The SKEWNESS function computes the statistical skewness of an n-element vector. Skewness determines whether a distribution is symmetric about its maximum. Positive skewness indicates the distribution is skewed to the right, with a longer tail to the right of the distribution maximum. Negative skewness indicates the distribution is skewed to the left, with a longer tail to the left of the distribution maximum.
SKEWNESS calls the IDL function MOMENT.
Examples
x = [65, 63, 67, 64, 68, 62, 70, 66, 68, 67, 69, 71, 66, 65, 70]
result = SKEWNESS(x)
PRINT, 'Skewness = ', result
IDL prints:
Skewness = -0.0942851
Syntax
Result = SKEWNESS( X
[, DIMENSION=value]
[, /DOUBLE] [, /NAN] )
Return Value
Returns the floating point or double precision statistical skewness. If the variance of the vector is zero, the skewness is not defined, and SKEWNESS returns !VALUES.F_NAN as the result.
Arguments
X
A numeric vector.
Keywords
DIMENSION
Set this keyword to a scalar indicating the dimension across which to calculate the skewness. If this keyword is not present or is zero, then the skewness is computed across all dimensions of the input array. If this keyword is present, then the skewness is only calculated only across a single dimension. In this case the result is an array with one less dimension than the input.
DOUBLE
Set this keyword to force computations to be done in double-precision arithmetic.
NAN
Set this keyword to cause the routine to check for occurrences of the IEEE floating-point values NaN or Infinity in the input data. Elements with the value NaN or Infinity are treated as missing data.
Version History
5.1 |
Introduced |
8.0 |
Added DIMENSION keyword
|
See Also
KURTOSIS, MEAN, MEANABSDEV, MOMENT, STDDEV, VARIANCE