# Chapter 6

### 27 terms by usneha92

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MR. Rod

### complement (def)

the opposite event

P(A)=1-P(Ac)

### conditional probability (def)

probability of one even happening under the condition that we know another event has already happened

### conditional probability (form)

P(A|B)=P(both happening)/P(given happening)

### conditional probability (table)

calculate the relative proportion by eliminating the rows or columns not given

### disjoint

same as mutually exclusive

P(A|B)=P(A)

### independent (def)

knowledge of one event does not effect another event

### independent (intersection form)

P(A int B)=P(A)P(B)

### intersection

probability of two or more events happening at the same time

### joint events

same as intersecting events

### multiplication principle

if one event can be done in x ways and another can be done in y ways then both can be done in xy ways

### mutually exclusive (def)

two or more events with no common or overlapping items

P(A int B)=0

### never

two events are never mutually exclusive and independent at the same time

### probabilitree diagram

a visual picture where the second branching represents conditional probability

### probability

the proportion of times an outcome occurs in a long series of repetitions

### random

individual outcomes are uncertain but there is a regular distribution of outcomes

### replacement

drawing an element from a sample space and returning it after recording its value. the probability of the next event doesn't change

### sample space (def)

a list of all possible events

P(S)=1

### simulation

using random charts or random generators to perform a large number of trials to approximate the probability

### union

P(AUB)=P(A)+P(B)-P(A int B)

P(AUB)=P(A)+P(B)

### union-independent

P(AUB)=P(A)+P(B)-P(A)P(B)

### venn diagram

a visual picture where circles are used and areas represent the probability of being in that section

### without replacement

drawing an element from a sample space and not returning it after recording its value. The probability of the next event does change

Example: