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# Section4.2posted - STOR155,Section2 Thursday,March4,2010...

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STOR 155, Section 2 Thursday, March 4, 2010 Finishing Section 4.2

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Recap of Sections 4.1 and 4.2 through p.  250 Probability models where  S  is a  finite  set of outcomes: Assign a probability to each outcome in  S , making sure they’re  all ≥ 0 and add to 1.   It’s the job of the person setting up the model to assure that the  assignment  applies  to the situation at hand. That is, that the probabilities represent  expected long-run  proportions . Then the probability of any event  A  (subset of  S )   is the sum of  the probabilities assigned to the outcomes in  A. A simple special case:  If we can assume all outcomes are  equally likely , then each has probability 1/ k  where  k  is the  number of outcomes in  S . Probability models where  S  is an  interval  of real numbers: Use some rule to assign probabilities to intervals (not to  individual outcomes).   Most important example:  normal probabilities, found from Table  A. In any model, use Rules 3 and 4 to find P(A or B) when A and B are disjoint c
4.2  Probability models: Independence and the multiplication rule Events  A  and  B  are  independent   if knowing  that one occurs does not change the  probability that the other occurs. Rule 5 :    If events  A  and  B  are  independent, then  P(A  and  B)  =  P(A)P(B).

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4.2  Probability models: Independence and the multiplication rule Rule 5:
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Section4.2posted - STOR155,Section2 Thursday,March4,2010...

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