# datasets: Datasets from the GUM "Guide to the expression of... In propagate: Propagation of Uncertainty

## Description

Several datasets found in "Annex H" of the GUM that are used in illustrating the different approaches to quantifying measurement uncertainty.

## Usage

 1 2 3 H.2 H.3 H.4 

## Details

H.2: Simultaneous resistance and reactance measurement, Table H.2
This example demonstrates the treatment of multiple measurands or output quantities determined simultaneously in the same measurement and the correlation of their estimates. It considers only the random variations of the observations; in actual practice, the uncertainties of corrections for systematic effects would also contribute to the uncertainty of the measurement results. The data are analysed in two different ways with each yielding essentially the same numerical values.
H.2.1 The measurement problem:
The resistance R and the reactance X of a circuit element are determined by measuring the amplitude V of a sinusoidally-alternating potential difference across its terminals, the amplitude I of the alternating current passing through it, and the phase-shift angle φ of the alternating potential difference relative to the alternating current. Thus the three input quantities are V, I, and φ and the three output quantities -the measurands- are the three impedance components R, X, and Z. Since Z^2 = R^2 + X^2, there are only two independent output quantities.
H.2.2 Mathematical model and data:
The measurands are related to the input quantities by Ohm's law:

H.3: Calibration of a thermometer, Table H.6
This example illustrates the use of the method of least squares to obtain a linear calibration curve and how the parameters of the fit, the intercept and slope, and their estimated variances and covariance, are used to obtain from the curve the value and standard uncertainty of a predicted correction.
H.3.1 The measurement problem:
A thermometer is calibrated by comparing n = 11 temperature readings t_k of the thermometer, each having negligible uncertainty, with corresponding known reference temperatures t_{R,k} in the temperature range 21°C to 27°C to obtain the corrections b_k = t_{R,k} - t_k to the readings. The measured corrections b_k and measured temperatures t_k are the input quantities of the evaluation. A linear calibration curve

b(t) = y_1 + y_2(t-t_0) \qquad (\mathrm{H.12})

is fitted to the measured corrections and temperatures by the method of least squares. The parameters y_1 and y_2, which are respectively the intercept and slope of the calibration curve, are the two measurands or output quantities to be determined. The temperature t_0 is a conveniently chosen exact reference temperature; it is not an independent parameter to be determined by the least-squares fit. Once y_1 and y_2 are found, along with their estimated variances and covariance, Equation (H.12) can be used to predict the value and standard uncertainty of the correction to be applied to the thermometer for any value t of the temperature.

H.4: Measurement of activity, Table H.7
This example is similar to example H.2, the simultaneous measurement of resistance and reactance, in that the data can be analysed in two different ways but each yields essentially the same numerical result. The first approach illustrates once again the need to take the observed correlations between input quantities into account.
H.4.1 The measurement problem:
The unknown radon ({}^{222}\mathrm{Rn}) activity concentration in a water sample is determined by liquid-scintillation counting against a radon-in-water standard sample having a known activity concentration. The unknown activity concentration is obtained by measuring three counting sources consisting of approximately 5g of water and 12g of organic emulsion scintillator in vials of volume 22ml:
Source (a) a standard consisting of a mass m_S of the standard solution with a known activity concentration;
Source (b) a matched blank water sample containing no radioactive material, used to obtain the background counting rate;
Source (c) the sample consisting of an aliquot of mass m_x with unknown activity concentration.
Six cycles of measurement of the three counting sources are made in the order standard - blank - sample; and each dead-time-corrected counting interval T_0 for each source during all six cycles is 60 minutes. Although the background counting rate cannot be assumed to be constant over the entire counting interval (65 hours), it is assumed that the number of counts obtained for each blank may be used as representative of the background counting rate during the measurements of the standard and sample in the same cycle. The data are given in Table H.7, where
t_S, t_B, t_x are the times from the reference time t = 0 to the midpoint of the dead-time-corrected counting intervals T_0 = 60 min for the standard, blank, and sample vials, respectively; although t_B is given for completeness, it is not needed in the analysis;
C_S, C_B, C_x are the number of counts recorded in the dead-time-corrected counting intervals T_0 = 60 min for the standard, blank, and sample vials, respectively.
The observed counts may be expressed as

C_S = C_B + \varepsilon A_S T_0 m_S e^{-λ t_S} \qquad (\mathrm{H.18a})

C_x = C_B + \varepsilon A_x T_0 m_x e^{-λ t_x} \qquad (\mathrm{H.18b})

where
\varepsilon is the liquid scintillation detection efficiency for {}^{222}\mathrm{Rn} for a given source composition, assumed to be independent of the activity level;
A_S is the activity concentration of the standard at the reference time t = 0;
A_x is the measurand and is defined as the unknown activity concentration of the sample at the reference time t = 0;
m_S is the mass of the standard solution;
m_x is the mass of the sample aliquot;
λ is the decay constant for {}^{222}\mathrm{Rn}: λ = (ln 2)/T_{1/2} = 1.25894 \cdot 10^{-4}\ \mathrm{min}^{-1} (T_{1/2} = 5505.8\ \mathrm{min}).
(...) This suggests combining Equations (H.18a) and (H.18b) to obtain the following expression for the unknown concentration in terms of the known quantities:

... = A_S \frac{m_S}{m_x}\frac{C_x - C_B}{C_S - C_B}e^{λ(t_x - t_S)} \qquad (\mathrm{H.19})

where (C_x - C_B)e^{λ t_x} and (C_S - C_B)e^{λ t_S} are, respectively, the background-corrected counts of the sample and the standard at the reference time t = 0 and for the time interval T_0 = 60 min.

## Author(s)

Andrej-Nikolai Spiess, taken mainly from the GUM 2008 manual.

## References

Evaluation of measurement data - Guide to the expression of uncertainty in measurement.
JCGM 100:2008 (GUM 1995 with minor corrections).
http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf.

Evaluation of measurement data - Supplement 1 to the Guide to the expression of uncertainty in measurement - Propagation of distributions using a Monte Carlo Method.
JCGM 101:2008.
http://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf.

## Examples

 1 ## See "Examples" in 'propagate'. 

propagate documentation built on May 7, 2018, 1:03 a.m.