Many data analysts are familiar with logistic regression, where the response variable, Y, has two observed values, often represented as Y=0 and Y=1. The case Y=0 encodes that an event did not happen. For example, a patient did not experience some disease or did not die. The opposite case (Y=1)
A previous article discusses various ways to overlay a density curve on a histogram in SAS. SAS provides several procedures that handle this task for common univariate probability distributions such as normal, lognormal, and gamma. If you define and use a less common distribution, you can write a GTL template
SAS has several procedures that can fit a probability distribution to data, plot a histogram, and overlay one or more density estimates: PROC UNIVARIATE in Base SAS enables you to overlay parametric density curves from about 20 common continuous probability distributions, such as normal, lognormal, and gamma. It also enables
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
Suppose you measure data weekly. According to the ISO standard, weeks are measured in the range 1-53, where most years have 52 weeks, but occasionally there is a "leap week." (The WEEK function in SAS implements the ISO standard to find the week-of-the-year for any date.) The heat map to
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
I have previously written about the moment-ratio diagram as a graphical tool for modeling univariate distributions and also as a tool for examining the distribution of the skewness and kurtosis statistics for distributions. The simple moment-ratio (M-R) diagram from my book (Wicklin, 2013, Simulating Data with SAS, Chapter 16) is
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
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
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
