Hi there. I’m Greg. I’m starting a trip. This is an educational journey through the concept of design of experiments (DOE). I’m doing this to better understand the company I work for (Stat-Ease), the product we create (Design-Expert® software), and the people we sell it to (industrial experimenters). I will be learning as much as I can on this topic, then I’ll write about it. So, hopefully, you can learn along with me. If you have any comments or questions, please feel free to comment at the bottom.
So, off we go. First things first.
What exactly is design of experiments (DOE)?
When I first decided to do this, I went to Wikipedia to see what they said about DOE. No help there.
“The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.” –Wikipedia
The what now?
That’s not what I would call a clearly conveyed message. After some more research, I have compiled this ‘definition’ of DOE:
Design of experiments (DOE), at its core, is a systematic method used to find cause-and-effect relationships. So, as you are running a process, DOE determines how changes in the inputs to that process change the output.
Obviously, that works for me since I wrote it. But does it work for you?
So, conceptually I’m off and running. But why do we need ‘designed experiments’? After all, isn’t all experimentation about combining some inputs, measuring the outputs, and looking at what happened?
The key words above are ‘systematic method’. Turns out, if we stick to statistical concepts we can get a lot more out of our experiments. That is what I’m here for. Understanding these ‘concepts’ within this ‘systematic method’ and how this is advantageous.
Well, off I go on my journey!
Recently, Stat-Ease Founding Principal, Pat Whitcomb, was interviewed to get his thoughts on design of experiments (DOE) and industrial analytics. It was very interesting, especially to this relative newbie to DOE. One passage really jumped out at me:
“Industrial analytics is all about getting meaning from data. Data is speaking and analytics is the listening device, but you need a hearing aid to distinguish correlation from causality. According to Pat Whitcomb, design of experiments (DOE) is exactly that. ‘Even though you have tons of data, you still have unanswered questions. You need to find the drivers, and then use them to advance the process in the desired direction. You need to be able to see what is truly important and what is not,’ says Pat Whitcomb, Stat-Ease founder and DOE expert. ‘Correlations between data may lead you to assume something and lead you on a wrong path. Design of experiments is about testing if a controlled change of input makes a difference in output. The method allows you to ask questions of your process and get a scientific answer. Having established a specific causality, you have a perfect point to use data, modelling and analytics to improve, secure and optimize the process.’"
It was the line ‘distinguish correlation from causality’ that got me thinking. It’s a powerful difference, one that most people don’t understand.
As I was mulling over this topic, I got into my car to drive home and played one of the podcasts I listen to regularly. It happened to be an interview with psychologist Dr. Fjola Helgadottir and her research into social media and mental health. As you may know, there has been a lot of attention paid to depression and social media use. When she brought up the concept of correlation and causality it naturally caught my attention. (And no, let’s not get into Jung’s concept of Synchronicity and whether this was a meaningful coincidence or not.)
The interesting thing that Dr. Helgadottir brought up was the correlation between social media and depression. That correlation is misunderstood by the general population as causality. She went on to say that recent research has not shown any causality between the two but has shown that people who are depressed are more likely to use social media more than other people. So there is a correlation between social media and depression, but one does not cause the other.
So, back to Pat’s comments. The data is speaking. We all need a listening device to tell us what it’s saying. For those of you in the world of industrial experimentation, experimental design can be that device that differentiates the correlations from the causality.
The situation: You have successfully run an experiment and analyzed the data. The results include a prediction equation with a high predicted R-squared that will be useful for many purposes. How can you share this with colleagues?
The solution: Design-Expert® software has a little-known but useful “Copy Equation” function that allows you to export the prediction equation to MS Excel so that others can use it for future work, without needing a copy of Design-Expert software. The advantage of using this function is that it brings in all the essential significant digits, including ones not showing on your screen. This accuracy is critical to getting correct predictive values.
3. Open Excel, position your mouse and use Ctrl-V to correctly paste the formula into Excel (Ctrl-V allows the spreadsheet functionality to work.)
4. As shown in the figure (coloration added within Excel), the blue cells allow the user to enter actual factor settings. These values are used in the prediction equation, with the result showing in the yellow cell.
You can also view this process in this video.
Good luck with your experimenting!
The upcoming 2019 Analytics Solutions Conference will be the premier meeting to learn about how others are using statistical tools (design of experiments (DOE), multivariate analysis (MVA) and process analytical technology (PAT)). Come see how these tools are used to dramatically impact the bottom line.
And registration is now open for the conference! Don’t miss out on early bird registration.
Reason #3 to be there: Sharon Flank’s keynote talk "Field Authentication and Adding Chemistry to Blockchain".
These days, companies need to find effective ways to secure quality and protect against counterfeit products. Whether you are the manufacturer or the end user, using the right product is important.
This complex problem requires an elegant solution. One that is non-destructive, fast, and user friendly. Dr. Flank takes us thru a solution that uses spectroscopy and analytics to identify a products chemical ‘fingerprint’. This can be applied on pharmaceuticals, cosmetics, spare parts, electronics, wine and additive manufacturing, and the talk will include use cases and real-life examples.
Reason #4 to be there: a dinner cruise down the Mississippi. That’s right. Network with your fellow attendees while cruising down the Mississippi river at sunset. Then have dinner and a drink before returning to shore.
Need more reasons to attend? More keynote speakers, more fun networking opportunities, and summer in Minnesota! And to top it off, early bird registration is still open.
Register now to ensure your seat!
General Info: https://www.statease.com/asc-2019
The upcoming 2019 Analytics Solutions Conference is your opportunity to discover how others are using statistical tools (design of experiments (DOE), multivariate analysis (MVA) and process analytical technology (PAT)) to dramatically impact the bottom line.
Reason #1 to be there: Keynote Speaker Dr. Geoff Vining. Jump-start your education by learning about leading-edge methodology with Geoff’s talk on “Solving Complex Problems”.
These days, organizations need solutions for increasingly complex problems that are critical to their operation. Statistical engineering is the discipline dedicated to the art and science of solving these complex problems. These problems almost always are unstructured and typically large, crossing several disciplines. Typically, the data associated with them come from a wide variety of sources and thus usually look like a “mess.” The key is how to provide enough definition, data analysis, and structure to create a reasonable path to a truly sustainable solution.
Statistical engineering provides a structure to determine which statistical and analytic tools/methods are appropriate depending on the circumstances, and it outlines how to create sustainable solutions efficiently and effectively. Ultimately, it is the discipline that helps practitioners determine “the right tool for the right job at the right time, properly applied.”
This talk introduces this new discipline at a high level. It then outlines the important roles that both DOE and data analytics play in the solution of complex problems. In the process it emphasizes the importance of strategy and understanding exactly what the tools can and cannot do.
Reason #2 to be there: Cost. At $495 (with the early bird special rates, in place until April 15), this is a truly inexpensive conference. With the tremendous value to be had in technical content for all audiences entry-level to advanced, and the great price point, this is a no brainer.
Need more reasons to attend? More keynote speakers, fun networking opportunities, and summer in Minnesota!
General Info: https://www.statease.com/asc-2019