Entries by Gail Massari

Recently, JMP has been deluged with requests for information about Design of Experiments (DOE or DOX). Was it due to atmospheric disruption when NASA hit the lunar south pole last week? Or shall we just chalk it up to a growing desire to work smart and make better use of resources in the workplace?

Never fear, JMP is responding. On October 28, author and Arizona State University professor Douglas Montgomery and JMP’s Brad Jones are offering a free seminar on DOE in Phoenix. At the seminar we will give away some copies of several books, including Montgomery’s latest edition of Design and Analysis of Experiments and a new SAS Press book by W.L. Gore employees and JMP users José and Brenda Ramírez, Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide.

Join Doug, Brad, Susan Glick, John Guerrero and the southwest US JMP team on Thursday, October 28. Seats are filling fast so register today. We also just added a December 3 live DOE Webcast hosted by JMP’s Tom Donnelly. It is part of our Master Your JMP Skills series. Sit back at your desk, put your feet up and join this DOE event. Registration is required.

The curious vintage junkie in me borrowed the 20-year-old JMP box that John Sall holds up in his 20th anniversary video.

The description of JMP on the back cover was a surprise. Why? That description still applies today, 20 years later:

• Classical statistics combined with today’s most interactive graphics. • Data analysis with a visual difference for viewing among groups. • One powerful platform. • Multidimensional data views that make it easy to identify outliers. • Designed for immediate productivity – no programs to write. • Yours from SAS – the respected name in data analysis software. True, the number of platforms, plots and analyses offered in JMP has grown to include Tabulate, Bubble Plots, a drag-and-drop Graph Builder, new reliability tools and more. And for nigh on 10 years, JMP has offered cutting-edge design of experiments approaches, both custom and textbook. JMP now runs on Windows and Linux in addition to Mac, and with every release JMP integrates better and more seamlessly with SAS. Like anything vintage, JMP has touched and been touched by a host of people with stories to tell. I invite you to share some of your stories about JMP. How has it helped you in your job? What ideas have you shared with us that are now built into JMP? What has held up over the years? What has made you smile or say, “aha”?

Feel free to post a comment here. Or post a video response to Sall’s 20th Anniversary video or to our global thank you on the SAS YouTube Channel.

You can also tweet your comment using the Twitter tag #JMP20.

Want to automate JMP analyses that you perform routinely? Need a way to add a bit of customization to your JMP reports? Learn best from examples?

A new book, Jump Into JMP® Scripting, by Wendy Murphrey and Rosemary Lucas, two members of the JMP Technical Support team, will give you a good start.

Weighing in at about 200 pages (less than one-third the size of the JMP Scripting Guide), this book is for people who don’t know how to use JMP Scripting Language (JSL) and are not interested in heavy programming.

Part 1 of the book walks you through short tutorials to learn practical scripting basics. The first chapter (which you can download as a sample chapter) is aptly titled 'Make JMP Work for You'. It describes how to access and then make slight edits to the scripts JMP captures in the background as you point and click your way through analyses.

Part 2 teaches you to write 60 short scripts that answer some of the most common questions first-time scripter writers ask, such as, “How can I specify the order of an axis?” and “How can I populate a Combo Box based on a user selection of another Combo Box?”.

In The Wall Street Journal, Michael Schrage and MIT colleague Erik Brynjolfsson describe how technology now enables all employees -- not just R&D scientists or market researchers - to test new ideas faster and cheaper than ever before.

The key? Faster experimentation for hypothesis testing. The promise? Innovation at warp speed.

Erik suggests two requirements for innovation -- a creative flash and a quantitative mindset that translates the flash into measurable and testable experiments.

Michael suggests that new electronic technologies for capturing and examining results almost immediately from small, targeted experiments lay the groundwork for innovation for companies (like Google) that are bold enough to breed a culture of experimentation.

Want to learn more? Meet and speak with Michael Schrage at the JMP Innovators' Summit in Chicago on Friday and Saturday, September 18 and 19. Michael is there both days -- opening the sessions each day, hosting a group of attendees at Friday’s dinner and moderating the closing panel session on Saturday.

Consider coming a few days early to attend the Discovery 2009 conference and learn about how people use JMP, including for measurable, testable experiments. The creative flash? Well, that’s up to you.

