s = std(X)
s = std(X,flag)
s = std(X,flag,dim)
There are two common textbook definitions for the standard deviation s of a data vector X.
and n is the number of elements in the sample. The two forms of the equation differ only in n – 1 versus n in the divisor.
s = std(X), where X is a vector, returns the standard deviation using (1) above. The result s is the square root of an unbiased estimator of the variance of the population from which X is drawn, as long as X consists of independent, identically distributed samples.
If X is a matrix, std(X) returns a row vector containing the standard deviation of the elements of each column of X. If X is a multidimensional array, std(X) is the standard deviation of the elements along the first nonsingleton dimension of X.
The input array, X, must be of type double or single for all syntaxes.
For matrix X
X = 1 5 9 7 15 22 s = std(X,0,1) s = 4.2426 7.0711 9.1924 s = std(X,0,2) s = 4.000 7.5056