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| Summary |
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| Let A and B be two events. Then, |
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| where S is the sample space. |
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| Note that simple events of a sample space are always mutually exclusive. |
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 Sample space: Set of all possible outcomes of a random experiment. |
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Event : An event of a random experiment is defined as a subset of the sample space. |
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Equally likely Events: Outcomes of a random experiment are called equally likely events, if all of these have equal frequencies. |
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Exhaustive outcomes: All the outcomes of a random experiment. |
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Probability of an event: P(A) |
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P(AC) = Probability of the non-occurrence of A |
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| = 1- P(A) |
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Addition Theorem: If A and B are any two events of a random  |
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If A, B, C are there events of a random experiment then |
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If A, B and C are mutually exclusive then |
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Total Probability: |
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| P(A) = P(E1) P(A|E1) + P(E2) P(A|E2)+ … +P(En) P(A|En) |
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Random variable: A real valued function 'X' defined on the sample space is called a random variable. |
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Discrete random variable: A random variable which can assume only finitely or infinitely many distinct values. |
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Continuous random variable: A random variable which can take any value over an interval is called a continuous random variable. |
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Probability distribution of a discrete random variable is of the form |
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