The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
While many applications of Monte Carlo techniques use pseudorandom numbers, some applications that involve integrals are more accurate when you use quasirandom numbers, which, despite their names, are not random but are deterministic sequences of numbers. Many of these sequences are constructed by representing base-10 numbers in a different base.
Many data analysts are familiar with logistic regression, where the response variable, Y, has two observed values, often represented as Y=0 and Y=1. The case Y=0 encodes that an event did not happen. For example, a patient did not experience some disease or did not die. The opposite case (Y=1)
A previous article discusses various ways to overlay a density curve on a histogram in SAS. SAS provides several procedures that handle this task for common univariate probability distributions such as normal, lognormal, and gamma. If you define and use a less common distribution, you can write a GTL template