The definitions below apply to all of the probability distribution functions:

For discrete distributions, the probability density is the likelihood that a random variable takes on a particular value.

For continuous distributions, the probability density is the likelihood-per-unit-x that a random variable takes on a particular value within a particular distribution. The area under this curve between two x values corresponds to the probability of having a future measurement in the given distribution fall between these values. As the ends of the integration interval converge, the probability of achieving a particular value is 0 since the area under the curve drops to zero.

The cumulative probability is the probability that a random variable takes on a value less than or equal to a specified value. The cumulative probability functions calculate the cumulative probability by integrating (or summing for a discrete distribution) the corresponding probability density over an appropriate range.

Set the probability that a random variable is less than or equal to a value. You can then calculate this value with the inverse cumulative probability functions.

The random number generators use a seed value to generate a sequence of quasi-random numbers. To generate a different sequence of random numbers, use the seed function in your worksheet.