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Gilson and Voss - Voss Associates

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MDA when background <strong>and</strong> sample count 3 + 4.65 R B<br />

times are one minute <strong>and</strong> k is 1.645. Eff<br />

MDA when background count time is ten<br />

minutes <strong>and</strong> sample count time is one 3 + 3.45 R B<br />

minute <strong>and</strong> 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 <strong>and</strong> 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 <strong>and</strong> sample count 3 + 4.65 R B<br />

times are one minute <strong>and</strong> k is 1.645. Eff<br />

MDA when background count time is ten<br />

minutes <strong>and</strong> sample count time is one 3 + 3.45 R B<br />

minute <strong>and</strong> 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 <strong>and</strong> 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 />

Page 108<br />

Page 108<br />

MDA when background <strong>and</strong> sample count 3 + 4.65 R B<br />

times are one minute <strong>and</strong> k is 1.645. Eff<br />

MDA when background count time is ten<br />

minutes <strong>and</strong> sample count time is one 3 + 3.45 R B<br />

minute <strong>and</strong> 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 <strong>and</strong> 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 <strong>and</strong> sample count 3 + 4.65 R B<br />

times are one minute <strong>and</strong> k is 1.645. Eff<br />

MDA when background count time is ten<br />

minutes <strong>and</strong> sample count time is one 3 + 3.45 R B<br />

minute <strong>and</strong> 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 <strong>and</strong> 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 />

Page 108<br />

Page 108

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