Overall Satisfaction with JMP
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.
They continue to evolve the program. Offering meaningful new features with nearly every release.
JMP is a powerful data visualization tool. It likewise is a powerful DOE tool. For these applications, I think it is appropriate for all. As you dive deeper into JMP capabilities, I think it becomes more appropriate for user to have at least some formal training in statistics.