[…] Why would you want to use a regression procedure instead of PROC UNIVARIATE? One reason is that the response variable (failure or survival) might depend on additional covariates. A regression model enables you to account for additional covariates and still understand the underlying distribution of the random errors. A second reason is that the FMM procedure can fit a mixture of distributions. To make sense of the results, you must be able to interpret the regression output in terms of the usual parameters for the probability distributions. In a second article, I show how to fit a mixture of Weibull distributions. …