Lecture-5-Summer2010-1

Lecture-5-Summer2010-1 - LECTURE 5 Discrete Probability...

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LECTURE 5 Discrete Probability Distributions This lecture covers material on Random Variables, discrete probability distributions, and computing the expected value, variance and standard deviation for discrete probability distributions. Read: Chapter 5, Sections 5.1, 5.2, and 5.3. Experiment: Any process that generates well-defined outcomes. For example the process of selling shares of a particular company is an experiment if it has clearly defined outcomes - sell at a gain, sell at a loss, or sell with no change in the share price. Random Variable: A random variable is a rule or function that assigns one (and only) numerical value to the outcome of an experiment. For example, x can be a random variable whose value indicates the number of shares sold at a loss. Note: A random variable can be either discrete or continuous. Discrete Random Variable: Random variables that can assume a finite number or an infinite sequence of numerical values such as 0, 1, 2. ... . If a brokerage house has four traders, and y is a discrete random variable whose value indicates the number of traders who sold shares of ABC company at a loss in one day, then y can assume only one of the values: 0, 1, 2, 3, 4. (In this case
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This note was uploaded on 09/21/2011 for the course OM 210 taught by Professor Singer during the Summer '08 term at George Mason.

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Lecture-5-Summer2010-1 - LECTURE 5 Discrete Probability...

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