Hello . I'm Ryan Lekivetz , Manager of the DOE and Reliability team at JMP .
And I'm all Rory Lekivetz.
We're here today to talk to you about Easy DOE . The question is it easy enough for a seven- year- old? N ow that you're eight years older you're a lot wiser to answer that question.
For those of you who don't know about Easy DOE, so it's a new platform and JMP 17 . Now , the idea with Easy DOE is it's going to be a new file type that encompasses the design through the analysis of a designed experiment . No more do you need to worry about splitting up , going from the DOE platform to a data table and then running the analysis separately .
Now , the idea with Easy DOE is that we're trying to aid novice users through that entire workflow . There's going to be a guided mode where we've tried to add hints and useful defaults to guide those users while at the same time having a flexible mode for those who are more comfortable with Easy DOE .
Now before we started doing this idea with Easy DOE and running our experiment, I did talk to Rory about the daily workflow . I f you open up the DOE documentation , we outline this idea of a Easy DOE workflow which goes through the described phase , which is where we identify the goal and the responses and the different factors, specify where we're looking at our model . We create the design , collect the data , fit a model to that data , and then use that model to predict .
Right now , if you think about the way traditionally we've done this in JMP, at that design phase is where we create the data table . Using that data table the experimental go and collect the data and then perform the remaining steps ending it. N ow what you'll see in Easy DOE , there's the tabbed interface where each tab represents one of these steps in the DOE workflow .
Now what was the experiment that we did ?
Paper airplanes .
Rory had found a website that talked about different ways to create paper airplanes . You want to tell them what was the response ? What were you trying to measure ?
We were trying to measure the distance which was inches .
What factors did you end up deciding that we could change ?
For factors we decided on war plane type, paper type, flying force and paperclip .
Yeah . Now you to tell them about some of these different tabs . Okay , so let's start . What was the define tab ?
The define tab was where you got to choose your factors and your responses .
That's right . I should mention here as well that when we were using Easy DOE , I left Lory in control of the entire platform . She launched it . She was the one entering everything and clicking between tabs and all of that . I think after the define tab , we moved to the next. What was that next tab?
Model. F or the model tab , you had to choose w hich one of these four was the best for your experiment.
Now I'll say too, on this one this is where we had to talk a little bit more about what these different model types mean . Of course , for a seven- year- old and even an eight- year- old , now that idea of understanding interactions can be a difficult thing .
Now , the main effect versus the interaction . One of the nice things was the website that we had found about creating paper airplanes . It talked about how some of the different types of paper airplanes do better when you throw it hard versus light .
It already had discussed that idea of interactions, so that's why ultimately, I helped her decide on picking that two- factor interaction model with the main effects .
Once we had that model, then what happened?
Then was the design . The design shows you what you're going to be making . Since we were doing paper airplanes and we entered the factors for tight paper throwing for some paper clip , then it sounds like different types , different papers, different throwing forces like route and paper clip or no paper clip.
Yes , I think we made the 16 different paper airplanes and so each one was a different one . I think we put a number on it . Is that right ? We label it with a number one . Yeah . Then what happens after we have that design, what do we do with that ?
Then we do good data entry . With data entry is where you enter in how many inches you want.
Yeah . I think we went outside and we took those paper airplanes and we flew them anyway and then just measured that . Yeah , that's right . W hat happened after we had that data entry ?
Then we go to be analyzed . Analyze is where you figure out which ones were the best.
Yeah , which ones really were impacting that distance flown . Now , I should mention here , so this is a novel thing in Easy DOE . The confidence intervals for each of our different effects are clickable .
All right, I had actually thought that this was going to be a really difficult time to talk about the analyzed . But as soon as we got there, so it starts with all of the terms in the model and very quickly figured out that you could click on them . She looked at the ones that were close to zero and just removed them .
On the top was actually her model . She actually picked a much simpler model than even what the best model . You'll notice there is a best model one . But one could argue that I actually might even prefer her model to the one that was picked by the best model .
But again , still a very nice way to play around with your model and see what happens if term enter o r are removed just by clicking on those confidence intervals . T hen after we moved to the analysis , what was that tab they had?
The predict tab was where you could see which types or which t hings were the best . The best look like it would probably be a dirt metal construction and differently for us since it was like, the hard in blue light was like…
Did it matter or not really?
Not really . It would be like you could do hard or light in your paper .
I should mention here, so it was interesting to see she hadn't really seen the prediction profiler so much before . I mean , wasn't familiar with it . S he did have to be told to click in there to see what happens . But even for a seven- year- old , it's interesting to see once they have that sense that they can click within that prediction profiler , she was really able to get the hang of it .
J ust some final thoughts from Easy DOE . I asked Rory a few questions ahead of time . What would you like to tell people about Easy DOE ?
It was really fun .
Yeah . If you were to do this experiment again , would you change or what would you change ?
The factors , maybe different days for the weather .
You think like it might be windy on some days and not on others . F or my own perspective , you know , so she was actually able to complete this with minimal help from me . I mean , she was in control the entire time of the Easy DOE platform . A lot of these different choices she was making on her own . Even when it came to the factors that she picked .
As well she actually did help us find some usability issues . There were pieces like in the design tab that I think we improve throughout because of users trying this out not just for her , but as well as other users that we had in the DOE program . The model she definitely needed help with , but the analysis was easier than expected .
Just some references , acknowledgments . Really , I just want to thank all the members of JMP that helped in the development of Easy DOE . There's a huge list that you'll actually see . We have a Discovery America presentation there as well , where we talk about this in a little bit more detail . Again , all the feedback from external and internal users that have seen this before the release of 17 and since it's been released .
Thank you for your time and joining us today . We hope you'll join us during Discovery where we can discuss this poster . Not sure yet if you'll be able to join us , but I definitely will be and hopefully you as well . Thank you .
Thank you .
