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Fit probability distribution object to data

`pd = fitdist(x,distname)`

`pd = fitdist(x,distname,Name,Value)`

```
[pdca,gn,gl]
= fitdist(x,distname,'By',groupvar)
```

```
[pdca,gn,gl]
= fitdist(x,distname,'By',groupvar,Name,Value)
```

creates
the probability distribution object with additional options specified
by one or more name-value pair arguments. For example, you can indicate
censored data or specify control parameters for the iterative fitting
algorithm.`pd`

= fitdist(`x`

,`distname`

,`Name,Value`

)

`[`

creates
probability distribution objects by fitting the distribution specified
by `pdca`

,`gn`

,`gl`

]
= fitdist(`x`

,`distname`

,'By',`groupvar`

)`distname`

to the data in `x`

based
on the grouping variable `groupvar`

. It returns
a cell array of fitted probability distribution objects, `pdca`

,
a cell array of group labels, `gn`

, and a cell
array of grouping variable levels, `gl`

.

The `fitdist`

function fits most distributions
using maximum likelihood estimation. Two exceptions are the normal
and lognormal distributions with uncensored data.

For the uncensored normal distribution, the estimated value of the sigma parameter is the square root of the unbiased estimate of the variance.

For the uncensored lognormal distribution, the estimated value of the sigma parameter is the square root of the unbiased estimate of the variance of the log of the data.

The **Distribution Fitter** app opens a graphical user interface
for you to import data from the workspace and interactively fit a probability distribution to
that data. You can then save the distribution to the workspace as a probability distribution
object. Open the Distribution Fitter app using `distributionFitter`

, or click Distribution Fitter on the Apps tab.

[1] Johnson, N. L., S. Kotz, and N. Balakrishnan. *Continuous
Univariate Distributions*. Vol. 1, Hoboken, NJ: Wiley-Interscience,
1993.

[2] Johnson, N. L., S. Kotz, and N. Balakrishnan. *Continuous
Univariate Distributions*. Vol. 2, Hoboken, NJ: Wiley-Interscience,
1994.

[3] Bowman, A. W., and A. Azzalini. *Applied
Smoothing Techniques for Data Analysis*. New York: Oxford
University Press, 1997.

`distributionFitter`

| `histfit`

| `makedist`

| `mle`

| `paramci`