Integral_Theorem {ExpRep} | R Documentation |
Given n Bernoulli experiments, with success probability p, this function calculates the probability that a successful event occurs between linf and lsup times.
Integral_Theorem(n = 100, p = 0.5, linf = 0, lsup = 50)
n |
An integer value representing the number of repetitions of the Bernoulli experiment. |
p |
A real value with the probability that a successful event will happen in any single Bernoulli experiment (called the probability of success). |
linf |
An integer value representing the minimum number of times that the successful event should happen. |
lsup |
An integer value representing the maximum number of times that the successful event should happen. |
Bernoulli experiments are sequences of events, in which successive experiments are independent and at each experiment the probability of appearance of a "successful" event (p) remains constant. The value of n must be high and the value of p must be small. It is necessary that linf < lsup.
A real value representing the approximate probability that a successful event occurs between linf and lsup times, in n repetitions of a Bernoulli experiment.
Department of Mathematics. University of Oriente. Cuba.
Larisa Zamora and Jorge Diaz
Gnedenko, B. V. (1978). The Theory of Probability. Mir Publishers. Moscow.
Poisson_Theorem, Local_Theorem.
Prob<-Integral_Theorem(n=100,p=0.5,linf=0,lsup=50)
Prob
## The function is currently defined as
function (n = 100, p = 0.5, linf = 0, lsup = 50)
{
A <- (linf - n * p)/sqrt(n * p * (1 - p))
B <- (lsup - n * p)/sqrt(n * p * (1 - p))
P <- pnorm(B) - pnorm(A)
return(P)
}