5: Continuous Random Variables
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- 5.1: 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. In this chapter and the next, we will study the uniform distribution, the exponential distribution, and the normal distribution.
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- 5.2: Continuous Probability Functions
- The probability density function (pdf) is used to describe probabilities for continuous random variables. The area under the density curve between two points corresponds to the probability that the variable falls between those two values. In other words, the area under the density curve between points a and b is equal to P(a<x<b)P(a<x<b) . The cumulative distribution function (cdf) gives the probability as an area.
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- 5.4: The Exponential Distribution
- The exponential distribution is often concerned with the amount of time until some specific event occurs. Values for an exponential random variable occur in the following way. There are fewer large values and more small values. The exponential distribution is widely used in the field of reliability. Reliability deals with the amount of time a product lasts.
Contributors
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Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/30189442-699...b91b9de@18.114 .