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.
One of the joys of statistics is that you can often use different methods to estimate the same quantity. Last week I described how to compute a parametric density estimate for univariate data, and use the parameters estimates to compute the area under the probability density function (PDF). This article
If you create a scatter plot of highly correlated data, you will see little more than a thin cloud of points. Small-scale relationships in the data might be masked by the correlation. For example, Luke Miller recently posted a scatter plot that compares the body temperature of snails when they
In a previous article, I discussed random jittering as a technique to reduce overplotting in scatter plots. The example used data that are rounded to the nearest unit, although the idea applies equally well to ordinal data in general. The act of jittering (adding random noise to data) is a
Jittering. To a statistician, it is more than what happens when you drink too much coffee. Jittering is the act of adding random noise to data in order to prevent overplotting in statistical graphs. Overplotting can occur when a continuous measurement is rounded to some convenient unit. This has the
The area under a density estimate curve gives information about the probability that an event occurs. The simplest density estimate is a histogram, and last week I described a few ways to compute empirical estimates of probabilities from histograms and from the data themselves, including how to construct the empirical
In my statistical analysis of coupons article, I presented a scatter plot that includes the identity line, y=x. This post describes how to write a general program that uses the SGPLOT procedure in SAS 9.2. By a "general program," I mean that the program produces the result based on the
In Base SAS you can use the DATASETS procedure to determine the SAS data sets in a library, and you can use the DELETE statement to delete data sets. Did you know that you can do the same operations from within the SAS/IML language? The following DATA step creates four
Readers' comments indicate that my previous blog article about computing the area under an ROC curve was helpful. Great! There is another common application of numerical integration: finding the area under a density estimation curve. This article provides an overview of density estimation and computes an empirical cumulative density function.
This is Part 4 of my response to Charlie Huang's interesting article titled Top 10 most powerful functions for PROC SQL. As I did for eaerlier topics, I will examine one of the "powerful" SQL functions that Charlie mentions and show how to do the same computation in SAS/IML software.
A reader commented to me that he wants to use the HISTOGRAM statement of the SGPLOT procedure to overlay two histograms on a single plot. He could do it, but unfortunately SAS was choosing a large bin width for one of the variables and a small bin width for the
