When designing an experiment, a common diagnostic is the statistical power of effects. Bradley Jones has written a number of blog posts on this very topic. In essence, what is the probability that we can detect non-negligible effects given a specified model? Of course, there are a set of assumptions/specifications
Last year, I wrote two blog posts about power analysis for designed experiments. Since then, JMP 11 was released, and the user interface for power analysis in the Custom Designer changed. This post introduces the new interface and shows how to use it profitably. Why did you change the interface?
In my previous post, I talked about the fundamental quantities that affect the ability of a designed experiment to detect non-negligible effects of the factors. These are: 1) The size of the effect 2) The root mean squared error (RMSE) of the fitted model 3) The significance level of the
When I took my first course in linear models and design of experiments, my professor told the class that the most common question that he encountered in his statistical consulting was, “How many samples do I need [for my results to be statistically significant]?” This question comes out of a