Dear All:
I am currently reading a journal regarding the tolerancing of parts for assembled products. That is, to what specifications should the components be made to ensure that the assembled product meets the required standard.
The experimenter has a total of 7 specs for 3 components to look at and runs a 2^(7-1) experiment. A GLM is estimated from the experimental data.
Now, the whole point is to identify which inputs are causing the greatest variability in the output. Intuitively, this suggests an ANOVA by treatment combination. However, rather than do this the author uses the GLM in conjunction with the Error Propagation Formula ("EPF") and the variances of the inputs to find the overall variability in Y and each of the 7 inputs' contributions to it.
My question is, "Is there a particular reason for using this method rather than ANOVA"?
ANOVA could have been used to find the overall varaibility in Y (SStot/N-1) and each of the MStreats could have been used to give an estimate of the contribution to variability. Does anyone know of a good reason why the author would instead find this information using the EPF, which is only an approximate relationship?
Additionally, does anyone know of a good reference which explains the advantages of the EPF and also states what the second-order EPF is?
Thanks in advance.
Bryan.
