mean

Mean of probability distribution

Syntax

m = mean(pd)

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

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

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

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

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

Usage notes and limitations:

The input argument pd can be a fitted probability distribution object for beta, exponential, extreme value, lognormal, normal, and Weibull distributions. Create pd by fitting a probability distribution to sample data from the fitdist function. For an example, see Code Generation for Probability Distribution Objects.