Launch the Life Distribution platform by selecting Analyze > Reliability and Survival > Life Distribution.
The time to event (such as the time to failure) or time to censoring. With interval censoring, specify two Y variables, where one Y variable gives the lower limit and the other Y variable gives the upper limit for each unit. For details about censoring, see Event Plot.
Identifies censored observations. In your data table, 1 indicates a censored observation; 0 indicates an uncensored observation.
The column that contains multiple failure causes. This data helps you identify the most influential causes. If a Failure Cause column is selected, then a section is added to the window:
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Distribution specifies the distribution to fit for each failure cause. Select one distribution to fit for all causes; select Individual Best to let the platform automatically choose the best fit for each cause; or select Manual Pick to manually choose the distribution to fit for each failure cause after JMP creates the Life Distribution report. You can also change the distribution fits on the Life Distribution report itself.

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Comparison Criterion is an option only when you choose the Individual Best distribution fit. Select the method by which JMP chooses the best distribution: Akaike Information Criterion Corrected (AICc), Bayesian Information Criterion (BIC), or 2Loglikelihood. You can change the method later in the Model Comparisons report. (See Model Comparisons for details.)

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Censor Indicator in Failure Cause Column identifies the indicator used in the Failure Cause column for observations that did not fail. Select this option and then enter the indicator in the box that appears.

See Meeker and Escobar (1998, chap. 15) for a discussion of multiple failure causes. Omit Competing Causes also illustrates how to analyze multiple causes.
Is an identifier other than the row number. These labels appear on the y axis in the event plot.
Defines the method for computing confidence intervals for the parameters. The default is Wald, but you can select Likelihood instead. For the Custom Estimation report, for Wald type interval methods, the computations for the confidence intervals for the cumulative distribution function (cdf) start with Wald confidence intervals on the standardized variable. Next, the intervals are transformed to the cdf scale. The confidence intervals given in the other graphs and profilers are transformed Wald intervals. Joint confidence intervals for the parameters of a twoparameter distribution are shown in the loglikelihood contour plots. They are based on approximate likelihood ratios for the parameters. For more information, refer to Statistical Details.