Most statistical programmers have seen a graph of a normal distribution that approximates a binomial distribution. The figure is often accompanied by a statement that gives guidelines for when the approximation is valid. For example, if the binomial distribution describes an experiment with n trials and the probability of success
SAS provides several ways to compute sample quantiles of data. The UNIVARIATE procedure can compute quantiles (also called percentiles), but you can also compute them in the SAS/IML language. Prior to SAS/IML 9.22 (released in 2010) statistical programmers could call a SAS/IML module that computes sample quantiles. With the release
In the United States, this upcoming weekend is when we turn our clocks forward one hour as we adopt daylight saving time. (Some people will also flip their mattresses this weekend!) Daylight saving time (DST) in the US begins on the second Sunday in March and ends on the first
I work with continuous distributions more often than with discrete distributions. Consequently, I am used to thinking of the quantile function as being an inverse cumulative distribution function (CDF). (These functions are described in my article, "Four essential functions for statistical programmers.") For discrete distributions, they are not. To quote
As a SAS developer, I am always looking ahead to the next release of SAS. However, many SAS customer sites migrate to new releases slowly and are just now adopting versions of SAS that were released in 2010 or 2011. Consequently, I want to write a few articles that discuss
I've blogged several times about multivariate normality, including how to generate random values from a multivariate normal distribution. But given a set of multivariate data, how can you determine if it is likely to have come from a multivariate normal distribution? The answer, of course, is to run a goodness-of-fit
Sometimes in matrix computations you need to obtain the values of certain submatrices such as the diagonal elements or the super- or subdiagonal elements. About a year ago, I showed one way to do that: convert subscripts to indices and vice-versa. However, a tip from @RLangTip on Twitter got me
I recently saw a SAS Knowledge Base article called "How to stop processing your code if a certain condition is met." The article discusses the use of the %RETURN macro statement to abort the execution of a SAS program if some condition occurs. The "condition" is usually an error that
I recently blogged about Mahalanobis distance and what it means geometrically. I also previously showed how Mahalanobis distance can be used to compute outliers in multivariate data. But how do you compute Mahalanobis distance in SAS? Computing Mahalanobis distance with built-in SAS procedures and functions There are several ways to
The SAS DATA step supports a special syntax for determining whether a value is contained in an interval: y = (-2 < x < 2); This expression creates an indicator variable with the value 1 if x is in the interval (-2,2) and 0 otherwise. There is not a standard
