In the previous chapter we briefly summarized the theory of general linear models (classical GLM). The assumptions underlying a classical GLM are that (at least to a good approximation) the errors are normally distributed, the error variances are constant and independent of the mean, and the systematic effects combine additively, for short: normality, homoscedasticity and linearity.