Fitted means use the coefficients from the fitted model to compute the mean response for each level combination of a factor or interaction. Data means use the raw response variable means for each factor level combination. The two types of means are identical for balanced designs but can be different for unbalanced designs.
The fitted means estimate the average response at different levels of one factor while averaging over the levels of the other factors.
Use the Means table to understand the statistically significant differences between the factor levels in your data. The mean of each group provides an estimate of each population mean. Look for differences between group means for terms that are statistically significant.
For main effects, the table displays the groups within each factor and their means. For interaction effects, the table displays all possible combinations of the groups. If an interaction term is statistically significant, do not interpret the main effects without considering the interaction effects.
In these results, the Means table shows how the mean thickness varies by time, machine setting, and each combination of time and machine setting. Setting is statistically significant and the means differ between the machine settings. However, because the Time*Setting interaction term is also statistically significant, do not interpret the main effects without considering the interaction effects. For example, the table for the interaction term shows that with a setting of 44, time 2 is associated with a thicker coating. However, with a setting of 52, time 1 is associated with a thicker coating.
General Linear Model: Thickness versus Time, Operator, Setting
Term Fitted Mean
1 35 40.6667
1 44 70.1667
1 52 92.3333
2 35 40.5000
2 44 76.0000
2 52 89.6667