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Original author(s) | Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner |
---|---|
Developer(s) | Minitab, LLC |
Initial release | 1972 |
Stable release | |
Operating system | Windows |
Type | Statistical analysis |
License | Trialware |
Website | minitab.com |
Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in 1972. It began as a light version of OMNITAB 80, a statistical analysis program by NIST. Statistical analysis software such as Minitab automates calculations and the creation of graphs, allowing the user to focus more on the analysis of data and the interpretation of results. It is compatible with other Minitab, LLC software.
History[edit]
Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner in 1972. It began as a light version of OMNITAB 80, a statistical analysis program by NIST, which was conceived by Joseph Hilsenrath in years 1962-1964 as OMNITAB program for IBM 7090.[1][2]The documentation for OMNITAB 80 was last published 1986, and there has been no significant development since then.[3]
Minitab is distributed by Minitab, LLC, a privately owned company headquartered in State College, Pennsylvania.
Interoperability[edit]
Minitab, LLC also produces other software that can be used in conjunction with Minitab;[4] Quality Trainer is an eLearning package that teaches statistical tools and concepts in the context of quality improvement and Companion by Minitab is a tool for managing Six Sigma and Lean manufacturing.
See also[edit]
References[edit]
- ^Peavy, Sally T. (1986). 'OMNITAB 80'. NBS Special Publication. 701: 1–2.
- ^'OMNITAB'. Digital Computer Newsletter :: Digital Computer Newsletter. 16 (1): 4–6. October 1962 – January 1964.CS1 maint: date format (link)
- ^'NIST OMNITAB 80'. Nist.gov. Retrieved 2018-01-30.
- ^'Minitab Products'. Minitab.com. Retrieved 2018-01-30.
Further reading[edit]
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- 'Minitab Statistical Software Features – Minitab.' Software for Statistics, Process Improvement, Six Sigma, Quality – Minitab. N.p., n.d. Web. 11 Apr. 2011.
- Groebner, David F., Mark L. Berenson, David M. Levine, Timothy C. Krehbiel, and Hang Lau. Applied management statistics. Custom ed. Boston, MA: Pearson Custom Publishing/Pearson/Prentice Hall, 2008. Print
- Akers, Michael D (2018), Exploring, Analysing and Interpreting Data with Minitab 18 (1st ed.), United Kingdom, Compass Publishing. ISBN978-1-912009-19-0
- Brook, Quentin (2010). Lean Six Sigma and Minitab: The Complete Toolbox Guide for All Lean Six Sigma Practitioners (3rd ed.). United Kingdom: OPEX Resources Ltd. ISBN978-0-9546813-6-4.
- Bryman, Alan; Cramer, Duncan (1996). Quantitative Data Analysis with Minitab: A Guide for Social Scientists. London: Routledge. ISBN0-415-12323-2.
- Hardwick, Colin (2013). Practical Design of Experiments: DoE Made Easy! (1st ed.). United Kingdom: Liberation Books Ltd. ISBN978-1-4827-6099-6.
- Khan, Rehman M. (2013). Problem solving and data analysis using Minitab : a clear and easy guide to Six Sigma methodology (1st ed.). New York: Wiley. ISBN978-1-118-30757-1.
- Meyer, Ruth K.; David D. Krueger (2004). A Minitab Guide to Statistics (3rd ed.). Upper Saddle River, NJ: Prentice-Hall Publishing. ISBN978-0-13-149272-1.
- Stein, Philip G.; Matey, James R.; Pitts, Karen (1997). 'A Review of Statistical Software for the Apple Macintosh'. The American Statistician. 51 (1): 67–82. doi:10.1080/00031305.1997.10473593.
- Roberts, Dennis. 'Minitab resource website'. Penn State.
External links[edit]
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Use the power curve to assess the appropriate properties for your design.
The power curve represents the relationship between power and maximum difference for each number of replicates. Each symbol on the power curve represents a calculated value based on the properties that you enter. For example, if you enter a number of replicates and a power value, Minitab calculates the corresponding maximum difference and displays the calculated value on the graph.
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Examine the values on the curve to determine the difference between the largest and smallest means for the factor with the most levels that the experiment detects at a certain power value and number of replicates. A power value of 0.9 is usually considered adequate. However, some practitioners consider a power value of 0.8 to be adequate. If a design has low power, you might fail to detect a difference that is practically significant. Increasing the total number of experimental runs increases the power of your design. You want enough experimental runs in your design to achieve adequate power. A design has more power to detect a larger difference than a smaller difference.