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 standard deviation of the fitted distribution.

s = std(pd)

s = 8.7202

For a normal distribution, the standard deviation is equal to 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 standard deviation of the distribution.

s = std(pd)

s = 2.3163

创建一个三角形分布object.

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

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

Compute the standard deviation of the distribution.

s = std(pd)

s = 1.2472

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

loadexamgrades; x = grades(:,1);

Create a probability distribution object by fitting a kernel distribution to the data.

pd = fitdist (x,'Kernel')

pd = KernelDistribution Kernel = normal Bandwidth = 3.61677 Support = unbounded

Compute the standard deviation of the fitted distribution.

s = std(pd)

s = 9.4069