Register now for one or both events.

In the past two years, close to 1000 professors and students have attended JMP workshops held by Melodie Rush on U.S. college campuses. As the 2009-2010 academic year approaches, Mia Stephens joins the workshop circuit.

Mia, a new member of our JMP academic team, is an applied statistician who is no stranger to JMP. You may have seen her present at a JMP User Conference or be familiar with a data mining white paper she co-authored with her colleagues at North Haven Group.

I spoke with Mel and Mia after a campus visit.

Gail: What is the purpose of the workshops?
Mel: When a university licenses JMP, unlimited numbers of professors and students in the licensing group, say a Math Department or a whole campus, have access to the software. Some professors know JMP; others don’t. We want to arm the new professors with the JMP basics they need to teach the statistics covered in their courses.
Mia: And we want to show the JMP-savvy professors new features that they might use in teaching, like the reliability capabilities added in JMP 8.

Gail: What do you cover?
Mel: We demonstrate how to use JMP for basic stats; how to interact with the graphics JMP generates for almost all statistics; and how the data, graphs and statistics are all linked. This always creates a lot of energy in the room!
Mia: We make sure to cover at least univariate, bivariate, and multivariate summary statistics; t-test; basic regression; ANOVA; and contingency tables. Often, there are professors in the room who also teach or use SAS, and we show them how JMP integrates with SAS.

Gail: I've heard a lot about JMP from business schools.
Mel: We get asked about Biz Viz all the time. The JMP Graph Builder is a great way to compare data, so we show people how to analyze business and other data by dragging and dropping onto graphs. Everyone loves this!

Gail: How long are the workshops?
Mia: Two hours is about right to cover the basics and still keep people engaged.

Gail: Does each participant have JMP on a computer?
Mia: No, these aren't training sessions. We use JMP on a laptop connected to a projector.

Gail: How do you decide where to hold workshops?
Mel: Time and travel costs are a consideration. So, we take requests from campuses that can provide a room and are sure there will be at least 15-20 professors attending the session. They can bring as many students as the room will hold.
Mia: Our academic account team offers workshops to new sites that license JMP. We also get requests directly from professors.

Interested? To inquire about a workshop for your campus, send an email to the JMP academic team.

Hear Mel and Mia talk about the workshops.

More than 70 people gathered at SAS headquarters last Friday to hear Dr. William Meeker, Professor of Statistics at Iowa State University, talk about reliability analysis. Following a hot breakfast, attendees watched Meeker's presentation covering the principles behind probability plots, multiple failure modes analysis and accelerated life testing.

For each topic, JMP Statistical R&D Director Brad Jones showed JMP software's capabilities to analyze, plot and provide relevant statistics about the variety of distributions available to help users understand their data.

Of particular interest to the audience was the discussion of multiple failure modes analysis, which is important for understanding the impact of the wear of different parts on the performance of a product. Among the attendees were engineers who help their companies determine reasonable warranty periods, so this topic was particularly relevant to them.

This was the first live seminar in the JMP Explorers Series. Upcoming speakers include Richard DeVeaux, Sam Savage and Stephen Few.

While he was at SAS headquarters, Meeker recorded four videos about reliability analysis, and Leo Wright from JMP recorded four accompanying JMP demos. View the webcast videos and demos. You will need to log in using a SAS profile, or set up a profile and then log in. The data for the demos is also available for download. Why not use the data to run the reliability analyses yourself?

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After the sticker shock has worn off or the glow of a good deal fades into dullness, customers are left with a product that they expect will meet their needs. A large number of product designers and manufacturers that deliver great products that their customers love use JMP for reliability analysis.

JMP 8 integrates new, valuable reliability capabilities that are interactive and graphical. Many are based on the guidance of a leading expert on statistical methods for reliability, Dr. Bill Meeker, Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. Meeker has written a number of textbooks, including Statistical Methods for Reliability Data, which he co-wrote with Luis A. Escobar.

By the way, if you are in or near Cary on March 20, we invite you to hear Bill Meeker talk about reliability.

So what’s new for reliability in JMP? Chris Gotwalt, JMP Software Development Manager and Senior Research Statistician, described and walked me through some of the new features.

