Next: Adattamento di curve ai
Up: Matrice di covarianza di
Previous: Normalization uncertainty
Indice
Let us assume there are independently
measured values and
calibration constants
with their covariance matrix
. The latter
can also be theoretical parameters influencing the data, and
moreover they may be
correlated, as usually
happens if, for example, they are parameters of a calibration fit.
We can then include the in the vector that contains the
measurements and in the covariance matrix :

(14.33) 
The corrected quantities are obtained from the most general
function

(14.34) 
and the covariance matrix
from the covariance propagation
.
As a frequently encountered example, we can think of several
normalization constants, each affecting a subsample of the data 
as is
the case where each of several detectors
measures a set of physical quantities.
Let us consider just three quantities
() and three
uncorrelated
normalization standard uncertainties (
),
the first common to
and , the second to
and and the third to all three.
We get the following covariance matrix:

(14.35) 
Next: Adattamento di curve ai
Up: Matrice di covarianza di
Previous: Normalization uncertainty
Indice
Giulio D'Agostini
20010402