- Thread starter George Kraemer
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N = aM +b, where N = number of eggs, M = mass of a small quantity of eggs

T = cS^f, where T = total mass of eggs, S = size of crab

so substituting produces the model estimating number of eggs N from size (S): N = a(cS^f)+b

what I'd like to be able to show are confidence envelope around the model predictions

thanks

T = cS^f, where T = total mass of eggs, S = size of crab

so substituting produces the model estimating number of eggs N from size (S): N = a(cS^f)+b

what I'd like to be able to show are confidence envelope around the model predictions

thanks

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This should (possibly could?) work -

Re-sample the first set of data, do the regression and get an a,b pair.

Re-sample the second set of data (log-logged), and do the regression to get a c,f pair.

For a particular size S, use a,b,c, and f to calculate N.

Repeat the three steps a few 1000 times to get a distribution for N. Find the 2.5% and 97.5%tiles.

Are you looking for a confidence interval or are you actually interested in a prediction interval?

one more question; what's the rule - assuming there is one - on the resample n (i.e., how many observations from the full data set used to estimate the constants each time?)?. The power curve data set has 258 observations. The linear data set has 102 observations

Do the same with the 258 pairs for the other set (after log-logging) to get a plausible c, f pair.

Find N.

Repeat.