# mean

Mean of probability distribution

## Syntax

``m = mean(pd)``

## Description

example

````m = mean(pd)` returns the mean `m` of the probability distribution `pd`.```

## Examples

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Load the sample data. Create a vector containing the first column of students’ exam grade data.

```load examgrades x = grades(:,1);```

Create a normal distribution object by fitting it to the data.

`pd = fitdist(x,'Normal')`
```pd = NormalDistribution Normal distribution mu = 75.0083 [73.4321, 76.5846] sigma = 8.7202 [7.7391, 9.98843] ```

Compute the mean of the fitted distribution.

`m = mean(pd)`
```m = 75.0083 ```

The mean of the normal distribution is equal to the parameter `mu`.

Create a Weibull probability distribution object.

`pd = makedist('Weibull','a',5,'b',2)`
```pd = WeibullDistribution Weibull distribution A = 5 B = 2 ```

Compute the mean of the distribution.

`mean = mean(pd)`
```mean = 4.4311 ```

Create a uniform distribution object

`pd = makedist('Uniform','lower',-3,'upper',5)`
```pd = UniformDistribution Uniform distribution Lower = -3 Upper = 5 ```

Compute the mean of the distribution.

`m = mean(pd)`
```m = 1 ```

Load the sample data. Create a probability distribution object by fitting a kernel distribution to the miles per gallon (`MPG`) data.

```load carsmall; pd = fitdist(MPG,'Kernel')```
```pd = KernelDistribution Kernel = normal Bandwidth = 4.11428 Support = unbounded ```

Compute the mean of the distribution.

`mean(pd)`
```ans = 23.7181 ```

## Input Arguments

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Probability distribution, specified as a probability distribution object created using one of the following.

Function or AppDescription
`makedist`Create a probability distribution object using specified parameter values.
`fitdist`Fit a probability distribution object to sample data.
Distribution FitterFit a probability distribution to sample data using the interactive Distribution Fitter app and export the fitted object to the workspace.

## Output Arguments

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Mean of the probability distribution, returned as a scalar value.