Chapter 13: Probability Rules!

Question 1

Define ‘Conditional probability’.

Answer 1

P(B | A) = P(A AND B) / P(A)

P(B | A) is read “the probability of B GIVEN A”.

Question 2

Define ‘Independence’.

Answer 2

Events A and B are independent when P(B | A) = P(B | A^C) = P(B).

Question 3

Define ‘Independence Assumption’.

Answer 3

We often require events to be independent. (So you should think about whether this assumption is reasonable.)

Question 4

Define ‘General multiplication rule’.

Answer 4

For any two events, A and B, the probability of A AND B is P(A AND B) =P(A) x P(B | A).

Question 5

Define ‘Multiplication rule’.

Answer 5

If A and B are independent events, then the probability of A AND B is P(A AND B) = P(A) x P(B). This is easily extended to any number of independent events.

Question 6

Define ‘Tree diagram’.

Answer 6

A display of conditional events or probabilities that is helpful in thinking through conditioning.

Question 7

What should you use to solve problems involving probability? To reverse the conditioning?

Answer 7

• tables, Venn diagrams, and tree diagrams

- tree diagrams and Bayes’ Rule