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
“Multifactor testing via design of experiments (DOE) is the secret weapon for medical-device developers,” says Stat-Ease Principal Mark Anderson, lead author of the DOE and RSM Simplified series*. “They tend to keep their success to themselves to keep their competitive edge, but one story, that we can share, documents how a multinational manufacturer doubled their production rate while halving the variation of critical-to-quality performance. That’s powerful stuff!”
The case study that Mark is referring to can be found here: RSM for Med Devices.
Stat-Ease has guided many manufacturers in the medical device industry in the streamlining of their products and processes. Now, we have put that experience together into one place, the Designed Experiments for Medical Devices workshop.
In this one-of-a-kind workshop, learn how to optimize your medical device or process. Our DOE experts will show you how to use Design-Expert® software to help you save money or time while ensuring the quality of your product. We welcome scientists, engineers, and technical professionals working in this field, as well as organizations and institutions that devote most of their efforts to research, development, technology transfer, or commercialization of medical devices. Throughout this course you will get hands-on experience while using cases that come directly from this industry.
During a fast-paced two days, explore the use of fractional factorial designs for the screening and characterization of products or processes. Also see how to achieve top performance via response surface designs and multiple response optimization. Practice applying all these DOE tools while working through cases involving medical device design, pacemaker lead stress testing, and typical processes that may be used in the testing or production of these devices such as seal strength, soldering, dimensional analysis, and more.
For dates, location, cost, and more information about this workshop, visit: www.statease.com/training/workshops/class-demd-adin.html
About Stat-Ease and DOE
Based in Minneapolis, Stat-Ease has been a leading provider of design of experiments (DOE) software, training, and consulting since its founding in 1982. Using these powerful statistical tools, industrial experimenters can greatly accelerate the pace of process and product development, manufacturing troubleshooting and overall quality improvement. Via its multifactor testing methods, DOE quickly leads users to the elusive sweet spot where all requirements are met at minimal cost. The key to DOE is that by varying many factors, not just one, simultaneously, it enables breakthrough discoveries of previously unknown synergisms.