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# var() - Signal Processing

### Syntax

### Example

### Output / Return Value

### Limitations

### Alternatives / See Also

### Reference

V = var(A) returns the variance of the elements of A along the first array dimension whose size does not equal 1. If A is a vector of observations, the variance is a scalar.If A is a matrix whose columns are random variables and whose rows are observations, V is a row vector containing the variances corresponding to each column.If A is a multidimensional array, then var(A) treats the values along the first array dimension whose size does not equal 1 as vectors. The size of this dimension becomes 1 while the sizes of all other dimensions remain the same.The variance is normalized by the number of observations-1 by default.If A is a scalar, var(A) returns 0. If A is a 0-by-0 empty array, var(A) returns NaN.exampleV = var(___,w) specifies a weighting scheme for any of the previous syntaxes. When w = 0 (default), V is normalized by the number of observations-1. When w = 1, it is normalized by the number of observations. w can also be a weight vector containing nonnegative elements. In this case, the length of w must equal the length of the dimension over which var is operating. exampleV = var(___,dim) returns the variance along the 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.exampleV = var(___,nanflag) specifies whether to include or omit NaN values from the calculation for any of the previous syntaxes. For example, var(A,'includenan') includes all NaN values in A while var(A,'omitnan') ignores them.

V = var(A) exampleV = var(___,w) exampleV = var(___,dim) exampleV = var(___,nanflag) example

Variance of MatrixOpen This ExampleCreate a matrix and compute its variance.A = [4 -7 3; 1 4 -2; 10 7 9]; var(A) ans = 21.0000 54.3333 30.3333 Variance of ArrayOpen This ExampleCreate a 3-D array and compute its variance.A(:,:,1) = [1 3; 8 4]; A(:,:,2) = [3 -4; 1 2]; var(A) ans(:,:,1) = 24.5000 0.5000 ans(:,:,2) = 2 18 Specify Variance Weight VectorOpen This ExampleCreate a matrix and compute its variance according to a weight vector w.A = [5 -4 6; 2 3 9; -1 1 2]; w = [0.5 0.25 0.25]; var(A,w) ans = 6.1875 9.5000 6.1875 Specify Dimension for VarianceOpen This ExampleCreate a matrix and compute its variance along the first dimension.A = [4 -2 1; 9 5 7]; var(A,0,1) ans = 12.5000 24.5000 18.0000 Compute the variance of A along the second dimension.var(A,0,2) ans = 9 4 Variance Excluding NaNOpen This ExampleCreate a vector and compute its variance, excluding NaN values.A = [1.77 -0.005 3.98 -2.95 NaN 0.34 NaN 0.19]; V = var(A,'omitnan') V = 5.1970