Sharp JX-9400 Technical Information Seite 179

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more detail in statistical tables such as Zwillinger (2003)) and in most
mathematical software packages.
Confidence interval of the Gaussian distribution
Assuming that a measurement, x
i
, is a combination of the ‘true’ value and a
random error, e
i
, we have:
x
i
¼ þ e
i
ð7:62Þ
By measuring x
i
, we expect to find the best estimate of . This can be done by
performing N > 1 measurements and computing their average. This average x
is the estimate of the ‘true’ value :
ffihx
P
i
x
i
N
ð7:63Þ
Let us recall that the confidence interval is the interval that has a given
probability, P (for example, 95 per cent), to contain the ‘true’ value. In other
terms, the probability of it being wrong, that is that the ‘true’ value being
outside this confidence interval, is the error probability p ¼ 1 P.
What we need now is precisely to give the confidence interval around x that
will contain with a fair probability. The value of this confidence interval
depends on the probability distribution of the measured values. In principle,
a reasonable distribution function should be chosen, adjusted on the measur e-
ments and the validit y of this adjustment should be tested with the
2
test.
In most cases, however, and mainly when the number of the measurements
is large, a normal distribution with a mean and a standard deviation can be
assumed for the results of the meas urements. Under this assumption, the
confidence limit of the ‘true’ value is given by:
I
c
¼
s
ffiffiffiffi
N
p
TðP; N 1Þð7:64Þ
where s is the estimate of , and TðP; N 1Þ is the confidence interval of the
Student distribution with N 1 degrees of freedom. These are shown in
Figure 7.5.
0
0.1
0.2
0.3
0.4
–4 –3 –2 –1 0 1 2 3 4
Normal
5
2
1
Figure 7.4 Student distribution for 1, 2 and 5 degrees of freedom
compared to the normal distribution
158 Ventilation and Airflow in Buildings
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