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.

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Detecting and Adjusting Structural Breaks in Time Series and Panel Data Using the SSM Procedure

This paper provides an introduction to singular spectrum analysis and demonstrates how to use SAS/ETS software to perform it. To illustrate, monthly data on temperatures in the United States for about the last 100 years are analyzed to discover significant patterns.

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blog The DO Loop

Often your analysis produces prediction regions for classification problems, and it's nice to visualize them. Rick Wicklin describes three methods of doing this. He also discusses how to choose a seed for generating random numbers in SAS. Also, he shows how to perform a robust principal component analysis by using SAS/IML.

Blog Regularization, Regression Penalties, LASSO, Ridging, and Elastic Net

Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. As discussed by Agresti (2013), one such situation occurs when there are a large number of covariates, of which only a small subset are strongly associated with the response, and the sample size is not large. In this case, the maximum likelihood estimates can be inflated. Parameter estimates can also be inflated by collinearity and by separation, in the case of logistic regression, among other reasons. Learn about regularization, regression penalties, and other ways to shrink model parameter estimates.

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Telling the Story of Your Process with Graphical Enhancements of Control Charts

This paper explains how you can use the SHEWHART procedure in SAS/QC software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and-whisker plots, and add information that improves the interpretability of the chart.

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