Hello,
Assuming I design an experiment containing N separate measurements to measure something (different samples, different factors etc).
Each of these measurements has the same standard deviation of SD.
How would I calculate the standard error of the final result after fitting the data to a model?
I assume this will be better than the standard deviation SD.
I know when repeating the exact same measurement N times, the standard error of the result is: SD /sqrt(N).
But how is this for a DoE with N experiments?
In particular I'm looking into a D-Optimal design, and the question is how many experiments do I have to do in order to achieve a certain accuracy (standard error) of the results.
Thanks very much for your help,
Dom
