Trying to keep the comparison of like models, here is the SAS output for weight = sex with an intercept:
![Capture.JPG Capture.JPG](https://community.jmp.com/t5/image/serverpage/image-id/19317iC494587B8DA6E131/image-size/large?v=v2&px=999)
So the mean weight for sex = F is equal to 108.3181818 - 7.3737374 = 100.94444 (prediction).
Here is the JMP output for the same model:
![Capture.JPG Capture.JPG](https://community.jmp.com/t5/image/serverpage/image-id/19318i50860894DAB5E7B8/image-size/large?v=v2&px=999)
So the mean weight for sex = F is equal to 104.63131 - 3.686869 = 100.94444, the same predicted value
Let's remove the intercept term from the model and see what happens. Here is the SAS output for the new model:
![Capture.JPG Capture.JPG](https://community.jmp.com/t5/image/serverpage/image-id/19319i70390B6D407B0BDE/image-size/large?v=v2&px=999)
The mean weight for sex = F is 100.94444 again.
How about JMP?
![Capture.JPG Capture.JPG](https://community.jmp.com/t5/image/serverpage/image-id/19320i05CD2722496D5EDB/image-size/large?v=v2&px=999)
Hmm, that's different. This estimate is not wrong, though! JMP uses effect coding for the linear predictor and this constrained model model is not the same as the constrained model in SAS GLM,. This estimate is the best in the least squares sense but it is not a good fit with only sex for the predictor. You should not remove the intercept. That is the lesson here.