Probability Chapter
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Questions and Answers

What is the condition for two events A and B to be independent?

  • P(A) = P(B)
  • P(A B) = P(A) + P(B)
  • P(A|B) = P(A) and P(B|A) = P(B) (correct)
  • P(A|B) = P(B|A)
  • What is the purpose of Bayes' Theorem?

  • To calculate the probability of a single event
  • To determine if two events are dependent or independent
  • To find the probability of two independent events
  • To find the probability of another conditional event given the total sum of partitions' probability (correct)
  • If P(A) = 0.4, P(B) = 0.8, and P(A B) = 0.36, are events A and B independent?

  • No, because P(A) ≠ P(B)
  • Yes, because P(A) = P(B)
  • No, because P(A B) ≠ P(A)P(B) (correct)
  • Yes, because P(A B) = P(A)P(B)
  • What is the probability of event A in Example 1?

    <p>1/3</p> Signup and view all the answers

    What is the probability of event C in Example 1?

    <p>1/2</p> Signup and view all the answers

    If P(A) = 0.4, P(B) = 0.8, and P(A|B) = 0.45, are events A and B independent?

    <p>No, because P(A|B) ≠ P(A)</p> Signup and view all the answers

    If P(A) = 0.4, P(B) = 0.8, and P(A B) = 0.32, are events A and B independent?

    <p>Yes, because P(A B) = P(A)P(B)</p> Signup and view all the answers

    Who developed Bayes' Theorem?

    <p>Thomas Bayes</p> Signup and view all the answers

    What is the formula to check for independence between events A and B?

    <p>P(A|B) = P(A) and P(B|A) = P(B)</p> Signup and view all the answers

    What is the purpose of the denominator in Bayes' Theorem?

    <p>To find the total sum of partitions' probability</p> Signup and view all the answers

    In Example 1, are events A and C independent?

    <p>Yes, because P(A|C) = P(A)</p> Signup and view all the answers

    Study Notes

    Probability

    • Probability is used to quantify the likelihood or chance that an outcome of a random experiment will occur.
    • It is the branch of mathematics concerning events and numerical descriptions of how likely they are to happen.
    • Probability is essential in assessing risks and making better decisions throughout scientific and engineering disciplines.

    Sample Spaces and Events

    • A random experiment is an experiment that can result in different outcomes, even though it is repeated in the same manner every time.
    • A permutation can be constructed by selecting the element to be placed in the first position of the sequence from the n elements, then selecting the element for the second position from the n-1 remaining elements, and so forth.
    • Permutations are sometimes referred to as linear permutations.

    Combinations

    • A combination is the number of subsets of r elements that can be selected from a set of n elements, where order is not important.
    • Every subset of r elements can be indicated by listing the elements in the set and marking each element with a “*” if it is to be included in the subset.

    Interpretations and Axioms of Probability

    • The first axiom states that the probability of the sample space is equal to 1.
    • The second axiom states that a probability is nonnegative.
    • The third axiom states that for every collection of mutually exclusive events, the probability of their union is the sum of the individual probabilities.

    Total Probability Rule

    • The total probability rule is used to find the total probability of either of two events occurring.
    • P(A) + P(B) - P(A∩B) = P(A) + P(B) - P(A)P(B) if they are not mutually exclusive.

    Multiple Events

    • To find the total probability of any of the events A1, A2, …, An occurring, we apply the principle of inclusion-exclusion, summing individual probabilities and subtracting the probabilities of all possible intersections.
    • P(F) = P(F|H)P(H) + P(F|M)P(M) + P(F|L)P(L)

    Independence of Conditional Probabilities

    • Two events A and B are independent if P(A|B) = P(A) and P(B|A) = P(B).
    • If P(A|B) = P(A) and P(B|A) = P(B), then A and B are independent.

    Bayes' Theorem

    • Bayes' theorem is used to find the probability of another conditional event given the initial conditional event.
    • The theorem was developed by Thomas Bayes around the 1700s.

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