# Documentation

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# Kernel Distribution

Fit a smoothed distribution based on a kernel function and evaluate the distribution

## Functions

 `fitdist` Fit probability distribution object to data `dfittool` Open Distribution Fitting app `ksdensity` Kernel smoothing function estimate for univariate and bivariate data `mvksdensity` Kernel smoothing function estimate for multivariate data
 `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 `pdf` Probability density functions `random` Random numbers `std` Standard deviation of probability distribution `truncate` Truncate probability distribution object `var` Variance of probability distribution

## Using Objects

 `KernelDistribution` Kernel probability distribution object

## Examples and How To

Fit Kernel Distribution Object to Data

Fit a kernel probability distribution object to sample data.

Fit Kernel Distribution Using ksdensity

Generate a kernel probability density estimate from sample data using the `ksdensity` function.

Fit Probability Distribution Objects to Grouped Data

Fit probability distribution objects to grouped sample data, and create a plot to visually compare the pdf of each group.

Fit Distributions to Grouped Data Using ksdensity

Fit kernel distributions to grouped sample data using the `ksdensity` function.

Compare Multiple Distribution Fits

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

Kernel Distribution

A kernel distribution is a nonparametric representation of the probability density function of a random variable.

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.