Hello,
a customer wants to visualize if their curves are within the expected tolerance. She uses Non Linear fit for which JMP does not provide neither Confidence Interval nor Prediction Interval. To achieve that goal I’ve been through Non Linear Fit and used the predictor formula. I created a new table with 200,000 rows and 3 columns :Y-formula, :Y_formula_Conf_Int and :Y_formula_Pred_Int.
:Y-formula depict the fit using parameter estimates.
:Y_formula_Conf_Int depict the confidence interval around the fit using a normal random distribution for each parameter based on their estimate and ApproxStdErr.
:Y_formula_Pred_Int depict the prediction interval using a normal random distribution for each parameter based on their estimate and ApproxStdErr + the RMSE.
In order to visualize the 95% Conf. Int. & Pred. Int. I selected values within -2.2 and -1.8 SD (Lower Limit) and values within 1.8 and 2.2 SD (Upper Limit) for both :Y_formula_Conf_Int and :Y_formula_Pred_Int.
Is that a reasonable way of getting 95% intervals ? I used a normal random distribution but I’m wondering if parameters are supposed to be normally (or something else) distributed ?
The script and dataset are for illustration.
Is that correct ? good enough approximation ? totally wrong ?
Keep in mind that the 3P exponential used on this data is just as example, I would like something working for any fit (if possible).
Thanks for the help.
Emmanuel