**Model**: The
hypothesised effect(s) on a response, which can be tested with
ANOVA for
evidence of pattern in the data. An ANOVA model contains one or more terms,
each having an effect on the response that is tested against unmeasured error or
residual variation. A model with a single factor (whether categorical or
covariate) is written: Y = A + ε, and the ANOVA tests the term A against
the residual ε. A fully-replicated model with
two crossed factors is written: Y = A + B + B*A + ε, and the two-way ANOVA tests
each main effect A and B, and the interaction B*A, against the residual ε.

Models with multiple factors require care with declaring all terms in a statistical package. For example, the cross factored with nesting model: Y = C|B'(A) + ε is analysed by declaring the terms: C|A + C|B(A). The two-factor randomised block model: Y = S'|B|A is analysed by declaring the terms: S|B|A - S*B*A for a Model-1 analysis, or the terms: S + B|A for a Model-2 analysis.

Doncaster, C. P. & Davey, A. J. H. (2007) *Analysis of Variance and Covariance: How to
Choose and Construct Models for the Life Sciences*. Cambridge: Cambridge
University Press.

http://www.southampton.ac.uk/~cpd/anovas/datasets/