Two-Way Crossed Classification without Interaction

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 3 in Analysis of Variance for Random Models, 2004, pp 115-169 from Springer

Abstract: Abstract The one-way classification discussed in Chapter 2 involved the levels of only a single factor. It is the simplest model in terms of experimental layout, assumptions, computations, and analyses. However, in many investigations, it is desirable to measure response at combinations of levels of two or more factors considered simultaneously. Two factors are said to be crossed if the data contain observations at each combination of a level of one factor with a level of the other factor. Consider two factors A and B, where a levels are sampled from a large population of levels of A and b levels are sampled from a large population of levels of B, and one observation is made on each of the ab cells. This type of layout is commonly known as the balanced two-way crossed random model with one observation per cell. It can also be viewed as a randomized complete block design where both blocks and treatments are regarded as random.

Keywords: Mean Square Error; Variance Component; Computing Software; Approximate Confidence Interval; Simultaneous Confidence Interval (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1007/978-0-8176-8168-5_3

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