Computes the skewness.

`skewness(x, na.rm = FALSE, type = 3)`

x

a numeric vector containing the values whose skewness is to be computed.

na.rm

a logical value indicating whether `NA`

values
should be stripped before the computation proceeds.

type

an integer between 1 and 3 selecting one of the algorithms for computing skewness detailed below.

The estimated skewness of `x`

.

If `x`

contains missings and these are not removed, the skewness
is `NA`

.

Otherwise, write \(x_i\) for the non-missing elements of `x`

,
\(n\) for their number, \(\mu\) for their mean, \(s\) for
their standard deviation, and
\(m_r = \sum_i (x_i - \mu)^r / n\)
for the sample moments of order \(r\).

Joanes and Gill (1998) discuss three methods for estimating skewness:

- Type 1:
\(g_1 = m_3 / m_2^{3/2}\). This is the typical definition used in many older textbooks.

- Type 2:
\(G_1 = g_1 \sqrt{n(n-1)} / (n-2)\). Used in SAS and SPSS.

- Type 3:
\(b_1 = m_3 / s^3 = g_1 ((n-1)/n)^{3/2}\). Used in MINITAB and BMDP.

All three skewness measures are unbiased under normality.

D. N. Joanes and C. A. Gill (1998),
Comparing measures of sample skewness and kurtosis.
*The Statistician*, **47**, 183--189.

```
# NOT RUN {
x <- rnorm(100)
skewness(x)
# }
```

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