How do I compute Akaike's information criterion (AIC) to compare regression models?

Akaike's information criterion is used to compare both the efficiency of multivariate models looking at the same data combining the degree of fit with the number of terms in the model. Better fitting simpler models are preferred with smaller AICs. AIC can be used as an alternative to the F ratio in stepwise regressions investigating the effectiveness of adding or subtracting one or more predictors from a model (see an example in the Regression Grad talk).

AIC = n ln(RSS/n) + 2 df(model)

where RSS is the Residual Sum of Squares which is routinely outputted from the regression analysis and n is the total sample size. This formula is given on page 63 of Burnham and Anderson (2002).

Reference

Burnham, K.P., and Anderson, D.R. 2002. Model selection and multimodel inference: a practical information-theoretic approach, second edition. Springer-Verlag, New York.

(A pdf copy of the above book may also be downloaded for free from here.)