# Ch06 - Chapter 6 Discrete Distributions Chapter 6 1...

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Chapter 6 1 Chapter 6 Discrete Distributions

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Probability Models Probability to understand business processes (no a priori) Repeatable random experiments Roll 3 times, flip 10 times, observe # of customers on 10 different days
Discrete Distributions Discrete random variable Upper limits Customers, service calls, takeoffs Discrete probability distribution Same probability rules Assigns probabilities to each value of X Values of X not always equally likely

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Chapter 6 4 Central Tendency Given a discrete random variable X with p(x) we can determine what we expect X to be (expected value or mean): E X x p x i i i n ( ) ( ) = = 1 TVs, baristas, siblings
Chapter 6 5 Dispersion: The variance of X is: V(X) = σ 2 = ∑ (x i - µ) 2 P(x i ) TVs, baristas, siblings 2 ( ) V X σ = σ =

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Compound events Probability of less than 5 customers P(X<5) Probability of more than 2 TVs P(X>2) Clicker… Chapter 6 6
7 Discrete Uniform Distribution

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## This note was uploaded on 12/13/2010 for the course LEEDS BCOR 1020 taught by Professor Heatheradams during the Spring '08 term at Colorado.

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Ch06 - Chapter 6 Discrete Distributions Chapter 6 1...

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