加载样本数据。创建一个包含学生考试成绩数据的第一列的向量。

load考试x = 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分布a = 5 b = 2

计算分布的方差。

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

计算分布的方差。

v = var(pd)

v = 1.5556

加载样本数据。Create a vector containing the first column of students’ exam grade data.

load考试; 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