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.
In a previous post I described how to simulate random samples from an urn that contains colored balls. The previous article described the case where the balls can be either of two colors. In that csae, all the distributions are univariate. In this article I examine the case where the
If not for probability theory, urns would appear only in funeral homes and anthologies of British poetry. But in probability and statistics, urns are ever present and contain colored balls. The removal and inspection of colored balls from an urn is a classic way to demonstrate probability, sampling, variation, and
You've had a long day. You've implemented a custom algorithm in the SAS/IML language. But before you go home, you want to generate some matrices and test your program. If you are like me, you prefer a short statement—one line would be best. However, you also want the flexibility to
Occasionally a SAS statistical programmer will ask me, "How can I construct a large correlation matrix?" Often they are simulating data with SAS or developing a matrix algorithm that involves a correlation matrix. Typically they want a correlation matrix that is too large to input by hand, such as a
Dear Rick, I have a data set with 1,001 numerical variables. One variable is the response, the others are explanatory variable. How can I read the 1,000 explanatory variables into an IML matrix without typing every name? That's a good question. You need to be able to perform two sub-tasks:
Last week I discussed ordinary least squares (OLS) regression models and showed how to illustrate the assumptions about the conditional distribution of the response variable. For a single continuous explanatory variable, the illustration is a scatter plot with a regression line and several normal probability distributions along the line. The
I've previously written about how to generate a sequence of evenly spaced points in an interval. Evenly spaced data is useful for scoring a regression model on an interval. In the previous articles the endpoints of the interval were hard-coded. However, it is common to want to evaluate a function
A friend who teaches courses about statistical regression asked me how to create a graph in SAS that illustrates an important concept: the conditional distribution of the response variable. The basic idea is to draw a scatter plot with a regression line, then overlay several probability distributions along the line,
Statistical programmers often have to use the results from one SAS procedure as the input to another SAS procedure. Because ODS enables you to you to create a SAS data set from any ODS table or graph, it is easy to obtain a data set that contains the value of
In my previous blog post, I showed how you can use SAS to program a "weaving" algorithm that takes an image, cuts it into strips, and weaves the strips together to create mathematical art. I used matrices and heat maps for the computations and visualization. At the end of the
