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std
Standard deviation
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:
where
and n is the number of elements in the sample. The two forms of the equation differ only in 
versus 
in the divisor.
s = std(X),
where X is a vector, returns the standard deviation using (1) above. If X is a random sample of data from a normal distribution, s2 is the best unbiased estimate of its variance.
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 th elements along the first nonsingleton dimension of X.
s = std(X,flag)
for flag = 0, is the same as std(X). For flag = 1, std(X,1) returns the standard deviation using (2) above, producing the second moment of the sample about its mean.
s = std(X,flag,dim)
computes the standard deviations along the dimension of X specified by scalar dim.
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
corrcoef, cov, mean, median