Standard Regression Assumptions

The underlying mathematics of all types of regression assume that there are no patterns in the residuals. That is, they assume that the difference between the observed and predicted values of the model reflect only random noise. The reason that they make such an assumption is that if the errors of the model reflect something other than random noise, then the model is failing to account for something important.

The various tests that exist of the assumptions of regression mainly relate to looking for different types of patterns in the residuals.

Missing values are missing Completely At Random

extreme case: sample is random.

within sample.

Homogeneity of variance

The homogeneity of variance assumption is that the amount of error in the model is variance of the residuals is constant (i.e., homogeneous). That is, that there is no relationship between the amount of error,