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#reading-9-probability-concepts

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**Summary **

erwise, the events are dependent. The multiplication rule for independent events states that if A and B are independent events, P(AB) = P(A)P(B). The rule generalizes in similar fashion to more than two events. <span>According to the total probability rule, if S 1 , S 2 , …, S n are mutually exclusive and exhaustive scenarios or events, then P(A) = P(A | S 1 )P(S 1 ) + P(A | S 2 )P(S 2 ) + … + P(A | S n )P(S n ). The expected value of a random variable is a probability-weighted average of the possible outcomes of the random variable. For a random variable X, the expected value of

erwise, the events are dependent. The multiplication rule for independent events states that if A and B are independent events, P(AB) = P(A)P(B). The rule generalizes in similar fashion to more than two events. <span>According to the total probability rule, if S 1 , S 2 , …, S n are mutually exclusive and exhaustive scenarios or events, then P(A) = P(A | S 1 )P(S 1 ) + P(A | S 2 )P(S 2 ) + … + P(A | S n )P(S n ). The expected value of a random variable is a probability-weighted average of the possible outcomes of the random variable. For a random variable X, the expected value of

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