Panasonic PT-DZ12000U

B
MDA when background and sample count 3 + 4.65 %R
times are one minute and k is 1.645. Eff
MDA when background count time is ten
B
minutes and sample count time is one 3 + 3.45 %R
minute and k is 1.645. Eff
POISSON STATISTICS
For Poisson distributions the following logic applies.
n
P is the probability of getting count “n”
n
P = : e / n!
n-
:
n = the hypothetical count
: = true mean counts
If the true mean, :, is 3, then there is a 5% probability that we will
get a zero count and a 95% probability that we will get greater than
zero counts. There is a 65% probability that we will get 3 or more
counts.
181
B
MDA when background and sample count 3 + 4.65 %R
times are one minute and k is 1.645. Eff
MDA when background count time is ten
B
minutes and sample count time is one 3 + 3.45 %R
minute and k is 1.645. Eff
POISSON STATISTICS
For Poisson distributions the following logic applies.
n
P is the probability of getting count “n”
n
P = : e / n!
n-
:
n = the hypothetical count
: = true mean counts
If the true mean, :, is 3, then there is a 5% probability that we will
get a zero count and a 95% probability that we will get greater
than zero counts. There is a 65% probability that we will get 3 or
more counts.
181
B
MDA when background and sample count 3 + 4.65 %R
times are one minute and k is 1.645. Eff
MDA when background count time is ten
B
minutes and sample count time is one 3 + 3.45 %R
minute and k is 1.645. Eff
POISSON STATISTICS
For Poisson distributions the following logic applies.
n
P is the probability of getting count “n”
n
P = : e / n!
n-
:
n = the hypothetical count
: = true mean counts
If the true mean, :, is 3, then there is a 5% probability that we will
get a zero count and a 95% probability that we will get greater than
zero counts. There is a 65% probability that we will get 3 or more
counts.
181
B
MDA when background and sample count 3 + 4.65 %R
times are one minute and k is 1.645. Eff
MDA when background count time is ten
B
minutes and sample count time is one 3 + 3.45 %R
minute and k is 1.645. Eff
POISSON STATISTICS
For Poisson distributions the following logic applies.
n
P is the probability of getting count “n”
n
P = : e / n!
n-
:
n = the hypothetical count
: = true mean counts
If the true mean, :, is 3, then there is a 5% probability that we will
get a zero count and a 95% probability that we will get greater
than zero counts. There is a 65% probability that we will get 3 or
more counts.
181