JMP 13 Preview: Improvements to the Analyze menu for a better user experience

From time to time, the addition of new features requires a review of how capabilities are organized and presented in JMP. Are they located where it makes the most sense and where users would expect to find them? For example, in JMP 12 there was enough new material combined with existing functionality to warrant a Consumer Research submenu in the Analyze menu.

In JMP 13, users will also see some changes to the Analyze menu:

  • The old Modeling submenu has been replaced by two new submenus: Predictive Modeling and Specialized Modeling.
  • A Clustering submenu has been added so that you can quickly find your favorite clustering technique.
You can easily find the Fit Curve platform under Specialized Modeling in JMP 13.

You can easily find the Fit Curve platform under Specialized Modeling in JMP 13.

The Predictive Modeling submenu is the new home for a variety of modeling platforms that emphasize prediction, such as Neural, Partition, Random Forest, and K-Nearest Neighbors. The Specialized Modeling menu is where you will find platforms like Fit Curve, Nonlinear, and Time Series.

Senior Research Statistician Developer Clay Barker, who wrote the Fit Curve and Normal Mixture platforms, was happy to see these platforms find a home in the reorganized menus. “They used to be buried inside other platforms (Fit Curve inside Nonlinear and Normal Mixtures inside k-Means), which made them harder to find. Now they will get more exposure, and hopefully more people will start using them,” Clay says.

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The new Clustering submenu includes Normal Mixtures and Cluster Variables among other platforms.

The new Clustering submenu also contains a new platform: Latent Class Analysis or LCA for short. The addition of the generalized LCA for categorical clustering didn’t fit with the continuous response clustering methods that already existed. This was also the case with the new specialized implementation of LCA for text analytics in JMP Pro. Some of the output for the new LCA clustering includes Multidimensional Scaling (MDS) plots and “share” charts.

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Multi-Dimensional Scaling plot allows you to see distance between clusters.

While JMP includes a number of good clustering methods, they were hard to find especially if you didn’t know where to look, so many users didn’t know about them. Some, like Normal Mixture clustering or Variable Clustering, were “platform personalities” or in a list of red-triangle menus. The new Clustering submenu groups new and existing clustering methods together.

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The new share charts in the LCA report show the conditional probabilities given cluster membership for each cluster and each Y, plotted as a horizontal stacked bar chart.

The addition of LCA will enable users to do more with their text data. Chris Gotwalt, director of statistical R&D at JMP, enjoyed working on the sparse-matrix LCA, as he had never done anything like applying sparse methods to a clustering algorithm.

“The response so far has been positive on the new functionality as well as the new visuals. Having the other clustering methods more prominently featured in the submenu will hopefully lead to greater use,” Chris says.

Both Clay and Chris will be leading tutorials at Discovery Summit 2016, and it's not too late to sign up for these special sessions. In addition, they'll be presenting a paper titled "Visually Exploring Design of Experiments Models with the Generalized Regression Platform" at conference.

To learn more about what's coming in JMP 13, visit the preview page at our website.

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JMP 13 Preview: Dashboard Builder for creating interactive dashboards quickly

A sample dashboard created with Dashboard Builder in JMP 13.

Dashboard Builder is a drag-and-drop tool in JMP 13 for creating dashboards that can display JMP data tables and reports. This dashboard combines a data table, a Graph Builder visualization and a Distribution report.

Dashboards are such a popular way to keep an eye on important metrics and share the findings of analysis, and for years JMP users have been creating dashboards using JMP Scripting Language and tools such as Application Builder. But now in JMP 13, there’s an even easier way to make dashboards, and it requires no scripting!

Dashboard Builder is a flexible way to arrange multiple JMP reports and data tables in a single window. You can select from a variety of templates to make a dashboard quickly. But you can also simply drag and drop to create custom dashboards without using the templates. You can also save a dashboard as interactive HTML so others can view it using a web browser.

“With just a few clicks, you can create an interactive dashboard that you can use as part of your process or for communicating results,” says developer Dan Schikore, who worked on Dashboard Builder.

While the ease of use is very important, Dan says that the most exciting part of this new feature is its potential impact on the day-to-day work of scientists, engineers and analysts. “You might not build dashboards every day, but you and your colleagues may very well interact with them every day,” Dan says.

Templates in Dashboard Builder in JMP 13

Choose from among a variety of dashboard templates in Dashboard Builder in JMP 13 -- or make a custom dashboard.

The response from early adopters of JMP 13 has been overwhelmingly positive because so many users want to make dashboards and were looking for a quick way to create them. “People have a lot of suggestions for this feature, so it will be something we continue to build on,” Dan says.

Dan says it was fun to work on the modern drag-and-drop interface of Dashboard Builder, noting that he’s heard some people calling it “Graph Builder for reports.”

You can hear directly from Dan at Discovery Summit 2016 in a few weeks. He’ll be presenting a poster session on “Building Dashboards in JMP 13” and a tutorial titled “Building Dashboards and Applications.”

To learn more about what’s coming in JMP 13, stop by the preview page on our website. There, you can sign up to watch a live stream of JMP chief architect John Sall’s tour of JMP 13 on Sept. 21, as well as watch short videos about JMP 13 and JMP Pro 13.

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Now you can contact JMP Technical Support from within JMP

While you have long been able to reach JMP Technical Support by phone, email and the JMP website, you can now submit a support request from the Help menu in JMP!

We know that switching out of JMP can be inconvenient and disruptive to your thought process. Whether you have a question about how to accomplish a task in JMP, need additional details about a statistical method, or need clarification about something in the documentation, you can easily reach out to JMP experts without leaving the software.


How do you do that?

Simply download and install the Contact JMP Technical Support Add-In from the File Exchange. Once installed, the Contact JMP Technical Support item will appear on your Help menu in JMP. Select this menu item to open a dialog where you can select a topic and subtopic, provide details of your question, and even attach files. There are also links to additional resources, such as the online documentation and the JMP User Community.


Now, more than ever, JMP Technical Support is only a click away!

At JMP Technical Support, our mission is to:

Help our customers make the best use of our software products through effective and responsive support, active advocacy, and a broad and flexible range of self-help resources.

You can find a full description of the support services and support policies on the JMP website.

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Looking at Summer Games data with JMP

You might say I love sports. I began swimming at a very early age and participated on swim teams for many years. Gymnastics, volleyball, softball, basketball and even track teams were all part of my life, and I loved playing and competing. So maybe that is why I always love watching the Summer Games!

I thought it might be interesting to look at data that gave details about the athletes themselves. In terms of height, weight and age, what can we learn about these athletes from around the world?

Using JMP, I looked at countries represented, ages of the athletes and BMI (body mass index). I found the most detail for the London 2012 summer games, and I decided to focus my analysis on medal winners.

First look at age of medal winners

The first graph I made was this Distribution, which showed that the median age of the 2012 medal winners was 26 years.


In this age Distribution, we can see the youngest athletes were 15 years old, and there were three of them. Two of them won gold, and the other took home a silver. The oldest was 56 and won a bronze medal.

Exploring sports and age

I wondered which sports have the oldest and youngest athletes. The heatmap below helps us see that. What jumps out right away are the bright red spots, which show us that somewhat older athletes appear in the sport of equestrian. The average age for equestrian athletes is 38. In 2012, Mark Todd from New Zealand was the 56-year-old bronze medal winner in equestrian. What about the youngest athletes? The darker blue areas show the youngest to be the gymnasts.


Exploring geography

Next, I thought I’d add a geographic component to my data exploration. The graph below shows a world map of medal winners for 2012 athletes by median age.


Here, we can see the older medal winners come from Sweden, one in shooting and the other in equestrian. I found this visualization helpful because the green area shows that most of the participants are in the 25-30 age range.

Exploring BMI

If you look at the heatmap above you and see that the tallest athletes are in athletics (track and field), judo, rowing, and swimming.

With the height and weight of athletes, I calculated their BMI using a formula in JMP. Most people know this metric as a way to understand whether their weight is healthy or not. According to the NIH website:

  • Underweight = <BMI of 18.5
  • Normal weight = BMI of 18.5–24.9
  • Overweight = BMI of 25–29.9
  • Obesity = BMI of 30 or greater

A BMI of 20-25 would be considered good, depending on your fitness level. So, what does the BMI of elite athletes look like?



The median BMI is 22.7! And the lowest is 15.2!

It was surprising to see this, so I selected the lowest values of the Distribution and discovered that they were in the sports of athletics and diving (see below).


Exploring total medal counts

I’ll end my exploration of the data with a look at which country has the most medals. Some countries no longer exist and/or have become other nations. I didn’t recode the data in any way, but rather analyzed it as is, using the original name of the country that won the medals.

Currently, the United States is more than 1,000 medals ahead in total medal counts for the Summer Games, with the USSR (Russian Federation) in second. This graph shows totals through 2012, with any country that had won 250 or more medals.


I am presenting a poster on Summer Games data at Discovery Summit 2016, so look for it there if you are going.

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When are the Summer Games, historically?

The Summer Games are over, and here's one thing that surprised me. I had assumed that since Rio is in the southern hemisphere, where it’s currently winter, the Games would be shifted a couple months, as they were for Sydney. I’ve since learned that Rio is very pleasant in the winter with highs typically in the 70s Fahrenheit, so no delay was necessary.

How did the Rio opening date compare to past Games? I set about to collect Summer Games dates and discovered a few interesting things in the process.

Getting the Data

I found a few sources of date information, and naturally they didn’t always agree with each other. One source of discrepancy is that there is more than one way to define the “opening” of the Games: The opening of the competitions sometimes happens before the actual opening ceremony. In fact, this year soccer matches started two days ago.

I decided to collect dates for the opening and closing ceremonies, and started with dates from Wikipedia pages. The information there follows a regular pattern, and I was able to scan the dates with JSL (JMP Scripting Language) using regular expressions to tease out the date from inside the "td" tags:

For Each Row(
url = "<a href=""></a>" || Char( :year ) || "_Summer_Olympics";
text = Load Text File( url );
:Opening Date = Try( Regex Match( text, "Opening ceremony&lt;.+?&lt;td&gt;(.+?)&lt;/td&gt;" )[2], "" );
:Closing Date = Try( Regex Match( text, "Closing ceremony&lt;.+?&lt;td&gt;(.+?)&lt;/td&gt;" )[2], "" );
Wait( 5 );

That worked well, but there was one glitch: The pages didn’t agree on the date formats, as you can see from this snippet of the imported table:



Not terrible, but it did complicate my parsing a little. I thought about editing the Wikipedia pages myself to standardize the dates, but I got only as far as checking the Wikipedia style guide , where it turns out both formats are perfectly acceptable.

The next issue was that some of the early Summer Games durations were suspiciously long. The Wikipedia page for Paris 1924, for instance, has the opening ceremony date as May 4 and the closing on July 27. For those, I dug a little deeper. Fortunately, many of the official reports have been scanned and made available online. In the report for Paris 1924, I found this wonderful table of events.


If you look closely, you can see the ceremonie d’ouverture on July 5, along with competitions happening well in advance of that, including art competitions in March and April.

Pulling It Together

Back to my data quest ... I stuck with using opening ceremony dates for showing the timing of the core of the Summer Games even if not capturing all events. Research led to a few other refinements, and I excluded the first three modern Games (Athens, Paris, and St. Louis) since they did not have a similar structure. Some sources say the London 1908 were the first truly modern Summer Games in structure.

Here is a chart of the durations of each Summer Games since 1908, based on the opening and closing ceremony dates.


The Rio dates are indeed typical (only a few days off from the median, in fact). We can see expectedly shifted dates for other southern hemisphere hosts (Sydney 2000 and Melbourne 1956), but I don’t know what accounts for the late starts for Tokyo 1964 and Mexico City 1968.

Interesting Finds

The earlier Summer Games didn’t call out the opening and closing ceremonies as distinctly as we do today, so sometimes it took a little digging to identify the final “soirée” or “farewell banquet.” Along the way, I found a few gems. The official reports sometimes ran 1,000 pages and contained many photographs. The Stockholm 1912 report was particularly complete, including details on the stadium showers, turf construction and a photo of the royal box.

Olympic Mail

There is even a table showing the number of mailings sent by the Swedish Olympic Committee by month.


I just had to take a look at the data on a graph:


While you can see the increase in activity leading up to the actual Summer Games by looking at the numbers in the table, the pattern is much clearer in the chart: a steady build-up and then an explosion for the first six months of the year. It’s interesting that the drop-off precedes the games by a month or so. Is that a reflection of slower international mail delivery in 1912?

Suspicious Outlier

The Antwerp 1920 Summer Games went bankrupt and didn’t produce an official report, but one was compiled decades later. This table has an outlier that has to be an error:


Gymnasts numbering 1,648 would give Belgium as many competitors as all the other nations combined. And elsewhere I saw that the maximum size for the gymnastics team was 60 athletes. Still, if it’s an error, it’s hard to believe it wasn’t questioned when making the totals in the bottom row. Any data sleuths out there to track down the true number?

Walking Race

I love this photo of a walking race from Stockholm 1912 and had to include it:


Notice the judge checking the form of the walkers! And I happened upon this related comment in the London 1908 report:


I hope you enjoyed the Summer Games as much as I did, and I'm still hoping for Ultimate Frisbee to make it into the competitions someday.

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JMP 13 Preview: Now you can “textcavate” your data with the new Text Explorer

JMP users might notice that new versions of the software often bring the ability to support new kinds of data. The ability to incorporate image data came with JMP 12, and with JMP 13 comes support for text data.

In the early days of this platform’s development, we were brainstorming ideas for what to name it. I proposed “Textcavator” as the platform would help you dig a little deeper and expose more value in your text data. Text Explorer is much clearer and better reflects what the platform does, and fittingly, the name came from someone who greatly influenced its development — read on to find out who that is.

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“Our customers have a lot of data in verbatim fields — in surveys, repair records, incident reports, etc.— and they have no means to analyze this data. A great deal of effort goes into collecting this text data, and we wanted to include a basic facility so our customers could derive more value from their text data,” explains John Sall, SAS co-founder and Executive VP, and chief architect of JMP.

Customers said they wanted to not only digest text data, but also interact with and explore it so they could see important phrases, create new columns and new graphics. Early adopters of JMP 13 were impressed with the speed, the interaction and the graphics, and they liked that it provided a rich facility for specifying regular expressions, like part numbers and failure codes.

Its use in education was also important to John. “We felt that Text Explorer was an important element of our data mining methods, as it’s commonly taught in data mining courses,” John says.

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An example of a Word Cloud made with the new Text Explorer in JMP 13

The platform includes standard features you would expect: word frequencies, word clouds (either count ordered or centered) and stemming to handle word endings. In addition, the platform includes these features:

  • Very good regular expression facility with a library of pre-defined as well as user-defined regular expressions.
  • Multi-word phrase detection and their use as tokens.
  • Built-in Recode to consolidate multiple terms.
  • Highly interactive command set (e.g., show the text containing phrases to easily see context).
  • Good performance (for single machine).

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With just a click on a phrase or term in the list, you can see how the phrases or terms are used in context.

This new platform was a team effort, with seven main developers:

Because the Text Explorer platform is a basic text exploration facility, it has no understanding of vocabulary, parts of speech or syntax, has no spelling correction, and does not do sentiment analysis. The non-language specific approach that is based on the document term matrix and derived methods like the SVD and LCA is generally called a “bag-of-words” approach in that the order of the words is ignored, and only their presence and our count in the documents is analyzed. It is quite easy to use with very limited customization features to worry about (no ontologies). As the focus is to enable users to gain more value from verbatim fields, there are no extensive tools to access text in various file formats (e.g., PDF, word processing documents or web crawlers).


You can learn more about the new Text Explorer platform and see it in action in an Analytically Speaking webcast with Adsurgo co-founder, Heath Rushing, who was very influential in the development of Text Explorer — and he is the one we have to thank for the name, Text Explorer (thank you, Heath!).

Heath also shares two more extensive demos in a Technically Speaking webcast. And along with James Wisnowski, Heath is presenting a session titled “Mind the Gap: JMP on the Text Explorer Express” in a few weeks at Discovery Summit. Developers and other early-adopter customers will be presenting more about text exploration at Discovery Summit, including a tutorial by Chris Gotwalt titled, “The U-to-the-V: A Hitchhiker’s Guide to JMP 13 Text Explorer.”

For more information on what's coming in JMP 13, visit the preview page at our website.

* We will explore the text analytics capabilities in JMP Pro 13 in a future post.

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Former RSS president on statistics and science

David J HAND (10 of 16)_croppedI’ve had the opportunity to hear The Improbability Principle author David Hand speak three times now, most recently at Discovery Summit Europe in Amsterdam. We are featuring this particular talk on Analytically Speaking, Sept. 14, at 1 p.m. ET.

As a child, Hand thought a scientist unearthed dinosaurs before lunch and discovered new planets before dinner. Later, as he entered the final year of his master's program, he decided that with statistics, he’d at least have the advantage of finding a job afterward. He learned more about statistics and decided this was a great way to be an indispensable member of teams of scientists in a variety of areas.

Over the course of Hand’s career, which includes two terms as president of the Royal Statistical Society and his current posts at Winton Capital Management and Imperial College London, his applied research has covered the fields of psychology, medicine, official statistics, finance, astronomy and more.

He explains that the use of statistics is not in the mathematics behind it, but in its ability to help extract information from data. His experience in a wide number of fields is a testament to this.

"Statisticians are the modern equivalent of Victorian explorers," says Hand. "They are there when the discoveries are made."

Sign up to watch the full talk, and you’ll also hear his take on:

  • The advancement of statistics with new applications, bigger data storage and better data capture technology.
  • Why statistics is a language, rather than a set of isolated concepts and methods.
  • The importance of understanding the problem before making statistical choices.
  • Natural data versus human-generated data.
  • The dangers of selection bias.
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JMP 13 Preview: New MaxDiff platform for consumer research

MaxDiff (maximum difference scaling) is a new platform in JMP 13 that will be helpful to anyone who does consumer research. It enables a specialized type of choice model where respondents are asked to evaluate items (product attributes, …) in sets of three to five, choosing the most preferred and least preferred in each set.

This relatively recent methodology developed by Jordan Louviere et al. is very effective in ranking existing products and avoids the ambiguity respondents encounter when trying to do a forced ranking of a large number of items. The most and least important things are clear, but the relative rankings in the middle tend to be unclear.

MaxDiff screenshot from the potato chip taste test

MaxDiff can help rank items that don't easily break down into features.

This platform was added because JMP users who were doing consumer research said it was high on their priority list. They really wanted to stay in JMP to do this rather than use R or another software package.

Regular readers of the JMP Blog may recall the Potato chip smackdown: Winners and losers post a few years ago by Melinda Theilbar, JMP research statistician developer. That was actually a MaxDiff analysis using JMP 12, and it took a LOT of data work. So Melinda was motivated to make life easier for customers who need to do this kind of choice modeling.

In fact, she enjoys using MaxDiff regularly for a project outside of her JMP work. She co-founded Research Triangle Analysts, a nonprofit forum for data enthusiasts to promote useful techniques, meaningful analysis and effective communication of findings. Melinda surveys this group each year, asking members to rate talks, topics and speakers. MaxDiff lets her easily see what are the most and least important of these among members of this group.

While there are users who already appreciate what MaxDiff choice models yield, Melinda believes others will get access to a great new method they may not have had in their analytical tool set.

“It’s hard to get data on things you most need to measure. This method provides a way to get a handle on data that you really need, but is often difficult to effectively gather,” Melinda says.

We are very appreciative of the input we received during our Early Adopter program on this new platform, in particular from Walt Paczkowski, founder of Data Analytics Corp.:

"JMP continued to push the envelope in consumer research with the addition of two new MaxDiff modeling platforms in its newest release, JMP 13. One  allows you to create a MaxDiff design matrix while the other gives you the ability to estimate MaxDiff utilities both at the aggregate and disaggregate levels. As should be expected from all the JMP platforms, these new additions are fully integrated with all the power and versatility of JMP making JMP the only software that allows you to design, estimate and analyze a range of choice models. Anyone who works in the consumer choice area will find these new additions to be a great advance for their work."

For Melinda, what's most exciting about MaxDiff is its ease of use and the opportunity to introduce our users to this technique: "Many could really benefit from it but may not have invested in the tools or the time needed to try it."

Now, it’s been implemented in an intuitive form you would expect in JMP. Melinda will blog about how to use Max Diff later this fall. And as we noted in another post about association analysis (which Melinda also worked on), she will be presenting at Discovery Summit next month on new features in choice modeling in JMP and JMP Pro.

For more about what's coming in JMP 13, visit the preview site.

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At Discovery Summit, JMP users show how to solve problems

JMP-Discovery2015_50B4370Most Discovery Summit attendees name the user-led breakout sessions as a top reason for attending the conference. There’s a lot of value in these talks, which showcase first-rate statistical techniques and novel applications of the software.

Presenters will share real-world case studies about how they’ve used JMP to solve problems, Sept. 21-23 at SAS world headquarters. The agenda spells it out for you.

Before you arrive, be sure to review the abstracts to determine which talks you’re JMP-Discovery2015_50B4296most interested in attending. Topics include:

  • An adventure in semiconductor data visualization.
  • The new JMP Text Explorer.
  • Speeding up the dirty work of analytics.
  • Letting go of Excel.
  • Storytelling with data to executives.
  • Lessons from definitive screening designs.
  • Discovering consumer survey insights.
  • Is my model valid?

If you download our conference mobile app, you’ll have the chance to rate the sessions you attend to help select the best papers and posters. We’ll announce the winners during the closing remarks. The app is available on Apple and Android devices -- just search “JMP Discovery.”

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JMP 13 Preview: New Parallel Plot option in Graph Builder for visualizing any number of variables

Do you ever need to visualize more than three dimensions in your graphs? If so, you likely know about parallel plots, or parallel coordinate plots. They’ve been available in JMP for a long time.

Parallel Plot in Graph Builder

Parallel plot is new in Graph Builder for JMP 13 and better than ever.

In the new version of JMP, parallel plots are even easier and more fun to use because you can create parallel plots with Graph Builder, the drag-and-drop graph creation tool.

“Parallel plots are not for everybody. But for some people, parallel plots are one of only a few graphing options available,” says Xan Gregg, JMP developer and creator of Graph Builder.

“Traditional graphs can handle only three dimensions. But because there’s no limit to the number of axes in a parallel plot, it’s scalable to any number of variables. If you want to show 10 variables in a graph, for example, a parallel plot is a good option,” Xan explains.

A parallel plot is very useful for seeing which of the many variables move together, he adds.

How does working with parallel plots in Graph Builder compare to the older Parallel Plot platform? There are more options for labeling. The Graph Builder version handles categorical variables better. And there are options for controlling the amount of “curviness” between connecting lines.

“And of course, you can directly interact with it, like you can with all graphs in Graph Builder,” Xan points out.

Look for posts by Xan about Graph Builder in JMP 13 in the months ahead. And if you’re planning on attending Discovery Summit in September, come early and attend his tutorial, “Creating Effective Visualizations Using Graph Builder.”

And for more information on what's coming in JMP 13 and JMP Pro 13, visit the  preview site.

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