The means table displays the fitted means of the observations within groups based on one or more categorical variables. Fitted means use least squares to predict the mean response values of a balanced design.
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 usability and quality ratings varies by method, plant, and the method*plant interaction. Method and the interaction term are statistically significant at the 0.10 level. The table shows that method 1 and method 2 are associated with mean usability ratings of 4.819 and 6.212 respectively. The difference between these means is larger than the difference between the corresponding means for quality rating. This confirms the interpretation of the eigen analysis.
However, because the Method*Plant 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 method 1, plant C is associated with the highest usability rating and the lowest quality rating. However, with method 2, plant A is associated with the highest usability rating and a quality rating that is nearly equal to the highest quality rating.
Least Squares Means for Responses
Rating Quality Rating
Mean SE Mean Mean SE Mean
Method 1 4.819 0.1645 5.242 0.1932
Method 2 6.212 0.1794 6.026 0.2107
Plant A 5.708 0.1924 5.833 0.2259
Plant B 5.493 0.2323 5.914 0.2727
Plant C 5.345 0.2059 5.155 0.2418
Method 1 Plant A 4.667 0.2721 5.417 0.3195
Method 1 Plant B 4.700 0.2981 5.400 0.3500
Method 1 Plant C 5.091 0.2842 4.909 0.3337
Method 2 Plant A 6.750 0.2721 6.250 0.3195
Method 2 Plant B 6.286 0.3563 6.429 0.4183
Method 2 Plant C 5.600 0.2981 5.400 0.3500