Statistics Toolbox | ![]() ![]() |
Syntax
Description
computes the variance of the data in X. For vectors, var(x) is the variance of the elements in y = var(X)
x
. For matrices, var(X) is a row vector containing the variance of each column of X
.
normalizes by n-1 where n is the sequence length. For normally distributed data, this makes y = var(x)
var(x)
the minimum variance unbiased estimator MVUE of 2(the second parameter).
normalizes by n and yields the second moment of the sample data about its mean (moment of inertia).y = var(x,1)
computes the variance using the vector of positive weights y = var(X,w)
w
. The number of elements in w
must equal the number of rows in the matrix X
. For vector x
, w
and x
must match in length.
var
supports both common definitions of variance. Let SS be the sum of
the squared deviations of the elements of a vector x
from their mean. Then, var(x)
= SS/(n-1) is the MVUE, and var(x,1)
= SS/n is the maximum likelihood estimator (MLE) of 2.
Examples
![]() | unifstat | weibcdf | ![]() |