Stats cheat sheet 2 - Chapter 14 Law of Large Numbers...

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Chapter 14 Law of Large Numbers – states that the long-run relative frequency of repeated independent events gets closer and closer to the true relative frequency as the number of trials increases Independence – two events are independent if learning that one event occurs does not change the probability that the other event occurs P(A) = The probability that event A will occur. Theoretical Probability – When the probability comes from a model Empirical Probability – When the probability comes from the long-run relative frequency of the event’s occurrence Personal Probability – When the probability is subjective and represents your personal degree of belief P(A or B) = P(A) + P(B), provided A and B are disjoint P(A and B) = P(A) x P(B), provided that A and B are independent Disjoint (Mutually exclusive) – Two events are disjoint if they share no outcomes in common. If A and B are disjoint then knowing that A occurs tells us that B cannot occur. * - Disjoint events can’t be independent Chapter 15 P(A or B) = P(A) + P(B) – P(A and B) P(B|A) – The conditional probability of B given A. P(A and B) = P(A) x P(B|A) P(B|A) = P(A and B) / P(A) Independence – Events A and B are independent if P(B|A) = P(B) Chapter 18 = = σp SDp pqn Independence Assumption – the sampled values must be independent of each other Sample Size Assumption –
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This note was uploaded on 04/04/2008 for the course ILRST 2100 taught by Professor Vellemanp during the Fall '07 term at Cornell University (Engineering School).

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Stats cheat sheet 2 - Chapter 14 Law of Large Numbers...

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