Density and Distribution Definitions |
These definitions apply to all of the density and distribution functions.
In continuous distributions, this is the likelihood-per-unit-x (the probability density) that a random variable will take on a particular value within a particular distribution. The area under this curve between two values of x corresponds to the probability of having a future measurement in the given distribution fall between these values. Note that, in the limit as the ends of the integration interval converge, the probability is 0 of achieving a particular value, since the area under the curve drops to zero. In discrete distributions, the probability density is simply the likelihood that a random variable will take a particular value.
The probability that a random variable will take on a value less than or equal to a specified value. This is obtained by simply integrating (or summing, for a discrete distribution) the corresponding probability density over an appropriate range.
These functions take a probability as an argument and return a value such that the probability that a random variable will be less than or equal to that value is whatever probability you supplied as an argument.
The random number generators use a seed value to generate a sequence of quasi-random numbers. To generate a different sequence of random numbers, go to the Tools menu, click Worksheet Options, and change the seed value on the Built-in Variables tab, or use the seed function in your worksheet.