SAS Institute Inc. - SAS/STAT® Software

Why is SAS/STAT software important? Increasingly organizations are turning to statistical analysis to guide decision-making processes. Using optimal statistical techniques can provide new information that improves processes, drives development and revenue, and helps you retain valued and satisfied customers. SAS/STAT software provides a complete, comprehensive set of tools that can meet the data analysis needs of the entire organization.

For whom is SAS/STAT software designed? SAS/STAT software is designed for use by business analysts, statisticians, researchers and engineers.

Key Benefits

• Take advantage of all data in order to uncover new business opportunities and increase revenue. SAS/STAT software is designed to handle large data sets from disparate sources, enabling you to take advantage of all data that is available for your analyses. Analysts are freed to focus on analysis rather than data issues.

   

• Move the scientific discovery process forward by applying the latest statistical techniques. Statistical procedures in SAS are constantly being updated to reflect the latest advances in statistical methodology, thus enabling you to go beyond the basics for more advanced statistical analyses. In addition, technical support is provided by experienced master’s- and doctorate-level statisticians who provide a level of service not often found with other software vendors.

   

• Achieve corporate and governmental compliance. SAS has nearly three decades of experience developing analytic software and a proven reputation for delivering superior, reliable results. With SAS/STAT software, you can produce repeatable code that is easily documented and verified for corporate and governmental compliance issues.

• Analysis of variance. Analysis of variance is a technique for analyzing experimental data. With SAS/STAT software, you can perform analysis of variance for balanced or unbalanced designs, multivariate analysis of variance and repeated measurements analysis of variance.
• Regression. SAS/STAT software’s general regression procedure uses least squares to estimate parameters, includes nine different model selection methods and produces a variety of diagnostic measures. More specialized procedures fit generalized linear models, mixed linear models, nonlinear models and quadratic response surface models.
• Categorical data analysis. In categorical data the outcome of interest reflects categories with data often presented in tabular form, known as contingency tables. With SAS/STAT software, you can investigate the association in a contingency table as well as produce measures that indicate the strength of that relationship.
• Multivariate analysis. Multivariate analyses encompass a variety of methods for modeling data with two or more response variables or for identifying relationships among several variables without designating particular variables as response or explanatory variables. You can use common factor analysis to explain the correlations among a set of variables in terms of a limited number of unobservable, or latent, variables.

• Survival analysis.
• Psychometric analysis.
• Cluster analysis.
• Nonparametric analysis.
• Survey data analysis.
• Multiple imputation for missing values.
• Study planning.