JMP® Blog

Did you see the cover article in yesterday's New York Times Sunday Book Review? Harvard psychologist Steven Pinker reviewed the new book by Malcolm Gladwell, who was the keynote speaker for the Innovators' Summit in Chicago.

Pinker writes that Gladwell is "a minor genius who unwittingly demonstrates the hazards of statistical reasoning and who occasionally blunders into spectacular failures."

Also worth a read is the editors' note for the book review section, which points out that Gladwell says getting a graduate degree in statistics may be the best way to start a career in journalism now.

The editors' note also quotes Pinker as saying statistical reasoning is the most important scientific concept that non-scientists lack.

While the chief architect of JMP, John Sall, remains heavily involved in the day-to-day development of our product here in Cary, North Carolina, he also travels the world to show what JMP can do for organizations of all kinds.

Sall flew to China and Japan recently with Steve Adams, JMP's Director of International Sales. In Beijing, Sall spent time with influential professors from Beijing University of Technology, Peking University, the Chinese Academy of Sciences and the Capital University of Economics and Business. He gave a JMP demo at the China Association for Quality and took part in a pharmaceutical conference hosted at the SAS Beijing offices. In Shanghai, Sall was a keynote speaker at the International Forum of Quantitative Decision-making and Continuous Process Improvement at Shanghai University.

In Japan, Sall presented a demo at a users group meeting with about 70 JMP users. Attendees also had a chance to chat individually with Sall and Adams at a reception following the meeting. During his visit, Sall was interviewed by the chief editor of IT Media Enterprise, the largest online news media in Japan, which comprehensively covers IT-related topics.

Scroll through the photos below for a look at some of the highlights of the trip.


At the entrance to Shanghai University's main campus, John Sall (left) and Steve Adams (right) stand by a billboard for the International Forum of Quantitative Decision-making and Continuous Process Improvement, where Sall gave a keynote speech.


A balloon-festooned sign outside the conference center celebrates John Sall's visit to Shanghai University.


Provost and Executive Vice President of Shanghai University, Zhou Zewei (left), welcomes John Sall (right).


The conference at Shanghai University took place in a circular meeting room where rows were arranged in concentric circles and each seat had a monitor in front of it.


John Sall (front row, center) and Steve Adams (front row, left) and JMP Japan staff celebrate a successful JMP users group meeting in Japan.

If you have updated your copy of JMP 8 to 8.0.2, you already have access to a brand new book in the JMP documentation set called Discovering JMP -- though you may not have known it!

To find out a bit more about this new book, I sent a few questions to Jonathan Gatlin, a JMP technical writer who has written several posts for this blog. Here's what you need to know about Discovering JMP.

Arati: What is the book about?
Jonathan: Discovering JMP provides a general overview of JMP software. The chapters cover the following information:

Chapter 1: Introducing JMP – basic concepts like what is a JMP platform, and how JMP is different from Excel.
Chapter 2: Preparing, interacting with, and summarizing your data.
Chapter 3: Visualizing your data – using graphics to explore your data.
Chapter 4: Analyzing your data – looking at distributions, relationships, and models.
Chapter 5: Customizing JMP – saving JMP results, creating JSL scripts, and setting preferences.
Chapter 6: Special JMP features – connecting JMP with SAS, the automatic update feature, and showing interactive results outside of JMP.

Arati: Why did you and the documentation team write it?
Jonathan: We wrote this book to assist new JMP users in getting started quickly with JMP. The examples should give the user a good start on understanding navigation through JMP, as well as an introduction to analyzing data with the most commonly used JMP platforms. We also wanted to provide a general purpose quick start book for international markets. We plan to translate this book into all the supported JMP languages.

Arati: How is it different from the other JMP books?
Jonathan: Discovering JMP is one of five books in the JMP documentation set. The other books provide complete details on all of JMP’s platforms and features. For example, the Statistics and Graphics Guide provides a full description of all statistical and graphical features in JMP. The purpose of Discovering JMP is not to give complete details, but to introduce the user to some commonly used JMP platforms and to provide simple examples of each.

