JMP (pronounced jump) is a powerful and interactive data visualization and statistical analysis tool. Use JMP to learn more about your data by performing analyses and interacting with the data using data tables, graphs, charts, and reports. SAS JMP enables researchers to perform a wide range of statistical analyses and modeling. JMP is equally useful to the business analyst who wants to quickly uncover trends and patterns in data. With JMP, you do not have to be an expert in statistics to get information from your data.

For example, you can use JMP to do the following:
Create interactive graphs and charts to explore your data and discover relationships.Discover patterns of variation across many variables at once.Explore and summarize large amounts of data.Develop powerful statistical models to predict the future.
JMP 14 highlights
JMP Projects to organize files and manage multiple open windows with a drag-and-drop tabbed interface.New Recode tools and automated routines to get data ready for analysis more quickly.Multiple file import to quickly and accurately combine hundreds - or even thousands - of files into one JMP data table.Graph Builder improvements including the new packed bars chart, which combines elements from treemaps and Pareto Descriptions; finer graph customization; and easy creation of custom error bars.Interface to Python to connect to Python, send data, execute Python code and return data to JMP for analysis or visualization.HTTP Request to communicate with external web servers through JSL. Addition of JSON parsing functions makes it much easier to get web data into JMP.A-optimality criterion in Custom Design for putting different emphasis on groups of parameters through weighting.New Multiple Factor Analysis for sensory analysis to help identify groupings of similar products and detect outliers that can skew results.Drift detection, Goal Description, three-way chart calculations and numerous UI improvements in Process Screening platform.Bayesian Inference for Fit Life by X.
JMP Pro 14 highlights
New Functional Data Explorer to understand, clean, align and build models from sensor streams or batch process data.Discriminant analysis in Text Explorer to dig deeper into text.Validation column in Text Explorer for use in a predictive modeling workflow.More distributions, including more censoring, and multinomial response in Generalized Regression to build models for diverse data.Ability to publish recode columns to the Formula DepotImprovements to K-NN and Naïve Bayes. K-NN adds model selection, and both add profilers.

Citación :

rapidgator_net:
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