Main Content

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
Probability Distribution FunctionInteractive density and distribution plots


expand all

boxplotVisualize summary statistics with box plot
histfitHistogram with a distribution fit
normplotNormal probability plot
normspecNormal density plot shading between specifications
plotPlot probability distribution object (Since R2022b)
probplotProbability plots
qqplotQuantile-quantile plot
wblplotWeibull probability plot
cdfplotEmpirical cumulative distribution function (cdf) plot
ecdfEmpirical cumulative distribution function
ecdfhistHistogram based on empirical cumulative distribution function
ksdensityKernel smoothing function estimate for univariate and bivariate data
fsurfhtInteractive contour plot
randtoolInteractive random number generation
surfhtInteractive contour plot