# Continuous Probability Distributions

We now extend the definition of probability distribution from discrete (see Discrete Probability Distributions) to continuous random variables. Such variables take on an infinite range of values even in a finite interval (weight of rice, room temperature, etc.).

While for a discrete random variable x, the probability that x assumes a value between a and b (exclusive) is given by

the frequency function f of a continuous random variable can assume an infinite number of values (even in a finite interval) and so we can’t simply sum up the values in the ordinary way. For continuous variables, the equivalent formulation is that the probability that x assumes a value between a and b is given by

i.e. the area under the graph of y = f(x) bounded by the x-axis and the lines x = a and x = b.

Figure 1 – Probability as area under a curve

Figure 1 – Area under the curve

Definition 1: For a continuous random variable x is a frequency function, also called the probability density function (pdf) provided:

The corresponding (cumulative) distribution function F(x)  is defined by

Property 2: For any continuous random variable x with distribution function F(x)

Observation: f is a valid probability density function provided that f always takes non-negative values and the area between the curve and the x-axis is 1. f is the probability density function for a particular random variable x provided the area of the region indicated in Figure 1 represents the probability that x assumes a value between a and b inclusively. Note that the probability that f takes any particular value a is not f(a). In fact for any specific value a, the probability that x takes the value a is considered to be 0.

Essentially the area under a curve is a way of summing when dealing with an infinite range of values in a continuum. For those of you familiar with calculus