(Edited to include systematic error) I'm running some predictive experiments on a quantitative variable. The dataset is made mostly of data coming from sensors, and the outcome variable is the result of a mechanical test. So both Xs and Y are affected by some systematic error: I guess this means I will never have predictions more accurate than the total systematic error involved in process. How can I detect this error, so I can have some threshold for the maximum accuracy for the model? I don't need a perfect measure (that would be included in instruments documentation, currently not available) but at least some degree of reliability for the model.
First thing that came to my mind is to find some observations with a really similar profile, and check if the real Y has some variability.
Any suggestions? some literature about this? Thanks