One-Way Classification

Hardeo Sahai and Mario Miguel Ojeda
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Hardeo Sahai: Universidad Central del Caribe, Center for Addiction Studies School of Medicine
Mario Miguel Ojeda: Universidad Veracruzana, Económico Administrativa

Chapter 2 in Analysis of Variance for Random Models, 2004, pp 21-114 from Springer

Abstract: 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.

Keywords: Mean Square Error; Variance Component; Interclass Correlation; Negative Estimate; Likelihood Equation (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1007/978-0-8176-8168-5_2

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