chisqTest | R Documentation |

This function computes the chi-square test for normality.

chisqTest(data, alpha = 0.05, j = 1, df = 3)

`data` |
Data of a univariate distribution for which the test statistic is computed (vector) |

`alpha` |
The two-sided decision threshold used for hypothesis-testing |

`j` |
The # hypotheses tested; used to compute a Bonferonni correction, if applicable; should remain at its default if multiple testing is not an issue (scalar) |

`df` |
The degrees of freedom used to test for significance against the sampling distribution (scalar) |

Bins are created by cutting the data to ensure that values within these intervals would be equally probable if data are normal (Moore, 1986). By default, this function assumes that all relevant parameters (mu, sigma) are estimators, fixing the degrees of freedom at df = 3.

An object including the test statistic, p-value, and a significance flag (list)

Moore, D.S., (1986) Tests of the chi-squared type. In: D'agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York.

values <- rnorm(100) x <- chisqTest(data = values)

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