What is PROC UNIVARIATE used for?

PROC UNIVARIATE is primarily used for analyzing the distribution of a single variable within a dataset. It provides detailed statistical summaries and visualizations that help in understanding the characteristics of the variable, including measures of central tendency, dispersion, and shape of the distribution.

One of the key features of PROC UNIVARIATE is its ability to detect outliers and extreme values. It generates diagnostic plots, such as box plots and quantile plots, which visually highlight points that fall outside the expected range of values. Furthermore, it calculates statistical measures, including skewness and kurtosis, which provide additional insight into the distribution's shape and potential anomalies.

This focus on distribution analysis and outlier detection sets PROC UNIVARIATE apart from the other options. For instance, while regression analysis involves exploring relationships between dependent and independent variables, generating frequency tables pertains to categorical data representation, and comparing means often relates to performing statistical tests like ANOVA. Each of these tasks is conducted using different procedures specifically designed for their purposes, unlike PROC UNIVARIATE, which emphasizes the univariate aspects of a dataset.