Hello @Justin_Bui,
Welcome to the Community !
For information about Full Factorial designs, you can check the JMP help : Full Factorial Designs (jmp.com)
Just to add some information and explanations to the great work provided by @ian_jmp :
1) There are several options to deal with "repetitions" (depending if it is replicate runs or repeats):
- Repetition is making multiple response(s) measurements on the same experimental run, while replication is making multiple experimental runs for each treatment combination.
In your case, you have done several times (20 times in total) each treatment from the design, so you are in the situation of replicates. You specify 19 as your number of replicates: you have done the original treatment combination from the design, and then replicate it 19 times, so you end up with 20 pieces/experiments per treatment. So here, you just add a row for each experimental run you have done with its response, since it is done on a different piece for each treatment of the design. No need to aggregate the results with a mean or std deviation here.
2) For the second part, your model certainly needs some "refining"/improvement.
For example, your last interaction "Materials*Assy side" doesn't seem significant (p-value 0,659 > 0,05), so it can be removed from the model. To have an example on how to remove non-significant terms from your model, please check Reduce the Model (jmp.com)
Once you have introduced all your experimental runs (20 experimental runs per treatments in the DOE) as rows in your datatable and removed non-significant terms, it will be a lot easier for you to assess/evaluate the DoE model :
- Do you have a significant model ? Does it make sense with your domain expertise ?
- How much do the factors and model explain the variability in your response measurements ?
- Are there interactions ?
- What are the effect sizes of the factors ?
- ...
...and for us to help you further in the interpretation if needed.
Hope this answer will help you,
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)