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Rick WicklinSeptember 15, 2025 0

Stirling numbers in SAS

In probability and statistics, special numbers are used to compute probabilities by counting the number of ways certain events can occur. The most famous are combinations and permutations. Both are used to count the ways to arrange or select items from a set. If a set contains n elements: A

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Analytics | Learn SAS | Programming Tips

Rick WicklinAugust 25, 2025 1

Implement the generalized extreme value distribution in SAS

SAS supports more than 25 common probability distributions for the PDF, CDF, QUANTILE, and RAND functions. If you need a less-common distribution, you can implement new distributions by using Base SAS (specifically, PROC FCMP) or the SAS/IML language. On the SAS Support Communities, a SAS programmer asked how to implement

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Analytics | Learn SAS | Programming Tips

Rick WicklinAugust 18, 2025 0

Confidence intervals for Cohen's d statistic in SAS

A previous article discusses Cohen's d statistic and how to compute it in SAS. For a two-sample independent design, Cohen's d estimates the standardized mean difference (SMD). Because Cohen's d is a biased statistic, the previous article also computes Hedges' g, which is an unbiased estimate of the SMD. Lastly,

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Analytics | Learn SAS | Programming Tips

Rick WicklinAugust 6, 2025 0

3 ways to estimate parameters when fitting a distribution to data

SAS provides procedures to fit common probability distributions to sample data. You can use PROC UNIVARIATE in Base SAS or PROC SEVERITY in SAS/ETS software to estimate the distribution parameters for approximately 20 common distributions, including normal, lognormal, beta, gamma, and Weibull. Since there are infinitely many distributions, you may

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Analytics | Learn SAS

Rick WicklinJuly 7, 2025 3

Noncentrality parameters and confidence intervals

While researching a topic on effect sizes, I learned about a SAS function that is related to noncentrality parameters. I previously wrote an article about the noncentral t distribution, which is one of several well-known distributions that contains an optional noncentrality parameter. I mentioned that the PDF, CDF, and QUANTILE

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Rick WicklinJune 16, 2025 0

Find the orthant that contains a point

This article shows how to classify a set of high-dimensional data into orthants. An orthant is the d-dimensional generalization of a quadrant. For 2-D Euclidean space, there are four quadrants, often labeled by Roman numerals I-IV. The quadrants are open sets that are defined by the signs of each coordinate

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Analytics | Data Visualization | Programming Tips

Rick WicklinMay 27, 2025 0

Visualize the Gini-Simpson diversity index

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

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Analytics | Learn SAS

Rick WicklinMay 12, 2025 0

Stratified bootstrapping and when to use it

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

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Analytics | Data Visualization | Programming Tips

Rick WicklinApril 21, 2025 1

Updating old SAS programs: A case study with PROC LOESS

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

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Analytics | Programming Tips

Rick WicklinApril 14, 2025 0

Newton's minimization method

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

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