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|
The Distribution Fitting app provides a visual, interactive approach to fitting univariate distributions to data.
Use the Distribution Fitting app to interactively fit a probability distribution to data.
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.
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.