mean

Mean of probability distribution

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
makedistCreate a probability distribution object using specified parameter values.
fitdistFit 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.

Extended Capabilities

Introduced in R2013a