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# SAS/STAT Software

## Descriptive Statistics

Below are highlights of the capabilities of the SAS/STAT procedures that compute descriptive statistics:

• BOXPLOT Procedure — Creates side-by-side box-and-whiskers 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 goodness-of-fit tests

## BOXPLOT Procedure

The BOXPLOT procedure creates side-by-side box-and-whiskers plots of measurements organized in groups. A box-and-whiskers 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 box-and-whiskers 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 product-moment correlation Spearman rank-order correlation Kendall's tau-b 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 p-values 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 Mantel-Haenszel 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 Mantel-Haenszel 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 stratum-specific rate estimates and their confidence limits of populations stratum-specific 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 quantile-quantile plots (Q-Q plots), probability plots, and probability-probability plots (P-P plots). produces goodness-of-fit 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