# Continuous Random Variables

## Introduction

Continuous random variables have many applications. Baseball batting averages, IQ scores, the length of time a long-distance telephone call lasts, the amount of money a person carries, the length of time a computer chip lasts, and SAT scores are just a few. The field of reliability depends on a variety of continuous random variables.

### Properties of Continuous Probability Distributions

The graph of a continuous probability distribution is a curve. Probability is represented by the area under the curve.
The curve is called the probability density function (abbreviated as pdf). We use the symbol f(x) to represent the curve. f(x) is the function that corresponds to the graph; we use the density function f(x) to draw the graph of the probability distribution. 
Area under the curve is given by a different function called the  cumulative distribution function  (abbreviated as cdf). The cumulative distribution function is used to evaluate probability as area.


We will find the area that represents probability by using geometry, formulas, technology, or probability tables.  In general, calculus is needed to find the area under the curve for many probability density functions.  When we use formulas to find the area in this textbook, we are using formulas that were found by using the techniques of integral calculus.  However, because most students taking this course have not studied calculus, we will not be using calculus in this textbook.
There are many continuous probability distributions. When probability is modeled by use of a continuous probability distribution, the distribution used is selected to model and fit the particular situation in the best way.
In this chapter and the next, we will study the uniform distribution, the exponential distribution, and the normal distribution. The following graphs illustrate these distributions:






