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AP Statistics - Chapter 6


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conditional probability
when knowing one probability greatly effects the chances of the occurrence of another event
Bayes' Rule
P(A | B) = [ P(A|B1) * P(B1) ] / [ P(A|B1) * P(B1) + P(A|B2) * P(B2) ]
P(A | B)
P(A /\ B) / P(B)
simple event
any event consisting of exactly one outcome
sample space
the collection of all possible outcomes from a chance experiment
A and B, A /\ B
the event consisting of outcomes common to both events
P(A U B)
P(A) + P(B) - P(A /\ B)
disjoint or mutually exclusive
two events that have no common outcomes
chance experiment
any experiment for which there is uncertainty concerning the resulting outcome
A or B, A U B
the event consisting of all outcomes in at least one of the two events
Law of Large Numbers
as the number of repetitions of a chance experiment increases, the chance that the relative frequency of occurrence for an event will differ from the true probability of the event by more than any small number approaches 0
not A, A^c
the event consisting of all outcomes not in A
P(A /\ B)
P(A) * P(B), A and B are independent
any collection of outcomes from the sample space of a chance experiment
independent events
P(A | B) = P(A), knowledge that some number of the events have occurred does not change the probabilities that any particular one or more of the other events has occurred

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