Documentation

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

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

Topics

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.

Define 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 compare the empirical distribution of sample data with a specified distribution.

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.