# ISO 16269-6 PDF

Partie 6: Détermination des intervalles statistiques de dispersion. STANDARD. ISO. Second edition. Reference number. STANDARD. ISO. Second edition. Reference number. ISO (E). This is a free 6 page sample. Access the full version online. Purchase your copy of BS ISO as a PDF download or hard copy directly from the official BSI Shop. All BSI British Standards. Author: Zolokasa Nim Country: Pacific Islands Language: English (Spanish) Genre: Sex Published (Last): 14 June 2005 Pages: 428 PDF File Size: 18.89 Mb ePub File Size: 5.46 Mb ISBN: 980-5-41071-988-5 Downloads: 69097 Price: Free* [*Free Regsitration Required] Uploader: Kazihn A tolerance interval is a statistical interval within which, with some confidence level, a specified proportion of a sampled population falls.

A tolerance 16296-6 can be seen as a statistical version of a probability interval. The tolerance interval is less widely known than the confidence interval and prediction intervala situation some educators have lamented, as it can lead to misuse of the other intervals where a tolerance interval is more appropriate.

### GSO ISO – Standards Store – GCC Standardization Organization

The tolerance interval differs from a confidence interval in that the confidence interval bounds a single-valued population parameter the mean or the variancefor 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 errorand 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.

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.

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In other words, a prediction interval covers a specified proportion of a population on averagewhereas a tolerance interval covers it with a certain confidence levelmaking the tolerance interval more appropriate if a single interval is intended to bound multiple future samples.

### Tolerance interval – Wikipedia

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 1y 2.

Such an interval, would however, not be of much help to a person renting one of these cars and wondering whether the full gallon tank of gas will suffice to carry him the miles to his destination. For that job, a prediction interval would be much more useful.

Another example is given by: It was noted that the log-transformed lead levels fitted a normal distribution well that is, the data are from a lognormal distribution. 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. Now suppose we want to predict the air lead level at a particular area within the laboratory. A two-sided prediction interval can be similarly computed.

The meaning and interpretation of these intervals are well known. A prediction interval has a similar interpretation, and is meant to provide information concerning a single lead level only. 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.

One-sided normal tolerance intervals have an exact solution in terms of the sample mean and sample variance based on the noncentral t -distribution. From Wikipedia, the free encyclopedia. Not to be confused with Engineering tolerance. This section needs expansion with: You can help by adding to it.

ALBERTO GIRRI PDF YoungBook Reviews: Ryan 22 June Retrieved 22 February Determination of statistical tolerance intervals”. A tutorial on tolerance intervals for ordinary least-squares regression”. Chemometrics and Intelligent Laboratory Systems. Theory, Applications, and Computation. John Wiley and Sons. Journal of Statistical Software. Retrieved 19 February Mean arithmetic geometric harmonic Median Mode. Central limit theorem Moments Skewness Kurtosis L-moments. Grouped data Frequency distribution Contingency table. Pearson product-moment correlation Rank correlation Spearman’s rho Kendall’s tau Partial correlation Scatter plot.

Sampling stratified cluster Standard error Opinion poll Questionnaire.

Observational study Natural experiment Quasi-experiment. Z -test normal Student’s t -test F -test. Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator. Pearson product-moment Partial correlation Confounding variable Coefficient of determination.

Simple linear regression Ordinary least squares General linear model Bayesian regression. Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal.

Spectral density estimation Fourier analysis Wavelet Whittle likelihood.

## ISO 16269-6:2014

Cartography Environmental statistics Geographic information system Geostatistics Kriging. Category Portal Commons WikiProject. Retrieved from ” https: Engineering concepts Statistical intervals Statistical forecasting. Pages using web citations with no URL Articles to be expanded from July All articles to be expanded Articles using small message boxes. Views Read Edit View history. This page was last edited on 11 Juneat By using this site, you agree to the Terms of Use and Privacy Policy.

Correlation Regression analysis Correlation Pearson product-moment Partial correlation Confounding variable Coefficient of determination.