If your response is really two levels (not satisfied, satisfied), then use a nominal modeling type and treat it as a categorical response. This will change your linear regression to logistic regression.
If your response is really continuous (satisfaction from 0 to 1), then linear regression will have difficulty. Regression assumes that the response is unbounded and has a range of negative infinity to positive infinity. You can use a transformation of the response that is built into the Fit Model launch dialog that should remedy the disparity. The Logit transform is Log( satisfaction / (1-satisfaction) ). (Note that function is the natural logarithm).
Simply select the response in the Y role and then click the red triangle near the bottom center for Transforms and select Logit. It works like this:
![Capture.PNG Capture.PNG](https://community.jmp.com/t5/image/serverpage/image-id/10169i23B2E791AEF36A2B/image-size/large?v=v2&px=999)
This example has a response Y that is continuous but bounded between 0 and 1 like your response. The predictor X is normally distributed. Now set up the Fit Model launch I as instructed:
![Capture.PNG Capture.PNG](https://community.jmp.com/t5/image/serverpage/image-id/10170i90D046E8E9B3171F/image-size/large?v=v2&px=999)
I am using a linear model of second order but that fact is not important. This approach works with any linear predictor such as your model. Click Run:
![Capture.PNG Capture.PNG](https://community.jmp.com/t5/image/serverpage/image-id/10171iBEA6800342681F12/image-size/large?v=v2&px=999)
(Note that I first changed the Emphasis setting to Minimal Report.)
You can see that this transformation helps the regression deal with the lower and upper bounds of satisfaction.