EDA and the routine checking of underlying assumptions provides insight into all of the above. Location and variation checks provide information as to whether C is really constant. Distributional checks indicate whether is the best estimator. Techniques for distributional checking include histograms , normal probability plots , and probability plot correlation coefficient plots . Randomness checks ascertain whether the usual is valid.
Distributional tests assist in determining a better estimator, if needed.
Assumptions not satisfied If one or more of the above assumptions is not satisfied, then we use EDA techniques, or some mix of EDA and classical techniques, to find a more appropriate model for the data. If the data are not random, then we may investigate fitting some simple time series models to the data. If the constant location and scale assumptions are violated, we may need to investigate the measurement process to see if there is an explanation. The assumptions above are still quite relevant in the sense that for an approriate model the error component should follow the assumptions. The criterion for validating the model, or comparing competing models, is framed in terms of these assumptions.