# distfreeest: Estimating Various Quantities for Distribution-Free Tolerance... In tolerance: Statistical Tolerance Intervals and Regions

## Description

When providing two of the three quantities `n`, `alpha`, and `P`, this function solves for the third quantity in the context of distribution-free tolerance intervals.

## Usage

 `1` ```distfree.est(n = NULL, alpha = NULL, P = NULL, side = 1) ```

## Arguments

 `n` The necessary sample size to cover a proportion `P` of the population with confidence `1-alpha`. Can be a vector. `alpha` 1 minus the confidence level attained when it is desired to cover a proportion `P` of the population and a sample size `n` is provided. Can be a vector. `P` The proportion of the population to be covered with confidence `1-alpha` when a sample size `n` is provided. Can be a vector. `side` Whether a 1-sided or 2-sided tolerance interval is assumed (determined by `side = 1` or `side = 2`, respectively).

## Value

When providing two of the three quantities `n`, `alpha`, and `P`, `distfree.est` returns the third quantity. If more than one value of a certain quantity is specified, then a table will be returned.

## References

Natrella, M. G. (1963), Experimental Statistics: National Bureau of Standards - Handbook No. 91, United States Government Printing Office, Washington, D.C.

## See Also

`nptol.int`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ``` ## Solving for 1 minus the confidence level. distfree.est(n = 59, P = 0.95, side = 1) ## Solving for the sample size. distfree.est(alpha = 0.05, P = 0.95, side = 1) ## Solving for the proportion of the population to cover. distfree.est(n = 59, alpha = 0.05, side = 1) ## Solving for sample sizes for many tolerance specifications. distfree.est(alpha = seq(0.01, 0.05, 0.01), P = seq(0.80, 0.99, 0.01), side = 2) ```

tolerance documentation built on Feb. 6, 2020, 5:08 p.m.