahai & Mario Miguel Ojeda, 2004. "One-Way Classification," Springer Books, in: Analysis of Variance for Random Models, chapter 2, pages 21-114, Springer.
    • Handle: RePEc:spr:sprchp:978-0-8176-8168-5_2
    DOI: 10.1007/978-0-8176-8168-5_2
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