# shapiro.test: Shapiro-Wilk Normality Test

## Description

Performs the Shapiro-Wilk test of normality.

## Usage

 `1` ```shapiro.test(x) ```

## Arguments

 `x` a numeric vector of data values. Missing values are allowed, but the number of non-missing values must be between 3 and 5000.

## Value

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

 `statistic` the value of the Shapiro-Wilk statistic. `p.value` an approximate p-value for the test. This is said in Royston (1995) to be adequate for `p.value < 0.1`. `method` the character string `"Shapiro-Wilk normality test"`. `data.name` a character string giving the name(s) of the data.

## Source

The algorithm used is a C translation of the Fortran code described in Royston (1995). The calculation of the p value is exact for n = 3, otherwise approximations are used, separately for 4 ≤ n ≤ 11 and n ≥ 12.

## References

Patrick Royston (1982). An extension of Shapiro and Wilk's W test for normality to large samples. Applied Statistics, 31, 115–124. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.2307/2347973")}.

Patrick Royston (1982). Algorithm AS 181: The W test for Normality. Applied Statistics, 31, 176–180. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.2307/2347986")}.

Patrick Royston (1995). Remark AS R94: A remark on Algorithm AS 181: The W test for normality. Applied Statistics, 44, 547–551. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.2307/2986146")}.

`qqnorm` for producing a normal quantile-quantile plot.
 ```1 2``` ```shapiro.test(rnorm(100, mean = 5, sd = 3)) shapiro.test(runif(100, min = 2, max = 4)) ```