SAS

Statistics

Description

From traditional analysis of variance and predictive modeling to exact methods and statistical visualization techniques, SAS software provides tools for both specialized and enterprisewide analytical needs.

Key Features

• Analysis of variance. Analysis of variance is a technique for analyzing experimental data. With SAS software, you can perform analysis of variance for balanced or unbalanced designs, multivariate analysis of variance and repeated measurements analysis of variance.
• Regression. SAS 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 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.

More information:
SAS on Hippu
SAS on Vuori