Before trying to interpret the rest of the results, first look at the best-fit value of each parameter in your model. First make sure you know what units each parameter is expressed in (if there are any units; some parameters are unitless constants). Next, make sure each value makes biological or scientific sense.
If a value doesn't make any sense, ask your self these questions:
•Does your data actually define all the parameters?
•Should you be constraining a parameter (or several) to constant values? If not, should you be constraining them to have a range of values (only positive numbers, perhaps)?
•Should you be fitting a family of datasets together using global nonlinear regression?
On the confidence tab of the nonlinear regression dialog, you choose how Prism should deal with difficult fits. If you choose the recommended method, difficult parameters (and their confidence interval) will be marked as "unstable".
If you choose (on the Confidence tab) to use the older method of identifying some fits as "ambiguous", then if a value is preceded by ~, it means the results are 'ambiguous'. Changing the value of any parameter will always move the curve further from the data and increase the sum-of-squares. But when the fit is 'ambiguous', changing other parameters can move the curve so it is near the data again. In other words, many combinations of parameter values lead to curves that fit equally well.
The parameter values preceded by ~ define the curve that Prism creates, but aren't actually useful in other ways. Other values would generate the same curve, or one that fits just as well.
When the best fit value is preceded by ~, so is the standard error. The corresponding confidence intervals are shown as "very wide" with no numerical range (the range would be infinitely wide).