New in JMP 14 and JMP Pro 14 (PDF)
Scientists, engineers and other data explorers need a highly visual and interactive way to discover what’s hidden in their data. The latest version of JMP offers new and enhanced ways to do just that. JMP 14 includes new ways to access rich data sources and new tools to streamline the data cleanup process. For those scientists and engineers who have chosen JMP Pro 14 for their data analysis needs, modeling tools are available to take analyses to the next level—no matter what form the data comes in. See what's new in JMP 14 and JMP Pro 14—with many of the features requested by users themselves.
Projects afford a new way to organize files and use JMP with a tabbed, web browser-like interface and can ease the pain of managing your workspace when many tables, scripts and reports are open at once.
The single-document interface provides a place to bookmark important files and displays a window list to let you see and easily move to any open window, data table or script. If you run repeated data queries, analyses and reports, using a Project is a quick way to collect a set of workflow tasks for streamlined sharing, saving and collaborating. With Projects, you can:
For many users, each analysis begins with importing dozens, hundreds or even thousands of files. Before JMP 14, these users needed to write JSL scripts to perform such imports. With JMP 14, the Multiple File Import platform gives users a point-and-click way to quickly and accurately combine hundreds— or even thousands—of files into a single one.
Import multiple files into a single data table, or a collection of data tables (for example many free-text documents that need to be concatenated for use with Text Explorer), with just a few clicks. Specify import options, filter files by a variety of criteria (including size, date, name and type), and import to a single column (for use with Text Explorer) or into a multi-column table. Use Multiple File Import interactively, or have the platform write you a script to automate future imports.
Graph Builder is the graph creation toolbox in JMP that helps you discover and share the stories within your data. With JMP 14, Graph Builder has new capabilities that enhance the data exploration process and further the publication-readiness of your findings. Among these new features is a new bar type that combines elements from treemaps and Pareto plots and many new options that give you finer control over graph customizations. Graph Builder receives many other enhancements in JMP 14, including:
With direct database access and an ODBC driver, it is easy to query, join and filter data using Query Builder—but what happens when you'd like to perform an analysis using one of the many data sources available through web APIs?
JMP 14 introduces the JSL HTTP Request function. HTTP Request allows you to write custom scripts to communicate with external web servers (for example, Salesforce, Google Analytics or Twitter) through REST APIs. By using HTTP Request and the new JSON parsing functions, you can automate the process of fetching data from the web and placing it into JMP tables.
Every JMP user works with data tables, and in JMP 14, they are even more powerful and easier to use. Now tables can use the values of any column—even an image column—as markers. They can also store hyperlinks to web pages, files and JSL scripts and perform several new single-click transforms. Additionally, more powerful Virtual Joins are possible with just a few clicks and a new column utility can easily convert between value labels and codes. A few other new features to enhance your use of tables include:
Ensuring the consistency and accuracy of categorical data has always been one of the most time-consuming and tedious aspects of data cleanup. Recode is a tool that many scientists and engineers utilize daily to expedite the data cleanup process. In JMP 14, you'll find that Recode has additional tools and automated routines to get you analysis-ready more quickly than ever before.
With improved filtering and text parsing options, support for multiple response data and value labels and integration with the Formula Depot in JMP Pro, you won’t trust your data cleansing to any other tool. Here are just a few of the Recode enhancements in JMP 14:
JMP 14 introduces Python into the suite of programming language interfaces in JMP, adding to the existing ability JMP has to connect to SAS, MATLAB and R. With the Python interface, JMP users can leverage in-house Python experts and pre-existing libraries, without leaving JMP. It also allows users to connect to a local install of Python, send data from JMP to Python, execute Python code from a JSL script and return data to JMP for data visualization, analysis and further exploration.
The DOE platform in JMP is world-class, better enabling users to solve challenging problems in a wide variety of real-world settings. In JMP 14, numerous improvements to the DOE platform include:
JMP Pro 14 brings improvements to the Naïve Bayes functionality introduced in JMP Pro 13. This classic approach allows classification of categorical responses in the data and works well with large data sets. It uses a simple estimate of the probability of conditional distributions of X given Y and reverses the conditional probability using Bayes' rule. In JMP Pro 14, Naïve Bayes is rendered as a fully featured platform and offers the functionality available in other predictive modeling platforms, such as profilers and diagnostic plots.
As with the Naive Bayes platform, JMP Pro 14 improves upon its K-Nearest Neighbors (K-NN) functionality. In JMP Pro 14, K-NN is now a complete platform with features available in other predictive modeling platforms, including profilers, a model selection tool that lets you interactively select K and diagnostic plots to help you build more robust models.
Text Explorer was an exciting addition to JMP 13, letting users explore unstructured text data in a point-and-click environment. Now, Text Explorer has enhancements that make it even more useful for digging deeper into text. Some of the updates available in JMP 14 include:
Quality engineers will find a variety of new diagnostics and time-saving tools in JMP 14. CUSUM charts in JMP now have the familiar look, feel and interpretation of traditional control charts and the Process Screening platform adds Drift Detection, Goal Plots and the ability to generate three-way control charts directly from platform launch. The Process Capability platform adds new distributions and measures of variability, while a new platform, Manage Spec Limits, lets users easily import, export and modify spec limits.
Multiple Factor Analysis (MFA) is a sensory analysis technique whose goals are to find groupings of products that are similar and to identify outlier panelists—the panelists who are so different from the rest of the group that their values skew the results of the analysis. MFA allows the researcher to perform a PCA-like analysis with untrained panelists. It is often used for pre-evaluation studies, where many different products are being tested, as well as for later-stage consumer panel studies where many consumers—many of whom have no training on standard sensory measurements—rate the products.
Scientists and engineers are dealing with more and more data streamed from device-based sensors or batch process monitors. Hundreds or thousands of such data streams can create a large volume of data, very quickly, posing a unique set of challenges.
No matter what your industry, common difficulties exist with this type of data: simplifying and cleaning up messy data, removing outliers and building models that characterize an underlying function or relate a continuous data stream to measures of performance, such as yield, defect rate or product quality.
With Functional Data Explorer in JMP Pro 14, you can take advantage of this functional data, easily performing data cleanup, transformation, visualization and feature extraction. Ultimately, this allows you to more easily build models you can use to design experiments, characterize processes or make predictions.
Generalized regression is the premier linear modeling platform in JMP Pro. With Generalized Regression, you can build models for many kinds of data—for example, DOE data, observational data, data with categorical responses, messy data, text data, highly correlated data and more. It is a tool that fits models, selects variables, handles multicollinearity and provides diagnostic reports—all in one place. JMP Pro 14 introduces some key enhancements to the Generalized Regression functionality, including:
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