Two New Platforms Predict Events

Life Distribution and Fit Life By X are two new platforms. Users start by fitting multiple time-to-event distributions to their data. Plots of the distributions are overlaid with a plot of the data, all in the same window. Then, with a few mouse clicks, users visually and statistically examine and compare the distributions to determine which ones offer good explanations for the data. JMP makes this easy by overlaying all the distributions onto one graph.

After choosing a model, JMP profilers determine the probability that an event will occur. Based on criteria specified by the users, the profilers extract from the model relevant quantities of interest, such as estimates of median time to failure or the probability that an event will have happened by a certain time.

The new platforms are analogous to the Distribution and Fit Y By X platforms in JMP, in that Life Distribution provides graphical and analytical tools for examining a single variable, while Fit Life By X allows the user to explore the relationship between a response and an explanatory variable. Fit Life By X offers accelerated life testing. Both new platforms provide the censoring that is typical of reliability data.

New ‘Distribution Dredger’ Recommends a Distribution

For users who want guidance selecting the distribution that best fits the data, a great new interactive interface fits all distributions behind the scenes, and then it suggests a distribution by identifying the distribution with the best AICc score.

Here’s an Example

Using fan.jmp data found in the JMP Sample Data, we used Life Distribution to create a model, for which we compared four different distributions on a lognormal scale simply by checking boxes next to the distribution types and selecting a radio button for the probability scale type.

JMP displayed a graph that linearized the data. The graph is analogous to a probability paper plot. JMP also displayed the AICc values and profilers for each distribution.





At this point, we looked at graphs and AICc values to determine which distribution explains the data best. In this case, Lognormal looked best.

We didn’t stop there, however. We used the Fit All Distributions option, found under the red triangle menu, to fit all the distributions automatically. JMP identified Threshold Loglogistic as the best-fitting distribution because it had the lowest AICc value.



16 Interactive Distributions in All

The JMP 8 Life Distribution platform includes 14 new distributions plus two distributions that were formerly available elsewhere in JMP and are now available in the survival reliability context. The new distributions are:

• Frechet
• Log Logistic
• Smallest Extreme Value
• Largest Extreme Value
• Logistic
• Threshold Frechet
• Threshold Lognormal
• Threshold Loglogistic
• Zero Inflated Weibull
• Zero Inflated Frechet
• Zero Inflated Lognormal
• Zero Inflated Loglogistic
• Generalized Gamma
• Log Generalized Gamma

The two distributions that were formerly available elsewhere in JMP but are now available in the survival reliability context are:

• Threshold Weibull
• Normal

Want to see for yourself? View a video that includes more examples.

JMP® 8 offers a new capability to generate simultaneous prediction intervals, which are very useful when making claims about the future performance of small lots of products. In the latest edition of his white paper Statistical Intervals: Confidence, Prediction, Enclosure (PDF file), José Ramírez updates his examples to incorporate simultaneous prediction intervals and summarizes the corresponding conclusions.

José also adds a new section titled "How Much Can We Trust Our Claims?" in which he describes how to determine whether the data used to make the claims based on statistical intervals are homogenous. He uses JMP to create a process behavior chart (control chart) for individual measurements. The chart captures a running record with limits that allow the analyst to decide whether the data is homogeneous, whether the process is stable and whether the claims made based on the prediction intervals are, therefore, valid.

Why not download the paper for some good reading?

Experts agree that Six Sigma is a highly disciplined process that helps everyone in an organization develop and deliver near-perfect products and services.

Discipline? Necessary, sure. But that doesn't sound energizing or fun.

That's where Visual Six Sigma, a phrase JMP coined, comes in. JMP Visual Six Sigma is fun for novices, and even for Master Black Belts.

THE SAS UK office is offering seminars that show Visual Six Sigma in action using real world case studies. An optional workshop the same day as the seminars allows you to try out the Visual Six Sigma Roadmap for yourself.

Both are free of charge.

Wednesday, 16th July: SAS Manchester, Salford Quays
Tuesday, 7th October: SAS Marlow, Wittington House

I invite you to learn more and register for a seminar.

NNE Pharmaplan is an engineering and consulting company headquartered in Denmark, with 20 locations in Europe, North America and Asia.

Its expertise? Pharmaceutical, biotech and medical device manufacturing.

Its purpose? To enable clients to establish state-of-the-art production facilities and use methods that meet the myriad of production and regulatory demands -- and to equip those clients with the knowledge and tools to adapt to future needs.

Analytics is vital to the success of NNE Pharmaplan's clients. So is Six Sigma. Ergo, so is JMP.

The NNE Pharmaplan global academy team just notified me about two upcoming courses using JMP for teaching and implementing core statistical concepts and techniques. Courses are held in Denmark in English.

• In August, the company is offering a three-day course in applied statistics using JMP for Green Belts.
  
  
• In September, a three-week Black Belt course using JMP begins.
  

If you ever called or e-mailed a problem to JMP Technical Support, you may have been in contact with Duane Hayes. Duane manages JMP Technical Support for SAS. We recently discussed a tip that also may be helpful when sharing JMP reports with colleagues.

Duane: People often send us JMP reports, .JRP files, so we can recreate and solve their problem. They don't realize that the .JRP file is just a script, like the one shown below. To recreate the problem we also need the data table saved as a .JMP file.

Open("C:\Documents and Settings\hayes\Desktop\snapdragon");


Oneway(
	Y( :Y ),
	X( :Soil ),
	Block( :Block ),
	Means( 1 ),
	Box Plots( 0 ),
	Mean Diamonds( 1 )
);

Gail: What should they send you instead?

Duane: Nothing instead. Something in addition. They must also send us the data - the .JMP file. We then save the .JMP file and edit the Open statement in the .JRP file to point to the location where we saved the data file. JMP users need to remember this when sharing reports with colleagues.

Gail: So, what must they do to share report files?

Duane: Users have three options. Option 1: Put the data in a shared directory to which their colleague has access, run the analyses and then save the .JRP (report) file to that same directory. That way, when the colleague opens the .JRP file from JMP, everything will work. Option 2: Send the .JRP file and the .JMP file to the colleague, who saves the .JMP file to a location of choice. In the .JRP (report) file, the colleague then edits the Open statement to point to that location.

Gail: And I bet Option 3 is to create a JMP project.

Duane: Right. This allows users to bundle and share everything related to their analyses, including documents, PowerPoint presentations, animations and more. Richard Potter described this in detail in his blog Projects in JMP 7. Projects are really in line with the spirit of JMP, which lets you build upon one analysis with another, or subset data, without having to start the analysis anew. Projects are a great way to leverage work you have already done and make it accessible to someone else for review or the next step in the analysis.

Statistical intervals can be confusing, even in the minds of those who use them often.

José Ramirez, from W.L. Gore & Associates of GORE-TEX® fabric fame, offers a white paper that uses an easy-to-understand manufacturing example to describe the differences between confidence, prediction and tolerance (enclosure) intervals. He provides formulas plus the simple steps for implementing each interval type using JMP menus.

See if you find José's style as engaging and easy to follow as I did. If so, I have good news for you. He covers more statistical concepts in Chapter 2 of a book he is co-authoring with Brenda Ramirez for SAS Press. Their step-by-step guide for analyzing and interpreting continuous data using JMP will be in print at the end of this year.

Interested in meeting José? He's presenting at Discovery 2008 June 16-17 at SAS headquarters in Cary, NC. Register by May 15 for a discount. Or register with a friend, and you'll each get an iPod Shuffle.

Randomizing an experiment completely is often either impossible or prohibitively expensive. That's where split-plot designs can be valuable. Split-plot designs allow you to fix certain factors for several runs in a row. Within each block of runs (or whole plot), the factors that are hard to change remain fixed while the others vary at random from run to run. This makes the logistics of running a design simpler.

If you’ve had the opportunity to see JMP R&D Director Bradley Jones demonstrate how JMP Custom Designer handles split-plot designs elegantly and efficiently, you will want to read the paper he recently co-authored.

In the May 2007 issue of Journal of the Royal Statistical Society: Series C (Applied Statistics), Brad and Professor Peter Goos from Universiteit Antwerpen introduce a new method for generating optimal split-plot designs. In the paper, they demonstrate the usefulness of this flexibility with a 100-run polypropylene experiment involving 11 factors. In the experiment, they found a design that is substantially more efficient than designs produced using other approaches.

We also have more resources on design of experiments (DOE or DOX).

