You can calculate values for probability density functions, cumulative distribution functions, or inverse cumulative probabilities of your data, for the distribution you choose from the menu.

- Probability distribution function (PDF)
- The probability distribution function (PDF) curve indicates regions of higher and lower probabilities for values of the random variable. For example, for a normal distribution, the highest PDF value is at the mean, while lower PDF values are in the tails of the distribution.
- Cumulative distribution function (CDF)
- The cumulative distribution function (CDF) calculates the cumulative probability up to the variable value that you specify. Use the CDF to determine the probability that a random observation that is taken from the population will be less than or equal to a certain value. For example, a cumulative distribution function can show the proportion of trees in a forest that have diameter measurements of 10 inches or less.
- Inverse cumulative distribution function (ICDF)
- The inverse cumulative distribution function (ICDF) gives the value of the variable associated with a specific cumulative probability. For example, a reliability engineer wants to determine the time by which specific proportions of components fail. The engineer can use the ICDF to determine the 95th percentile of the failure time distribution.