kurtosis.norm.test: Kurtosis test for normality
In normtest: Tests for Normality

Description Usage Arguments Details Value Author(s) References Examples

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

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

`x`	a numeric vector of data values.

nrepl

the number of replications in Monte Carlo simulation.

The kurtosis test for normality is based on the following statistic:

b_2 = \frac{\frac{1}{n}∑_{i=1}^n(X_i - \overline{X})^4}{≤ft(\frac{1}{n}∑_{i=1}^n(X_i - \overline{X})^2\right)^2},

The p-value is computed by Monte Carlo simulation.

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

`statistic`	the value of the test statistic.
`p.value`	the p-value for the test.
`method`	the character string "Kurtosis 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	kurtosis.norm.test(rnorm(100)) kurtosis.norm.test(runif(100,-1,1))

	Kurtosis test for normality

data:  rnorm(100)
T = 2.8356, p-value = 0.7275


	Kurtosis test for normality

data:  runif(100, -1, 1)
T = 1.8465, p-value = 0.016