Summary of Features
Stat Studio provides tools for exploring data, analyzing
distributions, fitting parametric and nonparametric regression models,
and analyzing multivariate relationships. In addition, you can extend
the set of available analyses by writing programs.
To explore data, you can do the following:
- identify observations in plots
- select observations in linked data tables, bar charts, box plots, contour
plots, histograms, line plots, mosaic plots, and two- and
three-dimensional scatter plots
- exclude observations from graphs and analyses
- search, sort, subset, and extract data
- transform variables
- change the color and shape of observation markers based on the
value of a variable
To analyze distributions, you can do the following:
- compute descriptive statistics
- create quantile-quantile plots
- create mosaic plots of cross-classified data
- fit parametric and kernel density estimates for distributions
- detect outliers in contaminated Gaussian data
To fit parametric and nonparametric regression models, you can do the
following:
- smooth two-dimensional data by using polynomials, loess curves, and
thin-plate splines
- add confidence bands for mean and predicted values
- create residual and influence diagnostic plots
- fit robust regression models, and detect outliers and
high-leverage observations
- fit logistic models
- fit the general linear model with a wide variety of response
and link functions
- include classification effects in logistic and generalized
linear models
To analyze multivariate relationships, you can do the following:
- calculate correlation matrices and scatter plot matrices with
confidence ellipses for relationships among pairs of variables
- reduce dimensionality with principal component analysis
- examine relationships between a nominal variable and a set
of interval variables with discriminant analysis
- examine relationships between two sets of interval variables
with canonical correlation analysis
- reduce dimensionality by computing common factors for a set of
interval variables with factor analysis
- reduce dimensionality and graphically examine relationships
between categorical variables in a contingency table with
correspondence analysis
To extend the set of available analyses, you can do the following:
- write, debug, and execute IMLPlus programs in an integrated
development environment
- add legends, curves, maps, or other custom features to
statistical graphics
- create new static graphics
- animate graphics
- execute SAS procedures or DATA steps from within your IMLPlus programs
- develop interactive data analysis programs that use dialog boxes
- call computational routines written in IML, C, FORTRAN,
or Java
Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.