Arati: Who is the intended audience for the book?
Jonathan: Discovering JMP is written to introduce JMP to a new user. It assumes the reader has no knowledge of statistics or JMP. The book will be useful to anyone who needs to learn JMP, whether that person is a statistician, researcher, or business analyst.

Arati: How can JMP users find the new book?
Jonathan: This book is available with JMP 8.0.2, and can be found with the other books at the JMP install location, usually at C:\Program Files\SAS\JMP\8\Support Files English\Documentation. You can also download a PDF of the book from the JMP Documentation page on our Web site.

Bradley Jones, Director of R&D for JMP, and Christopher Nachtsheim, Professor in the Carlson School of Management at the University of Minnesota, collaborated on a paper that was published this month in the Journal of Quality Technology. Both authors have published widely on the subject of design of experiments and are recognized as experts in the field.

Their new paper is titled “Split-Plot Designs: What, Why and How,” and in this interview with Brad, I asked him those same questions contained in the paper title. Subscribers to the Journal of Quality Technology can read the full article via the ASQ Web site.

Arati: What is a split-plot design?

Brad: A split-plot experiment is a statistically designed study where the experimental runs are grouped so that certain variables do not change their settings within a group. The experimenter only changes these variables or factors between groups of runs. Holding these factors constant for an entire group of runs means that the run order for these experiment is not completely random. Statisticians often recommend the use of completely randomized designs rather than split-plot designs.

Arati: Why are split-plot designs important for practicing statisticians to know about?

Brad: Though complete randomization avoids certain problems in the analysis and interpretation of results from experiments, it often requires substantial extra effort. In many processes, certain factors are hard to change from one processing run to the next. To change some factors is as easy as turning a dial or flipping a switch. Changing others can require making time-consuming and expensive alterations to the system. It makes sense to structure your experimental runs to take this practical constraint into account. That is, you would like to do several runs in a row only changing factors that are easy to manipulate before stopping the system to make a big change. Slavish insistence on complete randomization has sometimes resulted in operational people sorting a randomized design for logistical convenience without informing the principal investigator. This sorting makes the design a split-plot design but generally a poorly constructed one. Worse yet, the principal investigator, being unaware of the sorting, analyzes the data as though the run order was random. Since the appropriate analysis of a completely randomized design is different from that of a split-plot design, the consequence of the run-sorting subterfuge can invalidate the results and make for poor decisions.

Arati: Tell me about one or two of the recent developments in designing and analyzing split-plot experiments.

Brad: In the last decade, the design and analysis of split-plot experiments has been a hot topic in research literature. For my money, the most exciting developments have been the methods for design and analysis of optimal split-plot experiments. It is not really the optimality of the designs that makes this approach exciting, though optimal designs certainly have desirable properties. The real value of these methods it that they allow for much more flexible problem specification and thus much wider applicability.

Arati: What was your purpose in writing this article with Professor Nachtsheim?

Brad: A couple of years ago I had a conversation with two past editors of the Journal of Quality Technology. They noted the resurgence of interest in split-plot designs. They also were concerned that the mathematical complexity of some of the publications were leaving most practitioners behind. There was the fact that different researchers often recommend slightly different approaches. This can confuse practitioners who do not have the background to discriminate between competing methods. Professor Nachtsheim and I wrote the article to cover all the major research lines pursued in the last decade or so and to present even-handedly their strong and weak points. I included Professor Nachtsheim in the project because I have been an advocate for one approach, and I thought it would be more objective to include an author who had no strong prior convictions.

Arati: Who is the audience for this article, and how do you hope they will use the information in it?

Brad: We wrote the article for two audiences. For the statistician who is unfamiliar with the recent trend in this area, our article provides a reference list and a guide to the main lines of research. The more important audience, however, is the community of practitioners. We wanted to provide information to empower this community to use these new methods profitably.

I met José Ramírez, PhD, in Chicago at the JMP Discovery conference and Innovators’ Summit. An industrial statistician and longtime JMP and SAS user, he was quite the celebrity at the conference, where he gave a well-attended talk about designing experiments using JMP and SAS.

José told me about his new book, co-authored with his wife Brenda Ramírez, who is also an industrial statistician and expert user of JMP and SAS. The pair wrote the book, Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide, over two years, on weekends and evenings. They also write a blog called Stat Insights that includes excerpts from their book and discusses “statistics as a catalyst for engineering and scientific discoveries.”

