On social media, a SAS user reported that SAS could not compute the modulo of an extremely large integer. In SAS, the modulo operation is usually performed by using the MOD function, which computes the remainder of dividing an integer, N, by another integer, d. (In symbols, the remainder is
A previous article discusses the Gini-Simpson diversity index and how to compute it in SAS. Suppose you have a sample that contains R classes. (Classes are also called groups or categories.) Intuitively, the sample exhibits "high diversity" if the class sizes are approximately equal. The sample shows "low diversity" if
An article by David Corliss in Amstat News (Corliss D. (2025) "Quantifying Diversity: Calculating the Gini-Simpson Diversity Index") discusses a new statistical measure of diversity that was adopted by the US Census Bureau. The statistic is called the Gini-Simpson diversity index. The Census Bureau has published an article about how
When you use the bootstrap method in statistics, the most common resampling method is called case resampling. For data that has N observations, each bootstrap sample is created by sampling with replacement from the N observations (or "cases") in the data. However, if the data set includes categorical variables, it
A recent article describes the main features of simulation by using the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE was created to oversample from a set of rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) provides a way
The Synthetic Minority Over-sampling Technique (SMOTE) was created to address class-imbalance problems in machine learning algorithms. The idea is to oversample from the rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) is essentially a way to simulate
SAS programmers love to brag that the SAS will still run a program they wrote twenty or forty years. This is both a blessing and a curse. It's a blessing because it frees the statistical programmer from needing to revisit and rewrite code that was written long ago. It's a
Isaac Newton had many amazing scientific and mathematical accomplishments. His law of universal gravitation and his creation of calculus are at the top of the list! But in the field of numerical analysis, "Newton's Method" was a groundbreaking advancement for solving for a root of a nonlinear smooth function. The
Newton's method was in the news this week. Not the well-known linear method for finding roots, but a more complicated method for finding minima, sometimes called the method of successive parabolic approximations. Newton's parabolic method was recently improved by modern researchers who extended the method to use higher-dimensional polynomials. The
Nearly every statistician has heard the aphorism, "All models are wrong, but some are useful." The quote is attributed to George Box, an early and influential thinker about statistics. Did George Box actually say this quote? Yes, he did. The first part of the quote ("All models are wrong") appeared
