The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
A previous article shows how to compute various robust estimates of scale in SAS. In that article, I show how to scale these robust estimators so that they become consistent estimators of the standard deviation (σ) when the data are normally distributed. The scaling factor is related to the expected
Years ago, I wrote an article about the "trap and cap" programming technique. The idea is that programmers should "trap" inputs to functions (like SQRT, LOG, and QUANTILE functions) to avoid domain errors. In addition, when visualizing a function's range, you should "cap" the output to improve graphs of functions
In statistics, the normal (Gaussian) distribution serves as a reference for many statistical quantities. For example, a normal distribution has excess kurtosis equal to zero, and other distributions are classified as leptokurtic (heavier-than-normal tails) or platykurtic (lighter-than-normal tails) in comparison. Similarly, the standard deviation of a normal distribution (σ) is