You might have attended a workshop by Distinguished Research Statistician Developer Warren Kuhfeld on statistical graphics using ODS. More recent workshops have focused on advanced methods, and Kuhfeld has put the material into book form. These examples illustrate the use of the Graph Template Language, PROC SGPLOT, and SG annotation. They are a great source of information for those users who understand the basics of ODS Graphics and want to increase their skills. For more information, see this blog post.

Proving difference is the point of most statistical testing. In contrast, the point of equivalence and noninferiority tests is to prove that results are substantially the same, or at least not appreciably worse. An equivalence test can show that a new treatment, one that is less expensive or causes fewer side effects, can replace a standard treatment. A noninferiority test can show that a faster manufacturing process creates no more product defects or industrial waste than the standard process. This paper reviews familiar and new methods for planning and analyzing equivalence and noninferiority studies in the POWER, TTEST, and FREQ procedures in SAS/STAT software.

SAS Simulation Studio, a component of SAS/OR^® software for Microsoft Windows environments, provides powerful and versatile capabilities for building, executing, and analyzing discrete event simulation models in a graphical environment. Its object-oriented, drag-and-drop modeling makes building and working with simulation models accessible to novice users, and its broad range of model configuration options and advanced capabilities makes SAS Simulation Studio suitable for sophisticated simulation modeling and analysis. This paper explores some of the modeling methods that have proven useful in practical experience.

In many spatial analysis applications (including crime analysis, epidemiology, ecology, and forestry), spatial point process modeling can help you study the interaction between different events and help you model the process intensity (the rate of event occurrence per unit area). For example, crime analysts might want to estimate where crimes are likely to occur in a city and whether they are associated with locations of public features such as bars and bus stops. Forestry researchers might want to estimate where trees grow best and test for association with covariates such as elevation and gradient. This paper describes the SPP procedure for exploring and modeling spatial point pattern data.

Generalized additive models (GAMs) are very flexible statistical models that are applicable to a number of fields. They can be thought of as extensions to generalized linear models where the response variable depends on predictors in both parametric and nonparametric ways. GAMs can handle response variables from many distributions, such as the normal, binomial, Poisson, gamma, and negative binomial distributions, and a link function transforms the mean of the response variable to the scale of the linear predictor. These models can include the usual linear predictors as well as nonlinear transformations of continuous variables with controlled smoothness. The GAMPL procedure fits generalized additive models based on penalized likelihood. Listen to Senior Manager and PROC GAMPL developer Weijie Cai present an overview of his new software.

When you create a statistical graphic such as a line plot or a scatter plot, it is sometimes important to preserve the aspect ratio of the data. For example, if the ranges of the X and Y variables are equal, it can be useful to display the data in a square region. This is important when you want to visualize the distance between points, as in certain multivariate statistics, or to visually compare variances. This article presents two ways to create ODS statistical graphics in SAS^® in which the scale of the data is preserved by the plot. You can use the ASPECT= option in PROC SGPLOT and the OVERLAYEQUATED layout in the Graph Template Language (GTL).

Also, if you are still catching up on 2015 like me, this post describes articles from 2015 that deserve a second look.

The most popular talks in recent years, sought out by local SAS users groups here and abroad, have been the mixed models presentations by the Tech Support staff. And this should be no surprise, because who is better versed to describe the ins and outs of some of our most popular procedures? Now you too can learn from these experts in two recorded presentations by Principal Technical Support Statistician Jill Tao on troubleshooting convergence and tips and strategies for improving performance.

In the last several years, we have built our video portal to include over 80 presentations (usually 8–15 minutes) on topics ranging from statistics to data mining to operations research. You will find information about new releases, overviews of new focus areas, how-to-get-started talks, discussions about specialized techniques, and more. We are adding to the library all the time, so check back. Instructors may find the videos a useful adjunct to teaching materials.

Please let us know what you think, and feel free to suggest additional topics.

April 18­-21, 2016
Las Vegas, Nevada

In some modeling situations, you might want to restrict the estimates of one or more model parameters. There might be known science that dictates the effect that increasing a predictor has on the response. For example, increasing temperature should cause an increase in a particular chemical response, or increasing a car's weight should decrease its gas mileage. Or you might want to restrict parameters to particular values that have been determined in previous studies or by some underlying theory. This note discusses the various tools that you can use in SAS/STAT procedures to restrict model parameters.