Application Guide

Binomial Pdf (binomPdf())
Computes a probability at x for the discrete binomial distribution with the specified
numtrials and probability of success (p) on each trial. The x parameter can be an
integer or a list of integers. 0{p{1 must be true. numtrials must be an integer >0. If
you do not specify x, a list of probabilities from 0 to numtrials is returned. The
probability density function (pdf) is:
where n = numtrials
This distribution is useful in determining the probability of success in a success/failure
trial, at trial n. For example, you could use this distribution to predict the probability of
getting heads in a coin toss on the fifth toss.
Binomial Cdf (binomCdf())
Computes a cumulative probability for the discrete binomial distribution with n number
of trials and probability p of success on each trial.
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.
Inverse Binomial (invBinom())
Given the number of trials (NumTrials) and the probability of success of each trial
(Prob), this function returns the minimum number of successes, k, such that the value,
k, is greater than or equal to the given cumulative probability (CumulativeProb).
This distribution is useful in determining the upper bound input of the binomial cdf. For
example, if you flip a coin 10 times, and you want the probability of getting x heads or
less to be greater than 75%, this distribution will help determine what x should be.
Inverse Binomial with respect to N (invBinomN())
Given the probability of success of each trial (Prob), and the number of successes
(NumSuccess), this function returns the minimum number of trials, N, such that the
value, N, is less than or equal to the given cumulative probability (CumulativeProb).
This distribution is useful in determining the number of trials of the binomial cdf. For
example, if you flip a coin several times and you want the number of heads to be 6 or
less with a probability of less than 25%, this distribution will help determine how many
times to flip the coin.
Lists & Spreadsheet Application 116