Below are highlights of the capabilities of the SAS/STAT procedures that compute descriptive statistics:
 BOXPLOT Procedure — Creates sidebyside boxandwhiskers plots of measurements organized in groups
 CORR Procedure — Computes correlation coefficients, nonparametric measures of association, and the probabilities associated with these statistics
 STDRATE Procedure — Computes directly and indirectly standardized rates and risks
 UNIVARIATE Procedure — Descriptive statistics based on moments, graphical representations of distributions, and goodnessoffit tests
BOXPLOT Procedure
The BOXPLOT procedure creates sidebyside boxandwhiskers plots of measurements organized in groups.
A boxandwhiskers plot displays the mean, quartiles, and minimum and maximum observations for a group.
The procedure enables you to do the following:
 control the style of the boxandwhiskers plots
 specify one of several methods for calculating quantile statistics (percentiles)
 add block legends and symbol markers to reveal stratification in data
 display vertical and horizontal reference lines

 control axis values and labels
 overlay the box plot with plots of additional variables
 control the layout and appearance of the plot

For further details, see
BOXPLOT Procedure
CORR Procedure
The CORR procedure computes Pearson correlation coefficients, three nonparametric measures of association, and the probabilities
associated with these statistics.
The following are highlights of the CORR procedure's features:
 produces the following correlation statistics:
 Pearson productmoment correlation
 Spearman rankorder correlation
 Kendall's taub coefficient
 Hoeffding's measure of dependence, D
 Pearson, Spearman, and Kendall partial correlation
 polychoric correlation
 polyserial correlation
 computes Cronbach's coefficient alpha for estimating reliability
 saves the correlation statistics in a SAS data set for use with other statistical and reporting procedures

 enables you to use Fisher's z transformation to derive confidence limits and pvalues under a specified
null hypothesis for a Pearson or Spearman correlation
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
CORR Procedure
STDRATE Procedure
The STDRATE procedure computes direct standardized rates and risks for study populations, rate and risk differences, and rate and risk ratios.
The following are highlights of the STDRATE procedure's features:
 computes normal, lognormal, and gamma distribution confidence intervals for directly
standardized rates and risks
 compares two directly standardized rates or risks from different populations
 provides MantelHaenszel estimates for homogeneous effects across strata
 computes indirect standardized rates and risks, including standardized morbidity/mortality ratio(SMR)
 computes normal, lognormal, and Poisson distribution confidence intervals for SMR
 computes the attributable fraction and population attributable fraction by using either indirect
standardization or MantelHaenszel estimation

 automatically produces the following plots by using ODS Graphics:
 proportion of exposed time or sample size for each stratum in the populations
 proportion of exposed time or sample size for each stratum in the populations
 stratumspecific rate estimates and their confidence limits of populations
 stratumspecific risk estimates and their confidence limits of populations
 SMR for each stratum in the populations
 performs BY group processing, which enables you to obtain separate analyses on grouped observations

For further details, see
STDRATE Procedure
UNIVARIATE Procedure
The UNIVARIATE procedure produces descriptive statistics based on moments (including skewness and kurtosis), quantiles or
percentiles (such as the median), frequency tables, and extreme values. The following are highlights of the UNIVARIATE procedure's
features:
 produces histograms that optionally can be fitted with probability density curves for various distributions and with kernel density estimates
 produces cumulative distribution function plots (cdf plots). Optionally, these can be superimposed with probability distribution curves for various distributions.
 produces quantilequantile plots (QQ plots), probability plots, and probabilityprobability plots (PP plots).
 produces goodnessoffit tests for a variety of distributions
 provides the ability to inset summary statistics on plots
 provides the ability to analyze data sets with a frequency variable

 provides the ability to create output data sets containing summary statistics, histogram intervals, and parameters of fitted curves
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 performs weighted analysis
 creates a SAS data set that corresponds to any output table
 produces two kinds of graphical output:
 traditional graphics
 ODS Statistical Graphics

For further details, see
UNIVARIATE Procedure