For more than 20 years, JMP statistical discovery software from SAS has been the tool of choice for scientists, engineers, social scientists, market research analysts and other data explorers in almost every industry and government sector. JMP links powerful statistics with interactive graphics, in memory and on the desktop. It demystifies data, producing visual representations that reveal context and insight impossible to see in a table of numbers. With JMP, you can be more efficient, tackle difficult statistical problems, communicate findings and bring your data analysis to a whole new level. JMP offers a seamless interface to the unparalleled richness of SAS. You can also work with your other favorite tools, including Microsoft Excel and R, when you make JMP your analytic hub.

This software is also referenced in ORMS.

References in zbMATH (referenced in 33 articles , 1 standard article )

Showing results 1 to 20 of 33.
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  1. Fasshauer, Gregory; McCourt, Michael: Kernel-based approximation methods using MATLAB (2016)
  2. Goos, Peter; Meintrup, David: Statistics with JMP: hypothesis tests, ANOVA and regression (2016)
  3. Myers, Raymond H.; Montgomery, Douglas C.; Anderson-Cook, Christine M.: Response surface methodology. Process and product optimization using designed experiments (2016)
  4. Goos, Peter; Meintrup, David: Statistics with JMP: graphs, descriptive statistics and probability (2015)
  5. Montgomery, Douglas C.; Jennings, Cheryl L.; Kulahci, Murat: Introduction to time series analysis and forecasting (2015)
  6. Geller, Nancy L.; Wu, Colin O.: In memoriam: Gang Zheng (May 6, 1965 -- January 9, 2014) (2014)
  7. Kenett, Ron; Zacks, Shelemyahu; Amberti, Daniele: Modern industrial statistics. With applications in R, MINITAB and JMP (2014)
  8. Pardo, Scott: Equivalence and noninferiority tests for quality, manufacturing and test engineers (2014)
  9. Yihui Xie, Heike Hofmann, Xiaoyue Cheng: Reactive Programming for Interactive Graphics (2014) arXiv
  10. Hocking, Ronald R.: Methods and applications of linear models. Regression and the analysis of variance (2013)
  11. Woodruff, Matthew J.; Reed, Patrick M.; Simpson, Timothy W.: Many objective visual analytics: rethinking the design of complex engineered systems (2013) ioport
  12. Montgomery, Douglas C.; Peck, Elizabeth A.; Vining, G. Geoffrey: Introduction to linear regression analysis. (2012)
  13. Pardoe, Iain: Applied regression modeling (2012)
  14. Silva, Valter Bruno; Rouboa, Abel: Optimizing the DMFC operating conditions using a response surface method (2012)
  15. Tobias, Paul A.; Trindade, David C.: Applied reliability (2012)
  16. Spiegelman, Clifford H.; Park, Eun Sug; Rilett, Laurence R.: Transportation statistics and microsimulation (2011)
  17. Myers, Raymond H.; Montgomery, Douglas C.; Vining, G. Geoffrey; Robinson, Timothy J.: Generalized linear models. With applications in engineering and the sciences. (2010)
  18. Ross, Sheldon M.: Introduction to probability models (2010)
  19. Almiron, Marcelo G.; Almeida, Eliana S.; Miranda, Marcio N.: The reliability of statistical functions in four software packages freely used in numerical computation (2009)
  20. Montgomery, Douglas C.; Jennings, Cheryl L.; Kulahci, Murat: Introduction to time series analysis and forecasting (2008)

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