In statistics, model specification is part of the process of building a statistical model: specification consists of selecting an appropriate functional form for the model and choosing which variables to include. For example, given personal income y {\displaystyle y} together with years of schooling s {\displaystyle s} and on-the-job experience x {\displaystyle x} , we might specify a functional relationship y = f ( s , x ) {\displaystyle y=f(s,x)} as follows:
where ε {\displaystyle \varepsilon } is the unexplained error term that is supposed to comprise independent and identically distributed Gaussian variables.
The statistician Sir David Cox has said, "How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis".