# Documentation

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# Exploration and Visualization

Plot distribution functions, interactively fit distributions, create plots, and generate random numbers

Interactively fit probability distributions to sample data and export a probability distribution object to the MATLAB® workspace using the Distribution Fitter app. Explore the data range and identify potential outliers using box plots and quantile-quantile plots. Visualize the overall distribution by plotting a histogram with a fitted normal density function line. Assess whether your sample data comes from a population with a particular distribution, such as normal or Weibull, using probability plots. If a parametric distribution cannot adequately describe the sample data, compute and plot the empirical cumulative distribution function based on the sample data. Alternatively, estimate the cdf using a kernel smoothing function.

## Apps

 Distribution Fitter Fit probability distributions to data

## Functions

 `boxplot` Box plot `histfit` Histogram with a distribution fit `normplot` Normal probability plot `normspec` Normal density plot between specifications `probplot` Probability plots `qqplot` Quantile-quantile plot `wblplot` Weibull probability plot
 `cdfplot` Empirical cumulative distribution function plot `ecdf` Empirical cumulative distribution function `ecdfhist` Histogram based on empirical cumulative distribution function `ksdensity` Kernel smoothing function estimate for univariate and bivariate data
 Probability Distribution Function Interactive density and distribution plots `fsurfht` Interactive contour plot `randtool` Interactive random number generation `surfht` Interactive contour plot

## Examples and How To

Model Data Using the Distribution Fitter App

The Distribution Fitter app provides a visual, interactive approach to fitting univariate distributions to data.

Fit a Distribution Using the Distribution Fitter App

Use the Distribution Fitter app to interactively fit a probability distribution to data.

Custom Distributions Using the Distribution Fitter App

Use the Distribution Fitter app to fit distributions not supported by the Statistics and Machine Learning Toolbox™ by defining a custom distribution.

## Concepts

Distribution Plots

Visually determine data distributions.

Nonparametric and Empirical Probability Distributions

Estimate a probability density function or a cumulative distribution function from sample data.

Grouping Variables

Grouping variables are utility variables used to group or categorize observations.

Generating Random Data

Pseudorandom numbers are generated by deterministic algorithms.