# Skewness: Compute the Skewness In SciencesPo: A Tool Set for Analyzing Political Behavior Data

## Description

Provides three methods for performing skewness test.

## Usage

 `1` ```Skewness(x, na.rm = TRUE, type = 3) ```

## Arguments

 `x` a numeric vector containing the values whose skewness is to be computed. `na.rm` a logical value for `na.rm`, default is `na.rm=TRUE`. `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, [email protected]

## References

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

## Examples

 ```1 2 3 4 5``` ```w <-sample(4,10, TRUE) x <- sample(10, 1000, replace=TRUE, prob=w) Skewness(x, type = 1) Skewness(x, type = 2) Skewness(x) ```

SciencesPo documentation built on May 29, 2017, 9:28 p.m.