The power of a statistical test measures the test's ability to detect a specific alternate hypothesis. For example, educational researchers might want to compare the mean scores of boys and girls on a standardized test. They plan to use the well-known two-sample t test. The null hypothesis is that the
In my article "Simulation in SAS: The slow way or the BY way," I showed how to use BY-group processing rather than a macro loop in order to efficiently analyze simulated data with SAS. In the example, I analyzed the simulated data by using PROC MEANS, and I use the
In my constant effort to keep pace with Chris Hemedinger, I am pleased to announce the availability of my new book, Simulating Data with SAS. 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
A SAS customer asks: How do I use SAS to generate multiple samples of size N from a multivariate normal distribution? Suppose that you want to simulate k samples (each with N observations) from a multivariate normal distribution with a given mean vector and covariance matrix. Because all of the
I have previously written about how to use the "table" distribution to generate random values from a discrete probability distribution. For example, if there are 50 black marbles, 20 red marbles, and 30 white marbles in a box, the following SAS/IML program simulates random draws (with replacement) of 1,000 marbles:
I wanted to write a blog post about the "Table distribution" in SAS. The Table distribution, which is supported by the RAND and the RANDGEN function, enables you to specify the probability of selecting each of k items. Therefore you can use the Table distribution to sample, with replacement, from
A while ago I saw a blog post on how to simulate Bernoulli outcomes when the probability of generating a 1 (success) varies from observation to observation. I've done this often in SAS, both in the DATA step and in the SAS/IML language. For example, when simulating data that satisfied
Last week I wrote about using acceptance-rejection algorithms in vector languages to simulate data. The main point I made is that in a vector language it is efficient to generate many more variates than are needed, with the knowledge that a certain proportion will be rejected. In last week's article,
A few days ago on the SAS/IML Support Community, there was an interesting discussion about how to simulate data from a truncated Poisson distribution. The SAS/IML user wanted to generate values from a Poisson distribution, but discard any zeros that are generated. This kind of simulation is known as an
I recently encountered a SUGI30 paper by Chuck Kincaid entitled "Guidelines for Selecting the Covariance Structure in Mixed Model Analysis." I think Kincaid does a good job of describing some common covariance structures that are used in mixed models. One of the many uses for SAS/IML is as a language
