New in JMP 13 and JMP Pro 13 (PDF)
JMP 13 helps you make your own luck; discover how new analysis platforms, feature enhancements, and improvements across the entire analytic workflow—from importing data to sharing analysis results—help you find the unexpected in your data.
The Query Builder for relational databases and SAS data sets has quickly become the preferred way to easily create accurate, repeatable and sharable queries—without having to write SQL code. However, until now you could not use Query Builder to join JMP tables already in memory, and nobody wants to create a database just to join data that is already in JMP. The JMP 13 Query Builder for JMP tables provides multi-table query and join capability through the same interface as the SQL Query Builder and SAS Query Builder. Once you start using this Query Builder, you’ll wonder how you ever lived without it.
In addition, JMP 13 has many improvements that serve to streamline the data preparation workflow. Here are just a few:
Often, scientists and engineers must sell the change solution to others who may lack their level of analytics expertise. For clear and efficient communication, a single presentation-ready dashboard is the gold standard. In previous versions of JMP, the process of creating a dashboard required many clicks. In JMP 13, the Dashboard Builder aggregates JMP reports into a presentation-ready dashboard with only a few clicks and includes such time-savings features as:
A virtual join eases the pain of joining and/or maintaining large (or many) tables. In previous versions of JMP, tables had to be physically joined. In some cases, this required more memory than could comfortably be spared. Consider the scenario of joining a tall table to a wide one (for example, joining weekly summary values to data collected every second). Even in the best cases, the memory demand was high. In the worst cases, the join was impossible. With the virtual join in JMP 13 you get the benefits of a join, without creating a new table or expending memory. The resulting memory savings can be significant–both virtually and physically–especially when columns contain large strings or images.
In previous versions of JMP, there was no way to screen hundreds—let alone thousands—of processes automatically. As a result, process control analysts, Six-Sigma practitioners, semiconductor engineers and other quality engineers had to manually triage these reports, which meant that most of the time allotted to searching for “problem” processes was consumed in viewing processes that were well under control. In JMP 13, there is a better way.
The JMP 13 Process Screening platform lets you quickly determine overall process health with the Process Performance Graph, and easily identify, using a customizable combination of tests and metrics, which processes warrant extra attention. It also lets you easily create control charts and process capability reports for any processes you choose.
With all the time you will save, you can focus more on problem solving, and less on fire-fighting.
The Process Screening platform in JMP 13 joins Predictor Screening (formally known as Screen Predictors) and Response Screening, as well as other modeling utilities (such as Explore outliers and Explore missing values) in the new Screening submenu of the Analyze menu.
Free text data exists in many forms: survey responses, repair logs, engineering reports and free-response fields are just a few examples. In previous versions of JMP, market researchers, warranty engineers, medical professionals, engineers and scientists who had mountains of text-based data could do very little with it in JMP besides counting and recoding words. Text Explorer uses a “bag of words” approach to parse text into tokens to build a document term matrix. With Text Explorer, you can easily triage and uncover the meaning in your text data, rather than having to choose to either process it manually, or ignore it altogether.
Text Explorer in JMP:
Anyone who shares JMP results outside of JMP can do so more easily and effectively in JMP 13.
In the past, it was time-consuming to build a summary website of JMP results—but without this, an individual report could lack context. Interactive HTML reports are useful, but a single report usually cannot capture the entire analytic process. HTML Auto-Reports in JMP solved this problem by providing a linked, thumbnail-indexed collection of HTML reports—but installation of an add-in was required. In JMP 13, the HTML Auto-Report feature is built-in.
Also, the tedious and error-prone work of copying and pasting multiple JMP tables into Excel workbooks is now a thing of the past: create, update or append to an Excel workbook of multiple JMP tables—each in its own worksheet—with a single click.
We asked users which JMP reports should be available as interactive HTML, and the answer was nearly unanimous: Graph Builder! Message received. Now interactive HTML exports support most of the Graph Builder elements. We love granting wishes.
You’ll get interactive versions of Points, Smoothers, Ellipses, Lines, Bars, Areas, Box Plots, Histograms, Heatmaps, Mosaic Plots, Caption Boxes and Map Shapes. We’ve also worked to ensure your eye-catching dashboards built with the Dashboard Builder remain just as attractive when they are rendered as interactive HTML.
One of the most loved tools in JMP, Graph Builder, brings value to nearly every user, helping them make beautiful graphs to communicate their data’s story as effortlessly as possible. In JMP 13, a host of new improvements and additions appear:
In previous versions of JMP, it was difficult for DOE practitioners to compare designs easily, especially when there were more than two designs – it was a manual process requiring copy/paste or shuffling views among multiple design evaluations. Now, compare up to three competing designs and get all of the diagnostics in one report. Compare the designs with respect to power, correlation, prediction variance, aliasing and efficiency.
With each new version, JMP improves upon its world-class design of experiments capabilities. In JMP 13, key improvements include:
In previous versions of JMP, quality assurance analysts, quality engineers, supplier quality engineers and Six Sigma professionals could perform capability analysis for normally distributed processes in the process capability platform, but for non-normal processes, had to turn to the Distribution platform. Also, JMP provided standard capability indices, but these only apply when the process’ distribution is normal. In JMP 13, Capability reports can be created in a single platform, regardless of the processes’ distributions. You can also:
Design, manufacturing and test engineers need to be able to analyze time-to-event data under non-constant stress situations, but classical Accelerated Life Tests (ALTs) assume constant stress over the life of any given unit. The JMP 13 Cumulative Damage platform:
Before, engineers analyzing reliability growth on concurrent or parallel systems often had to use competing software. Now, the Reliability Growth platform offers the following new capabilities:
Market researchers and social scientists need tools to reduce complexity in their data. Latent Class Analysis (LCA) is a statistical method for identifying unobserved class membership among subjects, using categorical observed variables. For example, an insurer may wish to categorize people into different driver categories (latent classes), based on the type of cars they drive, their accident records, and other publicly available data. JMP 13 adds LCA to the newly reorganized cluster submenu, which includes a suite of techniques: Hierarchical, K Means, Normal Mixtures and Cluster Variables.
MDS is a popular multivariate statistical technique used by sensory analysts, social scientists, market researchers and biologists to create a visual representation of the similarities among a set of objects. For example, given a matrix of perceived similarities among various brands of cars, MDS produces a map where points corresponding to similar cars are closer together and points corresponding to dissimilar cars are further apart. There is little formal inference, such as hypothesis testing, associated with MDS, but it is a good tool for comparing products and making judgments about similarities without having to list product attributes, as other techniques, like conjoint analysis and factor analysis, require.
MaxDiff analysis is a technique used to analyze customer preference. Similar to choice analysis, respondents simply select the best and worst choice from a set of fixed options. MaxDiff studies are often easier to execute than standard choice experiments, and in certain contexts, they can be more informative. With JMP 13, researchers can not only analyze MaxDiff studies, they can design them using the DOE platform.
Many market researchers conduct choice experiments where “no preference” may be selected, instead of any of the other options. Ignoring this possibility, where it exists, produces biased analysis results. JMP 13 now supports this important option.
For many users, scripting takes the everyday drudgery out of repetitive tasks, letting them focus on more interesting things: building models, designing and running experiments, and finding useful ways to communicate results. For others, scripting extends the JMP feature set, taking JMP beyond its native capabilities. New features and architectural changes in JSL include:
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