Rad Data Handbook 20.. - Voss Associates
Rad Data Handbook 20.. - Voss Associates
Rad Data Handbook 20.. - Voss Associates
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
MDA when background and sample count 3 + 4.65 R B<br />
times are one minute and k is 1.645. Eff<br />
MDA when background count time is ten<br />
minutes and sample count time is one 3 + 3.45 R B<br />
minute and k is 1.645. Eff<br />
POISSON STATISTICS<br />
For Poisson distributions the following logic applies.<br />
P<br />
n<br />
is the probability of getting count “n”<br />
n -<br />
P<br />
n<br />
= e / n!<br />
n = the hypothetical count<br />
= true mean counts<br />
If the true mean, , is 3, then there is a 5% probability that we will<br />
get a zero count and a 95% probability that we will get greater than<br />
zero counts. There is a 65% probability that we will get 3 or more<br />
counts.<br />
MDA when background and sample count 3 + 4.65 R B<br />
times are one minute and k is 1.645. Eff<br />
MDA when background count time is ten<br />
minutes and sample count time is one 3 + 3.45 R B<br />
minute and k is 1.645. Eff<br />
POISSON STATISTICS<br />
For Poisson distributions the following logic applies.<br />
P<br />
n<br />
is the probability of getting count “n”<br />
n -<br />
P<br />
n<br />
= e / n!<br />
n = the hypothetical count<br />
= true mean counts<br />
If the true mean, , is 3, then there is a 5% probability that we will<br />
get a zero count and a 95% probability that we will get greater<br />
than zero counts. There is a 65% probability that we will get 3 or<br />
more counts.<br />
108<br />
108<br />
MDA when background and sample count 3 + 4.65 R B<br />
times are one minute and k is 1.645. Eff<br />
MDA when background count time is ten<br />
minutes and sample count time is one 3 + 3.45 R B<br />
minute and k is 1.645. Eff<br />
POISSON STATISTICS<br />
For Poisson distributions the following logic applies.<br />
P<br />
n<br />
is the probability of getting count “n”<br />
n -<br />
P<br />
n<br />
= e / n!<br />
n = the hypothetical count<br />
= true mean counts<br />
If the true mean, , is 3, then there is a 5% probability that we will<br />
get a zero count and a 95% probability that we will get greater than<br />
zero counts. There is a 65% probability that we will get 3 or more<br />
counts.<br />
MDA when background and sample count 3 + 4.65 R B<br />
times are one minute and k is 1.645. Eff<br />
MDA when background count time is ten<br />
minutes and sample count time is one 3 + 3.45 R B<br />
minute and k is 1.645. Eff<br />
POISSON STATISTICS<br />
For Poisson distributions the following logic applies.<br />
P<br />
n<br />
is the probability of getting count “n”<br />
n -<br />
P<br />
n<br />
= e / n!<br />
n = the hypothetical count<br />
= true mean counts<br />
If the true mean, , is 3, then there is a 5% probability that we will<br />
get a zero count and a 95% probability that we will get greater<br />
than zero counts. There is a 65% probability that we will get 3 or<br />
more counts.<br />
108<br />
108
Change language