  An appeal for symmetry is made to build established notions of specific
representation and specific nonlinearity of measurement (often called model
error) into a canonical linear regression model. Additive components are
derived from the trivially complete model M = m. Factor analysis and equation
error motivate corresponding notions of representation and nonlinearity in an
errors-in-variables framework, with a novel interpretation of terms. It is
suggested that a modern interpretation of correlation involves both linear and
nonlinear association.
