Example of a Distribution Overview Plot with right-censored data
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Suppose you work for a company that manufactures engine windings for turbine assemblies. Engine windings may decompose at an unacceptable rate at high temperatures. You want to know, at given high temperatures, at what time do 1% of the engine windings fail. You plan to get this information by using Parametric Distribution Analysis (Right Censoring), but you first want to have a quick look at your data from different perspectives.

First you collect data for times to failure for the engine windings at two temperatures. In the first sample, you test 50 windings at 80° C; in the second sample, you test 40 windings at 100° C. Some of the units drop out of the test due to failures from other causes. These units are considered to be right censored because their failures were not due to the cause of interest. In the Minitab worksheet, you use a column of censoring indicators to designate which times are actual failures (1) and which are censored units removed from the test before failure (0).

1    Open the worksheet RELIABLE.MTW.

2    Choose Stat > Reliability/Survival > Distribution Analysis (Right Censoring) > Distribution Overview Plot.

3    In Variables, enter Temp80 and Temp100.

4    In Distribution, choose Lognormal.

5    Click Censor. Choose Use censoring columns and enter Cens80 and Cens100. Click OK in each dialog box.

Session window output

Graph window output

Interpreting the results

These four plots describe the failure rate of engine windings at two different temperatures. With these plots, you can determine how much more likely it is that engine windings will fail when running at 100° C as opposed to 80° C.