Anne Milley

11月 052016
 

bhutanGalit Shmueli, National Tsing Hua University’s Distinguished Professor of Service Science, will be visiting the SAS campus this month for an interview for an Analytically Speaking webcast.

Her research interests span a number of interesting topics, most notably her acclaimed research, To Explain or Predict, as well as noteworthy research on statistical strategy, bio-surveillance, online auctions, count data models, quality control and more.

In the Analytically Speaking interview, we’ll focus on her most interesting Explain or Predict work as well as her research on Information Quality and Behavioral Big Data, which was the basis of her plenary talk at the Stu Hunter conference earlier this year. I'll also ask about her books and teaching.

Galit has authored and co-authored many books, two of which — just out this year — include some JMP. First is Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, with co-authors, Peter C. Bruce, Nitin R. Patel, and Mia Stephens of JMP. This first edition release coincides with the third edition release of Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, with the first two co-authors listed above. As Michael Rappa says so well in the foreword of the JMP Pro version of the book, “Learning analytics is ultimately about doing things to and with data to generate insights.  Mastering one's dexterity with powerful statistical tools is a necessary and critical step in the learning process.”

The second book is Information Quality: The Potential of Data and Analytics to Generate Knowledge, which Galit co-authored with Professor Ron S. Kenett, CEO and founder of KPA and research professor at the University of Turin in Italy (you may recognize Ron and KPA colleagues as guest bloggers on the JMP Blog on the topic of QbD). As David Hand notes in his foreword, the book explains that “the same data may be high quality for one purpose and low quality for another, and that the adequacy of an analysis depends on the data and the goal, as well as depending on other less obvious aspects, such as the accessibility, completeness, and confidentiality of the data.”

Both Ron and Galit will be plenary speakers at Discovery Summit Prague in March. You can download a chapter from their book, which discusses information quality support with JMP and features an add-in for Information Quality, both written by Ian Cox of JMP. You can see a short demo of JMP support for information quality during the Analytically Speaking webcast on Nov. 16.

Whether your analysis is seeking to explain some phenomena and/or to make useful predictions, you will want to hear Galit’s thoughtful perspective on the tensions between these two goals, as well as what Galit has to say on other topics up for discussion. Join us! If Nov. 16 doesn’t suit your schedule, you can always view the archived version when convenient.

tags: Analytically Speaking, Analytics, Books, Discovery Summit, Statistics

The post To explain or predict with Galit Shmueli appeared first on JMP Blog.

10月 052016
 

iconPopular xkcd comic and author, Randall Munroe, delivered a fantastic closing plenary, Complicated Stuff in Simple Words, at JMP Discovery Summit last month. Based on his very popular second book, Thing Explainer: Complicated Stuff in Simple Words, it was hugely entertaining, and we are sharing it as this month’s episode of our web series Analytically Speaking.

Because Randall’s talk was followed by the book signing for his newest book, many didn’t submit feedback on his talk in the JMP Discovery Summit app (probably so they could quickly get in line for the book). I took the comments submitted and used the new Text Explorer platform in JMP 13 to show you the very positive terms from the comments and how the powerful regex handles all those enthusiastic exclamation points!!!!

Below left, you see the most popular terms and phrases listed. And at right, you can see the regular expression editor with default tokenizing options highlighted in different colors under the Word Separator List. These settings can be further customized, but for this simple example, we see that Randall Munroe (using simple words) evoked very positive and enthusiastic comments. For more on text exploration, check out these previous posts.

screen-shot-2016-10-03-at-2-38-03-pm

The "Bang!!! cleaner" nicely handles a sequence of exclamation points for this text exploration.

During the book signing, Randall asked what JMP users do at their organizations. Upon hearing a few answers about some of the “complicated stuff“ JMP users do, he actually flipped to a place in his book where he “explained” what they did!

booksigning

Many attendees were looking forward to Randall's talk and book signing, and had planned ahead by bringing copies of his other books to be autographed.

At the close of his talk, I tweeted, “Awesome keynote by Randall Munroe @ #jmpDiscoverySummit. His curiosity is contagious.” We hope you will tune in Oct. 12 for some entertaining thing-explaining! Or, you can watch the archive along with other episodes of Analytically Speaking.

tags: Analytically Speaking, Discovery Summit, Discovery Summit Keynote, Text Analysis, Text Explorer

The post Entertaining thing-explaining with Randall Munroe appeared first on JMP Blog.

