var

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

load examgrades
x = grades(:,1);

Fit a normal distribution object 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 variance of the fitted distribution.

v = var(pd)

v = 
76.0419

For a normal distribution, the variance is equal to the square of the parameter sigma.

Create a Weibull probability distribution object.

pd = makedist('Weibull','A',5,'B',2)

pd = 
  WeibullDistribution

  Weibull distribution
    A = 5
    B = 2

Compute the variance of the distribution.

v = var(pd)

v = 
5.3650

Create a triangular distribution object.

pd = makedist('Triangular','A',-3,'B',1,'C',3)

pd = 
  TriangularDistribution

A = -3, B = 1, C = 3

Compute the variance of the distribution.

v = var(pd)

v = 
1.5556

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

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

Fit a kernel distribution object to the data.

pd = fitdist(x,'Kernel')

pd = 
  KernelDistribution

    Kernel = normal
    Bandwidth = 3.61677
    Support = unbounded

Compute the variance of the fitted distribution.

v = var(pd)

v = 
88.4893