Description Usage Arguments Details Value References See Also Examples

Tabulated summary of k-factors for tolerance intervals based on normality. The user can specify multiple values for each of the three inputs.

1 2 3 |

`n` |
A vector of (effective) sample sizes. |

`alpha` |
The level chosen such that |

`P` |
The proportion of the population to be covered by this tolerance interval. Can be a vector. |

`side` |
Whether a 1-sided or 2-sided tolerance interval is required (determined by |

`f` |
The number of degrees of freedom associated with calculating the estimate of the population standard deviation.
If |

`method` |
The method for calculating the k-factors. The k-factor for the 1-sided tolerance intervals
is performed exactly and thus is the same for the chosen method. |

`m` |
The maximum number of subintervals to be used in the |

`by.arg` |
How you would like the output organized. If |

The method used for estimating the k-factors is that due to Howe as it is generally viewed as more accurate than the Weissberg-Beatty method.

`K.table`

returns a list with a structure determined by the argument `by.arg`

described above.

Howe, W. G. (1969), Two-Sided Tolerance Limits for Normal Populations - Some Improvements, *Journal of the
American Statistical Association*, **64**, 610–620.

Weissberg, A. and Beatty, G. (1969), Tables of Tolerance Limit Factors for Normal Distributions, *Technometrics*,
**2**, 483–500.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Tables generated for each value of the sample size.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "n")
## Tables generated for each value of the confidence level.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "alpha")
## Tables generated for each value of the coverage proportion.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "P")
``` |

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