# Documentation

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# Weibull Distribution

Fit, evaluate, and generate random samples from Weibull distribution

## Functions

 `makedist` Create probability distribution object `fitdist` Fit probability distribution object to data `distributionFitter` Open Distribution Fitter app
 `cdf` Cumulative distribution functions `icdf` Inverse cumulative distribution functions `iqr` Interquartile range `mean` Mean of probability distribution `median` Median of probability distribution `negloglik` Negative log likelihood of probability distribution `paramci` Confidence intervals for probability distribution parameters `pdf` Probability density functions `proflik` Profile likelihood function for probability distribution `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution
 `wblcdf` Weibull cumulative distribution function `wblpdf` Weibull probability density function `wblinv` Weibull inverse cumulative distribution function `wbllike` Weibull negative log-likelihood `wblstat` Weibull mean and variance `wblfit` Weibull parameter estimates `wblrnd` Weibull random numbers

## Using Objects

 `WeibullDistribution` Weibull probability distribution object

## Examples and How To

Fit Probability Distribution Objects to Grouped Data

This example shows how to fit probability distribution objects to grouped sample data, and create a plot to visually compare the pdf of each group.

Compare Multiple Distribution Fits

This example shows how to fit multiple probability distribution objects to the same set of sample data, and obtain a visual comparison of how well each distribution fits the data.

## Concepts

Weibull Distribution

The Weibull pdf is an appropriate analytical tool for modeling the breaking strength of materials. Current usage also includes reliability and lifetime modeling.

Grouping Variables

Grouping variables are utility variables used to group or categorize observations.