# Compute the Skewness

### Description

Provides three methods for performing skewness test.

### Usage

1 |

### Arguments

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

`na.rm` |
a logical value for |

`type` |
an integer between 1 and 3 for selecting the algorithms for computing the skewness, see details below. |

### Details

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.

### Value

An object of the same type as `x`

### Note

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).

### Author(s)

Daniel Marcelino, dmarcelino@live.com

### References

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

### Examples

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