Re: Recording DOE Club with Jacqueline Asscher and Phil KayThis was a great session of the DOE Club. So nice to see so many of you.
I know there were lots of questions that did not get answered, so feel free to ask them here.
Let's keep the discussion going...
Recording DOE Club with Jacqueline Asscher and Phil KayThanks @J_Asscher and @Phil_Kay for this session, and to all Users who participated!
Video:
Resoults from the Poll Questions:
The discussion continued though the chat:
Q: Do you always accept the default number of whole plots? Would you ever increase it?
A1: Very useful indeed, but sometimes a bit more problematic for modelling
A2: You may have to increase the number of whole plot if you want more power and more precision in the estimates of your main effects for factors that are hard to change
Q: What is the implication of running a standard design by grouping experiments? is it ever acceptable?
A1: You may have lurking variables influencing your responses, like time, temperature, operator, etc... and the error is not random anymore (it may correspond to changes due to the "block changes"), which may hide some important factors or reveal non-important ones correlated to lurking variables
A2: imagine if you make two blocks and in the first you have X1 as -1 and on the second part as +1. If you have an hidden effect ore something degrade over time that increase over time, this effect is attributed to X1 even if its not. This can lead to false models.
Some further information was shared during the session:
- White Paper on Split Plot: https://www.stat.purdue.edu/~kuczek/stat514/Split%20plot%20example.pdf
- Follow Phil on LinkedIn: #DOEbyPhilKay
- Follow Victor Guiller on LinkedIn: https://www.linkedin.com/in/victorguiller/
Re: Recording DOE Club with Jacqueline Asscher and Phil KayAll, Hi ... Regarding the question on the Implication of running a standard design by grouping experiments ...
Let's say you group all the high temperature runs on Monday, all the low temperature runs on Tuesday. If you see a difference between results at low and high temperature, how will you know if the difference is due to the temperature change, not just a difference between days? (I have seen daily differences, and Mondays can be "unique".)
You may have to group low and high temperatures due to logistics, safety, and/or cost so it's OK to do that. What the split plot design does is add another group of high temp runs (say Wednesday) and of low temp runs (say Thursday) so that we can separate day-to-day (or batch-to-batch or vessel-to-vessel or ...) variation from the true temperature effect.
As for Friday, well ... ?
