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Summary

e-FITS (link currently only accessible to NIST staff) is a web-based tool used to generate graphs, tables, and random numbers for a large number of probability distributions. In addition, it can fit distributions to user-supplied data.

The methodolgy used to develop e-FITS is being extended to the e-Metrology project (link currently restricted to NIST staff). e-Metrology provides forms for common metrology problems encountered by NIST scientists and engineers.

RELATED PROGRAM / PROJECTS:

e-Handbook of Statistical Methods

Description

DESCRIPTION:

e-FITS is a web-based tool, currently available to NIST staff, used to perform the following tasks for over 100 probability distributions.

  • Generate graphs of probability functions (probability density, cumulative distribution, inverse cumulative distribution, hazard, cumulative hazard, survival, inverse survival).  
  • Generate tables for each of these probability functions.  
  • Generate random numbers from the specified distribution.  
  • Fit the distribution to user-supplied data. The fit analysis will include parameter estimation and diagnostic analysis of the fit.

e-Metrology is also a web-based tool currently available to NIST staff that can be used to perform the following tasks.

  • Uncertainty following the ISO Guide to the Expression of Uncertainty in Measurement (GUM). Utilizes the R-based gummer routines written by Hung-Kung Liu, Will Guthrie and Antonio Possolo.  
  • Consensus means utilizing various methods. Consensus means are a key component of many SRM analyses.  
  • Interlaboratory analysis based on ASTM standard E-691 and proficiency testing based on ASTM standards E-2489A and E-2489B. Youden plots and bivariate normal tolerance region plots.  
  • A limit of detection analysis based on the proposed ASTM WK 19817. This implements a method developed by Andrew Rukhin, Stefan Leigh and Michael Verkouteren (of CSTL).  
  • Outlier detection for univariate normal data.  
  • Jim Filliben's 10-step analysis of full and fractional factorial designs.  
  • Linear and quadratic calibration and errors-in-variables regression.  
  • One and two factor analysis of variance with supporting graphics.
Created August 26, 2010, Updated April 27, 2016