Abstract
The two-way crossed model considered in Chapter 3 uses the simple additive model, which makes an important assumption that the value of the difference between the mean responses at two levels of A is the same at each level B. However, in many situations, this simple additive model may not be appropriate. When the differences between the mean response at different levels of A tend to vary over the different levels of B, it is said that the two factors interact. If an experimenter makes more than one observation per cell, it permits him to investigate not only the main effects of both factors but also their interaction. In this chapter, we consider a two-way crossed model with more than one observation per cell, which allows the investigation of interaction terms between the two factors.
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Sahai, H., Ojeda, M.M. (2004). Two-Way Crossed Classification with Interaction. In: Analysis of Variance for Random Models. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8168-5_4
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DOI: https://doi.org/10.1007/978-0-8176-8168-5_4
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