Exploration and Visualization
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.
|Visualize summary statistics with box plot|
|Histogram with a distribution fit|
|Normal probability plot|
|Normal density plot shading between specifications|
|Plot probability distribution object (Since R2022b)|
|Weibull probability plot|
Nonparametric and Empirical Distribution Plots
- 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.