Select the method or formula of your choice.

Minitab calculates both parametric and nonparametric tolerance intervals. The calculations for the parametric tolerance intervals assume that the parent distribution of the sample is normally distributed. The calculations for the nonparametric tolerance intervals assume only that the parent distribution is continuous.

Let *X* _{1}, *X* _{2}, ..., *X* _{ n } be the ordered statistics based on random sample of size *n* from some continuous distribution.

Let the distribution function be F(*x*;*θ*) for Ω in some parameter space with dimension greater than or equal to 1.

Let *L* < *U* be two statistics based on the sample such that for any given values *α* and *P*, with 0 < *α* < 1 and 0 < *P* < 1, the following holds for every *θ* in Ω:

Then, the interval [*L*, *U*] is a two-sided tolerance interval with content = *P* x 100% and confidence level = 100(1 – *α*)%. Such an interval can be called a two-sided (1 – *α*, *P*) tolerance interval. For example, if *α* = 0.10 and *P* = 0.85, then the resulting interval is called a two-sided (90% , 0.85) tolerance interval.

If *L* = –∞ and *U* < +∞, then the interval (-∞, *U*] is called a one-sided (1 – *α*, *P*) upper tolerance bound. If *L* > -∞ and *U* = +∞, then the interval [L, +∞) is called a one-sided (1 – *α*, *P*) lower tolerance bound.

Tolerance intervals possess the following interesting and useful properties:

- A one-sided (1 –
*α*,*P*) lower tolerance bound is also a one-sided (*α*, 1 –*P*) upper tolerance bound. - A one-sided (1 –
*α*)100% lower confidence bound of the (1 –*P*)^{th}percentile of the distribution of the data is also a one-sided (1 – α,*P*) lower tolerance bound. Similarly, a one-sided (1 –*α*)100% upper confidence bound of the*P*^{th}percentile of the distribution of the data is also a one-sided (1 –*α*,*P*) upper tolerance bound. - If
*L*and*U*are one-sided (1 –*α*/2 , (1 +*P*)/2) lower and upper tolerance bounds, then [*L*,*U*] is an approximate two-sided (1 –*α*,*P*) tolerance interval. This method may be used in cases where two-sided tolerance intervals cannot be directly obtained. The resulting two-sided tolerance intervals are generally conservative. See Guenther^{1}and Hahn and Meeker^{2}.

- Guenther, W. C. (1972). Tolerance intervals for univariate distributions. Naval Research Logistics, 19: 309–333.
- Hahn G. J. and Meeker W. Q. (1991). Statistical Intervals: A Guide for Practitioners John Wiley & Sons, New York.

Minitab calculates exact (1 –
*α*,
*P*) tolerance intervals, where 1 –
*α* is the confidence level and
*P* is the coverage (the target minimum percentage of population in the
interval). The lower limit,
*L*, and the upper limit,
*U*, for all tolerance intervals are given by the following formulas:

where
*t*_{n-1,1-α}(*δ*) is the 1 –
*α* percentile of the noncentral t-distribution with
*n* – 1 degrees of freedom and noncentrality parameter,
*δ*, which is given by the following formula:

The exact tolerance factor for a two-sided interval is obtained by solving
the following equation for
*k*. See Krishnamoorthy and Mathew^{1}.

where F_{n – 1} is the cumulative distribution function for
a chi-square distribution with
*n* – 1 degrees of freedom, and
*χ*^{2}_{1,p} is the
*P*^{th} percentile of the noncentral chi-square distribution
with 1 degree of freedom and noncentrality parameter
*z*^{2}. The left-hand side of the equation can be rewritten
as:

where:

where Φ(*z*) is the probability density function of the standard
normal distribution. Minitab uses a 36-point Gauss-Legendre quadrature to
evaluate I(*k*,
*n*,
*P*).

Term | Description |
---|---|

1 -
α | the confidence level of the tolerance interval |

P | the coverage of the tolerance interval (the target minimum percentage of population in the interval) |

L | the lower limit of the tolerance interval |

U | the upper limit of the tolerance interval |

the mean of the sample | |

k | the tolerance factor (also called k-factor) |

S | the standard deviation of the sample |

n | the number of observations in the sample |

Z_{P} | the
P^{th} percentile of the standard normal
distribution |

- Krishnamoorthy, K. and Mathew, T. (2009). Statistical Tolerance Regions: Theory, Applications, and Computation. Wiley, Hoboken, NJ.

Minitab calculates exact (1 – *α*, *P*) nonparametric tolerance intervals, where 1 – *α* is the confidence level and *P* is the coverage (the target minimum percentage of population in the interval). The nonparametric method for tolerance intervals is a distribution free method. That is, the nonparametric tolerance interval does not depend on the parent population of your sample. Minitab uses an exact method for both one-sided and two-sided intervals.

Let *X* _{1}, *X* _{2} , ... , *X* _{ n } be the ordered statistics based on a random sample from some continuously distributed population F(*x*;*θ*). Then, based on the findings of Wilks^{1, 2} and Robbins^{3}, it can be shown that:

where *B* denotes the cumulative distribution function of the beta distribution with parameters *a* = *r* and *b* = *n* – *s* + 1. Thus ( *X _{r} * ,

Let *k* be the largest integer that satisfies the following:

where *Y* is a binomial random variable with parameters *n* and 1 – *P*. It can be shown (see Krishnamoorthy and Mathew^{4}) that a one-sided (1 – *α*, *P*) lower tolerance bound is given by *X _{k} *. Similarly, a one-sided (1 –

Let *k* be the smallest integer that satisfies the following:

where *V* is a binomial random variable with parameters *n* and *P*. Thus,

where F_{ V } ^{-1}(x) is the inverse cumulative distribution function of *V*. It can be shown (see Krishnamoorthy and Mathew^{4}) that a two-sided (1 – *α*, *P*) tolerance interval may be given as ( *X _{r} * ,

Term | Description |
---|---|

1 – α | the confidence level of the tolerance interval |

P | the coverage of the tolerance interval (the target minimum percentage of population in the interval) |

n | the number of observations in the sample |

- Wilks, S. S. (1941). Sample size for tolerance limits on a normal distribution. The Annals of Mathematical Statistics, 12, 91–96.
- Wilks, S. S. (1941). Statistical prediction with special reference to the problem of tolerance limits. The Annals of Mathematical Statistics, 13, 400–409.
- Robbins, H. (1944). On distribution-free tolerance limits in random sampling. The Annals of Mathematical Statistics, 15, 214–216.