I couldn't quite think of how best to title this, so recommendations are welcome. Same goes for the tags (I don't have the reputation to use the tags that I thought were appropriate). The question is this:Suppose you have N pairs of observations, (x,y), and you have a model with some unknown parameters, B, that estimates the relationship between x and y, F(x,B) -> y. Now suppose you determine B using the method of least-squares (and, implicitly, that all the assumptions of least-squares are satisfied). The parameters, B, are themselves random variables, each with its own variance. Is there any way to estimate the reduction (or increase) in the variance of B that would result from applying the same method of least-squares to N+1 pairs of observations?The question is asked in the context of experimentation. If each data point costs $X, an affirmative answer to the question would go a long way in determining whether or not to continue testing.
Estimating Variance Reduction Resultant from Additional Data
statistics;algorithms;experiments