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Rick WicklinApril 3, 2023 0

Why use rank correlation?

A previous article discusses rank correlation and lists some advantages of using rank correlation. However, the article does not show examples where an analyst might prefer to report the rank correlation instead of the traditional Pearson product-moment correlation. This article provides three examples where the rank correlation is a better

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Analytics

Rick WicklinMarch 27, 2023 1

Simpson's paradox and confounding variables

A previous article discusses the issue of a confounding variable and uses correlation to give an example. The example shows that the correlation between two variables might be affected by a third variable, which is called a confounding variable. The article mentions that you can use the PARTIAL statement in

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Analytics | Learn SAS | Programming Tips

Rick WicklinMarch 22, 2023 4

Partial correlation: controlling for confounding variables

A data analyst wanted to estimate the correlation between two variables, but he was concerned about the influence of a confounding variable that is correlated with them. The correlation might affect the apparent relationship between main two variables in the study. A common confounding variable is age because young people

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Analytics | Learn SAS | Programming Tips

Rick WicklinMarch 15, 2023 2

Fitting a distribution to an expert's opinion: An application of the metalog distribution

Most homeowners know that large home improvement projects can take longer than you expect. Whether it's remodeling a kitchen, adding a deck, or landscaping a yard, big projects are expensive and subject to a lot of uncertainty. Factors such as weather, the availability of labor, and the supply of materials,

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Analytics | Learn SAS | Programming Tips

Rick WicklinMarch 13, 2023 1

Use the metalog distribution in SAS

A previous article describes the metalog distribution (Keelin, 2016). The metalog distribution is a flexible family of distributions that can model a wide range of shapes for data distributions. The metalog system can model bounded, semibounded, and unbounded continuous distributions. This article shows how to use the metalog distribution in

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Analytics | Programming Tips

Rick WicklinMarch 1, 2023 0

The distribution of the difference between two beta random variables

A SAS programmer wanted to compute the distribution of X-Y, where X and Y are two beta-distributed random variables. Pham-Gia and Turkkan (1993) derive a formula for the PDF of this distribution. Unfortunately, the PDF involves evaluating a two-dimensional generalized hypergeometric function, which is not available in all programming languages.

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Analytics | Programming Tips

Rick WicklinFebruary 27, 2023 0

Compute hypergeometric functions in SAS

In applied mathematics, there is a large collection of "special functions." These function appera in certain applications, often as the solution to a differential equation, but also in the definition of probability distributions. For example, I have written about Bessel functions, the complete and incomplete gamma function, and the complete

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Analytics

Rick WicklinFebruary 22, 2023 3

What is the metalog distribution?

The metalog family of distributions (Keelin, Decision Analysis, 2016) is a flexible family that can model a wide range of continuous univariate data distributions when the data-generating mechanism is unknown. This article provides an overview of the metalog distributions. A subsequent article shows how to download and use a library

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Analytics | Learn SAS | Programming Tips

Rick WicklinFebruary 20, 2023 2

Simulate from a bounded distribution that has a specified mean

A SAS programmer asked for help to simulate data from a distribution that has certain properties. The distribution must be supported on the interval [a, b] and have a specified mean, μ, where a < μ < b. It turns out that there are infinitely many distributions that satisfy these

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Analytics | Programming Tips

Rick WicklinFebruary 13, 2023 0

Estimate the pth root of a Markov transition matrix

You can use a Markov transition matrix to model the transition of an entity between a set of discrete states. A transition matrix is also called a stochastic matrix. A previous article describes how to use transition matrices for stochastic modeling. You can estimate a Markov transition matrix by using

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