Hello @KernDawg, The convergence criteria and optimization algorithm for Fit Mixed and Fit Least Squares (with a random effect specified) are going to be different; which I suspect is also part of the problem here. Another issue with this dataset is the covariance structure (high confounding risk), which can cause convergence problems in my experience:
![PatrickGiuliano_1-1646714315406.png PatrickGiuliano_1-1646714315406.png](https://community.jmp.com/t5/image/serverpage/image-id/40628i4A32B67392047FBD/image-size/medium?v=v2&px=400)
Note that as a rule of thumb, we might consider a Pearson correlation of ~ 0.3 to indicate moderate risk, with ~0.5 or higher to indicate higher risk (but this will depend on subject-matter-knowledge and your own personal risk-definition).
The Fit Mixed platform may be more sensitive to this confounding risk and hence, what appears to be a convergence problem (where effect estimates are not reported). Agree with @Phil_Kay that low signal to noise ratio in this model may contribute to the problem we are seeing.
The Fit Least Squares personality with a random effect specified looks like a perfectly acceptable model specification for your modeling scenario here; in which case, none of your effects are statistically significant on your Yield Actual response (except for the Intercept term).
![PatrickGiuliano_2-1646714518880.png PatrickGiuliano_2-1646714518880.png](https://community.jmp.com/t5/image/serverpage/image-id/40629iFF6CB5DF65EE3C21/image-size/large?v=v2&px=999)
The fact that only the intercept is "significant" is likely another clue. You might want to look at a better-fitting model here, or consider why your expected model is not fitting your data particularly well (Adj-R-squared = 6% and is less than 1/2 of R-squared = 13%):
![PatrickGiuliano_3-1646714932424.png PatrickGiuliano_3-1646714932424.png](https://community.jmp.com/t5/image/serverpage/image-id/40630i2D90377ACCF75612/image-size/large?v=v2&px=999)
-@PatrickGiuliano