Description Usage Format Details Source References

Calibration of an end gauge for length measurement. Notation is based on the presentation in chapter 3 of
*Data Modeling for Metrology and Testing in Measurement Science*.

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A list containing 10 components that give the uncertainty budget, measurement function, and other quantities needed to carry out an uncertainty analysis using the methods from the Guide to the Expression of Uncertainty in Measurement:

- var.name
Character vector giving the name of each input variable included in the analysis.

- unit
Expression giving the units of each input variable

- x.i
Vector giving the reported value for each input variable.

- u.i
Vector giving the standard uncertainty associated with each reported value.

- nu.i
Vector giving the degrees of freedom associated with each standard uncertainty.

- type
Character vector indicating the method of evaluation (Type A or Type B) for each standard uncertainty.

- distribution
Character vector listing the probability distribution assumed to describe each variable value.

- measurement.fnc
Character string giving the measurement function for the output variable.

- correlation
Matrix giving the correlations between input variable values.

- shared.u.i
Character vector describing which standard uncertainties, if any, are based on a common underlying standard uncertainty. A vector that is the same as var.name indicates that all standard uncertainties are based on independent assessments of standard uncertainty. A vector with fewer names than in var.names indicates that one or more variables are derived from a common uncertainty assessment.

Details can be found in the GUM and in Data Modeling for Metrology and Testing in Measurement Science.

Joint Committee on Guides in Metrology (JCGM), *Evaluation of Measurement Data Guide to the Expression of
Uncertainty in Measurement*, http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf, 2008.

Guthrie, W.F. et. al., "Three Statistical Paradigms for Assessment and Interpretation of Measurement Uncertainty" in
*Data Modeling for Metrology and Testing in Measurement Science*, F. Pavese and A.B. Forbes, eds., Birkhauser,
Boston, 2009.

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