Discrete and Continuous Distributions

Continuous Beta Distribution

Models proportions

Continuous Poisson Distribution

Count certain events that are random but appear to happen at a certain rate (i.e. arrival times of customers, car crashes in an area)

Discrete Hypergeometric Distribution

Dependent events with finite possibilities that are not constant. Sample, then re-sample (n) without replacement to find x number of items that are different (r) from the total population (N).

Continuous Exponential Distribution

How likely you are to succeed in a given time period x with a given independent event probability of lambda.

Discrete Poisson Distribution

Independent events that have a constant rate and lambda = E(x) = Var(x) = time period. Probability of x number of trials in a specific time period.

Discrete Geometric Distribution

Independent events with only 2 outcomes and infinite possibilities. Count x trials until the first success.

Discrete Negative Binomial Distribution

Independent events with only 2 outcomes, a constant probability, and infinite possibilities. The x number of repeated trials until a set number of successes is reached.

discrete binomial distribution

Independent trials with only 2 outcomes, a constant probability of success, and finite possibilities. X number of successes out of a set number of trials.

Continuous Weibull Distribution

Models wait times, failure times, survival times

Continuous Gamma Distribution

Waiting time (interval between 0 and infinity) until an event occurs

continuous uniform distribution

describes a random variable that has an equally likely chance of assuming a value within a specified range

Discrete Uniform Distribution

the distribution has a finite number of specified values, each value is equally likely, the distribution is symmetric