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344 Lists&Spreadsheet Application
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())
Computes a probability at x for the discrete Poisson distribution with the
specified mean, μ, 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 eight tosses of a
coin.
Poisson Cdf (poissCdf())
Computes a cumulative probability for the discrete Poisson distribution with
specified mean, x.
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.
Geometric Pdf (geomPdf())
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 . 0{ p {1 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.
Geometric Cdf (geomCdf())
Computes a cumulative geometric probability from lowBound to upBound with
the specified probability of success, p.