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.
When comparing scores from different subjects, it is often useful to rank the subjects. A rank is the order of a subject when the associated score is listed in ascending order. I've written a few articles about the importance of including confidence intervals when you display rankings, but I haven't
In my article on computing confidence intervals for rankings, I had to generate p random vectors that each contained N random numbers. Each vector was generated from normal distribution with different parameters. This post compares two different ways to generate p vectors that are sampled from independent normal distributions. Sampling
Editor's Note: This article was an April Fool's prank from 2011. The entire article is fake. Today, SAS, the leader in business analytics announces significant changes to two popular SAS blogs, The DO Loop (written by Rick Wicklin) and The SAS Dummy (previously written by Chris Hemedinger). The two blogs
This week, I posted the 100th article to The DO Loop. To celebrate, I'm going to analyze the content of my first 100 articles. In December 2010, I compiled a list of The DO Loop's most-read posts, so I won't repeat that exercise. Instead, I thought it would be interesting
In a previous post, I described how to compute means and standard errors for data that I want to rank. The example data (which are available for download) are mean daily delays for 20 US airlines in 2007. The previous post carried out steps 1 and 2 of the method
When you create a character matrix in SAS/IML software, the initial values determine the number of characters that can fit into any element of the matrix. For example, the following statements define a 1x3 character matrix: proc iml; m = {"Low" "Med" "High"}; After the matrix is defined, at most
I recently posted an article about representing uncertainty in rankings on the blog of the ASA Section for Statistical Programmers and Analysts (SSPA). The posting discusses the importance of including confidence intervals or other indicators of uncertainty when you display rankings. Today's article complements the SSPA post by showing how
I recently blogged about how to eliminate a macro loop in favor of using SAS/IML language statements. The purpose of the program was to extract N 3x3 matrices from a big 3Nx3 matrix. The main portion of my PROC IML program looked something like this: proc iml; ... do i=0
Statistical programmers can be creative and innovative. But when it comes to choosing names of variables, often x1, x2, x3,... works as well as any other choice. In this blog post, I have two tips that are related to constructing variable names of the form x1, x2,..., xn. Both tips
Loony. Zany. Brilliant. Hysterical. Those are some of the adjectives I use to describe The Far Side® cartoons by Gary Larson from the 1980s and early '90s. I recently rediscovered an old book, The Far Side Gallery 2, which collects some of the best of Larson's wonderfully wacky cartoons. Every
