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e-Statistics

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.

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.

Lead Organizational Unit:

itl

Staff:

Alan Heckert, ITL
Antonio Possolo, ITL
Charles Hagwood, ITL
Will Guthrie, ITL
Andrew Rukhin, ITL
James Filliben, ITL
Hung-Kung Liu, ITL
Bill Strawderman, ITL
Michael Verkouteren, CSTL
Noorulain Saddiqi, ITL SURF Student
Cameron Rose, ITL SURF Student
Guy Cao, ITL SURF Student
Svletlana Rabinovich, ITL SURF Student

Related Programs and Projects:

Contact

Alan Heckert
301-975-2899
alan.heckert@nist.gov
100 Bureau Drive, M/S 8980
Gaithersburg, MD 20899-8980