Chapter 5: Probability

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22 Terms

1

random process

generates outcomes purely by chance

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2

probability

 a number between 0 and 1 that describes the proportion of times the outcome would occur in a very long series of trials

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3

law of large numbers

 if we observe more and more trials of any random process, the proportion of times that a specific outcome occurs approaches its probability

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4

simulation

 process that imitates a random process in such a way that simulated outcomes are consistent with real-world outcomes. 

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5

simulation process

describe, perform, use results

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6

probability model

 a description of some random process that consists of two parts: a list of all possible outcomes and the probability for each outcome

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7

sample space

 list of all possible outcomes

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8

event

any collection of outcomes from some random process

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9

complement rule

 P(AC)= 1 - P(A), where AC is the complement of event A

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10

complement

the event that a certain outcome does not occur

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11

mutually exclusive (disjoint)

no outcomes in common and so can never occur together - that is, if P(A and B) = 0

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12

addition rule for mutually exclusive events

P(A or B) = P(A) + P(B)

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13

general addition rule

 P(A or B) = P(A) + P(B) - P(A and B)

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14

intersection of A and B

“A and B”, consists of outcomes that are common to both events, notated as A ∩ B

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15

union of A and B

“A or B”, consists of outcomes that are in either event or both, notated as A ∪ B

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16

conditional probability

 probability that one event happens given that another event is known to have happened

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conditional probability of A given B

notated as A | B, solved with this equation <math xmlns="http://www.w3.org/1998/Math/MathML" display="block" data-is-equatio="1" data-latex="P\left(A\mid B\right)\ =\ \frac{P\left(A\ and\ B\right)}{P\left(B\right)}=\ \frac{P\left(A\ \cap B\right)}{P\left(B\right)}=\ \frac{P\left(both\ events\ occur\right)}{P\left(given\ event\ occurs\right)}"><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>A</mi><mo>∣</mo><mi>B</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow><mtext></mtext><mo>=</mo><mtext></mtext><mfrac><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>A</mi><mtext></mtext><mi>a</mi><mi>n</mi><mi>d</mi><mtext></mtext><mi>B</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>B</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow></mfrac><mo>=</mo><mtext></mtext><mfrac><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>A</mi><mtext></mtext><mo>∩</mo><mi>B</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>B</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow></mfrac><mo>=</mo><mtext></mtext><mfrac><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>b</mi><mi>o</mi><mi>t</mi><mi>h</mi><mtext></mtext><mi>e</mi><mi>v</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>s</mi><mtext></mtext><mi>o</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow><mrow><mi>P</mi><mrow data-mjx-texclass="INNER"><mo data-mjx-texclass="OPEN">(</mo><mi>g</mi><mi>i</mi><mi>v</mi><mi>e</mi><mi>n</mi><mtext></mtext><mi>e</mi><mi>v</mi><mi>e</mi><mi>n</mi><mi>t</mi><mtext></mtext><mi>o</mi><mi>c</mi><mi>c</mi><mi>u</mi><mi>r</mi><mi>s</mi><mo data-mjx-texclass="CLOSE">)</mo></mrow></mrow></mfrac></math>


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18

independent events

 if knowing whether or not one event has occurred does not change the probability that the other event will happen

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19

A and B are independent if

P(A|B) = P(A|BC) = P(A) OR P(B|A) = P(B|AC) = P(B)

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20

general multiplication rule

 P(A and B) = P(A ∩ B) = P(A) x P(B|A)

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21

all probabilities after the ____ stage on a tree diagram are conditional

first

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22

if A and B are independent, the probability that A and B both occur

P(A and B) = P(A ∩ B) = P(A) x P(B)

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