The prediction confidence interval is the predicted mean response plus or minus a multiple of the standard error of the predicted response. The standard error is the square root of the variance of the predicted response, which is a function of the predictor levels and the covariance matrix for the parameter estimates.
Here is a simple example in which I regressed :weight versus :height from the Big Class sample data table. I set the :height = 59, the same as the first observation.
![predict.PNG predict.PNG](https://community.jmp.com/t5/image/serverpage/image-id/30653i094A4E860FA171BA/image-size/large?v=v2&px=999)
Then I held the Alt key on Windows (Option key on Macintosh) and clicked the red triangle at the top of Fit Least Squares to get this dialog from which I selected Mean Confidence Interval Formula.
![menu.PNG menu.PNG](https://community.jmp.com/t5/image/serverpage/image-id/30654iD219AD5F873C55A1/image-size/large?v=v2&px=999)
You can examine the column formula to see the calculation.
![formula.PNG formula.PNG](https://community.jmp.com/t5/image/serverpage/image-id/30655i06142E1D6101BD73/image-size/large?v=v2&px=999)
The linear predictor starts the formula to estimate the mean, then the rest is subtracted for the lower confidence bound. The 2.024 multiplier is the t quantile for 95% confidence and the error degrees of freedom.
What quantity did you expect for the interval?