Here, José and Brenda share details about the book for readers of the JMP Blog.

Arati: Why did you decide to write this book?

José & Brenda: A few years ago, the JMP team approached us with the idea to write a book for engineers and scientists. This seemed like a natural progression in our careers, since we have been collaborating with engineers and scientists for many years and we have developed and delivered countless hours of training in statistics and continuous improvement. In addition, we are big fans of JMP software and have been using it for a long time.

So writing this book seemed like the perfect opportunity for us to consolidate the significant knowledge we have gained as practicing industrial statisticians, and share it in a way that is far-reaching and useful to this community. An additional inspiration for our book comes from the National Bureau of Standards Handbook 91 Experimental Statistics by Mary Natrella. We wanted to bring the same spirit and utility of the NBS Handbook 91 to the countless engineers, scientists and data analysts whose work requires them to transform data into actionable information.

Arati: Who, specifically, will benefit from reading and using your book? And how do you hope they will use the book?

Brenda: The book is primarily written for engineers and scientists who need to use statistics and JMP to make sense of data and make sound decisions based on their analyses. This includes, for example, people working in semiconductor, automotive, chemical and aerospace industries. Other professionals in these industries who will find it valuable include quality engineers, reliability engineers, Six Sigma Black Belts and statisticians.

In addition to the working professional, those who are studying to become engineers, scientists or even statisticians, as well as those teaching them, should get a copy of our book. It is a great teaching aid.

For those who want a reference for how to solve common problems using statistics and JMP, we walk through different case studies using a seven-step problem-solving framework, with heavy emphasis on the problem setup, interpretation, and translation of the results in the context of the problem.

For those who want to learn more about the statistical techniques and concepts, we provide a practical overview of the underpinnings and provide appropriate references. Finally, for those who want to learn how to benefit from the power of JMP, we have loaded the book with many step-by-step instructions and tips and tricks.

Arati: What kinds of case studies or problems do you discuss in the book?

José: In Chapters 3 through 7, we start with a problem description, setting the stage for the uncertainties that need to be solved using the statistical techniques described in the chapter.

All of the case studies in the book are based upon common problems that engineers or scientist will come across at some point in their careers, and the chapter headings reflect the specific application.

For example, in Chapter 4, “Comparing the Measured Performance of a Material, Process, or Product to a Standard,” we use a semiconductor example involving a new three-zone vertical furnace for thin film deposition of waters to illustrate the usefulness of one-sample significance tests to qualify a new piece of equipment.

In Chapter 5, “Comparing the Measured Performance of Two Materials, Processes, or Products,” we compare the performance of two mass spectrometers in an analytical laboratory using the atomic weight of silver to determine if a bias exists and to understand their measurement error.

Although it is not officially a case study, we are thrilled to include in Chapter 7 the data from Albert Einstein’s first published paper. In his 1901 paper, a young Einstein used least squares to fit a model to investigate the nature of intermolecular forces.

Arati: It’s pretty cool that you had Professor Douglas Montgomery write the foreword to your book. How did you make that happen?

José: Ever since we were students, we have been using and following the work of Professor Montgomery, and we believe his books are excellent references for engineers and scientists. We also share a passion for industrial statistics and Doug and I have crossed paths many times over the years at various statistical conferences and events, including, more recently, at JMP conferences.

When we put all of these pieces together – statistics, engineering and JMP – Professor Montgomery seemed like the perfect person to entrust with this important part of our book. So we just had to find a way to ask him if he would be willing to write the foreword to our book. Luckily for us, that opportunity arose at the Quality & Productivity Research conference in June 2008 in Madison, WI. At that event, I was able to discuss this possibility with him, and without hesitation he said, “Yes.”

Arati: How will you use this book going forward in your professional career?

Brenda: This book is a reflection of how we collaborate with engineers and scientists to use statistics as a catalyst for new discoveries and insights. Having the book will make it easier to share our statistical engineering philosophy with others.

Arati: Where is your book sold?

José: Our book is available online from the SAS Web site or Amazon.com. Both Web sites allow the reader to view the table of contents and a sample chapter from the book.