Which analysis is commonly performed using the PROC UNIVARIATE procedure in SAS?

The PROC UNIVARIATE procedure in SAS is primarily designed for analyzing the distribution of a single variable. This includes generating descriptive statistics that summarize the characteristics of the data. Key outputs from PROC UNIVARIATE include measures such as mean, median, mode, standard deviation, skewness, and kurtosis, which collectively provide a comprehensive overview of the data's distribution.

Additionally, PROC UNIVARIATE also offers graphical representations, such as histograms and box plots, which further aid in understanding the distribution and identifying any potential outliers in the data. This focus on univariate analysis and descriptive statistics is what makes it particularly useful for exploring and summarizing the properties of individual variables within a dataset.

In contrast, the other analysis types mentioned are not suited for PROC UNIVARIATE. For instance, multivariate regression involves the relationship between multiple variables and is typically analyzed using procedures designed for multivariable analysis. Logistic regression is specifically used for binary outcome variables and utilizes different procedures more suitable for that kind of analysis. Frequency distributions, while they may seem related, are often handled through other commands in SAS that are more specialized for categorical data rather than the comprehensive descriptive analysis provided by PROC UNIVARIATE.