Compute the Skewness
Provides three methods for performing skewness test.
a numeric vector containing the values whose skewness is to be computed.
a logical value for
an integer between 1 and 3 for selecting the algorithms for computing the skewness, see details below.
Skewness is a measure of symmetry distribution. Negative skewness (g_1 < 0) indicates that the mean of the data distribution is less than the median, and the data distribution is left-skewed. Positive skewness (g_1 > 0) indicates that the mean of the data values is larger than the median, and the data distribution is right-skewed. Values of g_1 near zero indicate a symmetric distribution.
An object of the same type as
There are several methods to compute skewness, Joanes and Gill (1998) discuss three of the most traditional methods. According to them, type 3 performs better in non-normal population distribution, whereas in normal-like population distribution type 2 fits better the data. Such difference between the two formulae tend to disappear in large samples. Type 1: g_1 = m_3/m_2^(3/2).
Type 2: G_1 = g_1*sqrt(n(n-1))/(n-2).
Type 3: b_1 = m_3/s^3 = g_1 ((n-1)/n)^(3/2).
Daniel Marcelino, email@example.com
Joanes, D. N. and C. A. Gill. (1998) Comparing measures of sample skewness and kurtosis. The Statistician, 47, 183–189.
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