Hello everyone,
I beg your pardon for my dummy question.
I am fitting a response surface model (glm including main effects, interactions and quadratic effects) on results of a central composite design with three parameters noted X1, X2 and X3 for the sake of the example. While looking at the parameters estimates, some of them are significant and some are not significant, at the alpha risk of 5%. My question is related to a parameter, let's say X3, where neither the main effect, the interactions nor the quadratic effect are significant.
I am interested in analysing the prediction profiler as it gives me indications on the ranges of the predicted Y-values on the design space considering all parameters X1, X2 and X3 (at given locations).
As none of the X3 estimates was significant I am not willing to interpret the estimates (even though is appropriately expressed that any interpretation is unreliable due to random variation and that there is no conclusive evidence that the variable has any effect at all).
My question is then the following one: Does it still make sense to interpret the confidence intervals of a prediction profiler for this parameter X3? Is this confidence interval somehow reliable even when none of the estimates is significant?
Many thanks in advance for your help,
Chris
