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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
(1-5 of 5)Companies can't remove reviews or game the system. Here's why
August 09, 2019
JMP - A user friendly statistical analysis program
We started out with only a small group using JMP, but due to its ease of use, we have now expanded it so that almost everyone doing any kind of statistical analysis is using JMP. It is our go-to choice for fast and easy statistical analysis products and is the favorite for new workers.
- No coding required!
- Fast, easy, and simple.
- Microsoft Excel compatible.
- Not many in-depth tools compared to other programs.
- Non-open source for quick and easy fixes to bugs.
- Expensive compared to other programs.
90%
9.0
80%
8.0
85%
8.5
90%
9.0
- Much wider use of statistical analysis across the whole organization.
- Streamlined regular data processing.
- Led to better coordination among other programs such as Excel.
Compared to other, similar programs, JMP is outstanding in ease of use and ability to be used by almost anyone across an organization. It is more fluid, user friendly, and, most importantly, requires no coding experience. The only two areas where it is not as good as competitors is in its cost and comparatively more shallow analytical capabilities
November 11, 2015
Jmp your way to success in analyzing your data
Score 8 out of 10
Vetted Review
Verified User
JMP Pro is being used across the whole organization. It is mostly used to solve engineering problems. It is used as a statistical and analytical tool to troubleshoot problems related to manufacturing.
- Very powerful visual analytical tool
- Easy capability for creating reports in HTML
- Integration with R, Matlab
- Statistical modeling capability
- Very good documentation on well established solutions
- Scripting capability is very poor, they have introduced application builder to do GUI based application but still needs more flexibility in terms of scripting as its lot tedious to do simple things
- Need improved documentation on features that are newer
- Its not easy to develop certain analysis and takes a while to screen out data and build it
- Need some more Data mining and Machine learning algorithms
Compared to:
MSExcel - Useful from engineering data analysis perspective
Matlab - cost/ expensive licensing
R - Open source may not have support at the right time when needed for an organization (disclaimer: I have novice R experience)
Python - Open source may not have support at the right time when needed for an organization (disclaimer: I have novice R experience)
MSExcel - Useful from engineering data analysis perspective
Matlab - cost/ expensive licensing
R - Open source may not have support at the right time when needed for an organization (disclaimer: I have novice R experience)
Python - Open source may not have support at the right time when needed for an organization (disclaimer: I have novice R experience)
- Faster conversion
- easy visual analytics
- Decision making through data became easy
- Visual Analytics
- Data Filtering
- Graph Builder
- Analyzing
- Gage RnR
- Modeling and Simulations
- JSL, Scripting
- Application Builder
No
September 30, 2015
JMP for Process Engineers in Semiconductor Manufaturing
Score 9 out of 10
Vetted Review
Verified User
JMP is a software suite utilized by a handful of engineers in our Semiconductor manufacturing facility. It competes directly with another alternative that many have converted to but I have found JMP to be much more intuitive and friendlier to manipulate and analyze data that I specifically promoted and was granted a user license. We have scores of data on our products that we manufacture, the process is extremely complex and JMP helps to build insight quicker into the problems we face on a daily basis.
- Semiconductor Manufacturing Process data is not always statistically compatible, meaning classical statistical approaches are insufficient to recognize anomalies and discrepant product that could deteriorate customer satisfaction. Process data often requires an experienced eye to observe the historical trends graphically, and I've come to rely heavily on JMP's ability to simplify graphing while also enabling continued manipulation and analysis of the data through the graphical interface. I can quickly highlight discrepant material, modify the display characteristics, redisplay, update, republish....and the cycle continues until I have a clean set of data that is intuitive and insightful.
- My JMP use is "ALWAYS" closely tied with MS Excel because raw data manipulation within JMP is rather cumbersome and clunky. The freedom and flexibility that Excel offers allows me to organize the data easier then when I'm ready I export the data set to JMP for analysis. If I didn't have Excel to assist in building the data set, I don't know how long it would take me to become proficient in JMP. Were JMP to adopt the flexible features that Excel has for housing data and manipulating arrays, then I wouldn't even need to use Excel...the distinct advantage in my mind of JMP over Excel is its analytic and graphic features but JMP's limitation is its rigid requirements for data structure and the high hurdle for common Excel users to work with data in the bounds JMP requires.
- I do many repetitive tasks in JMP, I wish the ability to build a script from my operations was easier and more intuitive. Many users are either unaware of the scripting capabilities or afraid to begin learning about the features because the learning curve is too steep. Some efforts to cater the power of scripting to the less astute audience will build more loyal followers and further widen the customer base.
- As I reflected over the use of JMP from the past several years, I realized that JMP is usually a tool used in tandem with another. The more independent JMP can become to perform more tasks the less I rely on secondary software applications and ultimately the greater cost advantage for the organization because it could reduce licensing fees for software tools that support its operations. Typically I must query a database for the data, manipulate it with MS Excel, then export the queried information into JMP for analysis and display...this is 3 sets of software that I must navigate before I can actually make a decision on what the data is telling me. If JMP could query a database better, allow easier data manipulation then an organization would only need to purchase the JMP license as a complete solution to its organizational needs.
MS Excel is good for manipulating data and providing flexible data arrays, but has serious deficiencies in its graphical displays and analytic capabilities. This is where JMP has its greatest advantages...see some of my previous comments, but I see these software applications working well in tandem and not independently of each other.
- JMP has allowed myself and other engineering peers to solve process problems faster and identify issues that lead to customer dissatisfaction.
Score 9 out of 10
Vetted Review
Verified User
We use JMP as a statistical tool to manage data. It is used across our department and multiple other departments across the university. JMP provides us with a powerful tool to conduct any number of meaningful statistical analyses on any kind of data. We use JMP to provide powerful, interactive visuals for presentation and publishing purposes.
- JMP can handle a seemingly infinite amount of data.
- The statistical tests included in JMP are all-inclusive.
- The learning curve can be very, very steep.
- IBM SPSS,Excel
We actually use both JMP and IBM SPSS, but I think JMP's complexity lends itself to more in-depth statistical analyses. SPSS is designed for that as well, but we tend to use it more for quicker analyses, and we have found that JMP is far more powerful.
60%
6.0
N/A
N/A
N/A
N/A
N/A
N/A
No
- Price
- Product Features
The product features were definitely the most important factor in our decision to use JMP, primarily the statistical analytics tools.
I wouldn't; I think JMP is a perfect fit for what we do.
- High-level inferential statistical analyses
- Predictive Models
- Overall UI/UX
No
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