Entries by Arati Bechtel

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.

While at Discovery 2009 and Innovators' Summit in Chicago last week, John Sall, chief architect of JMP, spread the word about JMP, SAS and the value of analytics. He met with journalists and bloggers, and some of the coverage has already been published online. Take a look:

• In a story for Computerworld, reporter Eric Lai asked Sall why JMP chose best-selling author Malcolm Gladwell to headline the Chicago conferences. "Journalists are like detectives. A lot of our customers are like that," said Sall, who is co-founder and Executive Vice President of SAS. "They don't just want to prove things you already know. That's lawyer stuff. They want to look at statistical outliers and figure out new things."
  
  
• Greg Burns, Senior Business Correspondent for the Chicago Tribune, talked with both Sall and Gladwell during the conferences. He wrote up his interview with Sall in two parts, posting it Tuesday and Wednesday this week. It is part of what Burns is calling Big Brain Week, with Sall representing left-brain thinking and Gladwell representing right-brain thinking. Burns' interview with Gladwell appears in two parts today and tomorrow.
  
  
• Data visualization guru Stephen Few, Principal of Perceptual Edge, wrote about the conferences and Gladwell's keynote speech in his influential blog, Visual Business Intelligence. Few applauded Gladwell's insight that modern problems present the challenges of too much information and too little understanding of that data, pointing to a need for good analytics. "More directly related to my work in BI, I’ve stated countless times that this industry has done a wonderful job of giving us technologies for collecting and storing enormous quantities of data, but has largely failed to provide the data sense-making tools that are needed to put data to use for decision-making," Few writes.
  

From what I understand, there's more coverage to come. Please let me know if you've written or know of a blog post or other online content about the conferences.

And if you didn't get to go to Chicago last week for Discovery and the Innovators' Summit, you can get a sense of why some attendees said it was the best conference they'd ever been to by checking out our own coverage; it includes photos, live blogs, audio, video and Twitter updates.

We hope you'll be at our analytics conferences in Chicago later this week, but if you can't make it, you can follow the action via the JMP Web site.

On Thursday, Sept. 17, at 8:30 a.m. Central Time, our live conference page will start to fill up with Twitter updates, live blogging, photos, video and audio podcasts from Discovery 2009 and Innovators' Summit. (At the moment, you'll see little going on at the live conference page -- but you can take a peek at what we have planned!)

You can use Twitter to send us a question for a particular speaker during the conferences. Just add the hashtag #JMPcon to your tweet, and we'll do our best to ask your question for you.

You can also send questions and comments via our live blog, which will be hosted here at the JMP Blog. We'll be live blogging all of the keynote presentations, including the speech by Malcolm Gladwell on Friday afternoon.

Did you miss last week's Q&A with John Sall, chief architect of JMP, in DecisionStats, an analytics blog by Ajay Ohri? It's worth a read.

"In a free-wheeling and exclusive interview, John talks of the long journey within SAS and his experiences in helping make JMP the data visualizaton software of choice," Ajay says in his introduction to the post.

Ajay's questions cover such topics as Sall's educational background, his "Eureka!" moments while developing SAS with CEO Jim Goodnight and why Sall created JMP software. He even asks Sall what he does in his free time when he isn't creating "world-class companies or groovy statistical discovery software." (As a former technology writer for a daily newspaper, I give kudos to Ajay for working the word "groovy" into his interview!)

Other SAS leaders featured in DecisionStats interviews include Jim Davis, chief marketing officer, and Anne Milley, marketing director, with whom Ajay did a two-part interview.

JMP user Archana Pawse, PhD, is a Six Sigma Black Belt who has worked for Applied Magnetics, Superconductor Technologies and JDS Uniphase as a Product/Process/Reliability Engineer.

What follows is Dr. Pawse's description of how she uses JMP in her line of work, which she wanted to share with others. Feel free to leave a question or comment for her to respond to or contact her directly by e-mail.

How I Use JMP for Root Cause Analysis

In a manufacturing environment, it is very critical to find the root cause of failures quickly. Delay in identifying the root cause can result in wasted money and resources. JMP is useful for root cause investigation because you can easily explore and graph the data in multiple ways. I have found three JMP features/techniques -- selecting cells, partition plots and variability charts -- to be useful in this investigation.

To demonstrate these techniques, I have generated some hypothetical data. In this example, Y1, Y2 and Y3 are performance parameters for a product that is made using three processes P1, P2 and P3.

P1-Machine, P1-X1 and P1-X2 are the parameters for process P1. P2-Machine, P2-X1, P2-X2 are the parameters for process P2. P3-Machine, P3-X1 are the parameters for process P3. Parameter Y3 has 15 failures (value <= 0.7).

