skewness.norm.test: Skewness test for normality
In normtest: Tests for Normality

Description Usage Arguments Details Value Author(s) References Examples

Performs skewness test for the composite hypothesis of normality, see, e.g., Shapiro, Wilk and Chen (1968).

1	skewness.norm.test(x, nrepl=2000)

`x`	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

The skewness test for normality is based on the sample skewness:

√{b_1} = \frac{\frac{1}{n}∑_{i=1}^n(X_i - \overline{X})^3}{≤ft(\frac{1}{n}∑_{i=1}^n(X_i - \overline{X})^2\right)^{3/2}},

The p-value is computed by Monte Carlo simulation.

A list with class "htest" containing the following components:

`statistic`	the value of the sample skewness.
`p.value`	the p-value for the test.
`method`	the character string "Skewness test for normality".
`data.name`	a character string giving the name(s) of the data.

Ilya Gavrilov and Ruslan Pusev

Shapiro, S. S., Wilk, M. B. and Chen, H. J. (1968): A comparative study of various tests for normality. — Journal of the American Statistical Association, vol. 63, pp. 1343–1372.

1 2	skewness.norm.test(rnorm(100)) skewness.norm.test(abs(runif(100,-2,5)))

	Skewness test for normality

data:  rnorm(100)
T = 0.16845, p-value = 0.4925


	Skewness test for normality

data:  abs(runif(100, -2, 5))
T = 0.26609, p-value = 0.2395