Products & Solutions / Statistical Analysis

Statistical Analysis with SAS/STAT® Software

Providing the foundation for SAS® Analytics

From traditional statistical analysis of variance and predictive modeling to exact methods and statistical visualization techniques, SAS/STAT software is designed for both specialized and enterprisewide analytical needs. SAS/STAT software provides a complete, comprehensive set of tools that can meet the data analysis needs of the entire organization.  

Benefits

  • Take advantage of all data in order to uncover new business opportunities and increase revenue.
  • Simplify through a single software environment.
  • Advance the scientific discovery process by applying the latest statistical techniques.
  • Achieve corporate and governmental compliance.
  • Gain higher model-scoring performance and faster time to results.

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Features

  • Analysis of Variance
  • Mixed Models
  • Regression
  • Categorical Data Analysis
  • Bayesian Analysis
  • Multivariate Analysis
  • Survival Analysis
  • Psychometric Analysis
  • Cluster Analysis
  • Nonparametric Analysis
  • Survey Data Analysis
  • Multiple Imputation
  • Study Planning
  • Multithreaded Procedures
  • Statistical Graphics
  • Postfitting Inference

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How SAS® Is Different

  • Stability. SAS has more than 37 years of experience developing and delivering advanced statistical analysis software. Statistical procedures in SAS are constantly updated to reflect the latest advances in statistical methodology, thus enabling you to go beyond the basics for more advanced analyses to solve the most complex challenges.
  • Comprehensive. SAS/STAT provides a comprehensive range of statistical methods that are applicable in businesses, research organizations and the public sector. And it works in both specialized and general enterprise application environments.
  • Reliable. SAS statistical software has a proven reputation for delivering superior-quality, reliable results. Technical support for our statistical software at SAS is provided by master's- and doctorate-level statisticians who can help address almost any issue quickly and effectively.

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 analyses. As the value of big data analytics continues to grow, SAS remains a leader in freeing analysts to focus on analysis rather than data issues.
  • Simplify through a single software environment. With SAS, data access, data management, statistical analysis and reporting are all available within a single software environment.
  • Advance the scientific discovery process by applying the latest statistical techniques. Statistical procedures in SAS are constantly updated to reflect the latest advances in statistical methodology, enabling you to go beyond the basics for more advanced analyses. Technical support for our statistical software is provided by experienced master's- and doctorate-level statisticians who provide a level of service rarely found with other software vendors.
  • Achieve corporate and governmental compliance. SAS has more than 37 years of experience developing advanced statistical analysis software and a proven reputation for delivering superior, reliable results. With SAS/STAT software, you can produce code that is easily documented and verified for corporate and governmental compliance issues.
  • Gain higher model-scoring performance and faster time to results. When licensed with SAS Model Manager and SAS Scoring Accelerator, SAS/STAT linear models can be published into database-specific functions and in-database processing. This eliminates the need to move data between SAS and the database for scoring purposes, reducing cost, complexity and latency of the scoring process. Performance of the entire modeling process is improved, enabling faster predictive results and competitive advantage.

Features

Analysis of Variance
  • Balanced and unbalanced designs.
  • Multivariate analysis of variance and repeated measurements.
  • Linear models.
  • More analysis of variance features.
Mixed Models
  • Linear mixed models.
  • Nonlinear mixed models.
  • Generalized linear mixed models.
  • More mixed models features.
Regression
  • Least squares regression with model selection techniques.
  • Diagnostic measures.
  • Robust regression.
  • Loess regression.
  • Nonlinear regression and quadratic response surface models.
  • Partial least squares regression.
  • Quantile regression.
  • Multivariate adaptive regression splines.
  • More regression features.
Categorical Data Analysis
  • Contingency tables and measures of association.
  • Logistic regression and log linear models.
  • Bioassay analysis.
  • Generalized estimating equations.
  • Generalized linear models.
  • Exact methods.
  • Zero-inflated Poisson regression.
  • Zero-inflated negative binomial regression.
  • More categorical data analysis features.
Bayesian Analysis
  • Bayesian modeling and inference for generalized linear models, accelerated failure time models, Cox regression models (piecewise constant baseline hazard) and finite mixture models.
  • General Bayesian statistical models with user-specified priors and likelihood functions.
  • More Bayesian analysis features.
Multivariate Analysis
  • Factor analysis.
  • Principal components.
  • Canonical correlation and discriminant analysis.
  • Path analysis.
  • Structural equation modeling.
  • More multivariate analysis features.
Survival Analysis
  • Nonparametric estimation of survivor function.
  • Accelerated failure time models.
  • Proportional hazards models.
  • Quantile regression models.
  • More survival analysis features.
Psychometric Analysis
  • Multidimensional scaling.
  • Conjoint analysis with variable transformations.
  • Correspondence analysis.
  • More psychometric analysis features.
Cluster Analysis
  • Hierarchical clustering of multivariate data or distance data.
  • Disjoint clustering of large data sets.
  • Nonparametric clustering with hypothesis tests for the number of clusters.
  • More cluster analysis features.
Nonparametric Analysis
  • Nonparametric analysis of variance. Exact probabilities computed for many nonparametric statistics.
  • Kruskal-Wallis, Wilcoxon-Mann-Whitney and Friedman tests.
  • Other rank tests for balanced or unbalanced one-way or two-way designs.
  • More nonparametric analysis features.
Survey Data Analysis
  • Sample selection.
  • Descriptive statistics and t-tests.
  • Linear and logistic regression.
  • Frequency table analysis.
  • Cox proportional hazards model.
  • More survey data analysis features.
Multiple Imputation
  • Regression and propensity score methods for monotone missing patterns.
  • MCMC method for arbitrary missing patterns.
  • Combine results for statistically valid inferences.
  • More missing value imputation features.
Study Planning
  • Power and Sample Size application provides interface for computation of sample sizes and characterization of power for t-tests, confidence intervals, linear models, tests of proportions and rank tests for survival analysis.
  • More study planning features.
Multithreaded Procedures
  • Numerous traditional SAS/STAT procedures are multithreaded.
  • The procedures that make up the SAS High-Performance Statistics product are also available with SAS/STAT software.
Statistical Graphics
  • ODS Statistical Graphics.
  • Scatter plots, diagnostic plots, histograms, box-and-whisker plots and more.
Postfitting Inference
  • A breadth of postfitting analyses available once model is fitted and parameters estimated.
  • Includes model fit information stored from these same procedures to perform additional analyses without refitting the model.

For more information, see the SAS/STAT documentation.

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Contour plot for analysis of spatial data created by KRIGE2D procedure.

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Statistical procedures create graphics as automatically as they create tables.

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System Requirements

Host Platforms/Server Tier
  • HP/UX on Itanium: 11iv3 (11.31)
  • IBM AIX R64 on POWER architecture 7.1
  • IBM z/OS: V1R11 and higher
  • Linux x64 (64-bit): Novell SuSE 11 SP1; Red Hat Enterprise Linux 6.1; Oracle Linux 6.1
  • Microsoft Windows on x64 (64-bit):
    Desktop: Windows 7* x64 SP1; Windows 8** x64
    Server: Windows Server 2008 x64 SP2 Family; Windows Server 2008 R2 SP1 Family; Windows Server 2012 Family
  • Solaris on SPARC: Version 10 Update 9
  • Solaris on x64 (x64-86): Version 10 Update 9; Version 11
Client Tier
  • Microsoft Windows (64-bit): Windows 7* x64 SP1; Windows 8** x64
Middle Tier
  • HP/UX on Itanium
  • IBM AIX on POWER
  • Linux x64 (x86-64)
  • Microsoft Windows x64 (x86-64)
  • Solaris (SPARC and x64)
Supported Web Browsers
  • Internet Explorer 9: Windows 7 (32-bit and x64 32-bit Web browsers)
  • Internet Explorer 10: Windows 7 and Windows 8 (32-bit and x64 32-bit Web browsers)
  • Firefox 6 and up: Windows 7 and Windows 8 (32-bit and x64 32-bit Web browsers); Linux x64: RHEL 6 and SLES 11 (32-bit Web browsers)
  • Chrome 15 and up: Windows 7 and Windows 8 (32-bit and x64 32-bit Web browsers); Linux x64: RHEL 6.1 and SLES 11 SP 1 (32-bit Web browsers)
Required software
  • Base SAS®
RDMS Support
  • PostgreSQL 9.0+ (SAS default)
  • Oracle 11g FP2
  • MySQL 5.0 and 5.5+
  • IBM DB2 FP2 and 10
  • Microsoft SQL Server 2008 and 2010
  • Teradata 13.10 and 13.10.02.01

* NOTE: Windows 7 supported editions are: Professional, Ultimate and Enterprise.
** NOTE: Supported editions include: Windows 8, Windows 8 Pro, Windows 8 Enterprise.

Please consult your local SAS sales representative if you have questions about your platform requirements. Also, for more detailed information, please visit our support site at http://support.sas.com/resources/sysreq/.

Ready to learn more?

Call us at 1-800-727-0025 (US and Canada) or request more information.