1. Selecting cells

In JMP, once you have graphed all relevant parameters, you can select failed data points on a graph or cells in the table, and they are highlighted on all the graphs in the session. This option is not available in some other software. By highlighting the failures, you can immediately see if there is any correlation between various parameters or if outliers on one graph are also outliers on other graphs.

In this example, I have graphed distributions of parameters Y1, Y2 and Y3, and I have selected all the failures for the parameter Y3. These failures are also highlighted on distributions of Y1 and Y2, which show that the failures are also outliers for parameter Y1. This kind of information can give more insight into the cause of failures.



2. Partition plots

If your final product performance depends on various process parameters and its interactions, then it is very time-consuming to review all process parameters to find the cause of a product failure. Partition analysis can narrow this list down to a few parameters, so you can investigate these few parameters in detail. In some cases, the partition analysis can even identify the root cause.

In this example, the Y3 parameter is the response variable, and all the process parameters are entered in the X factor field. The best split option in partition analysis shows that the product fails (has a low Y3 value) when it is processed in machine B at P2 process and machine 3 at the P1 process.






3. Variability charts

Variability charts are useful for visualizing failures. This graph is very effective at displaying interactions between various parameters because you can plot Y versus multiple X parameters. In addition, by using multiple colors, symbols, symbol sizes, you can show at least four parameters on this graph.

In this example, it’s easy to see from the graph that all failed product (Y3<=0.7) is made using machine B at step P1 and machine 3 at step P2. In addition, by color-coding the data for the different P3 machines, you can see that these failures are not related to the machine used at step P3. Different symbols are used for the passed and failed units for easier identification.



The techniques/features described here are not limited only to root cause investigation. They can also be used for other manufacturing applications like process optimizations as well as in marketing applications to identify a target market or cross-sell opportunities.

In case you missed my Twitter update about it last week, a JMP tree map created by Daniel Arneman of UNC Energy Services was featured in a recent data visualization report by Intelligent Enterprise titled "Seeing Connections: Visualization Makes Sense of Data." The report, by Seth Grimes, is available as a free download, with registration, and it's definitely worth a read.

Arneman's tree map visualizes greenhouse gas emissions by buildings on the campus of the University of North Carolina at Chapel Hill. It is also featured in the "Visualization Image Gallery" that accompanies the report (registration isn't required to view this), along with an explanation of the value of this type of data visualization:

"Cell Size Indicates Variable Contribution: The University of North Carolina at Chapel Hill developed this treemap visualization using SAS JMP software to show that certain buildings and classes of buildings are responsible for more greenhouse gas emissions than others. Treemaps are not designed to convey numerical quantities; the intent is to show relative rankings...."

You can read more about how Arneman uses JMP to assess, monitor and help reduce greenhouse gas emissions at UNC in our story about his work. You'll see the same tree map in our image gallery (though, using different colors), and you'll also see lots of other cool visualizations by Arneman, including a series of Bubble Plots and the Profiler.

Referring to JMP as "an old friend," statistician John Wass reviewed JMP 8 for Scientific Computing and calls it a "major upgrade." The review includes several visualizations and covers many of the major new features of JMP 8.

Wass concludes: "This latest version is stunning in the quality of the graphics, and JMP has pioneered the advancement of statistical graphics by heavily linking most number-crunching operations to a graphic. Interested parties are highly encouraged to download a trial version."

Here's where you can get the fully functional 30-day trial version of JMP 8, for Windows, Mac and Linux.

NOTE: This entry comes to the JMP Blog from our colleague Jerome Bryssinck of SAS Belgium. Jerome had seen Jeff Perkinson's examples of basketball analytics using JMP and created his own example using football (or soccer) data. In response to comments from readers, Jerome updated his model on May 26, and this blog post now reflects those changes.

THE QUESTION: Has the game been decided yet? HTGBD

This is the question that most people constantly ask themselves when they are watching a football game. This question can take different forms depending on the circumstances. If you're lucky to support the winning team, you might ask yourself: "How secure is the lead?" And for the less fortunate of us: "Is there still a chance for my team to win?"

THE ANSWER: Analytics


Graph1: Probability of the game having been decided in function of the elapsed time and the number of goals difference.

Graph1 shows the probablility of the game having been decided in function of the elapsed time and the number of goals difference. It is possible to change the elapsed time and the number of goal difference on the graph by clicking on a different value.

Some interpretation examples:

If Time=45 and Goal Difference=0: The game has been going on for 45 minutes, and the number of goal difference is 0. There is a 23% probability that the outcome of the game won't change. Here, as the teams are even (0 goal difference), this would mean that there is a 23% probability the game will end in a tie.

If Time=45 and Goal Difference=1: The game has been going on for 45 minutes, and one of the teams is leading by 1 goal difference, then we have a 60% probability that the outcome of the game won't change. Here, this would mean that the leading team has a 60% probability to win.

More Details about the Answer

The model used above has been built using data from the UK Premier League from 2002 to 2006. The type of model used is a regression model.

