I have two questions regarding design of experiment (custom design) using JMP 12.1.0:
Thank you in advance!
So in conclusion please keep in mind that the experimental design is the result that answers the question, "What is the best data to fit my model?"
Mark offers some great advice and counsel. Based on your second reply...if you really suspect no valuable information to be obtained with any treatment combinations at the zero level, then why include that level at all? Generally speaking one shouldn't include treatment combinations in an experiment that, through prior knowledge or domain expertise, would result in abject failures providing no information upon which to contribute to your ability to address the practical problem. Since the factor is discrete numeric, I suggest picking a low level which will provide valuable empirical information.
So you expect "the most interesting correlations in the range 20-100" based on experience. Good.
You extended this range down to 0 and up to 150. That change might be good, too.
It is OK to include 'control' runs but you might want to add them after the custom design is made and exclude them from the analysis (fitting the model). This way you have them for comparison but they don't have to meet the needs of or detract from the modeling.
By 'negative control' and 'don't expect any informative response' do you mean that you won't get a response at all or that the nature of what you are studying will fundamentally change from the nature obtained with non-zero levels and won't be relevant or useful? Sorry I am not clear about your point.
Again, the point of the factor range and design levels in your experiment is to support fitting the model. For example, the most informative runs (highest leverage) for estimating the linear parameter are at the extremes of the range and nowhere in between.
You will use the model to find the most satisfactory factor level for the desired response. (This prediction will be confirmed empirically with more tests.)
Mark, Peter, thank you for your comments and suggestions. Very appreciated!
I am going to exclude zero level from DoE. The response is boolean (object survived/failed) and I will monitor samples over time. Zero level will bring no benefit, as it is known from the nature of failure, that it is impossible to fail with the zero-level of concentration factor.
I have several more factors and want to check if they have any interactions with the main factor (failure will happen later, or earlier).
Your decision to omit a zero level makes perfect sense.
So the outcome is binary (survived, failed)? What is the response to be modeled?
The goal of the experiment is to identify active factors apart from main one.
Response will be a life time for particular factor combinations.
Currently, with the binary outcome there will be no much use of the model (in sence of prediction).
In order to give answers on all other questions I will need to dive into extensive reading :)
You can use Life Distribution to explore candidate distribution models for your life data.
You can use Fit Model > Fit Parametric Survival to model the data with a selected distribution for errors.
The best layout of your data table might need some help. This kind of analysis is a bit different than the usual regression modeling.