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gail_massari

Community Manager

Joined:

Feb 27, 2013

Reliability Analysis with JMP

After the sticker shock has worn off or the glow of a good deal fades into dullness, customers are left with a product that they expect will meet their needs. A large number of product designers and manufacturers that deliver great products that their customers love use JMP for reliability analysis.


JMP 8 integrates new, valuable reliability capabilities that are interactive and graphical. Many are based on the guidance of a leading expert on statistical methods for reliability, Dr. Bill Meeker, Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. Meeker has written a number of textbooks, including Statistical Methods for Reliability Data, which he co-wrote with Luis A. Escobar.


By the way, if you are in or near Cary on March 20, we invite you to hear Bill Meeker talk about reliability.


So what’s new for reliability in JMP? Chris Gotwalt, JMP Software Development Manager and Senior Research Statistician, described and walked me through some of the new features.


Two New Platforms Predict Events


Life Distribution and Fit Life By X are two new platforms. Users start by fitting multiple time-to-event distributions to their data. Plots of the distributions are overlaid with a plot of the data, all in the same window. Then, with a few mouse clicks, users visually and statistically examine and compare the distributions to determine which ones offer good explanations for the data. JMP makes this easy by overlaying all the distributions onto one graph.


After choosing a model, JMP profilers determine the probability that an event will occur. Based on criteria specified by the users, the profilers extract from the model relevant quantities of interest, such as estimates of median time to failure or the probability that an event will have happened by a certain time.


The new platforms are analogous to the Distribution and Fit Y By X platforms in JMP, in that Life Distribution provides graphical and analytical tools for examining a single variable, while Fit Life By X allows the user to explore the relationship between a response and an explanatory variable. Fit Life By X offers accelerated life testing. Both new platforms provide the censoring that is typical of reliability data.


New ‘Distribution Dredger’ Recommends a Distribution


For users who want guidance selecting the distribution that best fits the data, a great new interactive interface fits all distributions behind the scenes, and then it suggests a distribution by identifying the distribution with the best AICc score.


Here’s an Example


Using fan.jmp data found in the JMP Sample Data, we used Life Distribution to create a model, for which we compared four different distributions on a lognormal scale simply by checking boxes next to the distribution types and selecting a radio button for the probability scale type.


JMP displayed a graph that linearized the data. The graph is analogous to a probability paper plot. JMP also displayed the AICc values and profilers for each distribution.






At this point, we looked at graphs and AICc values to determine which distribution explains the data best. In this case, Lognormal looked best.


We didn’t stop there, however. We used the Fit All Distributions option, found under the red triangle menu, to fit all the distributions automatically. JMP identified Threshold Loglogistic as the best-fitting distribution because it had the lowest AICc value.




16 Interactive Distributions in All


The JMP 8 Life Distribution platform includes 14 new distributions plus two distributions that were formerly available elsewhere in JMP and are now available in the survival reliability context. The new distributions are:


• Frechet

• Log Logistic

• Smallest Extreme Value

• Largest Extreme Value

• Logistic

• Threshold Frechet

• Threshold Lognormal

• Threshold Loglogistic

• Zero Inflated Weibull

• Zero Inflated Frechet

• Zero Inflated Lognormal

• Zero Inflated Loglogistic

• Generalized Gamma

• Log Generalized Gamma


The two distributions that were formerly available elsewhere in JMP but are now available in the survival reliability context are:


• Threshold Weibull

• Normal


Want to see for yourself? View a video that includes more examples.


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