Description Usage Arguments Value References See Also Examples

Provides minimum sample sizes for a future sample size when constructing normal tolerance intervals. Various strategies are available for determining the sample size, including strategies that incorporate known specification limits.

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`x` |
A vector of current data that is distributed according to a normal distribution. This is only required for |

`alpha` |
The level chosen such that |

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

`delta` |
The precision measure for the future tolerance interval as specified under the Faulkenberry-Weeks method. |

`P.prime` |
The proportion of the population (greater than |

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

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

`spec` |
A vector of length 2 given known specification limits. These are required when |

`hyper.par` |
Necessary parameter values for the different methods. If |

`method` |
The method for performing the sample size determination. |

`norm.ss`

returns a data frame with items:

`alpha` |
The specified significance level. |

`P` |
The proportion of the population covered by this tolerance interval. |

`delta` |
The user-specified or calculated precision measure. Not returned if |

`P.prime` |
The user-specified or calculated closeness measure. Not returned if |

`n` |
The minimum sample size determined using the conditions specified for this function. |

Faulkenberry, G. D. and Weeks, D. L. (1968), Sample Size Determination for Tolerance Limits, *Technometrics*, **10**, 343–348.

Young, D. S., Gordon, C. M., Zhu, S., and Olin, B. D. (2016), Sample Size Determination Strategies for Normal Tolerance Intervals Using Historical Data, *Quality Engineering*, **28**, 337–351.

`bayesnormtol.int`

, `Normal`

, `normtol.int`

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