Chapter 14
Law of Large Numbers –
states that the longrun 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 longrun 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(BA) –
The conditional probability of B given A.
P(A and B) = P(A) x P(BA)
P(BA) = P(A and B) / P(A)
Independence –
Events A and B are independent if P(BA) = 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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 Fall '07
 VELLEMANP
 Statistics, Normal Distribution, Null hypothesis, Statistical hypothesis testing, longrun relative frequency

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