where \(P(X)\) is the probability of \(X\) successes in \(n\) trials when the probability of a success in ANY ONE TRIAL is \(p\). \(X \sim P(\mu )\) means that \(X\) has a Poisson probability distribu...where \(P(X)\) is the probability of \(X\) successes in \(n\) trials when the probability of a success in ANY ONE TRIAL is \(p\). \(X \sim P(\mu )\) means that \(X\) has a Poisson probability distribution where \(X =\) the number of occurrences in the interval of interest. When \(P(\mu)\) is used to approximate a binomial distribution, \(\mu = np\) where n represents the number of independent trials and \(p\) represents the probability of success in a single trial.
where \(P(X)\) is the probability of \(X\) successes in \(n\) trials when the probability of a success in ANY ONE TRIAL is \(p\). \(X \sim P(\mu )\) means that \(X\) has a Poisson probability distribu...where \(P(X)\) is the probability of \(X\) successes in \(n\) trials when the probability of a success in ANY ONE TRIAL is \(p\). \(X \sim P(\mu )\) means that \(X\) has a Poisson probability distribution where \(X =\) the number of occurrences in the interval of interest. When \(P(\mu)\) is used to approximate a binomial distribution, \(\mu = np\) where n represents the number of independent trials and \(p\) represents the probability of success in a single trial.
