If X has mean μ and standard deviation σ , the ratio r ≡ | μ | σ is called the measurement signal-to-noise ratio of X. The idea is that X can be expressed as X = μ + ( X − μ ) , with μ representing the signal and X − μ the noise. If we define | ( X − μ ) μ | ≡ D as the relative deviation of X from its signal (or mean) μ , show that for a > 0 , P { D ≤ a } ≥ 1 − 1 r 2 a 2
If X has mean μ and standard deviation σ , the ratio r ≡ | μ | σ is called the measurement signal-to-noise ratio of X. The idea is that X can be expressed as X = μ + ( X − μ ) , with μ representing the signal and X − μ the noise. If we define | ( X − μ ) μ | ≡ D as the relative deviation of X from its signal (or mean) μ , show that for a > 0 , P { D ≤ a } ≥ 1 − 1 r 2 a 2
Solution Summary: The author explains how to prove the signal to noise ratio of X.
If X has mean
μ
and standard deviation
σ
, the ratio
r
≡
|
μ
|
σ
is called the measurement signal-to-noise ratio of X. The idea is that X can be expressed as
X
=
μ
+
(
X
−
μ
)
, with
μ
representing the signal and
X
−
μ
the noise. If we define
|
(
X
−
μ
)
μ
|
≡
D
as the relative deviation of X from its signal (or mean)
μ
, show that for
a
>
0
,
P
{
D
≤
a
}
≥
1
−
1
r
2
a
2
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