Abstract
Every probability model we’ve seen so far has been discrete—the possible values are separated from each other as “discrete points” on a number line. With this section, we begin our study of the so-called “continuous” distributions. A continuously distributed random variable on an interval [a, b] is a random variable which takes on any possible value in the interval [a, b] of real numbers. Random variables whose values are measurements of time, weight, size, and so on, are typical examples of situations which may give rise to continuous distributions.
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© 1994 Springer Science+Business Media New York
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Creighton, J.H.C. (1994). Continuous Probability Models. In: A First Course in Probability Models and Statistical Inference. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8540-8_4
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DOI: https://doi.org/10.1007/978-1-4419-8540-8_4
Publisher Name: Springer, New York, NY
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