Using Lists & Spreadsheet 227
This distribution is useful in determining the probability of a success on
one trial before all trials are completed. For example, if heads is a
successful coin toss and you plan to toss the coin 10 times, this
distribution would predict the chance of obtaining heads at least once in
the 10 tosses.
Poisson Pdf (poissPdf())
Poisson Pdf computes a probability at x for the discrete Poisson
distribution with the specified mean, m, which must be a real number > 0.
x can be an integer or a list of integers. The probability density function
(pdf) is:
This distribution is useful in determining the probability of obtaining a
certain number of successes before a trial begins. For example, you could
use this calculation to predict the number of heads that would occur in 8
tosses of a coin.
poissoncdf (poissCdf())
poissoncdf( computes a cumulative probability at x for the discrete
Poisson distribution with the specified mean, m, which must be a real
number > 0. x can be a real number or a list of real numbers.
This distribution is useful in determining the probability that a certain
number of successes occur between the upper and lower bounds of a
trial. For example, you could use this calculation to predict the number of
heads displayed between coin toss #3 and toss #8.
geometpdf (geomPdf())
geometpdf (computes a probability at x, the number of the trial on
which the first success occurs, for the discrete geometric distribution with
the specified probability of success p. 0p1 must be true. x can be an
integer or a list of integers. The probability density function (pdf) is:
This distribution is useful in determining the likeliest number of trials
before a success is obtained. For example, you could use this calculation
to predict the number of coin tosses that would be made before a heads
resulted.
x() e
μ–
μ
x
x!⁄ x, 0,1,2,...==
fx() p 1 p–()
x 1–
x, 1,2,...==
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