This is an excellent lecture on a very important concept in DOE. I have 2 questions. 1. Adding additional model points can increase the desired FDS. What if there are some practical constraints in not adding additional model points like time, or budget? What trade-offs we need to make to ensure we get the same precision? 2. What if I don't have an historical data of the std deviation? Let's say the process is a new one. Which std dev should I use? Should a separate ANOVA study be conducted and then use this std dev?
If you do not want to add more runs, then finding ways to decrease the noise in the system, or increasing the acceptable "d" will both change the signal to noise ratio. A larger S/N ratio will increase the FDS calculation.
Hello , At RSM you talk about predictions. Isn't a prediction per measurement? So if you're going to do more measurements, isn't it called confidence interval instead of prediction interval?
Good question: A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed.
can you further clarify on the "estimated standard should be taken from historical data "? btw can i just use the anova standard deviation in previous study for power calculation?
This is an excellent lecture on a very important concept in DOE. I have 2 questions.
1. Adding additional model points can increase the desired FDS. What if there are some practical constraints in not adding additional model points like time, or budget? What trade-offs we need to make to ensure we get the same precision?
2. What if I don't have an historical data of the std deviation? Let's say the process is a new one. Which std dev should I use? Should a separate ANOVA study be conducted and then use this std dev?
If you do not want to add more runs, then finding ways to decrease the noise in the system, or increasing the acceptable "d" will both change the signal to noise ratio. A larger S/N ratio will increase the FDS calculation.
Hello , At RSM you talk about predictions. Isn't a prediction per measurement? So if you're going to do more measurements, isn't it called confidence interval instead of prediction interval?
Good question: A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed.
can you further clarify on the "estimated standard should be taken from historical data "? btw can i just use the anova standard deviation in previous study for power calculation?
Yes, the standard deviation from a previous study could be a good estimate