Chapter 5 slides - Slides by JOHN LOUCKS St. Edwards...

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1 Slide Slides by JOHN LOUCKS St. Edward’s University
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2 Slide Discrete Probability Distributions Random Variables Discrete Probability Distributions Expected Value and Variance .10 .20 .30 .40 0 1 2 3 4 TVs Sold Probability
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3 Slide Random Variables A random variable is a numerical description 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. A discrete random variable may assume either a finite number of values or an infinite sequence of values (takes on integer values). A continuous random variable may assume any numerical value in an interval or collection of intervals (takes on any numerical value).
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4 Slide Let x = number of TVs sold at the store in one day, where x can take on 5 values (0, 1, 2, 3, 4) Example: JSL Appliances Discrete random variable with a finite number of values
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5 Slide Let x = number of customers arriving in one day, where x can take on the values 0, 1, 2, . . . Example: JSL Appliances Discrete random variable with an infinite sequence of values We can count the customers arriving, but there is no finite upper limit on the number that might arrive.
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This note was uploaded on 11/17/2011 for the course ECONOMICS 103 taught by Professor Tohmass during the Spring '11 term at University of Houston-Victoria.

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Chapter 5 slides - Slides by JOHN LOUCKS St. Edwards...

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