In one-way classification models, the interest is in comparing the treatment effects which correspond to a single variable. When there are two variables, say A and B, various treatments are obtained by combining the various levels of variable A with those of variable B. If A is at a different levels and B is at b levels, then assuming that all possible levels of A can be attempted with those of B, the experiment consists of ab treatment combinations. In such a case, we say that A and B are crossed with each other, and the design is often referred to as a two-way classification.

If each ab treatment is tried an equal number of times, then the resulting design is balanced. Such designs usually lead to a simpler analysis ...

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