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 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


Distribution Plots

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

Nonparametric and Empirical Distribution Plots

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

Interactive Tools

Probability Distribution Function Interactive density and distribution plots
fsurfht Interactive contour plot
randtool Interactive random number generation
surfht Interactive contour plot
Was this topic helpful?