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Ch. 3 - Probability
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470당신이 사용하는 게 아니라요?교과서 변경
3장, 문제 3.2.5

"True or False? In Exercises 5 and 6, determine whether the statement is true or false. If it is false, rewrite it as a true statement.
5. If two events are independent, then P(A|B) = P(B)."

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Step 1: Begin by recalling the definition of independent events. Two events A and B are independent if the occurrence of one does not affect the probability of the other. Mathematically, this is expressed as P(A ∩ B) = P(A) * P(B).
Step 2: Understand the conditional probability formula. Conditional probability P(A|B) is defined as P(A|B) = P(A ∩ B) / P(B), provided P(B) > 0.
Step 3: Substitute the independence condition into the conditional probability formula. Since P(A ∩ B) = P(A) * P(B) for independent events, the formula becomes P(A|B) = (P(A) * P(B)) / P(B).
Step 4: Simplify the expression. Cancel out P(B) in the numerator and denominator, which results in P(A|B) = P(A). This shows that for independent events, P(A|B) equals P(A), not P(B).
Step 5: Conclude that the given statement is false. Rewrite the true statement: 'If two events are independent, then P(A|B) = P(A).'

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주요 개념

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Independent Events

Independent events are those whose occurrence does not affect the probability of the other event occurring. In probability theory, two events A and B are independent if the probability of A occurring given that B has occurred is equal to the probability of A occurring alone, expressed as P(A|B) = P(A). This concept is crucial for understanding how probabilities interact in different scenarios.
추천 영상:
05:54
Probability of Multiple Independent Events

Conditional Probability

Conditional probability refers to the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), which reads as 'the probability of A given B.' Understanding this concept is essential for analyzing relationships between events and determining how the occurrence of one event influences the likelihood of another.
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03:53
Conditional Probability Rule

Probability Notation

Probability notation is a standardized way of expressing probabilities and relationships between events. For example, P(A) represents the probability of event A occurring, while P(A|B) indicates the probability of A occurring under the condition that B has occurred. Familiarity with this notation is important for interpreting statements and solving problems in probability.
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5:37
Introduction to Probability
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교과서 질문

According to Bayes’ Theorem, the probability of event A , given that event B has occurred, is

P(A|B) = P(A) * P(B|A)P(A) * P(B|A) + P(A') * P(B|A').

In Exercises 33–38, use Bayes’ Theorem to find P(A|B).

33. P(A) = 2/3, P(A') = 1/3, P(B|A) = 1/5 , and P(B|A') = 1/2

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교과서 질문

Identifying Simple Events In Exercises 33-36, determine the number of outcomes in the event. Then decide whether the event is a simple event or not. Explain your reasoning.

34. A spreadsheet is used to randomly generate a number from 1 to 4000. Event B is generating a number less than 500.

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교과서 질문

Using a Tree Diagram In Exercises 67-70, a probability experiment consists of rolling a six-sided die and spinning the spinner shown at the left. The spinner is equally likely to land on each color. Use a tree diagram to find the probability of the event. Then explain whether the event can be considered unusual.

68. Event B: rolling an odd number and the spinner landing on green

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교과서 질문

Finding Classical Probabilities In Exercises 41-46, a probability experiment consists of rolling a 12-sided die numbered 1 to 12. Find the probability of the event.

43. Event C: rolling a number greater than 4

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교과서 질문

Finding the Probability of an Event In Exercises 21-24, the probability that an event will not happen is given. Find the probability that the event will happen. 

23. P(E')=3/4

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교과서 질문

"According to Bayes’ Theorem, the probability of event A , given that event B has occurred, is

P(A|B) = P(A) * P(B|A)P(A) * P(B|A) + P(A') * P(B|A').

In Exercises 33–38, use Bayes’ Theorem to find P(A|B).

36. P(A) = 0.62, P(A') = 0.38, P(B|A) = 0.41 , and P(B|A') = 0.17 "

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