If you have JMP, check out sample data and instructions for generating a split-plot design.

If you are a university student, your department or campus probably licenses JMP® for courses and research. We also make it really easy for you to license your own fully functional 6- or 12-month copy of JMP for your PC or Mac. That license for JMP is between you and SAS, not the university and SAS.

Last year, thousands of students in the US, Israel, Italy, Singapore, the United Kingdom and the US Virgin Islands purchased and downloaded JMP from e-academy.com. We’ve just added 20 more countries to the eligibility list. To get your personal copy of JMP, you must be a current post-secondary student at one of the countries on the list and have a valid campus e-mail address.



If you do not have an account at e-academy.com already, just follow e-academy’s two step process:

1. Register as a student using your campus e-mail address. e-academy will send your login information to you at that e-mail address.

2. Log into www.e-academy.com/jmp and then select your JMP product (6- or 12-month version; Mac or PC), pay by credit card and download JMP.

After you get JMP, remember:

• The point-and-click interface selects the appropriate analysis based on the types and numbers of variables you select.
• Statistics display in the same window as the graph
• Hover help assists you interpret the statistics. Just move your cursor over the statistic of interest.
• Click on points in a graph to highlight the data on the data table, and vice versa.
• The JMP Starter defines analyses and then takes you to the analysis with the correct setup already in place.
• You can view instructional videos or attend our weekly live Webcast on Getting Started with JMP.
• Look for seven ways to get started with JMP® 7 using sample data, tutorials and scripts in the JMP online HELP.

We offer JMP licenses through e-academy to students in these countries:

Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
Colombia
Czech Republic
France
Ireland
Israel
Italy
Luxembourg
Mexico
Netherlands
New Zealand
Portugal
Singapore
Slovakia (Slovak Republic)
Switzerland
Turkey
United Kingdom
United States
Venezuela
Virgin Islands (US)

If your country is not on this list, ask your professor or department head how to access JMP on your campus.

Marie Gaudard, Phil Ramsey and Mia Stephens have taught JMP and used it in their North Haven Group Six Sigma consulting practice since the release of JMP Version 4 in the 1990s. All three are strong believers in the value of the JMP Partition platform for novice to expert users.

Marie is an ardent data miner. She recently added a new file to our File Exchange. It‘s a JMP data table that will help you learn how to use partitioning to mine data.

The problem is easy to understand: Some print jobs are ruined by a band of ink on the pages, and the production team wants to identify the factors that may be causing the ‘banding problem’.

The data is easy to use: Marie provides data on more than 500 print runs and she includes embedded scripts that get you started.

There is a tutorial to help you along: North Haven Group wrote a white paper that gives background on data mining and partitioning. The paper is in the form of a tutorial for implementing the techniques using JMP.

I talked with Marie last week about why she likes JMP’s Partition platform.

Marie: It is intuitive, powerful, easy to understand and users love it! It’s powerful because it handles large amounts of data and it’s trustworthy because it examines a very large number of possible splits and picks the optimum one. It is especially useful when the explanatory variables are nominal and have many levels.

Me: Is it for JMP power users only?

Marie: Emphatically, no! Wearing my trainer’s hat, I love it because it is so easy for my clients to use, and it gives them incredible insight into their data. We just teach a new user the basics of opening, saving, and navigating the interface and about the convention of the ‘red triangle’. The red triangle reveals lots of options available to them after they do their first split – options like small tree views and a leaf report. They just click SPLIT to see, in a graphical tree view, the variables that are most likely to affect the outcome in which they are interested, and the nodes that describe how the variables are related to the outcome. Then, they can easily click PRUNE when they want to reverse the operation. Your readers will know what I mean as soon as they open the data and run our scripts.

Me: How does it compare to other data mining tools you’ve used?

Marie: Many other data mining tools that do this kind of analysis are largely inflexible. For example, they give you the final tree based on their built-in stopping rules. But you know more about your data and even about the constraints of the organization that you must consider. JMP lets you lock out a variable that may be interesting, but not useful for understanding the problem. Then you can, very easily, go back and split on other variables that may be more valuable. You can also split at specific nodes. We find this very valuable for gaining a deeper understanding of the data. And you can decide when to stop splitting, based on knowledge of the process or using criteria provided by the Partition platform itself.