`pearson.test(x, n.classes = ceiling(2 * (n^(2/5))), adjust = TRUE)`

x

a numeric vector of data values. Missing values are allowed.

n.classes

The number of classes. The default is due to Moore (1986).

adjust

logical; if

`TRUE`

(default), the p-value is computed from
a chi-square distribution with `n.classes`

-3 degrees of freedom, otherwise
from a chi-square distribution with `n.classes`

-1 degrees of freedom.-
A list with class “htest” containing the following components:
- statistic
- the value of the Pearson chi-square statistic.
- p.value
- the p-value for the test.
- method
- the character string “Pearson chi-square normality test”.
- data.name
- a character string giving the name(s) of the data.
- n.classes
- the number of classes used for the test.
- df
- the degress of freedom of the chi-square distribution used to compute the p-value.

`n.classes`

-3 degrees of freedom
if `adjust`

is `TRUE`

and from a chi-square distribution with `n.classes`

-1
degrees of freedom otherwise. In both cases this is not (!) the correct p-value,
lying somewhere between the two, see also Moore (1986).
Thode Jr., H.C. (2002): Testing for Normality. Marcel Dekker, New York.

`shapiro.test`

for performing the Shapiro-Wilk test for normality.
`ad.test`

, `cvm.test`

,
`lillie.test`

, `sf.test`

for performing further tests for normality.
`qqnorm`

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

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