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Welcome to Statistics and Operations Research

SAS has long developed software for data analysis, econometrics, operations research, and quality improvement. The purpose of these pages is to provide our users with technical information about using this software, including details about software capabilities, examples, papers, e-newsletter, and communities.

Featured News

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Partial Proportional Odds Modeling with the LOGISTIC Procedure

Bob Derr demonstrates what to do when PROC LOGISTIC fits the default proportional odds model to multilevel response data but the proportional odds assumption is rejected. He shows how to use the UNEQUALSLOPES option, introduced in SAS/STAT 12.1, and the EQUALSLOPES option, introduced in SAS/STAT 13.2, to fit a partial proportional odds model to these data. He also discusses graphical and selection methods to determine which covariates have proportional odds and shows how to use likelihood-ratio tests to test for goodness of fit of the models.


The Do Loop

Distinguished Research Statistician Developer Rick Wicklin shows how to create a discrete heat map with PROC SGPLOT, offers tips about anchor points and rotated text in PROC SGPLOT, and demonstrates how to conditionally append observations to a SAS data set. He also presents the essential guide to binning in SAS as well as how to compute and visualize the similarities among recipes using cosine similarity.

Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models

PROC BGLIMM is a new, high-performance, sampling-based procedure that provides full Bayesian inference for generalized linear mixed models, new in SAS/STAT 15.1. This paper provides a brief overview of GLMMs; introduces important features, statements, and options in PROC BGLIMM; and covers high-level simulation and algorithm details of the procedure. It also presents three examples, from simple to complex, to demonstrate how to use PROC BGLIMM.

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tech support tip Nonconvergence in Log-Linked Poisson and Negative Binomial Models

This note discusses and illustrates the conditions under which nonconvergence occurs in log-linked count models.

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