The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
The other day I was at the grocery store buying a week's worth of groceries. When the cashier, Kurt (not his real name), totaled my bill, he announced, "That'll be ninety-six dollars, even." "Even?" I asked incredulously. "You mean no cents?" "Yup," he replied. "It happens." "Wow," I said, with
Chris started a tradition for SAS Press authors to post a photo of themselves with their new book. Thanks to everyone who helped with the production of Statistical Programming with SAS/IML Software.
Suppose that you compute the coefficients of a polynomial regression by using a certain set of polynomial effects and that I compute coefficients for a different set of polynomial effects. Can I use my coefficients to find your coefficients? The answer is yes, and this article explains how. Standard Polynomial
I just got back from a great conference in San Diego at the 2010 meeting of the Western Users of SAS Software (WUSS) where I gave several presentations on PROC IML and SAS/IML Studio. If you didn't make it to San Diego, you can still read my 2010 paper on
Sampling with replacement is a useful technique for simulations and for resampling from data. Over at the SAS/IML Discussion Forum, there was a recent question about how to use SAS/IML software to sample with replacement from a set of events. I have previously blogged about efficient sampling, but this topic
This post is about an estimate, but not the statistical kind. It also provides yet another example in which the arithmetic mean is not the appropriate measure for a computation. First, some background. Last week I read a blog post by Peter Flom that reminded me that it is wrong