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# std() - Signal Processing

### Syntax

### Example

### Output / Return Value

### Limitations

### Alternatives / See Also

### Reference

S = std(A) returns the standard deviation of the elements of A along the first array dimension whose size does not equal 1. If A is a vector of observations, then the standard deviation is a scalar. If A is a matrix whose columns are random variables and whose rows are observations, then S is a row vector containing the standard deviations corresponding to each column.If A is a multidimensional array, then std(A) operates along the first array dimension whose size does not equal 1, treating the elements as vectors. The size of this dimension becomes 1 while the sizes of all other dimensions remain the same.By default, the standard deviation is normalized by N-1, where N is the number of observations.exampleS = std(A,w) specifies a weighting scheme for any of the previous syntaxes. When w = 0 (default), S is normalized by N-1. When w = 1, S is normalized by the number of observations, N. w also can be a weight vector containing nonnegative elements. In this case, the length of w must equal the length of the dimension over which std is operating. exampleS = std(A,w,dim) returns the standard deviation along dimension dim for any of the previous syntaxes. To maintain the default normalization while specifying the dimension of operation, set w = 0 in the second argument.exampleS = std(___,nanflag) specifies whether to include or omit NaN values from the calculation for any of the previous syntaxes. For example, std(A,'includenan') includes all NaN values in A while std(A,'omitnan') ignores them.

S = std(A) exampleS = std(A,w) exampleS = std(A,w,dim) exampleS = std(___,nanflag) example

Standard Deviation of Matrix ColumnsOpen This ExampleCreate a matrix and compute the standard deviation of each column.A = [4 -5 1; 2 3 5; -9 1 7]; S = std(A) S = 7.0000 4.1633 3.0551 Standard Deviation of 3-D ArrayOpen This ExampleCreate a 3-D array and compute the standard deviation along the first dimension.A(:,:,1) = [2 4; -2 1]; A(:,:,2) = [9 13; -5 7]; A(:,:,3) = [4 4; 8 -3]; S = std(A) S(:,:,1) = 2.8284 2.1213 S(:,:,2) = 9.8995 4.2426 S(:,:,3) = 2.8284 4.9497 Specify Standard Deviation WeightsOpen This ExampleCreate a matrix and compute the standard deviation of each column according to a weight vector w.A = [1 5; 3 7; -9 2]; w = [1 1 0.5]; S = std(A,w) S = 4.4900 1.8330 Standard Deviation Along Matrix RowsOpen This ExampleCreate a matrix and calculate the standard deviation along each row.A = [6 4 23 -3; 9 -10 4 11; 2 8 -5 1]; S = std(A,0,2) S = 11.0303 9.4692 5.3229 Standard Deviation Excluding NaNOpen This ExampleCreate a vector and compute its standard deviation, excluding NaN values.A = [1.77 -0.005 3.98 -2.95 NaN 0.34 NaN 0.19]; S = std(A,'omitnan') S = 2.2797