Two Pitfalls of Using Standard Regression Diagnostics When Both X and Y Have Measurement Error

Modern exploratory data analysis produces models that are not based on physical theory but that are consistent with pictures of the data.When both X and Y have error this can be risky, because important features are hidden.Two examples are given that show that systematic model departures and heteroscedasticity may not be detectable with standard regression diagnostics.