9月 142016
 

Clay Barker has been busy extending the usefulness of the Generalized Regression platform in JMP Pro, adding many new models and enhancing ease of use. Generalized Regression (or GenReg for short) debuted in JMP Pro 11 as the place to do a trio of popular penalized regression techniques: Lasso, Elastic Net and Ridge. These penalized techniques are attractive because they lead to simpler models that are less prone to overfitting. After adding more modeling and selection techniques in JMP Pro 12 and now JMP 13, GenReg has become a place to do variable selection quickly and easily for a wide variety of problems.

Variable selection is where much of the “art” is in model building, and even more so with ever-wider data.

“Customers have been asking for variable selection for time-to-event data for some time, and now GenReg will be able to do that. There are not a lot of easy options for doing variable selection across so many different scenarios” says Clay, a Senior Research Statistician Developer at JMP.

Screen Shot 2016-08-29 at 4.01.45 PM

Now you can use Generalized Regression on time-to-event data.

One of the biggest enhancements in the Generalized Regression platform for JMP Pro 13 is the ability to handle censored data to do parametric survival analysis and proportional hazards models. Fellow developer Peng Liu even added a link to the Generalized Regression platform from within the Survival Analysis platform (in JMP Pro).

Variable selection is an active area of research in statistics. To do variable selection well and build really useful models, you need a breadth of tools and even hybrid approaches using automated selection techniques like the Lasso and Elastic Net. But the interactive nature of GenReg makes it easy to adopt a hybrid strategy where you can easily explore alternative models supported by the automated selection technique. Now JMP Pro provides some powerful new variable selection methods, including a modified two-stage forward selection method — first on the main effects and then on the higher-order effects involving the main effects, making Generalized Regression a premier tool for analyzing designed experiments.

Also new in JMP Pro 13 is the Double Lasso, a two-stage modeling technique where a first pass of the Lasso screens for variables to select and then a second pass of the Lasso is done on the variables selected in the first pass. Doing two passes of the lasso effectively separates the selection and shrinkage process of the Lasso, which can lead to better predictions. Another highlight is the addition of the Extended Regularized Information Criterion (ERIC). ERIC is similar in spirit to the Bayesian Information Criterion, but it was derived specifically for the Adaptive Lasso.

You can find out more about what’s coming in JMP Pro 13 by visiting 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.

tags: Generalized Regression, JMP 13, JMP Pro, Statistics

The post JMP 13 Preview: More enhancements to generalized regression appeared first on JMP Blog.

9月 092016
 

Building on the new features in JMP 13 for exploring unstructured text data, JMP Pro 13 enables you to do more with text data, like cluster terms and phrases and use text in predictive models. You’ll be able to answer more questions, scale to larger data and stay in flow. […]

The post JMP 13 Preview: New text analytics in JMP Pro appeared first on JMP Blog.

9月 092016
 

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 […]

The post JMP 13 Preview: Improvements to the Analyze menu for a better user experience appeared first on JMP Blog.

9月 022016
 

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 […]

The post JMP 13 Preview: Now you can “textcavate” your data with the new Text Explorer appeared first on JMP Blog.

8月 312016
 

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 […]

The post JMP 13 Preview: New MaxDiff platform for consumer research appeared first on JMP Blog.

8月 262016
 

Have you ever wanted to include data in an analysis without having to subset it from different tables and put it all together in a new table? Have you wanted to “see” how your data will come together before committing to joining many tables to make sure you get it […]

The post JMP 13 Preview: The power of the new Virtual Join appeared first on JMP Blog.

7月 302016
 

Heath Rushing is someone I count myself very fortunate to know — first as a colleague at SAS and now as co-founder of Adsurgo, a successful consultancy. Over years of JMP use, Heath has enthusiastically taught classes using JMP, written papers and the book, Design and Analysis of Experiments by […]

The post Exploring text and other data with Heath Rushing appeared first on JMP Blog.

5月 252016
 

Dr. Karen Copeland will be our featured guest on Analytically Speaking on June 8. She is the owner of Boulder Statistics, a successful consultancy to a wide array of industry sectors around the world — medical device, diagnostics, chemicals, marketing, environmental, consumer and food products, pharmaceuticals, and web analytics, among […]

The post “Bolder” statistics with Karen Copeland appeared first on JMP Blog.