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

loadexamgradesx = grades(:,1);

对象的数据符合正态分布。

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 parametersigma.

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.

loadexamgrades; 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