Texas Instruments TINSPIRE Teacher Software Guidebook - Page 592

F Cdf (F Cdf()) computes the F distribution probability between lowBound and upBound for the specified dfnumer (degrees of freedom) and dfDenom. You can click the Draw (Shade area) check box to shade the area between the lower and upper bounds. Changes to the initial lowBound and upBound automatically update the distribution. 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: f(x) = ⎛ ⎝ n⎞ x⎠ px ( 1 - p )n - x ,x = 0,1,...,n 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. Poisson Pdf (poissPdf()) 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: f(x) = e-μμx ⁄ x!,x = 0,1,2,... 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. 580 Using Lists & Spreadsheet