The following representations are useful to understand the underlying data.



Graph2: Has the Game Been Decided vs. Time

Graph2 shows the percentage of the games that have been decided in function of the Elapsed Time. I must say that I wasn't surprised by this graph, which basically states that the Elapsed Time and the HTGBD (Has The Game Been Decided) are directly proportional.



Graph3: Has the Game Been Decided vs. Time By Goal Difference

Graph3 shows the percentage of the games that have been decided in function of the Elapsed Time by the number of goal difference. According to this graph, the number of goal difference is an excellent predictor for the HTGBD.

Additional readings:

Similar models are available for basketball. Check out Bill James and Jeff Perkinson if you want to learn more.

This entry was first published in Jerome Bryssinck's blog, Brisink. It is republished here with his permission.

A recent article in SEEDMAGAZINE.COM laments that many people misuse and rely too heavily on static data visualizations, such as the pie chart. "The pie chart is intended to display proportions of a whole within a single, small data set, but overzealous Excel users dump in large data sets or stack multiple pies. The resulting complex defeats the purpose of using a picture: simplification," writes Veronique Greenwood in "Getting Past the Pie Chart."

Greenwood is, of course, not alone is expressing concerns about the pie chart. Stephen Few -- Principal of Perceptual Edge, biz viz guru and Discovery 2009 speaker -- has written eloquently about why we should "Save the Pies for Dessert" [PDF].

The SEEDMAGAZINE.COM article quotes big names in data viz, such as Colin Ware (Director of the Data Visualization Research Lab at the University of New Hampshire and author of Visual Design for Thinking) and Bill Cleveland (statistican at Bell Labs and Professor at Purdue University and author of Visualizing Data) in its useful discussion of how to improve new forms of data visualization. Although the article doesn't include any visuals, the author makes some good points:

• "One way to solve the problem of overly complicated diagrams is to introduce interactivity."
  
• According to Cleveland, "an effective way of detecting patterns in massive data sets is to make a simple chart for each subset and view the hundreds of charts in quick succession."
  
• "Numerical tools — statistical tests of variance and significance — are just as important in assessing trends" (also attributed to Cleveland).

These ideas are in line with the way JMP approaches data visualization. "JMP emphasizes interactivity over perfectly styled static graphs and the use of statistics alongside data visualization," says Xan Gregg, a JMP developer and data visualization expert.

Thanks to Steve Baker, author of The Numerati and a speaker at Innovators' Summit, for mentioning this article in his Twitter feed last week.

Six regional JMP users groups have formed over the past year or so, and it looks like Atlanta may get the next one. JMP user Kevin Holston is spreading the word that he'd like to start a group there.

To join Kevin in creating the group, send him a message via Twitter (Twitter ID: holstonk) or e-mail.

As Kevin begins gathering other JMP users together, I asked him about his use of JMP and why he is starting a users group:

1. Where do you work?

I work for NASCO LLC, a healthcare payer solutions company within a consulting group that ensures our customer’s experience high performance levels with our claims processing system. So my practice area focuses on performance management and healthcare payer analytics.

2. How long have you been using JMP? Do you also use SAS?

I started using JMP with version 7, so I am a relatively new user. I had used Base SAS with other employers and in graduate school.

3. What do you use JMP and SAS for?

Business visualization and Visual Six Sigma purposes. JMP stands up to the marketing claims and really does enable greater understanding of business data with the ease of point-and-click functionality. I am tired of static spreadsheets, built from simple queries and presenting pieces of information. I must drill deeper into our customers data, explore relationships and trends.

4. Why do you want to start a JMP users group in Atlanta?

I am the sole person who uses JMP within my company, so I do not have others to share ideas with. I want to learn, grow and network with other users.

5. What do you think people could get out of meeting with other JMP users?

I think establishing a JMP users group will be a great way to:

• Provide a friendly environment where people can learn more about JMP.
• Network and share ideas.
• Get help with all kinds of technical questions.
• Get honest advice.

Good news: JMP 8 for Macintosh begins shipping April 28. So Mac users will soon have access to all of the new capabilities of JMP 8, like exporting Bubble Plots and Profiler for use in PowerPoint and Web pages; designing and analyzing choice studies; conducting more types of reliability analysis; and understanding data through drag-and-drop graph creation.

More good news: You can get a discount on single-user licenses of JMP 8 for Mac right now.

Last fall, we offered a JMP 8 for Windows pre-order deal for single-user licenses, and now we extend a similar discount on JMP 8 for Mac.

If you pre-order a single-user license of JMP 8 for Mac by April 24, you can save more than 25 percent. Our Web site has all of the details on this.

Of course, if you have two or more users of JMP 8 for Mac at your organization, you'll want the JMP annual license instead. Call 1-877-594-6567 for more information on that.