Sawa (1978) developed a model selection criterion that was derived from a
Bayesian modification of the AIC criterion. Sawa's Bayesian Information
Criterion (BIC) is a function of the number of observations n, the SSE, the
pure error variance fitting the full model, and the number of independent
variables including the intercept.

$$SBIC = n * ln(SSE / n) + 2(p + 2)q - 2(q^2)$$

where \(q = n(\sigma^2)/SSE\), *n* is the sample size, *p* is the number of model parameters including intercept
*SSE* is the residual sum of squares.