 We can derive the probability distribution of any linear combination of the dependent variables if the probability distribution of experimental errors is known or assumed.Inferring is easy when assuming that the errors follow a normal distribution, consequently implying that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.A special case of generalized least squares called weighted least squares occurs when all the off-diagonal entries of "Ω" (the correlation matrix of the residuals) are null; the variances of the observations (along the covariance matrix diagonal) may still be unequal (heteroscedasticity).