In a General Full Factorial design, the experimental factors can have any number of levels. For example, Factor A may have two levels, Factor B may have three levels, and Factor C may have five levels. The experimental runs include all combinations of these factor levels.

Minitab's power and sample size capabilities allow you to examine how the following test properties affect each other:

·    power - the probability of being able to detect an effect of a given size.

·    replicates - the number of times each run is repeated.

·    maximum difference between main effect means - the difference in your variable that you want the test to be able to detect.

If you enter values (or sets of values) for any two of these properties, Minitab calculates the associated value(s) for the remaining property.

By default, all calculations are based on an a-level of 0.05. However, you can select any value between 0 and 1.

A metal parts supplier is developing a new part to be incorporated into the production of a motor. They would like to study the effects of three exterior coatings and three alloys on the corrosion resistance of the new part. They are interested in detecting a maximum difference of at least 0.4. The estimated standard deviation is 0.15.