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Moving from Minitab to JMP: A Transition Guide, Version 2

By Kevin Potcner


Easy to use statistical software has become a essential tool in almost all businesses as more companies seek to have their employees basing decisions on data. Comprehensive statistical analyses, building of sophisticated descriptive and predictive models, and mining data to discover insights are no longer just done by statisticians but by those across many functional areas within an organization. Academia is experiencing a similar need. There has been a substantial increase in the number of programs and courses across a wide range of disciplines exposing students to the tools and techniques of the statistical sciences. Interactive point-and-click software can greatly reduce the barrier for students to develop the necessary skills and enjoy the data exploration process without the need for programming.

This white paper explores two popular software packages – Minitab and JMP. Differences between the two will be illustrated with the goal of easing the transition for experienced Minitab users to move over to JMP. Focus is on the user interface, how tools are accessed, how analyses are performed, and the general workflow that a user follows to analyze data.

Minitab is a graphical user interface-based (GUI) statistical software program first developed in 1978 as a computing tool to support introductory statistics courses. Minitab v19, released in June 2020, is the version featured in this white paper.

JMP and JMP Pro are also GUI-based software, first developed by SAS in 1989 to support engineers, scientists and researchers in applying the tools of the statistical sciences in a nonprogramming, highly interactive environment. JMP and JMP Pro are used across a wide range of industries and academic disciplines. JMP 16 and JMP Pro 16, released in March 2021, are the versions featured in this paper.

As this is a transition guide rather than a comprehensive coverage of their individual capabilities, we are unable to cover all the differences between the two in this white paper. Visit minitab.com and jmp.com to learn more.


About the Author

Kevin Potcner is an Academic Ambassador for JMP. He has over 25 years experience teaching and consulting within industry and academia. In 2000, he started working at Minitab as a full time employee and consultant, before joining JMP in 2018. Kevin has held faculty positions at The Rochester Institute of Technology, University of Florida, University of San Francisco and is currently an instructor for California State University Data Science program. He holds a BS in Printing Sciences and an MS in Applied Statistics both from The Rochester Institute of Technology. Connect with him on LinkedIn.

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