This is documentation for Mathematica 8, which was
based on an earlier version of the Wolfram Language.
View current documentation (Version 11.2)
Statistical Visualization
Statistical visualization is used to understand how data is distributed and how that compares to other datasets and distributions. Histograms and smooth histograms both effectively estimate the various distribution functions, either through binning or smoothing. Quantile and related plots compare data to a reference distribution. Box-and-whisker and distribution charts compare a number of data distributions to each other. All the statistical visualization functions provide high levels of automation of aesthetics and statistical computations including automatic bin selection, bandwidth determination, and distribution parameter estimation. All functions also give detailed access to customize both aesthetics and statistical computations.
Distribution Shapes
Histogram plot a histogram of data
SmoothHistogram plot a density estimate of data
BarSpacing  ▪ BarOrigin  ▪ GridLines  ▪ ScalingFunctions  ▪
Distribution Fits
QuantilePlot quantile-quantile plot of data or distributions
ProbabilityPlot probability-probability plot of data or distributions
ProbabilityScalePlot normal plot, Weibull plot, Gumbel plot, etc.
Distribution Comparisons
BoxWhiskerChart box-and-whisker chart of multiple datasets
DistributionChart distribution chart of multiple datasets
BarChart  ▪ PieChart  ▪ BubbleChart  ▪ ...
ListPlot  ▪ ListPlot3D  ▪ ListContourPlot  ▪ ...
Plot  ▪ Plot3D  ▪ DiscretePlot  ▪ DiscretePlot3D  ▪ ContourPlot  ▪ ...