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A dot chart or dot plot is a statistical chart consisting of data points plotted on a fairly simple scale, typically using filled in circles. There are two common, yet very different, versions of the dot chart. The first has been used in hand-drawn (pre-computer era) graphs to depict distributions going back to 1884. [1]
Because the whiskers must end at an observed data point, the whisker lengths can look unequal, even though 1.5 IQR is the same for both sides. All other observed data points outside the boundary of the whiskers are plotted as outliers. [10] The outliers can be plotted on the box-plot as a dot, a small circle, a star, etc. (see example below).
use of visuals over the tabular presentation of data; bar charts: to visualize one or more series of data; line charts: to track changes in several dependent data sets over a period of time; sparklines: to show the trend in a single data set; scorecards: to monitor KPIs and trends; use of legends anytime more than one color or shape is present ...
Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
Drain plot : A two-dimensional plot where the data are presented in a hierarchy with multiple levels. The levels are nested in the sense that the pieces in each pie chart add up to 100%. A waterfall or waterdrop metaphor is used to link each layer to the one below visually conveying the hierarchical structure. Drain Plot. [4]
The radar chart is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points.
By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]
In some cases, the software's default settings may produce incorrect results; in others, user modifications to the settings could result in incorrect results. Sample data and results are presented by Wheeler for the explicit purpose of testing SPC software. [7] Performing such software validation is generally a good idea with any SPC software.