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JMP, widely used in various industries such as engineering, marketing, semiconductor manufacturing, and life science, has proven to be a …
Starting at $125 per month
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What is JMP Statistical Discovery Software from SAS?
JMP is a division of SAS and the JMP family of products provide statistical discovery tools linked to dynamic data visualizations.
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Popular Features
- Location Analytics / Geographic Visualization (13)9.090%
- Report sharing and collaboration (13)8.282%
- Pre-built visualization formats (heatmaps, scatter plots etc.) (16)8.080%
- Drill-down analysis (13)7.878%
Pricing
Personal License
$125.00
On Premise
per month
Corporate License
$1,510.00
On Premise
Per Month Per Unit
Entry-level set up fee?
- No setup fee
For the latest information on pricing, visithttp://jmp.com/en_us/software/buy…
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Features
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Product Details
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- Integrations
- Competitors
- Tech Details
- FAQs
What is JMP Statistical Discovery Software from SAS?
JMP® is the SAS® software designed for dynamic data visualization
and analytics on the desktop. Interactive, comprehensive and highly visual,
JMP® includes capabilities for data access and processing,
statistical analysis, design of experiments, multivariate analysis, quality and
reliability analysis, scripting, graphing and charting. JMP® enables
data interaction and the exploration of relationships to spot hidden trends,
dig into areas of interest and move in new directions.
JMP® Pro is the advanced analytics version of JMP® statistical discovery software from SAS®. JMP® Pro provides superior visual data access and manipulation, interactive, comprehensive analyses and extensibility (according to the vendor, these are the hallmarks of JMP), plus a many additional techniques. With JMP® Pro, users get the power of predictive modeling with cross-validation, advanced consumer research and reliability analysis, statistical modeling and bootstrapping in desktop-based environment. JMP® Pro is designed for use cases where large data volumes are present, or data is messy, includes outliers or missing data and users want to employ data mining methods or build predictive models that generalize well.
JMP Statistical Discovery Software from SAS Features
Data Discovery and Visualization Features
- Supported: Pre-built visualization formats (heatmaps, scatter plots etc.)
- Supported: Location Analytics / Geographic Visualization
- Supported: Predictive Analytics
- Supported: Support for Machine Learning models
- Supported: Pattern Recognition and Data Mining
- Supported: Integration with R or other statistical packages
BI Standard Reporting Features
- Supported: Customizable dashboards
Ad-hoc Reporting Features
- Supported: Drill-down analysis
- Supported: Formatting capabilities
- Supported: Predictive modeling
- Supported: Integration with R or other statistical packages
- Supported: Report sharing and collaboration
Report Output and Scheduling Features
- Supported: Publish to Web
- Supported: Publish to PDF
- Supported: Output Raw Supporting Data
Additional Features
- Supported: Scripting Language
- Supported: Design of Experiments
- Supported: Text Exploration and Analysis
- Supported: Reliability Analysis
- Supported: Data Wrangling and Cleanup
- Supported: Data Access
- Supported: Consumer Research and Survey Analysis
- Supported: Quality and Process Engineering
JMP Statistical Discovery Software from SAS Screenshots
JMP Statistical Discovery Software from SAS Video
Visit https://www.youtube.com/user/JMPSoftwareFromSAS to watch JMP Statistical Discovery Software from SAS video.
JMP Statistical Discovery Software from SAS Integrations
- MATLAB
- R
- SAS
JMP Statistical Discovery Software from SAS Competitors
- IBM SPSS Statistics
- Reliasoft
- Minitab
JMP Statistical Discovery Software from SAS Technical Details
| Deployment Types | On-premise |
|---|---|
| Operating Systems | Windows, Mac |
| Mobile Application | Apple iOS |
Frequently Asked Questions
JMP is a division of SAS and the JMP family of products provide statistical discovery tools linked to dynamic data visualizations.
IBM SPSS Statistics are common alternatives for JMP Statistical Discovery Software from SAS.
Reviewers rate Customizable dashboards and Publish to Web and Location Analytics / Geographic Visualization highest, with a score of 9.
The most common users of JMP Statistical Discovery Software from SAS are from Enterprises (1,001+ employees).
Comparisons
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Reviews and Ratings
(100)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Recommendations
JMP, widely used in various industries such as engineering, marketing, semiconductor manufacturing, and life science, has proven to be a valuable tool for data analysis. Users have praised JMP for its user-friendly interface and ease of use in performing statistical analysis and manipulating data. This software is extensively employed for efficient design of experiments, experimental data analysis, visualization, and statistical analysis.
