Mathematics for Business and Economics: Conditional Probability

Mathematics for Business and Economics: Conditional Probability

Recap Section 4.5

  • Elementary Rules:

    • 0P(E)10 \le P(E) \le 1

    • P(S)=1P(S) = 1 (Probability of the sample space is 1)

    • P()=0P(\emptyset) = 0 (Probability of the impossible event is 0)

  • Union Rule: For any two events E and F,
    P(EF)=P(E)+P(F)P(EF)P(E \cup F) = P(E) + P(F) - P(E \cap F)

  • Mutually Exclusive Events: If E and F are mutually exclusive (they cannot occur at the same time, meaning EF=E \cap F = \emptyset),
    P(EF)=P(E)+P(F)P(E \cup F) = P(E) + P(F)

  • Complement Rule: For any event E,
    P(Ec)=1P(E)P(E^c) = 1 - P(E)

This Lecture's Content

This lecture focuses on:

  • Defining conditional probability

  • The Product Rule

  • Using probability trees

  • Understanding independent events

Conditional Probability: Motivation Example (1)

Scenario: If 1%1\% of UConn students are student-athletes, and 100%100\% of these student-athletes live in the United States.

Questions:

  1. What is the probability of meeting a UConn student who lives in the United States AND is a student-athlete?

  2. What is the probability of meeting a UConn student who lives in the United States GIVEN THAT that student is a student-athlete?

Solution:

  • Let E: A student lives in the United States.

  • Let F: A student is a student-athlete.

  1. P(EF)P(E \cap F): This is the probability that a student is both a student-athlete AND lives in the United States. Since all student-athletes live in the USA, this is simply the probability of being a student-athlete.
    P(EF)=P(F)=1%P(E \cap F) = P(F) = 1\%

  2. P(E given F)P(E \text{ given } F): This is the probability that a student lives in the United States, given they are a student-athlete. Since ALL student-athletes live in the USA, this probability is 100%100\%.

    • Reasoning: If we know for certain a student is a student-athlete, and all student-athletes live in the USA, then it is certain that this student lives in the USA.

    • This demonstrates that more information leads to a better understanding of an event, and probabilities are