Binomial cumulative distribution function
y = binocdf(x,N,p)
y = binocdf(x,N,p,'upper')
y = binocdf(x,N,p) computes a binomial cdf at each of the values in x using the corresponding number of trials in N and probability of success for each trial in p. x, N, and p can be vectors, matrices, or multidimensional arrays that are all the same size. A scalar input is expanded to a constant array with the same dimensions of the other inputs. The values in N must all be positive integers, the values in x must lie on the interval [0,N], and the values in p must lie on the interval [0, 1].
The binomial cdf for a given value x and a given pair of parameters n and p is
The result, y, is the probability of observing up to x successes in n independent trials, where the probability of success in any given trial is p. The indicator function ensures that x only adopts values of 0,1,...,n.
If a baseball team plays 162 games in a season and has a 50-50 chance of winning any game, then the probability of that team winning more than 100 games in a season is:
1 - binocdf(100,162,0.5)
ans = 0.0010
The result is 0.001 (i.e., 1-0.999). If a team wins 100 or more games in a season, this result suggests that it is likely that the team's true probability of winning any game is greater than 0.5.