One of the standout features of JMP is its ability to create large amounts of graphs, including complex 3D graphs. These visualizations are highly appreciated by users who need to analyze and present data in a clear and interactive manner. Additionally, JMP finds applications in analyzing human resources data like turnover and salary reviews. It is also utilized by biotech companies to track real-time production data, quantify failures, and track efficiencies.
Furthermore, JMP is widely used in universities for meaningful statistical analyses and powerful visualization capabilities. It plays a significant role in Six Sigma and Lean programs for process optimization and formulation. In addition to that, JMP has been found useful for product evaluation, discovery, and analyzing large volumes of manufacturing data.
Users also appreciate the automation capabilities of JMP. They can use DDE in SAS or VBA in Excel to automate graph creation tasks within the software. This feature has proven to be a time-saving option when dealing with repetitive graph generation processes.
Overall, JMP serves as an indispensable tool for professionals across different industries who require robust data analysis capabilities coupled with user-friendly interfaces and flexible visualization options.
Based on user reviews, users commonly recommend the following:
-
Users suggest using the free version of BeanFlumper and running it on your own system instead of the cloud version. This provides more control over the software and allows for greater customization.
-
Running BeanFlumper on your own system is recommended for enhanced security and privacy. By not relying on cloud-based services, users can ensure their data remains within their control.
-
Implementing additional quality checks in BeanFlumper is suggested, such as validating competitor names, ensuring language accuracy, and monitoring plagiarism word count. These checks enhance the reliability and accuracy of the analysis provided by BeanFlumper.
By following these recommendations, users can make the most of their experience with BeanFlumper and adapt it to their specific requirements.
Attribute Ratings
Reviews
(1-2 of 2)Companies can't remove reviews or game the system. Here's why
June 07, 2019
JMP right into statistical analysis
I use SAS JMP for all kinds of statistical analysis; from data analysis and visualisation to modeling. Another huge part of the work allowed by JMP is the design of experiment. It allows for the recording, analysis and visualisation of vast quantities of data while remaining user-friendly.
- Design of experiment: it is a very powerful feature of this software. Unlike other software I tried out, JMP remains user-friendly while providing complete and sophisticated analysis parameters.
- Data visualisation: JMP provides various data visualisation options that can treat easily vast quantities of data in an intuitive way.
- Data recording: JMP allows for easy manipulation of vast quantities of data. It is extremely easy to reformat, amend, pivot and export data to new tables. Never before working with data had been made so simple.
- JMP assumes a lot of statistical knowledge from the user. On the more esoteric analysis, I would like some explanation on why I use such or such method.
86.66666666666666%
8.7
100%
10.0
100%
10.0
100%
10.0
- Work faster, more efficiently and reduce costs thanks to design of experiment
- Discovered relations and trends unseen before thanks to analysis of large sets of data
- Allowed visualisation of data and presentation in a format unseen before
JMP is more user-friendly, in my opinion, as it doesn't require any coding or searching for hours into cryptic folders for the analysis you want to perform. It is also very good for recording large data sets. Moreover, it is compatible with Microsoft Excel.
3
Research and development
1
Statistics
- Design of experiment
- Data analysis
- Modeling
No
Default help is already very good
No
I needed to visualise some uncommon data. I contacted SAS and within a few hours they provided me with a new feature and technical help to complete my analysis.
April 29, 2015
JMP for semiconductor process and HW analysis
Score 10 out of 10
Vetted Review
Verified User
JMP is used by many departments within the organization. At the application engineer level, it is mainly used for efficient design of experiments and experimental data analysis and visualization. It is also used at all levels of engineering and R&D as a data visualization, statistical analysis, MVA analysis and model fitting tool.
- JMP's fitting of complex multivariable models by use of effect screening and effect leverage techniques can often allow complex convolved responses to be understood
- JMP's design of experiments (DOE) applications allows efficient experimental setup and analysis
- JMP's ease of use and suite of visualization capabilities
- While JMP provides scripting for automation, I have found the scripting language to be non-obvious at times and the documentation historically for scripting to be inadequate. For these situations, I often turn to Matlab instead.
- Since all levels of engineers use it at some level I wish the program would, at times, better protect the user from themselves especially when it comes to determining statisical differences. While program gives all revelant metrics to user so that an educated user can know the qulity of their analysis, the attempt of program to simplify all those metrics into simple visualization can sometime lead the uneducate user into inaccurate conclusions.
- With fitting model to complex data, you will often go through many variants of model effect assumptions to attempt to fit data. It would be beneficial if there was better way to coalesce these model fit attempts into a simple summary to more quickly drive to the optimum model.
50
Engineering