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.
When data contain outliers, medians estimate the center of the data better than means do. In general, robust estimates of location and sale are preferred over classical moment-based estimates when the data contain outliers or are from a heavy-tailed distribution. Thus, instead of using the mean and standard deviation of
I refer to the SAS documentation every day. Usually, I want information about SAS syntax and the statistical formulas and algorithms for various options and statements. Although I have bookmarked common documentation books and chapters, sometimes it is easier to perform an internet search to find information. I've discovered a
A SAS programmer noticed that there is not a built-in function in the SAS DATA step that computes the product for each row across a specified set of variables. There are built-in functions for various statistics such as the SUM, MAX, MIN, MEAN, and MEDIAN functions. But no DATA step
I previously wrote about how to understand standardized regression coefficients in PROC REG in SAS. You can obtain the standardized estimates by using the STB option on the MODEL statement in PROC REG. Several readers have written to ask whether I could write a similar article about the STDCOEF option
You can standardize a numerical variable by subtracting a location parameter from each observation and then dividing by a scale parameter. Often, the parameters depend on the data that you are standardizing. For example, the most common way to standardize a variable is to subtract the sample mean and divide
Odani's truism is a mathematical result that says that if you want to compare the fractions a/b and c/d, it often is sufficient to compare the sums (a+d) and (b+c) rather than the products a*d and b*c. (All of the integers a, b, c, and d are positive.) If you
Quick! Which fraction is bigger, 40/83 or 27/56? It's not always easy to mentally compare two fractions to determine which is larger. For this example, you can easily see that both fractions are a little less than 1/2, but to compare the numbers you need to compare the products 40*56
A previous article discusses the definition of the Hoeffding D statistic and how to compute it in SAS. The letter D stands for "dependence." Unlike the Pearson correlation, which measures linear relationships, the Hoeffding D statistic tests whether two random variables are independent. Dependent variables have a Hoeffding D statistic
There are many statistics that measure whether two continuous random variables are independent or whether they are related to each other in some way. The most well-known statistic is Pearson's correlation, which is a parametric measure of the linear relationship between two variables. A related measure is Spearman's rank correlation,
SAS/IML programmers often create and call user-defined modules. Recall that a module is a user-defined subroutine or function. A function returns a value; a subroutine can change one or more of its input arguments. I have written a complete guide to understanding SAS/IML modules, which contains many tips for working
