Rick Wicklin
Distinguished Researcher in Computational Statistics

Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.

Learn SAS | Programming Tips
Rick Wicklin 0
The joy of sets

The fundamental operations on sets are union, intersection, and set difference, all of which are supported directly in the SAS IML language. While studying another programming language, I noticed that the language supports an additional operation, namely the symmetric difference between two sets. The language also supports query functions to

Analytics | Learn SAS | Programming Tips
Rick Wicklin 0
Should you use the Wald confidence interval for a binomial proportion?

The "Teacher’s Corner" of The American Statistician enables statisticians to discuss topics that are relevant to teaching and learning statistics. Sometimes, the articles have practical relevance, too. Andersson (2023) "The Wald Confidence Interval for a Binomial p as an Illuminating 'Bad' Example," is intended for professors and masters-level students in

Rick Wicklin 0
Means and medians of subgroups

A journal article listed the mean, median, and size for subgroups of the data, but did not report the overall mean or median. A SAS programmer wondered what, if any, inferences could be made about the overall mean and median for the data. The answer is that you can calculate

Analytics | Learn SAS
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Why use rank correlation?

A previous article discusses rank correlation and lists some advantages of using rank correlation. However, the article does not show examples where an analyst might prefer to report the rank correlation instead of the traditional Pearson product-moment correlation. This article provides three examples where the rank correlation is a better

Rick Wicklin 0
What is the metalog distribution?

The metalog family of distributions (Keelin, Decision Analysis, 2016) is a flexible family that can model a wide range of continuous univariate data distributions when the data-generating mechanism is unknown. This article provides an overview of the metalog distributions. A subsequent article shows how to download and use a library

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