New in JMP 12 and JMP Pro 12 (PDF)
From data import to analysis to sharing results, enhancements in JMP 12 speed the learning cycle and make it easier to reach breakthrough statistical discoveries.
Until now, those who work with databases have often needed to write their own SQL code to join database tables and query them – a tedious and error-prone process requiring a skill set most analysts lack. Query Builder allows users to connect to databases and perform joins and queries using intuitive dialogs. Users unfamiliar with SQL can now get their work done without relying on IT, while advanced SQL programmers can augment the generated code with custom code.
Supported in many Fit Model personalities, interactive model editing lets you quickly run and compare various models with a drag-and-drop interface within the report. This keeps you in the analysis flow, allowing you to build a model that performs well with fewer clicks, while emphasizing the interactive nature of JMP.
It is always nice to show your data analysis results from JMP directly in JMP. But in some cases, you may need to prepare a slide deck to present your information to others. Now, JMP makes it easy to export your reports as PowerPoint slides. You can reformat slide decks created by JMP with a company template. Tables include editable text, and JMP automatically transfers graph titles to slide titles.
The biggest news for JMP 12 data tables is support for a new data type: Expression. Images can be dragged and dropped into table cells, while lists and matrices can be stored in their “native” forms. The Summary platform can also use this feature to display histograms in the summary table. Graph Builder can export images to a data table. Finally, you can now drag to change row height.
Other ways to use expression columns include:
Data cleaning/preparation prior to modeling can take up to three times as long as the modeling or analysis itself. Recoding columns that contain character data is often responsible for much of the preparation time. Categorical data is often messy due to misspellings, inconsistent capitalization, abbreviations and structure that require cleaning in preparation for successful modeling.
“Everyone knows that most of the time on a data project is spent cleaning and preparing the data,” says Dick De Veaux, Professor of Statistics at Williams College. “And how many times have you wanted an easier way to group categories that are 'nearly the same'? Now with the Smart Recode tool you can do it automatically."
The new Recode:
Graph Builder was already one of the most-loved tools in JMP. Now it gets a host of new features in JMP 12:
Profiler and Bubble Plot are two of the most popular tools for communicating results with others outside of JMP. Previously, interactive versions of these platforms were available only as Flash, which many devices (especially mobile) cannot play. Now they are available to output as interactive HTML.
The Local Data Filter is a powerful visualization aid. Now you can perform the same type of filtering using platforms. Application Builder’s new Data Filter Source Box lets you filter points and output in one area of a report by selecting items from a platform in another area of the report.
JMP software’s world-class DOE features continue to evolve. Definitive screening designs now support blocking, while space-filling designs support categorical factors, the Augment feature, and a new criterion for optimizing these designs, MaxPro. Split plots can now work with covariate factors, and a Data Filter-like interface now allows disallowed combinations to be specified via drag and drop.
Multiple Correspondence Analysis is a data analysis technique that’s similar to principal component analysis, but for categorical data. It helps to visualize and uncover hidden structures in a data set consisting of more than two categorical columns. Such information is useful in market research to identify target audiences for advertisements.
Enjoy a unified platform for assessing process capability. JMP reports within (short-term) capability and overall (long-term) capability. You can subgroup a process by nesting the column in a categorical variable, or set a constant subgroup size for a process. The graphics are updated, and you see both CPK and PPK by default. Finally, it’s extremely easy to import and export spec limits. You can also run a capability report directly from Control Chart Builder.
To measure a product characteristic, sometimes the product must be destroyed. For example, when measuring breaking strength, the product is stressed until it breaks. The new Destructive Degradation platform for reliability studies handles this type of analysis and has a built-in model library with visual representations of many standard models, transformations and data distributions. Includes a model comparison, CDF and Quantile Profilers.
“We are treated to an array of new features with each new version of JMP,” says William Q. Meeker, Professor of Statistics at Iowa State University. “I am particularly impressed by the new destructive degradation platform. With the slick interface, analysis of destructive degradation data will be remarkably simple, allowing useful inferences on both degradation metrics and failure-time metrics. As usual, the JMP developers have made effective use of the ubiquitous and powerful JMP Profiler.”
An updated Cluster platform helps address common issues in data preparation prior to clustering. This includes reporting, data manipulation, dimension reduction and summarization.
The Cluster platform better addresses these common analysis issues:
The updated Discriminant platform includes a number of new features, making it a more useful tool in a predictive modeler’s toolbox, especially with larger problems.
New features include:
• Linear Wide, a new method for wide data cases. Useful when the covariance matrix is too big to calculate (thousands of variables).
• Make Scoring Script lets you create a script that has the same effect as the Save command if you run it, but a script can be applied to a separate data table.
A number of utilities help prepare data sets for analysis automatically, making data preparation more efficient – where you spend most data analysis time.
Modeling utilities in JMP 12 include:
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