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Weibull++ Life Data Folio

ReliaSoft Plot Utilities

Life Data Analysis Plots

To create a plot in a Weibull++ life data folio, choose Life Data > Analysis > Plot or click the icon on the Main page of the control panel.

Tip: You can add additional plot sheets to the folio by choosing Life Data > Folio Sheets > Insert Additional Plot. The additional sheets can function as overlay plots to display results from multiple data sheets in the current folio on a single plot.

The following is a description of the different types of plots that can be created in a Weibull++ life data folio. (For general information on working with plots, see ReliaSoft Plot Utilities.)

Note: Unlike the probability plots for other distributions, the y-axis in an exponential probability plot always indicates the reliability instead of the unreliability. This tradition arose from the time when probability plotting was performed "by hand." The exponential reliability model starts with R = 1 at T = 0 (or gamma). Thus, if the unreliability were plotted, the axis would start at Q = 1 - R = 0, which is not possible, given that the y-axis scale is logarithmic.

When the parameters have been calculated using rank regression analysis, the regression line is fitted to the data points on the plot in order to obtain the distribution parameters. Therefore, the plot can also be used to compare how different distributions fit a particular data set. The closer the regression line tracks the points on the plots, the better the fit.

In contrast, the maximum likelihood estimation (MLE) method obtains the solution of the line from the likelihood function, not by the plotting positions of the data points. Therefore, the line is not expected to track the points on the plot, and the plot should not be used to evaluate the fit of a distribution when using MLE.

The following examples show the rank regression analysis of single data set using a Weibull distribution and a lognormal distribution. As you can see, the probability plot shows that the Weibull distribution presents the better fit to this particular data set.

 

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