ABSTRACT
In this chapter, we show various methods for visualizing the shapes of distributions. The ability to visualize the distribution shape in exploratory data analysis is important for several reasons. First, we can use it to summarize a data set to better understand general characteristics such as shape, spread, or location. In turn, this information can be used to suggest transformations or probabilistic models for the data. Second, we can use these methods to check model assumptions, such as symmetry, normality, etc. We present several techniques for visualizing univariate and bivariate distributions. These include 1-D and 2-D histograms, boxplots, quantilebased plots, and bagplots.
