A tolerance interval is a statistical interval within which, with some confidence level, a specified proportion of a sampled population falls. "More specifically, a 100×p%/100×(1−α) tolerance interval provides limits within which at least a certain proportion (p) of the population falls with a given level of confidence (1−α)."[1] "A (p, 1−α) tolerance interval (TI) based on a sample is constructed so that it would include at least a proportion p of the sampled population with confidence 1−α; such a TI is usually referred to as p-content − (1−α) coverage TI."[2] "A (p, 1−α) upper tolerance limit (TL) is simply an 1−α upper confidence limit for the 100 p percentile of the population."[2] A tolerance interval can be seen as a statistical version of a probability interval. "In the parameters-known case, a 95% tolerance interval and a 95% prediction interval are the same."[3] If we knew a population's exact parameters, we would be able to compute a range within which a certain proportion of the population falls. For example, if we know a population is normally distributed with mean μ displaystyle mu and standard deviation σ displaystyle sigma , then the interval μ ± 1.96 σ displaystyle mu pm 1.96sigma includes 95% of the population (1.96 is the z-score for 95% coverage of a normally distributed population). However, if we have only a sample from the population, we know only the sample mean μ ^ displaystyle hat mu and sample standard deviation σ ^ displaystyle hat sigma , which are only estimates of the true parameters. In that case, μ ^ ± 1.96 σ ^ displaystyle hat mu pm 1.96 hat sigma will not necessarily include 95% of the population, due to variance in these estimates. A tolerance interval bounds this variance by introducing a confidence level γ displaystyle gamma , which is the confidence with which this interval actually includes the specified proportion of the population. For a normally distributed population, a z-score can be transformed into a "k factor" or tolerance factor[4] for a given γ displaystyle gamma via lookup tables or several approximation formulas.[5] "As the degrees of freedom approach infinity, the prediction and tolerance intervals become equal."[6] Contents 1 Formulas 1.1 Normal case 2 Relation to other intervals 2.1 Examples 3 Calculation 4 See also 5 References 6 Further reading Formulas[edit] This section needs expansion with: mathematical equations. You can help by adding to it. (July 2014) Normal case[edit] Relation to other intervals[edit] Main article: Interval estimation The tolerance interval is less widely known than the confidence interval and prediction interval, a situation some educators have lamented, as it can lead to misuse of the other intervals where a tolerance interval is more appropriate.[7][8] The tolerance interval differs from a confidence interval in that the confidence interval bounds a single-valued population parameter (the mean or the variance, for example) with some confidence, while the tolerance interval bounds the range of data values that includes a specific proportion of the population. Whereas a confidence interval's size is entirely due to sampling error, and will approach a zero-width interval at the true population parameter as sample size increases, a tolerance interval's size is due partly to sampling error and partly to actual variance in the population, and will approach the population's probability interval as sample size increases.[7][8] The tolerance interval is related to a prediction interval in that both put bounds on variation in future samples. The prediction interval only bounds a single future sample, however, whereas a tolerance interval bounds the entire population (equivalently, an arbitrary sequence of future samples). In other words, a prediction interval covers a specified proportion of a population on average, whereas a tolerance interval covers it with a certain confidence level, making the tolerance interval more appropriate if a single interval is intended to bound multiple future samples.[8][9] Examples[edit] [7] gives the following example: So consider once again a proverbial EPA mileage test scenario, in which several nominally identical autos of a particular model are tested to produce mileage figures y 1 , y 2 , . . . , y n displaystyle y_ 1 ,y_ 2 ,...,y_ n . If such data are processed to produce a 95% confidence interval for the mean mileage of the model, it is, for example, possible to use it to project the mean or total gasoline consumption for the manufactured fleet of such autos over their first 5,000 miles of use. Such an interval, would however, not be of much help to a person renting one of these cars and wondering whether the (full) 10-gallon tank of gas will suffice to carry him the 350 miles to his destination. For that job, a prediction interval would be much more useful. (Consider the differing implications of being "95% sure" that μ ≥ 35 displaystyle mu geq 35 as opposed to being "95% sure" that y n + 1 ≥ 35 displaystyle y_ n+1 geq 35 .) But neither a confidence interval for μ displaystyle mu nor a prediction interval for a single additional mileage is exactly what is needed by a design engineer charged with determining how large a gas tank the model really needs to guarantee that 99% of the autos produced will have a 400-mile cruising range. What the engineer really needs is