Explore: Theoretical Distributions

plots are also used to compare two theoretical distributions to each other. Since Q–Q plots compare distributions, there is no need for the values to

distribution. Various methods exist for quantifying kurtosis in theoretical distributions, and corresponding techniques allow estimation based on sample

In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier

calculated for either a finite set of values or for a theoretical distribution, such as the normal distribution. Occasionally authors use central tendency to

Many probability distributions that are important in theory or applications have been given specific names. The Bernoulli distribution, which takes value

value 0 if and only if the two distributions in question are identical. It has diverse applications, both theoretical, such as characterizing the relative

sample set and the theoretical distribution. A P–P plot can be used as a graphical adjunct to a tests of the fit of probability distributions, with additional

such as measurement errors, often have distributions that are nearly normal. Moreover, Gaussian distributions have some unique properties that are valuable

family of distributions, but then it must be compared against the critical values appropriate to that family of theoretical distributions and dependent

gamma distribution is a versatile two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution, and