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A bit of a silly question.

We have a model, which can be described as y = M(x) + some significant levels of noise.

We see that if we repeat the measurements of y N times, we can see a clear correlation between the measurements and M.

However we want to present the data in a different format, mainly answering the question "how many repeated measurements of the same object x are needed to get a good estimate of M(x)?"

Is there a statistical framework for that?

user1384636
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  • Here are some related questions, hope this is helpful: https://stats.stackexchange.com/questions/451223/uncertainty-of-parameters-estimated-by-maximum-likelihood/451239#451239 – Camille Gontier Sep 27 '21 at 09:55
  • https://stats.stackexchange.com/questions/491275/does-sampling-more-frequently-reduce-variance/491386#491386 – Camille Gontier Sep 27 '21 at 09:55

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