a tolerance interval for a fraction p = .99 displaystyle p=.99 of mileages of such autos. Another example is given by:[9] The air lead levels were collected from n = 15 displaystyle n=15 different areas within the facility. It was noted that the log-transformed lead levels fitted a normal distribution well (that is, the data are from a lognormal distribution. Let μ displaystyle mu and σ 2 displaystyle sigma ^ 2 , respectively, denote the population mean and variance for the log-transformed data. If X displaystyle X denotes the corresponding random variable, we thus have X ∼ N ( μ , σ 2 ) displaystyle Xsim mathcal N (mu ,sigma ^ 2 ) . We note that exp(mu) is the median air lead level. A confidence interval for mu can be constructed the usual way, based on the t-distribution; this in turn will provide a confidence interval for the median air lead level. If X ¯ displaystyle bar X and S denote the sample mean and standard deviation of the log-transformed data for a sample of size n, a 95% confidence interval for mu is given by X ¯ ± t n − 1 , 0.975 S / ( n ) displaystyle bar X pm t_ n-1,0.975 S/ sqrt ( n) , where t m , 1 − α displaystyle t_ m,1-alpha denotes the 1-alpha quantile of a t-distribution with m degrees of freedom. It may also be of interest to derive a 95% upper confidence bound for the median air lead level. Such a bound for mu is given by X ¯ + t n − 1 , 0.95 S / n displaystyle bar X +t_ n-1,0.95 S/ sqrt n . Consequently, a 95% upper confidence bound for the median air lead is given by exp ( X ¯ + t n − 1 , 0.95 S / n ) displaystyle exp left( bar X +t_ n-1,0.95 S/ sqrt n right) . Now suppose we want to predict the air lead level at a particular area within the laboratory. A 95% upper prediction limit for the log-transformed lead level is given by X ¯ + t n − 1 , 0.95 S ( 1 + 1 / n ) displaystyle bar X +t_ n-1,0.95 S sqrt left(1+1/nright) . A two-sided prediction interval can be similarly computed. The meaning and interpretation of these intervals are well known. For example, if the confidence interval X ¯ ± t n − 1 , 0.975 S / n displaystyle bar X pm t_ n-1,0.975 S/ sqrt n is computed repeatedly from independent samples, 95% of the intervals so computed will include the true value of μ displaystyle mu , in the long run. In other words, the interval is meant to provide information concerning the parameter μ displaystyle mu only. A prediction interval has a similar interpretation, and is meant to provide information concerning a single lead level only. Now suppose we want to use the sample to conclude whether or not at least 95% of the population lead levels are below a threshold. The confidence interval and prediction interval cannot answer this question, since the confidence interval is only for the median lead level, and the prediction interval is only for a single lead level. What is required is a tolerance interval; more specifically, an upper tolerance limit. The upper tolerance limit is to be computed subject to the condition that at least 95% of the population lead levels is below the limit, with a certain confidence level, say 99%. Calculation[edit] One-sided normal tolerance intervals have an exact solution in terms of the sample mean and sample variance based on the noncentral t-distribution.[10] Two-sided normal tolerance intervals can be obtained based on the noncentral chi-squared distribution.[10] See also[edit] Engineering tolerance References[edit] ^ D. S. Young (2010), Book Reviews: "Statistical Tolerance Regions:
Theory, Applications, and Computation", TECHNOMETRICS, FEBRUARY 2010,
VOL. 52, NO. 1, pp.143-144.
^ a b Krishnamoorthy, K. and Lian, Xiaodong(2011) 'Closed-form
approximate tolerance intervals for some general linear models and
comparison studies', Journal of Statistical Computation and
Simulation,, First published on: 13 June 2011
doi:10.1080/00949655.2010.545061
^ Thomas P. Ryan (22 June 2007). Modern Engineering Statistics. John
Wiley & Sons. pp. 222–. ISBN 978-0-470-12843-5.
Retrieved 22 February 2013.
^ "Statistical interpretation of data — Part 6: Determination of
statistical tolerance intervals". ISO 16269-6. 2005. p. 64.
Missing or empty url= (help)
^ "Tolerance intervals for a normal distribution". Engineering
Further reading[edit] K. Krishnamoorthy (2009). Statistical Tolerance Regions: Theory, Applications, and Computation. John Wiley and Sons. ISBN 0-470-38026-8. ; Chap. 1, "Preliminaries", is available at http://media.wiley.com/product_data/excerpt/68/04703802/0470380268.pdf Derek S. Young (August 2010). "tolerance: An R Package for Estimating Tolerance Intervals". Journal of Statistical Software. 36 (5): 1–39. ISSN 1548-7660. Retrieved 19 February 2013. ISO 16269-6, Statistical interpretation of data, Part 6: Determination of statistical tolerance intervals, Technical Committee ISO/TC 69, Applications of statistical methods. Available at http://standardsproposals.bsigroup.com/home/getpdf/458 v t e Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L-moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q–Q plot Run chart Scatter plot Stem-and-leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model Lp space Parameter location scale shape Parametric family Likelihood (monotone) Location–scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization Lehmann–Scheffé theorem
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