Dot plot (statistics)

[1] The other version is described by William S. Cleveland as an alternative to the bar chart, in which dots are used to depict the quantitative values (e.g. counts) associated with categorical variables.

The algorithm for computing a dot plot is closely related to kernel density estimation.

Choice of dot size is equivalent to choosing the bandwidth for a kernel density estimate.

Compared to (vertical) bar charts and pie charts, Cleveland argues that dot plots allow more accurate interpretation of the graph by readers by making the labels easier to read, reducing non-data ink (or graph clutter) and supporting table look-up.

In other words, it is an extensive RACI table with additional information about the sequence of steps in the process.