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Select Analyze > Reliability and Survival > Survival.
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Select the check box for Plot Failure instead of Survival.
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Click OK.
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Notice that the probability of failure increases over time. Often the next step is to explore distributional fits, such as a Weibull model. From the red triangle menu, select Weibull Plot and Weibull Fit.
Because the fit is reasonable and the Beta estimate is near 1, you can conclude that this looks like an exponential distribution, which has a constant hazard rate. From the red triangle menu, select Fitted Distribution Plots. Three views of the Weibull fit appear.
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Select Analyze > Reliability and Survival > Survival.
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Click OK.
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From the red triangle menu, select Competing Causes.
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Fit Y by X Plot of Time Cycles by Cause Code shows the Fit Y by X plot of Time Cycles by Cause Code with the Quantiles option in effect. This plot further illustrates how the alphas and betas relate to the failure distribution.
In this example, recall that cause 9 was the source of most of the failures. If cause 9 was corrected, how would that affect the survival due to the remaining causes? Select the Omit Causes option to remove a cause value and recalculate the survival estimates.
Survival Plots with Omitted Causes shows the survival plots with all competing causes and without cause 9. You can see that the survival rate (represented by the dashed line) without cause 9 does not improve much until 2,000 cycles, but then becomes much better and remains improved, even after 10,000 cycles.
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Select Analyze > Reliability and Survival > Survival.
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Select the check box next to Plot Failure instead of Survival.
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Click OK.
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From the red triangle menu, select LogNormal Fit.
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