Disc-Prob-Distrs-1 - STATISTICS FOR MGMT DECISIONS DSC 325...

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STATISTICS FOR MGMT DECISIONS – DSC 325 Discrete Probability Distributions Part I Random Variables: A random variable is defined as a numerical example of the outcome of an experiment. A random variable can be classified as being either discrete or continuous depending on the numerical values it assumes. Discrete Random Variables: A random variable that may assume either a finite number of values such as 0, 1, 2, 3, ….. Is referred to as a discrete random variable . For example, if we toss a die once, if the outcome is assigned a letter X, the possible outcomes are: {1, 2, 3, 4, 5, and 6}. X is a discrete random variable. If we toss a coin 8 times, the number heads “Y” we get are: {1, 2, 3, 4, 5, 6, 7, 8}. Y is a discrete random variable. Continuous Random Variables: A random variable that assumes any numerical value in an interval or collection of intervals is called a continuous random variable. Experiments that are based on measurement scales such as time, weight, distance, and temperature can be described by continuous variables.
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