# Statistics/Distributions/Continuous

A continuous statistic is a random variable that does not have any points at which there is any distinct probability that the variable will be the corresponding number.

### General Properties[edit | edit source]

#### Cumulative Distribution Function[edit | edit source]

A continuous random variable, like a discrete random variable, has a cumulative distribution function. Like the one for a discrete random variable, it also increases towards 1. Depending on the random variable, it may reach one at a finite number, or it may not. The cdf is represented by a capital F.

#### Probability Distribution Function[edit | edit source]

Unlike a discrete random variable, a continuous random variable has a probability density function instead of a probability mass function. The difference is that the former must integrate to 1, while the latter must have a total value of 1. The two are very similar, otherwise. The pdf is represented by a lowercase f.

#### Special Values[edit | edit source]

Let *R* be the set of points of the distribution.

The expected value for a continuous variable *X* with probability density function *f* is defined as
.

More generally, the expected value of any continuously transformed variable *g(X)* with probability density function *f* is defined as
.

The mean of a continuous or discrete distribution is defined as .

The variance of a continuous or discrete distribution is defined as .

Expectations can also be derived by producing the Moment Generating Function for the distribution in question. This is done by finding the expected value . Once the Moment Generating Function has been created, each derivative of the function gives a different piece of information about the distribution function.

= mean

= variance

= skewness

= kurtosis