Buffon's needle experiment for estimating π is a classical example of using an experiment (or a simulation) to estimate a probability. This example is presented in many books on statistical simulation and is famous enough that Brian Ripley in his book Stochastic Simulation states that the problem is "well known
Hello, 2012! It's a New Year and I'm flushed with ideas for new blog articles. (You can also read about The DO Loop's most popular posts of 2011.) The fundamental purpose of my blog is to present tips and techniques for writing efficient statistical programs in SAS. I pledge to
At the beginning of 2011, I made four New Year's resolutions for my blog. As the year draws to a close, it's time to see how I did: Resolution: 100 blog posts in 2011: Completed. I blew by this goal by posting 165 articles. I recently compiled a list of
The other day someone posted the following question to the SAS-L discussion list: Is there a SAS PROC out there that takes a multi-category discrete variable with character categories and converts it to a single numeric coded variable (not a set of dummy variables) with the character categories assigned as
I have previously written about how to create funnel plots in SAS software. A funnel plot is a way to compare the aggregated performance of many groups without ranking them. The groups can be states, counties, schools, hospitals, doctors, airlines, and so forth. A funnel plot graphs a performance metric
I was at the Wikipedia site the other day, looking up properties of the Chi-square distribution. I noticed that the formula for the median of the chi-square distribution with d degrees of freedom is given as ≈ d(1-2/(9d))3. However, there is no mention of how well this formula approximates the
Last week I showed how to use the UNIQUE-LOC technique to iterate over categories in a SAS/IML program. The observant reader might have noticed that the algorithm, although general, could be made more efficient if the data are sorted by categories. The UNIQUEBY Technique Suppose that you want to compute
Being able to reshape data is a useful skill in data analysis. Most of the time you can use the TRANSPOSE procedure or the SAS DATA step to reshape your data. But the SAS/IML language can be handy, too. I only use PROC TRANSPOSE a few times per year, so
When you analyze data, you will occasionally have to deal with categorical variables. The typical situation is that you want to repeat an analysis or computation for each level (category) of a categorical variable. For example, you might want to analyze males separately from females. Unlike most other SAS procedures,
In SAS/IML 9.22 and beyond, you can call the R statistical programming language from within a SAS/IML program. The syntax is similar to the syntax for calling SAS from SAS/IML: You use a SUBMIT statement, but add the R option: SUBMIT / R. All statements in the program between the
