Understanding Fit Response Screening Effect Tests

[#1]

Hello! 

I am new to using jmp as a whole and this is m first time using it for DOE. I used the software for a fractional factorial screening design. I had three different factors to evaluate all of which were categorical. My data was collected/recorded using 0 and 1 to represent fail or pass respectively. 

I have now inputted the data into my design and I am trying to understand the analysis provided by JMP. I saw a tutorial online that said to just right click model and hit run script. I then changed the personality to to Response Screening  After running the script I got a table and some plots attached below. 

I am just looking for some guidance on how to interpret the data since I am just trying discern which of the factors had the most effect. or if i am just using the wrong analysis. 

[#2]

Response Screening is intended to quickly identify the few responses of interest out of many possible responses. You have a single response.

Is the pass or fail indication a result of a rule applied to a continuous measurement? If so, the measurement is more informative and should be used as the response in your analysis. If not, then change the Modeling Type of the data column with the responses to Nominal before the analysis. This change will cause JMP to perform nominal logistic regression instead of ordinary least squares regression.

What size fraction did you use? What was the resolution? Are there potential interaction or non-linear effects of these three factors?

[#3]

Thank you so much for the reply! 

So, Yeah we tested out three different factors against one response which would be the sample passed or failed evaluation. 

i apologize for not answering your other questions, I am just unsure as to what those are. This is my first time using JMP. If going based off of the color map on correlations is correct, there was no overlap with the factors using the design outputted by JMP. 

[#4]

Hi @MedianPuppy4982 .  It May be helpful if you post your design so that we can understand the design better, and answer @Mark_Bailey ‘s questions. His questions are really about how you designed the experiment, and what your thought process(es) was. 

[#5]

I apologize for the delay! Thank your for trying to help! I have attached some screenshots of my design hopefully this will help