Our World Statistics Day conversations have been a great reminder of how much statistics can inform our lives. Do you have an example of how statistics has made a difference in your life? Share your story with the Community!
This is part of a series of Q&As with the JMP development team as we near the release of JMP 9 on Oct. 12. In previous interviews, I talked with Xan Gregg and Chung-Wei Ng.
Laura Lancaster is a statistical developer at JMP. She has been with JMP for six years, starting as a statistical tester and then moving into statistical development.
Arati: What do you like about JMP?
Laura: I like how JMP makes it easy to explore and understand you data without much statistical knowledge, yet is deep and statistically powerful as well. I also love the fact that I can extend the capabilities of JMP with JSL.
Arati: Do you use JMP in your job or for personal projects? If so, how?
Laura: I use JMP for researching methods to implement in JMP and for testing what I have already implemented in JMP. I make extensive use of existing platforms as well as JSL in my research and testing.
Arati: What are your favorite JMP features that you wish more people knew about and used?
Laura: Some of my favorite features of JMP are the profilers. The Profilers are very rich in their capabilities, and I believe that more users could benefit from using features such as Profiler simulation and optimization.
Arati: What’s new in JMP 9 in your area of focus?
Laura: Several Analysis of Means (ANOM) methods were added to Oneway and Contingency to compare means, variances and proportions. Additionally, a Heterogeneity of Variances test that uses Random ANOM for variances was added to the Variability/Gauge R&R platform that tests for unequal error variance.
Arati: What was the reason for adding those to JMP? How will they be helpful to customers?
Laura: Analysis of Means (ANOM) shows results in a graphical form, making it easy to see which means are significantly different from the overall mean. The graph looks somewhat like a control chart and is easy to learn to read. ANOM for Variances provides a Homogeneity of Variance test, making it easy to see which variances are significantly different from the Mean Square Error.
The Heterogeneity of Variances test in Variability is an important test that can reveal unequal variances in the data. This is an assumption that should be checked before proceeding with a Gauge measurement systems analysis. Also, this test could reveal information about the measurement system and possibly point to areas of improvement. The Heterogeneity of Variances tests are a form of Random ANOM for variances and look like ANOM charts. Thus, they are easy to read and reveal which variances are different from the overall variance.
Arati: What’s most exciting to you as a developer?
Laura: It is great to hear from JMP customers that the features you helped add to JMP have made their work easier, more effective and more efficient.
Arati: What do you like to do in your free time?
Laura: I like to travel, especially on a sailboat. I also enjoy playing the violin, reading, exercising and volunteering with a couple of local organizations that assist the needy.
Arati: Pick two of the following to identify: your favorite programming language, favorite algorithm, favorite formula, favorite theorem or favorite software tool.
Laura: My favorite programming language is C++ and my favorite algorithm is Dynamic Programming.