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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.


Distribution FitterFit probability distributions to data


boxplotBox plot
histfitHistogram with a distribution fit
normplotNormal probability plot
normspecNormal density plot between specifications
probplotProbability plots
qqplotQuantile-quantile plot
wblplotWeibull probability plot
cdfplotEmpirical cumulative distribution function plot
ecdfEmpirical cumulative distribution function
ecdfhistHistogram based on empirical cumulative distribution function
ksdensityKernel smoothing function estimate for univariate and bivariate data
Probability Distribution FunctionInteractive density and distribution plots
fsurfhtInteractive contour plot
randtoolInteractive random number generation
surfhtInteractive 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.


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.

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