Description Usage Arguments Details Value References Examples

Compute Pearson's coefficient of skewness.

1 | ```
PearsonSkew(x)
``` |

`x` |
a vector of original observations. |

Pearson's coefficient of skewness is defined as

*γ_1 = \frac{E[(X - μ)^3]}{(σ^3)}*

where *μ = E(X)* and *σ^2 = E[(X - μ)^2]*. The sample version based on a random sample *x_1,x_2,…,x_n* is defined as

*\hat{γ_1} = \frac{∑_{i=1}^n (x_i - \bar{x})^3}{n s^3}*

where *\bar{x}* is the sample mean and *s* is the sample standard deviation of the data, respectively.

`PearsonSkew`

gives the sample Pearson's univariate skewness.

Pearson, K. (1894). Contributions to the mathematical theory of evolution. *Philos. Trans. R. Soc. Lond.* A 185, 71-110.

Pearson, K. (1895). Contributions to the mathematical theory of evolution II: skew variation in homogeneous material. *Philos. Trans. R. Soc. Lond.* A 86, 343-414.

1 2 3 4 5 6 7 8 9 | ```
# Compute Pearson's univariate skewness
set.seed(2019)
x <- rnorm(1000) # Normal Distribution
PearsonSkew(x)
set.seed(2019)
y <- rlnorm(1000, meanlog = 1, sdlog = 0.25) # Log-normal Distribution
PearsonSkew(y)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.