This is documentation for Mathematica 9, which was
based on an earlier version of the Wolfram Language.
MATHEMATICA GUIDE

# 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.

## Learning ResourcesLearning Resources

### How Tos

Related Web Resources

Training CoursesCommunity

## ReferenceReference

### Distribution Shapes

Histogram plot a histogram of data

SmoothHistogram plot a density estimate of data

### 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