Application Guide

334 Lists&Spreadsheet Application
This distribution is useful in determining the probability that a single observation falls
within the range between the lower bound and upper bound.
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
cumulative probability of k successes is greater than or equal to the given
cumulative probability (CumulativeProb).
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
cumulative probability of x successes is less than or equal to the given cumulative
probability (CumulativeProb).