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This module serves as an introduction to the Continuous Random Variables chapter in the Elementary Statistics textbook.

Student learning objectives

By the end of this chapter, the student should be able to:

  • Recognize and understand continuous probability density functions in general.
  • Recognize the uniform probability distribution and apply it appropriately.

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, thelength 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.

This chapter gives an introduction to continuous random variables and the many continuous distributions. We will be studying these continuous distributions for several chapters.

The characteristics of continuous random variables are:

  • The outcomes are measured, not counted.
  • Geometrically, the probability of an outcome is equal to an area under a mathematical curve called the density curve, f x .
  • Each individual value has zero probability of occurring. Instead we find the probability that the value is between two endpoints.

We will start with a simplest continuous distributions, the Uniform .

The values of discrete and continuous random variables can be ambiguous. For example, if X is equal to the number of miles (to the nearest mile) you drive to work, then X is a discrete random variable. You count the miles. If X is the distance you drive to work, then you measure values of X and X is a continuous random variable. How the random variable is defined is very important.

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Source:  OpenStax, Elementary statistics. OpenStax CNX. Dec 30, 2013 Download for free at http://cnx.org/content/col10966/1.4
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