Continuous Random Variables

Wrap-Up

The Uniform, Normal and Exponential distributions are three of the most important continuous random variables, and can be used to model a number of real-life phenomenon. The Uniform (0,1) random variable is useful in simulation studies, as most other random variables can be generated or approximated by mathematical manipulation of Uniform random numbers. The Normal distribution is a good model for many real-world phenomenon, especially many biological and psychological characteristics (e.g. crop yields, height or intelligence scores). Many statistical techniques also presume that data or the sampling distribution of a statistic is normally distributed. Finally, the Normal distribution is a good approximation for many other symmetric or nearly symmetric distributions, including (under specific conditions) the Binomial and Poisson distributions. Exponential random variables are frequently used to describe lifetimes of manufactured products, or the time/distance between events that have a Poisson distribution.

You can learn more about the distributions discussed in this module in other Project Links Probability and Statistics modules, including Poisson and Exponential Module and the Random Variable Relationships Module..

The following textbooks are also excellent sources of information about the random variables introduced here, as well as other discrete and continuous distributions not discussed:


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