JMP

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 58 articles , 1 standard article )

Showing results 1 to 20 of 58.
Sorted by year (citations)

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  1. Cho, Kyung Serk; Tony Ng, Hon Keung: Tolerance intervals in statistical software and robustness under model misspecification (2021)
  2. Kenett, Ron S.; Zacks, Shelemyahu; Amberti, Daniele: Modern industrial statistics. With applications in R, MINITAB and JMP (2021)
  3. Kevin Kunzmann, Maximilian Pilz, Carolin Herrmann, Geraldine Rauch, Meinhard Kieser: The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R (2021) not zbMATH
  4. Montgomery, Douglas C.; Peck, Elizabeth A.; Vining, G. Geoffrey: Introduction to linear regression analysis (2021)
  5. Tyler Morgan-Wall, George Khoury: Optimal Design Generation and Power Evaluation in R: The skpr Package (2021) not zbMATH
  6. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  7. Lin Wang, Wei Zhang, Qizhai Li: AssocTests: An R Package for Genetic Association Studies (2020) not zbMATH
  8. Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert: LocalControl: An R Package for Comparative Safety and Effectiveness Research (2020) not zbMATH
  9. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  10. P.T. Eendebak, A.R. Vazquez: OApackage: A Python package for generation and analysis of orthogonal arrays, optimal designs and conference designs (2019) not zbMATH
  11. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  12. Ulrike Grömping: R Package DoE.base for Factorial Experiments (2018) not zbMATH
  13. Fasshauer, Gregory; McCourt, Michael: Kernel-based approximation methods using MATLAB (2016)
  14. Goos, Peter; Meintrup, David: Statistics with JMP: hypothesis tests, ANOVA and regression (2016)
  15. John Lawson, Cameron Willden: Mixture Experiments in R Using mixexp (2016) not zbMATH
  16. Myers, Raymond H.; Montgomery, Douglas C.; Anderson-Cook, Christine M.: Response surface methodology. Process and product optimization using designed experiments (2016)
  17. Tang, Yongqiang: Notes on Kolassa’s method for estimating the power of Wilcoxon-Mann-Whitney test (2016)
  18. Goos, Peter; Meintrup, David: Statistics with JMP: graphs, descriptive statistics and probability (2015)
  19. Mark A. Wolters: A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems (2015) not zbMATH
  20. Montgomery, Douglas C.; Jennings, Cheryl L.; Kulahci, Murat: Introduction to time series analysis and forecasting (2015)

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