Fitting the model using REML in the Standard Least Squares personality lets you view the variation in intercepts and slopes (Standard Least Squares Regression). Note that the slopes do not have much variability, but the intercepts have quite a bit. The intercept and slope might be negatively correlated; varieties with lower intercepts seem to have higher slopes.
Standard Least Squares Regression
Select Help > Sample Data Library and open
Select Analyze > Fit Model.
Select Yield and click Y.
Select Mixed Model from the Personality list. Alternatively, you can select the Mixed Model personality first, and then click Y to add Yield.
Select Moisture and click Add on the Fixed Effects tab.
Completed Fit Model Launch Window Showing Fixed Effects
Select the Random Effects tab.
Select Moisture and click Add.
Select Variety from the Select Columns list, select Moisture from the Random Effects tab, and then click Nest Random Coefficients.
Completed Fit Model Launch Window Showing Random Effects Tab
Click Run.
The Fit Mixed report is shown in Fit Mixed Report. Note that some of the constituent reports are closed because of space considerations. The Actual by Predicted plot shows no discrepancy in terms of model fit and underlying assumptions.
Yield = 33.43 + 0.66 * Moisture
Fit Mixed Report
Random Coefficients Report
In the Model Specification window, the Center Polynomials option is selected by default. Because of this, the Moisture effect is centered at its mean of 35.583, as stated in the note at the top of the Variety report. From the Fixed Effects Parameter Estimates and Random Coefficients reports, you obtain the following prediction equation for Variety 2: