SAS/IML 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 C, FORTRAN, Java, R, or the SAS/IML language