Random Variables and Discrete Probability Distributions

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What are the expected winnings for our raffle example? • μX = E(X) = 499(1/1000) + 199(1/1000) + 49(2/1000) + 9(3/1000) + (-1)(993/1000) ( 1)(993/1000) = (499 + 199 + 98 + 27 - 993)/1000 = -170/1000 170/1000 = -0.17 0.17 • Someone buying a ticket for our raffle can expect to lose $0.17.
For our raffle example, can we use our expected prize calculation to determine the expected winnings?
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Random Variables and Discrete Proba
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Random Variable • A random variab
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Random Variable • A discrete rand
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Random Variable • A continuous ra
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Probability Distribution for a Disc
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What is the probability distributio
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Did you notice that each example me
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Requirements for a probability dist
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Requirements for a probability dist
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Did you notice that if the examples
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Did you notice that if the examples
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Notation • For clarity, we will u
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Does the table below represent a pr
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Probability Histogram • As with a
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Raffle Example • Although there a
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Raffle Example • Since not not wi
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Raffle Example • Next, we determi
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Raffle Example • Finally, we comp
Page 91 and 92: Raffle Example Probability Distribu
Page 93 and 94: Raffle Example • We do not need t
Page 95 and 96: Raffle Example • Winnings Winning
Page 97 and 98: Raffle Example • Alternate Altern
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Page 105 and 106: Raffle Example • Note: The point
Page 107 and 108: Raffle Example • Did you notice t
Page 109 and 110: Raffle Example • Did you notice t
Page 111 and 112: Expected Value for a Discrete Rando
Page 119 and 120: How many Heads should we expect if
Page 125 and 126: What is the expected value for the
Page 131 and 132: What is the expected prize for our
Page 137 and 138: What are the expected winnings for
Page 139 and 140: What are the expected winnings for
Page 141: What are the expected winnings for
Page 145 and 146: For our raffle example, can we use
Page 147 and 148: For our raffle example, can we use
Page 149 and 150: Variance and Standard Deviation for
Page 151 and 152: What is the standard deviation for
Page 167: Variance and Standard Deviation for
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Random Variables and Discrete Probability Distributions

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