There are many ways to visualize the distribution of univariate data. A previous article presents an overview and shows how to use SAS to create histograms, kernel density estimates, box plots, and cumulative distribution plots. This article continues the visualization journey, with an emphasis on dot plots and heat maps.

There are many ways to visualize the distribution of univariate data: histograms, kernel density estimates, box plots, and more. Visualizing a distribution leads to better insights than merely displaying statistics such as the sample mean, standard deviation, and quantiles. In fact, there are many well-known examples of data sets that

I've often wondered about the logic that the SGPLOT procedure in SAS uses to determine whether a set of graphical overlays will receive identical attributes or different attributes. (Recall that color, size, line style, and marker symbol are all examples of attributes.) I know that when you plot grouped data