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Introduction
Probability distributions arise from experiments where the outcome is subject to chance. The nature of the experiment dictates which probability distributions may be appropriate for modeling the resulting random outcomes. There are two types of probability distributions - continuous and discrete.
Suppose you are studying a machine that produces videotape. One measure of the quality of the tape is the number of visual defects per hundred feet of tape. The result of this experiment is an integer, since you cannot observe 1.5 defects. To model this experiment you should use a discrete probability distribution.
A measure affecting the cost and quality of videotape is its thickness. Thick tape is more expensive to produce, while variation in the thickness of the tape on the reel increases the likelihood of breakage. Suppose you measure the thickness of the tape every 1000 feet. The resulting numbers can take a continuum of possible values, which suggests using a continuous probability distribution to model the results.
Using a probability model does not allow you to predict the result of any individual experiment but you can determine the probability that a given outcome will fall inside a specific range of values.
This following two sections provide more information about the available distributions:
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