PROBABILITY RULES
PROBABILITY DEFINITIONS
PROBABILITY LAWS
COUNTING RULES
DISCRETE PROBABILITY DISTRIBUTIONS
In probability, a discrete distribution has either a finite or a countably infinite number of possible values. That means you can enumerate or make a listing of all possible values, such as 1, 2, 3, 4, 5, 6 or 1, 2, 3, . . .
There are several kinds of discrete probability distributions, including discrete uniform, binomial, Poisson, geometric, negative binomial, and hypergeometric.
CONTINUOUS PROBABILITY DISTRIBUTIONS
When you work with continuous probability distributions, the functions can take many forms. These include continuous uniform, exponential, normal, standard normal (Z), binomial approximation, Poisson approximation, and distributions for the sample mean and
sample proportion.
When you work with the normal distribution, you need to keep in mind that it’s a continuous distribution, not a discrete one. A continuous distribution’s probability function takes the form of a continuous curve, and its random variable
takes on an uncountably infinite number of possible values. This means the set of possible values is written as an interval, such as negative infinity to positive infinity, zero to infinity, or an interval like [0, 10], which represents all real numbers from
0 to 10, including 0 and 10.
