PyGSL Reference Manual
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11.2.2 Absolute deviation
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11.2 Available functions
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11.2.4 Autocorrelation
11.2.3 Higher moments (skewness and kurtosis)
skew
(
data
)
Compute the skewness of
data
. The skewness is defined as
(11.7)
where
are the elements of the dataset
data
. The skewness measures the asymmetry of the tails of a distribution.
The function computes the mean and estimated standard deviation of
data
via calls to
mean
and
sd
.
skew_m_sd
(
data, mean, sd
)
Compute the skewness of the dataset
data
using the given values of the mean
mean
and standard deviation varsd
(11.8)
These functions are useful if you have already computed the mean and standard deviation of
data
and want to avoid recomputing them.
kurtosis
(
data
)
Compute the kurtosis of
data
. The kurtosis is defined as
(11.9)
The kurtosis measures how sharply peaked a distribution is, relative to its width. The kurtosis is normalized to zero for a gaussian distribution.
kurtosis_m_sd
(
data, mean, sd
)
This function computes the kurtosis of the dataset
data
using the given values of the mean
mean
and standard deviation
sd
(11.10)
This function is useful if you have already computed the mean and standard deviation of
data
and want to avoid recomputing them.
PyGSL Reference Manual
Previous:
11.2.2 Absolute deviation
Up:
11.2 Available functions
Next:
11.2.4 Autocorrelation
Release 0.9, documentation updated on October, 2008.