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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

Release 14.2 Release 14.2 is here!

There are new 14.2 releases of SAS/STAT, SAS/ETS, SAS/IML, SAS/OR, and other analytical products accompanying the fourth maintenance release of SAS® 9.4. Here are highlights of these new releases:

  • Propensity score analysis (SAS/STAT)
  • Estimation of causal treatment effect (SAS/STAT)
  • Time-dependent ROC curve analysis for Cox regression (SAS/STAT)
  • Spatial econometric models for cross-sectional data (SAS/ETS)
  • Support for tables and lists (SAS/IML)
  • Performance improvements in the LP, MILP, network, and NLP solvers (SAS/OR)

In addition, you will notice a new framework for the online documentation called the Help Center. Documentation such as the SAS/STAT User's Guide now provides links to example code right in the "Examples" section, and the SAS/STAT documentation also provides easy access to videos that pertain to each chapter.

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The DO Loop

Distinguished Research Statistician Developer Rick Wicklin shows you how to compute the coverage probability of confidence with a simulation approach and how to construct an empty-space plot that shows the distance from every point in a region to the nearest reference site (such as a hospital or store).


Tech Support tip Estimating Relative Risks in a Multinomial Response Model

There are two types of relative risks that might be of interest when you are modeling a multinomial response. You might want to compare two populations with respect to an individual response level probability (P(Y=i|X=j)/P(Y=i|X=k)), or you might want to compare response level probabilities in a given population (P(Y=i|X=j)/P(Y=k|X=j). Both situations are discussed in this usage note. In the multinomial case, relative risk estimates are nonlinear functions of the parameters in a generalized logit model, which can be fit using PROC LOGISTIC and a macro, the CATMOD procedure, or the NLMIXED procedure.

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