# Mean or average of two partially correlated measurements

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See this article for background material or a similar article with an example application from particle physics.

Use the root macro to perform an average of two measurements:
x1 +- dx1s (statistical) +- dx1u (uncorrelated systematic) +- dx1c (correlated systematic)
x2 +- dx2s (statistical) +- dx2u (uncorrelated systematic) +- dx2c (correlated systematic)

resulting in mean m +- dmstat +- dmsyst. The systematic errors in each channel are decomposed in uncorrelated and (100%) correlated components. The correlation matrix which appears in the solution is composed of the sum of a diagonal covariance matrix with elements given by the uncorrelated uncertainties summed in quadrature

 dx1s2 + dx1u2 0 0 dx2s2 + dx2u2

and a non-diagonal covariance matrix for the correlated uncertainties with correlation coefficient rho=1

 dx1c2 dx1c * dx2c dx1c * dx2c dx2c2

. From the form of the covariance matrix C

 dx12 rho * dx1 * dx2 rho * dx1 * dx2 dx22

we can identify dxi2 = dxis2 + dxiu2 + dxic2 and rho = dx1c * dx2c / (dx1 * dx2).

Minimizing the generalized chi-squared XTC-1X, where X is a column vector

 x1 - m x2 - m

, we get for the minimum variance estimate of m

m = (x1/dx12 + x2/dx22 - rho * (x1 + x2) /(dx1 * dx2)) / (1/dx12 + 1/dx22 - 2 * rho/(dx1 * dx2))

and the variance of m

dm2 = (1-rho2) / ( 1/dx12 + 1/dx22 - 2 * rho/(dx1 * dx2)). We decompose the variance into statistical and systematic components by subtraction in quadrature of the statistical uncertainty dmstat2 = 1/(1/dx1s2 + 1/dx2s2), dmsyst2 = dm2 - dmstat 2.

There is protection in the code against the very special case that there is only a 100% correlated uncertainty. If this is truely the case then the 2 measurements must be equal by construction and the uncertainty may be taken as the smaller of the 2.

There is also code to show the results of a popular approximation that does not properly take into account the correlation between the 2 measurements.

Here is a sample output from the macro for some (almost) randomly chosen measurement results:

*******************************************
* *
* W E L C O M E to R O O T *
* *
* Version 5.28/00 14 December 2010 *
* *
* http://root.cern.ch *
* *
*******************************************

ROOT 5.28/00 (trunk@37585, Dec 14 2010, 15:20:27 on linuxx8664gcc)

CINT/ROOT C/C++ Interpreter version 5.18.00, July 2, 2010
Type ? for help. Commands must be C++ statements.
Enclose multiple statements between { }.
root 
Processing AverageMeasurements.C...
The measurements being averaged:
-------------------------------
x1 = 58.9 +- 3.4 (stat) +- 1.5 (uncorr syst) +- 2.4 (corr syst)
= 58.9 +- 4.4238 (total)

x2 = 68.7 +- 2.8 (stat) +- 0.3 (uncorr syst) +- 3.9 (corr syst)
= 68.7 +- 4.81041 (total)

Results for the generalized weighted average
--------------------------------------------
Correlation coefficient (rho) = 0.439844

m = 63.0708 +- 2.1614 (stat) +- 3.24854 (syst)
= 63.0708 +- 3.90188 (total)

Generalized chi-squared = 4.00333

Approximate, simple formulae
----------------------------
m = 65.1253 +- 2.1614 (stat) +- 3.20753 (syst)
= 65.1253 +- 3.8678 (total)

Generalized chi-squared = 4.28057 (for the approximate minimum)
Delta chi-squared with respect to exact minimum = 0.277241


### The derivation of the terms in the correlation matrix

The covariance matrix is
.
Suppose are three independent sources of normally-distributed unit fluctuations (with < δi > = 0 and < δi * δj > = δij and where is the kroneker delta function (1 for and zero for ).

Pseudo-measurements of (x1,x2) can be generated from the expressions (x1,x2) = (x10 + α * δ1 + β * δ2,x20 + γ * δ3 + λ * δ2).

Expanding the covariance matrix one finds .

Using the properties of above and , one finds ; so that C12 = C21 = β * λ. Similar substitution gives for the diagonal matrix elements

and

so that C=

 α2 + β2 β * λ