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Scilab Help >> Statistics > Dispersion widths > variance


variance (and mean) of a vector or matrix (or hypermatrix) of real or complex numbers


[s [,mc]] = variance(x [,orien [,m]])

[s, mc] = variance(x)
[s, mc] = variance(x, "r"|1 )
[s, mc] = variance(x, "c"|2 )
[s, mc] = variance(x, "*"  , %nan)
[s, mc] = variance(x, "r"|1, %nan)
[s, mc] = variance(x, "c"|2, %nan)
s = variance(x, "*", m)
s = variance(x, "r"|1, m)
s = variance(x, "c"|2, m)



real or complex vector or matrix. A hypermatrix is accepted only for undirectional computations variance(x) or variance(x,"*",m)


the orientation of the computation. Valid values are

  • 1 or "r": result is a row, after a column-wise computation.
  • 2 or "c": result is a column, after a row-wise computation.
  • "*": full undirectional computation (default); explicitly required when m is used.


The known mean of the underlying statistical distribution law (assuming that it is known).

  • "*" mode (default): m must be scalar
  • "r" or 1 mode: m is a row of length size(x,2). The variance along the column #j is computed using m(j) as the mean for the considered column. If m(j) is the same for all columns, it can be provided as a scalar m.
  • "c" or 2 mode: m is a column of length size(x,1). The variance along the row #i is computed using m(i) as the mean for the considered row. If m(i) is the same for all rows, it can be provided as a scalar m.

When m is not provided, the variance is built dividing the quadratic distance of n values to mean(x) (or mean(x,"c") or mean(x,"r")) by n-1 (n being length(x) or size(x,1) or size(x,2)). If the elements of x are mutually independent, the result is then statistically unbiased.

Else, the variance is built dividing the quadratic distance of values to m by the number n of considered values (n being length(x) or size(x,1) or size(x,2)).

If a true value m independent from x elements is used, x and m values are mutually independent, and the result is then unbiased.

When the special value m = %nan is provided, the variance is still normalized by n (not n-1) but is computed using m=mean(x) instead (or m = mean(x,"c") or m = mean(x,"r")). This m does not bring independent information, and yields a statistically biased result.

The variance of unweighted values of x elements. It is a scalar or a column vector or a row vector according to orien.
Scalar or orien-wise mean of x elements (unweighted) (= mean(x,..)), as computed before and used as reference in the variance.


This function computes the variance of the real or complex numbers stored into a vector or matrix x. If x is complex, variance(x,..) = variance(real(x),..) + variance(imag(x),..) is returned.

For a vector, a matrix, or a hypermatrix x, s = variance(x) returns in the scalar s the variance of all the entries of x.

s = variance(x,"c") (or, equivalently, s = variance(x,2)) is the columnwise variance: s is a column vector, with s(j) = variance(x(j,:)).

s = variance(x,"r") (or, equivalently, s = variance(x,1)) is the rowwise variance: s is a row vector, with s(i) = variance(x(:,i)).

The second output argument m is the mean of the input, with respect to the orien parameter.

The variance(x, "*"|"c"|"r", 1) synopsis used only in Scilab 5.4.1 must be replaced with variance(x, "*"|"c"|"r", %nan). variance(x, "*"|"c"|"r", 1) will warn the user until Scilab 6.0. Indeed, 1 will be now considered as m=1. If 1 is the true value provided as m, the warning may be avoided entering 1+%eps instead of 1.


x = [ 0.2113249 0.0002211 0.6653811; 0.7560439 0.4453586 0.6283918 ]
s = variance(x)
s = variance(x, "r")
s = variance(x, "c")

// The underlying statistical distribution and its mean are known:
x = grand(100, 5, "unf", 0, 7);      // Uniform distribution on [0, 7]
// => the true asymptotic mean is (0+7)/2 = 3.5 and variance = (7-0)^2/12
(7-0)^2/12                  // True asymptotic variance
s = variance(x)             // Unbiased (division by n-1).
s = variance(x, "*", 3.5)   // Unbiased (division by n). Always >= variance(x)
s = variance(x, "*", %nan)    // Biased   (division by n). Always <= variance(x)
// Across columns:
s = variance(x, "c")
s = variance(x, "c", 3.5)
s = variance(x, "c", %nan)

// With complex numbers uniformly distributed on [0,1] + [0,1].i:
x = rand(4, 3) + rand(4, 3)*%i
s = variance(x)
s = variance(x, "*", 0.5 + 0.5*%i)
s = variance(x, "*", %nan)
s = variance(x, "r")
s = variance(x, "c")

// With a hypermatrix
x = rand(3, 2, 2)    // Uniform distribution on [0, 1]
s = variance(x)
s = variance(x, "*", 0.5)
s = variance(x, "*", %nan)
// s = variance(x, "r")  // Is not supported for a hypermatrix
// s = variance(x, "c")  // Is not supported for a hypermatrix

See also

  • variancef — variance (and mean) of a vector or matrix of frequency-weighted real or complex numbers
  • mtlb_var — Matlab var emulation function
  • stdev — standard deviation (row or column-wise) of vector/matrix entries


Wonacott, T.H. & Wonacott, R.J.; Introductory Statistics, fifth edition, J.Wiley & Sons, 1990.


  • variance(x, orien, 0|1) removed (as introduced in Scilab 5.4.1)

  • variance(x, orien, m) introduced: the true mean m of the underlying statistical law can be used.

  • variance(x, orien, %nan) introduced: mean(x,..) is used but divided by n values (instead of n-1)

  • [s, mc] = variance(x,..) introduced: the mean mc computed from x is now also returned

  • variance(complexes) fixed. variance(x,"*",1) introduced. Vectorization of the code for directional usages variance(x,"r"|"c"). Full revision of the help page

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