When you do a general MANOVA, you can choose to calculate the univariate statistics to examine the individual responses.

The most important statistic in the analysis of variance table is the p-value (P), which exists for each term in the model (except for the error term). The p-value for a term tells you whether the effect for that term is significant:

·    If P is less than or equal to the a-level you selected, then the effect for the term is significant.

·    If P is larger than the a-level you selected, the effect is not significant.

If a factor is significant, the level means for the factor are significantly different from each other.

If an interaction term is significant, the effects of each factor are different at each level of the other factors. For this reason, you should not analyze the individual effects of terms involved in significant higher-order interactions.

In the door lock analysis, you assessed the effects of method, plant, and the method- by-plant interaction for the quality response. Assuming that you chose the common a-level of 0.05 for the test, the results indicate that:

·    The p-value for the interaction term (0.750) is greater than 0.05. Thus, the interaction is not significant, and you can consider the effects of the method and quality separately.

·    The p-value for the method factor is 0.008. Because this value is less than the chosen a-level of 0.05, the effect of method on quality is significant. In other words, the mean quality score is different for the two production methods.

·    The p-value for degree is greater than 0.05 (0.064), indicating that there is no significant effect of plant location on quality.