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 Fitting 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.

Distribution Fitting | Fit probability distributions to data |

Probability Distribution Function | Interactive density and distribution plots |

`fsurfht` |
Interactive contour plot |

`randtool` |
Interactive random number generation |

`surfht` |
Interactive contour plot |

**Model Data Using the Distribution Fitting App**

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

**Fit a Distribution Using the Distribution Fitting App**

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

**Custom Distributions Using the Distribution Fitting App**

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

Visually determine data distributions.

**Nonparametric and Empirical Probability Distributions**

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

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

Pseudorandom numbers are generated by deterministic algorithms.

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