Dear JMP® User,

According to the Merriam-Webster dictionary, synergy means a mutually advantageous conjunction or compatibility of distinct business participants or elements (such as resources or efforts).  In layman’s terms, it means that the sum of the parts is greater than the whole. As any chemist could tell you, synergy is a valuable thing to have. JMP and chemistry have this synergy – here are a few that come to mind.

For any chemistry-related project, there are unique features in JMP to help you analyze your data. Start with analytical chemistry. How do you know that you are getting reproducible results from your measurement device? JMP has advanced tools for evaluating your measurement process, allowing you to pinpoint and quantify the sources of variability in your measurement system.

Many chemical processes need to be monitored to ensure that they are producing quality products that are in specification. JMP has the tools for monitoring these processes. With the Control Chart Builder, analysts can drag and drop the variables they want on the chart and JMP will determine the appropriate control chart. Find an out-of-control condition? JMP’s dynamic linking allows you to quickly find all of the information related to it and then find a remedy.

To truly understand complex chemical reactions or to optimize the way a plant is operating, design of experiments is a critical tool. Designing experiments is one of JMP’s strengths. The Custom Designer will quickly allow a researcher to plan experiments for complicated scenarios and then help plan the analysis by building an appropriate model for analyzing the experiment. Plus, JMP provides all of the tools necessary for understanding the experimental results and optimizing responses.

There are so many other ways that chemists and chemical engineers can benefit from using JMP software. So many, in fact, JMP has a website dedicated to chemists.
Check it out!

And if you are interested in design and analysis of mixture experiments, want to learn how to analyze multidimensional data AND are attending the JMP Discovery Summit in September – sign up for one of our pre-conference courses.

I’ll leave you with this –

Q: What did the bartender say when oxygen, hydrogen, sulfur, sodium and phosphorous walked into his bar? 
A: OH SNaP!
  Brooke_Fortson.jpg  


Best regards,
Brooke Fortson
Editor

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Blogger’s Corner

  blogger
“Easy moving average using Summation function” by Byron Wingerd

Scientists and engineers use online sensors and measurement devices to monitor an almost infinite array of process attributes. When analyzing time series data captured by historians and data loggers, two questions come up regularly: “How can I calculate a moving average?” and “How do I flag rows after a gap occurs in my time series data?” Learn how.

    Course Highlights

Custom Designs for Industrial Experiments Using JMP Software JMP® Software

Learn a state-of-the-art approach to designing industrial laboratory experiments that is based on the latest statistical theory and numerical methods. Listen to course developer Mark Bailey give an overview of his class. Next offering is May 13.

 
Article Highlight

“Design of Experiments Makes a Comeback” by Rick Mullin

Read article.

 
Book Highlights

JMP® for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition by Ann Lehman, Norm O'Rourke, Larry Hatcher and Edward Stepanski

Fundamentals of Predictive Analytics with JMP® by Ron Klimberg and B. D. McCullough

 
Tips and Tricks

In my 30 years of applying design of experiments methodology, how to efficiently investigate a process or system in a reasonable number of experiments, given the time, material or cost constraints was a frequent challenge. Many times, we would have to prune our brainstormed factors down to six or seven to stay within our budget for the number of runs that we could afford.

Unfortunately, that prevented us from considering some potentially important factors and exploring how those factors were affected by the other investigated factors. Thanks to a brand-new class of screening designs by Bradley Jones and Christopher Nachtsheim called definitive screening designs, this is no longer the case.

A definitive screening design requires only 2N+1 experiments. For example, a 10-factor investigation requires only 21 experiments. In addition, these experiments can delineate main effects clear of confounding with two-way interactions and often can even delineate polynomial terms. In contrast, a classical design of experiments would often include center-point replication, but this would give only a global assessment of the lack of fit. On the other hand, the construction of the definitive screening design can often identify the active polynomial term.

A paper by Jones and Nachtsheim on definitive screening designs won the prestigious ASQ Brumbaugh Award because it made the largest single contribution to the development of industrial application of quality control in 2011. In addition, the first application of this new design type by Novomer in 2012 was recognized by the ASA with the Statistics in Chemistry Award

Below is a color map of the correlations for a 10-factor definitive screening design. All of the main effects are clear of confounding, with all two-way interactions and polynomial terms.

Tip image 1


Best of all, this new class of designs is available to you on the JMP File Exchange as an add-in.


This tip was provided by Louis Valente, JMP Senior Manager, Technical Enablement.


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