Skip to main content
6: Continuous Random Variables
-
-
Last updated
-
-
Save as PDF
-
-
-
6.1: 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.
-
-
6.2: 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.
-
-
6.3: The Uniform Distribution
-
The uniform distribution is a continuous probability distribution and is concerned with events that are equally likely to occur. When working out problems that have a uniform distribution, be careful to note if the data is inclusive or exclusive.
-
-
6.4: Continuous Distribution (Worksheet)
-
A statistics Worksheet: The student will compare and contrast empirical data from a random number generator with the uniform distribution.
-
-
6.5: Continuous Random Variables (Exercises)
-
These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.
-
-
6.6: Exercises
-
These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.
Contributors and Attributions